toolbar module

Module for dealing with interactive GUIs.

PlanetaryComputerEndpoint

Bases: TitilerEndpoint

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

Source code in geemap/common.py

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

    def __init__(
        self,
        endpoint="https://planetarycomputer.microsoft.com/api/data/v1",
        name="item",
        TileMatrixSetId="WebMercatorQuad",
    ):
        """Initialize the PlanetaryComputerEndpoint object.

        Args:
            endpoint (str, optional): The endpoint of the titiler server. Defaults to "https://planetarycomputer.microsoft.com/api/data/v1".
            name (str, optional): The name to be used in the file path. Defaults to "item".
            TileMatrixSetId (str, optional): The TileMatrixSetId to be used in the file path. Defaults to "WebMercatorQuad".
        """
        super().__init__(endpoint, name, TileMatrixSetId)

    def url_for_stac_collection(self):
        return f"{self.endpoint}/collection/{self.TileMatrixSetId}/tilejson.json"

    def url_for_collection_assets(self):
        return f"{self.endpoint}/collection/assets"

    def url_for_collection_bounds(self):
        return f"{self.endpoint}/collection/bounds"

    def url_for_collection_info(self):
        return f"{self.endpoint}/collection/info"

    def url_for_collection_info_geojson(self):
        return f"{self.endpoint}/collection/info.geojson"

    def url_for_collection_pixel_value(self, lon, lat):
        return f"{self.endpoint}/collection/point/{lon},{lat}"

    def url_for_collection_wmts(self):
        return f"{self.endpoint}/collection/{self.TileMatrixSetId}/WMTSCapabilities.xml"

    def url_for_collection_lat_lon_assets(self, lng, lat):
        return f"{self.endpoint}/collection/{lng},{lat}/assets"

    def url_for_collection_bbox_assets(self, minx, miny, maxx, maxy):
        return f"{self.endpoint}/collection/{minx},{miny},{maxx},{maxy}/assets"

    def url_for_stac_mosaic(self, searchid):
        return f"{self.endpoint}/mosaic/{searchid}/{self.TileMatrixSetId}/tilejson.json"

    def url_for_mosaic_info(self, searchid):
        return f"{self.endpoint}/mosaic/{searchid}/info"

    def url_for_mosaic_lat_lon_assets(self, searchid, lon, lat):
        return f"{self.endpoint}/mosaic/{searchid}/{lon},{lat}/assets"

__init__(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 geemap/common.py

def __init__(
    self,
    endpoint="https://planetarycomputer.microsoft.com/api/data/v1",
    name="item",
    TileMatrixSetId="WebMercatorQuad",
):
    """Initialize the PlanetaryComputerEndpoint object.

    Args:
        endpoint (str, optional): The endpoint of the titiler server. Defaults to "https://planetarycomputer.microsoft.com/api/data/v1".
        name (str, optional): The name to be used in the file path. Defaults to "item".
        TileMatrixSetId (str, optional): The TileMatrixSetId to be used in the file path. Defaults to "WebMercatorQuad".
    """
    super().__init__(endpoint, name, TileMatrixSetId)

TitilerEndpoint

This class contains the methods for the titiler endpoint.

Source code in geemap/common.py

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

    def __init__(
        self,
        endpoint="https://titiler.xyz",
        name="stac",
        TileMatrixSetId="WebMercatorQuad",
    ):
        """Initialize the TitilerEndpoint object.

        Args:
            endpoint (str, optional): The endpoint of the titiler server. Defaults to "https://titiler.xyz".
            name (str, optional): The name to be used in the file path. Defaults to "stac".
            TileMatrixSetId (str, optional): The TileMatrixSetId to be used in the file path. Defaults to "WebMercatorQuad".
        """
        self.endpoint = endpoint
        self.name = name
        self.TileMatrixSetId = TileMatrixSetId

    def url_for_stac_item(self):
        return f"{self.endpoint}/{self.name}/{self.TileMatrixSetId}/tilejson.json"

    def url_for_stac_assets(self):
        return f"{self.endpoint}/{self.name}/assets"

    def url_for_stac_bounds(self):
        return f"{self.endpoint}/{self.name}/bounds"

    def url_for_stac_info(self):
        return f"{self.endpoint}/{self.name}/info"

    def url_for_stac_info_geojson(self):
        return f"{self.endpoint}/{self.name}/info.geojson"

    def url_for_stac_statistics(self):
        return f"{self.endpoint}/{self.name}/statistics"

    def url_for_stac_pixel_value(self, lon, lat):
        return f"{self.endpoint}/{self.name}/point/{lon},{lat}"

    def url_for_stac_wmts(self):
        return (
            f"{self.endpoint}/{self.name}/{self.TileMatrixSetId}/WMTSCapabilities.xml"
        )

__init__(endpoint='https://titiler.xyz', 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".	'https://titiler.xyz'
name	str	The name to be used in the file path. Defaults to "stac".	'stac'
TileMatrixSetId	str	The TileMatrixSetId to be used in the file path. Defaults to "WebMercatorQuad".	'WebMercatorQuad'

Source code in geemap/common.py

def __init__(
    self,
    endpoint="https://titiler.xyz",
    name="stac",
    TileMatrixSetId="WebMercatorQuad",
):
    """Initialize the TitilerEndpoint object.

    Args:
        endpoint (str, optional): The endpoint of the titiler server. Defaults to "https://titiler.xyz".
        name (str, optional): The name to be used in the file path. Defaults to "stac".
        TileMatrixSetId (str, optional): The TileMatrixSetId to be used in the file path. Defaults to "WebMercatorQuad".
    """
    self.endpoint = endpoint
    self.name = name
    self.TileMatrixSetId = TileMatrixSetId

Toolbar

Bases: AnyWidget

A toolbar that can be added to the map.

Source code in geemap/toolbar.py

class Toolbar(anywidget.AnyWidget):
    """A toolbar that can be added to the map."""

    _esm = pathlib.Path(__file__).parent / "static" / "toolbar.js"

    # The list of main tools.
    main_tools = map_widgets.TypedTuple(
        trait=traitlets.Instance(widgets.Widget),
        help="List of main tools",
    ).tag(sync=True, **widgets.widget_serialization)

    # The list of extra tools.
    extra_tools = map_widgets.TypedTuple(
        trait=traitlets.Instance(widgets.Widget),
        help="List of extra tools",
    ).tag(sync=True, **widgets.widget_serialization)

    # Whether the toolbar is expanded.
    expanded = traitlets.Bool(False).tag(sync=True)

    _TOGGLE_EXPAND_ICON = "add"
    _TOGGLE_EXPAND_TOOLTIP = "Expand toolbar"
    _TOGGLE_COLLAPSE_ICON = "remove"
    _TOGGLE_COLLAPSE_TOOLTIP = "Collapse toolbar"

    def __init__(
        self,
        host_map: "geemap.Map",
        main_tools: List[ToolbarItem],
        extra_tools: List[ToolbarItem],
    ):
        """Adds a toolbar with `main_tools` and `extra_tools` to the `host_map`."""
        super().__init__()
        if not main_tools:
            raise ValueError("A toolbar cannot be initialized without `main_tools`.")
        self.host_map = host_map
        self.toggle_widget = ToolbarItem(
            icon=self._TOGGLE_EXPAND_ICON,
            tooltip=self._TOGGLE_EXPAND_TOOLTIP,
            callback=self._toggle_callback,
            reset=True,
        )
        self.main_tools = main_tools + ([self.toggle_widget] if extra_tools else [])
        self.extra_tools = extra_tools
        for widget in self.main_tools + self.extra_tools:
            widget.callback_wrapper = lambda callback, value, tool: callback(
                self.host_map, value, tool
            )

    def reset(self):
        """Resets the toolbar so that no widget is selected."""
        for widget in self.main_tools + self.extra_tools:
            widget.value = False

    def _toggle_callback(self, m, selected, item):
        del m, item  # unused
        if not selected:
            return
        if self.toggle_widget.icon == self._TOGGLE_EXPAND_ICON:
            self.expanded = True
            self.toggle_widget.tooltip_text = self._TOGGLE_COLLAPSE_TOOLTIP
            self.toggle_widget.icon = self._TOGGLE_COLLAPSE_ICON
        elif self.toggle_widget.icon == self._TOGGLE_COLLAPSE_ICON:
            self.expanded = False
            self.toggle_widget.tooltip_text = self._TOGGLE_EXPAND_TOOLTIP
            self.toggle_widget.icon = self._TOGGLE_EXPAND_ICON

__init__(host_map, main_tools, extra_tools)

Adds a toolbar with main_tools and extra_tools to the host_map.

Source code in geemap/toolbar.py

def __init__(
    self,
    host_map: "geemap.Map",
    main_tools: List[ToolbarItem],
    extra_tools: List[ToolbarItem],
):
    """Adds a toolbar with `main_tools` and `extra_tools` to the `host_map`."""
    super().__init__()
    if not main_tools:
        raise ValueError("A toolbar cannot be initialized without `main_tools`.")
    self.host_map = host_map
    self.toggle_widget = ToolbarItem(
        icon=self._TOGGLE_EXPAND_ICON,
        tooltip=self._TOGGLE_EXPAND_TOOLTIP,
        callback=self._toggle_callback,
        reset=True,
    )
    self.main_tools = main_tools + ([self.toggle_widget] if extra_tools else [])
    self.extra_tools = extra_tools
    for widget in self.main_tools + self.extra_tools:
        widget.callback_wrapper = lambda callback, value, tool: callback(
            self.host_map, value, tool
        )

reset()

Resets the toolbar so that no widget is selected.

Source code in geemap/toolbar.py

def reset(self):
    """Resets the toolbar so that no widget is selected."""
    for widget in self.main_tools + self.extra_tools:
        widget.value = False

ToolbarItem

Bases: AnyWidget

A toolbar item widget for geemap.

Source code in geemap/toolbar.py

@map_widgets.Theme.apply
class ToolbarItem(anywidget.AnyWidget):
    """A toolbar item widget for geemap."""

    _esm = pathlib.Path(__file__).parent / "static" / "toolbar_item.js"
    active = traitlets.Bool(False).tag(sync=True)
    primary = traitlets.Bool(True).tag(sync=True)
    icon = traitlets.Unicode("").tag(sync=True)
    # Note: "tooltip" is already defined on ipywidgets.Widget.
    tooltip_text = traitlets.Unicode("").tag(sync=True)

    def __init__(
        self,
        icon: str,
        tooltip: str,
        callback: Callable[[Any, bool, Any], None],
        control: Optional[widgets.Widget] = None,
        reset=False,
        active=False,
    ):
        """A togglable, toolbar item.

        Args:
            icon (str): The icon name to use, from https://fonts.google.com/icons.
            tooltip: The tooltip text to show a user on hover.
            callback: A callback function to execute when the item icon is clicked.
                Its signature should be `callback(map, selected, item)`, where
                `map` is the host map, `selected` is a boolean indicating if the
                user selected or unselected the tool, and `item` is this object.
            control: The control widget associated with this item. Used to
                cleanup state when toggled off.
            reset: Whether to reset the selection after the callback has finished.
            active: Whether the tool is currently active.
        """
        super().__init__()
        self.icon = icon
        self.tooltip_text = tooltip
        self.callback = callback
        self.callback_wrapper = lambda *args: None
        self.control = control
        self.reset = reset
        self.active = active

    def toggle_off(self):
        if self.active:
            self.active = False

    @traitlets.observe("active")
    def _observe_value(self, change: Dict[str, Any]) -> None:
        if (value := change.get("new")) is not None:
            self.callback_wrapper(self.callback, value, self)
        if self.active and self.reset:
            self.active = False

__init__(icon, tooltip, callback, control=None, reset=False, active=False)

A togglable, toolbar item.

Parameters:

Name	Type	Description	Default
icon	str	The icon name to use, from https://fonts.google.com/icons.	required
tooltip	str	The tooltip text to show a user on hover.	required
callback	Callable[[Any, bool, Any], None]	A callback function to execute when the item icon is clicked. Its signature should be callback(map, selected, item), where map is the host map, selected is a boolean indicating if the user selected or unselected the tool, and item is this object.	required
control	Optional[Widget]	The control widget associated with this item. Used to cleanup state when toggled off.	None
reset		Whether to reset the selection after the callback has finished.	False
active		Whether the tool is currently active.	False

Source code in geemap/toolbar.py

def __init__(
    self,
    icon: str,
    tooltip: str,
    callback: Callable[[Any, bool, Any], None],
    control: Optional[widgets.Widget] = None,
    reset=False,
    active=False,
):
    """A togglable, toolbar item.

    Args:
        icon (str): The icon name to use, from https://fonts.google.com/icons.
        tooltip: The tooltip text to show a user on hover.
        callback: A callback function to execute when the item icon is clicked.
            Its signature should be `callback(map, selected, item)`, where
            `map` is the host map, `selected` is a boolean indicating if the
            user selected or unselected the tool, and `item` is this object.
        control: The control widget associated with this item. Used to
            cleanup state when toggled off.
        reset: Whether to reset the selection after the callback has finished.
        active: Whether the tool is currently active.
    """
    super().__init__()
    self.icon = icon
    self.tooltip_text = tooltip
    self.callback = callback
    self.callback_wrapper = lambda *args: None
    self.control = control
    self.reset = reset
    self.active = active

add_crs(filename, epsg)

Add a CRS to a raster dataset.

Parameters:

Name	Type	Description	Default
filename	str	The filename of the raster dataset.	required
epsg	int | str	The EPSG code of the CRS.	required

Source code in geemap/common.py

def add_crs(filename, epsg):
    """Add a CRS to a raster dataset.

    Args:
        filename (str): The filename of the raster dataset.
        epsg (int | str): The EPSG code of the CRS.

    """
    try:
        import rasterio
    except ImportError:
        raise ImportError(
            "rasterio is required for adding a CRS to a raster. Please install it using 'pip install rasterio'."
        )

    if not os.path.exists(filename):
        raise ValueError("filename must exist.")

    if isinstance(epsg, int):
        epsg = f"EPSG:{epsg}"
    elif isinstance(epsg, str):
        epsg = "EPSG:" + epsg
    else:
        raise ValueError("epsg must be an integer or string.")

    crs = rasterio.crs.CRS({"init": epsg})
    with rasterio.open(filename, mode="r+") as src:
        src.crs = crs

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 geemap/timelapse.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
    import warnings

    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.LANCZOS)

    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_overlay(collection, overlay_data, color='black', width=1, opacity=1.0, region=None)

Adds an overlay to an image collection.

Parameters:

Name	Type	Description	Default
collection	ImageCollection	The image collection to add the overlay to.	required
overlay_data	str | Geometry | FeatureCollection	The overlay data to add to the image collection. It can be an HTTP URL to a GeoJSON file.	required
color	str	The color of the overlay. Defaults to 'black'.	'black'
width	int	The width of the overlay. Defaults to 1.	1
opacity	float	The opacity of the overlay. Defaults to 1.0.	1.0
region	Geometry | FeatureCollection	The region of interest to add the overlay to. Defaults to None.	None

Returns:

Type	Description
ImageCollection	ee.ImageCollection: An ImageCollection with the overlay added.

Source code in geemap/timelapse.py

def add_overlay(
    collection: ee.ImageCollection,
    overlay_data: Union[str, ee.Geometry, ee.FeatureCollection],
    color: str = "black",
    width: int = 1,
    opacity: float = 1.0,
    region: Union[ee.Geometry, ee.FeatureCollection] = None,
) -> ee.ImageCollection:
    """Adds an overlay to an image collection.

    Args:
        collection (ee.ImageCollection): The image collection to add the overlay to.
        overlay_data (str | ee.Geometry | ee.FeatureCollection): The overlay data to add to the image collection. It can be an HTTP URL to a GeoJSON file.
        color (str, optional): The color of the overlay. Defaults to 'black'.
        width (int, optional): The width of the overlay. Defaults to 1.
        opacity (float, optional): The opacity of the overlay. Defaults to 1.0.
        region (ee.Geometry | ee.FeatureCollection, optional): The region of interest to add the overlay to. Defaults to None.

    Returns:
        ee.ImageCollection: An ImageCollection with the overlay added.
    """

    # Some common administrative boundaries.
    public_assets = ["continents", "countries", "us_states", "china"]

    if not isinstance(collection, ee.ImageCollection):
        raise Exception("The collection must be an ee.ImageCollection.")

    if not isinstance(overlay_data, ee.FeatureCollection):
        if isinstance(overlay_data, str):
            try:
                if overlay_data.lower() in public_assets:
                    overlay_data = ee.FeatureCollection(
                        f"users/giswqs/public/{overlay_data.lower()}"
                    )
                elif overlay_data.startswith("http") and overlay_data.endswith(
                    ".geojson"
                ):
                    overlay_data = geojson_to_ee(overlay_data)
                else:
                    overlay_data = ee.FeatureCollection(overlay_data)

            except Exception as e:
                print(
                    "The overlay_data must be a valid ee.FeatureCollection, a valid ee.FeatureCollection asset id, or http url to a geojson file."
                )
                raise Exception(e)
        elif isinstance(overlay_data, ee.Feature):
            overlay_data = ee.FeatureCollection([overlay_data])
        elif isinstance(overlay_data, ee.Geometry):
            overlay_data = ee.FeatureCollection([ee.Feature(overlay_data)])
        else:
            raise Exception(
                "The overlay_data must be a valid ee.FeatureCollection or a valid ee.FeatureCollection asset id."
            )

    try:
        if region is not None:
            overlay_data = overlay_data.filterBounds(region)

        empty = ee.Image().byte()
        image = empty.paint(
            **{
                "featureCollection": overlay_data,
                "color": 1,
                "width": width,
            }
        ).visualize(**{"palette": check_color(color), "opacity": opacity})
        blend_col = collection.map(
            lambda img: img.blend(image).set(
                "system:time_start", img.get("system:time_start")
            )
        )
        return blend_col
    except Exception as e:
        print("Error in add_overlay:")
        raise Exception(e)

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 geemap/timelapse.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
    import warnings

    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 geemap/timelapse.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 warnings

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

    warnings.simplefilter("ignore")

    pkg_dir = str(importlib.resources.files("geemap").joinpath("geemap.py").parent)
    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 geemap/common.py

def adjust_longitude(in_fc):
    """Adjusts longitude if it is less than -180 or greater than 180.

    Args:
        in_fc (dict): The input dictionary containing coordinates.

    Returns:
        dict: A dictionary containing the converted longitudes
    """
    try:
        keys = in_fc.keys()

        if "geometry" in keys:
            coordinates = in_fc["geometry"]["coordinates"]

            if in_fc["geometry"]["type"] == "Point":
                longitude = coordinates[0]
                if longitude < -180:
                    longitude = 360 + longitude
                elif longitude > 180:
                    longitude = longitude - 360
                in_fc["geometry"]["coordinates"][0] = longitude

            elif in_fc["geometry"]["type"] == "Polygon":
                for index1, item in enumerate(coordinates):
                    for index2, element in enumerate(item):
                        longitude = element[0]
                        if longitude < -180:
                            longitude = 360 + longitude
                        elif longitude > 180:
                            longitude = longitude - 360
                        in_fc["geometry"]["coordinates"][index1][index2][0] = longitude

            elif in_fc["geometry"]["type"] == "LineString":
                for index, element in enumerate(coordinates):
                    longitude = element[0]
                    if longitude < -180:
                        longitude = 360 + longitude
                    elif longitude > 180:
                        longitude = longitude - 360
                    in_fc["geometry"]["coordinates"][index][0] = longitude

        elif "type" in keys:
            coordinates = in_fc["coordinates"]

            if in_fc["type"] == "Point":
                longitude = coordinates[0]
                if longitude < -180:
                    longitude = 360 + longitude
                elif longitude > 180:
                    longitude = longitude - 360
                in_fc["coordinates"][0] = longitude

            elif in_fc["type"] == "Polygon":
                for index1, item in enumerate(coordinates):
                    for index2, element in enumerate(item):
                        longitude = element[0]
                        if longitude < -180:
                            longitude = 360 + longitude
                        elif longitude > 180:
                            longitude = longitude - 360
                        in_fc["coordinates"][index1][index2][0] = longitude

            elif in_fc["type"] == "LineString":
                for index, element in enumerate(coordinates):
                    longitude = element[0]
                    if longitude < -180:
                        longitude = 360 + longitude
                    elif longitude > 180:
                        longitude = longitude - 360
                    in_fc["coordinates"][index][0] = longitude

        return in_fc

    except Exception as e:
        print(e)
        return None

annual_NAIP(year, region)

Create an NAIP mosaic of a specified year for a specified region.

Parameters:

Name	Type	Description	Default
year	int	The specified year to create the mosaic for.	required
region	object	ee.Geometry	required

Returns:

Name	Type	Description
object		ee.Image

Source code in geemap/common.py

def annual_NAIP(year, region):
    """Create an NAIP mosaic of a specified year for a specified region.

    Args:
        year (int): The specified year to create the mosaic for.
        region (object): ee.Geometry

    Returns:
        object: ee.Image
    """

    start_date = ee.Date.fromYMD(year, 1, 1)
    end_date = ee.Date.fromYMD(year, 12, 31)
    collection = (
        ee.ImageCollection("USDA/NAIP/DOQQ")
        .filterDate(start_date, end_date)
        .filterBounds(region)
    )

    time_start = ee.Date(
        ee.List(collection.aggregate_array("system:time_start")).sort().get(0)
    )
    time_end = ee.Date(
        ee.List(collection.aggregate_array("system:time_end")).sort().get(-1)
    )
    image = ee.Image(collection.mosaic().clip(region))
    NDWI = ee.Image(image).normalizedDifference(["G", "N"]).select(["nd"], ["ndwi"])
    NDVI = ee.Image(image).normalizedDifference(["N", "R"]).select(["nd"], ["ndvi"])
    image = image.addBands(NDWI)
    image = image.addBands(NDVI)
    return image.set({"system:time_start": time_start, "system:time_end": time_end})

api_docs()

Open a browser and navigate to the geemap API documentation.

Source code in geemap/common.py

def api_docs():
    """Open a browser and navigate to the geemap API documentation."""
    import webbrowser

    url = "https://geemap.org/geemap"
    webbrowser.open_new_tab(url)

arc_active_map()

Get the active map in ArcGIS Pro.

Returns:

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

Source code in geemap/common.py

def arc_active_map():
    """Get the active map in ArcGIS Pro.

    Returns:
        arcpy.Map: The active map in ArcGIS Pro.
    """
    if is_arcpy():
        import arcpy

        aprx = arcpy.mp.ArcGISProject("CURRENT")
        m = aprx.activeMap
        return m
    else:
        return None

arc_active_view()

Get the active view in ArcGIS Pro.

Returns:

Type	Description
	arcpy.MapView: The active view in ArcGIS Pro.

Source code in geemap/common.py

def arc_active_view():
    """Get the active view in ArcGIS Pro.

    Returns:
        arcpy.MapView: The active view in ArcGIS Pro.
    """
    if is_arcpy():
        import arcpy

        aprx = arcpy.mp.ArcGISProject("CURRENT")
        view = aprx.activeView
        return view
    else:
        return None

arc_add_layer(url, name=None, shown=True, opacity=1.0)

Add a layer to the active map in ArcGIS Pro.

Parameters:

Name	Type	Description	Default
url	str	The URL of the tile layer to add.	required
name	str	The name of the layer. Defaults to None.	None
shown	bool	Whether the layer is shown. Defaults to True.	True
opacity	float	The opacity of the layer. Defaults to 1.0.	1.0

Source code in geemap/common.py

def arc_add_layer(url, name=None, shown=True, opacity=1.0):
    """Add a layer to the active map in ArcGIS Pro.

    Args:
        url (str): The URL of the tile layer to add.
        name (str, optional): The name of the layer. Defaults to None.
        shown (bool, optional): Whether the layer is shown. Defaults to True.
        opacity (float, optional): The opacity of the layer. Defaults to 1.0.
    """
    if is_arcpy():
        m = arc_active_map()
        if m is not None:
            m.addDataFromPath(url)
            if isinstance(name, str):
                layers = m.listLayers("Tiled service layer")
                if len(layers) > 0:
                    layer = layers[0]
                    layer.name = name
                    layer.visible = shown
                    layer.transparency = 100 - (opacity * 100)

arc_zoom_to_extent(xmin, ymin, xmax, ymax)

Zoom to an extent in ArcGIS Pro.

Parameters:

Name	Type	Description	Default
xmin	float	The minimum x value of the extent.	required
ymin	float	The minimum y value of the extent.	required
xmax	float	The maximum x value of the extent.	required
ymax	float	The maximum y value of the extent.	required

Source code in geemap/common.py

def arc_zoom_to_extent(xmin, ymin, xmax, ymax):
    """Zoom to an extent in ArcGIS Pro.

    Args:
        xmin (float): The minimum x value of the extent.
        ymin (float): The minimum y value of the extent.
        xmax (float): The maximum x value of the extent.
        ymax (float): The maximum y value of the extent.
    """
    if is_arcpy():
        import arcpy

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

array_mean(arr)

Calculates the mean of an array along the given axis.

Parameters:

Name	Type	Description	Default
arr	object	Array to calculate mean.	required

Returns:

Name	Type	Description
object		ee.Number

Source code in geemap/common.py

def array_mean(arr):
    """Calculates the mean of an array along the given axis.

    Args:
        arr (object): Array to calculate mean.

    Returns:
        object: ee.Number
    """
    total = ee.Array(arr).accum(0).get([-1])
    size = arr.length()
    return ee.Number(total.divide(size))

array_sum(arr)

Accumulates elements of an array along the given axis.

Parameters:

Name	Type	Description	Default
arr	object	Array to accumulate.	required

Returns:

Name	Type	Description
object		ee.Number

Source code in geemap/common.py

def array_sum(arr):
    """Accumulates elements of an array along the given axis.

    Args:
        arr (object): Array to accumulate.

    Returns:
        object: ee.Number
    """
    return ee.Array(arr).accum(0).get([-1])

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

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

Parameters:

Name	Type	Description	Default
array	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
driver	str	The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG".	'COG'
**kwargs		Additional keyword arguments to be passed to the rasterio.open() function.	{}

Source code in geemap/common.py

def array_to_image(
    array,
    output: str = None,
    source: str = None,
    dtype: str = None,
    compress: str = "deflate",
    transpose: bool = True,
    cellsize: float = None,
    crs: str = None,
    driver: str = "COG",
    **kwargs,
) -> str:
    """Save a NumPy array as a GeoTIFF using the projection information from an existing GeoTIFF file.

    Args:
        array (np.ndarray): The NumPy array to be saved as a GeoTIFF.
        output (str): The path to the output image. If None, a temporary file will be created. Defaults to None.
        source (str, optional): The path to an existing GeoTIFF file with map projection information. Defaults to None.
        dtype (np.dtype, optional): The data type of the output array. Defaults to None.
        compress (str, optional): The compression method. Can be one of the following: "deflate", "lzw", "packbits", "jpeg". Defaults to "deflate".
        transpose (bool, optional): Whether to transpose the array from (bands, rows, columns) to (rows, columns, bands). Defaults to True.
        cellsize (float, optional): The resolution of the output image in meters. Defaults to None.
        crs (str, optional): The CRS of the output image. Defaults to None.
        driver (str, optional): The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG".
        **kwargs: Additional keyword arguments to be passed to the rasterio.open() function.
    """

    import numpy as np
    import rasterio
    import xarray as xr

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

    if isinstance(array, xr.DataArray):
        coords = [coord for coord in array.coords]
        if coords[0] == "time":
            x_dim = coords[1]
            y_dim = coords[2]
            if array.dims[0] == "time":
                array = array.isel(time=0)

            array = array.rename({y_dim: "y", x_dim: "x"}).transpose("y", "x")
        array = array.values

    if array.ndim == 3 and transpose:
        array = np.transpose(array, (1, 2, 0))

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

    if not output.endswith(".tif"):
        output += ".tif"

    if source is not None:
        with rasterio.open(source) as src:
            crs = src.crs
            transform = src.transform
            if compress is None:
                compress = src.compression
    else:
        if cellsize is None:
            raise ValueError("resolution must be provided if source is not provided")
        if crs is None:
            raise ValueError(
                "crs must be provided if source is not provided, such as EPSG:3857"
            )

        if "transform" not in kwargs:
            # Define the geotransformation parameters
            xmin, ymin, xmax, ymax = (
                0,
                0,
                cellsize * array.shape[1],
                cellsize * array.shape[0],
            )
            transform = rasterio.transform.from_bounds(
                xmin, ymin, xmax, ymax, array.shape[1], array.shape[0]
            )
        else:
            transform = kwargs["transform"]

    if dtype is None:
        # Determine the minimum and maximum values in the array
        min_value = np.min(array)
        max_value = np.max(array)
        # Determine the best dtype for the array
        if min_value >= 0 and max_value <= 1:
            dtype = np.float32
        elif min_value >= 0 and max_value <= 255:
            dtype = np.uint8
        elif min_value >= -128 and max_value <= 127:
            dtype = np.int8
        elif min_value >= 0 and max_value <= 65535:
            dtype = np.uint16
        elif min_value >= -32768 and max_value <= 32767:
            dtype = np.int16
        else:
            dtype = np.float64

    # Convert the array to the best dtype
    array = array.astype(dtype)

    # Define the GeoTIFF metadata
    metadata = {
        "driver": driver,
        "height": array.shape[0],
        "width": array.shape[1],
        "dtype": array.dtype,
        "crs": crs,
        "transform": transform,
    }

    if array.ndim == 2:
        metadata["count"] = 1
    elif array.ndim == 3:
        metadata["count"] = array.shape[2]
    if compress is not None:
        metadata["compress"] = compress

    metadata.update(**kwargs)

    # Create a new GeoTIFF file and write the array to it
    with rasterio.open(output, "w", **metadata) as dst:
        if array.ndim == 2:
            dst.write(array, 1)
        elif array.ndim == 3:
            for i in range(array.shape[2]):
                dst.write(array[:, :, i], i + 1)

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

Convert a NumPy array to a memory file.

Parameters:

Name	Type	Description	Default
array	ndarray	The input NumPy array.	required
source	str	Path to the source file to extract metadata from. Defaults to None.	None
dtype	str	The desired data type of the array. Defaults to None.	None
compress	str	The compression method for the output file. Defaults to "deflate".	'deflate'
transpose	bool	Whether to transpose the array from (bands, rows, columns) to (rows, columns, bands). Defaults to True.	True
cellsize	float	The cell size of the array if source is not provided. Defaults to None.	None
crs	str	The coordinate reference system of the array if source is not provided. Defaults to None.	None
transform	tuple	The affine transformation matrix if source is not provided. Defaults to None.	None
driver	str	The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG".	'COG'
**kwargs		Additional keyword arguments to be passed to the rasterio.open() function.	{}

Returns:

Type	Description
	rasterio.DatasetReader: The rasterio dataset reader object for the converted array.

Source code in geemap/common.py

def array_to_memory_file(
    array,
    source: str = None,
    dtype: str = None,
    compress: str = "deflate",
    transpose: bool = True,
    cellsize: float = None,
    crs: str = None,
    transform: tuple = None,
    driver="COG",
    **kwargs,
):
    """Convert a NumPy array to a memory file.

    Args:
        array (numpy.ndarray): The input NumPy array.
        source (str, optional): Path to the source file to extract metadata from. Defaults to None.
        dtype (str, optional): The desired data type of the array. Defaults to None.
        compress (str, optional): The compression method for the output file. Defaults to "deflate".
        transpose (bool, optional): Whether to transpose the array from (bands, rows, columns) to (rows, columns, bands). Defaults to True.
        cellsize (float, optional): The cell size of the array if source is not provided. Defaults to None.
        crs (str, optional): The coordinate reference system of the array if source is not provided. Defaults to None.
        transform (tuple, optional): The affine transformation matrix if source is not provided. Defaults to None.
        driver (str, optional): The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG".
        **kwargs: Additional keyword arguments to be passed to the rasterio.open() function.

    Returns:
        rasterio.DatasetReader: The rasterio dataset reader object for the converted array.
    """
    import rasterio
    import numpy as np
    import xarray as xr

    if isinstance(array, xr.DataArray):
        coords = [coord for coord in array.coords]
        if coords[0] == "time":
            x_dim = coords[1]
            y_dim = coords[2]
            if array.dims[0] == "time":
                array = array.isel(time=0)

            array = array.rename({y_dim: "y", x_dim: "x"}).transpose("y", "x")
        array = array.values

    if array.ndim == 3 and transpose:
        array = np.transpose(array, (1, 2, 0))

    if source is not None:
        with rasterio.open(source) as src:
            crs = src.crs
            transform = src.transform
            if compress is None:
                compress = src.compression
    else:
        if cellsize is None:
            raise ValueError("cellsize must be provided if source is not provided")
        if crs is None:
            raise ValueError(
                "crs must be provided if source is not provided, such as EPSG:3857"
            )

        if "transform" not in kwargs:
            # Define the geotransformation parameters
            xmin, ymin, xmax, ymax = (
                0,
                0,
                cellsize * array.shape[1],
                cellsize * array.shape[0],
            )
            # (west, south, east, north, width, height)
            transform = rasterio.transform.from_bounds(
                xmin, ymin, xmax, ymax, array.shape[1], array.shape[0]
            )
        else:
            transform = kwargs["transform"]

    if dtype is None:
        # Determine the minimum and maximum values in the array
        min_value = np.min(array)
        max_value = np.max(array)
        # Determine the best dtype for the array
        if min_value >= 0 and max_value <= 1:
            dtype = np.float32
        elif min_value >= 0 and max_value <= 255:
            dtype = np.uint8
        elif min_value >= -128 and max_value <= 127:
            dtype = np.int8
        elif min_value >= 0 and max_value <= 65535:
            dtype = np.uint16
        elif min_value >= -32768 and max_value <= 32767:
            dtype = np.int16
        else:
            dtype = np.float64

    # Convert the array to the best dtype
    array = array.astype(dtype)

    # Define the GeoTIFF metadata
    metadata = {
        "driver": driver,
        "height": array.shape[0],
        "width": array.shape[1],
        "dtype": array.dtype,
        "crs": crs,
        "transform": transform,
    }

    if array.ndim == 2:
        metadata["count"] = 1
    elif array.ndim == 3:
        metadata["count"] = array.shape[2]
    if compress is not None:
        metadata["compress"] = compress

    metadata.update(**kwargs)

    # Create a new memory file and write the array to it
    memory_file = rasterio.MemoryFile()
    dst = memory_file.open(**metadata)

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

    dst.close()

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

    return dataset_reader

bands_to_image_collection(img)

Converts all bands in an image to an image collection.

Parameters:

Name	Type	Description	Default
img	object	The image to convert.	required

Returns:

Name	Type	Description
object		ee.ImageCollection

Source code in geemap/common.py

def bands_to_image_collection(img):
    """Converts all bands in an image to an image collection.

    Args:
        img (object): The image to convert.

    Returns:
        object: ee.ImageCollection
    """
    collection = ee.ImageCollection(img.bandNames().map(lambda b: img.select([b])))
    return collection

bbox_coords(geometry, decimals=4)

Get the bounding box coordinates of a geometry.

Parameters:

Name	Type	Description	Default
geometry	Geometry | FeatureCollection	The input geometry.	required
decimals	int	The number of decimals to round to. Defaults to 4.	4

Returns:

Name	Type	Description
list		The bounding box coordinates in the form [west, south, east, north].

Source code in geemap/common.py

def bbox_coords(geometry, decimals=4):
    """Get the bounding box coordinates of a geometry.

    Args:
        geometry (ee.Geometry | ee.FeatureCollection): The input geometry.
        decimals (int, optional): The number of decimals to round to. Defaults to 4.

    Returns:
        list: The bounding box coordinates in the form [west, south, east, north].
    """
    if isinstance(geometry, ee.FeatureCollection):
        geometry = geometry.geometry()

    if geometry is not None:
        if not isinstance(geometry, ee.Geometry):
            raise ValueError("geometry must be an ee.Geometry.")

        coords = geometry.bounds().coordinates().getInfo()[0]
        x = [p[0] for p in coords]
        y = [p[1] for p in coords]
        west = round(min(x), decimals)
        east = round(max(x), decimals)
        south = round(min(y), decimals)
        north = round(max(y), decimals)
        return [west, south, east, north]
    else:
        return None

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

Converts a bounding box to a GeoDataFrame.

Parameters:

Name	Type	Description	Default
bbox	tuple	A bounding box in the form of a tuple (minx, miny, maxx, maxy).	required
crs	str	The coordinate reference system of the bounding box to convert to. Defaults to "EPSG:4326".	'EPSG:4326'

Returns:

Type	Description
	geopandas.GeoDataFrame: A GeoDataFrame containing the bounding box.

Source code in geemap/common.py

def bbox_to_gdf(bbox, crs="EPSG:4326"):
    """Converts a bounding box to a GeoDataFrame.

    Args:
        bbox (tuple): A bounding box in the form of a tuple (minx, miny, maxx, maxy).
        crs (str, optional): The coordinate reference system of the bounding box to convert to. Defaults to "EPSG:4326".

    Returns:
        geopandas.GeoDataFrame: A GeoDataFrame containing the bounding box.
    """
    check_package(name="geopandas", URL="https://geopandas.org")
    from shapely.geometry import box
    import geopandas as gpd

    minx, miny, maxx, maxy = bbox
    geometry = box(minx, miny, maxx, maxy)
    d = {"geometry": [geometry]}
    gdf = gpd.GeoDataFrame(d, crs="EPSG:4326")
    gdf.to_crs(crs=crs, inplace=True)
    return gdf

bbox_to_geojson(bounds)

Convert coordinates of a bounding box to a geojson.

Parameters:

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

Returns:

Name	Type	Description
dict		A geojson feature.

Source code in geemap/common.py

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

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

    Returns:
        dict: A geojson feature.
    """
    return {
        "geometry": {
            "type": "Polygon",
            "coordinates": [
                [
                    [bounds[0], bounds[3]],
                    [bounds[0], bounds[1]],
                    [bounds[2], bounds[1]],
                    [bounds[2], bounds[3]],
                    [bounds[0], bounds[3]],
                ]
            ],
        },
        "type": "Feature",
    }

blend(top_layer, bottom_layer=None, top_vis=None, bottom_vis=None, hillshade=True, expression='a*b', **kwargs)

Create a blended image that is a combination of two images, e.g., DEM and hillshade. This function was inspired by Jesse Anderson. See https://github.com/jessjaco/gee-blend.

Parameters:

Name	Type	Description	Default
top_layer	Image	The top layer image, e.g., ee.Image("CGIAR/SRTM90_V4")	required
bottom_layer	Image	The bottom layer image. If not specified, it will use the top layer image.	None
top_vis	dict	The top layer image vis parameters as a dictionary. Defaults to None.	None
bottom_vis	dict	The bottom layer image vis parameters as a dictionary. Defaults to None.	None
hillshade	bool	Flag to use hillshade. Defaults to True.	True
expression	str	The expression to use for the blend. Defaults to 'a*b'.	'a*b'

Returns:

Type	Description
	ee.Image: The blended image.

Source code in geemap/common.py

def blend(
    top_layer,
    bottom_layer=None,
    top_vis=None,
    bottom_vis=None,
    hillshade=True,
    expression="a*b",
    **kwargs,
):
    """Create a blended image that is a combination of two images, e.g., DEM and hillshade. This function was inspired by Jesse Anderson. See https://github.com/jessjaco/gee-blend.

    Args:
        top_layer (ee.Image): The top layer image, e.g., ee.Image("CGIAR/SRTM90_V4")
        bottom_layer (ee.Image, optional): The bottom layer image. If not specified, it will use the top layer image.
        top_vis (dict, optional): The top layer image vis parameters as a dictionary. Defaults to None.
        bottom_vis (dict, optional): The bottom layer image vis parameters as a dictionary. Defaults to None.
        hillshade (bool, optional): Flag to use hillshade. Defaults to True.
        expression (str, optional): The expression to use for the blend. Defaults to 'a*b'.

    Returns:
        ee.Image: The blended image.
    """
    from box import Box

    if not isinstance(top_layer, ee.Image):
        raise ValueError("top_layer must be an ee.Image.")

    if bottom_layer is None:
        bottom_layer = top_layer

    if not isinstance(bottom_layer, ee.Image):
        raise ValueError("bottom_layer must be an ee.Image.")

    if top_vis is not None:
        if not isinstance(top_vis, dict):
            raise ValueError("top_vis must be a dictionary.")
        elif "palette" in top_vis and isinstance(top_vis["palette"], Box):
            try:
                top_vis["palette"] = top_vis["palette"]["default"]
            except Exception as e:
                print("The provided palette is invalid.")
                raise Exception(e)

    if bottom_vis is not None:
        if not isinstance(bottom_vis, dict):
            raise ValueError("top_vis must be a dictionary.")
        elif "palette" in bottom_vis and isinstance(bottom_vis["palette"], Box):
            try:
                bottom_vis["palette"] = bottom_vis["palette"]["default"]
            except Exception as e:
                print("The provided palette is invalid.")
                raise Exception(e)

    if top_vis is None:
        top_bands = top_layer.bandNames().getInfo()
        top_vis = {"bands": top_bands}
        if hillshade:
            top_vis["palette"] = ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"]
            top_vis["min"] = 0
            top_vis["max"] = 6000

    if bottom_vis is None:
        bottom_bands = bottom_layer.bandNames().getInfo()
        bottom_vis = {"bands": bottom_bands}
        if hillshade:
            bottom_vis["bands"] = ["hillshade"]

    top = top_layer.visualize(**top_vis).divide(255)

    if hillshade:
        bottom = ee.Terrain.hillshade(bottom_layer).visualize(**bottom_vis).divide(255)
    else:
        bottom = bottom_layer.visualize(**bottom_vis).divide(255)

    if "a" not in expression or ("b" not in expression):
        raise ValueError("expression must contain 'a' and 'b'.")

    result = ee.Image().expression(expression, {"a": top, "b": bottom})
    return result

bounds_to_xy_range(bounds)

Convert bounds to x and y range to be used as input to bokeh map.

Parameters:

Name	Type	Description	Default
bounds	list	A list of bounds in the form [(south, west), (north, east)] or [xmin, ymin, xmax, ymax].	required

Returns:

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

Source code in geemap/common.py

def bounds_to_xy_range(bounds):
    """Convert bounds to x and y range to be used as input to bokeh map.

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

    Returns:
        tuple: A tuple of (x_range, y_range).
    """

    if isinstance(bounds, tuple):
        bounds = list(bounds)
    elif not isinstance(bounds, list):
        raise TypeError("bounds must be a list")

    if len(bounds) == 4:
        west, south, east, north = bounds
    elif len(bounds) == 2:
        south, west = bounds[0]
        north, east = bounds[1]

    xmin, ymin = lnglat_to_meters(west, south)
    xmax, ymax = lnglat_to_meters(east, north)
    x_range = (xmin, xmax)
    y_range = (ymin, ymax)
    return x_range, y_range

build_api_tree(api_dict, output_widget, layout_width='100%')

Builds an Earth Engine API tree view.

Parameters:

Name	Type	Description	Default
api_dict	dict	The dictionary containing information about each Earth Engine API function.	required
output_widget	object	An Output widget.	required
layout_width	str	The percentage width of the widget. Defaults to '100%'.	'100%'

Returns:

Name	Type	Description
tuple		Returns a tuple containing two items: a tree Output widget and a tree dictionary.

Source code in geemap/common.py

def build_api_tree(api_dict, output_widget, layout_width="100%"):
    """Builds an Earth Engine API tree view.

    Args:
        api_dict (dict): The dictionary containing information about each Earth Engine API function.
        output_widget (object): An Output widget.
        layout_width (str, optional): The percentage width of the widget. Defaults to '100%'.

    Returns:
        tuple: Returns a tuple containing two items: a tree Output widget and a tree dictionary.
    """

    warnings.filterwarnings("ignore")

    tree = Tree()
    tree_dict = {}

    names = api_dict.keys()

    def handle_click(event):
        if event["new"]:
            name = event["owner"].name
            values = api_dict[name]

            with output_widget:
                output_widget.outputs = ()
                html_widget = widgets.HTML(value=values["html"])
                display(html_widget)

    for name in names:
        func_list = ee_function_tree(name)
        first = func_list[0]

        if first not in tree_dict.keys():
            tree_dict[first] = Node(first)
            tree_dict[first].opened = False
            tree.add_node(tree_dict[first])

        for index, func in enumerate(func_list):
            if index > 0:
                if func not in tree_dict.keys():
                    node = tree_dict[func_list[index - 1]]
                    node.opened = False
                    tree_dict[func] = Node(func)
                    node.add_node(tree_dict[func])

                    if index == len(func_list) - 1:
                        node = tree_dict[func_list[index]]
                        node.icon = "file"
                        node.observe(handle_click, "selected")

    return tree, tree_dict

build_computed_object_tree(ee_object, layer_name='', opened=False)

Return a tree structure representing an EE object.

The source code was adapted from https://github.com/google/earthengine-jupyter. Credits to Tyler Erickson.

Parameters:

Name	Type	Description	Default
ee_object	Union[FeatureCollection, Image, Geometry, Feature]	The Earth Engine object.	required
layer_name	str	The name of the layer. Defaults to "".	''
opened	bool	Whether to expand the tree. Defaults to False.	False

Returns:

Type	Description
dict[str, Any]	dict[str, Any]: The node representing the Earth Engine object information.

Source code in geemap/coreutils.py

def build_computed_object_tree(
    ee_object: Union[ee.FeatureCollection, ee.Image, ee.Geometry, ee.Feature],
    layer_name: str = "",
    opened: bool = False,
) -> dict[str, Any]:
    """Return a tree structure representing an EE object.

    The source code was adapted from https://github.com/google/earthengine-jupyter.
    Credits to Tyler Erickson.

    Args:
        ee_object (Union[ee.FeatureCollection, ee.Image, ee.Geometry, ee.Feature]):
            The Earth Engine object.
        layer_name (str, optional): The name of the layer. Defaults to "".
        opened (bool, optional): Whether to expand the tree. Defaults to False.

    Returns:
        dict[str, Any]: The node representing the Earth Engine object information.
    """

    # Convert EE object props to dicts. It's easier to traverse the nested structure.
    if isinstance(ee_object, ee.FeatureCollection):
        ee_object = ee_object.map(lambda f: ee.Feature(None, f.toDictionary()))

    layer_info = ee_object.getInfo()
    if not layer_info:
        return {}

    # Strip geometries because they're slow to render as text.
    if "geometry" in layer_info:
        layer_info.pop("geometry")

    # Sort the keys in layer_info and the nested properties.
    if properties := layer_info.get("properties"):
        layer_info["properties"] = dict(sorted(properties.items()))
    ordering_list = ["type", "id", "version", "bands", "properties"]
    layer_info = _order_items(layer_info, ordering_list)

    ee_type = layer_info.get("type", ee_object.__class__.__name__)

    band_info = ""
    if bands := layer_info.get("bands"):
        band_info = f" ({len(bands)} bands)"
    if layer_name:
        layer_name = f"{layer_name}: "

    return new_tree_node(
        f"{layer_name}{ee_type}{band_info}",
        _generate_tree(layer_info, opened),
        expanded=opened,
    )

build_repo_tree(out_dir=None, name='gee_repos')

Builds a repo tree for GEE account.

Parameters:

Name	Type	Description	Default
out_dir	str	The output directory for the repos. Defaults to None.	None
name	str	The output name for the repo directory. Defaults to 'gee_repos'.	'gee_repos'

Returns:

Name	Type	Description
tuple		Returns a tuple containing a tree widget, an output widget, and a tree dictionary containing nodes.

Source code in geemap/common.py

def build_repo_tree(out_dir=None, name="gee_repos"):
    """Builds a repo tree for GEE account.

    Args:
        out_dir (str): The output directory for the repos. Defaults to None.
        name (str, optional): The output name for the repo directory. Defaults to 'gee_repos'.

    Returns:
        tuple: Returns a tuple containing a tree widget, an output widget, and a tree dictionary containing nodes.
    """

    warnings.filterwarnings("ignore")

    if out_dir is None:
        out_dir = os.path.join(os.path.expanduser("~"))

    repo_dir = os.path.join(out_dir, name)
    if not os.path.exists(repo_dir):
        os.makedirs(repo_dir)

    URLs = {
        # 'Owner': 'https://earthengine.googlesource.com/{ee_user_id()}/default',
        "Writer": "",
        "Reader": "https://github.com/gee-community/geemap",
        "Examples": "https://github.com/giswqs/earthengine-py-examples",
        "Archive": "https://earthengine.googlesource.com/EGU2017-EE101",
    }

    user_id = ee_user_id()
    if user_id is not None:
        URLs["Owner"] = f"https://earthengine.googlesource.com/{ee_user_id()}/default"

    path_widget = widgets.Text(placeholder="Enter the link to a Git repository here...")
    path_widget.layout.width = "475px"
    clone_widget = widgets.Button(
        description="Clone",
        button_style="primary",
        tooltip="Clone the repository to folder.",
    )
    info_widget = widgets.HBox()

    groups = ["Owner", "Writer", "Reader", "Examples", "Archive"]
    for group in groups:
        group_dir = os.path.join(repo_dir, group)
        if not os.path.exists(group_dir):
            os.makedirs(group_dir)

    example_dir = os.path.join(repo_dir, "Examples/earthengine-py-examples")
    if not os.path.exists(example_dir):
        clone_github_repo(URLs["Examples"], out_dir=example_dir)

    left_widget, right_widget, tree_dict = file_browser(
        in_dir=repo_dir,
        add_root_node=False,
        search_description="Filter scripts...",
        use_import=True,
        return_sep_widgets=True,
    )
    info_widget.children = [right_widget]

    def handle_folder_click(event):
        if event["new"]:
            url = ""
            selected = event["owner"]
            if selected.name in URLs.keys():
                url = URLs[selected.name]

            path_widget.value = url
            clone_widget.disabled = False
            info_widget.children = [path_widget, clone_widget]
        else:
            info_widget.children = [right_widget]

    for group in groups:
        dirname = os.path.join(repo_dir, group)
        node = tree_dict[dirname]
        node.observe(handle_folder_click, "selected")

    def handle_clone_click(b):
        url = path_widget.value
        default_dir = os.path.join(repo_dir, "Examples")
        if url == "":
            path_widget.value = "Please enter a valid URL to the repository."
        else:
            for group in groups:
                key = os.path.join(repo_dir, group)
                node = tree_dict[key]
                if node.selected:
                    default_dir = key
            try:
                path_widget.value = "Cloning..."
                clone_dir = os.path.join(default_dir, os.path.basename(url))
                if url.find("github.com") != -1:
                    clone_github_repo(url, out_dir=clone_dir)
                elif url.find("googlesource") != -1:
                    clone_google_repo(url, out_dir=clone_dir)
                path_widget.value = "Cloned to {}".format(clone_dir)
                clone_widget.disabled = True
            except Exception as e:
                path_widget.value = (
                    "An error occurred when trying to clone the repository " + str(e)
                )
                clone_widget.disabled = True

    clone_widget.on_click(handle_clone_click)

    return left_widget, info_widget, tree_dict

build_toolbox(tools_dict, max_width='1080px', max_height='600px')

Build the GEE toolbox.

Parameters:

Name	Type	Description	Default
tools_dict	dict	A dictionary containing information for all tools.	required
max_width	str	The maximum width of the widget.	'1080px'
max_height	str	The maximum height of the widget.	'600px'

Returns:

Name	Type	Description
object		An ipywidget representing the toolbox.

Source code in geemap/toolbar.py

def build_toolbox(tools_dict, max_width="1080px", max_height="600px"):
    """Build the GEE toolbox.

    Args:
        tools_dict (dict): A dictionary containing information for all tools.
        max_width (str, optional): The maximum width of the widget.
        max_height (str, optional): The maximum height of the widget.

    Returns:
        object: An ipywidget representing the toolbox.
    """
    left_widget = widgets.VBox(layout=widgets.Layout(min_width="175px"))
    center_widget = widgets.VBox(
        layout=widgets.Layout(min_width="200px", max_width="200px")
    )
    right_widget = widgets.Output(
        layout=widgets.Layout(width="630px", max_height=max_height)
    )
    full_widget = widgets.HBox(
        [left_widget, center_widget, right_widget],
        layout=widgets.Layout(max_width=max_width, max_height=max_height),
    )

    search_widget = widgets.Text(
        placeholder="Search tools ...", layout=widgets.Layout(width="170px")
    )
    label_widget = widgets.Label(layout=widgets.Layout(width="170px"))
    label_widget.value = f"{len(tools_dict)} Available Tools"
    close_btn = widgets.Button(
        description="Close Toolbox", icon="close", layout=widgets.Layout(width="170px")
    )

    categories = {}
    categories["All Tools"] = []
    for key in tools_dict.keys():
        category = tools_dict[key]["category"]
        if category not in categories.keys():
            categories[category] = []
        categories[category].append(tools_dict[key]["name"])
        categories["All Tools"].append(tools_dict[key]["name"])

    options = list(categories.keys())
    all_tools = categories["All Tools"]
    all_tools.sort()
    category_widget = widgets.Select(
        options=options, layout=widgets.Layout(width="170px", height="165px")
    )
    tools_widget = widgets.Select(
        options=[], layout=widgets.Layout(width="195px", height="400px")
    )

    def category_selected(change):
        if change["new"]:
            selected = change["owner"].value
            options = categories[selected]
            options.sort()
            tools_widget.options = options
            label_widget.value = f"{len(options)} Available Tools"

    category_widget.observe(category_selected, "value")

    def tool_selected(change):
        if change["new"]:
            selected = change["owner"].value
            tool_dict = tools_dict[selected]
            with right_widget:
                right_widget.outputs = ()
                display(tool_gui(tool_dict, max_height=max_height))

    tools_widget.observe(tool_selected, "value")

    def search_changed(change):
        if change["new"]:
            keyword = change["owner"].value
            if len(keyword) > 0:
                selected_tools = []
                for tool in all_tools:
                    if keyword.lower() in tool.lower():
                        selected_tools.append(tool)
                if len(selected_tools) > 0:
                    tools_widget.options = selected_tools
                label_widget.value = f"{len(selected_tools)} Available Tools"
        else:
            tools_widget.options = all_tools
            label_widget.value = f"{len(tools_dict)} Available Tools"

    search_widget.observe(search_changed, "value")

    def cleanup():
        full_widget.close()

    full_widget.cleanup = cleanup

    def close_btn_clicked(b):
        full_widget.cleanup()

    close_btn.on_click(close_btn_clicked)

    category_widget.value = list(categories.keys())[0]
    tools_widget.options = all_tools
    left_widget.children = [category_widget, search_widget, label_widget, close_btn]
    center_widget.children = [tools_widget]

    return full_widget

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 geemap/common.py

def center_zoom_to_xy_range(center, zoom):
    """Convert center and zoom to x and y range to be used as input to bokeh map.

    Args:
        center (tuple): A tuple of (latitude, longitude).
        zoom (int): The zoom level.

    Returns:
        tuple: A tuple of (x_range, y_range).
    """

    if isinstance(center, tuple) or isinstance(center, list):
        pass
    else:
        raise TypeError("center must be a tuple or list")

    if not isinstance(zoom, int):
        raise TypeError("zoom must be an integer")

    latitude, longitude = center
    x_range = (-179, 179)
    y_range = (-70, 70)
    x_full_length = x_range[1] - x_range[0]
    y_full_length = y_range[1] - y_range[0]

    x_length = x_full_length / 2 ** (zoom - 2)
    y_length = y_full_length / 2 ** (zoom - 2)

    south = latitude - y_length / 2
    north = latitude + y_length / 2
    west = longitude - x_length / 2
    east = longitude + x_length / 2

    xmin, ymin = lnglat_to_meters(west, south)
    xmax, ymax = lnglat_to_meters(east, north)

    x_range = (xmin, xmax)
    y_range = (ymin, ymax)

    return x_range, y_range

check_basemap(basemap)

Check Google basemaps

Parameters:

Name	Type	Description	Default
basemap	str	The basemap name.	required

Returns:

Name	Type	Description
str		The basemap name.

Source code in geemap/common.py

def check_basemap(basemap):
    """Check Google basemaps

    Args:
        basemap (str): The basemap name.

    Returns:
        str: The basemap name.
    """
    if isinstance(basemap, str):
        map_dict = {
            "ROADMAP": "Google Maps",
            "SATELLITE": "Google Satellite",
            "TERRAIN": "Google Terrain",
            "HYBRID": "Google Hybrid",
        }

        if basemap.upper() in map_dict.keys():
            return map_dict[basemap.upper()]
        else:
            return basemap
    else:
        return basemap

check_cmap(cmap)

Check the colormap and return a list of colors.

Parameters:

Name	Type	Description	Default
cmap	Union[str, List[str], Box]	The colormap to check.	required

Returns:

Type	Description
List[str]	List[str]: A list of colors.

Source code in geemap/coreutils.py

def check_cmap(cmap: Union[str, List[str]]) -> List[str]:
    """Check the colormap and return a list of colors.

    Args:
        cmap (Union[str, List[str], Box]): The colormap to check.

    Returns:
        List[str]: A list of colors.
    """

    from box import Box
    from .colormaps import get_palette

    if isinstance(cmap, str):
        try:
            palette = get_palette(cmap)
            if isinstance(palette, dict):
                palette = palette["default"]
            return palette
        except Exception as e:
            try:
                return check_color(cmap)
            except Exception as e:
                raise Exception(f"{cmap} is not a valid colormap.")
    elif isinstance(cmap, Box):
        return list(cmap["default"])
    elif isinstance(cmap, list) or isinstance(cmap, tuple):
        return cmap
    else:
        raise Exception(f"{cmap} is not a valid colormap.")

check_color(in_color)

Checks the input color and returns the corresponding hex color code.

Parameters:

Name	Type	Description	Default
in_color	str or tuple or list	It can be a string (e.g., 'red', '#ffff00', 'ffff00', 'ff0') or RGB tuple/list (e.g., (255, 127, 0)).	required

Returns:

Name	Type	Description
str	str	A hex color code.

Source code in geemap/coreutils.py

def check_color(in_color: Union[str, Tuple, List]) -> 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/list (e.g., (255, 127, 0)).

    Returns:
        str: A hex color code.
    """
    from matplotlib import colors

    out_color = "#000000"  # default black color
    # Handle RGB tuple or list
    if isinstance(in_color, (tuple, list)) and len(in_color) == 3:
        # rescale color if necessary
        if all(isinstance(item, int) for item in in_color):
            # Ensure values are floats between 0 and 1 for to_hex
            in_color = [c / 255.0 for c in in_color]
        try:
            return colors.to_hex(in_color)
        except ValueError:
            print(
                f"The provided RGB color ({in_color}) is invalid. Using the default black color."
            )
            return out_color

    # Handle string color input
    elif isinstance(in_color, str):
        try:
            # Try converting directly (handles color names and hex with #)
            return colors.to_hex(in_color)
        except ValueError:
            try:
                # Try again by adding an extra # (handles hex without #)
                return colors.to_hex(f"#{in_color}")
            except ValueError:
                print(
                    f"The provided color string ({in_color}) is invalid. Using the default black color."
                )
                return out_color
    else:
        print(
            f"The provided color type ({type(in_color)}) is invalid. Using the default black color."
        )
        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 geemap/common.py

def check_dir(dir_path, make_dirs=True):
    """Checks if a directory exists and creates it if it does not.

    Args:
        dir_path ([str): The path to the directory.
        make_dirs (bool, optional): Whether to create the directory if it does not exist. Defaults to True.

    Raises:
        FileNotFoundError: If the directory could not be found.
        TypeError: If the input directory path is not a string.

    Returns:
        str: The path to the directory.
    """

    if isinstance(dir_path, str):
        if dir_path.startswith("~"):
            dir_path = os.path.expanduser(dir_path)
        else:
            dir_path = os.path.abspath(dir_path)

        if not os.path.exists(dir_path) and make_dirs:
            os.makedirs(dir_path)

        if os.path.exists(dir_path):
            return dir_path
        else:
            raise FileNotFoundError("The provided directory could not be found.")
    else:
        raise TypeError("The provided directory path must be a string.")

check_file_path(file_path, make_dirs=True)

Gets the absolute file path.

Parameters:

Name	Type	Description	Default
file_path	[str	The path to the file.	required
make_dirs	bool	Whether to create the directory if it does not exist. Defaults to True.	True

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 geemap/common.py

def check_file_path(file_path, make_dirs=True):
    """Gets the absolute file path.

    Args:
        file_path ([str): The path to the file.
        make_dirs (bool, optional): Whether to create the directory if it does not exist. Defaults to True.

    Raises:
        FileNotFoundError: If the directory could not be found.
        TypeError: If the input directory path is not a string.

    Returns:
        str: The absolute path to the file.
    """
    if isinstance(file_path, str):
        if file_path.startswith("~"):
            file_path = os.path.expanduser(file_path)
        else:
            file_path = os.path.abspath(file_path)

        file_dir = os.path.dirname(file_path)
        if not os.path.exists(file_dir) and make_dirs:
            os.makedirs(file_dir)

        return file_path

    else:
        raise TypeError("The provided file path must be a string.")

check_git_install()

Checks if Git is installed.

Returns:

Name	Type	Description
bool		Returns True if Git is installed, otherwise returns False.

Source code in geemap/common.py

def check_git_install():
    """Checks if Git is installed.

    Returns:
        bool: Returns True if Git is installed, otherwise returns False.
    """
    import webbrowser

    cmd = "git --version"
    output = os.popen(cmd).read()

    if "git version" in output:
        return True
    else:
        url = "https://git-scm.com/downloads"
        print(f"Git is not installed. Please download Git from {url} and install it.")
        webbrowser.open_new_tab(url)
        return False

check_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 geemap/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_install(package)

Checks whether a package is installed. If not, it will install the package.

Parameters:

Name	Type	Description	Default
package	str	The name of the package to check.	required

Source code in geemap/common.py

def check_install(package):
    """Checks whether a package is installed. If not, it will install the package.

    Args:
        package (str): The name of the package to check.
    """
    import subprocess

    try:
        __import__(package)
        # print('{} is already installed.'.format(package))
    except ImportError:
        print(f"{package} is not installed. Installing ...")
        try:
            subprocess.check_call(["python", "-m", "pip", "install", package])
        except Exception as e:
            print(f"Failed to install {package}")
            print(e)
        print(f"{package} has been installed successfully.")

check_titiler_endpoint(titiler_endpoint=None)

Returns the default titiler endpoint.

Returns:

Name	Type	Description
object		A titiler endpoint.

Source code in geemap/common.py

def check_titiler_endpoint(titiler_endpoint=None):
    """Returns the default titiler endpoint.

    Returns:
        object: A titiler endpoint.
    """
    if titiler_endpoint is None:
        if os.environ.get("TITILER_ENDPOINT") is not None:
            titiler_endpoint = os.environ.get("TITILER_ENDPOINT")

            if titiler_endpoint == "planetary-computer":
                titiler_endpoint = PlanetaryComputerEndpoint()
        else:
            titiler_endpoint = "https://titiler.xyz"
    elif titiler_endpoint in ["planetary-computer", "pc"]:
        titiler_endpoint = PlanetaryComputerEndpoint()

    return titiler_endpoint

classify(data, column, cmap=None, colors=None, labels=None, scheme='Quantiles', k=5, legend_kwds=None, classification_kwds=None)

Classify a dataframe column using a variety of classification schemes.

Parameters:

Name	Type	Description	Default
data	str | 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 geemap/common.py

def classify(
    data,
    column,
    cmap=None,
    colors=None,
    labels=None,
    scheme="Quantiles",
    k=5,
    legend_kwds=None,
    classification_kwds=None,
):
    """Classify a dataframe column using a variety of classification schemes.

    Args:
        data (str | pd.DataFrame | gpd.GeoDataFrame): The data to classify. It can be a filepath to a vector dataset, a pandas dataframe, or a geopandas geodataframe.
        column (str): The column to classify.
        cmap (str, optional): The name of a colormap recognized by matplotlib. Defaults to None.
        colors (list, optional): A list of colors to use for the classification. Defaults to None.
        labels (list, optional): A list of labels to use for the legend. Defaults to None.
        scheme (str, optional): Name of a choropleth classification scheme (requires mapclassify).
            Name of a choropleth classification scheme (requires mapclassify).
            A mapclassify.MapClassifier object will be used
            under the hood. Supported are all schemes provided by mapclassify (e.g.
            'BoxPlot', 'EqualInterval', 'FisherJenks', 'FisherJenksSampled',
            'HeadTailBreaks', 'JenksCaspall', 'JenksCaspallForced',
            'JenksCaspallSampled', 'MaxP', 'MaximumBreaks',
            'NaturalBreaks', 'Quantiles', 'Percentiles', 'StdMean',
            'UserDefined'). Arguments can be passed in classification_kwds.
        k (int, optional): Number of classes (ignored if scheme is None or if column is categorical). Default to 5.
        legend_kwds (dict, optional): Keyword arguments to pass to :func:`matplotlib.pyplot.legend` or `matplotlib.pyplot.colorbar`. Defaults to None.
            Keyword arguments to pass to :func:`matplotlib.pyplot.legend` or
            Additional accepted keywords when `scheme` is specified:
            fmt : string
                A formatting specification for the bin edges of the classes in the
                legend. For example, to have no decimals: ``{"fmt": "{:.0f}"}``.
            labels : list-like
                A list of legend labels to override the auto-generated labblels.
                Needs to have the same number of elements as the number of
                classes (`k`).
            interval : boolean (default False)
                An option to control brackets from mapclassify legend.
                If True, open/closed interval brackets are shown in the legend.
        classification_kwds (dict, optional): Keyword arguments to pass to mapclassify. Defaults to None.

    Returns:
        pd.DataFrame, dict: A pandas dataframe with the classification applied and a legend dictionary.
    """

    import numpy as np
    import pandas as pd
    import geopandas as gpd
    import matplotlib as mpl
    import matplotlib.pyplot as plt

    try:
        import mapclassify
    except ImportError:
        raise ImportError(
            'mapclassify is required for this function. Install with "pip install mapclassify".'
        )

    if isinstance(data, gpd.GeoDataFrame) or isinstance(data, pd.DataFrame):
        df = data
    else:
        try:
            df = gpd.read_file(data)
        except Exception:
            raise TypeError(
                "Data must be a GeoDataFrame or a path to a file that can be read by geopandas.read_file()."
            )

    if df.empty:
        warnings.warn(
            "The GeoDataFrame you are attempting to plot is "
            "empty. Nothing has been displayed.",
            UserWarning,
        )
        return

    columns = df.columns.values.tolist()
    if column not in columns:
        raise ValueError(
            f"{column} is not a column in the GeoDataFrame. It must be one of {columns}."
        )

    # Convert categorical data to numeric
    init_column = None
    value_list = None
    if np.issubdtype(df[column].dtype, np.object0):
        value_list = df[column].unique().tolist()
        value_list.sort()
        df["category"] = df[column].replace(value_list, range(0, len(value_list)))
        init_column = column
        column = "category"
        k = len(value_list)

    if legend_kwds is not None:
        legend_kwds = legend_kwds.copy()

    # To accept pd.Series and np.arrays as column
    if isinstance(column, (np.ndarray, pd.Series)):
        if column.shape[0] != df.shape[0]:
            raise ValueError(
                "The dataframe and given column have different number of rows."
            )
        else:
            values = column

            # Make sure index of a Series matches index of df
            if isinstance(values, pd.Series):
                values = values.reindex(df.index)
    else:
        values = df[column]

    values = df[column]
    nan_idx = np.asarray(pd.isna(values), dtype="bool")

    if cmap is None:
        cmap = "Blues"
    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)

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

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 geemap/common.py

def clip_image(image, mask, output):
    """Clip an image by mask.

    Args:
        image (str): Path to the image file in GeoTIFF format.
        mask (str | list | dict): The mask used to extract the image. It can be a path to vector datasets (e.g., GeoJSON, Shapefile), a list of coordinates, or m.user_roi.
        output (str): Path to the output file.

    Raises:
        ImportError: If the fiona or rasterio package is not installed.
        FileNotFoundError: If the image is not found.
        ValueError: If the mask is not a valid GeoJSON or raster file.
        FileNotFoundError: If the mask file is not found.
    """
    try:
        import fiona
        import rasterio
        import rasterio.mask
    except ImportError as e:
        raise ImportError(e)

    if not os.path.exists(image):
        raise FileNotFoundError(f"{image} does not exist.")

    if not output.endswith(".tif"):
        raise ValueError("Output must be a tif file.")

    output = check_file_path(output)

    if isinstance(mask, ee.Geometry):
        mask = mask.coordinates().getInfo()[0]

    if isinstance(mask, str):
        if mask.startswith("http"):
            mask = download_file(mask, output)
        if not os.path.exists(mask):
            raise FileNotFoundError(f"{mask} does not exist.")
    elif isinstance(mask, list) or isinstance(mask, dict):
        if isinstance(mask, list):
            geojson = {
                "type": "FeatureCollection",
                "features": [
                    {
                        "type": "Feature",
                        "properties": {},
                        "geometry": {"type": "Polygon", "coordinates": [mask]},
                    }
                ],
            }
        else:
            geojson = {
                "type": "FeatureCollection",
                "features": [mask],
            }
        mask = temp_file_path(".geojson")
        with open(mask, "w") as f:
            json.dump(geojson, f)

    with fiona.open(mask, "r") as shapefile:
        shapes = [feature["geometry"] for feature in shapefile]

    with rasterio.open(image) as src:
        out_image, out_transform = rasterio.mask.mask(src, shapes, crop=True)
        out_meta = src.meta

    out_meta.update(
        {
            "driver": "GTiff",
            "height": out_image.shape[1],
            "width": out_image.shape[2],
            "transform": out_transform,
        }
    )

    with rasterio.open(output, "w", **out_meta) as dest:
        dest.write(out_image)

clone_github_repo(url, out_dir)

Clones a GitHub repository.

Parameters:

Name	Type	Description	Default
url	str	The link to the GitHub repository	required
out_dir	str	The output directory for the cloned repository.	required

Source code in geemap/common.py

def clone_github_repo(url, out_dir):
    """Clones a GitHub repository.

    Args:
        url (str): The link to the GitHub repository
        out_dir (str): The output directory for the cloned repository.
    """

    repo_name = os.path.basename(url)
    # url_zip = os.path.join(url, 'archive/master.zip')
    url_zip = url + "/archive/master.zip"

    if os.path.exists(out_dir):
        print(
            "The specified output directory already exists. Please choose a new directory."
        )
        return

    parent_dir = os.path.dirname(out_dir)
    out_file_path = os.path.join(parent_dir, repo_name + ".zip")

    try:
        urllib.request.urlretrieve(url_zip, out_file_path)
    except Exception:
        print("The provided URL is invalid. Please double check the URL.")
        return

    with zipfile.ZipFile(out_file_path, "r") as zip_ref:
        zip_ref.extractall(parent_dir)

    src = out_file_path.replace(".zip", "-master")
    os.rename(src, out_dir)
    os.remove(out_file_path)

clone_google_repo(url, out_dir=None)

Clones an Earth Engine repository from https://earthengine.googlesource.com, such as https://earthengine.googlesource.com/users/google/datasets

Parameters:

Name	Type	Description	Default
url	str	The link to the Earth Engine repository	required
out_dir	str	The output directory for the cloned repository. Defaults to None.	None

Source code in geemap/common.py

def clone_google_repo(url, out_dir=None):
    """Clones an Earth Engine repository from https://earthengine.googlesource.com, such as https://earthengine.googlesource.com/users/google/datasets

    Args:
        url (str): The link to the Earth Engine repository
        out_dir (str, optional): The output directory for the cloned repository. Defaults to None.
    """
    repo_name = os.path.basename(url)

    if out_dir is None:
        out_dir = os.path.join(os.getcwd(), repo_name)

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

    if os.path.exists(out_dir):
        print(
            "The specified output directory already exists. Please choose a new directory."
        )
        return

    if check_git_install():
        cmd = f'git clone "{url}" "{out_dir}"'
        os.popen(cmd).read()

clone_repo(out_dir='.', unzip=True)

Clones the geemap GitHub repository.

Parameters:

Name	Type	Description	Default
out_dir	str	Output folder for the repo. Defaults to '.'.	'.'
unzip	bool	Whether to unzip the repository. Defaults to True.	True

Source code in geemap/common.py

def clone_repo(out_dir=".", unzip=True):
    """Clones the geemap GitHub repository.

    Args:
        out_dir (str, optional): Output folder for the repo. Defaults to '.'.
        unzip (bool, optional): Whether to unzip the repository. Defaults to True.
    """
    url = "https://github.com/gee-community/geemap/archive/master.zip"
    filename = "geemap-master.zip"
    download_from_url(url, out_file_name=filename, out_dir=out_dir, unzip=unzip)

cog_bands(url, titiler_endpoint=None, timeout=300)

Get band names of a Cloud Optimized GeoTIFF (COG).

Parameters:

Name	Type	Description	Default
url	str	HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif	required
titiler_endpoint	str	Titiler endpoint. Defaults to "https://titiler.xyz".	None
timeout	int	Timeout in seconds. Defaults to 300.	300

Returns:

Name	Type	Description
list		A list of band names

Source code in geemap/common.py

def cog_bands(url, titiler_endpoint=None, timeout=300):
    """Get band names of a Cloud Optimized GeoTIFF (COG).

    Args:
        url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
        timeout (int, optional): Timeout in seconds. Defaults to 300.

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

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

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

cog_bounds(url, titiler_endpoint=None, timeout=300)

Get the bounding box of a Cloud Optimized GeoTIFF (COG).

Parameters:

Name	Type	Description	Default
url	str	HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif	required
titiler_endpoint	str	Titiler endpoint. Defaults to "https://titiler.xyz".	None
timeout	int	Timeout in seconds. Defaults to 300.	300

Returns:

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

Source code in geemap/common.py

def cog_bounds(url, titiler_endpoint=None, timeout=300):
    """Get the bounding box of a Cloud Optimized GeoTIFF (COG).

    Args:
        url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
        timeout (int, optional): Timeout in seconds. Defaults to 300.

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

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    url = get_direct_url(url)

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

    if "bounds" in r.keys():
        bounds = r["bounds"]
    else:
        bounds = None
    return bounds

cog_center(url, titiler_endpoint=None)

Get the centroid of a Cloud Optimized GeoTIFF (COG).

Parameters:

Name	Type	Description	Default
url	str	HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif	required
titiler_endpoint	str	Titiler endpoint. Defaults to "https://titiler.xyz".	None

Returns:

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

Source code in geemap/common.py

def cog_center(url, titiler_endpoint=None):
    """Get the centroid of a Cloud Optimized GeoTIFF (COG).

    Args:
        url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".

    Returns:
        tuple: A tuple representing (longitude, latitude)
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    url = get_direct_url(url)
    bounds = cog_bounds(url, titiler_endpoint)
    center = ((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2)  # (lat, lon)
    return center

cog_info(url, titiler_endpoint=None, return_geojson=False, timeout=300)

Get band statistics of a Cloud Optimized GeoTIFF (COG).

Parameters:

Name	Type	Description	Default
url	str	HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif	required
titiler_endpoint	str	Titiler endpoint. Defaults to "https://titiler.xyz".	None
timeout	int	Timeout in seconds. Defaults to 300.	300

Returns:

Name	Type	Description
list		A dictionary of band info.

Source code in geemap/common.py

def cog_info(url, titiler_endpoint=None, return_geojson=False, timeout=300):
    """Get band statistics of a Cloud Optimized GeoTIFF (COG).

    Args:
        url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
        timeout (int, optional): Timeout in seconds. Defaults to 300.

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

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

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

    return r

cog_mosaic(links, titiler_endpoint=None, username='anonymous', layername=None, overwrite=False, verbose=True, timeout=300, **kwargs)

Creates a COG mosaic from a list of COG URLs.

Parameters:

Name	Type	Description	Default
links	list	A list containing COG HTTP URLs.	required
titiler_endpoint	str	Titiler endpoint. Defaults to "https://titiler.xyz".	None
username	str	User name for the titiler endpoint. Defaults to "anonymous".	'anonymous'
layername	[type]	Layer name to use. Defaults to None.	None
overwrite	bool	Whether to overwrite the layer name if existing. Defaults to False.	False
verbose	bool	Whether to print out descriptive information. Defaults to True.	True
timeout	int	Timeout in seconds. Defaults to 300.	300

Raises:

Type	Description
Exception	If the COG mosaic fails to create.

Returns:

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

Source code in geemap/common.py

def cog_mosaic(
    links,
    titiler_endpoint=None,
    username="anonymous",
    layername=None,
    overwrite=False,
    verbose=True,
    timeout=300,
    **kwargs,
):
    """Creates a COG mosaic from a list of COG URLs.

    Args:
        links (list): A list containing COG HTTP URLs.
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
        username (str, optional): User name for the titiler endpoint. Defaults to "anonymous".
        layername ([type], optional): Layer name to use. Defaults to None.
        overwrite (bool, optional): Whether to overwrite the layer name if existing. Defaults to False.
        verbose (bool, optional): Whether to print out descriptive information. Defaults to True.
        timeout (int, optional): Timeout in seconds. Defaults to 300.

    Raises:
        Exception: If the COG mosaic fails to create.

    Returns:
        str: The tile URL for the COG mosaic.
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    if layername is None:
        layername = "layer_" + random_string(5)

    try:
        if verbose:
            print("Creating COG masaic ...")

        # Create token
        r = requests.post(
            f"{titiler_endpoint}/tokens/create",
            json={"username": username, "scope": ["mosaic:read", "mosaic:create"]},
        ).json()
        token = r["token"]

        # Create mosaic
        requests.post(
            f"{titiler_endpoint}/mosaicjson/create",
            json={
                "username": username,
                "layername": layername,
                "files": links,
                # "overwrite": overwrite
            },
            params={
                "access_token": token,
            },
        ).json()

        r2 = requests.get(
            f"{titiler_endpoint}/mosaicjson/{username}.{layername}/tilejson.json",
            timeout=timeout,
        ).json()

        return r2["tiles"][0]

    except Exception as e:
        raise Exception(e)

cog_mosaic_from_file(filepath, skip_rows=0, titiler_endpoint=None, username='anonymous', layername=None, overwrite=False, verbose=True, **kwargs)

Creates a COG mosaic from a csv/txt file stored locally for through HTTP URL.

Parameters:

Name	Type	Description	Default
filepath	str	Local path or HTTP URL to the csv/txt file containing COG URLs.	required
skip_rows	int	The number of rows to skip in the file. Defaults to 0.	0
titiler_endpoint	str	Titiler endpoint. Defaults to "https://titiler.xyz".	None
username	str	User name for the titiler endpoint. Defaults to "anonymous".	'anonymous'
layername	[type]	Layer name to use. Defaults to None.	None
overwrite	bool	Whether to overwrite the layer name if existing. Defaults to False.	False
verbose	bool	Whether to print out descriptive information. Defaults to True.	True

Returns:

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

Source code in geemap/common.py

def cog_mosaic_from_file(
    filepath,
    skip_rows=0,
    titiler_endpoint=None,
    username="anonymous",
    layername=None,
    overwrite=False,
    verbose=True,
    **kwargs,
):
    """Creates a COG mosaic from a csv/txt file stored locally for through HTTP URL.

    Args:
        filepath (str): Local path or HTTP URL to the csv/txt file containing COG URLs.
        skip_rows (int, optional): The number of rows to skip in the file. Defaults to 0.
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
        username (str, optional): User name for the titiler endpoint. Defaults to "anonymous".
        layername ([type], optional): Layer name to use. Defaults to None.
        overwrite (bool, optional): Whether to overwrite the layer name if existing. Defaults to False.
        verbose (bool, optional): Whether to print out descriptive information. Defaults to True.

    Returns:
        str: The tile URL for the COG mosaic.
    """
    import urllib

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    links = []
    if filepath.startswith("http"):
        data = urllib.request.urlopen(filepath)
        for line in data:
            links.append(line.decode("utf-8").strip())

    else:
        with open(filepath) as f:
            links = [line.strip() for line in f.readlines()]

    links = links[skip_rows:]
    # print(links)
    mosaic = cog_mosaic(
        links, titiler_endpoint, username, layername, overwrite, verbose, **kwargs
    )
    return mosaic

cog_pixel_value(lon, lat, url, bidx=None, titiler_endpoint=None, timeout=300, **kwargs)

Get pixel value from COG.

Parameters:

Name	Type	Description	Default
lon	float	Longitude of the pixel.	required
lat	float	Latitude of the pixel.	required
url	str	HTTP URL to a COG, e.g., 'https://github.com/opengeos/data/releases/download/raster/Libya-2023-07-01.tif'	required
bidx	str	Dataset band indexes (e.g bidx=1, bidx=1&bidx=2&bidx=3). Defaults to None.	None
titiler_endpoint	str	Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.	None
timeout	int	Timeout in seconds. Defaults to 300.	300

Returns:

Name	Type	Description
list		A dictionary of band info.

Source code in geemap/common.py

def cog_pixel_value(
    lon,
    lat,
    url,
    bidx=None,
    titiler_endpoint=None,
    timeout=300,
    **kwargs,
):
    """Get pixel value from COG.

    Args:
        lon (float): Longitude of the pixel.
        lat (float): Latitude of the pixel.
        url (str): HTTP URL to a COG, e.g., 'https://github.com/opengeos/data/releases/download/raster/Libya-2023-07-01.tif'
        bidx (str, optional): Dataset band indexes (e.g bidx=1, bidx=1&bidx=2&bidx=3). Defaults to None.
        titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.
        timeout (int, optional): Timeout in seconds. Defaults to 300.

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

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    url = get_direct_url(url)
    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    kwargs["url"] = url
    if bidx is not None:
        kwargs["bidx"] = bidx

    r = requests.get(
        f"{titiler_endpoint}/cog/point/{lon},{lat}", params=kwargs, timeout=timeout
    ).json()
    bands = cog_bands(url, titiler_endpoint)
    # if isinstance(titiler_endpoint, str):
    #     r = requests.get(f"{titiler_endpoint}/cog/point/{lon},{lat}", params=kwargs).json()
    # else:
    #     r = requests.get(
    #         titiler_endpoint.url_for_stac_pixel_value(lon, lat), params=kwargs
    #     ).json()

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

cog_stats(url, titiler_endpoint=None, timeout=300)

Get band statistics of a Cloud Optimized GeoTIFF (COG).

Parameters:

Name	Type	Description	Default
url	str	HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif	required
titiler_endpoint	str	Titiler endpoint. Defaults to "https://titiler.xyz".	None
timeout	int	Timeout in seconds. Defaults to 300.	300

Returns:

Name	Type	Description
list		A dictionary of band statistics.

Source code in geemap/common.py

def cog_stats(url, titiler_endpoint=None, timeout=300):
    """Get band statistics of a Cloud Optimized GeoTIFF (COG).

    Args:
        url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
        timeout (int, optional): Timeout in seconds. Defaults to 300.

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

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

    return r

cog_tile(url, bands=None, titiler_endpoint=None, timeout=300, proxies=None, **kwargs)

Get a tile layer from a Cloud Optimized GeoTIFF (COG). Source code adapted from https://developmentseed.org/titiler/examples/notebooks/Working_with_CloudOptimizedGeoTIFF_simple/

Parameters:

Name	Type	Description	Default
url	str	HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif	required
titiler_endpoint	str	Titiler endpoint. Defaults to "https://titiler.xyz".	None
timeout	int	Timeout in seconds. Defaults to 300.	300
proxies	dict	Proxies to use. Defaults to None.	None

Returns:

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

Source code in geemap/common.py

def cog_tile(
    url,
    bands=None,
    titiler_endpoint=None,
    timeout=300,
    proxies=None,
    **kwargs,
):
    """Get a tile layer from a Cloud Optimized GeoTIFF (COG).
        Source code adapted from https://developmentseed.org/titiler/examples/notebooks/Working_with_CloudOptimizedGeoTIFF_simple/

    Args:
        url (str): HTTP URL to a COG, e.g., https://opendata.digitalglobe.com/events/mauritius-oil-spill/post-event/2020-08-12/105001001F1B5B00/105001001F1B5B00.tif
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://titiler.xyz".
        timeout (int, optional): Timeout in seconds. Defaults to 300.
        proxies (dict, optional): Proxies to use. Defaults to None.

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

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)
    url = get_direct_url(url)

    kwargs["url"] = url

    band_names = cog_bands(url, titiler_endpoint)

    if bands is None and "bidx" not in kwargs:
        if len(band_names) >= 3:
            kwargs["bidx"] = [1, 2, 3]
    elif bands is not None and "bidx" not in kwargs:
        if all(isinstance(x, int) for x in bands):
            kwargs["bidx"] = bands
        elif all(isinstance(x, str) for x in bands):
            kwargs["bidx"] = [band_names.index(x) + 1 for x in bands]
        else:
            raise ValueError("Bands must be a list of integers or strings.")

    if "palette" in kwargs:
        kwargs["colormap_name"] = kwargs.pop("palette")

    if "colormap" in kwargs:
        kwargs["colormap_name"] = kwargs.pop("colormap")

    if "rescale" not in kwargs:
        stats = cog_stats(url, titiler_endpoint)
        percentile_2 = min([stats[s]["percentile_2"] for s in stats])
        percentile_98 = max([stats[s]["percentile_98"] for s in stats])
        kwargs["rescale"] = f"{percentile_2},{percentile_98}"

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

    r = requests.get(
        f"{titiler_endpoint}/cog/{TileMatrixSetId}/tilejson.json",
        params=kwargs,
        timeout=timeout,
        proxies=proxies,
    ).json()

    return r["tiles"][0]

cog_validate(source, verbose=False)

Validate Cloud Optimized Geotiff.

Parameters:

Name	Type	Description	Default
source	str	A dataset path or URL. Will be opened in "r" mode.	required
verbose	bool	Whether to print the output of the validation. Defaults to False.	False

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 geemap/common.py

def cog_validate(source, verbose=False):
    """Validate Cloud Optimized Geotiff.

    Args:
        source (str): A dataset path or URL. Will be opened in "r" mode.
        verbose (bool, optional): Whether to print the output of the validation. Defaults to False.

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

    Returns:
        tuple: A tuple containing the validation results (True is src_path is a valid COG, List of validation errors, and a list of validation warnings).
    """
    try:
        from rio_cogeo.cogeo import cog_validate, cog_info
    except ImportError:
        raise ImportError(
            "The rio-cogeo package is not installed. Please install it with `pip install rio-cogeo` or `conda install rio-cogeo -c conda-forge`."
        )

    if not source.startswith("http"):
        source = check_file_path(source)

        if not os.path.exists(source):
            raise FileNotFoundError("The provided input file could not be found.")

    if verbose:
        return cog_info(source)
    else:
        return cog_validate(source)

column_stats(collection, column, stats_type)

Aggregates over a given property of the objects in a collection, calculating the sum, min, max, mean, sample standard deviation, sample variance, total standard deviation and total variance of the selected property.

Parameters:

Name	Type	Description	Default
collection	FeatureCollection	The input feature collection to calculate statistics.	required
column	str	The name of the column to calculate statistics.	required
stats_type	str	The type of statistics to calculate.	required

Returns:

Name	Type	Description
dict		The dictionary containing information about the requested statistics.

Source code in geemap/common.py

def column_stats(collection, column, stats_type):
    """Aggregates over a given property of the objects in a collection, calculating the sum, min, max, mean,
    sample standard deviation, sample variance, total standard deviation and total variance of the selected property.

    Args:
        collection (FeatureCollection): The input feature collection to calculate statistics.
        column (str): The name of the column to calculate statistics.
        stats_type (str): The type of statistics to calculate.

    Returns:
        dict: The dictionary containing information about the requested statistics.
    """
    stats_type = stats_type.lower()
    allowed_stats = ["min", "max", "mean", "median", "sum", "stdDev", "variance"]
    if stats_type not in allowed_stats:
        print(
            "The stats type must be one of the following: {}".format(
                ",".join(allowed_stats)
            )
        )
        return

    stats_dict = {
        "min": ee.Reducer.min(),
        "max": ee.Reducer.max(),
        "mean": ee.Reducer.mean(),
        "median": ee.Reducer.median(),
        "sum": ee.Reducer.sum(),
        "stdDev": ee.Reducer.stdDev(),
        "variance": ee.Reducer.variance(),
    }

    selectors = [column]
    stats = collection.reduceColumns(
        **{"selectors": selectors, "reducer": stats_dict[stats_type]}
    )

    return stats

connect_postgis(database, host='localhost', user=None, password=None, port=5432, use_env_var=False)

Connects to a PostGIS database.

Parameters:

Name	Type	Description	Default
database	str	Name of the database	required
host	str	Hosting server for the database. Defaults to "localhost".	'localhost'
user	str	User name to access the database. Defaults to None.	None
password	str	Password to access the database. Defaults to None.	None
port	int	Port number to connect to at the server host. Defaults to 5432.	5432
use_env_var	bool	Whether to use environment variables. It set to True, user and password are treated as an environment variables with default values user="SQL_USER" and password="SQL_PASSWORD". Defaults to False.	False

Raises:

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

Returns:

Type	Description

Source code in geemap/common.py

def connect_postgis(
    database, host="localhost", user=None, password=None, port=5432, use_env_var=False
):
    """Connects to a PostGIS database.

    Args:
        database (str): Name of the database
        host (str, optional): Hosting server for the database. Defaults to "localhost".
        user (str, optional): User name to access the database. Defaults to None.
        password (str, optional): Password to access the database. Defaults to None.
        port (int, optional): Port number to connect to at the server host. Defaults to 5432.
        use_env_var (bool, optional): Whether to use environment variables. It set to True, user and password are treated as an environment variables with default values user="SQL_USER" and password="SQL_PASSWORD". Defaults to False.

    Raises:
        ValueError: If user is not specified.
        ValueError: If password is not specified.

    Returns:
        [type]: [description]
    """
    check_package(name="geopandas", URL="https://geopandas.org")
    check_package(
        name="sqlalchemy",
        URL="https://docs.sqlalchemy.org/en/14/intro.html#installation",
    )

    from sqlalchemy import create_engine

    if use_env_var:
        if user is not None:
            user = os.getenv(user)
        else:
            user = os.getenv("SQL_USER")

        if password is not None:
            password = os.getenv(password)
        else:
            password = os.getenv("SQL_PASSWORD")

        if user is None:
            raise ValueError("user is not specified.")
        if password is None:
            raise ValueError("password is not specified.")

    connection_string = f"postgresql://{user}:{password}@{host}:{port}/{database}"
    engine = create_engine(connection_string)

    return engine

convert_js2py(m)

A widget for converting Earth Engine JavaScript to Python.

Parameters:

Name	Type	Description	Default
m	object	geemap.Map	required

Source code in geemap/toolbar.py

def convert_js2py(m):
    """A widget for converting Earth Engine JavaScript to Python.

    Args:
        m (object): geemap.Map
    """

    full_widget = widgets.VBox(layout=widgets.Layout(width="465px", height="350px"))

    text_widget = widgets.Textarea(
        placeholder="Paste your Earth Engine JavaScript into this textbox and click the Convert button below to convert the Javascript to Python",
        layout=widgets.Layout(width="455px", height="310px"),
    )

    buttons = widgets.ToggleButtons(
        value=None,
        options=["Convert", "Clear", "Close"],
        tooltips=["Convert", "Clear", "Close"],
        button_style="primary",
    )
    buttons.style.button_width = "128px"

    def cleanup():
        if m._convert_ctrl is not None and m._convert_ctrl in m.controls:
            m.remove_control(m._convert_ctrl)
        full_widget.close()

    def button_clicked(change):
        if change["new"] == "Convert":
            from .conversion import create_new_cell, js_snippet_to_py

            if len(text_widget.value) > 0:
                out_lines = js_snippet_to_py(
                    text_widget.value,
                    add_new_cell=False,
                    import_ee=False,
                    import_geemap=False,
                    show_map=False,
                    Map=m._var_name,
                )
                if len(out_lines) > 0 and len(out_lines[0].strip()) == 0:
                    out_lines = out_lines[1:]

                prefix = "# The code has been copied to the clipboard. \n# Press Ctrl+V to in a code cell to paste it.\n"
                text_widget.value = "".join([prefix] + out_lines)
                create_code_cell("".join(out_lines))

        elif change["new"] == "Clear":
            text_widget.value = ""
        elif change["new"] == "Close":
            m._convert_ctrl.cleanup()
        buttons.value = None

    buttons.observe(button_clicked, "value")

    full_widget.children = [text_widget, buttons]
    widget_control = ipyleaflet.WidgetControl(widget=full_widget, position="topright")
    widget_control.cleanup = cleanup
    m.add_control(widget_control)
    m._convert_ctrl = widget_control

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 geemap/common.py

def convert_lidar(
    source, destination=None, point_format_id=None, file_version=None, **kwargs
):
    """Converts a Las from one point format to another Automatically upgrades the file version if source file version
        is not compatible with the new point_format_id

    Args:
        source (str | laspy.lasdatas.base.LasBase): The source data to be converted.
        destination (str, optional): The destination file path. Defaults to None.
        point_format_id (int, optional): The new point format id (the default is None, which won't change the source format id).
        file_version (str, optional): The new file version. None by default which means that the file_version may be upgraded
            for compatibility with the new point_format. The file version will not be downgraded.

    Returns:
        aspy.lasdatas.base.LasBase: The converted LasData object.
    """
    try:
        import laspy
    except ImportError:
        print(
            "The laspy package is required for this function. Use `pip install laspy[lazrs,laszip]` to install it."
        )
        return

    if isinstance(source, str):
        source = read_lidar(source)

    las = laspy.convert(
        source, point_format_id=point_format_id, file_version=file_version
    )

    if destination is None:
        return las
    else:
        destination = check_file_path(destination)
        write_lidar(las, destination, **kwargs)
        return destination

coords_to_geojson(coords)

Convert a list of bbox coordinates representing [left, bottom, right, top] to geojson FeatureCollection.

Parameters:

Name	Type	Description	Default
coords	list	A list of bbox coordinates representing [left, bottom, right, top].	required

Returns:

Name	Type	Description
dict		A geojson FeatureCollection.

Source code in geemap/common.py

def coords_to_geojson(coords):
    """Convert a list of bbox coordinates representing [left, bottom, right, top] to geojson FeatureCollection.

    Args:
        coords (list): A list of bbox coordinates representing [left, bottom, right, top].

    Returns:
        dict: A geojson FeatureCollection.
    """

    features = []
    for bbox in coords:
        features.append(bbox_to_geojson(bbox))
    return {"type": "FeatureCollection", "features": features}

copy_credentials_to_colab()

Copies ee credentials from Google Drive to Google Colab.

Source code in geemap/common.py

def copy_credentials_to_colab():
    """Copies ee credentials from Google Drive to Google Colab."""
    src = "/content/drive/My Drive/.config/earthengine/credentials"
    dst = "/root/.config/earthengine/credentials"

    wd = os.path.dirname(dst)
    if not os.path.exists(wd):
        os.makedirs(wd)

    shutil.copyfile(src, dst)

copy_credentials_to_drive()

Copies ee credentials from Google Colab to Google Drive.

Source code in geemap/common.py

def copy_credentials_to_drive():
    """Copies ee credentials from Google Colab to Google Drive."""
    src = "/root/.config/earthengine/credentials"
    dst = "/content/drive/My Drive/.config/earthengine/credentials"

    wd = os.path.dirname(dst)
    if not os.path.exists(wd):
        os.makedirs(wd)

    shutil.copyfile(src, dst)

create_code_cell(code='', where='below')

Creates a code cell in the IPython Notebook.

Parameters:

Name	Type	Description	Default
code	str	Code to fill the new code cell with. Defaults to ''.	''
where	str	Where to add the new code cell. It can be one of the following: above, below, at_bottom. Defaults to 'below'.	'below'

Source code in geemap/coreutils.py

def create_code_cell(code: str = "", where: str = "below") -> None:
    """Creates a code cell in the IPython Notebook.

    Args:
        code (str, optional): Code to fill the new code cell with. Defaults to ''.
        where (str, optional): Where to add the new code cell. It can be one of
            the following: above, below, at_bottom. Defaults to 'below'.
    """

    import base64
    import pyperclip

    try:
        pyperclip.copy(str(code))
    except Exception as e:
        pass

    encoded_code = (base64.b64encode(str.encode(code))).decode()
    display(
        Javascript(
            """
        var code = IPython.notebook.insert_cell_{0}('code');
        code.set_text(atob("{1}"));
    """.format(
                where, encoded_code
            )
        )
    )

create_colorbar(width=150, height=30, palette=['blue', 'green', 'red'], add_ticks=True, add_labels=True, labels=None, vertical=False, out_file=None, font_type='arial.ttf', font_size=12, font_color='black', add_outline=True, outline_color='black')

Creates a colorbar based on the provided palette.

Parameters:

Name	Type	Description	Default
width	int	Width of the colorbar in pixels. Defaults to 150.	150
height	int	Height of the colorbar in pixels. Defaults to 30.	30
palette	list	Palette for the colorbar. Each color can be provided as a string (e.g., 'red'), a hex string (e.g., '#ff0000'), or an RGB tuple (255, 0, 255). Defaults to ['blue', 'green', 'red'].	['blue', 'green', 'red']
add_ticks	bool	Whether to add tick markers to the colorbar. Defaults to True.	True
add_labels	bool	Whether to add labels to the colorbar. Defaults to True.	True
labels	list	A list of labels to add to the colorbar. Defaults to None.	None
vertical	bool	Whether to rotate the colorbar vertically. Defaults to False.	False
out_file	str	File path to the output colorbar in png format. Defaults to None.	None
font_type	str	Font type to use for labels. Defaults to 'arial.ttf'.	'arial.ttf'
font_size	int	Font size to use for labels. Defaults to 12.	12
font_color	str	Font color to use for labels. Defaults to 'black'.	'black'
add_outline	bool	Whether to add an outline to the colorbar. Defaults to True.	True
outline_color	str	Color for the outline of the colorbar. Defaults to 'black'.	'black'

Returns:

Name	Type	Description
str		File path of the output colorbar in png format.

Source code in geemap/common.py

def create_colorbar(
    width=150,
    height=30,
    palette=["blue", "green", "red"],
    add_ticks=True,
    add_labels=True,
    labels=None,
    vertical=False,
    out_file=None,
    font_type="arial.ttf",
    font_size=12,
    font_color="black",
    add_outline=True,
    outline_color="black",
):
    """Creates a colorbar based on the provided palette.

    Args:
        width (int, optional): Width of the colorbar in pixels. Defaults to 150.
        height (int, optional): Height of the colorbar in pixels. Defaults to 30.
        palette (list, optional): Palette for the colorbar. Each color can be provided as a string (e.g., 'red'), a hex string (e.g., '#ff0000'), or an RGB tuple (255, 0, 255). Defaults to ['blue', 'green', 'red'].
        add_ticks (bool, optional): Whether to add tick markers to the colorbar. Defaults to True.
        add_labels (bool, optional): Whether to add labels to the colorbar. Defaults to True.
        labels (list, optional): A list of labels to add to the colorbar. Defaults to None.
        vertical (bool, optional): Whether to rotate the colorbar vertically. Defaults to False.
        out_file (str, optional): File path to the output colorbar in png format. Defaults to None.
        font_type (str, optional): Font type to use for labels. Defaults to 'arial.ttf'.
        font_size (int, optional): Font size to use for labels. Defaults to 12.
        font_color (str, optional): Font color to use for labels. Defaults to 'black'.
        add_outline (bool, optional): Whether to add an outline to the colorbar. Defaults to True.
        outline_color (str, optional): Color for the outline of the colorbar. Defaults to 'black'.

    Returns:
        str: File path of the output colorbar in png format.

    """
    import decimal

    # import io
    from matplotlib import colors
    from PIL import Image, ImageDraw, ImageFont

    warnings.simplefilter("ignore")
    pkg_dir = str(importlib.resources.files("geemap").joinpath("geemap.py").parent)

    if out_file is None:
        filename = "colorbar_" + random_string() + ".png"
        out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
        out_file = os.path.join(out_dir, filename)
    elif not out_file.endswith(".png"):
        print("The output file must end with .png")
        return
    else:
        out_file = os.path.abspath(out_file)

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

    im = Image.new("RGBA", (width, height))
    ld = im.load()

    def float_range(start, stop, step):
        while start < stop:
            yield float(start)
            start += decimal.Decimal(step)

    n_colors = len(palette)
    decimal_places = 2
    rgb_colors = [colors.to_rgb(c) for c in palette]
    keys = [
        round(c, decimal_places)
        for c in list(float_range(0, 1.0001, 1.0 / (n_colors - 1)))
    ]

    heatmap = []
    for index, item in enumerate(keys):
        pair = [item, rgb_colors[index]]
        heatmap.append(pair)

    def gaussian(x, a, b, c, d=0):
        return a * math.exp(-((x - b) ** 2) / (2 * c**2)) + d

    def pixel(x, width=100, map=[], spread=1):
        width = float(width)
        r = sum(
            [
                gaussian(x, p[1][0], p[0] * width, width / (spread * len(map)))
                for p in map
            ]
        )
        g = sum(
            [
                gaussian(x, p[1][1], p[0] * width, width / (spread * len(map)))
                for p in map
            ]
        )
        b = sum(
            [
                gaussian(x, p[1][2], p[0] * width, width / (spread * len(map)))
                for p in map
            ]
        )
        return min(1.0, r), min(1.0, g), min(1.0, b)

    for x in range(im.size[0]):
        r, g, b = pixel(x, width=width, map=heatmap)
        r, g, b = [int(256 * v) for v in (r, g, b)]
        for y in range(im.size[1]):
            ld[x, y] = r, g, b

    if add_outline:
        draw = ImageDraw.Draw(im)
        draw.rectangle(
            [(0, 0), (width - 1, height - 1)], outline=check_color(outline_color)
        )
        del draw

    if add_ticks:
        tick_length = height * 0.1
        x = [key * width for key in keys]
        y_top = height - tick_length
        y_bottom = height
        draw = ImageDraw.Draw(im)
        for i in x:
            shape = [(i, y_top), (i, y_bottom)]
            draw.line(shape, fill="black", width=0)
        del draw

    if vertical:
        im = im.transpose(Image.ROTATE_90)

    width, height = im.size

    if labels is None:
        labels = [str(c) for c in keys]
    elif len(labels) == 2:
        try:
            lowerbound = float(labels[0])
            upperbound = float(labels[1])
            step = (upperbound - lowerbound) / (len(palette) - 1)
            labels = [str(lowerbound + c * step) for c in range(0, len(palette))]
        except Exception as e:
            print(e)
            print("The labels are invalid.")
            return
    elif len(labels) == len(palette):
        labels = [str(c) for c in labels]
    else:
        print("The labels must have the same length as the palette.")
        return

    if add_labels:
        default_font = os.path.join(pkg_dir, "data/fonts/arial.ttf")
        if font_type == "arial.ttf":
            font = ImageFont.truetype(default_font, font_size)
        else:
            try:
                font_list = system_fonts(show_full_path=True)
                font_names = [os.path.basename(f) for f in font_list]
                if (font_type in font_list) or (font_type in font_names):
                    font = ImageFont.truetype(font_type, font_size)
                else:
                    print(
                        "The specified font type could not be found on your system. Using the default font instead."
                    )
                    font = ImageFont.truetype(default_font, font_size)
            except Exception as e:
                print(e)
                font = ImageFont.truetype(default_font, font_size)

        font_color = check_color(font_color)

        draw = ImageDraw.Draw(im)
        w, h = draw.textsize(labels[0], font=font)

        for label in labels:
            w_tmp, h_tmp = draw.textsize(label, font)
            if w_tmp > w:
                w = w_tmp
            if h_tmp > h:
                h = h_tmp

        W, H = width + w * 2, height + h * 2
        background = Image.new("RGBA", (W, H))
        draw = ImageDraw.Draw(background)

        if vertical:
            xy = (0, h)
        else:
            xy = (w, 0)
        background.paste(im, xy, im)

        for index, label in enumerate(labels):
            w_tmp, h_tmp = draw.textsize(label, font)

            if vertical:
                spacing = 5
                x = width + spacing
                y = int(height + h - keys[index] * height - h_tmp / 2 - 1)
                draw.text((x, y), label, font=font, fill=font_color)

            else:
                x = int(keys[index] * width + w - w_tmp / 2)
                spacing = int(h * 0.05)
                y = height + spacing
                draw.text((x, y), label, font=font, fill=font_color)

        im = background.copy()

    im.save(out_file)
    return out_file

create_contours(image, min_value, max_value, interval, kernel=None, region=None, values=None)

Creates contours from an image. Code adapted from https://mygeoblog.com/2017/01/28/contour-lines-in-gee. Credits to MyGeoBlog.

Parameters:

Name	Type	Description	Default
image	Image	An image to create contours.	required
min_value	float	The minimum value of contours.	required
max_value	float	The maximum value of contours.	required
interval	float	The interval between contours.	required
kernel	Kernel	The kernel to use for smoothing image. Defaults to None.	None
region	Geometry | FeatureCollection	The region of interest. Defaults to None.	None
values	list	A list of values to create contours for. Defaults to None.	None

Raises:

Type	Description
TypeError	The image must be an ee.Image.
TypeError	The region must be an ee.Geometry or ee.FeatureCollection.

Returns:

Type	Description
	ee.Image: The image containing contours.

Source code in geemap/common.py

def create_contours(
    image, min_value, max_value, interval, kernel=None, region=None, values=None
):
    """Creates contours from an image. Code adapted from https://mygeoblog.com/2017/01/28/contour-lines-in-gee. Credits to MyGeoBlog.

    Args:
        image (ee.Image): An image to create contours.
        min_value (float): The minimum value of contours.
        max_value (float): The maximum value of contours.
        interval (float):  The interval between contours.
        kernel (ee.Kernel, optional): The kernel to use for smoothing image. Defaults to None.
        region (ee.Geometry | ee.FeatureCollection, optional): The region of interest. Defaults to None.
        values (list, optional): A list of values to create contours for. Defaults to None.

    Raises:
        TypeError: The image must be an ee.Image.
        TypeError: The region must be an ee.Geometry or ee.FeatureCollection.

    Returns:
        ee.Image: The image containing contours.
    """
    if not isinstance(image, ee.Image):
        raise TypeError("The image must be an ee.Image.")
    if region is not None:
        if isinstance(region, ee.FeatureCollection) or isinstance(region, ee.Geometry):
            pass
        else:
            raise TypeError(
                "The region must be an ee.Geometry or ee.FeatureCollection."
            )

    if kernel is None:
        kernel = ee.Kernel.gaussian(5, 3)

    if isinstance(values, list):
        values = ee.List(values)
    elif isinstance(values, ee.List):
        pass

    if values is None:
        values = ee.List.sequence(min_value, max_value, interval)

    def contouring(value):
        mycountour = (
            image.convolve(kernel)
            .subtract(ee.Image.constant(value))
            .zeroCrossing()
            .multiply(ee.Image.constant(value).toFloat())
        )
        return mycountour.mask(mycountour)

    contours = values.map(contouring)

    if region is not None:
        if isinstance(region, ee.FeatureCollection):
            return ee.ImageCollection(contours).mosaic().clipToCollection(region)
        elif isinstance(region, ee.Geometry):
            return ee.ImageCollection(contours).mosaic().clip(region)
    else:
        return ee.ImageCollection(contours).mosaic()

create_download_button(label, data, file_name=None, mime=None, key=None, help=None, on_click=None, args=None, **kwargs)

Streamlit function to create a download button.

Parameters:

Name	Type	Description	Default
label	str	A short label explaining to the user what this button is for..	required
data	str | list	The contents of the file to be downloaded. See example below for caching techniques to avoid recomputing this data unnecessarily.	required
file_name	str	An optional string to use as the name of the file to be downloaded, such as 'my_file.csv'. If not specified, the name will be automatically generated. Defaults to None.	None
mime	str	The MIME type of the data. If None, defaults to "text/plain" (if data is of type str or is a textual file) or "application/octet-stream" (if data is of type bytes or is a binary file). Defaults to None.	None
key	str	An optional string or integer to use as the unique key for the widget. If this is omitted, a key will be generated for the widget based on its content. Multiple widgets of the same type may not share the same key. Defaults to None.	None
help	str	An optional tooltip that gets displayed when the button is hovered over. Defaults to None.	None
on_click	str	An optional callback invoked when this button is clicked. Defaults to None.	None
args	list	An optional tuple of args to pass to the callback. Defaults to None.	None
kwargs	dict	An optional tuple of args to pass to the callback.	{}

Source code in geemap/common.py

def create_download_button(
    label,
    data,
    file_name=None,
    mime=None,
    key=None,
    help=None,
    on_click=None,
    args=None,
    **kwargs,
):
    """Streamlit function to create a download button.

    Args:
        label (str): A short label explaining to the user what this button is for..
        data (str | list): The contents of the file to be downloaded. See example below for caching techniques to avoid recomputing this data unnecessarily.
        file_name (str, optional): An optional string to use as the name of the file to be downloaded, such as 'my_file.csv'. If not specified, the name will be automatically generated. Defaults to None.
        mime (str, optional): The MIME type of the data. If None, defaults to "text/plain" (if data is of type str or is a textual file) or "application/octet-stream" (if data is of type bytes or is a binary file). Defaults to None.
        key (str, optional): An optional string or integer to use as the unique key for the widget. If this is omitted, a key will be generated for the widget based on its content. Multiple widgets of the same type may not share the same key. Defaults to None.
        help (str, optional): An optional tooltip that gets displayed when the button is hovered over. Defaults to None.
        on_click (str, optional): An optional callback invoked when this button is clicked. Defaults to None.
        args (list, optional): An optional tuple of args to pass to the callback. Defaults to None.
        kwargs (dict, optional): An optional tuple of args to pass to the callback.

    """
    try:
        import streamlit as st
        import pandas as pd

        if isinstance(data, str):
            if file_name is None:
                file_name = data.split("/")[-1]

            if data.endswith(".csv"):
                data = pd.read_csv(data).to_csv()
                if mime is None:
                    mime = "text/csv"
                return st.download_button(
                    label, data, file_name, mime, key, help, on_click, args, **kwargs
                )
            elif (
                data.endswith(".gif") or data.endswith(".png") or data.endswith(".jpg")
            ):
                if mime is None:
                    mime = f"image/{os.path.splitext(data)[1][1:]}"

                with open(data, "rb") as file:
                    return st.download_button(
                        label,
                        file,
                        file_name,
                        mime,
                        key,
                        help,
                        on_click,
                        args,
                        **kwargs,
                    )

            else:
                return st.download_button(
                    label,
                    label,
                    data,
                    file_name,
                    mime,
                    key,
                    help,
                    on_click,
                    args,
                    **kwargs,
                )

    except ImportError:
        print("Streamlit is not installed. Please run 'pip install streamlit'.")
        return
    except Exception as e:
        raise Exception(e)

create_download_link(filename, title='Click here to download: ')

Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578

Parameters:

Name	Type	Description	Default
filename	str	The file path to the file to download	required
title	str	str. Defaults to "Click here to download: ".	'Click here to download: '

Returns:

Name	Type	Description
str		HTML download URL.

Source code in geemap/common.py

def create_download_link(filename, title="Click here to download: "):
    """Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578

    Args:
        filename (str): The file path to the file to download
        title (str, optional): str. Defaults to "Click here to download: ".

    Returns:
        str: HTML download URL.
    """
    import base64

    from IPython.display import HTML

    data = open(filename, "rb").read()
    b64 = base64.b64encode(data)
    payload = b64.decode()
    basename = os.path.basename(filename)
    html = '<a download="{filename}" href="data:text/csv;base64,{payload}" style="color:#0000FF;" target="_blank">{title}</a>'
    html = html.format(payload=payload, title=title + f" {basename}", filename=basename)
    return HTML(html)

create_grid(ee_object, scale, proj=None)

Create a grid covering an Earth Engine object.

Parameters:

Name	Type	Description	Default
ee_object	Image | Geometry | FeatureCollection	The Earth Engine object.	required
scale	float	The grid cell size.	required
proj	str	The projection. Defaults to None.	None

Returns:

Type	Description
	ee.FeatureCollection: The grid as a feature collection.

Source code in geemap/common.py

def create_grid(ee_object, scale, proj=None):
    """Create a grid covering an Earth Engine object.

    Args:
        ee_object (ee.Image | ee.Geometry | ee.FeatureCollection): The Earth Engine object.
        scale (float): The grid cell size.
        proj (str, optional): The projection. Defaults to None.


    Returns:
        ee.FeatureCollection: The grid as a feature collection.
    """

    if isinstance(ee_object, ee.FeatureCollection) or isinstance(ee_object, ee.Image):
        geometry = ee_object.geometry()
    elif isinstance(ee_object, ee.Geometry):
        geometry = ee_object
    else:
        raise ValueError(
            "ee_object must be an ee.FeatureCollection, ee.Image, or ee.Geometry"
        )

    if proj is None:
        proj = geometry.projection()

    grid = geometry.coveringGrid(proj, scale)

    return grid

create_legend(title='Legend', labels=None, colors=None, legend_dict=None, builtin_legend=None, opacity=1.0, position='bottomright', draggable=True, output=None, style={})

Create a legend in HTML format. Reference: https://bit.ly/3oV6vnH

Parameters:

Name	Type	Description	Default
title	str	Title of the legend. Defaults to 'Legend'. Defaults to "Legend".	'Legend'
colors	list	A list of legend colors. Defaults to None.	None
labels	list	A list of legend labels. Defaults to None.	None
legend_dict	dict	A dictionary containing legend items as keys and color as values. If provided, legend_keys and legend_colors will be ignored. Defaults to None.	None
builtin_legend	str	Name of the builtin legend to add to the map. Defaults to None.	None
opacity	float	The opacity of the legend. Defaults to 1.0.	1.0
position	str	The position of the legend, can be one of the following: "topleft", "topright", "bottomleft", "bottomright". Defaults to "bottomright".	'bottomright'
draggable	bool	If True, the legend can be dragged to a new position. Defaults to True.	True
output	str	The output file path (*.html) to save the legend. Defaults to None.	None
style		Additional keyword arguments to style the legend, such as position, bottom, right, z-index, border, background-color, border-radius, padding, font-size, etc. The default style is: style = { 'position': 'fixed', 'z-index': '9999', 'border': '2px solid grey', 'background-color': 'rgba(255, 255, 255, 0.8)', 'border-radius': '5px', 'padding': '10px', 'font-size': '14px', 'bottom': '20px', 'right': '5px' }	{}

Returns:

Name	Type	Description
str		The HTML code of the legend.

Source code in geemap/common.py

def create_legend(
    title="Legend",
    labels=None,
    colors=None,
    legend_dict=None,
    builtin_legend=None,
    opacity=1.0,
    position="bottomright",
    draggable=True,
    output=None,
    style={},
):
    """Create a legend in HTML format. Reference: https://bit.ly/3oV6vnH

    Args:
        title (str, optional): Title of the legend. Defaults to 'Legend'. Defaults to "Legend".
        colors (list, optional): A list of legend colors. Defaults to None.
        labels (list, optional): A list of legend labels. Defaults to None.
        legend_dict (dict, optional): A dictionary containing legend items as keys and color as values.
            If provided, legend_keys and legend_colors will be ignored. Defaults to None.
        builtin_legend (str, optional): Name of the builtin legend to add to the map. Defaults to None.
        opacity (float, optional): The opacity of the legend. Defaults to 1.0.
        position (str, optional): The position of the legend, can be one of the following:
            "topleft", "topright", "bottomleft", "bottomright". Defaults to "bottomright".
        draggable (bool, optional): If True, the legend can be dragged to a new position. Defaults to True.
        output (str, optional): The output file path (*.html) to save the legend. Defaults to None.
        style: Additional keyword arguments to style the legend, such as position, bottom, right, z-index,
            border, background-color, border-radius, padding, font-size, etc. The default style is:
            style = {
                'position': 'fixed',
                'z-index': '9999',
                'border': '2px solid grey',
                'background-color': 'rgba(255, 255, 255, 0.8)',
                'border-radius': '5px',
                'padding': '10px',
                'font-size': '14px',
                'bottom': '20px',
                'right': '5px'
            }

    Returns:
        str: The HTML code of the legend.
    """

    from .legends import builtin_legends

    pkg_dir = str(importlib.resources.files("geemap").joinpath("geemap.py").parent)
    legend_template = os.path.join(pkg_dir, "data/template/legend_style.html")

    if draggable:
        legend_template = os.path.join(pkg_dir, "data/template/legend.txt")

    if not os.path.exists(legend_template):
        raise FileNotFoundError("The legend template does not exist.")

    if labels is not None:
        if not isinstance(labels, list):
            print("The legend keys must be a list.")
            return
    else:
        labels = ["One", "Two", "Three", "Four", "etc"]

    if colors is not None:
        if not isinstance(colors, list):
            print("The legend colors must be a list.")
            return
        elif all(isinstance(item, tuple) for item in colors):
            try:
                colors = [rgb_to_hex(x) for x in colors]
            except Exception as e:
                print(e)
        elif all((item.startswith("#") and len(item) == 7) for item in colors):
            pass
        elif all((len(item) == 6) for item in colors):
            pass
        else:
            print("The legend colors must be a list of tuples.")
            return
    else:
        colors = [
            "#8DD3C7",
            "#FFFFB3",
            "#BEBADA",
            "#FB8072",
            "#80B1D3",
        ]

    if len(labels) != len(colors):
        print("The legend keys and values must be the same length.")
        return

    allowed_builtin_legends = builtin_legends.keys()
    if builtin_legend is not None:
        if builtin_legend not in allowed_builtin_legends:
            print(
                "The builtin legend must be one of the following: {}".format(
                    ", ".join(allowed_builtin_legends)
                )
            )
            return
        else:
            legend_dict = builtin_legends[builtin_legend]
            labels = list(legend_dict.keys())
            colors = list(legend_dict.values())

    if legend_dict is not None:
        if not isinstance(legend_dict, dict):
            print("The legend dict must be a dictionary.")
            return
        else:
            labels = list(legend_dict.keys())
            colors = list(legend_dict.values())
            if all(isinstance(item, tuple) for item in colors):
                try:
                    colors = [rgb_to_hex(x) for x in colors]
                except Exception as e:
                    print(e)

    allowed_positions = [
        "topleft",
        "topright",
        "bottomleft",
        "bottomright",
    ]
    if position not in allowed_positions:
        raise ValueError(
            "The position must be one of the following: {}".format(
                ", ".join(allowed_positions)
            )
        )

    if position == "bottomright":
        if "bottom" not in style:
            style["bottom"] = "20px"
        if "right" not in style:
            style["right"] = "5px"
        if "left" in style:
            del style["left"]
        if "top" in style:
            del style["top"]
    elif position == "bottomleft":
        if "bottom" not in style:
            style["bottom"] = "5px"
        if "left" not in style:
            style["left"] = "5px"
        if "right" in style:
            del style["right"]
        if "top" in style:
            del style["top"]
    elif position == "topright":
        if "top" not in style:
            style["top"] = "5px"
        if "right" not in style:
            style["right"] = "5px"
        if "left" in style:
            del style["left"]
        if "bottom" in style:
            del style["bottom"]
    elif position == "topleft":
        if "top" not in style:
            style["top"] = "5px"
        if "left" not in style:
            style["left"] = "5px"
        if "right" in style:
            del style["right"]
        if "bottom" in style:
            del style["bottom"]

    if "position" not in style:
        style["position"] = "fixed"
    if "z-index" not in style:
        style["z-index"] = "9999"
    if "background-color" not in style:
        style["background-color"] = "rgba(255, 255, 255, 0.8)"
    if "padding" not in style:
        style["padding"] = "10px"
    if "border-radius" not in style:
        style["border-radius"] = "5px"
    if "font-size" not in style:
        style["font-size"] = "14px"

    content = []

    with open(legend_template) as f:
        lines = f.readlines()

    if draggable:
        for index, line in enumerate(lines):
            if index < 36:
                content.append(line)
            elif index == 36:
                line = lines[index].replace("Legend", title)
                content.append(line)
            elif index < 39:
                content.append(line)
            elif index == 39:
                for i, color in enumerate(colors):
                    item = f"    <li><span style='background:{check_color(color)};opacity:{opacity};'></span>{labels[i]}</li>\n"
                    content.append(item)
            elif index > 41:
                content.append(line)
        content = content[3:-1]

    else:
        for index, line in enumerate(lines):
            if index < 8:
                content.append(line)
            elif index == 8:
                for key, value in style.items():
                    content.append(
                        "              {}: {};\n".format(key.replace("_", "-"), value)
                    )
            elif index < 17:
                pass
            elif index < 19:
                content.append(line)
            elif index == 19:
                content.append(line.replace("Legend", title))
            elif index < 22:
                content.append(line)
            elif index == 22:
                for index, key in enumerate(labels):
                    color = colors[index]
                    if not color.startswith("#"):
                        color = "#" + color
                    item = "                    <li><span style='background:{};opacity:{};'></span>{}</li>\n".format(
                        color, opacity, key
                    )
                    content.append(item)
            elif index < 33:
                pass
            else:
                content.append(line)

    legend_text = "".join(content)

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

create_nlcd_qml(out_qml)

Create a QGIS Layer Style (.qml) for NLCD data

Parameters:

Name	Type	Description	Default
out_qml	str	File path to the output qml.	required

Source code in geemap/common.py

def create_nlcd_qml(out_qml):
    """Create a QGIS Layer Style (.qml) for NLCD data

    Args:
        out_qml (str): File path to the output qml.
    """
    pkg_dir = str(importlib.resources.files("geemap").joinpath("geemap.py").parent)
    data_dir = os.path.join(pkg_dir, "data")
    template_dir = os.path.join(data_dir, "template")
    qml_template = os.path.join(template_dir, "NLCD.qml")

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

    shutil.copyfile(qml_template, out_qml)

create_timelapse(collection, start_date, end_date, region=None, bands=None, frequency='year', reducer='median', date_format=None, out_gif=None, palette=None, vis_params=None, dimensions=768, frames_per_second=10, crs='EPSG:3857', overlay_data=None, overlay_color='black', overlay_width=1, overlay_opacity=1.0, title=None, title_xy=('2%', '90%'), add_text=True, text_xy=('2%', '2%'), text_sequence=None, font_type='arial.ttf', font_size=20, font_color='white', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, add_colorbar=False, colorbar_width=6.0, colorbar_height=0.4, colorbar_label=None, colorbar_label_size=12, colorbar_label_weight='normal', colorbar_tick_size=10, colorbar_bg_color=None, colorbar_orientation='horizontal', colorbar_dpi='figure', colorbar_xy=None, colorbar_size=(300, 300), loop=0, mp4=False, fading=False, parallel_scale=1, step=1)

Create a timelapse from any ee.ImageCollection.

Parameters:

Name	Type	Description	Default
collection	str | ImageCollection	The collection of images to create a timeseries from. It can be a string representing the collection ID or an ee.ImageCollection object.	required
start_date	str	The start date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.	required
end_date	str	The end date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.	required
region	Geometry	The region to use to filter the collection of images. It must be an ee.Geometry object. Defaults to None.	None
bands	list	A list of band names to use in the timelapse. Defaults to None.	None
frequency	str	The frequency of the timeseries. It must be one of the following: 'year', 'month', 'day', 'hour', 'minute', 'second'. Defaults to 'year'.	'year'
reducer	str	The reducer to use to reduce the collection of images to a single value. It can be one of the following: 'median', 'mean', 'min', 'max', 'variance', 'sum'. Defaults to 'median'.	'median'
drop_empty	bool	Whether to drop empty images from the timeseries. Defaults to True.	required
date_format	str	A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.	None
out_gif	str	The output gif file path. Defaults to None.	None
palette	list	A list of colors to render a single-band image in the timelapse. Defaults to None.	None
vis_params	dict	A dictionary of visualization parameters to use in the timelapse. Defaults to None. See more at https://developers.google.com/earth-engine/guides/image_visualization.	None
dimensions	int	a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.	768
frames_per_second	int	Animation speed. Defaults to 10.	10
crs	str	The coordinate reference system to use. Defaults to "EPSG:3857".	'EPSG:3857'
overlay_data	(int, str, list)	Administrative boundary to be drawn on the timelapse. Defaults to None.	None
overlay_color	str	Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.	'black'
overlay_width	int	Width of the overlay. Defaults to 1.	1
overlay_opacity	float	Opacity of the overlay. Defaults to 1.0.	1.0
title	str	The title of the timelapse. Defaults to None.	None
title_xy	tuple	Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
add_text	bool	Whether to add animated text to the timelapse. Defaults to True.	True
title_xy	tuple	Lower left corner of the text sequency. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
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'.	'white'
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
add_colorbar	bool	Whether to add a colorbar to the timelapse. Defaults to False.	False
colorbar_width	float	Width of the colorbar. Defaults to 6.0.	6.0
colorbar_height	float	Height of the colorbar. Defaults to 0.4.	0.4
colorbar_label	str	Label for the colorbar. Defaults to None.	None
colorbar_label_size	int	Font size for the colorbar label. Defaults to 12.	12
colorbar_label_weight	str	Font weight for the colorbar label. Defaults to 'normal'.	'normal'
colorbar_tick_size	int	Font size for the colorbar ticks. Defaults to 10.	10
colorbar_bg_color	str	Background color for the colorbar, can be color like "white", "black". Defaults to None.	None
colorbar_orientation	str	Orientation of the colorbar. Defaults to 'horizontal'.	'horizontal'
colorbar_dpi	str	DPI for the colorbar, can be numbers like 100, 300. Defaults to 'figure'.	'figure'
colorbar_xy	tuple	Lower left corner of the colorbar. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	None
colorbar_size	tuple	Size of the colorbar. It can be formatted like this: (300, 300). Defaults to (300, 300).	(300, 300)
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
mp4	bool	Whether to create an mp4 file. Defaults to False.	False
fading	int | bool	If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).	False
parallel_scale	int	A scaling factor used to limit memory use; using a larger parallel_scale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.	1
step	int	The step size to use when creating the date sequence. Defaults to 1.	1

Returns:

Name	Type	Description
str		File path to the timelapse gif.

Source code in geemap/timelapse.py

def create_timelapse(
    collection,
    start_date,
    end_date,
    region=None,
    bands=None,
    frequency="year",
    reducer="median",
    date_format=None,
    out_gif=None,
    palette=None,
    vis_params=None,
    dimensions=768,
    frames_per_second=10,
    crs="EPSG:3857",
    overlay_data=None,
    overlay_color="black",
    overlay_width=1,
    overlay_opacity=1.0,
    title=None,
    title_xy=("2%", "90%"),
    add_text=True,
    text_xy=("2%", "2%"),
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="white",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    add_colorbar=False,
    colorbar_width=6.0,
    colorbar_height=0.4,
    colorbar_label=None,
    colorbar_label_size=12,
    colorbar_label_weight="normal",
    colorbar_tick_size=10,
    colorbar_bg_color=None,
    colorbar_orientation="horizontal",
    colorbar_dpi="figure",
    colorbar_xy=None,
    colorbar_size=(300, 300),
    loop=0,
    mp4=False,
    fading=False,
    parallel_scale=1,
    step=1,
):
    """Create a timelapse from any ee.ImageCollection.

    Args:
        collection (str | ee.ImageCollection): The collection of images to create a timeseries from. It can be a string representing the collection ID or an ee.ImageCollection object.
        start_date (str): The start date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.
        end_date (str): The end date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.
        region (ee.Geometry, optional): The region to use to filter the collection of images. It must be an ee.Geometry object. Defaults to None.
        bands (list, optional): A list of band names to use in the timelapse. Defaults to None.
        frequency (str, optional): The frequency of the timeseries. It must be one of the following: 'year', 'month', 'day', 'hour', 'minute', 'second'. Defaults to 'year'.
        reducer (str, optional):  The reducer to use to reduce the collection of images to a single value. It can be one of the following: 'median', 'mean', 'min', 'max', 'variance', 'sum'. Defaults to 'median'.
        drop_empty (bool, optional): Whether to drop empty images from the timeseries. Defaults to True.
        date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.
        out_gif (str): The output gif file path. Defaults to None.
        palette (list, optional): A list of colors to render a single-band image in the timelapse. Defaults to None.
        vis_params (dict, optional): A dictionary of visualization parameters to use in the timelapse. Defaults to None. See more at https://developers.google.com/earth-engine/guides/image_visualization.
        dimensions (int, optional): a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
        frames_per_second (int, optional): Animation speed. Defaults to 10.
        crs (str, optional): The coordinate reference system to use. Defaults to "EPSG:3857".
        overlay_data (int, str, list, optional): Administrative boundary to be drawn on the timelapse. Defaults to None.
        overlay_color (str, optional): Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.
        overlay_width (int, optional): Width of the overlay. Defaults to 1.
        overlay_opacity (float, optional): Opacity of the overlay. Defaults to 1.0.
        title (str, optional): The title of the timelapse. Defaults to None.
        title_xy (tuple, optional): Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        add_text (bool, optional): Whether to add animated text to the timelapse. Defaults to True.
        title_xy (tuple, optional): Lower left corner of the text sequency. 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.
        add_colorbar (bool, optional): Whether to add a colorbar to the timelapse. Defaults to False.
        colorbar_width (float, optional): Width of the colorbar. Defaults to 6.0.
        colorbar_height (float, optional): Height of the colorbar. Defaults to 0.4.
        colorbar_label (str, optional): Label for the colorbar. Defaults to None.
        colorbar_label_size (int, optional): Font size for the colorbar label. Defaults to 12.
        colorbar_label_weight (str, optional): Font weight for the colorbar label. Defaults to 'normal'.
        colorbar_tick_size (int, optional): Font size for the colorbar ticks. Defaults to 10.
        colorbar_bg_color (str, optional): Background color for the colorbar, can be color like "white", "black". Defaults to None.
        colorbar_orientation (str, optional): Orientation of the colorbar. Defaults to 'horizontal'.
        colorbar_dpi (str, optional): DPI for the colorbar, can be numbers like 100, 300. Defaults to 'figure'.
        colorbar_xy (tuple, optional): Lower left corner of the colorbar. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        colorbar_size (tuple, optional): Size of the colorbar. It can be formatted like this: (300, 300). Defaults to (300, 300).
        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.
        mp4 (bool, optional): Whether to create an mp4 file. Defaults to False.
        fading (int | bool, optional): If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).
        parallel_scale (int, optional): A scaling factor used to limit memory use; using a larger parallel_scale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.
        step (int, optional): The step size to use when creating the date sequence. Defaults to 1.

    Returns:
        str: File path to the timelapse gif.
    """
    import geemap.colormaps as cm

    if not isinstance(collection, ee.ImageCollection):
        if isinstance(collection, str):
            collection = ee.ImageCollection(collection)
        else:
            raise Exception(
                "The collection must be an ee.ImageCollection object or asset id."
            )

    col = create_timeseries(
        collection,
        start_date,
        end_date,
        region=region,
        bands=bands,
        frequency=frequency,
        reducer=reducer,
        drop_empty=True,
        date_format=date_format,
        parallel_scale=parallel_scale,
        step=step,
    )

    # rename the bands to remove the '_reducer' characters from the band names.
    col = col.map(
        lambda img: img.rename(
            img.bandNames().map(lambda name: ee.String(name).replace(f"_{reducer}", ""))
        )
    )

    if out_gif is None:
        out_gif = temp_file_path(".gif")
    else:
        out_gif = check_file_path(out_gif)

    out_dir = os.path.dirname(out_gif)

    if bands is None:
        names = col.first().bandNames().getInfo()
        if len(names) < 3:
            bands = [names[0]]
        else:
            bands = names[:3][::-1]
    elif isinstance(bands, str):
        bands = [bands]
    elif not isinstance(bands, list):
        raise Exception("The bands must be a string or a list of strings.")

    if isinstance(palette, str):
        palette = cm.get_palette(palette, 15)
    elif isinstance(palette, list) or isinstance(palette, tuple):
        pass
    elif palette is not None:
        raise Exception("The palette must be a string or a list of strings.")

    if vis_params is None:
        img = col.first().select(bands)
        scale = collection.first().select(0).projection().nominalScale().multiply(10)
        min_value = min(
            image_min_value(img, region=region, scale=scale).getInfo().values()
        )
        max_value = max(
            image_max_value(img, region=region, scale=scale).getInfo().values()
        )
        vis_params = {"bands": bands, "min": min_value, "max": max_value}

        if len(bands) == 1:
            if palette is not None:
                vis_params["palette"] = palette
            else:
                vis_params["palette"] = cm.palettes.ndvi
    elif isinstance(vis_params, dict):
        if "bands" not in vis_params:
            vis_params["bands"] = bands
        if "min" not in vis_params:
            img = col.first().select(bands)
            scale = (
                collection.first().select(0).projection().nominalScale().multiply(10)
            )
            vis_params["min"] = min(
                image_min_value(img, region=region, scale=scale).getInfo().values()
            )
        if "max" not in vis_params:
            img = col.first().select(bands)
            scale = (
                collection.first().select(0).projection().nominalScale().multiply(10)
            )
            vis_params["max"] = max(
                image_max_value(img, region=region, scale=scale).getInfo().values()
            )
        if palette is None and (len(bands) == 1) and ("palette" not in vis_params):
            vis_params["palette"] = cm.palettes.ndvi
        elif palette is not None and ("palette" not in vis_params):
            vis_params["palette"] = palette
        if len(bands) > 1 and "palette" in vis_params:
            del vis_params["palette"]
    else:
        raise Exception("The vis_params must be a dictionary.")

    col = col.select(bands).map(
        lambda img: img.visualize(**vis_params).set(
            {
                "system:time_start": img.get("system:time_start"),
                "system:date": img.get("system:date"),
            }
        )
    )

    if overlay_data is not None:
        col = add_overlay(
            col, overlay_data, overlay_color, overlay_width, overlay_opacity
        )

    video_args = {}
    video_args["dimensions"] = dimensions
    video_args["region"] = region
    video_args["framesPerSecond"] = frames_per_second
    video_args["crs"] = crs
    video_args["min"] = 0
    video_args["max"] = 255

    # if crs is not None:
    #     video_args["crs"] = crs

    if "palette" in vis_params or len(bands) > 1:
        video_args["bands"] = ["vis-red", "vis-green", "vis-blue"]
    else:
        video_args["bands"] = ["vis-gray"]

    if (
        isinstance(dimensions, int)
        and dimensions > 768
        or isinstance(dimensions, str)
        and any(dim > 768 for dim in list(map(int, dimensions.split("x"))))
    ):
        count = col.size().getInfo()
        basename = os.path.basename(out_gif)[:-4]
        names = [
            os.path.join(
                out_dir, f"{basename}_{str(i+1).zfill(int(len(str(count))))}.jpg"
            )
            for i in range(count)
        ]
        get_image_collection_thumbnails(
            col,
            out_dir,
            vis_params={
                "min": 0,
                "max": 255,
                "bands": video_args["bands"],
            },
            dimensions=dimensions,
            names=names,
        )
        make_gif(
            names,
            out_gif,
            fps=frames_per_second,
            loop=loop,
            mp4=False,
            clean_up=True,
        )
    else:
        download_ee_video(col, video_args, out_gif)

    if title is not None and isinstance(title, str):
        add_text_to_gif(
            out_gif,
            out_gif,
            xy=title_xy,
            text_sequence=title,
            font_type=font_type,
            font_size=font_size,
            font_color=font_color,
            add_progress_bar=add_progress_bar,
            progress_bar_color=progress_bar_color,
            progress_bar_height=progress_bar_height,
            duration=1000 / frames_per_second,
            loop=loop,
        )
    if add_text:
        if text_sequence is None:
            text_sequence = col.aggregate_array("system:date").getInfo()
        add_text_to_gif(
            out_gif,
            out_gif,
            xy=text_xy,
            text_sequence=text_sequence,
            font_type=font_type,
            font_size=font_size,
            font_color=font_color,
            add_progress_bar=add_progress_bar,
            progress_bar_color=progress_bar_color,
            progress_bar_height=progress_bar_height,
            duration=1000 / frames_per_second,
            loop=loop,
        )
    if add_colorbar:
        colorbar = save_colorbar(
            None,
            colorbar_width,
            colorbar_height,
            vis_params["min"],
            vis_params["max"],
            vis_params["palette"],
            label=colorbar_label,
            label_size=colorbar_label_size,
            label_weight=colorbar_label_weight,
            tick_size=colorbar_tick_size,
            bg_color=colorbar_bg_color,
            orientation=colorbar_orientation,
            dpi=colorbar_dpi,
            show_colorbar=False,
        )
        add_image_to_gif(out_gif, out_gif, colorbar, colorbar_xy, colorbar_size)

    if os.path.exists(out_gif):
        reduce_gif_size(out_gif)

    if isinstance(fading, bool):
        fading = int(fading)
    if fading > 0:
        gif_fading(out_gif, out_gif, duration=fading, verbose=False)

    if mp4:
        out_mp4 = out_gif.replace(".gif", ".mp4")
        gif_to_mp4(out_gif, out_mp4)

    return out_gif

create_timeseries(collection, start_date, end_date, region=None, bands=None, frequency='year', reducer='median', drop_empty=True, date_format=None, parallel_scale=1, step=1)

Creates a timeseries from a collection of images by a specified frequency and reducer.

Parameters:

Name	Type	Description	Default
collection	str | ImageCollection	The collection of images to create a timeseries from. It can be a string representing the collection ID or an ee.ImageCollection object.	required
start_date	str	The start date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.	required
end_date	str	The end date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.	required
region	Geometry	The region to use to filter the collection of images. It must be an ee.Geometry object. Defaults to None.	None
bands	list	The list of bands to use to create the timeseries. It must be a list of strings. Defaults to None.	None
frequency	str	The frequency of the timeseries. It must be one of the following: 'year', 'month', 'day', 'hour', 'minute', 'second'. Defaults to 'year'.	'year'
reducer	str	The reducer to use to reduce the collection of images to a single value. It can be one of the following: 'median', 'mean', 'min', 'max', 'variance', 'sum'. Defaults to 'median'.	'median'
drop_empty	bool	Whether to drop empty images from the timeseries. Defaults to True.	True
date_format	str	A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.	None
parallel_scale	int	A scaling factor used to limit memory use; using a larger parallel_scale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.	1
step	int	The step size to use when creating the date sequence. Defaults to 1.	1

Returns:

Type	Description
	ee.ImageCollection: The timeseries.

Source code in geemap/timelapse.py

def create_timeseries(
    collection,
    start_date,
    end_date,
    region=None,
    bands=None,
    frequency="year",
    reducer="median",
    drop_empty=True,
    date_format=None,
    parallel_scale=1,
    step=1,
):
    """Creates a timeseries from a collection of images by a specified frequency and reducer.

    Args:
        collection (str | ee.ImageCollection): The collection of images to create a timeseries from. It can be a string representing the collection ID or an ee.ImageCollection object.
        start_date (str): The start date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.
        end_date (str): The end date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.
        region (ee.Geometry, optional): The region to use to filter the collection of images. It must be an ee.Geometry object. Defaults to None.
        bands (list, optional): The list of bands to use to create the timeseries. It must be a list of strings. Defaults to None.
        frequency (str, optional): The frequency of the timeseries. It must be one of the following: 'year', 'month', 'day', 'hour', 'minute', 'second'. Defaults to 'year'.
        reducer (str, optional):  The reducer to use to reduce the collection of images to a single value. It can be one of the following: 'median', 'mean', 'min', 'max', 'variance', 'sum'. Defaults to 'median'.
        drop_empty (bool, optional): Whether to drop empty images from the timeseries. Defaults to True.
        date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.
        parallel_scale (int, optional): A scaling factor used to limit memory use; using a larger parallel_scale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.
        step (int, optional): The step size to use when creating the date sequence. Defaults to 1.

    Returns:
        ee.ImageCollection: The timeseries.
    """
    if not isinstance(collection, ee.ImageCollection):
        if isinstance(collection, str):
            collection = ee.ImageCollection(collection)
        else:
            raise Exception(
                "The collection must be an ee.ImageCollection object or asset id."
            )

    if bands is not None:
        collection = collection.select(bands)
    else:
        bands = collection.first().bandNames()

    feq_dict = {
        "year": "YYYY",
        "month": "YYYY-MM",
        "quarter": "YYYY-MM",
        "week": "YYYY-MM-dd",
        "day": "YYYY-MM-dd",
        "hour": "YYYY-MM-dd HH",
        "minute": "YYYY-MM-dd HH:mm",
        "second": "YYYY-MM-dd HH:mm:ss",
    }

    if date_format is None:
        date_format = feq_dict[frequency]

    dates = date_sequence(start_date, end_date, frequency, date_format, step)

    try:
        reducer = eval(f"ee.Reducer.{reducer}()")
    except Exception as e:
        print("The provided reducer is invalid.")
        raise Exception(e)

    def create_image(date):
        start = ee.Date(date)
        if frequency == "quarter":
            end = start.advance(3, "month")
        else:
            end = start.advance(1, frequency)

        if region is None:
            sub_col = collection.filterDate(start, end)
            image = sub_col.reduce(reducer, parallel_scale)

        else:
            sub_col = collection.filterDate(start, end).filterBounds(region)
            image = ee.Image(
                ee.Algorithms.If(
                    ee.Algorithms.ObjectType(region).equals("FeatureCollection"),
                    sub_col.reduce(reducer, parallel_scale).clipToCollection(region),
                    sub_col.reduce(reducer, parallel_scale).clip(region),
                )
            )
        return image.set(
            {
                "system:time_start": ee.Date(date).millis(),
                "system:date": ee.Date(date).format(date_format),
                "empty": sub_col.limit(1).size().eq(0),
            }
        ).rename(bands)

    try:
        images = ee.ImageCollection(dates.map(create_image))
        if drop_empty:
            return images.filterMetadata("empty", "equals", 0)
        else:
            return images
    except Exception as e:
        raise Exception(e)

credentials_in_colab()

Checks if the ee credentials file exists in Google Colab.

Returns:

Name	Type	Description
bool		Returns True if Google Drive is mounted, False otherwise.

Source code in geemap/common.py

def credentials_in_colab():
    """Checks if the ee credentials file exists in Google Colab.

    Returns:
        bool: Returns True if Google Drive is mounted, False otherwise.
    """
    credentials_path = "/root/.config/earthengine/credentials"
    if os.path.exists(credentials_path):
        return True
    else:
        return False

credentials_in_drive()

Checks if the ee credentials file exists in Google Drive.

Returns:

Name	Type	Description
bool		Returns True if Google Drive is mounted, False otherwise.

Source code in geemap/common.py

def credentials_in_drive():
    """Checks if the ee credentials file exists in Google Drive.

    Returns:
        bool: Returns True if Google Drive is mounted, False otherwise.
    """
    credentials_path = "/content/drive/My Drive/.config/earthengine/credentials"
    if os.path.exists(credentials_path):
        return True
    else:
        return False

csv_points_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude')

Converts a csv file containing points (latitude, longitude) into a shapefile.

Parameters:

Name	Type	Description	Default
in_csv	str	File path or HTTP URL to the input csv file. For example, https://raw.githubusercontent.com/giswqs/data/main/world/world_cities.csv	required
out_shp	str	File path to the output shapefile.	required
latitude	str	Column name for the latitude column. Defaults to 'latitude'.	'latitude'
longitude	str	Column name for the longitude column. Defaults to 'longitude'.	'longitude'

Source code in geemap/common.py

def csv_points_to_shp(in_csv, out_shp, latitude="latitude", longitude="longitude"):
    """Converts a csv file containing points (latitude, longitude) into a shapefile.

    Args:
        in_csv (str): File path or HTTP URL to the input csv file. For example, https://raw.githubusercontent.com/giswqs/data/main/world/world_cities.csv
        out_shp (str): File path to the output shapefile.
        latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.
        longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.

    """
    import whitebox

    if in_csv.startswith("http") and in_csv.endswith(".csv"):
        out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
        out_name = os.path.basename(in_csv)

        if not os.path.exists(out_dir):
            os.makedirs(out_dir)
        download_from_url(in_csv, out_dir=out_dir, verbose=False)
        in_csv = os.path.join(out_dir, out_name)

    wbt = whitebox.WhiteboxTools()
    in_csv = os.path.abspath(in_csv)
    out_shp = os.path.abspath(out_shp)

    if not os.path.exists(in_csv):
        raise Exception("The provided csv file does not exist.")

    with open(in_csv, encoding="utf-8") as csv_file:
        reader = csv.DictReader(csv_file)
        fields = reader.fieldnames
        xfield = fields.index(longitude)
        yfield = fields.index(latitude)

    wbt.csv_points_to_vector(in_csv, out_shp, xfield=xfield, yfield=yfield, epsg=4326)

csv_to_df(in_csv, **kwargs)

Converts a CSV file to pandas dataframe.

Parameters:

Name	Type	Description	Default
in_csv	str	File path to the input CSV.	required

Returns:

Type	Description
	pd.DataFrame: pandas DataFrame

Source code in geemap/common.py

def csv_to_df(in_csv, **kwargs):
    """Converts a CSV file to pandas dataframe.

    Args:
        in_csv (str): File path to the input CSV.

    Returns:
        pd.DataFrame: pandas DataFrame
    """
    import pandas as pd

    in_csv = github_raw_url(in_csv)

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

csv_to_ee(in_csv, latitude='latitude', longitude='longitude', encoding='utf-8', geodesic=True)

Creates points for a CSV file and exports data as a GeoJSON.

Parameters:

Name	Type	Description	Default
in_csv	str	The file path to the input CSV file.	required
latitude	str	The name of the column containing latitude coordinates. Defaults to "latitude".	'latitude'
longitude	str	The name of the column containing longitude coordinates. Defaults to "longitude".	'longitude'
encoding	str	The encoding of characters. Defaults to "utf-8".	'utf-8'
geodesic	bool	Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.	True

Returns:

Name	Type	Description
ee_object		An ee.Geometry object

Source code in geemap/common.py

def csv_to_ee(
    in_csv, latitude="latitude", longitude="longitude", encoding="utf-8", geodesic=True
):
    """Creates points for a CSV file and exports data as a GeoJSON.

    Args:
        in_csv (str): The file path to the input CSV file.
        latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
        longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
        encoding (str, optional): The encoding of characters. Defaults to "utf-8".
        geodesic (bool, optional): Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.

    Returns:
        ee_object: An ee.Geometry object
    """

    geojson = csv_to_geojson(
        in_csv, latitude=latitude, longitude=longitude, encoding=encoding
    )
    fc = geojson_to_ee(geojson, geodesic=geodesic)
    return fc

csv_to_gdf(in_csv, latitude='latitude', longitude='longitude', encoding='utf-8')

Creates points for a CSV file and converts them to a GeoDataFrame.

Parameters:

Name	Type	Description	Default
in_csv	str	The file path to the input CSV file.	required
latitude	str	The name of the column containing latitude coordinates. Defaults to "latitude".	'latitude'
longitude	str	The name of the column containing longitude coordinates. Defaults to "longitude".	'longitude'
encoding	str	The encoding of characters. Defaults to "utf-8".	'utf-8'

Returns:

Name	Type	Description
object		GeoDataFrame.

Source code in geemap/common.py

def csv_to_gdf(in_csv, latitude="latitude", longitude="longitude", encoding="utf-8"):
    """Creates points for a CSV file and converts them to a GeoDataFrame.

    Args:
        in_csv (str): The file path to the input CSV file.
        latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
        longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
        encoding (str, optional): The encoding of characters. Defaults to "utf-8".

    Returns:
        object: GeoDataFrame.
    """

    check_package(name="geopandas", URL="https://geopandas.org")

    import geopandas as gpd

    out_dir = os.getcwd()

    out_geojson = os.path.join(out_dir, random_string() + ".geojson")
    csv_to_geojson(in_csv, out_geojson, latitude, longitude, encoding)

    gdf = gpd.read_file(out_geojson)
    os.remove(out_geojson)
    return gdf

csv_to_geojson(in_csv, out_geojson=None, latitude='latitude', longitude='longitude', encoding='utf-8')

Creates points for a CSV file and exports data as a GeoJSON.

Parameters:

Name	Type	Description	Default
in_csv	str	The file path to the input CSV file.	required
out_geojson	str	The file path to the exported GeoJSON. Default to None.	None
latitude	str	The name of the column containing latitude coordinates. Defaults to "latitude".	'latitude'
longitude	str	The name of the column containing longitude coordinates. Defaults to "longitude".	'longitude'
encoding	str	The encoding of characters. Defaults to "utf-8".	'utf-8'

Source code in geemap/common.py

def csv_to_geojson(
    in_csv,
    out_geojson=None,
    latitude="latitude",
    longitude="longitude",
    encoding="utf-8",
):
    """Creates points for a CSV file and exports data as a GeoJSON.

    Args:
        in_csv (str): The file path to the input CSV file.
        out_geojson (str): The file path to the exported GeoJSON. Default to None.
        latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
        longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
        encoding (str, optional): The encoding of characters. Defaults to "utf-8".

    """

    import pandas as pd

    in_csv = github_raw_url(in_csv)

    if out_geojson is not None:
        out_geojson = check_file_path(out_geojson)

    df = pd.read_csv(in_csv)
    geojson = df_to_geojson(
        df, latitude=latitude, longitude=longitude, encoding=encoding
    )

    if out_geojson is None:
        return geojson
    else:
        with open(out_geojson, "w", encoding=encoding) as f:
            f.write(json.dumps(geojson))

csv_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude', encoding='utf-8')

Converts a csv file with latlon info to a point shapefile.

Parameters:

Name	Type	Description	Default
in_csv	str	The input csv file containing longitude and latitude columns.	required
out_shp	str	The file path to the output shapefile.	required
latitude	str	The column name of the latitude column. Defaults to 'latitude'.	'latitude'
longitude	str	The column name of the longitude column. Defaults to 'longitude'.	'longitude'

Source code in geemap/common.py

def csv_to_shp(
    in_csv, out_shp, latitude="latitude", longitude="longitude", encoding="utf-8"
):
    """Converts a csv file with latlon info to a point shapefile.

    Args:
        in_csv (str): The input csv file containing longitude and latitude columns.
        out_shp (str): The file path to the output shapefile.
        latitude (str, optional): The column name of the latitude column. Defaults to 'latitude'.
        longitude (str, optional): The column name of the longitude column. Defaults to 'longitude'.
    """
    import shapefile as shp

    if in_csv.startswith("http") and in_csv.endswith(".csv"):
        in_csv = github_raw_url(in_csv)
        in_csv = download_file(in_csv, quiet=True, overwrite=True)

    try:
        points = shp.Writer(out_shp, shapeType=shp.POINT)
        with open(in_csv, encoding=encoding) as csvfile:
            csvreader = csv.DictReader(csvfile)
            header = csvreader.fieldnames
            [points.field(field) for field in header]
            for row in csvreader:
                points.point((float(row[longitude])), (float(row[latitude])))
                points.record(*tuple([row[f] for f in header]))

        out_prj = out_shp.replace(".shp", ".prj")
        with open(out_prj, "w") as f:
            prj_str = 'GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.0174532925199433]] '
            f.write(prj_str)

    except Exception as e:
        raise Exception(e)

csv_to_vector(in_csv, output, latitude='latitude', longitude='longitude', encoding='utf-8', **kwargs)

Creates points for a CSV file and converts them to a vector dataset.

Parameters:

Name	Type	Description	Default
in_csv	str	The file path to the input CSV file.	required
output	str	The file path to the output vector dataset.	required
latitude	str	The name of the column containing latitude coordinates. Defaults to "latitude".	'latitude'
longitude	str	The name of the column containing longitude coordinates. Defaults to "longitude".	'longitude'
encoding	str	The encoding of characters. Defaults to "utf-8".	'utf-8'

Source code in geemap/common.py

def csv_to_vector(
    in_csv,
    output,
    latitude="latitude",
    longitude="longitude",
    encoding="utf-8",
    **kwargs,
):
    """Creates points for a CSV file and converts them to a vector dataset.

    Args:
        in_csv (str): The file path to the input CSV file.
        output (str): The file path to the output vector dataset.
        latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
        longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
        encoding (str, optional): The encoding of characters. Defaults to "utf-8".

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

date_sequence(start, end, unit, date_format='YYYY-MM-dd', step=1)

Creates a date sequence.

Parameters:

Name	Type	Description	Default
start	str	The start date, e.g., '2000-01-01'.	required
end	str	The end date, e.g., '2000-12-31'.	required
unit	str	One of 'year', 'quarter', 'month' 'week', 'day', 'hour', 'minute', or 'second'.	required
date_format	str	A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.	'YYYY-MM-dd'
step	int	The step size. Defaults to 1.	1

Returns:

Type	Description
	ee.List: A list of date sequence.

Source code in geemap/common.py

def date_sequence(start, end, unit, date_format="YYYY-MM-dd", step=1):
    """Creates a date sequence.

    Args:
        start (str): The start date, e.g., '2000-01-01'.
        end (str): The end date, e.g., '2000-12-31'.
        unit (str): One of 'year', 'quarter', 'month' 'week', 'day', 'hour', 'minute', or 'second'.
        date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.
        step (int, optional): The step size. Defaults to 1.

    Returns:
        ee.List: A list of date sequence.
    """

    def get_quarter(d):
        return str((int(d[5:7]) - 1) // 3 * 3 + 1).zfill(2)

    def get_monday(d):
        date_obj = datetime.datetime.strptime(d, "%Y-%m-%d")
        start_of_week = date_obj - datetime.timedelta(days=date_obj.weekday())
        return start_of_week.strftime("%Y-%m-%d")

    if unit == "year":
        start = start[:4] + "-01-01"
    elif unit == "month":
        start = start[:7] + "-01"
    elif unit == "quarter":
        start = start[:5] + get_quarter(start) + "-01"
    elif unit == "week":
        start = get_monday(start)

    start_date = ee.Date(start)
    end_date = ee.Date(end)

    if unit != "quarter":
        count = ee.Number(end_date.difference(start_date, unit)).toInt()
        num_seq = ee.List.sequence(0, count)
        if step > 1:
            num_seq = num_seq.slice(0, num_seq.size(), step)
        date_seq = num_seq.map(
            lambda d: start_date.advance(d, unit).format(date_format)
        )

    else:
        unit = "month"
        count = ee.Number(end_date.difference(start_date, unit)).divide(3).toInt()
        num_seq = ee.List.sequence(0, count.multiply(3), 3)
        date_seq = num_seq.map(
            lambda d: start_date.advance(d, unit).format(date_format)
        )

    return date_seq

delete_shp(in_shp, verbose=False)

Deletes a shapefile.

Parameters:

Name	Type	Description	Default
in_shp	str	The input shapefile to delete.	required
verbose	bool	Whether to print out descriptive text. Defaults to False.	False

Source code in geemap/common.py

def delete_shp(in_shp, verbose=False):
    """Deletes a shapefile.

    Args:
        in_shp (str): The input shapefile to delete.
        verbose (bool, optional): Whether to print out descriptive text. Defaults to False.
    """
    from pathlib import Path

    in_shp = os.path.abspath(in_shp)
    in_dir = os.path.dirname(in_shp)
    basename = os.path.basename(in_shp).replace(".shp", "")

    files = Path(in_dir).rglob(basename + ".*")

    for file in files:
        filepath = os.path.join(in_dir, str(file))
        try:
            os.remove(filepath)
            if verbose:
                print(f"Deleted {filepath}")
        except Exception as e:
            if verbose:
                print(e)

df_to_ee(df, latitude='latitude', longitude='longitude', **kwargs)

Converts a pandas DataFrame to ee.FeatureCollection.

Parameters:

Name	Type	Description	Default
df	DataFrame	An input pandas.DataFrame.	required
latitude	str	Column name for the latitude column. Defaults to 'latitude'.	'latitude'
longitude	str	Column name for the longitude column. Defaults to 'longitude'.	'longitude'

Raises:

Type	Description
TypeError	The input data type must be pandas.DataFrame.

Returns:

Type	Description
	ee.FeatureCollection: The ee.FeatureCollection converted from the input pandas DataFrame.

Source code in geemap/common.py

def df_to_ee(df, latitude="latitude", longitude="longitude", **kwargs):
    """Converts a pandas DataFrame to ee.FeatureCollection.

    Args:
        df (pandas.DataFrame): An input pandas.DataFrame.
        latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.
        longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.

    Raises:
        TypeError: The input data type must be pandas.DataFrame.

    Returns:
        ee.FeatureCollection: The ee.FeatureCollection converted from the input pandas DataFrame.
    """
    import pandas as pd

    if not isinstance(df, pd.DataFrame):
        raise TypeError("The input data type must be pandas.DataFrame.")

    geojson = df_to_geojson(df, latitude=latitude, longitude=longitude)
    fc = geojson_to_ee(geojson)

    return fc

df_to_geojson(df, out_geojson=None, latitude='latitude', longitude='longitude', encoding='utf-8')

Creates points for a Pandas DataFrame and exports data as a GeoJSON.

Parameters:

Name	Type	Description	Default
df	DataFrame	The input Pandas DataFrame.	required
out_geojson	str	The file path to the exported GeoJSON. Default to None.	None
latitude	str	The name of the column containing latitude coordinates. Defaults to "latitude".	'latitude'
longitude	str	The name of the column containing longitude coordinates. Defaults to "longitude".	'longitude'
encoding	str	The encoding of characters. Defaults to "utf-8".	'utf-8'

Source code in geemap/common.py

def df_to_geojson(
    df,
    out_geojson=None,
    latitude="latitude",
    longitude="longitude",
    encoding="utf-8",
):
    """Creates points for a Pandas DataFrame and exports data as a GeoJSON.

    Args:
        df (pandas.DataFrame): The input Pandas DataFrame.
        out_geojson (str): The file path to the exported GeoJSON. Default to None.
        latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
        longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
        encoding (str, optional): The encoding of characters. Defaults to "utf-8".

    """

    from geojson import Feature, FeatureCollection, Point

    if out_geojson is not None:
        out_dir = os.path.dirname(os.path.abspath(out_geojson))
        if not os.path.exists(out_dir):
            os.makedirs(out_dir)

    features = df.apply(
        lambda row: Feature(
            geometry=Point((float(row[longitude]), float(row[latitude]))),
            properties=dict(row),
        ),
        axis=1,
    ).tolist()

    geojson = FeatureCollection(features=features)

    if out_geojson is None:
        return geojson
    else:
        with open(out_geojson, "w", encoding=encoding) as f:
            f.write(json.dumps(geojson))

dict_to_csv(data_dict, out_csv, by_row=False, timeout=300, proxies=None)

Downloads an ee.Dictionary as a CSV file.

Parameters:

Name	Type	Description	Default
data_dict	Dictionary	The input ee.Dictionary.	required
out_csv	str	The output file path to the CSV file.	required
by_row	bool	Whether to use by row or by column. Defaults to False.	False
timeout	int	Timeout in seconds. Defaults to 300 seconds.	300
proxies	dict	Proxy settings. Defaults to None.	None

Source code in geemap/common.py

def dict_to_csv(data_dict, out_csv, by_row=False, timeout=300, proxies=None):
    """Downloads an ee.Dictionary as a CSV file.

    Args:
        data_dict (ee.Dictionary): The input ee.Dictionary.
        out_csv (str): The output file path to the CSV file.
        by_row (bool, optional): Whether to use by row or by column. Defaults to False.
        timeout (int, optional): Timeout in seconds. Defaults to 300 seconds.
        proxies (dict, optional): Proxy settings. Defaults to None.
    """

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

    if not by_row:
        csv_feature = ee.Feature(None, data_dict)
        csv_feat_col = ee.FeatureCollection([csv_feature])
    else:
        keys = data_dict.keys()
        data = keys.map(lambda k: ee.Dictionary({"name": k, "value": data_dict.get(k)}))
        csv_feature = data.map(lambda f: ee.Feature(None, f))
        csv_feat_col = ee.FeatureCollection(csv_feature)

    ee_export_vector(csv_feat_col, out_csv, timeout=timeout, proxies=proxies)

display_html(src, width=950, height=600)

Display an HTML file in a Jupyter Notebook.

Args src (str): File path to HTML file. width (int, optional): Width of the map. Defaults to 950. height (int, optional): Height of the map. Defaults to 600.

Source code in geemap/common.py

def display_html(src, width=950, height=600):
    """Display an HTML file in a Jupyter Notebook.

    Args
        src (str): File path to HTML file.
        width (int, optional): Width of the map. Defaults to 950.
        height (int, optional): Height of the map. Defaults to 600.
    """
    if not os.path.isfile(src):
        raise ValueError(f"{src} is not a valid file path.")
    display(IFrame(src=src, width=width, height=height))

download_ee_image(image, filename, region=None, crs=None, crs_transform=None, scale=None, resampling='near', dtype=None, overwrite=True, num_threads=None, max_tile_size=None, max_tile_dim=None, shape=None, scale_offset=False, unmask_value=None, **kwargs)

Download an Earth Engine Image as a GeoTIFF. Images larger than the `Earth Engine size limit are split and downloaded as separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574

Parameters:

Name	Type	Description	Default
image	Image	The image to be downloaded.	required
filename	str	Name of the destination file.	required
region	Geometry	Region defined by geojson polygon in WGS84. Defaults to the entire image granule.	None
crs	str	Reproject image(s) to this EPSG or WKT CRS. Where image bands have different CRSs, all are re-projected to this CRS. Defaults to the CRS of the minimum scale band.	None
crs_transform	list	tuple of float, list of float, rio.Affine, optional List of 6 numbers specifying an affine transform in the specified CRS. In row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. All bands are re-projected to this transform.	None
scale	float	Resample image(s) to this pixel scale (size) (m). Where image bands have different scales, all are resampled to this scale. Defaults to the minimum scale of image bands.	None
resampling	ResamplingMethod	Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None.	'near'
dtype	str	Convert to this data type (uint8, int8, uint16, int16, uint32, int32, float32 or float64). Defaults to auto select a minimum size type that can represent the range of pixel values.	None
overwrite	bool	Overwrite the destination file if it exists. Defaults to True.	True
num_threads	int	Number of tiles to download concurrently. Defaults to a sensible auto value.	None
max_tile_size		int, optional Maximum tile size (MB). If None, defaults to the Earth Engine download size limit (32 MB).	None
max_tile_dim		int, optional Maximum tile width/height (pixels). If None, defaults to Earth Engine download limit (10000).	None
shape		tuple of int, optional (height, width) dimensions to export (pixels).	None
scale_offset		bool, optional Whether to apply any EE band scales and offsets to the image.	False
unmask_value	float	The value to use for pixels that are masked in the input image. If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None.	None

Source code in geemap/common.py

def download_ee_image(
    image,
    filename,
    region=None,
    crs=None,
    crs_transform=None,
    scale=None,
    resampling="near",
    dtype=None,
    overwrite=True,
    num_threads=None,
    max_tile_size=None,
    max_tile_dim=None,
    shape=None,
    scale_offset=False,
    unmask_value=None,
    **kwargs,
):
    """Download an Earth Engine Image as a GeoTIFF. Images larger than the `Earth Engine size limit are split and downloaded as
        separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574

    Args:
        image (ee.Image): The image to be downloaded.
        filename (str): Name of the destination file.
        region (ee.Geometry, optional): Region defined by geojson polygon in WGS84. Defaults to the entire image granule.
        crs (str, optional): Reproject image(s) to this EPSG or WKT CRS.  Where image bands have different CRSs, all are
            re-projected to this CRS. Defaults to the CRS of the minimum scale band.
        crs_transform (list, optional): tuple of float, list of float, rio.Affine, optional
            List of 6 numbers specifying an affine transform in the specified CRS.  In row-major order:
            [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation].  All bands are re-projected to
            this transform.
        scale (float, optional): Resample image(s) to this pixel scale (size) (m).  Where image bands have different scales,
            all are resampled to this scale.  Defaults to the minimum scale of image bands.
        resampling (ResamplingMethod, optional): Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None.
        dtype (str, optional): Convert to this data type (`uint8`, `int8`, `uint16`, `int16`, `uint32`, `int32`, `float32`
            or `float64`).  Defaults to auto select a minimum size type that can represent the range of pixel values.
        overwrite (bool, optional): Overwrite the destination file if it exists. Defaults to True.
        num_threads (int, optional): Number of tiles to download concurrently. Defaults to a sensible auto value.
        max_tile_size: int, optional
            Maximum tile size (MB).  If None, defaults to the Earth Engine download size limit (32 MB).
        max_tile_dim: int, optional
            Maximum tile width/height (pixels).  If None, defaults to Earth Engine download limit (10000).
        shape: tuple of int, optional
            (height, width) dimensions to export (pixels).
        scale_offset: bool, optional
            Whether to apply any EE band scales and offsets to the image.
        unmask_value (float, optional): The value to use for pixels that are masked in the input image. If the exported image contains
            zero values, you should set the unmask value to a  non-zero value so that the zero values are not treated as missing data. Defaults to None.

    """

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

    try:
        import geedim as gd
    except ImportError:
        raise ImportError(
            "Please install geedim using `pip install geedim` or `conda install -c conda-forge geedim`"
        )

    if not isinstance(image, ee.Image):
        raise ValueError("image must be an ee.Image.")

    if unmask_value is not None:
        if isinstance(region, ee.Geometry):
            image = image.clip(region)
        elif isinstance(region, ee.FeatureCollection):
            image = image.clipToCollection(region)
        image = image.unmask(unmask_value, sameFootprint=False)

    if region is not None:
        kwargs["region"] = region

    if crs is not None:
        kwargs["crs"] = crs

    if crs_transform is not None:
        kwargs["crs_transform"] = crs_transform

    if scale is not None:
        kwargs["scale"] = scale

    if resampling is not None:
        kwargs["resampling"] = resampling

    if dtype is not None:
        kwargs["dtype"] = dtype

    if max_tile_size is not None:
        kwargs["max_tile_size"] = max_tile_size

    if max_tile_dim is not None:
        kwargs["max_tile_dim"] = max_tile_dim

    if shape is not None:
        kwargs["shape"] = shape

    if scale_offset:
        kwargs["scale_offset"] = scale_offset

    img = gd.download.BaseImage(image)
    img.download(filename, overwrite=overwrite, num_threads=num_threads, **kwargs)

download_ee_image_collection(collection, out_dir=None, filenames=None, region=None, crs=None, crs_transform=None, scale=None, resampling='near', dtype=None, overwrite=True, num_threads=None, max_tile_size=None, max_tile_dim=None, shape=None, scale_offset=False, unmask_value=None, **kwargs)

Download an Earth Engine ImageCollection as GeoTIFFs. Images larger than the `Earth Engine size limit are split and downloaded as separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574

Parameters:

Name	Type	Description	Default
collection	ImageCollection	The image collection to be downloaded.	required
out_dir	str	The directory to save the downloaded images. Defaults to the current directory.	None
filenames	list	A list of filenames to use for the downloaded images. Defaults to the image ID.	None
region	Geometry	Region defined by geojson polygon in WGS84. Defaults to the entire image granule.	None
crs	str	Reproject image(s) to this EPSG or WKT CRS. Where image bands have different CRSs, all are re-projected to this CRS. Defaults to the CRS of the minimum scale band.	None
crs_transform	list	tuple of float, list of float, rio.Affine, optional List of 6 numbers specifying an affine transform in the specified CRS. In row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. All bands are re-projected to this transform.	None
scale	float	Resample image(s) to this pixel scale (size) (m). Where image bands have different scales, all are resampled to this scale. Defaults to the minimum scale of image bands.	None
resampling	ResamplingMethod	Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None.	'near'
dtype	str	Convert to this data type (uint8, int8, uint16, int16, uint32, int32, float32 or float64). Defaults to auto select a minimum size type that can represent the range of pixel values.	None
overwrite	bool	Overwrite the destination file if it exists. Defaults to True.	True
num_threads	int	Number of tiles to download concurrently. Defaults to a sensible auto value.	None
max_tile_size		int, optional Maximum tile size (MB). If None, defaults to the Earth Engine download size limit (32 MB).	None
max_tile_dim		int, optional Maximum tile width/height (pixels). If None, defaults to Earth Engine download limit (10000).	None
shape		tuple of int, optional (height, width) dimensions to export (pixels).	None
scale_offset		bool, optional Whether to apply any EE band scales and offsets to the image.	False
unmask_value	float	The value to use for pixels that are masked in the input image. If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None.	None

Source code in geemap/common.py

def download_ee_image_collection(
    collection,
    out_dir=None,
    filenames=None,
    region=None,
    crs=None,
    crs_transform=None,
    scale=None,
    resampling="near",
    dtype=None,
    overwrite=True,
    num_threads=None,
    max_tile_size=None,
    max_tile_dim=None,
    shape=None,
    scale_offset=False,
    unmask_value=None,
    **kwargs,
):
    """Download an Earth Engine ImageCollection as GeoTIFFs. Images larger than the `Earth Engine size limit are split and downloaded as
        separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574

    Args:
        collection (ee.ImageCollection): The image collection to be downloaded.
        out_dir (str, optional): The directory to save the downloaded images. Defaults to the current directory.
        filenames (list, optional): A list of filenames to use for the downloaded images. Defaults to the image ID.
        region (ee.Geometry, optional): Region defined by geojson polygon in WGS84. Defaults to the entire image granule.
        crs (str, optional): Reproject image(s) to this EPSG or WKT CRS.  Where image bands have different CRSs, all are
            re-projected to this CRS. Defaults to the CRS of the minimum scale band.
        crs_transform (list, optional): tuple of float, list of float, rio.Affine, optional
            List of 6 numbers specifying an affine transform in the specified CRS.  In row-major order:
            [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation].  All bands are re-projected to
            this transform.
        scale (float, optional): Resample image(s) to this pixel scale (size) (m).  Where image bands have different scales,
            all are resampled to this scale.  Defaults to the minimum scale of image bands.
        resampling (ResamplingMethod, optional): Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None.
        dtype (str, optional): Convert to this data type (`uint8`, `int8`, `uint16`, `int16`, `uint32`, `int32`, `float32`
            or `float64`).  Defaults to auto select a minimum size type that can represent the range of pixel values.
        overwrite (bool, optional): Overwrite the destination file if it exists. Defaults to True.
        num_threads (int, optional): Number of tiles to download concurrently. Defaults to a sensible auto value.
        max_tile_size: int, optional
            Maximum tile size (MB).  If None, defaults to the Earth Engine download size limit (32 MB).
        max_tile_dim: int, optional
            Maximum tile width/height (pixels).  If None, defaults to Earth Engine download limit (10000).
        shape: tuple of int, optional
            (height, width) dimensions to export (pixels).
        scale_offset: bool, optional
            Whether to apply any EE band scales and offsets to the image.
        unmask_value (float, optional): The value to use for pixels that are masked in the input image. If the exported image contains zero values,
            you should set the unmask value to a  non-zero value so that the zero values are not treated as missing data. Defaults to None.
    """

    if not isinstance(collection, ee.ImageCollection):
        raise ValueError("ee_object must be an ee.ImageCollection.")

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

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

    try:
        count = int(collection.size().getInfo())
        print(f"Total number of images: {count}\n")

        if filenames is not None:
            if len(filenames) != count:
                raise ValueError(
                    f"The number of filenames must match the number of image: {count}"
                )

        for i in range(0, count):
            image = ee.Image(collection.toList(count).get(i))
            if filenames is not None:
                name = filenames[i]
                if not name.endswith(".tif"):
                    name = name + ".tif"
            else:
                name = image.get("system:index").getInfo() + ".tif"
            filename = os.path.join(os.path.abspath(out_dir), name)
            print(f"Downloading {i + 1}/{count}: {name}")
            download_ee_image(
                image,
                filename,
                region,
                crs,
                crs_transform,
                scale,
                resampling,
                dtype,
                overwrite,
                num_threads,
                max_tile_size,
                max_tile_dim,
                shape,
                scale_offset,
                unmask_value,
                **kwargs,
            )

    except Exception as e:
        raise Exception(f"Error downloading image collection: {e}")

download_ee_image_tiles(image, features, out_dir=None, prefix=None, crs=None, crs_transform=None, scale=None, resampling='near', dtype=None, overwrite=True, num_threads=None, max_tile_size=None, max_tile_dim=None, shape=None, scale_offset=False, unmask_value=None, column=None, **kwargs)

Download an Earth Engine Image as small tiles based on ee.FeatureCollection. Images larger than the `Earth Engine size limit are split and downloaded as separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574

Parameters:

Name	Type	Description	Default
image	Image	The image to be downloaded.	required
features	FeatureCollection	The features to loop through to download image.	required
out_dir	str	The output directory. Defaults to None.	None
prefix	str	The prefix for the output file. Defaults to None.	None
crs	str	Reproject image(s) to this EPSG or WKT CRS. Where image bands have different CRSs, all are re-projected to this CRS. Defaults to the CRS of the minimum scale band.	None
crs_transform	list	tuple of float, list of float, rio.Affine, optional List of 6 numbers specifying an affine transform in the specified CRS. In row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. All bands are re-projected to this transform.	None
scale	float	Resample image(s) to this pixel scale (size) (m). Where image bands have different scales, all are resampled to this scale. Defaults to the minimum scale of image bands.	None
resampling	ResamplingMethod	Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None.	'near'
dtype	str	Convert to this data type (uint8, int8, uint16, int16, uint32, int32, float32 or float64). Defaults to auto select a minimum size type that can represent the range of pixel values.	None
overwrite	bool	Overwrite the destination file if it exists. Defaults to True.	True
num_threads	int	Number of tiles to download concurrently. Defaults to a sensible auto value.	None
max_tile_size		int, optional Maximum tile size (MB). If None, defaults to the Earth Engine download size limit (32 MB).	None
max_tile_dim		int, optional Maximum tile width/height (pixels). If None, defaults to Earth Engine download limit (10000).	None
shape		tuple of int, optional (height, width) dimensions to export (pixels).	None
scale_offset		bool, optional Whether to apply any EE band scales and offsets to the image.	False
unmask_value	float	The value to use for pixels that are masked in the input image. If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None.	None
column	str	The column name to use for the filename. Defaults to None.	None

Source code in geemap/common.py

def download_ee_image_tiles(
    image,
    features,
    out_dir=None,
    prefix=None,
    crs=None,
    crs_transform=None,
    scale=None,
    resampling="near",
    dtype=None,
    overwrite=True,
    num_threads=None,
    max_tile_size=None,
    max_tile_dim=None,
    shape=None,
    scale_offset=False,
    unmask_value=None,
    column=None,
    **kwargs,
):
    """Download an Earth Engine Image as small tiles based on ee.FeatureCollection. Images larger than the `Earth Engine size limit are split and downloaded as
        separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574

    Args:
        image (ee.Image): The image to be downloaded.
        features (ee.FeatureCollection): The features to loop through to download image.
        out_dir (str, optional): The output directory. Defaults to None.
        prefix (str, optional): The prefix for the output file. Defaults to None.
        crs (str, optional): Reproject image(s) to this EPSG or WKT CRS.  Where image bands have different CRSs, all are
            re-projected to this CRS. Defaults to the CRS of the minimum scale band.
        crs_transform (list, optional): tuple of float, list of float, rio.Affine, optional
            List of 6 numbers specifying an affine transform in the specified CRS.  In row-major order:
            [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation].  All bands are re-projected to
            this transform.
        scale (float, optional): Resample image(s) to this pixel scale (size) (m).  Where image bands have different scales,
            all are resampled to this scale.  Defaults to the minimum scale of image bands.
        resampling (ResamplingMethod, optional): Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None.
        dtype (str, optional): Convert to this data type (`uint8`, `int8`, `uint16`, `int16`, `uint32`, `int32`, `float32`
            or `float64`).  Defaults to auto select a minimum size type that can represent the range of pixel values.
        overwrite (bool, optional): Overwrite the destination file if it exists. Defaults to True.
        num_threads (int, optional): Number of tiles to download concurrently. Defaults to a sensible auto value.
        max_tile_size: int, optional
            Maximum tile size (MB).  If None, defaults to the Earth Engine download size limit (32 MB).
        max_tile_dim: int, optional
            Maximum tile width/height (pixels).  If None, defaults to Earth Engine download limit (10000).
        shape: tuple of int, optional
            (height, width) dimensions to export (pixels).
        scale_offset: bool, optional
            Whether to apply any EE band scales and offsets to the image.
        unmask_value (float, optional): The value to use for pixels that are masked in the input image. If the exported image contains zero values,
            you should set the unmask value to a  non-zero value so that the zero values are not treated as missing data. Defaults to None.
        column (str, optional): The column name to use for the filename. Defaults to None.

    """
    import time

    start = time.time()

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

    if not isinstance(features, ee.FeatureCollection):
        raise ValueError("features must be an ee.FeatureCollection.")

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

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

    if prefix is None:
        prefix = ""

    count = features.size().getInfo()
    collection = features.toList(count)

    if column is not None:
        names = features.aggregate_array(column).getInfo()
    else:
        names = [str(i + 1).zfill(len(str(count))) for i in range(count)]

    for i in range(count):
        region = ee.Feature(collection.get(i)).geometry()
        filename = os.path.join(
            out_dir, "{}{}.tif".format(prefix, names[i].replace("/", "_"))
        )
        print(f"Downloading {i + 1}/{count}: {filename}")
        download_ee_image(
            image,
            filename,
            region,
            crs,
            crs_transform,
            scale,
            resampling,
            dtype,
            overwrite,
            num_threads,
            max_tile_size,
            max_tile_dim,
            shape,
            scale_offset,
            unmask_value,
            **kwargs,
        )

    print(f"Downloaded {count} tiles in {time.time() - start} seconds.")

download_ee_image_tiles_parallel(image, features, out_dir=None, prefix=None, crs=None, crs_transform=None, scale=None, resampling='near', dtype=None, overwrite=True, num_threads=None, max_tile_size=None, max_tile_dim=None, shape=None, scale_offset=False, unmask_value=None, column=None, job_args={'n_jobs': -1}, ee_init=True, project_id=None, **kwargs)

Download an Earth Engine Image as small tiles based on ee.FeatureCollection. Images larger than the `Earth Engine size limit are split and downloaded as separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574

Parameters:

Name	Type	Description	Default
image	Image	The image to be downloaded.	required
features	FeatureCollection	The features to loop through to download image.	required
out_dir	str	The output directory. Defaults to None.	None
prefix	str	The prefix for the output file. Defaults to None.	None
crs	str	Reproject image(s) to this EPSG or WKT CRS. Where image bands have different CRSs, all are re-projected to this CRS. Defaults to the CRS of the minimum scale band.	None
crs_transform	list	tuple of float, list of float, rio.Affine, optional List of 6 numbers specifying an affine transform in the specified CRS. In row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. All bands are re-projected to this transform.	None
scale	float	Resample image(s) to this pixel scale (size) (m). Where image bands have different scales, all are resampled to this scale. Defaults to the minimum scale of image bands.	None
resampling	ResamplingMethod	Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None.	'near'
dtype	str	Convert to this data type (uint8, int8, uint16, int16, uint32, int32, float32 or float64). Defaults to auto select a minimum size type that can represent the range of pixel values.	None
overwrite	bool	Overwrite the destination file if it exists. Defaults to True.	True
num_threads	int	Number of tiles to download concurrently. Defaults to a sensible auto value.	None
max_tile_size		int, optional Maximum tile size (MB). If None, defaults to the Earth Engine download size limit (32 MB).	None
max_tile_dim		int, optional Maximum tile width/height (pixels). If None, defaults to Earth Engine download limit (10000).	None
shape		tuple of int, optional (height, width) dimensions to export (pixels).	None
scale_offset		bool, optional Whether to apply any EE band scales and offsets to the image.	False
unmask_value	float	The value to use for pixels that are masked in the input image. If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None.	None
column	str	The column name in the feature collection to use as the filename. Defaults to None.	None
job_args	dict	The arguments to pass to joblib.Parallel. Defaults to {"n_jobs": -1}.	{'n_jobs': -1}
ee_init	bool	Whether to initialize Earth Engine. Defaults to True.	True
project_id	str	The Earth Engine project ID. Defaults to None.	None

Source code in geemap/common.py

def download_ee_image_tiles_parallel(
    image,
    features,
    out_dir=None,
    prefix=None,
    crs=None,
    crs_transform=None,
    scale=None,
    resampling="near",
    dtype=None,
    overwrite=True,
    num_threads=None,
    max_tile_size=None,
    max_tile_dim=None,
    shape=None,
    scale_offset=False,
    unmask_value=None,
    column=None,
    job_args={"n_jobs": -1},
    ee_init=True,
    project_id=None,
    **kwargs,
):
    """Download an Earth Engine Image as small tiles based on ee.FeatureCollection. Images larger than the `Earth Engine size limit are split and downloaded as
        separate tiles, then re-assembled into a single GeoTIFF. See https://github.com/dugalh/geedim/blob/main/geedim/download.py#L574

    Args:
        image (ee.Image): The image to be downloaded.
        features (ee.FeatureCollection): The features to loop through to download image.
        out_dir (str, optional): The output directory. Defaults to None.
        prefix (str, optional): The prefix for the output file. Defaults to None.
        crs (str, optional): Reproject image(s) to this EPSG or WKT CRS.  Where image bands have different CRSs, all are
            re-projected to this CRS. Defaults to the CRS of the minimum scale band.
        crs_transform (list, optional): tuple of float, list of float, rio.Affine, optional
            List of 6 numbers specifying an affine transform in the specified CRS.  In row-major order:
            [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation].  All bands are re-projected to
            this transform.
        scale (float, optional): Resample image(s) to this pixel scale (size) (m).  Where image bands have different scales,
            all are resampled to this scale.  Defaults to the minimum scale of image bands.
        resampling (ResamplingMethod, optional): Resampling method, can be 'near', 'bilinear', 'bicubic', or 'average'. Defaults to None.
        dtype (str, optional): Convert to this data type (`uint8`, `int8`, `uint16`, `int16`, `uint32`, `int32`, `float32`
            or `float64`).  Defaults to auto select a minimum size type that can represent the range of pixel values.
        overwrite (bool, optional): Overwrite the destination file if it exists. Defaults to True.
        num_threads (int, optional): Number of tiles to download concurrently. Defaults to a sensible auto value.
        max_tile_size: int, optional
            Maximum tile size (MB).  If None, defaults to the Earth Engine download size limit (32 MB).
        max_tile_dim: int, optional
            Maximum tile width/height (pixels).  If None, defaults to Earth Engine download limit (10000).
        shape: tuple of int, optional
            (height, width) dimensions to export (pixels).
        scale_offset: bool, optional
            Whether to apply any EE band scales and offsets to the image.
        unmask_value (float, optional): The value to use for pixels that are masked in the input image. If the exported image contains zero values,
            you should set the unmask value to a  non-zero value so that the zero values are not treated as missing data. Defaults to None.
        column (str, optional): The column name in the feature collection to use as the filename. Defaults to None.
        job_args (dict, optional): The arguments to pass to joblib.Parallel. Defaults to {"n_jobs": -1}.
        ee_init (bool, optional): Whether to initialize Earth Engine. Defaults to True.
        project_id (str, optional): The Earth Engine project ID. Defaults to None.

    """
    import joblib
    import time

    start = time.time()

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

    if not isinstance(features, ee.FeatureCollection):
        raise ValueError("features must be an ee.FeatureCollection.")

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

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

    if prefix is None:
        prefix = ""

    count = features.size().getInfo()
    if column is not None:
        names = features.aggregate_array(column).getInfo()
    else:
        names = [str(i + 1).zfill(len(str(count))) for i in range(count)]
    collection = features.toList(count)

    def download_data(index):
        if ee_init:
            ee_initialize(
                opt_url="https://earthengine-highvolume.googleapis.com",
                project=project_id,
            )
        region = ee.Feature(collection.get(index)).geometry()
        filename = os.path.join(
            out_dir, "{}{}.tif".format(prefix, names[index].replace("/", "_"))
        )
        print(f"Downloading {index + 1}/{count}: {filename}")

        download_ee_image(
            image,
            filename,
            region,
            crs,
            crs_transform,
            scale,
            resampling,
            dtype,
            overwrite,
            num_threads,
            max_tile_size,
            max_tile_dim,
            shape,
            scale_offset,
            unmask_value,
            **kwargs,
        )

    with joblib.Parallel(**job_args) as parallel:
        parallel(joblib.delayed(download_data)(index) for index in range(count))

    end = time.time()
    print(f"Finished in {end - start} seconds.")

download_ee_video(collection, video_args, out_gif, timeout=300, proxies=None)

Downloads a video thumbnail as a GIF image from Earth Engine.

Parameters:

Name	Type	Description	Default
collection	object	An ee.ImageCollection.	required
video_args	object	Parameters for expring the video thumbnail.	required
out_gif	str	File path to the output GIF.	required
timeout	int	The number of seconds the request will be timed out. Defaults to 300.	300
proxies	dict	A dictionary of proxy servers to use. Defaults to None.	None

Source code in geemap/common.py

def download_ee_video(collection, video_args, out_gif, timeout=300, proxies=None):
    """Downloads a video thumbnail as a GIF image from Earth Engine.

    Args:
        collection (object): An ee.ImageCollection.
        video_args (object): Parameters for expring the video thumbnail.
        out_gif (str): File path to the output GIF.
        timeout (int, optional): The number of seconds the request will be timed out. Defaults to 300.
        proxies (dict, optional): A dictionary of proxy servers to use. Defaults to None.
    """

    out_gif = os.path.abspath(out_gif)
    if not out_gif.endswith(".gif"):
        print("The output file must have an extension of .gif.")
        return

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

    if "region" in video_args.keys():
        roi = video_args["region"]

        if not isinstance(roi, ee.Geometry):
            try:
                roi = roi.geometry()
            except Exception as e:
                print("Could not convert the provided roi to ee.Geometry")
                print(e)
                return

        video_args["region"] = roi
    if "dimensions" not in video_args:
        video_args["dimensions"] = 768

    try:
        print("Generating URL...")
        url = collection.getVideoThumbURL(video_args)

        print(f"Downloading GIF image from {url}\nPlease wait ...")
        r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)

        if r.status_code != 200:
            print("An error occurred while downloading.")
            print(r.json()["error"]["message"])
            return
        else:
            with open(out_gif, "wb") as fd:
                for chunk in r.iter_content(chunk_size=1024):
                    fd.write(chunk)
            print(f"The GIF image has been saved to: {out_gif}")
    except Exception as e:
        print(e)

download_file(url=None, output=None, quiet=False, proxy=None, speed=None, use_cookies=True, verify=True, id=None, fuzzy=False, resume=False, unzip=True, overwrite=False)

Download a file from URL, including Google Drive shared URL.

Parameters:

Name	Type	Description	Default
url	Optional[str]	Google Drive URL is also supported. Defaults to None.	None
output	Optional[str]	Output filename. Default is basename of URL.	None
quiet	bool	Suppress terminal output. Default is False.	False
proxy	Optional[str]	Proxy. Defaults to None.	None
speed	Optional[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	Union[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.	True
id	Optional[str]	Google Drive's file ID. Defaults to None.	None
fuzzy	bool	Fuzzy extraction of Google Drive's file Id. Defaults to False.	False
resume	bool	Resume the download from existing tmp file if possible. Defaults to False.	False
unzip	bool	Unzip the file. Defaults to True.	True
overwrite	bool	Overwrite the file if it already exists. Defaults to False.	False

Returns:

Name	Type	Description
str	str	The output file path.

Source code in geemap/coreutils.py

def download_file(
    url: Optional[str] = None,
    output: Optional[str] = None,
    quiet: bool = False,
    proxy: Optional[str] = None,
    speed: Optional[float] = None,
    use_cookies: bool = True,
    verify: Union[bool, str] = True,
    id: Optional[str] = None,
    fuzzy: bool = False,
    resume: bool = False,
    unzip: bool = True,
    overwrite: bool = False,
) -> str:
    """Download a file from URL, including Google Drive shared URL.

    Args:
        url (Optional[str], optional): Google Drive URL is also supported.
            Defaults to None.
        output (Optional[str], optional): Output filename. Default is basename of URL.
        quiet (bool, optional): Suppress terminal output. Default is False.
        proxy (Optional[str], optional): Proxy. Defaults to None.
        speed (Optional[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 (Union[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.
        id (Optional[str], optional): Google Drive's file ID. Defaults to None.
        fuzzy (bool, optional): Fuzzy extraction of Google Drive's file Id.
            Defaults to False.
        resume (bool, optional): Resume the download from existing tmp file if
            possible. Defaults to False.
        unzip (bool, optional): Unzip the file. Defaults to True.
        overwrite (bool, optional): Overwrite the file if it already exists.
            Defaults to False.

    Returns:
        str: The output file path.
    """

    import gdown

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

    if isinstance(url, str):
        if os.path.exists(os.path.abspath(output)) and (not overwrite):
            print(
                f"{output} already exists. Skip downloading. Set overwrite=True to overwrite."
            )
            return os.path.abspath(output)
        else:
            url = github_raw_url(url)

    if "https://drive.google.com/file/d/" in url:
        fuzzy = True

    output = gdown.download(
        url, output, quiet, proxy, speed, use_cookies, verify, id, fuzzy, resume
    )

    if unzip and output.endswith(".zip"):
        with zipfile.ZipFile(output, "r") as zip_ref:
            if not quiet:
                print("Extracting files...")
            zip_ref.extractall(os.path.dirname(output))

    return os.path.abspath(output)

download_folder(url=None, id=None, output=None, quiet=False, proxy=None, speed=None, use_cookies=True, remaining_ok=False)

Downloads the entire folder from URL.

Parameters:

Name	Type	Description	Default
url	str	URL of the Google Drive folder. Must be of the format 'https://drive.google.com/drive/folders/{url}'. Defaults to None.	None
id	str	Google Drive's folder ID. Defaults to None.	None
output	str	String containing the path of the output folder. Defaults to current working directory.	None
quiet	bool	Suppress terminal output. Defaults to False.	False
proxy	str	Proxy. Defaults to None.	None
speed	float	Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.	None
use_cookies	bool	Flag to use cookies. Defaults to True.	True
resume	bool	Resume the download from existing tmp file if possible. Defaults to False.	required

Returns:

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

Source code in geemap/common.py

def download_folder(
    url=None,
    id=None,
    output=None,
    quiet=False,
    proxy=None,
    speed=None,
    use_cookies=True,
    remaining_ok=False,
):
    """Downloads the entire folder from URL.

    Args:
        url (str, optional): URL of the Google Drive folder. Must be of the format 'https://drive.google.com/drive/folders/{url}'. Defaults to None.
        id (str, optional): Google Drive's folder ID. Defaults to None.
        output (str, optional):  String containing the path of the output folder. Defaults to current working directory.
        quiet (bool, optional): Suppress terminal output. Defaults to False.
        proxy (str, optional): Proxy. Defaults to None.
        speed (float, optional): Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.
        use_cookies (bool, optional): Flag to use cookies. Defaults to True.
        resume (bool, optional): Resume the download from existing tmp file if possible. Defaults to False.

    Returns:
        list: List of files downloaded, or None if failed.
    """
    import gdown

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

download_from_gdrive(gfile_url, file_name, out_dir='.', unzip=True, verbose=True)

Download a file shared via Google Drive (e.g., https://drive.google.com/file/d/18SUo_HcDGltuWYZs1s7PpOmOq_FvFn04/view?usp=sharing)

Parameters:

Name	Type	Description	Default
gfile_url	str	The Google Drive shared file URL	required
file_name	str	The output file name to use.	required
out_dir	str	The output directory. Defaults to '.'.	'.'
unzip	bool	Whether to unzip the output file if it is a zip file. Defaults to True.	True
verbose	bool	Whether to display or not the output of the function	True

Source code in geemap/common.py

def download_from_gdrive(gfile_url, file_name, out_dir=".", unzip=True, verbose=True):
    """Download a file shared via Google Drive
       (e.g., https://drive.google.com/file/d/18SUo_HcDGltuWYZs1s7PpOmOq_FvFn04/view?usp=sharing)

    Args:
        gfile_url (str): The Google Drive shared file URL
        file_name (str): The output file name to use.
        out_dir (str, optional): The output directory. Defaults to '.'.
        unzip (bool, optional): Whether to unzip the output file if it is a zip file. Defaults to True.
        verbose (bool, optional): Whether to display or not the output of the function
    """
    try:
        from google_drive_downloader import GoogleDriveDownloader as gdd
    except ImportError:
        raise Exception(
            "Please install the google_drive_downloader package using `pip install googledrivedownloader`"
        )

    file_id = gfile_url.split("/")[5]
    if verbose:
        print(f"Google Drive file id: {file_id}")

    dest_path = os.path.join(out_dir, file_name)
    gdd.download_file_from_google_drive(file_id, dest_path, True, unzip)

    return

download_from_url(url, out_file_name=None, out_dir='.', unzip=True, verbose=True)

Download a file from a URL (e.g., https://github.com/giswqs/whitebox/raw/master/examples/testdata.zip)

Parameters:

Name	Type	Description	Default
url	str	The HTTP URL to download.	required
out_file_name	str	The output file name to use. Defaults to None.	None
out_dir	str	The output directory to use. Defaults to '.'.	'.'
unzip	bool	Whether to unzip the downloaded file if it is a zip file. Defaults to True.	True
verbose	bool	Whether to display or not the output of the function	True

Source code in geemap/common.py

def download_from_url(url, out_file_name=None, out_dir=".", unzip=True, verbose=True):
    """Download a file from a URL (e.g., https://github.com/giswqs/whitebox/raw/master/examples/testdata.zip)

    Args:
        url (str): The HTTP URL to download.
        out_file_name (str, optional): The output file name to use. Defaults to None.
        out_dir (str, optional): The output directory to use. Defaults to '.'.
        unzip (bool, optional): Whether to unzip the downloaded file if it is a zip file. Defaults to True.
        verbose (bool, optional): Whether to display or not the output of the function
    """
    in_file_name = os.path.basename(url)

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

    if verbose:
        print(f"Downloading {url} ...")

    try:
        urllib.request.urlretrieve(url, out_file_path)
    except Exception:
        raise Exception("The URL is invalid. Please double check the URL.")

    final_path = out_file_path

    if unzip:
        # if it is a zip file
        if ".zip" in out_file_name:
            if verbose:
                print(f"Unzipping {out_file_name} ...")
            with zipfile.ZipFile(out_file_path, "r") as zip_ref:
                zip_ref.extractall(out_dir)
            final_path = os.path.join(
                os.path.abspath(out_dir), out_file_name.replace(".zip", "")
            )

        # if it is a tar file
        if ".tar" in out_file_name:
            if verbose:
                print(f"Unzipping {out_file_name} ...")
            with tarfile.open(out_file_path, "r") as tar_ref:
                with tarfile.open(out_file_path, "r") as tar_ref:

                    def is_within_directory(directory, target):
                        abs_directory = os.path.abspath(directory)
                        abs_target = os.path.abspath(target)

                        prefix = os.path.commonprefix([abs_directory, abs_target])

                        return prefix == abs_directory

                    def safe_extract(
                        tar, path=".", members=None, *, numeric_owner=False
                    ):
                        for member in tar.getmembers():
                            member_path = os.path.join(path, member.name)
                            if not is_within_directory(path, member_path):
                                raise Exception("Attempted Path Traversal in Tar File")

                        tar.extractall(path, members, numeric_owner=numeric_owner)

                    safe_extract(tar_ref, out_dir)
            final_path = os.path.join(
                os.path.abspath(out_dir), out_file_name.replace(".tar", "")
            )

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

    return

download_ned(region, out_dir=None, return_url=False, download_args={}, **kwargs)

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

Parameters:

Name	Type	Description	Default
region	str | list	A filepath to a vector dataset or a list of bounds in the form of [minx, miny, maxx, maxy].	required
out_dir	str	The directory to download the files to. Defaults to None, which uses the current working directory.	None
return_url	bool	Whether to return the download URLs of the files. Defaults to False.	False
download_args	dict	A dictionary of arguments to pass to the download_file function. Defaults to {}.	{}

Returns:

Name	Type	Description
list		A list of the download URLs of the files if return_url is True.

Source code in geemap/common.py

def download_ned(region, out_dir=None, return_url=False, download_args={}, **kwargs):
    """Download the US National Elevation Datasets (NED) for a region.

    Args:
        region (str | list): A filepath to a vector dataset or a list of bounds in the form of [minx, miny, maxx, maxy].
        out_dir (str, optional): The directory to download the files to. Defaults to None, which uses the current working directory.
        return_url (bool, optional): Whether to return the download URLs of the files. Defaults to False.
        download_args (dict, optional): A dictionary of arguments to pass to the download_file function. Defaults to {}.

    Returns:
        list: A list of the download URLs of the files if return_url is True.
    """
    import geopandas as gpd

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

    if isinstance(region, str):
        if region.startswith("http"):
            region = github_raw_url(region)
            region = download_file(region)
        elif not os.path.exists(region):
            raise ValueError("region must be a path or a URL to a vector dataset.")

        roi = gpd.read_file(region, **kwargs)
        roi = roi.to_crs(epsg=4326)
        bounds = roi.total_bounds

    elif isinstance(region, list):
        bounds = region

    else:
        raise ValueError(
            "region must be a filepath or a list of bounds in the form of [minx, miny, maxx, maxy]."
        )
    minx, miny, maxx, maxy = [float(x) for x in bounds]
    tiles = []
    left = abs(math.floor(minx))
    right = abs(math.floor(maxx)) - 1
    upper = math.ceil(maxy)
    bottom = math.ceil(miny) - 1

    for y in range(upper, bottom, -1):
        for x in range(left, right, -1):
            tile_id = "n{}w{}".format(str(y).zfill(2), str(x).zfill(3))
            tiles.append(tile_id)

    links = []
    filepaths = []

    for index, tile in enumerate(tiles):
        tif_url = f"https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/13/TIFF/current/{tile}/USGS_13_{tile}.tif"

        r = requests.head(tif_url)
        if r.status_code == 200:
            tif = os.path.join(out_dir, os.path.basename(tif_url))
            links.append(tif_url)
            filepaths.append(tif)
        else:
            print(f"{tif_url} does not exist.")

    if return_url:
        return links
    else:
        for index, link in enumerate(links):
            print(f"Downloading {index + 1} of {len(links)}: {os.path.basename(link)}")
            download_file(link, filepaths[index], **download_args)

dynamic_world(region=None, start_date='2020-01-01', end_date='2021-01-01', clip=False, reducer=None, projection='EPSG:3857', scale=10, return_type='hillshade')

Create 10-m land cover composite based on Dynamic World. The source code is adapted from the following tutorial by Spatial Thoughts: https://developers.google.com/earth-engine/tutorials/community/introduction-to-dynamic-world-pt-1

Parameters:

Name	Type	Description	Default
region	Geometry | FeatureCollection	The region of interest.	None
start_date	str | Date	The start date of the query. Default to "2020-01-01".	'2020-01-01'
end_date	str | Date	The end date of the query. Default to "2021-01-01".	'2021-01-01'
clip	bool	Whether to clip the image to the region. Default to False.	False
reducer	Reducer	The reducer to be used. Default to None.	None
projection	str	The projection to be used for creating hillshade. Default to "EPSG:3857".	'EPSG:3857'
scale	int	The scale to be used for creating hillshade. Default to 10.	10
return_type	str	The type of image to be returned. Can be one of 'hillshade', 'visualize', 'class', or 'probability'. Default to "hillshade".	'hillshade'

Returns:

Type	Description
	ee.Image: The image with the specified return_type.

Source code in geemap/common.py

def dynamic_world(
    region=None,
    start_date="2020-01-01",
    end_date="2021-01-01",
    clip=False,
    reducer=None,
    projection="EPSG:3857",
    scale=10,
    return_type="hillshade",
):
    """Create 10-m land cover composite based on Dynamic World. The source code is adapted from the following tutorial by Spatial Thoughts:
    https://developers.google.com/earth-engine/tutorials/community/introduction-to-dynamic-world-pt-1

    Args:
        region (ee.Geometry | ee.FeatureCollection): The region of interest.
        start_date (str | ee.Date): The start date of the query. Default to "2020-01-01".
        end_date (str | ee.Date): The end date of the query. Default to "2021-01-01".
        clip (bool, optional): Whether to clip the image to the region. Default to False.
        reducer (ee.Reducer, optional): The reducer to be used. Default to None.
        projection (str, optional): The projection to be used for creating hillshade. Default to "EPSG:3857".
        scale (int, optional): The scale to be used for creating hillshade. Default to 10.
        return_type (str, optional): The type of image to be returned. Can be one of 'hillshade', 'visualize', 'class', or 'probability'. Default to "hillshade".

    Returns:
        ee.Image: The image with the specified return_type.
    """

    if return_type not in ["hillshade", "visualize", "class", "probability"]:
        raise ValueError(
            f"{return_type} must be one of 'hillshade', 'visualize', 'class', or 'probability'."
        )

    if reducer is None:
        reducer = ee.Reducer.mode()

    dw = ee.ImageCollection("GOOGLE/DYNAMICWORLD/V1").filter(
        ee.Filter.date(start_date, end_date)
    )

    if isinstance(region, ee.FeatureCollection) or isinstance(region, ee.Geometry):
        dw = dw.filterBounds(region)
    else:
        raise ValueError("region must be an ee.FeatureCollection or ee.Geometry.")

    # Create a Mode Composite
    classification = dw.select("label")
    dwComposite = classification.reduce(reducer)
    if clip and (region is not None):
        if isinstance(region, ee.Geometry):
            dwComposite = dwComposite.clip(region)
        elif isinstance(region, ee.FeatureCollection):
            dwComposite = dwComposite.clipToCollection(region)
        elif isinstance(region, ee.Feature):
            dwComposite = dwComposite.clip(region.geometry())

    dwVisParams = {
        "min": 0,
        "max": 8,
        "palette": [
            "#419BDF",
            "#397D49",
            "#88B053",
            "#7A87C6",
            "#E49635",
            "#DFC35A",
            "#C4281B",
            "#A59B8F",
            "#B39FE1",
        ],
    }

    if return_type == "class":
        return dwComposite
    elif return_type == "visualize":
        return dwComposite.visualize(**dwVisParams)
    else:
        # Create a Top-1 Probability Hillshade Visualization
        probabilityBands = [
            "water",
            "trees",
            "grass",
            "flooded_vegetation",
            "crops",
            "shrub_and_scrub",
            "built",
            "bare",
            "snow_and_ice",
        ]

        # Select probability bands
        probabilityCol = dw.select(probabilityBands)

        # Create a multi-band image with the average pixel-wise probability
        # for each band across the time-period
        meanProbability = probabilityCol.reduce(ee.Reducer.mean())

        # Composites have a default projection that is not suitable
        # for hillshade computation.
        # Set a EPSG:3857 projection with 10m scale
        proj = ee.Projection(projection).atScale(scale)
        meanProbability = meanProbability.setDefaultProjection(proj)

        # Create the Top1 Probability Hillshade
        top1Probability = meanProbability.reduce(ee.Reducer.max())

        if clip and (region is not None):
            if isinstance(region, ee.Geometry):
                top1Probability = top1Probability.clip(region)
            elif isinstance(region, ee.FeatureCollection):
                top1Probability = top1Probability.clipToCollection(region)
            elif isinstance(region, ee.Feature):
                top1Probability = top1Probability.clip(region.geometry())

        if return_type == "probability":
            return top1Probability
        else:
            top1Confidence = top1Probability.multiply(100).int()
            hillshade = ee.Terrain.hillshade(top1Confidence).divide(255)
            rgbImage = dwComposite.visualize(**dwVisParams).divide(255)
            probabilityHillshade = rgbImage.multiply(hillshade)

            return probabilityHillshade

dynamic_world_s2(region=None, start_date='2020-01-01', end_date='2021-01-01', clip=False, cloud_pct=0.35, reducer=None)

Create Sentinel-2 composite for the Dynamic World Land Cover product.

Parameters:

Name	Type	Description	Default
region	Geometry | FeatureCollection	The region of interest. Default to None.	None
start_date	str | Date	The start date of the query. Default to "2020-01-01".	'2020-01-01'
end_date	str | Date	The end date of the query. Default to "2021-01-01".	'2021-01-01'
clip	bool	Whether to clip the image to the region. Default to False.	False
cloud_pct	float	The percentage of cloud cover to be used for filtering. Default to 0.35.	0.35
reducer	Reducer	The reducer to be used for creating image composite. Default to None.	None

Returns:

Type	Description
	ee.Image: The Sentinel-2 composite.

Source code in geemap/common.py

def dynamic_world_s2(
    region=None,
    start_date="2020-01-01",
    end_date="2021-01-01",
    clip=False,
    cloud_pct=0.35,
    reducer=None,
):
    """Create Sentinel-2 composite for the Dynamic World Land Cover product.

    Args:
        region (ee.Geometry | ee.FeatureCollection): The region of interest. Default to None.
        start_date (str | ee.Date): The start date of the query. Default to "2020-01-01".
        end_date (str | ee.Date): The end date of the query. Default to "2021-01-01".
        clip (bool, optional): Whether to clip the image to the region. Default to False.
        cloud_pct (float, optional): The percentage of cloud cover to be used for filtering. Default to 0.35.
        reducer (ee.Reducer, optional): The reducer to be used for creating image composite. Default to None.

    Returns:
        ee.Image: The Sentinel-2 composite.
    """
    s2 = (
        ee.ImageCollection("COPERNICUS/S2_HARMONIZED")
        .filterDate(start_date, end_date)
        .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", cloud_pct * 100))
    )

    if isinstance(region, ee.FeatureCollection) or isinstance(region, ee.Geometry):
        s2 = s2.filterBounds(region)
    else:
        raise ValueError("region must be an ee.FeatureCollection or ee.Geometry.")

    if reducer is None:
        reducer = ee.Reducer.median()

    image = s2.reduce(reducer).rename(s2.first().bandNames())

    if clip and (region is not None):
        if isinstance(region, ee.Geometry):
            image = image.clip(region)
        elif isinstance(region, ee.FeatureCollection):
            image = image.clipToCollection(region)

    return image

dynamic_world_timeseries(region, start_date='2016-01-01', end_date='2021-12-31', cloud_pct=30, frequency='year', reducer='mode', drop_empty=True, date_format=None, return_type='hillshade', parallel_scale=1)

Create Dynamic World timeseries.

Parameters:

Name	Type	Description	Default
region	Geometry | FeatureCollection	The region of interest.	required
start_date	str | Date	The start date of the query. Default to "2016-01-01".	'2016-01-01'
end_date	str | Date	The end date of the query. Default to "2021-12-31".	'2021-12-31'
cloud_pct	int	The cloud percentage threshold (<=). Defaults to 30.	30
frequency	str	The frequency of the timeseries. It must be one of the following: 'year', 'month', 'day', 'hour', 'minute', 'second'. Defaults to 'year'.	'year'
reducer	str	The reducer to be used. Defaults to "mode".	'mode'
drop_empty	bool	Whether to drop empty images from the timeseries. Defaults to True.	True
date_format	str	A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.	None
return_type	str	The type of image to be returned. Can be one of 'hillshade', 'visualize', 'class', or 'probability'. Default to "hillshade".	'hillshade'
parallel_scale	int	A scaling factor used to limit memory use; using a larger parallel_scale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.	1

Returns:

Type	Description
	ee.ImageCollection: An ImageCollection of the Dynamic World land cover timeseries.

Source code in geemap/timelapse.py

def dynamic_world_timeseries(
    region,
    start_date="2016-01-01",
    end_date="2021-12-31",
    cloud_pct=30,
    frequency="year",
    reducer="mode",
    drop_empty=True,
    date_format=None,
    return_type="hillshade",
    parallel_scale=1,
):
    """Create Dynamic World timeseries.

    Args:
        region (ee.Geometry | ee.FeatureCollection): The region of interest.
        start_date (str | ee.Date): The start date of the query. Default to "2016-01-01".
        end_date (str | ee.Date): The end date of the query. Default to "2021-12-31".
        cloud_pct (int, optional): The cloud percentage threshold (<=). Defaults to 30.
        frequency (str, optional): The frequency of the timeseries. It must be one of the following: 'year', 'month', 'day', 'hour', 'minute', 'second'. Defaults to 'year'.
        reducer (str, optional): The reducer to be used. Defaults to "mode".
        drop_empty (bool, optional): Whether to drop empty images from the timeseries. Defaults to True.
        date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.
        return_type (str, optional): The type of image to be returned. Can be one of 'hillshade', 'visualize', 'class', or 'probability'. Default to "hillshade".
        parallel_scale (int, optional): A scaling factor used to limit memory use; using a larger parallel_scale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.

    Returns:
        ee.ImageCollection: An ImageCollection of the Dynamic World land cover timeseries.
    """
    if return_type not in ["hillshade", "visualize", "class", "probability"]:
        raise ValueError(
            f"{return_type} must be one of 'hillshade', 'visualize', 'class', or 'probability'."
        )

    if (
        isinstance(region, ee.FeatureCollection)
        or isinstance(region, ee.Feature)
        or isinstance(region, ee.Geometry)
    ):
        pass
    else:
        raise ValueError(
            f"{region} must be one of ee.FeatureCollection, ee.Feature, or ee.Geometry."
        )

    if cloud_pct < 0 or cloud_pct > 100:
        raise ValueError(f"{cloud_pct} must be between 0 and 100.")

    s2 = (
        ee.ImageCollection("COPERNICUS/S2_HARMONIZED")
        .filterDate(start_date, end_date)
        .filterBounds(region)
        .filter(ee.Filter.lte("CLOUDY_PIXEL_PERCENTAGE", cloud_pct))
    )

    ids = s2.aggregate_array("system:index")

    dw = ee.ImageCollection("GOOGLE/DYNAMICWORLD/V1").filter(
        ee.Filter.inList("system:index", ids)
    )

    collection = dw.select("label")

    dwVisParams = {
        "min": 0,
        "max": 8,
        "palette": [
            "#419BDF",
            "#397D49",
            "#88B053",
            "#7A87C6",
            "#E49635",
            "#DFC35A",
            "#C4281B",
            "#A59B8F",
            "#B39FE1",
        ],
    }

    images = create_timeseries(
        collection,
        start_date,
        end_date,
        region,
        None,
        frequency,
        reducer,
        drop_empty,
        date_format,
        parallel_scale,
    )

    if return_type == "class":
        return images
    elif return_type == "visualize":
        result = images.map(lambda img: img.visualize(**dwVisParams))
        return result
    else:
        # Create a Top-1 Probability Hillshade Visualization
        probabilityBands = [
            "water",
            "trees",
            "grass",
            "flooded_vegetation",
            "crops",
            "shrub_and_scrub",
            "built",
            "bare",
            "snow_and_ice",
        ]

        # Select probability bands
        probabilityCol = dw.select(probabilityBands)

        prob_col = create_timeseries(
            probabilityCol,
            start_date,
            end_date,
            region,
            None,
            frequency,
            "mean",
            drop_empty,
            date_format,
            parallel_scale,
        )

        prob_images = ee.ImageCollection(
            prob_col.map(
                lambda img: img.reduce(ee.Reducer.max()).set(
                    "system:time_start", img.get("system:time_start")
                )
            )
        )

        if return_type == "probability":
            return prob_images

        elif return_type == "hillshade":
            count = prob_images.size()
            nums = ee.List.sequence(0, count.subtract(1))

            def create_hillshade(d):
                proj = ee.Projection("EPSG:3857").atScale(10)
                img = ee.Image(images.toList(images.size()).get(d))
                prob_img = ee.Image(prob_images.toList(prob_images.size()).get(d))
                prob_img = prob_img.setDefaultProjection(proj)
                top1Confidence = prob_img.multiply(100).int()
                hillshade = ee.Terrain.hillshade(top1Confidence).divide(255)
                rgbImage = img.visualize(**dwVisParams).divide(255)
                probabilityHillshade = rgbImage.multiply(hillshade)
                return probabilityHillshade.set(
                    "system:time_start", img.get("system:time_start")
                )

            result = ee.ImageCollection(nums.map(create_hillshade))
            return result

edit_download_html(htmlWidget, filename, title='Click here to download: ')

Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578#issuecomment-617668058

Parameters:

Name	Type	Description	Default
htmlWidget	object	The HTML widget to display the URL.	required
filename	str	File path to download.	required
title	str	Download description. Defaults to "Click here to download: ".	'Click here to download: '

Source code in geemap/common.py

def edit_download_html(htmlWidget, filename, title="Click here to download: "):
    """Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578#issuecomment-617668058

    Args:
        htmlWidget (object): The HTML widget to display the URL.
        filename (str): File path to download.
        title (str, optional): Download description. Defaults to "Click here to download: ".
    """

    # from IPython.display import HTML
    # import ipywidgets as widgets
    import base64

    # Change widget html temporarily to a font-awesome spinner
    htmlWidget.value = '<i class="fa fa-spinner fa-spin fa-2x fa-fw"></i><span class="sr-only">Loading...</span>'

    # Process raw data
    data = open(filename, "rb").read()
    b64 = base64.b64encode(data)
    payload = b64.decode()

    basename = os.path.basename(filename)

    # Create and assign html to widget
    html = '<a download="{filename}" href="data:text/csv;base64,{payload}" target="_blank">{title}</a>'
    htmlWidget.value = html.format(
        payload=payload, title=title + basename, filename=basename
    )

ee_api_to_csv(outfile=None, timeout=300, proxies=None)

Extracts Earth Engine API documentation from https://developers.google.com/earth-engine/api_docs as a csv file.

Parameters:

Name	Type	Description	Default
outfile	str	The output file path to a csv file. Defaults to None.	None
timeout	int	Timeout in seconds. Defaults to 300.	300
proxies	dict	Proxy settings. Defaults to None.	None

Source code in geemap/common.py

def ee_api_to_csv(outfile=None, timeout=300, proxies=None):
    """Extracts Earth Engine API documentation from https://developers.google.com/earth-engine/api_docs as a csv file.

    Args:
        outfile (str, optional): The output file path to a csv file. Defaults to None.
        timeout (int, optional): Timeout in seconds. Defaults to 300.
        proxies (dict, optional): Proxy settings. Defaults to None.
    """
    from bs4 import BeautifulSoup

    pkg_dir = str(importlib.resources.files("geemap").joinpath("geemap.py").parent)
    data_dir = os.path.join(pkg_dir, "data")
    template_dir = os.path.join(data_dir, "template")
    csv_file = os.path.join(template_dir, "ee_api_docs.csv")

    if outfile is None:
        outfile = csv_file
    else:
        if not outfile.endswith(".csv"):
            print("The output file must end with .csv")
            return
        else:
            out_dir = os.path.dirname(outfile)
            if not os.path.exists(out_dir):
                os.makedirs(out_dir)

    url = "https://developers.google.com/earth-engine/api_docs"

    try:
        r = requests.get(url, timeout=timeout, proxies=proxies)
        soup = BeautifulSoup(r.content, "html.parser")

        names = []
        descriptions = []
        functions = []
        returns = []
        arguments = []
        types = []
        details = []

        names = [h2.text for h2 in soup.find_all("h2")]
        descriptions = [h2.next_sibling.next_sibling.text for h2 in soup.find_all("h2")]
        func_tables = soup.find_all("table", class_="blue")
        functions = [func_table.find("code").text for func_table in func_tables]
        returns = [func_table.find_all("td")[1].text for func_table in func_tables]

        detail_tables = []
        tables = soup.find_all("table", class_="blue")

        for table in tables:
            item = table.next_sibling
            if item.attrs == {"class": ["details"]}:
                detail_tables.append(item)
            else:
                detail_tables.append("")

        for detail_table in detail_tables:
            if detail_table != "":
                items = [item.text for item in detail_table.find_all("code")]
            else:
                items = ""
            arguments.append(items)

        for detail_table in detail_tables:
            if detail_table != "":
                items = [item.text for item in detail_table.find_all("td")]
                items = items[1::3]
            else:
                items = ""
            types.append(items)

        for detail_table in detail_tables:
            if detail_table != "":
                items = [item.text for item in detail_table.find_all("p")]
            else:
                items = ""
            details.append(items)

        with open(outfile, "w", encoding="utf-8") as csv_file:
            csv_writer = csv.writer(csv_file, delimiter="\t")

            csv_writer.writerow(
                [
                    "name",
                    "description",
                    "function",
                    "returns",
                    "argument",
                    "type",
                    "details",
                ]
            )

            for i in range(len(names)):
                name = names[i]
                description = descriptions[i]
                function = functions[i]
                return_type = returns[i]
                argument = "|".join(arguments[i])
                argu_type = "|".join(types[i])
                detail = "|".join(details[i])

                csv_writer.writerow(
                    [
                        name,
                        description,
                        function,
                        return_type,
                        argument,
                        argu_type,
                        detail,
                    ]
                )

    except Exception as e:
        print(e)

ee_data_html(asset)

Generates HTML from an asset to be used in the HTML widget.

Parameters:

Name	Type	Description	Default
asset	dict	A dictionary containing an Earth Engine asset.	required

Returns:

Name	Type	Description
str		A string containing HTML.

Source code in geemap/common.py

def ee_data_html(asset):
    """Generates HTML from an asset to be used in the HTML widget.

    Args:
        asset (dict): A dictionary containing an Earth Engine asset.

    Returns:
        str: A string containing HTML.
    """
    try:
        asset_title = asset.get("title", "Unknown")
        asset_dates = asset.get("dates", "Unknown")
        ee_id_snippet = asset.get("id", "Unknown")
        asset_uid = asset.get("uid", None)
        asset_url = asset.get("asset_url", "")
        code_url = asset.get("sample_code", None)
        thumbnail_url = asset.get("thumbnail_url", None)
        asset_type = asset.get("type", "Unknown")

        if asset_type == "image":
            ee_id_snippet = "ee.Image('{}')".format(ee_id_snippet)
        elif asset_type == "image_collection":
            ee_id_snippet = "ee.ImageCollection('{}')".format(ee_id_snippet)
        elif asset_type == "table":
            ee_id_snippet = "ee.FeatureCollection('{}')".format(ee_id_snippet)

        if not code_url and asset_uid:
            coder_url = f"""https://code.earthengine.google.com/?scriptPath=Examples%3ADatasets%2F{asset_uid}"""
        else:
            coder_url = code_url

        ## ee datasets always have a asset_url, and should have a thumbnail
        catalog = (
            bool(asset_url)
            * f"""
                    <h4>Data Catalog</h4>
                        <p style="margin-left: 40px"><a href="{asset_url.replace('terms-of-use','description')}" target="_blank">Description</a></p>
                        <p style="margin-left: 40px"><a href="{asset_url.replace('terms-of-use','bands')}" target="_blank">Bands</a></p>
                        <p style="margin-left: 40px"><a href="{asset_url.replace('terms-of-use','image-properties')}" target="_blank">Properties</a></p>
                        <p style="margin-left: 40px"><a href="{coder_url}" target="_blank">Example</a></p>
                    """
        )
        thumbnail = (
            bool(thumbnail_url)
            * f"""
                    <h4>Dataset Thumbnail</h4>
                    <img src="{thumbnail_url}">
                    """
        )
        ## only community datasets have a code_url
        alternative = (
            bool(code_url)
            * f"""
                    <h4>Community Catalog</h4>
                        <p style="margin-left: 40px">{asset.get('provider','Provider unknown')}</p>
                        <p style="margin-left: 40px">{asset.get('tags','Tags unknown')}</p>
                        <p style="margin-left: 40px"><a href="{coder_url}" target="_blank">Example</a></p>
                    """
        )

        template = f"""
            <html>
            <body>
                <h3>{asset_title}</h3>
                <h4>Dataset Availability</h4>
                    <p style="margin-left: 40px">{asset_dates}</p>
                <h4>Earth Engine Snippet</h4>
                    <p style="margin-left: 40px">{ee_id_snippet}</p>
                {catalog}
                {alternative}
                {thumbnail}
            </body>
            </html>
        """
        return template

    except Exception as e:
        print(e)

ee_data_thumbnail(asset_id, timeout=300, proxies=None)

Retrieves the thumbnail URL of an Earth Engine asset.

Parameters:

Name	Type	Description	Default
asset_id	str	An Earth Engine asset id.	required
timeout	int	Timeout in seconds. Defaults to 300.	300
proxies	dict	Proxy settings. Defaults to None.	None

Returns:

Name	Type	Description
str		An http url of the thumbnail.

Source code in geemap/common.py

def ee_data_thumbnail(asset_id, timeout=300, proxies=None):
    """Retrieves the thumbnail URL of an Earth Engine asset.

    Args:
        asset_id (str): An Earth Engine asset id.
        timeout (int, optional): Timeout in seconds. Defaults to 300.
        proxies (dict, optional): Proxy settings. Defaults to None.

    Returns:
        str: An http url of the thumbnail.
    """
    import urllib

    from bs4 import BeautifulSoup

    asset_uid = asset_id.replace("/", "_")
    asset_url = "https://developers.google.com/earth-engine/datasets/catalog/{}".format(
        asset_uid
    )
    thumbnail_url = "https://mw1.google.com/ges/dd/images/{}_sample.png".format(
        asset_uid
    )

    r = requests.get(thumbnail_url, timeout=timeout, proxies=proxies)

    try:
        if r.status_code != 200:
            html_page = urllib.request.urlopen(asset_url)
            soup = BeautifulSoup(html_page, features="html.parser")

            for img in soup.findAll("img"):
                if "sample.png" in img.get("src"):
                    thumbnail_url = img.get("src")
                    return thumbnail_url

        return thumbnail_url
    except Exception as e:
        print(e)

ee_export_geojson(ee_object, filename=None, selectors=None, timeout=300, proxies=None)

Exports Earth Engine FeatureCollection to geojson.

Parameters:

Name	Type	Description	Default
ee_object	object	ee.FeatureCollection to export.	required
filename	str	Output file name. Defaults to None.	None
selectors	list	A list of attributes to export. Defaults to None.	None
timeout	int	Timeout in seconds. Defaults to 300 seconds.	300
proxies	dict	Proxy settings. Defaults to None.	None

Source code in geemap/common.py

def ee_export_geojson(
    ee_object, filename=None, selectors=None, timeout=300, proxies=None
):
    """Exports Earth Engine FeatureCollection to geojson.

    Args:
        ee_object (object): ee.FeatureCollection to export.
        filename (str): Output file name. Defaults to None.
        selectors (list, optional): A list of attributes to export. Defaults to None.
        timeout (int, optional): Timeout in seconds. Defaults to 300 seconds.
        proxies (dict, optional): Proxy settings. Defaults to None.
    """

    if not isinstance(ee_object, ee.FeatureCollection):
        print("The ee_object must be an ee.FeatureCollection.")
        return

    if filename is None:
        out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
        filename = os.path.join(out_dir, random_string(6) + ".geojson")

    allowed_formats = ["geojson"]
    filename = os.path.abspath(filename)
    basename = os.path.basename(filename)
    name = os.path.splitext(basename)[0]
    filetype = os.path.splitext(basename)[1][1:].lower()

    if not (filetype.lower() in allowed_formats):
        print("The output file type must be geojson.")
        return

    if selectors is None:
        selectors = ee_object.first().propertyNames().getInfo()
        selectors = [".geo"] + selectors

    elif not isinstance(selectors, list):
        print("selectors must be a list, such as ['attribute1', 'attribute2']")
        return
    else:
        allowed_attributes = ee_object.first().propertyNames().getInfo()
        for attribute in selectors:
            if not (attribute in allowed_attributes):
                print(
                    "Attributes must be one chosen from: {} ".format(
                        ", ".join(allowed_attributes)
                    )
                )
                return

    try:
        # print('Generating URL ...')
        url = ee_object.getDownloadURL(
            filetype=filetype, selectors=selectors, filename=name
        )
        # print('Downloading data from {}\nPlease wait ...'.format(url))
        r = None
        r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)

        if r.status_code != 200:
            print("An error occurred while downloading. \n Retrying ...")
            try:
                new_ee_object = ee_object.map(filter_polygons)
                print("Generating URL ...")
                url = new_ee_object.getDownloadURL(
                    filetype=filetype, selectors=selectors, filename=name
                )
                print(f"Downloading data from {url}\nPlease wait ...")
                r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)
            except Exception as e:
                print(e)

        with open(filename, "wb") as fd:
            for chunk in r.iter_content(chunk_size=1024):
                fd.write(chunk)
    except Exception as e:
        print("An error occurred while downloading.")
        if r is not None:
            print(r.json()["error"]["message"])

        return

    with open(filename) as f:
        geojson = f.read()

    return geojson

ee_export_image(ee_object, filename, scale=None, crs=None, crs_transform=None, region=None, dimensions=None, file_per_band=False, format='ZIPPED_GEO_TIFF', unzip=True, unmask_value=None, timeout=300, proxies=None, verbose=True)

Exports an ee.Image as a GeoTIFF.

Parameters:

Name	Type	Description	Default
ee_object	object	The ee.Image to download.	required
filename	str	Output filename for the exported image.	required
scale	float	A default scale to use for any bands that do not specify one; ignored if crs and crs_transform is specified. Defaults to None.	None
crs	str	A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.	None
crs_transform	list	a default affine transform to use for any bands that do not specify one, of the same format as the crs_transform of bands. Defaults to None.	None
region	object	A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.	None
dimensions	list	An optional array of two integers defining the width and height to which the band is cropped. Defaults to None.	None
file_per_band	bool	Whether to produce a different GeoTIFF per band. Defaults to False.	False
format	str	One of: "ZIPPED_GEO_TIFF" (GeoTIFF file(s) wrapped in a zip file, default), "GEO_TIFF" (GeoTIFF file), "NPY" (NumPy binary format). If "GEO_TIFF" or "NPY", filePerBand and all band-level transformations will be ignored. Loading a NumPy output results in a structured array.	'ZIPPED_GEO_TIFF'
unzip	bool	Whether to unzip the downloaded file. Defaults to True.	True
unmask_value	float	The value to use for pixels that are masked in the input image. If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None.	None
timeout	int	The timeout in seconds for the request. Defaults to 300.	300
proxies	dict	A dictionary of proxy servers to use. Defaults to None.	None
verbose	bool	Whether to print out descriptive text. Defaults to True.	True

Source code in geemap/common.py

def ee_export_image(
    ee_object,
    filename,
    scale=None,
    crs=None,
    crs_transform=None,
    region=None,
    dimensions=None,
    file_per_band=False,
    format="ZIPPED_GEO_TIFF",
    unzip=True,
    unmask_value=None,
    timeout=300,
    proxies=None,
    verbose=True,
):
    """Exports an ee.Image as a GeoTIFF.

    Args:
        ee_object (object): The ee.Image to download.
        filename (str): Output filename for the exported image.
        scale (float, optional): A default scale to use for any bands that do not specify one; ignored if crs and crs_transform is specified. Defaults to None.
        crs (str, optional): A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.
        crs_transform (list, optional): a default affine transform to use for any bands that do not specify one, of the same format as the crs_transform of bands. Defaults to None.
        region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.
        dimensions (list, optional): An optional array of two integers defining the width and height to which the band is cropped. Defaults to None.
        file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.
        format (str, optional):  One of: "ZIPPED_GEO_TIFF" (GeoTIFF file(s) wrapped in a zip file, default), "GEO_TIFF" (GeoTIFF file), "NPY" (NumPy binary format). If "GEO_TIFF" or "NPY",
            filePerBand and all band-level transformations will be ignored. Loading a NumPy output results in a structured array.
        unzip (bool, optional): Whether to unzip the downloaded file. Defaults to True.
        unmask_value (float, optional): The value to use for pixels that are masked in the input image.
            If the exported image contains zero values, you should set the unmask value to a  non-zero value so that the zero values are not treated as missing data. Defaults to None.
        timeout (int, optional): The timeout in seconds for the request. Defaults to 300.
        proxies (dict, optional): A dictionary of proxy servers to use. Defaults to None.
        verbose (bool, optional): Whether to print out descriptive text. Defaults to True.
    """

    if not isinstance(ee_object, ee.Image):
        print("The ee_object must be an ee.Image.")
        return

    if unmask_value is not None:
        ee_object = ee_object.selfMask().unmask(unmask_value)
        if isinstance(region, ee.Geometry):
            ee_object = ee_object.clip(region)
        elif isinstance(region, ee.FeatureCollection):
            ee_object = ee_object.clipToCollection(region)

    filename = os.path.abspath(filename)
    basename = os.path.basename(filename)
    name = os.path.splitext(basename)[0]
    filetype = os.path.splitext(basename)[1][1:].lower()
    filename_zip = filename.replace(".tif", ".zip")

    if filetype != "tif":
        print("The filename must end with .tif")
        return

    try:
        if verbose:
            print("Generating URL ...")
        params = {"name": name, "filePerBand": file_per_band}

        params["scale"] = scale
        if region is None:
            region = ee_object.geometry()
        if dimensions is not None:
            params["dimensions"] = dimensions
        if region is not None:
            params["region"] = region
        if crs is not None:
            params["crs"] = crs
        if crs_transform is not None:
            params["crs_transform"] = crs_transform
        if format != "ZIPPED_GEO_TIFF":
            params["format"] = format

        try:
            url = ee_object.getDownloadURL(params)
        except Exception as e:
            print("An error occurred while downloading.")
            print(e)
            return

        if verbose:
            print(f"Downloading data from {url}\nPlease wait ...")
        # Need to initialize r to something because of how we currently handle errors
        # We should aim to refactor the code such that only one try block is needed
        r = None
        r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)

        if r.status_code != 200:
            print("An error occurred while downloading.")
            return

        with open(filename_zip, "wb") as fd:
            for chunk in r.iter_content(chunk_size=1024):
                fd.write(chunk)

    except Exception as e:
        print("An error occurred while downloading.")
        if r is not None:
            print(r.json()["error"]["message"])
        return

    try:
        if unzip:
            with zipfile.ZipFile(filename_zip) as z:
                z.extractall(os.path.dirname(filename))
            os.remove(filename_zip)

        if verbose:
            if file_per_band:
                print(f"Data downloaded to {os.path.dirname(filename)}")
            else:
                print(f"Data downloaded to {filename}")
    except Exception as e:
        print(e)

ee_export_image_collection(ee_object, out_dir, scale=None, crs=None, crs_transform=None, region=None, dimensions=None, file_per_band=False, format='ZIPPED_GEO_TIFF', unmask_value=None, filenames=None, timeout=300, proxies=None, verbose=True)

Exports an ImageCollection as GeoTIFFs.

Parameters:

Name	Type	Description	Default
ee_object	object	The ee.Image to download.	required
out_dir	str	The output directory for the exported images.	required
scale	float	A default scale to use for any bands that do not specify one; ignored if crs and crs_transform is specified. Defaults to None.	None
crs	str	A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.	None
crs_transform	list	a default affine transform to use for any bands that do not specify one, of the same format as the crs_transform of bands. Defaults to None.	None
region	object	A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.	None
dimensions	list	An optional array of two integers defining the width and height to which the band is cropped. Defaults to None.	None
file_per_band	bool	Whether to produce a different GeoTIFF per band. Defaults to False.	False
format	str	One of: "ZIPPED_GEO_TIFF" (GeoTIFF file(s) wrapped in a zip file, default), "GEO_TIFF" (GeoTIFF file), "NPY" (NumPy binary format). If "GEO_TIFF" or "NPY", filePerBand and all band-level transformations will be ignored. Loading a NumPy output results in a structured array.	'ZIPPED_GEO_TIFF'
unmask_value	float	The value to use for pixels that are masked in the input image. If the exported image contains zero values, you should set the unmask value to a non-zero value so that the zero values are not treated as missing data. Defaults to None.	None
filenames	list | int	A list of filenames to use for the exported images. Defaults to None.	None
timeout	int	The timeout in seconds for the request. Defaults to 300.	300
proxies	dict	A dictionary of proxy servers to use. Defaults to None.	None
verbose	bool	Whether to print out descriptive text. Defaults to True.	True

Source code in geemap/common.py

def ee_export_image_collection(
    ee_object,
    out_dir,
    scale=None,
    crs=None,
    crs_transform=None,
    region=None,
    dimensions=None,
    file_per_band=False,
    format="ZIPPED_GEO_TIFF",
    unmask_value=None,
    filenames=None,
    timeout=300,
    proxies=None,
    verbose=True,
):
    """Exports an ImageCollection as GeoTIFFs.

    Args:
        ee_object (object): The ee.Image to download.
        out_dir (str): The output directory for the exported images.
        scale (float, optional): A default scale to use for any bands that do not specify one; ignored if crs and crs_transform is specified. Defaults to None.
        crs (str, optional): A default CRS string to use for any bands that do not explicitly specify one. Defaults to None.
        crs_transform (list, optional): a default affine transform to use for any bands that do not specify one, of the same format as the crs_transform of bands. Defaults to None.
        region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.
        dimensions (list, optional): An optional array of two integers defining the width and height to which the band is cropped. Defaults to None.
        file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.
        format (str, optional):  One of: "ZIPPED_GEO_TIFF" (GeoTIFF file(s) wrapped in a zip file, default), "GEO_TIFF" (GeoTIFF file), "NPY" (NumPy binary format). If "GEO_TIFF" or "NPY",
            filePerBand and all band-level transformations will be ignored. Loading a NumPy output results in a structured array.
        unmask_value (float, optional): The value to use for pixels that are masked in the input image.
            If the exported image contains zero values, you should set the unmask value to a  non-zero value so that the zero values are not treated as missing data. Defaults to None.
        filenames (list | int, optional): A list of filenames to use for the exported images. Defaults to None.
        timeout (int, optional): The timeout in seconds for the request. Defaults to 300.
        proxies (dict, optional): A dictionary of proxy servers to use. Defaults to None.
        verbose (bool, optional): Whether to print out descriptive text. Defaults to True.
    """

    if not isinstance(ee_object, ee.ImageCollection):
        print("The ee_object must be an ee.ImageCollection.")
        return

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

    try:
        count = int(ee_object.size().getInfo())
        if verbose:
            print(f"Total number of images: {count}\n")

        if filenames is None:
            filenames = ee_object.aggregate_array("system:index").getInfo()
        elif isinstance(filenames, int):
            filenames = [str(f + filenames) for f in range(0, count)]

        if len(filenames) != count:
            raise Exception(
                "The number of filenames must be equal to the number of images."
            )

        filenames = [str(f) + ".tif" for f in filenames if not str(f).endswith(".tif")]

        for i in range(0, count):
            image = ee.Image(ee_object.toList(count).get(i))
            filename = os.path.join(out_dir, filenames[i])
            if verbose:
                print(f"Exporting {i + 1}/{count}: {filename}")
            ee_export_image(
                image,
                filename=filename,
                scale=scale,
                crs=crs,
                crs_transform=crs_transform,
                region=region,
                dimensions=dimensions,
                file_per_band=file_per_band,
                format=format,
                unmask_value=unmask_value,
                timeout=timeout,
                proxies=proxies,
            )
            print("\n")

    except Exception as e:
        print(e)

ee_export_image_collection_to_asset(ee_object, descriptions=None, assetIds=None, pyramidingPolicy=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, **kwargs)

Creates a batch task to export an ImageCollection as raster images to Google Drive.

Parameters:

Name	Type	Description	Default
ee_object		The image collection to export.	required
descriptions		A list of human-readable names of the tasks.	None
assetIds		The destination asset ID.	None
pyramidingPolicy		The pyramiding policy to apply to each band in the image, a dictionary keyed by band name. Values must be one of: "mean", "sample", "min", "max", or "mode". Defaults to "mean". A special key, ".default", may be used to change the default for all bands.	None
dimensions		The dimensions of the exported image. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers.	None
region		The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the image's region.	None
scale		The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified.	None
crs		The coordinate reference system of the exported image's projection. Defaults to the image's default projection.	None
crsTransform		A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image's native CRS transform.	None
maxPixels		The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000.	None
**kwargs		Holds other keyword arguments that may have been deprecated such as 'crs_transform'.	{}

Source code in geemap/common.py

def ee_export_image_collection_to_asset(
    ee_object,
    descriptions=None,
    assetIds=None,
    pyramidingPolicy=None,
    dimensions=None,
    region=None,
    scale=None,
    crs=None,
    crsTransform=None,
    maxPixels=None,
    **kwargs,
):
    """Creates a batch task to export an ImageCollection as raster images to Google Drive.

    Args:
        ee_object: The image collection to export.
        descriptions: A list of human-readable names of the tasks.
        assetIds: The destination asset ID.
        pyramidingPolicy: The pyramiding policy to apply to each band in the
            image, a dictionary keyed by band name. Values must be
            one of: "mean", "sample", "min", "max", or "mode".
            Defaults to "mean". A special key, ".default", may be used to
            change the default for all bands.
        dimensions: The dimensions of the exported image. Takes either a
            single positive integer as the maximum dimension or "WIDTHxHEIGHT"
            where WIDTH and HEIGHT are each positive integers.
        region: The lon,lat coordinates for a LinearRing or Polygon
            specifying the region to export. Can be specified as a nested
            lists of numbers or a serialized string. Defaults to the image's
            region.
        scale: The resolution in meters per pixel. Defaults to the
            native resolution of the image asset unless a crsTransform
            is specified.
        crs: The coordinate reference system of the exported image's
            projection. Defaults to the image's default projection.
        crsTransform: A comma-separated string of 6 numbers describing
            the affine transform of the coordinate reference system of the
            exported image's projection, in the order: xScale, xShearing,
            xTranslation, yShearing, yScale and yTranslation. Defaults to
            the image's native CRS transform.
        maxPixels: The maximum allowed number of pixels in the exported
            image. The task will fail if the exported region covers more
            pixels in the specified projection. Defaults to 100,000,000.
        **kwargs: Holds other keyword arguments that may have been deprecated
            such as 'crs_transform'.
    """

    if not isinstance(ee_object, ee.ImageCollection):
        raise ValueError("The ee_object must be an ee.ImageCollection.")

    try:
        count = int(ee_object.size().getInfo())
        print(f"Total number of images: {count}\n")

        if (descriptions is not None) and (len(descriptions) != count):
            print("The number of descriptions is not equal to the number of images.")
            return

        if descriptions is None:
            descriptions = ee_object.aggregate_array("system:index").getInfo()

        if assetIds is None:
            assetIds = descriptions

        images = ee_object.toList(count)

        if os.environ.get("USE_MKDOCS") is not None:  # skip if running GitHub CI.
            return

        for i in range(0, count):
            image = ee.Image(images.get(i))
            description = descriptions[i]
            assetId = assetIds[i]
            ee_export_image_to_asset(
                image,
                description,
                assetId,
                pyramidingPolicy,
                dimensions,
                region,
                scale,
                crs,
                crsTransform,
                maxPixels,
                **kwargs,
            )

    except Exception as e:
        print(e)

ee_export_image_collection_to_cloud_storage(ee_object, descriptions=None, bucket=None, fileNamePrefix=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, shardSize=None, fileDimensions=None, skipEmptyTiles=None, fileFormat=None, formatOptions=None, **kwargs)

Creates a batch task to export an ImageCollection as raster images to a Google Cloud bucket.

Parameters:

Name	Type	Description	Default
ee_object		The image collection to export.	required
descriptions		A list of human-readable names of the tasks.	None
bucket		The name of a Cloud Storage bucket for the export.	None
fileNamePrefix		Cloud Storage object name prefix for the export. Defaults to the name of the task.	None
dimensions		The dimensions of the exported image. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers.	None
region		The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the image's region.	None
scale		The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified.	None
crs		The coordinate reference system of the exported image's projection. Defaults to the image's default projection.	None
crsTransform		A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image's native CRS transform.	None
maxPixels		The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000.	None
shardSize		Size in pixels of the tiles in which this image will be computed. Defaults to 256.	None
fileDimensions		The dimensions in pixels of each image file, if the image is too large to fit in a single file. May specify a single number to indicate a square shape, or a tuple of two dimensions to indicate (width,height). Note that the image will still be clipped to the overall image dimensions. Must be a multiple of shardSize.	None
skipEmptyTiles		If true, skip writing empty (i.e. fully-masked) image tiles. Defaults to false.	None
fileFormat		The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to 'GeoTIFF'.	None
formatOptions		A dictionary of string keys to format specific options.	None
**kwargs		Holds other keyword arguments that may have been deprecated such as 'crs_transform'.	{}

Source code in geemap/common.py

def ee_export_image_collection_to_cloud_storage(
    ee_object,
    descriptions=None,
    bucket=None,
    fileNamePrefix=None,
    dimensions=None,
    region=None,
    scale=None,
    crs=None,
    crsTransform=None,
    maxPixels=None,
    shardSize=None,
    fileDimensions=None,
    skipEmptyTiles=None,
    fileFormat=None,
    formatOptions=None,
    **kwargs,
):
    """Creates a batch task to export an ImageCollection as raster images to a Google Cloud bucket.

    Args:
        ee_object: The image collection to export.
        descriptions: A list of human-readable names of the tasks.
        bucket: The name of a Cloud Storage bucket for the export.
        fileNamePrefix: Cloud Storage object name prefix for the export.
            Defaults to the name of the task.
        dimensions: The dimensions of the exported image. Takes either a
            single positive integer as the maximum dimension or "WIDTHxHEIGHT"
            where WIDTH and HEIGHT are each positive integers.
        region: The lon,lat coordinates for a LinearRing or Polygon
            specifying the region to export. Can be specified as a nested
            lists of numbers or a serialized string. Defaults to the image's
            region.
        scale: The resolution in meters per pixel. Defaults to the
            native resolution of the image asset unless a crsTransform
            is specified.
        crs: The coordinate reference system of the exported image's
            projection. Defaults to the image's default projection.
        crsTransform: A comma-separated string of 6 numbers describing
            the affine transform of the coordinate reference system of the
            exported image's projection, in the order: xScale, xShearing,
            xTranslation, yShearing, yScale and yTranslation. Defaults to
            the image's native CRS transform.
        maxPixels: The maximum allowed number of pixels in the exported
            image. The task will fail if the exported region covers more
            pixels in the specified projection. Defaults to 100,000,000.
        shardSize: Size in pixels of the tiles in which this image will be
            computed. Defaults to 256.
        fileDimensions: The dimensions in pixels of each image file, if the
            image is too large to fit in a single file. May specify a
            single number to indicate a square shape, or a tuple of two
            dimensions to indicate (width,height). Note that the image will
            still be clipped to the overall image dimensions. Must be a
            multiple of shardSize.
        skipEmptyTiles: If true, skip writing empty (i.e. fully-masked)
            image tiles. Defaults to false.
        fileFormat: The string file format to which the image is exported.
            Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to
            'GeoTIFF'.
        formatOptions: A dictionary of string keys to format specific options.
        **kwargs: Holds other keyword arguments that may have been deprecated
            such as 'crs_transform'.
    """

    if not isinstance(ee_object, ee.ImageCollection):
        raise ValueError("The ee_object must be an ee.ImageCollection.")

    try:
        count = int(ee_object.size().getInfo())
        print(f"Total number of images: {count}\n")

        if (descriptions is not None) and (len(descriptions) != count):
            print("The number of descriptions is not equal to the number of images.")
            return

        if descriptions is None:
            descriptions = ee_object.aggregate_array("system:index").getInfo()

        images = ee_object.toList(count)

        if os.environ.get("USE_MKDOCS") is not None:  # skip if running GitHub CI.
            return

        for i in range(0, count):
            image = ee.Image(images.get(i))
            description = descriptions[i]
            ee_export_image_to_cloud_storage(
                image,
                description,
                bucket,
                fileNamePrefix,
                dimensions,
                region,
                scale,
                crs,
                crsTransform,
                maxPixels,
                shardSize,
                fileDimensions,
                skipEmptyTiles,
                fileFormat,
                formatOptions,
                **kwargs,
            )

    except Exception as e:
        print(e)

ee_export_image_collection_to_drive(ee_object, descriptions=None, folder=None, fileNamePrefix=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, shardSize=None, fileDimensions=None, skipEmptyTiles=None, fileFormat=None, formatOptions=None, **kwargs)

Creates a batch task to export an ImageCollection as raster images to Google Drive.

Parameters:

Name	Type	Description	Default
ee_object		The image collection to export.	required
descriptions		A list of human-readable names of the tasks.	None
folder		The name of a unique folder in your Drive account to export into. Defaults to the root of the drive.	None
fileNamePrefix		The Google Drive filename for the export. Defaults to the name of the task.	None
dimensions		The dimensions of the exported image. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers.	None
region		The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the image's region.	None
scale		The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified.	None
crs		The coordinate reference system of the exported image's projection. Defaults to the image's default projection.	None
crsTransform		A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image's native CRS transform.	None
maxPixels		The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000.	None
shardSize		Size in pixels of the tiles in which this image will be computed. Defaults to 256.	None
fileDimensions		The dimensions in pixels of each image file, if the image is too large to fit in a single file. May specify a single number to indicate a square shape, or a tuple of two dimensions to indicate (width,height). Note that the image will still be clipped to the overall image dimensions. Must be a multiple of shardSize.	None
skipEmptyTiles		If true, skip writing empty (i.e. fully-masked) image tiles. Defaults to false.	None
fileFormat		The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to 'GeoTIFF'.	None
formatOptions		A dictionary of string keys to format specific options.	None
**kwargs		Holds other keyword arguments that may have been deprecated such as 'crs_transform', 'driveFolder', and 'driveFileNamePrefix'.	{}

Source code in geemap/common.py

def ee_export_image_collection_to_drive(
    ee_object,
    descriptions=None,
    folder=None,
    fileNamePrefix=None,
    dimensions=None,
    region=None,
    scale=None,
    crs=None,
    crsTransform=None,
    maxPixels=None,
    shardSize=None,
    fileDimensions=None,
    skipEmptyTiles=None,
    fileFormat=None,
    formatOptions=None,
    **kwargs,
):
    """Creates a batch task to export an ImageCollection as raster images to Google Drive.

    Args:
        ee_object: The image collection to export.
        descriptions: A list of human-readable names of the tasks.
        folder: The name of a unique folder in your Drive account to
            export into. Defaults to the root of the drive.
        fileNamePrefix: The Google Drive filename for the export.
            Defaults to the name of the task.
        dimensions: The dimensions of the exported image. Takes either a
            single positive integer as the maximum dimension or "WIDTHxHEIGHT"
            where WIDTH and HEIGHT are each positive integers.
        region: The lon,lat coordinates for a LinearRing or Polygon
            specifying the region to export. Can be specified as a nested
            lists of numbers or a serialized string. Defaults to the image's
            region.
        scale: The resolution in meters per pixel. Defaults to the
            native resolution of the image asset unless a crsTransform
            is specified.
        crs: The coordinate reference system of the exported image's
            projection. Defaults to the image's default projection.
        crsTransform: A comma-separated string of 6 numbers describing
            the affine transform of the coordinate reference system of the
            exported image's projection, in the order: xScale, xShearing,
            xTranslation, yShearing, yScale and yTranslation. Defaults to
            the image's native CRS transform.
        maxPixels: The maximum allowed number of pixels in the exported
            image. The task will fail if the exported region covers more
            pixels in the specified projection. Defaults to 100,000,000.
        shardSize: Size in pixels of the tiles in which this image will be
            computed. Defaults to 256.
        fileDimensions: The dimensions in pixels of each image file, if the
            image is too large to fit in a single file. May specify a
            single number to indicate a square shape, or a tuple of two
            dimensions to indicate (width,height). Note that the image will
            still be clipped to the overall image dimensions. Must be a
            multiple of shardSize.
        skipEmptyTiles: If true, skip writing empty (i.e. fully-masked)
            image tiles. Defaults to false.
        fileFormat: The string file format to which the image is exported.
            Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to
            'GeoTIFF'.
        formatOptions: A dictionary of string keys to format specific options.
        **kwargs: Holds other keyword arguments that may have been deprecated
            such as 'crs_transform', 'driveFolder', and 'driveFileNamePrefix'.
    """

    if not isinstance(ee_object, ee.ImageCollection):
        raise ValueError("The ee_object must be an ee.ImageCollection.")

    try:
        count = int(ee_object.size().getInfo())
        print(f"Total number of images: {count}\n")

        if (descriptions is not None) and (len(descriptions) != count):
            raise ValueError(
                "The number of descriptions is not equal to the number of images."
            )

        if descriptions is None:
            descriptions = ee_object.aggregate_array("system:index").getInfo()

        images = ee_object.toList(count)

        if os.environ.get("USE_MKDOCS") is not None:  # skip if running GitHub CI.
            return

        for i in range(0, count):
            image = ee.Image(images.get(i))
            description = descriptions[i]
            ee_export_image_to_drive(
                image,
                description,
                folder,
                fileNamePrefix,
                dimensions,
                region,
                scale,
                crs,
                crsTransform,
                maxPixels,
                shardSize,
                fileDimensions,
                skipEmptyTiles,
                fileFormat,
                formatOptions,
                **kwargs,
            )

    except Exception as e:
        print(e)

ee_export_image_to_asset(image, description='myExportImageTask', assetId=None, pyramidingPolicy=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, **kwargs)

Creates a task to export an EE Image to an EE Asset.

Parameters:

Name	Type	Description	Default
image		The image to be exported.	required
description		Human-readable name of the task.	'myExportImageTask'
assetId		The destination asset ID.	None
pyramidingPolicy		The pyramiding policy to apply to each band in the image, a dictionary keyed by band name. Values must be one of: "mean", "sample", "min", "max", or "mode". Defaults to "mean". A special key, ".default", may be used to change the default for all bands.	None
dimensions		The dimensions of the exported image. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers.	None
region		The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the image's region.	None
scale		The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified.	None
crs		The coordinate reference system of the exported image's projection. Defaults to the image's default projection.	None
crsTransform		A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image's native CRS transform.	None
maxPixels		The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000.	None
**kwargs		Holds other keyword arguments that may have been deprecated such as 'crs_transform'.	{}

Source code in geemap/common.py

def ee_export_image_to_asset(
    image,
    description="myExportImageTask",
    assetId=None,
    pyramidingPolicy=None,
    dimensions=None,
    region=None,
    scale=None,
    crs=None,
    crsTransform=None,
    maxPixels=None,
    **kwargs,
):
    """Creates a task to export an EE Image to an EE Asset.

    Args:
        image: The image to be exported.
        description: Human-readable name of the task.
        assetId: The destination asset ID.
        pyramidingPolicy: The pyramiding policy to apply to each band in the
            image, a dictionary keyed by band name. Values must be
            one of: "mean", "sample", "min", "max", or "mode".
            Defaults to "mean". A special key, ".default", may be used to
            change the default for all bands.
        dimensions: The dimensions of the exported image. Takes either a
            single positive integer as the maximum dimension or "WIDTHxHEIGHT"
            where WIDTH and HEIGHT are each positive integers.
        region: The lon,lat coordinates for a LinearRing or Polygon
            specifying the region to export. Can be specified as a nested
            lists of numbers or a serialized string. Defaults to the image's
            region.
        scale: The resolution in meters per pixel. Defaults to the
            native resolution of the image asset unless a crsTransform
            is specified.
        crs: The coordinate reference system of the exported image's
            projection. Defaults to the image's default projection.
        crsTransform: A comma-separated string of 6 numbers describing
            the affine transform of the coordinate reference system of the
            exported image's projection, in the order: xScale, xShearing,
            xTranslation, yShearing, yScale and yTranslation. Defaults to
            the image's native CRS transform.
        maxPixels: The maximum allowed number of pixels in the exported
            image. The task will fail if the exported region covers more
            pixels in the specified projection. Defaults to 100,000,000.
        **kwargs: Holds other keyword arguments that may have been deprecated
            such as 'crs_transform'.
    """

    if isinstance(image, ee.Image) or isinstance(image, ee.image.Image):
        pass
    else:
        raise ValueError("Input image must be an instance of ee.Image")

    if isinstance(assetId, str):
        if assetId.startswith("users/") or assetId.startswith("projects/"):
            pass
        else:
            assetId = f"{ee_user_id()}/{assetId}"

    task = ee.batch.Export.image.toAsset(
        image,
        description,
        assetId,
        pyramidingPolicy,
        dimensions,
        region,
        scale,
        crs,
        crsTransform,
        maxPixels,
        **kwargs,
    )
    task.start()

ee_export_image_to_cloud_storage(image, description='myExportImageTask', bucket=None, fileNamePrefix=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, shardSize=None, fileDimensions=None, skipEmptyTiles=None, fileFormat=None, formatOptions=None, **kwargs)

Creates a task to export an EE Image to Google Cloud Storage.

Parameters:

Name	Type	Description	Default
image		The image to be exported.	required
description		Human-readable name of the task.	'myExportImageTask'
bucket		The name of a Cloud Storage bucket for the export.	None
fileNamePrefix		Cloud Storage object name prefix for the export. Defaults to the name of the task.	None
dimensions		The dimensions of the exported image. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers.	None
region		The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the image's region.	None
scale		The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified.	None
crs		The coordinate reference system of the exported image's projection. Defaults to the image's default projection.	None
crsTransform		A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image's native CRS transform.	None
maxPixels		The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000.	None
shardSize		Size in pixels of the tiles in which this image will be computed. Defaults to 256.	None
fileDimensions		The dimensions in pixels of each image file, if the image is too large to fit in a single file. May specify a single number to indicate a square shape, or a tuple of two dimensions to indicate (width,height). Note that the image will still be clipped to the overall image dimensions. Must be a multiple of shardSize.	None
skipEmptyTiles		If true, skip writing empty (i.e. fully-masked) image tiles. Defaults to false.	None
fileFormat		The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to 'GeoTIFF'.	None
formatOptions		A dictionary of string keys to format specific options.	None
**kwargs		Holds other keyword arguments that may have been deprecated such as 'crs_transform'.	{}

Source code in geemap/common.py

def ee_export_image_to_cloud_storage(
    image,
    description="myExportImageTask",
    bucket=None,
    fileNamePrefix=None,
    dimensions=None,
    region=None,
    scale=None,
    crs=None,
    crsTransform=None,
    maxPixels=None,
    shardSize=None,
    fileDimensions=None,
    skipEmptyTiles=None,
    fileFormat=None,
    formatOptions=None,
    **kwargs,
):
    """Creates a task to export an EE Image to Google Cloud Storage.

    Args:
        image: The image to be exported.
        description: Human-readable name of the task.
        bucket: The name of a Cloud Storage bucket for the export.
        fileNamePrefix: Cloud Storage object name prefix for the export.
            Defaults to the name of the task.
        dimensions: The dimensions of the exported image. Takes either a
            single positive integer as the maximum dimension or "WIDTHxHEIGHT"
            where WIDTH and HEIGHT are each positive integers.
        region: The lon,lat coordinates for a LinearRing or Polygon
            specifying the region to export. Can be specified as a nested
            lists of numbers or a serialized string. Defaults to the image's
            region.
        scale: The resolution in meters per pixel. Defaults to the
            native resolution of the image asset unless a crsTransform
            is specified.
        crs: The coordinate reference system of the exported image's
            projection. Defaults to the image's default projection.
        crsTransform: A comma-separated string of 6 numbers describing
            the affine transform of the coordinate reference system of the
            exported image's projection, in the order: xScale, xShearing,
            xTranslation, yShearing, yScale and yTranslation. Defaults to
            the image's native CRS transform.
        maxPixels: The maximum allowed number of pixels in the exported
            image. The task will fail if the exported region covers more
            pixels in the specified projection. Defaults to 100,000,000.
        shardSize: Size in pixels of the tiles in which this image will be
            computed. Defaults to 256.
        fileDimensions: The dimensions in pixels of each image file, if the
            image is too large to fit in a single file. May specify a
            single number to indicate a square shape, or a tuple of two
            dimensions to indicate (width,height). Note that the image will
            still be clipped to the overall image dimensions. Must be a
            multiple of shardSize.
        skipEmptyTiles: If true, skip writing empty (i.e. fully-masked)
            image tiles. Defaults to false.
        fileFormat: The string file format to which the image is exported.
            Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to
            'GeoTIFF'.
        formatOptions: A dictionary of string keys to format specific options.
        **kwargs: Holds other keyword arguments that may have been deprecated
            such as 'crs_transform'.
    """

    if not isinstance(image, ee.Image):
        raise ValueError("Input image must be an instance of ee.Image")

    try:
        task = ee.batch.Export.image.toCloudStorage(
            image,
            description,
            bucket,
            fileNamePrefix,
            dimensions,
            region,
            scale,
            crs,
            crsTransform,
            maxPixels,
            shardSize,
            fileDimensions,
            skipEmptyTiles,
            fileFormat,
            formatOptions,
            **kwargs,
        )
        task.start()
    except Exception as e:
        print(e)

ee_export_image_to_drive(image, description='myExportImageTask', folder=None, fileNamePrefix=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, shardSize=None, fileDimensions=None, skipEmptyTiles=None, fileFormat=None, formatOptions=None, **kwargs)

Creates a batch task to export an Image as a raster to Google Drive.

Parameters:

Name	Type	Description	Default
image		The image to be exported.	required
description		Human-readable name of the task.	'myExportImageTask'
folder		The name of a unique folder in your Drive account to export into. Defaults to the root of the drive.	None
fileNamePrefix		The Google Drive filename for the export. Defaults to the name of the task.	None
dimensions		The dimensions of the exported image. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers.	None
region		The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the image's region.	None
scale		The resolution in meters per pixel. Defaults to the native resolution of the image asset unless a crsTransform is specified.	None
crs		The coordinate reference system of the exported image's projection. Defaults to the image's default projection.	None
crsTransform		A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported image's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image's native CRS transform.	None
maxPixels		The maximum allowed number of pixels in the exported image. The task will fail if the exported region covers more pixels in the specified projection. Defaults to 100,000,000.	None
shardSize		Size in pixels of the tiles in which this image will be computed. Defaults to 256.	None
fileDimensions		The dimensions in pixels of each image file, if the image is too large to fit in a single file. May specify a single number to indicate a square shape, or a tuple of two dimensions to indicate (width,height). Note that the image will still be clipped to the overall image dimensions. Must be a multiple of shardSize.	None
skipEmptyTiles		If true, skip writing empty (i.e. fully-masked) image tiles. Defaults to false.	None
fileFormat		The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to 'GeoTIFF'.	None
formatOptions		A dictionary of string keys to format specific options.	None
**kwargs		Holds other keyword arguments that may have been deprecated such as 'crs_transform', 'driveFolder', and 'driveFileNamePrefix'.	{}

Source code in geemap/common.py

def ee_export_image_to_drive(
    image,
    description="myExportImageTask",
    folder=None,
    fileNamePrefix=None,
    dimensions=None,
    region=None,
    scale=None,
    crs=None,
    crsTransform=None,
    maxPixels=None,
    shardSize=None,
    fileDimensions=None,
    skipEmptyTiles=None,
    fileFormat=None,
    formatOptions=None,
    **kwargs,
):
    """Creates a batch task to export an Image as a raster to Google Drive.

    Args:
        image: The image to be exported.
        description: Human-readable name of the task.
        folder: The name of a unique folder in your Drive account to
            export into. Defaults to the root of the drive.
        fileNamePrefix: The Google Drive filename for the export.
            Defaults to the name of the task.
        dimensions: The dimensions of the exported image. Takes either a
            single positive integer as the maximum dimension or "WIDTHxHEIGHT"
            where WIDTH and HEIGHT are each positive integers.
        region: The lon,lat coordinates for a LinearRing or Polygon
            specifying the region to export. Can be specified as a nested
            lists of numbers or a serialized string. Defaults to the image's
            region.
        scale: The resolution in meters per pixel. Defaults to the
            native resolution of the image asset unless a crsTransform
            is specified.
        crs: The coordinate reference system of the exported image's
            projection. Defaults to the image's default projection.
        crsTransform: A comma-separated string of 6 numbers describing
            the affine transform of the coordinate reference system of the
            exported image's projection, in the order: xScale, xShearing,
            xTranslation, yShearing, yScale and yTranslation. Defaults to
            the image's native CRS transform.
        maxPixels: The maximum allowed number of pixels in the exported
            image. The task will fail if the exported region covers more
            pixels in the specified projection. Defaults to 100,000,000.
        shardSize: Size in pixels of the tiles in which this image will be
            computed. Defaults to 256.
        fileDimensions: The dimensions in pixels of each image file, if the
            image is too large to fit in a single file. May specify a
            single number to indicate a square shape, or a tuple of two
            dimensions to indicate (width,height). Note that the image will
            still be clipped to the overall image dimensions. Must be a
            multiple of shardSize.
        skipEmptyTiles: If true, skip writing empty (i.e. fully-masked)
            image tiles. Defaults to false.
        fileFormat: The string file format to which the image is exported.
            Currently only 'GeoTIFF' and 'TFRecord' are supported, defaults to
            'GeoTIFF'.
        formatOptions: A dictionary of string keys to format specific options.
        **kwargs: Holds other keyword arguments that may have been deprecated
            such as 'crs_transform', 'driveFolder', and 'driveFileNamePrefix'.
    """

    if not isinstance(image, ee.Image):
        raise ValueError("Input image must be an instance of ee.Image")

    task = ee.batch.Export.image.toDrive(
        image,
        description,
        folder,
        fileNamePrefix,
        dimensions,
        region,
        scale,
        crs,
        crsTransform,
        maxPixels,
        shardSize,
        fileDimensions,
        skipEmptyTiles,
        fileFormat,
        formatOptions,
        **kwargs,
    )
    task.start()

ee_export_map_to_cloud_storage(image, description='myExportMapTask', bucket=None, fileFormat=None, path=None, writePublicTiles=None, maxZoom=None, scale=None, minZoom=None, region=None, skipEmptyTiles=None, mapsApiKey=None, **kwargs)

Creates a task to export an Image as a pyramid of map tiles.

Exports a rectangular pyramid of map tiles for use with web map viewers. The map tiles will be accompanied by a reference index.html file that displays them using the Google Maps API, and an earth.html file for opening the map on Google Earth.

Parameters:

Name	Type	Description	Default
image		The image to export as tiles.	required
description		Human-readable name of the task.	'myExportMapTask'
bucket		The destination bucket to write to.	None
fileFormat		The map tiles' file format, one of 'auto', 'png', or 'jpeg'. Defaults to 'auto', which means that opaque tiles will be encoded as 'jpg' and tiles with transparency will be encoded as 'png'.	None
path		The string used as the output's path. A trailing '/' is optional. Defaults to the task's description.	None
writePublicTiles		Whether to write public tiles instead of using the bucket's default object ACL. Defaults to True and requires the invoker to be an OWNER of bucket.	None
maxZoom		The maximum zoom level of the map tiles to export.	None
scale		The max image resolution in meters per pixel, as an alternative to 'maxZoom'. The scale will be converted to the most appropriate maximum zoom level at the equator.	None
minZoom		The optional minimum zoom level of the map tiles to export.	None
region		The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Map tiles will be produced in the rectangular region containing this geometry. Defaults to the image's region.	None
skipEmptyTiles		If true, skip writing empty (i.e. fully-transparent) map tiles. Defaults to false.	None
mapsApiKey		Used in index.html to initialize the Google Maps API. This removes the "development purposes only" message from the map.	None
**kwargs		Holds other keyword arguments that may have been deprecated such as 'crs_transform'.	{}

Source code in geemap/common.py

def ee_export_map_to_cloud_storage(
    image,
    description="myExportMapTask",
    bucket=None,
    fileFormat=None,
    path=None,
    writePublicTiles=None,
    maxZoom=None,
    scale=None,
    minZoom=None,
    region=None,
    skipEmptyTiles=None,
    mapsApiKey=None,
    **kwargs,
):
    """Creates a task to export an Image as a pyramid of map tiles.

    Exports a rectangular pyramid of map tiles for use with web map
    viewers. The map tiles will be accompanied by a reference
    index.html file that displays them using the Google Maps API,
    and an earth.html file for opening the map on Google Earth.

    Args:
        image: The image to export as tiles.
        description: Human-readable name of the task.
        bucket: The destination bucket to write to.
        fileFormat: The map tiles' file format, one of 'auto', 'png',
            or 'jpeg'. Defaults to 'auto', which means that opaque tiles
            will be encoded as 'jpg' and tiles with transparency will be
            encoded as 'png'.
        path: The string used as the output's path. A trailing '/'
            is optional. Defaults to the task's description.
        writePublicTiles: Whether to write public tiles instead of using the
            bucket's default object ACL. Defaults to True and requires the
            invoker to be an OWNER of bucket.
        maxZoom: The maximum zoom level of the map tiles to export.
        scale: The max image resolution in meters per pixel, as an alternative
            to 'maxZoom'. The scale will be converted to the most appropriate
            maximum zoom level at the equator.
        minZoom: The optional minimum zoom level of the map tiles to export.
        region: The lon,lat coordinates for a LinearRing or Polygon
            specifying the region to export. Can be specified as a nested
            lists of numbers or a serialized string. Map tiles will be
            produced in the rectangular region containing this geometry.
            Defaults to the image's region.
        skipEmptyTiles: If true, skip writing empty (i.e. fully-transparent)
            map tiles. Defaults to false.
        mapsApiKey: Used in index.html to initialize the Google Maps API. This
            removes the "development purposes only" message from the map.
        **kwargs: Holds other keyword arguments that may have been deprecated
            such as 'crs_transform'.

    """
    if not isinstance(image, ee.Image):
        raise TypeError("image must be an ee.Image")

    if os.environ.get("USE_MKDOCS") is not None:  # skip if running GitHub CI.
        return

    print(
        f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
    )

    task = ee.batch.Export.map.toCloudStorage(
        image,
        description,
        bucket,
        fileFormat,
        path,
        writePublicTiles,
        maxZoom,
        scale,
        minZoom,
        region,
        skipEmptyTiles,
        mapsApiKey,
        **kwargs,
    )
    task.start()

ee_export_vector(ee_object, filename, selectors=None, verbose=True, keep_zip=False, timeout=300, proxies=None)

Exports Earth Engine FeatureCollection to other formats, including shp, csv, json, kml, and kmz.

Parameters:

Name	Type	Description	Default
ee_object	object	ee.FeatureCollection to export.	required
filename	str	Output file name.	required
selectors	list	A list of attributes to export. Defaults to None.	None
verbose	bool	Whether to print out descriptive text.	True
keep_zip	bool	Whether to keep the downloaded shapefile as a zip file.	False
timeout	int	Timeout in seconds. Defaults to 300 seconds.	300
proxies	dict	A dictionary of proxies to use. Defaults to None.	None

Source code in geemap/common.py

def ee_export_vector(
    ee_object,
    filename,
    selectors=None,
    verbose=True,
    keep_zip=False,
    timeout=300,
    proxies=None,
):
    """Exports Earth Engine FeatureCollection to other formats, including shp, csv, json, kml, and kmz.

    Args:
        ee_object (object): ee.FeatureCollection to export.
        filename (str): Output file name.
        selectors (list, optional): A list of attributes to export. Defaults to None.
        verbose (bool, optional): Whether to print out descriptive text.
        keep_zip (bool, optional): Whether to keep the downloaded shapefile as a zip file.
        timeout (int, optional): Timeout in seconds. Defaults to 300 seconds.
        proxies (dict, optional): A dictionary of proxies to use. Defaults to None.
    """

    if not isinstance(ee_object, ee.FeatureCollection):
        raise ValueError("ee_object must be an ee.FeatureCollection")

    allowed_formats = ["csv", "geojson", "json", "kml", "kmz", "shp"]
    # allowed_formats = ['csv', 'kml', 'kmz']
    filename = os.path.abspath(filename)
    basename = os.path.basename(filename)
    name = os.path.splitext(basename)[0]
    filetype = os.path.splitext(basename)[1][1:].lower()

    if filetype == "shp":
        filename = filename.replace(".shp", ".zip")

    if not (filetype.lower() in allowed_formats):
        raise ValueError(
            "The file type must be one of the following: {}".format(
                ", ".join(allowed_formats)
            )
        )

    if selectors is None:
        selectors = ee_object.first().propertyNames().getInfo()
        if filetype == "csv":
            # remove .geo coordinate field
            ee_object = ee_object.select([".*"], None, False)

    if filetype == "geojson":
        selectors = [".geo"] + selectors

    elif not isinstance(selectors, list):
        raise ValueError(
            "selectors must be a list, such as ['attribute1', 'attribute2']"
        )
    else:
        allowed_attributes = ee_object.first().propertyNames().getInfo()
        for attribute in selectors:
            if not (attribute in allowed_attributes):
                raise ValueError(
                    "Attributes must be one chosen from: {} ".format(
                        ", ".join(allowed_attributes)
                    )
                )

    try:
        if verbose:
            print("Generating URL ...")
        url = ee_object.getDownloadURL(
            filetype=filetype, selectors=selectors, filename=name
        )
        if verbose:
            print(f"Downloading data from {url}\nPlease wait ...")
        r = None
        r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)

        if r.status_code != 200:
            print("An error occurred while downloading. \n Retrying ...")
            try:
                new_ee_object = ee_object.map(filter_polygons)
                print("Generating URL ...")
                url = new_ee_object.getDownloadURL(
                    filetype=filetype, selectors=selectors, filename=name
                )
                print(f"Downloading data from {url}\nPlease wait ...")
                r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)
            except Exception as e:
                print(e)
                raise ValueError

        with open(filename, "wb") as fd:
            for chunk in r.iter_content(chunk_size=1024):
                fd.write(chunk)
    except Exception as e:
        print("An error occurred while downloading.")
        if r is not None:
            print(r.json()["error"]["message"])
        raise ValueError(e)

    try:
        if filetype == "shp":
            with zipfile.ZipFile(filename) as z:
                z.extractall(os.path.dirname(filename))
            if not keep_zip:
                os.remove(filename)
            filename = filename.replace(".zip", ".shp")
        if verbose:
            print(f"Data downloaded to {filename}")
    except Exception as e:
        raise ValueError(e)

ee_export_vector_to_asset(collection, description='myExportTableTask', assetId=None, maxVertices=None, **kwargs)

Creates a task to export a FeatureCollection to Asset.

Parameters:

Name	Type	Description	Default
collection		The feature collection to be exported.	required
description		Human-readable name of the task.	'myExportTableTask'
assetId		The destination asset ID.	None
maxVertices		Max number of uncut vertices per geometry; geometries with more vertices will be cut into pieces smaller than this size.	None
**kwargs		Holds other keyword arguments that may have been deprecated.	{}

Source code in geemap/common.py

def ee_export_vector_to_asset(
    collection,
    description="myExportTableTask",
    assetId=None,
    maxVertices=None,
    **kwargs,
):
    """Creates a task to export a FeatureCollection to Asset.

    Args:
        collection: The feature collection to be exported.
        description: Human-readable name of the task.
        assetId: The destination asset ID.
        maxVertices:
            Max number of uncut vertices per geometry; geometries with more
            vertices will be cut into pieces smaller than this size.
        **kwargs: Holds other keyword arguments that may have been deprecated.
    """
    if not isinstance(collection, ee.FeatureCollection):
        raise ValueError("The collection must be an ee.FeatureCollection.")

    if os.environ.get("USE_MKDOCS") is not None:  # skip if running GitHub CI.
        return

    if isinstance(assetId, str):
        if assetId.startswith("users/") or assetId.startswith("projects/"):
            pass
        else:
            assetId = f"{ee_user_id()}/{assetId}"

    print(assetId)
    print(
        f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
    )

    task = ee.batch.Export.table.toAsset(
        collection,
        description,
        assetId,
        maxVertices,
        **kwargs,
    )
    task.start()

ee_export_vector_to_cloud_storage(collection, description='myExportTableTask', bucket=None, fileNamePrefix=None, fileFormat=None, selectors=None, maxVertices=None, **kwargs)

Creates a task to export a FeatureCollection to Google Cloud Storage.

Parameters:

Name	Type	Description	Default
collection		The feature collection to be exported.	required
description		Human-readable name of the task.	'myExportTableTask'
bucket		The name of a Cloud Storage bucket for the export.	None
fileNamePrefix		Cloud Storage object name prefix for the export. Defaults to the name of the task.	None
fileFormat		The output format: "CSV" (default), "GeoJSON", "KML", "KMZ", "SHP", or "TFRecord".	None
selectors		The list of properties to include in the output, as a list of strings or a comma-separated string. By default, all properties are included.	None
maxVertices		Max number of uncut vertices per geometry; geometries with more vertices will be cut into pieces smaller than this size.	None
**kwargs		Holds other keyword arguments that may have been deprecated such as 'outputBucket'.	{}

Source code in geemap/common.py

def ee_export_vector_to_cloud_storage(
    collection,
    description="myExportTableTask",
    bucket=None,
    fileNamePrefix=None,
    fileFormat=None,
    selectors=None,
    maxVertices=None,
    **kwargs,
):
    """Creates a task to export a FeatureCollection to Google Cloud Storage.

    Args:
        collection: The feature collection to be exported.
        description: Human-readable name of the task.
        bucket: The name of a Cloud Storage bucket for the export.
        fileNamePrefix: Cloud Storage object name prefix for the export.
            Defaults to the name of the task.
        fileFormat: The output format: "CSV" (default), "GeoJSON", "KML", "KMZ",
            "SHP", or "TFRecord".
        selectors: The list of properties to include in the output, as a list
            of strings or a comma-separated string. By default, all properties
            are included.
        maxVertices:
            Max number of uncut vertices per geometry; geometries with more
            vertices will be cut into pieces smaller than this size.
        **kwargs: Holds other keyword arguments that may have been deprecated
            such as 'outputBucket'.
    """
    if not isinstance(collection, ee.FeatureCollection):
        raise ValueError("The collection must be an ee.FeatureCollection.")

    allowed_formats = ["csv", "geojson", "kml", "kmz", "shp", "tfrecord"]
    if not (fileFormat.lower() in allowed_formats):
        raise ValueError(
            "The file type must be one of the following: {}".format(
                ", ".join(allowed_formats)
            )
        )

    if os.environ.get("USE_MKDOCS") is not None:  # skip if running GitHub CI.
        return

    print(
        f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
    )

    task = ee.batch.Export.table.toCloudStorage(
        collection,
        description,
        bucket,
        fileNamePrefix,
        fileFormat,
        selectors,
        maxVertices,
        **kwargs,
    )
    task.start()

ee_export_vector_to_drive(collection, description='myExportTableTask', folder=None, fileNamePrefix=None, fileFormat=None, selectors=None, maxVertices=None, **kwargs)

Creates a task to export a FeatureCollection to Drive.

Parameters:

Name	Type	Description	Default
collection		The feature collection to be exported.	required
description		Human-readable name of the task.	'myExportTableTask'
folder		The name of a unique folder in your Drive account to export into. Defaults to the root of the drive.	None
fileNamePrefix		The Google Drive filename for the export. Defaults to the name of the task.	None
fileFormat		The output format: "CSV" (default), "GeoJSON", "KML", "KMZ", "SHP", or "TFRecord".	None
selectors		The list of properties to include in the output, as a list of strings or a comma-separated string. By default, all properties are included.	None
maxVertices		Max number of uncut vertices per geometry; geometries with more vertices will be cut into pieces smaller than this size.	None
**kwargs		Holds other keyword arguments that may have been deprecated such as 'driveFolder' and 'driveFileNamePrefix'.	{}

Source code in geemap/common.py

def ee_export_vector_to_drive(
    collection,
    description="myExportTableTask",
    folder=None,
    fileNamePrefix=None,
    fileFormat=None,
    selectors=None,
    maxVertices=None,
    **kwargs,
):
    """Creates a task to export a FeatureCollection to Drive.

    Args:
        collection: The feature collection to be exported.
        description: Human-readable name of the task.
        folder: The name of a unique folder in your Drive account to
            export into. Defaults to the root of the drive.
        fileNamePrefix: The Google Drive filename for the export.
            Defaults to the name of the task.
        fileFormat: The output format: "CSV" (default), "GeoJSON", "KML",
            "KMZ", "SHP", or "TFRecord".
        selectors: The list of properties to include in the output, as a list
            of strings or a comma-separated string. By default, all properties
            are included.
        maxVertices:
            Max number of uncut vertices per geometry; geometries with more
            vertices will be cut into pieces smaller than this size.
        **kwargs: Holds other keyword arguments that may have been deprecated
            such as 'driveFolder' and 'driveFileNamePrefix'.
    """
    if not isinstance(collection, ee.FeatureCollection):
        raise ValueError("The collection must be an ee.FeatureCollection.")

    allowed_formats = ["csv", "geojson", "kml", "kmz", "shp", "tfrecord"]
    if not (fileFormat.lower() in allowed_formats):
        raise ValueError(
            "The file type must be one of the following: {}".format(
                ", ".join(allowed_formats)
            )
        )

    if os.environ.get("USE_MKDOCS") is not None:  # skip if running GitHub CI.
        return

    print(
        f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
    )

    task = ee.batch.Export.table.toDrive(
        collection,
        description,
        folder,
        fileNamePrefix,
        fileFormat,
        selectors,
        maxVertices,
        **kwargs,
    )
    task.start()

ee_export_vector_to_feature_view(collection, description='myExportTableTask', assetId=None, ingestionTimeParameters=None, **kwargs)

Creates a task to export a FeatureCollection to a FeatureView.

Parameters:

Name	Type	Description	Default
collection		The feature collection to be exported.	required
description		Human-readable name of the task.	'myExportTableTask'
assetId		The destination asset ID.	None
ingestionTimeParameters		The FeatureView ingestion time parameters.	None
**kwargs		Holds other keyword arguments that may have been deprecated.	{}

Source code in geemap/common.py

def ee_export_vector_to_feature_view(
    collection,
    description="myExportTableTask",
    assetId=None,
    ingestionTimeParameters=None,
    **kwargs,
):
    """Creates a task to export a FeatureCollection to a FeatureView.

    Args:
        collection: The feature collection to be exported.
        description: Human-readable name of the task.
        assetId: The destination asset ID.
        ingestionTimeParameters: The FeatureView ingestion time parameters.
        **kwargs: Holds other keyword arguments that may have been deprecated.
    """
    if not isinstance(collection, ee.FeatureCollection):
        raise ValueError("The collection must be an ee.FeatureCollection.")

    if os.environ.get("USE_MKDOCS") is not None:  # skip if running GitHub CI.
        return

    print(
        f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
    )

    task = ee.batch.Export.table.toFeatureView(
        collection,
        description,
        assetId,
        ingestionTimeParameters,
        **kwargs,
    )
    task.start()

ee_export_video_to_cloud_storage(collection, description='myExportVideoTask', bucket=None, fileNamePrefix=None, framesPerSecond=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, maxFrames=None, **kwargs)

Creates a task to export an ImageCollection as a video to Cloud Storage.

Parameters:

Name	Type	Description	Default
collection		The image collection to be exported. The collection must only contain RGB images.	required
description		Human-readable name of the task.	'myExportVideoTask'
bucket		The name of a Cloud Storage bucket for the export.	None
fileNamePrefix		Cloud Storage object name prefix for the export. Defaults to the task's description.	None
framesPerSecond		A number between .1 and 120 describing the framerate of the exported video.	None
dimensions		The dimensions of the exported video. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers.	None
region		The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the first image's region.	None
scale		The resolution in meters per pixel.	None
crs		The coordinate reference system of the exported video's projection. Defaults to SR-ORG:6627.	None
crsTransform		A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported video's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image collection's native CRS transform.	None
maxPixels		The maximum number of pixels per frame. Defaults to 1e8 pixels per frame. By setting this explicitly, you may raise or lower the limit.	None
maxFrames		The maximum number of frames to export. Defaults to 1000 frames. By setting this explicitly, you may raise or lower the limit.	None
**kwargs		Holds other keyword arguments that may have been deprecated such as 'crs_transform'.	{}

Source code in geemap/common.py

def ee_export_video_to_cloud_storage(
    collection,
    description="myExportVideoTask",
    bucket=None,
    fileNamePrefix=None,
    framesPerSecond=None,
    dimensions=None,
    region=None,
    scale=None,
    crs=None,
    crsTransform=None,
    maxPixels=None,
    maxFrames=None,
    **kwargs,
):
    """Creates a task to export an ImageCollection as a video to Cloud Storage.

    Args:
        collection: The image collection to be exported. The collection must
            only contain RGB images.
        description: Human-readable name of the task.
        bucket: The name of a Cloud Storage bucket for the export.
        fileNamePrefix: Cloud Storage object name prefix for the export.
            Defaults to the task's description.
        framesPerSecond: A number between .1 and 120 describing the
            framerate of the exported video.
        dimensions: The dimensions of the exported video. Takes either a
            single positive integer as the maximum dimension or "WIDTHxHEIGHT"
            where WIDTH and HEIGHT are each positive integers.
        region: The lon,lat coordinates for a LinearRing or Polygon
            specifying the region to export. Can be specified as a nested
            lists of numbers or a serialized string. Defaults to the first
            image's region.
        scale: The resolution in meters per pixel.
        crs: The coordinate reference system of the exported video's
            projection. Defaults to SR-ORG:6627.
        crsTransform: A comma-separated string of 6 numbers describing
            the affine transform of the coordinate reference system of the
            exported video's projection, in the order: xScale, xShearing,
            xTranslation, yShearing, yScale and yTranslation. Defaults to
            the image collection's native CRS transform.
        maxPixels: The maximum number of pixels per frame.
            Defaults to 1e8 pixels per frame. By setting this explicitly,
            you may raise or lower the limit.
        maxFrames: The maximum number of frames to export.
            Defaults to 1000 frames. By setting this explicitly, you may
            raise or lower the limit.
        **kwargs: Holds other keyword arguments that may have been deprecated
            such as 'crs_transform'.

    """
    if not isinstance(collection, ee.ImageCollection):
        raise TypeError("collection must be an ee.ImageCollection")

    if os.environ.get("USE_MKDOCS") is not None:  # skip if running GitHub CI.
        return

    print(
        f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
    )

    task = ee.batch.Export.video.toCloudStorage(
        collection,
        description,
        bucket,
        fileNamePrefix,
        framesPerSecond,
        dimensions,
        region,
        scale,
        crs,
        crsTransform,
        maxPixels,
        maxFrames,
        **kwargs,
    )
    task.start()

ee_export_video_to_drive(collection, description='myExportVideoTask', folder=None, fileNamePrefix=None, framesPerSecond=None, dimensions=None, region=None, scale=None, crs=None, crsTransform=None, maxPixels=None, maxFrames=None, **kwargs)

Creates a task to export an ImageCollection as a video to Drive.

Parameters:

Name	Type	Description	Default
collection		The image collection to be exported. The collection must only contain RGB images.	required
description		Human-readable name of the task.	'myExportVideoTask'
folder		The name of a unique folder in your Drive account to export into. Defaults to the root of the drive.	None
fileNamePrefix		The Google Drive filename for the export. Defaults to the name of the task.	None
framesPerSecond		A number between .1 and 120 describing the framerate of the exported video.	None
dimensions		The dimensions of the exported video. Takes either a single positive integer as the maximum dimension or "WIDTHxHEIGHT" where WIDTH and HEIGHT are each positive integers.	None
region		The lon,lat coordinates for a LinearRing or Polygon specifying the region to export. Can be specified as a nested lists of numbers or a serialized string. Defaults to the first image's region.	None
scale		The resolution in meters per pixel.	None
crs		The coordinate reference system of the exported video's projection. Defaults to SR-ORG:6627.	None
crsTransform		A comma-separated string of 6 numbers describing the affine transform of the coordinate reference system of the exported video's projection, in the order: xScale, xShearing, xTranslation, yShearing, yScale and yTranslation. Defaults to the image collection's native CRS transform.	None
maxPixels		The maximum number of pixels per frame. Defaults to 1e8 pixels per frame. By setting this explicitly, you may raise or lower the limit.	None
maxFrames		The maximum number of frames to export. Defaults to 1000 frames. By setting this explicitly, you may raise or lower the limit.	None
**kwargs		Holds other keyword arguments that may have been deprecated such as 'crs_transform'.	{}

Source code in geemap/common.py

def ee_export_video_to_drive(
    collection,
    description="myExportVideoTask",
    folder=None,
    fileNamePrefix=None,
    framesPerSecond=None,
    dimensions=None,
    region=None,
    scale=None,
    crs=None,
    crsTransform=None,
    maxPixels=None,
    maxFrames=None,
    **kwargs,
):
    """Creates a task to export an ImageCollection as a video to Drive.

    Args:
        collection: The image collection to be exported. The collection must
            only contain RGB images.
        description: Human-readable name of the task.
        folder: The name of a unique folder in your Drive account to
            export into. Defaults to the root of the drive.
        fileNamePrefix: The Google Drive filename for the export.
            Defaults to the name of the task.
        framesPerSecond: A number between .1 and 120 describing the
            framerate of the exported video.
        dimensions: The dimensions of the exported video. Takes either a
            single positive integer as the maximum dimension or "WIDTHxHEIGHT"
            where WIDTH and HEIGHT are each positive integers.
        region: The lon,lat coordinates for a LinearRing or Polygon
            specifying the region to export. Can be specified as a nested
            lists of numbers or a serialized string. Defaults to the first
            image's region.
        scale: The resolution in meters per pixel.
        crs: The coordinate reference system of the exported video's
            projection. Defaults to SR-ORG:6627.
        crsTransform: A comma-separated string of 6 numbers describing
            the affine transform of the coordinate reference system of the
            exported video's projection, in the order: xScale, xShearing,
            xTranslation, yShearing, yScale and yTranslation. Defaults to
            the image collection's native CRS transform.
        maxPixels: The maximum number of pixels per frame.
            Defaults to 1e8 pixels per frame. By setting this explicitly,
            you may raise or lower the limit.
        maxFrames: The maximum number of frames to export.
            Defaults to 1000 frames. By setting this explicitly, you may
            raise or lower the limit.
        **kwargs: Holds other keyword arguments that may have been deprecated
            such as 'crs_transform'.

    """
    if not isinstance(collection, ee.ImageCollection):
        raise TypeError("collection must be an ee.ImageCollection")

    if os.environ.get("USE_MKDOCS") is not None:  # skip if running GitHub CI.
        return

    print(
        f"Exporting {description}... Please check the Task Manager from the JavaScript Code Editor."
    )

    task = ee.batch.Export.video.toDrive(
        collection,
        description,
        folder,
        fileNamePrefix,
        framesPerSecond,
        dimensions,
        region,
        scale,
        crs,
        crsTransform,
        maxPixels,
        maxFrames,
        **kwargs,
    )
    task.start()

ee_function_tree(name)

Construct the tree structure based on an Earth Engine function. For example, the function "ee.Algorithms.FMask.matchClouds" will return a list ["ee.Algorithms", "ee.Algorithms.FMask", "ee.Algorithms.FMask.matchClouds"]

Parameters:

Name	Type	Description	Default
name	str	The name of the Earth Engine function	required

Returns:

Name	Type	Description
list		The list for parent functions.

Source code in geemap/common.py

def ee_function_tree(name):
    """Construct the tree structure based on an Earth Engine function. For example, the function "ee.Algorithms.FMask.matchClouds" will return a list ["ee.Algorithms", "ee.Algorithms.FMask", "ee.Algorithms.FMask.matchClouds"]

    Args:
        name (str): The name of the Earth Engine function

    Returns:
        list: The list for parent functions.
    """
    func_list = []
    try:
        items = name.split(".")
        if items[0] == "ee":
            for i in range(2, len(items) + 1):
                func_list.append(".".join(items[0:i]))
        else:
            for i in range(1, len(items) + 1):
                func_list.append(".".join(items[0:i]))

        return func_list
    except Exception as e:
        print(e)
        print("The provided function name is invalid.")

ee_initialize(token_name='EARTHENGINE_TOKEN', auth_mode=None, auth_args=None, user_agent_prefix='geemap', project=None, **kwargs)

Authenticates Earth Engine and initialize an Earth Engine session

Parameters:

Name	Type	Description	Default
token_name	str	The name of the Earth Engine token. Defaults to "EARTHENGINE_TOKEN". In Colab, you can also set a secret named "EE_PROJECT_ID" to initialize Earth Engine.	'EARTHENGINE_TOKEN'
auth_mode	str	The authentication mode, can be one of colab, notebook, localhost, or gcloud. See https://developers.google.com/earth-engine/guides/auth for more details. Defaults to None.	None
auth_args	dict	Additional authentication parameters for aa.Authenticate(). Defaults to {}.	None
user_agent_prefix	str	If set, the prefix (version-less) value used for setting the user-agent string. Defaults to "geemap".	'geemap'
project	str	The Google cloud project ID for Earth Engine. Defaults to None.	None
kwargs	dict	Additional parameters for ee.Initialize(). For example, opt_url='https://earthengine-highvolume.googleapis.com' to use the Earth Engine High-Volume platform. Defaults to {}.	{}

Source code in geemap/coreutils.py

def ee_initialize(
    token_name: str = "EARTHENGINE_TOKEN",
    auth_mode: Optional[str] = None,
    auth_args: Optional[Dict[str, Any]] = None,
    user_agent_prefix: str = "geemap",
    project: Optional[str] = None,
    **kwargs: Any,
) -> None:
    """Authenticates Earth Engine and initialize an Earth Engine session

    Args:
        token_name (str, optional): The name of the Earth Engine token.
            Defaults to "EARTHENGINE_TOKEN". In Colab, you can also set a secret
            named "EE_PROJECT_ID" to initialize Earth Engine.
        auth_mode (str, optional): The authentication mode, can be one of colab,
            notebook, localhost, or gcloud.
            See https://developers.google.com/earth-engine/guides/auth for more
            details. Defaults to None.
        auth_args (dict, optional): Additional authentication parameters for
            aa.Authenticate(). Defaults to {}.
        user_agent_prefix (str, optional): If set, the prefix (version-less)
            value used for setting the user-agent string. Defaults to "geemap".
        project (str, optional): The Google cloud project ID for Earth Engine.
            Defaults to None.
        kwargs (dict, optional): Additional parameters for ee.Initialize().
            For example, opt_url='https://earthengine-highvolume.googleapis.com'
            to use the Earth Engine High-Volume platform. Defaults to {}.
    """
    import google.oauth2.credentials
    from .__init__ import __version__

    user_agent = f"{user_agent_prefix}/{__version__}"
    ee.data.setUserAgent(user_agent)

    if ee.data._credentials is not None:
        return

    ee_token = get_env_var(token_name)
    if ee_token is not None:

        stored = json.loads(ee_token)
        credentials = google.oauth2.credentials.Credentials(
            None,
            token_uri="https://oauth2.googleapis.com/token",
            client_id=stored["client_id"],
            client_secret=stored["client_secret"],
            refresh_token=stored["refresh_token"],
            quota_project_id=stored["project"],
        )

        ee.Initialize(credentials=credentials, **kwargs)
        return

    if auth_args is None:
        auth_args = {}

    if project is None:
        kwargs["project"] = get_env_var("EE_PROJECT_ID")
    else:
        kwargs["project"] = project

    if auth_mode is None:
        if in_colab_shell() and (ee.data._credentials is None):
            ee.Authenticate()
            ee.Initialize(**kwargs)
            return
        else:
            auth_mode = "notebook"

    auth_args["auth_mode"] = auth_mode

    ee.Authenticate(**auth_args)
    ee.Initialize(**kwargs)

ee_join_table(ee_object, data, src_key, dst_key=None)

Join a table to an ee.FeatureCollection attribute table.

Parameters:

Name	Type	Description	Default
ee_object	FeatureCollection	The ee.FeatureCollection to be joined by a table.	required
data	str | DataFraem | GeoDataFrame	The table to join to the ee.FeatureCollection.	required
src_key	str	The key of ee.FeatureCollection attribute table to join.	required
dst_key	str	The key of the table to be joined to the ee.FeatureCollection. Defaults to None.	None

Returns:

Type	Description
	ee.FeatureCollection: The joined ee.FeatureCollection.

Source code in geemap/common.py

def ee_join_table(ee_object, data, src_key, dst_key=None):
    """Join a table to an ee.FeatureCollection attribute table.

    Args:
        ee_object (ee.FeatureCollection): The ee.FeatureCollection to be joined by a table.
        data (str | pd.DataFraem | gpd.GeoDataFrame): The table to join to the ee.FeatureCollection.
        src_key (str): The key of ee.FeatureCollection attribute table to join.
        dst_key (str, optional): The key of the table to be joined to the ee.FeatureCollection. Defaults to None.

    Returns:
        ee.FeatureCollection: The joined ee.FeatureCollection.
    """
    import pandas as pd

    if not isinstance(ee_object, ee.FeatureCollection):
        raise TypeError("The input ee_object must be of type ee.FeatureCollection.")

    if not isinstance(src_key, str):
        raise TypeError("The input src_key must be of type str.")

    if dst_key is None:
        dst_key = src_key

    if isinstance(data, str):
        data = github_raw_url(data)
        if data.endswith(".csv"):
            df = pd.read_csv(data)
        elif data.endswith(".geojson"):
            df = geojson_to_df(data)
        else:
            import geopandas as gpd

            gdf = gpd.read_file(data)
            df = gdf_to_df(gdf)
    elif isinstance(data, pd.DataFrame):
        if "geometry" in data.columns:
            df = data.drop(columns=["geometry"])
        elif "geom" in data.columns:
            df = data.drop(columns=["geom"])
        else:
            df = data
    else:
        raise TypeError("The input data must be of type str or pandas.DataFrame.")

    df[dst_key] = df[dst_key].astype(str)
    df.set_index(dst_key, inplace=True)
    df = df[~df.index.duplicated(keep="first")]
    table = ee.Dictionary(df.to_dict("index"))

    fc = ee_object.map(lambda f: f.set(table.get(f.get(src_key), ee.Dictionary())))
    return fc

ee_num_round(num, decimal=2)

Rounds a number to a specified number of decimal places.

Parameters:

Name	Type	Description	Default
num	Number	The number to round.	required
decimal	int	The number of decimal places to round. Defaults to 2.	2

Returns:

Type	Description
	ee.Number: The number with the specified decimal places rounded.

Source code in geemap/common.py

def ee_num_round(num, decimal=2):
    """Rounds a number to a specified number of decimal places.

    Args:
        num (ee.Number): The number to round.
        decimal (int, optional): The number of decimal places to round. Defaults to 2.

    Returns:
        ee.Number: The number with the specified decimal places rounded.
    """
    format_str = "%.{}f".format(decimal)
    return ee.Number.parse(ee.Number(num).format(format_str))

ee_plot_gui(m, position='topright', **kwargs)

Widget for plotting Earth Engine data.

Parameters:

Name	Type	Description	Default
m	object	geemap.Map.	required
position	str	Position of the widget. Defaults to "topright".	'topright'

Source code in geemap/toolbar.py

def ee_plot_gui(m, position="topright", **kwargs):
    """Widget for plotting Earth Engine data.

    Args:
        m (object): geemap.Map.
        position (str, optional): Position of the widget. Defaults to "topright".
    """
    close_btn = widgets.Button(
        icon="times",
        tooltip="Close the plot widget",
        button_style="primary",
        layout=widgets.Layout(width="32px"),
    )

    m._plot_checked = True
    dropdown = widgets.Dropdown(options=list(m.ee_raster_layers.keys()))
    dropdown.layout.width = "18ex"
    m._plot_dropdown_widget = dropdown

    widget = widgets.HBox([dropdown, close_btn])

    plot_dropdown_control = ipyleaflet.WidgetControl(widget=widget, position=position)
    m._plot_dropdown_control = plot_dropdown_control
    m.add(plot_dropdown_control)

    old_draw_control = m.get_draw_control()
    m.remove_draw_control()
    m.add_draw_control_lite()
    draw_control = m.get_draw_control()

    if not hasattr(m, "_chart_points"):
        m._chart_points = []
    if not hasattr(m, "_chart_values"):
        m._chart_values = []
    if not hasattr(m, "_chart_labels"):
        m._chart_labels = None

    def get_layer_name_and_ee_object():
        if not m._plot_checked or not len(m.ee_raster_layers) > 0:
            raise AssertionError(
                "Plot widget must be active and one raster layer available."
            )
        plot_layer_name = m._plot_dropdown_widget.value
        ee_object = m.ee_layers.get(plot_layer_name)["ee_object"]
        if isinstance(ee_object, ee.ImageCollection):
            ee_object = ee_object.mosaic()
        return plot_layer_name, ee_object

    def generate_chart(dict_values, chart_point):
        try:
            plot_layer_name, ee_object = get_layer_name_and_ee_object()
            m.default_style = {"cursor": "wait"}
            plot_options = {}
            if hasattr(m, "_plot_options"):
                plot_options = m._plot_options
            if "title" not in plot_options.keys():
                plot_options["title"] = plot_layer_name
            if ("add_marker_cluster" in plot_options.keys()) and plot_options[
                "add_marker_cluster"
            ]:
                if not hasattr(m, "_plot_markers"):
                    m._plot_markers = []
                markers = m._plot_markers
                marker_cluster = m._plot_marker_cluster
                markers.append(ipyleaflet.Marker(location=chart_point))
                marker_cluster.markers = markers
                m._plot_marker_cluster = marker_cluster

            band_names = ee_object.bandNames().getInfo()
            if any(len(name) > 3 for name in band_names):
                band_names = list(range(1, len(band_names) + 1))

            m._chart_labels = band_names
            m._chart_points.append(chart_point)

            band_values = list(dict_values.values())
            m._chart_values.append(band_values)
            m.plot(band_names, band_values, **plot_options)
            if plot_options["title"] == plot_layer_name:
                del plot_options["title"]
            m.default_style = {"cursor": "crosshair"}
        except Exception as e:
            if not hasattr(m, "_plot_widget"):
                m._plot_widget = None
            if m._plot_widget is not None:
                with m._plot_widget:
                    m._plot_widget.outputs = ()
                    print("No data for the clicked location.")
            else:
                print(e)
            m.default_style = {"cursor": "crosshair"}

    def handle_interaction(**kwargs):
        try:
            _, ee_object = get_layer_name_and_ee_object()
        except AssertionError:
            return

        latlon = kwargs.get("coordinates")
        if kwargs.get("type") == "click":
            xy = ee.Geometry.Point(latlon[::-1])
            plot_options = {}
            if hasattr(m, "_plot_options"):
                plot_options = m._plot_options
            sample_scale = m.getScale()
            if "sample_scale" in plot_options.keys() and (
                plot_options["sample_scale"] is not None
            ):
                sample_scale = plot_options["sample_scale"]
            try:
                dict_values_tmp = (
                    ee_object.sample(xy, scale=sample_scale)
                    .first()
                    .toDictionary()
                    .getInfo()
                )
                b_names = ee_object.bandNames().getInfo()
                dict_values = dict(zip(b_names, [dict_values_tmp[b] for b in b_names]))
                generate_chart(dict_values, latlon)
            except Exception as e:
                if hasattr(m, "_plot_widget") and m._plot_widget is not None:
                    m._plot_widget.clear_output()
                    with m._plot_widget:
                        print("No data for the clicked location.")
                else:
                    pass
                m.default_style = {"cursor": "crosshair"}

    m.on_interaction(handle_interaction)

    def handle_draw(_, geometry):
        try:
            _, ee_object = get_layer_name_and_ee_object()
        except AssertionError:
            return

        if m.roi_reducer_scale is None:
            scale = ee_object.select(0).projection().nominalScale()
        else:
            scale = m.roi_reducer_scale
        dict_values_tmp = ee_object.reduceRegion(
            reducer=m.roi_reducer,
            geometry=geometry,
            scale=scale,
            bestEffort=True,
        ).getInfo()
        b_names = ee_object.bandNames().getInfo()
        dict_values = dict(zip(b_names, [dict_values_tmp[b] for b in b_names]))
        chart_point = geometry.centroid(1).coordinates().getInfo()
        generate_chart(dict_values, chart_point)

    draw_control.on_geometry_create(handle_draw)

    def cleanup():
        m._plot_checked = False

        if (
            hasattr(m, "plot_control")
            and (m._plot_control is not None)
            and (m._plot_control in m.controls)
        ):
            m._plot_widget.outputs = ()
            m.remove_control(m._plot_control)

        if (
            m._plot_dropdown_control is not None
            and m._plot_dropdown_control in m.controls
        ):
            m.remove_control(m._plot_dropdown_control)

        widget.close()

        m.on_interaction(handle_interaction, remove=True)
        m._plot_widget = None
        m.default_style = {"cursor": "default"}
        if old_draw_control:
            old_draw_control.open()
            m.substitute(m.get_draw_control(), old_draw_control)
        else:
            m.remove_draw_control()

    m._plot_dropdown_control.cleanup = cleanup

    def cleanup():
        if not hasattr(m, "_plot_dropdown_widget"):
            m._plot_dropdown_widget = None
        if not hasattr(m, "_plot_dropdown_control"):
            m._plot_dropdown_control = None
        plot_dropdown_widget = m._plot_dropdown_widget
        plot_dropdown_control = m._plot_dropdown_control
        if plot_dropdown_control in m.controls:
            m.remove_control(plot_dropdown_control)
        del plot_dropdown_widget
        del plot_dropdown_control

        if not hasattr(m, "_plot_widget"):
            m._plot_widget = None
        if not hasattr(m, "_plot_control"):
            m._plot_control = None

        if m._plot_control in m.controls:
            plot_control = m._plot_control
            plot_widget = m._plot_widget
            m.remove_control(plot_control)
            m._plot_control = None
            m._plot_widget = None
            del plot_control
            del plot_widget
        if (
            hasattr(m, "_plot_marker_cluster")
            and m._plot_marker_cluster is not None
            and m._plot_marker_cluster in m.layers
        ):
            m.remove_layer(m._plot_marker_cluster)
        if hasattr(m, "_chart_points"):
            m._chart_points = []
        if hasattr(m, "_chart_values"):
            m._chart_values = []
        if hasattr(m, "_chart_labels"):
            m._chart_labels = None

    m._plot_dropdown_control.cleanup = cleanup

    def close_click(_):
        m._plot_dropdown_control.cleanup()

    close_btn.on_click(close_click)

ee_search(asset_limit=100)

Search Earth Engine API and user assets. If you received a warning (IOPub message rate exceeded) in Jupyter notebook, you can relaunch Jupyter notebook using the following command: jupyter notebook --NotebookApp.iopub_msg_rate_limit=10000

Parameters:

Name	Type	Description	Default
asset_limit	int	The number of assets to display for each asset type, i.e., Image, ImageCollection, and FeatureCollection. Defaults to 100.	100

Source code in geemap/common.py

def ee_search(asset_limit=100):
    """Search Earth Engine API and user assets. If you received a warning (IOPub message rate exceeded) in Jupyter notebook, you can relaunch Jupyter notebook using the following command:
        jupyter notebook --NotebookApp.iopub_msg_rate_limit=10000

    Args:
        asset_limit (int, optional): The number of assets to display for each asset type, i.e., Image, ImageCollection, and FeatureCollection. Defaults to 100.
    """

    warnings.filterwarnings("ignore")

    class Flags:
        def __init__(
            self,
            repos=None,
            docs=None,
            assets=None,
            docs_dict=None,
            asset_dict=None,
            asset_import=None,
        ):
            self.repos = repos
            self.docs = docs
            self.assets = assets
            self.docs_dict = docs_dict
            self.asset_dict = asset_dict
            self.asset_import = asset_import

    flags = Flags()

    search_type = widgets.ToggleButtons(
        options=["Scripts", "Docs", "Assets"],
        tooltips=[
            "Search Earth Engine Scripts",
            "Search Earth Engine API",
            "Search Earth Engine Assets",
        ],
        button_style="primary",
    )
    search_type.style.button_width = "100px"

    search_box = widgets.Text(placeholder="Filter scripts...", value="Loading...")
    search_box.layout.width = "310px"

    tree_widget = widgets.Output()

    left_widget = widgets.VBox()
    right_widget = widgets.VBox()
    output_widget = widgets.Output()
    output_widget.layout.max_width = "650px"

    search_widget = widgets.HBox()
    search_widget.children = [left_widget, right_widget]
    display(search_widget)

    repo_tree, repo_output, _ = build_repo_tree()
    left_widget.children = [search_type, repo_tree]
    right_widget.children = [repo_output]

    flags.repos = repo_tree
    search_box.value = ""

    def search_type_changed(change):
        search_box.value = ""

        output_widget.outputs = ()
        tree_widget.outputs = ()
        if change["new"] == "Scripts":
            search_box.placeholder = "Filter scripts..."
            left_widget.children = [search_type, repo_tree]
            right_widget.children = [repo_output]
        elif change["new"] == "Docs":
            search_box.placeholder = "Filter methods..."
            search_box.value = "Loading..."
            left_widget.children = [search_type, search_box, tree_widget]
            right_widget.children = [output_widget]
            if flags.docs is None:
                api_dict = read_api_csv()
                ee_api_tree, tree_dict = build_api_tree(api_dict, output_widget)
                flags.docs = ee_api_tree
                flags.docs_dict = tree_dict
            else:
                ee_api_tree = flags.docs
            with tree_widget:
                tree_widget.outputs = ()
                display(ee_api_tree)
                right_widget.children = [output_widget]
            search_box.value = ""
        elif change["new"] == "Assets":
            search_box.placeholder = "Filter assets..."
            left_widget.children = [search_type, search_box, tree_widget]
            right_widget.children = [output_widget]
            search_box.value = "Loading..."
            if flags.assets is None:
                asset_tree, asset_widget, asset_dict = build_asset_tree(
                    limit=asset_limit
                )
                flags.assets = asset_tree
                flags.asset_dict = asset_dict
                flags.asset_import = asset_widget

            with tree_widget:
                tree_widget.outputs = ()
                display(flags.assets)
            right_widget.children = [flags.asset_import]
            search_box.value = ""

    search_type.observe(search_type_changed, names="value")

    def search_box_callback(text):
        if search_type.value == "Docs":
            with tree_widget:
                if text.value == "":
                    print("Loading...")
                    tree_widget.outputs = ()
                    display(flags.docs)
                else:
                    tree_widget.outputs = ()
                    print("Searching...")
                    tree_widget.outputs = ()
                    sub_tree = search_api_tree(text.value, flags.docs_dict)
                    display(sub_tree)
        elif search_type.value == "Assets":
            with tree_widget:
                if text.value == "":
                    print("Loading...")
                    tree_widget.outputs = ()
                    display(flags.assets)
                else:
                    tree_widget.outputs = ()
                    print("Searching...")
                    tree_widget.outputs = ()
                    sub_tree = search_api_tree(text.value, flags.asset_dict)
                    display(sub_tree)

    search_box.on_submit(search_box_callback)

ee_to_bbox(ee_object)

Get the bounding box of an Earth Engine object as a list in the format [xmin, ymin, xmax, ymax].

Parameters:

Name	Type	Description	Default
ee_object	Image | Geometry | Feature | FeatureCollection	The input Earth Engine object.	required

Returns:

Name	Type	Description
list		The bounding box of the Earth Engine object in the format [xmin, ymin, xmax, ymax].

Source code in geemap/common.py

def ee_to_bbox(ee_object):
    """Get the bounding box of an Earth Engine object as a list in the format [xmin, ymin, xmax, ymax].

    Args:
        ee_object (ee.Image | ee.Geometry | ee.Feature | ee.FeatureCollection): The input Earth Engine object.

    Returns:
        list: The bounding box of the Earth Engine object in the format [xmin, ymin, xmax, ymax].
    """
    if (
        isinstance(ee_object, ee.Image)
        or isinstance(ee_object, ee.Feature)
        or isinstance(ee_object, ee.FeatureCollection)
    ):
        geometry = ee_object.geometry()
    elif isinstance(ee_object, ee.Geometry):
        geometry = ee_object
    else:
        raise Exception(
            "The ee_object must be an ee.Image, ee.Feature, ee.FeatureCollection or ee.Geometry object."
        )

    bounds = geometry.bounds().getInfo()["coordinates"][0]
    xmin = bounds[0][0]
    ymin = bounds[0][1]
    xmax = bounds[1][0]
    ymax = bounds[2][1]
    bbox = [xmin, ymin, xmax, ymax]
    return bbox

ee_to_csv(ee_object, filename, columns=None, remove_geom=True, sort_columns=False, **kwargs)

Downloads an ee.FeatureCollection as a CSV file.

Parameters:

Name	Type	Description	Default
ee_object	object	ee.FeatureCollection	required
filename	str	The output filepath of the CSV file.	required
columns	list	A list of attributes to export. Defaults to None.	None
remove_geom	bool	Whether to remove the geometry column. Defaults to True.	True
sort_columns	bool	Whether to sort the columns alphabetically. Defaults to False.	False
kwargs		Additional arguments passed to ee_to_df().	{}

Source code in geemap/common.py

def ee_to_csv(
    ee_object,
    filename,
    columns=None,
    remove_geom=True,
    sort_columns=False,
    **kwargs,
):
    """Downloads an ee.FeatureCollection as a CSV file.

    Args:
        ee_object (object): ee.FeatureCollection
        filename (str): The output filepath of the CSV file.
        columns (list, optional): A list of attributes to export. Defaults to None.
        remove_geom (bool, optional): Whether to remove the geometry column. Defaults to True.
        sort_columns (bool, optional): Whether to sort the columns alphabetically. Defaults to False.
        kwargs: Additional arguments passed to ee_to_df().

    """
    try:
        if filename.lower().endswith(".csv"):
            df = ee_to_df(ee_object, columns, remove_geom, sort_columns, **kwargs)
            df.to_csv(filename, index=False)
        else:
            print("The filename must end with .csv")

    except Exception as e:
        print(e)

ee_to_df(ee_object, columns=None, remove_geom=True, sort_columns=False, **kwargs)

Converts an ee.FeatureCollection to pandas dataframe.

Parameters:

Name	Type	Description	Default
ee_object	FeatureCollection	ee.FeatureCollection.	required
columns	list	List of column names. Defaults to None.	None
remove_geom	bool	Whether to remove the geometry column. Defaults to True.	True
sort_columns	bool	Whether to sort the column names. Defaults to False.	False
kwargs		Additional arguments passed to ee.data.computeFeature.	{}

Raises:

Type	Description
TypeError	ee_object must be an ee.FeatureCollection

Returns:

Type	Description
	pd.DataFrame: pandas DataFrame

Source code in geemap/common.py

def ee_to_df(
    ee_object,
    columns=None,
    remove_geom=True,
    sort_columns=False,
    **kwargs,
):
    """Converts an ee.FeatureCollection to pandas dataframe.

    Args:
        ee_object (ee.FeatureCollection): ee.FeatureCollection.
        columns (list): List of column names. Defaults to None.
        remove_geom (bool): Whether to remove the geometry column. Defaults to True.
        sort_columns (bool): Whether to sort the column names. Defaults to False.
        kwargs: Additional arguments passed to ee.data.computeFeature.

    Raises:
        TypeError: ee_object must be an ee.FeatureCollection

    Returns:
        pd.DataFrame: pandas DataFrame
    """
    if isinstance(ee_object, ee.Feature):
        ee_object = ee.FeatureCollection([ee_object])

    if not isinstance(ee_object, ee.FeatureCollection):
        raise TypeError("ee_object must be an ee.FeatureCollection")

    try:
        if remove_geom:
            data = ee_object.map(
                lambda f: ee.Feature(None, f.toDictionary(f.propertyNames().sort()))
            )
        else:
            data = ee_object

        kwargs["expression"] = data
        kwargs["fileFormat"] = "PANDAS_DATAFRAME"

        df = ee.data.computeFeatures(kwargs)

        if isinstance(columns, list):
            df = df[columns]

        if remove_geom and ("geo" in df.columns):
            df = df.drop(columns=["geo"], axis=1)

        if sort_columns:
            df = df.reindex(sorted(df.columns), axis=1)

        return df
    except Exception as e:
        raise Exception(e)

ee_to_gdf(ee_object, columns=None, sort_columns=False, **kwargs)

Converts an ee.FeatureCollection to GeoPandas GeoDataFrame.

Parameters:

Name	Type	Description	Default
ee_object	FeatureCollection	ee.FeatureCollection.	required
columns	list	List of column names. Defaults to None.	None
sort_columns	bool	Whether to sort the column names. Defaults to False.	False
kwargs		Additional arguments passed to ee.data.computeFeature.	{}

Raises:

Type	Description
TypeError	ee_object must be an ee.FeatureCollection

Returns:

Type	Description
	gpd.GeoDataFrame: GeoPandas GeoDataFrame

Source code in geemap/common.py

def ee_to_gdf(
    ee_object,
    columns=None,
    sort_columns=False,
    **kwargs,
):
    """Converts an ee.FeatureCollection to GeoPandas GeoDataFrame.

    Args:
        ee_object (ee.FeatureCollection): ee.FeatureCollection.
        columns (list): List of column names. Defaults to None.
        sort_columns (bool): Whether to sort the column names. Defaults to False.
        kwargs: Additional arguments passed to ee.data.computeFeature.

    Raises:
        TypeError: ee_object must be an ee.FeatureCollection

    Returns:
        gpd.GeoDataFrame: GeoPandas GeoDataFrame
    """
    if isinstance(ee_object, ee.Feature):
        ee_object = ee.FeatureCollection([ee_object])

    if not isinstance(ee_object, ee.FeatureCollection):
        raise TypeError("ee_object must be an ee.FeatureCollection")

    try:
        kwargs["expression"] = ee_object
        kwargs["fileFormat"] = "GEOPANDAS_GEODATAFRAME"

        crs = ee_object.first().geometry().projection().crs().getInfo()
        gdf = ee.data.computeFeatures(kwargs)

        if isinstance(columns, list):
            gdf = gdf[columns]

        if sort_columns:
            gdf = gdf.reindex(sorted(gdf.columns), axis=1)

        gdf.crs = crs
        return gdf
    except Exception as e:
        raise Exception(e)

ee_to_geojson(ee_object, filename=None, indent=2, **kwargs)

Converts Earth Engine object to geojson.

Parameters:

Name	Type	Description	Default
ee_object	object	An Earth Engine object.	required
filename	str	The file path to save the geojson. Defaults to None.	None

Returns:

Name	Type	Description
object		GeoJSON object.

Source code in geemap/common.py

def ee_to_geojson(ee_object, filename=None, indent=2, **kwargs):
    """Converts Earth Engine object to geojson.

    Args:
        ee_object (object): An Earth Engine object.
        filename (str, optional): The file path to save the geojson. Defaults to None.

    Returns:
        object: GeoJSON object.
    """

    try:
        if (
            isinstance(ee_object, ee.Geometry)
            or isinstance(ee_object, ee.Feature)
            or isinstance(ee_object, ee.FeatureCollection)
        ):
            json_object = ee_object.getInfo()
            if filename is not None:
                filename = os.path.abspath(filename)
                if not os.path.exists(os.path.dirname(filename)):
                    os.makedirs(os.path.dirname(filename))
                with open(filename, "w") as f:
                    f.write(json.dumps(json_object, indent=indent, **kwargs) + "\n")
            else:
                return json_object
        else:
            print("Could not convert the Earth Engine object to geojson")
    except Exception as e:
        raise Exception(e)

ee_to_geotiff(ee_object, output, bbox=None, vis_params={}, zoom=None, resolution=None, crs='EPSG:3857', to_cog=False, quiet=False, **kwargs)

Downloads an Earth Engine object as GeoTIFF.

Parameters:

Name	Type	Description	Default
ee_object	Image | FeatureCollection	The Earth Engine object to download.	required
output	str	The output path for the GeoTIFF.	required
bbox	str	The bounding box in the format [xmin, ymin, xmax, ymax]. Defaults to None, which is the bounding box of the Earth Engine object.	None
vis_params	dict	Visualization parameters. Defaults to {}.	{}
zoom	int	The zoom level to download the image at. Defaults to None.	None
resolution	float	The resolution in meters to download the image at. Defaults to None.	None
crs	str	The CRS of the output image. Defaults to "EPSG:3857".	'EPSG:3857'
to_cog	bool	Whether to convert the image to Cloud Optimized GeoTIFF. Defaults to False.	False
quiet	bool	Whether to hide the download progress bar. Defaults to False.	False

Source code in geemap/common.py

def ee_to_geotiff(
    ee_object,
    output,
    bbox=None,
    vis_params={},
    zoom=None,
    resolution=None,
    crs="EPSG:3857",
    to_cog=False,
    quiet=False,
    **kwargs,
):
    """Downloads an Earth Engine object as GeoTIFF.

    Args:
        ee_object (ee.Image | ee.FeatureCollection): The Earth Engine object to download.
        output (str): The output path for the GeoTIFF.
        bbox (str, optional): The bounding box in the format [xmin, ymin, xmax, ymax]. Defaults to None,
            which is the bounding box of the Earth Engine object.
        vis_params (dict, optional): Visualization parameters. Defaults to {}.
        zoom (int, optional): The zoom level to download the image at. Defaults to None.
        resolution (float, optional): The resolution in meters to download the image at. Defaults to None.
        crs (str, optional): The CRS of the output image. Defaults to "EPSG:3857".
        to_cog (bool, optional): Whether to convert the image to Cloud Optimized GeoTIFF. Defaults to False.
        quiet (bool, optional): Whether to hide the download progress bar. Defaults to False.

    """

    from box import Box

    image = None

    if (
        not isinstance(ee_object, ee.Image)
        and not isinstance(ee_object, ee.ImageCollection)
        and not isinstance(ee_object, ee.FeatureCollection)
        and not isinstance(ee_object, ee.Feature)
        and not isinstance(ee_object, ee.Geometry)
    ):
        err_str = "\n\nThe image argument in 'addLayer' function must be an instance of one of ee.Image, ee.Geometry, ee.Feature or ee.FeatureCollection."
        raise AttributeError(err_str)

    if (
        isinstance(ee_object, ee.geometry.Geometry)
        or isinstance(ee_object, ee.feature.Feature)
        or isinstance(ee_object, ee.featurecollection.FeatureCollection)
    ):
        features = ee.FeatureCollection(ee_object)

        width = 2

        if "width" in vis_params:
            width = vis_params["width"]

        color = "000000"

        if "color" in vis_params:
            color = vis_params["color"]

        image_fill = features.style(**{"fillColor": color}).updateMask(
            ee.Image.constant(0.5)
        )
        image_outline = features.style(
            **{"color": color, "fillColor": "00000000", "width": width}
        )

        image = image_fill.blend(image_outline)
    elif isinstance(ee_object, ee.image.Image):
        image = ee_object
    elif isinstance(ee_object, ee.imagecollection.ImageCollection):
        image = ee_object.mosaic()

    if "palette" in vis_params:
        if isinstance(vis_params["palette"], Box):
            try:
                vis_params["palette"] = vis_params["palette"]["default"]
            except Exception as e:
                print("The provided palette is invalid.")
                raise Exception(e)
        elif isinstance(vis_params["palette"], str):
            vis_params["palette"] = check_cmap(vis_params["palette"])
        elif not isinstance(vis_params["palette"], list):
            raise ValueError(
                "The palette must be a list of colors or a string or a Box object."
            )

    map_id_dict = ee.Image(image).getMapId(vis_params)
    url = map_id_dict["tile_fetcher"].url_format

    if bbox is None:
        bbox = ee_to_bbox(image)

    if zoom is None and resolution is None:
        raise ValueError("Either zoom level or resolution must be specified.")

    tms_to_geotiff(output, bbox, zoom, resolution, url, crs, to_cog, quiet, **kwargs)

ee_to_numpy(ee_object, region=None, scale=None, bands=None, **kwargs)

Extracts a rectangular region of pixels from an image into a numpy array.

Parameters:

Name	Type	Description	Default
ee_object	Image	The image to sample.	required
region	Geometry	The region to sample. Defaults to None.	None
bands	list	The list of band names to extract. Defaults to None.	None
scale	int	A nominal scale in meters of the projection to sample in. Defaults to None.	None

Returns:

Type	Description
	np.ndarray: A 3D numpy array in the format of [row, column, band].

Source code in geemap/common.py

def ee_to_numpy(ee_object, region=None, scale=None, bands=None, **kwargs):
    """Extracts a rectangular region of pixels from an image into a numpy array.

    Args:
        ee_object (ee.Image): The image to sample.
        region (ee.Geometry, optional): The region to sample. Defaults to None.
        bands (list, optional): The list of band names to extract. Defaults to None.
        scale (int, optional): A nominal scale in meters of the projection to sample in. Defaults to None.

    Returns:
        np.ndarray: A 3D numpy array in the format of [row, column, band].
    """
    import numpy as np

    if (region is not None) or (scale is not None):
        ee_object = ee_object.clipToBoundsAndScale(geometry=region, scale=scale)

    kwargs["expression"] = ee_object
    kwargs["fileFormat"] = "NUMPY_NDARRAY"
    if bands is not None:
        kwargs["bandIds"] = bands

    try:
        struct_array = ee.data.computePixels(kwargs)
        array = np.dstack(([struct_array[band] for band in struct_array.dtype.names]))
        return array
    except Exception as e:
        raise Exception(e)

ee_to_shp(ee_object, filename, columns=None, sort_columns=False, **kwargs)

Downloads an ee.FeatureCollection as a shapefile.

Parameters:

Name	Type	Description	Default
ee_object	object	ee.FeatureCollection	required
filename	str	The output filepath of the shapefile.	required
columns	list	A list of attributes to export. Defaults to None.	None
sort_columns	bool	Whether to sort the columns alphabetically. Defaults to False.	False
kwargs		Additional arguments passed to ee_to_gdf().	{}

Source code in geemap/common.py

def ee_to_shp(
    ee_object,
    filename,
    columns=None,
    sort_columns=False,
    **kwargs,
):
    """Downloads an ee.FeatureCollection as a shapefile.

    Args:
        ee_object (object): ee.FeatureCollection
        filename (str): The output filepath of the shapefile.
        columns (list, optional): A list of attributes to export. Defaults to None.
        sort_columns (bool, optional): Whether to sort the columns alphabetically. Defaults to False.
        kwargs: Additional arguments passed to ee_to_gdf().

    """
    try:
        if filename.lower().endswith(".shp"):
            gdf = ee_to_gdf(ee_object, columns, sort_columns, **kwargs)
            gdf.to_file(filename)
        else:
            print("The filename must end with .shp")

    except Exception as e:
        print(e)

ee_to_xarray(dataset, drop_variables=None, io_chunks=None, n_images=-1, mask_and_scale=True, decode_times=True, decode_timedelta=None, use_cftime=None, concat_characters=True, decode_coords=True, crs=None, scale=None, projection=None, geometry=None, primary_dim_name=None, primary_dim_property=None, ee_mask_value=None, ee_initialize=True, **kwargs)

Open an Earth Engine ImageCollection as an Xarray Dataset. This function is a wrapper for xee. EarthEngineBackendEntrypoint.open_dataset(). See https://github.com/google/Xee/blob/main/xee/ext.py#L886

Parameters:

Name	Type	Description	Default
dataset		An asset ID for an ImageCollection, or an ee.ImageCollection object.	required
drop_variables	optional	Variables or bands to drop before opening.	None
io_chunks	optional	Specifies the chunking strategy for loading data from EE. By default, this automatically calculates optional chunks based on the request_byte_limit.	None
n_images	optional	The max number of EE images in the collection to open. Useful when there are a large number of images in the collection since calculating collection size can be slow. -1 indicates that all images should be included.	-1
mask_and_scale	optional	Lazily scale (using scale_factor and add_offset) and mask (using _FillValue).	True
decode_times	optional	Decode cf times (e.g., integers since "hours since 2000-01-01") to np.datetime64.	True
decode_timedelta	optional	If True, decode variables and coordinates with time units in {"days", "hours", "minutes", "seconds", "milliseconds", "microseconds"} into timedelta objects. If False, leave them encoded as numbers. If None (default), assume the same value of decode_time.	None
use_cftime	optional	Only relevant if encoded dates come from a standard calendar (e.g. "gregorian", "proleptic_gregorian", "standard", or not specified). If None (default), attempt to decode times to np.datetime64[ns] objects; if this is not possible, decode times to cftime.datetime objects. If True, always decode times to cftime.datetime objects, regardless of whether or not they can be represented using np.datetime64[ns] objects. If False, always decode times to np.datetime64[ns] objects; if this is not possible raise an error.	None
concat_characters	optional	Should character arrays be concatenated to strings, for example: ["h", "e", "l", "l", "o"] -> "hello"	True
decode_coords	optional	bool or {"coordinates", "all"}, Controls which variables are set as coordinate variables: - "coordinates" or True: Set variables referred to in the 'coordinates' attribute of the datasets or individual variables as coordinate variables. - "all": Set variables referred to in 'grid_mapping', 'bounds' and other attributes as coordinate variables.	True
crs	optional	The coordinate reference system (a CRS code or WKT string). This defines the frame of reference to coalesce all variables upon opening. By default, data is opened with `EPSG:4326'.	None
scale	optional	The scale in the crs or projection's units of measure -- either meters or degrees. This defines the scale that all data is represented in upon opening. By default, the scale is 1° when the CRS is in degrees or 10,000 when in meters.	None
projection	optional	Specify an ee.Projection object to define the scale and crs (or other coordinate reference system) with which to coalesce all variables upon opening. By default, the scale and reference system is set by the the crs and scale arguments.	None
geometry	optional	Specify an ee.Geometry to define the regional bounds when opening the data. When not set, the bounds are defined by the CRS's 'area_of_use` boundaries. If those aren't present, the bounds are derived from the geometry of the first image of the collection.	None
primary_dim_name	optional	Override the name of the primary dimension of the output Dataset. By default, the name is 'time'.	None
primary_dim_property	optional	Override the ee.Image property for which to derive the values of the primary dimension. By default, this is 'system:time_start'.	None
ee_mask_value	optional	Value to mask to EE nodata values. By default, this is 'np.iinfo(np.int32).max' i.e. 2147483647.	None
request_byte_limit		the max allowed bytes to request at a time from Earth Engine. By default, it is 48MBs.	required
ee_initialize	optional	Whether to initialize ee with the high-volume endpoint. Defaults to True.	True

Returns:

Type	Description
	An xarray.Dataset that streams in remote data from Earth Engine.

Source code in geemap/common.py

def ee_to_xarray(
    dataset,
    drop_variables=None,
    io_chunks=None,
    n_images=-1,
    mask_and_scale=True,
    decode_times=True,
    decode_timedelta=None,
    use_cftime=None,
    concat_characters=True,
    decode_coords=True,
    crs=None,
    scale=None,
    projection=None,
    geometry=None,
    primary_dim_name=None,
    primary_dim_property=None,
    ee_mask_value=None,
    ee_initialize=True,
    **kwargs,
):
    """Open an Earth Engine ImageCollection as an Xarray Dataset. This function is a wrapper for
        xee. EarthEngineBackendEntrypoint.open_dataset().
        See https://github.com/google/Xee/blob/main/xee/ext.py#L886

    Args:
        dataset: An asset ID for an ImageCollection, or an
            ee.ImageCollection object.
        drop_variables (optional): Variables or bands to drop before opening.
        io_chunks (optional): Specifies the chunking strategy for loading data
            from EE. By default, this automatically calculates optional chunks based
            on the `request_byte_limit`.
        n_images (optional): The max number of EE images in the collection to
            open. Useful when there are a large number of images in the collection
            since calculating collection size can be slow. -1 indicates that all
            images should be included.
        mask_and_scale (optional): Lazily scale (using scale_factor and
            add_offset) and mask (using _FillValue).
        decode_times (optional): Decode cf times (e.g., integers since "hours
            since 2000-01-01") to np.datetime64.
        decode_timedelta (optional): If True, decode variables and coordinates
            with time units in {"days", "hours", "minutes", "seconds",
            "milliseconds", "microseconds"} into timedelta objects. If False, leave
            them encoded as numbers. If None (default), assume the same value of
            decode_time.
        use_cftime (optional): Only relevant if encoded dates come from a standard
            calendar (e.g. "gregorian", "proleptic_gregorian", "standard", or not
            specified).  If None (default), attempt to decode times to
            ``np.datetime64[ns]`` objects; if this is not possible, decode times to
            ``cftime.datetime`` objects. If True, always decode times to
            ``cftime.datetime`` objects, regardless of whether or not they can be
            represented using ``np.datetime64[ns]`` objects.  If False, always
            decode times to ``np.datetime64[ns]`` objects; if this is not possible
            raise an error.
        concat_characters (optional): Should character arrays be concatenated to
            strings, for example: ["h", "e", "l", "l", "o"] -> "hello"
        decode_coords (optional): bool or {"coordinates", "all"}, Controls which
            variables are set as coordinate variables: - "coordinates" or True: Set
            variables referred to in the ``'coordinates'`` attribute of the datasets
            or individual variables as coordinate variables. - "all": Set variables
            referred to in  ``'grid_mapping'``, ``'bounds'`` and other attributes as
            coordinate variables.
        crs (optional): The coordinate reference system (a CRS code or WKT
            string). This defines the frame of reference to coalesce all variables
            upon opening. By default, data is opened with `EPSG:4326'.
        scale (optional): The scale in the `crs` or `projection`'s units of
            measure -- either meters or degrees. This defines the scale that all
            data is represented in upon opening. By default, the scale is 1° when
            the CRS is in degrees or 10,000 when in meters.
        projection (optional): Specify an `ee.Projection` object to define the
            `scale` and `crs` (or other coordinate reference system) with which to
            coalesce all variables upon opening. By default, the scale and reference
            system is set by the the `crs` and `scale` arguments.
        geometry (optional): Specify an `ee.Geometry` to define the regional
            bounds when opening the data. When not set, the bounds are defined by
            the CRS's 'area_of_use` boundaries. If those aren't present, the bounds
            are derived from the geometry of the first image of the collection.
        primary_dim_name (optional): Override the name of the primary dimension of
            the output Dataset. By default, the name is 'time'.
        primary_dim_property (optional): Override the `ee.Image` property for
            which to derive the values of the primary dimension. By default, this is
            'system:time_start'.
        ee_mask_value (optional): Value to mask to EE nodata values. By default,
            this is 'np.iinfo(np.int32).max' i.e. 2147483647.
        request_byte_limit: the max allowed bytes to request at a time from Earth
            Engine. By default, it is 48MBs.
        ee_initialize (optional): Whether to initialize ee with the high-volume endpoint. Defaults to True.

    Returns:
      An xarray.Dataset that streams in remote data from Earth Engine.
    """
    try:
        import xee
    except ImportError:
        install_package("xee")
        import xee

    import xarray as xr

    kwargs["drop_variables"] = drop_variables
    kwargs["io_chunks"] = io_chunks
    kwargs["n_images"] = n_images
    kwargs["mask_and_scale"] = mask_and_scale
    kwargs["decode_times"] = decode_times
    kwargs["decode_timedelta"] = decode_timedelta
    kwargs["use_cftime"] = use_cftime
    kwargs["concat_characters"] = concat_characters
    kwargs["decode_coords"] = decode_coords
    kwargs["crs"] = crs
    kwargs["scale"] = scale
    kwargs["projection"] = projection
    kwargs["geometry"] = geometry
    kwargs["primary_dim_name"] = primary_dim_name
    kwargs["primary_dim_property"] = primary_dim_property
    kwargs["ee_mask_value"] = ee_mask_value
    kwargs["engine"] = "ee"

    if ee_initialize:
        opt_url = "https://earthengine-highvolume.googleapis.com"
        ee.Initialize(opt_url=opt_url)

    if isinstance(dataset, str):
        if not dataset.startswith("ee://"):
            dataset = "ee://" + dataset
    elif isinstance(dataset, ee.Image):
        dataset = ee.ImageCollection(dataset)
    elif isinstance(dataset, ee.ImageCollection):
        pass
    elif isinstance(dataset, list):
        items = []
        for item in dataset:
            if isinstance(item, str) and not item.startswith("ee://"):
                item = "ee://" + item
            items.append(item)
        dataset = items
    else:
        raise ValueError(
            "The dataset must be an ee.Image, ee.ImageCollection, or a list of ee.Image."
        )

    if isinstance(dataset, list):
        ds = xr.open_mfdataset(dataset, **kwargs)
    else:
        ds = xr.open_dataset(dataset, **kwargs)

    return ds

ee_user_id()

Gets Earth Engine account user id.

Returns:

Name	Type	Description
str		A string containing the user id.

Source code in geemap/common.py

def ee_user_id():
    """Gets Earth Engine account user id.

    Returns:
        str: A string containing the user id.
    """
    # ee_initialize()
    roots = ee.data.getAssetRoots()
    if len(roots) == 0:
        return None
    else:
        root = ee.data.getAssetRoots()[0]
        user_id = root["id"].replace("projects/earthengine-legacy/assets/", "")
        return user_id

ee_vector_style(collection, column, labels=None, color='black', pointSize=3, pointShape='circle', width=2, fillColor=None, lineType='solid', neighborhood=5, return_fc=False)

Create a vector style for a feature collection.

Parameters:

Name	Type	Description	Default
collection	FeatureCollection	The input feature collection.	required
column	str	The name of the column to use for styling.	required
labels	list	A list of labels to use for styling. Defaults to None.	None
color	str | list	A default color (CSS 3.0 color value e.g. 'FF0000' or 'red') to use for drawing the features. Supports opacity (e.g.: 'FF000088' for 50% transparent red). Defaults to "black".	'black'
pointSize	int | list	The default size in pixels of the point markers. Defaults to 3.	3
pointShape	str | list	The default shape of the marker to draw at each point location. One of: circle, square, diamond, cross, plus, pentagram, hexagram, triangle, triangle_up, triangle_down, triangle_left, triangle_right, pentagon, hexagon, star5, star6. This argument also supports the following Matlab marker abbreviations: o, s, d, x, +, p, h, ^, v, <, >. Defaults to "circle".	'circle'
width	int | list	The default line width for lines and outlines for polygons and point shapes. Defaults to 2.	2
fillColor	str | list	The color for filling polygons and point shapes. Defaults to 'color' at 0.66 opacity. Defaults to None.	None
lineType	str | list	The default line style for lines and outlines of polygons and point shapes. Defaults to 'solid'. One of: solid, dotted, dashed. Defaults to "solid".	'solid'
neighborhood	int	If styleProperty is used and any feature has a pointSize or width larger than the defaults, tiling artifacts can occur. Specifies the maximum neighborhood (pointSize + width) needed for any feature. Defaults to 5.	5
return_fc	bool	If True, return an ee.FeatureCollection with a style property. Otherwise, return a styled ee.Image. Defaults to False.	False

Returns:

Type	Description
	ee.FeatureCollection | ee.Image: The styled Earth Engine FeatureCollection or Image.

Source code in geemap/common.py

def ee_vector_style(
    collection,
    column,
    labels=None,
    color="black",
    pointSize=3,
    pointShape="circle",
    width=2,
    fillColor=None,
    lineType="solid",
    neighborhood=5,
    return_fc=False,
):
    """Create a vector style for a feature collection.

    Args:
        collection (ee.FeatureCollection): The input feature collection.
        column (str): The name of the column to use for styling.
        labels (list, optional): A list of labels to use for styling. Defaults to None.
        color (str | list, optional): A default color (CSS 3.0 color value e.g. 'FF0000' or 'red') to use for drawing the features. Supports opacity (e.g.: 'FF000088' for 50% transparent red). Defaults to "black".
        pointSize (int | list, optional): The default size in pixels of the point markers. Defaults to 3.
        pointShape (str | list, optional): The default shape of the marker to draw at each point location. One of: circle, square, diamond, cross, plus, pentagram, hexagram, triangle, triangle_up, triangle_down, triangle_left, triangle_right, pentagon, hexagon, star5, star6. This argument also supports the following Matlab marker abbreviations: o, s, d, x, +, p, h, ^, v, <, >. Defaults to "circle".
        width (int | list, optional): The default line width for lines and outlines for polygons and point shapes. Defaults to 2.
        fillColor (str | list, optional): The color for filling polygons and point shapes. Defaults to 'color' at 0.66 opacity. Defaults to None.
        lineType (str | list, optional): The default line style for lines and outlines of polygons and point shapes. Defaults to 'solid'. One of: solid, dotted, dashed. Defaults to "solid".
        neighborhood (int, optional): If styleProperty is used and any feature has a pointSize or width larger than the defaults, tiling artifacts can occur. Specifies the maximum neighborhood (pointSize + width) needed for any feature. Defaults to 5.
        return_fc (bool, optional): If True, return an ee.FeatureCollection with a style property. Otherwise, return a styled ee.Image. Defaults to False.

    Returns:
        ee.FeatureCollection | ee.Image: The styled Earth Engine FeatureCollection or Image.
    """
    if not isinstance(collection, ee.FeatureCollection):
        raise ValueError("collection must be an ee.FeatureCollection.")

    if not isinstance(column, str):
        raise ValueError("column must be a string.")

    prop_names = ee.Feature(collection.first()).propertyNames().getInfo()
    if column not in prop_names:
        raise ValueError(
            f"{column} is not a property name of the collection. It must be one of {','.join(prop_names)}."
        )

    if labels is None:
        labels = collection.aggregate_array(column).distinct().sort().getInfo()
    elif isinstance(labels, list):
        collection = collection.filter(ee.Filter.inList(column, labels))
    elif not isinstance(labels, list):
        raise ValueError("labels must be a list.")

    size = len(labels)
    if isinstance(color, str):
        color = [color] * size
    elif size != len(color):
        raise ValueError("labels and color must be the same length.")
    elif not isinstance(color, list):
        raise ValueError("color must be a string or a list.")

    if isinstance(pointSize, int):
        pointSize = [pointSize] * size
    elif not isinstance(pointSize, list):
        raise ValueError("pointSize must be an integer or a list.")

    if isinstance(pointShape, str):
        pointShape = [pointShape] * size
    elif not isinstance(pointShape, list):
        raise ValueError("pointShape must be a string or a list.")

    if isinstance(width, int):
        width = [width] * size
    elif not isinstance(width, list):
        raise ValueError("width must be an integer or a list.")

    if fillColor is None:
        fillColor = color
    elif isinstance(fillColor, str):
        fillColor = [fillColor] * size
    elif not isinstance(fillColor, list):
        raise ValueError("fillColor must be a list.")

    if not isinstance(neighborhood, int):
        raise ValueError("neighborhood must be an integer.")

    if isinstance(lineType, str):
        lineType = [lineType] * size
    elif not isinstance(lineType, list):
        raise ValueError("lineType must be a string or list.")

    style_dict = {}

    for i, label in enumerate(labels):
        style_dict[label] = {
            "color": color[i],
            "pointSize": pointSize[i],
            "pointShape": pointShape[i],
            "width": width[i],
            "fillColor": fillColor[i],
            "lineType": lineType[i],
        }

    style = ee.Dictionary(style_dict)

    result = collection.map(lambda f: f.set("style", style.get(f.get(column))))

    if return_fc:
        return result
    else:
        return result.style(**{"styleProperty": "style", "neighborhood": neighborhood})

explode(coords)

Explode a GeoJSON geometry's coordinates object and yield coordinate tuples. As long as the input is conforming, the type of the geometry doesn't matter. From Fiona 1.4.8

Parameters:

Name	Type	Description	Default
coords	list	A list of coordinates.	required

Yields:

Type	Description

Source code in geemap/common.py

def explode(coords):
    """Explode a GeoJSON geometry's coordinates object and yield
    coordinate tuples. As long as the input is conforming, the type of
    the geometry doesn't matter.  From Fiona 1.4.8

    Args:
        coords (list): A list of coordinates.

    Yields:
        [type]: [description]
    """

    for e in coords:
        if isinstance(e, (float, int)):
            yield coords
            break
        else:
            for f in explode(e):
                yield f

extract_pixel_values(ee_object, region, scale=None, projection=None, tileScale=1, getInfo=False)

Samples the pixels of an image, returning them as a ee.Dictionary.

Parameters:

Name	Type	Description	Default
ee_object	Image | ImageCollection	The ee.Image or ee.ImageCollection to sample.	required
region	Geometry	The region to sample from. If unspecified, uses the image's whole footprint.	required
scale	float	A nominal scale in meters of the projection to sample in. Defaults to None.	None
projection	str	The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.	None
tileScale	int	A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.	1
getInfo	bool	Whether to use getInfo with the results, i.e., returning the values a list. Default to False.	False

Raises:

Type	Description
TypeError	The image must be an instance of ee.Image.
TypeError	Region must be an instance of ee.Geometry.

Returns:

Type	Description
	ee.Dictionary: The dictionary containing band names and pixel values.

Source code in geemap/common.py

def extract_pixel_values(
    ee_object, region, scale=None, projection=None, tileScale=1, getInfo=False
):
    """Samples the pixels of an image, returning them as a ee.Dictionary.

    Args:
        ee_object (ee.Image | ee.ImageCollection): The ee.Image or ee.ImageCollection to sample.
        region (ee.Geometry): The region to sample from. If unspecified, uses the image's whole footprint.
        scale (float, optional): A nominal scale in meters of the projection to sample in. Defaults to None.
        projection (str, optional): The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
        tileScale (int, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.
        getInfo (bool, optional): Whether to use getInfo with the results, i.e., returning the values a list. Default to False.

    Raises:
        TypeError: The image must be an instance of ee.Image.
        TypeError: Region must be an instance of ee.Geometry.

    Returns:
        ee.Dictionary: The dictionary containing band names and pixel values.
    """
    if isinstance(ee_object, ee.ImageCollection):
        ee_object = ee_object.toBands()

    if not isinstance(ee_object, ee.Image):
        raise TypeError("The image must be an instance of ee.Image.")

    if not isinstance(region, ee.Geometry):
        raise TypeError("Region must be an instance of ee.Geometry.")

    dict_values = (
        ee_object.sample(region, scale, projection, tileScale=tileScale)
        .first()
        .toDictionary()
    )

    if getInfo:
        band_names = ee_object.bandNames().getInfo()
        values_tmp = dict_values.getInfo()
        values = [values_tmp[i] for i in band_names]
        return dict(zip(band_names, values))
    else:
        return dict_values

extract_transect(image, line, reducer='mean', n_segments=100, dist_interval=None, scale=None, crs=None, crsTransform=None, tileScale=1.0, to_pandas=False, **kwargs)

Extracts transect from an image. Credits to Gena for providing the JavaScript example https://code.earthengine.google.com/b09759b8ac60366ee2ae4eccdd19e615.

Parameters:

Name	Type	Description	Default
image	Image	The image to extract transect from.	required
line	LineString	The LineString used to extract transect from an image.	required
reducer	str	The ee.Reducer to use, e.g., 'mean', 'median', 'min', 'max', 'stdDev'. Defaults to "mean".	'mean'
n_segments	int	The number of segments that the LineString will be split into. Defaults to 100.	100
dist_interval	float	The distance interval used for splitting the LineString. If specified, the n_segments parameter will be ignored. Defaults to None.	None
scale	float	A nominal scale in meters of the projection to work in. Defaults to None.	None
crs	Projection	The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.	None
crsTransform	list	The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and will replace any transform already set on the projection. Defaults to None.	None
tileScale	float	A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.	1.0
to_pandas	bool	Whether to convert the result to a pandas dataframe. Default to False.	False

Raises:

Type	Description
TypeError	If the geometry type is not LineString.
Exception	If the program fails to compute.

Returns:

Type	Description
	ee.FeatureCollection: The FeatureCollection containing the transect with distance and reducer values.

Source code in geemap/common.py

def extract_transect(
    image,
    line,
    reducer="mean",
    n_segments=100,
    dist_interval=None,
    scale=None,
    crs=None,
    crsTransform=None,
    tileScale=1.0,
    to_pandas=False,
    **kwargs,
):
    """Extracts transect from an image. Credits to Gena for providing the JavaScript example https://code.earthengine.google.com/b09759b8ac60366ee2ae4eccdd19e615.

    Args:
        image (ee.Image): The image to extract transect from.
        line (ee.Geometry.LineString): The LineString used to extract transect from an image.
        reducer (str, optional): The ee.Reducer to use, e.g., 'mean', 'median', 'min', 'max', 'stdDev'. Defaults to "mean".
        n_segments (int, optional): The number of segments that the LineString will be split into. Defaults to 100.
        dist_interval (float, optional): The distance interval used for splitting the LineString. If specified, the n_segments parameter will be ignored. Defaults to None.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
        crs (ee.Projection, optional): The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
        crsTransform (list, optional): The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and will replace any transform already set on the projection. Defaults to None.
        tileScale (float, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.
        to_pandas (bool, optional): Whether to convert the result to a pandas dataframe. Default to False.

    Raises:
        TypeError: If the geometry type is not LineString.
        Exception: If the program fails to compute.

    Returns:
        ee.FeatureCollection: The FeatureCollection containing the transect with distance and reducer values.
    """
    try:
        geom_type = line.type().getInfo()
        if geom_type != "LineString":
            raise TypeError("The geometry type must be LineString.")

        reducer = eval("ee.Reducer." + reducer + "()")
        maxError = image.projection().nominalScale().divide(5)

        length = line.length(maxError)
        if dist_interval is None:
            dist_interval = length.divide(n_segments)

        distances = ee.List.sequence(0, length, dist_interval)
        lines = line.cutLines(distances, maxError).geometries()

        def set_dist_attr(l):
            l = ee.List(l)
            geom = ee.Geometry(l.get(0))
            distance = ee.Number(l.get(1))
            geom = ee.Geometry.LineString(geom.coordinates())
            return ee.Feature(geom, {"distance": distance})

        lines = lines.zip(distances).map(set_dist_attr)
        lines = ee.FeatureCollection(lines)

        transect = image.reduceRegions(
            **{
                "collection": ee.FeatureCollection(lines),
                "reducer": reducer,
                "scale": scale,
                "crs": crs,
                "crsTransform": crsTransform,
                "tileScale": tileScale,
            }
        )

        if to_pandas:
            return ee_to_df(transect)
        return transect

    except Exception as e:
        raise Exception(e)

extract_values_to_points(in_fc, image, out_fc=None, scale=None, crs=None, crsTransform=None, tileScale=1, stats_type='FIRST', timeout=300, proxies=None, **kwargs)

Extracts image values to points.

Parameters:

Name	Type	Description	Default
in_fc	object	ee.FeatureCollection.	required
image	object	The ee.Image to extract pixel values.	required
out_fc	object	The output feature collection. Defaults to None.	None
scale	Projectoin	A nominal scale in meters of the projection to sample in. If unspecified,the scale of the image's first band is used.	None
crs	str	The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.	None
crsTransform	list	The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and will replace any transform already set on the projection.	None
tile_scale	float	A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default.	required
stats_type	str	Statistic type to be calculated. Defaults to 'FIRST'.	'FIRST'
timeout	int	The number of seconds after which the request will be terminated. Defaults to 300.	300
proxies	dict	A dictionary of proxy servers to use for each request. Defaults to None.	None

Returns:

Name	Type	Description
object		ee.FeatureCollection

Source code in geemap/common.py

def extract_values_to_points(
    in_fc,
    image,
    out_fc=None,
    scale=None,
    crs=None,
    crsTransform=None,
    tileScale=1,
    stats_type="FIRST",
    timeout=300,
    proxies=None,
    **kwargs,
):
    """Extracts image values to points.

    Args:
        in_fc (object): ee.FeatureCollection.
        image (object): The ee.Image to extract pixel values.
        out_fc (object, optional): The output feature collection. Defaults to None.
        scale (ee.Projectoin, optional): A nominal scale in meters of the projection to sample in. If unspecified,the scale of the image's first band is used.
        crs (str, optional): The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
        crsTransform (list, optional): The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and will replace any transform already set on the projection.
        tile_scale (float, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default.
        stats_type (str, optional): Statistic type to be calculated. Defaults to 'FIRST'.
        timeout (int, optional): The number of seconds after which the request will be terminated. Defaults to 300.
        proxies (dict, optional): A dictionary of proxy servers to use for each request. Defaults to None.

    Returns:
        object: ee.FeatureCollection
    """

    if "tile_scale" in kwargs:
        tileScale = kwargs["tile_scale"]
    if "crs_transform" in kwargs:
        crsTransform = kwargs["crs_transform"]

    allowed_stats = {
        "FIRST": ee.Reducer.first(),
        "MEAN": ee.Reducer.mean(),
        "MAXIMUM": ee.Reducer.max(),
        "MEDIAN": ee.Reducer.median(),
        "MINIMUM": ee.Reducer.min(),
        "MODE": ee.Reducer.mode(),
        "STD": ee.Reducer.stdDev(),
        "MIN_MAX": ee.Reducer.minMax(),
        "SUM": ee.Reducer.sum(),
        "VARIANCE": ee.Reducer.variance(),
    }

    if stats_type.upper() not in allowed_stats:
        raise ValueError(
            f"The statistics_type must be one of the following {', '.join(allowed_stats.keys())}"
        )

    if not isinstance(in_fc, ee.FeatureCollection):
        try:
            in_fc = shp_to_ee(in_fc)
        except Exception as e:
            print(e)
            return

    if not isinstance(image, ee.Image):
        print("The image must be an instance of ee.Image.")
        return

    result = image.reduceRegions(
        collection=in_fc,
        reducer=allowed_stats[stats_type.upper()],
        scale=scale,
        crs=crs,
        crsTransform=crsTransform,
        tileScale=tileScale,
    )

    if out_fc is not None:
        ee_export_vector(result, out_fc, timeout=timeout, proxies=proxies)
    else:
        return result

file_browser(in_dir=None, show_hidden=False, add_root_node=True, search_description=None, use_import=False, return_sep_widgets=False, node_icon='file')

Creates a simple file browser and text editor.

Parameters:

Name	Type	Description	Default
in_dir	str	The input directory. Defaults to None, which will use the current working directory.	None
show_hidden	bool	Whether to show hidden files/folders. Defaults to False.	False
add_root_node	bool	Whether to add the input directory as a root node. Defaults to True.	True
search_description	str	The description of the search box. Defaults to None.	None
use_import	bool	Whether to show the import button. Defaults to False.	False
return_sep_widgets	bool	Whether to return the results as separate widgets. Defaults to False.	False

Returns:

Name	Type	Description
object		An ipywidget.

Source code in geemap/common.py

def file_browser(
    in_dir=None,
    show_hidden=False,
    add_root_node=True,
    search_description=None,
    use_import=False,
    return_sep_widgets=False,
    node_icon="file",
):
    """Creates a simple file browser and text editor.

    Args:
        in_dir (str, optional): The input directory. Defaults to None, which will use the current working directory.
        show_hidden (bool, optional): Whether to show hidden files/folders. Defaults to False.
        add_root_node (bool, optional): Whether to add the input directory as a root node. Defaults to True.
        search_description (str, optional): The description of the search box. Defaults to None.
        use_import (bool, optional): Whether to show the import button. Defaults to False.
        return_sep_widgets (bool, optional): Whether to return the results as separate widgets. Defaults to False.

    Returns:
        object: An ipywidget.
    """
    import platform

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

    if not os.path.exists(in_dir):
        print("The provided directory does not exist.")
        return
    elif not os.path.isdir(in_dir):
        print("The provided path is not a valid directory.")
        return

    sep = "/"
    if platform.system() == "Windows":
        sep = "\\"

    if in_dir.endswith(sep):
        in_dir = in_dir[:-1]

    full_widget = widgets.HBox()
    left_widget = widgets.VBox()

    right_widget = widgets.VBox()

    import_btn = widgets.Button(
        description="import",
        button_style="primary",
        tooltip="import the content to a new cell",
        disabled=True,
    )
    import_btn.layout.width = "70px"
    path_widget = widgets.Text()
    path_widget.layout.min_width = "400px"
    # path_widget.layout.max_width = '400px'
    save_widget = widgets.Button(
        description="Save",
        button_style="primary",
        tooltip="Save edits to file.",
        disabled=True,
    )
    info_widget = widgets.HBox()
    info_widget.children = [path_widget, save_widget]
    if use_import:
        info_widget.children = [import_btn, path_widget, save_widget]

    text_widget = widgets.Textarea()
    text_widget.layout.width = "630px"
    text_widget.layout.height = "600px"

    right_widget.children = [info_widget, text_widget]
    full_widget.children = [left_widget]

    if search_description is None:
        search_description = "Search files/folders..."
    search_box = widgets.Text(placeholder=search_description)
    search_box.layout.width = "310px"
    tree_widget = widgets.Output()
    tree_widget.layout.max_width = "310px"
    tree_widget.overflow = "auto"

    left_widget.children = [search_box, tree_widget]

    tree = Tree(multiple_selection=False)
    tree_dict = {}

    def on_button_clicked(b):
        content = text_widget.value
        out_file = path_widget.value

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

        with open(out_file, "w") as f:
            f.write(content)

        text_widget.disabled = True
        text_widget.value = "The content has been saved successfully."
        save_widget.disabled = True
        path_widget.disabled = True

        if (out_file not in tree_dict.keys()) and (out_dir in tree_dict.keys()):
            node = Node(os.path.basename(out_file))
            tree_dict[out_file] = node
            parent_node = tree_dict[out_dir]
            parent_node.add_node(node)

    save_widget.on_click(on_button_clicked)

    def import_btn_clicked(b):
        if (text_widget.value != "") and (path_widget.value.endswith(".py")):
            create_code_cell(text_widget.value)

    import_btn.on_click(import_btn_clicked)

    def search_box_callback(text):
        with tree_widget:
            if text.value == "":
                print("Loading...")
                tree_widget.outputs = ()
                display(tree)
            else:
                tree_widget.outputs = ()
                print("Searching...")
                tree_widget.outputs = ()
                sub_tree = search_api_tree(text.value, tree_dict)
                display(sub_tree)

    search_box.on_submit(search_box_callback)

    def handle_file_click(event):
        if event["new"]:
            cur_node = event["owner"]
            for key in tree_dict.keys():
                if (cur_node is tree_dict[key]) and (os.path.isfile(key)):
                    if key.endswith(".py"):
                        import_btn.disabled = False
                    else:
                        import_btn.disabled = True
                    try:
                        with open(key) as f:
                            content = f.read()
                            text_widget.value = content
                            text_widget.disabled = False
                            path_widget.value = key
                            path_widget.disabled = False
                            save_widget.disabled = False
                            full_widget.children = [left_widget, right_widget]
                    except Exception as e:
                        path_widget.value = key
                        path_widget.disabled = True
                        save_widget.disabled = True
                        text_widget.disabled = True
                        text_widget.value = (
                            "Failed to open {}.".format(cur_node.name) + "\n\n" + str(e)
                        )
                        full_widget.children = [left_widget, right_widget]
                        return
                    break

    def handle_folder_click(event):
        if event["new"]:
            full_widget.children = [left_widget]
            text_widget.value = ""

    if add_root_node:
        root_name = in_dir.split(sep)[-1]
        root_node = Node(root_name)
        tree_dict[in_dir] = root_node
        tree.add_node(root_node)
        root_node.observe(handle_folder_click, "selected")

    for root, d_names, f_names in os.walk(in_dir):
        if not show_hidden:
            folders = root.split(sep)
            for folder in folders:
                if folder.startswith("."):
                    continue
            for d_name in d_names:
                if d_name.startswith("."):
                    d_names.remove(d_name)
            for f_name in f_names:
                if f_name.startswith("."):
                    f_names.remove(f_name)

        d_names.sort()
        f_names.sort()

        if (not add_root_node) and (root == in_dir):
            for d_name in d_names:
                node = Node(d_name)
                tree_dict[os.path.join(in_dir, d_name)] = node
                tree.add_node(node)
                node.opened = False
                node.observe(handle_folder_click, "selected")

        if (root != in_dir) and (root not in tree_dict.keys()):
            name = root.split(sep)[-1]
            dir_name = os.path.dirname(root)
            parent_node = tree_dict[dir_name]
            node = Node(name)
            tree_dict[root] = node
            parent_node.add_node(node)
            node.observe(handle_folder_click, "selected")

        if len(f_names) > 0:
            parent_node = tree_dict[root]
            parent_node.opened = False
            for f_name in f_names:
                node = Node(f_name)
                node.icon = node_icon
                full_path = os.path.join(root, f_name)
                tree_dict[full_path] = node
                parent_node.add_node(node)
                node.observe(handle_file_click, "selected")

    with tree_widget:
        tree_widget.outputs = ()
        display(tree)

    if return_sep_widgets:
        return left_widget, right_widget, tree_dict
    else:
        return full_widget

filter_HUC08(region)

Filters HUC08 watersheds intersecting a given region.

Parameters:

Name	Type	Description	Default
region	object	ee.Geometry	required

Returns:

Name	Type	Description
object		ee.FeatureCollection

Source code in geemap/common.py

def filter_HUC08(region):
    """Filters HUC08 watersheds intersecting a given region.

    Args:
        region (object): ee.Geometry

    Returns:
        object: ee.FeatureCollection
    """

    USGS_HUC08 = ee.FeatureCollection("USGS/WBD/2017/HUC08")  # Subbasins
    HUC08 = USGS_HUC08.filterBounds(region)
    return HUC08

filter_HUC10(region)

Filters HUC10 watersheds intersecting a given region.

Parameters:

Name	Type	Description	Default
region	object	ee.Geometry	required

Returns:

Name	Type	Description
object		ee.FeatureCollection

Source code in geemap/common.py

def filter_HUC10(region):
    """Filters HUC10 watersheds intersecting a given region.

    Args:
        region (object): ee.Geometry

    Returns:
        object: ee.FeatureCollection
    """

    USGS_HUC10 = ee.FeatureCollection("USGS/WBD/2017/HUC10")  # Watersheds
    HUC10 = USGS_HUC10.filterBounds(region)
    return HUC10

filter_NWI(HUC08_Id, region, exclude_riverine=True)

Retrieves NWI dataset for a given HUC8 watershed.

Parameters:

Name	Type	Description	Default
HUC08_Id	str	The HUC8 watershed id.	required
region	object	ee.Geometry	required
exclude_riverine	bool	Whether to exclude riverine wetlands. Defaults to True.	True

Returns:

Name	Type	Description
object		ee.FeatureCollection

Source code in geemap/common.py

def filter_NWI(HUC08_Id, region, exclude_riverine=True):
    """Retrieves NWI dataset for a given HUC8 watershed.

    Args:
        HUC08_Id (str): The HUC8 watershed id.
        region (object): ee.Geometry
        exclude_riverine (bool, optional): Whether to exclude riverine wetlands. Defaults to True.

    Returns:
        object: ee.FeatureCollection
    """
    nwi_asset_prefix = "users/wqs/NWI-HU8/HU8_"
    nwi_asset_suffix = "_Wetlands"
    nwi_asset_path = nwi_asset_prefix + HUC08_Id + nwi_asset_suffix
    nwi_huc = ee.FeatureCollection(nwi_asset_path).filterBounds(region)

    if exclude_riverine:
        nwi_huc = nwi_huc.filter(
            ee.Filter.notEquals(**{"leftField": "WETLAND_TY", "rightValue": "Riverine"})
        )
    return nwi_huc

filter_polygons(ftr)

Converts GeometryCollection to Polygon/MultiPolygon

Parameters:

Name	Type	Description	Default
ftr	object	ee.Feature	required

Returns:

Name	Type	Description
object		ee.Feature

Source code in geemap/common.py

def filter_polygons(ftr):
    """Converts GeometryCollection to Polygon/MultiPolygon

    Args:
        ftr (object): ee.Feature

    Returns:
        object: ee.Feature
    """
    # ee_initialize()
    geometries = ftr.geometry().geometries()
    geometries = geometries.map(
        lambda geo: ee.Feature(ee.Geometry(geo)).set("geoType", ee.Geometry(geo).type())
    )

    polygons = (
        ee.FeatureCollection(geometries)
        .filter(ee.Filter.eq("geoType", "Polygon"))
        .geometry()
    )
    return ee.Feature(polygons).copyProperties(ftr)

find_HUC08(HUC08_Id)

Finds a HUC08 watershed based on a given HUC08 ID

Parameters:

Name	Type	Description	Default
HUC08_Id	str	The HUC08 ID.	required

Returns:

Name	Type	Description
object		ee.FeatureCollection

Source code in geemap/common.py

def find_HUC08(HUC08_Id):
    """Finds a HUC08 watershed based on a given HUC08 ID

    Args:
        HUC08_Id (str): The HUC08 ID.

    Returns:
        object: ee.FeatureCollection
    """

    USGS_HUC08 = ee.FeatureCollection("USGS/WBD/2017/HUC08")  # Subbasins
    HUC08 = USGS_HUC08.filter(ee.Filter.eq("huc8", HUC08_Id))
    return HUC08

find_HUC10(HUC10_Id)

Finds a HUC10 watershed based on a given HUC08 ID

Parameters:

Name	Type	Description	Default
HUC10_Id	str	The HUC10 ID.	required

Returns:

Name	Type	Description
object		ee.FeatureCollection

Source code in geemap/common.py

def find_HUC10(HUC10_Id):
    """Finds a HUC10 watershed based on a given HUC08 ID

    Args:
        HUC10_Id (str): The HUC10 ID.

    Returns:
        object: ee.FeatureCollection
    """

    USGS_HUC10 = ee.FeatureCollection("USGS/WBD/2017/HUC10")  # Watersheds
    HUC10 = USGS_HUC10.filter(ee.Filter.eq("huc10", HUC10_Id))
    return HUC10

find_NAIP(region, add_NDVI=True, add_NDWI=True)

Create annual NAIP mosaic for a given region.

Parameters:

Name	Type	Description	Default
region	object	ee.Geometry	required
add_NDVI	bool	Whether to add the NDVI band. Defaults to True.	True
add_NDWI	bool	Whether to add the NDWI band. Defaults to True.	True

Returns:

Name	Type	Description
object		ee.ImageCollection

Source code in geemap/common.py

def find_NAIP(region, add_NDVI=True, add_NDWI=True):
    """Create annual NAIP mosaic for a given region.

    Args:
        region (object): ee.Geometry
        add_NDVI (bool, optional): Whether to add the NDVI band. Defaults to True.
        add_NDWI (bool, optional): Whether to add the NDWI band. Defaults to True.

    Returns:
        object: ee.ImageCollection
    """

    init_collection = (
        ee.ImageCollection("USDA/NAIP/DOQQ")
        .filterBounds(region)
        .filterDate("2009-01-01", "2019-12-31")
        .filter(ee.Filter.listContains("system:band_names", "N"))
    )

    yearList = ee.List(
        init_collection.distinct(["system:time_start"]).aggregate_array(
            "system:time_start"
        )
    )
    init_years = yearList.map(lambda y: ee.Date(y).get("year"))

    # remove duplicates
    init_years = ee.Dictionary(
        init_years.reduce(ee.Reducer.frequencyHistogram())
    ).keys()
    years = init_years.map(lambda x: ee.Number.parse(x))
    # years = init_years.map(lambda x: x)

    # Available NAIP years with NIR band
    def NAIPAnnual(year):
        start_date = ee.Date.fromYMD(year, 1, 1)
        end_date = ee.Date.fromYMD(year, 12, 31)
        collection = init_collection.filterDate(start_date, end_date)
        # .filterBounds(geometry)
        # .filter(ee.Filter.listContains("system:band_names", "N"))
        time_start = ee.Date(
            ee.List(collection.aggregate_array("system:time_start")).sort().get(0)
        ).format("YYYY-MM-dd")
        time_end = ee.Date(
            ee.List(collection.aggregate_array("system:time_end")).sort().get(-1)
        ).format("YYYY-MM-dd")
        col_size = collection.size()
        image = ee.Image(collection.mosaic().clip(region))

        if add_NDVI:
            NDVI = (
                ee.Image(image)
                .normalizedDifference(["N", "R"])
                .select(["nd"], ["ndvi"])
            )
            image = image.addBands(NDVI)

        if add_NDWI:
            NDWI = (
                ee.Image(image)
                .normalizedDifference(["G", "N"])
                .select(["nd"], ["ndwi"])
            )
            image = image.addBands(NDWI)

        return image.set(
            {
                "system:time_start": time_start,
                "system:time_end": time_end,
                "tiles": col_size,
            }
        )

    # remove years with incomplete coverage
    naip = ee.ImageCollection(years.map(NAIPAnnual))
    mean_size = ee.Number(naip.aggregate_mean("tiles"))
    total_sd = ee.Number(naip.aggregate_total_sd("tiles"))
    threshold = mean_size.subtract(total_sd.multiply(1))
    naip = naip.filter(
        ee.Filter.Or(ee.Filter.gte("tiles", threshold), ee.Filter.gte("tiles", 15))
    )
    naip = naip.filter(ee.Filter.gte("tiles", 7))

    naip_count = naip.size()
    naip_seq = ee.List.sequence(0, naip_count.subtract(1))

    def set_index(index):
        img = ee.Image(naip.toList(naip_count).get(index))
        return img.set({"system:uid": ee.Number(index).toUint8()})

    naip = naip_seq.map(set_index)

    return ee.ImageCollection(naip)

find_NWI(HUC08_Id, exclude_riverine=True)

Finds NWI dataset for a given HUC08 watershed.

Parameters:

Name	Type	Description	Default
HUC08_Id	str	The HUC08 watershed ID.	required
exclude_riverine	bool	Whether to exclude riverine wetlands. Defaults to True.	True

Returns:

Name	Type	Description
object		ee.FeatureCollection

Source code in geemap/common.py

def find_NWI(HUC08_Id, exclude_riverine=True):
    """Finds NWI dataset for a given HUC08 watershed.

    Args:
        HUC08_Id (str): The HUC08 watershed ID.
        exclude_riverine (bool, optional): Whether to exclude riverine wetlands. Defaults to True.

    Returns:
        object: ee.FeatureCollection
    """

    nwi_asset_prefix = "users/wqs/NWI-HU8/HU8_"
    nwi_asset_suffix = "_Wetlands"
    nwi_asset_path = nwi_asset_prefix + HUC08_Id + nwi_asset_suffix
    nwi_huc = ee.FeatureCollection(nwi_asset_path)
    if exclude_riverine:
        nwi_huc = nwi_huc.filter(
            ee.Filter.notEquals(**{"leftField": "WETLAND_TY", "rightValue": "Riverine"})
        )
    return nwi_huc

find_files(input_dir, ext=None, fullpath=True, recursive=True)

Find files in a directory.

Parameters:

Name	Type	Description	Default
input_dir	str	The input directory.	required
ext	str	The file extension to match. Defaults to None.	None
fullpath	bool	Whether to return the full path. Defaults to True.	True
recursive	bool	Whether to search recursively. Defaults to True.	True

Returns:

Name	Type	Description
list		A list of matching files.

Source code in geemap/common.py

def find_files(input_dir, ext=None, fullpath=True, recursive=True):
    """Find files in a directory.

    Args:
        input_dir (str): The input directory.
        ext (str, optional): The file extension to match. Defaults to None.
        fullpath (bool, optional): Whether to return the full path. Defaults to True.
        recursive (bool, optional): Whether to search recursively. Defaults to True.

    Returns:
        list: A list of matching files.
    """

    from pathlib import Path

    files = []

    if ext is None:
        ext = "*"
    else:
        ext = ext.replace(".", "")

    ext = f"*.{ext}"

    if recursive:
        if fullpath:
            files = [str(path.joinpath()) for path in Path(input_dir).rglob(ext)]
        else:
            files = [str(path.name) for path in Path(input_dir).rglob(ext)]
    else:
        if fullpath:
            files = [str(path.joinpath()) for path in Path(input_dir).glob(ext)]
        else:
            files = [path.name for path in Path(input_dir).glob(ext)]

    return files

find_landsat_by_path_row(landsat_col, path_num, row_num)

Finds Landsat images by WRS path number and row number.

Parameters:

Name	Type	Description	Default
landsat_col	str	The image collection id of Landsat.	required
path_num	int	The WRS path number.	required
row_num	int	the WRS row number.	required

Returns:

Name	Type	Description
object		ee.ImageCollection

Source code in geemap/common.py

def find_landsat_by_path_row(landsat_col, path_num, row_num):
    """Finds Landsat images by WRS path number and row number.

    Args:
        landsat_col (str): The image collection id of Landsat.
        path_num (int): The WRS path number.
        row_num (int): the WRS row number.

    Returns:
        object: ee.ImageCollection
    """
    try:
        if isinstance(landsat_col, str):
            landsat_col = ee.ImageCollection(landsat_col)
            collection = landsat_col.filter(ee.Filter.eq("WRS_PATH", path_num)).filter(
                ee.Filter.eq("WRS_ROW", row_num)
            )
            return collection
    except Exception as e:
        print(e)

fishnet(data, h_interval=1.0, v_interval=1.0, rows=None, cols=None, delta=1.0, intersect=True, output=None, **kwargs)

Create a fishnet (i.e., rectangular grid) based on an input vector dataset.

Parameters:

Name	Type	Description	Default
data	str | Geometry | Feature | FeatureCollection	The input vector dataset. It can be a file path, HTTP URL, ee.Geometry, ee.Feature, or ee.FeatureCollection.	required
h_interval	float	The horizontal interval in degrees. It will be ignored if rows and cols are specified. Defaults to 1.0.	1.0
v_interval	float	The vertical interval in degrees. It will be ignored if rows and cols are specified. Defaults to 1.0.	1.0
rows	int	The number of rows. Defaults to None.	None
cols	int	The number of columns. Defaults to None.	None
delta	float	The buffer distance in degrees. Defaults to 1.0.	1.0
intersect	bool	If True, the output will be a feature collection of intersecting polygons. Defaults to True.	True
output	str	The output file path. Defaults to None.	None

Returns:

Type	Description
	ee.FeatureCollection: The fishnet as an ee.FeatureCollection.

Source code in geemap/common.py

def fishnet(
    data,
    h_interval=1.0,
    v_interval=1.0,
    rows=None,
    cols=None,
    delta=1.0,
    intersect=True,
    output=None,
    **kwargs,
):
    """Create a fishnet (i.e., rectangular grid) based on an input vector dataset.

    Args:
        data (str | ee.Geometry | ee.Feature | ee.FeatureCollection): The input vector dataset. It can be a file path, HTTP URL, ee.Geometry, ee.Feature, or ee.FeatureCollection.
        h_interval (float, optional): The horizontal interval in degrees. It will be ignored if rows and cols are specified. Defaults to 1.0.
        v_interval (float, optional): The vertical interval in degrees. It will be ignored if rows and cols are specified. Defaults to 1.0.
        rows (int, optional): The number of rows. Defaults to None.
        cols (int, optional): The number of columns. Defaults to None.
        delta (float, optional): The buffer distance in degrees. Defaults to 1.0.
        intersect (bool, optional): If True, the output will be a feature collection of intersecting polygons. Defaults to True.
        output (str, optional): The output file path. Defaults to None.


    Returns:
        ee.FeatureCollection: The fishnet as an ee.FeatureCollection.
    """
    if isinstance(data, str):
        data = vector_to_ee(data, **kwargs)

    if isinstance(data, ee.FeatureCollection) or isinstance(data, ee.Feature):
        data = data.geometry()
    elif isinstance(data, ee.Geometry):
        pass
    else:
        raise ValueError(
            "data must be a string, ee.FeatureCollection, ee.Feature, or ee.Geometry."
        )

    coords = data.bounds().coordinates().getInfo()

    west = coords[0][0][0]
    east = coords[0][1][0]
    south = coords[0][0][1]
    north = coords[0][2][1]

    if rows is not None and cols is not None:
        v_interval = (north - south) / rows
        h_interval = (east - west) / cols

    # west = west - delta * h_interval
    east = east + delta * h_interval
    # south = south - delta * v_interval
    north = north + delta * v_interval

    grids = latlon_grid(v_interval, h_interval, west, east, south, north)

    if intersect:
        grids = grids.filterBounds(data)

    if output is not None:
        ee_export_vector(grids, output)

    else:
        return grids

gdf_bounds(gdf, return_geom=False)

Returns the bounding box of a GeoDataFrame.

Parameters:

Name	Type	Description	Default
gdf	GeoDataFrame	A GeoDataFrame.	required
return_geom	bool	Whether to return the bounding box as a GeoDataFrame. Defaults to False.	False

Returns:

Type	Description
	list | gpd.GeoDataFrame: A bounding box in the form of a list (minx, miny, maxx, maxy) or GeoDataFrame.

Source code in geemap/common.py

def gdf_bounds(gdf, return_geom=False):
    """Returns the bounding box of a GeoDataFrame.

    Args:
        gdf (gpd.GeoDataFrame): A GeoDataFrame.
        return_geom (bool, optional): Whether to return the bounding box as a GeoDataFrame. Defaults to False.

    Returns:
        list | gpd.GeoDataFrame: A bounding box in the form of a list (minx, miny, maxx, maxy) or GeoDataFrame.
    """
    bounds = gdf.total_bounds
    if return_geom:
        return bbox_to_gdf(bbox=bounds)
    else:
        return bounds

gdf_centroid(gdf, return_geom=False)

Returns the centroid of a GeoDataFrame.

Parameters:

Name	Type	Description	Default
gdf	GeoDataFrame	A GeoDataFrame.	required
return_geom	bool	Whether to return the bounding box as a GeoDataFrame. Defaults to False.	False

Returns:

Type	Description
	list | gpd.GeoDataFrame: A bounding box in the form of a list (lon, lat) or GeoDataFrame.

Source code in geemap/common.py

def gdf_centroid(gdf, return_geom=False):
    """Returns the centroid of a GeoDataFrame.

    Args:
        gdf (gpd.GeoDataFrame): A GeoDataFrame.
        return_geom (bool, optional): Whether to return the bounding box as a GeoDataFrame. Defaults to False.

    Returns:
        list | gpd.GeoDataFrame: A bounding box in the form of a list (lon, lat) or GeoDataFrame.
    """

    warnings.filterwarnings("ignore")

    centroid = gdf_bounds(gdf, return_geom=True).centroid
    if return_geom:
        return centroid
    else:
        return centroid.x[0], centroid.y[0]

gdf_geom_type(gdf, first_only=True)

Returns the geometry type of a GeoDataFrame.

Parameters:

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

Returns:

Name	Type	Description
str		The geometry type of the GeoDataFrame.

Source code in geemap/common.py

def gdf_geom_type(gdf, first_only=True):
    """Returns the geometry type of a GeoDataFrame.

    Args:
        gdf (gpd.GeoDataFrame): A GeoDataFrame.
        first_only (bool, optional): Whether to return the geometry type of the first feature in the GeoDataFrame. Defaults to True.

    Returns:
        str: The geometry type of the GeoDataFrame.
    """

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

gdf_to_df(gdf, drop_geom=True)

Converts a GeoDataFrame to a pandas DataFrame.

Parameters:

Name	Type	Description	Default
gdf	GeoDataFrame	A GeoDataFrame.	required
drop_geom	bool	Whether to drop the geometry column. Defaults to True.	True

Returns:

Type	Description
	pd.DataFrame: A pandas DataFrame containing the GeoDataFrame.

Source code in geemap/common.py

def gdf_to_df(gdf, drop_geom=True):
    """Converts a GeoDataFrame to a pandas DataFrame.

    Args:
        gdf (gpd.GeoDataFrame): A GeoDataFrame.
        drop_geom (bool, optional): Whether to drop the geometry column. Defaults to True.

    Returns:
        pd.DataFrame: A pandas DataFrame containing the GeoDataFrame.
    """
    import pandas as pd

    if drop_geom:
        df = pd.DataFrame(gdf.drop(columns=["geometry"]))
    else:
        df = pd.DataFrame(gdf)

    return df

gdf_to_ee(gdf, geodesic=True, date=None, date_format='YYYY-MM-dd')

Converts a GeoPandas GeoDataFrame to ee.FeatureCollection.

Parameters:

Name	Type	Description	Default
gdf	GeoDataFrame	The input geopandas.GeoDataFrame to be converted ee.FeatureCollection.	required
geodesic	bool	Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. Defaults to True.	True
date	str	Column name for the date column. Defaults to None.	None
date_format	str	Date format. A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.	'YYYY-MM-dd'

Raises:

Type	Description
TypeError	The input data type must be geopandas.GeoDataFrame.

Returns:

Type	Description
	ee.FeatureCollection: The output ee.FeatureCollection converted from the input geopandas.GeoDataFrame.

Source code in geemap/common.py

def gdf_to_ee(gdf, geodesic=True, date=None, date_format="YYYY-MM-dd"):
    """Converts a GeoPandas GeoDataFrame to ee.FeatureCollection.

    Args:
        gdf (geopandas.GeoDataFrame): The input geopandas.GeoDataFrame to be converted ee.FeatureCollection.
        geodesic (bool, optional): Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. Defaults to True.
        date (str, optional): Column name for the date column. Defaults to None.
        date_format (str, optional): Date format. A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.

    Raises:
        TypeError: The input data type must be geopandas.GeoDataFrame.

    Returns:
        ee.FeatureCollection: The output ee.FeatureCollection converted from the input geopandas.GeoDataFrame.
    """
    check_package(name="geopandas", URL="https://geopandas.org")

    import geopandas as gpd

    if not isinstance(gdf, gpd.GeoDataFrame):
        raise TypeError("The input data type must be geopandas.GeoDataFrame.")

    out_json = os.path.join(os.getcwd(), random_string(6) + ".geojson")
    gdf = gdf.to_crs(4326)
    gdf.to_file(out_json, driver="GeoJSON")

    fc = geojson_to_ee(out_json, geodesic=geodesic)

    if date is not None:
        try:
            fc = fc.map(
                lambda x: x.set(
                    "system:time_start",
                    ee.Date.parse(date_format, x.get(date)).millis(),
                )
            )
        except Exception as e:
            raise Exception(e)

    os.remove(out_json)

    return fc

gdf_to_geojson(gdf, out_geojson=None, epsg=None)

Converts a GeoDataFame to GeoJSON.

Parameters:

Name	Type	Description	Default
gdf	GeoDataFrame	A GeoPandas GeoDataFrame.	required
out_geojson	str	File path to he output GeoJSON. Defaults to None.	None
epsg	str	An EPSG string, e.g., "4326". Defaults to None.	None

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 geemap/common.py

def gdf_to_geojson(gdf, out_geojson=None, epsg=None):
    """Converts a GeoDataFame to GeoJSON.

    Args:
        gdf (GeoDataFrame): A GeoPandas GeoDataFrame.
        out_geojson (str, optional): File path to he output GeoJSON. Defaults to None.
        epsg (str, optional): An EPSG string, e.g., "4326". Defaults to None.

    Raises:
        TypeError: When the output file extension is incorrect.
        Exception: When the conversion fails.

    Returns:
        dict: When the out_json is None returns a dict.
    """
    check_package(name="geopandas", URL="https://geopandas.org")

    try:
        if epsg is not None:
            gdf = gdf.to_crs(epsg=epsg)
        geojson = gdf.__geo_interface__

        if out_geojson is None:
            return geojson
        else:
            ext = os.path.splitext(out_geojson)[1]
            if ext.lower() not in [".json", ".geojson"]:
                raise TypeError(
                    "The output file extension must be either .json or .geojson"
                )
            out_dir = os.path.dirname(out_geojson)
            if not os.path.exists(out_dir):
                os.makedirs(out_dir)

            gdf.to_file(out_geojson, driver="GeoJSON")
    except Exception as e:
        raise Exception(e)

geocode(location, max_rows=10, reverse=False)

Search location by address and lat/lon coordinates.

Parameters:

Name	Type	Description	Default
location	str	Place name or address	required
max_rows	int	Maximum number of records to return. Defaults to 10.	10
reverse	bool	Search place based on coordinates. Defaults to False.	False

Source code in geemap/common.py

def geocode(location, max_rows=10, reverse=False):
    """Search location by address and lat/lon coordinates.

    Args:
        location (str): Place name or address
        max_rows (int, optional): Maximum number of records to return. Defaults to 10.
        reverse (bool, optional): Search place based on coordinates. Defaults to False.
    Returns:
        list: Returns a list of locations.
    """
    import geocoder

    if not isinstance(location, str):
        print("The location must be a string.")
        return None

    if not reverse:
        locations = []
        addresses = set()
        g = geocoder.arcgis(location, maxRows=max_rows)

        for result in g:
            address = result.address
            if address not in addresses:
                addresses.add(address)
                locations.append(result)

        if len(locations) > 0:
            return locations
        else:
            return None

    else:
        try:
            if "," in location:
                latlon = [float(x) for x in location.split(",")]
            elif " " in location:
                latlon = [float(x) for x in location.split(" ")]
            else:
                return
            g = geocoder.arcgis(latlon, method="reverse")
            locations = []
            addresses = set()

            for result in g:
                address = result.address
                if address not in addresses:
                    addresses.add(address)
                    locations.append(result)

            if len(locations) > 0:
                return locations
            else:
                return None

        except Exception as e:
            print(e)
            return None

geojson_to_df(in_geojson, encoding='utf-8', drop_geometry=True)

Converts a GeoJSON object to a pandas DataFrame.

Parameters:

Name	Type	Description	Default
in_geojson	str | dict	The input GeoJSON file or dict.	required
encoding	str	The encoding of the GeoJSON object. Defaults to "utf-8".	'utf-8'
drop_geometry	bool	Whether to drop the geometry column. Defaults to True.	True

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 geemap/common.py

def geojson_to_df(in_geojson, encoding="utf-8", drop_geometry=True):
    """Converts a GeoJSON object to a pandas DataFrame.

    Args:
        in_geojson (str | dict): The input GeoJSON file or dict.
        encoding (str, optional): The encoding of the GeoJSON object. Defaults to "utf-8".
        drop_geometry (bool, optional): Whether to drop the geometry column. Defaults to True.

    Raises:
        FileNotFoundError: If the input GeoJSON file could not be found.

    Returns:
        pd.DataFrame: A pandas DataFrame containing the GeoJSON object.
    """

    import pandas as pd
    from urllib.request import urlopen

    if isinstance(in_geojson, str):
        if in_geojson.startswith("http"):
            in_geojson = github_raw_url(in_geojson)
            with urlopen(in_geojson) as f:
                data = json.load(f)
        else:
            in_geojson = os.path.abspath(in_geojson)
            if not os.path.exists(in_geojson):
                raise FileNotFoundError("The provided GeoJSON file could not be found.")

            with open(in_geojson, encoding=encoding) as f:
                data = json.load(f)

    elif isinstance(in_geojson, dict):
        data = in_geojson

    df = pd.json_normalize(data["features"])
    df.columns = [col.replace("properties.", "") for col in df.columns]
    if drop_geometry:
        df = df[df.columns.drop(list(df.filter(regex="geometry")))]
    return df

geojson_to_ee(geo_json, geodesic=False, encoding='utf-8')

Converts a GeoJSON to an Earth Engine Geometry or FeatureCollection.

Parameters:

Name	Type	Description	Default
geo_json	Union[Dict[str, Any], str]	A GeoJSON geometry dictionary or file path.	required
geodesic	bool	Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected. Defaults to False.	False
encoding	str	The encoding of characters. Defaults to "utf-8".	'utf-8'

Returns:

Type	Description
Union[Geometry, FeatureCollection]	Union[ee.Geometry, ee.FeatureCollection]: An Earth Engine Geometry or FeatureCollection.

Raises:

Type	Description
Exception	If the GeoJSON cannot be converted to an Earth Engine object.

Source code in geemap/coreutils.py

def geojson_to_ee(
    geo_json: Union[Dict[str, Any], str],
    geodesic: bool = False,
    encoding: str = "utf-8",
) -> Union[ee.Geometry, ee.FeatureCollection]:
    """Converts a GeoJSON to an Earth Engine Geometry or FeatureCollection.

    Args:
        geo_json (Union[Dict[str, Any], str]): A GeoJSON geometry dictionary or
            file path.
        geodesic (bool, optional): Whether line segments should be interpreted
            as spherical geodesics. If false, indicates that line segments
            should be interpreted as planar lines in the specified CRS. If
            absent, defaults to true if the CRS is geographic (including the
            default EPSG:4326), or to false if the CRS is projected. Defaults to False.
        encoding (str, optional): The encoding of characters. Defaults to "utf-8".

    Returns:
        Union[ee.Geometry, ee.FeatureCollection]: An Earth Engine Geometry or FeatureCollection.

    Raises:
        Exception: If the GeoJSON cannot be converted to an Earth Engine object.
    """

    try:
        if isinstance(geo_json, str):
            if geo_json.startswith("http") and geo_json.endswith(".geojson"):
                geo_json = github_raw_url(geo_json)
                out_geojson = temp_file_path(extension=".geojson")
                download_file(geo_json, out_geojson)
                with open(out_geojson, "r", encoding=encoding) as f:
                    geo_json = json.loads(f.read())
                os.remove(out_geojson)

            elif os.path.isfile(geo_json):
                with open(os.path.abspath(geo_json), encoding=encoding) as f:
                    geo_json = json.load(f)

        # geo_json["geodesic"] = geodesic
        if geo_json["type"] == "FeatureCollection":
            for feature in geo_json["features"]:
                if feature["geometry"]["type"] != "Point":
                    feature["geometry"]["geodesic"] = geodesic
            features = ee.FeatureCollection(geo_json)
            return features
        elif geo_json["type"] == "Feature":
            geom = None
            if "style" in geo_json["properties"]:
                keys = geo_json["properties"]["style"].keys()
                if "radius" in keys:  # Checks whether it is a circle
                    geom = ee.Geometry(geo_json["geometry"])
                    radius = geo_json["properties"]["style"]["radius"]
                    geom = geom.buffer(radius)
                elif geo_json["geometry"]["type"] == "Point":
                    geom = ee.Geometry(geo_json["geometry"])
                else:
                    geom = ee.Geometry(geo_json["geometry"], "", geodesic)
            elif (
                geo_json["geometry"]["type"] == "Point"
            ):  # Checks whether it is a point
                coordinates = geo_json["geometry"]["coordinates"]
                longitude = coordinates[0]
                latitude = coordinates[1]
                geom = ee.Geometry.Point(longitude, latitude)
            else:
                geom = ee.Geometry(geo_json["geometry"], "", geodesic)
            return geom
        else:
            raise Exception("Could not convert the geojson to ee.Geometry()")

    except Exception as e:
        print("Could not convert the geojson to ee.Geometry()")
        raise Exception(e)

geometry_type(ee_object)

Get geometry type of an Earth Engine object.

Parameters:

Name	Type	Description	Default
ee_object	Any	An Earth Engine object.	required

Returns:

Name	Type	Description
str	str	Returns geometry type. One of Point, MultiPoint, LineString, LinearRing, MultiLineString, BBox, Rectangle, Polygon, MultiPolygon.

Raises:

Type	Description
TypeError	If the ee_object is not one of ee.Geometry, ee.Feature, ee.FeatureCollection.

Source code in geemap/coreutils.py

def geometry_type(ee_object: Any) -> str:
    """Get geometry type of an Earth Engine object.

    Args:
        ee_object (Any): An Earth Engine object.

    Returns:
        str: Returns geometry type. One of Point, MultiPoint, LineString,
            LinearRing, MultiLineString, BBox, Rectangle, Polygon, MultiPolygon.

    Raises:
        TypeError: If the ee_object is not one of ee.Geometry, ee.Feature,
            ee.FeatureCollection.
    """
    if isinstance(ee_object, ee.Geometry):
        return ee_object.type().getInfo()
    elif isinstance(ee_object, ee.Feature):
        return ee_object.geometry().type().getInfo()
    elif isinstance(ee_object, ee.FeatureCollection):
        return ee.Feature(ee_object.first()).geometry().type().getInfo()
    else:
        raise TypeError(
            "The ee_object must be one of ee.Geometry, ee.Feature, ee.FeatureCollection."
        )

geotiff_to_image(image, output)

Converts a GeoTIFF file to a JPEG/PNG image.

Parameters:

Name	Type	Description	Default
image	str	The path to the input GeoTIFF file.	required
output	str	The path to save the output JPEG/PNG file.	required

Returns:

Type	Description
None	None

Source code in geemap/common.py

def geotiff_to_image(image: str, output: str) -> None:
    """
    Converts a GeoTIFF file to a JPEG/PNG image.

    Args:
        image (str): The path to the input GeoTIFF file.
        output (str): The path to save the output JPEG/PNG file.

    Returns:
        None
    """

    import rasterio
    from PIL import Image

    # Open the GeoTIFF file
    with rasterio.open(image) as dataset:
        # Read the image data
        data = dataset.read()

        # Convert the image data to 8-bit format (assuming it's not already)
        if dataset.dtypes[0] != "uint8":
            data = (data / data.max() * 255).astype("uint8")

        # Convert the image data to RGB format if it's a single band image
        if dataset.count == 1:
            data = data.squeeze()
            data = data.reshape((1, data.shape[0], data.shape[1]))
            data = data.repeat(3, axis=0)

        # Create a PIL Image object from the image data
        image = Image.fromarray(data.transpose(1, 2, 0))

        # Save the image as a JPEG file
        image.save(output)

get_all_NAIP(start_year=2009, end_year=2019)

Creates annual NAIP imagery mosaic.

Parameters:

Name	Type	Description	Default
start_year	int	The starting year. Defaults to 2009.	2009
end_year	int	The ending year. Defaults to 2019.	2019

Returns:

Name	Type	Description
object		ee.ImageCollection

Source code in geemap/common.py

def get_all_NAIP(start_year=2009, end_year=2019):
    """Creates annual NAIP imagery mosaic.

    Args:
        start_year (int, optional): The starting year. Defaults to 2009.
        end_year (int, optional): The ending year. Defaults to 2019.

    Returns:
        object: ee.ImageCollection
    """
    try:

        def get_annual_NAIP(year):
            try:
                collection = ee.ImageCollection("USDA/NAIP/DOQQ")
                start_date = ee.Date.fromYMD(year, 1, 1)
                end_date = ee.Date.fromYMD(year, 12, 31)
                naip = collection.filterDate(start_date, end_date).filter(
                    ee.Filter.listContains("system:band_names", "N")
                )
                return ee.ImageCollection(naip)
            except Exception as e:
                print(e)

        years = ee.List.sequence(start_year, end_year)
        collection = years.map(get_annual_NAIP)
        return collection

    except Exception as e:
        print(e)

get_annual_NAIP(year, RGBN=True)

Filters NAIP ImageCollection by year.

Parameters:

Name	Type	Description	Default
year	int	The year to filter the NAIP ImageCollection.	required
RGBN	bool	Whether to retrieve 4-band NAIP imagery only. Defaults to True.	True

Returns:

Name	Type	Description
object		ee.ImageCollection

Source code in geemap/common.py

def get_annual_NAIP(year, RGBN=True):
    """Filters NAIP ImageCollection by year.

    Args:
        year (int): The year to filter the NAIP ImageCollection.
        RGBN (bool, optional): Whether to retrieve 4-band NAIP imagery only. Defaults to True.

    Returns:
        object: ee.ImageCollection
    """
    try:
        collection = ee.ImageCollection("USDA/NAIP/DOQQ")
        start_date = str(year) + "-01-01"
        end_date = str(year) + "-12-31"
        naip = collection.filterDate(start_date, end_date)
        if RGBN:
            naip = naip.filter(ee.Filter.listContains("system:band_names", "N"))
        return naip
    except Exception as e:
        print(e)

get_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 geemap/common.py

def get_bounds(geometry, north_up=True, transform=None):
    """Bounding box of a GeoJSON geometry, GeometryCollection, or FeatureCollection.
    left, bottom, right, top
    *not* xmin, ymin, xmax, ymax
    If not north_up, y will be switched to guarantee the above.
    Source code adapted from https://github.com/mapbox/rasterio/blob/master/rasterio/features.py#L361

    Args:
        geometry (dict): A GeoJSON dict.
        north_up (bool, optional): . Defaults to True.
        transform ([type], optional): . Defaults to None.

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

    if "bbox" in geometry:
        return tuple(geometry["bbox"])

    geometry = geometry.get("geometry") or geometry

    # geometry must be a geometry, GeometryCollection, or FeatureCollection
    if not (
        "coordinates" in geometry or "geometries" in geometry or "features" in geometry
    ):
        raise ValueError(
            "geometry must be a GeoJSON-like geometry, GeometryCollection, "
            "or FeatureCollection"
        )

    if "features" in geometry:
        # Input is a FeatureCollection
        xmins = []
        ymins = []
        xmaxs = []
        ymaxs = []
        for feature in geometry["features"]:
            xmin, ymin, xmax, ymax = get_bounds(feature["geometry"])
            xmins.append(xmin)
            ymins.append(ymin)
            xmaxs.append(xmax)
            ymaxs.append(ymax)
        if north_up:
            return min(xmins), min(ymins), max(xmaxs), max(ymaxs)
        else:
            return min(xmins), max(ymaxs), max(xmaxs), min(ymins)

    elif "geometries" in geometry:
        # Input is a geometry collection
        xmins = []
        ymins = []
        xmaxs = []
        ymaxs = []
        for geometry in geometry["geometries"]:
            xmin, ymin, xmax, ymax = get_bounds(geometry)
            xmins.append(xmin)
            ymins.append(ymin)
            xmaxs.append(xmax)
            ymaxs.append(ymax)
        if north_up:
            return min(xmins), min(ymins), max(xmaxs), max(ymaxs)
        else:
            return min(xmins), max(ymaxs), max(xmaxs), min(ymins)

    elif "coordinates" in geometry:
        # Input is a singular geometry object
        if transform is not None:
            xyz = list(explode(geometry["coordinates"]))
            xyz_px = [transform * point for point in xyz]
            xyz = tuple(zip(*xyz_px))
            return min(xyz[0]), max(xyz[1]), max(xyz[0]), min(xyz[1])
        else:
            xyz = tuple(zip(*list(explode(geometry["coordinates"]))))
            if north_up:
                return min(xyz[0]), min(xyz[1]), max(xyz[0]), max(xyz[1])
            else:
                return min(xyz[0]), max(xyz[1]), max(xyz[0]), min(xyz[1])

    # all valid inputs returned above, so whatever falls through is an error
    raise ValueError(
        "geometry must be a GeoJSON-like geometry, GeometryCollection, "
        "or FeatureCollection"
    )

get_census_dict(reset=False)

Returns a dictionary of Census data.

Parameters:

Name	Type	Description	Default
reset	bool	Reset the dictionary. Defaults to False.	False

Returns:

Name	Type	Description
dict		A dictionary of Census data.

Source code in geemap/common.py

def get_census_dict(reset=False):
    """Returns a dictionary of Census data.

    Args:
        reset (bool, optional): Reset the dictionary. Defaults to False.

    Returns:
        dict: A dictionary of Census data.
    """
    pkg_dir = str(importlib.resources.files("geemap").joinpath("geemap.py").parent)
    census_data = os.path.join(pkg_dir, "data/census_data.json")

    if reset:
        try:
            from owslib.wms import WebMapService
        except ImportError:
            raise ImportError(
                'The owslib package must be installed to use this function. Install with "pip install owslib"'
            )

        census_dict = {}

        names = [
            "Current",
            "ACS 2021",
            "ACS 2019",
            "ACS 2018",
            "ACS 2017",
            "ACS 2016",
            "ACS 2015",
            "ACS 2014",
            "ACS 2013",
            "ACS 2012",
            "ECON 2012",
            "Census 2020",
            "Census 2010",
            "Physical Features",
            "Decennial Census 2020",
            "Decennial Census 2010",
            "Decennial Census 2000",
            "Decennial Physical Features",
        ]

        links = {}

        print("Retrieving data. Please wait ...")
        for name in names:
            if "Decennial" not in name:
                links[name] = (
                    f"https://tigerweb.geo.census.gov/arcgis/services/TIGERweb/tigerWMS_{name.replace(' ', '')}/MapServer/WMSServer"
                )
            else:
                links[name] = (
                    f"https://tigerweb.geo.census.gov/arcgis/services/Census2020/tigerWMS_{name.replace('Decennial', '').replace(' ', '')}/MapServer/WMSServer"
                )

            wms = WebMapService(links[name], timeout=300)
            layers = list(wms.contents)
            layers.sort()
            census_dict[name] = {
                "url": links[name],
                "layers": layers,
                # "title": wms.identification.title,
                # "abstract": wms.identification.abstract,
            }

        with open(census_data, "w") as f:
            json.dump(census_dict, f, indent=4)

    else:
        with open(census_data, "r") as f:
            census_dict = json.load(f)

    return census_dict

get_center(geometry, north_up=True, transform=None)

Get the centroid of a GeoJSON.

Parameters:

Name	Type	Description	Default
geometry	dict	A GeoJSON dict.	required
north_up	bool	. Defaults to True.	True
transform	[type]	. Defaults to None.	None

Returns:

Name	Type	Description
list		[lon, lat]

Source code in geemap/common.py

def get_center(geometry, north_up=True, transform=None):
    """Get the centroid of a GeoJSON.

    Args:
        geometry (dict): A GeoJSON dict.
        north_up (bool, optional): . Defaults to True.
        transform ([type], optional): . Defaults to None.

    Returns:
        list: [lon, lat]
    """
    bounds = get_bounds(geometry, north_up, transform)
    center = ((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2)  # (lat, lon)
    return center

get_current_latlon()

Get the current latitude and longitude based on the user's location.

Source code in geemap/common.py

def get_current_latlon():
    """Get the current latitude and longitude based on the user's location."""
    import geocoder

    g = geocoder.ip("me")
    props = g.geojson["features"][0]["properties"]
    lat = props["lat"]
    lon = props["lng"]
    return lat, lon

get_current_year()

Get the current year.

Returns:

Name	Type	Description
int		The current year.

Source code in geemap/common.py

def get_current_year():
    """Get the current year.

    Returns:
        int: The current year.
    """
    today = datetime.date.today()
    return today.year

get_direct_url(url)

Get the direct URL for a given URL.

Parameters:

Name	Type	Description	Default
url	str	The URL to get the direct URL for.	required

Returns:

Name	Type	Description
str		The direct URL.

Source code in geemap/common.py

def get_direct_url(url):
    """Get the direct URL for a given URL.

    Args:
        url (str): The URL to get the direct URL for.

    Returns:
        str: The direct URL.
    """

    if not isinstance(url, str):
        raise ValueError("url must be a string.")

    if not url.startswith("http"):
        raise ValueError("url must start with http.")

    r = requests.head(url, allow_redirects=True)
    return r.url

get_ee_token()

Get Earth Engine token.

Returns:

Name	Type	Description
dict		The Earth Engine token.

Source code in geemap/common.py

def get_ee_token():
    """Get Earth Engine token.

    Returns:
        dict: The Earth Engine token.
    """
    credential_file_path = os.path.expanduser("~/.config/earthengine/credentials")

    if os.path.exists(credential_file_path):
        with open(credential_file_path, "r") as f:
            credentials = json.load(f)
            return credentials
    else:
        print("Earth Engine credentials not found. Please run ee.Authenticate()")
        return None

get_env_var(key)

Retrieves an environment variable or Colab secret for the given key.

Colab secrets have precedence over environment variables.

Parameters:

Name	Type	Description	Default
key	str	The key that's used to fetch the environment variable.	required

Returns:

Type	Description
Optional[str]	Optional[str]: The retrieved key, or None if no environment variable was found.

Source code in geemap/coreutils.py

def get_env_var(key: str) -> Optional[str]:
    """Retrieves an environment variable or Colab secret for the given key.

    Colab secrets have precedence over environment variables.

    Args:
        key (str): The key that's used to fetch the environment variable.

    Returns:
        Optional[str]: The retrieved key, or None if no environment variable was found.
    """
    if not key:
        return None

    if in_colab_shell():
        from google.colab import userdata

        try:
            return userdata.get(key)
        except (userdata.SecretNotFoundError, userdata.NotebookAccessError):
            pass

    return os.environ.get(key)

get_geometry_coords(row, geom, coord_type, shape_type, mercator=False)

Returns the coordinates ('x' or 'y') of edges of a Polygon exterior.

:param: (GeoPandas Series) row : The row of each of the GeoPandas DataFrame. :param: (str) geom : The column name. :param: (str) coord_type : Whether it's 'x' or 'y' coordinate. :param: (str) shape_type

Source code in geemap/common.py

def get_geometry_coords(row, geom, coord_type, shape_type, mercator=False):
    """
    Returns the coordinates ('x' or 'y') of edges of a Polygon exterior.

    :param: (GeoPandas Series) row : The row of each of the GeoPandas DataFrame.
    :param: (str) geom : The column name.
    :param: (str) coord_type : Whether it's 'x' or 'y' coordinate.
    :param: (str) shape_type
    """

    # Parse the exterior of the coordinate
    if shape_type.lower() in ["polygon", "multipolygon"]:
        exterior = row[geom].geoms[0].exterior
        if coord_type == "x":
            # Get the x coordinates of the exterior
            coords = list(exterior.coords.xy[0])
            if mercator:
                coords = [lnglat_to_meters(x, 0)[0] for x in coords]
            return coords

        elif coord_type == "y":
            # Get the y coordinates of the exterior
            coords = list(exterior.coords.xy[1])
            if mercator:
                coords = [lnglat_to_meters(0, y)[1] for y in coords]
            return coords

    elif shape_type.lower() in ["linestring", "multilinestring"]:
        if coord_type == "x":
            coords = list(row[geom].coords.xy[0])
            if mercator:
                coords = [lnglat_to_meters(x, 0)[0] for x in coords]
            return coords
        elif coord_type == "y":
            coords = list(row[geom].coords.xy[1])
            if mercator:
                coords = [lnglat_to_meters(0, y)[1] for y in coords]
            return coords

    elif shape_type.lower() in ["point", "multipoint"]:
        exterior = row[geom]

        if coord_type == "x":
            # Get the x coordinates of the exterior
            coords = exterior.coords.xy[0][0]
            if mercator:
                coords = lnglat_to_meters(coords, 0)[0]
            return coords

        elif coord_type == "y":
            # Get the y coordinates of the exterior
            coords = exterior.coords.xy[1][0]
            if mercator:
                coords = lnglat_to_meters(0, coords)[1]
            return coords

get_google_maps_api_key(key='GOOGLE_MAPS_API_KEY')

Retrieves the Google Maps API key from the environment or Colab user data.

Parameters:

Name	Type	Description	Default
key	str	The name of the environment variable or Colab user data key where the API key is stored. Defaults to 'GOOGLE_MAPS_API_KEY'.	'GOOGLE_MAPS_API_KEY'

Returns:

Name	Type	Description
str	Optional[str]	The API key, or None if it could not be found.

Source code in geemap/coreutils.py

def get_google_maps_api_key(key: str = "GOOGLE_MAPS_API_KEY") -> Optional[str]:
    """
    Retrieves the Google Maps API key from the environment or Colab user data.

    Args:
        key (str, optional): The name of the environment variable or Colab user
            data key where the API key is stored. Defaults to
            'GOOGLE_MAPS_API_KEY'.

    Returns:
        str: The API key, or None if it could not be found.
    """
    if api_key := get_env_var(key):
        return api_key
    return os.environ.get(key, None)

get_image_collection_thumbnails(ee_object, out_dir, vis_params, dimensions=500, region=None, format='jpg', names=None, verbose=True, timeout=300, proxies=None)

Download thumbnails for all images in an ImageCollection.

Parameters:

Name	Type	Description	Default
ee_object	object	The ee.ImageCollection instance.	required
out_dir	[str	The output directory to store thumbnails.	required
vis_params	dict	The visualization parameters.	required
dimensions	int	(a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 500.	500
region	object	Geospatial region of the image to render, it may be an ee.Geometry, GeoJSON, or an array of lat/lon points (E,S,W,N). If not set the default is the bounds image. Defaults to None.	None
format	str	Either 'png' or 'jpg'. Default to 'jpg'.	'jpg'
names	list	The list of output file names. Defaults to None.	None
verbose	bool	Whether or not to print hints. Defaults to True.	True
timeout	int	The number of seconds after which the request will be terminated. Defaults to 300.	300
proxies	dict	A dictionary of proxy servers to use for the request. Defaults to None.	None

Source code in geemap/common.py

def get_image_collection_thumbnails(
    ee_object,
    out_dir,
    vis_params,
    dimensions=500,
    region=None,
    format="jpg",
    names=None,
    verbose=True,
    timeout=300,
    proxies=None,
):
    """Download thumbnails for all images in an ImageCollection.

    Args:
        ee_object (object): The ee.ImageCollection instance.
        out_dir ([str): The output directory to store thumbnails.
        vis_params (dict): The visualization parameters.
        dimensions (int, optional):(a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 500.
        region (object, optional): Geospatial region of the image to render, it may be an ee.Geometry, GeoJSON, or an array of lat/lon points (E,S,W,N). If not set the default is the bounds image. Defaults to None.
        format (str, optional): Either 'png' or 'jpg'. Default to 'jpg'.
        names (list, optional): The list of output file names. Defaults to None.
        verbose (bool, optional): Whether or not to print hints. Defaults to True.
        timeout (int, optional): The number of seconds after which the request will be terminated. Defaults to 300.
        proxies (dict, optional): A dictionary of proxy servers to use for the request. Defaults to None.
    """
    if not isinstance(ee_object, ee.ImageCollection):
        print("The ee_object must be an ee.ImageCollection.")
        raise TypeError("The ee_object must be an ee.Image.")

    if format not in ["png", "jpg"]:
        raise ValueError("The output image format must be png or jpg.")

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

    try:
        count = int(ee_object.size().getInfo())
        if verbose:
            print(f"Total number of images: {count}\n")

        if (names is not None) and (len(names) != count):
            print("The number of names is not equal to the number of images.")
            return

        if names is None:
            names = ee_object.aggregate_array("system:index").getInfo()

        images = ee_object.toList(count)

        for i in range(0, count):
            image = ee.Image(images.get(i))
            name = str(names[i])
            ext = os.path.splitext(name)[1][1:]
            if ext != format:
                name = name + "." + format
            out_img = os.path.join(out_dir, name)
            if verbose:
                print(f"Downloading {i+1}/{count}: {name} ...")

            get_image_thumbnail(
                image,
                out_img,
                vis_params,
                dimensions,
                region,
                format,
                timeout=timeout,
                proxies=proxies,
            )

    except Exception as e:
        print(e)

get_image_thumbnail(ee_object, out_img, vis_params, dimensions=500, region=None, format='jpg', crs='EPSG:3857', timeout=300, proxies=None)

Download a thumbnail for an ee.Image.

Parameters:

Name	Type	Description	Default
ee_object	object	The ee.Image instance.	required
out_img	str	The output file path to the png thumbnail.	required
vis_params	dict	The visualization parameters.	required
dimensions	int	(a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 500.	500
region	object	Geospatial region of the image to render, it may be an ee.Geometry, GeoJSON, or an array of lat/lon points (E,S,W,N). If not set the default is the bounds image. Defaults to None.	None
format	str	Either 'png' or 'jpg'. Default to 'jpg'.	'jpg'
timeout	int	The number of seconds after which the request will be terminated. Defaults to 300.	300
proxies	dict	A dictionary of proxy servers to use for the request. Defaults to None.	None

Source code in geemap/common.py

def get_image_thumbnail(
    ee_object,
    out_img,
    vis_params,
    dimensions=500,
    region=None,
    format="jpg",
    crs="EPSG:3857",
    timeout=300,
    proxies=None,
):
    """Download a thumbnail for an ee.Image.

    Args:
        ee_object (object): The ee.Image instance.
        out_img (str): The output file path to the png thumbnail.
        vis_params (dict): The visualization parameters.
        dimensions (int, optional):(a number or pair of numbers in format WIDTHxHEIGHT) Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 500.
        region (object, optional): Geospatial region of the image to render, it may be an ee.Geometry, GeoJSON, or an array of lat/lon points (E,S,W,N). If not set the default is the bounds image. Defaults to None.
        format (str, optional): Either 'png' or 'jpg'. Default to 'jpg'.
        timeout (int, optional): The number of seconds after which the request will be terminated. Defaults to 300.
        proxies (dict, optional): A dictionary of proxy servers to use for the request. Defaults to None.
    """

    if not isinstance(ee_object, ee.Image):
        raise TypeError("The ee_object must be an ee.Image.")

    ext = os.path.splitext(out_img)[1][1:]
    if ext not in ["png", "jpg"]:
        raise ValueError("The output image format must be png or jpg.")
    else:
        format = ext

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

    if region is not None:
        vis_params["region"] = region

    vis_params["dimensions"] = dimensions
    vis_params["format"] = format
    vis_params["crs"] = crs
    url = ee_object.getThumbURL(vis_params)

    try:
        r = requests.get(url, stream=True, timeout=timeout, proxies=proxies)
    except Exception as e:
        print("An error occurred while downloading.")
        print(e)

    if r.status_code != 200:
        print("An error occurred while downloading.")
        print(r.json()["error"]["message"])

    else:
        with open(out_img, "wb") as fd:
            for chunk in r.iter_content(chunk_size=1024):
                fd.write(chunk)

get_info(ee_object, layer_name='', opened=False, return_node=False)

Print out the information for an Earth Engine object using a tree structure. The source code was adapted from https://github.com/google/earthengine-jupyter. Credits to Tyler Erickson.

Parameters:

Name	Type	Description	Default
ee_object	Union[FeatureCollection, Image, Geometry, Feature]	The Earth Engine object.	required
layer_name	str	The name of the layer. Defaults to "".	''
opened	bool	Whether to expand the tree. Defaults to False.	False
return_node	bool	Whether to return the widget as ipytree.Node. If False, returns the widget as ipytree.Tree. Defaults to False.	False

Returns:

Type	Description
Union[Node, Tree, None]	Union[Node, Tree, None]: The tree or node representing the Earth Engine object information.

Source code in geemap/coreutils.py

def get_info(
    ee_object: Union[ee.FeatureCollection, ee.Image, ee.Geometry, ee.Feature],
    layer_name: str = "",
    opened: bool = False,
    return_node: bool = False,
) -> Union[Node, Tree, None]:
    """Print out the information for an Earth Engine object using a tree structure.
    The source code was adapted from https://github.com/google/earthengine-jupyter.
    Credits to Tyler Erickson.

    Args:
        ee_object (Union[ee.FeatureCollection, ee.Image, ee.Geometry, ee.Feature]):
            The Earth Engine object.
        layer_name (str, optional): The name of the layer. Defaults to "".
        opened (bool, optional): Whether to expand the tree. Defaults to False.
        return_node (bool, optional): Whether to return the widget as ipytree.Node.
            If False, returns the widget as ipytree.Tree. Defaults to False.

    Returns:
        Union[Node, Tree, None]: The tree or node representing the Earth Engine
            object information.
    """

    tree_json = build_computed_object_tree(ee_object, layer_name, opened)

    def _create_node(data):
        """Create a widget for the computed object tree."""
        node = Node(data.get("label", "Node"), opened=data.get("expanded", False))
        if children := data.get("children"):
            for child in children:
                node.add_node(_create_node(child))
        else:
            node.icon = "file"
            node.value = str(data)  # Store the entire data as a string
        return node

    root_node = _create_node(tree_json)
    if return_node:
        return root_node
    else:
        tree = Tree()
        tree.add_node(root_node)
        return tree

get_local_tile_layer(source, port='default', debug=False, indexes=None, colormap=None, vmin=None, vmax=None, nodata=None, attribution=None, tile_format='ipyleaflet', layer_name='Local COG', return_client=False, quiet=False, **kwargs)

Generate an ipyleaflet/folium TileLayer from a local raster dataset or remote Cloud Optimized GeoTIFF (COG). If you are using this function in JupyterHub on a remote server and the raster does not render properly, try running the following two lines before calling this function:

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'
return_client	bool	If True, the tile client will be returned. Defaults to False.	False
quiet	bool	If True, the error messages will be suppressed. Defaults to False.	False

Returns:

Type	Description
	ipyleaflet.TileLayer | folium.TileLayer: An ipyleaflet.TileLayer or folium.TileLayer.

Source code in geemap/common.py

def get_local_tile_layer(
    source,
    port="default",
    debug=False,
    indexes=None,
    colormap=None,
    vmin=None,
    vmax=None,
    nodata=None,
    attribution=None,
    tile_format="ipyleaflet",
    layer_name="Local COG",
    return_client=False,
    quiet=False,
    **kwargs,
):
    """Generate an ipyleaflet/folium TileLayer from a local raster dataset or remote Cloud Optimized GeoTIFF (COG).
        If you are using this function in JupyterHub on a remote server and the raster does not render properly, try
        running the following two lines before calling this function:

        import os
        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'

    Args:
        source (str): The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.
        port (str, optional): The port to use for the server. Defaults to "default".
        debug (bool, optional): If True, the server will be started in debug mode. Defaults to False.
        indexes (int, optional): The band(s) to use. Band indexing starts at 1. Defaults to None.
        colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
        vmin (float, optional): The minimum value to use when colormapping the colormap when plotting a single band. Defaults to None.
        vmax (float, optional): The maximum value to use when colormapping the colormap when plotting a single band. Defaults to None.
        nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
        attribution (str, optional): Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.
        tile_format (str, optional): The tile layer format. Can be either ipyleaflet or folium. Defaults to "ipyleaflet".
        layer_name (str, optional): The layer name to use. Defaults to None.
        return_client (bool, optional): If True, the tile client will be returned. Defaults to False.
        quiet (bool, optional): If True, the error messages will be suppressed. Defaults to False.

    Returns:
        ipyleaflet.TileLayer | folium.TileLayer: An ipyleaflet.TileLayer or folium.TileLayer.
    """
    import rasterio

    check_package(
        "localtileserver", URL="https://github.com/banesullivan/localtileserver"
    )

    # Handle legacy localtileserver kwargs
    if "cmap" in kwargs:
        warnings.warn(
            "`cmap` is a deprecated keyword argument for get_local_tile_layer. Please use `colormap`."
        )
    if "palette" in kwargs:
        warnings.warn(
            "`palette` is a deprecated keyword argument for get_local_tile_layer. Please use `colormap`."
        )
    if "band" in kwargs or "bands" in kwargs:
        warnings.warn(
            "`band` and `bands` are deprecated keyword arguments for get_local_tile_layer. Please use `indexes`."
        )
    if "projection" in kwargs:
        warnings.warn(
            "`projection` is a deprecated keyword argument for get_local_tile_layer and will be ignored."
        )
    if "style" in kwargs:
        warnings.warn(
            "`style` is a deprecated keyword argument for get_local_tile_layer and will be ignored."
        )

    if "max_zoom" not in kwargs:
        kwargs["max_zoom"] = 30
    if "max_native_zoom" not in kwargs:
        kwargs["max_native_zoom"] = 30
    if "cmap" in kwargs:
        colormap = kwargs.pop("cmap")
    if "palette" in kwargs:
        colormap = kwargs.pop("palette")
    if "band" in kwargs:
        indexes = kwargs.pop("band")
    if "bands" in kwargs:
        indexes = kwargs.pop("bands")

    # Make it compatible with binder and JupyterHub
    if os.environ.get("JUPYTERHUB_SERVICE_PREFIX") is not None:
        os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = (
            f"{os.environ['JUPYTERHUB_SERVICE_PREFIX'].lstrip('/')}/proxy/{{port}}"
        )

    if is_studio_lab():
        os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = (
            f"studiolab/default/jupyter/proxy/{{port}}"
        )
    elif is_on_aws():
        os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = "proxy/{port}"
    elif "prefix" in kwargs:
        os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = kwargs["prefix"]
        kwargs.pop("prefix")

    from localtileserver import (
        get_leaflet_tile_layer,
        get_folium_tile_layer,
        TileClient,
    )

    # if "show_loading" not in kwargs:
    #     kwargs["show_loading"] = False

    if isinstance(source, str):
        if not source.startswith("http"):
            if source.startswith("~"):
                source = os.path.expanduser(source)
            # else:
            #     source = os.path.abspath(source)
            # if not os.path.exists(source):
            #     raise ValueError("The source path does not exist.")
        else:
            source = github_raw_url(source)
    elif isinstance(source, TileClient) or isinstance(
        source, rasterio.io.DatasetReader
    ):
        pass

    else:
        raise ValueError("The source must either be a string or TileClient")

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    if layer_name is None:
        if source.startswith("http"):
            layer_name = "RemoteTile_" + random_string(3)
        else:
            layer_name = "LocalTile_" + random_string(3)

    if isinstance(source, str) or isinstance(source, rasterio.io.DatasetReader):
        tile_client = TileClient(source, port=port, debug=debug)
    else:
        tile_client = source

    if quiet:
        output = widgets.Output()
        with output:
            if tile_format == "ipyleaflet":
                tile_layer = get_leaflet_tile_layer(
                    tile_client,
                    port=port,
                    debug=debug,
                    indexes=indexes,
                    colormap=colormap,
                    vmin=vmin,
                    vmax=vmax,
                    nodata=nodata,
                    attribution=attribution,
                    name=layer_name,
                    **kwargs,
                )
            else:
                tile_layer = get_folium_tile_layer(
                    tile_client,
                    port=port,
                    debug=debug,
                    indexes=indexes,
                    colormap=colormap,
                    vmin=vmin,
                    vmax=vmax,
                    nodata=nodata,
                    attr=attribution,
                    overlay=True,
                    name=layer_name,
                    **kwargs,
                )
    else:
        if tile_format == "ipyleaflet":
            tile_layer = get_leaflet_tile_layer(
                tile_client,
                port=port,
                debug=debug,
                indexes=indexes,
                colormap=colormap,
                vmin=vmin,
                vmax=vmax,
                nodata=nodata,
                attribution=attribution,
                name=layer_name,
                **kwargs,
            )
        else:
            tile_layer = get_folium_tile_layer(
                tile_client,
                port=port,
                debug=debug,
                indexes=indexes,
                colormap=colormap,
                vmin=vmin,
                vmax=vmax,
                nodata=nodata,
                attr=attribution,
                overlay=True,
                name=layer_name,
                **kwargs,
            )

    if return_client:
        return tile_layer, tile_client
    else:
        return tile_layer

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 geemap/common.py

def get_palettable(types=None):
    """Get a list of palettable color palettes.

    Args:
        types (list, optional): A list of palettable types to return, e.g., types=['matplotlib', 'cartocolors']. Defaults to None.

    Returns:
        list: A list of palettable color palettes.
    """
    try:
        import palettable
    except ImportError:
        raise ImportError(
            "The palettable package is not installed. Please install it with `pip install palettable`."
        )

    if types is not None and (not isinstance(types, list)):
        raise ValueError("The types must be a list.")

    allowed_palettes = [
        "cartocolors",
        "cmocean",
        "colorbrewer",
        "cubehelix",
        "lightbartlein",
        "matplotlib",
        "mycarta",
        "scientific",
        "tableau",
        "wesanderson",
    ]

    if types is None:
        types = allowed_palettes[:]

    if all(x in allowed_palettes for x in types):
        pass
    else:
        raise ValueError(
            "The types must be one of the following: " + ", ".join(allowed_palettes)
        )

    palettes = []

    if "cartocolors" in types:
        cartocolors_diverging = [
            f"cartocolors.diverging.{c}"
            for c in dir(palettable.cartocolors.diverging)[:-19]
        ]
        cartocolors_qualitative = [
            f"cartocolors.qualitative.{c}"
            for c in dir(palettable.cartocolors.qualitative)[:-19]
        ]
        cartocolors_sequential = [
            f"cartocolors.sequential.{c}"
            for c in dir(palettable.cartocolors.sequential)[:-41]
        ]

        palettes = (
            palettes
            + cartocolors_diverging
            + cartocolors_qualitative
            + cartocolors_sequential
        )

    if "cmocean" in types:
        cmocean_diverging = [
            f"cmocean.diverging.{c}" for c in dir(palettable.cmocean.diverging)[:-19]
        ]
        cmocean_sequential = [
            f"cmocean.sequential.{c}" for c in dir(palettable.cmocean.sequential)[:-19]
        ]

        palettes = palettes + cmocean_diverging + cmocean_sequential

    if "colorbrewer" in types:
        colorbrewer_diverging = [
            f"colorbrewer.diverging.{c}"
            for c in dir(palettable.colorbrewer.diverging)[:-19]
        ]
        colorbrewer_qualitative = [
            f"colorbrewer.qualitative.{c}"
            for c in dir(palettable.colorbrewer.qualitative)[:-19]
        ]
        colorbrewer_sequential = [
            f"colorbrewer.sequential.{c}"
            for c in dir(palettable.colorbrewer.sequential)[:-41]
        ]

        palettes = (
            palettes
            + colorbrewer_diverging
            + colorbrewer_qualitative
            + colorbrewer_sequential
        )

    if "cubehelix" in types:
        cubehelix = [
            "classic_16",
            "cubehelix1_16",
            "cubehelix2_16",
            "cubehelix3_16",
            "jim_special_16",
            "perceptual_rainbow_16",
            "purple_16",
            "red_16",
        ]
        cubehelix = [f"cubehelix.{c}" for c in cubehelix]
        palettes = palettes + cubehelix

    if "lightbartlein" in types:
        lightbartlein_diverging = [
            f"lightbartlein.diverging.{c}"
            for c in dir(palettable.lightbartlein.diverging)[:-19]
        ]
        lightbartlein_sequential = [
            f"lightbartlein.sequential.{c}"
            for c in dir(palettable.lightbartlein.sequential)[:-19]
        ]

        palettes = palettes + lightbartlein_diverging + lightbartlein_sequential

    if "matplotlib" in types:
        matplotlib_colors = [
            f"matplotlib.{c}" for c in dir(palettable.matplotlib)[:-16]
        ]
        palettes = palettes + matplotlib_colors

    if "mycarta" in types:
        mycarta = [f"mycarta.{c}" for c in dir(palettable.mycarta)[:-16]]
        palettes = palettes + mycarta

    if "scientific" in types:
        scientific_diverging = [
            f"scientific.diverging.{c}"
            for c in dir(palettable.scientific.diverging)[:-19]
        ]
        scientific_sequential = [
            f"scientific.sequential.{c}"
            for c in dir(palettable.scientific.sequential)[:-19]
        ]

        palettes = palettes + scientific_diverging + scientific_sequential

    if "tableau" in types:
        tableau = [f"tableau.{c}" for c in dir(palettable.tableau)[:-14]]
        palettes = palettes + tableau

    return palettes

get_palette_colors(cmap_name=None, n_class=None, hashtag=False)

Get a palette from a matplotlib colormap. See the list of colormaps at https://matplotlib.org/stable/tutorials/colors/colormaps.html.

Parameters:

Name	Type	Description	Default
cmap_name	str	The name of the matplotlib colormap. Defaults to None.	None
n_class	int	The number of colors. Defaults to None.	None
hashtag	bool	Whether to return a list of hex colors. Defaults to False.	False

Returns:

Name	Type	Description
list		A list of hex colors.

Source code in geemap/common.py

def get_palette_colors(cmap_name=None, n_class=None, hashtag=False):
    """Get a palette from a matplotlib colormap. See the list of colormaps at https://matplotlib.org/stable/tutorials/colors/colormaps.html.

    Args:
        cmap_name (str, optional): The name of the matplotlib colormap. Defaults to None.
        n_class (int, optional): The number of colors. Defaults to None.
        hashtag (bool, optional): Whether to return a list of hex colors. Defaults to False.

    Returns:
        list: A list of hex colors.
    """
    import matplotlib as mpl
    import matplotlib.pyplot as plt

    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_temp_dir()

Returns the temporary directory.

Returns:

Name	Type	Description
str		The temporary directory.

Source code in geemap/common.py

def get_temp_dir():
    """Returns the temporary directory.

    Returns:
        str: The temporary directory.
    """

    import tempfile

    return tempfile.gettempdir()

get_wms_layers(url, return_titles=False)

Returns a list of WMS layers from a WMS service.

Parameters:

Name	Type	Description	Default
url	str	The URL of the WMS service.	required
return_titles	bool	If True, the titles of the layers will be returned. Defaults to False.	False

Returns:

Name	Type	Description
list		A list of WMS layers.

Source code in geemap/common.py

def get_wms_layers(url, return_titles=False):
    """Returns a list of WMS layers from a WMS service.

    Args:
        url (str): The URL of the WMS service.
        return_titles (bool, optional): If True, the titles of the layers will be returned. Defaults to False.

    Returns:
        list: A list of WMS layers.
    """
    from owslib.wms import WebMapService

    wms = WebMapService(url)
    layers = list(wms.contents)
    layers.sort()
    if return_titles:
        return layers, [wms[layer].title for layer in layers]
    else:
        return layers

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 geemap/timelapse.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}"

    # if fade >= duration:
    #     duration = fade + 1

    # files = []
    # for i in range(1, count + 1):
    #     files.append(f"-framerate {framerate} -loop 1 -t {duration} -i {i}.png")

    # inputs = " ".join(files)

    # filters = []
    # for i in range(count):
    #     if i == 0:
    #         filters.append(f'"[0:v]fade=t=out:st=4:d={fade}[v0];')
    #     else:
    #         filters.append(
    #             f"[{i}:v]fade=t=in:st=0:d={fade},fade=t=out:st=4:d={fade}[v{i}];"
    #         )

    # last_filter = ""
    # for i in range(count):
    #     last_filter += f"[v{i}]"
    # last_filter += f"concat=n={count}:v=1:a=0,split[v0][v1];"
    # filters.append(last_filter)
    # palette = f'[v0]palettegen[p];[v1][p]paletteuse[v]" -map "[v]"'
    # filters.append(palette)
    # filters = " ".join(filters)

    # cmd = f"ffmpeg -y {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 geemap/timelapse.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 geemap/timelapse.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_raw_url(url)

Get the raw URL for a GitHub file.

Parameters:

Name	Type	Description	Default
url	str	The GitHub URL.	required

Returns:

Name	Type	Description
str	str	The raw URL.

Source code in geemap/coreutils.py

def github_raw_url(url: str) -> str:
    """Get the raw URL for a GitHub file.

    Args:
        url (str): The GitHub URL.

    Returns:
        str: The raw URL.
    """
    if isinstance(url, str) and url.startswith("https://github.com/") and "blob" in url:
        url = url.replace("github.com", "raw.githubusercontent.com").replace(
            "blob/", "", 1
        )
    return url

goes_fire_timelapse(roi=None, out_gif=None, start_date='2020-09-05T15:00', end_date='2020-09-06T02:00', data='GOES-17', scan='full_disk', dimensions=768, framesPerSecond=10, date_format='YYYY-MM-dd HH:mm', xy=('3%', '3%'), text_sequence=None, font_type='arial.ttf', font_size=20, font_color='#ffffff', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, loop=0, crs=None, overlay_data=None, overlay_color='#000000', overlay_width=1, overlay_opacity=1.0, mp4=False, fading=False, **kwargs)

Create a timelapse of GOES fire data. The code is adapted from Justin Braaten's code: https://code.earthengine.google.com/8a083a7fb13b95ad4ba148ed9b65475e. Credits to Justin Braaten. See also https://jstnbraaten.medium.com/goes-in-earth-engine-53fbc8783c16

Parameters:

Name	Type	Description	Default
out_gif	str	The file path to save the gif.	None
start_date	str	The start date of the time series. Defaults to "2021-10-24T14:00:00".	'2020-09-05T15:00'
end_date	str	The end date of the time series. Defaults to "2021-10-25T01:00:00".	'2020-09-06T02:00'
data	str	The GOES satellite data to use. Defaults to "GOES-17".	'GOES-17'
scan	str	The GOES scan to use. Defaults to "full_disk".	'full_disk'
region	Geometry	The region of interest. Defaults to None.	required
dimensions	int	a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.	768
frames_per_second	int	Animation speed. Defaults to 10.	required
date_format	str	The date format to use. Defaults to "YYYY-MM-dd HH:mm".	'YYYY-MM-dd HH:mm'
xy	tuple	Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('3%', '3%')
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'.	'#ffffff'
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
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
crs	str	The coordinate reference system to use, e.g., "EPSG:3857". Defaults to None.	None
overlay_data	(int, str, list)	Administrative boundary to be drawn on the timelapse. Defaults to None.	None
overlay_color	str	Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.	'#000000'
overlay_width	int	Width of the overlay. Defaults to 1.	1
overlay_opacity	float	Opacity of the overlay. Defaults to 1.0.	1.0
mp4	bool	Whether to convert the GIF to MP4. Defaults to False.	False
fading	int | bool	If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).	False

Raises:

Type	Description
Exception	Raise exception.

Source code in geemap/timelapse.py

def goes_fire_timelapse(
    roi=None,
    out_gif=None,
    start_date="2020-09-05T15:00",
    end_date="2020-09-06T02:00",
    data="GOES-17",
    scan="full_disk",
    dimensions=768,
    framesPerSecond=10,
    date_format="YYYY-MM-dd HH:mm",
    xy=("3%", "3%"),
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="#ffffff",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    loop=0,
    crs=None,
    overlay_data=None,
    overlay_color="#000000",
    overlay_width=1,
    overlay_opacity=1.0,
    mp4=False,
    fading=False,
    **kwargs,
):
    """Create a timelapse of GOES fire data. The code is adapted from Justin Braaten's code: https://code.earthengine.google.com/8a083a7fb13b95ad4ba148ed9b65475e.
    Credits to Justin Braaten. See also https://jstnbraaten.medium.com/goes-in-earth-engine-53fbc8783c16

    Args:
        out_gif (str): The file path to save the gif.
        start_date (str, optional): The start date of the time series. Defaults to "2021-10-24T14:00:00".
        end_date (str, optional): The end date of the time series. Defaults to "2021-10-25T01:00:00".
        data (str, optional): The GOES satellite data to use. Defaults to "GOES-17".
        scan (str, optional): The GOES scan to use. Defaults to "full_disk".
        region (ee.Geometry, optional): The region of interest. Defaults to None.
        dimensions (int, optional): a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
        frames_per_second (int, optional): Animation speed. Defaults to 10.
        date_format (str, optional): The date format to use. Defaults to "YYYY-MM-dd HH:mm".
        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.
        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.
        crs (str, optional): The coordinate reference system to use, e.g., "EPSG:3857". Defaults to None.
        overlay_data (int, str, list, optional): Administrative boundary to be drawn on the timelapse. Defaults to None.
        overlay_color (str, optional): Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.
        overlay_width (int, optional): Width of the overlay. Defaults to 1.
        overlay_opacity (float, optional): Opacity of the overlay. Defaults to 1.0.
        mp4 (bool, optional): Whether to convert the GIF to MP4. Defaults to False.
        fading (int | bool, optional): If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).

    Raises:
        Exception: Raise exception.
    """

    try:
        if "region" in kwargs:
            roi = kwargs["region"]

        if out_gif is None:
            out_gif = os.path.abspath(f"goes_fire_{random_string(3)}.gif")

        if roi is None:
            roi = ee.Geometry.BBox(-123.17, 36.56, -118.22, 40.03)

        col = goes_fire_timeseries(start_date, end_date, data, scan, roi)
        if overlay_data is not None:
            col = add_overlay(
                col, overlay_data, overlay_color, overlay_width, overlay_opacity
            )

        # visParams = {
        #     "bands": ["CMI_C02", "CMI_GREEN", "CMI_C01"],
        #     "min": 0,
        #     "max": 0.8,
        #     "dimensions": dimensions,
        #     "framesPerSecond": framesPerSecond,
        #     "region": region,
        #     "crs": col.first().projection(),
        # }

        if crs is None:
            crs = col.first().projection()

        cmiFdcVisParams = {
            "dimensions": dimensions,
            "framesPerSecond": framesPerSecond,
            "region": roi,
            "crs": crs,
        }

        if text_sequence is None:
            text_sequence = image_dates(col, date_format=date_format).getInfo()

        download_ee_video(col, cmiFdcVisParams, out_gif)

        if os.path.exists(out_gif):
            add_text_to_gif(
                out_gif,
                out_gif,
                xy,
                text_sequence,
                font_type,
                font_size,
                font_color,
                add_progress_bar,
                progress_bar_color,
                progress_bar_height,
                duration=1000 / framesPerSecond,
                loop=loop,
            )

            try:
                reduce_gif_size(out_gif)
                if isinstance(fading, bool):
                    fading = int(fading)
                if fading > 0:
                    gif_fading(out_gif, out_gif, duration=fading, verbose=False)

            except Exception as _:
                pass

            if mp4:
                out_mp4 = out_gif.replace(".gif", ".mp4")
                gif_to_mp4(out_gif, out_mp4)

            return out_gif

    except Exception as e:
        raise Exception(e)

goes_fire_timeseries(start_date='2020-09-05T15:00', end_date='2020-09-06T02:00', data='GOES-17', scan='full_disk', region=None, merge=True)

Create a time series of GOES Fire data. The code is adapted from Justin Braaten's code: https://code.earthengine.google.com/8a083a7fb13b95ad4ba148ed9b65475e. Credits to Justin Braaten. See also https://jstnbraaten.medium.com/goes-in-earth-engine-53fbc8783c16

Parameters:

Name	Type	Description	Default
start_date	str	The start date of the time series. Defaults to "2020-09-05T15:00".	'2020-09-05T15:00'
end_date	str	The end date of the time series. Defaults to "2020-09-06T02:00".	'2020-09-06T02:00'
data	str	The GOES satellite data to use. Defaults to "GOES-17".	'GOES-17'
scan	str	The GOES scan to use. Defaults to "full_disk".	'full_disk'
region	Geometry	The region of interest. Defaults to None.	None
merge	bool	Whether to merge the fire timeseries with GOES CMI timeseries. Defaults to True.	True

Raises:

Type	Description
ValueError	The data must be either GOES-16 or GOES-17.
ValueError	The scan must be either full_disk or conus.

Returns:

Type	Description
	ee.ImageCollection: GOES fire timeseries.

Source code in geemap/timelapse.py

def goes_fire_timeseries(
    start_date="2020-09-05T15:00",
    end_date="2020-09-06T02:00",
    data="GOES-17",
    scan="full_disk",
    region=None,
    merge=True,
):
    """Create a time series of GOES Fire data. The code is adapted from Justin Braaten's code: https://code.earthengine.google.com/8a083a7fb13b95ad4ba148ed9b65475e.
    Credits to Justin Braaten. See also https://jstnbraaten.medium.com/goes-in-earth-engine-53fbc8783c16

    Args:
        start_date (str, optional): The start date of the time series. Defaults to "2020-09-05T15:00".
        end_date (str, optional): The end date of the time series. Defaults to "2020-09-06T02:00".
        data (str, optional): The GOES satellite data to use. Defaults to "GOES-17".
        scan (str, optional): The GOES scan to use. Defaults to "full_disk".
        region (ee.Geometry, optional): The region of interest. Defaults to None.
        merge (bool, optional): Whether to merge the fire timeseries with GOES CMI timeseries. Defaults to True.

    Raises:
        ValueError: The data must be either GOES-16 or GOES-17.
        ValueError: The scan must be either full_disk or conus.

    Returns:
        ee.ImageCollection: GOES fire timeseries.
    """

    if data not in ["GOES-16", "GOES-17"]:
        raise ValueError("The data must be either GOES-16 or GOES-17.")

    if scan.lower() not in ["full_disk", "conus"]:
        raise ValueError("The scan must be either full_disk or conus.")

    scan_types = {
        "full_disk": "FDCF",
        "conus": "FDCC",
    }

    if region is None:
        region = ee.Geometry.BBox(-123.17, 36.56, -118.22, 40.03)

    # Get the fire/hotspot characterization dataset.
    col = ee.ImageCollection(f"NOAA/GOES/{data[-2:]}/{scan_types[scan.lower()]}")
    fdcCol = col.filterDate(start_date, end_date)

    # Identify fire-detected pixels of medium to high confidence.
    fireMaskCodes = [10, 30, 11, 31, 12, 32, 13, 33, 14, 34, 15, 35]
    confVals = [1.0, 1.0, 0.9, 0.9, 0.8, 0.8, 0.5, 0.5, 0.3, 0.3, 0.1, 0.1]
    defaultConfVal = 0

    def fdcVis(img):
        confImg = img.remap(fireMaskCodes, confVals, defaultConfVal, "Mask")
        return (
            confImg.gte(0.3)
            .selfMask()
            .set("system:time_start", img.get("system:time_start"))
        )

    fdcVisCol = fdcCol.map(fdcVis)
    if not merge:
        return fdcVisCol
    else:
        geosVisCol = goes_timeseries(start_date, end_date, data, scan, region)
        # Join the fire collection to the CMI collection.
        joinFilter = ee.Filter.equals(
            **{"leftField": "system:time_start", "rightField": "system:time_start"}
        )
        joinedCol = ee.Join.saveFirst("match").apply(geosVisCol, fdcVisCol, joinFilter)

        def overlayVis(img):
            cmi = ee.Image(img).visualize(
                **{
                    "bands": ["CMI_C02", "CMI_GREEN", "CMI_C01"],
                    "min": 0,
                    "max": 0.8,
                    "gamma": 0.8,
                }
            )
            fdc = ee.Image(img.get("match")).visualize(
                **{"palette": ["ff5349"], "min": 0, "max": 1, "opacity": 0.7}
            )
            return cmi.blend(fdc).set("system:time_start", img.get("system:time_start"))

        cmiFdcVisCol = ee.ImageCollection(joinedCol.map(overlayVis))
        return cmiFdcVisCol

goes_timelapse(roi=None, out_gif=None, start_date='2021-10-24T14:00:00', end_date='2021-10-25T01:00:00', data='GOES-17', scan='full_disk', bands=['CMI_C02', 'CMI_GREEN', 'CMI_C01'], dimensions=768, framesPerSecond=10, date_format='YYYY-MM-dd HH:mm', xy=('3%', '3%'), text_sequence=None, font_type='arial.ttf', font_size=20, font_color='#ffffff', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, loop=0, crs=None, overlay_data=None, overlay_color='black', overlay_width=1, overlay_opacity=1.0, mp4=False, fading=False, **kwargs)

Create a timelapse of GOES data. The code is adapted from Justin Braaten's code: https://code.earthengine.google.com/57245f2d3d04233765c42fb5ef19c1f4. Credits to Justin Braaten. See also https://jstnbraaten.medium.com/goes-in-earth-engine-53fbc8783c16

Parameters:

Name	Type	Description	Default
roi	Geometry	The region of interest. Defaults to None.	None
out_gif	str	The file path to save the gif.	None
start_date	str	The start date of the time series. Defaults to "2021-10-24T14:00:00".	'2021-10-24T14:00:00'
end_date	str	The end date of the time series. Defaults to "2021-10-25T01:00:00".	'2021-10-25T01:00:00'
data	str	The GOES satellite data to use. Defaults to "GOES-17".	'GOES-17'
scan	str	The GOES scan to use. Defaults to "full_disk".	'full_disk'
bands	list	The bands to visualize. Defaults to ["CMI_C02", "CMI_GREEN", "CMI_C01"].	['CMI_C02', 'CMI_GREEN', 'CMI_C01']
dimensions	int	a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.	768
frames_per_second	int	Animation speed. Defaults to 10.	required
date_format	str	The date format to use. Defaults to "YYYY-MM-dd HH:mm".	'YYYY-MM-dd HH:mm'
xy	tuple	Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('3%', '3%')
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'.	'#ffffff'
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. 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.	5
crs	str	The coordinate reference system to use, e.g., "EPSG:3857". Defaults to None.	None
overlay_data	(int, str, list)	Administrative boundary to be drawn on the timelapse. Defaults to None.	None
overlay_color	str	Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.	'black'
overlay_width	int	Line width of the overlay. Defaults to 1.	1
overlay_opacity	float	Opacity of the overlay. Defaults to 1.0.	1.0
mp4	bool	Whether to save the animation as an mp4 file. Defaults to False.	False
fading	int | bool	If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).	False

Source code in geemap/timelapse.py

def goes_timelapse(
    roi=None,
    out_gif=None,
    start_date="2021-10-24T14:00:00",
    end_date="2021-10-25T01:00:00",
    data="GOES-17",
    scan="full_disk",
    bands=["CMI_C02", "CMI_GREEN", "CMI_C01"],
    dimensions=768,
    framesPerSecond=10,
    date_format="YYYY-MM-dd HH:mm",
    xy=("3%", "3%"),
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="#ffffff",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    loop=0,
    crs=None,
    overlay_data=None,
    overlay_color="black",
    overlay_width=1,
    overlay_opacity=1.0,
    mp4=False,
    fading=False,
    **kwargs,
):
    """Create a timelapse of GOES data. The code is adapted from Justin Braaten's code: https://code.earthengine.google.com/57245f2d3d04233765c42fb5ef19c1f4.
    Credits to Justin Braaten. See also https://jstnbraaten.medium.com/goes-in-earth-engine-53fbc8783c16

    Args:
        roi (ee.Geometry, optional): The region of interest. Defaults to None.
        out_gif (str): The file path to save the gif.
        start_date (str, optional): The start date of the time series. Defaults to "2021-10-24T14:00:00".
        end_date (str, optional): The end date of the time series. Defaults to "2021-10-25T01:00:00".
        data (str, optional): The GOES satellite data to use. Defaults to "GOES-17".
        scan (str, optional): The GOES scan to use. Defaults to "full_disk".
        bands (list, optional): The bands to visualize. Defaults to ["CMI_C02", "CMI_GREEN", "CMI_C01"].
        dimensions (int, optional): a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
        frames_per_second (int, optional): Animation speed. Defaults to 10.
        date_format (str, optional): The date format to use. Defaults to "YYYY-MM-dd HH:mm".
        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.        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.
        crs (str, optional): The coordinate reference system to use, e.g., "EPSG:3857". Defaults to None.
        overlay_data (int, str, list, optional): Administrative boundary to be drawn on the timelapse. Defaults to None.
        overlay_color (str, optional): Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.
        overlay_width (int, optional): Line width of the overlay. Defaults to 1.
        overlay_opacity (float, optional): Opacity of the overlay. Defaults to 1.0.
        mp4 (bool, optional): Whether to save the animation as an mp4 file. Defaults to False.
        fading (int | bool, optional): If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).
    Raises:
        Exception: Raise exception.
    """

    try:
        if "region" in kwargs:
            roi = kwargs["region"]

        if out_gif is None:
            out_gif = os.path.abspath(f"goes_{random_string(3)}.gif")

        visParams = {
            "bands": bands,
            "min": 0,
            "max": 0.8,
        }
        col = goes_timeseries(start_date, end_date, data, scan, roi)
        col = col.select(bands).map(
            lambda img: img.visualize(**visParams).set(
                {
                    "system:time_start": img.get("system:time_start"),
                }
            )
        )
        if overlay_data is not None:
            col = add_overlay(
                col, overlay_data, overlay_color, overlay_width, overlay_opacity
            )

        if roi is None:
            roi = ee.Geometry.Polygon(
                [
                    [
                        [-159.5954, 60.4088],
                        [-159.5954, 24.5178],
                        [-114.2438, 24.5178],
                        [-114.2438, 60.4088],
                    ]
                ],
                None,
                False,
            )

        if crs is None:
            crs = col.first().projection()

        videoParams = {
            "bands": ["vis-red", "vis-green", "vis-blue"],
            "min": 0,
            "max": 255,
            "dimensions": dimensions,
            "framesPerSecond": framesPerSecond,
            "region": roi,
            "crs": crs,
        }

        if text_sequence is None:
            text_sequence = image_dates(col, date_format=date_format).getInfo()

        download_ee_video(col, videoParams, out_gif)

        if os.path.exists(out_gif):
            add_text_to_gif(
                out_gif,
                out_gif,
                xy,
                text_sequence,
                font_type,
                font_size,
                font_color,
                add_progress_bar,
                progress_bar_color,
                progress_bar_height,
                duration=1000 / framesPerSecond,
                loop=loop,
            )

            try:
                reduce_gif_size(out_gif)

                if isinstance(fading, bool):
                    fading = int(fading)
                if fading > 0:
                    gif_fading(out_gif, out_gif, duration=fading, verbose=False)

            except Exception as _:
                pass

            if mp4:
                out_mp4 = out_gif.replace(".gif", ".mp4")
                gif_to_mp4(out_gif, out_mp4)

            return out_gif

    except Exception as e:
        raise Exception(e)

goes_timeseries(start_date='2021-10-24T14:00:00', end_date='2021-10-25T01:00:00', data='GOES-17', scan='full_disk', region=None, show_night=[False, 'a_mode'])

Create a time series of GOES data. The code is adapted from Justin Braaten's code: https://code.earthengine.google.com/57245f2d3d04233765c42fb5ef19c1f4. Credits to Justin Braaten. See also https://jstnbraaten.medium.com/goes-in-earth-engine-53fbc8783c16

Parameters:

Name	Type	Description	Default
start_date	str	The start date of the time series. Defaults to "2021-10-24T14:00:00".	'2021-10-24T14:00:00'
end_date	str	The end date of the time series. Defaults to "2021-10-25T01:00:00".	'2021-10-25T01:00:00'
data	str	The GOES satellite data to use. Defaults to "GOES-17".	'GOES-17'
scan	str	The GOES scan to use. Defaults to "full_disk".	'full_disk'
region	Geometry	The region of interest. Defaults to None.	None
show_night	list	Show the clouds at night through [True, "a_mode"] o [True, "b_mode"]. Defaults to [False, "a_mode"]	[False, 'a_mode']

Returns:

Type	Description
	ee.ImageCollection: GOES timeseries.

Source code in geemap/timelapse.py

def goes_timeseries(
    start_date="2021-10-24T14:00:00",
    end_date="2021-10-25T01:00:00",
    data="GOES-17",
    scan="full_disk",
    region=None,
    show_night=[False, "a_mode"],
):
    """Create a time series of GOES data. The code is adapted from Justin Braaten's code: https://code.earthengine.google.com/57245f2d3d04233765c42fb5ef19c1f4.
    Credits to Justin Braaten. See also https://jstnbraaten.medium.com/goes-in-earth-engine-53fbc8783c16

    Args:
        start_date (str, optional): The start date of the time series. Defaults to "2021-10-24T14:00:00".
        end_date (str, optional): The end date of the time series. Defaults to "2021-10-25T01:00:00".
        data (str, optional): The GOES satellite data to use. Defaults to "GOES-17".
        scan (str, optional): The GOES scan to use. Defaults to "full_disk".
        region (ee.Geometry, optional): The region of interest. Defaults to None.
        show_night (list, optional): Show the clouds at night through [True, "a_mode"] o [True, "b_mode"].  Defaults to [False, "a_mode"]
    Raises:
        ValueError: The data must be either GOES-16 or GOES-17.
        ValueError: The scan must be either full_disk, conus, or mesoscale.

    Returns:
        ee.ImageCollection: GOES timeseries.
    """

    if data not in ["GOES-16", "GOES-17"]:
        raise ValueError("The data must be either GOES-16 or GOES-17.")

    if scan.lower() not in ["full_disk", "conus", "mesoscale"]:
        raise ValueError("The scan must be either full_disk, conus, or mesoscale.")

    scan_types = {
        "full_disk": "MCMIPF",
        "conus": "MCMIPC",
        "mesoscale": "MCMIPM",
    }

    col = ee.ImageCollection(f"NOAA/GOES/{data[-2:]}/{scan_types[scan.lower()]}")

    if region is None:
        region = ee.Geometry.Polygon(
            [
                [
                    [-159.5954379282731, 60.40883060191719],
                    [-159.5954379282731, 24.517881970830725],
                    [-114.2438754282731, 24.517881970830725],
                    [-114.2438754282731, 60.40883060191719],
                ]
            ],
            None,
            False,
        )

    # Applies scaling factors.
    def applyScaleAndOffset(img):
        def getFactorImg(factorNames):
            factorList = img.toDictionary().select(factorNames).values()
            return ee.Image.constant(factorList)

        scaleImg = getFactorImg(["CMI_C.._scale"])
        offsetImg = getFactorImg(["CMI_C.._offset"])
        scaled = img.select("CMI_C..").multiply(scaleImg).add(offsetImg)
        return img.addBands(**{"srcImg": scaled, "overwrite": True})

    # Adds a synthetic green band.
    def addGreenBand(img):
        green = img.expression(
            "CMI_GREEN = 0.45 * red + 0.10 * nir + 0.45 * blue",
            {
                "blue": img.select("CMI_C01"),
                "red": img.select("CMI_C02"),
                "nir": img.select("CMI_C03"),
            },
        )
        return img.addBands(green)

    # Show at clouds at night (a-mode)
    def showNighta(img):
        # Make normalized infrared
        IR_n = img.select("CMI_C13").unitScale(ee.Number(90), ee.Number(313))
        IR_n = IR_n.expression(
            "ir_p = (1 -IR_n)/1.4",
            {
                "IR_n": IR_n.select("CMI_C13"),
            },
        )

        # Add infrared to rgb bands
        R_ir = img.select("CMI_C02").max(IR_n)
        G_ir = img.select("CMI_GREEN").max(IR_n)
        B_ir = img.select("CMI_C01").max(IR_n)

        return img.addBands([R_ir, G_ir, B_ir], overwrite=True)

    # Show at clouds at night (b-mode)
    def showNightb(img):
        night = img.select("CMI_C03").unitScale(0, 0.016).subtract(1).multiply(-1)

        cmi11 = img.select("CMI_C11").unitScale(100, 310)
        cmi13 = img.select("CMI_C13").unitScale(100, 300)
        cmi15 = img.select("CMI_C15").unitScale(100, 310)
        iNight = cmi15.addBands([cmi13, cmi11]).clamp(0, 1).subtract(1).multiply(-1)

        iRGBNight = iNight.visualize(**{"min": 0, "max": 1, "gamma": 1.4}).updateMask(
            night
        )

        iRGB = img.visualize(
            **{
                "bands": ["CMI_C02", "CMI_C03", "CMI_C01"],
                "min": 0.15,
                "max": 1,
                "gamma": 1.4,
            }
        )
        return iRGB.blend(iRGBNight).set(
            "system:time_start", img.get("system:time_start")
        )

    # Scales select bands for visualization.
    def scaleForVis(img):
        return (
            img.select(["CMI_C01", "CMI_GREEN", "CMI_C02", "CMI_C03", "CMI_C05"])
            .resample("bicubic")
            .log10()
            .interpolate([-1.6, 0.176], [0, 1], "clamp")
            .unmask(0)
            .set("system:time_start", img.get("system:time_start"))
        )

    # Wraps previous functions.
    def processForVis(img):
        if show_night[0]:
            if show_night[1] == "a_mode":
                return scaleForVis(showNighta(addGreenBand(applyScaleAndOffset(img))))

            else:
                return showNightb(applyScaleAndOffset(img))

        else:
            return scaleForVis(addGreenBand(applyScaleAndOffset(img)))

    return col.filterDate(start_date, end_date).filterBounds(region).map(processForVis)

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		True if it has transparency, False otherwise.

Source code in geemap/common.py

def has_transparency(img):
    """Checks whether an image has transparency.

    Args:
        img (object):  a PIL Image object.

    Returns:
        bool: True if it has transparency, False otherwise.
    """

    if img.mode == "P":
        transparent = img.info.get("transparency", -1)
        for _, index in img.getcolors():
            if index == transparent:
                return True
    elif img.mode == "RGBA":
        extrema = img.getextrema()
        if extrema[3][0] < 255:
            return True

    return False

hex_to_rgb(value='FFFFFF')

Converts hex color to RGB color.

Parameters:

Name	Type	Description	Default
value	str	Hex color code as a string. Defaults to 'FFFFFF'.	'FFFFFF'

Returns:

Type	Description
Tuple[int, int, int]	Tuple[int, int, int]: RGB color as a tuple.

Source code in geemap/coreutils.py

def hex_to_rgb(value: 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[int, int, int]: 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))

hex_to_rgba(hex_color, opacity)

Converts a hex color code to an RGBA color string.

Parameters:

Name	Type	Description	Default
hex_color	str	The hex color code to convert. It can be in the format '#RRGGBB' or 'RRGGBB'.	required
opacity	float	The opacity value for the RGBA color. It should be a float between 0.0 (completely transparent) and 1.0 (completely opaque).	required

Returns:

Name	Type	Description
str	str	The RGBA color string in the format 'rgba(R, G, B, A)'.

Source code in geemap/common.py

def hex_to_rgba(hex_color: str, opacity: float) -> str:
    """
    Converts a hex color code to an RGBA color string.

    Args:
        hex_color (str): The hex color code to convert. It can be in the format
            '#RRGGBB' or 'RRGGBB'.
        opacity (float): The opacity value for the RGBA color. It should be a
            float between 0.0 (completely transparent) and 1.0 (completely opaque).

    Returns:
        str: The RGBA color string in the format 'rgba(R, G, B, A)'.
    """
    hex_color = hex_color.lstrip("#")
    h_len = len(hex_color)
    r, g, b = (
        int(hex_color[i : i + h_len // 3], 16) for i in range(0, h_len, h_len // 3)
    )
    return f"rgba({r},{g},{b},{opacity})"

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 geemap/common.py

def html_to_gradio(html, width="100%", height="500px", **kwargs):
    """Converts the map to an HTML string that can be used in Gradio. Removes unsupported elements, such as
        attribution and any code blocks containing functions. See https://github.com/gradio-app/gradio/issues/3190

    Args:
        width (str, optional): The width of the map. Defaults to '100%'.
        height (str, optional): The height of the map. Defaults to '500px'.

    Returns:
        str: The HTML string to use in Gradio.
    """

    if isinstance(width, int):
        width = f"{width}px"

    if isinstance(height, int):
        height = f"{height}px"

    if isinstance(html, str):
        with open(html, "r") as f:
            lines = f.readlines()
    elif isinstance(html, list):
        lines = html
    else:
        raise TypeError("html must be a file path or a list of strings")

    output = []
    skipped_lines = []
    for index, line in enumerate(lines):
        if index in skipped_lines:
            continue
        if line.lstrip().startswith('{"attribution":'):
            continue
        elif "on(L.Draw.Event.CREATED, function(e)" in line:
            for i in range(14):
                skipped_lines.append(index + i)
        elif "L.Control.geocoder" in line:
            for i in range(5):
                skipped_lines.append(index + i)
        elif "function(e)" in line:
            print(
                f"Warning: The folium plotting backend does not support functions in code blocks. Please delete line {index + 1}."
            )
        else:
            output.append(line + "\n")

    return f"""<iframe style="width: {width}; height: {height}" name="result" allow="midi; geolocation; microphone; camera;
    display-capture; encrypted-media;" sandbox="allow-modals allow-forms
    allow-scripts allow-same-origin allow-popups
    allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
    allowpaymentrequest="" frameborder="0" srcdoc='{"".join(output)}'></iframe>"""

html_to_streamlit(filename, width=None, height=None, scrolling=False, replace_dict={})

Renders an HTML file as a Streamlit component. Args: filename (str): The filename of the HTML file. width (int, optional): Width of the map. Defaults to None. height (int, optional): Height of the map. Defaults to 600. scrolling (bool, optional): Whether to allow the map to scroll. Defaults to False. replace_dict (dict, optional): A dictionary of strings to replace in the HTML file. Defaults to {}.

Raises:

Type	Description
ValueError	If the filename does not exist.

Returns:

Type	Description
	streamlit.components: components.html object.

Source code in geemap/common.py

def html_to_streamlit(
    filename, width=None, height=None, scrolling=False, replace_dict={}
):
    """Renders an HTML file as a Streamlit component.
    Args:
        filename (str): The filename of the HTML file.
        width (int, optional): Width of the map. Defaults to None.
        height (int, optional): Height of the map. Defaults to 600.
        scrolling (bool, optional): Whether to allow the map to scroll. Defaults to False.
        replace_dict (dict, optional): A dictionary of strings to replace in the HTML file. Defaults to {}.

    Raises:
        ValueError: If the filename does not exist.

    Returns:
        streamlit.components: components.html object.
    """

    import streamlit.components.v1 as components

    if not os.path.exists(filename):
        raise ValueError("filename must exist.")

    f = open(filename, "r")

    html = f.read()

    for key, value in replace_dict.items():
        html = html.replace(key, value)

    f.close()
    return components.html(html, width=width, height=height, scrolling=scrolling)

image_area(img, region=None, scale=None, denominator=1.0)

Calculates the area of an image.

Parameters:

Name	Type	Description	Default
img	object	ee.Image	required
region	object	The region over which to reduce data. Defaults to the footprint of the image's first band.	None
scale	float	A nominal scale in meters of the projection to work in. Defaults to None.	None
denominator	float	The denominator to use for converting size from square meters to other units. Defaults to 1.0.	1.0

Returns:

Name	Type	Description
object		ee.Dictionary

Source code in geemap/common.py

def image_area(img, region=None, scale=None, denominator=1.0):
    """Calculates the area of an image.

    Args:
        img (object): ee.Image
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
        denominator (float, optional): The denominator to use for converting size from square meters to other units. Defaults to 1.0.

    Returns:
        object: ee.Dictionary
    """
    if region is None:
        region = img.geometry()

    if scale is None:
        scale = image_scale(img)

    pixel_area = (
        img.unmask().neq(ee.Image(0)).multiply(ee.Image.pixelArea()).divide(denominator)
    )
    img_area = pixel_area.reduceRegion(
        **{
            "geometry": region,
            "reducer": ee.Reducer.sum(),
            "scale": scale,
            "maxPixels": 1e12,
        }
    )
    return img_area

image_area_by_group(img, groups=None, region=None, scale=None, denominator=1.0, out_csv=None, labels=None, decimal_places=4, verbose=True)

Calculates the area of each class of an image.

Parameters:

Name	Type	Description	Default
img	object	ee.Image	required
groups	object	The groups to use for the area calculation. Defaults to None.	None
region	object	The region over which to reduce data. Defaults to the footprint of the image's first band.	None
scale	float	A nominal scale in meters of the projection to work in. Defaults to None.	None
denominator	float	The denominator to use for converting size from square meters to other units. Defaults to 1.0.	1.0
out_csv	str	The path to the output CSV file. Defaults to None.	None
labels	object	The class labels to use in the output CSV file. Defaults to None.	None
decimal_places	int	The number of decimal places to use for the output. Defaults to 2.	4
verbose	bool	If True, print the progress. Defaults to True.	True

Returns:

Name	Type	Description
object		pandas.DataFrame

Source code in geemap/common.py

def image_area_by_group(
    img,
    groups=None,
    region=None,
    scale=None,
    denominator=1.0,
    out_csv=None,
    labels=None,
    decimal_places=4,
    verbose=True,
):
    """Calculates the area of each class of an image.

    Args:
        img (object): ee.Image
        groups (object, optional): The groups to use for the area calculation. Defaults to None.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
        denominator (float, optional): The denominator to use for converting size from square meters to other units. Defaults to 1.0.
        out_csv (str, optional): The path to the output CSV file. Defaults to None.
        labels (object, optional): The class labels to use in the output CSV file. Defaults to None.
        decimal_places (int, optional): The number of decimal places to use for the output. Defaults to 2.
        verbose (bool, optional): If True, print the progress. Defaults to True.

    Returns:
        object: pandas.DataFrame
    """
    import pandas as pd

    values = []
    if region is None:
        region = ee.Geometry.BBox(-179.9, -89.5, 179.9, 89.5)

    if groups is None:
        groups = image_value_list(img, region, scale)

    if not isinstance(groups, list):
        groups = groups.getInfo()

    groups.sort(key=int)

    for group in groups:
        if verbose:
            print(f"Calculating area for group {group} ...")
        area = image_area(img.eq(float(group)), region, scale, denominator)
        values.append(area.values().get(0).getInfo())

    d = {"group": groups, "area": values}
    df = pd.DataFrame(data=d)
    df = df.set_index("group")
    df["percentage"] = df["area"] / df["area"].sum()
    df = df.astype(float).round(decimal_places)
    if isinstance(labels, list) and len(labels) == len(values):
        df["labels"] = labels

    if out_csv is not None:
        df.to_csv(out_csv)
    else:
        return df

image_band_names(img)

Gets image band names.

Parameters:

Name	Type	Description	Default
img	Image	The input image.	required

Returns:

Type	Description
	ee.List: The returned list of image band names.

Source code in geemap/common.py

def image_band_names(img):
    """Gets image band names.

    Args:
        img (ee.Image): The input image.

    Returns:
        ee.List: The returned list of image band names.
    """
    return img.bandNames()

image_bandcount(image, **kwargs)

Get the number of bands in an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath or URL.	required

Returns:

Name	Type	Description
int		The number of bands in the image.

Source code in geemap/common.py

def image_bandcount(image, **kwargs):
    """Get the number of bands in an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        int: The number of bands in the image.
    """

    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    return len(client.metadata()["bands"])

image_bounds(image, **kwargs)

Get the bounds of an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath or URL.	required

Returns:

Name	Type	Description
list		A list of bounds in the form of [(south, west), (north, east)].

Source code in geemap/common.py

def image_bounds(image, **kwargs):
    """Get the bounds of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        list: A list of bounds in the form of [(south, west), (north, east)].
    """

    image_check(image)
    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    bounds = client.bounds()
    return [(bounds[0], bounds[2]), (bounds[1], bounds[3])]

image_cell_size(img)

Retrieves the image cell size (e.g., spatial resolution)

Parameters:

Name	Type	Description	Default
img	object	ee.Image	required

Returns:

Name	Type	Description
float		The nominal scale in meters.

Source code in geemap/common.py

def image_cell_size(img):
    """Retrieves the image cell size (e.g., spatial resolution)

    Args:
        img (object): ee.Image

    Returns:
        float: The nominal scale in meters.
    """
    bands = img.bandNames()
    scales = bands.map(lambda b: img.select([b]).projection().nominalScale())
    scale = ee.Algorithms.If(
        scales.distinct().size().gt(1),
        ee.Dictionary.fromLists(bands.getInfo(), scales),
        scales.get(0),
    )
    return scale

image_center(image, **kwargs)

Get the center of an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath or URL.	required

Returns:

Name	Type	Description
tuple		A tuple of (latitude, longitude).

Source code in geemap/common.py

def image_center(image, **kwargs):
    """Get the center of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        tuple: A tuple of (latitude, longitude).
    """
    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    return client.center()

image_client(image, **kwargs)

Get a LocalTileserver TileClient from an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath or URL.	required

Returns:

Name	Type	Description
TileClient		A LocalTileserver TileClient.

Source code in geemap/common.py

def image_client(image, **kwargs):
    """Get a LocalTileserver TileClient from an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        TileClient: A LocalTileserver TileClient.
    """
    image_check(image)

    _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    return client

image_convolution(image, kernel=None, resample=None, projection='EPSG:3857', **kwargs)

Performs a convolution on an image.

Parameters:

Name	Type	Description	Default
image	Image | ImageCollection	The image to convolve.	required
kernel	Kernel	The kernel to convolve with. Defaults to None, a 7x7 gaussian kernel.	None
resample	str	The resample method to use. It can be either 'bilinear' or 'bicubic'". Defaults to None, which uses the image's resample method.	None
projection	str	The projection to use. Defaults to 'EPSG:3857'.	'EPSG:3857'

Returns:

Type	Description
	ee.Image: The convolved image.

Source code in geemap/common.py

def image_convolution(
    image, kernel=None, resample=None, projection="EPSG:3857", **kwargs
):
    """Performs a convolution on an image.

    Args:
        image (ee.Image | ee.ImageCollection): The image to convolve.
        kernel (ee.Kernel, optional): The kernel to convolve with. Defaults to None, a 7x7 gaussian kernel.
        resample (str, optional): The resample method to use. It can be either 'bilinear' or 'bicubic'". Defaults to None, which uses the image's resample method.
        projection (str, optional): The projection to use. Defaults to 'EPSG:3857'.

    Returns:
        ee.Image: The convolved image.
    """
    if isinstance(image, ee.ImageCollection):
        image = image.mosaic()
    elif not isinstance(image, ee.Image):
        raise ValueError("image must be an ee.Image or ee.ImageCollection.")

    if kernel is None:
        kernel = ee.Kernel.gaussian(radius=3, sigma=2, units="pixels", normalize=True)
    elif not isinstance(kernel, ee.Kernel):
        raise ValueError("kernel must be an ee.Kernel.")

    if resample is not None:
        if resample not in ["bilinear", "bicubic"]:
            raise ValueError("resample must be one of 'bilinear' or 'bicubic'")

    result = image.convolve(kernel)

    if resample is not None:
        result = result.resample(resample)

    return result.setDefaultProjection(projection)

image_count(collection, region=None, band=None, start_date=None, end_date=None, clip=False)

Create an image with the number of available images for a specific region. Args: collection (ee.ImageCollection): The collection to be queried. region (ee.Geometry | ee.FeatureCollection, optional): The region to be queried. start_date (str | ee.Date, optional): The start date of the query. band (str, optional): The band to be queried. end_date (str | ee.Date, optional): The end date of the query. clip (bool, optional): Whether to clip the image to the region.

Returns:

Type	Description
	ee.Image: The image with each pixel value representing the number of available images.

Source code in geemap/common.py

def image_count(
    collection, region=None, band=None, start_date=None, end_date=None, clip=False
):
    """Create an image with the number of available images for a specific region.
    Args:
        collection (ee.ImageCollection): The collection to be queried.
        region (ee.Geometry | ee.FeatureCollection, optional): The region to be queried.
        start_date (str | ee.Date, optional): The start date of the query.
        band (str, optional): The band to be queried.
        end_date (str | ee.Date, optional): The end date of the query.
        clip (bool, optional): Whether to clip the image to the region.

    Returns:
        ee.Image: The image with each pixel value representing the number of available images.
    """
    if not isinstance(collection, ee.ImageCollection):
        raise TypeError("collection must be an ee.ImageCollection.")

    if region is not None:
        if isinstance(region, ee.Geometry) or isinstance(region, ee.FeatureCollection):
            pass
        else:
            raise TypeError("region must be an ee.Geometry or ee.FeatureCollection.")

    if (start_date is not None) and (end_date is not None):
        pass
    elif (start_date is None) and (end_date is None):
        pass
    else:
        raise ValueError("start_date and end_date must be provided.")

    if band is None:
        first_image = collection.first()
        band = first_image.bandNames().get(0)

    if region is not None:
        collection = collection.filterBounds(region)

    if start_date is not None and end_date is not None:
        collection = collection.filterDate(start_date, end_date)

    image = (
        collection.filter(ee.Filter.listContains("system:band_names", band))
        .select([band])
        .reduce(ee.Reducer.count())
    )

    if clip:
        image = image.clip(region)

    return image

image_date(img, date_format='YYYY-MM-dd')

Retrieves the image acquisition date.

Parameters:

Name	Type	Description	Default
img	object	ee.Image	required
date_format	str	The date format to use. Defaults to 'YYYY-MM-dd'.	'YYYY-MM-dd'

Returns:

Name	Type	Description
str		A string representing the acquisition of the image.

Source code in geemap/common.py

def image_date(img, date_format="YYYY-MM-dd"):
    """Retrieves the image acquisition date.

    Args:
        img (object): ee.Image
        date_format (str, optional): The date format to use. Defaults to 'YYYY-MM-dd'.

    Returns:
        str: A string representing the acquisition of the image.
    """
    return ee.Date(img.get("system:time_start")).format(date_format)

image_dates(img_col, date_format='YYYY-MM-dd')

Get image dates of all images in an ImageCollection.

Parameters:

Name	Type	Description	Default
img_col	object	ee.ImageCollection	required
date_format	str	A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html; if omitted will use ISO standard date formatting. Defaults to 'YYYY-MM-dd'.	'YYYY-MM-dd'

Returns:

Name	Type	Description
object		ee.List

Source code in geemap/common.py

def image_dates(img_col, date_format="YYYY-MM-dd"):
    """Get image dates of all images in an ImageCollection.

    Args:
        img_col (object): ee.ImageCollection
        date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html; if omitted will use ISO standard date formatting. Defaults to 'YYYY-MM-dd'.

    Returns:
        object: ee.List
    """
    dates = img_col.aggregate_array("system:time_start")
    new_dates = dates.map(lambda d: ee.Date(d).format(date_format))
    return new_dates

image_geotransform(image, **kwargs)

Get the geotransform of an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath or URL.	required

Returns:

Name	Type	Description
list		A list of geotransform values.

Source code in geemap/common.py

def image_geotransform(image, **kwargs):
    """Get the geotransform of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        list: A list of geotransform values.
    """
    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    return client.metadata()["GeoTransform"]

image_histogram(img, region=None, scale=None, x_label=None, y_label=None, title=None, width=None, height=500, plot_args={}, layout_args={}, return_df=False, **kwargs)

Create a histogram of an image.

Parameters:

Name	Type	Description	Default
img	Image	The image to calculate the histogram.	required
region	Geometry | FeatureCollection	The region over which to reduce data. Defaults to the footprint of the image's first band.	None
scale	float	A nominal scale in meters of the projection to work in. Defaults to None.	None
x_label	str	Label for the x axis. Defaults to None.	None
y_label	str	Label for the y axis. Defaults to None.	None
title	str	Title for the plot. Defaults to None.	None
width	int	Width of the plot in pixels. Defaults to None.	None
height	int	Height of the plot in pixels. Defaults to 500.	500
layout_args	dict	Layout arguments for the plot to be passed to fig.update_layout(),	{}
return_df	bool	If True, return a pandas dataframe. Defaults to False.	False

Returns:

Type	Description
	pandas DataFrame | plotly figure object: A dataframe or plotly figure object.

Source code in geemap/common.py

def image_histogram(
    img,
    region=None,
    scale=None,
    x_label=None,
    y_label=None,
    title=None,
    width=None,
    height=500,
    plot_args={},
    layout_args={},
    return_df=False,
    **kwargs,
):
    """Create a histogram of an image.

    Args:
        img (ee.Image): The image to calculate the histogram.
        region (ee.Geometry | ee.FeatureCollection, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
        x_label (str, optional): Label for the x axis. Defaults to None.
        y_label (str, optional): Label for the y axis. Defaults to None.
        title (str, optional): Title for the plot. Defaults to None.
        width (int, optional): Width of the plot in pixels. Defaults to None.
        height (int, optional): Height of the plot in pixels. Defaults to 500.
        layout_args (dict, optional): Layout arguments for the plot to be passed to fig.update_layout(),
        return_df (bool, optional): If True, return a pandas dataframe. Defaults to False.

    Returns:
        pandas DataFrame | plotly figure object: A dataframe or plotly figure object.
    """
    import pandas as pd
    import plotly.express as px

    hist = image_value_list(img, region, scale, return_hist=True, **kwargs).getInfo()
    keys = sorted(hist, key=int)
    values = [hist.get(key) for key in keys]

    data = pd.DataFrame({"key": keys, "value": values})

    if return_df:
        return data
    else:
        labels = {}

        if x_label is not None:
            labels["key"] = x_label
        if y_label is not None:
            labels["value"] = y_label

        try:
            fig = px.bar(
                data,
                x="key",
                y="value",
                labels=labels,
                title=title,
                width=width,
                height=height,
                **plot_args,
            )

            if isinstance(layout_args, dict):
                fig.update_layout(**layout_args)

            return fig
        except Exception as e:
            raise Exception(e)

image_max_value(img, region=None, scale=None)

Retrieves the maximum value of an image.

Parameters:

Name	Type	Description	Default
img	object	The image to calculate the maximum value.	required
region	object	The region over which to reduce data. Defaults to the footprint of the image's first band.	None
scale	float	A nominal scale in meters of the projection to work in. Defaults to None.	None

Returns:

Name	Type	Description
object		ee.Number

Source code in geemap/common.py

def image_max_value(img, region=None, scale=None):
    """Retrieves the maximum value of an image.

    Args:
        img (object): The image to calculate the maximum value.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.

    Returns:
        object: ee.Number
    """
    if region is None:
        region = img.geometry()

    if scale is None:
        scale = image_scale(img)

    max_value = img.reduceRegion(
        **{
            "reducer": ee.Reducer.max(),
            "geometry": region,
            "scale": scale,
            "maxPixels": 1e12,
            "bestEffort": True,
        }
    )
    return max_value

image_mean_value(img, region=None, scale=None)

Retrieves the mean value of an image.

Parameters:

Name	Type	Description	Default
img	object	The image to calculate the mean value.	required
region	object	The region over which to reduce data. Defaults to the footprint of the image's first band.	None
scale	float	A nominal scale in meters of the projection to work in. Defaults to None.	None

Returns:

Name	Type	Description
object		ee.Number

Source code in geemap/common.py

def image_mean_value(img, region=None, scale=None):
    """Retrieves the mean value of an image.

    Args:
        img (object): The image to calculate the mean value.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.

    Returns:
        object: ee.Number
    """
    if region is None:
        region = img.geometry()

    if scale is None:
        scale = image_scale(img)

    mean_value = img.reduceRegion(
        **{
            "reducer": ee.Reducer.mean(),
            "geometry": region,
            "scale": scale,
            "maxPixels": 1e12,
            "bestEffort": True,
        }
    )
    return mean_value

image_metadata(image, **kwargs)

Get the metadata of an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath or URL.	required

Returns:

Name	Type	Description
dict		A dictionary of image metadata.

Source code in geemap/common.py

def image_metadata(image, **kwargs):
    """Get the metadata of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        dict: A dictionary of image metadata.
    """
    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    return client.metadata()

image_min_value(img, region=None, scale=None)

Retrieves the minimum value of an image.

Parameters:

Name	Type	Description	Default
img	object	The image to calculate the minimum value.	required
region	object	The region over which to reduce data. Defaults to the footprint of the image's first band.	None
scale	float	A nominal scale in meters of the projection to work in. Defaults to None.	None

Returns:

Name	Type	Description
object		ee.Number

Source code in geemap/common.py

def image_min_value(img, region=None, scale=None):
    """Retrieves the minimum value of an image.

    Args:
        img (object): The image to calculate the minimum value.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.

    Returns:
        object: ee.Number
    """
    if region is None:
        region = img.geometry()

    if scale is None:
        scale = image_scale(img)

    min_value = img.reduceRegion(
        **{
            "reducer": ee.Reducer.min(),
            "geometry": region,
            "scale": scale,
            "maxPixels": 1e12,
            "bestEffort": True,
        }
    )
    return min_value

image_projection(image, **kwargs)

Get the projection of an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath or URL.	required

Returns:

Name	Type	Description
str		The projection of the image.

Source code in geemap/common.py

def image_projection(image, **kwargs):
    """Get the projection of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        str: The projection of the image.
    """
    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    return client.metadata()["Projection"]

image_props(img, date_format='YYYY-MM-dd')

Gets image properties.

Parameters:

Name	Type	Description	Default
img	Image	The input image.	required
date_format	str	The output date format. Defaults to 'YYYY-MM-dd HH:mm:ss'.	'YYYY-MM-dd'

Returns:

Type	Description
	dd.Dictionary: The dictionary containing image properties.

Source code in geemap/common.py

def image_props(img, date_format="YYYY-MM-dd"):
    """Gets image properties.

    Args:
        img (ee.Image): The input image.
        date_format (str, optional): The output date format. Defaults to 'YYYY-MM-dd HH:mm:ss'.

    Returns:
        dd.Dictionary: The dictionary containing image properties.
    """
    if not isinstance(img, ee.Image):
        print("The input object must be an ee.Image")
        return

    keys = img.propertyNames().remove("system:footprint").remove("system:bands")
    values = keys.map(lambda p: img.get(p))
    props = ee.Dictionary.fromLists(keys, values)

    names = keys.getInfo()

    bands = img.bandNames()
    scales = bands.map(lambda b: img.select([b]).projection().nominalScale())
    scale = ee.Algorithms.If(
        scales.distinct().size().gt(1),
        ee.Dictionary.fromLists(bands.getInfo(), scales),
        scales.get(0),
    )

    props = props.set("NOMINAL_SCALE", scale)

    if "system:time_start" in names:
        image_date = ee.Date(img.get("system:time_start")).format(date_format)
        time_start = ee.Date(img.get("system:time_start")).format("YYYY-MM-dd HH:mm:ss")
        # time_end = ee.Date(img.get('system:time_end')).format('YYYY-MM-dd HH:mm:ss')
        time_end = ee.Algorithms.If(
            ee.List(img.propertyNames()).contains("system:time_end"),
            ee.Date(img.get("system:time_end")).format("YYYY-MM-dd HH:mm:ss"),
            time_start,
        )
        props = props.set("system:time_start", time_start)
        props = props.set("system:time_end", time_end)
        props = props.set("IMAGE_DATE", image_date)

    if "system:asset_size" in names:
        asset_size = (
            ee.Number(img.get("system:asset_size"))
            .divide(1e6)
            .format()
            .cat(ee.String(" MB"))
        )

        props = props.set("system:asset_size", asset_size)

    return props

image_reclassify(img, in_list, out_list)

Reclassify an image.

Parameters:

Name	Type	Description	Default
img	object	The image to which the remapping is applied.	required
in_list	list	The source values (numbers or EEArrays). All values in this list will be mapped to the corresponding value in 'out_list'.	required
out_list	list	The destination values (numbers or EEArrays). These are used to replace the corresponding values in 'from'. Must have the same number of values as 'in_list'.	required

Returns:

Name	Type	Description
object		ee.Image

Source code in geemap/common.py

def image_reclassify(img, in_list, out_list):
    """Reclassify an image.

    Args:
        img (object): The image to which the remapping is applied.
        in_list (list): The source values (numbers or EEArrays). All values in this list will be mapped to the corresponding value in 'out_list'.
        out_list (list): The destination values (numbers or EEArrays). These are used to replace the corresponding values in 'from'. Must have the same number of values as 'in_list'.

    Returns:
        object: ee.Image
    """
    image = img.remap(in_list, out_list)
    return image

image_resolution(image, **kwargs)

Get the resolution of an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath or URL.	required

Returns:

Name	Type	Description
float		The resolution of the image.

Source code in geemap/common.py

def image_resolution(image, **kwargs):
    """Get the resolution of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        float: The resolution of the image.
    """
    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    return client.metadata()["GeoTransform"][1]

image_scale(img)

Retrieves the image cell size (e.g., spatial resolution)

Parameters:

Name	Type	Description	Default
img	object	ee.Image	required

Returns:

Name	Type	Description
float		The nominal scale in meters.

Source code in geemap/common.py

def image_scale(img):
    """Retrieves the image cell size (e.g., spatial resolution)

    Args:
        img (object): ee.Image

    Returns:
        float: The nominal scale in meters.
    """
    # bands = img.bandNames()
    # scales = bands.map(lambda b: img.select([b]).projection().nominalScale())
    # scale = ee.Algorithms.If(scales.distinct().size().gt(1), ee.Dictionary.fromLists(bands.getInfo(), scales), scales.get(0))
    return img.select(0).projection().nominalScale()

image_set_crs(image, epsg)

Define the CRS of an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath	required
epsg	int	The EPSG code of the CRS to set.	required

Source code in geemap/common.py

def image_set_crs(image, epsg):
    """Define the CRS of an image.

    Args:
        image (str): The input image filepath
        epsg (int): The EPSG code of the CRS to set.
    """

    from rasterio.crs import CRS
    import rasterio

    with rasterio.open(image, "r+") as rds:
        rds.crs = CRS.from_epsg(epsg)

image_size(image, **kwargs)

Get the size (width, height) of an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath or URL.	required

Returns:

Name	Type	Description
tuple		A tuple of (width, height).

Source code in geemap/common.py

def image_size(image, **kwargs):
    """Get the size (width, height) of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        tuple: A tuple of (width, height).
    """
    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image

    metadata = client.metadata()
    return metadata["sourceSizeX"], metadata["sourceSizeY"]

image_smoothing(img, reducer, kernel)

Smooths an image.

Parameters:

Name	Type	Description	Default
img	object	The image to be smoothed.	required
reducer	object	ee.Reducer	required
kernel	object	ee.Kernel	required

Returns:

Name	Type	Description
object		ee.Image

Source code in geemap/common.py

def image_smoothing(img, reducer, kernel):
    """Smooths an image.

    Args:
        img (object): The image to be smoothed.
        reducer (object): ee.Reducer
        kernel (object): ee.Kernel

    Returns:
        object: ee.Image
    """
    image = img.reduceNeighborhood(
        **{
            "reducer": reducer,
            "kernel": kernel,
        }
    )
    return image

image_stats(img, region=None, scale=None)

Gets image descriptive statistics.

Parameters:

Name	Type	Description	Default
img	Image	The input image to calculate descriptive statistics.	required
region	object	The region over which to reduce data. Defaults to the footprint of the image's first band.	None
scale	float	A nominal scale in meters of the projection to work in. Defaults to None.	None

Returns:

Type	Description
	ee.Dictionary: A dictionary containing the description statistics of the input image.

Source code in geemap/common.py

def image_stats(img, region=None, scale=None):
    """Gets image descriptive statistics.

    Args:
        img (ee.Image): The input image to calculate descriptive statistics.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.

    Returns:
        ee.Dictionary: A dictionary containing the description statistics of the input image.
    """

    if not isinstance(img, ee.Image):
        print("The input object must be an ee.Image")
        return

    stat_types = ["min", "max", "mean", "std", "sum"]

    image_min = image_min_value(img, region, scale)
    image_max = image_max_value(img, region, scale)
    image_mean = image_mean_value(img, region, scale)
    image_std = image_std_value(img, region, scale)
    image_sum = image_sum_value(img, region, scale)

    stat_results = ee.List([image_min, image_max, image_mean, image_std, image_sum])

    stats = ee.Dictionary.fromLists(stat_types, stat_results)

    return stats

image_stats_by_zone(image, zones, out_csv=None, labels=None, region=None, scale=None, reducer='MEAN', bestEffort=True, **kwargs)

Calculate statistics for an image by zone.

Parameters:

Name	Type	Description	Default
image	Image	The image to calculate statistics for.	required
zones	Image	The zones to calculate statistics for.	required
out_csv	str	The path to the output CSV file. Defaults to None.	None
labels	list	The list of zone labels to use for the output CSV. Defaults to None.	None
region	Geometry	The region over which to reduce data. Defaults to the footprint of zone image.	None
scale	float	A nominal scale in meters of the projection to work in. Defaults to None.	None
reducer	str | Reducer	The reducer to use. It can be one of MEAN, MAXIMUM, MINIMUM, MODE, STD, MIN_MAX, SUM, VARIANCE. Defaults to MEAN.	'MEAN'
bestEffort	bool	If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. Defaults to True.	True

Returns:

Type	Description
	str | pd.DataFrame: The path to the output CSV file or a pandas DataFrame.

Source code in geemap/common.py

def image_stats_by_zone(
    image,
    zones,
    out_csv=None,
    labels=None,
    region=None,
    scale=None,
    reducer="MEAN",
    bestEffort=True,
    **kwargs,
):
    """Calculate statistics for an image by zone.

    Args:
        image (ee.Image): The image to calculate statistics for.
        zones (ee.Image): The zones to calculate statistics for.
        out_csv (str, optional): The path to the output CSV file. Defaults to None.
        labels (list, optional): The list of zone labels to use for the output CSV. Defaults to None.
        region (ee.Geometry, optional): The region over which to reduce data. Defaults to the footprint of zone image.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
        reducer (str | ee.Reducer, optional): The reducer to use. It can be one of MEAN, MAXIMUM, MINIMUM, MODE, STD, MIN_MAX, SUM, VARIANCE. Defaults to MEAN.
        bestEffort (bool, optional): If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. Defaults to True.

    Returns:
        str | pd.DataFrame: The path to the output CSV file or a pandas DataFrame.
    """
    import pandas as pd

    if region is not None:
        if isinstance(region, ee.Geometry):
            pass
        elif isinstance(region, ee.FeatureCollection):
            region = region.geometry()
        else:
            raise ValueError("region must be an ee.Geometry or ee.FeatureCollection")

    if scale is None:
        scale = image_scale(image)

    allowed_stats = {
        "MEAN": ee.Reducer.mean(),
        "MAXIMUM": ee.Reducer.max(),
        "MEDIAN": ee.Reducer.median(),
        "MINIMUM": ee.Reducer.min(),
        "MODE": ee.Reducer.mode(),
        "STD": ee.Reducer.stdDev(),
        "MIN_MAX": ee.Reducer.minMax(),
        "SUM": ee.Reducer.sum(),
        "VARIANCE": ee.Reducer.variance(),
    }

    if isinstance(reducer, str):
        if reducer.upper() not in allowed_stats:
            raise ValueError(
                "reducer must be one of: {}".format(", ".join(allowed_stats.keys()))
            )
        else:
            reducer = allowed_stats[reducer.upper()]
    elif isinstance(reducer, ee.Reducer):
        pass
    else:
        raise ValueError(
            "reducer must be one of: {}".format(", ".join(allowed_stats.keys()))
        )

    values = image_value_list(zones, region=region)
    values = values.map(lambda x: ee.Number.parse(x))

    def get_stats(value):
        img = image.updateMask(zones.eq(ee.Number(value)))
        kwargs["reducer"] = reducer
        kwargs["scale"] = scale
        kwargs["geometry"] = region
        kwargs["bestEffort"] = bestEffort
        stat = img.reduceRegion(**kwargs)
        return ee.Image().set({"zone": value}).set({"stat": stat.values().get(0)})

    collection = ee.ImageCollection(values.map(lambda x: get_stats(x)))
    keys = collection.aggregate_array("zone").getInfo()
    values = collection.aggregate_array("stat").getInfo()

    if labels is not None and isinstance(labels, list):
        if len(labels) != len(keys):
            warnings.warn("labels are not the same length as keys, ignoring labels.")
            df = pd.DataFrame({"zone": keys, "stat": values})
        else:
            df = pd.DataFrame({"zone": keys, "label": labels, "stat": values})
    else:
        df = pd.DataFrame({"zone": keys, "stat": values})

    if out_csv is not None:
        check_file_path(out_csv)
        df.to_csv(out_csv, index=False)
        return out_csv
    else:
        return df

image_std_value(img, region=None, scale=None)

Retrieves the standard deviation of an image.

Parameters:

Name	Type	Description	Default
img	object	The image to calculate the standard deviation.	required
region	object	The region over which to reduce data. Defaults to the footprint of the image's first band.	None
scale	float	A nominal scale in meters of the projection to work in. Defaults to None.	None

Returns:

Name	Type	Description
object		ee.Number

Source code in geemap/common.py

def image_std_value(img, region=None, scale=None):
    """Retrieves the standard deviation of an image.

    Args:
        img (object): The image to calculate the standard deviation.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.

    Returns:
        object: ee.Number
    """
    if region is None:
        region = img.geometry()

    if scale is None:
        scale = image_scale(img)

    std_value = img.reduceRegion(
        **{
            "reducer": ee.Reducer.stdDev(),
            "geometry": region,
            "scale": scale,
            "maxPixels": 1e12,
            "bestEffort": True,
        }
    )
    return std_value

image_sum_value(img, region=None, scale=None)

Retrieves the sum of an image.

Parameters:

Name	Type	Description	Default
img	object	The image to calculate the standard deviation.	required
region	object	The region over which to reduce data. Defaults to the footprint of the image's first band.	None
scale	float	A nominal scale in meters of the projection to work in. Defaults to None.	None

Returns:

Name	Type	Description
object		ee.Number

Source code in geemap/common.py

def image_sum_value(img, region=None, scale=None):
    """Retrieves the sum of an image.

    Args:
        img (object): The image to calculate the standard deviation.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.

    Returns:
        object: ee.Number
    """
    if region is None:
        region = img.geometry()

    if scale is None:
        scale = image_scale(img)

    sum_value = img.reduceRegion(
        **{
            "reducer": ee.Reducer.sum(),
            "geometry": region,
            "scale": scale,
            "maxPixels": 1e12,
            "bestEffort": True,
        }
    )
    return sum_value

image_to_cog(source, dst_path=None, profile='deflate', **kwargs)

Converts an image to a COG file.

Parameters:

Name	Type	Description	Default
source	str	A dataset path, URL or rasterio.io.DatasetReader object.	required
dst_path	str	An output dataset path or or PathLike object. Defaults to None.	None
profile	str	COG profile. More at https://cogeotiff.github.io/rio-cogeo/profile. Defaults to "deflate".	'deflate'

Raises:

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

Source code in geemap/common.py

def image_to_cog(source, dst_path=None, profile="deflate", **kwargs):
    """Converts an image to a COG file.

    Args:
        source (str): A dataset path, URL or rasterio.io.DatasetReader object.
        dst_path (str, optional): An output dataset path or or PathLike object. Defaults to None.
        profile (str, optional): COG profile. More at https://cogeotiff.github.io/rio-cogeo/profile. Defaults to "deflate".

    Raises:
        ImportError: If rio-cogeo is not installed.
        FileNotFoundError: If the source file could not be found.
    """
    try:
        from rio_cogeo.cogeo import cog_translate
        from rio_cogeo.profiles import cog_profiles

    except ImportError:
        raise ImportError(
            "The rio-cogeo package is not installed. Please install it with `pip install rio-cogeo` or `conda install rio-cogeo -c conda-forge`."
        )

    if not source.startswith("http"):
        source = check_file_path(source)

        if not os.path.exists(source):
            raise FileNotFoundError("The provided input file could not be found.")

    if dst_path is None:
        if not source.startswith("http"):
            dst_path = os.path.splitext(source)[0] + "_cog.tif"
        else:
            dst_path = temp_file_path(extension=".tif")

    dst_path = check_file_path(dst_path)

    dst_profile = cog_profiles.get(profile)
    cog_translate(source, dst_path, dst_profile, **kwargs)

image_to_numpy(image)

Converts an image to a numpy array.

Parameters:

Name	Type	Description	Default
image	str	A dataset path, URL or rasterio.io.DatasetReader object.	required

Raises:

Type	Description
FileNotFoundError	If the provided file could not be found.

Returns:

Type	Description
	np.array: A numpy array.

Source code in geemap/common.py

def image_to_numpy(image):
    """Converts an image to a numpy array.

    Args:
        image (str): A dataset path, URL or rasterio.io.DatasetReader object.

    Raises:
        FileNotFoundError: If the provided file could not be found.

    Returns:
        np.array: A numpy array.
    """
    import rasterio
    from osgeo import gdal
    from contextlib import contextmanager

    @contextmanager
    def gdal_error_handler():
        """Context manager for GDAL error handler."""
        gdal.PushErrorHandler("CPLQuietErrorHandler")
        try:
            yield
        finally:
            gdal.PopErrorHandler()

    gdal.UseExceptions()

    with gdal_error_handler():

        if not os.path.exists(image):
            raise FileNotFoundError("The provided input file could not be found.")

        with rasterio.open(image, "r") as ds:
            arr = ds.read()  # read all raster values

    return arr

image_value_list(img, region=None, scale=None, return_hist=False, **kwargs)

Get the unique values of an image.

Parameters:

Name	Type	Description	Default
img	Image	The image to calculate the unique values.	required
region	Geometry | FeatureCollection	The region over which to reduce data. Defaults to the footprint of the image's first band.	None
scale	float	A nominal scale in meters of the projection to work in. Defaults to None.	None
return_hist	bool	If True, return a histogram of the values. Defaults to False.	False

Returns:

Type	Description
	ee.List | ee.Dictionary: A list of unique values or a dictionary containing a list of unique values and a histogram.

Source code in geemap/common.py

def image_value_list(img, region=None, scale=None, return_hist=False, **kwargs):
    """Get the unique values of an image.

    Args:
        img (ee.Image): The image to calculate the unique values.
        region (ee.Geometry | ee.FeatureCollection, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
        return_hist (bool, optional): If True, return a histogram of the values. Defaults to False.

    Returns:
        ee.List | ee.Dictionary: A list of unique values or a dictionary containing a list of unique values and a histogram.
    """
    if region is None:
        geom = img.geometry().bounds()
        region = ee.FeatureCollection([ee.Feature(geom)])
    elif isinstance(region, ee.Geometry):
        region = ee.FeatureCollection([ee.Feature(region)])
    elif isinstance(region, ee.FeatureCollection):
        pass
    else:
        raise ValueError("region must be an ee.Geometry or ee.FeatureCollection")

    if scale is None:
        scale = img.select(0).projection().nominalScale().multiply(10)

    reducer = ee.Reducer.frequencyHistogram()
    kwargs["scale"] = scale
    kwargs["reducer"] = reducer
    kwargs["collection"] = region

    result = img.reduceRegions(**kwargs)
    hist = ee.Dictionary(result.first().get("histogram"))
    if return_hist:
        return hist
    else:
        return hist.keys()

in_colab_shell()

Checks if the code is running in a Google Colab environment.

Returns:

Name	Type	Description
bool	bool	True if running in Google Colab, False otherwise.

Source code in geemap/coreutils.py

def in_colab_shell() -> bool:
    """
    Checks if the code is running in a Google Colab environment.

    Returns:
        bool: True if running in Google Colab, False otherwise.
    """
    return "google.colab" in sys.modules

inspector_gui(m=None)

Generates a tool GUI template using ipywidgets.

Parameters:

Name	Type	Description	Default
m	Map	The leaflet Map object. Defaults to None.	None

Returns:

Name	Type	Description
ipywidgets		The tool GUI widget.

Source code in geemap/toolbar.py

def inspector_gui(m=None):
    """Generates a tool GUI template using ipywidgets.

    Args:
        m (geemap.Map, optional): The leaflet Map object. Defaults to None.

    Returns:
        ipywidgets: The tool GUI widget.
    """
    import pandas as pd

    widget_width = "250px"
    padding = "0px 5px 0px 5px"  # upper, right, bottom, left
    style = {"description_width": "initial"}

    if m is not None:
        marker_cluster = ipyleaflet.MarkerCluster(name="Inspector Markers")
        setattr(m, "pixel_values", [])
        setattr(m, "marker_cluster", marker_cluster)

        if not hasattr(m, "interact_mode"):
            setattr(m, "interact_mode", False)

        if not hasattr(m, "inspector_output"):
            inspector_output = widgets.Output(
                layout=widgets.Layout(
                    width=widget_width,
                    padding="0px 5px 5px 5px",
                    max_width=widget_width,
                )
            )
            setattr(m, "inspector_output", inspector_output)

        output = m.inspector_output
        output.outputs = ()

        if not hasattr(m, "inspector_add_marker"):
            inspector_add_marker = widgets.Checkbox(
                description="Add Marker at clicked location",
                value=True,
                indent=False,
                layout=widgets.Layout(padding=padding, width=widget_width),
            )
            setattr(m, "inspector_add_marker", inspector_add_marker)
        add_marker = m.inspector_add_marker

        if not hasattr(m, "inspector_bands_chk"):
            inspector_bands_chk = widgets.Checkbox(
                description="Get pixel value for visible bands only",
                indent=False,
                layout=widgets.Layout(padding=padding, width=widget_width),
            )
            setattr(m, "inspector_bands_chk", inspector_bands_chk)
        bands_chk = m.inspector_bands_chk

        if not hasattr(m, "inspector_class_label"):
            inspector_label = widgets.Text(
                value="",
                description="Class label:",
                placeholder="Add a label to the marker",
                style=style,
                layout=widgets.Layout(width=widget_width, padding=padding),
            )
            setattr(m, "inspector_class_label", inspector_label)
        label = m.inspector_class_label

        options = []
        if hasattr(m, "cog_layer_dict"):
            options = list(m.cog_layer_dict.keys())
            options.sort()
        if len(options) == 0:
            default_option = None
        else:
            default_option = options[0]
        if not hasattr(m, "inspector_dropdown"):
            inspector_dropdown = widgets.Dropdown(
                options=options,
                value=default_option,
                description="Select a layer:",
                layout=widgets.Layout(width=widget_width, padding=padding),
                style=style,
            )
            setattr(m, "inspector_dropdown", inspector_dropdown)

        dropdown = m.inspector_dropdown

    toolbar_button = widgets.ToggleButton(
        value=False,
        tooltip="Toolbar",
        icon="info-circle",
        layout=widgets.Layout(width="28px", height="28px", padding="0px 0px 0px 4px"),
    )

    close_button = widgets.ToggleButton(
        value=False,
        tooltip="Close the tool",
        icon="times",
        button_style="primary",
        layout=widgets.Layout(height="28px", width="28px", padding="0px 0px 0px 4px"),
    )

    buttons = widgets.ToggleButtons(
        value=None,
        options=["Download", "Reset", "Close"],
        tooltips=["Download", "Reset", "Close"],
        button_style="primary",
    )
    buttons.style.button_width = "80px"

    if len(options) == 0:
        with output:
            print("No COG/STAC layers available")

    toolbar_widget = widgets.VBox()
    toolbar_widget.children = [toolbar_button]
    toolbar_header = widgets.HBox()
    toolbar_header.children = [close_button, toolbar_button]
    toolbar_footer = widgets.VBox()
    toolbar_footer.children = [
        add_marker,
        label,
        dropdown,
        bands_chk,
        buttons,
        output,
    ]

    toolbar_event = ipyevents.Event(
        source=toolbar_widget, watched_events=["mouseenter", "mouseleave"]
    )

    def chk_change(change):
        if hasattr(m, "pixel_values"):
            m.pixel_values = []
        if hasattr(m, "marker_cluster"):
            m.marker_cluster.markers = []
        output.outputs = ()

    bands_chk.observe(chk_change, "value")

    def handle_toolbar_event(event):
        if event["type"] == "mouseenter":
            toolbar_widget.children = [toolbar_header, toolbar_footer]
        elif event["type"] == "mouseleave":
            if not toolbar_button.value:
                toolbar_widget.children = [toolbar_button]
                toolbar_button.value = False
                close_button.value = False

    toolbar_event.on_dom_event(handle_toolbar_event)

    def toolbar_btn_click(change):
        if change["new"]:
            close_button.value = False
            toolbar_widget.children = [toolbar_header, toolbar_footer]
        else:
            if not close_button.value:
                toolbar_widget.children = [toolbar_button]

    toolbar_button.observe(toolbar_btn_click, "value")

    def cleanup():
        toolbar_button.value = False
        if m is not None:
            if hasattr(m, "inspector_mode"):
                delattr(m, "inspector_mode")
            if m.tool_control is not None and m.tool_control in m.controls:
                m.remove_control(m.tool_control)
                m.tool_control = None
            m.default_style = {"cursor": "default"}

            m.marker_cluster.markers = []
            m.pixel_values = []
            marker_cluster_layer = m.find_layer("Inspector Markers")
            if marker_cluster_layer is not None:
                m.remove_layer(marker_cluster_layer)

            if hasattr(m, "pixel_values"):
                delattr(m, "pixel_values")

            if hasattr(m, "marker_cluster"):
                delattr(m, "marker_cluster")

        toolbar_widget.close()

    def close_btn_click(change):
        if change["new"]:
            m.tool_control.cleanup()

    close_button.observe(close_btn_click, "value")

    def button_clicked(change):
        if change["new"] == "Download":
            with output:
                output.outputs = ()
                if len(m.pixel_values) == 0:
                    print(
                        "No pixel values available. Click on the map to start collection data."
                    )
                else:
                    print("Downloading pixel values...")
                    df = pd.DataFrame(m.pixel_values)
                    temp_csv = temp_file_path("csv")
                    df.to_csv(temp_csv, index=False)
                    link = create_download_link(temp_csv)
                    with output:
                        output.outputs = ()
                        display(link)
        elif change["new"] == "Reset":
            label.value = ""
            output.outputs = ()
            if hasattr(m, "pixel_values"):
                m.pixel_values = []
            if hasattr(m, "marker_cluster"):
                m.marker_cluster.markers = []
        elif change["new"] == "Close":
            m.tool_control.cleanup()

        buttons.value = None

    buttons.observe(button_clicked, "value")

    toolbar_button.value = True

    def handle_interaction(**kwargs):
        latlon = kwargs.get("coordinates")
        lat = round(latlon[0], 4)
        lon = round(latlon[1], 4)
        if (
            kwargs.get("type") == "click"
            and hasattr(m, "inspector_mode")
            and m.inspector_mode
        ):
            m.default_style = {"cursor": "wait"}

            with output:
                output.outputs = ()
                print("Getting pixel value ...")

                layer_dict = m.cog_layer_dict[dropdown.value]

            if layer_dict["type"] == "STAC":
                if bands_chk.value:
                    assets = layer_dict["assets"]
                else:
                    assets = None

                result = stac_pixel_value(
                    lon,
                    lat,
                    layer_dict["url"],
                    layer_dict["collection"],
                    layer_dict["items"],
                    assets,
                    layer_dict["titiler_endpoint"],
                    verbose=False,
                )
                if result is not None:
                    with output:
                        output.outputs = ()
                        print(f"lat/lon: {lat:.4f}, {lon:.4f}\n")
                        for key in result:
                            print(f"{key}: {result[key]}")

                        result["latitude"] = lat
                        result["longitude"] = lon
                        result["label"] = label.value
                        m.pixel_values.append(result)
                    if add_marker.value:
                        markers = list(m.marker_cluster.markers)
                        markers.append(ipyleaflet.Marker(location=latlon))
                        m.marker_cluster.markers = markers

                else:
                    with output:
                        output.outputs = ()
                        print("No pixel value available")
                        bounds = m.cog_layer_dict[m.inspector_dropdown.value]["bounds"]
                        m.zoom_to_bounds(bounds)
            elif layer_dict["type"] == "COG":
                result = cog_pixel_value(lon, lat, layer_dict["url"], verbose=False)
                if result is not None:
                    with output:
                        output.outputs = ()
                        print(f"lat/lon: {lat:.4f}, {lon:.4f}\n")
                        for key in result:
                            print(f"{key}: {result[key]}")

                        result["latitude"] = lat
                        result["longitude"] = lon
                        result["label"] = label.value
                        m.pixel_values.append(result)
                    if add_marker.value:
                        markers = list(m.marker_cluster.markers)
                        markers.append(ipyleaflet.Marker(location=latlon))
                        m.marker_cluster.markers = markers
                else:
                    with output:
                        output.outputs = ()
                        print("No pixel value available")
                        bounds = m.cog_layer_dict[m.inspector_dropdown.value]["bounds"]
                        m.zoom_to_bounds(bounds)

            elif layer_dict["type"] == "LOCAL":
                result = local_tile_pixel_value(
                    lon, lat, layer_dict["tile_client"], verbose=False
                )
                if result is not None:
                    if m.inspector_bands_chk.value:
                        band = m.cog_layer_dict[m.inspector_dropdown.value]["band"]
                        band_names = m.cog_layer_dict[m.inspector_dropdown.value][
                            "band_names"
                        ]
                        if band is not None:
                            sel_bands = [band_names[b - 1] for b in band]
                            result = {k: v for k, v in result.items() if k in sel_bands}
                    with output:
                        output.outputs = ()
                        print(f"lat/lon: {lat:.4f}, {lon:.4f}\n")
                        for key in result:
                            print(f"{key}: {result[key]}")

                        result["latitude"] = lat
                        result["longitude"] = lon
                        result["label"] = label.value
                        m.pixel_values.append(result)
                    if add_marker.value:
                        markers = list(m.marker_cluster.markers)
                        markers.append(ipyleaflet.Marker(location=latlon))
                        m.marker_cluster.markers = markers
                else:
                    with output:
                        output.outputs = ()
                        print("No pixel value available")
                        bounds = m.cog_layer_dict[m.inspector_dropdown.value]["bounds"]
                        m.zoom_to_bounds(bounds)
            m.default_style = {"cursor": "crosshair"}

    if m is not None:
        if not hasattr(m, "marker_cluster"):
            setattr(m, "marker_cluster", marker_cluster)
        m.add_layer(marker_cluster)

        if not m.interact_mode:
            m.on_interaction(handle_interaction)
            m.interact_mode = True

    if m is not None:
        toolbar_control = ipyleaflet.WidgetControl(
            widget=toolbar_widget, position="topright"
        )
        toolbar_control.cleanup = cleanup

        if toolbar_control not in m.controls:
            m.add_control(toolbar_control)
            m.tool_control = toolbar_control

        if not hasattr(m, "inspector_mode"):
            if hasattr(m, "cog_layer_dict"):
                setattr(m, "inspector_mode", True)
            else:
                setattr(m, "inspector_mode", False)

    else:
        return toolbar_widget

install_from_github(url)

Install a package from a GitHub repository.

Parameters:

Name	Type	Description	Default
url	str	The URL of the GitHub repository.	required

Source code in geemap/common.py

def install_from_github(url):
    """Install a package from a GitHub repository.

    Args:
        url (str): The URL of the GitHub repository.
    """

    try:
        download_dir = os.path.join(os.path.expanduser("~"), "Downloads")
        if not os.path.exists(download_dir):
            os.makedirs(download_dir)

        repo_name = os.path.basename(url)
        zip_url = os.path.join(url, "archive/master.zip")
        filename = repo_name + "-master.zip"
        download_from_url(
            url=zip_url, out_file_name=filename, out_dir=download_dir, unzip=True
        )

        pkg_dir = os.path.join(download_dir, repo_name + "-master")
        pkg_name = os.path.basename(url)
        work_dir = os.getcwd()
        os.chdir(pkg_dir)
        print(f"Installing {pkg_name}...")
        cmd = "pip install ."
        os.system(cmd)
        os.chdir(work_dir)
        print(f"{pkg_name} has been installed successfully.")
        # print("\nPlease comment out 'install_from_github()' and restart the kernel to take effect:\nJupyter menu -> Kernel -> Restart & Clear Output")

    except Exception as e:
        print(e)

install_package(package)

Install a Python package.

Parameters:

Name	Type	Description	Default
package	str | list	The package name or a GitHub URL or a list of package names or GitHub URLs.	required

Source code in geemap/common.py

def install_package(package):
    """Install a Python package.

    Args:
        package (str | list): The package name or a GitHub URL or a list of package names or GitHub URLs.
    """
    import subprocess

    if isinstance(package, str):
        packages = [package]

    for package in packages:
        if package.startswith("https"):
            package = f"git+{package}"

        # Execute pip install command and show output in real-time
        command = f"pip install {package}"
        process = subprocess.Popen(command.split(), stdout=subprocess.PIPE)

        # Print output in real-time
        while True:
            output = process.stdout.readline()
            if output == b"" and process.poll() is not None:
                break
            if output:
                print(output.decode("utf-8").strip())

        # Wait for process to complete
        process.wait()

is_arcpy()

Check if arcpy is available.

Returns:

Name	Type	Description
book		True if arcpy is available, False otherwise.

Source code in geemap/common.py

def is_arcpy():
    """Check if arcpy is available.

    Returns:
        book: True if arcpy is available, False otherwise.
    """
    import sys

    if "arcpy" in sys.modules:
        return True
    else:
        return False

is_drive_mounted()

Checks whether Google Drive is mounted in Google Colab.

Returns:

Name	Type	Description
bool		Returns True if Google Drive is mounted, False otherwise.

Source code in geemap/common.py

def is_drive_mounted():
    """Checks whether Google Drive is mounted in Google Colab.

    Returns:
        bool: Returns True if Google Drive is mounted, False otherwise.
    """
    drive_path = "/content/drive/My Drive"
    if os.path.exists(drive_path):
        return True
    else:
        return False

is_latlon_valid(location)

Checks whether a pair of coordinates is valid.

Parameters:

Name	Type	Description	Default
location	str	A pair of latlon coordinates separated by comma or space.	required

Returns:

Name	Type	Description
bool		Returns True if valid.

Source code in geemap/common.py

def is_latlon_valid(location):
    """Checks whether a pair of coordinates is valid.

    Args:
        location (str): A pair of latlon coordinates separated by comma or space.

    Returns:
        bool: Returns True if valid.
    """
    latlon = []
    if "," in location:
        latlon = [float(x) for x in location.split(",")]
    elif " " in location:
        latlon = [float(x) for x in location.split(" ")]
    else:
        print(
            "The coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
        )
        return False

    try:
        lat, lon = float(latlon[0]), float(latlon[1])
        if lat >= -90 and lat <= 90 and lon >= -180 and lon <= 180:
            return True
        else:
            return False
    except Exception as e:
        print(e)
        return False

is_on_aws()

Check if the current notebook is running on AWS.

Returns:

Name	Type	Description
bool		True if the notebook is running on AWS.

Source code in geemap/common.py

def is_on_aws():
    """Check if the current notebook is running on AWS.

    Returns:
        bool: True if the notebook is running on AWS.
    """

    import psutil

    output = psutil.Process().parent().cmdline()

    on_aws = False
    for item in output:
        if item.endswith(".aws") or "ec2-user" in item:
            on_aws = True
    return on_aws

is_studio_lab()

Check if the current notebook is running on Studio Lab.

Returns:

Name	Type	Description
bool		True if the notebook is running on Studio Lab.

Source code in geemap/common.py

def is_studio_lab():
    """Check if the current notebook is running on Studio Lab.

    Returns:
        bool: True if the notebook is running on Studio Lab.
    """

    import psutil

    output = psutil.Process().parent().cmdline()

    on_studio_lab = False
    for item in output:
        if "studiolab/bin" in item:
            on_studio_lab = True
    return on_studio_lab

is_tool(name)

Check whether name is on PATH and marked as executable.

Source code in geemap/common.py

def is_tool(name):
    """Check whether `name` is on PATH and marked as executable."""

    # from shutil import which

    return shutil.which(name) is not None

jpg_to_gif(in_dir, out_gif, fps=10, loop=0)

Convert a list of jpg images to gif.

Parameters:

Name	Type	Description	Default
in_dir	str	The input directory containing jpg images.	required
out_gif	str	The output file path to the gif.	required
fps	int	Frames per second. Defaults to 10.	10
loop	bool	controls how many times the animation repeats. 1 means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0.	0

Raises:

Type	Description
FileNotFoundError	No jpg images could be found.

Source code in geemap/common.py

def jpg_to_gif(in_dir, out_gif, fps=10, loop=0):
    """Convert a list of jpg images to gif.

    Args:
        in_dir (str): The input directory containing jpg images.
        out_gif (str): The output file path to the gif.
        fps (int, optional): Frames per second. Defaults to 10.
        loop (bool, optional): controls how many times the animation repeats. 1 means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0.

    Raises:
        FileNotFoundError: No jpg images could be found.
    """
    import glob

    from PIL import Image

    if not out_gif.endswith(".gif"):
        raise ValueError("The out_gif must be a gif file.")

    out_gif = os.path.abspath(out_gif)

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

    # Create the frames
    frames = []
    imgs = list(glob.glob(os.path.join(in_dir, "*.jpg")))
    imgs.sort()

    if len(imgs) == 0:
        raise FileNotFoundError(f"No jpg could be found in {in_dir}.")

    for i in imgs:
        new_frame = Image.open(i)
        frames.append(new_frame)

    # Save into a GIF file that loops forever
    frames[0].save(
        out_gif,
        format="GIF",
        append_images=frames[1:],
        save_all=True,
        duration=1000 / fps,
        loop=loop,
    )

jrc_hist_monthly_history(collection=None, region=None, start_date='1984-03-16', end_date=None, start_month=1, end_month=12, scale=None, frequency='year', reducer='mean', denominator=10000.0, x_label=None, y_label=None, title=None, width=None, height=None, layout_args={}, return_df=False, **kwargs)

Create a JRC monthly history plot.

Parameters:

Name	Type	Description	Default
collection	ImageCollection	The image collection of JRC surface water monthly history. Default to ee.ImageCollection('JRC/GSW1_4/MonthlyHistory')	None
region	Geometry | FeatureCollection	The region to plot. Default to None.	None
start_date	str	The start date of the plot. Default to '1984-03-16'.	'1984-03-16'
end_date	str	The end date of the plot. Default to the current date.	None
start_month	int	The start month of the plot. Default to 1.	1
end_month	int	The end month of the plot. Default to 12.	12
scale	float	The scale to compute the statistics. Default to None.	None
frequency	str	The frequency of the plot. Can be either 'year' or 'month', Default to 'year'.	'year'
reducer	str	The reducer to compute the statistics. Can be either 'mean', 'min', 'max', 'median', etc. Default to 'mean'.	'mean'
denominator	int	The denominator to convert area from square meters to other units. Default to 1e4, converting to hectares.	10000.0
x_label	str	Label for the x axis. Defaults to None.	None
y_label	str	Label for the y axis. Defaults to None.	None
title	str	Title for the plot. Defaults to None.	None
width	int	Width of the plot in pixels. Defaults to None.	None
height	int	Height of the plot in pixels. Defaults to 500.	None
layout_args	dict	Layout arguments for the plot to be passed to fig.update_layout(),	{}
return_df	bool	Whether to return the dataframe of the plot. Defaults to False.	False

Returns:

Type	Description
	pd.DataFrame: Pandas dataframe of the plot.

Source code in geemap/common.py

def jrc_hist_monthly_history(
    collection=None,
    region=None,
    start_date="1984-03-16",
    end_date=None,
    start_month=1,
    end_month=12,
    scale=None,
    frequency="year",
    reducer="mean",
    denominator=1e4,
    x_label=None,
    y_label=None,
    title=None,
    width=None,
    height=None,
    layout_args={},
    return_df=False,
    **kwargs,
):
    """Create a JRC monthly history plot.

    Args:
        collection (ee.ImageCollection, optional): The image collection of JRC surface water monthly history.
            Default to ee.ImageCollection('JRC/GSW1_4/MonthlyHistory')
        region (ee.Geometry | ee.FeatureCollection, optional): The region to plot. Default to None.
        start_date (str, optional): The start date of the plot. Default to '1984-03-16'.
        end_date (str, optional): The end date of the plot. Default to the current date.
        start_month (int, optional): The start month of the plot. Default to 1.
        end_month (int, optional): The end month of the plot. Default to 12.
        scale (float, optional): The scale to compute the statistics. Default to None.
        frequency (str, optional): The frequency of the plot. Can be either 'year' or 'month', Default to 'year'.
        reducer (str, optional): The reducer to compute the statistics. Can be either 'mean', 'min', 'max', 'median', etc. Default to 'mean'.
        denominator (int, optional): The denominator to convert area from square meters to other units. Default to 1e4, converting to hectares.
        x_label (str, optional): Label for the x axis. Defaults to None.
        y_label (str, optional): Label for the y axis. Defaults to None.
        title (str, optional): Title for the plot. Defaults to None.
        width (int, optional): Width of the plot in pixels. Defaults to None.
        height (int, optional): Height of the plot in pixels. Defaults to 500.
        layout_args (dict, optional): Layout arguments for the plot to be passed to fig.update_layout(),
        return_df (bool, optional): Whether to return the dataframe of the plot. Defaults to False.

    Returns:
        pd.DataFrame: Pandas dataframe of the plot.
    """

    from datetime import date
    import pandas as pd
    import plotly.express as px

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

    if collection is None:
        collection = ee.ImageCollection("JRC/GSW1_4/MonthlyHistory")

    if frequency not in ["year", "month"]:
        raise ValueError("frequency must be 'year' or 'month'.")

    images = (
        collection.filterDate(start_date, end_date)
        .filter(ee.Filter.calendarRange(start_month, end_month, "month"))
        .map(lambda img: img.eq(2).selfMask())
    )

    def cal_area(img):
        pixel_area = img.multiply(ee.Image.pixelArea()).divide(denominator)
        img_area = pixel_area.reduceRegion(
            **{
                "geometry": region,
                "reducer": ee.Reducer.sum(),
                "scale": scale,
                "maxPixels": 1e12,
                "bestEffort": True,
            }
        )
        return img.set({"area": img_area})

    areas = images.map(cal_area)
    stats = areas.aggregate_array("area").getInfo()
    values = [item["water"] for item in stats]
    labels = areas.aggregate_array("system:index").getInfo()
    months = [label.split("_")[1] for label in labels]

    if frequency == "month":
        area_df = pd.DataFrame({"Month": labels, "Area": values, "month": months})
    else:
        dates = [d[:4] for d in labels]
        data_dict = {"Date": labels, "Year": dates, "Area": values}
        df = pd.DataFrame(data_dict)
        result = df.groupby("Year").agg(reducer)
        area_df = pd.DataFrame({"Year": result.index, "Area": result["Area"]})
        area_df = area_df.reset_index(drop=True)

    if return_df:
        return area_df
    else:
        labels = {}

        if x_label is not None:
            labels[frequency.title()] = x_label
        if y_label is not None:
            labels["Area"] = y_label

        fig = px.bar(
            area_df,
            x=frequency.title(),
            y="Area",
            labels=labels,
            title=title,
            width=width,
            height=height,
            **kwargs,
        )

        fig.update_layout(**layout_args)

        return fig

jslink_slider_label(slider, label)

Link a slider and a label.

Parameters:

Name	Type	Description	Default
slider	IntSlider | FloatSlider	The slider.	required
label	Label	The label.	required

Source code in geemap/common.py

def jslink_slider_label(slider, label):
    """Link a slider and a label.

    Args:
        slider (ipywidgets.IntSlider | ipywidgets.FloatSlider): The slider.
        label (ipywidgets.Label): The label.
    """

    def update_label(change):
        if change["name"]:
            label.value = str(change["new"])

    slider.observe(update_label, "value")

kml_to_ee(in_kml, **kwargs)

Converts a KML to ee.FeatureCollection.

Parameters:

Name	Type	Description	Default
in_kml	str	The file path to the input KML.	required

Raises:

Type	Description
FileNotFoundError	The input KML could not be found.

Returns:

Name	Type	Description
object		ee.FeatureCollection

Source code in geemap/common.py

def kml_to_ee(in_kml, **kwargs):
    """Converts a KML to ee.FeatureCollection.

    Args:
        in_kml (str): The file path to the input KML.

    Raises:
        FileNotFoundError: The input KML could not be found.

    Returns:
        object: ee.FeatureCollection
    """

    warnings.filterwarnings("ignore")

    in_kml = os.path.abspath(in_kml)
    if not os.path.exists(in_kml):
        raise FileNotFoundError("The input KML could not be found.")

    out_json = os.path.join(os.getcwd(), "tmp.geojson")

    check_package(name="geopandas", URL="https://geopandas.org")

    kml_to_geojson(in_kml, out_json, **kwargs)
    ee_object = geojson_to_ee(out_json)
    os.remove(out_json)
    return ee_object

kml_to_geojson(in_kml, out_geojson=None, **kwargs)

Converts a KML to GeoJSON.

Parameters:

Name	Type	Description	Default
in_kml	str	The file path to the input KML.	required
out_geojson	str	The file path to the output GeoJSON. Defaults to None.	None

Raises:

Type	Description
FileNotFoundError	The input KML could not be found.
TypeError	The output must be a GeoJSON.

Source code in geemap/common.py

def kml_to_geojson(in_kml, out_geojson=None, **kwargs):
    """Converts a KML to GeoJSON.

    Args:
        in_kml (str): The file path to the input KML.
        out_geojson (str): The file path to the output GeoJSON. Defaults to None.

    Raises:
        FileNotFoundError: The input KML could not be found.
        TypeError: The output must be a GeoJSON.
    """

    warnings.filterwarnings("ignore")

    in_kml = os.path.abspath(in_kml)
    if not os.path.exists(in_kml):
        raise FileNotFoundError("The input KML could not be found.")

    if out_geojson is not None:
        out_geojson = os.path.abspath(out_geojson)
        ext = os.path.splitext(out_geojson)[1].lower()
        if ext not in [".json", ".geojson"]:
            raise TypeError("The output file must be a GeoJSON.")

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

    check_package(name="geopandas", URL="https://geopandas.org")

    import geopandas as gpd

    gdf = gpd.read_file(in_kml, driver="KML", **kwargs)

    if out_geojson is not None:
        gdf.to_file(out_geojson, driver="GeoJSON", **kwargs)
    else:
        return gdf.__geo_interface__

kml_to_shp(in_kml, out_shp, **kwargs)

Converts a KML to shapefile.

Parameters:

Name	Type	Description	Default
in_kml	str	The file path to the input KML.	required
out_shp	str	The file path to the output shapefile.	required

Raises:

Type	Description
FileNotFoundError	The input KML could not be found.
TypeError	The output must be a shapefile.

Source code in geemap/common.py

def kml_to_shp(in_kml, out_shp, **kwargs):
    """Converts a KML to shapefile.

    Args:
        in_kml (str): The file path to the input KML.
        out_shp (str): The file path to the output shapefile.

    Raises:
        FileNotFoundError: The input KML could not be found.
        TypeError: The output must be a shapefile.
    """

    warnings.filterwarnings("ignore")

    in_kml = os.path.abspath(in_kml)
    if not os.path.exists(in_kml):
        raise FileNotFoundError("The input KML could not be found.")

    out_shp = os.path.abspath(out_shp)
    if not out_shp.endswith(".shp"):
        raise TypeError("The output must be a shapefile.")

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

    check_package(name="geopandas", URL="https://geopandas.org")

    import geopandas as gpd
    import fiona

    df = gpd.read_file(in_kml, driver="KML", **kwargs)
    df.to_file(out_shp, **kwargs)

kmz_to_ee(in_kmz, **kwargs)

Converts a KMZ to ee.FeatureCollection.

Parameters:

Name	Type	Description	Default
in_kmz	str	The file path to the input KMZ.	required

Raises:

Type	Description
FileNotFoundError	The input KMZ could not be found.

Returns:

Name	Type	Description
object		ee.FeatureCollection

Source code in geemap/common.py

def kmz_to_ee(in_kmz, **kwargs):
    """Converts a KMZ to ee.FeatureCollection.

    Args:
        in_kmz (str): The file path to the input KMZ.

    Raises:
        FileNotFoundError: The input KMZ could not be found.

    Returns:
        object: ee.FeatureCollection
    """
    in_kmz = os.path.abspath(in_kmz)
    if not os.path.exists(in_kmz):
        raise FileNotFoundError("The input KMZ could not be found.")

    out_dir = os.path.dirname(in_kmz)
    out_kml = os.path.join(out_dir, "doc.kml")
    with zipfile.ZipFile(in_kmz, "r") as zip_ref:
        zip_ref.extractall(out_dir)

    fc = kml_to_ee(out_kml, **kwargs)
    os.remove(out_kml)
    return fc

landsat_scaling(image, thermal_bands=True, apply_fmask=False)

Apply scaling factors to a Landsat image. See an example at https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2

Parameters:

Name	Type	Description	Default
image	Image	The input Landsat image.	required
thermal_bands	bool	Whether to apply scaling to thermal bands. Defaults to True.	True
apply_fmask	bool	Whether to apply Fmask cloud mask. Defaults to False.	False

Returns:

Type	Description
	ee.Image: The scaled Landsat image.

Source code in geemap/common.py

def landsat_scaling(image, thermal_bands=True, apply_fmask=False):
    """Apply scaling factors to a Landsat image. See an example at
        https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2

    Args:
        image (ee.Image): The input Landsat image.
        thermal_bands (bool, optional): Whether to apply scaling to thermal bands. Defaults to True.
        apply_fmask (bool, optional): Whether to apply Fmask cloud mask. Defaults to False.

    Returns:
        ee.Image: The scaled Landsat image.
    """

    # Apply the scaling factors to the appropriate bands.
    opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
    if thermal_bands:
        thermalBands = image.select("ST_B.*").multiply(0.00341802).add(149)

    if apply_fmask:
        # Replace the original bands with the scaled ones and apply the masks.
        # Bit 0 - Fill
        # Bit 1 - Dilated Cloud
        # Bit 2 - Cirrus
        # Bit 3 - Cloud
        # Bit 4 - Cloud Shadow
        qaMask = image.select("QA_PIXEL").bitwiseAnd(int("11111", 2)).eq(0)
        if thermal_bands:
            return (
                image.addBands(thermalBands, None, True)
                .addBands(opticalBands, None, True)
                .updateMask(qaMask)
            )
        else:
            return image.addBands(opticalBands, None, True).updateMask(qaMask)

    else:
        if thermal_bands:
            return image.addBands(thermalBands, None, True).addBands(
                opticalBands, None, True
            )
        else:
            return image.addBands(opticalBands, None, True)

landsat_timelapse(roi=None, out_gif=None, start_year=1984, end_year=None, start_date='06-10', end_date='09-20', bands=['NIR', 'Red', 'Green'], vis_params=None, dimensions=768, frames_per_second=5, crs='EPSG:3857', apply_fmask=True, nd_bands=None, nd_threshold=0, nd_palette=['black', 'blue'], overlay_data=None, overlay_color='black', overlay_width=1, overlay_opacity=1.0, frequency='year', date_format=None, title=None, title_xy=('2%', '90%'), add_text=True, text_xy=('2%', '2%'), text_sequence=None, font_type='arial.ttf', font_size=20, font_color='white', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, loop=0, mp4=False, fading=False, step=1)

Generates a Landsat timelapse GIF image. This function is adapted from https://emaprlab.users.earthengine.app/view/lt-gee-time-series-animator. A huge thank you to Justin Braaten for sharing his fantastic work.

Parameters:

Name	Type	Description	Default
roi	object	Region of interest to create the timelapse. Defaults to None.	None
out_gif	str	File path to the output animated GIF. Defaults to None.	None
start_year	int	Starting year for the timelapse. Defaults to 1984.	1984
end_year	int	Ending year for the timelapse. Defaults to None, which will use the current year.	None
start_date	str	Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.	'06-10'
end_date	str	Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.	'09-20'
bands	list	Three bands selected from ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa']. Defaults to ['NIR', 'Red', 'Green'].	['NIR', 'Red', 'Green']
vis_params	dict	Visualization parameters. Defaults to None.	None
dimensions	int	a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.	768
frames_per_second	int	Animation speed. Defaults to 5.	5
crs	str	The coordinate reference system to use. Defaults to "EPSG:3857".	'EPSG:3857'
apply_fmask	bool	Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.	True
nd_bands	list	A list of names specifying the bands to use, e.g., ['Green', 'SWIR1']. The normalized difference is computed as (first − second) / (first + second). Note that negative input values are forced to 0 so that the result is confined to the range (-1, 1).	None
nd_threshold	float	The threshold for extracting pixels from the normalized difference band.	0
nd_palette	list	The color palette to use for displaying the normalized difference band.	['black', 'blue']
overlay_data	(int, str, list)	Administrative boundary to be drawn on the timelapse. Defaults to None.	None
overlay_color	str	Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.	'black'
overlay_width	int	Line width of the overlay. Defaults to 1.	1
overlay_opacity	float	Opacity of the overlay. Defaults to 1.0.	1.0
frequency	str	Frequency of the timelapse. Defaults to 'year'.	'year'
date_format	str	Date format for the timelapse. Defaults to None.	None
title	str	The title of the timelapse. Defaults to None.	None
title_xy	tuple	Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
add_text	bool	Whether to add animated text to the timelapse. Defaults to True.	True
title_xy	tuple	Lower left corner of the text sequency. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
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'.	'white'
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
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
mp4	bool	Whether to convert the GIF to MP4. Defaults to False.	False
fading	int | bool	If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).	False
step	int	Step size for the timelapse. Defaults to 1.	1

Returns:

Name	Type	Description
str		File path to the output GIF image.

Source code in geemap/timelapse.py

def landsat_timelapse(
    roi=None,
    out_gif=None,
    start_year=1984,
    end_year=None,
    start_date="06-10",
    end_date="09-20",
    bands=["NIR", "Red", "Green"],
    vis_params=None,
    dimensions=768,
    frames_per_second=5,
    crs="EPSG:3857",
    apply_fmask=True,
    nd_bands=None,
    nd_threshold=0,
    nd_palette=["black", "blue"],
    overlay_data=None,
    overlay_color="black",
    overlay_width=1,
    overlay_opacity=1.0,
    frequency="year",
    date_format=None,
    title=None,
    title_xy=("2%", "90%"),
    add_text=True,
    text_xy=("2%", "2%"),
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="white",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    loop=0,
    mp4=False,
    fading=False,
    step=1,
):
    """Generates a Landsat timelapse GIF image. This function is adapted from https://emaprlab.users.earthengine.app/view/lt-gee-time-series-animator. A huge thank you to Justin Braaten for sharing his fantastic work.

    Args:
        roi (object, optional): Region of interest to create the timelapse. Defaults to None.
        out_gif (str, optional): File path to the output animated GIF. Defaults to None.
        start_year (int, optional): Starting year for the timelapse. Defaults to 1984.
        end_year (int, optional): Ending year for the timelapse. Defaults to None, which will use the current year.
        start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.
        end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.
        bands (list, optional): Three bands selected from ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa']. Defaults to ['NIR', 'Red', 'Green'].
        vis_params (dict, optional): Visualization parameters. Defaults to None.
        dimensions (int, optional): a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
        frames_per_second (int, optional): Animation speed. Defaults to 5.
        crs (str, optional): The coordinate reference system to use. Defaults to "EPSG:3857".
        apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.
        nd_bands (list, optional): A list of names specifying the bands to use, e.g., ['Green', 'SWIR1']. The normalized difference is computed as (first − second) / (first + second). Note that negative input values are forced to 0 so that the result is confined to the range (-1, 1).
        nd_threshold (float, optional): The threshold for extracting pixels from the normalized difference band.
        nd_palette (list, optional): The color palette to use for displaying the normalized difference band.
        overlay_data (int, str, list, optional): Administrative boundary to be drawn on the timelapse. Defaults to None.
        overlay_color (str, optional): Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.
        overlay_width (int, optional): Line width of the overlay. Defaults to 1.
        overlay_opacity (float, optional): Opacity of the overlay. Defaults to 1.0.
        frequency (str, optional): Frequency of the timelapse. Defaults to 'year'.
        date_format (str, optional): Date format for the timelapse. Defaults to None.
        title (str, optional): The title of the timelapse. Defaults to None.
        title_xy (tuple, optional): Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        add_text (bool, optional): Whether to add animated text to the timelapse. Defaults to True.
        title_xy (tuple, optional): Lower left corner of the text sequency. 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.
        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.
        mp4 (bool, optional): Whether to convert the GIF to MP4. Defaults to False.
        fading (int | bool, optional): If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).
        step (int, optional): Step size for the timelapse. Defaults to 1.

    Returns:
        str: File path to the output GIF image.
    """

    if roi is None:
        roi = ee.Geometry.Polygon(
            [
                [
                    [-115.471773, 35.892718],
                    [-115.471773, 36.409454],
                    [-114.271283, 36.409454],
                    [-114.271283, 35.892718],
                    [-115.471773, 35.892718],
                ]
            ],
            None,
            False,
        )
    elif isinstance(roi, ee.Feature) or isinstance(roi, ee.FeatureCollection):
        roi = roi.geometry()
    elif isinstance(roi, ee.Geometry):
        pass
    else:
        raise ValueError("The provided roi is invalid. It must be an ee.Geometry")

    if out_gif is None:
        out_gif = temp_file_path(".gif")
    elif not out_gif.endswith(".gif"):
        raise ValueError("The output file must end with .gif")
    else:
        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)

    if end_year is None:
        end_year = get_current_year()

    allowed_bands = ["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2", "pixel_qa"]

    if len(bands) == 3 and all(x in allowed_bands for x in bands):
        pass
    else:
        raise Exception(
            "You can only select 3 bands from the following: {}".format(
                ", ".join(allowed_bands)
            )
        )

    if nd_bands is not None:
        if len(nd_bands) == 2 and all(x in allowed_bands[:-1] for x in nd_bands):
            pass
        else:
            raise Exception(
                "You can only select two bands from the following: {}".format(
                    ", ".join(allowed_bands[:-1])
                )
            )

    try:
        if vis_params is None:
            vis_params = {}
            vis_params["bands"] = bands
            vis_params["min"] = 0
            vis_params["max"] = 0.4
            vis_params["gamma"] = [1, 1, 1]
        raw_col = landsat_timeseries(
            roi,
            start_year,
            end_year,
            start_date,
            end_date,
            apply_fmask,
            frequency,
            date_format,
            step,
        )

        col = raw_col.select(bands).map(
            lambda img: img.visualize(**vis_params).set(
                {
                    "system:time_start": img.get("system:time_start"),
                    "system:date": img.get("system:date"),
                }
            )
        )
        if overlay_data is not None:
            col = add_overlay(
                col, overlay_data, overlay_color, overlay_width, overlay_opacity
            )

        if (
            isinstance(dimensions, int)
            and dimensions > 768
            or isinstance(dimensions, str)
            and any(dim > 768 for dim in list(map(int, dimensions.split("x"))))
        ):
            count = col.size().getInfo()
            basename = os.path.basename(out_gif)[:-4]
            names = [
                os.path.join(
                    out_dir, f"{basename}_{str(i+1).zfill(int(len(str(count))))}.jpg"
                )
                for i in range(count)
            ]
            get_image_collection_thumbnails(
                col,
                out_dir,
                vis_params={
                    "min": 0,
                    "max": 255,
                    "bands": ["vis-red", "vis-green", "vis-blue"],
                },
                dimensions=dimensions,
                names=names,
            )
            make_gif(
                names,
                out_gif,
                fps=frames_per_second,
                loop=loop,
                mp4=False,
                clean_up=True,
            )

        else:
            video_args = vis_params.copy()
            video_args["dimensions"] = dimensions
            video_args["region"] = roi
            video_args["framesPerSecond"] = frames_per_second
            video_args["crs"] = crs
            video_args["bands"] = ["vis-red", "vis-green", "vis-blue"]
            video_args["min"] = 0
            video_args["max"] = 255

            download_ee_video(col, video_args, out_gif)

        if os.path.exists(out_gif):
            if title is not None and isinstance(title, str):
                add_text_to_gif(
                    out_gif,
                    out_gif,
                    xy=title_xy,
                    text_sequence=title,
                    font_type=font_type,
                    font_size=font_size,
                    font_color=font_color,
                    add_progress_bar=add_progress_bar,
                    progress_bar_color=progress_bar_color,
                    progress_bar_height=progress_bar_height,
                    duration=1000 / frames_per_second,
                    loop=loop,
                )
            if add_text:
                if text_sequence is None:
                    text_sequence = col.aggregate_array("system:date").getInfo()
                add_text_to_gif(
                    out_gif,
                    out_gif,
                    xy=text_xy,
                    text_sequence=text_sequence,
                    font_type=font_type,
                    font_size=font_size,
                    font_color=font_color,
                    add_progress_bar=add_progress_bar,
                    progress_bar_color=progress_bar_color,
                    progress_bar_height=progress_bar_height,
                    duration=1000 / frames_per_second,
                    loop=loop,
                )

        if nd_bands is not None:
            nd_images = landsat_ts_norm_diff(
                raw_col, bands=nd_bands, threshold=nd_threshold
            )
            out_nd_gif = out_gif.replace(".gif", "_nd.gif")
            landsat_ts_norm_diff_gif(
                nd_images,
                out_gif=out_nd_gif,
                vis_params=None,
                palette=nd_palette,
                dimensions=dimensions,
                frames_per_second=frames_per_second,
            )

        if os.path.exists(out_gif):
            reduce_gif_size(out_gif)

        if isinstance(fading, bool):
            fading = int(fading)
        if fading > 0:
            gif_fading(out_gif, out_gif, duration=fading, verbose=False)

        if mp4:
            out_mp4 = out_gif.replace(".gif", ".mp4")
            gif_to_mp4(out_gif, out_mp4)

        return out_gif

    except Exception as e:
        raise Exception(e)

landsat_timelapse_legacy(roi=None, out_gif=None, start_year=1984, end_year=None, start_date='06-10', end_date='09-20', bands=['NIR', 'Red', 'Green'], vis_params=None, dimensions=768, frames_per_second=5, crs='EPSG:3857', apply_fmask=True, nd_bands=None, nd_threshold=0, nd_palette=['black', 'blue'], overlay_data=None, overlay_color='black', overlay_width=1, overlay_opacity=1.0, frequency='year', date_format=None, title=None, title_xy=('2%', '90%'), add_text=True, text_xy=('2%', '2%'), text_sequence=None, font_type='arial.ttf', font_size=20, font_color='white', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, loop=0, mp4=False, fading=False)

Generates a Landsat timelapse GIF image. This function is adapted from https://emaprlab.users.earthengine.app/view/lt-gee-time-series-animator. A huge thank you to Justin Braaten for sharing his fantastic work.

Parameters:

Name	Type	Description	Default
roi	object	Region of interest to create the timelapse. Defaults to None.	None
out_gif	str	File path to the output animated GIF. Defaults to None.	None
start_year	int	Starting year for the timelapse. Defaults to 1984.	1984
end_year	int	Ending year for the timelapse. Defaults to None, which means the current year.	None
start_date	str	Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.	'06-10'
end_date	str	Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.	'09-20'
bands	list	Three bands selected from ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa']. Defaults to ['NIR', 'Red', 'Green'].	['NIR', 'Red', 'Green']
vis_params	dict	Visualization parameters. Defaults to None.	None
dimensions	int	a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.	768
frames_per_second	int	Animation speed. Defaults to 5.	5
crs	str	The coordinate reference system to use. Defaults to "EPSG:3857".	'EPSG:3857'
apply_fmask	bool	Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.	True
nd_bands	list	A list of names specifying the bands to use, e.g., ['Green', 'SWIR1']. The normalized difference is computed as (first − second) / (first + second). Note that negative input values are forced to 0 so that the result is confined to the range (-1, 1).	None
nd_threshold	float	The threshold for extracting pixels from the normalized difference band.	0
nd_palette	list	The color palette to use for displaying the normalized difference band.	['black', 'blue']
overlay_data	(int, str, list)	Administrative boundary to be drawn on the timelapse. Defaults to None.	None
overlay_color	str	Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.	'black'
overlay_width	int	Line width of the overlay. Defaults to 1.	1
overlay_opacity	float	Opacity of the overlay. Defaults to 1.0.	1.0
frequency	str	Frequency of the timelapse. Defaults to 'year'.	'year'
date_format	str	Date format for the timelapse. Defaults to None.	None
title	str	The title of the timelapse. Defaults to None.	None
title_xy	tuple	Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
add_text	bool	Whether to add animated text to the timelapse. Defaults to True.	True
title_xy	tuple	Lower left corner of the text sequency. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
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'.	'white'
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
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
mp4	bool	Whether to convert the GIF to MP4. Defaults to False.	False
fading	int | bool	If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).	False

Returns:

Name	Type	Description
str		File path to the output GIF image.

Source code in geemap/timelapse.py

def landsat_timelapse_legacy(
    roi=None,
    out_gif=None,
    start_year=1984,
    end_year=None,
    start_date="06-10",
    end_date="09-20",
    bands=["NIR", "Red", "Green"],
    vis_params=None,
    dimensions=768,
    frames_per_second=5,
    crs="EPSG:3857",
    apply_fmask=True,
    nd_bands=None,
    nd_threshold=0,
    nd_palette=["black", "blue"],
    overlay_data=None,
    overlay_color="black",
    overlay_width=1,
    overlay_opacity=1.0,
    frequency="year",
    date_format=None,
    title=None,
    title_xy=("2%", "90%"),
    add_text=True,
    text_xy=("2%", "2%"),
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="white",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    loop=0,
    mp4=False,
    fading=False,
):
    """Generates a Landsat timelapse GIF image. This function is adapted from https://emaprlab.users.earthengine.app/view/lt-gee-time-series-animator. A huge thank you to Justin Braaten for sharing his fantastic work.

    Args:
        roi (object, optional): Region of interest to create the timelapse. Defaults to None.
        out_gif (str, optional): File path to the output animated GIF. Defaults to None.
        start_year (int, optional): Starting year for the timelapse. Defaults to 1984.
        end_year (int, optional): Ending year for the timelapse. Defaults to None, which means the current year.
        start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.
        end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.
        bands (list, optional): Three bands selected from ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'pixel_qa']. Defaults to ['NIR', 'Red', 'Green'].
        vis_params (dict, optional): Visualization parameters. Defaults to None.
        dimensions (int, optional): a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
        frames_per_second (int, optional): Animation speed. Defaults to 5.
        crs (str, optional): The coordinate reference system to use. Defaults to "EPSG:3857".
        apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.
        nd_bands (list, optional): A list of names specifying the bands to use, e.g., ['Green', 'SWIR1']. The normalized difference is computed as (first − second) / (first + second). Note that negative input values are forced to 0 so that the result is confined to the range (-1, 1).
        nd_threshold (float, optional): The threshold for extracting pixels from the normalized difference band.
        nd_palette (list, optional): The color palette to use for displaying the normalized difference band.
        overlay_data (int, str, list, optional): Administrative boundary to be drawn on the timelapse. Defaults to None.
        overlay_color (str, optional): Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.
        overlay_width (int, optional): Line width of the overlay. Defaults to 1.
        overlay_opacity (float, optional): Opacity of the overlay. Defaults to 1.0.
        frequency (str, optional): Frequency of the timelapse. Defaults to 'year'.
        date_format (str, optional): Date format for the timelapse. Defaults to None.
        title (str, optional): The title of the timelapse. Defaults to None.
        title_xy (tuple, optional): Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        add_text (bool, optional): Whether to add animated text to the timelapse. Defaults to True.
        title_xy (tuple, optional): Lower left corner of the text sequency. 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.
        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.
        mp4 (bool, optional): Whether to convert the GIF to MP4. Defaults to False.
        fading (int | bool, optional): If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).

    Returns:
        str: File path to the output GIF image.
    """

    if end_year is None:
        end_year = get_current_year()

    if roi is None:
        roi = ee.Geometry.Polygon(
            [
                [
                    [-115.471773, 35.892718],
                    [-115.471773, 36.409454],
                    [-114.271283, 36.409454],
                    [-114.271283, 35.892718],
                    [-115.471773, 35.892718],
                ]
            ],
            None,
            False,
        )
    elif isinstance(roi, ee.Feature) or isinstance(roi, ee.FeatureCollection):
        roi = roi.geometry()
    elif isinstance(roi, ee.Geometry):
        pass
    else:
        raise ValueError("The provided roi is invalid. It must be an ee.Geometry")

    if out_gif is None:
        out_gif = temp_file_path(".gif")
    elif not out_gif.endswith(".gif"):
        raise ValueError("The output file must end with .gif")
    else:
        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)

    allowed_bands = ["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2", "pixel_qa"]

    if len(bands) == 3 and all(x in allowed_bands for x in bands):
        pass
    else:
        raise Exception(
            "You can only select 3 bands from the following: {}".format(
                ", ".join(allowed_bands)
            )
        )

    if nd_bands is not None:
        if len(nd_bands) == 2 and all(x in allowed_bands[:-1] for x in nd_bands):
            pass
        else:
            raise Exception(
                "You can only select two bands from the following: {}".format(
                    ", ".join(allowed_bands[:-1])
                )
            )

    try:
        if vis_params is None:
            vis_params = {}
            vis_params["bands"] = bands
            vis_params["min"] = 0
            vis_params["max"] = 4000
            vis_params["gamma"] = [1, 1, 1]
        raw_col = landsat_timeseries(
            roi,
            start_year,
            end_year,
            start_date,
            end_date,
            apply_fmask,
            frequency,
            date_format,
        )

        col = raw_col.select(bands).map(
            lambda img: img.visualize(**vis_params).set(
                {
                    "system:time_start": img.get("system:time_start"),
                    "system:date": img.get("system:date"),
                }
            )
        )
        if overlay_data is not None:
            col = add_overlay(
                col, overlay_data, overlay_color, overlay_width, overlay_opacity
            )

        if (
            isinstance(dimensions, int)
            and dimensions > 768
            or isinstance(dimensions, str)
            and any(dim > 768 for dim in list(map(int, dimensions.split("x"))))
        ):
            count = col.size().getInfo()
            basename = os.path.basename(out_gif)[:-4]
            names = [
                os.path.join(
                    out_dir, f"{basename}_{str(i+1).zfill(int(len(str(count))))}.jpg"
                )
                for i in range(count)
            ]
            get_image_collection_thumbnails(
                col,
                out_dir,
                vis_params={
                    "min": 0,
                    "max": 255,
                    "bands": ["vis-red", "vis-green", "vis-blue"],
                },
                dimensions=dimensions,
                names=names,
            )
            make_gif(
                names,
                out_gif,
                fps=frames_per_second,
                loop=loop,
                mp4=False,
                clean_up=True,
            )

        else:
            video_args = vis_params.copy()
            video_args["dimensions"] = dimensions
            video_args["region"] = roi
            video_args["framesPerSecond"] = frames_per_second
            video_args["crs"] = crs
            video_args["bands"] = ["vis-red", "vis-green", "vis-blue"]
            video_args["min"] = 0
            video_args["max"] = 255

            download_ee_video(col, video_args, out_gif)

        if os.path.exists(out_gif):
            if title is not None and isinstance(title, str):
                add_text_to_gif(
                    out_gif,
                    out_gif,
                    xy=title_xy,
                    text_sequence=title,
                    font_type=font_type,
                    font_size=font_size,
                    font_color=font_color,
                    add_progress_bar=add_progress_bar,
                    progress_bar_color=progress_bar_color,
                    progress_bar_height=progress_bar_height,
                    duration=1000 / frames_per_second,
                    loop=loop,
                )
            if add_text:
                if text_sequence is None:
                    text_sequence = col.aggregate_array("system:date").getInfo()
                add_text_to_gif(
                    out_gif,
                    out_gif,
                    xy=text_xy,
                    text_sequence=text_sequence,
                    font_type=font_type,
                    font_size=font_size,
                    font_color=font_color,
                    add_progress_bar=add_progress_bar,
                    progress_bar_color=progress_bar_color,
                    progress_bar_height=progress_bar_height,
                    duration=1000 / frames_per_second,
                    loop=loop,
                )

        if nd_bands is not None:
            nd_images = landsat_ts_norm_diff(
                raw_col, bands=nd_bands, threshold=nd_threshold
            )
            out_nd_gif = out_gif.replace(".gif", "_nd.gif")
            landsat_ts_norm_diff_gif(
                nd_images,
                out_gif=out_nd_gif,
                vis_params=None,
                palette=nd_palette,
                dimensions=dimensions,
                frames_per_second=frames_per_second,
            )

        if os.path.exists(out_gif):
            reduce_gif_size(out_gif)

        if isinstance(fading, bool):
            fading = int(fading)
        if fading > 0:
            gif_fading(out_gif, out_gif, duration=fading, verbose=False)

        if mp4:
            out_mp4 = out_gif.replace(".gif", ".mp4")
            gif_to_mp4(out_gif, out_mp4)

        return out_gif

    except Exception as e:
        raise Exception(e)

landsat_timeseries(roi=None, start_year=1984, end_year=None, start_date='06-10', end_date='09-20', apply_fmask=True, frequency='year', date_format=None, step=1)

Generates an annual Landsat ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work.

Parameters:

Name	Type	Description	Default
roi	object	Region of interest to create the timelapse. Defaults to None.	None
start_year	int	Starting year for the timelapse. Defaults to 1984.	1984
end_year	int	Ending year for the timelapse. Defaults to None, which means the current year.	None
start_date	str	Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.	'06-10'
end_date	str	Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.	'09-20'
apply_fmask	bool	Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.	True
frequency	str	Frequency of the timelapse. Defaults to 'year'.	'year'
date_format	str	Format of the date. Defaults to None.	None
step	int	The step size to use when creating the date sequence. Defaults to 1.	1

Source code in geemap/timelapse.py

def landsat_timeseries(
    roi=None,
    start_year=1984,
    end_year=None,
    start_date="06-10",
    end_date="09-20",
    apply_fmask=True,
    frequency="year",
    date_format=None,
    step=1,
):
    """Generates an annual Landsat ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work.

    Args:
        roi (object, optional): Region of interest to create the timelapse. Defaults to None.
        start_year (int, optional): Starting year for the timelapse. Defaults to 1984.
        end_year (int, optional): Ending year for the timelapse. Defaults to None, which means the current year.
        start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.
        end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.
        apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.
        frequency (str, optional): Frequency of the timelapse. Defaults to 'year'.
        date_format (str, optional): Format of the date. Defaults to None.
        step (int, optional): The step size to use when creating the date sequence. Defaults to 1.
    Returns:
        object: Returns an ImageCollection containing annual Landsat images.
    """

    # Input and output parameters.
    import re

    if roi is None:
        roi = ee.Geometry.Polygon(
            [
                [
                    [-115.471773, 35.892718],
                    [-115.471773, 36.409454],
                    [-114.271283, 36.409454],
                    [-114.271283, 35.892718],
                    [-115.471773, 35.892718],
                ]
            ],
            None,
            False,
        )

    if end_year is None:
        end_year = get_current_year()

    if not isinstance(roi, ee.Geometry):
        try:
            roi = roi.geometry()
        except Exception as e:
            print("Could not convert the provided roi to ee.Geometry")
            print(e)
            return

    feq_dict = {
        "year": "YYYY",
        "month": "YYYY-MM",
        "quarter": "YYYY-MM",
    }

    if date_format is None:
        date_format = feq_dict[frequency]

    if frequency not in feq_dict:
        raise ValueError("frequency must be year, quarter, or month.")

    # Setup vars to get dates.
    if (
        isinstance(start_year, int)
        and (start_year >= 1984)
        and (start_year < get_current_year())
    ):
        pass
    else:
        print("The start year must be an integer >= 1984.")
        return

    if (
        isinstance(end_year, int)
        and (end_year > 1984)
        and (end_year <= get_current_year())
    ):
        pass
    else:
        print(f"The end year must be an integer <= {get_current_year()}.")
        return

    if re.match(r"[0-9]{2}-[0-9]{2}", start_date) and re.match(
        r"[0-9]{2}-[0-9]{2}", end_date
    ):
        pass
    else:
        print("The start date and end date must be month-day, such as 06-10, 09-20")
        return

    try:
        datetime.datetime(int(start_year), int(start_date[:2]), int(start_date[3:5]))
        datetime.datetime(int(end_year), int(end_date[:2]), int(end_date[3:5]))
    except Exception as e:
        print("The input dates are invalid.")
        raise Exception(e)

    def days_between(d1, d2):
        d1 = datetime.datetime.strptime(d1, "%Y-%m-%d")
        d2 = datetime.datetime.strptime(d2, "%Y-%m-%d")
        return abs((d2 - d1).days)

    n_days = days_between(
        str(start_year) + "-" + start_date, str(start_year) + "-" + end_date
    )
    start_month = int(start_date[:2])
    start_day = int(start_date[3:5])

    # Landsat collection preprocessingEnabled
    # Get Landsat surface reflectance collections for OLI, ETM+ and TM sensors.
    LC09col = ee.ImageCollection("LANDSAT/LC09/C02/T1_L2")
    LC08col = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2")
    LE07col = ee.ImageCollection("LANDSAT/LE07/C02/T1_L2")
    LT05col = ee.ImageCollection("LANDSAT/LT05/C02/T1_L2")
    LT04col = ee.ImageCollection("LANDSAT/LT04/C02/T1_L2")

    # Define a collection filter by date, bounds, and quality.
    def colFilter(col, roi, start_date, end_date):
        return col.filterBounds(roi).filterDate(start_date, end_date)

    # Function to get and rename bands of interest from OLI.
    def renameOli(img):
        return img.select(
            ["SR_B2", "SR_B3", "SR_B4", "SR_B5", "SR_B6", "SR_B7"],
            ["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2"],
        )

    # Function to get and rename bands of interest from ETM+.
    def renameEtm(img):
        return img.select(
            ["SR_B1", "SR_B2", "SR_B3", "SR_B4", "SR_B5", "SR_B7"],
            ["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2"],
        )

    def fmask(image):
        # see https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2
        # Bit 0 - Fill
        # Bit 1 - Dilated Cloud
        # Bit 2 - Cirrus
        # Bit 3 - Cloud
        # Bit 4 - Cloud Shadow
        qaMask = image.select("QA_PIXEL").bitwiseAnd(int("11111", 2)).eq(0)

        # Apply the scaling factors to the appropriate bands.
        opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)

        # Replace the original bands with the scaled ones and apply the masks.
        return image.addBands(opticalBands, None, True).updateMask(qaMask)

    # Define function to prepare OLI images.
    def prepOli(img):
        orig = img
        if apply_fmask:
            img = fmask(img)
        else:
            img = img.select("SR_B.").multiply(0.0000275).add(-0.2)
        img = renameOli(img)
        return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample(
            "bicubic"
        )

    # Define function to prepare ETM+ images.
    def prepEtm(img):
        orig = img
        if apply_fmask:
            img = fmask(img)
        else:
            img = img.select("SR_B.").multiply(0.0000275).add(-0.2)
        img = renameEtm(img)
        return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample(
            "bicubic"
        )

    # Get annual median collection.
    def getAnnualComp(y):
        startDate = ee.Date.fromYMD(
            ee.Number(y), ee.Number(start_month), ee.Number(start_day)
        )
        endDate = startDate.advance(ee.Number(n_days), "day")

        # Filter collections and prepare them for merging.
        LC09coly = colFilter(LC09col, roi, startDate, endDate).map(prepOli)
        LC08coly = colFilter(LC08col, roi, startDate, endDate).map(prepOli)
        LE07coly = colFilter(LE07col, roi, startDate, endDate).map(prepEtm)
        LT05coly = colFilter(LT05col, roi, startDate, endDate).map(prepEtm)
        LT04coly = colFilter(LT04col, roi, startDate, endDate).map(prepEtm)

        # Merge the collections.
        col = LC09coly.merge(LC08coly).merge(LE07coly).merge(LT05coly).merge(LT04coly)

        yearImg = col.median()
        nBands = yearImg.bandNames().size()
        yearImg = ee.Image(ee.Algorithms.If(nBands, yearImg, dummyImg))
        return yearImg.set(
            {
                "year": y,
                "system:time_start": startDate.millis(),
                "nBands": nBands,
                "system:date": ee.Date(startDate).format(date_format),
            }
        )

    # Get monthly median collection.
    def getMonthlyComp(startDate):
        startDate = ee.Date(startDate)
        endDate = startDate.advance(1, "month")

        # Filter collections and prepare them for merging.
        LC09coly = colFilter(LC09col, roi, startDate, endDate).map(prepOli)
        LC08coly = colFilter(LC08col, roi, startDate, endDate).map(prepOli)
        LE07coly = colFilter(LE07col, roi, startDate, endDate).map(prepEtm)
        LT05coly = colFilter(LT05col, roi, startDate, endDate).map(prepEtm)
        LT04coly = colFilter(LT04col, roi, startDate, endDate).map(prepEtm)

        # Merge the collections.
        col = LC09coly.merge(LC08coly).merge(LE07coly).merge(LT05coly).merge(LT04coly)

        monthImg = col.median()
        nBands = monthImg.bandNames().size()
        monthImg = ee.Image(ee.Algorithms.If(nBands, monthImg, dummyImg))
        return monthImg.set(
            {
                "system:time_start": startDate.millis(),
                "nBands": nBands,
                "system:date": ee.Date(startDate).format(date_format),
            }
        )

    # Get quarterly median collection.
    def getQuarterlyComp(startDate):
        startDate = ee.Date(startDate)

        endDate = startDate.advance(3, "month")

        # Filter collections and prepare them for merging.
        LC09coly = colFilter(LC09col, roi, startDate, endDate).map(prepOli)
        LC08coly = colFilter(LC08col, roi, startDate, endDate).map(prepOli)
        LE07coly = colFilter(LE07col, roi, startDate, endDate).map(prepEtm)
        LT05coly = colFilter(LT05col, roi, startDate, endDate).map(prepEtm)
        LT04coly = colFilter(LT04col, roi, startDate, endDate).map(prepEtm)

        # Merge the collections.
        col = LC09coly.merge(LC08coly).merge(LE07coly).merge(LT05coly).merge(LT04coly)

        quarter = col.median()
        nBands = quarter.bandNames().size()
        quarter = ee.Image(ee.Algorithms.If(nBands, quarter, dummyImg))
        return quarter.set(
            {
                "system:time_start": startDate.millis(),
                "nBands": nBands,
                "system:date": ee.Date(startDate).format(date_format),
            }
        )

    # Make a dummy image for missing years.
    bandNames = ee.List(["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2", "pixel_qa"])
    fillerValues = ee.List.repeat(0, bandNames.size())
    dummyImg = ee.Image.constant(fillerValues).rename(bandNames).selfMask().int16()

    # Make list of /quarterly/monthly image composites.

    if frequency == "year":
        years = ee.List.sequence(start_year, end_year, step)
        imgList = years.map(getAnnualComp)
    elif frequency == "quarter":
        quarters = date_sequence(
            str(start_year) + "-01-01",
            str(end_year) + "-12-31",
            "quarter",
            date_format,
            step,
        )
        imgList = quarters.map(getQuarterlyComp)
    elif frequency == "month":
        months = date_sequence(
            str(start_year) + "-01-01",
            str(end_year) + "-12-31",
            "month",
            date_format,
            step,
        )
        imgList = months.map(getMonthlyComp)

    # Convert image composite list to collection
    imgCol = ee.ImageCollection.fromImages(imgList)

    imgCol = imgCol.map(
        lambda img: img.clip(roi).set({"coordinates": roi.coordinates()})
    )

    return imgCol

landsat_timeseries_legacy(roi=None, start_year=1984, end_year=2021, start_date='06-10', end_date='09-20', apply_fmask=True, frequency='year', date_format=None)

Generates an annual Landsat ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work. Args: roi (object, optional): Region of interest to create the timelapse. Defaults to None. start_year (int, optional): Starting year for the timelapse. Defaults to 1984. end_year (int, optional): Ending year for the timelapse. Defaults to 2021. start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'. end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'. apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking. frequency (str, optional): Frequency of the timelapse. Defaults to 'year'. date_format (str, optional): Format of the date. Defaults to None. Returns: object: Returns an ImageCollection containing annual Landsat images.

Source code in geemap/timelapse.py

def landsat_timeseries_legacy(
    roi=None,
    start_year=1984,
    end_year=2021,
    start_date="06-10",
    end_date="09-20",
    apply_fmask=True,
    frequency="year",
    date_format=None,
):
    """Generates an annual Landsat ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work.
    Args:
        roi (object, optional): Region of interest to create the timelapse. Defaults to None.
        start_year (int, optional): Starting year for the timelapse. Defaults to 1984.
        end_year (int, optional): Ending year for the timelapse. Defaults to 2021.
        start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.
        end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.
        apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.
        frequency (str, optional): Frequency of the timelapse. Defaults to 'year'.
        date_format (str, optional): Format of the date. Defaults to None.
    Returns:
        object: Returns an ImageCollection containing annual Landsat images.
    """

    ################################################################################
    # Input and output parameters.
    import re

    # import datetime

    if roi is None:
        roi = ee.Geometry.Polygon(
            [
                [
                    [-115.471773, 35.892718],
                    [-115.471773, 36.409454],
                    [-114.271283, 36.409454],
                    [-114.271283, 35.892718],
                    [-115.471773, 35.892718],
                ]
            ],
            None,
            False,
        )

    if not isinstance(roi, ee.Geometry):
        try:
            roi = roi.geometry()
        except Exception as e:
            print("Could not convert the provided roi to ee.Geometry")
            print(e)
            return

    feq_dict = {
        "year": "YYYY",
        "month": "YYYY-MM",
        "quarter": "YYYY-MM",
    }

    if date_format is None:
        date_format = feq_dict[frequency]

    if frequency not in feq_dict:
        raise ValueError("frequency must be year, quarter, or month.")

    ################################################################################

    # Setup vars to get dates.
    if isinstance(start_year, int) and (start_year >= 1984) and (start_year < 2021):
        pass
    else:
        print("The start year must be an integer >= 1984.")
        return

    if isinstance(end_year, int) and (end_year > 1984) and (end_year <= 2021):
        pass
    else:
        print("The end year must be an integer <= 2021.")
        return

    if re.match(r"[0-9]{2}-[0-9]{2}", start_date) and re.match(
        r"[0-9]{2}-[0-9]{2}", end_date
    ):
        pass
    else:
        print("The start date and end date must be month-day, such as 06-10, 09-20")
        return

    try:
        datetime.datetime(int(start_year), int(start_date[:2]), int(start_date[3:5]))
        datetime.datetime(int(end_year), int(end_date[:2]), int(end_date[3:5]))
    except Exception as e:
        print("The input dates are invalid.")
        raise Exception(e)

    def days_between(d1, d2):
        d1 = datetime.datetime.strptime(d1, "%Y-%m-%d")
        d2 = datetime.datetime.strptime(d2, "%Y-%m-%d")
        return abs((d2 - d1).days)

    n_days = days_between(
        str(start_year) + "-" + start_date, str(start_year) + "-" + end_date
    )
    start_month = int(start_date[:2])
    start_day = int(start_date[3:5])
    # start_date = str(start_year) + "-" + start_date
    # end_date = str(end_year) + "-" + end_date

    # # Define a collection filter by date, bounds, and quality.
    # def colFilter(col, aoi):  # , startDate, endDate):
    #     return(col.filterBounds(aoi))

    # Landsat collection preprocessingEnabled
    # Get Landsat surface reflectance collections for OLI, ETM+ and TM sensors.
    LC08col = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
    LE07col = ee.ImageCollection("LANDSAT/LE07/C01/T1_SR")
    LT05col = ee.ImageCollection("LANDSAT/LT05/C01/T1_SR")
    LT04col = ee.ImageCollection("LANDSAT/LT04/C01/T1_SR")

    # Define a collection filter by date, bounds, and quality.
    def colFilter(col, roi, start_date, end_date):
        return col.filterBounds(roi).filterDate(start_date, end_date)
        # .filter('CLOUD_COVER < 5')
        # .filter('GEOMETRIC_RMSE_MODEL < 15')
        # .filter('IMAGE_QUALITY == 9 || IMAGE_QUALITY_OLI == 9'))

    # Function to get and rename bands of interest from OLI.
    def renameOli(img):
        return img.select(
            ["B2", "B3", "B4", "B5", "B6", "B7", "pixel_qa"],
            ["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2", "pixel_qa"],
        )

    # Function to get and rename bands of interest from ETM+.
    def renameEtm(img):
        return img.select(
            ["B1", "B2", "B3", "B4", "B5", "B7", "pixel_qa"],
            ["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2", "pixel_qa"],
        )

    # Add NBR for LandTrendr segmentation.
    def calcNbr(img):
        return img.addBands(
            img.normalizedDifference(["NIR", "SWIR2"]).multiply(-10000).rename("NBR")
        ).int16()

    # Define function to mask out clouds and cloud shadows in images.
    # Use CFmask band included in USGS Landsat SR image product.
    def fmask(img):
        cloudShadowBitMask = 1 << 3
        cloudsBitMask = 1 << 5
        qa = img.select("pixel_qa")
        mask = (
            qa.bitwiseAnd(cloudShadowBitMask)
            .eq(0)
            .And(qa.bitwiseAnd(cloudsBitMask).eq(0))
        )
        return img.updateMask(mask)

    # Define function to prepare OLI images.
    def prepOli(img):
        orig = img
        img = renameOli(img)
        if apply_fmask:
            img = fmask(img)
        return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample(
            "bicubic"
        )

    # Define function to prepare ETM+ images.
    def prepEtm(img):
        orig = img
        img = renameEtm(img)
        if apply_fmask:
            img = fmask(img)
        return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample(
            "bicubic"
        )

    # Get annual median collection.
    def getAnnualComp(y):
        startDate = ee.Date.fromYMD(
            ee.Number(y), ee.Number(start_month), ee.Number(start_day)
        )
        endDate = startDate.advance(ee.Number(n_days), "day")

        # Filter collections and prepare them for merging.
        LC08coly = colFilter(LC08col, roi, startDate, endDate).map(prepOli)
        LE07coly = colFilter(LE07col, roi, startDate, endDate).map(prepEtm)
        LT05coly = colFilter(LT05col, roi, startDate, endDate).map(prepEtm)
        LT04coly = colFilter(LT04col, roi, startDate, endDate).map(prepEtm)

        # Merge the collections.
        col = LC08coly.merge(LE07coly).merge(LT05coly).merge(LT04coly)

        yearImg = col.median()
        nBands = yearImg.bandNames().size()
        yearImg = ee.Image(ee.Algorithms.If(nBands, yearImg, dummyImg))
        return calcNbr(yearImg).set(
            {
                "year": y,
                "system:time_start": startDate.millis(),
                "nBands": nBands,
                "system:date": ee.Date(startDate).format(date_format),
            }
        )

    # Get monthly median collection.
    def getMonthlyComp(startDate):
        startDate = ee.Date(startDate)
        endDate = startDate.advance(1, "month")

        # Filter collections and prepare them for merging.
        LC08coly = colFilter(LC08col, roi, startDate, endDate).map(prepOli)
        LE07coly = colFilter(LE07col, roi, startDate, endDate).map(prepEtm)
        LT05coly = colFilter(LT05col, roi, startDate, endDate).map(prepEtm)
        LT04coly = colFilter(LT04col, roi, startDate, endDate).map(prepEtm)

        # Merge the collections.
        col = LC08coly.merge(LE07coly).merge(LT05coly).merge(LT04coly)

        monthImg = col.median()
        nBands = monthImg.bandNames().size()
        monthImg = ee.Image(ee.Algorithms.If(nBands, monthImg, dummyImg))
        return calcNbr(monthImg).set(
            {
                "system:time_start": startDate.millis(),
                "nBands": nBands,
                "system:date": ee.Date(startDate).format(date_format),
            }
        )

    # Get quarterly median collection.
    def getQuarterlyComp(startDate):
        startDate = ee.Date(startDate)

        endDate = startDate.advance(3, "month")

        # Filter collections and prepare them for merging.
        LC08coly = colFilter(LC08col, roi, startDate, endDate).map(prepOli)
        LE07coly = colFilter(LE07col, roi, startDate, endDate).map(prepEtm)
        LT05coly = colFilter(LT05col, roi, startDate, endDate).map(prepEtm)
        LT04coly = colFilter(LT04col, roi, startDate, endDate).map(prepEtm)

        # Merge the collections.
        col = LC08coly.merge(LE07coly).merge(LT05coly).merge(LT04coly)

        quarter = col.median()
        nBands = quarter.bandNames().size()
        quarter = ee.Image(ee.Algorithms.If(nBands, quarter, dummyImg))
        return calcNbr(quarter).set(
            {
                "system:time_start": startDate.millis(),
                "nBands": nBands,
                "system:date": ee.Date(startDate).format(date_format),
            }
        )

    ################################################################################

    # Make a dummy image for missing years.
    bandNames = ee.List(["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2", "pixel_qa"])
    fillerValues = ee.List.repeat(0, bandNames.size())
    dummyImg = ee.Image.constant(fillerValues).rename(bandNames).selfMask().int16()

    # ################################################################################
    # # Get a list of years
    # years = ee.List.sequence(start_year, end_year)

    # ################################################################################
    # # Make list of annual image composites.
    # imgList = years.map(getAnnualComp)

    if frequency == "year":
        years = ee.List.sequence(start_year, end_year)
        imgList = years.map(getAnnualComp)
    elif frequency == "quarter":
        quarters = date_sequence(
            str(start_year) + "-01-01", str(end_year) + "-12-31", "quarter", date_format
        )
        imgList = quarters.map(getQuarterlyComp)
    elif frequency == "month":
        months = date_sequence(
            str(start_year) + "-01-01", str(end_year) + "-12-31", "month", date_format
        )
        imgList = months.map(getMonthlyComp)

    # Convert image composite list to collection
    imgCol = ee.ImageCollection.fromImages(imgList)

    imgCol = imgCol.map(
        lambda img: img.clip(roi).set({"coordinates": roi.coordinates()})
    )

    return imgCol

landsat_ts_norm_diff(collection, bands=['Green', 'SWIR1'], threshold=0)

Computes a normalized difference index based on a Landsat timeseries.

Parameters:

Name	Type	Description	Default
collection	ImageCollection	A Landsat timeseries.	required
bands	list	The bands to use for computing normalized difference. Defaults to ['Green', 'SWIR1'].	['Green', 'SWIR1']
threshold	float	The threshold to extract features. Defaults to 0.	0

Returns:

Type	Description
	ee.ImageCollection: An ImageCollection containing images with values greater than the specified threshold.

Source code in geemap/timelapse.py

def landsat_ts_norm_diff(collection, bands=["Green", "SWIR1"], threshold=0):
    """Computes a normalized difference index based on a Landsat timeseries.

    Args:
        collection (ee.ImageCollection): A Landsat timeseries.
        bands (list, optional): The bands to use for computing normalized difference. Defaults to ['Green', 'SWIR1'].
        threshold (float, optional): The threshold to extract features. Defaults to 0.

    Returns:
        ee.ImageCollection: An ImageCollection containing images with values greater than the specified threshold.
    """
    nd_images = collection.map(
        lambda img: img.normalizedDifference(bands)
        .gt(threshold)
        .copyProperties(img, img.propertyNames())
    )
    return nd_images

landsat_ts_norm_diff_gif(collection, out_gif=None, vis_params=None, palette=['black', 'blue'], dimensions=768, frames_per_second=10, mp4=False)

[summary]

Parameters:

Name	Type	Description	Default
collection	ImageCollection	The normalized difference Landsat timeseires.	required
out_gif	str	File path to the output animated GIF. Defaults to None.	None
vis_params	dict	Visualization parameters. Defaults to None.	None
palette	list	The palette to use for visualizing the timelapse. Defaults to ['black', 'blue']. The first color in the list is the background color.	['black', 'blue']
dimensions	int	a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.	768
frames_per_second	int	Animation speed. Defaults to 10.	10
mp4	bool	If True, the output gif will be converted to mp4. Defaults to False.	False

Returns:

Name	Type	Description
str		File path to the output animated GIF.

Source code in geemap/timelapse.py

def landsat_ts_norm_diff_gif(
    collection,
    out_gif=None,
    vis_params=None,
    palette=["black", "blue"],
    dimensions=768,
    frames_per_second=10,
    mp4=False,
):
    """[summary]

    Args:
        collection (ee.ImageCollection): The normalized difference Landsat timeseires.
        out_gif (str, optional): File path to the output animated GIF. Defaults to None.
        vis_params (dict, optional): Visualization parameters. Defaults to None.
        palette (list, optional): The palette to use for visualizing the timelapse. Defaults to ['black', 'blue']. The first color in the list is the background color.
        dimensions (int, optional): a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
        frames_per_second (int, optional): Animation speed. Defaults to 10.
        mp4 (bool, optional): If True, the output gif will be converted to mp4. Defaults to False.

    Returns:
        str: File path to the output animated GIF.
    """
    coordinates = ee.Image(collection.first()).get("coordinates")
    roi = ee.Geometry.Polygon(coordinates, None, False)

    if out_gif is None:
        out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
        filename = "landsat_ts_nd_" + random_string() + ".gif"
        out_gif = os.path.join(out_dir, filename)
    elif not out_gif.endswith(".gif"):
        raise Exception("The output file must end with .gif")

    bands = ["nd"]
    if vis_params is None:
        vis_params = {}
        vis_params["bands"] = bands
        vis_params["palette"] = palette

    video_args = vis_params.copy()
    video_args["dimensions"] = dimensions
    video_args["region"] = roi
    video_args["framesPerSecond"] = frames_per_second
    video_args["crs"] = "EPSG:3857"

    if "bands" not in video_args.keys():
        video_args["bands"] = bands

    download_ee_video(collection, video_args, out_gif)

    if os.path.exists(out_gif):
        reduce_gif_size(out_gif)

    if mp4:
        out_mp4 = out_gif.replace(".gif", ".mp4")
        gif_to_mp4(out_gif, out_mp4)

latitude_grid(step=1.0, west=-180, east=180, south=-85, north=85)

Create a latitude grid.

Parameters:

Name	Type	Description	Default
step	float	The step size in degrees. Defaults to 1.0.	1.0
west	int	The west boundary in degrees. Defaults to -180.	-180
east	int	The east boundary in degrees. Defaults to 180.	180
south	int	The south boundary in degrees. Defaults to -85.	-85
north	int	The north boundary in degrees. Defaults to 85.	85

Returns:

Type	Description
	ee.FeatureCollection: A feature collection of latitude grids.

Source code in geemap/common.py

def latitude_grid(step=1.0, west=-180, east=180, south=-85, north=85):
    """Create a latitude grid.

    Args:
        step (float, optional): The step size in degrees. Defaults to 1.0.
        west (int, optional): The west boundary in degrees. Defaults to -180.
        east (int, optional): The east boundary in degrees. Defaults to 180.
        south (int, optional): The south boundary in degrees. Defaults to -85.
        north (int, optional): The north boundary in degrees. Defaults to 85.

    Returns:
        ee.FeatureCollection: A feature collection of latitude grids.
    """
    values = ee.List.sequence(south, north - step, step)

    def create_feature(lat):
        return ee.Feature(
            ee.Geometry.BBox(west, lat, east, ee.Number(lat).add(step))
        ).set(
            {
                "south": lat,
                "west": west,
                "north": ee.Number(lat).add(step),
                "east": east,
            }
        )

    features = ee.FeatureCollection(values.map(create_feature))
    return features

latlon_from_text(location)

Extracts latlon from text.

Parameters:

Name	Type	Description	Default
location	str	A pair of latlon coordinates separated by comma or space.	required

Returns:

Name	Type	Description
bool		Returns (lat, lon) if valid.

Source code in geemap/common.py

def latlon_from_text(location):
    """Extracts latlon from text.

    Args:
        location (str): A pair of latlon coordinates separated by comma or space.

    Returns:
        bool: Returns (lat, lon) if valid.
    """
    latlon = []
    try:
        if "," in location:
            latlon = [float(x) for x in location.split(",")]
        elif " " in location:
            latlon = [float(x) for x in location.split(" ")]
        else:
            print(
                "The lat-lon coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
            )
            return None

        lat, lon = latlon[0], latlon[1]
        if lat >= -90 and lat <= 90 and lon >= -180 and lon <= 180:
            return lat, lon
        else:
            return None

    except Exception as e:
        print(e)
        print(
            "The lat-lon coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
        )
        return None

latlon_grid(lat_step=1.0, lon_step=1.0, west=-180, east=180, south=-85, north=85)

Create a rectangular grid of latitude and longitude.

Parameters:

Name	Type	Description	Default
lat_step	float	The step size in degrees. Defaults to 1.0.	1.0
lon_step	float	The step size in degrees. Defaults to 1.0.	1.0
west	int	The west boundary in degrees. Defaults to -180.	-180
east	int	The east boundary in degrees. Defaults to 180.	180
south	int	The south boundary in degrees. Defaults to -85.	-85
north	int	The north boundary in degrees. Defaults to 85.	85

Returns:

Type	Description
	ee.FeatureCollection: A feature collection of latitude and longitude grids.

Source code in geemap/common.py

def latlon_grid(lat_step=1.0, lon_step=1.0, west=-180, east=180, south=-85, north=85):
    """Create a rectangular grid of latitude and longitude.

    Args:
        lat_step (float, optional): The step size in degrees. Defaults to 1.0.
        lon_step (float, optional): The step size in degrees. Defaults to 1.0.
        west (int, optional): The west boundary in degrees. Defaults to -180.
        east (int, optional): The east boundary in degrees. Defaults to 180.
        south (int, optional): The south boundary in degrees. Defaults to -85.
        north (int, optional): The north boundary in degrees. Defaults to 85.

    Returns:
        ee.FeatureCollection: A feature collection of latitude and longitude grids.
    """
    longitudes = ee.List.sequence(west, east - lon_step, lon_step)
    latitudes = ee.List.sequence(south, north - lat_step, lat_step)

    def create_lat_feature(lat):
        def create_lon_features(lon):
            return ee.Feature(
                ee.Geometry.BBox(
                    lon, lat, ee.Number(lon).add(lon_step), ee.Number(lat).add(lat_step)
                )
            ).set(
                {
                    "south": lat,
                    "west": lon,
                    "north": ee.Number(lat).add(lat_step),
                    "east": ee.Number(lon).add(lon_step),
                }
            )

        return ee.FeatureCollection(longitudes.map(create_lon_features))

    return ee.FeatureCollection(latitudes.map(create_lat_feature)).flatten()

legend_from_ee(ee_class_table)

Extract legend from an Earth Engine class table on the Earth Engine Data Catalog page such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1

Parameters:

Name	Type	Description	Default
ee_class_table	str	An Earth Engine class table with triple quotes.	required

Returns:

Name	Type	Description
dict		Returns a legend dictionary that can be used to create a legend.

Source code in geemap/common.py

def legend_from_ee(ee_class_table):
    """Extract legend from an Earth Engine class table on the Earth Engine Data Catalog page
    such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1

    Args:
        ee_class_table (str): An Earth Engine class table with triple quotes.

    Returns:
        dict: Returns a legend dictionary that can be used to create a legend.
    """
    try:
        ee_class_table = ee_class_table.strip()
        lines = ee_class_table.split("\n")[1:]

        if lines[0] == "Value\tColor\tDescription":
            lines = lines[1:]

        legend_dict = {}
        for _, line in enumerate(lines):
            items = line.split("\t")
            items = [item.strip() for item in items]
            color = items[1]
            key = items[0] + " " + items[2]
            legend_dict[key] = color

        return legend_dict

    except Exception as e:
        print(e)

list_vars(var_type=None)

Lists all defined avariables.

Parameters:

Name	Type	Description	Default
var_type	object	The object type of variables to list. Defaults to None.	None

Returns:

Name	Type	Description
list		A list of all defined variables.

Source code in geemap/common.py

def list_vars(var_type=None):
    """Lists all defined avariables.

    Args:
        var_type (object, optional): The object type of variables to list. Defaults to None.

    Returns:
        list: A list of all defined variables.
    """
    result = []

    for var in globals():
        reserved_vars = [
            "In",
            "Out",
            "get_ipython",
            "exit",
            "quit",
            "json",
            "getsizeof",
            "NamespaceMagics",
            "np",
            "var_dic_list",
            "list_vars",
            "ee",
            "geemap",
        ]

        if (not var.startswith("_")) and (var not in reserved_vars):
            if var_type is not None and isinstance(eval(var), var_type):
                result.append(var)
            elif var_type is None:
                result.append(var)

    return result

lnglat_to_meters(longitude, latitude)

coordinate conversion between lat/lon in decimal degrees to web mercator

Parameters:

Name	Type	Description	Default
longitude	float	The longitude.	required
latitude	float	The latitude.	required

Returns:

Name	Type	Description
tuple		A tuple of (x, y) in meters.

Source code in geemap/common.py

def lnglat_to_meters(longitude, latitude):
    """coordinate conversion between lat/lon in decimal degrees to web mercator

    Args:
        longitude (float): The longitude.
        latitude (float): The latitude.

    Returns:
        tuple: A tuple of (x, y) in meters.
    """
    import numpy as np

    origin_shift = np.pi * 6378137
    easting = longitude * origin_shift / 180.0
    northing = np.log(np.tan((90 + latitude) * np.pi / 360.0)) * origin_shift / np.pi

    if np.isnan(easting):
        if longitude > 0:
            easting = 20026376
        else:
            easting = -20026376

    if np.isnan(northing):
        if latitude > 0:
            northing = 20048966
        else:
            northing = -20048966

    return (easting, northing)

load_GeoTIFF(URL)

Loads a Cloud Optimized GeoTIFF (COG) as an Image. Only Google Cloud Storage is supported. The URL can be one of the following formats: Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif Option 3: https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/044/034/LC08_L1TP_044034_20131228_20170307_01_T1/LC08_L1TP_044034_20131228_20170307_01_T1_B5.TIF

Parameters:

Name	Type	Description	Default
URL	str	The Cloud Storage URL of the GeoTIFF to load.	required

Returns:

Type	Description
	ee.Image: an Earth Engine image.

Source code in geemap/common.py

def load_GeoTIFF(URL):
    """Loads a Cloud Optimized GeoTIFF (COG) as an Image. Only Google Cloud Storage is supported. The URL can be one of the following formats:
    Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif
    Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif
    Option 3: https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/044/034/LC08_L1TP_044034_20131228_20170307_01_T1/LC08_L1TP_044034_20131228_20170307_01_T1_B5.TIF

    Args:
        URL (str): The Cloud Storage URL of the GeoTIFF to load.

    Returns:
        ee.Image: an Earth Engine image.
    """

    uri = URL.strip()

    if uri.startswith("http"):
        uri = get_direct_url(uri)

    if uri.startswith("https://storage.googleapis.com/"):
        uri = uri.replace("https://storage.googleapis.com/", "gs://")
    elif uri.startswith("https://storage.cloud.google.com/"):
        uri = uri.replace("https://storage.cloud.google.com/", "gs://")

    if not uri.startswith("gs://"):
        raise Exception(
            f'Invalid GCS URL: {uri}. Expected something of the form "gs://bucket/path/to/object.tif".'
        )

    if not uri.lower().endswith(".tif"):
        raise Exception(
            f'Invalid GCS URL: {uri}. Expected something of the form "gs://bucket/path/to/object.tif".'
        )

    cloud_image = ee.Image.loadGeoTIFF(uri)
    return cloud_image

load_GeoTIFFs(URLs)

Loads a list of Cloud Optimized GeoTIFFs (COG) as an ImageCollection. URLs is a list of URL, which can be one of the following formats: Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif Option 3: https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/044/034/LC08_L1TP_044034_20131228_20170307_01_T1/LC08_L1TP_044034_20131228_20170307_01_T1_B5.TIF

Parameters:

Name	Type	Description	Default
URLs	list	A list of Cloud Storage URL of the GeoTIFF to load.	required

Returns:

Type	Description
	ee.ImageCollection: An Earth Engine ImageCollection.

Source code in geemap/common.py

def load_GeoTIFFs(URLs):
    """Loads a list of Cloud Optimized GeoTIFFs (COG) as an ImageCollection. URLs is a list of URL, which can be one of the following formats:
    Option 1: gs://pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif
    Option 2: https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3B_AnalyticMS.tif
    Option 3: https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/044/034/LC08_L1TP_044034_20131228_20170307_01_T1/LC08_L1TP_044034_20131228_20170307_01_T1_B5.TIF

    Args:
        URLs (list): A list of Cloud Storage URL of the GeoTIFF to load.

    Returns:
        ee.ImageCollection: An Earth Engine ImageCollection.
    """

    if not isinstance(URLs, list):
        raise Exception("The URLs argument must be a list.")

    URIs = []
    for URL in URLs:
        uri = URL.strip()

        if uri.startswith("http"):
            uri = get_direct_url(uri)

        if uri.startswith("https://storage.googleapis.com/"):
            uri = uri.replace("https://storage.googleapis.com/", "gs://")
        elif uri.startswith("https://storage.cloud.google.com/"):
            uri = uri.replace("https://storage.cloud.google.com/", "gs://")

        if not uri.startswith("gs://"):
            raise Exception(
                f'Invalid GCS URL: {uri}. Expected something of the form "gs://bucket/path/to/object.tif".'
            )

        if not uri.lower().endswith(".tif"):
            raise Exception(
                f'Invalid GCS URL: {uri}. Expected something of the form "gs://bucket/path/to/object.tif".'
            )

        URIs.append(uri)

    URIs = ee.List(URIs)
    collection = URIs.map(lambda uri: ee.Image.loadGeoTIFF(uri))
    return ee.ImageCollection(collection)

local_tile_bands(source)

Get band names from COG.

Parameters:

Name	Type	Description	Default
source	str | TileClient	A local COG file path or TileClient	required

Returns:

Name	Type	Description
list		A list of band names.

Source code in geemap/common.py

def local_tile_bands(source):
    """Get band names from COG.

    Args:
        source (str | TileClient): A local COG file path or TileClient

    Returns:
        list: A list of band names.
    """
    check_package("localtileserver", "https://github.com/banesullivan/localtileserver")
    from localtileserver import TileClient

    if isinstance(source, str):
        tile_client = TileClient(source)
    elif isinstance(source, TileClient):
        tile_client = source
    else:
        raise ValueError("source must be a string or TileClient object.")

    return tile_client.band_names

local_tile_pixel_value(lon, lat, tile_client, verbose=True, **kwargs)

Get pixel value from COG.

Parameters:

Name	Type	Description	Default
lon	float	Longitude of the pixel.	required
lat	float	Latitude of the pixel.	required
url	str	HTTP URL to a COG, e.g., 'https://github.com/opengeos/data/releases/download/raster/Libya-2023-07-01.tif'	required
bidx	str	Dataset band indexes (e.g bidx=1, bidx=1&bidx=2&bidx=3). Defaults to None.	required
titiler_endpoint	str	Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.	required
verbose	bool	Print status messages. Defaults to True.	True

Returns:

Name	Type	Description
PointData		rio-tiler point data.

Source code in geemap/common.py

def local_tile_pixel_value(
    lon,
    lat,
    tile_client,
    verbose=True,
    **kwargs,
):
    """Get pixel value from COG.

    Args:
        lon (float): Longitude of the pixel.
        lat (float): Latitude of the pixel.
        url (str): HTTP URL to a COG, e.g., 'https://github.com/opengeos/data/releases/download/raster/Libya-2023-07-01.tif'
        bidx (str, optional): Dataset band indexes (e.g bidx=1, bidx=1&bidx=2&bidx=3). Defaults to None.
        titiler_endpoint (str, optional): Titiler endpoint, e.g., "https://titiler.xyz", "planetary-computer", "pc". Defaults to None.
        verbose (bool, optional): Print status messages. Defaults to True.

    Returns:
        PointData: rio-tiler point data.
    """
    return tile_client.point(lon, lat, coord_crs="EPSG:4326", **kwargs)

local_tile_vmin_vmax(source, bands=None, **kwargs)

Get vmin and vmax from COG.

Parameters:

Name	Type	Description	Default
source	str | TileClient	A local COG file path or TileClient object.	required
bands	str | list	A list of band names. Defaults to None.	None

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 geemap/common.py

def local_tile_vmin_vmax(
    source,
    bands=None,
    **kwargs,
):
    """Get vmin and vmax from COG.

    Args:
        source (str | TileClient): A local COG file path or TileClient object.
        bands (str | list, optional): A list of band names. Defaults to None.

    Raises:
        ValueError: If source is not a TileClient object or a local COG file path.

    Returns:
        tuple: A tuple of vmin and vmax.
    """
    check_package("localtileserver", "https://github.com/banesullivan/localtileserver")
    from localtileserver import TileClient

    if isinstance(source, str):
        tile_client = TileClient(source)
    elif isinstance(source, TileClient):
        tile_client = source
    else:
        raise ValueError("source must be a string or TileClient object.")

    bandnames = tile_client.band_names
    stats = tile_client.reader.statistics()

    if isinstance(bands, str):
        bands = [bands]
    elif isinstance(bands, list):
        pass
    elif bands is None:
        bands = bandnames

    if all(b in bandnames for b in bands):
        vmin = min([stats[b]["min"] for b in bands])
        vmax = max([stats[b]["max"] for b in bands])
    else:
        vmin = min([stats[b]["min"] for b in bandnames])
        vmax = max([stats[b]["max"] for b in bandnames])
    return vmin, vmax

longitude_grid(step=1.0, west=-180, east=180, south=-85, north=85)

Create a longitude grid.

Parameters:

Name	Type	Description	Default
step	float	The step size in degrees. Defaults to 1.0.	1.0
west	int	The west boundary in degrees. Defaults to -180.	-180
east	int	The east boundary in degrees. Defaults to 180.	180
south	int	The south boundary in degrees. Defaults to -85.	-85
north	int	The north boundary in degrees. Defaults to 85.	85

Returns:

Type	Description
	ee.FeatureCollection: A feature collection of longitude grids.

Source code in geemap/common.py

def longitude_grid(step=1.0, west=-180, east=180, south=-85, north=85):
    """Create a longitude grid.

    Args:
        step (float, optional): The step size in degrees. Defaults to 1.0.
        west (int, optional): The west boundary in degrees. Defaults to -180.
        east (int, optional): The east boundary in degrees. Defaults to 180.
        south (int, optional): The south boundary in degrees. Defaults to -85.
        north (int, optional): The north boundary in degrees. Defaults to 85.

    Returns:
        ee.FeatureCollection: A feature collection of longitude grids.
    """

    values = ee.List.sequence(west, east - step, step)

    def create_feature(lon):
        return ee.Feature(
            ee.Geometry.BBox(lon, south, ee.Number(lon).add(step), north)
        ).set(
            {
                "south": south,
                "west": lon,
                "north": north,
                "east": ee.Number(lon).add(step),
            }
        )

    features = ee.FeatureCollection(values.map(create_feature))
    return features

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 geemap/timelapse.py

def make_gif(
    images: Union[List[str], str],
    out_gif: str,
    ext: str = "jpg",
    fps: int = 10,
    loop: int = 0,
    mp4: bool = False,
    clean_up: bool = False,
) -> None:
    """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.

    """
    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)

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 geemap/timelapse.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)

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 geemap/common.py

def meters_to_lnglat(x, y):
    """coordinate conversion between web mercator to lat/lon in decimal degrees

    Args:
        x (float): The x coordinate.
        y (float): The y coordinate.

    Returns:
        tuple: A tuple of (longitude, latitude) in decimal degrees.
    """
    import numpy as np

    origin_shift = np.pi * 6378137
    longitude = (x / origin_shift) * 180.0
    latitude = (y / origin_shift) * 180.0
    latitude = (
        180 / np.pi * (2 * np.arctan(np.exp(latitude * np.pi / 180.0)) - np.pi / 2.0)
    )
    return (longitude, latitude)

minimum_bounding_box(geojson)

Gets the minimum bounding box for a geojson polygon.

Parameters:

Name	Type	Description	Default
geojson	dict	A geojson dictionary.	required

Returns:

Name	Type	Description
tuple		Returns a tuple containing the minimum bounding box in the format of (lower_left(lat, lon), upper_right(lat, lon)), such as ((13, -130), (32, -120)).

Source code in geemap/common.py

def minimum_bounding_box(geojson):
    """Gets the minimum bounding box for a geojson polygon.

    Args:
        geojson (dict): A geojson dictionary.

    Returns:
        tuple: Returns a tuple containing the minimum bounding box in the format of (lower_left(lat, lon), upper_right(lat, lon)), such as ((13, -130), (32, -120)).
    """
    coordinates = []
    try:
        if "geometry" in geojson.keys():
            coordinates = geojson["geometry"]["coordinates"][0]
        else:
            coordinates = geojson["coordinates"][0]
        lower_left = min([x[1] for x in coordinates]), min(
            [x[0] for x in coordinates]
        )  # (lat, lon)
        upper_right = max([x[1] for x in coordinates]), max(
            [x[0] for x in coordinates]
        )  # (lat, lon)
        bounds = (lower_left, upper_right)
        return bounds
    except Exception as e:
        raise Exception(e)

modis_ndvi_doy_ts(data='Terra', band='NDVI', start_date=None, end_date=None, region=None)

Create MODIS NDVI timeseries. The source code is adapted from https://developers.google.com/earth-engine/tutorials/community/modis-ndvi-time-series-animation.

Parameters:

Name	Type	Description	Default
data	str	Either "Terra" or "Aqua". Defaults to "Terra".	'Terra'
band	str	Either the "NDVI" or "EVI" band. Defaults to "NDVI".	'NDVI'
start_date	str	The start date used to filter the image collection, e.g., "2013-01-01". Defaults to None.	None
end_date	str	The end date used to filter the image collection. Defaults to None.	None
region	Geometry	The geometry used to filter the image collection. Defaults to None.	None

Returns:

Type	Description
	ee.ImageCollection: The MODIS NDVI time series.

Source code in geemap/timelapse.py

def modis_ndvi_doy_ts(
    data="Terra", band="NDVI", start_date=None, end_date=None, region=None
):
    """Create MODIS NDVI timeseries. The source code is adapted from https://developers.google.com/earth-engine/tutorials/community/modis-ndvi-time-series-animation.

    Args:
        data (str, optional): Either "Terra" or "Aqua". Defaults to "Terra".
        band (str, optional): Either the "NDVI" or "EVI" band. Defaults to "NDVI".
        start_date (str, optional): The start date used to filter the image collection, e.g., "2013-01-01". Defaults to None.
        end_date (str, optional): The end date used to filter the image collection. Defaults to None.
        region (ee.Geometry, optional): The geometry used to filter the image collection. Defaults to None.

    Returns:
        ee.ImageCollection: The MODIS NDVI time series.
    """
    if data not in ["Terra", "Aqua"]:
        raise Exception("data must be 'Terra' or 'Aqua'.")

    if band not in ["NDVI", "EVI"]:
        raise Exception("band must be 'NDVI' or 'EVI'.")

    if region is not None:
        if isinstance(region, ee.Geometry) or isinstance(region, ee.FeatureCollection):
            pass
        else:
            raise Exception("region must be an ee.Geometry or ee.FeatureCollection.")

    if data == "Terra":
        col = ee.ImageCollection("MODIS/006/MOD13A2").select(band)
    else:
        col = ee.ImageCollection("MODIS/006/MYD13A2").select(band)

    if (start_date is not None) and (end_date is not None):
        col = col.filterDate(start_date, end_date)

    def set_doy(img):
        doy = ee.Date(img.get("system:time_start")).getRelative("day", "year")
        return img.set("doy", doy)

    col = col.map(set_doy)

    # Get a collection of distinct images by 'doy'.
    distinctDOY = col.filterDate("2013-01-01", "2014-01-01")

    # Define a filter that identifies which images from the complete
    # collection match the DOY from the distinct DOY collection.
    filter = ee.Filter.equals(**{"leftField": "doy", "rightField": "doy"})

    # Define a join.
    join = ee.Join.saveAll("doy_matches")

    # Apply the join and convert the resulting FeatureCollection to an
    # ImageCollection.
    joinCol = ee.ImageCollection(join.apply(distinctDOY, col, filter))

    # Apply median reduction among matching DOY collections.

    def match_doy(img):
        doyCol = ee.ImageCollection.fromImages(img.get("doy_matches"))
        return doyCol.reduce(ee.Reducer.median())

    comp = joinCol.map(match_doy)

    if region is not None:
        return comp.map(lambda img: img.clip(region))
    else:
        return comp

modis_ndvi_timelapse(roi=None, out_gif=None, data='Terra', band='NDVI', start_date=None, end_date=None, dimensions=768, framesPerSecond=10, crs='EPSG:3857', xy=('3%', '3%'), text_sequence=None, font_type='arial.ttf', font_size=20, font_color='#ffffff', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, loop=0, overlay_data=None, overlay_color='black', overlay_width=1, overlay_opacity=1.0, mp4=False, fading=False, **kwargs)

Create MODIS NDVI timelapse. The source code is adapted from https://developers.google.com/earth-engine/tutorials/community/modis-ndvi-time-series-animation.

Parameters:

Name	Type	Description	Default
roi	Geometry	The geometry used to filter the image collection. Defaults to None.	None
out_gif	str	The output gif file path. Defaults to None.	None
data	str	Either "Terra" or "Aqua". Defaults to "Terra".	'Terra'
band	str	Either the "NDVI" or "EVI" band. Defaults to "NDVI".	'NDVI'
start_date	str	The start date used to filter the image collection, e.g., "2013-01-01". Defaults to None.	None
end_date	str	The end date used to filter the image collection. Defaults to None.	None
dimensions	int	a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.	768
frames_per_second	int	Animation speed. Defaults to 10.	required
crs	str	The coordinate reference system to use. Defaults to "EPSG:3857".	'EPSG:3857'
xy	tuple	Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('3%', '3%')
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'.	'#ffffff'
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
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
overlay_data	(int, str, list)	Administrative boundary to be drawn on the timelapse. Defaults to None.	None
overlay_color	str	Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.	'black'
overlay_width	int	Width of the overlay. Defaults to 1.	1
overlay_opacity	float	Opacity of the overlay. Defaults to 1.0.	1.0
mp4	bool	Whether to convert the output gif to mp4. Defaults to False.	False
fading	int | bool	If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).	False

Source code in geemap/timelapse.py

def modis_ndvi_timelapse(
    roi=None,
    out_gif=None,
    data="Terra",
    band="NDVI",
    start_date=None,
    end_date=None,
    dimensions=768,
    framesPerSecond=10,
    crs="EPSG:3857",
    xy=("3%", "3%"),
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="#ffffff",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    loop=0,
    overlay_data=None,
    overlay_color="black",
    overlay_width=1,
    overlay_opacity=1.0,
    mp4=False,
    fading=False,
    **kwargs,
):
    """Create MODIS NDVI timelapse. The source code is adapted from https://developers.google.com/earth-engine/tutorials/community/modis-ndvi-time-series-animation.

    Args:
        roi (ee.Geometry, optional): The geometry used to filter the image collection. Defaults to None.
        out_gif (str): The output gif file path. Defaults to None.
        data (str, optional): Either "Terra" or "Aqua". Defaults to "Terra".
        band (str, optional): Either the "NDVI" or "EVI" band. Defaults to "NDVI".
        start_date (str, optional): The start date used to filter the image collection, e.g., "2013-01-01". Defaults to None.
        end_date (str, optional): The end date used to filter the image collection. Defaults to None.
        dimensions (int, optional): a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
        frames_per_second (int, optional): Animation speed. Defaults to 10.
        crs (str, optional): The coordinate reference system to use. Defaults to "EPSG:3857".
        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.
        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.
        overlay_data (int, str, list, optional): Administrative boundary to be drawn on the timelapse. Defaults to None.
        overlay_color (str, optional): Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.
        overlay_width (int, optional): Width of the overlay. Defaults to 1.
        overlay_opacity (float, optional): Opacity of the overlay. Defaults to 1.0.
        mp4 (bool, optional): Whether to convert the output gif to mp4. Defaults to False.
        fading (int | bool, optional): If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).

    """

    if roi is None:
        roi = ee.Geometry.Polygon(
            [
                [
                    [-18.6983, 38.1446],
                    [-18.6983, -36.1630],
                    [52.2293, -36.1630],
                    [52.2293, 38.1446],
                ]
            ],
            None,
            False,
        )

    if out_gif is None:
        out_gif = os.path.abspath(f"modis_ndvi_{random_string(3)}.gif")

    try:
        col = modis_ndvi_doy_ts(data, band, start_date, end_date, roi)

        # Define RGB visualization parameters.
        visParams = {
            "min": 0.0,
            "max": 9000.0,
            "palette": [
                "FFFFFF",
                "CE7E45",
                "DF923D",
                "F1B555",
                "FCD163",
                "99B718",
                "74A901",
                "66A000",
                "529400",
                "3E8601",
                "207401",
                "056201",
                "004C00",
                "023B01",
                "012E01",
                "011D01",
                "011301",
            ],
        }

        # Create RGB visualization images for use as animation frames.
        rgbVis = col.map(lambda img: img.visualize(**visParams).clip(roi))

        if overlay_data is not None:
            rgbVis = add_overlay(
                rgbVis,
                overlay_data,
                overlay_color,
                overlay_width,
                overlay_opacity,
                roi,
            )

        # Define GIF visualization arguments.
        videoArgs = {
            "region": roi,
            "dimensions": dimensions,
            "crs": crs,
            "framesPerSecond": framesPerSecond,
        }

        download_ee_video(rgbVis, videoArgs, out_gif)

        if text_sequence is None:
            text = rgbVis.aggregate_array("system:index").getInfo()
            text_sequence = [d.replace("_", "-")[5:] for d in text]

        if os.path.exists(out_gif):
            add_text_to_gif(
                out_gif,
                out_gif,
                xy,
                text_sequence,
                font_type,
                font_size,
                font_color,
                add_progress_bar,
                progress_bar_color,
                progress_bar_height,
                duration=1000 / framesPerSecond,
                loop=loop,
            )

            try:
                reduce_gif_size(out_gif)
                if isinstance(fading, bool):
                    fading = int(fading)
                if fading > 0:
                    gif_fading(out_gif, out_gif, duration=fading, verbose=False)

            except Exception as _:
                pass

        if mp4:
            out_mp4 = out_gif.replace(".gif", ".mp4")
            gif_to_mp4(out_gif, out_mp4)

        return out_gif

    except Exception as e:
        raise Exception(e)

modis_ocean_color_timelapse(satellite, start_date, end_date, roi=None, bands=None, frequency='year', reducer='median', date_format=None, out_gif=None, palette='coolwarm', vis_params=None, dimensions=768, frames_per_second=5, crs='EPSG:3857', overlay_data=None, overlay_color='black', overlay_width=1, overlay_opacity=1.0, title=None, title_xy=('2%', '90%'), add_text=True, text_xy=('2%', '2%'), text_sequence=None, font_type='arial.ttf', font_size=20, font_color='white', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, add_colorbar=True, colorbar_width=6.0, colorbar_height=0.4, colorbar_label='Sea Surface Temperature (°C)', colorbar_label_size=12, colorbar_label_weight='normal', colorbar_tick_size=10, colorbar_bg_color='white', colorbar_orientation='horizontal', colorbar_dpi='figure', colorbar_xy=None, colorbar_size=(300, 300), loop=0, mp4=False, fading=False)

Creates a ocean color timelapse from MODIS. https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI

Parameters:

Name	Type	Description	Default
satellite	str	The satellite to use, can be either "Terra" or "Aqua".	required
start_date	str	The start date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.	required
end_date	str	The end date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.	required
roi	Geometry	The region to use to filter the collection of images. It must be an ee.Geometry object. Defaults to None.	None
bands	list	A list of band names to use in the timelapse. Defaults to None.	None
frequency	str	The frequency of the timeseries. It must be one of the following: 'year', 'month', 'day', 'hour', 'minute', 'second'. Defaults to 'year'.	'year'
reducer	str	The reducer to use to reduce the collection of images to a single value. It can be one of the following: 'median', 'mean', 'min', 'max', 'variance', 'sum'. Defaults to 'median'.	'median'
drop_empty	bool	Whether to drop empty images from the timeseries. Defaults to True.	required
date_format	str	A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.	None
out_gif	str	The output gif file path. Defaults to None.	None
palette	list	A list of colors to render a single-band image in the timelapse. Defaults to None.	'coolwarm'
vis_params	dict	A dictionary of visualization parameters to use in the timelapse. Defaults to None. See more at https://developers.google.com/earth-engine/guides/image_visualization.	None
dimensions	int	a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.	768
frames_per_second	int	Animation speed. Defaults to 10.	5
crs	str	The coordinate reference system to use. Defaults to "EPSG:3857".	'EPSG:3857'
overlay_data	(int, str, list)	Administrative boundary to be drawn on the timelapse. Defaults to None.	None
overlay_color	str	Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.	'black'
overlay_width	int	Width of the overlay. Defaults to 1.	1
overlay_opacity	float	Opacity of the overlay. Defaults to 1.0.	1.0
title	str	The title of the timelapse. Defaults to None.	None
title_xy	tuple	Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
add_text	bool	Whether to add animated text to the timelapse. Defaults to True.	True
title_xy	tuple	Lower left corner of the text sequency. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
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'.	'white'
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
add_colorbar	bool	Whether to add a colorbar to the timelapse. Defaults to False.	True
colorbar_width	float	Width of the colorbar. Defaults to 6.0.	6.0
colorbar_height	float	Height of the colorbar. Defaults to 0.4.	0.4
colorbar_label	str	Label for the colorbar. Defaults to None.	'Sea Surface Temperature (°C)'
colorbar_label_size	int	Font size for the colorbar label. Defaults to 12.	12
colorbar_label_weight	str	Font weight for the colorbar label. Defaults to 'normal'.	'normal'
colorbar_tick_size	int	Font size for the colorbar ticks. Defaults to 10.	10
colorbar_bg_color	str	Background color for the colorbar, can be color like "white", "black". Defaults to None.	'white'
colorbar_orientation	str	Orientation of the colorbar. Defaults to 'horizontal'.	'horizontal'
colorbar_dpi	str	DPI for the colorbar, can be numbers like 100, 300. Defaults to 'figure'.	'figure'
colorbar_xy	tuple	Lower left corner of the colorbar. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	None
colorbar_size	tuple	Size of the colorbar. It can be formatted like this: (300, 300). Defaults to (300, 300).	(300, 300)
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
mp4	bool	Whether to create an mp4 file. Defaults to False.	False
fading	int | bool	If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).	False

Returns:

Name	Type	Description
str		File path to the timelapse gif.

Source code in geemap/timelapse.py

def modis_ocean_color_timelapse(
    satellite,
    start_date,
    end_date,
    roi=None,
    bands=None,
    frequency="year",
    reducer="median",
    date_format=None,
    out_gif=None,
    palette="coolwarm",
    vis_params=None,
    dimensions=768,
    frames_per_second=5,
    crs="EPSG:3857",
    overlay_data=None,
    overlay_color="black",
    overlay_width=1,
    overlay_opacity=1.0,
    title=None,
    title_xy=("2%", "90%"),
    add_text=True,
    text_xy=("2%", "2%"),
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="white",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    add_colorbar=True,
    colorbar_width=6.0,
    colorbar_height=0.4,
    colorbar_label="Sea Surface Temperature (°C)",
    colorbar_label_size=12,
    colorbar_label_weight="normal",
    colorbar_tick_size=10,
    colorbar_bg_color="white",
    colorbar_orientation="horizontal",
    colorbar_dpi="figure",
    colorbar_xy=None,
    colorbar_size=(300, 300),
    loop=0,
    mp4=False,
    fading=False,
):
    """Creates a ocean color timelapse from MODIS. https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI

    Args:
        satellite (str): The satellite to use, can be either "Terra" or "Aqua".
        start_date (str): The start date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.
        end_date (str): The end date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.
        roi (ee.Geometry, optional): The region to use to filter the collection of images. It must be an ee.Geometry object. Defaults to None.
        bands (list, optional): A list of band names to use in the timelapse. Defaults to None.
        frequency (str, optional): The frequency of the timeseries. It must be one of the following: 'year', 'month', 'day', 'hour', 'minute', 'second'. Defaults to 'year'.
        reducer (str, optional):  The reducer to use to reduce the collection of images to a single value. It can be one of the following: 'median', 'mean', 'min', 'max', 'variance', 'sum'. Defaults to 'median'.
        drop_empty (bool, optional): Whether to drop empty images from the timeseries. Defaults to True.
        date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.
        out_gif (str): The output gif file path. Defaults to None.
        palette (list, optional): A list of colors to render a single-band image in the timelapse. Defaults to None.
        vis_params (dict, optional): A dictionary of visualization parameters to use in the timelapse. Defaults to None. See more at https://developers.google.com/earth-engine/guides/image_visualization.
        dimensions (int, optional): a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
        frames_per_second (int, optional): Animation speed. Defaults to 10.
        crs (str, optional): The coordinate reference system to use. Defaults to "EPSG:3857".
        overlay_data (int, str, list, optional): Administrative boundary to be drawn on the timelapse. Defaults to None.
        overlay_color (str, optional): Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.
        overlay_width (int, optional): Width of the overlay. Defaults to 1.
        overlay_opacity (float, optional): Opacity of the overlay. Defaults to 1.0.
        title (str, optional): The title of the timelapse. Defaults to None.
        title_xy (tuple, optional): Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        add_text (bool, optional): Whether to add animated text to the timelapse. Defaults to True.
        title_xy (tuple, optional): Lower left corner of the text sequency. 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.
        add_colorbar (bool, optional): Whether to add a colorbar to the timelapse. Defaults to False.
        colorbar_width (float, optional): Width of the colorbar. Defaults to 6.0.
        colorbar_height (float, optional): Height of the colorbar. Defaults to 0.4.
        colorbar_label (str, optional): Label for the colorbar. Defaults to None.
        colorbar_label_size (int, optional): Font size for the colorbar label. Defaults to 12.
        colorbar_label_weight (str, optional): Font weight for the colorbar label. Defaults to 'normal'.
        colorbar_tick_size (int, optional): Font size for the colorbar ticks. Defaults to 10.
        colorbar_bg_color (str, optional): Background color for the colorbar, can be color like "white", "black". Defaults to None.
        colorbar_orientation (str, optional): Orientation of the colorbar. Defaults to 'horizontal'.
        colorbar_dpi (str, optional): DPI for the colorbar, can be numbers like 100, 300. Defaults to 'figure'.
        colorbar_xy (tuple, optional): Lower left corner of the colorbar. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        colorbar_size (tuple, optional): Size of the colorbar. It can be formatted like this: (300, 300). Defaults to (300, 300).
        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.
        mp4 (bool, optional): Whether to create an mp4 file. Defaults to False.
        fading (int | bool, optional): If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).

    Returns:
        str: File path to the timelapse gif.
    """
    collection = modis_ocean_color_timeseries(
        satellite, start_date, end_date, roi, bands, frequency, reducer, date_format
    )

    if bands is None:
        bands = ["sst"]

    if len(bands) == 1 and palette is None:
        palette = "coolwarm"

    if roi is None:
        roi = ee.Geometry.BBox(-99.755133, 18.316722, -79.761194, 31.206929)

    out_gif = create_timelapse(
        collection,
        start_date,
        end_date,
        roi,
        bands,
        frequency,
        reducer,
        date_format,
        out_gif,
        palette,
        vis_params,
        dimensions,
        frames_per_second,
        crs,
        overlay_data,
        overlay_color,
        overlay_width,
        overlay_opacity,
        title,
        title_xy,
        add_text,
        text_xy,
        text_sequence,
        font_type,
        font_size,
        font_color,
        add_progress_bar,
        progress_bar_color,
        progress_bar_height,
        add_colorbar,
        colorbar_width,
        colorbar_height,
        colorbar_label,
        colorbar_label_size,
        colorbar_label_weight,
        colorbar_tick_size,
        colorbar_bg_color,
        colorbar_orientation,
        colorbar_dpi,
        colorbar_xy,
        colorbar_size,
        loop,
        mp4,
        fading,
    )

    return out_gif

modis_ocean_color_timeseries(satellite, start_date, end_date, region=None, bands=None, frequency='year', reducer='median', drop_empty=True, date_format=None, parallel_scale=1)

Creates a ocean color timeseries from MODIS. https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI

Parameters:

Name	Type	Description	Default
satellite	str	The satellite to use, can be either "Terra" or "Aqua".	required
start_date	str	The start date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.	required
end_date	str	The end date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.	required
region	Geometry	The region to use to filter the collection of images. It must be an ee.Geometry object. Defaults to None.	None
bands	list	The list of bands to use to create the timeseries. It must be a list of strings. Defaults to None.	None
frequency	str	The frequency of the timeseries. It must be one of the following: 'year', 'month', 'day'. Defaults to 'year'.	'year'
reducer	str	The reducer to use to reduce the collection of images to a single value. It can be one of the following: 'median', 'mean', 'min', 'max', 'variance', 'sum'. Defaults to 'median'.	'median'
drop_empty	bool	Whether to drop empty images from the timeseries. Defaults to True.	True
date_format	str	A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.	None
parallel_scale	int	A scaling factor used to limit memory use; using a larger parallel_scale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.	1

Returns:

Type	Description
	ee.ImageCollection: The timeseries.

Source code in geemap/timelapse.py

def modis_ocean_color_timeseries(
    satellite,
    start_date,
    end_date,
    region=None,
    bands=None,
    frequency="year",
    reducer="median",
    drop_empty=True,
    date_format=None,
    parallel_scale=1,
):
    """Creates a ocean color timeseries from MODIS. https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI

    Args:
        satellite (str): The satellite to use, can be either "Terra" or "Aqua".
        start_date (str): The start date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.
        end_date (str): The end date of the timeseries. It must be formatted like this: 'YYYY-MM-dd'.
        region (ee.Geometry, optional): The region to use to filter the collection of images. It must be an ee.Geometry object. Defaults to None.
        bands (list, optional): The list of bands to use to create the timeseries. It must be a list of strings. Defaults to None.
        frequency (str, optional): The frequency of the timeseries. It must be one of the following: 'year', 'month', 'day'. Defaults to 'year'.
        reducer (str, optional):  The reducer to use to reduce the collection of images to a single value. It can be one of the following: 'median', 'mean', 'min', 'max', 'variance', 'sum'. Defaults to 'median'.
        drop_empty (bool, optional): Whether to drop empty images from the timeseries. Defaults to True.
        date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.
        parallel_scale (int, optional): A scaling factor used to limit memory use; using a larger parallel_scale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.

    Returns:
        ee.ImageCollection: The timeseries.
    """

    if satellite not in ["Terra", "Aqua"]:
        raise Exception("Satellite must be 'Terra' or 'Aqua'.")

    allowed_frequency = ["year", "quarter", "month", "week", "day"]
    if frequency not in allowed_frequency:
        raise Exception(
            "Frequency must be one of the following: {}".format(allowed_frequency)
        )

    if region is not None:
        if isinstance(region, ee.Geometry) or isinstance(region, ee.FeatureCollection):
            pass
        else:
            raise Exception("region must be an ee.Geometry or ee.FeatureCollection.")

    col = ee.ImageCollection(f"NASA/OCEANDATA/MODIS-{satellite}/L3SMI").filterDate(
        start_date, end_date
    )

    ts = create_timeseries(
        col,
        start_date,
        end_date,
        region,
        bands,
        frequency,
        reducer,
        drop_empty,
        date_format,
        parallel_scale,
    )

    return ts

modis_timeseries(asset_id='MODIS/006/MOD13A2', band_name=None, roi=None, start_year=2001, end_year=None, start_date='01-01', end_date='12-31')

Generates a Monthly MODIS ImageCollection. Args: asset_id (str, optional): The asset id the MODIS ImageCollection. band_name (str, optional): The band name of the image to use. roi (object, optional): Region of interest to create the timelapse. Defaults to None. start_year (int, optional): Starting year for the timelapse. Defaults to 1984. end_year (int, optional): Ending year for the timelapse. Defaults to None, which is the current year. start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'. end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'. Returns: object: Returns an ImageCollection containing month MODIS images.

Source code in geemap/timelapse.py

def modis_timeseries(
    asset_id="MODIS/006/MOD13A2",
    band_name=None,
    roi=None,
    start_year=2001,
    end_year=None,
    start_date="01-01",
    end_date="12-31",
):
    """Generates a Monthly MODIS ImageCollection.
    Args:
        asset_id (str, optional): The asset id the MODIS ImageCollection.
        band_name (str, optional): The band name of the image to use.
        roi (object, optional): Region of interest to create the timelapse. Defaults to None.
        start_year (int, optional): Starting year for the timelapse. Defaults to 1984.
        end_year (int, optional): Ending year for the timelapse. Defaults to None, which is the current year.
        start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.
        end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.
    Returns:
        object: Returns an ImageCollection containing month MODIS images.
    """

    try:
        if end_year is None:
            end_year = datetime.datetime.now().year

        collection = ee.ImageCollection(asset_id)
        if band_name is None:
            band_name = collection.first().bandNames().getInfo()[0]
        collection = collection.select(band_name)
        if roi is not None:
            if isinstance(roi, ee.Geometry):
                collection = ee.ImageCollection(
                    collection.map(lambda img: img.clip(roi))
                )
            elif isinstance(roi, ee.FeatureCollection):
                collection = ee.ImageCollection(
                    collection.map(lambda img: img.clipToCollection(roi))
                )

        start = str(start_year) + "-" + start_date
        end = str(end_year) + "-" + end_date

        seq = date_sequence(start, end, "month")

        def monthly_modis(start_d):
            end_d = ee.Date(start_d).advance(1, "month")
            return ee.Image(collection.filterDate(start_d, end_d).mean())

        images = ee.ImageCollection(seq.map(monthly_modis))
        return images

    except Exception as e:
        raise Exception(e)

mosaic(images, output, merge_args={}, verbose=True, **kwargs)

Mosaics a list of images into a single image. Inspired by https://bit.ly/3A6roDK.

Parameters:

Name	Type	Description	Default
images	str | list	An input directory containing images or a list of images.	required
output	str	The output image filepath.	required
merge_args	dict	A dictionary of arguments to pass to the rasterio.merge function. Defaults to {}.	{}
verbose	bool	Whether to print progress. Defaults to True.	True

Source code in geemap/common.py

def mosaic(images, output, merge_args={}, verbose=True, **kwargs):
    """Mosaics a list of images into a single image. Inspired by https://bit.ly/3A6roDK.

    Args:
        images (str | list): An input directory containing images or a list of images.
        output (str): The output image filepath.
        merge_args (dict, optional): A dictionary of arguments to pass to the rasterio.merge function. Defaults to {}.
        verbose (bool, optional): Whether to print progress. Defaults to True.

    """
    from rasterio.merge import merge
    import rasterio as rio
    from pathlib import Path

    output = os.path.abspath(output)

    if isinstance(images, str):
        path = Path(images)
        raster_files = list(path.iterdir())
    elif isinstance(images, list):
        raster_files = images
    else:
        raise ValueError("images must be a list of raster files.")

    raster_to_mosiac = []

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

    for index, p in enumerate(raster_files):
        if verbose:
            print(f"Reading {index+1}/{len(raster_files)}: {os.path.basename(p)}")
        raster = rio.open(p, **kwargs)
        raster_to_mosiac.append(raster)

    if verbose:
        print("Merging rasters...")
    arr, transform = merge(raster_to_mosiac, **merge_args)

    output_meta = raster.meta.copy()
    output_meta.update(
        {
            "driver": "GTiff",
            "height": arr.shape[1],
            "width": arr.shape[2],
            "transform": transform,
        }
    )

    with rio.open(output, "w", **output_meta) as m:
        m.write(arr)

naip_timelapse(roi, start_year=2003, end_year=None, out_gif=None, bands=None, palette=None, vis_params=None, dimensions=768, frames_per_second=3, crs='EPSG:3857', overlay_data=None, overlay_color='black', overlay_width=1, overlay_opacity=1.0, title=None, title_xy=('2%', '90%'), add_text=True, text_xy=('2%', '2%'), text_sequence=None, font_type='arial.ttf', font_size=20, font_color='white', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, loop=0, mp4=False, fading=False, step=1)

Create a timelapse from NAIP imagery.

Parameters:

Name	Type	Description	Default
roi	Geometry	The region to use to filter the collection of images. It must be an ee.Geometry object. Defaults to None.	required
start_year	int | str	The start year of the timeseries. It must be formatted like this: 'YYYY'. Defaults to 2003.	2003
end_year	int | str	The end year of the timeseries. It must be formatted like this: 'YYYY'. Defaults to None, which will use the current year.	None
out_gif	str	The output gif file path. Defaults to None.	None
bands	list	A list of band names to use in the timelapse. Defaults to None.	None
palette	list	A list of colors to render a single-band image in the timelapse. Defaults to None.	None
vis_params	dict	A dictionary of visualization parameters to use in the timelapse. Defaults to None. See more at https://developers.google.com/earth-engine/guides/image_visualization.	None
dimensions	int	a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.	768
frames_per_second	int	Animation speed. Defaults to 10.	3
crs	str	The coordinate reference system to use. Defaults to "EPSG:3857".	'EPSG:3857'
overlay_data	(int, str, list)	Administrative boundary to be drawn on the timelapse. Defaults to None.	None
overlay_color	str	Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.	'black'
overlay_width	int	Width of the overlay. Defaults to 1.	1
overlay_opacity	float	Opacity of the overlay. Defaults to 1.0.	1.0
title	str	The title of the timelapse. Defaults to None.	None
title_xy	tuple	Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
add_text	bool	Whether to add animated text to the timelapse. Defaults to True.	True
title_xy	tuple	Lower left corner of the text sequency. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
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'.	'white'
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
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
mp4	bool	Whether to create an mp4 file. Defaults to False.	False
fading	int | bool	If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).	False
step	int	The step size to use when creating the date sequence. Defaults to 1.	1

Returns:

Name	Type	Description
str		File path to the timelapse gif.

Source code in geemap/timelapse.py

def naip_timelapse(
    roi,
    start_year=2003,
    end_year=None,
    out_gif=None,
    bands=None,
    palette=None,
    vis_params=None,
    dimensions=768,
    frames_per_second=3,
    crs="EPSG:3857",
    overlay_data=None,
    overlay_color="black",
    overlay_width=1,
    overlay_opacity=1.0,
    title=None,
    title_xy=("2%", "90%"),
    add_text=True,
    text_xy=("2%", "2%"),
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="white",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    loop=0,
    mp4=False,
    fading=False,
    step=1,
):
    """Create a timelapse from NAIP imagery.

    Args:
        roi (ee.Geometry): The region to use to filter the collection of images. It must be an ee.Geometry object. Defaults to None.
        start_year (int | str, optional): The start year of the timeseries. It must be formatted like this: 'YYYY'. Defaults to 2003.
        end_year (int | str, optional): The end year of the timeseries. It must be formatted like this: 'YYYY'. Defaults to None, which will use the current year.
        out_gif (str): The output gif file path. Defaults to None.
        bands (list, optional): A list of band names to use in the timelapse. Defaults to None.
        palette (list, optional): A list of colors to render a single-band image in the timelapse. Defaults to None.
        vis_params (dict, optional): A dictionary of visualization parameters to use in the timelapse. Defaults to None. See more at https://developers.google.com/earth-engine/guides/image_visualization.
        dimensions (int, optional): a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
        frames_per_second (int, optional): Animation speed. Defaults to 10.
        crs (str, optional): The coordinate reference system to use. Defaults to "EPSG:3857".
        overlay_data (int, str, list, optional): Administrative boundary to be drawn on the timelapse. Defaults to None.
        overlay_color (str, optional): Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.
        overlay_width (int, optional): Width of the overlay. Defaults to 1.
        overlay_opacity (float, optional): Opacity of the overlay. Defaults to 1.0.
        title (str, optional): The title of the timelapse. Defaults to None.
        title_xy (tuple, optional): Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        add_text (bool, optional): Whether to add animated text to the timelapse. Defaults to True.
        title_xy (tuple, optional): Lower left corner of the text sequency. 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.
        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.
        mp4 (bool, optional): Whether to create an mp4 file. Defaults to False.
        fading (int | bool, optional): If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).
        step (int, optional): The step size to use when creating the date sequence. Defaults to 1.

    Returns:
        str: File path to the timelapse gif.
    """

    try:
        if end_year is None:
            end_year = datetime.datetime.now().year

        collection = ee.ImageCollection("USDA/NAIP/DOQQ")
        start_date = str(start_year) + "-01-01"
        end_date = str(end_year) + "-12-31"
        frequency = "year"
        reducer = "median"
        date_format = "YYYY"

        if bands is not None and isinstance(bands, list) and "N" in bands:
            collection = collection.filter(
                ee.Filter.listContains("system:band_names", "N")
            )

        return create_timelapse(
            collection,
            start_date,
            end_date,
            roi,
            bands,
            frequency,
            reducer,
            date_format,
            out_gif,
            palette,
            vis_params,
            dimensions,
            frames_per_second,
            crs,
            overlay_data,
            overlay_color,
            overlay_width,
            overlay_opacity,
            title,
            title_xy,
            add_text,
            text_xy,
            text_sequence,
            font_type,
            font_size,
            font_color,
            add_progress_bar,
            progress_bar_color,
            progress_bar_height,
            loop=loop,
            mp4=mp4,
            fading=fading,
            step=step,
        )

    except Exception as e:
        raise Exception(e)

naip_timeseries(roi=None, start_year=2003, end_year=None, RGBN=False, step=1)

Creates NAIP annual timeseries

Parameters:

Name	Type	Description	Default
roi	object	An ee.Geometry representing the region of interest. Defaults to None.	None
start_year	int	Starting year for the timeseries. Defaults to 2003.	2003
end_year	int	Ending year for the timeseries. Defaults to None, which will use the current year.	None
RGBN	bool	Whether to retrieve 4-band NAIP imagery only.	False
step	int	The step size to use when creating the date sequence. Defaults to 1.	1

Source code in geemap/timelapse.py

def naip_timeseries(roi=None, start_year=2003, end_year=None, RGBN=False, step=1):
    """Creates NAIP annual timeseries

    Args:
        roi (object, optional): An ee.Geometry representing the region of interest. Defaults to None.
        start_year (int, optional): Starting year for the timeseries. Defaults to 2003.
        end_year (int, optional): Ending year for the timeseries. Defaults to None, which will use the current year.
        RGBN (bool, optional): Whether to retrieve 4-band NAIP imagery only.
        step (int, optional): The step size to use when creating the date sequence. Defaults to 1.
    Returns:
        object: An ee.ImageCollection representing annual NAIP imagery.
    """
    try:
        if end_year is None:
            end_year = datetime.datetime.now().year

        def get_annual_NAIP(year):
            try:
                collection = ee.ImageCollection("USDA/NAIP/DOQQ")
                if roi is not None:
                    collection = collection.filterBounds(roi)
                start_date = ee.Date.fromYMD(year, 1, 1)
                end_date = ee.Date.fromYMD(year, 12, 31)
                naip = collection.filterDate(start_date, end_date)
                if RGBN:
                    naip = naip.filter(ee.Filter.listContains("system:band_names", "N"))
                if roi is not None:
                    if isinstance(roi, ee.Geometry):
                        image = ee.Image(ee.ImageCollection(naip).mosaic().clip(roi))
                    elif isinstance(roi, ee.FeatureCollection):
                        image = ee.Image(
                            ee.ImageCollection(naip).mosaic().clipToCollection(roi)
                        )
                else:
                    image = ee.Image(ee.ImageCollection(naip).mosaic())
                return image.set(
                    {
                        "system:time_start": ee.Date(start_date).millis(),
                        "system:time_end": ee.Date(end_date).millis(),
                        "empty": naip.size().eq(0),
                    }
                )
            except Exception as e:
                raise Exception(e)

        years = ee.List.sequence(start_year, end_year, step)
        collection = ee.ImageCollection(years.map(get_annual_NAIP))
        return collection.filterMetadata("empty", "equals", 0)

    except Exception as e:
        raise Exception(e)

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 geemap/common.py

def netcdf_tile_layer(
    filename,
    variables=None,
    colormap=None,
    vmin=None,
    vmax=None,
    nodata=None,
    port="default",
    debug=False,
    attribution=None,
    tile_format="ipyleaflet",
    layer_name="NetCDF layer",
    return_client=False,
    shift_lon=True,
    lat="lat",
    lon="lon",
    **kwargs,
):
    """Generate an ipyleaflet/folium TileLayer from a netCDF file.
        If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer),
        try adding to following two lines to the beginning of the notebook if the raster does not render properly.

        import os
        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = f'{os.environ['JUPYTERHUB_SERVICE_PREFIX'].lstrip('/')}/proxy/{{port}}'

    Args:
        filename (str): File path or HTTP URL to the netCDF file.
        variables (int, optional): The variable/band names to extract data from the netCDF file. Defaults to None. If None, all variables will be extracted.
        port (str, optional): The port to use for the server. Defaults to "default".
        colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
        vmin (float, optional): The minimum value to use when colormapping the colormap when plotting a single band. Defaults to None.
        vmax (float, optional): The maximum value to use when colormapping the colormap when plotting a single band. Defaults to None.
        nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
        debug (bool, optional): If True, the server will be started in debug mode. Defaults to False.
        projection (str, optional): The projection of the GeoTIFF. Defaults to "EPSG:3857".
        attribution (str, optional): Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.
        tile_format (str, optional): The tile layer format. Can be either ipyleaflet or folium. Defaults to "ipyleaflet".
        layer_name (str, optional): The layer name to use. Defaults to "NetCDF layer".
        return_client (bool, optional): If True, the tile client will be returned. Defaults to False.
        shift_lon (bool, optional): Flag to shift longitude values from [0, 360] to the range [-180, 180]. Defaults to True.
        lat (str, optional): Name of the latitude variable. Defaults to 'lat'.
        lon (str, optional): Name of the longitude variable. Defaults to 'lon'.

    Returns:
        ipyleaflet.TileLayer | folium.TileLayer: An ipyleaflet.TileLayer or folium.TileLayer.
    """

    check_package(
        "localtileserver", URL="https://github.com/banesullivan/localtileserver"
    )

    try:
        import xarray as xr
    except ImportError as e:
        raise ImportError(e)

    if filename.startswith("http"):
        filename = download_file(filename)

    if not os.path.exists(filename):
        raise FileNotFoundError(f"{filename} does not exist.")

    output = filename.replace(".nc", ".tif")

    xds = xr.open_dataset(filename, **kwargs)

    if shift_lon:
        xds.coords[lon] = (xds.coords[lon] + 180) % 360 - 180
        xds = xds.sortby(xds.lon)

    allowed_vars = list(xds.data_vars.keys())
    if isinstance(variables, str):
        if variables not in allowed_vars:
            raise ValueError(f"{variables} is not a subset of {allowed_vars}.")
        variables = [variables]

    if variables is not None and len(variables) > 3:
        raise ValueError("Only 3 variables can be plotted at a time.")

    if variables is not None and (not set(variables).issubset(allowed_vars)):
        raise ValueError(f"{variables} must be a subset of {allowed_vars}.")

    xds.rio.set_spatial_dims(x_dim=lon, y_dim=lat).rio.to_raster(output)
    if variables is None:
        if len(allowed_vars) >= 3:
            band_idx = [1, 2, 3]
        else:
            band_idx = [1]
    else:
        band_idx = [allowed_vars.index(var) + 1 for var in variables]

    tile_layer = get_local_tile_layer(
        output,
        port=port,
        debug=debug,
        indexes=band_idx,
        colormap=colormap,
        vmin=vmin,
        vmax=vmax,
        nodata=nodata,
        attribution=attribution,
        tile_format=tile_format,
        layer_name=layer_name,
        return_client=return_client,
    )
    return tile_layer

netcdf_to_ee(nc_file, var_names, band_names=None, lon='lon', lat='lat', decimal=2)

Creates an ee.Image from netCDF variables band_names that are read from nc_file. Currently only supports variables in a regular longitude/latitude grid (EPSG:4326).

Parameters:

Name	Type	Description	Default
nc_file	str	the name of the netCDF file to read	required
var_names	str or list	the name(s) of the variable(s) to read	required
band_names	list	if given, the bands are renamed to band_names. Defaults to the original var_names	None
lon	str	the name of the longitude variable in the netCDF file. Defaults to "lon"	'lon'
lat	str	the name of the latitude variable in the netCDF file. Defaults to "lat"	'lat'
decimal	int	the number of decimal places to round the longitude and latitude values to. Defaults to 2.	2

Returns:

Name	Type	Description
image		An ee.Image

Source code in geemap/common.py

def netcdf_to_ee(nc_file, var_names, band_names=None, lon="lon", lat="lat", decimal=2):
    """
    Creates an ee.Image from netCDF variables band_names that are read from nc_file. Currently only supports variables in a regular longitude/latitude grid (EPSG:4326).

    Args:
        nc_file (str): the name of the netCDF file to read
        var_names (str or list): the name(s) of the variable(s) to read
        band_names (list, optional): if given, the bands are renamed to band_names. Defaults to the original var_names
        lon (str, optional): the name of the longitude variable in the netCDF file. Defaults to "lon"
        lat (str, optional): the name of the latitude variable in the netCDF file. Defaults to "lat"
        decimal (int, optional): the number of decimal places to round the longitude and latitude values to. Defaults to 2.

    Returns:
        image: An ee.Image

    """
    try:
        import xarray as xr

    except Exception:
        raise ImportError(
            "You need to install xarray first. See https://github.com/pydata/xarray"
        )

    import numpy as np
    from collections import Counter

    def most_common_value(lst):
        counter = Counter(lst)
        most_common = counter.most_common(1)
        return float(format(most_common[0][0], f".{decimal}f"))

    try:
        if not isinstance(nc_file, str):
            print("The input file must be a string.")
            return
        if band_names and not isinstance(band_names, (list, str)):
            print("Band names must be a string or list.")
            return
        if not isinstance(lon, str) or not isinstance(lat, str):
            print("The longitude and latitude variable names must be a string.")
            return

        ds = xr.open_dataset(nc_file)
        data = ds[var_names]

        lon_data = data[lon]
        lat_data = data[lat]

        dim_lon = np.unique(np.ediff1d(lon_data))
        dim_lat = np.unique(np.ediff1d(lat_data))
        dim_lon = [most_common_value(dim_lon)]
        dim_lat = [most_common_value(dim_lat)]

        # if (len(dim_lon) != 1) or (len(dim_lat) != 1):
        #     print("The netCDF file is not a regular longitude/latitude grid")
        #     return

        try:
            data = data.to_array()
            # ^ this is only needed (and works) if we have more than 1 variable
            # axis_for_roll will be used in case we need to use np.roll
            # and should be 1 for the case with more than 1 variable
            axis_for_roll = 1
        except Exception:
            axis_for_roll = 0
            # .to_array() does not work (and is not needed!) if there is only 1 variable
            # in this case, the axis_for_roll needs to be 0

        data_np = np.array(data)

        do_transpose = True  # To do: figure out if we need to transpose the data or not
        if do_transpose:
            try:
                data_np = np.transpose(data_np, (0, 2, 1))
            except Exception:
                data_np = np.transpose(data_np)

        # Figure out if we need to roll the data or not
        # (see https://github.com/gee-community/geemap/issues/285#issuecomment-791385176)
        if np.max(lon_data) > 180:
            data_np = np.roll(data_np, 180, axis=axis_for_roll)
            west_lon = lon_data[0] - 180
        else:
            west_lon = lon_data[0]

        transform = [dim_lon[0], 0, float(west_lon), 0, dim_lat[0], float(lat_data[0])]

        if band_names is None:
            band_names = var_names

        image = numpy_to_ee(
            data_np, "EPSG:4326", transform=transform, band_names=band_names
        )

        return image

    except Exception as e:
        print(e)

netcdf_to_tif(filename, output=None, variables=None, shift_lon=True, lat='lat', lon='lon', return_vars=False, **kwargs)

Convert a netcdf file to a GeoTIFF file.

Parameters:

Name	Type	Description	Default
filename	str	Path to the netcdf file.	required
output	str	Path to the output GeoTIFF file. Defaults to None. If None, the output file will be the same as the input file with the extension changed to .tif.	None
variables	str | list	Name of the variable or a list of variables to extract. Defaults to None. If None, all variables will be extracted.	None
shift_lon	bool	Flag to shift longitude values from [0, 360] to the range [-180, 180]. Defaults to True.	True
lat	str	Name of the latitude variable. Defaults to 'lat'.	'lat'
lon	str	Name of the longitude variable. Defaults to 'lon'.	'lon'
return_vars	bool	Flag to return all variables. Defaults to False.	False

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 geemap/common.py

def netcdf_to_tif(
    filename,
    output=None,
    variables=None,
    shift_lon=True,
    lat="lat",
    lon="lon",
    return_vars=False,
    **kwargs,
):
    """Convert a netcdf file to a GeoTIFF file.

    Args:
        filename (str): Path to the netcdf file.
        output (str, optional): Path to the output GeoTIFF file. Defaults to None. If None, the output file will be the same as the input file with the extension changed to .tif.
        variables (str | list, optional): Name of the variable or a list of variables to extract. Defaults to None. If None, all variables will be extracted.
        shift_lon (bool, optional): Flag to shift longitude values from [0, 360] to the range [-180, 180]. Defaults to True.
        lat (str, optional): Name of the latitude variable. Defaults to 'lat'.
        lon (str, optional): Name of the longitude variable. Defaults to 'lon'.
        return_vars (bool, optional): Flag to return all variables. Defaults to False.

    Raises:
        ImportError: If the xarray or rioxarray package is not installed.
        FileNotFoundError: If the netcdf file is not found.
        ValueError: If the variable is not found in the netcdf file.
    """
    try:
        import xarray as xr
    except ImportError as e:
        raise ImportError(e)

    if filename.startswith("http"):
        filename = download_file(filename)

    if not os.path.exists(filename):
        raise FileNotFoundError(f"{filename} does not exist.")

    if output is None:
        output = filename.replace(".nc", ".tif")
    else:
        output = check_file_path(output)

    xds = xr.open_dataset(filename, **kwargs)

    if shift_lon:
        xds.coords[lon] = (xds.coords[lon] + 180) % 360 - 180
        xds = xds.sortby(xds.lon)

    allowed_vars = list(xds.data_vars.keys())
    if isinstance(variables, str):
        if variables not in allowed_vars:
            raise ValueError(f"{variables} is not a valid variable.")
        variables = [variables]

    if variables is not None and (not set(variables).issubset(allowed_vars)):
        raise ValueError(f"{variables} must be a subset of {allowed_vars}.")

    if variables is None:
        xds.rio.set_spatial_dims(x_dim=lon, y_dim=lat).rio.to_raster(output)
    else:
        xds[variables].rio.set_spatial_dims(x_dim=lon, y_dim=lat).rio.to_raster(output)

    if return_vars:
        return output, allowed_vars
    else:
        return output

new_tree_node(label, children=None, expanded=False, top_level=False)

Returns node JSON for an interactive representation of an EE ComputedObject.

Source code in geemap/coreutils.py

def new_tree_node(
    label: str,
    children: Optional[list[dict[str, Any]]] = None,
    expanded: bool = False,
    top_level: bool = False,
) -> Dict[str, Any]:
    """Returns node JSON for an interactive representation of an EE ComputedObject."""
    return {
        "label": label,
        "children": children or [],
        "expanded": expanded,
        "topLevel": top_level,
    }

num_round(num, decimal=2)

Rounds a number to a specified number of decimal places.

Parameters:

Name	Type	Description	Default
num	float	The number to round.	required
decimal	int	The number of decimal places to round. Defaults to 2.	2

Returns:

Name	Type	Description
float		The number with the specified decimal places rounded.

Source code in geemap/common.py

def num_round(num, decimal=2):
    """Rounds a number to a specified number of decimal places.

    Args:
        num (float): The number to round.
        decimal (int, optional): The number of decimal places to round. Defaults to 2.

    Returns:
        float: The number with the specified decimal places rounded.
    """
    return round(num, decimal)

numpy_to_cog(np_array, out_cog, bounds=None, profile=None, dtype=None, crs='epsg:4326')

Converts a numpy array to a COG file.

Parameters:

Name	Type	Description	Default
np_array	array	A numpy array representing the image.	required
out_cog	str	The output COG file path.	required
bounds	tuple	The bounds of the image in the format of (minx, miny, maxx, maxy). Defaults to None.	None
profile	str | dict	File path to an existing COG file or a dictionary representing the profile. Defaults to None.	None
dtype	str	The data type of the output COG file. Defaults to None.	None
crs	str	The coordinate reference system of the output COG file. Defaults to "epsg:4326".	'epsg:4326'

Source code in geemap/common.py

def numpy_to_cog(
    np_array, out_cog, bounds=None, profile=None, dtype=None, crs="epsg:4326"
):
    """Converts a numpy array to a COG file.

    Args:
        np_array (np.array): A numpy array representing the image.
        out_cog (str): The output COG file path.
        bounds (tuple, optional): The bounds of the image in the format of (minx, miny, maxx, maxy). Defaults to None.
        profile (str | dict, optional): File path to an existing COG file or a dictionary representing the profile. Defaults to None.
        dtype (str, optional): The data type of the output COG file. Defaults to None.
        crs (str, optional): The coordinate reference system of the output COG file. Defaults to "epsg:4326".

    """

    import numpy as np
    import rasterio
    from rasterio.io import MemoryFile
    from rasterio.transform import from_bounds

    from rio_cogeo.cogeo import cog_translate
    from rio_cogeo.profiles import cog_profiles

    warnings.filterwarnings("ignore")

    if not isinstance(np_array, np.ndarray):
        raise TypeError("The input array must be a numpy array.")

    out_dir = os.path.dirname(out_cog)
    check_dir(out_dir)

    if profile is not None:
        if isinstance(profile, str):
            if not os.path.exists(profile):
                raise FileNotFoundError("The provided file could not be found.")
            with rasterio.open(profile) as ds:
                bounds = ds.bounds
        elif isinstance(profile, rasterio.profiles.Profile):
            profile = dict(profile)
        elif not isinstance(profile, dict):
            raise TypeError("The provided profile must be a file path or a dictionary.")

    if bounds is None:
        bounds = (-180.0, -85.0511287798066, 180.0, 85.0511287798066)

    if not isinstance(bounds, tuple) and len(bounds) != 4:
        raise TypeError("The provided bounds must be a tuple of length 4.")

    # Rasterio uses numpy array of shape of `(bands, height, width)`

    if len(np_array.shape) == 3:
        nbands = np_array.shape[0]
        height = np_array.shape[1]
        width = np_array.shape[2]
    elif len(np_array.shape) == 2:
        nbands = 1
        height = np_array.shape[0]
        width = np_array.shape[1]
        np_array = np_array.reshape((1, height, width))
    else:
        raise ValueError("The input array must be a 2D or 3D numpy array.")

    src_transform = from_bounds(*bounds, width=width, height=height)
    if dtype is None:
        dtype = str(np_array.dtype)

    if isinstance(profile, dict):
        src_profile = profile
        src_profile["count"] = nbands
    else:
        src_profile = dict(
            driver="GTiff",
            dtype=dtype,
            count=nbands,
            height=height,
            width=width,
            crs=crs,
            transform=src_transform,
        )

    with MemoryFile() as memfile:
        with memfile.open(**src_profile) as mem:
            # Populate the input file with numpy array
            mem.write(np_array)

            dst_profile = cog_profiles.get("deflate")
            cog_translate(
                mem,
                out_cog,
                dst_profile,
                in_memory=True,
                quiet=True,
            )

numpy_to_ee(np_array, crs=None, transform=None, transformWkt=None, band_names=None)

Creates an ee.Image from a 3D numpy array where each 2D numpy slice is added to a band, and a geospatial transform that indicates where to put the data. If the np_array is already 2D only, then it is only a one-band image.

Parameters:

Name	Type	Description	Default
np_array	array	the 3D (or 2D) numpy array to add to an image	required
crs	str	The base coordinate reference system of this Projection, given as a well-known authority code (e.g. 'EPSG:4326') or a WKT string.	None
transform	list	The transform between projected coordinates and the base coordinate system, specified as a 2x3 affine transform matrix in row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. May not specify both this and 'transformWkt'.	None
transformWkt	str	The transform between projected coordinates and the base coordinate system, specified as a WKT string. May not specify both this and 'transform'.	None
band_names	str or list	The list of names for the bands. The default names are 'constant', and 'constant_1', 'constant_2', etc.	None

Returns:

Name	Type	Description
image		An ee.Image

Source code in geemap/common.py

def numpy_to_ee(np_array, crs=None, transform=None, transformWkt=None, band_names=None):
    """
    Creates an ee.Image from a 3D numpy array where each 2D numpy slice is added to a band, and a geospatial transform that indicates where to put the data. If the np_array is already 2D only, then it is only a one-band image.

    Args:
        np_array (np.array): the 3D (or 2D) numpy array to add to an image
        crs (str): The base coordinate reference system of this Projection, given as a well-known authority code (e.g. 'EPSG:4326') or a WKT string.
        transform (list): The transform between projected coordinates and the base coordinate system, specified as a 2x3 affine transform matrix in row-major order: [xScale, xShearing, xTranslation, yShearing, yScale, yTranslation]. May not specify both this and 'transformWkt'.
        transformWkt (str): The transform between projected coordinates and the base coordinate system, specified as a WKT string. May not specify both this and 'transform'.
        band_names (str or list, optional): The list of names for the bands. The default names are 'constant', and 'constant_1', 'constant_2', etc.

    Returns:
        image: An ee.Image

    """
    import numpy as np

    if not isinstance(np_array, np.ndarray):
        print("The input must be a numpy.ndarray.")
        return
    if not len(np_array.shape) in [2, 3]:
        print("The input must have 2 or 3 dimensions")
        return
    if band_names and not isinstance(band_names, (list, str)):
        print("Band names must be a str or list")
        return

    try:
        projection = ee.Projection(crs, transform, transformWkt)
        coords = ee.Image.pixelCoordinates(projection).floor().int32()
        x = coords.select("x")
        y = coords.select("y")
        s = np_array.shape
        if len(s) < 3:
            dimx = s[0]
            dimy = s[1]
        else:
            dimx = s[1]
            dimy = s[2]
            dimz = s[0]

        coord_mask = x.gte(0).And(y.gte(0)).And(x.lt(dimx)).And(y.lt(dimy))
        coords = coords.updateMask(coord_mask)

        def list_to_ee(a_list):
            ee_data = ee.Array(a_list)
            image = ee.Image(ee_data).arrayGet(coords)
            return image

        if len(s) < 3:
            image = list_to_ee(np_array.tolist())
        else:
            image = list_to_ee(np_array[0].tolist())
            for z in np.arange(1, dimz):
                image = image.addBands(list_to_ee(np_array[z].tolist()))

        if band_names:
            image = image.rename(band_names)

        return image

    except Exception as e:
        print(e)

nwi_add_color(fc)

Converts NWI vector dataset to image and add color to it.

Parameters:

Name	Type	Description	Default
fc	object	ee.FeatureCollection	required

Returns:

Name	Type	Description
object		ee.Image

Source code in geemap/common.py

def nwi_add_color(fc):
    """Converts NWI vector dataset to image and add color to it.

    Args:
        fc (object): ee.FeatureCollection

    Returns:
        object: ee.Image
    """
    emergent = ee.FeatureCollection(
        fc.filter(ee.Filter.eq("WETLAND_TY", "Freshwater Emergent Wetland"))
    )
    emergent = emergent.map(lambda f: f.set("R", 127).set("G", 195).set("B", 28))
    # print(emergent.first())

    forested = fc.filter(
        ee.Filter.eq("WETLAND_TY", "Freshwater Forested/Shrub Wetland")
    )
    forested = forested.map(lambda f: f.set("R", 0).set("G", 136).set("B", 55))

    pond = fc.filter(ee.Filter.eq("WETLAND_TY", "Freshwater Pond"))
    pond = pond.map(lambda f: f.set("R", 104).set("G", 140).set("B", 192))

    lake = fc.filter(ee.Filter.eq("WETLAND_TY", "Lake"))
    lake = lake.map(lambda f: f.set("R", 19).set("G", 0).set("B", 124))

    riverine = fc.filter(ee.Filter.eq("WETLAND_TY", "Riverine"))
    riverine = riverine.map(lambda f: f.set("R", 1).set("G", 144).set("B", 191))

    fc = ee.FeatureCollection(
        emergent.merge(forested).merge(pond).merge(lake).merge(riverine)
    )

    #   base = ee.Image(0).mask(0).toInt8()
    base = ee.Image().byte()
    img = base.paint(fc, "R").addBands(
        base.paint(fc, "G").addBands(base.paint(fc, "B"))
    )

    return img

open_data_widget(m)

A widget for opening local vector/raster data.

Parameters:

Name	Type	Description	Default
m	object	geemap.Map	required

Source code in geemap/toolbar.py

def open_data_widget(m):
    """A widget for opening local vector/raster data.

    Args:
        m (object): geemap.Map
    """

    padding = "0px 0px 0px 5px"
    style = {"description_width": "initial"}

    tool_output = widgets.Output()
    tool_output_ctrl = ipyleaflet.WidgetControl(widget=tool_output, position="topright")

    if (
        hasattr(m, "_tool_output_ctrl")
        and m._tool_output_ctrl is not None
        and m._tool_output_ctrl in m.controls
    ):
        m.remove_control(m._tool_output_ctrl)

    file_type = widgets.ToggleButtons(
        options=["Shapefile", "GeoJSON", "CSV", "Vector", "Raster"],
        tooltips=[
            "Open a shapefile",
            "Open a GeoJSON file",
            "Open a vector dataset",
            "Create points from CSV",
            "Open a vector dataset",
            "Open a raster dataset",
        ],
    )
    file_type.style.button_width = "88px"

    filepath = widgets.Text(
        value="",
        description="File path or http URL:",
        tooltip="Enter a file path or http URL to vector data",
        style=style,
        layout=widgets.Layout(width="454px", padding=padding),
    )
    http_widget = widgets.HBox()

    file_chooser = FileChooser(
        os.getcwd(), sandbox_path=m.sandbox_path, layout=widgets.Layout(width="454px")
    )
    file_chooser.filter_pattern = "*.shp"
    file_chooser.use_dir_icons = True

    style = {"description_width": "initial"}
    layer_name = widgets.Text(
        value="Shapefile",
        description="Enter a layer name:",
        tooltip="Enter a layer name for the selected file",
        style=style,
        layout=widgets.Layout(width="454px", padding="0px 0px 0px 5px"),
    )

    longitude = widgets.Dropdown(
        options=[],
        value=None,
        description="Longitude:",
        layout=widgets.Layout(width="149px", padding="0px 0px 0px 5px"),
        style={"description_width": "initial"},
    )

    latitude = widgets.Dropdown(
        options=[],
        value=None,
        description="Latitude:",
        layout=widgets.Layout(width="149px", padding="0px 0px 0px 5px"),
        style={"description_width": "initial"},
    )

    label = widgets.Dropdown(
        options=[],
        value=None,
        description="Label:",
        layout=widgets.Layout(width="149px", padding="0px 0px 0px 5px"),
        style={"description_width": "initial"},
    )

    csv_widget = widgets.HBox()

    convert_bool = widgets.Checkbox(
        description="Convert to ee.FeatureCollection?",
        indent=False,
        layout=widgets.Layout(padding="0px 0px 0px 5px"),
    )
    convert_hbox = widgets.HBox([convert_bool])

    ok_cancel = widgets.ToggleButtons(
        value=None,
        options=["Apply", "Reset", "Close"],
        tooltips=["Apply", "Reset", "Close"],
        button_style="primary",
    )
    # ok_cancel.style.button_width = "133px"

    bands = widgets.Text(
        value=None,
        description="Band:",
        tooltip="Enter a list of band indices",
        style=style,
        layout=widgets.Layout(width="150px", padding=padding),
    )

    vmin = widgets.Text(
        value=None,
        description="vmin:",
        tooltip="Minimum value of the raster to visualize",
        style=style,
        layout=widgets.Layout(width="148px"),
    )

    vmax = widgets.Text(
        value=None,
        description="vmax:",
        tooltip="Maximum value of the raster to visualize",
        style=style,
        layout=widgets.Layout(width="148px"),
    )

    nodata = widgets.Text(
        value=None,
        description="Nodata:",
        tooltip="Nodata the raster to visualize",
        style=style,
        layout=widgets.Layout(width="150px", padding=padding),
    )

    palette = widgets.Dropdown(
        options=[],
        value=None,
        description="palette:",
        layout=widgets.Layout(width="300px"),
        style=style,
    )

    raster_options = widgets.VBox()

    main_widget = widgets.VBox(
        [
            file_type,
            file_chooser,
            http_widget,
            csv_widget,
            layer_name,
            convert_hbox,
            raster_options,
            ok_cancel,
        ]
    )

    tool_output.outputs = ()
    with tool_output:
        display(main_widget)

    def bands_changed(change):
        if change["new"] and "," in change["owner"].value:
            palette.value = None
            palette.disabled = True
        else:
            palette.disabled = False

    bands.observe(bands_changed, "value")

    def chooser_callback(chooser):
        filepath.value = file_chooser.selected

        if file_type.value == "CSV":
            import pandas as pd

            df = pd.read_csv(filepath.value)
            col_names = df.columns.values.tolist()
            longitude.options = col_names
            latitude.options = col_names
            label.options = col_names

            if "longitude" in col_names:
                longitude.value = "longitude"
            if "latitude" in col_names:
                latitude.value = "latitude"
            if "name" in col_names:
                label.value = "name"

    file_chooser.register_callback(chooser_callback)

    def file_type_changed(change):
        ok_cancel.value = None
        file_chooser.default_path = os.getcwd()
        file_chooser.reset()
        layer_name.value = file_type.value
        csv_widget.children = []
        filepath.value = ""

        if change["new"] == "Shapefile":
            file_chooser.filter_pattern = "*.shp"
            raster_options.children = []
            convert_hbox.children = [convert_bool]
            http_widget.children = []
        elif change["new"] == "GeoJSON":
            file_chooser.filter_pattern = "*.geojson"
            raster_options.children = []
            convert_hbox.children = [convert_bool]
            http_widget.children = [filepath]
        elif change["new"] == "Vector":
            file_chooser.filter_pattern = "*.*"
            raster_options.children = []
            convert_hbox.children = [convert_bool]
            http_widget.children = [filepath]
        elif change["new"] == "CSV":
            file_chooser.filter_pattern = ["*.csv", "*.CSV"]
            csv_widget.children = [longitude, latitude, label]
            raster_options.children = []
            convert_hbox.children = [convert_bool]
            http_widget.children = [filepath]
        elif change["new"] == "Raster":
            if not hasattr(m, "_colormaps"):
                from .colormaps import list_colormaps

                m._colormaps = list_colormaps(add_extra=True)
            file_chooser.filter_pattern = ["*.tif", "*.img"]
            palette.options = m._colormaps
            palette.value = None
            raster_options.children = [
                widgets.HBox([bands, vmin, vmax]),
                widgets.HBox([nodata, palette]),
            ]
            convert_hbox.children = []
            http_widget.children = [filepath]

    def cleanup():
        if (
            hasattr(m, "_tool_output_ctrl")
            and m._tool_output_ctrl is not None
            and m._tool_output_ctrl in m.controls
        ):
            m.remove_control(m._tool_output_ctrl)
            m._tool_output_ctrl = None

    tool_output_ctrl.cleanup = cleanup

    def ok_cancel_clicked(change):
        if change["new"] == "Apply":
            m.default_style = {"cursor": "wait"}
            file_path = filepath.value

            if file_path is not None:
                ext = os.path.splitext(file_path)[1]
                with tool_output:
                    if ext.lower() == ".shp":
                        if convert_bool.value:
                            ee_object = shp_to_ee(file_path)
                            m.addLayer(ee_object, {}, layer_name.value)
                        else:
                            m.add_shapefile(
                                file_path, style={}, layer_name=layer_name.value
                            )
                    elif ext.lower() == ".geojson":
                        if convert_bool.value:
                            ee_object = geojson_to_ee(file_path)
                            m.addLayer(ee_object, {}, layer_name.value)
                        else:
                            m.add_geojson(
                                file_path, style={}, layer_name=layer_name.value
                            )

                    elif ext.lower() == ".csv":
                        if convert_bool.value:
                            ee_object = csv_to_ee(
                                file_path, latitude.value, longitude.value
                            )
                            m.addLayer(ee_object, {}, layer_name.value)
                        else:
                            m.add_xy_data(
                                file_path,
                                x=longitude.value,
                                y=latitude.value,
                                label=label.value,
                                layer_name=layer_name.value,
                            )

                    elif ext.lower() in [".tif", "img"] and file_type.value == "Raster":
                        band = None
                        vis_min = None
                        vis_max = None
                        vis_nodata = None

                        try:
                            if len(bands.value) > 0:
                                band = bands.value.split(",")
                            if len(vmin.value) > 0:
                                vis_min = float(vmin.value)
                            if len(vmax.value) > 0:
                                vis_max = float(vmax.value)
                            if len(nodata.value) > 0:
                                vis_nodata = float(nodata.value)
                        except Exception as _:
                            pass

                        m.add_raster(
                            file_path,
                            layer_name=layer_name.value,
                            band=band,
                            palette=palette.value,
                            vmin=vis_min,
                            vmax=vis_max,
                            nodata=vis_nodata,
                        )
                    else:
                        m.add_vector(file_path, style={}, layer_name=layer_name.value)
            else:
                print("Please select a file to open.")

            m.default_style = {"cursor": "default"}

        elif change["new"] == "Reset":
            file_chooser.reset()
            tool_output.outputs = ()
            with tool_output:
                display(main_widget)
        elif change["new"] == "Close":
            tool_output_ctrl.cleanup()
        ok_cancel.value = None

    file_type.observe(file_type_changed, names="value")
    ok_cancel.observe(ok_cancel_clicked, names="value")
    # file_chooser.register_callback(chooser_callback)

    m.add_control(tool_output_ctrl)
    m._tool_output_ctrl = tool_output_ctrl

open_github(subdir=None)

Opens the GitHub repository for this package.

Parameters:

Name	Type	Description	Default
subdir	str	Sub-directory of the repository. Defaults to None.	None

Source code in geemap/common.py

def open_github(subdir=None):
    """Opens the GitHub repository for this package.

    Args:
        subdir (str, optional): Sub-directory of the repository. Defaults to None.
    """
    import webbrowser

    url = "https://github.com/gee-community/geemap"

    if subdir == "source":
        url += "/tree/master/geemap/"
    elif subdir == "examples":
        url += "/tree/master/examples"
    elif subdir == "tutorials":
        url += "/tree/master/tutorials"

    webbrowser.open_new_tab(url)

open_image_from_url(url, timeout=300, proxies=None)

Loads an image from the specified URL.

Parameters:

Name	Type	Description	Default
url	str	URL of the image.	required
timeout	int	Timeout in seconds. Defaults to 300.	300
proxies	dict	Dictionary of proxies. Defaults to None.	None

Returns:

Name	Type	Description
object		Image object.

Source code in geemap/common.py

def open_image_from_url(url, timeout=300, proxies=None):
    """Loads an image from the specified URL.

    Args:
        url (str): URL of the image.
        timeout (int, optional): Timeout in seconds. Defaults to 300.
        proxies (dict, optional): Dictionary of proxies. Defaults to None.

    Returns:
        object: Image object.
    """
    from PIL import Image

    # from io import BytesIO
    # from urllib.parse import urlparse

    try:
        url = get_direct_url(url)
        response = requests.get(url, timeout=timeout, proxies=proxies)
        img = Image.open(io.BytesIO(response.content))
        return img
    except Exception as e:
        print(e)

open_url(url)

Opens the URL in a new browser tab.

Parameters:

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

Source code in geemap/coreutils.py

def open_url(url: str) -> None:
    """Opens the URL in a new browser tab.

    Args:
        url (str): The URL to open.
    """
    if in_colab_shell():
        display(
            Javascript('window.open("{url}", "_blank", "noopener")'.format(url=url))
        )
    else:
        import webbrowser

        webbrowser.open_new_tab(url)

open_youtube()

Opens the YouTube tutorials for geemap.

Source code in geemap/common.py

def open_youtube():
    """Opens the YouTube tutorials for geemap."""
    import webbrowser

    url = "https://www.youtube.com/playlist?list=PLAxJ4-o7ZoPccOFv1dCwvGI6TYnirRTg3"
    webbrowser.open_new_tab(url)

osm_to_ee(query, which_result=None, by_osmid=False, buffer_dist=None, geodesic=True)

Retrieves place(s) by name or ID from the Nominatim API as an ee.FeatureCollection.

Parameters:

Name	Type	Description	Default
query	str | dict | list	Query string(s) or structured dict(s) to geocode.	required
which_result	INT	Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None.	None
by_osmid	bool	If True, handle query as an OSM ID for lookup rather than text search. Defaults to False.	False
buffer_dist	float	Distance to buffer around the place geometry, in meters. Defaults to None.	None
geodesic	bool	Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.	True

Returns:

Type	Description
	ee.FeatureCollection: An Earth Engine FeatureCollection.

Source code in geemap/common.py

def osm_to_ee(
    query, which_result=None, by_osmid=False, buffer_dist=None, geodesic=True
):
    """Retrieves place(s) by name or ID from the Nominatim API as an ee.FeatureCollection.

    Args:
        query (str | dict | list): Query string(s) or structured dict(s) to geocode.
        which_result (INT, optional): Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None.
        by_osmid (bool, optional): If True, handle query as an OSM ID for lookup rather than text search. Defaults to False.
        buffer_dist (float, optional): Distance to buffer around the place geometry, in meters. Defaults to None.
        geodesic (bool, optional): Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.

    Returns:
        ee.FeatureCollection: An Earth Engine FeatureCollection.
    """
    gdf = osm_to_gdf(query, which_result, by_osmid, buffer_dist)
    fc = gdf_to_ee(gdf, geodesic)
    return fc

osm_to_gdf(query, which_result=None, by_osmid=False, buffer_dist=None)

Retrieves place(s) by name or ID from the Nominatim API as a GeoDataFrame.

Parameters:

Name	Type	Description	Default
query	str | dict | list	Query string(s) or structured dict(s) to geocode.	required
which_result	INT	Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None.	None
by_osmid	bool	If True, handle query as an OSM ID for lookup rather than text search. Defaults to False.	False
buffer_dist	float	Distance to buffer around the place geometry, in meters. Defaults to None.	None

Returns:

Name	Type	Description
GeoDataFrame		A GeoPandas GeoDataFrame.

Source code in geemap/common.py

def osm_to_gdf(
    query,
    which_result=None,
    by_osmid=False,
    buffer_dist=None,
):
    """Retrieves place(s) by name or ID from the Nominatim API as a GeoDataFrame.

    Args:
        query (str | dict | list): Query string(s) or structured dict(s) to geocode.
        which_result (INT, optional): Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None.
        by_osmid (bool, optional): If True, handle query as an OSM ID for lookup rather than text search. Defaults to False.
        buffer_dist (float, optional): Distance to buffer around the place geometry, in meters. Defaults to None.

    Returns:
        GeoDataFrame: A GeoPandas GeoDataFrame.
    """
    check_package(
        "geopandas", "https://geopandas.org/getting_started.html#installation"
    )
    check_package("osmnx", "https://osmnx.readthedocs.io/en/stable/")

    try:
        import osmnx as ox

        gdf = ox.geocode_to_gdf(query, which_result, by_osmid, buffer_dist)
        return gdf
    except Exception as e:
        raise Exception(e)

osm_to_geojson(query, which_result=None, by_osmid=False, buffer_dist=None)

Retrieves place(s) by name or ID from the Nominatim API as an ee.FeatureCollection.

Parameters:

Name	Type	Description	Default
query	str | dict | list	Query string(s) or structured dict(s) to geocode.	required
which_result	INT	Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None.	None
by_osmid	bool	If True, handle query as an OSM ID for lookup rather than text search. Defaults to False.	False
buffer_dist	float	Distance to buffer around the place geometry, in meters. Defaults to None.	None

Returns:

Type	Description
	ee.FeatureCollection: An Earth Engine FeatureCollection.

Source code in geemap/common.py

def osm_to_geojson(query, which_result=None, by_osmid=False, buffer_dist=None):
    """Retrieves place(s) by name or ID from the Nominatim API as an ee.FeatureCollection.

    Args:
        query (str | dict | list): Query string(s) or structured dict(s) to geocode.
        which_result (INT, optional): Which geocoding result to use. if None, auto-select the first (Multi)Polygon or raise an error if OSM doesn't return one. to get the top match regardless of geometry type, set which_result=1. Defaults to None.
        by_osmid (bool, optional): If True, handle query as an OSM ID for lookup rather than text search. Defaults to False.
        buffer_dist (float, optional): Distance to buffer around the place geometry, in meters. Defaults to None.

    Returns:
        ee.FeatureCollection: An Earth Engine FeatureCollection.
    """
    gdf = osm_to_gdf(query, which_result, by_osmid, buffer_dist)
    return gdf.__geo_interface__

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 geemap/common.py

def planet_biannual_tiles_tropical(
    api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
    """Generates Planet  bi-annual imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """

    import folium
    import ipyleaflet

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    tiles = {}
    link = planet_biannual_tropical(api_key, token_name)
    for url in link:
        index = url.find("20")
        name = "Planet_" + url[index : index + 15]
        if tile_format == "ipyleaflet":
            tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
        else:
            tile = folium.TileLayer(
                tiles=url,
                attr="Planet",
                name=name,
                overlay=True,
                control=True,
            )
        tiles[name] = tile

    return tiles

planet_biannual_tropical(api_key=None, token_name='PLANET_API_KEY')

Generates Planet bi-annual imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

Parameters:

Name	Type	Description	Default
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'

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 geemap/common.py

def planet_biannual_tropical(api_key=None, token_name="PLANET_API_KEY"):
    """Generates Planet bi-annual imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Raises:
        ValueError: If the API key could not be found.

    Returns:
        list: A list of tile URLs.
    """

    if api_key is None:
        api_key = os.environ.get(token_name)
        if api_key is None:
            raise ValueError("The Planet API Key must be provided.")

    dates = [
        "2015-12_2016-05",
        "2016-06_2016-11",
        "2016-12_2017-05",
        "2017-06_2017-11",
        "2017-12_2018-05",
        "2018-06_2018-11",
        "2018-12_2019-05",
        "2019-06_2019-11",
        "2019-12_2020-05",
        "2020-06_2020-08",
    ]

    link = []
    prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/planet_medres_normalized_analytic_"
    subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="

    for d in dates:
        url = f"{prefix}{d}{subfix}{api_key}"
        link.append(url)

    return link

planet_by_month(year=2016, month=1, api_key=None, token_name='PLANET_API_KEY')

Gets Planet global mosaic tile url by month. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

Parameters:

Name	Type	Description	Default
year	int	The year of Planet global mosaic, must be >=2016. Defaults to 2016.	2016
month	int	The month of Planet global mosaic, must be 1-12. Defaults to 1.	1
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'

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 geemap/common.py

def planet_by_month(
    year=2016,
    month=1,
    api_key=None,
    token_name="PLANET_API_KEY",
):
    """Gets Planet global mosaic tile url by month. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        year (int, optional): The year of Planet global mosaic, must be >=2016. Defaults to 2016.
        month (int, optional): The month of Planet global mosaic, must be 1-12. Defaults to 1.
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Raises:
        ValueError: The Planet API key is not provided.
        ValueError: The year is invalid.
        ValueError: The month is invalid.
        ValueError: The month is invalid.

    Returns:
        str: A Planet global mosaic tile url.
    """
    # from datetime import date

    if api_key is None:
        api_key = os.environ.get(token_name)
        if api_key is None:
            raise ValueError("The Planet API Key must be provided.")

    today = datetime.date.today()
    year_now = int(today.strftime("%Y"))
    month_now = int(today.strftime("%m"))
    # quarter_now = (month_now - 1) // 3 + 1

    if year > year_now:
        raise ValueError(f"Year must be between 2016 and {year_now}.")
    elif year == year_now and month >= month_now:
        raise ValueError(f"Month must be less than {month_now} for year {year_now}")

    if month < 1 or month > 12:
        raise ValueError("Month must be between 1 and 12.")

    prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/global_monthly_"
    subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="

    m_str = str(year) + "_" + str(month).zfill(2)
    url = f"{prefix}{m_str}{subfix}{api_key}"

    return url

planet_by_quarter(year=2016, quarter=1, api_key=None, token_name='PLANET_API_KEY')

Gets Planet global mosaic tile url by quarter. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

Parameters:

Name	Type	Description	Default
year	int	The year of Planet global mosaic, must be >=2016. Defaults to 2016.	2016
quarter	int	The quarter of Planet global mosaic, must be 1-4. Defaults to 1.	1
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'

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 geemap/common.py

def planet_by_quarter(
    year=2016,
    quarter=1,
    api_key=None,
    token_name="PLANET_API_KEY",
):
    """Gets Planet global mosaic tile url by quarter. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        year (int, optional): The year of Planet global mosaic, must be >=2016. Defaults to 2016.
        quarter (int, optional): The quarter of Planet global mosaic, must be 1-4. Defaults to 1.
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Raises:
        ValueError: The Planet API key is not provided.
        ValueError: The year is invalid.
        ValueError: The quarter is invalid.
        ValueError: The quarter is invalid.

    Returns:
        str: A Planet global mosaic tile url.
    """
    # from datetime import date

    if api_key is None:
        api_key = os.environ.get(token_name)
        if api_key is None:
            raise ValueError("The Planet API Key must be provided.")

    today = datetime.date.today()
    year_now = int(today.strftime("%Y"))
    month_now = int(today.strftime("%m"))
    quarter_now = (month_now - 1) // 3 + 1

    if year > year_now:
        raise ValueError(f"Year must be between 2016 and {year_now}.")
    elif year == year_now and quarter >= quarter_now:
        raise ValueError(f"Quarter must be less than {quarter_now} for year {year_now}")

    if quarter < 1 or quarter > 4:
        raise ValueError("Quarter must be between 1 and 4.")

    prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/global_quarterly_"
    subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="

    m_str = str(year) + "q" + str(quarter)
    url = f"{prefix}{m_str}{subfix}{api_key}"

    return url

planet_catalog(api_key=None, token_name='PLANET_API_KEY')

Generates Planet bi-annual and monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

Parameters:

Name	Type	Description	Default
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'

Returns:

Name	Type	Description
list		A list of tile URLs.

Source code in geemap/common.py

def planet_catalog(api_key=None, token_name="PLANET_API_KEY"):
    """Generates Planet bi-annual and monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Returns:
        list: A list of tile URLs.
    """
    quarterly = planet_quarterly(api_key, token_name)
    monthly = planet_monthly(api_key, token_name)
    return quarterly + monthly

planet_catalog_tropical(api_key=None, token_name='PLANET_API_KEY')

Generates Planet bi-annual and monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

Parameters:

Name	Type	Description	Default
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'

Returns:

Name	Type	Description
list		A list of tile URLs.

Source code in geemap/common.py

def planet_catalog_tropical(api_key=None, token_name="PLANET_API_KEY"):
    """Generates Planet bi-annual and monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Returns:
        list: A list of tile URLs.
    """
    biannual = planet_biannual_tropical(api_key, token_name)
    monthly = planet_monthly_tropical(api_key, token_name)
    return biannual + monthly

planet_monthly(api_key=None, token_name='PLANET_API_KEY')

Generates Planet monthly imagery URLs based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

Parameters:

Name	Type	Description	Default
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'

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 geemap/common.py

def planet_monthly(api_key=None, token_name="PLANET_API_KEY"):
    """Generates Planet monthly imagery URLs based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Raises:
        ValueError: If the API key could not be found.

    Returns:
        list: A list of tile URLs.
    """
    # from datetime import date

    if api_key is None:
        api_key = os.environ.get(token_name)
        if api_key is None:
            raise ValueError("The Planet API Key must be provided.")

    today = datetime.date.today()
    year_now = int(today.strftime("%Y"))
    month_now = int(today.strftime("%m"))

    link = []
    prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/global_monthly_"
    subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="

    for year in range(2016, year_now + 1):
        for month in range(1, 13):
            m_str = str(year) + "_" + str(month).zfill(2)

            if year == year_now and month >= month_now:
                break

            url = f"{prefix}{m_str}{subfix}{api_key}"
            link.append(url)

    return link

planet_monthly_tiles(api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')

Generates Planet monthly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

Parameters:

Name	Type	Description	Default
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'
tile_format	str	The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".	'ipyleaflet'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in geemap/common.py

def planet_monthly_tiles(
    api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
    """Generates Planet  monthly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """
    import folium
    import ipyleaflet

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    tiles = {}
    link = planet_monthly(api_key, token_name)

    for url in link:
        index = url.find("20")
        name = "Planet_" + url[index : index + 7]

        if tile_format == "ipyleaflet":
            tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
        else:
            tile = folium.TileLayer(
                tiles=url,
                attr="Planet",
                name=name,
                overlay=True,
                control=True,
            )

        tiles[name] = tile

    return tiles

planet_monthly_tiles_tropical(api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')

Generates Planet monthly imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

Parameters:

Name	Type	Description	Default
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'
tile_format	str	The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".	'ipyleaflet'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in geemap/common.py

def planet_monthly_tiles_tropical(
    api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
    """Generates Planet  monthly imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """
    import folium
    import ipyleaflet

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    tiles = {}
    link = planet_monthly_tropical(api_key, token_name)
    for url in link:
        index = url.find("20")
        name = "Planet_" + url[index : index + 7]

        if tile_format == "ipyleaflet":
            tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
        else:
            tile = folium.TileLayer(
                tiles=url,
                attr="Planet",
                name=name,
                overlay=True,
                control=True,
            )

        tiles[name] = tile

    return tiles

planet_monthly_tropical(api_key=None, token_name='PLANET_API_KEY')

Generates Planet monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

Parameters:

Name	Type	Description	Default
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'

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 geemap/common.py

def planet_monthly_tropical(api_key=None, token_name="PLANET_API_KEY"):
    """Generates Planet monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Raises:
        ValueError: If the API key could not be found.

    Returns:
        list: A list of tile URLs.
    """
    # from datetime import date

    if api_key is None:
        api_key = os.environ.get(token_name)
        if api_key is None:
            raise ValueError("The Planet API Key must be provided.")

    today = datetime.date.today()
    year_now = int(today.strftime("%Y"))
    month_now = int(today.strftime("%m"))

    links = []
    prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/planet_medres_normalized_analytic_"
    subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="

    for year in range(2020, year_now + 1):
        for month in range(1, 13):
            m_str = str(year) + "-" + str(month).zfill(2)

            if year == 2020 and month < 9:
                continue
            if year == year_now and month >= month_now:
                break

            url = f"{prefix}{m_str}{subfix}{api_key}"
            links.append(url)

    return links

planet_quarterly(api_key=None, token_name='PLANET_API_KEY')

Generates Planet quarterly imagery URLs based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

Parameters:

Name	Type	Description	Default
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'

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 geemap/common.py

def planet_quarterly(api_key=None, token_name="PLANET_API_KEY"):
    """Generates Planet quarterly imagery URLs based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Raises:
        ValueError: If the API key could not be found.

    Returns:
        list: A list of tile URLs.
    """
    # from datetime import date

    if api_key is None:
        api_key = os.environ.get(token_name)
        if api_key is None:
            raise ValueError("The Planet API Key must be provided.")

    today = datetime.date.today()
    year_now = int(today.strftime("%Y"))
    month_now = int(today.strftime("%m"))
    quarter_now = (month_now - 1) // 3 + 1

    link = []
    prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/global_quarterly_"
    subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="

    for year in range(2016, year_now + 1):
        for quarter in range(1, 5):
            m_str = str(year) + "q" + str(quarter)

            if year == year_now and quarter >= quarter_now:
                break

            url = f"{prefix}{m_str}{subfix}{api_key}"
            link.append(url)

    return link

planet_quarterly_tiles(api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')

Generates Planet quarterly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

Parameters:

Name	Type	Description	Default
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'
tile_format	str	The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".	'ipyleaflet'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in geemap/common.py

def planet_quarterly_tiles(
    api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
    """Generates Planet  quarterly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """
    import folium
    import ipyleaflet

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    tiles = {}
    links = planet_quarterly(api_key, token_name)

    for url in links:
        index = url.find("20")
        name = "Planet_" + url[index : index + 6]

        if tile_format == "ipyleaflet":
            tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
        else:
            tile = folium.TileLayer(
                tiles=url,
                attr="Planet",
                name=name,
                overlay=True,
                control=True,
            )

        tiles[name] = tile

    return tiles

planet_tile_by_month(year=2016, month=1, name=None, api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')

Generates Planet monthly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis

Parameters:

Name	Type	Description	Default
year	int	The year of Planet global mosaic, must be >=2016. Defaults to 2016.	2016
month	int	The month of Planet global mosaic, must be 1-12. Defaults to 1.	1
name	str	The layer name to use. Defaults to None.	None
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'
tile_format	str	The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".	'ipyleaflet'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in geemap/common.py

def planet_tile_by_month(
    year=2016,
    month=1,
    name=None,
    api_key=None,
    token_name="PLANET_API_KEY",
    tile_format="ipyleaflet",
):
    """Generates Planet monthly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis

    Args:
        year (int, optional): The year of Planet global mosaic, must be >=2016. Defaults to 2016.
        month (int, optional): The month of Planet global mosaic, must be 1-12. Defaults to 1.
        name (str, optional): The layer name to use. Defaults to None.
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """
    import folium
    import ipyleaflet

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    url = planet_by_month(year, month, api_key, token_name)

    if name is None:
        name = "Planet_" + str(year) + "_" + str(month).zfill(2)

    if tile_format == "ipyleaflet":
        tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
    else:
        tile = folium.TileLayer(
            tiles=url,
            attr="Planet",
            name=name,
            overlay=True,
            control=True,
        )

    return tile

planet_tile_by_quarter(year=2016, quarter=1, name=None, api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')

Generates Planet quarterly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis

Parameters:

Name	Type	Description	Default
year	int	The year of Planet global mosaic, must be >=2016. Defaults to 2016.	2016
quarter	int	The quarter of Planet global mosaic, must be 1-4. Defaults to 1.	1
name	str	The layer name to use. Defaults to None.	None
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'
tile_format	str	The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".	'ipyleaflet'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in geemap/common.py

def planet_tile_by_quarter(
    year=2016,
    quarter=1,
    name=None,
    api_key=None,
    token_name="PLANET_API_KEY",
    tile_format="ipyleaflet",
):
    """Generates Planet quarterly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis

    Args:
        year (int, optional): The year of Planet global mosaic, must be >=2016. Defaults to 2016.
        quarter (int, optional): The quarter of Planet global mosaic, must be 1-4. Defaults to 1.
        name (str, optional): The layer name to use. Defaults to None.
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """

    import folium
    import ipyleaflet

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    url = planet_by_quarter(year, quarter, api_key, token_name)

    if name is None:
        name = "Planet_" + str(year) + "_q" + str(quarter)

    if tile_format == "ipyleaflet":
        tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
    else:
        tile = folium.TileLayer(
            tiles=url,
            attr="Planet",
            name=name,
            overlay=True,
            control=True,
        )

    return tile

planet_tiles(api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')

Generates Planet imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

Parameters:

Name	Type	Description	Default
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'
tile_format	str	The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".	'ipyleaflet'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in geemap/common.py

def planet_tiles(api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"):
    """Generates Planet imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """

    catalog = {}
    quarterly = planet_quarterly_tiles(api_key, token_name, tile_format)
    monthly = planet_monthly_tiles(api_key, token_name, tile_format)

    for key in quarterly:
        catalog[key] = quarterly[key]

    for key in monthly:
        catalog[key] = monthly[key]

    return catalog

planet_tiles_tropical(api_key=None, token_name='PLANET_API_KEY', tile_format='ipyleaflet')

Generates Planet monthly imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

Parameters:

Name	Type	Description	Default
api_key	str	The Planet API key. Defaults to None.	None
token_name	str	The environment variable name of the API key. Defaults to "PLANET_API_KEY".	'PLANET_API_KEY'
tile_format	str	The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".	'ipyleaflet'

Raises:

Type	Description
ValueError	If the tile layer format is invalid.

Returns:

Name	Type	Description
dict		A dictionary of TileLayer.

Source code in geemap/common.py

def planet_tiles_tropical(
    api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
    """Generates Planet  monthly imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """

    catalog = {}
    biannul = planet_biannual_tiles_tropical(api_key, token_name, tile_format)
    monthly = planet_monthly_tiles_tropical(api_key, token_name, tile_format)

    for key in biannul:
        catalog[key] = biannul[key]

    for key in monthly:
        catalog[key] = monthly[key]

    return catalog

plot_raster(image, band=None, cmap='terrain', proj='EPSG:3857', figsize=None, open_kwargs={}, **kwargs)

Plot a raster image.

Parameters:

Name	Type	Description	Default
image	str | DataArray	The input raster image, can be a file path, HTTP URL, or xarray.DataArray.	required
band	int	The band index, starting from zero. Defaults to None.	None
cmap	str	The matplotlib colormap to use. Defaults to "terrain".	'terrain'
proj	str	The EPSG projection code. Defaults to "EPSG:3857".	'EPSG:3857'
figsize	tuple	The figure size as a tuple, such as (10, 8). Defaults to None.	None
open_kwargs	dict	The keyword arguments to pass to rioxarray.open_rasterio. Defaults to {}.	{}
**kwargs		Additional keyword arguments to pass to xarray.DataArray.plot().	{}

Source code in geemap/common.py

def plot_raster(
    image,
    band=None,
    cmap="terrain",
    proj="EPSG:3857",
    figsize=None,
    open_kwargs={},
    **kwargs,
):
    """Plot a raster image.

    Args:
        image (str | xarray.DataArray ): The input raster image, can be a file path, HTTP URL, or xarray.DataArray.
        band (int, optional): The band index, starting from zero. Defaults to None.
        cmap (str, optional): The matplotlib colormap to use. Defaults to "terrain".
        proj (str, optional): The EPSG projection code. Defaults to "EPSG:3857".
        figsize (tuple, optional): The figure size as a tuple, such as (10, 8). Defaults to None.
        open_kwargs (dict, optional): The keyword arguments to pass to rioxarray.open_rasterio. Defaults to {}.
        **kwargs: Additional keyword arguments to pass to xarray.DataArray.plot().

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

    if in_colab_shell():
        print("The plot_raster() function is not supported in Colab.")
        return

    try:
        import pvxarray
        import rioxarray
        import xarray
    except ImportError:
        raise ImportError(
            "pyxarray and rioxarray are required for plotting. Please install them using 'pip install rioxarray pyvista-xarray'."
        )

    if isinstance(image, str):
        da = rioxarray.open_rasterio(image, **open_kwargs)
    elif isinstance(image, xarray.DataArray):
        da = image
    else:
        raise ValueError("image must be a string or xarray.Dataset.")

    if band is not None:
        da = da[dict(band=band)]

    da = da.rio.reproject(proj)
    kwargs["cmap"] = cmap
    kwargs["figsize"] = figsize
    da.plot(**kwargs)

plot_raster_3d(image, band=None, cmap='terrain', factor=1.0, proj='EPSG:3857', background=None, x=None, y=None, z=None, order=None, component=None, open_kwargs={}, mesh_kwargs={}, **kwargs)

Plot a raster image in 3D.

Parameters:

Name	Type	Description	Default
image	str | DataArray	The input raster image, can be a file path, HTTP URL, or xarray.DataArray.	required
band	int	The band index, starting from zero. Defaults to None.	None
cmap	str	The matplotlib colormap to use. Defaults to "terrain".	'terrain'
factor	float	The scaling factor for the raster. Defaults to 1.0.	1.0
proj	str	The EPSG projection code. Defaults to "EPSG:3857".	'EPSG:3857'
background	str	The background color. Defaults to None.	None
x	str	The x coordinate. Defaults to None.	None
y	str	The y coordinate. Defaults to None.	None
z	str	The z coordinate. Defaults to None.	None
order	str	The order of the coordinates. Defaults to None.	None
component	str	The component of the coordinates. Defaults to None.	None
open_kwargs	dict	The keyword arguments to pass to rioxarray.open_rasterio. Defaults to {}.	{}
mesh_kwargs	dict	The keyword arguments to pass to pyvista.mesh.warp_by_scalar(). Defaults to {}.	{}
**kwargs		Additional keyword arguments to pass to xarray.DataArray.plot().	{}

Source code in geemap/common.py

def plot_raster_3d(
    image,
    band=None,
    cmap="terrain",
    factor=1.0,
    proj="EPSG:3857",
    background=None,
    x=None,
    y=None,
    z=None,
    order=None,
    component=None,
    open_kwargs={},
    mesh_kwargs={},
    **kwargs,
):
    """Plot a raster image in 3D.

    Args:
        image (str | xarray.DataArray): The input raster image, can be a file path, HTTP URL, or xarray.DataArray.
        band (int, optional): The band index, starting from zero. Defaults to None.
        cmap (str, optional): The matplotlib colormap to use. Defaults to "terrain".
        factor (float, optional): The scaling factor for the raster. Defaults to 1.0.
        proj (str, optional): The EPSG projection code. Defaults to "EPSG:3857".
        background (str, optional): The background color. Defaults to None.
        x (str, optional): The x coordinate. Defaults to None.
        y (str, optional): The y coordinate. Defaults to None.
        z (str, optional): The z coordinate. Defaults to None.
        order (str, optional): The order of the coordinates. Defaults to None.
        component (str, optional): The component of the coordinates. Defaults to None.
        open_kwargs (dict, optional): The keyword arguments to pass to rioxarray.open_rasterio. Defaults to {}.
        mesh_kwargs (dict, optional): The keyword arguments to pass to pyvista.mesh.warp_by_scalar(). Defaults to {}.
        **kwargs: Additional keyword arguments to pass to xarray.DataArray.plot().
    """

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

    if in_colab_shell():
        print("The plot_raster_3d() function is not supported in Colab.")
        return

    try:
        import pvxarray
        import pyvista
        import rioxarray
        import xarray
    except ImportError:
        raise ImportError(
            "pyxarray and rioxarray are required for plotting. Please install them using 'pip install rioxarray pyvista-xarray'."
        )

    if isinstance(background, str):
        pyvista.global_theme.background = background

    if isinstance(image, str):
        da = rioxarray.open_rasterio(image, **open_kwargs)
    elif isinstance(image, xarray.DataArray):
        da = image
    else:
        raise ValueError("image must be a string or xarray.Dataset.")

    if band is not None:
        da = da[dict(band=band)]

    da = da.rio.reproject(proj)
    mesh_kwargs["factor"] = factor
    kwargs["cmap"] = cmap

    coords = list(da.coords)

    if x is None:
        if "x" in coords:
            x = "x"
        elif "lon" in coords:
            x = "lon"
    if y is None:
        if "y" in coords:
            y = "y"
        elif "lat" in coords:
            y = "lat"
    if z is None:
        if "z" in coords:
            z = "z"
        elif "elevation" in coords:
            z = "elevation"
        elif "band" in coords:
            z = "band"

    # Grab the mesh object for use with PyVista
    mesh = da.pyvista.mesh(x=x, y=y, z=z, order=order, component=component)

    # Warp top and plot in 3D
    mesh.warp_by_scalar(**mesh_kwargs).plot(**kwargs)

plotly_basemap_gui(canvas, map_min_width='78%', map_max_width='98%')

Widget for changing basemaps.

Parameters:

Name	Type	Description	Default
m	object	geemap.Map.	required

Source code in geemap/toolbar.py

def plotly_basemap_gui(canvas, map_min_width="78%", map_max_width="98%"):
    """Widget for changing basemaps.

    Args:
        m (object): geemap.Map.
    """
    from .plotlymap import basemaps

    m = canvas.map
    layer_count = len(m.layout.mapbox.layers)
    container_widget = canvas.container_widget
    map_widget = canvas.map_widget

    map_widget.layout.width = map_min_width

    value = "Esri.WorldTopoMap"
    m.add_basemap(value)

    dropdown = widgets.Dropdown(
        options=list(basemaps.keys()),
        value=value,
        layout=widgets.Layout(width="200px"),
    )

    close_btn = widgets.Button(
        icon="times",
        tooltip="Close the basemap widget",
        button_style="primary",
        layout=widgets.Layout(width="32px"),
    )

    basemap_widget = widgets.HBox([dropdown, close_btn])
    container_widget.children = [basemap_widget]

    def on_click(change):
        basemap_name = change["new"]
        m.layout.mapbox.layers = m.layout.mapbox.layers[:layer_count]
        m.add_basemap(basemap_name)

    dropdown.observe(on_click, "value")

    def close_click(change):
        container_widget.children = []
        basemap_widget.close()
        map_widget.layout.width = map_max_width
        canvas.toolbar_reset()
        canvas.toolbar_button.value = False

    close_btn.on_click(close_click)

plotly_search_basemaps(canvas)

The widget for search XYZ tile services.

Parameters:

Name	Type	Description	Default
m	Map	The Plotly Map object. Defaults to None.	required

Returns:

Name	Type	Description
ipywidgets		The tool GUI widget.

Source code in geemap/toolbar.py

def plotly_search_basemaps(canvas):
    """The widget for search XYZ tile services.

    Args:
        m (plotlymap.Map, optional): The Plotly Map object. Defaults to None.

    Returns:
        ipywidgets: The tool GUI widget.
    """
    import xyzservices.providers as xyz
    from xyzservices import TileProvider

    m = canvas.map
    container_widget = canvas.container_widget
    map_widget = canvas.map_widget
    map_widget.layout.width = "75%"

    # map_widget.layout.width = map_min_width

    widget_width = "250px"
    padding = "0px 0px 0px 5px"  # upper, right, bottom, left
    style = {"description_width": "initial"}

    toolbar_button = widgets.ToggleButton(
        value=False,
        tooltip="Toolbar",
        icon="search",
        layout=widgets.Layout(width="28px", height="28px", padding="0px 0px 0px 4px"),
    )

    close_button = widgets.ToggleButton(
        value=False,
        tooltip="Close the tool",
        icon="times",
        button_style="primary",
        layout=widgets.Layout(height="28px", width="28px", padding="0px 0px 0px 4px"),
    )

    checkbox = widgets.Checkbox(
        description="Search Quick Map Services (QMS)",
        indent=False,
        layout=widgets.Layout(padding=padding, width=widget_width),
    )

    providers = widgets.Dropdown(
        options=[],
        value=None,
        description="XYZ Tile:",
        layout=widgets.Layout(width=widget_width, padding=padding),
        style=style,
    )

    keyword = widgets.Text(
        value="",
        description="Search keyword:",
        placeholder="OpenStreetMap",
        style=style,
        layout=widgets.Layout(width=widget_width, padding=padding),
    )

    def search_callback(change):
        providers.options = []
        if keyword.value != "":
            tiles = search_xyz_services(keyword=keyword.value)
            if checkbox.value:
                tiles = tiles + search_qms(keyword=keyword.value)
            providers.options = tiles

    keyword.on_submit(search_callback)

    buttons = widgets.ToggleButtons(
        value=None,
        options=["Search", "Reset", "Close"],
        tooltips=["Search", "Reset", "Close"],
        button_style="primary",
    )
    buttons.style.button_width = "80px"

    output = widgets.Output(layout=widgets.Layout(width=widget_width, padding=padding))

    def providers_change(change):
        if change["new"] != "":
            provider = change["new"]
            if provider is not None:
                if provider.startswith("qms"):
                    with output:
                        output.outputs = ()
                        print("Adding data. Please wait...")
                    name = provider[4:]
                    qms_provider = TileProvider.from_qms(name)
                    url = qms_provider.build_url()
                    attribution = qms_provider.attribution
                    m.add_tile_layer(url, name, attribution)
                    output.outputs = ()
                elif provider.startswith("xyz"):
                    name = provider[4:]
                    xyz_provider = xyz.flatten()[name]
                    url = xyz_provider.build_url()
                    attribution = xyz_provider.attribution
                    if xyz_provider.requires_token():
                        with output:
                            output.outputs = ()
                            print(f"{provider} requires an API Key.")
                    m.add_tile_layer(url, name, attribution)

    providers.observe(providers_change, "value")

    toolbar_widget = widgets.VBox()
    toolbar_widget.children = [toolbar_button]
    toolbar_header = widgets.HBox()
    toolbar_header.children = [close_button, toolbar_button]
    toolbar_footer = widgets.VBox()
    toolbar_footer.children = [
        checkbox,
        keyword,
        providers,
        buttons,
        output,
    ]

    toolbar_event = ipyevents.Event(
        source=toolbar_widget, watched_events=["mouseenter", "mouseleave"]
    )

    def handle_toolbar_event(event):
        if event["type"] == "mouseenter":
            toolbar_widget.children = [toolbar_header, toolbar_footer]
        elif event["type"] == "mouseleave":
            if not toolbar_button.value:
                toolbar_widget.children = [toolbar_button]
                toolbar_button.value = False
                close_button.value = False

    toolbar_event.on_dom_event(handle_toolbar_event)

    def toolbar_btn_click(change):
        if change["new"]:
            close_button.value = False
            toolbar_widget.children = [toolbar_header, toolbar_footer]
        else:
            if not close_button.value:
                toolbar_widget.children = [toolbar_button]

    toolbar_button.observe(toolbar_btn_click, "value")

    def close_btn_click(change):
        if change["new"]:
            toolbar_button.value = False
            canvas.toolbar_reset()
            toolbar_widget.close()

    close_button.observe(close_btn_click, "value")

    def button_clicked(change):
        if change["new"] == "Search":
            providers.options = []
            output.outputs = ()
            if keyword.value != "":
                tiles = search_xyz_services(keyword=keyword.value)
                if checkbox.value:
                    tiles = tiles + search_qms(keyword=keyword.value)
                providers.options = tiles
            else:
                with output:
                    print("Please enter a search keyword.")
        elif change["new"] == "Reset":
            keyword.value = ""
            providers.options = []
            output.outputs = ()
        elif change["new"] == "Close":
            canvas.toolbar_reset()
            toolbar_widget.close()

        buttons.value = None

    buttons.observe(button_clicked, "value")

    toolbar_button.value = True
    container_widget.children = [toolbar_widget]

plotly_toolbar(canvas)

Creates the main toolbar and adds it to the map.

Parameters:

Name	Type	Description	Default
m	Map	The plotly Map object.	required

Source code in geemap/toolbar.py

def plotly_toolbar(
    canvas,
):
    """Creates the main toolbar and adds it to the map.

    Args:
        m (plotlymap.Map): The plotly Map object.
    """
    m = canvas.map
    map_min_width = canvas.map_min_width
    map_max_width = canvas.map_max_width
    map_refresh = canvas.map_refresh
    map_widget = canvas.map_widget

    if not map_refresh:
        width = int(map_min_width.replace("%", ""))
        if width > 90:
            map_min_width = "90%"

    tools = {
        "map": {
            "name": "basemap",
            "tooltip": "Change basemap",
        },
        "search": {
            "name": "search_xyz",
            "tooltip": "Search XYZ tile services",
        },
        "gears": {
            "name": "whitebox",
            "tooltip": "WhiteboxTools for local geoprocessing",
        },
        "folder-open": {
            "name": "vector",
            "tooltip": "Open local vector/raster data",
        },
        "picture-o": {
            "name": "raster",
            "tooltip": "Open COG/STAC dataset",
        },
        "question": {
            "name": "help",
            "tooltip": "Get help",
        },
    }

    icons = list(tools.keys())
    tooltips = [item["tooltip"] for item in list(tools.values())]

    icon_width = "32px"
    icon_height = "32px"
    n_cols = 3
    n_rows = math.ceil(len(icons) / n_cols)

    toolbar_grid = widgets.GridBox(
        children=[
            widgets.ToggleButton(
                layout=widgets.Layout(
                    width="auto", height="auto", padding="0px 0px 0px 4px"
                ),
                button_style="primary",
                icon=icons[i],
                tooltip=tooltips[i],
            )
            for i in range(len(icons))
        ],
        layout=widgets.Layout(
            width="115px",
            grid_template_columns=(icon_width + " ") * n_cols,
            grid_template_rows=(icon_height + " ") * n_rows,
            grid_gap="1px 1px",
            padding="5px",
        ),
    )
    canvas.toolbar = toolbar_grid

    def tool_callback(change):
        if change["new"]:
            current_tool = change["owner"]
            for tool in toolbar_grid.children:
                if tool is not current_tool:
                    tool.value = False
            tool = change["owner"]
            tool_name = tools[tool.icon]["name"]
            canvas.container_widget.children = []

            if tool_name == "basemap":
                plotly_basemap_gui(canvas)
            elif tool_name == "search_xyz":
                plotly_search_basemaps(canvas)
            elif tool_name == "whitebox":
                plotly_whitebox_gui(canvas)
            elif tool_name == "vector":
                plotly_tool_template(canvas)
            elif tool_name == "raster":
                plotly_tool_template(canvas)
            elif tool_name == "help":
                import webbrowser

                webbrowser.open_new_tab("https://geemap.org")
                tool.value = False
        else:
            canvas.container_widget.children = []
            map_widget.layout.width = map_max_width

    for tool in toolbar_grid.children:
        tool.observe(tool_callback, "value")

    toolbar_button = widgets.ToggleButton(
        value=False,
        tooltip="Toolbar",
        icon="wrench",
        layout=widgets.Layout(width="28px", height="28px", padding="0px 0px 0px 4px"),
    )
    canvas.toolbar_button = toolbar_button

    layers_button = widgets.ToggleButton(
        value=False,
        tooltip="Layers",
        icon="server",
        layout=widgets.Layout(height="28px", width="72px"),
    )
    canvas.layers_button = layers_button

    toolbar_widget = widgets.VBox(layout=widgets.Layout(overflow="hidden"))
    toolbar_widget.children = [toolbar_button]
    toolbar_header = widgets.HBox(layout=widgets.Layout(overflow="hidden"))
    toolbar_header.children = [layers_button, toolbar_button]
    toolbar_footer = widgets.VBox(layout=widgets.Layout(overflow="hidden"))
    toolbar_footer.children = [toolbar_grid]

    toolbar_event = ipyevents.Event(
        source=toolbar_widget, watched_events=["mouseenter", "mouseleave"]
    )

    def handle_toolbar_event(event):
        if event["type"] == "mouseenter":
            toolbar_widget.children = [toolbar_header, toolbar_footer]
            # map_widget.layout.width = "85%"
        elif event["type"] == "mouseleave":
            if not toolbar_button.value:
                toolbar_widget.children = [toolbar_button]
                toolbar_button.value = False
                layers_button.value = False
                # map_widget.layout.width = map_max_width

    toolbar_event.on_dom_event(handle_toolbar_event)

    def toolbar_btn_click(change):
        if change["new"]:
            map_widget.layout.width = map_min_width
            if map_refresh:
                with map_widget:
                    map_widget.outputs = ()
                    display(m)
            layers_button.value = False
            toolbar_widget.children = [toolbar_header, toolbar_footer]
        else:
            canvas.toolbar_reset()
            map_widget.layout.width = map_max_width
            if not layers_button.value:
                toolbar_widget.children = [toolbar_button]
            if map_refresh:
                with map_widget:
                    map_widget.outputs = ()
                    display(m)

    toolbar_button.observe(toolbar_btn_click, "value")

    def layers_btn_click(change):
        if change["new"]:
            layer_names = list(m.get_layers().keys())
            layers_hbox = []
            all_layers_chk = widgets.Checkbox(
                value=True,
                description="All layers on/off",
                indent=False,
                layout=widgets.Layout(height="18px", padding="0px 8px 25px 8px"),
            )
            all_layers_chk.layout.width = "30ex"
            layers_hbox.append(all_layers_chk)

            layer_chk_dict = {}

            for name in layer_names:
                if name in m.get_tile_layers():
                    index = m.find_layer_index(name)
                    layer = m.layout.mapbox.layers[index]
                elif name in m.get_data_layers():
                    index = m.find_layer_index(name)
                    layer = m.data[index]

                layer_chk = widgets.Checkbox(
                    value=layer.visible,
                    description=name,
                    indent=False,
                    layout=widgets.Layout(height="18px"),
                )
                layer_chk.layout.width = "25ex"
                layer_chk_dict[name] = layer_chk

                if hasattr(layer, "opacity"):
                    opacity = layer.opacity
                elif hasattr(layer, "marker"):
                    opacity = layer.marker.opacity
                else:
                    opacity = 1.0

                layer_opacity = widgets.FloatSlider(
                    value=opacity,
                    description_tooltip=name,
                    min=0,
                    max=1,
                    step=0.01,
                    readout=False,
                    layout=widgets.Layout(width="80px"),
                )

                layer_settings = widgets.ToggleButton(
                    icon="gear",
                    tooltip=name,
                    layout=widgets.Layout(
                        width="25px", height="25px", padding="0px 0px 0px 5px"
                    ),
                )

                def layer_chk_change(change):
                    if change["new"]:
                        m.set_layer_visibility(change["owner"].description, True)
                    else:
                        m.set_layer_visibility(change["owner"].description, False)

                layer_chk.observe(layer_chk_change, "value")

                def layer_opacity_change(change):
                    if change["new"]:
                        m.set_layer_opacity(
                            change["owner"].description_tooltip, change["new"]
                        )

                layer_opacity.observe(layer_opacity_change, "value")

                hbox = widgets.HBox(
                    [layer_chk, layer_settings, layer_opacity],
                    layout=widgets.Layout(padding="0px 8px 0px 8px"),
                )
                layers_hbox.append(hbox)

            def all_layers_chk_changed(change):
                if change["new"]:
                    for name in layer_names:
                        m.set_layer_visibility(name, True)
                        layer_chk_dict[name].value = True
                else:
                    for name in layer_names:
                        m.set_layer_visibility(name, False)
                        layer_chk_dict[name].value = False

            all_layers_chk.observe(all_layers_chk_changed, "value")

            toolbar_footer.children = layers_hbox
            toolbar_button.value = False
        else:
            toolbar_footer.children = [toolbar_grid]

    layers_button.observe(layers_btn_click, "value")

    return toolbar_widget

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 geemap/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 geemap/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 geemap/common.py

def png_to_gif(in_dir, out_gif, fps=10, loop=0):
    """Convert a list of png images to gif.

    Args:
        in_dir (str): The input directory containing png images.
        out_gif (str): The output file path to the gif.
        fps (int, optional): Frames per second. Defaults to 10.
        loop (bool, optional): controls how many times the animation repeats. 1 means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0.

    Raises:
        FileNotFoundError: No png images could be found.
    """
    import glob

    from PIL import Image

    if not out_gif.endswith(".gif"):
        raise ValueError("The out_gif must be a gif file.")

    out_gif = os.path.abspath(out_gif)

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

    # Create the frames
    frames = []
    imgs = list(glob.glob(os.path.join(in_dir, "*.png")))
    imgs.sort()

    if len(imgs) == 0:
        raise FileNotFoundError(f"No png could be found in {in_dir}.")

    for i in imgs:
        new_frame = Image.open(i)
        frames.append(new_frame)

    # Save into a GIF file that loops forever
    frames[0].save(
        out_gif,
        format="GIF",
        append_images=frames[1:],
        save_all=True,
        duration=1000 / fps,
        loop=loop,
    )

points_from_xy(data, x='longitude', y='latitude', z=None, crs=None, **kwargs)

Create a GeoPandas GeoDataFrame from a csv or Pandas DataFrame containing x, y, z values.

Parameters:

Name	Type	Description	Default
data	str | DataFrame	A csv or Pandas DataFrame containing x, y, z values.	required
x	str	The column name for the x values. Defaults to "longitude".	'longitude'
y	str	The column name for the y values. Defaults to "latitude".	'latitude'
z	str	The column name for the z values. Defaults to None.	None
crs	str | int	The coordinate reference system for the GeoDataFrame. Defaults to None.	None

Returns:

Type	Description
	geopandas.GeoDataFrame: A GeoPandas GeoDataFrame containing x, y, z values.

Source code in geemap/common.py

def points_from_xy(data, x="longitude", y="latitude", z=None, crs=None, **kwargs):
    """Create a GeoPandas GeoDataFrame from a csv or Pandas DataFrame containing x, y, z values.

    Args:
        data (str | pd.DataFrame): A csv or Pandas DataFrame containing x, y, z values.
        x (str, optional): The column name for the x values. Defaults to "longitude".
        y (str, optional): The column name for the y values. Defaults to "latitude".
        z (str, optional): The column name for the z values. Defaults to None.
        crs (str | int, optional): The coordinate reference system for the GeoDataFrame. Defaults to None.

    Returns:
        geopandas.GeoDataFrame: A GeoPandas GeoDataFrame containing x, y, z values.
    """
    check_package(name="geopandas", URL="https://geopandas.org")
    import geopandas as gpd
    import pandas as pd

    if crs is None:
        crs = "epsg:4326"

    data = github_raw_url(data)

    if isinstance(data, pd.DataFrame):
        df = data
    elif isinstance(data, str):
        if not data.startswith("http") and (not os.path.exists(data)):
            raise FileNotFoundError("The specified input csv does not exist.")
        else:
            df = pd.read_csv(data, **kwargs)
    else:
        raise TypeError("The data must be a pandas DataFrame or a csv file path.")

    gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df[x], df[y], z=z, crs=crs))

    return gdf

postgis_to_ee(sql, con, geom_col='geom', crs=None, geodestic=False, **kwargs)

Reads data from a PostGIS database and returns a GeoDataFrame.

Parameters:

Name	Type	Description	Default
sql	str	SQL query to execute in selecting entries from database, or name of the table to read from the database.	required
con	Engine	Active connection to the database to query.	required
geom_col	str	Column name to convert to shapely geometries. Defaults to "geom".	'geom'
crs	str | dict	CRS to use for the returned GeoDataFrame; if not set, tries to determine CRS from the SRID associated with the first geometry in the database, and assigns that to all geometries. Defaults to None.	None
geodestic	bool	Whether to use geodestic coordinates. Defaults to False.	False

Returns:

Type	Description

Source code in geemap/common.py

def postgis_to_ee(sql, con, geom_col="geom", crs=None, geodestic=False, **kwargs):
    """Reads data from a PostGIS database and returns a GeoDataFrame.

    Args:
        sql (str): SQL query to execute in selecting entries from database, or name of the table to read from the database.
        con (sqlalchemy.engine.Engine): Active connection to the database to query.
        geom_col (str, optional): Column name to convert to shapely geometries. Defaults to "geom".
        crs (str | dict, optional): CRS to use for the returned GeoDataFrame; if not set, tries to determine CRS from the SRID associated with the first geometry in the database, and assigns that to all geometries. Defaults to None.
        geodestic (bool, optional): Whether to use geodestic coordinates. Defaults to False.

    Returns:
        [type]: [description]
    """
    check_package(name="geopandas", URL="https://geopandas.org")

    gdf = read_postgis(sql, con, geom_col, crs=crs, **kwargs)
    fc = gdf_to_ee(gdf, geodesic=geodestic)
    return fc

random_sampling(image, region=None, scale=None, projection=None, factor=None, numPixels=None, seed=0, dropNulls=True, tileScale=1.0, geometries=True, to_pandas=False)

Samples the pixels of an image, returning them as a FeatureCollection. Each feature will have 1 property per band in the input image. Note that the default behavior is to drop features that intersect masked pixels, which result in null-valued properties (see dropNulls argument).

Parameters:

Name	Type	Description	Default
image	Image	The image to sample.	required
region	Geometry	The region to sample from. If unspecified, uses the image's whole footprint. Defaults to None.	None
scale	float	A nominal scale in meters of the projection to sample in.. Defaults to None.	None
projection	Projection	The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.. Defaults to None.	None
factor	float	A subsampling factor, within (0, 1]. If specified, 'numPixels' must not be specified. Defaults to no subsampling. Defaults to None.	None
numPixels	int	The approximate number of pixels to sample. If specified, 'factor' must not be specified. Defaults to None.	None
seed	int	A randomization seed to use for subsampling. Defaults to True. Defaults to 0.	0
dropNulls	bool	Post filter the result to drop features that have null-valued properties. Defaults to True.	True
tileScale	float	Post filter the result to drop features that have null-valued properties. Defaults to 1.	1.0
geometries	bool	If true, adds the center of the sampled pixel as the geometry property of the output feature. Otherwise, geometries will be omitted (saving memory). Defaults to True.	True
to_pandas	bool	Whether to return the result as a pandas dataframe. Defaults to False.	False

Raises:

Type	Description
TypeError	If the input image is not an ee.Image.

Returns:

Type	Description
	ee.FeatureCollection: Random sampled points.

Source code in geemap/common.py

def random_sampling(
    image,
    region=None,
    scale=None,
    projection=None,
    factor=None,
    numPixels=None,
    seed=0,
    dropNulls=True,
    tileScale=1.0,
    geometries=True,
    to_pandas=False,
):
    """Samples the pixels of an image, returning them as a FeatureCollection. Each feature will have 1 property per band in the input image. Note that the default behavior is to drop features that intersect masked pixels, which result in null-valued properties (see dropNulls argument).

    Args:
        image (ee.Image): The image to sample.
        region (ee.Geometry, optional): The region to sample from. If unspecified, uses the image's whole footprint. Defaults to None.
        scale (float, optional): A nominal scale in meters of the projection to sample in.. Defaults to None.
        projection (ee.Projection, optional): The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.. Defaults to None.
        factor (float, optional): A subsampling factor, within (0, 1]. If specified, 'numPixels' must not be specified. Defaults to no subsampling. Defaults to None.
        numPixels (int, optional): The approximate number of pixels to sample. If specified, 'factor' must not be specified. Defaults to None.
        seed (int, optional): A randomization seed to use for subsampling. Defaults to True. Defaults to 0.
        dropNulls (bool, optional): Post filter the result to drop features that have null-valued properties. Defaults to True.
        tileScale (float, optional): Post filter the result to drop features that have null-valued properties. Defaults to 1.
        geometries (bool, optional): If true, adds the center of the sampled pixel as the geometry property of the output feature. Otherwise, geometries will be omitted (saving memory). Defaults to True.
        to_pandas (bool, optional): Whether to return the result as a pandas dataframe. Defaults to False.

    Raises:
        TypeError: If the input image is not an ee.Image.

    Returns:
        ee.FeatureCollection: Random sampled points.
    """
    if not isinstance(image, ee.Image):
        raise TypeError("The image must be ee.Image")

    points = image.sample(
        **{
            "region": region,
            "scale": scale,
            "projection": projection,
            "factor": factor,
            "numPixels": numPixels,
            "seed": seed,
            "dropNulls": dropNulls,
            "tileScale": tileScale,
            "geometries": geometries,
        }
    )

    if to_pandas:
        return ee_to_df(points)
    else:
        return points

random_string(string_length=3)

Generates a random string of fixed length.

Parameters:

Name	Type	Description	Default
string_length	int	Fixed length. Defaults to 3.	3

Returns:

Name	Type	Description
str	str	A random string.

Source code in geemap/coreutils.py

def random_string(string_length: 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))

read_api_csv()

Extracts Earth Engine API from a csv file and returns a dictionary containing information about each function.

Returns:

Name	Type	Description
dict		The dictionary containing information about each function, including name, description, function form, return type, arguments, html.

Source code in geemap/common.py

def read_api_csv():
    """Extracts Earth Engine API from a csv file and returns a dictionary containing information about each function.

    Returns:
        dict: The dictionary containing information about each function, including name, description, function form, return type, arguments, html.
    """
    import copy

    pkg_dir = str(importlib.resources.files("geemap").joinpath("geemap.py").parent)
    data_dir = os.path.join(pkg_dir, "data")
    template_dir = os.path.join(data_dir, "template")
    csv_file = os.path.join(template_dir, "ee_api_docs.csv")
    html_file = os.path.join(template_dir, "ee_api_docs.html")

    with open(html_file) as f:
        in_html_lines = f.readlines()

    api_dict = {}

    with open(csv_file, "r", encoding="utf-8") as f:
        csv_reader = csv.DictReader(f, delimiter="\t")

        for line in csv_reader:
            out_html_lines = copy.copy(in_html_lines)
            out_html_lines[65] = in_html_lines[65].replace(
                "function_name", line["name"]
            )
            out_html_lines[66] = in_html_lines[66].replace(
                "function_description", line.get("description")
            )
            out_html_lines[74] = in_html_lines[74].replace(
                "function_usage", line.get("function")
            )
            out_html_lines[75] = in_html_lines[75].replace(
                "function_returns", line.get("returns")
            )

            arguments = line.get("argument")
            types = line.get("type")
            details = line.get("details")

            if "|" in arguments:
                argument_items = arguments.split("|")
            else:
                argument_items = [arguments]

            if "|" in types:
                types_items = types.split("|")
            else:
                types_items = [types]

            if "|" in details:
                details_items = details.split("|")
            else:
                details_items = [details]

            out_argument_lines = []

            for index in range(len(argument_items)):
                in_argument_lines = in_html_lines[87:92]
                in_argument_lines[1] = in_argument_lines[1].replace(
                    "function_argument", argument_items[index]
                )
                in_argument_lines[2] = in_argument_lines[2].replace(
                    "function_type", types_items[index]
                )
                in_argument_lines[3] = in_argument_lines[3].replace(
                    "function_details", details_items[index]
                )
                out_argument_lines.append("".join(in_argument_lines))

            out_html_lines = (
                out_html_lines[:87] + out_argument_lines + out_html_lines[92:]
            )

            contents = "".join(out_html_lines)

            api_dict[line["name"]] = {
                "description": line.get("description"),
                "function": line.get("function"),
                "returns": line.get("returns"),
                "argument": line.get("argument"),
                "type": line.get("type"),
                "details": line.get("details"),
                "html": contents,
            }

    return api_dict

read_file_from_url(url, return_type='list', encoding='utf-8')

Reads a file from a URL.

Parameters:

Name	Type	Description	Default
url	str	The URL of the file.	required
return_type	str	The return type, can either be string or list. Defaults to "list".	'list'
encoding	str	The encoding of the file. Defaults to "utf-8".	'utf-8'

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 geemap/common.py

def read_file_from_url(url, return_type="list", encoding="utf-8"):
    """Reads a file from a URL.

    Args:
        url (str): The URL of the file.
        return_type (str, optional): The return type, can either be string or list. Defaults to "list".
        encoding (str, optional): The encoding of the file. Defaults to "utf-8".

    Raises:
        ValueError: The return type must be either list or string.

    Returns:
        str | list: The contents of the file.
    """
    from urllib.request import urlopen

    if return_type == "list":
        return [line.decode(encoding).rstrip() for line in urlopen(url).readlines()]
    elif return_type == "string":
        return urlopen(url).read().decode(encoding)
    else:
        raise ValueError("The return type must be either list or string.")

read_lidar(filename, **kwargs)

Read a LAS file.

Parameters:

Name	Type	Description	Default
filename	str	A local file path or HTTP URL to a LAS file.	required

Returns:

Name	Type	Description
LasData		The LasData object return by laspy.read.

Source code in geemap/common.py

def read_lidar(filename, **kwargs):
    """Read a LAS file.

    Args:
        filename (str): A local file path or HTTP URL to a LAS file.

    Returns:
        LasData: The LasData object return by laspy.read.
    """
    try:
        import laspy
    except ImportError:
        print(
            "The laspy package is required for this function. Use `pip install laspy[lazrs,laszip]` to install it."
        )
        return

    if (
        isinstance(filename, str)
        and filename.startswith("http")
        and (filename.endswith(".las") or filename.endswith(".laz"))
    ):
        filename = github_raw_url(filename)
        filename = download_file(filename)

    return laspy.read(filename, **kwargs)

read_netcdf(filename, **kwargs)

Read a netcdf file.

Parameters:

Name	Type	Description	Default
filename	str	File path or HTTP URL to the netcdf file.	required

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 geemap/common.py

def read_netcdf(filename, **kwargs):
    """Read a netcdf file.

    Args:
        filename (str): File path or HTTP URL to the netcdf file.

    Raises:
        ImportError: If the xarray or rioxarray package is not installed.
        FileNotFoundError: If the netcdf file is not found.

    Returns:
        xarray.Dataset: The netcdf file as an xarray dataset.
    """
    try:
        import xarray as xr
    except ImportError as e:
        raise ImportError(e)

    if filename.startswith("http"):
        filename = download_file(filename)

    if not os.path.exists(filename):
        raise FileNotFoundError(f"{filename} does not exist.")

    xds = xr.open_dataset(filename, **kwargs)
    return xds

read_postgis(sql, con, geom_col='geom', crs=None, **kwargs)

Reads data from a PostGIS database and returns a GeoDataFrame.

Parameters:

Name	Type	Description	Default
sql	str	SQL query to execute in selecting entries from database, or name of the table to read from the database.	required
con	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 geemap/common.py

def read_postgis(sql, con, geom_col="geom", crs=None, **kwargs):
    """Reads data from a PostGIS database and returns a GeoDataFrame.

    Args:
        sql (str): SQL query to execute in selecting entries from database, or name of the table to read from the database.
        con (sqlalchemy.engine.Engine): Active connection to the database to query.
        geom_col (str, optional): Column name to convert to shapely geometries. Defaults to "geom".
        crs (str | dict, optional): CRS to use for the returned GeoDataFrame; if not set, tries to determine CRS from the SRID associated with the first geometry in the database, and assigns that to all geometries. Defaults to None.

    Returns:
        [type]: [description]
    """
    check_package(name="geopandas", URL="https://geopandas.org")

    import geopandas as gpd

    gdf = gpd.read_postgis(sql, con, geom_col, crs, **kwargs)
    return gdf

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 geemap/timelapse.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.
    """
    import ffmpeg
    import warnings

    warnings.filterwarnings("ignore")

    if not is_tool("ffmpeg"):
        print("ffmpeg is not installed on your computer.")
        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)

remove_geometry(fc)

Remove .geo coordinate field from a FeatureCollection

Parameters:

Name	Type	Description	Default
fc	object	The input FeatureCollection.	required

Returns:

Name	Type	Description
object		The output FeatureCollection without the geometry field.

Source code in geemap/common.py

def remove_geometry(fc):
    """Remove .geo coordinate field from a FeatureCollection

    Args:
        fc (object): The input FeatureCollection.

    Returns:
        object: The output FeatureCollection without the geometry field.
    """
    return fc.select([".*"], None, False)

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 geemap/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

rename_bands(img, in_band_names, out_band_names)

Renames image bands.

Parameters:

Name	Type	Description	Default
img	object	The image to be renamed.	required
in_band_names	list	The list of input band names.	required
out_band_names	list	The list of output band names.	required

Returns:

Name	Type	Description
object		The output image with the renamed bands.

Source code in geemap/common.py

def rename_bands(img, in_band_names, out_band_names):
    """Renames image bands.

    Args:
        img (object): The image to be renamed.
        in_band_names (list): The list of input band names.
        out_band_names (list): The list of output band names.

    Returns:
        object: The output image with the renamed bands.
    """
    return img.select(in_band_names, out_band_names)

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 geemap/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 geemap/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

reproject(image, output, dst_crs='EPSG:4326', resampling='nearest', **kwargs)

Reprojects an image.

Parameters:

Name	Type	Description	Default
image	str	The input image filepath.	required
output	str	The output image filepath.	required
dst_crs	str	The destination CRS. Defaults to "EPSG:4326".	'EPSG:4326'
resampling	Resampling	The resampling method. Defaults to "nearest".	'nearest'
**kwargs		Additional keyword arguments to pass to rasterio.open.	{}

Source code in geemap/common.py

def reproject(image, output, dst_crs="EPSG:4326", resampling="nearest", **kwargs):
    """Reprojects an image.

    Args:
        image (str): The input image filepath.
        output (str): The output image filepath.
        dst_crs (str, optional): The destination CRS. Defaults to "EPSG:4326".
        resampling (Resampling, optional): The resampling method. Defaults to "nearest".
        **kwargs: Additional keyword arguments to pass to rasterio.open.

    """
    import rasterio as rio
    from rasterio.warp import calculate_default_transform, reproject, Resampling

    if isinstance(resampling, str):
        resampling = getattr(Resampling, resampling)

    image = os.path.abspath(image)
    output = os.path.abspath(output)

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

    with rio.open(image, **kwargs) as src:
        transform, width, height = calculate_default_transform(
            src.crs, dst_crs, src.width, src.height, *src.bounds
        )
        kwargs = src.meta.copy()
        kwargs.update(
            {
                "crs": dst_crs,
                "transform": transform,
                "width": width,
                "height": height,
            }
        )

        with rio.open(output, "w", **kwargs) as dst:
            for i in range(1, src.count + 1):
                reproject(
                    source=rio.band(src, i),
                    destination=rio.band(dst, i),
                    src_transform=src.transform,
                    src_crs=src.crs,
                    dst_transform=transform,
                    dst_crs=dst_crs,
                    resampling=resampling,
                    **kwargs,
                )

requireJS(lib_path=None, Map=None)

Import Earth Engine JavaScript libraries. Based on the Open Earth Engine Library (OEEL). For more info, visit https://www.open-geocomputing.org/OpenEarthEngineLibrary.

Parameters:

Name	Type	Description	Default
lib_path	str	A local file path or HTTP URL to a JavaScript library. It can also be in a format like 'users/gena/packages:grid'. Defaults to None.	None
Map	Map	An geemap.Map object. Defaults to None.	None

Returns:

Name	Type	Description
object		oeel object.

Source code in geemap/common.py

def requireJS(lib_path=None, Map=None):
    """Import Earth Engine JavaScript libraries. Based on the Open Earth Engine Library (OEEL).
        For more info, visit https://www.open-geocomputing.org/OpenEarthEngineLibrary.

    Args:
        lib_path (str, optional): A local file path or HTTP URL to a JavaScript library. It can also be in a format like 'users/gena/packages:grid'. Defaults to None.
        Map (geemap.Map, optional): An geemap.Map object. Defaults to None.

    Returns:
        object: oeel object.
    """
    try:
        from oeel import oeel
    except ImportError:
        raise ImportError(
            "oeel is required for requireJS. Please install it using 'pip install oeel'."
        )

    ee_initialize()

    if lib_path is None:
        if Map is not None:
            oeel.setMap(Map)
        return oeel
    elif isinstance(lib_path, str):
        if lib_path.startswith("http"):
            lib_path = get_direct_url(lib_path)

        lib_path = change_require(lib_path)

        if Map is not None:
            oeel.setMap(Map)
        return oeel.requireJS(lib_path)

    else:
        raise ValueError("lib_path must be a string.")

rgb_to_hex(rgb=(255, 255, 255))

Converts RGB to hex color. In RGB color, R stands for Red, G stands for Green, and B stands for Blue, and it ranges from the decimal value of 0 – 255.

Parameters:

Name	Type	Description	Default
rgb	Tuple[int, int, int]	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 geemap/coreutils.py

def rgb_to_hex(rgb: 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[int, int, int], optional): RGB color code as a tuple of
            (red, green, blue). Defaults to (255, 255, 255).

    Returns:
        str: Hex color code.
    """
    return "%02x%02x%02x" % rgb

save_colorbar(out_fig=None, width=4.0, height=0.3, vmin=0, vmax=1.0, palette=None, vis_params=None, cmap='gray', discrete=False, label=None, label_size=10, label_weight='normal', tick_size=8, bg_color='white', orientation='horizontal', dpi='figure', transparent=False, show_colorbar=True, **kwargs)

Create a standalone colorbar and save it as an image.

Parameters:

Name	Type	Description	Default
out_fig	str	Path to the output image.	None
width	float	Width of the colorbar in inches. Default is 4.0.	4.0
height	float	Height of the colorbar in inches. Default is 0.3.	0.3
vmin	float	Minimum value of the colorbar. Default is 0.	0
vmax	float	Maximum value of the colorbar. Default is 1.0.	1.0
palette	list	List of colors to use for the colorbar. It can also be a cmap name, such as ndvi, ndwi, dem, coolwarm. Default is None.	None
vis_params	dict	Visualization parameters as a dictionary. See https://developers.google.com/earth-engine/guides/image_visualization for options.	None
cmap	str	Matplotlib colormap. Defaults to "gray". See https://matplotlib.org/3.3.4/tutorials/colors/colormaps.html#sphx-glr-tutorials-colors-colormaps-py for options.	'gray'
discrete	bool	Whether to create a discrete colorbar. Defaults to False.	False
label	str	Label for the colorbar. Defaults to None.	None
label_size	int	Font size for the colorbar label. Defaults to 12.	10
label_weight	str	Font weight for the colorbar label, can be "normal", "bold", etc. Defaults to "normal".	'normal'
tick_size	int	Font size for the colorbar tick labels. Defaults to 10.	8
bg_color	str	Background color for the colorbar. Defaults to "white".	'white'
orientation	str	Orientation of the colorbar, such as "vertical" and "horizontal". Defaults to "horizontal".	'horizontal'
dpi	float | str	The resolution in dots per inch. If 'figure', use the figure's dpi value. Defaults to "figure".	'figure'
transparent	bool	Whether to make the background transparent. Defaults to False.	False
show_colorbar	bool	Whether to show the colorbar. Defaults to True.	True
**kwargs		Other keyword arguments to pass to matplotlib.pyplot.savefig().	{}

Returns:

Name	Type	Description
str		Path to the output image.

Source code in geemap/common.py

def save_colorbar(
    out_fig=None,
    width=4.0,
    height=0.3,
    vmin=0,
    vmax=1.0,
    palette=None,
    vis_params=None,
    cmap="gray",
    discrete=False,
    label=None,
    label_size=10,
    label_weight="normal",
    tick_size=8,
    bg_color="white",
    orientation="horizontal",
    dpi="figure",
    transparent=False,
    show_colorbar=True,
    **kwargs,
):
    """Create a standalone colorbar and save it as an image.

    Args:
        out_fig (str): Path to the output image.
        width (float): Width of the colorbar in inches. Default is 4.0.
        height (float): Height of the colorbar in inches. Default is 0.3.
        vmin (float): Minimum value of the colorbar. Default is 0.
        vmax (float): Maximum value of the colorbar. Default is 1.0.
        palette (list): List of colors to use for the colorbar. It can also be a cmap name, such as ndvi, ndwi, dem, coolwarm. Default is None.
        vis_params (dict): Visualization parameters as a dictionary. See https://developers.google.com/earth-engine/guides/image_visualization for options.
        cmap (str, optional): Matplotlib colormap. Defaults to "gray". See https://matplotlib.org/3.3.4/tutorials/colors/colormaps.html#sphx-glr-tutorials-colors-colormaps-py for options.
        discrete (bool, optional): Whether to create a discrete colorbar. Defaults to False.
        label (str, optional): Label for the colorbar. Defaults to None.
        label_size (int, optional): Font size for the colorbar label. Defaults to 12.
        label_weight (str, optional): Font weight for the colorbar label, can be "normal", "bold", etc. Defaults to "normal".
        tick_size (int, optional): Font size for the colorbar tick labels. Defaults to 10.
        bg_color (str, optional): Background color for the colorbar. Defaults to "white".
        orientation (str, optional): Orientation of the colorbar, such as "vertical" and "horizontal". Defaults to "horizontal".
        dpi (float | str, optional): The resolution in dots per inch.  If 'figure', use the figure's dpi value. Defaults to "figure".
        transparent (bool, optional): Whether to make the background transparent. Defaults to False.
        show_colorbar (bool, optional): Whether to show the colorbar. Defaults to True.
        **kwargs: Other keyword arguments to pass to matplotlib.pyplot.savefig().

    Returns:
        str: Path to the output image.
    """
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    import numpy as np
    from .colormaps import palettes, get_palette

    if out_fig is None:
        out_fig = temp_file_path("png")
    else:
        out_fig = check_file_path(out_fig)

    if vis_params is None:
        vis_params = {}
    elif not isinstance(vis_params, dict):
        raise TypeError("The vis_params must be a dictionary.")

    if palette is not None:
        if palette in ["ndvi", "ndwi", "dem"]:
            palette = palettes[palette]
        elif palette in list(palettes.keys()):
            palette = get_palette(palette)
        vis_params["palette"] = palette

    orientation = orientation.lower()
    if orientation not in ["horizontal", "vertical"]:
        raise ValueError("The orientation must be either horizontal or vertical.")

    if "opacity" in vis_params:
        alpha = vis_params["opacity"]
        if type(alpha) not in (int, float):
            raise ValueError("The provided opacity value must be type scalar.")
    else:
        alpha = 1

    if cmap is not None:
        cmap = mpl.pyplot.get_cmap(cmap)
        norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)

    if "palette" in vis_params:
        hexcodes = to_hex_colors(vis_params["palette"])
        if discrete:
            cmap = mpl.colors.ListedColormap(hexcodes)
            vals = np.linspace(vmin, vmax, cmap.N + 1)
            norm = mpl.colors.BoundaryNorm(vals, cmap.N)

        else:
            cmap = mpl.colors.LinearSegmentedColormap.from_list(
                "custom", hexcodes, N=256
            )
            norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)

    elif cmap is not None:
        cmap = mpl.pyplot.get_cmap(cmap)
        norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)

    else:
        raise ValueError(
            'cmap keyword or "palette" key in vis_params must be provided.'
        )

    fig, ax = plt.subplots(figsize=(width, height))
    cb = mpl.colorbar.ColorbarBase(
        ax, norm=norm, alpha=alpha, cmap=cmap, orientation=orientation, **kwargs
    )
    if label is not None:
        cb.set_label(label=label, size=label_size, weight=label_weight)
    cb.ax.tick_params(labelsize=tick_size)

    if transparent:
        bg_color = None

    if bg_color is not None:
        kwargs["facecolor"] = bg_color
    if "bbox_inches" not in kwargs:
        kwargs["bbox_inches"] = "tight"

    fig.savefig(out_fig, dpi=dpi, transparent=transparent, **kwargs)
    if not show_colorbar:
        plt.close(fig)
    return out_fig

screen_capture(filename, monitor=1)

Takes a full screenshot of the selected monitor.

Parameters:

Name	Type	Description	Default
filename	str	The output file path to the screenshot.	required
monitor	int	The monitor to take the screenshot. Defaults to 1.	1

Source code in geemap/common.py

def screen_capture(filename, monitor=1):
    """Takes a full screenshot of the selected monitor.

    Args:
        filename (str): The output file path to the screenshot.
        monitor (int, optional): The monitor to take the screenshot. Defaults to 1.
    """
    try:
        from mss import mss
    except ImportError:
        raise ImportError("Please install mss package using 'pip install mss'")

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

    if not isinstance(monitor, int):
        print("The monitor number must be an integer.")
        return

    try:
        with mss() as sct:
            sct.shot(output=filename, mon=monitor)
            return filename

    except Exception as e:
        print(e)

search_api_tree(keywords, api_tree)

Search Earth Engine API and return functions containing the specified keywords

Parameters:

Name	Type	Description	Default
keywords	str	The keywords to search for.	required
api_tree	dict	The dictionary containing the Earth Engine API tree.	required

Returns:

Name	Type	Description
object		An ipytree object/widget.

Source code in geemap/common.py

def search_api_tree(keywords, api_tree):
    """Search Earth Engine API and return functions containing the specified keywords

    Args:
        keywords (str): The keywords to search for.
        api_tree (dict): The dictionary containing the Earth Engine API tree.

    Returns:
        object: An ipytree object/widget.
    """

    warnings.filterwarnings("ignore")

    sub_tree = Tree()

    for key in api_tree.keys():
        if keywords.lower() in key.lower():
            sub_tree.add_node(api_tree[key])

    return sub_tree

search_ee_data(keywords, regex=False, source='ee', types=None, keys=['id', 'provider', 'tags', 'title'])

Searches Earth Engine data catalog.

Parameters:

Name	Type	Description	Default
keywords	str | list	Keywords to search for can be id, provider, tag and so on. Split by space if string, e.g. "1 2" becomes ['1','2'].	required
regex	bool	Allow searching for regular expressions. Defaults to false.	False
source	str	Can be 'ee', 'community' or 'all'. Defaults to 'ee'. For more details, see https://github.com/samapriya/awesome-gee-community-datasets/blob/master/community_datasets.json	'ee'
types	list	List of valid collection types. Defaults to None so no filter is applied. A possible filter ['image_collection']	None
keys	list	List of metadata fields to search from. Defaults to ['id','provider','tags','title']	['id', 'provider', 'tags', 'title']

Returns:

Name	Type	Description
list		Returns a list of assets.

Source code in geemap/common.py

def search_ee_data(
    keywords,
    regex=False,
    source="ee",
    types=None,
    keys=["id", "provider", "tags", "title"],
):
    """Searches Earth Engine data catalog.

    Args:
        keywords (str | list): Keywords to search for can be id, provider, tag and so on. Split by space if string, e.g. "1 2" becomes ['1','2'].
        regex (bool, optional): Allow searching for regular expressions. Defaults to false.
        source (str, optional): Can be 'ee', 'community' or 'all'. Defaults to 'ee'. For more details, see https://github.com/samapriya/awesome-gee-community-datasets/blob/master/community_datasets.json
        types (list, optional): List of valid collection types. Defaults to None so no filter is applied. A possible filter ['image_collection']
        keys (list, optional): List of metadata fields to search from.  Defaults to ['id','provider','tags','title']

    Returns:
        list: Returns a list of assets.
    """
    if isinstance(keywords, str):
        keywords = keywords.split(" ")

    import re
    from functools import reduce

    def search_collection(pattern, dict_):
        if regex:
            if any(re.match(pattern, dict_[key]) for key in keys):
                return dict_
        elif any(pattern in dict_[key] for key in keys):
            return dict_
        return {}

    def search_all(pattern):
        # updated daily
        a = "https://raw.githubusercontent.com/samapriya/Earth-Engine-Datasets-List/master/gee_catalog.json"
        b = "https://raw.githubusercontent.com/samapriya/awesome-gee-community-datasets/master/community_datasets.json"
        sources = {"ee": [a], "community": [b], "all": [a, b]}
        matches = []
        for link in sources[source]:
            r = requests.get(link)
            catalog_list = r.json()
            matches += [search_collection(pattern, x) for x in catalog_list]
        matches = [x for x in matches if x]
        if types:
            return [x for x in matches if x["type"] in types]
        return matches

    try:
        assets = list(
            {json.dumps(match) for match in search_all(pattern=k)} for k in keywords
        )
        assets = sorted(list(reduce(set.intersection, assets)))
        assets = [json.loads(x) for x in assets]

        results = []
        for asset in assets:
            asset_dates = (
                asset.get("start_date", "Unknown")
                + " - "
                + asset.get("end_date", "Unknown")
            )
            asset_snippet = asset["id"]
            if "ee." in asset_snippet:
                start_index = asset_snippet.index("'") + 1
                end_index = asset_snippet.index("'", start_index)
                asset_id = asset_snippet[start_index:end_index]
            else:
                asset_id = asset_snippet

            asset["dates"] = asset_dates
            asset["id"] = asset_id
            asset["uid"] = asset_id.replace("/", "_")

            results.append(asset)

        return results

    except Exception as e:
        print(e)

search_qms(keyword, limit=10, list_only=True, add_prefix=True, timeout=300)

Search for QMS tile providers from Quick Map Services.

Parameters:

Name	Type	Description	Default
keyword	str	The keyword to search for.	required
limit	int	The maximum number of results to return. Defaults to 10.	10
list_only	bool	If True, only the list of services will be returned. Defaults to True.	True
add_prefix	bool	If True, the prefix "qms." will be added to the service name. Defaults to True.	True
timeout	int	The timeout in seconds. Defaults to 300.	300

Returns:

Name	Type	Description
list		A list of QMS tile providers.

Source code in geemap/common.py

def search_qms(keyword, limit=10, list_only=True, add_prefix=True, timeout=300):
    """Search for QMS tile providers from Quick Map Services.

    Args:
        keyword (str): The keyword to search for.
        limit (int, optional): The maximum number of results to return. Defaults to 10.
        list_only (bool, optional): If True, only the list of services will be returned. Defaults to True.
        add_prefix (bool, optional): If True, the prefix "qms." will be added to the service name. Defaults to True.
        timeout (int, optional): The timeout in seconds. Defaults to 300.

    Returns:
        list: A list of QMS tile providers.
    """

    QMS_API = "https://qms.nextgis.com/api/v1/geoservices"
    services = requests.get(
        f"{QMS_API}/?search={keyword}&type=tms&epsg=3857&limit={limit}", timeout=timeout
    )
    services = services.json()
    if services["results"]:
        providers = services["results"]
        if list_only:
            if add_prefix:
                return ["qms." + provider["name"] for provider in providers]
            else:
                return [provider["name"] for provider in providers]
        else:
            return providers
    else:
        return None

search_xyz_services(keyword, name=None, list_only=True, add_prefix=True)

Search for XYZ tile providers from xyzservices.

Parameters:

Name	Type	Description	Default
keyword	str	The keyword to search for.	required
name	str	The name of the xyz tile. Defaults to None.	None
list_only	bool	If True, only the list of services will be returned. Defaults to True.	True
add_prefix	bool	If True, the prefix "xyz." will be added to the service name. Defaults to True.	True

Returns:

Name	Type	Description
list		A list of XYZ tile providers.

Source code in geemap/common.py

def search_xyz_services(keyword, name=None, list_only=True, add_prefix=True):
    """Search for XYZ tile providers from xyzservices.

    Args:
        keyword (str): The keyword to search for.
        name (str, optional): The name of the xyz tile. Defaults to None.
        list_only (bool, optional): If True, only the list of services will be returned. Defaults to True.
        add_prefix (bool, optional): If True, the prefix "xyz." will be added to the service name. Defaults to True.

    Returns:
        list: A list of XYZ tile providers.
    """

    import xyzservices.providers as xyz

    if name is None:
        providers = xyz.filter(keyword=keyword).flatten()
    else:
        providers = xyz.filter(name=name).flatten()

    if list_only:
        if add_prefix:
            return ["xyz." + provider for provider in providers]
        else:
            return [provider for provider in providers]
    else:
        return providers

sentinel1_filtering(collection, band='VV', instrumentMode=None, orbitProperties_pass=None, transmitterReceiverPolarisation=None, remove_outliers=True, **kwargs)

Sentinel-1 data is collected with several different instrument configurations, resolutions, band combinations during both ascending and descending orbits. Because of this heterogeneity, it's usually necessary to filter the data down to a homogeneous subset before starting processing.

For more details, see https://developers.google.com/earth-engine/guides/sentinel1

Parameters:

Name	Type	Description	Default
collection		A Sentinel1 ImageCollection to filter.	required
band	str	Collection band. Can be one of ['HH','HV','VV','VH']. Defaults to 'VV' which is most commonly available on land.	'VV'
instrumentMode	str	Collection property. Can be one of ['IW','EW','SM']. Defaults to band default availability (IW for ['VV','VH'], EW for ['HH','HV']). IW is typically available for land. EW for icy regions.	None
orbitProperties_pass	str | None	Collection property. Can be one of ['ASCENDING', 'DESCENDING', None]. Default to 'ASCENDING'. Will return mixed property if set to None, which dampen elevation, and increase surface roughness/fractality visibility.	None
transmitterReceiverPolarisation		Collection property List contains this value. Can be one of ['HH','HV','VV','VH']. Defaults to band.	None
remove_outliers	bool	Remove pixels with extreme values (< -30). These can occur near the edge of an image. Default to True.	True
**kwargs	dict	All other arguments will be applied as filters to collection properties. F.e. {'resolution_meters':10} Full list properties: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD#image-properties	{}

Returns:

Name	Type	Description
object		Returns a homogeneous ImageCollection of Sentinel 1 images.

Source code in geemap/timelapse.py

def sentinel1_filtering(
    collection,
    band="VV",
    instrumentMode=None,
    orbitProperties_pass=None,
    transmitterReceiverPolarisation=None,
    remove_outliers=True,
    **kwargs,
):
    """
    Sentinel-1 data is collected with several different instrument configurations, resolutions,
    band combinations during both ascending and descending orbits. Because of this heterogeneity,
    it's usually necessary to filter the data down to a homogeneous subset before starting processing.

    For more details, see https://developers.google.com/earth-engine/guides/sentinel1

    Args:
        collection: A Sentinel1 ImageCollection to filter.
        band (str): Collection band. Can be one of ['HH','HV','VV','VH']. Defaults to 'VV' which is most commonly available on land.
        instrumentMode (str, optional): Collection property. Can be one of ['IW','EW','SM']. Defaults to band default availability (IW for ['VV','VH'], EW for ['HH','HV']). IW is typically available for land. EW for icy regions.
        orbitProperties_pass (str|None, optional): Collection property. Can be one of ['ASCENDING', 'DESCENDING', None]. Default to 'ASCENDING'.
            Will return mixed property if set to None, which dampen elevation, and increase surface roughness/fractality visibility.
        transmitterReceiverPolarisation: Collection property List contains this value. Can be one of ['HH','HV','VV','VH']. Defaults to band.
        remove_outliers (bool, optional): Remove pixels with extreme values (< -30). These can occur near the edge of an image. Default to True.
        **kwargs (dict, optional): All other arguments will be applied as filters to collection properties. F.e. {'resolution_meters':10}
            Full list properties: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD#image-properties

    Returns:
        object: Returns a homogeneous ImageCollection of Sentinel 1 images.
    """

    transmitterReceiverPolarisation = transmitterReceiverPolarisation or band
    instrumentMode = (
        instrumentMode or {"VV": "IW", "VH": "IW", "HH": "EW", "HV": "EW"}[band]
    )

    def remove_outliers_func(image):
        if not remove_outliers:
            return image
        edge = image.select(band).lt(-30.0)
        maskedimage = image.mask().And(edge.Not())
        return image.updateMask(maskedimage)

    col = collection.filter(ee.Filter.eq("instrumentMode", instrumentMode)).filter(
        ee.Filter.listContains(
            "transmitterReceiverPolarisation", transmitterReceiverPolarisation
        )
    )

    if remove_outliers:
        col = col.map(remove_outliers_func)

    for k, v in kwargs.items():
        col = col.filter(ee.Filter.eq(k, v))
    if orbitProperties_pass:
        col = col.filter(ee.Filter.eq("orbitProperties_pass", orbitProperties_pass))

    return col

sentinel1_timelapse(roi, out_gif=None, start_year=2015, end_year=None, start_date='01-01', end_date='12-31', bands=['VV'], frequency='year', reducer='median', date_format=None, palette='Greys', vis_params=None, dimensions=768, frames_per_second=10, crs='EPSG:3857', overlay_data=None, overlay_color='black', overlay_width=1, overlay_opacity=1.0, title=None, title_xy=('2%', '90%'), add_text=True, text_xy=('2%', '2%'), text_sequence=None, font_type='arial.ttf', font_size=20, font_color='white', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, add_colorbar=False, colorbar_width=6.0, colorbar_height=0.4, colorbar_label=None, colorbar_label_size=12, colorbar_label_weight='normal', colorbar_tick_size=10, colorbar_bg_color=None, colorbar_orientation='horizontal', colorbar_dpi='figure', colorbar_xy=None, colorbar_size=(300, 300), loop=0, mp4=False, fading=False, **kwargs)

Create a timelapse from any ee.ImageCollection.

Parameters:

Name	Type	Description	Default
roi	Geometry	The region to use to filter the collection of images. It must be an ee.Geometry object. Defaults to None.	required
out_gif	str	The output gif file path. Defaults to None.	None
start_year	int	Starting year for the timelapse. Defaults to 2015.	2015
end_year	int	Ending year for the timelapse. Defaults to the current year.	None
start_date	str	Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.	'01-01'
end_date	str	Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.	'12-31'
bands	list	A list of band names to use in the timelapse. Can be one of ['VV'],['HV'],['VH'],['HH'],['VV','VH'] or ['HH','HV']	['VV']
frequency	str	The frequency of the timeseries. It must be one of the following: 'year', 'month', 'day', 'hour', 'minute', 'second'. Defaults to 'year'.	'year'
reducer	str	The reducer to use to reduce the collection of images to a single value. It can be one of the following: 'median', 'mean', 'min', 'max', 'variance', 'sum'. Defaults to 'median'.	'median'
drop_empty	bool	Whether to drop empty images from the timeseries. Defaults to True.	required
date_format	str	A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.	None
palette	list	A list of colors to render a single-band image in the timelapse. Defaults to None.	'Greys'
vis_params	dict	A dictionary of visualization parameters to use in the timelapse. Defaults to None. See more at https://developers.google.com/earth-engine/guides/image_visualization.	None
dimensions	int	a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.	768
frames_per_second	int	Animation speed. Defaults to 10.	10
crs	str	The coordinate reference system to use. Defaults to "EPSG:3857".	'EPSG:3857'
overlay_data	(int, str, list)	Administrative boundary to be drawn on the timelapse. Defaults to None.	None
overlay_color	str	Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.	'black'
overlay_width	int	Width of the overlay. Defaults to 1.	1
overlay_opacity	float	Opacity of the overlay. Defaults to 1.0.	1.0
title	str	The title of the timelapse. Defaults to None.	None
title_xy	tuple	Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
add_text	bool	Whether to add animated text to the timelapse. Defaults to True.	True
title_xy	tuple	Lower left corner of the text sequency. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
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'.	'white'
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
add_colorbar	bool	Whether to add a colorbar to the timelapse. Defaults to False.	False
colorbar_width	float	Width of the colorbar. Defaults to 6.0.	6.0
colorbar_height	float	Height of the colorbar. Defaults to 0.4.	0.4
colorbar_label	str	Label for the colorbar. Defaults to None.	None
colorbar_label_size	int	Font size for the colorbar label. Defaults to 12.	12
colorbar_label_weight	str	Font weight for the colorbar label. Defaults to 'normal'.	'normal'
colorbar_tick_size	int	Font size for the colorbar ticks. Defaults to 10.	10
colorbar_bg_color	str	Background color for the colorbar, can be color like "white", "black". Defaults to None.	None
colorbar_orientation	str	Orientation of the colorbar. Defaults to 'horizontal'.	'horizontal'
colorbar_dpi	str	DPI for the colorbar, can be numbers like 100, 300. Defaults to 'figure'.	'figure'
colorbar_xy	tuple	Lower left corner of the colorbar. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	None
colorbar_size	tuple	Size of the colorbar. It can be formatted like this: (300, 300). Defaults to (300, 300).	(300, 300)
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
mp4	bool	Whether to create an mp4 file. Defaults to False.	False
fading	int | bool	If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).	False
**kwargs		Arguments for sentinel1_filtering(). Same filters will be applied to all bands.	{}

Returns:

Name	Type	Description
str		File path to the timelapse gif.

Source code in geemap/timelapse.py

def sentinel1_timelapse(
    roi,
    out_gif=None,
    start_year=2015,
    end_year=None,
    start_date="01-01",
    end_date="12-31",
    bands=["VV"],
    frequency="year",
    reducer="median",
    date_format=None,
    palette="Greys",
    vis_params=None,
    dimensions=768,
    frames_per_second=10,
    crs="EPSG:3857",
    overlay_data=None,
    overlay_color="black",
    overlay_width=1,
    overlay_opacity=1.0,
    title=None,
    title_xy=("2%", "90%"),
    add_text=True,
    text_xy=("2%", "2%"),
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="white",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    add_colorbar=False,
    colorbar_width=6.0,
    colorbar_height=0.4,
    colorbar_label=None,
    colorbar_label_size=12,
    colorbar_label_weight="normal",
    colorbar_tick_size=10,
    colorbar_bg_color=None,
    colorbar_orientation="horizontal",
    colorbar_dpi="figure",
    colorbar_xy=None,
    colorbar_size=(300, 300),
    loop=0,
    mp4=False,
    fading=False,
    **kwargs,
):
    """Create a timelapse from any ee.ImageCollection.

    Args:
        roi (ee.Geometry, optional): The region to use to filter the collection of images. It must be an ee.Geometry object. Defaults to None.
        out_gif (str): The output gif file path. Defaults to None.
        start_year (int, optional): Starting year for the timelapse. Defaults to 2015.
        end_year (int, optional): Ending year for the timelapse. Defaults to the current year.
        start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.
        end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.
        bands (list, optional): A list of band names to use in the timelapse. Can be one of ['VV'],['HV'],['VH'],['HH'],['VV','VH'] or ['HH','HV']
        frequency (str, optional): The frequency of the timeseries. It must be one of the following: 'year', 'month', 'day', 'hour', 'minute', 'second'. Defaults to 'year'.
        reducer (str, optional):  The reducer to use to reduce the collection of images to a single value. It can be one of the following: 'median', 'mean', 'min', 'max', 'variance', 'sum'. Defaults to 'median'.
        drop_empty (bool, optional): Whether to drop empty images from the timeseries. Defaults to True.
        date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html. Defaults to 'YYYY-MM-dd'.
        palette (list, optional): A list of colors to render a single-band image in the timelapse. Defaults to None.
        vis_params (dict, optional): A dictionary of visualization parameters to use in the timelapse. Defaults to None. See more at https://developers.google.com/earth-engine/guides/image_visualization.
        dimensions (int, optional): a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
        frames_per_second (int, optional): Animation speed. Defaults to 10.
        crs (str, optional): The coordinate reference system to use. Defaults to "EPSG:3857".
        overlay_data (int, str, list, optional): Administrative boundary to be drawn on the timelapse. Defaults to None.
        overlay_color (str, optional): Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.
        overlay_width (int, optional): Width of the overlay. Defaults to 1.
        overlay_opacity (float, optional): Opacity of the overlay. Defaults to 1.0.
        title (str, optional): The title of the timelapse. Defaults to None.
        title_xy (tuple, optional): Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        add_text (bool, optional): Whether to add animated text to the timelapse. Defaults to True.
        title_xy (tuple, optional): Lower left corner of the text sequency. 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.
        add_colorbar (bool, optional): Whether to add a colorbar to the timelapse. Defaults to False.
        colorbar_width (float, optional): Width of the colorbar. Defaults to 6.0.
        colorbar_height (float, optional): Height of the colorbar. Defaults to 0.4.
        colorbar_label (str, optional): Label for the colorbar. Defaults to None.
        colorbar_label_size (int, optional): Font size for the colorbar label. Defaults to 12.
        colorbar_label_weight (str, optional): Font weight for the colorbar label. Defaults to 'normal'.
        colorbar_tick_size (int, optional): Font size for the colorbar ticks. Defaults to 10.
        colorbar_bg_color (str, optional): Background color for the colorbar, can be color like "white", "black". Defaults to None.
        colorbar_orientation (str, optional): Orientation of the colorbar. Defaults to 'horizontal'.
        colorbar_dpi (str, optional): DPI for the colorbar, can be numbers like 100, 300. Defaults to 'figure'.
        colorbar_xy (tuple, optional): Lower left corner of the colorbar. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        colorbar_size (tuple, optional): Size of the colorbar. It can be formatted like this: (300, 300). Defaults to (300, 300).
        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.
        mp4 (bool, optional): Whether to create an mp4 file. Defaults to False.
        fading (int | bool, optional): If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).
        **kwargs: Arguments for sentinel1_filtering(). Same filters will be applied to all bands.

    Returns:
        str: File path to the timelapse gif.
    """
    from datetime import date

    assert bands in (
        ["VV"],
        ["VH"],
        ["HH"],
        ["HV"],
        ["VV", "VH"],
        ["HH", "HV"],
        ["VH", "VV"],
        ["HV", "HH"],
    ), "Not all Sentinel1 bands are available together."
    if bands in (["VH", "VV"], ["HV", "HH"]):
        bands[0], bands[1] = bands[1], bands[0]
    band = bands[0]

    if end_year is None:
        end_year = date.today().year

    start = f"{start_year}-{start_date}"
    end = f"{end_year}-{end_date}"

    if vis_params is None:
        vis_params = {"min": -30, "max": 0}

    collection = (
        ee.ImageCollection("COPERNICUS/S1_GRD").filterDate(start, end).filterBounds(roi)
    )
    collection = sentinel1_filtering(collection, band, **kwargs)

    return create_timelapse(
        collection,
        start,
        end,
        roi,
        bands,
        frequency,
        reducer,
        date_format,
        out_gif,
        palette,
        vis_params,
        dimensions,
        frames_per_second,
        crs,
        overlay_data,
        overlay_color,
        overlay_width,
        overlay_opacity,
        title,
        title_xy,
        add_text,
        text_xy,
        text_sequence,
        font_type,
        font_size,
        font_color,
        add_progress_bar,
        progress_bar_color,
        progress_bar_height,
        add_colorbar,
        colorbar_width,
        colorbar_height,
        colorbar_label,
        colorbar_label_size,
        colorbar_label_weight,
        colorbar_tick_size,
        colorbar_bg_color,
        colorbar_orientation,
        colorbar_dpi,
        colorbar_xy,
        colorbar_size,
        loop,
        mp4,
        fading,
    )

sentinel1_timelapse_legacy(roi=None, out_gif=None, start_year=2015, end_year=None, start_date='01-01', end_date='12-31', vis_params=None, dimensions=768, frames_per_second=5, crs='EPSG:3857', overlay_data=None, overlay_color='black', overlay_width=1, overlay_opacity=1.0, frequency='year', title=None, title_xy=('2%', '90%'), add_text=True, text_xy=('2%', '2%'), text_sequence=None, font_type='arial.ttf', font_size=20, font_color='white', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, loop=0, mp4=False, fading=False)

Generates a Sentinel-1 timelapse animated GIF or MP4.

Parameters:

Name	Type	Description	Default
roi	object	Region of interest to create the timelapse. Defaults to LV & Lake Mead.	None
out_gif	str	File path to the output animated GIF. Defaults to the Downloads folder s1_ts_*.gif.	None
start_year	int	Starting year for the timelapse. Defaults to 2015.	2015
end_year	int	Ending year for the timelapse. Defaults to current year.	None
start_date	str	Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.	'01-01'
end_date	str	Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.	'12-31'
vis_params	dict	Visualization parameters. Defaults to {'min':-18, 'max': -4}.	None
dimensions	int	a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.	768
frames_per_second	int	Animation speed. Defaults to 5.	5
crs	str	Coordinate reference system. Defaults to 'EPSG:3857'.	'EPSG:3857'
overlay_data	(int, str, list)	Administrative boundary to be drawn on the timelapse. Defaults to None.	None
overlay_color	str	Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.	'black'
overlay_width	int	Line width of the overlay. Defaults to 1.	1
overlay_opacity	float	Opacity of the overlay. Defaults to 1.0.	1.0
frequency	str	Frequency of the timelapse. Defaults to 'year'. Can be year, quarter or month.	'year'
title	str	The title of the timelapse. Defaults to None.	None
title_xy	tuple	Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
add_text	bool	Whether to add animated text to the timelapse. Defaults to True.	True
title_xy	tuple	Lower left corner of the text sequency. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
text_sequence	(int, str, list)	Text to be drawn. It can be an integer number, a string, or a list of strings. Defaults to image start dates.	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 'white'.	'white'
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
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
mp4	bool	Whether to convert the GIF to MP4. Defaults to False.	False
fading	int | bool	If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).	False

Returns:

Name	Type	Description
str		File path to the output GIF image.

Source code in geemap/timelapse.py

def sentinel1_timelapse_legacy(
    roi=None,
    out_gif=None,
    start_year=2015,
    end_year=None,
    start_date="01-01",
    end_date="12-31",
    vis_params=None,
    dimensions=768,
    frames_per_second=5,
    crs="EPSG:3857",
    overlay_data=None,
    overlay_color="black",
    overlay_width=1,
    overlay_opacity=1.0,
    frequency="year",
    title=None,
    title_xy=("2%", "90%"),
    add_text=True,
    text_xy=("2%", "2%"),
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="white",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    loop=0,
    mp4=False,
    fading=False,
):
    """Generates a Sentinel-1 timelapse animated GIF or MP4.

    Args:
        roi (object, optional): Region of interest to create the timelapse. Defaults to LV & Lake Mead.
        out_gif (str, optional): File path to the output animated GIF. Defaults to the Downloads folder s1_ts_*.gif.
        start_year (int, optional): Starting year for the timelapse. Defaults to 2015.
        end_year (int, optional): Ending year for the timelapse. Defaults to current year.
        start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.
        end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.
        vis_params (dict, optional): Visualization parameters. Defaults to {'min':-18, 'max': -4}.
        dimensions (int, optional): a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
        frames_per_second (int, optional): Animation speed. Defaults to 5.
        crs (str, optional): Coordinate reference system. Defaults to 'EPSG:3857'.
        overlay_data (int, str, list, optional): Administrative boundary to be drawn on the timelapse. Defaults to None.
        overlay_color (str, optional): Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.
        overlay_width (int, optional): Line width of the overlay. Defaults to 1.
        overlay_opacity (float, optional): Opacity of the overlay. Defaults to 1.0.
        frequency (str, optional): Frequency of the timelapse. Defaults to 'year'. Can be year, quarter or month.
        title (str, optional): The title of the timelapse. Defaults to None.
        title_xy (tuple, optional): Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        add_text (bool, optional): Whether to add animated text to the timelapse. Defaults to True.
        title_xy (tuple, optional): Lower left corner of the text sequency. 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 image start dates.
        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 'white'.
        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.
        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.
        mp4 (bool, optional): Whether to convert the GIF to MP4. Defaults to False.
        fading (int | bool, optional): If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).

    Returns:
        str: File path to the output GIF image.
    """

    CURRENT_YEAR, ROI_DEFAULT = sentinel1_defaults()
    roi = roi or ROI_DEFAULT
    end_year = end_year or CURRENT_YEAR

    col = sentinel1_timeseries(
        roi=roi,
        start_year=start_year,
        end_year=end_year,
        start_date=start_date,
        end_date=end_date,
        frequency=frequency,
        clip=True,
    )

    vis_params = vis_params or {"min": -25, "max": 5}

    if out_gif is None:
        out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
        filename = "s1_ts_" + random_string() + ".gif"
        out_gif = os.path.join(out_dir, filename)
    elif not out_gif.endswith(".gif"):
        print("The output file must end with .gif")
        return
    else:
        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)

    if overlay_data is not None:
        col = add_overlay(
            col, overlay_data, overlay_color, overlay_width, overlay_opacity
        )

    if (
        isinstance(dimensions, int)
        and dimensions > 768
        or isinstance(dimensions, str)
        and any(dim > 768 for dim in list(map(int, dimensions.split("x"))))
    ):
        count = col.size().getInfo()
        basename = os.path.basename(out_gif)[:-4]
        names = [
            os.path.join(
                out_dir, f"{basename}_{str(i+1).zfill(int(len(str(count))))}.jpg"
            )
            for i in range(count)
        ]
        get_image_collection_thumbnails(
            col,
            out_dir,
            vis_params=vis_params,
            dimensions=dimensions,
            names=names,
        )
        make_gif(
            names,
            out_gif,
            fps=frames_per_second,
            loop=loop,
            mp4=False,
            clean_up=True,
        )
    else:
        video_args = vis_params.copy()
        video_args["dimensions"] = dimensions
        video_args["region"] = roi
        video_args["framesPerSecond"] = frames_per_second
        video_args["crs"] = crs
        video_args["min"] = vis_params["min"]
        video_args["max"] = vis_params["max"]

        download_ee_video(col, video_args, out_gif)

    if os.path.exists(out_gif):
        if title is not None and isinstance(title, str):
            add_text_to_gif(
                out_gif,
                out_gif,
                xy=title_xy,
                text_sequence=title,
                font_type=font_type,
                font_size=font_size,
                font_color=font_color,
                add_progress_bar=add_progress_bar,
                progress_bar_color=progress_bar_color,
                progress_bar_height=progress_bar_height,
                duration=1000 / frames_per_second,
                loop=loop,
            )
        if add_text:
            if text_sequence is None:
                text_sequence = col.aggregate_array("system:date").getInfo()
            add_text_to_gif(
                out_gif,
                out_gif,
                xy=text_xy,
                text_sequence=text_sequence,
                font_type=font_type,
                font_size=font_size,
                font_color=font_color,
                add_progress_bar=add_progress_bar,
                progress_bar_color=progress_bar_color,
                progress_bar_height=progress_bar_height,
                duration=1000 / frames_per_second,
                loop=loop,
            )
        if os.path.exists(out_gif):
            reduce_gif_size(out_gif)
        if isinstance(fading, bool):
            fading = int(fading)
        if fading > 0:
            gif_fading(out_gif, out_gif, duration=fading, verbose=False)
        if mp4:
            out_mp4 = out_gif.replace(".gif", ".mp4")
            gif_to_mp4(out_gif, out_mp4)

    return out_gif

sentinel1_timeseries(roi=None, start_year=2015, end_year=None, start_date='01-01', end_date='12-31', frequency='year', clip=False, band='VV', **kwargs)

Generates a Sentinel 1 ImageCollection,
based on mean composites following a steady frequency (f.e. 1 image per month)

Adapted from https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS_S1_GRD

Parameters:

Name	Type	Description	Default
roi	object	Region of interest to create the timelapse. Defaults to a polygon partially covering Las Vegas and Lake Mead.	None
start_year	int	Starting year for the timelapse. Defaults to 2015.	2015
end_year	int	Ending year for the timelapse. Defaults to current year.	None
start_date	str	Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.	'01-01'
end_date	str	Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.	'12-31'
frequency	str	Frequency of the timelapse. Defaults to 'year'. Can be 'year', 'quarter' or 'month'.	'year'
band	str	Collection band. Can be one of ['HH','HV','VV','VH']. Defaults to 'VV' which is most commonly available on land.	'VV'
**kwargs		Arguments for sentinel1_filtering().	{}

Returns:

Name	Type	Description
object		Returns an ImageCollection of Sentinel 1 images.

Source code in geemap/timelapse.py

def sentinel1_timeseries(
    roi=None,
    start_year=2015,
    end_year=None,
    start_date="01-01",
    end_date="12-31",
    frequency="year",
    clip=False,
    band="VV",
    **kwargs,
):
    """
        Generates a Sentinel 1 ImageCollection,
        based on mean composites following a steady frequency (f.e. 1 image per month)
    Adapted from https://code.earthengine.google.com/?scriptPath=Examples:Datasets/COPERNICUS_S1_GRD

    Args:
        roi (object, optional): Region of interest to create the timelapse. Defaults to a polygon partially covering Las Vegas and Lake Mead.
        start_year (int, optional): Starting year for the timelapse. Defaults to 2015.
        end_year (int, optional): Ending year for the timelapse. Defaults to current year.
        start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.
        end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.
        frequency (str, optional): Frequency of the timelapse. Defaults to 'year'.  Can be 'year', 'quarter' or 'month'.
        band (str): Collection band. Can be one of ['HH','HV','VV','VH']. Defaults to 'VV' which is most commonly available on land.
        **kwargs: Arguments for sentinel1_filtering().

    Returns:
        object: Returns an ImageCollection of Sentinel 1 images.
    """

    CURRENT_YEAR, ROI_DEFAULT = sentinel1_defaults()
    roi = roi or ROI_DEFAULT
    end_year = end_year or CURRENT_YEAR
    roi = valid_roi(roi)

    start = f"{start_year}-{start_date}"
    end = f"{end_year}-{end_date}"

    dates = date_sequence(start, end, frequency)
    col = ee.ImageCollection("COPERNICUS/S1_GRD").filterBounds(roi)
    col = sentinel1_filtering(col, band=band, **kwargs).select(band)

    n = 1
    if frequency == "quarter":
        n = 3
        frequency = "month"

    def transform(date):  # coll, frequency
        start = date
        end = ee.Date(date).advance(n, frequency).advance(-1, "day")
        return (
            col.filterDate(start, end)
            .mean()
            .set(
                {
                    "system:time_start": ee.Date(start).millis(),
                    "system:time_end": ee.Date(end).millis(),
                    "system:date": start,
                }
            )
        )

    imgList = dates.map(lambda date: transform(date))
    imgColl = ee.ImageCollection.fromImages(imgList)
    if clip:
        imgColl = imgColl.map(lambda img: img.clip(roi))
    return imgColl

sentinel2_timelapse(roi=None, out_gif=None, start_year=2015, end_year=None, start_date='06-10', end_date='09-20', bands=['NIR', 'Red', 'Green'], vis_params=None, dimensions=768, frames_per_second=5, crs='EPSG:3857', apply_fmask=True, cloud_pct=30, overlay_data=None, overlay_color='black', overlay_width=1, overlay_opacity=1.0, frequency='year', date_format=None, title=None, title_xy=('2%', '90%'), add_text=True, text_xy=('2%', '2%'), text_sequence=None, font_type='arial.ttf', font_size=20, font_color='white', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, loop=0, mp4=False, fading=False, step=1, **kwargs)

Generates a Sentinel-2 timelapse GIF image. This function is adapted from https://emaprlab.users.earthengine.app/view/lt-gee-time-series-animator. A huge thank you to Justin Braaten for sharing his fantastic work.

Parameters:

Name	Type	Description	Default
roi	object	Region of interest to create the timelapse. Defaults to None.	None
out_gif	str	File path to the output animated GIF. Defaults to None.	None
start_year	int	Starting year for the timelapse. Defaults to 2015.	2015
end_year	int	Ending year for the timelapse. Defaults to None, which means the current year.	None
start_date	str	Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.	'06-10'
end_date	str	Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.	'09-20'
bands	list	Three bands selected from ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'Red Edge 1', 'Red Edge 2', 'Red Edge 3', 'Red Edge 4']. Defaults to ['NIR', 'Red', 'Green'].	['NIR', 'Red', 'Green']
vis_params	dict	Visualization parameters. Defaults to None.	None
dimensions	int	a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.	768
frames_per_second	int	Animation speed. Defaults to 10.	5
crs	str	Coordinate reference system. Defaults to 'EPSG:3857'.	'EPSG:3857'
apply_fmask	bool	Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.	True
cloud_pct	int	Maximum percentage of cloud coverage allowed. Defaults to 30.	30
overlay_data	(int, str, list)	Administrative boundary to be drawn on the timelapse. Defaults to None.	None
overlay_color	str	Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.	'black'
overlay_width	int	Line width of the overlay. Defaults to 1.	1
overlay_opacity	float	Opacity of the overlay. Defaults to 1.0.	1.0
frequency	str	Frequency of the timelapse. Defaults to 'year'.	'year'
date_format	str	Date format for the timelapse. Defaults to None.	None
title	str	The title of the timelapse. Defaults to None.	None
title_xy	tuple	Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
add_text	bool	Whether to add animated text to the timelapse. Defaults to True.	True
title_xy	tuple	Lower left corner of the text sequency. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.	('2%', '90%')
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'.	'white'
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
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
mp4	bool	Whether to convert the GIF to MP4. Defaults to False.	False
fading	int | bool	If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).	False
step	int	Step size for selecting images. Defaults to 1.	1
kwargs	optional	Additional arguments to pass the geemap.create_timeseries() function.	{}

Returns:

Name	Type	Description
str		File path to the output GIF image.

Source code in geemap/timelapse.py

def sentinel2_timelapse(
    roi=None,
    out_gif=None,
    start_year=2015,
    end_year=None,
    start_date="06-10",
    end_date="09-20",
    bands=["NIR", "Red", "Green"],
    vis_params=None,
    dimensions=768,
    frames_per_second=5,
    crs="EPSG:3857",
    apply_fmask=True,
    cloud_pct=30,
    overlay_data=None,
    overlay_color="black",
    overlay_width=1,
    overlay_opacity=1.0,
    frequency="year",
    date_format=None,
    title=None,
    title_xy=("2%", "90%"),
    add_text=True,
    text_xy=("2%", "2%"),
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="white",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    loop=0,
    mp4=False,
    fading=False,
    step=1,
    **kwargs,
):
    """Generates a Sentinel-2 timelapse GIF image. This function is adapted from https://emaprlab.users.earthengine.app/view/lt-gee-time-series-animator. A huge thank you to Justin Braaten for sharing his fantastic work.

    Args:
        roi (object, optional): Region of interest to create the timelapse. Defaults to None.
        out_gif (str, optional): File path to the output animated GIF. Defaults to None.
        start_year (int, optional): Starting year for the timelapse. Defaults to 2015.
        end_year (int, optional): Ending year for the timelapse. Defaults to None, which means the current year.
        start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '06-10'.
        end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '09-20'.
        bands (list, optional): Three bands selected from ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2', 'Red Edge 1', 'Red Edge 2', 'Red Edge 3', 'Red Edge 4']. Defaults to ['NIR', 'Red', 'Green'].
        vis_params (dict, optional): Visualization parameters. Defaults to None.
        dimensions (int, optional): a number or pair of numbers (in format 'WIDTHxHEIGHT') Maximum dimensions of the thumbnail to render, in pixels. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Defaults to 768.
        frames_per_second (int, optional): Animation speed. Defaults to 10.
        crs (str, optional): Coordinate reference system. Defaults to 'EPSG:3857'.
        apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.
        cloud_pct (int, optional): Maximum percentage of cloud coverage allowed. Defaults to 30.
        overlay_data (int, str, list, optional): Administrative boundary to be drawn on the timelapse. Defaults to None.
        overlay_color (str, optional): Color for the overlay data. Can be any color name or hex color code. Defaults to 'black'.
        overlay_width (int, optional): Line width of the overlay. Defaults to 1.
        overlay_opacity (float, optional): Opacity of the overlay. Defaults to 1.0.
        frequency (str, optional): Frequency of the timelapse. Defaults to 'year'.
        date_format (str, optional): Date format for the timelapse. Defaults to None.
        title (str, optional): The title of the timelapse. Defaults to None.
        title_xy (tuple, optional): Lower left corner of the title. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        add_text (bool, optional): Whether to add animated text to the timelapse. Defaults to True.
        title_xy (tuple, optional): Lower left corner of the text sequency. 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.
        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.
        mp4 (bool, optional): Whether to convert the GIF to MP4. Defaults to False.
        fading (int | bool, optional): If True, add fading effect to the timelapse. Defaults to False, no fading. To add fading effect, set it to True (1 second fading duration) or to an integer value (fading duration).
        step (int, optional): Step size for selecting images. Defaults to 1.
        kwargs (optional): Additional arguments to pass the geemap.create_timeseries() function.

    Returns:
        str: File path to the output GIF image.
    """

    if end_year is None:
        end_year = datetime.datetime.now().year

    if roi is None:
        roi = ee.Geometry.Polygon(
            [
                [
                    [-115.471773, 35.892718],
                    [-115.471773, 36.409454],
                    [-114.271283, 36.409454],
                    [-114.271283, 35.892718],
                    [-115.471773, 35.892718],
                ]
            ],
            None,
            False,
        )
    elif isinstance(roi, ee.Feature) or isinstance(roi, ee.FeatureCollection):
        roi = roi.geometry()
    elif isinstance(roi, ee.Geometry):
        pass
    else:
        print("The provided roi is invalid. It must be an ee.Geometry")
        return

    if out_gif is None:
        out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
        filename = "s2_ts_" + random_string() + ".gif"
        out_gif = os.path.join(out_dir, filename)
    elif not out_gif.endswith(".gif"):
        print("The output file must end with .gif")
        return
    else:
        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)

    allowed_bands = {
        "Blue": "B2",
        "Green": "B3",
        "Red": "B4",
        "Red Edge 1": "B5",
        "Red Edge 2": "B6",
        "Red Edge 3": "B7",
        "NIR": "B8",
        "Red Edge 4": "B8A",
        "SWIR1": "B11",
        "SWIR2": "B12",
        "QA60": "QA60",
    }

    if bands is None:
        bands = ["SWIR1", "NIR", "Red"]

    for index, band in enumerate(bands):
        if band in allowed_bands:
            bands[index] = allowed_bands[band]

    if len(bands) == 3:
        pass
    else:
        raise Exception(
            "You can only select 3 bands from the following: {}".format(
                ", ".join(allowed_bands)
            )
        )

    try:
        if vis_params is None:
            vis_params = {}
            vis_params["bands"] = bands
            vis_params["min"] = 0
            vis_params["max"] = 0.4
            vis_params["gamma"] = [1, 1, 1]

        if "reducer" not in kwargs:
            kwargs["reducer"] = "median"
        if "drop_empty" not in kwargs:
            kwargs["drop_empty"] = True
        if "parallel_scale" not in kwargs:
            kwargs["parallel_scale"] = 1
        kwargs["date_format"] = date_format
        col = sentinel2_timeseries(
            roi,
            start_year,
            end_year,
            start_date,
            end_date,
            bands,
            apply_fmask,
            cloud_pct,
            frequency,
            step=step,
            **kwargs,
        )
        col = col.select(bands).map(
            lambda img: img.visualize(**vis_params).set(
                {
                    "system:time_start": img.get("system:time_start"),
                    "system:date": img.get("system:date"),
                }
            )
        )
        if overlay_data is not None:
            col = add_overlay(
                col, overlay_data, overlay_color, overlay_width, overlay_opacity
            )

        if (
            isinstance(dimensions, int)
            and dimensions > 768
            or isinstance(dimensions, str)
            and any(dim > 768 for dim in list(map(int, dimensions.split("x"))))
        ):
            count = col.size().getInfo()
            basename = os.path.basename(out_gif)[:-4]
            names = [
                os.path.join(
                    out_dir, f"{basename}_{str(i+1).zfill(int(len(str(count))))}.jpg"
                )
                for i in range(count)
            ]
            get_image_collection_thumbnails(
                col,
                out_dir,
                vis_params={
                    "min": 0,
                    "max": 255,
                    "bands": ["vis-red", "vis-green", "vis-blue"],
                },
                dimensions=dimensions,
                names=names,
            )
            make_gif(
                names,
                out_gif,
                fps=frames_per_second,
                loop=loop,
                mp4=False,
                clean_up=True,
            )
        else:
            video_args = vis_params.copy()
            video_args["dimensions"] = dimensions
            video_args["region"] = roi
            video_args["framesPerSecond"] = frames_per_second
            video_args["crs"] = crs
            video_args["bands"] = ["vis-red", "vis-green", "vis-blue"]
            video_args["min"] = 0
            video_args["max"] = 255

            download_ee_video(col, video_args, out_gif)

        if os.path.exists(out_gif):
            if title is not None and isinstance(title, str):
                add_text_to_gif(
                    out_gif,
                    out_gif,
                    xy=title_xy,
                    text_sequence=title,
                    font_type=font_type,
                    font_size=font_size,
                    font_color=font_color,
                    add_progress_bar=add_progress_bar,
                    progress_bar_color=progress_bar_color,
                    progress_bar_height=progress_bar_height,
                    duration=1000 / frames_per_second,
                    loop=loop,
                )
            if add_text:
                if text_sequence is None:
                    text_sequence = col.aggregate_array("system:date").getInfo()
                add_text_to_gif(
                    out_gif,
                    out_gif,
                    xy=text_xy,
                    text_sequence=text_sequence,
                    font_type=font_type,
                    font_size=font_size,
                    font_color=font_color,
                    add_progress_bar=add_progress_bar,
                    progress_bar_color=progress_bar_color,
                    progress_bar_height=progress_bar_height,
                    duration=1000 / frames_per_second,
                    loop=loop,
                )

        if os.path.exists(out_gif):
            reduce_gif_size(out_gif)

        if isinstance(fading, bool):
            fading = int(fading)
        if fading > 0:
            gif_fading(out_gif, out_gif, duration=fading, verbose=False)

        if mp4:
            out_mp4 = out_gif.replace(".gif", ".mp4")
            gif_to_mp4(out_gif, out_mp4)

        return out_gif

    except Exception as e:
        print(e)

sentinel2_timeseries(roi, start_year=2015, end_year=None, start_date='01-01', end_date='12-31', bands=None, mask_cloud=True, cloud_pct=30, frequency='year', reducer='median', drop_empty=True, date_format=None, parallel_scale=1, step=1)

Generates an annual Sentinel 2 ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work. Images include both level 1C and level 2A imagery. Args:

roi (object, optional): Region of interest to create the timelapse. Defaults to None.
start_year (int, optional): Starting year for the timelapse. Defaults to 2015.
end_year (int, optional): Ending year for the timelapse. Defaults to None, which will use the current year.
start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.
mask_cloud (bool, optional): Whether to mask clouds. Defaults to True.
end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.
bands (list, optional): The list of bands to use to create the timeseries. It must be a list of strings. Defaults to None.
cloud_pct (int, optional): Maximum cloud percentage to include in the timelapse. Defaults to 30.
frequency (str, optional): Frequency of the timelapse. Defaults to 'year'.
reducer (str, optional):  The reducer to use to reduce the collection of images to a single value. It can be one of the following: 'median', 'mean', 'min', 'max', 'variance', 'sum'. Defaults to 'median'.
drop_empty (bool, optional): Whether to drop empty images from the timeseries. Defaults to True.
date_format (str, optional): Format of the date. Defaults to None.
parallel_scale (int, optional): A scaling factor used to limit memory use; using a larger parallel_scale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.
step (int, optional): The step size to use when creating the date sequence. Defaults to 1.

Returns:

Name	Type	Description
object		Returns an ImageCollection containing annual Sentinel 2 images.

Source code in geemap/timelapse.py

def sentinel2_timeseries(
    roi,
    start_year=2015,
    end_year=None,
    start_date="01-01",
    end_date="12-31",
    bands=None,
    mask_cloud=True,
    cloud_pct=30,
    frequency="year",
    reducer="median",
    drop_empty=True,
    date_format=None,
    parallel_scale=1,
    step=1,
):
    """Generates an annual Sentinel 2 ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work.
       Images include both level 1C and level 2A imagery.
    Args:

        roi (object, optional): Region of interest to create the timelapse. Defaults to None.
        start_year (int, optional): Starting year for the timelapse. Defaults to 2015.
        end_year (int, optional): Ending year for the timelapse. Defaults to None, which will use the current year.
        start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.
        mask_cloud (bool, optional): Whether to mask clouds. Defaults to True.
        end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.
        bands (list, optional): The list of bands to use to create the timeseries. It must be a list of strings. Defaults to None.
        cloud_pct (int, optional): Maximum cloud percentage to include in the timelapse. Defaults to 30.
        frequency (str, optional): Frequency of the timelapse. Defaults to 'year'.
        reducer (str, optional):  The reducer to use to reduce the collection of images to a single value. It can be one of the following: 'median', 'mean', 'min', 'max', 'variance', 'sum'. Defaults to 'median'.
        drop_empty (bool, optional): Whether to drop empty images from the timeseries. Defaults to True.
        date_format (str, optional): Format of the date. Defaults to None.
        parallel_scale (int, optional): A scaling factor used to limit memory use; using a larger parallel_scale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.
        step (int, optional): The step size to use when creating the date sequence. Defaults to 1.

    Returns:
        object: Returns an ImageCollection containing annual Sentinel 2 images.
    """
    if end_year is None:
        end_year = datetime.date.today().year

    def maskS2clouds(image):
        qa = image.select("QA60")

        # Bits 10 and 11 are clouds and cirrus, respectively.
        cloudBitMask = 1 << 10
        cirrusBitMask = 1 << 11

        # Both flags should be set to zero, indicating clear conditions.
        mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0))

        return (
            image.updateMask(mask)
            .divide(10000)
            .set(image.toDictionary(image.propertyNames()))
        )

    start = f"{start_year}-{start_date}"
    end = f"{end_year}-{end_date}"
    doy_start = ee.Number.parse(ee.Date(start).format("D"))
    doy_end = ee.Number.parse(ee.Date(end).format("D"))
    collection = (
        ee.ImageCollection("COPERNICUS/S2_HARMONIZED")
        .filterDate(start, end)
        .filter(ee.Filter.calendarRange(doy_start, doy_end, "day_of_year"))
        .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", cloud_pct))
        .filterBounds(roi)
    )

    if mask_cloud:
        collection = collection.map(maskS2clouds)
    else:
        collection = collection.map(
            lambda img: img.divide(10000).set(img.toDictionary(img.propertyNames()))
        )

    if bands is not None:
        allowed_bands = {
            "Blue": "B2",
            "Green": "B3",
            "Red": "B4",
            "Red Edge 1": "B5",
            "Red Edge 2": "B6",
            "Red Edge 3": "B7",
            "NIR": "B8",
            "Red Edge 4": "B8A",
            "SWIR1": "B11",
            "SWIR2": "B12",
            "QA60": "QA60",
        }

        for index, band in enumerate(bands):
            if band in allowed_bands:
                bands[index] = allowed_bands[band]

        collection = collection.select(bands)

    ts = create_timeseries(
        collection,
        start,
        end,
        roi,
        bands,
        frequency,
        reducer,
        drop_empty,
        date_format,
        parallel_scale,
        step,
    )
    return ts

sentinel2_timeseries_legacy(roi=None, start_year=2015, end_year=None, start_date='01-01', end_date='12-31', apply_fmask=True, frequency='year', date_format=None)

Generates an annual Sentinel 2 ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work. Images include both level 1C and level 2A imagery. Args:

roi (object, optional): Region of interest to create the timelapse. Defaults to None.
start_year (int, optional): Starting year for the timelapse. Defaults to 2015.
end_year (int, optional): Ending year for the timelapse. Defaults to None, which will use the current year.
start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.
end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.
apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.
frequency (str, optional): Frequency of the timelapse. Defaults to 'year'.
date_format (str, optional): Format of the date. Defaults to None.

Returns:

Name	Type	Description
object		Returns an ImageCollection containing annual Sentinel 2 images.

Source code in geemap/timelapse.py

def sentinel2_timeseries_legacy(
    roi=None,
    start_year=2015,
    end_year=None,
    start_date="01-01",
    end_date="12-31",
    apply_fmask=True,
    frequency="year",
    date_format=None,
):
    """Generates an annual Sentinel 2 ImageCollection. This algorithm is adapted from https://gist.github.com/jdbcode/76b9ac49faf51627ebd3ff988e10adbc. A huge thank you to Justin Braaten for sharing his fantastic work.
       Images include both level 1C and level 2A imagery.
    Args:

        roi (object, optional): Region of interest to create the timelapse. Defaults to None.
        start_year (int, optional): Starting year for the timelapse. Defaults to 2015.
        end_year (int, optional): Ending year for the timelapse. Defaults to None, which will use the current year.
        start_date (str, optional): Starting date (month-day) each year for filtering ImageCollection. Defaults to '01-01'.
        end_date (str, optional): Ending date (month-day) each year for filtering ImageCollection. Defaults to '12-31'.
        apply_fmask (bool, optional): Whether to apply Fmask (Function of mask) for automated clouds, cloud shadows, snow, and water masking.
        frequency (str, optional): Frequency of the timelapse. Defaults to 'year'.
        date_format (str, optional): Format of the date. Defaults to None.

    Returns:
        object: Returns an ImageCollection containing annual Sentinel 2 images.
    """
    ################################################################################

    ################################################################################
    # Input and output parameters.

    import re

    # import datetime

    if end_year is None:
        end_year = datetime.date.today().year

    if roi is None:
        # roi = ee.Geometry.Polygon(
        #     [[[-180, -80],
        #       [-180, 80],
        #         [180, 80],
        #         [180, -80],
        #         [-180, -80]]], None, False)
        roi = ee.Geometry.Polygon(
            [
                [
                    [-115.471773, 35.892718],
                    [-115.471773, 36.409454],
                    [-114.271283, 36.409454],
                    [-114.271283, 35.892718],
                    [-115.471773, 35.892718],
                ]
            ],
            None,
            False,
        )

    if not isinstance(roi, ee.Geometry):
        try:
            roi = roi.geometry()
        except Exception as e:
            print("Could not convert the provided roi to ee.Geometry")
            print(e)
            return

    # Adjusts longitudes less than -180 degrees or greater than 180 degrees.
    geojson = ee_to_geojson(roi)
    geojson = adjust_longitude(geojson)
    roi = ee.Geometry(geojson)

    feq_dict = {
        "year": "YYYY",
        "month": "YYYY-MM",
        "quarter": "YYYY-MM",
    }

    if date_format is None:
        date_format = feq_dict[frequency]

    if frequency not in feq_dict:
        raise ValueError("frequency must be year, quarter, or month.")

    ################################################################################
    # Setup vars to get dates.
    if (
        isinstance(start_year, int)
        and (start_year >= 2015)
        and (start_year <= get_current_year())
    ):
        pass
    else:
        print("The start year must be an integer >= 2015.")
        return

    if (
        isinstance(end_year, int)
        and (end_year >= 2015)
        and (end_year <= get_current_year())
    ):
        pass
    else:
        print(f"The end year must be an integer <= {get_current_year()}.")
        return

    if re.match(r"[0-9]{2}-[0-9]{2}", start_date) and re.match(
        r"[0-9]{2}-[0-9]{2}", end_date
    ):
        pass
    else:
        print("The start data and end date must be month-day, such as 06-10, 09-20")
        return

    try:
        datetime.datetime(int(start_year), int(start_date[:2]), int(start_date[3:5]))
        datetime.datetime(int(end_year), int(end_date[:2]), int(end_date[3:5]))
    except Exception as e:
        raise ValueError("The input dates are invalid.")

    try:
        start_test = datetime.datetime(
            int(start_year), int(start_date[:2]), int(start_date[3:5])
        )
        end_test = datetime.datetime(
            int(end_year), int(end_date[:2]), int(end_date[3:5])
        )
        if start_test > end_test:
            raise ValueError("Start date must be prior to end date")
    except Exception as e:
        raise Exception(e)

    def days_between(d1, d2):
        d1 = datetime.datetime.strptime(d1, "%Y-%m-%d")
        d2 = datetime.datetime.strptime(d2, "%Y-%m-%d")
        return abs((d2 - d1).days)

    n_days = days_between(
        str(start_year) + "-" + start_date, str(start_year) + "-" + end_date
    )
    start_month = int(start_date[:2])
    start_day = int(start_date[3:5])
    # start_date = str(start_year) + "-" + start_date
    # end_date = str(end_year) + "-" + end_date

    # # Define a collection filter by date, bounds, and quality.
    # def colFilter(col, aoi):  # , startDate, endDate):
    #     return(col.filterBounds(aoi))

    # Get Sentinel 2 collections, both Level-1C (top of atmophere) and Level-2A (surface reflectance)
    MSILCcol = ee.ImageCollection("COPERNICUS/S2")
    MSI2Acol = ee.ImageCollection("COPERNICUS/S2_SR")

    # Define a collection filter by date, bounds, and quality.
    def colFilter(col, roi, start_date, end_date):
        return col.filterBounds(roi).filterDate(start_date, end_date)
        # .filter('CLOUD_COVER < 5')
        # .filter('GEOMETRIC_RMSE_MODEL < 15')
        # .filter('IMAGE_QUALITY == 9 || IMAGE_QUALITY_OLI == 9'))

    # Function to get and rename bands of interest from MSI
    def renameMSI(img):
        return img.select(
            ["B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B11", "B12", "QA60"],
            [
                "Blue",
                "Green",
                "Red",
                "Red Edge 1",
                "Red Edge 2",
                "Red Edge 3",
                "NIR",
                "Red Edge 4",
                "SWIR1",
                "SWIR2",
                "QA60",
            ],
        )

    # Add NBR for LandTrendr segmentation.

    def calcNbr(img):
        return img.addBands(
            img.normalizedDifference(["NIR", "SWIR2"]).multiply(-10000).rename("NBR")
        ).int16()

    # Define function to mask out clouds and cloud shadows in images.
    # Use CFmask band included in USGS Landsat SR image product.

    def fmask(img):
        cloudOpaqueBitMask = 1 << 10
        cloudCirrusBitMask = 1 << 11
        qa = img.select("QA60")
        mask = (
            qa.bitwiseAnd(cloudOpaqueBitMask)
            .eq(0)
            .And(qa.bitwiseAnd(cloudCirrusBitMask).eq(0))
        )
        return img.updateMask(mask)

    # Define function to prepare MSI images.
    def prepMSI(img):
        orig = img
        img = renameMSI(img)
        if apply_fmask:
            img = fmask(img)
        return ee.Image(img.copyProperties(orig, orig.propertyNames())).resample(
            "bicubic"
        )

    # Get annual median collection.
    def getAnnualComp(y):
        startDate = ee.Date.fromYMD(
            ee.Number(y), ee.Number(start_month), ee.Number(start_day)
        )
        endDate = startDate.advance(ee.Number(n_days), "day")

        # Filter collections and prepare them for merging.
        MSILCcoly = colFilter(MSILCcol, roi, startDate, endDate).map(prepMSI)
        MSI2Acoly = colFilter(MSI2Acol, roi, startDate, endDate).map(prepMSI)

        # Merge the collections.
        col = MSILCcoly.merge(MSI2Acoly)

        yearImg = col.median()
        nBands = yearImg.bandNames().size()
        yearImg = ee.Image(ee.Algorithms.If(nBands, yearImg, dummyImg))
        return calcNbr(yearImg).set(
            {
                "year": y,
                "system:time_start": startDate.millis(),
                "nBands": nBands,
                "system:date": ee.Date(startDate).format(date_format),
            }
        )

    # Get quarterly median collection.
    def getQuarterlyComp(startDate):
        startDate = ee.Date(startDate)
        endDate = startDate.advance(3, "month")

        # Filter collections and prepare them for merging.
        MSILCcoly = colFilter(MSILCcol, roi, startDate, endDate).map(prepMSI)
        MSI2Acoly = colFilter(MSI2Acol, roi, startDate, endDate).map(prepMSI)

        # Merge the collections.
        col = MSILCcoly.merge(MSI2Acoly)

        yearImg = col.median()
        nBands = yearImg.bandNames().size()
        yearImg = ee.Image(ee.Algorithms.If(nBands, yearImg, dummyImg))
        return calcNbr(yearImg).set(
            {
                "system:time_start": startDate.millis(),
                "nBands": nBands,
                "system:date": ee.Date(startDate).format(date_format),
            }
        )

    # Get monthly median collection.
    def getMonthlyComp(startDate):
        startDate = ee.Date(startDate)
        endDate = startDate.advance(1, "month")

        # Filter collections and prepare them for merging.
        MSILCcoly = colFilter(MSILCcol, roi, startDate, endDate).map(prepMSI)
        MSI2Acoly = colFilter(MSI2Acol, roi, startDate, endDate).map(prepMSI)

        # Merge the collections.
        col = MSILCcoly.merge(MSI2Acoly)

        yearImg = col.median()
        nBands = yearImg.bandNames().size()
        yearImg = ee.Image(ee.Algorithms.If(nBands, yearImg, dummyImg))
        return calcNbr(yearImg).set(
            {
                "system:time_start": startDate.millis(),
                "nBands": nBands,
                "system:date": ee.Date(startDate).format(date_format),
            }
        )

    ################################################################################

    # Make a dummy image for missing years.
    bandNames = ee.List(
        [
            "Blue",
            "Green",
            "Red",
            "Red Edge 1",
            "Red Edge 2",
            "Red Edge 3",
            "NIR",
            "Red Edge 4",
            "SWIR1",
            "SWIR2",
            "QA60",
        ]
    )
    fillerValues = ee.List.repeat(0, bandNames.size())
    dummyImg = ee.Image.constant(fillerValues).rename(bandNames).selfMask().int16()

    # ################################################################################
    # # Get a list of years
    # years = ee.List.sequence(start_year, end_year)

    # ################################################################################
    # # Make list of annual image composites.
    # imgList = years.map(getAnnualComp)

    if frequency == "year":
        years = ee.List.sequence(start_year, end_year)
        imgList = years.map(getAnnualComp)
    elif frequency == "quarter":
        quarters = date_sequence(
            str(start_year) + "-01-01", str(end_year) + "-12-31", "quarter", date_format
        )
        imgList = quarters.map(getQuarterlyComp)
    elif frequency == "month":
        months = date_sequence(
            str(start_year) + "-01-01", str(end_year) + "-12-31", "month", date_format
        )
        imgList = months.map(getMonthlyComp)

    # Convert image composite list to collection
    imgCol = ee.ImageCollection.fromImages(imgList)

    imgCol = imgCol.map(lambda img: img.clip(roi))

    return imgCol

set_proxy(port=1080, ip='http://127.0.0.1', timeout=300)

Sets proxy if needed. This is only needed for countries where Google services are not available.

Parameters:

Name	Type	Description	Default
port	int	The proxy port number. Defaults to 1080.	1080
ip	str	The IP address. Defaults to 'http://127.0.0.1'.	'http://127.0.0.1'
timeout	int	The timeout in seconds. Defaults to 300.	300

Source code in geemap/common.py

1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1