common module¶

This module contains some common functions for both folium and ipyleaflet to interact with the Earth Engine Python API.

`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/common.py

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

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

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

    try:
        image = 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
    image_size = min(logo_raw_size[0], image_size[0]), min(
        logo_raw_size[1], image_size[1]
    )

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

    W, H = image.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.
        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(
                "xy is out of bounds. x must be within [0, {}], and y must be within [0, {}]".format(
                    W, 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:
        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(image)):
            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_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/common.py

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

    Args:
        in_gif (str): The file path to the input GIF image.
        out_gif (str): The file path to the output GIF image.
        xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        text_sequence (int, str, list, optional): Text to be drawn. It can be an integer number, a string, or a list of strings. Defaults to None.
        font_type (str, optional): Font type. Defaults to "arial.ttf".
        font_size (int, optional): Font size. Defaults to 20.
        font_color (str, optional): Font color. It can be a string (e.g., 'red'), rgb tuple (e.g., (255, 127, 0)), or hex code (e.g., '#ff00ff').  Defaults to '#000000'.
        add_progress_bar (bool, optional): Whether to add a progress bar at the bottom of the GIF. Defaults to True.
        progress_bar_color (str, optional): Color for the progress bar. Defaults to 'white'.
        progress_bar_height (int, optional): Height of the progress bar. Defaults to 5.
        duration (int, optional): controls how long each frame will be displayed for, in milliseconds. It is the inverse of the frame rate. Setting it to 100 milliseconds gives 10 frames per second. You can decrease the duration to give a smoother animation.. Defaults to 100.
        loop (int, optional): controls how many times the animation repeats. The default, 1, means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0.

    """
    # import io
    import warnings

    import pkg_resources
    from PIL import Image, ImageDraw, ImageFont, ImageSequence

    warnings.simplefilter("ignore")
    pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
    default_font = os.path.join(pkg_dir, "data/fonts/arial.ttf")

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

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

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

    if font_type == "arial.ttf":
        font = ImageFont.truetype(default_font, font_size)
    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:

Type	Description
`dict`	A dictionary containing the converted longitudes

Source code in geemap/common.py

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

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

    Returns:
        dict: A dictionary containing the converted longitudes
    """
    try:

        keys = in_fc.keys()

        if "geometry" in keys:

            coordinates = in_fc["geometry"]["coordinates"]

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

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

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

        elif "type" in keys:

            coordinates = in_fc["coordinates"]

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

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

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

        return in_fc

    except Exception as e:
        print(e)
        return None

`annual_NAIP(year, region)` ¶

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

Parameters:

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

Returns:

Type	Description
`object`	ee.Image

Source code in geemap/common.py

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

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

    Returns:
        object: ee.Image
    """

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

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

`api_docs()` ¶

Open a browser and navigate to the geemap API documentation.

Source code in geemap/common.py

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

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

`array_mean(arr)` ¶

Calculates the mean of an array along the given axis.

Parameters:

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

Returns:

Type	Description
`object`	ee.Number

Source code in geemap/common.py

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

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

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

`array_sum(arr)` ¶

Accumulates elements of an array along the given axis.

Parameters:

Name	Type	Description	Default
`arr`	`object`	Array to accumulate.	required

Returns:

Type	Description
`object`	ee.Number

Source code in geemap/common.py

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

    Args:
        arr (object): Array to accumulate.

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

`bands_to_image_collection(img)` ¶

Converts all bands in an image to an image collection.

Parameters:

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

Returns:

Type	Description
`object`	ee.ImageCollection

Source code in geemap/common.py

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

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

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

`bbox_to_geojson(bounds)` ¶

Convert coordinates of a bounding box to a geojson.

Parameters:

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

Returns:

Type	Description
`dict`	A geojson feature.

Source code in geemap/common.py

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

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

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

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

Builds an Earth Engine API tree view.

Parameters:

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

Returns:

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

Source code in geemap/common.py

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

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

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

    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.clear_output()
                html_widget = widgets.HTML(value=values["html"])
                display(html_widget)

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

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

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

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

    return tree, tree_dict

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

Builds a repo tree for GEE account.

Parameters:

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

Returns:

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

Source code in geemap/common.py

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

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

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

    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/giswqs/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

`check_color(in_color)` ¶

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

Parameters:

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

Returns:

Type	Description
`str`	A hex color code.

Source code in geemap/common.py

def check_color(in_color):
    """Checks the input color and returns the corresponding hex color code.

    Args:
        in_color (str or tuple): It can be a string (e.g., 'red', '#ffff00', 'ffff00', 'ff0') or RGB tuple (e.g., (255, 127, 0)).

    Returns:
        str: A hex color code.
    """
    import colour

    out_color = "#000000"  # default black color
    if isinstance(in_color, tuple) and len(in_color) == 3:

        # rescale color if necessary
        if all(isinstance(item, int) for item in in_color):
            in_color = [c / 255.0 for c in in_color]

        return colour.Color(rgb=tuple(in_color)).hex_l

    else:

        # try to guess the color system
        try:
            return colour.Color(in_color).hex_l

        except Exception as e:
            pass

        # try again by adding an extra # (GEE handle hex codes without #)
        try:
            return colour.Color(f"#{in_color}").hex_l

        except Exception as e:
            print(
                f"The provided color ({in_color}) is invalid. Using the default black color."
            )
            print(e)

        return out_color

`check_git_install()` ¶

Checks if Git is installed.

Returns:

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

Source code in geemap/common.py

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

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

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

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

`check_install(package)` ¶

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

Parameters:

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

Source code in geemap/common.py

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

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

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

`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/giswqs/geemap/archive/master.zip"
    filename = "geemap-master.zip"
    download_from_url(url, out_file_name=filename, out_dir=out_dir, unzip=unzip)

`column_stats(collection, column, stats_type)` ¶

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

Parameters:

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

Returns:

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

Source code in geemap/common.py

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

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

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

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

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

    return stats

`coords_to_geojson(coords)` ¶

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

Parameters:

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

Returns:

Type	Description
`dict`	A geojson FeatureCollection.

Source code in geemap/common.py

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

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

    Returns:
        dict: A geojson FeatureCollection.
    """

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

`copy_credentials_to_colab()` ¶

Copies ee credentials from Google Drive to Google Colab.

Source code in geemap/common.py

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

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

    shutil.copyfile(src, dst)

`copy_credentials_to_drive()` ¶

Copies ee credentials from Google Colab to Google Drive.

Source code in geemap/common.py

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

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

    shutil.copyfile(src, dst)

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

Creates a code cell in the IPython Notebook.

Parameters:

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

Source code in geemap/common.py

def create_code_cell(code="", where="below"):
    """Creates a code cell in the IPython Notebook.

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

    import base64

    from IPython.display import Javascript, display

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

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

Creates a colorbar based on the provided palette.

Parameters:

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

Returns:

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

Source code in geemap/common.py

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

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

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

    """
    import decimal
    import warnings

    # import io
    import pkg_resources
    from colour import Color
    from PIL import Image, ImageDraw, ImageFont

    warnings.simplefilter("ignore")
    pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))

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

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

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

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

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

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

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

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

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

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

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

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

    width, height = im.size

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

    if add_labels:

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

        font_color = check_color(font_color)

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

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

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

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

        for index, label in enumerate(labels):

            w_tmp, h_tmp = draw.textsize(label, font)

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

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

        im = background.copy()

    im.save(out_file)
    return out_file

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

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

Parameters:

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

Returns:

Type	Description
`str`	HTML download URL.

Source code in geemap/common.py

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

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

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

    from IPython.display import HTML

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

`create_nlcd_qml(out_qml)` ¶

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

Parameters:

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

Source code in geemap/common.py

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

    Args:
        out_qml (str): File path to the output qml.
    """
    import pkg_resources

    pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
    data_dir = os.path.join(pkg_dir, "data")
    template_dir = os.path.join(data_dir, "template")
    qml_template = os.path.join(template_dir, "NLCD.qml")

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

    shutil.copyfile(qml_template, out_qml)

`credentials_in_colab()` ¶

Checks if the ee credentials file exists in Google Colab.

Returns:

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

Source code in geemap/common.py

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

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

`credentials_in_drive()` ¶

Checks if the ee credentials file exists in Google Drive.

Returns:

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

Source code in geemap/common.py

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

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

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

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

Parameters:

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

Source code in geemap/common.py

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

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

    """
    import whitebox

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

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

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

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

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

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

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

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

Parameters:

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

Returns:

Type	Description
`ee_object`	An ee.Geometry object

Source code in geemap/common.py

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

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

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

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

`csv_to_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 json
    import pandas as pd

    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)

    df = pd.read_csv(in_csv)
    geojson = pandas_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_geopandas(in_csv, latitude='latitude', longitude='longitude', encoding='utf-8')` ¶

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

Parameters:

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

Returns:

Type	Description
`object`	GeoDataFrame.

Source code in geemap/common.py

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

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

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

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

`csv_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude')` ¶

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"):
    """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"):
        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)

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

    try:
        points = shp.Writer(out_shp, shapeType=shp.POINT)
        with open(in_csv, encoding="utf-8-sig") 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)

`date_sequence(start, end, unit, date_format='YYYY-MM-dd')` ¶

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', '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'`

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"):
    """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', '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'.

    Returns:
        ee.List: A list of date sequence.
    """
    start_date = ee.Date(start)
    end_date = ee.Date(end)
    count = ee.Number(end_date.difference(start_date, unit)).toInt()
    num_seq = ee.List.sequence(0, count)
    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)

`dict_to_csv(data_dict, out_csv, by_row=False)` ¶

Downloads an ee.Dictionary as a CSV file.

Parameters:

Name	Type	Description	Default
`data_dict`	`ee.Dictionary`	The input ee.Dictionary.	required
`out_csv`	`str`	The output file path to the CSV file.	required
`by_row`	`bool`	Whether to use by row or by column. Defaults to False.	`False`

Source code in geemap/common.py

def dict_to_csv(data_dict, out_csv, by_row=False):
    """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.
    """

    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)

`download_ee_video(collection, video_args, out_gif)` ¶

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

Source code in geemap/common.py

def download_ee_video(collection, video_args, out_gif):
    """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.
    """
    import requests

    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)

        if r.status_code != 200:
            print("An error occurred while downloading.")
            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_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
    """
    from google_drive_downloader import GoogleDriveDownloader as gdd

    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:
                tar_ref.extractall(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

`edit_download_html(htmlWidget, filename, title='Click here to download: ')` ¶

Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578#issuecomment-617668058

Parameters:

Name	Type	Description	Default
`htmlWidget`	`object`	The HTML widget to display the URL.	required
`filename`	`str`	File path to download.	required
`title`	`str`	Download description. Defaults to "Click here to download: ".	`'Click here to download: '`

Source code in geemap/common.py

def edit_download_html(htmlWidget, filename, title="Click here to download: "):
    """Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578#issuecomment-617668058

    Args:
        htmlWidget (object): The HTML widget to display the URL.
        filename (str): File path to download.
        title (str, optional): Download description. Defaults to "Click here to download: ".
    """

    # from IPython.display import HTML
    # import ipywidgets as widgets
    import base64

    # Change widget html temporarily to a font-awesome spinner
    htmlWidget.value = '<i class="fa fa-spinner fa-spin fa-2x fa-fw"></i><span class="sr-only">Loading...</span>'

    # Process raw data
    data = open(filename, "rb").read()
    b64 = base64.b64encode(data)
    payload = b64.decode()

    basename = os.path.basename(filename)

    # Create and assign html to widget
    html = '<a download="{filename}" href="data:text/csv;base64,{payload}" target="_blank">{title}</a>'
    htmlWidget.value = html.format(
        payload=payload, title=title + basename, filename=basename
    )

    # htmlWidget = widgets.HTML(value = '')
    # htmlWidget

`ee_api_to_csv(outfile=None)` ¶

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`

Source code in geemap/common.py

def ee_api_to_csv(outfile=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.
    """
    import pkg_resources
    import requests
    from bs4 import BeautifulSoup

    pkg_dir = os.path.dirname(pkg_resources.resource_filename("geemap", "geemap.py"))
    data_dir = os.path.join(pkg_dir, "data")
    template_dir = os.path.join(data_dir, "template")
    csv_file = os.path.join(template_dir, "ee_api_docs.csv")

    if outfile is None:
        outfile = csv_file
    else:
        if not outfile.endswith(".csv"):
            print("The output file must end with .csv")
            return
        else:
            out_dir = os.path.dirname(outfile)
            if not os.path.exists(out_dir):
                os.makedirs(out_dir)

    url = "https://developers.google.com/earth-engine/api_docs"

    try:

        r = requests.get(url)
        soup = BeautifulSoup(r.content, "html.parser")

        names = []
        descriptions = []
        functions = []
        returns = []
        arguments = []
        types = []
        details = []

        names = [h2.text for h2 in soup.find_all("h2")]
        descriptions = [h2.next_sibling.next_sibling.text for h2 in soup.find_all("h2")]
        func_tables = soup.find_all("table", class_="blue")
        functions = [func_table.find("code").text for func_table in func_tables]
        returns = [func_table.find_all("td")[1].text for func_table in func_tables]

        detail_tables = []
        tables = soup.find_all("table", class_="blue")

        for table in tables:
            item = table.next_sibling
            if item.attrs == {"class": ["details"]}:
                detail_tables.append(item)
            else:
                detail_tables.append("")

        for detail_table in detail_tables:
            if detail_table != "":
                items = [item.text for item in detail_table.find_all("code")]
            else:
                items = ""
            arguments.append(items)

        for detail_table in detail_tables:
            if detail_table != "":
                items = [item.text for item in detail_table.find_all("td")]
                items = items[1::3]
            else:
                items = ""
            types.append(items)

        for detail_table in detail_tables:
            if detail_table != "":
                items = [item.text for item in detail_table.find_all("p")]
            else:
                items = ""
            details.append(items)

        with open(outfile, "w", encoding="utf-8") as csv_file:
            csv_writer = csv.writer(csv_file, delimiter="\t")

            csv_writer.writerow(
                [
                    "name",
                    "description",
                    "function",
                    "returns",
                    "argument",
                    "type",
                    "details",
                ]
            )

            for i in range(len(names)):
                name = names[i]
                description = descriptions[i]
                function = functions[i]
                return_type = returns[i]
                argument = "|".join(arguments[i])
                argu_type = "|".join(types[i])
                detail = "|".join(details[i])

                csv_writer.writerow(
                    [
                        name,
                        description,
                        function,
                        return_type,
                        argument,
                        argu_type,
                        detail,
                    ]
                )

    except Exception as e:
        print(e)

`ee_data_html(asset)` ¶

Generates HTML from an asset to be used in the HTML widget.

Parameters:

Name	Type	Description	Default
`asset`	`dict`	A dictionary containing an Earth Engine asset.	required

Returns:

Type	Description
`str`	A string containing HTML.

Source code in geemap/common.py

def ee_data_html(asset):
    """Generates HTML from an asset to be used in the HTML widget.

    Args:
        asset (dict): A dictionary containing an Earth Engine asset.

    Returns:
        str: A string containing HTML.
    """
    template = """
        <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>
            <h4>Earth Engine Data Catalog</h4>
                <p style="margin-left: 40px"><a href="asset_url" target="_blank">asset_id</a></p>
            <h4>Dataset Thumbnail</h4>
                <img src="thumbnail_url">
        </body>
        </html>
    """

    try:

        text = template.replace("asset_title", asset["title"])
        text = text.replace("asset_dates", asset["dates"])
        text = text.replace("ee_id_snippet", asset["ee_id_snippet"])
        text = text.replace("asset_id", asset["id"])
        text = text.replace("asset_url", asset["asset_url"])
        # asset['thumbnail'] = ee_data_thumbnail(asset['id'])
        text = text.replace("thumbnail_url", asset["thumbnail_url"])

        return text

    except Exception as e:
        print(e)

`ee_data_thumbnail(asset_id)` ¶

Retrieves the thumbnail URL of an Earth Engine asset.

Parameters:

Name	Type	Description	Default
`asset_id`	`str`	An Earth Engine asset id.	required

Returns:

Type	Description
`str`	An http url of the thumbnail.

Source code in geemap/common.py

def ee_data_thumbnail(asset_id):
    """Retrieves the thumbnail URL of an Earth Engine asset.

    Args:
        asset_id (str): An Earth Engine asset id.

    Returns:
        str: An http url of the thumbnail.
    """
    import urllib

    import requests
    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)

    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)` ¶

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`

Source code in geemap/common.py

def ee_export_geojson(ee_object, filename=None, selectors=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.
    """
    import requests

    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 = requests.get(url, stream=True)

        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)
            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.")
        print(e)
        return

    with open(filename) as f:
        geojson = f.read()

    return geojson

`ee_export_image(ee_object, filename, scale=None, crs=None, region=None, file_per_band=False)` ¶

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`
`region`	`object`	A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.	`None`
`file_per_band`	`bool`	Whether to produce a different GeoTIFF per band. Defaults to False.	`False`

Source code in geemap/common.py

def ee_export_image(
    ee_object, filename, scale=None, crs=None, region=None, file_per_band=False
):
    """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.
        region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.
        file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.
    """
    import requests

    if not isinstance(ee_object, ee.Image):
        print("The ee_object must be an ee.Image.")
        return

    filename = os.path.abspath(filename)
    basename = os.path.basename(filename)
    name = os.path.splitext(basename)[0]
    filetype = os.path.splitext(basename)[1][1:].lower()
    filename_zip = filename.replace(".tif", ".zip")

    if filetype != "tif":
        print("The filename must end with .tif")
        return

    try:
        print("Generating URL ...")
        params = {"name": name, "filePerBand": file_per_band}
        if scale is None:
            scale = ee_object.projection().nominalScale().multiply(10)
        params["scale"] = scale
        if region is None:
            region = ee_object.geometry()
        params["region"] = region
        if crs is not None:
            params["crs"] = crs

        url = ee_object.getDownloadURL(params)
        print(f"Downloading data from {url}\nPlease wait ...")
        r = requests.get(url, stream=True)

        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.")
        print(e)
        return

    try:
        with zipfile.ZipFile(filename_zip) as z:
            z.extractall(os.path.dirname(filename))
        os.remove(filename_zip)

        if file_per_band:
            print(f"Data downloaded to {os.path.dirname(filename)}")
        else:
            print(f"Data downloaded to {filename}")
    except Exception as e:
        print(e)

`ee_export_image_collection(ee_object, out_dir, scale=None, crs=None, region=None, file_per_band=False)` ¶

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`
`region`	`object`	A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.	`None`
`file_per_band`	`bool`	Whether to produce a different GeoTIFF per band. Defaults to False.	`False`

Source code in geemap/common.py

def ee_export_image_collection(
    ee_object, out_dir, scale=None, crs=None, region=None, file_per_band=False
):
    """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.
        region (object, optional): A polygon specifying a region to download; ignored if crs and crs_transform is specified. Defaults to None.
        file_per_band (bool, optional): Whether to produce a different GeoTIFF per band. Defaults to False.
    """

    if not isinstance(ee_object, ee.ImageCollection):
        print("The ee_object must be an ee.ImageCollection.")
        return

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

    try:

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

        for i in range(0, count):
            image = ee.Image(ee_object.toList(count).get(i))
            name = image.get("system:index").getInfo() + ".tif"
            filename = os.path.join(os.path.abspath(out_dir), name)
            print(f"Exporting {i + 1}/{count}: {name}")
            ee_export_image(
                image,
                filename=filename,
                scale=scale,
                crs=crs,
                region=region,
                file_per_band=file_per_band,
            )
            print("\n")

    except Exception as e:
        print(e)

`ee_export_image_collection_to_drive(ee_object, descriptions=None, folder=None, region=None, scale=None, crs=None, max_pixels=10000000000000.0, file_format='GeoTIFF', format_options={})` ¶

Creates a batch task to export an ImageCollection as raster images to Google Drive.

Parameters:

Name	Type	Description	Default
`ee_object`	`object`	The image to export.	required
`descriptions`	`list`	A list of human-readable names of the tasks.	`None`
`folder`	`str`	The Google Drive Folder that the export will reside in. Defaults to None.	`None`
`region`	`object`	A LinearRing, Polygon, or coordinates representing region to export. These may be specified as the Geometry objects or coordinates serialized as a string. If not specified, the region defaults to the viewport at the time of invocation. Defaults to None.	`None`
`scale`	`float`	Resolution in meters per pixel. Defaults to 10 times of the image resolution.	`None`
`crs`	`str`	CRS to use for the exported image.. Defaults to None.	`None`
`max_pixels`	`int`	Restrict the number of pixels in the export. Defaults to 1.0E13.	`10000000000000.0`
`file_format`	`str`	The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'.	`'GeoTIFF'`
`format_options`	`dict`	A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True}	`{}`

Source code in geemap/common.py

def ee_export_image_collection_to_drive(
    ee_object,
    descriptions=None,
    folder=None,
    region=None,
    scale=None,
    crs=None,
    max_pixels=1.0e13,
    file_format="GeoTIFF",
    format_options={},
):
    """Creates a batch task to export an ImageCollection as raster images to Google Drive.

    Args:
        ee_object (object): The image to export.
        descriptions (list): A list of human-readable names of the tasks.
        folder (str, optional): The Google Drive Folder that the export will reside in. Defaults to None.
        region (object, optional): A LinearRing, Polygon, or coordinates representing region to export. These may be specified as the Geometry objects or coordinates serialized as a string. If not specified, the region defaults to the viewport at the time of invocation. Defaults to None.
        scale (float, optional): Resolution in meters per pixel. Defaults to 10 times of the image resolution.
        crs (str, optional): CRS to use for the exported image.. Defaults to None.
        max_pixels (int, optional): Restrict the number of pixels in the export. Defaults to 1.0E13.
        file_format (str, optional): The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'.
        format_options (dict, optional): A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True}
    """
    # ee_initialize()

    if not isinstance(ee_object, ee.ImageCollection):
        print("The ee_object must be an ee.ImageCollection.")
        return

    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)

        for i in range(0, count):
            image = ee.Image(images.get(i))
            name = descriptions[i]
            ee_export_image_to_drive(
                image,
                name,
                folder,
                region,
                scale,
                crs,
                max_pixels,
                file_format,
                format_options,
            )

    except Exception as e:
        print(e)

`ee_export_image_to_drive(ee_object, description, folder=None, region=None, scale=None, crs=None, max_pixels=10000000000000.0, file_format='GeoTIFF', format_options={})` ¶

Creates a batch task to export an Image as a raster to Google Drive.

Parameters:

Name	Type	Description	Default
`ee_object`	`object`	The image to export.	required
`description`	`str`	A human-readable name of the task.	required
`folder`	`str`	The Google Drive Folder that the export will reside in. Defaults to None.	`None`
`region`	`object`	A LinearRing, Polygon, or coordinates representing region to export. These may be specified as the Geometry objects or coordinates serialized as a string. If not specified, the region defaults to the viewport at the time of invocation. Defaults to None.	`None`
`scale`	`float`	Resolution in meters per pixel. Defaults to 10 times of the image resolution.	`None`
`crs`	`str`	CRS to use for the exported image.. Defaults to None.	`None`
`max_pixels`	`int`	Restrict the number of pixels in the export. Defaults to 1.0E13.	`10000000000000.0`
`file_format`	`str`	The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'.	`'GeoTIFF'`
`format_options`	`dict`	A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True}	`{}`

Source code in geemap/common.py

def ee_export_image_to_drive(
    ee_object,
    description,
    folder=None,
    region=None,
    scale=None,
    crs=None,
    max_pixels=1.0e13,
    file_format="GeoTIFF",
    format_options={},
):
    """Creates a batch task to export an Image as a raster to Google Drive.

    Args:
        ee_object (object): The image to export.
        description (str): A human-readable name of the task.
        folder (str, optional): The Google Drive Folder that the export will reside in. Defaults to None.
        region (object, optional): A LinearRing, Polygon, or coordinates representing region to export. These may be specified as the Geometry objects or coordinates serialized as a string. If not specified, the region defaults to the viewport at the time of invocation. Defaults to None.
        scale (float, optional): Resolution in meters per pixel. Defaults to 10 times of the image resolution.
        crs (str, optional): CRS to use for the exported image.. Defaults to None.
        max_pixels (int, optional): Restrict the number of pixels in the export. Defaults to 1.0E13.
        file_format (str, optional): The string file format to which the image is exported. Currently only 'GeoTIFF' and 'TFRecord' are supported. Defaults to 'GeoTIFF'.
        format_options (dict, optional): A dictionary of string keys to format specific options, e.g., {'compressed': True, 'cloudOptimized': True}
    """
    # ee_initialize()

    if not isinstance(ee_object, ee.Image):
        print("The ee_object must be an ee.Image.")
        return

    try:
        params = {}

        if folder is not None:
            params["driveFolder"] = folder
        if region is not None:
            params["region"] = region
        if scale is None:
            scale = ee_object.projection().nominalScale().multiply(10)
        params["scale"] = scale
        if crs is not None:
            params["crs"] = crs
        params["maxPixels"] = max_pixels
        params["fileFormat"] = file_format
        params["formatOptions"] = format_options

        task = ee.batch.Export.image(ee_object, description, params)
        task.start()

        print(f"Exporting {description} ...")

    except Exception as e:
        print(e)

`ee_export_vector(ee_object, filename, selectors=None, verbose=True, keep_zip=False)` ¶

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`

Source code in geemap/common.py

def ee_export_vector(ee_object, filename, selectors=None, verbose=True, keep_zip=False):
    """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.
    """
    import requests

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

    allowed_formats = ["csv", "geojson", "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):
        print(
            "The file type must be one of the following: {}".format(
                ", ".join(allowed_formats)
            )
        )
        print(
            "Earth Engine no longer supports downloading featureCollection as shapefile or json. \nPlease use geemap.ee_export_vector_to_drive() to export featureCollection to Google Drive."
        )
        raise ValueError

    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 = requests.get(url, stream=True)

        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)
            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.")
        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_drive(ee_object, description, folder, file_format='shp', selectors=None)` ¶

Exports Earth Engine FeatureCollection to Google Drive. other formats, including shp, csv, json, kml, and kmz.

Parameters:

Name	Type	Description	Default
`ee_object`	`object`	ee.FeatureCollection to export.	required
`description`	`str`	File name of the output file.	required
`folder`	`str`	Folder name within Google Drive to save the exported file.	required
`file_format`	`str`	The supported file format include shp, csv, json, kml, kmz, and TFRecord. Defaults to 'shp'.	`'shp'`
`selectors`	`list`	The list of attributes to export. Defaults to None.	`None`

Source code in geemap/common.py

def ee_export_vector_to_drive(
    ee_object, description, folder, file_format="shp", selectors=None
):
    """Exports Earth Engine FeatureCollection to Google Drive. other formats, including shp, csv, json, kml, and kmz.

    Args:
        ee_object (object): ee.FeatureCollection to export.
        description (str): File name of the output file.
        folder (str): Folder name within Google Drive to save the exported file.
        file_format (str, optional): The supported file format include shp, csv, json, kml, kmz, and TFRecord. Defaults to 'shp'.
        selectors (list, optional): The list of attributes to export. Defaults to None.
    """
    if not isinstance(ee_object, ee.FeatureCollection):
        print("The ee_object must be an ee.FeatureCollection.")
        return

    allowed_formats = ["csv", "json", "kml", "kmz", "shp", "tfrecord"]
    if not (file_format.lower() in allowed_formats):
        print(
            "The file type must be one of the following: {}".format(
                ", ".join(allowed_formats)
            )
        )
        return

    task_config = {
        "folder": folder,
        "fileFormat": file_format,
    }

    if selectors is not None:
        task_config["selectors"] = selectors
    elif (selectors is None) and (file_format.lower() == "csv"):
        # remove .geo coordinate field
        ee_object = ee_object.select([".*"], None, False)

    print(f"Exporting {description}...")
    task = ee.batch.Export.table.toDrive(ee_object, description, **task_config)
    task.start()

`ee_function_tree(name)` ¶

Construct the tree structure based on an Earth Engine function. For example, the function "ee.Algorithms.FMask.matchClouds" will return a list ["ee.Algorithms", "ee.Algorithms.FMask", "ee.Algorithms.FMask.matchClouds"]

Parameters:

Name	Type	Description	Default
`name`	`str`	The name of the Earth Engine function	required

Returns:

Type	Description
`list`	The list for parent functions.

Source code in geemap/common.py

def ee_function_tree(name):
    """Construct the tree structure based on an Earth Engine function. For example, the function "ee.Algorithms.FMask.matchClouds" will return a list ["ee.Algorithms", "ee.Algorithms.FMask", "ee.Algorithms.FMask.matchClouds"]

    Args:
        name (str): The name of the Earth Engine function

    Returns:
        list: The list for parent functions.
    """
    func_list = []
    try:
        items = name.split(".")
        if items[0] == "ee":
            for i in range(2, len(items) + 1):
                func_list.append(".".join(items[0:i]))
        else:
            for i in range(1, len(items) + 1):
                func_list.append(".".join(items[0:i]))

        return func_list
    except Exception as e:
        print(e)
        print("The provided function name is invalid.")

`ee_initialize(token_name='EARTHENGINE_TOKEN')` ¶

Authenticates Earth Engine and initialize an Earth Engine session

Source code in geemap/common.py

def ee_initialize(token_name="EARTHENGINE_TOKEN"):
    """Authenticates Earth Engine and initialize an Earth Engine session"""
    if ee.data._credentials is None:
        try:
            ee_token = os.environ.get(token_name)
            if ee_token is not None:
                credential_file_path = os.path.expanduser("~/.config/earthengine/")
                if not os.path.exists(credential_file_path):
                    credential = '{"refresh_token":"%s"}' % ee_token
                    os.makedirs(credential_file_path, exist_ok=True)
                    with open(credential_file_path + "credentials", "w") as file:
                        file.write(credential)
            elif in_colab_shell():
                if credentials_in_drive() and (not credentials_in_colab()):
                    copy_credentials_to_colab()
                elif not credentials_in_colab:
                    ee.Authenticate()
                    if is_drive_mounted() and (not credentials_in_drive()):
                        copy_credentials_to_drive()
                else:
                    if is_drive_mounted():
                        copy_credentials_to_drive()

            ee.Initialize()
        except Exception:
            ee.Authenticate()
            ee.Initialize()

`ee_num_round(num, decimal=2)` ¶

Rounds a number to a specified number of decimal places.

Parameters:

Name	Type	Description	Default
`num`	`ee.Number`	The number to round.	required
`decimal`	`int`	The number of decimal places to round. Defaults to 2.	`2`

Returns:

Type	Description
`ee.Number`	The number with the specified decimal places rounded.

Source code in geemap/common.py

def ee_num_round(num, decimal=2):
    """Rounds a number to a specified number of decimal places.

    Args:
        num (ee.Number): The number to round.
        decimal (int, optional): The number of decimal places to round. Defaults to 2.

    Returns:
        ee.Number: The number with the specified decimal places rounded.
    """
    format_str = "%.{}f".format(decimal)
    return ee.Number.parse(ee.Number(num).format(format_str))

`ee_search(asset_limit=100)` ¶

Search Earth Engine API and user assets. If you received a warning (IOPub message rate exceeded) in Jupyter notebook, you can relaunch Jupyter notebook using the following command: jupyter notebook --NotebookApp.iopub_msg_rate_limit=10000

Parameters:

Name	Type	Description	Default
`asset_limit`	`int`	The number of assets to display for each asset type, i.e., Image, ImageCollection, and FeatureCollection. Defaults to 100.	`100`

Source code in geemap/common.py

def ee_search(asset_limit=100):
    """Search Earth Engine API and user assets. If you received a warning (IOPub message rate exceeded) in Jupyter notebook, you can relaunch Jupyter notebook using the following command:
        jupyter notebook --NotebookApp.iopub_msg_rate_limit=10000

    Args:
        asset_limit (int, optional): The number of assets to display for each asset type, i.e., Image, ImageCollection, and FeatureCollection. Defaults to 100.
    """

    import warnings

    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.clear_output()
        tree_widget.clear_output()
        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.clear_output()
                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.clear_output()
                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.clear_output(wait=True)
                    display(flags.docs)
                else:
                    tree_widget.clear_output()
                    print("Searching...")
                    tree_widget.clear_output(wait=True)
                    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.clear_output(wait=True)
                    display(flags.assets)
                else:
                    tree_widget.clear_output()
                    print("Searching...")
                    tree_widget.clear_output(wait=True)
                    sub_tree = search_api_tree(text.value, flags.asset_dict)
                    display(sub_tree)

    search_box.on_submit(search_box_callback)

`ee_to_csv(ee_object, filename, selectors=None, verbose=True)` ¶

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
`selectors`	`list`	A list of attributes to export. Defaults to None.	`None`
`verbose`	`bool`	Whether to print out descriptive text.	`True`

Source code in geemap/common.py

def ee_to_csv(ee_object, filename, selectors=None, verbose=True):
    """Downloads an ee.FeatureCollection as a CSV file.

    Args:
        ee_object (object): ee.FeatureCollection
        filename (str): The output filepath of the CSV file.
        selectors (list, optional): A list of attributes to export. Defaults to None.
        verbose (bool, optional): Whether to print out descriptive text.

    """
    # ee_initialize()
    try:
        if filename.lower().endswith(".csv"):
            ee_export_vector(
                ee_object=ee_object,
                filename=filename,
                selectors=selectors,
                verbose=verbose,
            )
        else:
            print("The filename must end with .csv")

    except Exception as e:
        print(e)

`ee_to_geojson(ee_object, out_json=None)` ¶

Converts Earth Engine object to geojson.

Parameters:

Name	Type	Description	Default
`ee_object`	`object`	An Earth Engine object.	required

Returns:

Type	Description
`object`	GeoJSON object.

Source code in geemap/common.py

def ee_to_geojson(ee_object, out_json=None):
    """Converts Earth Engine object to geojson.

    Args:
        ee_object (object): An Earth Engine object.

    Returns:
        object: GeoJSON object.
    """
    # from json import dumps

    # ee_initialize()

    try:
        if (
            isinstance(ee_object, ee.geometry.Geometry)
            or isinstance(ee_object, ee.feature.Feature)
            or isinstance(ee_object, ee.featurecollection.FeatureCollection)
        ):
            json_object = ee_object.getInfo()
            if out_json is not None:
                out_json = os.path.abspath(out_json)
                if not os.path.exists(os.path.dirname(out_json)):
                    os.makedirs(os.path.dirname(out_json))
                with open(out_json, "w") as geojson:
                    geojson.write(json.dumps(json_object, indent=2) + "\n")
            return json_object
        else:
            print("Could not convert the Earth Engine object to geojson")
    except Exception as e:
        print(e)

`ee_to_geopandas(ee_object, selectors=None, verbose=False)` ¶

Converts an ee.FeatureCollection to Geopandas dataframe.

Parameters:

Name	Type	Description	Default
`ee_object`	`ee.FeatureCollection`	ee.FeatureCollection.	required
`selectors`	`list`	A list of attributes to export. Defaults to None.	`None`
`verbose`	`bool`	Whether to print out descriptive text. Defaults to False.	`False`

Exceptions:

Type	Description
`TypeError`	ee_object must be an ee.FeatureCollection.

Returns:

Type	Description
`gpd.GeoDataFrame`	geopandas.GeoDataFrame

Source code in geemap/common.py

def ee_to_geopandas(ee_object, selectors=None, verbose=False):
    """Converts an ee.FeatureCollection to Geopandas dataframe.

    Args:
        ee_object (ee.FeatureCollection): ee.FeatureCollection.
        selectors (list, optional): A list of attributes to export. Defaults to None.
        verbose (bool, optional): Whether to print out descriptive text. Defaults to False.

    Raises:
        TypeError: ee_object must be an ee.FeatureCollection.

    Returns:
        gpd.GeoDataFrame: geopandas.GeoDataFrame
    """
    from pathlib import Path

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

    import geopandas as gpd

    if not isinstance(ee_object, ee.FeatureCollection):
        raise TypeError("ee_object must be an ee.FeatureCollection")

    collection = ee_to_geojson(ee_object)
    gdf = gpd.GeoDataFrame.from_features(collection["features"])

    return gdf

`ee_to_numpy(ee_object, bands=None, region=None, properties=None, default_value=None)` ¶

Extracts a rectangular region of pixels from an image into a 2D numpy array per band.

Parameters:

Name	Type	Description	Default
`ee_object`	`object`	The image to sample.	required
`bands`	`list`	The list of band names to extract. Please make sure that all bands have the same spatial resolution. Defaults to None.	`None`
`region`	`object`	The region whose projected bounding box is used to sample the image. The maximum number of pixels you can export is 262,144. Resampling and reprojecting all bands to a fixed scale can be useful. Defaults to the footprint in each band.	`None`
`properties`	`list`	The properties to copy over from the sampled image. Defaults to all non-system properties.	`None`
`default_value`	`float`	A default value used when a sampled pixel is masked or outside a band's footprint. Defaults to None.	`None`

Returns:

Type	Description
`array`	A 3D numpy array.

Source code in geemap/common.py

def ee_to_numpy(
    ee_object, bands=None, region=None, properties=None, default_value=None
):
    """Extracts a rectangular region of pixels from an image into a 2D numpy array per band.

    Args:
        ee_object (object): The image to sample.
        bands (list, optional): The list of band names to extract. Please make sure that all bands have the same spatial resolution. Defaults to None.
        region (object, optional): The region whose projected bounding box is used to sample the image. The maximum number of pixels you can export is 262,144. Resampling and reprojecting all bands to a fixed scale can be useful. Defaults to the footprint in each band.
        properties (list, optional): The properties to copy over from the sampled image. Defaults to all non-system properties.
        default_value (float, optional): A default value used when a sampled pixel is masked or outside a band's footprint. Defaults to None.

    Returns:
        array: A 3D numpy array.
    """
    import numpy as np

    if not isinstance(ee_object, ee.Image):
        print("The input must be an ee.Image.")
        return

    if region is None:
        region = ee_object.geometry()

    try:

        if bands is not None:
            ee_object = ee_object.select(bands)
        else:
            bands = ee_object.bandNames().getInfo()

        band_arrs = ee_object.sampleRectangle(
            region=region, properties=properties, defaultValue=default_value
        )
        band_values = []

        for band in bands:
            band_arr = band_arrs.get(band).getInfo()
            band_value = np.array(band_arr)
            band_values.append(band_value)

        image = np.dstack(band_values)
        return image

    except Exception as e:
        print(e)

`ee_to_pandas(ee_object, **kwargs)` ¶

Converts an ee.FeatureCollection to pandas dataframe.

Parameters:

Name	Type	Description	Default
`ee_object`	`ee.FeatureCollection`	ee.FeatureCollection.	required

Exceptions:

Type	Description
`TypeError`	ee_object must be an ee.FeatureCollection

Returns:

Type	Description
`pd.DataFrame`	pandas DataFrame

Source code in geemap/common.py

def ee_to_pandas(ee_object, **kwargs):
    """Converts an ee.FeatureCollection to pandas dataframe.

    Args:
        ee_object (ee.FeatureCollection): ee.FeatureCollection.

    Raises:
        TypeError: ee_object must be an ee.FeatureCollection

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

    if not isinstance(ee_object, ee.FeatureCollection):
        raise TypeError("ee_object must be an ee.FeatureCollection")

    try:
        data = ee_object.map(lambda f: ee.Feature(None, f.toDictionary()))
        data = [x["properties"] for x in data.getInfo()["features"]]
        df = pd.DataFrame(data)
        return df
    except Exception as e:
        raise Exception(e)

`ee_to_shp(ee_object, filename, selectors=None, verbose=True, keep_zip=False)` ¶

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
`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`

Source code in geemap/common.py

def ee_to_shp(ee_object, filename, selectors=None, verbose=True, keep_zip=False):
    """Downloads an ee.FeatureCollection as a shapefile.

    Args:
        ee_object (object): ee.FeatureCollection
        filename (str): The output filepath of the shapefile.
        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.
    """
    # ee_initialize()
    try:
        if filename.lower().endswith(".shp"):
            ee_export_vector(
                ee_object=ee_object,
                filename=filename,
                selectors=selectors,
                verbose=verbose,
                keep_zip=keep_zip,
            )
        else:
            print("The filename must end with .shp")

    except Exception as e:
        print(e)

`ee_user_id()` ¶

Gets Earth Engine account user id.

Returns:

Type	Description
`str`	A string containing the user id.

Source code in geemap/common.py

def ee_user_id():
    """Gets Earth Engine account user id.

    Returns:
        str: A string containing the user id.
    """
    # ee_initialize()
    roots = ee.data.getAssetRoots()
    if len(roots) == 0:
        return None
    else:
        root = ee.data.getAssetRoots()[0]
        user_id = root["id"].replace("projects/earthengine-legacy/assets/", "")
        return user_id

`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`	`ee.Image \| ee.ImageCollection`	The ee.Image or ee.ImageCollection to sample.	required
`region`	`ee.Geometry`	The region to sample from. If unspecified, uses the image's whole footprint.	required
`scale`	`float`	A nominal scale in meters of the projection to sample in. Defaults to None.	`None`
`projection`	`str`	The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.	`None`
`tileScale`	`int`	A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.	`1`
`getInfo`	`bool`	Whether to use getInfo with the results, i.e., returning the values a list. Default to False.	`False`

Exceptions:

Type	Description
`TypeError`	The image must be an instance of ee.Image.
`TypeError`	Region must be an instance of ee.Geometry.

Returns:

Type	Description
`ee.Dictionary`	The dictionary containing band names and pixel values.

Source code in geemap/common.py

def extract_pixel_values(
    ee_object, region, scale=None, projection=None, tileScale=1, getInfo=False
):
    """Samples the pixels of an image, returning them as a ee.Dictionary.

    Args:
        ee_object (ee.Image | ee.ImageCollection): The ee.Image or ee.ImageCollection to sample.
        region (ee.Geometry): The region to sample from. If unspecified, uses the image's whole footprint.
        scale (float, optional): A nominal scale in meters of the projection to sample in. Defaults to None.
        projection (str, optional): The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
        tileScale (int, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.
        getInfo (bool, optional): Whether to use getInfo with the results, i.e., returning the values a list. Default to False.

    Raises:
        TypeError: The image must be an instance of ee.Image.
        TypeError: Region must be an instance of ee.Geometry.

    Returns:
        ee.Dictionary: The dictionary containing band names and pixel values.
    """
    if isinstance(ee_object, ee.ImageCollection):
        ee_object = ee_object.toBands()

    if not isinstance(ee_object, ee.Image):
        raise TypeError("The image must be an instance of ee.Image.")

    if not isinstance(region, ee.Geometry):
        raise TypeError("Region must be an instance of ee.Geometry.")

    dict_values = (
        ee_object.sample(region, scale, projection, tileScale=tileScale)
        .first()
        .toDictionary()
    )

    if getInfo:
        band_names = ee_object.bandNames().getInfo()
        values_tmp = dict_values.getInfo()
        values = [values_tmp[i] for i in band_names]
        return dict(zip(band_names, values))
    else:
        return dict_values

`extract_transect(image, line, reducer='mean', n_segments=100, dist_interval=None, scale=None, crs=None, crsTransform=None, tileScale=1.0, to_pandas=False, **kwargs)` ¶

Extracts transect from an image. Credits to Gena for providing the JavaScript example https://code.earthengine.google.com/b09759b8ac60366ee2ae4eccdd19e615.

Parameters:

Name	Type	Description	Default
`image`	`ee.Image`	The image to extract transect from.	required
`line`	`ee.Geometry.LineString`	The LineString used to extract transect from an image.	required
`reducer`	`str`	The ee.Reducer to use, e.g., 'mean', 'median', 'min', 'max', 'stdDev'. Defaults to "mean".	`'mean'`
`n_segments`	`int`	The number of segments that the LineString will be split into. Defaults to 100.	`100`
`dist_interval`	`float`	The distance interval used for splitting the LineString. If specified, the n_segments parameter will be ignored. Defaults to None.	`None`
`scale`	`float`	A nominal scale in meters of the projection to work in. Defaults to None.	`None`
`crs`	`ee.Projection`	The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.	`None`
`crsTransform`	`list`	The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and will replace any transform already set on the projection. Defaults to None.	`None`
`tileScale`	`float`	A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.	`1.0`
`to_pandas`	`bool`	Whether to convert the result to a pandas dataframe. Default to False.	`False`

Exceptions:

Type	Description
`TypeError`	If the geometry type is not LineString.
`Exception`	If the program fails to compute.

Returns:

Type	Description
`ee.FeatureCollection`	The FeatureCollection containing the transect with distance and reducer values.

Source code in geemap/common.py

def extract_transect(
    image,
    line,
    reducer="mean",
    n_segments=100,
    dist_interval=None,
    scale=None,
    crs=None,
    crsTransform=None,
    tileScale=1.0,
    to_pandas=False,
    **kwargs,
):
    """Extracts transect from an image. Credits to Gena for providing the JavaScript example https://code.earthengine.google.com/b09759b8ac60366ee2ae4eccdd19e615.

    Args:
        image (ee.Image): The image to extract transect from.
        line (ee.Geometry.LineString): The LineString used to extract transect from an image.
        reducer (str, optional): The ee.Reducer to use, e.g., 'mean', 'median', 'min', 'max', 'stdDev'. Defaults to "mean".
        n_segments (int, optional): The number of segments that the LineString will be split into. Defaults to 100.
        dist_interval (float, optional): The distance interval used for splitting the LineString. If specified, the n_segments parameter will be ignored. Defaults to None.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.
        crs (ee.Projection, optional): The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.
        crsTransform (list, optional): The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and will replace any transform already set on the projection. Defaults to None.
        tileScale (float, optional): A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default. Defaults to 1.
        to_pandas (bool, optional): Whether to convert the result to a pandas dataframe. Default to False.

    Raises:
        TypeError: If the geometry type is not LineString.
        Exception: If the program fails to compute.

    Returns:
        ee.FeatureCollection: The FeatureCollection containing the transect with distance and reducer values.
    """
    try:

        geom_type = line.type().getInfo()
        if geom_type != "LineString":
            raise TypeError("The geometry type must be LineString.")

        reducer = eval("ee.Reducer." + reducer + "()")
        maxError = image.projection().nominalScale().divide(5)

        length = line.length(maxError)
        if dist_interval is None:
            dist_interval = length.divide(n_segments)

        distances = ee.List.sequence(0, length, dist_interval)
        lines = line.cutLines(distances, maxError).geometries()

        def set_dist_attr(l):
            l = ee.List(l)
            geom = ee.Geometry(l.get(0))
            distance = ee.Number(l.get(1))
            geom = ee.Geometry.LineString(geom.coordinates())
            return ee.Feature(geom, {"distance": distance})

        lines = lines.zip(distances).map(set_dist_attr)
        lines = ee.FeatureCollection(lines)

        transect = image.reduceRegions(
            **{
                "collection": ee.FeatureCollection(lines),
                "reducer": reducer,
                "scale": scale,
                "crs": crs,
                "crsTransform": crsTransform,
                "tileScale": tileScale,
            }
        )

        if to_pandas:
            return ee_to_pandas(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', **kwargs)` ¶

Extracts image values to points.

Parameters:

Name	Type	Description	Default
`in_fc`	`object`	ee.FeatureCollection.	required
`image`	`object`	The ee.Image to extract pixel values.	required
`out_fc`	`object`	The output feature collection. Defaults to None.	`None`
`scale`	`ee.Projectoin`	A nominal scale in meters of the projection to sample in. If unspecified,the scale of the image's first band is used.	`None`
`crs`	`str`	The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. Defaults to None.	`None`
`crsTransform`	`list`	The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and will replace any transform already set on the projection.	`None`
`tile_scale`	`float`	A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default.	required
`stats_type`	`str`	Statistic type to be calculated. Defaults to 'FIRST'.	`'FIRST'`

Returns:

Type	Description
`object`	ee.FeatureCollection

Source code in geemap/common.py

def extract_values_to_points(
    in_fc,
    image,
    out_fc=None,
    scale=None,
    crs=None,
    crsTransform=None,
    tileScale=1,
    stats_type="FIRST",
    **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'.

    Returns:
        object: ee.FeatureCollection
    """

    if "tile_scale" in kwargs:
        tileScale = kwargs["tile_scale"]
    if "crs_transform" in kwargs:
        crsTransform = kwargs["crs_transform"]

    allowed_stats = {
        "FIRST": ee.Reducer.first(),
        "MEAN": ee.Reducer.mean(),
        "MAXIMUM": ee.Reducer.max(),
        "MEDIAN": ee.Reducer.median(),
        "MINIMUM": ee.Reducer.min(),
        "STD": ee.Reducer.stdDev(),
        "MIN_MAX": ee.Reducer.minMax(),
        "SUM": ee.Reducer.sum(),
        "VARIANCE": ee.Reducer.variance(),
    }

    if stats_type.upper() not in allowed_stats:
        raise ValueError(
            f"The statistics_type must be one of the following {', '.join(allowed_stats.keys())}"
        )

    if not isinstance(in_fc, ee.FeatureCollection):
        try:
            in_fc = shp_to_ee(in_fc)
        except Exception as e:
            print(e)
            return

    if not isinstance(image, ee.Image):
        print("The image must be an instance of ee.Image.")
        return

    result = image.reduceRegions(
        collection=in_fc,
        reducer=allowed_stats[stats_type.upper()],
        scale=scale,
        crs=crs,
        crsTransform=crsTransform,
        tileScale=tileScale,
    )

    if out_fc is not None:
        ee_export_vector(result, out_fc)
    else:
        return result

`file_browser(in_dir=None, show_hidden=False, add_root_node=True, search_description=None, use_import=False, return_sep_widgets=False, node_icon='file')` ¶

Creates a simple file browser and text editor.

Parameters:

Name	Type	Description	Default
`in_dir`	`str`	The input directory. Defaults to None, which will use the current working directory.	`None`
`show_hidden`	`bool`	Whether to show hidden files/folders. Defaults to False.	`False`
`add_root_node`	`bool`	Whether to add the input directory as a root node. Defaults to True.	`True`
`search_description`	`str`	The description of the search box. Defaults to None.	`None`
`use_import`	`bool`	Whether to show the import button. Defaults to False.	`False`
`return_sep_widgets`	`bool`	Whether to return the results as separate widgets. Defaults to False.	`False`

Returns:

Type	Description
`object`	An ipywidget.

Source code in geemap/common.py

def file_browser(
    in_dir=None,
    show_hidden=False,
    add_root_node=True,
    search_description=None,
    use_import=False,
    return_sep_widgets=False,
    node_icon="file",
):
    """Creates a simple file browser and text editor.

    Args:
        in_dir (str, optional): The input directory. Defaults to None, which will use the current working directory.
        show_hidden (bool, optional): Whether to show hidden files/folders. Defaults to False.
        add_root_node (bool, optional): Whether to add the input directory as a root node. Defaults to True.
        search_description (str, optional): The description of the search box. Defaults to None.
        use_import (bool, optional): Whether to show the import button. Defaults to False.
        return_sep_widgets (bool, optional): Whether to return the results as separate widgets. Defaults to False.

    Returns:
        object: An ipywidget.
    """
    import platform

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

    if not os.path.exists(in_dir):
        print("The provided directory does not exist.")
        return
    elif not os.path.isdir(in_dir):
        print("The provided path is not a valid directory.")
        return

    sep = "/"
    if platform.system() == "Windows":
        sep = "\\"

    if in_dir.endswith(sep):
        in_dir = in_dir[:-1]

    full_widget = widgets.HBox()
    left_widget = widgets.VBox()

    right_widget = widgets.VBox()

    import_btn = widgets.Button(
        description="import",
        button_style="primary",
        tooltip="import the content to a new cell",
        disabled=True,
    )
    import_btn.layout.width = "70px"
    path_widget = widgets.Text()
    path_widget.layout.min_width = "400px"
    # path_widget.layout.max_width = '400px'
    save_widget = widgets.Button(
        description="Save",
        button_style="primary",
        tooltip="Save edits to file.",
        disabled=True,
    )
    info_widget = widgets.HBox()
    info_widget.children = [path_widget, save_widget]
    if use_import:
        info_widget.children = [import_btn, path_widget, save_widget]

    text_widget = widgets.Textarea()
    text_widget.layout.width = "630px"
    text_widget.layout.height = "600px"

    right_widget.children = [info_widget, text_widget]
    full_widget.children = [left_widget]

    if search_description is None:
        search_description = "Search files/folders..."
    search_box = widgets.Text(placeholder=search_description)
    search_box.layout.width = "310px"
    tree_widget = widgets.Output()
    tree_widget.layout.max_width = "310px"
    tree_widget.overflow = "auto"

    left_widget.children = [search_box, tree_widget]

    tree = Tree(multiple_selection=False)
    tree_dict = {}

    def on_button_clicked(b):
        content = text_widget.value
        out_file = path_widget.value

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

        with open(out_file, "w") as f:
            f.write(content)

        text_widget.disabled = True
        text_widget.value = "The content has been saved successfully."
        save_widget.disabled = True
        path_widget.disabled = True

        if (out_file not in tree_dict.keys()) and (out_dir in tree_dict.keys()):
            node = Node(os.path.basename(out_file))
            tree_dict[out_file] = node
            parent_node = tree_dict[out_dir]
            parent_node.add_node(node)

    save_widget.on_click(on_button_clicked)

    def import_btn_clicked(b):
        if (text_widget.value != "") and (path_widget.value.endswith(".py")):
            create_code_cell(text_widget.value)

    import_btn.on_click(import_btn_clicked)

    def search_box_callback(text):

        with tree_widget:
            if text.value == "":
                print("Loading...")
                tree_widget.clear_output(wait=True)
                display(tree)
            else:
                tree_widget.clear_output()
                print("Searching...")
                tree_widget.clear_output(wait=True)
                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.clear_output()
        display(tree)

    if return_sep_widgets:
        return left_widget, right_widget, tree_dict
    else:
        return full_widget

`filter_HUC08(region)` ¶

Filters HUC08 watersheds intersecting a given region.

Parameters:

Name	Type	Description	Default
`region`	`object`	ee.Geometry	required

Returns:

Type	Description
`object`	ee.FeatureCollection

Source code in geemap/common.py

def filter_HUC08(region):
    """Filters HUC08 watersheds intersecting a given region.

    Args:
        region (object): ee.Geometry

    Returns:
        object: ee.FeatureCollection
    """

    USGS_HUC08 = ee.FeatureCollection("USGS/WBD/2017/HUC08")  # Subbasins
    HUC08 = USGS_HUC08.filterBounds(region)
    return HUC08

`filter_HUC10(region)` ¶

Filters HUC10 watersheds intersecting a given region.

Parameters:

Name	Type	Description	Default
`region`	`object`	ee.Geometry	required

Returns:

Type	Description
`object`	ee.FeatureCollection

Source code in geemap/common.py

def filter_HUC10(region):
    """Filters HUC10 watersheds intersecting a given region.

    Args:
        region (object): ee.Geometry

    Returns:
        object: ee.FeatureCollection
    """

    USGS_HUC10 = ee.FeatureCollection("USGS/WBD/2017/HUC10")  # Watersheds
    HUC10 = USGS_HUC10.filterBounds(region)
    return HUC10

`filter_NWI(HUC08_Id, region, exclude_riverine=True)` ¶

Retrieves NWI dataset for a given HUC8 watershed.

Parameters:

Name	Type	Description	Default
`HUC08_Id`	`str`	The HUC8 watershed id.	required
`region`	`object`	ee.Geometry	required
`exclude_riverine`	`bool`	Whether to exclude riverine wetlands. Defaults to True.	`True`

Returns:

Type	Description
`object`	ee.FeatureCollection

Source code in geemap/common.py

def filter_NWI(HUC08_Id, region, exclude_riverine=True):
    """Retrieves NWI dataset for a given HUC8 watershed.

    Args:
        HUC08_Id (str): The HUC8 watershed id.
        region (object): ee.Geometry
        exclude_riverine (bool, optional): Whether to exclude riverine wetlands. Defaults to True.

    Returns:
        object: ee.FeatureCollection
    """
    nwi_asset_prefix = "users/wqs/NWI-HU8/HU8_"
    nwi_asset_suffix = "_Wetlands"
    nwi_asset_path = nwi_asset_prefix + HUC08_Id + nwi_asset_suffix
    nwi_huc = ee.FeatureCollection(nwi_asset_path).filterBounds(region)

    if exclude_riverine:
        nwi_huc = nwi_huc.filter(
            ee.Filter.notEquals(**{"leftField": "WETLAND_TY", "rightValue": "Riverine"})
        )
    return nwi_huc

`filter_polygons(ftr)` ¶

Converts GeometryCollection to Polygon/MultiPolygon

Parameters:

Name	Type	Description	Default
`ftr`	`object`	ee.Feature	required

Returns:

Type	Description
`object`	ee.Feature

Source code in geemap/common.py

def filter_polygons(ftr):
    """Converts GeometryCollection to Polygon/MultiPolygon

    Args:
        ftr (object): ee.Feature

    Returns:
        object: ee.Feature
    """
    # ee_initialize()
    geometries = ftr.geometry().geometries()
    geometries = geometries.map(
        lambda geo: ee.Feature(ee.Geometry(geo)).set("geoType", ee.Geometry(geo).type())
    )

    polygons = (
        ee.FeatureCollection(geometries)
        .filter(ee.Filter.eq("geoType", "Polygon"))
        .geometry()
    )
    return ee.Feature(polygons).copyProperties(ftr)

`find_HUC08(HUC08_Id)` ¶

Finds a HUC08 watershed based on a given HUC08 ID

Parameters:

Name	Type	Description	Default
`HUC08_Id`	`str`	The HUC08 ID.	required

Returns:

Type	Description
`object`	ee.FeatureCollection

Source code in geemap/common.py

def find_HUC08(HUC08_Id):
    """Finds a HUC08 watershed based on a given HUC08 ID

    Args:
        HUC08_Id (str): The HUC08 ID.

    Returns:
        object: ee.FeatureCollection
    """

    USGS_HUC08 = ee.FeatureCollection("USGS/WBD/2017/HUC08")  # Subbasins
    HUC08 = USGS_HUC08.filter(ee.Filter.eq("huc8", HUC08_Id))
    return HUC08

`find_HUC10(HUC10_Id)` ¶

Finds a HUC10 watershed based on a given HUC08 ID

Parameters:

Name	Type	Description	Default
`HUC10_Id`	`str`	The HUC10 ID.	required

Returns:

Type	Description
`object`	ee.FeatureCollection

Source code in geemap/common.py

def find_HUC10(HUC10_Id):
    """Finds a HUC10 watershed based on a given HUC08 ID

    Args:
        HUC10_Id (str): The HUC10 ID.

    Returns:
        object: ee.FeatureCollection
    """

    USGS_HUC10 = ee.FeatureCollection("USGS/WBD/2017/HUC10")  # Watersheds
    HUC10 = USGS_HUC10.filter(ee.Filter.eq("huc10", HUC10_Id))
    return HUC10

`find_landsat_by_path_row(landsat_col, path_num, row_num)` ¶

Finds Landsat images by WRS path number and row number.

Parameters:

Name	Type	Description	Default
`landsat_col`	`str`	The image collection id of Landsat.	required
`path_num`	`int`	The WRS path number.	required
`row_num`	`int`	the WRS row number.	required

Returns:

Type	Description
`object`	ee.ImageCollection

Source code in geemap/common.py

def find_landsat_by_path_row(landsat_col, path_num, row_num):
    """Finds Landsat images by WRS path number and row number.

    Args:
        landsat_col (str): The image collection id of Landsat.
        path_num (int): The WRS path number.
        row_num (int): the WRS row number.

    Returns:
        object: ee.ImageCollection
    """
    try:
        if isinstance(landsat_col, str):
            landsat_col = ee.ImageCollection(landsat_col)
            collection = landsat_col.filter(ee.Filter.eq("WRS_PATH", path_num)).filter(
                ee.Filter.eq("WRS_ROW", row_num)
            )
            return collection
    except Exception as e:
        print(e)

`find_NAIP(region, add_NDVI=True, add_NDWI=True)` ¶

Create annual NAIP mosaic for a given region.

Parameters:

Name	Type	Description	Default
`region`	`object`	ee.Geometry	required
`add_NDVI`	`bool`	Whether to add the NDVI band. Defaults to True.	`True`
`add_NDWI`	`bool`	Whether to add the NDWI band. Defaults to True.	`True`

Returns:

Type	Description
`object`	ee.ImageCollection

Source code in geemap/common.py

def find_NAIP(region, add_NDVI=True, add_NDWI=True):
    """Create annual NAIP mosaic for a given region.

    Args:
        region (object): ee.Geometry
        add_NDVI (bool, optional): Whether to add the NDVI band. Defaults to True.
        add_NDWI (bool, optional): Whether to add the NDWI band. Defaults to True.

    Returns:
        object: ee.ImageCollection
    """

    init_collection = (
        ee.ImageCollection("USDA/NAIP/DOQQ")
        .filterBounds(region)
        .filterDate("2009-01-01", "2019-12-31")
        .filter(ee.Filter.listContains("system:band_names", "N"))
    )

    yearList = ee.List(
        init_collection.distinct(["system:time_start"]).aggregate_array(
            "system:time_start"
        )
    )
    init_years = yearList.map(lambda y: ee.Date(y).get("year"))

    # remove duplicates
    init_years = ee.Dictionary(
        init_years.reduce(ee.Reducer.frequencyHistogram())
    ).keys()
    years = init_years.map(lambda x: ee.Number.parse(x))
    # years = init_years.map(lambda x: x)

    # Available NAIP years with NIR band
    def NAIPAnnual(year):
        start_date = ee.Date.fromYMD(year, 1, 1)
        end_date = ee.Date.fromYMD(year, 12, 31)
        collection = init_collection.filterDate(start_date, end_date)
        # .filterBounds(geometry)
        # .filter(ee.Filter.listContains("system:band_names", "N"))
        time_start = ee.Date(
            ee.List(collection.aggregate_array("system:time_start")).sort().get(0)
        ).format("YYYY-MM-dd")
        time_end = ee.Date(
            ee.List(collection.aggregate_array("system:time_end")).sort().get(-1)
        ).format("YYYY-MM-dd")
        col_size = collection.size()
        image = ee.Image(collection.mosaic().clip(region))

        if add_NDVI:
            NDVI = (
                ee.Image(image)
                .normalizedDifference(["N", "R"])
                .select(["nd"], ["ndvi"])
            )
            image = image.addBands(NDVI)

        if add_NDWI:
            NDWI = (
                ee.Image(image)
                .normalizedDifference(["G", "N"])
                .select(["nd"], ["ndwi"])
            )
            image = image.addBands(NDWI)

        return image.set(
            {
                "system:time_start": time_start,
                "system:time_end": time_end,
                "tiles": col_size,
            }
        )

    # remove years with incomplete coverage
    naip = ee.ImageCollection(years.map(NAIPAnnual))
    mean_size = ee.Number(naip.aggregate_mean("tiles"))
    total_sd = ee.Number(naip.aggregate_total_sd("tiles"))
    threshold = mean_size.subtract(total_sd.multiply(1))
    naip = naip.filter(
        ee.Filter.Or(ee.Filter.gte("tiles", threshold), ee.Filter.gte("tiles", 15))
    )
    naip = naip.filter(ee.Filter.gte("tiles", 7))

    naip_count = naip.size()
    naip_seq = ee.List.sequence(0, naip_count.subtract(1))

    def set_index(index):
        img = ee.Image(naip.toList(naip_count).get(index))
        return img.set({"system:uid": ee.Number(index).toUint8()})

    naip = naip_seq.map(set_index)

    return ee.ImageCollection(naip)

`find_NWI(HUC08_Id, exclude_riverine=True)` ¶

Finds NWI dataset for a given HUC08 watershed.

Parameters:

Name	Type	Description	Default
`HUC08_Id`	`str`	The HUC08 watershed ID.	required
`exclude_riverine`	`bool`	Whether to exclude riverine wetlands. Defaults to True.	`True`

Returns:

Type	Description
`object`	ee.FeatureCollection

Source code in geemap/common.py

def find_NWI(HUC08_Id, exclude_riverine=True):
    """Finds NWI dataset for a given HUC08 watershed.

    Args:
        HUC08_Id (str): The HUC08 watershed ID.
        exclude_riverine (bool, optional): Whether to exclude riverine wetlands. Defaults to True.

    Returns:
        object: ee.FeatureCollection
    """

    nwi_asset_prefix = "users/wqs/NWI-HU8/HU8_"
    nwi_asset_suffix = "_Wetlands"
    nwi_asset_path = nwi_asset_prefix + HUC08_Id + nwi_asset_suffix
    nwi_huc = ee.FeatureCollection(nwi_asset_path)
    if exclude_riverine:
        nwi_huc = nwi_huc.filter(
            ee.Filter.notEquals(**{"leftField": "WETLAND_TY", "rightValue": "Riverine"})
        )
    return nwi_huc

`geocode(location, max_rows=10, reverse=False)` ¶

Search location by address and lat/lon coordinates.

Parameters:

Name	Type	Description	Default
`location`	`str`	Place name or address	required
`max_rows`	`int`	Maximum number of records to return. Defaults to 10.	`10`
`reverse`	`bool`	Search place based on coordinates. Defaults to False.	`False`

Returns:

Type	Description
`list`	Returns a list of locations.

Source code in geemap/common.py

def geocode(location, max_rows=10, reverse=False):
    """Search location by address and lat/lon coordinates.

    Args:
        location (str): Place name or address
        max_rows (int, optional): Maximum number of records to return. Defaults to 10.
        reverse (bool, optional): Search place based on coordinates. Defaults to False.

    Returns:
        list: Returns a list of locations.
    """
    import geocoder

    if not isinstance(location, str):
        print("The location must be a string.")
        return None

    if not reverse:

        locations = []
        addresses = set()
        g = geocoder.arcgis(location, maxRows=max_rows)

        for result in g:
            address = result.address
            if address not in addresses:
                addresses.add(address)
                locations.append(result)

        if len(locations) > 0:
            return locations
        else:
            return None

    else:
        try:
            if "," in location:
                latlon = [float(x) for x in location.split(",")]
            elif " " in location:
                latlon = [float(x) for x in location.split(" ")]
            else:
                print(
                    "The lat-lon coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
                )
                return
            g = geocoder.arcgis(latlon, method="reverse")
            locations = []
            addresses = set()

            for result in g:
                address = result.address
                if address not in addresses:
                    addresses.add(address)
                    locations.append(result)

            if len(locations) > 0:
                return locations
            else:
                return None

        except Exception as e:
            print(e)
            return None

`geojson_to_ee(geo_json, geodesic=True)` ¶

Converts a geojson to ee.Geometry()

Parameters:

Name	Type	Description	Default
`geo_json`	`dict`	A geojson geometry dictionary or file path.	required
`geodesic`	`bool`	Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.	`True`

Returns:

Type	Description
`ee_object`	An ee.Geometry object

Source code in geemap/common.py

def geojson_to_ee(geo_json, geodesic=True):
    """Converts a geojson to ee.Geometry()

    Args:
        geo_json (dict): A geojson geometry dictionary or file path.
        geodesic (bool, optional): Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.

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

    try:

        # import json

        if not isinstance(geo_json, dict) and os.path.isfile(geo_json):
            with open(os.path.abspath(geo_json), encoding="utf-8") as f:
                geo_json = json.load(f)

        if geo_json["type"] == "FeatureCollection":
            features = ee.FeatureCollection(geo_json["features"])
            return features
        elif geo_json["type"] == "Feature":
            geom = None
            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"
            ):  # Checks whether it is a point
                coordinates = geo_json["geometry"]["coordinates"]
                longitude = coordinates[0]
                latitude = coordinates[1]
                geom = ee.Geometry.Point(longitude, latitude)
            else:
                geom = ee.Geometry(geo_json["geometry"], "", geodesic)
            return geom
        else:
            raise Exception("Could not convert the geojson to ee.Geometry()")

    except Exception as e:
        print("Could not convert the geojson to ee.Geometry()")
        raise Exception(e)

`geometry_type(ee_object)` ¶

Get geometry type of an Earth Engine object.

Parameters:

Name	Type	Description	Default
`ee_object`	`object`	An Earth Engine object.	required

Returns:

Type	Description
`str`	Returns geometry type. One of Point, MultiPoint, LineString, LinearRing, MultiLineString, BBox, Rectangle, Polygon, MultiPolygon.

Source code in geemap/common.py

def geometry_type(ee_object):
    """Get geometry type of an Earth Engine object.

    Args:
        ee_object (object): An Earth Engine object.

    Returns:
        str: Returns geometry type. One of Point, MultiPoint, LineString, LinearRing, MultiLineString, BBox, Rectangle, Polygon, MultiPolygon.
    """
    if isinstance(ee_object, ee.Geometry):
        return ee_object.type().getInfo()
    elif isinstance(ee_object, ee.Feature):
        return ee_object.geometry().type().getInfo()
    elif isinstance(ee_object, ee.FeatureCollection):
        return ee.Feature(ee_object.first()).geometry().type().getInfo()
    else:
        raise TypeError(
            "The ee_object must be one of ee.Geometry, ee.Feature, ee.FeatureCollection."
        )

`geopandas_to_ee(gdf, geodesic=True)` ¶

Converts a GeoPandas GeoDataFrame to ee.FeatureCollection.

Parameters:

Name	Type	Description	Default
`gdf`	`geopandas.GeoDataFrame`	The input geopandas.GeoDataFrame to be converted ee.FeatureCollection.	required
`geodesic`	`bool`	Whether line segments should be interpreted as spherical geodesics. If false, indicates that line segments should be interpreted as planar lines in the specified CRS. If absent, defaults to true if the CRS is geographic (including the default EPSG:4326), or to false if the CRS is projected.. Defaults to True.	`True`

Exceptions:

Type	Description
`TypeError`	The input data type must be geopandas.GeoDataFrame.

Returns:

Type	Description
`ee.FeatureCollection`	The output ee.FeatureCollection converted from the input geopandas.GeoDataFrame.

Source code in geemap/common.py

def geopandas_to_ee(gdf, geodesic=True):
    """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.

    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.to_file(out_json, driver="GeoJSON")

    fc = geojson_to_ee(out_json, geodesic=geodesic)
    os.remove(out_json)

    return fc

`get_all_NAIP(start_year=2009, end_year=2019)` ¶

Creates annual NAIP imagery mosaic.

Parameters:

Name	Type	Description	Default
`start_year`	`int`	The starting year. Defaults to 2009.	`2009`
`end_year`	`int`	The ending year. Defaults to 2019.	`2019`

Returns:

Type	Description
`object`	ee.ImageCollection

Source code in geemap/common.py

def get_all_NAIP(start_year=2009, end_year=2019):
    """Creates annual NAIP imagery mosaic.

    Args:
        start_year (int, optional): The starting year. Defaults to 2009.
        end_year (int, optional): The ending year. Defaults to 2019.

    Returns:
        object: ee.ImageCollection
    """
    try:

        def get_annual_NAIP(year):
            try:
                collection = ee.ImageCollection("USDA/NAIP/DOQQ")
                start_date = ee.Date.fromYMD(year, 1, 1)
                end_date = ee.Date.fromYMD(year, 12, 31)
                naip = collection.filterDate(start_date, end_date).filter(
                    ee.Filter.listContains("system:band_names", "N")
                )
                return ee.ImageCollection(naip)
            except Exception as e:
                print(e)

        years = ee.List.sequence(start_year, end_year)
        collection = years.map(get_annual_NAIP)
        return collection

    except Exception as e:
        print(e)

`get_annual_NAIP(year, RGBN=True)` ¶

Filters NAIP ImageCollection by year.

Parameters:

Name	Type	Description	Default
`year`	`int`	The year to filter the NAIP ImageCollection.	required
`RGBN`	`bool`	Whether to retrieve 4-band NAIP imagery only. Defaults to True.	`True`

Returns:

Type	Description
`object`	ee.ImageCollection

Source code in geemap/common.py

def get_annual_NAIP(year, RGBN=True):
    """Filters NAIP ImageCollection by year.

    Args:
        year (int): The year to filter the NAIP ImageCollection.
        RGBN (bool, optional): Whether to retrieve 4-band NAIP imagery only. Defaults to True.

    Returns:
        object: ee.ImageCollection
    """
    try:
        collection = ee.ImageCollection("USDA/NAIP/DOQQ")
        start_date = str(year) + "-01-01"
        end_date = str(year) + "-12-31"
        naip = collection.filterDate(start_date, end_date)
        if RGBN:
            naip = naip.filter(ee.Filter.listContains("system:band_names", "N"))
        return naip
    except Exception as e:
        print(e)

`get_api_key(token_name, m=None)` ¶

Retrieves an API key based on a system environmen variable.

Parameters:

Name	Type	Description	Default
`token_name`	`str`	The token name.	required
`m`	`ipyleaflet.Map \| folium.Map`	A Map instance. Defaults to None.	`None`

Returns:

Type	Description
`str`	The API key.

Source code in geemap/common.py

def get_api_key(token_name, m=None):
    """Retrieves an API key based on a system environmen variable.

    Args:
        token_name (str): The token name.
        m (ipyleaflet.Map | folium.Map, optional): A Map instance. Defaults to None.

    Returns:
        str: The API key.
    """
    api_key = os.environ.get(token_name)
    if m is not None and token_name in m.api_keys:
        api_key = m.api_keys[token_name]

    return api_key

`get_bounds(geometry, north_up=True, transform=None)` ¶

Bounding box of a GeoJSON geometry, GeometryCollection, or FeatureCollection. left, bottom, right, top not xmin, ymin, xmax, ymax If not north_up, y will be switched to guarantee the above. Source code adapted from https://github.com/mapbox/rasterio/blob/master/rasterio/features.py#L361

Parameters:

Name	Type	Description	Default
`geometry`	`dict`	A GeoJSON dict.	required
`north_up`	`bool`	. Defaults to True.	`True`
`transform`	`[type]`	. Defaults to None.	`None`

Returns:

Type	Description
`list`	A list of coordinates representing [left, bottom, right, top]

Source code in geemap/common.py

def get_bounds(geometry, north_up=True, transform=None):
    """Bounding box of a GeoJSON geometry, GeometryCollection, or FeatureCollection.
    left, bottom, right, top
    *not* xmin, ymin, xmax, ymax
    If not north_up, y will be switched to guarantee the above.
    Source code adapted from https://github.com/mapbox/rasterio/blob/master/rasterio/features.py#L361

    Args:
        geometry (dict): A GeoJSON dict.
        north_up (bool, optional): . Defaults to True.
        transform ([type], optional): . Defaults to None.

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

    if "bbox" in geometry:
        return tuple(geometry["bbox"])

    geometry = geometry.get("geometry") or geometry

    # geometry must be a geometry, GeometryCollection, or FeatureCollection
    if not (
        "coordinates" in geometry or "geometries" in geometry or "features" in geometry
    ):
        raise ValueError(
            "geometry must be a GeoJSON-like geometry, GeometryCollection, "
            "or FeatureCollection"
        )

    if "features" in geometry:
        # Input is a FeatureCollection
        xmins = []
        ymins = []
        xmaxs = []
        ymaxs = []
        for feature in geometry["features"]:
            xmin, ymin, xmax, ymax = get_bounds(feature["geometry"])
            xmins.append(xmin)
            ymins.append(ymin)
            xmaxs.append(xmax)
            ymaxs.append(ymax)
        if north_up:
            return min(xmins), min(ymins), max(xmaxs), max(ymaxs)
        else:
            return min(xmins), max(ymaxs), max(xmaxs), min(ymins)

    elif "geometries" in geometry:
        # Input is a geometry collection
        xmins = []
        ymins = []
        xmaxs = []
        ymaxs = []
        for geometry in geometry["geometries"]:
            xmin, ymin, xmax, ymax = get_bounds(geometry)
            xmins.append(xmin)
            ymins.append(ymin)
            xmaxs.append(xmax)
            ymaxs.append(ymax)
        if north_up:
            return min(xmins), min(ymins), max(xmaxs), max(ymaxs)
        else:
            return min(xmins), max(ymaxs), max(xmaxs), min(ymins)

    elif "coordinates" in geometry:
        # Input is a singular geometry object
        if transform is not None:
            xyz = list(explode(geometry["coordinates"]))
            xyz_px = [transform * point for point in xyz]
            xyz = tuple(zip(*xyz_px))
            return min(xyz[0]), max(xyz[1]), max(xyz[0]), min(xyz[1])
        else:
            xyz = tuple(zip(*list(explode(geometry["coordinates"]))))
            if north_up:
                return min(xyz[0]), min(xyz[1]), max(xyz[0]), max(xyz[1])
            else:
                return min(xyz[0]), max(xyz[1]), max(xyz[0]), min(xyz[1])

    # all valid inputs returned above, so whatever falls through is an error
    raise ValueError(
        "geometry must be a GeoJSON-like geometry, GeometryCollection, "
        "or FeatureCollection"
    )

`get_center(geometry, north_up=True, transform=None)` ¶

Get the centroid of a GeoJSON.

Parameters:

Name	Type	Description	Default
`geometry`	`dict`	A GeoJSON dict.	required
`north_up`	`bool`	. Defaults to True.	`True`
`transform`	`[type]`	. Defaults to None.	`None`

Returns:

Type	Description
`list`	[lon, lat]

Source code in geemap/common.py

def get_center(geometry, north_up=True, transform=None):
    """Get the centroid of a GeoJSON.

    Args:
        geometry (dict): A GeoJSON dict.
        north_up (bool, optional): . Defaults to True.
        transform ([type], optional): . Defaults to None.

    Returns:
        list: [lon, lat]
    """
    bounds = get_bounds(geometry, north_up, transform)
    center = ((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2)  # (lat, lon)
    return center

`get_cog_bands(url, titiler_endpoint='https://api.cogeo.xyz/')` ¶

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://api.cogeo.xyz/".	`'https://api.cogeo.xyz/'`

Returns:

Type	Description
`list`	A list of band names

Source code in geemap/common.py

def get_cog_bands(url, titiler_endpoint="https://api.cogeo.xyz/"):
    """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://api.cogeo.xyz/".

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

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

    bands = [b[1] for b in r["band_descriptions"]]
    return bands

`get_cog_bounds(url, titiler_endpoint='https://api.cogeo.xyz/')` ¶

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://api.cogeo.xyz/".	`'https://api.cogeo.xyz/'`

Returns:

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

Source code in geemap/common.py

def get_cog_bounds(url, titiler_endpoint="https://api.cogeo.xyz/"):
    """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://api.cogeo.xyz/".

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

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

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

`get_cog_center(url, titiler_endpoint='https://api.cogeo.xyz/')` ¶

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://api.cogeo.xyz/".	`'https://api.cogeo.xyz/'`

Returns:

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

Source code in geemap/common.py

def get_cog_center(url, titiler_endpoint="https://api.cogeo.xyz/"):
    """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://api.cogeo.xyz/".

    Returns:
        tuple: A tuple representing (longitude, latitude)
    """
    bounds = get_cog_bounds(url, titiler_endpoint)
    center = ((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2)  # (lat, lon)
    return center

`get_cog_tile(url, titiler_endpoint='https://api.cogeo.xyz/', **kwargs)` ¶

Get a tile layer from a Cloud Optimized GeoTIFF (COG). Source code adapted from https://developmentseed.org/titiler/examples/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://api.cogeo.xyz/".	`'https://api.cogeo.xyz/'`

Returns:

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

Source code in geemap/common.py

def get_cog_tile(url, titiler_endpoint="https://api.cogeo.xyz/", **kwargs):
    """Get a tile layer from a Cloud Optimized GeoTIFF (COG).
        Source code adapted from https://developmentseed.org/titiler/examples/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://api.cogeo.xyz/".

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

    params = {"url": url}

    TileMatrixSetId = "WebMercatorQuad"
    if "TileMatrixSetId" in kwargs.keys():
        TileMatrixSetId = kwargs["TileMatrixSetId"]
    if "tile_format" in kwargs.keys():
        params["tile_format"] = kwargs["tile_format"]
    if "tile_scale" in kwargs.keys():
        params["tile_scale"] = kwargs["tile_scale"]
    if "minzoom" in kwargs.keys():
        params["minzoom"] = kwargs["minzoom"]
    if "maxzoom" in kwargs.keys():
        params["maxzoom"] = kwargs["maxzoom"]

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

    return r["tiles"][0]

`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_image_collection_thumbnails(ee_object, out_dir, vis_params, dimensions=500, region=None, format='png', names=None, verbose=True)` ¶

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 'png'.	`'png'`
`names`	`list`	The list of output file names. Defaults to None.	`None`
`verbose`	`bool`	Whether or not to print hints. Defaults to True.	`True`

Source code in geemap/common.py

def get_image_collection_thumbnails(
    ee_object,
    out_dir,
    vis_params,
    dimensions=500,
    region=None,
    format="png",
    names=None,
    verbose=True,
):
    """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 'png'.
        names (list, optional): The list of output file names. Defaults to None.
        verbose (bool, optional): Whether or not to print hints. Defaults to True.
    """
    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)[0][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)

    except Exception as e:
        print(e)

`get_image_thumbnail(ee_object, out_img, vis_params, dimensions=500, region=None, format='png')` ¶

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 'png'.	`'png'`

Source code in geemap/common.py

def get_image_thumbnail(
    ee_object, out_img, vis_params, dimensions=500, region=None, format="png"
):
    """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 'png'.
    """
    import requests

    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
    url = ee_object.getThumbURL(vis_params)

    r = requests.get(url, stream=True)
    if r.status_code != 200:
        print("An error occurred while downloading.")
    else:
        with open(out_img, "wb") as fd:
            for chunk in r.iter_content(chunk_size=1024):
                fd.write(chunk)

`get_stac_bands(url, titiler_endpoint='https://api.cogeo.xyz/')` ¶

Get band names of a single SpatialTemporal Asset Catalog (STAC) item.

Parameters:

Name	Type	Description	Default
`url`	`str`	HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json	required
`titiler_endpoint`	`str`	Titiler endpoint. Defaults to "https://api.cogeo.xyz/".	`'https://api.cogeo.xyz/'`

Returns:

Type	Description
`list`	A list of band names

Source code in geemap/common.py

def get_stac_bands(url, titiler_endpoint="https://api.cogeo.xyz/"):
    """Get band names of a single SpatialTemporal Asset Catalog (STAC) item.

    Args:
        url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".

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

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

    return r

`get_stac_bounds(url, titiler_endpoint='https://api.cogeo.xyz/')` ¶

Get the bounding box of a single SpatialTemporal Asset Catalog (STAC) item.

Parameters:

Name	Type	Description	Default
`url`	`str`	HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json	required
`titiler_endpoint`	`str`	Titiler endpoint. Defaults to "https://api.cogeo.xyz/".	`'https://api.cogeo.xyz/'`

Returns:

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

Source code in geemap/common.py

def get_stac_bounds(url, titiler_endpoint="https://api.cogeo.xyz/"):
    """Get the bounding box of a single SpatialTemporal Asset Catalog (STAC) item.

    Args:
        url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".

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

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

    bounds = r["bounds"]
    return bounds

`get_stac_center(url, titiler_endpoint='https://api.cogeo.xyz/')` ¶

Get the centroid of a single SpatialTemporal Asset Catalog (STAC) item.

Parameters:

Name	Type	Description	Default
`url`	`str`	HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json	required
`titiler_endpoint`	`str`	Titiler endpoint. Defaults to "https://api.cogeo.xyz/".	`'https://api.cogeo.xyz/'`

Returns:

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

Source code in geemap/common.py

def get_stac_center(url, titiler_endpoint="https://api.cogeo.xyz/"):
    """Get the centroid of a single SpatialTemporal Asset Catalog (STAC) item.

    Args:
        url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".

    Returns:
        tuple: A tuple representing (longitude, latitude)
    """
    bounds = get_stac_bounds(url, titiler_endpoint)
    center = ((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2)  # (lat, lon)
    return center

`get_stac_tile(url, bands=None, titiler_endpoint='https://api.cogeo.xyz/', **kwargs)` ¶

Get a tile layer from a single SpatialTemporal Asset Catalog (STAC) item.

Parameters:

Name	Type	Description	Default
`url`	`str`	HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json	required
`titiler_endpoint`	`str`	Titiler endpoint. Defaults to "https://api.cogeo.xyz/".	`'https://api.cogeo.xyz/'`

Returns:

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

Source code in geemap/common.py

def get_stac_tile(url, bands=None, titiler_endpoint="https://api.cogeo.xyz/", **kwargs):
    """Get a tile layer from a single SpatialTemporal Asset Catalog (STAC) item.

    Args:
        url (str): HTTP URL to a STAC item, e.g., https://canada-spot-ortho.s3.amazonaws.com/canada_spot_orthoimages/canada_spot5_orthoimages/S5_2007/S5_11055_6057_20070622/S5_11055_6057_20070622.json
        titiler_endpoint (str, optional): Titiler endpoint. Defaults to "https://api.cogeo.xyz/".

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

    params = {"url": url}

    TileMatrixSetId = "WebMercatorQuad"
    if "TileMatrixSetId" in kwargs.keys():
        TileMatrixSetId = kwargs["TileMatrixSetId"]
    if "expression" in kwargs.keys():
        params["expression"] = kwargs["expression"]
    if "tile_format" in kwargs.keys():
        params["tile_format"] = kwargs["tile_format"]
    if "tile_scale" in kwargs.keys():
        params["tile_scale"] = kwargs["tile_scale"]
    if "minzoom" in kwargs.keys():
        params["minzoom"] = kwargs["minzoom"]
    if "maxzoom" in kwargs.keys():
        params["maxzoom"] = kwargs["maxzoom"]

    allowed_bands = get_stac_bands(url, titiler_endpoint)

    if bands is None:
        bands = [allowed_bands[0]]
    elif 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)
            )
        )

    assets = ",".join(bands)
    params["assets"] = assets

    r = requests.get(
        f"{titiler_endpoint}/stac/{TileMatrixSetId}/tilejson.json", params=params
    ).json()

    return r["tiles"][0]

`has_transparency(img)` ¶

Checks whether an image has transparency.

Parameters:

Name	Type	Description	Default
`img`	`object`	a PIL Image object.	required

Returns:

Type	Description
`bool`	True if it has transparency, False otherwise.

Source code in geemap/common.py

def has_transparency(img):
    """Checks whether an image has transparency.

    Args:
        img (object):  a PIL Image object.

    Returns:
        bool: True if it has transparency, False otherwise.
    """

    if img.mode == "P":
        transparent = img.info.get("transparency", -1)
        for _, index in img.getcolors():
            if index == transparent:
                return True
    elif img.mode == "RGBA":
        extrema = img.getextrema()
        if extrema[3][0] < 255:
            return True

    return False

`hex_to_rgb(value='FFFFFF')` ¶

Converts hex color to RGB color.

Parameters:

Name	Type	Description	Default
`value`	`str`	Hex color code as a string. Defaults to 'FFFFFF'.	`'FFFFFF'`

Returns:

Type	Description
`tuple`	RGB color as a tuple.

Source code in geemap/common.py

def hex_to_rgb(value="FFFFFF"):
    """Converts hex color to RGB color.

    Args:
        value (str, optional): Hex color code as a string. Defaults to 'FFFFFF'.

    Returns:
        tuple: RGB color as a tuple.
    """
    value = value.lstrip("#")
    lv = len(value)
    return tuple(int(value[i : i + lv // 3], 16) for i in range(0, lv, lv // 3))

`image_area(img, region=None, scale=None, denominator=1.0)` ¶

Calculates the the area of an image.

Parameters:

Name	Type	Description	Default
`img`	`object`	ee.Image	required
`region`	`object`	The region over which to reduce data. Defaults to the footprint of the image's first band.	`None`
`scale`	`float`	A nominal scale in meters of the projection to work in. Defaults to None.	`None`
`denominator`	`float`	The denominator to use for converting size from square meters to other units. Defaults to 1.0.	`1.0`

Returns:

Type	Description
`object`	ee.Dictionary

Source code in geemap/common.py

def image_area(img, region=None, scale=None, denominator=1.0):
    """Calculates the 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_band_names(img)` ¶

Gets image band names.

Parameters:

Name	Type	Description	Default
`img`	`ee.Image`	The input image.	required

Returns:

Type	Description
`ee.List`	The returned list of image band names.

Source code in geemap/common.py

def image_band_names(img):
    """Gets image band names.

    Args:
        img (ee.Image): The input image.

    Returns:
        ee.List: The returned list of image band names.
    """
    return img.bandNames()

`image_cell_size(img)` ¶

Retrieves the image cell size (e.g., spatial resolution)

Parameters:

Name	Type	Description	Default
`img`	`object`	ee.Image	required

Returns:

Type	Description
`float`	The nominal scale in meters.

Source code in geemap/common.py

def image_cell_size(img):
    """Retrieves the image cell size (e.g., spatial resolution)

    Args:
        img (object): ee.Image

    Returns:
        float: The nominal scale in meters.
    """
    bands = img.bandNames()
    scales = bands.map(lambda b: img.select([b]).projection().nominalScale())
    scale = ee.Algorithms.If(
        scales.distinct().size().gt(1),
        ee.Dictionary.fromLists(bands.getInfo(), scales),
        scales.get(0),
    )
    return scale

`image_date(img, date_format='YYYY-MM-dd')` ¶

Retrieves the image acquisition date.

Parameters:

Name	Type	Description	Default
`img`	`object`	ee.Image	required
`date_format`	`str`	The date format to use. Defaults to 'YYYY-MM-dd'.	`'YYYY-MM-dd'`

Returns:

Type	Description
`str`	A string representing the acquisition of the image.

Source code in geemap/common.py

def image_date(img, date_format="YYYY-MM-dd"):
    """Retrieves the image acquisition date.

    Args:
        img (object): ee.Image
        date_format (str, optional): The date format to use. Defaults to 'YYYY-MM-dd'.

    Returns:
        str: A string representing the acquisition of the image.
    """
    return ee.Date(img.get("system:time_start")).format(date_format)

`image_dates(img_col, date_format='YYYY-MM-dd')` ¶

Get image dates of all images in an ImageCollection.

Parameters:

Name	Type	Description	Default
`img_col`	`object`	ee.ImageCollection	required
`date_format`	`str`	A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html; if omitted will use ISO standard date formatting. Defaults to 'YYYY-MM-dd'.	`'YYYY-MM-dd'`

Returns:

Type	Description
`object`	ee.List

Source code in geemap/common.py

def image_dates(img_col, date_format="YYYY-MM-dd"):
    """Get image dates of all images in an ImageCollection.

    Args:
        img_col (object): ee.ImageCollection
        date_format (str, optional): A pattern, as described at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.html; if omitted will use ISO standard date formatting. Defaults to 'YYYY-MM-dd'.

    Returns:
        object: ee.List
    """
    dates = img_col.aggregate_array("system:time_start")
    new_dates = dates.map(lambda d: ee.Date(d).format(date_format))
    return new_dates

`image_max_value(img, region=None, scale=None)` ¶

Retrieves the maximum value of an image.

Parameters:

Name	Type	Description	Default
`img`	`object`	The image to calculate the maximum value.	required
`region`	`object`	The region over which to reduce data. Defaults to the footprint of the image's first band.	`None`
`scale`	`float`	A nominal scale in meters of the projection to work in. Defaults to None.	`None`

Returns:

Type	Description
`object`	ee.Number

Source code in geemap/common.py

def image_max_value(img, region=None, scale=None):
    """Retrieves the maximum value of an image.

    Args:
        img (object): The image to calculate the maximum value.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.

    Returns:
        object: ee.Number
    """
    if region is None:
        region = img.geometry()

    if scale is None:
        scale = image_scale(img)

    max_value = img.reduceRegion(
        **{
            "reducer": ee.Reducer.max(),
            "geometry": region,
            "scale": scale,
            "maxPixels": 1e12,
            "bestEffort": True,
        }
    )
    return max_value

`image_mean_value(img, region=None, scale=None)` ¶

Retrieves the mean value of an image.

Parameters:

Name	Type	Description	Default
`img`	`object`	The image to calculate the mean value.	required
`region`	`object`	The region over which to reduce data. Defaults to the footprint of the image's first band.	`None`
`scale`	`float`	A nominal scale in meters of the projection to work in. Defaults to None.	`None`

Returns:

Type	Description
`object`	ee.Number

Source code in geemap/common.py

def image_mean_value(img, region=None, scale=None):
    """Retrieves the mean value of an image.

    Args:
        img (object): The image to calculate the mean value.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.

    Returns:
        object: ee.Number
    """
    if region is None:
        region = img.geometry()

    if scale is None:
        scale = image_scale(img)

    mean_value = img.reduceRegion(
        **{
            "reducer": ee.Reducer.mean(),
            "geometry": region,
            "scale": scale,
            "maxPixels": 1e12,
            "bestEffort": True,
        }
    )
    return mean_value

`image_min_value(img, region=None, scale=None)` ¶

Retrieves the minimum value of an image.

Parameters:

Name	Type	Description	Default
`img`	`object`	The image to calculate the minimum value.	required
`region`	`object`	The region over which to reduce data. Defaults to the footprint of the image's first band.	`None`
`scale`	`float`	A nominal scale in meters of the projection to work in. Defaults to None.	`None`

Returns:

Type	Description
`object`	ee.Number

Source code in geemap/common.py

def image_min_value(img, region=None, scale=None):
    """Retrieves the minimum value of an image.

    Args:
        img (object): The image to calculate the minimum value.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.

    Returns:
        object: ee.Number
    """
    if region is None:
        region = img.geometry()

    if scale is None:
        scale = image_scale(img)

    min_value = img.reduceRegion(
        **{
            "reducer": ee.Reducer.min(),
            "geometry": region,
            "scale": scale,
            "maxPixels": 1e12,
            "bestEffort": True,
        }
    )
    return min_value

`image_props(img, date_format='YYYY-MM-dd')` ¶

Gets image properties.

Parameters:

Name	Type	Description	Default
`img`	`ee.Image`	The input image.	required
`date_format`	`str`	The output date format. Defaults to 'YYYY-MM-dd HH:mm:ss'.	`'YYYY-MM-dd'`

Returns:

Type	Description
`dd.Dictionary`	The dictionary containing image properties.

Source code in geemap/common.py

def image_props(img, date_format="YYYY-MM-dd"):
    """Gets image properties.

    Args:
        img (ee.Image): The input image.
        date_format (str, optional): The output date format. Defaults to 'YYYY-MM-dd HH:mm:ss'.

    Returns:
        dd.Dictionary: The dictionary containing image properties.
    """
    if not isinstance(img, ee.Image):
        print("The input object must be an ee.Image")
        return

    keys = img.propertyNames().remove("system:footprint").remove("system:bands")
    values = keys.map(lambda p: img.get(p))

    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),
    )
    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,
    )
    asset_size = (
        ee.Number(img.get("system:asset_size"))
        .divide(1e6)
        .format()
        .cat(ee.String(" MB"))
    )

    props = ee.Dictionary.fromLists(keys, values)
    props = props.set("system:time_start", time_start)
    props = props.set("system:time_end", time_end)
    props = props.set("system:asset_size", asset_size)
    props = props.set("NOMINAL_SCALE", scale)
    props = props.set("IMAGE_DATE", image_date)

    return props

`image_reclassify(img, in_list, out_list)` ¶

Reclassify an image.

Parameters:

Name	Type	Description	Default
`img`	`object`	The image to which the remapping is applied.	required
`in_list`	`list`	The source values (numbers or EEArrays). All values in this list will be mapped to the corresponding value in 'out_list'.	required
`out_list`	`list`	The destination values (numbers or EEArrays). These are used to replace the corresponding values in 'from'. Must have the same number of values as 'in_list'.	required

Returns:

Type	Description
`object`	ee.Image

Source code in geemap/common.py

def image_reclassify(img, in_list, out_list):
    """Reclassify an image.

    Args:
        img (object): The image to which the remapping is applied.
        in_list (list): The source values (numbers or EEArrays). All values in this list will be mapped to the corresponding value in 'out_list'.
        out_list (list): The destination values (numbers or EEArrays). These are used to replace the corresponding values in 'from'. Must have the same number of values as 'in_list'.

    Returns:
        object: ee.Image
    """
    image = img.remap(in_list, out_list)
    return image

`image_scale(img)` ¶

Retrieves the image cell size (e.g., spatial resolution)

Parameters:

Name	Type	Description	Default
`img`	`object`	ee.Image	required

Returns:

Type	Description
`float`	The nominal scale in meters.

Source code in geemap/common.py

def image_scale(img):
    """Retrieves the image cell size (e.g., spatial resolution)

    Args:
        img (object): ee.Image

    Returns:
        float: The nominal scale in meters.
    """
    # bands = img.bandNames()
    # scales = bands.map(lambda b: img.select([b]).projection().nominalScale())
    # scale = ee.Algorithms.If(scales.distinct().size().gt(1), ee.Dictionary.fromLists(bands.getInfo(), scales), scales.get(0))
    return img.select(0).projection().nominalScale()

`image_smoothing(img, reducer, kernel)` ¶

Smooths an image.

Parameters:

Name	Type	Description	Default
`img`	`object`	The image to be smoothed.	required
`reducer`	`object`	ee.Reducer	required
`kernel`	`object`	ee.Kernel	required

Returns:

Type	Description
`object`	ee.Image

Source code in geemap/common.py

def image_smoothing(img, reducer, kernel):
    """Smooths an image.

    Args:
        img (object): The image to be smoothed.
        reducer (object): ee.Reducer
        kernel (object): ee.Kernel

    Returns:
        object: ee.Image
    """
    image = img.reduceNeighborhood(
        **{
            "reducer": reducer,
            "kernel": kernel,
        }
    )
    return image

`image_stats(img, region=None, scale=None)` ¶

Gets image descriptive statistics.

Parameters:

Name	Type	Description	Default
`img`	`ee.Image`	The input image to calculate descriptive statistics.	required
`region`	`object`	The region over which to reduce data. Defaults to the footprint of the image's first band.	`None`
`scale`	`float`	A nominal scale in meters of the projection to work in. Defaults to None.	`None`

Returns:

Type	Description
`ee.Dictionary`	A dictionary containing the description statistics of the input image.

Source code in geemap/common.py

def image_stats(img, region=None, scale=None):
    """Gets image descriptive statistics.

    Args:
        img (ee.Image): The input image to calculate descriptive statistics.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.

    Returns:
        ee.Dictionary: A dictionary containing the description statistics of the input image.
    """

    if not isinstance(img, ee.Image):
        print("The input object must be an ee.Image")
        return

    stat_types = ["min", "max", "mean", "std", "sum"]

    image_min = image_min_value(img, region, scale)
    image_max = image_max_value(img, region, scale)
    image_mean = image_mean_value(img, region, scale)
    image_std = image_std_value(img, region, scale)
    image_sum = image_sum_value(img, region, scale)

    stat_results = ee.List([image_min, image_max, image_mean, image_std, image_sum])

    stats = ee.Dictionary.fromLists(stat_types, stat_results)

    return stats

`image_std_value(img, region=None, scale=None)` ¶

Retrieves the standard deviation of an image.

Parameters:

Name	Type	Description	Default
`img`	`object`	The image to calculate the standard deviation.	required
`region`	`object`	The region over which to reduce data. Defaults to the footprint of the image's first band.	`None`
`scale`	`float`	A nominal scale in meters of the projection to work in. Defaults to None.	`None`

Returns:

Type	Description
`object`	ee.Number

Source code in geemap/common.py

def image_std_value(img, region=None, scale=None):
    """Retrieves the standard deviation of an image.

    Args:
        img (object): The image to calculate the standard deviation.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.

    Returns:
        object: ee.Number
    """
    if region is None:
        region = img.geometry()

    if scale is None:
        scale = image_scale(img)

    std_value = img.reduceRegion(
        **{
            "reducer": ee.Reducer.stdDev(),
            "geometry": region,
            "scale": scale,
            "maxPixels": 1e12,
            "bestEffort": True,
        }
    )
    return std_value

`image_sum_value(img, region=None, scale=None)` ¶

Retrieves the sum of an image.

Parameters:

Name	Type	Description	Default
`img`	`object`	The image to calculate the standard deviation.	required
`region`	`object`	The region over which to reduce data. Defaults to the footprint of the image's first band.	`None`
`scale`	`float`	A nominal scale in meters of the projection to work in. Defaults to None.	`None`

Returns:

Type	Description
`object`	ee.Number

Source code in geemap/common.py

def image_sum_value(img, region=None, scale=None):
    """Retrieves the sum of an image.

    Args:
        img (object): The image to calculate the standard deviation.
        region (object, optional): The region over which to reduce data. Defaults to the footprint of the image's first band.
        scale (float, optional): A nominal scale in meters of the projection to work in. Defaults to None.

    Returns:
        object: ee.Number
    """
    if region is None:
        region = img.geometry()

    if scale is None:
        scale = image_scale(img)

    sum_value = img.reduceRegion(
        **{
            "reducer": ee.Reducer.sum(),
            "geometry": region,
            "scale": scale,
            "maxPixels": 1e12,
            "bestEffort": True,
        }
    )
    return sum_value

`in_colab_shell()` ¶

Tests if the code is being executed within Google Colab.

Source code in geemap/common.py

def in_colab_shell():
    """Tests if the code is being executed within Google Colab."""
    import sys

    if "google.colab" in sys.modules:
        return True
    else:
        return False

`install_from_github(url)` ¶

Install a package from a GitHub repository.

Parameters:

Name	Type	Description	Default
`url`	`str`	The URL of the GitHub repository.	required

Source code in geemap/common.py

def install_from_github(url):
    """Install a package from a GitHub repository.

    Args:
        url (str): The URL of the GitHub repository.
    """

    try:
        download_dir = os.path.join(os.path.expanduser("~"), "Downloads")
        if not os.path.exists(download_dir):
            os.makedirs(download_dir)

        repo_name = os.path.basename(url)
        zip_url = os.path.join(url, "archive/master.zip")
        filename = repo_name + "-master.zip"
        download_from_url(
            url=zip_url, out_file_name=filename, out_dir=download_dir, unzip=True
        )

        pkg_dir = os.path.join(download_dir, repo_name + "-master")
        pkg_name = os.path.basename(url)
        work_dir = os.getcwd()
        os.chdir(pkg_dir)
        print(f"Installing {pkg_name}...")
        cmd = "pip install ."
        os.system(cmd)
        os.chdir(work_dir)
        print(f"{pkg_name} has been installed successfully.")
        # print("\nPlease comment out 'install_from_github()' and restart the kernel to take effect:\nJupyter menu -> Kernel -> Restart & Clear Output")

    except Exception as e:
        print(e)

`is_drive_mounted()` ¶

Checks whether Google Drive is mounted in Google Colab.

Returns:

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

Source code in geemap/common.py

def is_drive_mounted():
    """Checks whether Google Drive is mounted in Google Colab.

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

`is_latlon_valid(location)` ¶

Checks whether a pair of coordinates is valid.

Parameters:

Name	Type	Description	Default
`location`	`str`	A pair of latlon coordinates separated by comma or space.	required

Returns:

Type	Description
`bool`	Returns True if valid.

Source code in geemap/common.py

def is_latlon_valid(location):
    """Checks whether a pair of coordinates is valid.

    Args:
        location (str): A pair of latlon coordinates separated by comma or space.

    Returns:
        bool: Returns True if valid.
    """
    latlon = []
    if "," in location:
        latlon = [float(x) for x in location.split(",")]
    elif " " in location:
        latlon = [float(x) for x in location.split(" ")]
    else:
        print(
            "The coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
        )
        return False

    try:
        lat, lon = float(latlon[0]), float(latlon[1])
        if lat >= -90 and lat <= 90 and lon >= -180 and lon <= 180:
            return True
        else:
            return False
    except Exception as e:
        print(e)
        return False

`is_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

`kml_to_ee(in_kml)` ¶

Converts a KML to ee.FeatureColleciton.

Parameters:

Name	Type	Description	Default
`in_kml`	`str`	The file path to the input KML.	required

Exceptions:

Type	Description
`FileNotFoundError`	The input KML could not be found.

Returns:

Type	Description
`object`	ee.FeatureCollection

Source code in geemap/common.py

def kml_to_ee(in_kml):
    """Converts a KML to ee.FeatureColleciton.

    Args:
        in_kml (str): The file path to the input KML.

    Raises:
        FileNotFoundError: The input KML could not be found.

    Returns:
        object: ee.FeatureCollection
    """
    import warnings

    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)
    ee_object = geojson_to_ee(out_json)
    os.remove(out_json)
    return ee_object

`kml_to_geojson(in_kml, out_geojson=None)` ¶

Converts a KML to GeoJSON.

Parameters:

Name	Type	Description	Default
`in_kml`	`str`	The file path to the input KML.	required
`out_geojson`	`str`	The file path to the output GeoJSON. Defaults to None.	`None`

Exceptions:

Type	Description
`FileNotFoundError`	The input KML could not be found.
`TypeError`	The output must be a GeoJSON.

Source code in geemap/common.py

def kml_to_geojson(in_kml, out_geojson=None):
    """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.
    """
    import warnings

    warnings.filterwarnings("ignore")

    in_kml = os.path.abspath(in_kml)
    if not os.path.exists(in_kml):
        raise FileNotFoundError("The input KML could not be found.")

    if out_geojson is not None:
        out_geojson = os.path.abspath(out_geojson)
        ext = os.path.splitext(out_geojson)[1].lower()
        if ext not in [".json", ".geojson"]:
            raise TypeError("The output file must be a GeoJSON.")

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

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

    import geopandas as gpd

    # import fiona
    # print(fiona.supported_drivers)
    gpd.io.file.fiona.drvsupport.supported_drivers["KML"] = "rw"
    gdf = gpd.read_file(in_kml, driver="KML")

    if out_geojson is not None:
        gdf.to_file(out_geojson, driver="GeoJSON")
    else:
        return gdf.__geo_interface__

`kml_to_shp(in_kml, out_shp)` ¶

Converts a KML to shapefile.

Parameters:

Name	Type	Description	Default
`in_kml`	`str`	The file path to the input KML.	required
`out_shp`	`str`	The file path to the output shapefile.	required

Exceptions:

Type	Description
`FileNotFoundError`	The input KML could not be found.
`TypeError`	The output must be a shapefile.

Source code in geemap/common.py

def kml_to_shp(in_kml, out_shp):
    """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.
    """
    import warnings

    warnings.filterwarnings("ignore")

    in_kml = os.path.abspath(in_kml)
    if not os.path.exists(in_kml):
        raise FileNotFoundError("The input KML could not be found.")

    out_shp = os.path.abspath(out_shp)
    if not out_shp.endswith(".shp"):
        raise TypeError("The output must be a shapefile.")

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

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

    import geopandas as gpd

    # import fiona
    # print(fiona.supported_drivers)
    gpd.io.file.fiona.drvsupport.supported_drivers["KML"] = "rw"
    df = gpd.read_file(in_kml, driver="KML")
    df.to_file(out_shp)

`kmz_to_ee(in_kmz)` ¶

Converts a KMZ to ee.FeatureColleciton.

Parameters:

Name	Type	Description	Default
`in_kmz`	`str`	The file path to the input KMZ.	required

Exceptions:

Type	Description
`FileNotFoundError`	The input KMZ could not be found.

Returns:

Type	Description
`object`	ee.FeatureCollection

Source code in geemap/common.py

def kmz_to_ee(in_kmz):
    """Converts a KMZ to ee.FeatureColleciton.

    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)
    os.remove(out_kml)
    return fc

`landsat_timeseries(roi=None, start_year=1984, end_year=2020, start_date='06-10', end_date='09-20', apply_fmask=True)` ¶

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 2020.	`2020`
`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`

Returns:

Type	Description
`object`	Returns an ImageCollection containing annual Landsat images.

Source code in geemap/common.py

def landsat_timeseries(
    roi=None,
    start_year=1984,
    end_year=2020,
    start_date="06-10",
    end_date="09-20",
    apply_fmask=True,
):
    """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 2020.
        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.
    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

    ################################################################################

    # 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("[0-9]{2}\-[0-9]{2}", start_date) and re.match(
        "[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}
        )

    ################################################################################

    # 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)

    # 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

    # ################################################################################
    # # Run LandTrendr.
    # lt = ee.Algorithms.TemporalSegmentation.LandTrendr(
    #     timeSeries=imgCol.select(['NBR', 'SWIR1', 'NIR', 'Green']),
    #     maxSegments=10,
    #     spikeThreshold=0.7,
    #     vertexCountOvershoot=3,
    #     preventOneYearRecovery=True,
    #     recoveryThreshold=0.5,
    #     pvalThreshold=0.05,
    #     bestModelProportion=0.75,
    #     minObservationsNeeded=6)

    # ################################################################################
    # # Get fitted imagery. This starts export tasks.
    # def getYearStr(year):
    #     return(ee.String('yr_').cat(ee.Algorithms.String(year).slice(0,4)))

    # yearsStr = years.map(getYearStr)

    # r = lt.select(['SWIR1_fit']).arrayFlatten([yearsStr]).toShort()
    # g = lt.select(['NIR_fit']).arrayFlatten([yearsStr]).toShort()
    # b = lt.select(['Green_fit']).arrayFlatten([yearsStr]).toShort()

    # for i, c in zip([r, g, b], ['r', 'g', 'b']):
    #     descr = 'mamore-river-'+c
    #     name = 'users/user/'+descr
    #     print(name)
    #     task = ee.batch.Export.image.toAsset(
    #     image=i,
    #     region=roi.getInfo()['coordinates'],
    #     assetId=name,
    #     description=descr,
    #     scale=30,
    #     crs='EPSG:3857',
    #     maxPixels=1e13)
    #     task.start()

`landsat_ts_gif(roi=None, out_gif=None, start_year=1984, end_year=2020, start_date='06-10', end_date='09-20', bands=['NIR', 'Red', 'Green'], vis_params=None, dimensions=768, frames_per_second=10, apply_fmask=True, nd_bands=None, nd_threshold=0, nd_palette=['black', 'blue'])` ¶

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 2019.	`2020`
`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 10.	`10`
`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 extacting pixels from the normalized difference band.	`0`
`nd_palette`	`list`	The color palette to use for displaying the normalized difference band.	`['black', 'blue']`

Returns:

Type	Description
`str`	File path to the output GIF image.

Source code in geemap/common.py

def landsat_ts_gif(
    roi=None,
    out_gif=None,
    start_year=1984,
    end_year=2020,
    start_date="06-10",
    end_date="09-20",
    bands=["NIR", "Red", "Green"],
    vis_params=None,
    dimensions=768,
    frames_per_second=10,
    apply_fmask=True,
    nd_bands=None,
    nd_threshold=0,
    nd_palette=["black", "blue"],
):
    """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 2019.
        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 10.
        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 extacting pixels from the normalized difference band.
        nd_palette (list, optional): The color palette to use for displaying the normalized difference band.

    Returns:
        str: File path to the output GIF image.
    """

    # ee_initialize()

    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 = "landsat_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
    # elif not os.path.isfile(out_gif):
    #     print('The output file must be a file')
    #     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", "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:
        col = landsat_timeseries(
            roi, start_year, end_year, start_date, end_date, apply_fmask
        )

        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]

        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

        if "min" not in video_args.keys():
            video_args["min"] = 0

        if "max" not in video_args.keys():
            video_args["max"] = 4000

        if "gamma" not in video_args.keys():
            video_args["gamma"] = [1, 1, 1]

        download_ee_video(col, video_args, out_gif)

        if nd_bands is not None:
            nd_images = landsat_ts_norm_diff(
                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,
            )

        return out_gif

    except Exception as e:
        print(e)

`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`	`ee.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/common.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)` ¶

[summary]

Parameters:

Name	Type	Description	Default
`collection`	`ee.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`

Returns:

Type	Description
`str`	File path to the output animated GIF.

Source code in geemap/common.py

def landsat_ts_norm_diff_gif(
    collection,
    out_gif=None,
    vis_params=None,
    palette=["black", "blue"],
    dimensions=768,
    frames_per_second=10,
):
    """[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.

    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)

    return out_gif

`latlon_from_text(location)` ¶

Extracts latlon from text.

Parameters:

Name	Type	Description	Default
`location`	`str`	A pair of latlon coordinates separated by comma or space.	required

Returns:

Type	Description
`bool`	Returns (lat, lon) if valid.

Source code in geemap/common.py

def latlon_from_text(location):
    """Extracts latlon from text.

    Args:
        location (str): A pair of latlon coordinates separated by comma or space.

    Returns:
        bool: Returns (lat, lon) if valid.
    """
    latlon = []
    try:
        if "," in location:
            latlon = [float(x) for x in location.split(",")]
        elif " " in location:
            latlon = [float(x) for x in location.split(" ")]
        else:
            print(
                "The lat-lon coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
            )
            return None

        lat, lon = latlon[0], latlon[1]
        if lat >= -90 and lat <= 90 and lon >= -180 and lon <= 180:
            return lat, lon
        else:
            return None

    except Exception as e:
        print(e)
        print(
            "The lat-lon coordinates should be numbers only and separated by comma or space, such as 40.2, -100.3"
        )
        return None

`legend_from_ee(ee_class_table)` ¶

Extract legend from an Earth Engine class table on the Earth Engine Data Catalog page such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1

Parameters:

Name	Type	Description	Default
`ee_class_table`	`str`	An Earth Engine class table with triple quotes.	required

Returns:

Type	Description
`dict`	Returns a legend dictionary that can be used to create a legend.

Source code in geemap/common.py

def legend_from_ee(ee_class_table):
    """Extract legend from an Earth Engine class table on the Earth Engine Data Catalog page
    such as https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1

    Args:
        ee_class_table (str): An Earth Engine class table with triple quotes.

    Returns:
        dict: Returns a legend dictionary that can be used to create a legend.
    """
    try:
        ee_class_table = ee_class_table.strip()
        lines = ee_class_table.split("\n")[1:]

        if lines[0] == "Value\tColor\tDescription":
            lines = lines[1:]

        legend_dict = {}
        for _, line in enumerate(lines):
            items = line.split("\t")
            items = [item.strip() for item in items]
            color = items[1]
            key = items[0] + " " + items[2]
            legend_dict[key] = color

        return legend_dict

    except Exception as e:
        print(e)

`list_vars(var_type=None)` ¶

Lists all defined avariables.

Parameters:

Name	Type	Description	Default
`var_type`	`object`	The object type of variables to list. Defaults to None.	`None`

Returns:

Type	Description
`list`	A list of all defined variables.

Source code in geemap/common.py

def list_vars(var_type=None):
    """Lists all defined avariables.

    Args:
        var_type (object, optional): The object type of variables to list. Defaults to None.

    Returns:
        list: A list of all defined variables.
    """
    result = []

    for var in globals():

        reserved_vars = [
            "In",
            "Out",
            "get_ipython",
            "exit",
            "quit",
            "json",
            "getsizeof",
            "NamespaceMagics",
            "np",
            "var_dic_list",
            "list_vars",
            "ee",
            "geemap",
        ]

        if (not var.startswith("_")) and (var not in reserved_vars):
            if var_type is not None and isinstance(eval(var), var_type):
                result.append(var)
            elif var_type is None:
                result.append(var)

    return result

`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("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("https://storage.googleapis.com/"):
            uri = uri.replace("https://storage.googleapis.com/",

common module¶

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

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) ¶

adjust_longitude(in_fc) ¶

annual_NAIP(year, region) ¶

api_docs() ¶

array_mean(arr) ¶

array_sum(arr) ¶

bands_to_image_collection(img) ¶

bbox_to_geojson(bounds) ¶

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

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

check_color(in_color) ¶

check_git_install() ¶

check_install(package) ¶

clone_github_repo(url, out_dir) ¶

clone_google_repo(url, out_dir=None) ¶

clone_repo(out_dir='.', unzip=True) ¶

column_stats(collection, column, stats_type) ¶

coords_to_geojson(coords) ¶

copy_credentials_to_colab() ¶

copy_credentials_to_drive() ¶

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

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') ¶

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

create_nlcd_qml(out_qml) ¶

credentials_in_colab() ¶

credentials_in_drive() ¶

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

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

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

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

csv_to_pandas(in_csv, **kwargs) ¶

csv_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude') ¶

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

delete_shp(in_shp, verbose=False) ¶

dict_to_csv(data_dict, out_csv, by_row=False) ¶

download_ee_video(collection, video_args, out_gif) ¶

download_from_gdrive(gfile_url, file_name, out_dir='.', unzip=True, verbose=True) ¶

download_from_url(url, out_file_name=None, out_dir='.', unzip=True, verbose=True) ¶

edit_download_html(htmlWidget, filename, title='Click here to download: ') ¶

ee_api_to_csv(outfile=None) ¶

ee_data_html(asset) ¶

ee_data_thumbnail(asset_id) ¶

ee_export_geojson(ee_object, filename=None, selectors=None) ¶

ee_export_image(ee_object, filename, scale=None, crs=None, region=None, file_per_band=False) ¶

ee_export_image_collection(ee_object, out_dir, scale=None, crs=None, region=None, file_per_band=False) ¶

ee_export_image_collection_to_drive(ee_object, descriptions=None, folder=None, region=None, scale=None, crs=None, max_pixels=10000000000000.0, file_format='GeoTIFF', format_options={}) ¶

ee_export_image_to_drive(ee_object, description, folder=None, region=None, scale=None, crs=None, max_pixels=10000000000000.0, file_format='GeoTIFF', format_options={}) ¶

ee_export_vector(ee_object, filename, selectors=None, verbose=True, keep_zip=False) ¶

ee_export_vector_to_drive(ee_object, description, folder, file_format='shp', selectors=None) ¶

ee_function_tree(name) ¶

ee_initialize(token_name='EARTHENGINE_TOKEN') ¶

ee_num_round(num, decimal=2) ¶

ee_search(asset_limit=100) ¶

ee_to_csv(ee_object, filename, selectors=None, verbose=True) ¶

ee_to_geojson(ee_object, out_json=None) ¶

ee_to_geopandas(ee_object, selectors=None, verbose=False) ¶

ee_to_numpy(ee_object, bands=None, region=None, properties=None, default_value=None) ¶

ee_to_pandas(ee_object, **kwargs) ¶

ee_to_shp(ee_object, filename, selectors=None, verbose=True, keep_zip=False) ¶

ee_user_id() ¶

explode(coords) ¶

extract_pixel_values(ee_object, region, scale=None, projection=None, tileScale=1, getInfo=False) ¶

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) ¶

extract_values_to_points(in_fc, image, out_fc=None, scale=None, crs=None, crsTransform=None, tileScale=1, stats_type='FIRST', **kwargs) ¶

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') ¶

filter_HUC08(region) ¶

filter_HUC10(region) ¶

filter_NWI(HUC08_Id, region, exclude_riverine=True) ¶

filter_polygons(ftr) ¶

find_HUC08(HUC08_Id) ¶

find_HUC10(HUC10_Id) ¶

find_landsat_by_path_row(landsat_col, path_num, row_num) ¶

find_NAIP(region, add_NDVI=True, add_NDWI=True) ¶

find_NWI(HUC08_Id, exclude_riverine=True) ¶

geocode(location, max_rows=10, reverse=False) ¶

geojson_to_ee(geo_json, geodesic=True) ¶

geometry_type(ee_object) ¶

geopandas_to_ee(gdf, geodesic=True) ¶

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

`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)` ¶

`adjust_longitude(in_fc)` ¶

`annual_NAIP(year, region)` ¶

`api_docs()` ¶

`array_mean(arr)` ¶

`array_sum(arr)` ¶

`bands_to_image_collection(img)` ¶

`bbox_to_geojson(bounds)` ¶

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

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

`check_color(in_color)` ¶

`check_git_install()` ¶

`check_install(package)` ¶

`clone_github_repo(url, out_dir)` ¶

`clone_google_repo(url, out_dir=None)` ¶

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

`column_stats(collection, column, stats_type)` ¶

`coords_to_geojson(coords)` ¶

`copy_credentials_to_colab()` ¶

`copy_credentials_to_drive()` ¶

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

`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')` ¶

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

`create_nlcd_qml(out_qml)` ¶

`credentials_in_colab()` ¶

`credentials_in_drive()` ¶

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

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

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

`csv_to_geopandas(in_csv, latitude='latitude', longitude='longitude', encoding='utf-8')` ¶

`csv_to_pandas(in_csv, **kwargs)` ¶

`csv_to_shp(in_csv, out_shp, latitude='latitude', longitude='longitude')` ¶

`date_sequence(start, end, unit, date_format='YYYY-MM-dd')` ¶

`delete_shp(in_shp, verbose=False)` ¶

`dict_to_csv(data_dict, out_csv, by_row=False)` ¶

`download_ee_video(collection, video_args, out_gif)` ¶

`download_from_gdrive(gfile_url, file_name, out_dir='.', unzip=True, verbose=True)` ¶

`download_from_url(url, out_file_name=None, out_dir='.', unzip=True, verbose=True)` ¶

`edit_download_html(htmlWidget, filename, title='Click here to download: ')` ¶

`ee_api_to_csv(outfile=None)` ¶

`ee_data_html(asset)` ¶

`ee_data_thumbnail(asset_id)` ¶

`ee_export_geojson(ee_object, filename=None, selectors=None)` ¶

`ee_export_image(ee_object, filename, scale=None, crs=None, region=None, file_per_band=False)` ¶

`ee_export_image_collection(ee_object, out_dir, scale=None, crs=None, region=None, file_per_band=False)` ¶

`ee_export_image_collection_to_drive(ee_object, descriptions=None, folder=None, region=None, scale=None, crs=None, max_pixels=10000000000000.0, file_format='GeoTIFF', format_options={})` ¶

`ee_export_image_to_drive(ee_object, description, folder=None, region=None, scale=None, crs=None, max_pixels=10000000000000.0, file_format='GeoTIFF', format_options={})` ¶

`ee_export_vector(ee_object, filename, selectors=None, verbose=True, keep_zip=False)` ¶

`ee_export_vector_to_drive(ee_object, description, folder, file_format='shp', selectors=None)` ¶

`ee_function_tree(name)` ¶

`ee_initialize(token_name='EARTHENGINE_TOKEN')` ¶

`ee_num_round(num, decimal=2)` ¶

`ee_search(asset_limit=100)` ¶

`ee_to_csv(ee_object, filename, selectors=None, verbose=True)` ¶

`ee_to_geojson(ee_object, out_json=None)` ¶

`ee_to_geopandas(ee_object, selectors=None, verbose=False)` ¶

`ee_to_numpy(ee_object, bands=None, region=None, properties=None, default_value=None)` ¶

`ee_to_pandas(ee_object, **kwargs)` ¶

`ee_to_shp(ee_object, filename, selectors=None, verbose=True, keep_zip=False)` ¶

`ee_user_id()` ¶

`explode(coords)` ¶

`extract_pixel_values(ee_object, region, scale=None, projection=None, tileScale=1, getInfo=False)` ¶

`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)` ¶

`extract_values_to_points(in_fc, image, out_fc=None, scale=None, crs=None, crsTransform=None, tileScale=1, stats_type='FIRST', **kwargs)` ¶

`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')` ¶

`filter_HUC08(region)` ¶

`filter_HUC10(region)` ¶

`filter_NWI(HUC08_Id, region, exclude_riverine=True)` ¶

`filter_polygons(ftr)` ¶

`find_HUC08(HUC08_Id)` ¶

`find_HUC10(HUC10_Id)` ¶

`find_landsat_by_path_row(landsat_col, path_num, row_num)` ¶

`find_NAIP(region, add_NDVI=True, add_NDWI=True)` ¶

`find_NWI(HUC08_Id, exclude_riverine=True)` ¶

`geocode(location, max_rows=10, reverse=False)` ¶

`geojson_to_ee(geo_json, geodesic=True)` ¶

`geometry_type(ee_object)` ¶

`geopandas_to_ee(gdf, geodesic=True)` ¶

`get_all_NAIP(start_year=2009, end_year=2019)` ¶