hypercoast module¶

Main module.

`Map (Map)` ¶

A class that extends leafmap.Map to provide additional functionality for hypercoast.

Methods

Any methods inherited from leafmap.Map.

Source code in hypercoast/hypercoast.py

class Map(leafmap.Map):
    """
    A class that extends leafmap.Map to provide additional functionality for hypercoast.

    Attributes:
        Any attributes inherited from leafmap.Map.

    Methods:
        Any methods inherited from leafmap.Map.
    """

    def __init__(self, **kwargs):
        """
        Initializes a new instance of the Map class.

        Args:
            **kwargs: Arbitrary keyword arguments that are passed to the parent class's constructor.
        """
        super().__init__(**kwargs)

    def add(self, obj, position="topright", **kwargs):
        """Add a layer to the map.

        Args:
            **kwargs: Arbitrary keyword arguments that are passed to the parent class's add_layer method.
        """

        if isinstance(obj, str):
            if obj == "spectral":

                SpectralWidget(self, position=position)
                self.set_plot_options(add_marker_cluster=True)
            else:
                super().add(obj, **kwargs)

        else:
            super().add(obj, **kwargs)

    def search_emit(self, default_dataset="EMITL2ARFL"):
        """
        Adds a NASA Earth Data search tool to the map with a default dataset for EMIT.

        Args:
            default_dataset (str, optional): The default dataset to search for. Defaults to "EMITL2ARFL".
        """
        self.add("nasa_earth_data", default_dataset=default_dataset)

    def search_pace(self, default_dataset="PACE_OCI_L2_AOP_NRT"):
        """
        Adds a NASA Earth Data search tool to the map with a default dataset for PACE.

        Args:
            default_dataset (str, optional): The default dataset to search for. Defaults to "PACE_OCI_L2_AOP_NRT".
        """
        self.add("nasa_earth_data", default_dataset=default_dataset)

    def add_raster(
        self,
        source,
        indexes=None,
        colormap=None,
        vmin=None,
        vmax=None,
        nodata=None,
        attribution=None,
        layer_name="Raster",
        zoom_to_layer=True,
        visible=True,
        array_args={},
        **kwargs,
    ):
        """Add a local raster dataset to the map.
            If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer) and
            if the raster does not render properly, try installing jupyter-server-proxy using `pip install jupyter-server-proxy`,
            then running the following code before calling this function. For more info, see https://bit.ly/3JbmF93.

            import os
            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'

        Args:
            source (str): The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.
            indexes (int, optional): The band(s) to use. Band indexing starts at 1. Defaults to None.
            colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
            vmin (float, optional): The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.
            vmax (float, optional): The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.
            nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
            attribution (str, optional): Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.
            layer_name (str, optional): The layer name to use. Defaults to 'Raster'.
            zoom_to_layer (bool, optional): Whether to zoom to the extent of the layer. Defaults to True.
            visible (bool, optional): Whether the layer is visible. Defaults to True.
            array_args (dict, optional): Additional arguments to pass to `array_to_memory_file` when reading the raster. Defaults to {}.
        """

        import numpy as np

        if nodata is None:
            nodata = np.nan
        super().add_raster(
            source,
            indexes=indexes,
            colormap=colormap,
            vmin=vmin,
            vmax=vmax,
            nodata=nodata,
            attribution=attribution,
            layer_name=layer_name,
            zoom_to_layer=zoom_to_layer,
            visible=visible,
            array_args=array_args,
            **kwargs,
        )

    def add_emit(
        self,
        source,
        wavelengths=None,
        indexes=None,
        colormap=None,
        vmin=None,
        vmax=None,
        nodata=np.nan,
        attribution=None,
        layer_name="EMIT",
        zoom_to_layer=True,
        visible=True,
        array_args={},
        **kwargs,
    ):
        """Add a local raster dataset to the map.
            If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer) and
            if the raster does not render properly, try installing jupyter-server-proxy using `pip install jupyter-server-proxy`,
            then running the following code before calling this function. For more info, see https://bit.ly/3JbmF93.

            import os
            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'

        Args:
            source (str): The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.
            indexes (int, optional): The band(s) to use. Band indexing starts at 1. Defaults to None.
            colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
            vmin (float, optional): The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.
            vmax (float, optional): The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.
            nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
            attribution (str, optional): Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.
            layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.
            zoom_to_layer (bool, optional): Whether to zoom to the extent of the layer. Defaults to True.
            visible (bool, optional): Whether the layer is visible. Defaults to True.
            array_args (dict, optional): Additional arguments to pass to `array_to_memory_file` when reading the raster. Defaults to {}.
        """

        xds = None
        if isinstance(source, str):

            xds = read_emit(source)
            source = emit_to_image(xds, wavelengths=wavelengths)
        elif isinstance(source, xr.Dataset):
            xds = source
            source = emit_to_image(xds, wavelengths=wavelengths)

        self.add_raster(
            source,
            indexes=indexes,
            colormap=colormap,
            vmin=vmin,
            vmax=vmax,
            nodata=nodata,
            attribution=attribution,
            layer_name=layer_name,
            zoom_to_layer=zoom_to_layer,
            visible=visible,
            array_args=array_args,
            **kwargs,
        )

        self.cog_layer_dict[layer_name]["xds"] = xds
        self.cog_layer_dict[layer_name]["type"] = "EMIT"

    def add_pace(
        self,
        source,
        wavelengths=None,
        indexes=None,
        colormap="jet",
        vmin=None,
        vmax=None,
        nodata=np.nan,
        attribution=None,
        layer_name="PACE",
        zoom_to_layer=True,
        visible=True,
        method="nearest",
        gridded=False,
        array_args={},
        **kwargs,
    ):
        """Add a PACE dataset to the map.
            If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer) and
            if the raster does not render properly, try installing jupyter-server-proxy using `pip install jupyter-server-proxy`,
            then running the following code before calling this function. For more info, see https://bit.ly/3JbmF93.

            import os
            os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'

        Args:
            source (str): The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.
            indexes (int, optional): The band(s) to use. Band indexing starts at 1. Defaults to None.
            colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
            vmin (float, optional): The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.
            vmax (float, optional): The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.
            nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
            attribution (str, optional): Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.
            layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.
            zoom_to_layer (bool, optional): Whether to zoom to the extent of the layer. Defaults to True.
            visible (bool, optional): Whether the layer is visible. Defaults to True.
            array_args (dict, optional): Additional arguments to pass to `array_to_memory_file` when reading the raster. Defaults to {}.
        """

        if isinstance(source, str):

            source = read_pace(source)

        image = pace_to_image(
            source, wavelengths=wavelengths, method=method, gridded=gridded
        )

        if isinstance(wavelengths, list) and len(wavelengths) > 1:
            colormap = None

        self.add_raster(
            image,
            indexes=indexes,
            colormap=colormap,
            vmin=vmin,
            vmax=vmax,
            nodata=nodata,
            attribution=attribution,
            layer_name=layer_name,
            zoom_to_layer=zoom_to_layer,
            visible=visible,
            array_args=array_args,
            **kwargs,
        )

        self.cog_layer_dict[layer_name]["xds"] = source
        self.cog_layer_dict[layer_name]["type"] = "PACE"

    def set_plot_options(
        self,
        add_marker_cluster=False,
        plot_type=None,
        overlay=False,
        position="bottomright",
        min_width=None,
        max_width=None,
        min_height=None,
        max_height=None,
        **kwargs,
    ):
        """Sets plotting options.

        Args:
            add_marker_cluster (bool, optional): Whether to add a marker cluster. Defaults to False.
            sample_scale (float, optional):  A nominal scale in meters of the projection to sample in . Defaults to None.
            plot_type (str, optional): The plot type can be one of "None", "bar", "scatter" or "hist". Defaults to None.
            overlay (bool, optional): Whether to overlay plotted lines on the figure. Defaults to False.
            position (str, optional): Position of the control, can be ‘bottomleft’, ‘bottomright’, ‘topleft’, or ‘topright’. Defaults to 'bottomright'.
            min_width (int, optional): Min width of the widget (in pixels), if None it will respect the content size. Defaults to None.
            max_width (int, optional): Max width of the widget (in pixels), if None it will respect the content size. Defaults to None.
            min_height (int, optional): Min height of the widget (in pixels), if None it will respect the content size. Defaults to None.
            max_height (int, optional): Max height of the widget (in pixels), if None it will respect the content size. Defaults to None.

        """
        plot_options_dict = {}
        plot_options_dict["add_marker_cluster"] = add_marker_cluster
        plot_options_dict["plot_type"] = plot_type
        plot_options_dict["overlay"] = overlay
        plot_options_dict["position"] = position
        plot_options_dict["min_width"] = min_width
        plot_options_dict["max_width"] = max_width
        plot_options_dict["min_height"] = min_height
        plot_options_dict["max_height"] = max_height

        for key in kwargs:
            plot_options_dict[key] = kwargs[key]

        self._plot_options = plot_options_dict

        if not hasattr(self, "_plot_marker_cluster"):
            self._plot_marker_cluster = ipyleaflet.MarkerCluster(name="Marker Cluster")

        if add_marker_cluster and (self._plot_marker_cluster not in self.layers):
            self.add(self._plot_marker_cluster)

    def spectral_to_df(self, **kwargs):
        """Converts the spectral data to a pandas DataFrame.

        Returns:
            pd.DataFrame: The spectral data as a pandas DataFrame.
        """
        import pandas as pd

        df = pd.DataFrame(self._spectral_data, **kwargs)
        return df

    def spectral_to_csv(self, filename, index=True, **kwargs):
        """Saves the spectral data to a CSV file.

        Args:
            filename (str): The output CSV file.
            index (bool, optional): Whether to write the index. Defaults to True.
        """
        df = self.spectral_to_df()
        df = df.rename_axis("band")
        df.to_csv(filename, index=index, **kwargs)

`init(self, **kwargs)` `special` ¶

Initializes a new instance of the Map class.

Parameters:

Name	Type	Description	Default
`**kwargs`		Arbitrary keyword arguments that are passed to the parent class's constructor.	`{}`

Source code in hypercoast/hypercoast.py

def __init__(self, **kwargs):
    """
    Initializes a new instance of the Map class.

    Args:
        **kwargs: Arbitrary keyword arguments that are passed to the parent class's constructor.
    """
    super().__init__(**kwargs)

`add(self, obj, position='topright', **kwargs)` ¶

Add a layer to the map.

Parameters:

Name	Type	Description	Default
`**kwargs`		Arbitrary keyword arguments that are passed to the parent class's add_layer method.	`{}`

Source code in hypercoast/hypercoast.py

def add(self, obj, position="topright", **kwargs):
    """Add a layer to the map.

    Args:
        **kwargs: Arbitrary keyword arguments that are passed to the parent class's add_layer method.
    """

    if isinstance(obj, str):
        if obj == "spectral":

            SpectralWidget(self, position=position)
            self.set_plot_options(add_marker_cluster=True)
        else:
            super().add(obj, **kwargs)

    else:
        super().add(obj, **kwargs)

`add_emit(self, source, wavelengths=None, indexes=None, colormap=None, vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='EMIT', zoom_to_layer=True, visible=True, array_args={}, **kwargs)` ¶

Add a local raster dataset to the map. If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer) and if the raster does not render properly, try installing jupyter-server-proxy using pip install jupyter-server-proxy, then running the following code before calling this function. For more info, see https://bit.ly/3JbmF93.

import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'

Parameters:

Name	Type	Description	Default
`source`	`str`	The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.	required
`indexes`	`int`	The band(s) to use. Band indexing starts at 1. Defaults to None.	`None`
`colormap`	`str`	The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.	`None`
`vmin`	`float`	The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.	`None`
`vmax`	`float`	The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.	`None`
`nodata`	`float`	The value from the band to use to interpret as not valid data. Defaults to None.	`nan`
`attribution`	`str`	Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.	`None`
`layer_name`	`str`	The layer name to use. Defaults to 'EMIT'.	`'EMIT'`
`zoom_to_layer`	`bool`	Whether to zoom to the extent of the layer. Defaults to True.	`True`
`visible`	`bool`	Whether the layer is visible. Defaults to True.	`True`
`array_args`	`dict`	Additional arguments to pass to `array_to_memory_file` when reading the raster. Defaults to {}.	`{}`

Source code in hypercoast/hypercoast.py

def add_emit(
    self,
    source,
    wavelengths=None,
    indexes=None,
    colormap=None,
    vmin=None,
    vmax=None,
    nodata=np.nan,
    attribution=None,
    layer_name="EMIT",
    zoom_to_layer=True,
    visible=True,
    array_args={},
    **kwargs,
):
    """Add a local raster dataset to the map.
        If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer) and
        if the raster does not render properly, try installing jupyter-server-proxy using `pip install jupyter-server-proxy`,
        then running the following code before calling this function. For more info, see https://bit.ly/3JbmF93.

        import os
        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'

    Args:
        source (str): The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.
        indexes (int, optional): The band(s) to use. Band indexing starts at 1. Defaults to None.
        colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
        vmin (float, optional): The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.
        vmax (float, optional): The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.
        nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
        attribution (str, optional): Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.
        layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.
        zoom_to_layer (bool, optional): Whether to zoom to the extent of the layer. Defaults to True.
        visible (bool, optional): Whether the layer is visible. Defaults to True.
        array_args (dict, optional): Additional arguments to pass to `array_to_memory_file` when reading the raster. Defaults to {}.
    """

    xds = None
    if isinstance(source, str):

        xds = read_emit(source)
        source = emit_to_image(xds, wavelengths=wavelengths)
    elif isinstance(source, xr.Dataset):
        xds = source
        source = emit_to_image(xds, wavelengths=wavelengths)

    self.add_raster(
        source,
        indexes=indexes,
        colormap=colormap,
        vmin=vmin,
        vmax=vmax,
        nodata=nodata,
        attribution=attribution,
        layer_name=layer_name,
        zoom_to_layer=zoom_to_layer,
        visible=visible,
        array_args=array_args,
        **kwargs,
    )

    self.cog_layer_dict[layer_name]["xds"] = xds
    self.cog_layer_dict[layer_name]["type"] = "EMIT"

`add_pace(self, source, wavelengths=None, indexes=None, colormap='jet', vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='PACE', zoom_to_layer=True, visible=True, method='nearest', gridded=False, array_args={}, **kwargs)` ¶

Add a PACE dataset to the map. If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer) and if the raster does not render properly, try installing jupyter-server-proxy using pip install jupyter-server-proxy, then running the following code before calling this function. For more info, see https://bit.ly/3JbmF93.

import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'

Parameters:

Name	Type	Description	Default
`source`	`str`	The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.	required
`indexes`	`int`	The band(s) to use. Band indexing starts at 1. Defaults to None.	`None`
`colormap`	`str`	The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.	`'jet'`
`vmin`	`float`	The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.	`None`
`vmax`	`float`	The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.	`None`
`nodata`	`float`	The value from the band to use to interpret as not valid data. Defaults to None.	`nan`
`attribution`	`str`	Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.	`None`
`layer_name`	`str`	The layer name to use. Defaults to 'EMIT'.	`'PACE'`
`zoom_to_layer`	`bool`	Whether to zoom to the extent of the layer. Defaults to True.	`True`
`visible`	`bool`	Whether the layer is visible. Defaults to True.	`True`
`array_args`	`dict`	Additional arguments to pass to `array_to_memory_file` when reading the raster. Defaults to {}.	`{}`

Source code in hypercoast/hypercoast.py

def add_pace(
    self,
    source,
    wavelengths=None,
    indexes=None,
    colormap="jet",
    vmin=None,
    vmax=None,
    nodata=np.nan,
    attribution=None,
    layer_name="PACE",
    zoom_to_layer=True,
    visible=True,
    method="nearest",
    gridded=False,
    array_args={},
    **kwargs,
):
    """Add a PACE dataset to the map.
        If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer) and
        if the raster does not render properly, try installing jupyter-server-proxy using `pip install jupyter-server-proxy`,
        then running the following code before calling this function. For more info, see https://bit.ly/3JbmF93.

        import os
        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'

    Args:
        source (str): The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.
        indexes (int, optional): The band(s) to use. Band indexing starts at 1. Defaults to None.
        colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
        vmin (float, optional): The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.
        vmax (float, optional): The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.
        nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
        attribution (str, optional): Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.
        layer_name (str, optional): The layer name to use. Defaults to 'EMIT'.
        zoom_to_layer (bool, optional): Whether to zoom to the extent of the layer. Defaults to True.
        visible (bool, optional): Whether the layer is visible. Defaults to True.
        array_args (dict, optional): Additional arguments to pass to `array_to_memory_file` when reading the raster. Defaults to {}.
    """

    if isinstance(source, str):

        source = read_pace(source)

    image = pace_to_image(
        source, wavelengths=wavelengths, method=method, gridded=gridded
    )

    if isinstance(wavelengths, list) and len(wavelengths) > 1:
        colormap = None

    self.add_raster(
        image,
        indexes=indexes,
        colormap=colormap,
        vmin=vmin,
        vmax=vmax,
        nodata=nodata,
        attribution=attribution,
        layer_name=layer_name,
        zoom_to_layer=zoom_to_layer,
        visible=visible,
        array_args=array_args,
        **kwargs,
    )

    self.cog_layer_dict[layer_name]["xds"] = source
    self.cog_layer_dict[layer_name]["type"] = "PACE"

`add_raster(self, source, indexes=None, colormap=None, vmin=None, vmax=None, nodata=None, attribution=None, layer_name='Raster', zoom_to_layer=True, visible=True, array_args={}, **kwargs)` ¶

Add a local raster dataset to the map. If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer) and if the raster does not render properly, try installing jupyter-server-proxy using pip install jupyter-server-proxy, then running the following code before calling this function. For more info, see https://bit.ly/3JbmF93.

import os
os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'

Parameters:

Name	Type	Description	Default
`source`	`str`	The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.	required
`indexes`	`int`	The band(s) to use. Band indexing starts at 1. Defaults to None.	`None`
`colormap`	`str`	The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.	`None`
`vmin`	`float`	The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.	`None`
`vmax`	`float`	The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.	`None`
`nodata`	`float`	The value from the band to use to interpret as not valid data. Defaults to None.	`None`
`attribution`	`str`	Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.	`None`
`layer_name`	`str`	The layer name to use. Defaults to 'Raster'.	`'Raster'`
`zoom_to_layer`	`bool`	Whether to zoom to the extent of the layer. Defaults to True.	`True`
`visible`	`bool`	Whether the layer is visible. Defaults to True.	`True`
`array_args`	`dict`	Additional arguments to pass to `array_to_memory_file` when reading the raster. Defaults to {}.	`{}`

Source code in hypercoast/hypercoast.py

def add_raster(
    self,
    source,
    indexes=None,
    colormap=None,
    vmin=None,
    vmax=None,
    nodata=None,
    attribution=None,
    layer_name="Raster",
    zoom_to_layer=True,
    visible=True,
    array_args={},
    **kwargs,
):
    """Add a local raster dataset to the map.
        If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer) and
        if the raster does not render properly, try installing jupyter-server-proxy using `pip install jupyter-server-proxy`,
        then running the following code before calling this function. For more info, see https://bit.ly/3JbmF93.

        import os
        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'

    Args:
        source (str): The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.
        indexes (int, optional): The band(s) to use. Band indexing starts at 1. Defaults to None.
        colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
        vmin (float, optional): The minimum value to use when colormapping the palette when plotting a single band. Defaults to None.
        vmax (float, optional): The maximum value to use when colormapping the palette when plotting a single band. Defaults to None.
        nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
        attribution (str, optional): Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.
        layer_name (str, optional): The layer name to use. Defaults to 'Raster'.
        zoom_to_layer (bool, optional): Whether to zoom to the extent of the layer. Defaults to True.
        visible (bool, optional): Whether the layer is visible. Defaults to True.
        array_args (dict, optional): Additional arguments to pass to `array_to_memory_file` when reading the raster. Defaults to {}.
    """

    import numpy as np

    if nodata is None:
        nodata = np.nan
    super().add_raster(
        source,
        indexes=indexes,
        colormap=colormap,
        vmin=vmin,
        vmax=vmax,
        nodata=nodata,
        attribution=attribution,
        layer_name=layer_name,
        zoom_to_layer=zoom_to_layer,
        visible=visible,
        array_args=array_args,
        **kwargs,
    )

`search_emit(self, default_dataset='EMITL2ARFL')` ¶

Adds a NASA Earth Data search tool to the map with a default dataset for EMIT.

Parameters:

Name	Type	Description	Default
`default_dataset`	`str`	The default dataset to search for. Defaults to "EMITL2ARFL".	`'EMITL2ARFL'`

Source code in hypercoast/hypercoast.py

def search_emit(self, default_dataset="EMITL2ARFL"):
    """
    Adds a NASA Earth Data search tool to the map with a default dataset for EMIT.

    Args:
        default_dataset (str, optional): The default dataset to search for. Defaults to "EMITL2ARFL".
    """
    self.add("nasa_earth_data", default_dataset=default_dataset)

`search_pace(self, default_dataset='PACE_OCI_L2_AOP_NRT')` ¶

Adds a NASA Earth Data search tool to the map with a default dataset for PACE.

Parameters:

Name	Type	Description	Default
`default_dataset`	`str`	The default dataset to search for. Defaults to "PACE_OCI_L2_AOP_NRT".	`'PACE_OCI_L2_AOP_NRT'`

Source code in hypercoast/hypercoast.py

def search_pace(self, default_dataset="PACE_OCI_L2_AOP_NRT"):
    """
    Adds a NASA Earth Data search tool to the map with a default dataset for PACE.

    Args:
        default_dataset (str, optional): The default dataset to search for. Defaults to "PACE_OCI_L2_AOP_NRT".
    """
    self.add("nasa_earth_data", default_dataset=default_dataset)

`set_plot_options(self, add_marker_cluster=False, plot_type=None, overlay=False, position='bottomright', min_width=None, max_width=None, min_height=None, max_height=None, **kwargs)` ¶

Sets plotting options.

Parameters:

Name	Type	Description	Default
`add_marker_cluster`	`bool`	Whether to add a marker cluster. Defaults to False.	`False`
`sample_scale`	`float`	A nominal scale in meters of the projection to sample in . Defaults to None.	required
`plot_type`	`str`	The plot type can be one of "None", "bar", "scatter" or "hist". Defaults to None.	`None`
`overlay`	`bool`	Whether to overlay plotted lines on the figure. Defaults to False.	`False`
`position`	`str`	Position of the control, can be ‘bottomleft’, ‘bottomright’, ‘topleft’, or ‘topright’. Defaults to 'bottomright'.	`'bottomright'`
`min_width`	`int`	Min width of the widget (in pixels), if None it will respect the content size. Defaults to None.	`None`
`max_width`	`int`	Max width of the widget (in pixels), if None it will respect the content size. Defaults to None.	`None`
`min_height`	`int`	Min height of the widget (in pixels), if None it will respect the content size. Defaults to None.	`None`
`max_height`	`int`	Max height of the widget (in pixels), if None it will respect the content size. Defaults to None.	`None`

Source code in hypercoast/hypercoast.py

def set_plot_options(
    self,
    add_marker_cluster=False,
    plot_type=None,
    overlay=False,
    position="bottomright",
    min_width=None,
    max_width=None,
    min_height=None,
    max_height=None,
    **kwargs,
):
    """Sets plotting options.

    Args:
        add_marker_cluster (bool, optional): Whether to add a marker cluster. Defaults to False.
        sample_scale (float, optional):  A nominal scale in meters of the projection to sample in . Defaults to None.
        plot_type (str, optional): The plot type can be one of "None", "bar", "scatter" or "hist". Defaults to None.
        overlay (bool, optional): Whether to overlay plotted lines on the figure. Defaults to False.
        position (str, optional): Position of the control, can be ‘bottomleft’, ‘bottomright’, ‘topleft’, or ‘topright’. Defaults to 'bottomright'.
        min_width (int, optional): Min width of the widget (in pixels), if None it will respect the content size. Defaults to None.
        max_width (int, optional): Max width of the widget (in pixels), if None it will respect the content size. Defaults to None.
        min_height (int, optional): Min height of the widget (in pixels), if None it will respect the content size. Defaults to None.
        max_height (int, optional): Max height of the widget (in pixels), if None it will respect the content size. Defaults to None.

    """
    plot_options_dict = {}
    plot_options_dict["add_marker_cluster"] = add_marker_cluster
    plot_options_dict["plot_type"] = plot_type
    plot_options_dict["overlay"] = overlay
    plot_options_dict["position"] = position
    plot_options_dict["min_width"] = min_width
    plot_options_dict["max_width"] = max_width
    plot_options_dict["min_height"] = min_height
    plot_options_dict["max_height"] = max_height

    for key in kwargs:
        plot_options_dict[key] = kwargs[key]

    self._plot_options = plot_options_dict

    if not hasattr(self, "_plot_marker_cluster"):
        self._plot_marker_cluster = ipyleaflet.MarkerCluster(name="Marker Cluster")

    if add_marker_cluster and (self._plot_marker_cluster not in self.layers):
        self.add(self._plot_marker_cluster)

`spectral_to_csv(self, filename, index=True, **kwargs)` ¶

Saves the spectral data to a CSV file.

Parameters:

Name	Type	Description	Default
`filename`	`str`	The output CSV file.	required
`index`	`bool`	Whether to write the index. Defaults to True.	`True`

Source code in hypercoast/hypercoast.py

def spectral_to_csv(self, filename, index=True, **kwargs):
    """Saves the spectral data to a CSV file.

    Args:
        filename (str): The output CSV file.
        index (bool, optional): Whether to write the index. Defaults to True.
    """
    df = self.spectral_to_df()
    df = df.rename_axis("band")
    df.to_csv(filename, index=index, **kwargs)

`spectral_to_df(self, **kwargs)` ¶

Converts the spectral data to a pandas DataFrame.

Returns:

Type	Description
`pd.DataFrame`	The spectral data as a pandas DataFrame.

Source code in hypercoast/hypercoast.py

def spectral_to_df(self, **kwargs):
    """Converts the spectral data to a pandas DataFrame.

    Returns:
        pd.DataFrame: The spectral data as a pandas DataFrame.
    """
    import pandas as pd

    df = pd.DataFrame(self._spectral_data, **kwargs)
    return df

hypercoast module¶

Map (Map) ¶

__init__(self, **kwargs) special ¶

add(self, obj, position='topright', **kwargs) ¶

add_emit(self, source, wavelengths=None, indexes=None, colormap=None, vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='EMIT', zoom_to_layer=True, visible=True, array_args={}, **kwargs) ¶

add_pace(self, source, wavelengths=None, indexes=None, colormap='jet', vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='PACE', zoom_to_layer=True, visible=True, method='nearest', gridded=False, array_args={}, **kwargs) ¶

add_raster(self, source, indexes=None, colormap=None, vmin=None, vmax=None, nodata=None, attribution=None, layer_name='Raster', zoom_to_layer=True, visible=True, array_args={}, **kwargs) ¶

search_emit(self, default_dataset='EMITL2ARFL') ¶

search_pace(self, default_dataset='PACE_OCI_L2_AOP_NRT') ¶

set_plot_options(self, add_marker_cluster=False, plot_type=None, overlay=False, position='bottomright', min_width=None, max_width=None, min_height=None, max_height=None, **kwargs) ¶

spectral_to_csv(self, filename, index=True, **kwargs) ¶

spectral_to_df(self, **kwargs) ¶

`Map (Map)` ¶

`init(self, **kwargs)` `special` ¶

`add(self, obj, position='topright', **kwargs)` ¶

`add_emit(self, source, wavelengths=None, indexes=None, colormap=None, vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='EMIT', zoom_to_layer=True, visible=True, array_args={}, **kwargs)` ¶

`add_pace(self, source, wavelengths=None, indexes=None, colormap='jet', vmin=None, vmax=None, nodata=nan, attribution=None, layer_name='PACE', zoom_to_layer=True, visible=True, method='nearest', gridded=False, array_args={}, **kwargs)` ¶

`add_raster(self, source, indexes=None, colormap=None, vmin=None, vmax=None, nodata=None, attribution=None, layer_name='Raster', zoom_to_layer=True, visible=True, array_args={}, **kwargs)` ¶

`search_emit(self, default_dataset='EMITL2ARFL')` ¶

`search_pace(self, default_dataset='PACE_OCI_L2_AOP_NRT')` ¶

`set_plot_options(self, add_marker_cluster=False, plot_type=None, overlay=False, position='bottomright', min_width=None, max_width=None, min_height=None, max_height=None, **kwargs)` ¶

`spectral_to_csv(self, filename, index=True, **kwargs)` ¶

`spectral_to_df(self, **kwargs)` ¶