common module¶

The common module contains common functions and classes used by the other modules.

`convert_coords(coords, from_epsg, to_epsg)` ¶

Convert a list of coordinates from one EPSG to another.

Parameters:

Name	Type	Description	Default
`coords`	`List[Tuple[float, float]]`	List of tuples containing coordinates in the format (latitude, longitude).	required
`from_epsg`	`str`	Source EPSG code (default is "epsg:4326").	required
`to_epsg`	`str`	Target EPSG code (default is "epsg:32615").	required

Returns:

Type	Description
`List[Tuple[float, float]]`	List of tuples containing converted coordinates in the format (x, y).

Source code in hypercoast/common.py

def convert_coords(
    coords: List[Tuple[float, float]], from_epsg: str, to_epsg: str
) -> List[Tuple[float, float]]:
    """
    Convert a list of coordinates from one EPSG to another.

    Args:
        coords: List of tuples containing coordinates in the format (latitude, longitude).
        from_epsg: Source EPSG code (default is "epsg:4326").
        to_epsg: Target EPSG code (default is "epsg:32615").

    Returns:
        List of tuples containing converted coordinates in the format (x, y).
    """
    import pyproj

    # Define the coordinate transformation
    transformer = pyproj.Transformer.from_crs(from_epsg, to_epsg, always_xy=True)

    # Convert each coordinate
    converted_coords = [transformer.transform(lon, lat) for lat, lon in coords]

    return converted_coords

`download_acolite(outdir='.', platform=None)` ¶

Downloads the Acolite release based on the OS platform and extracts it to the specified directory. For more information, see the Acolite manual https://github.com/acolite/acolite/releases.

Parameters:

Name	Type	Description	Default
`outdir`	`str`	The output directory where the file will be Acolite and extracted.	`'.'`
`platform`	`Optional[str]`	The platform for which to download acolite. If None, the current system platform is used. Valid values are 'linux', 'darwin', and 'windows'.	`None`

Returns:

Type	Description
`str`	The path to the extracted Acolite directory.

Exceptions:

Type	Description
`Exception`	If the platform is unsupported or the download fails.

Source code in hypercoast/common.py

def download_acolite(outdir: str = ".", platform: Optional[str] = None) -> str:
    """
    Downloads the Acolite release based on the OS platform and extracts it to the specified directory.
    For more information, see the Acolite manual https://github.com/acolite/acolite/releases.

    Args:
        outdir (str): The output directory where the file will be Acolite and extracted.
        platform (Optional[str]): The platform for which to download acolite. If None, the current system platform is used.
                                  Valid values are 'linux', 'darwin', and 'windows'.

    Returns:
        str: The path to the extracted Acolite directory.

    Raises:
        Exception: If the platform is unsupported or the download fails.
    """
    import platform as pf
    import requests
    import tarfile
    from tqdm import tqdm

    base_url = "https://github.com/acolite/acolite/releases/download/20231023.0/"

    if platform is None:
        platform = pf.system().lower()
    else:
        platform = platform.lower()

    if platform == "linux":
        download_url = base_url + "acolite_py_linux_20231023.0.tar.gz"
        root_dir = "acolite_py_linux"
    elif platform == "darwin":
        download_url = base_url + "acolite_py_mac_20231023.0.tar.gz"
        root_dir = "acolite_py_mac"
    elif platform == "windows":
        download_url = base_url + "acolite_py_win_20231023.0.tar.gz"
        root_dir = "acolite_py_win"
    else:
        raise Exception(f"Unsupported OS platform: {platform}")

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

    extracted_path = os.path.join(outdir, root_dir)
    file_name = os.path.join(outdir, download_url.split("/")[-1])

    if os.path.exists(file_name):
        print(f"{file_name} already exists. Skip downloading.")
        return extracted_path

    response = requests.get(download_url, stream=True)
    total_size = int(response.headers.get("content-length", 0))
    block_size = 8192

    if response.status_code == 200:
        with open(file_name, "wb") as file, tqdm(
            desc=file_name,
            total=total_size,
            unit="iB",
            unit_scale=True,
            unit_divisor=1024,
        ) as bar:
            for chunk in response.iter_content(chunk_size=block_size):
                if chunk:
                    file.write(chunk)
                    bar.update(len(chunk))
        print(f"Downloaded {file_name}")
    else:
        raise Exception(f"Failed to download file from {download_url}")

    # Unzip the file
    with tarfile.open(file_name, "r:gz") as tar:
        tar.extractall(path=outdir)

    print(f"Extracted to {extracted_path}")
    return extracted_path

`download_ecostress(granules, out_dir=None, threads=8)` ¶

Downloads NASA ECOSTRESS granules.

Parameters:

Name	Type	Description	Default
`granules`	`List[dict]`	The granules to download.	required
`out_dir`	`str`	The output directory where the granules will be downloaded. Defaults to None (current directory).	`None`
`threads`	`int`	The number of threads to use for downloading. Defaults to 8.	`8`

Source code in hypercoast/common.py

def download_ecostress(
    granules: List[dict],
    out_dir: Optional[str] = None,
    threads: int = 8,
) -> None:
    """Downloads NASA ECOSTRESS granules.

    Args:
        granules (List[dict]): The granules to download.
        out_dir (str, optional): The output directory where the granules will be
            downloaded. Defaults to None (current directory).
        threads (int, optional): The number of threads to use for downloading.
            Defaults to 8.
    """

    download_nasa_data(granules=granules, out_dir=out_dir, threads=threads)

`download_emit(granules, out_dir=None, threads=8)` ¶

Downloads NASA EMIT granules.

Parameters:

Name	Type	Description	Default
`granules`	`List[dict]`	The granules to download.	required
`out_dir`	`str`	The output directory where the granules will be downloaded. Defaults to None (current directory).	`None`
`threads`	`int`	The number of threads to use for downloading. Defaults to 8.	`8`

Source code in hypercoast/common.py

def download_emit(
    granules: List[dict],
    out_dir: Optional[str] = None,
    threads: int = 8,
) -> None:
    """Downloads NASA EMIT granules.

    Args:
        granules (List[dict]): The granules to download.
        out_dir (str, optional): The output directory where the granules will be
            downloaded. Defaults to None (current directory).
        threads (int, optional): The number of threads to use for downloading.
            Defaults to 8.
    """

    download_nasa_data(granules=granules, out_dir=out_dir, threads=threads)

`download_file(url=None, output=None, quiet=True, proxy=None, speed=None, use_cookies=True, verify=True, id=None, fuzzy=False, resume=False, unzip=True, overwrite=False, subfolder=False)` ¶

Download a file from URL, including Google Drive shared URL.

Parameters:

Name	Type	Description	Default
`url`	`str`	Google Drive URL is also supported. Defaults to None.	`None`
`output`	`str`	Output filename. Default is basename of URL.	`None`
`quiet`	`bool`	Suppress terminal output. Default is True.	`True`
`proxy`	`str`	Proxy. Defaults to None.	`None`
`speed`	`float`	Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.	`None`
`use_cookies`	`bool`	Flag to use cookies. Defaults to True.	`True`
`verify`	`bool \| str`	Either a bool, in which case it controls whether the server's TLS certificate is verified, or a string, in which case it must be a path to a CA bundle to use. Default is True.. Defaults to True.	`True`
`id`	`str`	Google Drive's file ID. Defaults to None.	`None`
`fuzzy`	`bool`	Fuzzy extraction of Google Drive's file Id. Defaults to False.	`False`
`resume`	`bool`	Resume the download from existing tmp file if possible. Defaults to False.	`False`
`unzip`	`bool`	Unzip the file. Defaults to True.	`True`
`overwrite`	`bool`	Overwrite the file if it already exists. Defaults to False.	`False`
`subfolder`	`bool`	Create a subfolder with the same name as the file. Defaults to False.	`False`

Returns:

Type	Description
`str`	The output file path.

Source code in hypercoast/common.py

def download_file(
    url: Optional[str] = None,
    output: Optional[str] = None,
    quiet: Optional[bool] = True,
    proxy: Optional[str] = None,
    speed: Optional[float] = None,
    use_cookies: Optional[bool] = True,
    verify: Optional[bool] = True,
    id: Optional[str] = None,
    fuzzy: Optional[bool] = False,
    resume: Optional[bool] = False,
    unzip: Optional[bool] = True,
    overwrite: Optional[bool] = False,
    subfolder: Optional[bool] = False,
) -> str:
    """Download a file from URL, including Google Drive shared URL.

    Args:
        url (str, optional): Google Drive URL is also supported. Defaults to None.
        output (str, optional): Output filename. Default is basename of URL.
        quiet (bool, optional): Suppress terminal output. Default is True.
        proxy (str, optional): Proxy. Defaults to None.
        speed (float, optional): Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.
        use_cookies (bool, optional): Flag to use cookies. Defaults to True.
        verify (bool | str, optional): Either a bool, in which case it controls whether the server's TLS certificate is verified, or a string,
            in which case it must be a path to a CA bundle to use. Default is True.. Defaults to True.
        id (str, optional): Google Drive's file ID. Defaults to None.
        fuzzy (bool, optional): Fuzzy extraction of Google Drive's file Id. Defaults to False.
        resume (bool, optional): Resume the download from existing tmp file if possible. Defaults to False.
        unzip (bool, optional): Unzip the file. Defaults to True.
        overwrite (bool, optional): Overwrite the file if it already exists. Defaults to False.
        subfolder (bool, optional): Create a subfolder with the same name as the file. Defaults to False.

    Returns:
        str: The output file path.
    """
    import zipfile
    import tarfile
    import gdown

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

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

    if isinstance(url, str):
        if os.path.exists(os.path.abspath(output)) and (not overwrite):
            print(
                f"{output} already exists. Skip downloading. Set overwrite=True to overwrite."
            )
            return os.path.abspath(output)
        else:
            url = github_raw_url(url)

    if "https://drive.google.com/file/d/" in url:
        fuzzy = True

    output = gdown.download(
        url, output, quiet, proxy, speed, use_cookies, verify, id, fuzzy, resume
    )

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

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

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

    return os.path.abspath(output)

`download_nasa_data(granules, out_dir=None, provider=None, threads=8)` ¶

Downloads NASA Earthdata granules.

Parameters:

Name	Type	Description	Default
`granules`	`List[dict]`	The granules to download.	required
`out_dir`	`str`	The output directory where the granules will be downloaded. Defaults to None (current directory).	`None`
`provider`	`str`	The provider of the granules.	`None`
`threads`	`int`	The number of threads to use for downloading. Defaults to 8.	`8`

Source code in hypercoast/common.py

def download_nasa_data(
    granules: List[dict],
    out_dir: Optional[str] = None,
    provider: Optional[str] = None,
    threads: int = 8,
) -> None:
    """Downloads NASA Earthdata granules.

    Args:
        granules (List[dict]): The granules to download.
        out_dir (str, optional): The output directory where the granules will be downloaded. Defaults to None (current directory).
        provider (str, optional): The provider of the granules.
        threads (int, optional): The number of threads to use for downloading. Defaults to 8.
    """

    leafmap.nasa_data_download(
        granules=granules, out_dir=out_dir, provider=provider, threads=threads
    )

`download_pace(granules, out_dir=None, threads=8)` ¶

Downloads NASA PACE granules.

Parameters:

Name	Type	Description	Default
`granules`	`List[dict]`	The granules to download.	required
`out_dir`	`str`	The output directory where the granules will be downloaded. Defaults to None (current directory).	`None`
`threads`	`int`	The number of threads to use for downloading. Defaults to 8.	`8`

Source code in hypercoast/common.py

def download_pace(
    granules: List[dict],
    out_dir: Optional[str] = None,
    threads: int = 8,
) -> None:
    """Downloads NASA PACE granules.

    Args:
        granules (List[dict]): The granules to download.
        out_dir (str, optional): The output directory where the granules will be
            downloaded. Defaults to None (current directory).
        threads (int, optional): The number of threads to use for downloading.
            Defaults to 8.
    """

    download_nasa_data(granules=granules, out_dir=out_dir, threads=threads)

`extract_date_from_filename(filename)` ¶

Extracts a date from a filename assuming the date is in 'YYYYMMDD' format.

This function searches the filename for a sequence of 8 digits that represent a date in 'YYYYMMDD' format. If such a sequence is found, it converts the sequence into a pandas Timestamp object. If no such sequence is found, the function returns None.

Parameters:

Name	Type	Description	Default
`filename`	`str`	The filename from which to extract the date.	required

Returns:

Type	Description
`Optional[pd.Timestamp]`	A pandas Timestamp object representing the date found in the filename, or None if no date in 'YYYYMMDD' format is found.

Examples:

>>> extract_date_from_filename("example_20230101.txt")
Timestamp('2023-01-01 00:00:00')

>>> extract_date_from_filename("no_date_in_this_filename.txt")
None

Source code in hypercoast/common.py

def extract_date_from_filename(filename: str):
    """
    Extracts a date from a filename assuming the date is in 'YYYYMMDD' format.

    This function searches the filename for a sequence of 8 digits that represent a date in
    'YYYYMMDD' format. If such a sequence is found, it converts the sequence into a pandas
    Timestamp object. If no such sequence is found, the function returns None.

    Args:
        filename (str): The filename from which to extract the date.

    Returns:
        Optional[pd.Timestamp]: A pandas Timestamp object representing the date found in the filename,
        or None if no date in 'YYYYMMDD' format is found.

    Examples:
        >>> extract_date_from_filename("example_20230101.txt")
        Timestamp('2023-01-01 00:00:00')

        >>> extract_date_from_filename("no_date_in_this_filename.txt")
        None
    """
    import re
    import pandas as pd

    # Assuming the date format in filename is 'YYYYMMDD'
    date_match = re.search(r"\d{8}", filename)
    if date_match:
        return pd.to_datetime(date_match.group(), format="%Y%m%d")
    else:
        return None

`extract_spectral(ds, lat, lon, name='data')` ¶

Extracts spectral signature from a given xarray Dataset.

Parameters:

Name	Type	Description	Default
`ds`	`xarray.Dataset`	The dataset containing the spectral data.	required
`lat`	`float`	The latitude of the point to extract.	required
`lon`	`float`	The longitude of the point to extract.	required

Returns:

Type	Description
`xarray.DataArray`	The extracted data.

Source code in hypercoast/common.py

def extract_spectral(
    ds: xr.Dataset, lat: float, lon: float, name: str = "data"
) -> xr.DataArray:
    """
    Extracts spectral signature from a given xarray Dataset.

    Args:
        ds (xarray.Dataset): The dataset containing the spectral data.
        lat (float): The latitude of the point to extract.
        lon (float): The longitude of the point to extract.

    Returns:
        xarray.DataArray: The extracted data.
    """

    crs = ds.rio.crs

    x, y = convert_coords([[lat, lon]], "epsg:4326", crs)[0]

    values = ds.sel(x=x, y=y, method="nearest")[name].values

    da = xr.DataArray(values, dims=["band"], coords={"band": ds.coords["band"]})

    return da

`github_raw_url(url)` ¶

Get the raw URL for a GitHub file.

Parameters:

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

Returns:

Type	Description
`str`	The raw URL.

Source code in hypercoast/common.py

def github_raw_url(url: str) -> str:
    """Get the raw URL for a GitHub file.

    Args:
        url (str): The GitHub URL.
    Returns:
        str: The raw URL.
    """
    if isinstance(url, str) and url.startswith("https://github.com/") and "blob" in url:
        url = url.replace("github.com", "raw.githubusercontent.com").replace(
            "blob/", ""
        )
    return url

`image_cube(dataset, variable='reflectance', cmap='jet', clim=(0, 0.5), title='Reflectance', rgb_bands=None, rgb_wavelengths=None, rgb_gamma=1.0, rgb_cmap=None, rgb_clim=None, rgb_args={}, widget=None, plotter_args={}, show_axes=True, grid_origin=(0, 0, 0), grid_spacing=(1, 1, 1), **kwargs)` ¶

Creates an image cube from a dataset and plots it using PyVista.

Parameters:

Name	Type	Description	Default
`dataset`	`Union[str, xr.Dataset]`	The dataset to plot. Can be a path to a NetCDF file or an xarray Dataset.	required
`variable`	`str`	The variable to plot. Defaults to "reflectance".	`'reflectance'`
`cmap`	`str`	The colormap to use. Defaults to "jet".	`'jet'`
`clim`	`Tuple[float, float]`	The color limits. Defaults to (0, 0.5).	`(0, 0.5)`
`title`	`str`	The title for the scalar bar. Defaults to "Reflectance".	`'Reflectance'`
`rgb_bands`	`Optional[List[int]]`	The bands to use for the RGB image. Defaults to None.	`None`
`rgb_wavelengths`	`Optional[List[float]]`	The wavelengths to use for the RGB image. Defaults to None.	`None`
`rgb_gamma`	`float`	The gamma correction for the RGB image. Defaults to 1.	`1.0`
`rgb_cmap`	`Optional[str]`	The colormap to use for the RGB image. Defaults to None.	`None`
`rgb_clim`	`Optional[Tuple[float, float]]`	The color limits for the RGB image. Defaults to None.	`None`
`rgb_args`	`Dict[str, Any]`	Additional arguments for the `add_mesh` method for the RGB image. Defaults to {}.	`{}`
`widget`	`Optional[str]`	The widget to use for the image cube. Can be one of the following: "box", "plane", "slice", "orthogonal", and "threshold". Defaults to None.	`None`
`plotter_args`	`Dict[str, Any]`	Additional arguments for the `pv.Plotter` constructor. Defaults to {}.	`{}`
`show_axes`	`bool`	Whether to show the axes. Defaults to True.	`True`
`grid_origin`	`Tuple[float, float, float]`	The origin of the grid. Defaults to (0, 0, 0).	`(0, 0, 0)`
`grid_spacing`	`Tuple[float, float, float]`	The spacing of the grid.	`(1, 1, 1)`
`**kwargs`	`Dict[str, Any]`	Additional arguments for the `add_mesh` method. Defaults to {}.	`{}`

Returns:

Type	Description
`pv.Plotter`	The PyVista Plotter with the image cube added.

Source code in hypercoast/common.py

def image_cube(
    dataset,
    variable: str = "reflectance",
    cmap: str = "jet",
    clim: Tuple[float, float] = (0, 0.5),
    title: str = "Reflectance",
    rgb_bands: Optional[List[int]] = None,
    rgb_wavelengths: Optional[List[float]] = None,
    rgb_gamma: float = 1.0,
    rgb_cmap: Optional[str] = None,
    rgb_clim: Optional[Tuple[float, float]] = None,
    rgb_args: Dict[str, Any] = {},
    widget=None,
    plotter_args: Dict[str, Any] = {},
    show_axes: bool = True,
    grid_origin=(0, 0, 0),
    grid_spacing=(1, 1, 1),
    **kwargs: Any,
):
    """
    Creates an image cube from a dataset and plots it using PyVista.

    Args:
        dataset (Union[str, xr.Dataset]): The dataset to plot. Can be a path to
            a NetCDF file or an xarray Dataset.
        variable (str, optional): The variable to plot. Defaults to "reflectance".
        cmap (str, optional): The colormap to use. Defaults to "jet".
        clim (Tuple[float, float], optional): The color limits. Defaults to (0, 0.5).
        title (str, optional): The title for the scalar bar. Defaults to "Reflectance".
        rgb_bands (Optional[List[int]], optional): The bands to use for the RGB
            image. Defaults to None.
        rgb_wavelengths (Optional[List[float]], optional): The wavelengths to
            use for the RGB image. Defaults to None.
        rgb_gamma (float, optional): The gamma correction for the RGB image.
            Defaults to 1.
        rgb_cmap (Optional[str], optional): The colormap to use for the RGB image.
            Defaults to None.
        rgb_clim (Optional[Tuple[float, float]], optional): The color limits for
            the RGB image. Defaults to None.
        rgb_args (Dict[str, Any], optional): Additional arguments for the
            `add_mesh` method for the RGB image. Defaults to {}.
        widget (Optional[str], optional): The widget to use for the image cube.
            Can be one of the following: "box", "plane", "slice", "orthogonal",
            and "threshold". Defaults to None.
        plotter_args (Dict[str, Any], optional): Additional arguments for the
            `pv.Plotter` constructor. Defaults to {}.
        show_axes (bool, optional): Whether to show the axes. Defaults to True.
        grid_origin (Tuple[float, float, float], optional): The origin of the grid.
            Defaults to (0, 0, 0).
        grid_spacing (Tuple[float, float, float], optional): The spacing of the grid.
        **kwargs (Dict[str, Any], optional): Additional arguments for the
            `add_mesh` method. Defaults to {}.

    Returns:
        pv.Plotter: The PyVista Plotter with the image cube added.
    """

    import pyvista as pv
    import xarray as xr

    allowed_widgets = ["box", "plane", "slice", "orthogonal", "threshold"]

    if widget is not None:
        if widget not in allowed_widgets:
            raise ValueError(f"widget must be one of the following: {allowed_widgets}")

    if isinstance(dataset, str):
        dataset = xr.open_dataset(dataset)

    da = dataset[variable]  # xarray DataArray
    values = da.to_numpy()

    # Create the spatial reference for the image cube
    grid = pv.ImageData()

    # Set the grid dimensions: shape because we want to inject our values on the POINT data
    grid.dimensions = values.shape

    # Edit the spatial reference
    grid.origin = grid_origin  # The bottom left corner of the data set
    grid.spacing = grid_spacing  # These are the cell sizes along each axis

    # Add the data values to the cell data
    grid.point_data["values"] = values.flatten(order="F")  # Flatten the array

    # Plot the image cube with the RGB image overlay
    p = pv.Plotter(**plotter_args)

    if "scalar_bar_args" not in kwargs:
        kwargs["scalar_bar_args"] = {"title": title}
    else:
        kwargs["scalar_bar_args"]["title"] = title

    if "show_edges" not in kwargs:
        kwargs["show_edges"] = False

    if widget == "box":
        p.add_mesh_clip_box(grid, cmap=cmap, clim=clim, **kwargs)
    elif widget == "plane":
        if "normal" not in kwargs:
            kwargs["normal"] = (0, 0, 1)
        if "invert" not in kwargs:
            kwargs["invert"] = True
        if "normal_rotation" not in kwargs:
            kwargs["normal_rotation"] = False
        p.add_mesh_clip_plane(grid, cmap=cmap, clim=clim, **kwargs)
    elif widget == "slice":
        if "normal" not in kwargs:
            kwargs["normal"] = (0, 0, 1)
        if "normal_rotation" not in kwargs:
            kwargs["normal_rotation"] = False
        p.add_mesh_slice(grid, cmap=cmap, clim=clim, **kwargs)
    elif widget == "orthogonal":
        p.add_mesh_slice_orthogonal(grid, cmap=cmap, clim=clim, **kwargs)
    elif widget == "threshold":
        p.add_mesh_threshold(grid, cmap=cmap, clim=clim, **kwargs)
    else:
        p.add_mesh(grid, cmap=cmap, clim=clim, **kwargs)

    if rgb_bands is not None or rgb_wavelengths is not None:

        if rgb_bands is not None:
            rgb_image = dataset.isel(wavelength=rgb_bands, method="nearest")[
                variable
            ].to_numpy()
        elif rgb_wavelengths is not None:
            rgb_image = dataset.sel(wavelength=rgb_wavelengths, method="nearest")[
                variable
            ].to_numpy()

        x_dim, y_dim = rgb_image.shape[0], rgb_image.shape[1]
        z_dim = 1
        im = pv.ImageData(dimensions=(x_dim, y_dim, z_dim))

        # Add scalar data, you may also need to flatten this
        im.point_data["rgb_image"] = (
            rgb_image.reshape(-1, rgb_image.shape[2], order="F") * rgb_gamma
        )

        grid_z_max = grid.bounds[5]
        im.origin = (0, 0, grid_z_max)

        if rgb_image.shape[2] < 3:
            if rgb_cmap is None:
                rgb_cmap = cmap
            if rgb_clim is None:
                rgb_clim = clim

            if "cmap" not in rgb_args:
                rgb_args["cmap"] = rgb_cmap
            if "clim" not in rgb_args:
                rgb_args["clim"] = rgb_clim
        else:
            if "rgb" not in rgb_args:
                rgb_args["rgb"] = True

        if "show_scalar_bar" not in rgb_args:
            rgb_args["show_scalar_bar"] = False
        if "show_edges" not in rgb_args:
            rgb_args["show_edges"] = False

        p.add_mesh(im, **rgb_args)

    if show_axes:
        p.show_axes()

    return p

`nasa_earth_login(strategy='all', persist=True, **kwargs)` ¶

Logs in to NASA Earthdata.

Parameters:

Name	Type	Description	Default
`strategy`	`str`	The login strategy. Defaults to "all".	`'all'`
`persist`	`bool`	Whether to persist the login. Defaults to True.	`True`

Source code in hypercoast/common.py

def nasa_earth_login(strategy: str = "all", persist: bool = True, **kwargs) -> None:
    """Logs in to NASA Earthdata.

    Args:
        strategy (str, optional): The login strategy. Defaults to "all".
        persist (bool, optional): Whether to persist the login. Defaults to True.
    """

    leafmap.nasa_data_login(strategy=strategy, persist=persist, **kwargs)

`netcdf_groups(filepath)` ¶

Get the list of groups in a NetCDF file.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	The path to the NetCDF file.	required

Returns:

Type	Description
`list`	A list of group names in the NetCDF file.

Examples:

>>> netcdf_groups('path/to/netcdf/file')
['group1', 'group2', 'group3']

Source code in hypercoast/common.py

def netcdf_groups(filepath: str) -> List[str]:
    """
    Get the list of groups in a NetCDF file.

    Args:
        filepath (str): The path to the NetCDF file.

    Returns:
        list: A list of group names in the NetCDF file.

    Example:
        >>> netcdf_groups('path/to/netcdf/file')
        ['group1', 'group2', 'group3']
    """
    import h5netcdf

    with h5netcdf.File(filepath) as file:
        groups = list(file)
    return groups

`open_dataset(filename, engine=None, chunks=None, **kwargs)` ¶

Opens and returns an xarray Dataset from a file.

This function is a wrapper around xarray.open_dataset that allows for additional customization through keyword arguments.

Parameters:

Name	Type	Description	Default
`filename`	`str`	Path to the file to open.	required
`engine`	`Optional[str]`	Name of the engine to use for reading the file. If None, xarray's default engine is used. Examples include 'netcdf4', 'h5netcdf', 'zarr', etc.	`None`
`chunks`	`Optional[Dict[str, int]]`	Dictionary specifying how to chunk the dataset along each dimension. For example, `{'time': 1}` would load the dataset in single-time-step chunks. If None, the dataset is not chunked.	`None`
`**kwargs`	`Any`	Additional keyword arguments passed to `xarray.open_dataset`.	`{}`

Returns:

Type	Description
`xr.Dataset`	The opened dataset.

Examples:

Open a NetCDF file without chunking:

>>> dataset = open_dataset('path/to/file.nc')

Open a Zarr dataset, chunking along the 'time' dimension:

>>> dataset = open_dataset('path/to/dataset.zarr', engine='zarr', chunks={'time': 10})

Source code in hypercoast/common.py

def open_dataset(
    filename: str,
    engine: Optional[str] = None,
    chunks: Optional[Dict[str, int]] = None,
    **kwargs: Any,
) -> xr.Dataset:
    """
    Opens and returns an xarray Dataset from a file.

    This function is a wrapper around `xarray.open_dataset` that allows for additional
    customization through keyword arguments.

    Args:
        filename (str): Path to the file to open.
        engine (Optional[str]): Name of the engine to use for reading the file. If None, xarray's
            default engine is used. Examples include 'netcdf4', 'h5netcdf', 'zarr', etc.
        chunks (Optional[Dict[str, int]]): Dictionary specifying how to chunk the dataset along each dimension.
            For example, `{'time': 1}` would load the dataset in single-time-step chunks. If None,
            the dataset is not chunked.
        **kwargs: Additional keyword arguments passed to `xarray.open_dataset`.

    Returns:
        xr.Dataset: The opened dataset.

    Examples:
        Open a NetCDF file without chunking:
        >>> dataset = open_dataset('path/to/file.nc')

        Open a Zarr dataset, chunking along the 'time' dimension:
        >>> dataset = open_dataset('path/to/dataset.zarr', engine='zarr', chunks={'time': 10})
    """

    try:
        dataset = xr.open_dataset(filename, engine=engine, chunks=chunks, **kwargs)
    except Exception as e:
        dataset = xr.open_dataset(filename, engine="h5netcdf", chunks=chunks, **kwargs)

    return dataset

`pca(input_file, output_file, n_components=3, **kwargs)` ¶

Performs Principal Component Analysis (PCA) on a dataset.

Parameters:

Name	Type	Description	Default
`input_file`	`str`	The input file containing the data to analyze.	required
`output_file`	`str`	The output file to save the PCA results.	required
`n_components`	`int`	The number of principal components to compute. Defaults to 3.	`3`
`**kwargs`		Additional keyword arguments to pass to the scikit-learn PCA function. For more info, see https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html.	`{}`

Returns:

Type	Description
`None`	This function does not return a value. It saves the PCA results to the output file.

Source code in hypercoast/common.py

def pca(input_file, output_file, n_components=3, **kwargs):
    """
    Performs Principal Component Analysis (PCA) on a dataset.

    Args:
        input_file (str): The input file containing the data to analyze.
        output_file (str): The output file to save the PCA results.
        n_components (int, optional): The number of principal components to compute. Defaults to 3.
        **kwargs: Additional keyword arguments to pass to the scikit-learn PCA function.
            For more info, see https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html.

    Returns:
        None: This function does not return a value. It saves the PCA results to the output file.
    """

    import rasterio
    from sklearn.decomposition import PCA

    # Function to load the GeoTIFF image
    def load_geotiff(file_path):
        with rasterio.open(file_path) as src:
            image = src.read()
            profile = src.profile
        return image, profile

    # Function to perform PCA
    def perform_pca(image, n_components=3, **kwargs):
        # Reshape the image to [n_bands, n_pixels]
        n_bands, width, height = image.shape
        image_reshaped = image.reshape(n_bands, width * height).T

        # Perform PCA
        pca = PCA(n_components=n_components, **kwargs)
        principal_components = pca.fit_transform(image_reshaped)

        # Reshape the principal components back to image dimensions
        pca_image = principal_components.T.reshape(n_components, width, height)
        return pca_image

    # Function to save the PCA-transformed image
    def save_geotiff(file_path, image, profile):
        profile.update(count=image.shape[0])
        with rasterio.open(file_path, "w", **profile) as dst:
            dst.write(image)

    image, profile = load_geotiff(input_file)
    pca_image = perform_pca(image, n_components, **kwargs)
    save_geotiff(output_file, pca_image, profile)

`run_acolite(acolite_dir, settings_file=None, input_file=None, out_dir=None, polygon=None, l2w_parameters=None, rgb_rhot=True, rgb_rhos=True, map_l2w=True, verbose=True, **kwargs)` ¶

Runs the Acolite software for atmospheric correction and water quality retrieval. For more information, see the Acolite manual https://github.com/acolite/acolite/releases

This function constructs and executes a command to run the Acolite software with the specified parameters. It supports running Acolite with a settings file or with individual parameters specified directly. Additional parameters can be passed as keyword arguments.

Parameters:

Name	Type	Description	Default
`acolite_dir`	`str`	The directory where Acolite is installed.	required
`settings_file`	`Optional[str]`	The path to the Acolite settings file. If provided, other parameters except `verbose` are ignored. Defaults to None.	`None`
`input_file`	`Optional[str]`	The path to the input file for processing. Defaults to None.	`None`
`out_dir`	`Optional[str]`	The directory where output files will be saved. Defaults to None.	`None`
`polygon`	`Optional[str]`	The path to a polygon file for spatial subset. Defaults to None.	`None`
`l2w_parameters`	`Optional[str]`	Parameters for L2W processing. Defaults to None.	`None`
`rgb_rhot`	`bool`	Flag to generate RGB images using rhot. Defaults to True.	`True`
`rgb_rhos`	`bool`	Flag to generate RGB images using rhos. Defaults to True.	`True`
`map_l2w`	`bool`	Flag to map L2W products. Defaults to True.	`True`
`verbose`	`bool`	If True, prints the command output; otherwise, suppresses it. Defaults to True.	`True`
`**kwargs`	`Any`	Additional command line arguments to pass to acolite. Such as --l2w_export_geotiff, --merge_tiles, etc.	`{}`

Returns:

Type	Description
`None`	This function does not return a value. It executes the Acolite software.

Examples:

>>> run_acolite("/path/to/acolite", input_file="/path/to/inputfile", output="/path/to/output")

Source code in hypercoast/common.py

def run_acolite(
    acolite_dir: str,
    settings_file: Optional[str] = None,
    input_file: Optional[str] = None,
    out_dir: Optional[str] = None,
    polygon: Optional[str] = None,
    l2w_parameters: Optional[str] = None,
    rgb_rhot: bool = True,
    rgb_rhos: bool = True,
    map_l2w: bool = True,
    verbose: bool = True,
    **kwargs: Any,
) -> None:
    """
    Runs the Acolite software for atmospheric correction and water quality retrieval.
    For more information, see the Acolite manual https://github.com/acolite/acolite/releases

    This function constructs and executes a command to run the Acolite software with the specified
    parameters. It supports running Acolite with a settings file or with individual parameters
    specified directly. Additional parameters can be passed as keyword arguments.

    Args:
        acolite_dir (str): The directory where Acolite is installed.
        settings_file (Optional[str], optional): The path to the Acolite settings file. If provided,
            other parameters except `verbose` are ignored. Defaults to None.
        input_file (Optional[str], optional): The path to the input file for processing. Defaults to None.
        out_dir (Optional[str], optional): The directory where output files will be saved. Defaults to None.
        polygon (Optional[str], optional): The path to a polygon file for spatial subset. Defaults to None.
        l2w_parameters (Optional[str], optional): Parameters for L2W processing. Defaults to None.
        rgb_rhot (bool, optional): Flag to generate RGB images using rhot. Defaults to True.
        rgb_rhos (bool, optional): Flag to generate RGB images using rhos. Defaults to True.
        map_l2w (bool, optional): Flag to map L2W products. Defaults to True.
        verbose (bool, optional): If True, prints the command output; otherwise, suppresses it. Defaults to True.
        **kwargs (Any): Additional command line arguments to pass to acolite. Such as
            --l2w_export_geotiff, --merge_tiles, etc.

    Returns:
        None: This function does not return a value. It executes the Acolite software.

    Example:
        >>> run_acolite("/path/to/acolite", input_file="/path/to/inputfile", output="/path/to/output")
    """

    import subprocess
    from datetime import datetime

    def get_formatted_current_time(format_str="%Y-%m-%d %H:%M:%S"):
        current_time = datetime.now()
        formatted_time = current_time.strftime(format_str)
        return formatted_time

    acolite_dir_name = os.path.split(os.path.dirname(acolite_dir))[-1]
    acolite_exe = "acolite"
    if acolite_dir_name.endswith("win"):
        acolite_exe += ".exe"

    if isinstance(input_file, list):
        input_file = ",".join(input_file)

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

    acolite_exe_path = os.path.join(acolite_dir, "dist", "acolite", acolite_exe)

    acolite_cmd = [acolite_exe_path, "--cli"]

    if settings_file is not None:
        acolite_cmd.extend(["--settings", settings_file])
    else:
        lines = []
        lines.append("## ACOLITE settings")
        lines.append(f"## Written at {get_formatted_current_time()}")
        if input_file is not None:
            lines.append(f"inputfile={input_file}")
        if out_dir is not None:
            lines.append(f"output={out_dir}")
        if polygon is not None:
            lines.append(f"polygon={polygon}")
        else:
            lines.append("polygon=None")
        if l2w_parameters is not None:
            lines.append(f"l2w_parameters={l2w_parameters}")
        if rgb_rhot:
            lines.append("rgb_rhot=True")
        else:
            lines.append("rgb_rhot=False")
        if rgb_rhos:
            lines.append("rgb_rhos=True")
        else:
            lines.append("rgb_rhos=False")
        if map_l2w:
            lines.append("map_l2w=True")
        else:
            lines.append("map_l2w=False")

        for key, value in kwargs.items():
            lines.append(f"{key}={value}")

        lines.append(f"runid={get_formatted_current_time('%Y%m%d_%H%M%S')}")
        settings_filename = f"acolite_run_{get_formatted_current_time('%Y%m%d_%H%M%S')}_settings_user.txt"
        settings_file = os.path.join(out_dir, settings_filename)
        with open(settings_file, "w") as f:
            f.write("\n".join(lines))
        acolite_cmd.extend(["--settings", settings_file])

    if verbose:
        subprocess.run(acolite_cmd)
    else:
        subprocess.run(
            acolite_cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL
        )

`search_datasets(count=-1, **kwargs)` ¶

Searches for datasets using the EarthAccess API with optional filters.

This function wraps the earthaccess.search_datasets function, allowing for customized search queries based on a count limit and additional keyword arguments which serve as filters for the search.

Parameters:

Name	Type	Description	Default
`count`	`int`	The maximum number of datasets to return. A value of -1 indicates no limit. Defaults to -1.	`-1`
`**kwargs`	`Any`	Additional keyword arguments to pass as search filters to the EarthAccess API. keyword: case-insensitive and supports wildcards ? and * short_name: e.g. ATL08 doi: DOI for a dataset daac: e.g. NSIDC or PODAAC provider: particular to each DAAC, e.g. POCLOUD, LPDAAC etc. temporal: a tuple representing temporal bounds in the form (date_from, date_to) bounding_box: a tuple representing spatial bounds in the form (lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat)	`{}`

Returns:

Type	Description
`List[Dict[str, Any]]`	A list of dictionaries, where each dictionary contains information about a dataset found in the search.

Examples:

>>> results = search_datasets(count=5, keyword='temperature')
>>> print(results)

Source code in hypercoast/common.py

def search_datasets(count: int = -1, **kwargs: Any) -> List[Dict[str, Any]]:
    """
    Searches for datasets using the EarthAccess API with optional filters.

    This function wraps the `earthaccess.search_datasets` function, allowing for
    customized search queries based on a count limit and additional keyword arguments
    which serve as filters for the search.

    Args:
        count (int, optional): The maximum number of datasets to return. A value of -1
            indicates no limit. Defaults to -1.
        **kwargs (Any): Additional keyword arguments to pass as search filters to the
            EarthAccess API.
            keyword: case-insensitive and supports wildcards ? and *
            short_name: e.g. ATL08
            doi: DOI for a dataset
            daac: e.g. NSIDC or PODAAC
            provider: particular to each DAAC, e.g. POCLOUD, LPDAAC etc.
            temporal: a tuple representing temporal bounds in the form (date_from, date_to)
            bounding_box: a tuple representing spatial bounds in the form
            (lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat)


    Returns:
        List[Dict[str, Any]]: A list of dictionaries, where each dictionary contains
            information about a dataset found in the search.

    Example:
        >>> results = search_datasets(count=5, keyword='temperature')
        >>> print(results)
    """

    import earthaccess

    return earthaccess.search_datasets(count=count, **kwargs)

`search_ecostress(bbox=None, temporal=None, count=-1, short_name='ECO_L2T_LSTE', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)` ¶

Searches for NASA ECOSTRESS granules.

Parameters:

Name	Type	Description	Default
`bbox`	`List[float]`	The bounding box coordinates [xmin, ymin, xmax, ymax].	`None`
`temporal`	`str`	The temporal extent of the data.	`None`
`count`	`int`	The number of granules to retrieve. Defaults to -1 (retrieve all).	`-1`
`short_name`	`str`	The short name of the dataset. Defaults to "ECO_L2T_LSTE".	`'ECO_L2T_LSTE'`
`output`	`str`	The output file path to save the GeoDataFrame as a file.	`None`
`crs`	`str`	The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".	`'EPSG:4326'`
`return_gdf`	`bool`	Whether to return the GeoDataFrame in addition to the granules. Defaults to False.	`False`
`**kwargs`		Additional keyword arguments for the earthaccess.search_data() function.	`{}`

Returns:

Type	Description
`Union[List[dict], tuple]`	The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.

Source code in hypercoast/common.py

def search_ecostress(
    bbox: Optional[List[float]] = None,
    temporal: Optional[str] = None,
    count: int = -1,
    short_name: Optional[str] = "ECO_L2T_LSTE",
    output: Optional[str] = None,
    crs: str = "EPSG:4326",
    return_gdf: bool = False,
    **kwargs,
) -> Union[List[dict], tuple]:
    """Searches for NASA ECOSTRESS granules.

    Args:
        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].
        temporal (str, optional): The temporal extent of the data.
        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).
        short_name (str, optional): The short name of the dataset. Defaults to "ECO_L2T_LSTE".
        output (str, optional): The output file path to save the GeoDataFrame as a file.
        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".
        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.
        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.

    Returns:
        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.
    """

    return search_nasa_data(
        count=count,
        short_name=short_name,
        bbox=bbox,
        temporal=temporal,
        output=output,
        crs=crs,
        return_gdf=return_gdf,
        **kwargs,
    )

`search_emit(bbox=None, temporal=None, count=-1, short_name='EMITL2ARFL', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)` ¶

Searches for NASA EMIT granules.

Parameters:

Name	Type	Description	Default
`bbox`	`List[float]`	The bounding box coordinates [xmin, ymin, xmax, ymax].	`None`
`temporal`	`str`	The temporal extent of the data.	`None`
`count`	`int`	The number of granules to retrieve. Defaults to -1 (retrieve all).	`-1`
`short_name`	`str`	The short name of the dataset. Defaults to "EMITL2ARFL".	`'EMITL2ARFL'`
`output`	`str`	The output file path to save the GeoDataFrame as a file.	`None`
`crs`	`str`	The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".	`'EPSG:4326'`
`return_gdf`	`bool`	Whether to return the GeoDataFrame in addition to the granules. Defaults to False.	`False`
`**kwargs`		Additional keyword arguments for the earthaccess.search_data() function.	`{}`

Returns:

Type	Description
`Union[List[dict], tuple]`	The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.

Source code in hypercoast/common.py

def search_emit(
    bbox: Optional[List[float]] = None,
    temporal: Optional[str] = None,
    count: int = -1,
    short_name: Optional[str] = "EMITL2ARFL",
    output: Optional[str] = None,
    crs: str = "EPSG:4326",
    return_gdf: bool = False,
    **kwargs,
) -> Union[List[dict], tuple]:
    """Searches for NASA EMIT granules.

    Args:
        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].
        temporal (str, optional): The temporal extent of the data.
        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).
        short_name (str, optional): The short name of the dataset. Defaults to "EMITL2ARFL".
        output (str, optional): The output file path to save the GeoDataFrame as a file.
        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".
        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.
        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.

    Returns:
        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.
    """

    return search_nasa_data(
        count=count,
        short_name=short_name,
        bbox=bbox,
        temporal=temporal,
        output=output,
        crs=crs,
        return_gdf=return_gdf,
        **kwargs,
    )

`search_nasa_data(count=-1, short_name=None, bbox=None, temporal=None, version=None, doi=None, daac=None, provider=None, output=None, crs='EPSG:4326', return_gdf=False, **kwargs)` ¶

Searches for NASA Earthdata granules.

Parameters:

Name	Type	Description	Default
`count`	`int`	The number of granules to retrieve. Defaults to -1 (retrieve all).	`-1`
`short_name`	`str`	The short name of the dataset.	`None`
`bbox`	`List[float]`	The bounding box coordinates [xmin, ymin, xmax, ymax].	`None`
`temporal`	`str`	The temporal extent of the data.	`None`
`version`	`str`	The version of the dataset.	`None`
`doi`	`str`	The Digital Object Identifier (DOI) of the dataset.	`None`
`daac`	`str`	The Distributed Active Archive Center (DAAC) of the dataset.	`None`
`provider`	`str`	The provider of the dataset.	`None`
`output`	`str`	The output file path to save the GeoDataFrame as a file.	`None`
`crs`	`str`	The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".	`'EPSG:4326'`
`return_gdf`	`bool`	Whether to return the GeoDataFrame in addition to the granules. Defaults to False.	`False`
`**kwargs`		Additional keyword arguments for the earthaccess.search_data() function.	`{}`

Returns:

Type	Description
`Union[List[dict], tuple]`	The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.

Source code in hypercoast/common.py

def search_nasa_data(
    count: int = -1,
    short_name: Optional[str] = None,
    bbox: Optional[List[float]] = None,
    temporal: Optional[str] = None,
    version: Optional[str] = None,
    doi: Optional[str] = None,
    daac: Optional[str] = None,
    provider: Optional[str] = None,
    output: Optional[str] = None,
    crs: str = "EPSG:4326",
    return_gdf: bool = False,
    **kwargs,
) -> Union[List[dict], tuple]:
    """Searches for NASA Earthdata granules.

    Args:
        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).
        short_name (str, optional): The short name of the dataset.
        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].
        temporal (str, optional): The temporal extent of the data.
        version (str, optional): The version of the dataset.
        doi (str, optional): The Digital Object Identifier (DOI) of the dataset.
        daac (str, optional): The Distributed Active Archive Center (DAAC) of the dataset.
        provider (str, optional): The provider of the dataset.
        output (str, optional): The output file path to save the GeoDataFrame as a file.
        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".
        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.
        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.

    Returns:
        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.
    """

    if isinstance(bbox, list):
        bbox = tuple(bbox)

    return leafmap.nasa_data_search(
        count=count,
        short_name=short_name,
        bbox=bbox,
        temporal=temporal,
        version=version,
        doi=doi,
        daac=daac,
        provider=provider,
        output=output,
        crs=crs,
        return_gdf=return_gdf,
        **kwargs,
    )

`search_pace(bbox=None, temporal=None, count=-1, short_name='PACE_OCI_L2_AOP_NRT', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)` ¶

Searches for NASA PACE granules.

Parameters:

Name	Type	Description	Default
`bbox`	`List[float]`	The bounding box coordinates [xmin, ymin, xmax, ymax].	`None`
`temporal`	`str`	The temporal extent of the data.	`None`
`count`	`int`	The number of granules to retrieve. Defaults to -1 (retrieve all).	`-1`
`short_name`	`str`	The short name of the dataset. Defaults to "PACE_OCI_L2_AOP_NRT".	`'PACE_OCI_L2_AOP_NRT'`
`output`	`str`	The output file path to save the GeoDataFrame as a file.	`None`
`crs`	`str`	The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".	`'EPSG:4326'`
`return_gdf`	`bool`	Whether to return the GeoDataFrame in addition to the granules. Defaults to False.	`False`
`**kwargs`		Additional keyword arguments for the earthaccess.search_data() function.	`{}`

Returns:

Type	Description
`Union[List[dict], tuple]`	The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.

Source code in hypercoast/common.py

def search_pace(
    bbox: Optional[List[float]] = None,
    temporal: Optional[str] = None,
    count: int = -1,
    short_name: Optional[str] = "PACE_OCI_L2_AOP_NRT",
    output: Optional[str] = None,
    crs: str = "EPSG:4326",
    return_gdf: bool = False,
    **kwargs,
) -> Union[List[dict], tuple]:
    """Searches for NASA PACE granules.

    Args:
        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].
        temporal (str, optional): The temporal extent of the data.
        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).
        short_name (str, optional): The short name of the dataset. Defaults to "PACE_OCI_L2_AOP_NRT".
        output (str, optional): The output file path to save the GeoDataFrame as a file.
        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".
        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.
        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.

    Returns:
        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.
    """

    return search_nasa_data(
        count=count,
        short_name=short_name,
        bbox=bbox,
        temporal=temporal,
        output=output,
        crs=crs,
        return_gdf=return_gdf,
        **kwargs,
    )

`search_pace_chla(bbox=None, temporal=None, count=-1, short_name='PACE_OCI_L3M_CHL_NRT', granule_name='.DAY..0p1deg.*', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)` ¶

Searches for NASA PACE Chlorophyll granules.

Parameters:

Name	Type	Description	Default
`bbox`	`List[float]`	The bounding box coordinates [xmin, ymin, xmax, ymax].	`None`
`temporal`	`str`	The temporal extent of the data.	`None`
`count`	`int`	The number of granules to retrieve. Defaults to -1 (retrieve all).	`-1`
`short_name`	`str`	The short name of the dataset. Defaults to "PACE_OCI_L3M_CHL_NRT".	`'PACE_OCI_L3M_CHL_NRT'`
`output`	`str`	The output file path to save the GeoDataFrame as a file.	`None`
`crs`	`str`	The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".	`'EPSG:4326'`
`return_gdf`	`bool`	Whether to return the GeoDataFrame in addition to the granules. Defaults to False.	`False`
`**kwargs`		Additional keyword arguments for the earthaccess.search_data() function.	`{}`

Returns:

Type	Description
`Union[List[dict], tuple]`	The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.

Source code in hypercoast/common.py

def search_pace_chla(
    bbox: Optional[List[float]] = None,
    temporal: Optional[str] = None,
    count: int = -1,
    short_name: Optional[str] = "PACE_OCI_L3M_CHL_NRT",
    granule_name: Optional[str] = "*.DAY.*.0p1deg.*",
    output: Optional[str] = None,
    crs: str = "EPSG:4326",
    return_gdf: bool = False,
    **kwargs,
) -> Union[List[dict], tuple]:
    """Searches for NASA PACE Chlorophyll granules.

    Args:
        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].
        temporal (str, optional): The temporal extent of the data.
        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).
        short_name (str, optional): The short name of the dataset. Defaults to "PACE_OCI_L3M_CHL_NRT".
        output (str, optional): The output file path to save the GeoDataFrame as a file.
        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".
        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.
        **kwargs: Additional keyword arguments for the earthaccess.search_data() function.

    Returns:
        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.
    """

    return search_nasa_data(
        count=count,
        short_name=short_name,
        bbox=bbox,
        temporal=temporal,
        granule_name=granule_name,
        output=output,
        crs=crs,
        return_gdf=return_gdf,
        **kwargs,
    )

`show_field_data(data, x_col='wavelength', y_col_prefix='(', x_label='Wavelengths (nm)', y_label='Reflectance', use_marker_cluster=True, min_width=400, max_width=600, min_height=200, max_height=250, layer_name='Marker Cluster', m=None, center=(20, 0), zoom=2)` ¶

Displays field data on a map with interactive markers and popups showing time series data.

Parameters:

Name	Type	Description	Default
`data`	`Union[str, pd.DataFrame]`	Path to the CSV file or a pandas DataFrame containing the data.	required
`x_col`	`str`	Column name to use for the x-axis of the charts. Default is "wavelength".	`'wavelength'`
`y_col_prefix`	`str`	Prefix to identify the columns that contain the location-specific data. Default is "(".	`'('`
`x_label`	`str`	Label for the x-axis of the charts. Default is "Wavelengths (nm)".	`'Wavelengths (nm)'`
`y_label`	`str`	Label for the y-axis of the charts. Default is "Reflectance".	`'Reflectance'`
`use_marker_cluster`	`bool`	Whether to use marker clustering. Default is True.	`True`
`min_width`	`int`	Minimum width of the popup. Default is 400.	`400`
`max_width`	`int`	Maximum width of the popup. Default is 600.	`600`
`min_height`	`int`	Minimum height of the popup. Default is 200.	`200`
`max_height`	`int`	Maximum height of the popup. Default is 250.	`250`
`layer_name`	`str`	Name of the marker cluster layer. Default is "Marker Cluster".	`'Marker Cluster'`
`m`	`Map`	An ipyleaflet Map instance to add the markers to. Default is None.	`None`
`center`	`Tuple[float, float]`	Center of the map as a tuple of (latitude, longitude). Default is (20, 0).	`(20, 0)`
`zoom`	`int`	Zoom level of the map. Default is 2.	`2`

Returns:

Type	Description
`Map`	An ipyleaflet Map with the added markers and popups.

Source code in hypercoast/common.py

def show_field_data(
    data: Union[str],
    x_col: str = "wavelength",
    y_col_prefix: str = "(",
    x_label: str = "Wavelengths (nm)",
    y_label: str = "Reflectance",
    use_marker_cluster: bool = True,
    min_width: int = 400,
    max_width: int = 600,
    min_height: int = 200,
    max_height: int = 250,
    layer_name: str = "Marker Cluster",
    m: object = None,
    center: Tuple[float, float] = (20, 0),
    zoom: int = 2,
):
    """
    Displays field data on a map with interactive markers and popups showing time series data.

    Args:
        data (Union[str, pd.DataFrame]): Path to the CSV file or a pandas DataFrame containing the data.
        x_col (str): Column name to use for the x-axis of the charts. Default is "wavelength".
        y_col_prefix (str): Prefix to identify the columns that contain the location-specific data. Default is "(".
        x_label (str): Label for the x-axis of the charts. Default is "Wavelengths (nm)".
        y_label (str): Label for the y-axis of the charts. Default is "Reflectance".
        use_marker_cluster (bool): Whether to use marker clustering. Default is True.
        min_width (int): Minimum width of the popup. Default is 400.
        max_width (int): Maximum width of the popup. Default is 600.
        min_height (int): Minimum height of the popup. Default is 200.
        max_height (int): Maximum height of the popup. Default is 250.
        layer_name (str): Name of the marker cluster layer. Default is "Marker Cluster".
        m (Map, optional): An ipyleaflet Map instance to add the markers to. Default is None.
        center (Tuple[float, float]): Center of the map as a tuple of (latitude, longitude). Default is (20, 0).
        zoom (int): Zoom level of the map. Default is 2.

    Returns:
        Map: An ipyleaflet Map with the added markers and popups.
    """
    import pandas as pd
    import matplotlib.pyplot as plt
    from ipyleaflet import Map, Marker, Popup, MarkerCluster
    from ipywidgets import Output, VBox

    # Read the CSV file
    if isinstance(data, str):
        data = pd.read_csv(data)
    elif isinstance(data, pd.DataFrame):
        pass
    else:
        raise ValueError("data must be a path to a CSV file or a pandas DataFrame")

    # Extract locations from columns
    locations = [col for col in data.columns if col.startswith(y_col_prefix)]
    coordinates = [tuple(map(float, loc.strip("()").split())) for loc in locations]

    # Create the map
    if m is None:
        m = Map(center=center, zoom=zoom)

    # Function to create the chart
    def create_chart(data, title):
        fig, ax = plt.subplots(figsize=(10, 6))  # Adjust the figure size here
        ax.plot(data[x_col], data["values"])
        ax.set_title(title)
        ax.set_xlabel(x_label)
        ax.set_ylabel(y_label)
        output = Output()  # Adjust the output widget size here
        with output:
            plt.show()
        return output

    # Define a callback function to create and show the popup
    def callback_with_popup_creation(location, values):
        def f(**kwargs):
            marker_center = kwargs["coordinates"]
            output = create_chart(values, f"Location: {location}")
            popup = Popup(
                location=marker_center,
                child=VBox([output]),
                min_width=min_width,
                max_width=max_width,
                min_height=min_height,
                max_height=max_height,
            )
            m.add_layer(popup)

        return f

    markers = []

    # Add points to the map
    for i, coord in enumerate(coordinates):
        location = f"{coord}"
        values = pd.DataFrame({x_col: data[x_col], "values": data[locations[i]]})
        marker = Marker(location=coord, title=location, name=f"Marker {i + 1}")
        marker.on_click(callback_with_popup_creation(location, values))
        markers.append(marker)

    if use_marker_cluster:
        marker_cluster = MarkerCluster(markers=markers, name=layer_name)
        m.add_layer(marker_cluster)
    else:
        for marker in markers:
            m.add_layer(marker)

    return m

common module¶

convert_coords(coords, from_epsg, to_epsg) ¶

download_acolite(outdir='.', platform=None) ¶

download_ecostress(granules, out_dir=None, threads=8) ¶

download_emit(granules, out_dir=None, threads=8) ¶

download_file(url=None, output=None, quiet=True, proxy=None, speed=None, use_cookies=True, verify=True, id=None, fuzzy=False, resume=False, unzip=True, overwrite=False, subfolder=False) ¶

download_nasa_data(granules, out_dir=None, provider=None, threads=8) ¶

download_pace(granules, out_dir=None, threads=8) ¶

extract_date_from_filename(filename) ¶

extract_spectral(ds, lat, lon, name='data') ¶

github_raw_url(url) ¶

image_cube(dataset, variable='reflectance', cmap='jet', clim=(0, 0.5), title='Reflectance', rgb_bands=None, rgb_wavelengths=None, rgb_gamma=1.0, rgb_cmap=None, rgb_clim=None, rgb_args={}, widget=None, plotter_args={}, show_axes=True, grid_origin=(0, 0, 0), grid_spacing=(1, 1, 1), **kwargs) ¶

nasa_earth_login(strategy='all', persist=True, **kwargs) ¶

netcdf_groups(filepath) ¶

open_dataset(filename, engine=None, chunks=None, **kwargs) ¶

pca(input_file, output_file, n_components=3, **kwargs) ¶

run_acolite(acolite_dir, settings_file=None, input_file=None, out_dir=None, polygon=None, l2w_parameters=None, rgb_rhot=True, rgb_rhos=True, map_l2w=True, verbose=True, **kwargs) ¶

search_datasets(count=-1, **kwargs) ¶

search_ecostress(bbox=None, temporal=None, count=-1, short_name='ECO_L2T_LSTE', output=None, crs='EPSG:4326', return_gdf=False, **kwargs) ¶

search_emit(bbox=None, temporal=None, count=-1, short_name='EMITL2ARFL', output=None, crs='EPSG:4326', return_gdf=False, **kwargs) ¶

search_nasa_data(count=-1, short_name=None, bbox=None, temporal=None, version=None, doi=None, daac=None, provider=None, output=None, crs='EPSG:4326', return_gdf=False, **kwargs) ¶

search_pace(bbox=None, temporal=None, count=-1, short_name='PACE_OCI_L2_AOP_NRT', output=None, crs='EPSG:4326', return_gdf=False, **kwargs) ¶

search_pace_chla(bbox=None, temporal=None, count=-1, short_name='PACE_OCI_L3M_CHL_NRT', granule_name='*.DAY.*.0p1deg.*', output=None, crs='EPSG:4326', return_gdf=False, **kwargs) ¶

show_field_data(data, x_col='wavelength', y_col_prefix='(', x_label='Wavelengths (nm)', y_label='Reflectance', use_marker_cluster=True, min_width=400, max_width=600, min_height=200, max_height=250, layer_name='Marker Cluster', m=None, center=(20, 0), zoom=2) ¶

`convert_coords(coords, from_epsg, to_epsg)` ¶

`download_acolite(outdir='.', platform=None)` ¶

`download_ecostress(granules, out_dir=None, threads=8)` ¶

`download_emit(granules, out_dir=None, threads=8)` ¶

`download_file(url=None, output=None, quiet=True, proxy=None, speed=None, use_cookies=True, verify=True, id=None, fuzzy=False, resume=False, unzip=True, overwrite=False, subfolder=False)` ¶

`download_nasa_data(granules, out_dir=None, provider=None, threads=8)` ¶

`download_pace(granules, out_dir=None, threads=8)` ¶

`extract_date_from_filename(filename)` ¶

`extract_spectral(ds, lat, lon, name='data')` ¶

`github_raw_url(url)` ¶

`image_cube(dataset, variable='reflectance', cmap='jet', clim=(0, 0.5), title='Reflectance', rgb_bands=None, rgb_wavelengths=None, rgb_gamma=1.0, rgb_cmap=None, rgb_clim=None, rgb_args={}, widget=None, plotter_args={}, show_axes=True, grid_origin=(0, 0, 0), grid_spacing=(1, 1, 1), **kwargs)` ¶

`nasa_earth_login(strategy='all', persist=True, **kwargs)` ¶

`netcdf_groups(filepath)` ¶

`open_dataset(filename, engine=None, chunks=None, **kwargs)` ¶

`pca(input_file, output_file, n_components=3, **kwargs)` ¶

`run_acolite(acolite_dir, settings_file=None, input_file=None, out_dir=None, polygon=None, l2w_parameters=None, rgb_rhot=True, rgb_rhos=True, map_l2w=True, verbose=True, **kwargs)` ¶

`search_datasets(count=-1, **kwargs)` ¶

`search_ecostress(bbox=None, temporal=None, count=-1, short_name='ECO_L2T_LSTE', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)` ¶

`search_emit(bbox=None, temporal=None, count=-1, short_name='EMITL2ARFL', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)` ¶

`search_nasa_data(count=-1, short_name=None, bbox=None, temporal=None, version=None, doi=None, daac=None, provider=None, output=None, crs='EPSG:4326', return_gdf=False, **kwargs)` ¶

`search_pace(bbox=None, temporal=None, count=-1, short_name='PACE_OCI_L2_AOP_NRT', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)` ¶

`search_pace_chla(bbox=None, temporal=None, count=-1, short_name='PACE_OCI_L3M_CHL_NRT', granule_name='.DAY..0p1deg.*', output=None, crs='EPSG:4326', return_gdf=False, **kwargs)` ¶

`show_field_data(data, x_col='wavelength', y_col_prefix='(', x_label='Wavelengths (nm)', y_label='Reflectance', use_marker_cluster=True, min_width=400, max_width=600, min_height=200, max_height=250, layer_name='Marker Cluster', m=None, center=(20, 0), zoom=2)` ¶