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darts_acquisition.load_tcvis

Load the TCVIS for the given geobox, fetch new data from GEE if necessary.

Parameters:

  • geobox (odc.geo.geobox.GeoBox) –

    The geobox to load the data for.

  • data_dir (pathlib.Path | str) –

    The directory to store the downloaded data for faster access for consecutive calls.

  • buffer (int, default: 0 ) –

    The buffer around the geobox in pixels. Defaults to 0.

  • persist (bool, default: True ) –

    If the data should be persisted in memory. If not, this will return a Dask backed Dataset. Defaults to True.

Returns:

Usage

Since the API of the load_tcvis is based on GeoBox, one can load a specific ROI based on an existing Xarray DataArray:

import xarray as xr
import odc.geo.xr

from darts_aquisition import load_tcvis

# Assume "optical" is an already loaded s2 based dataarray

tcvis = load_tcvis(
    optical.odc.geobox,
    "/path/to/tcvis-parent-directory",
)

# Now we can for example match the resolution and extent of the optical data:
tcvis = tcvis.odc.reproject(optical.odc.geobox, resampling="cubic")
Source code in darts-acquisition/src/darts_acquisition/tcvis.py 
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@stopuhr.funkuhr("Loading TCVIS", printer=logger.debug, print_kwargs=True)
def load_tcvis(
    geobox: GeoBox,
    data_dir: Path | str,
    buffer: int = 0,
    persist: bool = True,
) -> xr.Dataset:
    """Load the TCVIS for the given geobox, fetch new data from GEE if necessary.

    Args:
        geobox (GeoBox): The geobox to load the data for.
        data_dir (Path | str): The directory to store the downloaded data for faster access for consecutive calls.
        buffer (int, optional): The buffer around the geobox in pixels. Defaults to 0.
        persist (bool, optional): If the data should be persisted in memory.
            If not, this will return a Dask backed Dataset. Defaults to True.

    Returns:
        xr.Dataset: The TCVIS dataset.

    Usage:
        Since the API of the `load_tcvis` is based on GeoBox, one can load a specific ROI based on an existing Xarray DataArray:

        ```python
        import xarray as xr
        import odc.geo.xr

        from darts_aquisition import load_tcvis

        # Assume "optical" is an already loaded s2 based dataarray

        tcvis = load_tcvis(
            optical.odc.geobox,
            "/path/to/tcvis-parent-directory",
        )

        # Now we can for example match the resolution and extent of the optical data:
        tcvis = tcvis.odc.reproject(optical.odc.geobox, resampling="cubic")
        ```

    """  # noqa: E501
    accessor = smart_geocubes.TCTrend(data_dir, create_icechunk_storage=False)

    # We want to assume that the datacube is already created to be save in a multi-process environment
    accessor.assert_created()

    tcvis = accessor.load(geobox, buffer=buffer, persist=persist)

    # Rename to follow our conventions
    tcvis = tcvis.rename_vars(
        {
            "TCB_slope": "tc_brightness",
            "TCG_slope": "tc_greenness",
            "TCW_slope": "tc_wetness",
        }
    )

    return tcvis