Function open_groups

xarray/backends/api.py:1103–1348

Open and decode a file or file-like object, creating a dictionary containing one xarray Dataset for each group in the file. Useful for an HDF file ("netcdf4" or "h5netcdf") containing many groups that are not alignable with their parents and cannot be opened directly with ``open_datatr

(
    filename_or_obj: T_PathFileOrDataStore,
    *,
    engine: T_Engine = None,
    chunks: T_Chunks = None,
    cache: bool | None = None,
    decode_cf: bool | None = None,
    mask_and_scale: bool | Mapping[str, bool] | None = None,
    decode_times: (
        bool | CFDatetimeCoder | Mapping[str, bool | CFDatetimeCoder] | None
    ) = None,
    decode_timedelta: (
        bool | CFTimedeltaCoder | Mapping[str, bool | CFTimedeltaCoder] | None
    ) = None,
    use_cftime: bool | Mapping[str, bool] | None = None,
    concat_characters: bool | Mapping[str, bool] | None = None,
    decode_coords: Literal["coordinates", "all"] | bool | None = None,
    drop_variables: str | Iterable[str] | None = None,
    create_default_indexes: bool = True,
    inline_array: bool = False,
    chunked_array_type: str | None = None,
    from_array_kwargs: dict[str, Any] | None = None,
    backend_kwargs: dict[str, Any] | None = None,
    **kwargs,
)

def open_groups(
    filename_or_obj: T_PathFileOrDataStore,
    *,
    engine: T_Engine = None,
    chunks: T_Chunks = None,
    cache: bool | None = None,
    decode_cf: bool | None = None,
    mask_and_scale: bool | Mapping[str, bool] | None = None,
    decode_times: (
        bool | CFDatetimeCoder | Mapping[str, bool | CFDatetimeCoder] | None
    ) = None,
    decode_timedelta: (
        bool | CFTimedeltaCoder | Mapping[str, bool | CFTimedeltaCoder] | None
    ) = None,
    use_cftime: bool | Mapping[str, bool] | None = None,
    concat_characters: bool | Mapping[str, bool] | None = None,
    decode_coords: Literal["coordinates", "all"] | bool | None = None,
    drop_variables: str | Iterable[str] | None = None,
    create_default_indexes: bool = True,
    inline_array: bool = False,
    chunked_array_type: str | None = None,
    from_array_kwargs: dict[str, Any] | None = None,
    backend_kwargs: dict[str, Any] | None = None,
    **kwargs,
) -> dict[str, Dataset]:
    """
    Open and decode a file or file-like object, creating a dictionary containing one xarray Dataset for each group in the file.

    Useful for an HDF file ("netcdf4" or "h5netcdf") containing many groups that are not alignable with their parents
    and cannot be opened directly with ``open_datatree``. It is encouraged to use this function to inspect your data,
    then make the necessary changes to make the structure coercible to a `DataTree` object before calling `DataTree.from_dict()` and proceeding with your analysis.

    Parameters
    ----------
    filename_or_obj : str, Path, file-like, bytes, memoryview or DataStore
        Strings and Path objects are interpreted as a path to a netCDF file or
        Zarr store. Bytes and memoryview objects are interpreted as file
        contents.
    engine : {"netcdf4", "h5netcdf", "zarr", None}, \
             installed backend or xarray.backends.BackendEntrypoint, optional
        Engine to use when reading files. If not provided, the default engine
        is chosen based on available dependencies, by default preferring
        "h5netcdf" over "netcdf4" (customizable via ``netcdf_engine_order`` in
        ``xarray.set_options()``). A custom backend class (a subclass of
        ``BackendEntrypoint``) can also be used.
        can also be used.
    chunks : int, dict, 'auto' or None, default: None
        If provided, used to load the data into dask arrays.

        - ``chunks="auto"`` will use dask ``auto`` chunking taking into account the
          engine preferred chunks.
        - ``chunks=None`` skips using dask. This uses xarray's internally private
          :ref:`lazy indexing classes <internal design.lazy indexing>`,
          but data is eagerly loaded into memory as numpy arrays when accessed.
          This can be more efficient for smaller arrays, though results may vary.
        - ``chunks=-1`` loads the data with dask using a single chunk for all arrays.
        - ``chunks={}`` loads the data with dask using the engine&#x27;s preferred chunk
          size, generally identical to the format&#x27;s chunk size. If not available, a