Function open_datatree

xarray/backends/api.py:862–1100

Open and decode a DataTree from a file or file-like object, creating one tree node for each group in the file. Parameters ---------- filename_or_obj : str, Path, file-like, bytes or DataStore Strings and Path objects are interpreted as a path to a netCDF file or Zar

(
    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_datatree(
    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,
) -> DataTree:
    """
    Open and decode a DataTree from a file or file-like object, creating one tree node for each group in the file.

    Parameters
    ----------
    filename_or_obj : str, Path, file-like, bytes 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.
    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
          single chunk for all arrays.

        See dask chunking for more details.
    cache : bool, optional
        If True, cache data loaded from the underlying datastore in memory as