api.py

from __future__ import annotations

import os
from collections.abc import Hashable, Iterable, Mapping, MutableMapping, Sequence
from functools import partial
from io import BytesIO
from numbers import Number
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Final,
    Literal,
    Union,
    cast,
    overload,
)

import numpy as np

from xarray import backends, conventions
from xarray.backends import plugins
from xarray.backends.common import (
    AbstractDataStore,
    ArrayWriter,
    _find_absolute_paths,
    _normalize_path,
)
from xarray.backends.locks import _get_scheduler
from xarray.backends.zarr import open_zarr
from xarray.core import indexing
from xarray.core.combine import (
    _infer_concat_order_from_positions,
    _nested_combine,
    combine_by_coords,
)
from xarray.core.dataarray import DataArray
from xarray.core.dataset import Dataset, _get_chunk, _maybe_chunk
from xarray.core.indexes import Index
from xarray.core.types import ZarrWriteModes
from xarray.core.utils import is_remote_uri
from xarray.namedarray.daskmanager import DaskManager
from xarray.namedarray.parallelcompat import guess_chunkmanager

if TYPE_CHECKING:
    try:
        from dask.delayed import Delayed
    except ImportError:
        Delayed = None  # type: ignore
    from io import BufferedIOBase

    from xarray.backends.common import BackendEntrypoint
    from xarray.core.types import (
        CombineAttrsOptions,
        CompatOptions,
        JoinOptions,
        NestedSequence,
        T_Chunks,
    )

    T_NetcdfEngine = Literal["netcdf4", "scipy", "h5netcdf"]
    T_Engine = Union[
        T_NetcdfEngine,
        Literal["pydap", "pynio", "zarr"],
        type[BackendEntrypoint],
        str,  # no nice typing support for custom backends
        None,
    ]
    T_NetcdfTypes = Literal[
        "NETCDF4", "NETCDF4_CLASSIC", "NETCDF3_64BIT", "NETCDF3_CLASSIC"
    ]
    from xarray.core.datatree import DataTree

DATAARRAY_NAME = "__xarray_dataarray_name__"
DATAARRAY_VARIABLE = "__xarray_dataarray_variable__"

ENGINES = {
    "netcdf4": backends.NetCDF4DataStore.open,
    "scipy": backends.ScipyDataStore,
    "pydap": backends.PydapDataStore.open,
    "h5netcdf": backends.H5NetCDFStore.open,
    "pynio": backends.NioDataStore,
    "zarr": backends.ZarrStore.open_group,
}


def _get_default_engine_remote_uri() -> Literal["netcdf4", "pydap"]:
    engine: Literal["netcdf4", "pydap"]
    try:
        import netCDF4  # noqa: F401

        engine = "netcdf4"
    except ImportError:  # pragma: no cover
        try:
            import pydap  # noqa: F401

            engine = "pydap"
        except ImportError:
            raise ValueError(
                "netCDF4 or pydap is required for accessing "
                "remote datasets via OPeNDAP"
            )
    return engine


def _get_default_engine_gz() -> Literal["scipy"]:
    try:
        import scipy  # noqa: F401

        engine: Final = "scipy"
    except ImportError:  # pragma: no cover
        raise ValueError("scipy is required for accessing .gz files")
    return engine


def _get_default_engine_netcdf() -> Literal["netcdf4", "scipy"]:
    engine: Literal["netcdf4", "scipy"]
    try:
        import netCDF4  # noqa: F401

        engine = "netcdf4"
    except ImportError:  # pragma: no cover
        try:
            import scipy.io.netcdf  # noqa: F401

            engine = "scipy"
        except ImportError:
            raise ValueError(
                "cannot read or write netCDF files without "
                "netCDF4-python or scipy installed"
            )
    return engine


def _get_default_engine(path: str, allow_remote: bool = False) -> T_NetcdfEngine:
    if allow_remote and is_remote_uri(path):
        return _get_default_engine_remote_uri()  # type: ignore[return-value]
    elif path.endswith(".gz"):
        return _get_default_engine_gz()
    else:
        return _get_default_engine_netcdf()


def _validate_dataset_names(dataset: Dataset) -> None:
    """DataArray.name and Dataset keys must be a string or None"""

    def check_name(name: Hashable):
        if isinstance(name, str):
            if not name:
                raise ValueError(
                    f"Invalid name {name!r} for DataArray or Dataset key: "
                    "string must be length 1 or greater for "
                    "serialization to netCDF files"
                )
        elif name is not None:
            raise TypeError(
                f"Invalid name {name!r} for DataArray or Dataset key: "
                "must be either a string or None for serialization to netCDF "
                "files"
            )

    for k in dataset.variables:
        check_name(k)


def _validate_attrs(dataset, invalid_netcdf=False):
    """`attrs` must have a string key and a value which is either: a number,
    a string, an ndarray, a list/tuple of numbers/strings, or a numpy.bool_.

    Notes
    -----
    A numpy.bool_ is only allowed when using the h5netcdf engine with
    `invalid_netcdf=True`.
    """

    valid_types = (str, Number, np.ndarray, np.number, list, tuple)
    if invalid_netcdf:
        valid_types += (np.bool_,)

    def check_attr(name, value, valid_types):
        if isinstance(name, str):
            if not name:
                raise ValueError(
                    f"Invalid name for attr {name!r}: string must be "
                    "length 1 or greater for serialization to "
                    "netCDF files"
                )
        else:
            raise TypeError(
                f"Invalid name for attr: {name!r} must be a string for "
                "serialization to netCDF files"
            )

        if not isinstance(value, valid_types):
            raise TypeError(
                f"Invalid value for attr {name!r}: {value!r}. For serialization to "
                "netCDF files, its value must be of one of the following types: "
                f"{', '.join([vtype.__name__ for vtype in valid_types])}"
            )

    # Check attrs on the dataset itself
    for k, v in dataset.attrs.items():
        check_attr(k, v, valid_types)

    # Check attrs on each variable within the dataset
    for variable in dataset.variables.values():
        for k, v in variable.attrs.items():
            check_attr(k, v, valid_types)


def _resolve_decoders_kwargs(decode_cf, open_backend_dataset_parameters, **decoders):
    for d in list(decoders):
        if decode_cf is False and d in open_backend_dataset_parameters:
            decoders[d] = False
        if decoders[d] is None:
            decoders.pop(d)
    return decoders


def _get_mtime(filename_or_obj):
    # if passed an actual file path, augment the token with
    # the file modification time
    mtime = None

    try:
        path = os.fspath(filename_or_obj)
    except TypeError:
        path = None

    if path and not is_remote_uri(path):
        mtime = os.path.getmtime(os.path.expanduser(filename_or_obj))

    return mtime


def _protect_dataset_variables_inplace(dataset, cache):
    for name, variable in dataset.variables.items():
        if name not in dataset._indexes:
            # no need to protect IndexVariable objects
            data = indexing.CopyOnWriteArray(variable._data)
            if cache:
                data = indexing.MemoryCachedArray(data)
            variable.data = data


def _finalize_store(write, store):
    """Finalize this store by explicitly syncing and closing"""
    del write  # ensure writing is done first
    store.close()


def _multi_file_closer(closers):
    for closer in closers:
        closer()


def load_dataset(filename_or_obj, **kwargs) -> Dataset:
    """Open, load into memory, and close a Dataset from a file or file-like
    object.

    This is a thin wrapper around :py:meth:`~xarray.open_dataset`. It differs
    from `open_dataset` in that it loads the Dataset into memory, closes the
    file, and returns the Dataset. In contrast, `open_dataset` keeps the file
    handle open and lazy loads its contents. All parameters are passed directly
    to `open_dataset`. See that documentation for further details.

    Returns
    -------
    dataset : Dataset
        The newly created Dataset.

    See Also
    --------
    open_dataset
    """
    if "cache" in kwargs:
        raise TypeError("cache has no effect in this context")

    with open_dataset(filename_or_obj, **kwargs) as ds:
        return ds.load()


def load_dataarray(filename_or_obj, **kwargs):
    """Open, load into memory, and close a DataArray from a file or file-like
    object containing a single data variable.

    This is a thin wrapper around :py:meth:`~xarray.open_dataarray`. It differs
    from `open_dataarray` in that it loads the Dataset into memory, closes the
    file, and returns the Dataset. In contrast, `open_dataarray` keeps the file
    handle open and lazy loads its contents. All parameters are passed directly
    to `open_dataarray`. See that documentation for further details.

    Returns
    -------
    datarray : DataArray
        The newly created DataArray.

    See Also
    --------
    open_dataarray
    """
    if "cache" in kwargs:
        raise TypeError("cache has no effect in this context")

    with open_dataarray(filename_or_obj, **kwargs) as da:
        return da.load()


def _chunk_ds(
    backend_ds,
    filename_or_obj,
    engine,
    chunks,
    overwrite_encoded_chunks,
    inline_array,
    chunked_array_type,
    from_array_kwargs,
    **extra_tokens,
):
    chunkmanager = guess_chunkmanager(chunked_array_type)

    # TODO refactor to move this dask-specific logic inside the DaskManager class
    if isinstance(chunkmanager, DaskManager):
        from dask.base import tokenize

        mtime = _get_mtime(filename_or_obj)
        token = tokenize(filename_or_obj, mtime, engine, chunks, **extra_tokens)
        name_prefix = "open_dataset-"
    else:
        # not used
        token = (None,)
        name_prefix = None

    variables = {}
    for name, var in backend_ds.variables.items():
        var_chunks = _get_chunk(var, chunks, chunkmanager)
        variables[name] = _maybe_chunk(
            name,
            var,
            var_chunks,
            overwrite_encoded_chunks=overwrite_encoded_chunks,
            name_prefix=name_prefix,
            token=token,
            inline_array=inline_array,
            chunked_array_type=chunkmanager,
            from_array_kwargs=from_array_kwargs.copy(),
        )
    return backend_ds._replace(variables)


def _dataset_from_backend_dataset(
    backend_ds,
    filename_or_obj,
    engine,
    chunks,
    cache,
    overwrite_encoded_chunks,
    inline_array,
    chunked_array_type,
    from_array_kwargs,
    **extra_tokens,
):
    if not isinstance(chunks, (int, dict)) and chunks not in {None, "auto"}:
        raise ValueError(
            f"chunks must be an int, dict, 'auto', or None. Instead found {chunks}."
        )

    _protect_dataset_variables_inplace(backend_ds, cache)
    if chunks is None:
        ds = backend_ds
    else:
        ds = _chunk_ds(
            backend_ds,
            filename_or_obj,
            engine,
            chunks,
            overwrite_encoded_chunks,
            inline_array,
            chunked_array_type,
            from_array_kwargs,
            **extra_tokens,
        )

    ds.set_close(backend_ds._close)

    # Ensure source filename always stored in dataset object
    if "source" not in ds.encoding and isinstance(filename_or_obj, (str, os.PathLike)):
        ds.encoding["source"] = _normalize_path(filename_or_obj)

    return ds


def open_dataset(
    filename_or_obj: str | os.PathLike[Any] | BufferedIOBase | AbstractDataStore,
    *,
    engine: T_Engine = None,
    chunks: T_Chunks = None,
    cache: bool | None = None,
    decode_cf: bool | None = None,
    mask_and_scale: bool | None = None,
    decode_times: bool | None = None,
    decode_timedelta: bool | None = None,
    use_cftime: bool | None = None,
    concat_characters: bool | None = None,
    decode_coords: Literal["coordinates", "all"] | bool | None = None,
    drop_variables: str | Iterable[str] | None = None,
    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,
) -> Dataset:
    """Open and decode a dataset from a file or file-like object.

    Parameters
    ----------
    filename_or_obj : str, Path, file-like or DataStore
        Strings and Path objects are interpreted as a path to a netCDF file
        or an OpenDAP URL and opened with python-netCDF4, unless the filename
        ends with .gz, in which case the file is gunzipped and opened with
        scipy.io.netcdf (only netCDF3 supported). Byte-strings or file-like
        objects are opened by scipy.io.netcdf (netCDF3) or h5py (netCDF4/HDF).
    engine : {"netcdf4", "scipy", "pydap", "h5netcdf", "pynio", \
        "zarr", None}, installed backend \
        or subclass of xarray.backends.BackendEntrypoint, optional
        Engine to use when reading files. If not provided, the default engine
        is chosen based on available dependencies, with a preference for
        "netcdf4". A custom backend class (a subclass of ``BackendEntrypoint``)
        can also be used.
    chunks : int, dict, 'auto' or None, optional
        If chunks is provided, it is used to load the new dataset into dask
        arrays. ``chunks=-1`` loads the dataset with dask using a single
        chunk for all arrays. ``chunks={}`` loads the dataset with dask using
        engine preferred chunks if exposed by the backend, otherwise with
        a single chunk for all arrays. In order to reproduce the default behavior
        of ``xr.open_zarr(...)`` use ``xr.open_dataset(..., engine='zarr', chunks={})``.
        ``chunks='auto'`` will use dask ``auto`` chunking taking into account the
        engine preferred chunks. See dask chunking for more details.
    cache : bool, optional
        If True, cache data loaded from the underlying datastore in memory as
        NumPy arrays when accessed to avoid reading from the underlying data-
        store multiple times. Defaults to True unless you specify the `chunks`
        argument to use dask, in which case it defaults to False. Does not
        change the behavior of coordinates corresponding to dimensions, which
        always load their data from disk into a ``pandas.Index``.
    decode_cf : bool, optional
        Whether to decode these variables, assuming they were saved according
        to CF conventions.
    mask_and_scale : bool, optional
        If True, replace array values equal to `_FillValue` with NA and scale
        values according to the formula `original_values * scale_factor +
        add_offset`, where `_FillValue`, `scale_factor` and `add_offset` are
        taken from variable attributes (if they exist).  If the `_FillValue` or
        `missing_value` attribute contains multiple values a warning will be
        issued and all array values matching one of the multiple values will
        be replaced by NA. This keyword may not be supported by all the backends.
    decode_times : bool, optional
        If True, decode times encoded in the standard NetCDF datetime format
        into datetime objects. Otherwise, leave them encoded as numbers.
        This keyword may not be supported by all the backends.
    decode_timedelta : bool, optional
        If True, decode variables and coordinates with time units in
        {"days", "hours", "minutes", "seconds", "milliseconds", "microseconds"}
        into timedelta objects. If False, leave them encoded as numbers.
        If None (default), assume the same value of decode_time.
        This keyword may not be supported by all the backends.
    use_cftime: bool, optional
        Only relevant if encoded dates come from a standard calendar
        (e.g. "gregorian", "proleptic_gregorian", "standard", or not
        specified).  If None (default), attempt to decode times to
        ``np.datetime64[ns]`` objects; if this is not possible, decode times to
        ``cftime.datetime`` objects. If True, always decode times to
        ``cftime.datetime`` objects, regardless of whether or not they can be
        represented using ``np.datetime64[ns]`` objects.  If False, always
        decode times to ``np.datetime64[ns]`` objects; if this is not possible
        raise an error. This keyword may not be supported by all the backends.
    concat_characters : bool, optional
        If True, concatenate along the last dimension of character arrays to
        form string arrays. Dimensions will only be concatenated over (and
        removed) if they have no corresponding variable and if they are only
        used as the last dimension of character arrays.
        This keyword may not be supported by all the backends.
    decode_coords : bool or {"coordinates", "all"}, optional
        Controls which variables are set as coordinate variables:

        - "coordinates" or True: Set variables referred to in the
          ``'coordinates'`` attribute of the datasets or individual variables
          as coordinate variables.
        - "all": Set variables referred to in  ``'grid_mapping'``, ``'bounds'`` and
          other attributes as coordinate variables.

        Only existing variables can be set as coordinates. Missing variables
        will be silently ignored.
    drop_variables: str or iterable of str, optional
        A variable or list of variables to exclude from being parsed from the
        dataset. This may be useful to drop variables with problems or
        inconsistent values.
    inline_array: bool, default: False
        How to include the array in the dask task graph.
        By default(``inline_array=False``) the array is included in a task by
        itself, and each chunk refers to that task by its key. With
        ``inline_array=True``, Dask will instead inline the array directly
        in the values of the task graph. See :py:func:`dask.array.from_array`.
    chunked_array_type: str, optional
        Which chunked array type to coerce this datasets' arrays to.
        Defaults to 'dask' if installed, else whatever is registered via the `ChunkManagerEnetryPoint` system.
        Experimental API that should not be relied upon.
    from_array_kwargs: dict
        Additional keyword arguments passed on to the `ChunkManagerEntrypoint.from_array` method used to create
        chunked arrays, via whichever chunk manager is specified through the `chunked_array_type` kwarg.
        For example if :py:func:`dask.array.Array` objects are used for chunking, additional kwargs will be passed
        to :py:func:`dask.array.from_array`. Experimental API that should not be relied upon.
    backend_kwargs: dict
        Additional keyword arguments passed on to the engine open function,
        equivalent to `**kwargs`.
    **kwargs: dict
        Additional keyword arguments passed on to the engine open function.
        For example:

        - 'group': path to the netCDF4 group in the given file to open given as
          a str,supported by "netcdf4", "h5netcdf", "zarr".
        - 'lock': resource lock to use when reading data from disk. Only
          relevant when using dask or another form of parallelism. By default,
          appropriate locks are chosen to safely read and write files with the
          currently active dask scheduler. Supported by "netcdf4", "h5netcdf",
          "scipy", "pynio".

        See engine open function for kwargs accepted by each specific engine.

    Returns
    -------
    dataset : Dataset
        The newly created dataset.

    Notes
    -----
    ``open_dataset`` opens the file with read-only access. When you modify
    values of a Dataset, even one linked to files on disk, only the in-memory
    copy you are manipulating in xarray is modified: the original file on disk
    is never touched.

    See Also
    --------
    open_mfdataset
    """

    if cache is None:
        cache = chunks is None

    if backend_kwargs is not None:
        kwargs.update(backend_kwargs)

    if engine is None:
        engine = plugins.guess_engine(filename_or_obj)

    if from_array_kwargs is None:
        from_array_kwargs = {}

    backend = plugins.get_backend(engine)

    decoders = _resolve_decoders_kwargs(
        decode_cf,
        open_backend_dataset_parameters=backend.open_dataset_parameters,
        mask_and_scale=mask_and_scale,
        decode_times=decode_times,
        decode_timedelta=decode_timedelta,
        concat_characters=concat_characters,
        use_cftime=use_cftime,
        decode_coords=decode_coords,
    )

    overwrite_encoded_chunks = kwargs.pop("overwrite_encoded_chunks", None)
    backend_ds = backend.open_dataset(
        filename_or_obj,
        drop_variables=drop_variables,
        **decoders,
        **kwargs,
    )
    ds = _dataset_from_backend_dataset(
        backend_ds,
        filename_or_obj,
        engine,
        chunks,
        cache,
        overwrite_encoded_chunks,
        inline_array,
        chunked_array_type,
        from_array_kwargs,
        drop_variables=drop_variables,
        **decoders,
        **kwargs,
    )
    return ds


def open_dataarray(
    filename_or_obj: str | os.PathLike[Any] | BufferedIOBase | AbstractDataStore,
    *,
    engine: T_Engine | None = None,
    chunks: T_Chunks | None = None,
    cache: bool | None = None,
    decode_cf: bool | None = None,
    mask_and_scale: bool | None = None,
    decode_times: bool | None = None,
    decode_timedelta: bool | None = None,
    use_cftime: bool | None = None,
    concat_characters: bool | None = None,
    decode_coords: Literal["coordinates", "all"] | bool | None = None,
    drop_variables: str | Iterable[str] | None = None,
    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,
) -> DataArray:
    """Open an DataArray from a file or file-like object containing a single
    data variable.

    This is designed to read netCDF files with only one data variable. If
    multiple variables are present then a ValueError is raised.

    Parameters
    ----------
    filename_or_obj : str, Path, file-like or DataStore
        Strings and Path objects are interpreted as a path to a netCDF file
        or an OpenDAP URL and opened with python-netCDF4, unless the filename
        ends with .gz, in which case the file is gunzipped and opened with
        scipy.io.netcdf (only netCDF3 supported). Byte-strings or file-like
        objects are opened by scipy.io.netcdf (netCDF3) or h5py (netCDF4/HDF).
    engine : {"netcdf4", "scipy", "pydap", "h5netcdf", "pynio", \
        "zarr", None}, installed backend \
        or subclass of xarray.backends.BackendEntrypoint, optional
        Engine to use when reading files. If not provided, the default engine
        is chosen based on available dependencies, with a preference for
        "netcdf4".
    chunks : int, dict, 'auto' or None, optional
        If chunks is provided, it is used to load the new dataset into dask
        arrays. ``chunks=-1`` loads the dataset with dask using a single
        chunk for all arrays. `chunks={}`` loads the dataset with dask using
        engine preferred chunks if exposed by the backend, otherwise with
        a single chunk for all arrays.
        ``chunks='auto'`` will use dask ``auto`` chunking taking into account the
        engine preferred chunks. See dask chunking for more details.
    cache : bool, optional
        If True, cache data loaded from the underlying datastore in memory as
        NumPy arrays when accessed to avoid reading from the underlying data-
        store multiple times. Defaults to True unless you specify the `chunks`
        argument to use dask, in which case it defaults to False. Does not
        change the behavior of coordinates corresponding to dimensions, which
        always load their data from disk into a ``pandas.Index``.
    decode_cf : bool, optional
        Whether to decode these variables, assuming they were saved according
        to CF conventions.
    mask_and_scale : bool, optional
        If True, replace array values equal to `_FillValue` with NA and scale
        values according to the formula `original_values * scale_factor +
        add_offset`, where `_FillValue`, `scale_factor` and `add_offset` are
        taken from variable attributes (if they exist).  If the `_FillValue` or
        `missing_value` attribute contains multiple values a warning will be
        issued and all array values matching one of the multiple values will
        be replaced by NA. This keyword may not be supported by all the backends.
    decode_times : bool, optional
        If True, decode times encoded in the standard NetCDF datetime format
        into datetime objects. Otherwise, leave them encoded as numbers.
        This keyword may not be supported by all the backends.
    decode_timedelta : bool, optional
        If True, decode variables and coordinates with time units in
        {"days", "hours", "minutes", "seconds", "milliseconds", "microseconds"}
        into timedelta objects. If False, leave them encoded as numbers.
        If None (default), assume the same value of decode_time.
        This keyword may not be supported by all the backends.
    use_cftime: bool, optional
        Only relevant if encoded dates come from a standard calendar
        (e.g. "gregorian", "proleptic_gregorian", "standard", or not
        specified).  If None (default), attempt to decode times to
        ``np.datetime64[ns]`` objects; if this is not possible, decode times to
        ``cftime.datetime`` objects. If True, always decode times to
        ``cftime.datetime`` objects, regardless of whether or not they can be
        represented using ``np.datetime64[ns]`` objects.  If False, always
        decode times to ``np.datetime64[ns]`` objects; if this is not possible
        raise an error. This keyword may not be supported by all the backends.
    concat_characters : bool, optional
        If True, concatenate along the last dimension of character arrays to
        form string arrays. Dimensions will only be concatenated over (and
        removed) if they have no corresponding variable and if they are only
        used as the last dimension of character arrays.
        This keyword may not be supported by all the backends.
    decode_coords : bool or {"coordinates", "all"}, optional
        Controls which variables are set as coordinate variables:

        - "coordinates" or True: Set variables referred to in the
          ``'coordinates'`` attribute of the datasets or individual variables
          as coordinate variables.
        - "all": Set variables referred to in  ``'grid_mapping'``, ``'bounds'`` and
          other attributes as coordinate variables.

        Only existing variables can be set as coordinates. Missing variables
        will be silently ignored.
    drop_variables: str or iterable of str, optional
        A variable or list of variables to exclude from being parsed from the
        dataset. This may be useful to drop variables with problems or
        inconsistent values.
    inline_array: bool, default: False
        How to include the array in the dask task graph.
        By default(``inline_array=False``) the array is included in a task by
        itself, and each chunk refers to that task by its key. With
        ``inline_array=True``, Dask will instead inline the array directly
        in the values of the task graph. See :py:func:`dask.array.from_array`.
    chunked_array_type: str, optional
        Which chunked array type to coerce the underlying data array to.
        Defaults to 'dask' if installed, else whatever is registered via the `ChunkManagerEnetryPoint` system.
        Experimental API that should not be relied upon.
    from_array_kwargs: dict
        Additional keyword arguments passed on to the `ChunkManagerEntrypoint.from_array` method used to create
        chunked arrays, via whichever chunk manager is specified through the `chunked_array_type` kwarg.
        For example if :py:func:`dask.array.Array` objects are used for chunking, additional kwargs will be passed
        to :py:func:`dask.array.from_array`. Experimental API that should not be relied upon.
    backend_kwargs: dict
        Additional keyword arguments passed on to the engine open function,
        equivalent to `**kwargs`.
    **kwargs: dict
        Additional keyword arguments passed on to the engine open function.
        For example:

        - 'group': path to the netCDF4 group in the given file to open given as
          a str,supported by "netcdf4", "h5netcdf", "zarr".
        - 'lock': resource lock to use when reading data from disk. Only
          relevant when using dask or another form of parallelism. By default,
          appropriate locks are chosen to safely read and write files with the
          currently active dask scheduler. Supported by "netcdf4", "h5netcdf",
          "scipy", "pynio".

        See engine open function for kwargs accepted by each specific engine.

    Notes
    -----
    This is designed to be fully compatible with `DataArray.to_netcdf`. Saving
    using `DataArray.to_netcdf` and then loading with this function will
    produce an identical result.

    All parameters are passed directly to `xarray.open_dataset`. See that
    documentation for further details.

    See also
    --------
    open_dataset
    """

    dataset = open_dataset(
        filename_or_obj,
        decode_cf=decode_cf,
        mask_and_scale=mask_and_scale,
        decode_times=decode_times,
        concat_characters=concat_characters,
        decode_coords=decode_coords,
        engine=engine,
        chunks=chunks,
        cache=cache,
        drop_variables=drop_variables,
        inline_array=inline_array,
        chunked_array_type=chunked_array_type,
        from_array_kwargs=from_array_kwargs,
        backend_kwargs=backend_kwargs,
        use_cftime=use_cftime,
        decode_timedelta=decode_timedelta,
        **kwargs,
    )

    if len(dataset.data_vars) != 1:
        raise ValueError(
            "Given file dataset contains more than one data "
            "variable. Please read with xarray.open_dataset and "
            "then select the variable you want."
        )
    else:
        (data_array,) = dataset.data_vars.values()

    data_array.set_close(dataset._close)

    # Reset names if they were changed during saving
    # to ensure that we can 'roundtrip' perfectly
    if DATAARRAY_NAME in dataset.attrs:
        data_array.name = dataset.attrs[DATAARRAY_NAME]
        del dataset.attrs[DATAARRAY_NAME]

    if data_array.name == DATAARRAY_VARIABLE:
        data_array.name = None

    return data_array


def open_datatree(
    filename_or_obj: str | os.PathLike[Any] | BufferedIOBase | AbstractDataStore,
    engine: T_Engine = 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, or DataStore
        Strings and Path objects are interpreted as a path to a netCDF file or Zarr store.
    engine : str, optional
        Xarray backend engine to use. Valid options include `{"netcdf4", "h5netcdf", "zarr"}`.
    **kwargs : dict
        Additional keyword arguments passed to :py:func:`~xarray.open_dataset` for each group.
    Returns
    -------
    xarray.DataTree
    """
    if engine is None:
        engine = plugins.guess_engine(filename_or_obj)

    backend = plugins.get_backend(engine)

    return backend.open_datatree(filename_or_obj, **kwargs)


def open_mfdataset(
    paths: str | NestedSequence[str | os.PathLike],
    chunks: T_Chunks | None = None,
    concat_dim: (
        str
        | DataArray
        | Index
        | Sequence[str]
        | Sequence[DataArray]
        | Sequence[Index]
        | None
    ) = None,
    compat: CompatOptions = "no_conflicts",
    preprocess: Callable[[Dataset], Dataset] | None = None,
    engine: T_Engine | None = None,
    data_vars: Literal["all", "minimal", "different"] | list[str] = "all",
    coords="different",
    combine: Literal["by_coords", "nested"] = "by_coords",
    parallel: bool = False,
    join: JoinOptions = "outer",
    attrs_file: str | os.PathLike | None = None,
    combine_attrs: CombineAttrsOptions = "override",
    **kwargs,
) -> Dataset:
    """Open multiple files as a single dataset.

    If combine='by_coords' then the function ``combine_by_coords`` is used to combine
    the datasets into one before returning the result, and if combine='nested' then
    ``combine_nested`` is used. The filepaths must be structured according to which
    combining function is used, the details of which are given in the documentation for
    ``combine_by_coords`` and ``combine_nested``. By default ``combine='by_coords'``
    will be used. Requires dask to be installed. See documentation for
    details on dask [1]_. Global attributes from the ``attrs_file`` are used
    for the combined dataset.

    Parameters
    ----------
    paths : str or nested sequence of paths
        Either a string glob in the form ``"path/to/my/files/*.nc"`` or an explicit list of
        files to open. Paths can be given as strings or as pathlib Paths. If
        concatenation along more than one dimension is desired, then ``paths`` must be a
        nested list-of-lists (see ``combine_nested`` for details). (A string glob will
        be expanded to a 1-dimensional list.)
    chunks : int, dict, 'auto' or None, optional
        Dictionary with keys given by dimension names and values given by chunk sizes.
        In general, these should divide the dimensions of each dataset. If int, chunk
        each dimension by ``chunks``. By default, chunks will be chosen to load entire
        input files into memory at once. This has a major impact on performance: please
        see the full documentation for more details [2]_.
    concat_dim : str, DataArray, Index or a Sequence of these or None, optional
        Dimensions to concatenate files along.  You only need to provide this argument
        if ``combine='nested'``, and if any of the dimensions along which you want to
        concatenate is not a dimension in the original datasets, e.g., if you want to
        stack a collection of 2D arrays along a third dimension. Set
        ``concat_dim=[..., None, ...]`` explicitly to disable concatenation along a
        particular dimension. Default is None, which for a 1D list of filepaths is
        equivalent to opening the files separately and then merging them with
        ``xarray.merge``.
    combine : {"by_coords", "nested"}, optional
        Whether ``xarray.combine_by_coords`` or ``xarray.combine_nested`` is used to
        combine all the data. Default is to use ``xarray.combine_by_coords``.
    compat : {"identical", "equals", "broadcast_equals", \
              "no_conflicts", "override"}, default: "no_conflicts"
        String indicating how to compare variables of the same name for
        potential conflicts when merging:

         * "broadcast_equals": all values must be equal when variables are
           broadcast against each other to ensure common dimensions.
         * "equals": all values and dimensions must be the same.
         * "identical": all values, dimensions and attributes must be the
           same.
         * "no_conflicts": only values which are not null in both datasets
           must be equal. The returned dataset then contains the combination
           of all non-null values.
         * "override": skip comparing and pick variable from first dataset

    preprocess : callable, optional
        If provided, call this function on each dataset prior to concatenation.
        You can find the file-name from which each dataset was loaded in
        ``ds.encoding["source"]``.
    engine : {"netcdf4", "scipy", "pydap", "h5netcdf", "pynio", \
        "zarr", None}, installed backend \
        or subclass of xarray.backends.BackendEntrypoint, optional
        Engine to use when reading files. If not provided, the default engine
        is chosen based on available dependencies, with a preference for
        "netcdf4".
    data_vars : {"minimal", "different", "all"} or list of str, default: "all"
        These data variables will be concatenated together:
          * "minimal": Only data variables in which the dimension already
            appears are included.
          * "different": Data variables which are not equal (ignoring
            attributes) across all datasets are also concatenated (as well as
            all for which dimension already appears). Beware: this option may
            load the data payload of data variables into memory if they are not
            already loaded.
          * "all": All data variables will be concatenated.
          * list of str: The listed data variables will be concatenated, in
            addition to the "minimal" data variables.
    coords : {"minimal", "different", "all"} or list of str, optional
        These coordinate variables will be concatenated together:
         * "minimal": Only coordinates in which the dimension already appears
           are included.
         * "different": Coordinates which are not equal (ignoring attributes)
           across all datasets are also concatenated (as well as all for which
           dimension already appears). Beware: this option may load the data
           payload of coordinate variables into memory if they are not already
           loaded.
         * "all": All coordinate variables will be concatenated, except
           those corresponding to other dimensions.
         * list of str: The listed coordinate variables will be concatenated,
           in addition the "minimal" coordinates.
    parallel : bool, default: False
        If True, the open and preprocess steps of this function will be
        performed in parallel using ``dask.delayed``. Default is False.
    join : {"outer", "inner", "left", "right", "exact", "override"}, default: "outer"
        String indicating how to combine differing indexes
        (excluding concat_dim) in objects

        - "outer": use the union of object indexes
        - "inner": use the intersection of object indexes
        - "left": use indexes from the first object with each dimension
        - "right": use indexes from the last object with each dimension
        - "exact": instead of aligning, raise `ValueError` when indexes to be
          aligned are not equal
        - "override": if indexes are of same size, rewrite indexes to be
          those of the first object with that dimension. Indexes for the same
          dimension must have the same size in all objects.
    attrs_file : str or path-like, optional
        Path of the file used to read global attributes from.
        By default global attributes are read from the first file provided,
        with wildcard matches sorted by filename.
    combine_attrs : {"drop", "identical", "no_conflicts", "drop_conflicts", \
                     "override"} or callable, default: "override"
        A callable or a string indicating how to combine attrs of the objects being
        merged:

        - "drop": empty attrs on returned Dataset.
        - "identical": all attrs must be the same on every object.
        - "no_conflicts": attrs from all objects are combined, any that have
          the same name must also have the same value.
        - "drop_conflicts": attrs from all objects are combined, any that have
          the same name but different values are dropped.
        - "override": skip comparing and copy attrs from the first dataset to
          the result.

        If a callable, it must expect a sequence of ``attrs`` dicts and a context object
        as its only parameters.
    **kwargs : optional
        Additional arguments passed on to :py:func:`xarray.open_dataset`. For an
        overview of some of the possible options, see the documentation of
        :py:func:`xarray.open_dataset`

    Returns
    -------
    xarray.Dataset

    Notes
    -----
    ``open_mfdataset`` opens files with read-only access. When you modify values
    of a Dataset, even one linked to files on disk, only the in-memory copy you
    are manipulating in xarray is modified: the original file on disk is never
    touched.

    See Also
    --------
    combine_by_coords
    combine_nested
    open_dataset

    Examples
    --------
    A user might want to pass additional arguments into ``preprocess`` when
    applying some operation to many individual files that are being opened. One route
    to do this is through the use of ``functools.partial``.

    >>> from functools import partial
    >>> def _preprocess(x, lon_bnds, lat_bnds):
    ...     return x.sel(lon=slice(*lon_bnds), lat=slice(*lat_bnds))
    ...
    >>> lon_bnds, lat_bnds = (-110, -105), (40, 45)
    >>> partial_func = partial(_preprocess, lon_bnds=lon_bnds, lat_bnds=lat_bnds)
    >>> ds = xr.open_mfdataset(
    ...     "file_*.nc", concat_dim="time", preprocess=partial_func
    ... )  # doctest: +SKIP

    It is also possible to use any argument to ``open_dataset`` together
    with ``open_mfdataset``, such as for example ``drop_variables``:

    >>> ds = xr.open_mfdataset(
    ...     "file.nc", drop_variables=["varname_1", "varname_2"]  # any list of vars
    ... )  # doctest: +SKIP

    References
    ----------

    .. [1] https://docs.xarray.dev/en/stable/dask.html
    .. [2] https://docs.xarray.dev/en/stable/dask.html#chunking-and-performance
    """
    paths = _find_absolute_paths(paths, engine=engine, **kwargs)

    if not paths:
        raise OSError("no files to open")

    if combine == "nested":
        if isinstance(concat_dim, (str, DataArray)) or concat_dim is None:
            concat_dim = [concat_dim]  # type: ignore[assignment]

        # This creates a flat list which is easier to iterate over, whilst
        # encoding the originally-supplied structure as "ids".
        # The "ids" are not used at all if combine='by_coords`.
        combined_ids_paths = _infer_concat_order_from_positions(paths)
        ids, paths = (
            list(combined_ids_paths.keys()),
            list(combined_ids_paths.values()),
        )
    elif combine == "by_coords" and concat_dim is not None:
        raise ValueError(
            "When combine='by_coords', passing a value for `concat_dim` has no "
            "effect. To manually combine along a specific dimension you should "
            "instead specify combine='nested' along with a value for `concat_dim`.",
        )

    open_kwargs = dict(engine=engine, chunks=chunks or {}, **kwargs)

    if parallel:
        import dask

        # wrap the open_dataset, getattr, and preprocess with delayed
        open_ = dask.delayed(open_dataset)
        getattr_ = dask.delayed(getattr)
        if preprocess is not None:
            preprocess = dask.delayed(preprocess)
    else:
        open_ = open_dataset
        getattr_ = getattr

    datasets = [open_(p, **open_kwargs) for p in paths]
    closers = [getattr_(ds, "_close") for ds in datasets]
    if preprocess is not None:
        datasets = [preprocess(ds) for ds in datasets]

    if parallel:
        # calling compute here will return the datasets/file_objs lists,
        # the underlying datasets will still be stored as dask arrays
        datasets, closers = dask.compute(datasets, closers)

    # Combine all datasets, closing them in case of a ValueError
    try:
        if combine == "nested":
            # Combined nested list by successive concat and merge operations
            # along each dimension, using structure given by "ids"
            combined = _nested_combine(
                datasets,
                concat_dims=concat_dim,
                compat=compat,
                data_vars=data_vars,
                coords=coords,
                ids=ids,
                join=join,
                combine_attrs=combine_attrs,
            )
        elif combine == "by_coords":
            # Redo ordering from coordinates, ignoring how they were ordered
            # previously
            combined = combine_by_coords(
                datasets,
                compat=compat,
                data_vars=data_vars,
                coords=coords,
                join=join,
                combine_attrs=combine_attrs,
            )
        else:
            raise ValueError(
                f"{combine} is an invalid option for the keyword argument"
                " ``combine``"
            )
    except ValueError:
        for ds in datasets:
            ds.close()
        raise

    combined.set_close(partial(_multi_file_closer, closers))

    # read global attributes from the attrs_file or from the first dataset
    if attrs_file is not None:
        if isinstance(attrs_file, os.PathLike):
            attrs_file = cast(str, os.fspath(attrs_file))
        combined.attrs = datasets[paths.index(attrs_file)].attrs

    return combined


WRITEABLE_STORES: dict[T_NetcdfEngine, Callable] = {
    "netcdf4": backends.NetCDF4DataStore.open,
    "scipy": backends.ScipyDataStore,
    "h5netcdf": backends.H5NetCDFStore.open,
}


# multifile=True returns writer and datastore
@overload
def to_netcdf(
    dataset: Dataset,
    path_or_file: str | os.PathLike | None = None,
    mode: Literal["w", "a"] = "w",
    format: T_NetcdfTypes | None = None,
    group: str | None = None,
    engine: T_NetcdfEngine | None = None,
    encoding: Mapping[Hashable, Mapping[str, Any]] | None = None,
    unlimited_dims: Iterable[Hashable] | None = None,
    compute: bool = True,
    *,
    multifile: Literal[True],
    invalid_netcdf: bool = False,
) -> tuple[ArrayWriter, AbstractDataStore]: ...


# path=None writes to bytes
@overload
def to_netcdf(
    dataset: Dataset,
    path_or_file: None = None,
    mode: Literal["w", "a"] = "w",
    format: T_NetcdfTypes | None = None,
    group: str | None = None,
    engine: T_NetcdfEngine | None = None,
    encoding: Mapping[Hashable, Mapping[str, Any]] | None = None,
    unlimited_dims: Iterable[Hashable] | None = None,
    compute: bool = True,
    multifile: Literal[False] = False,
    invalid_netcdf: bool = False,
) -> bytes: ...


# compute=False returns dask.Delayed
@overload
def to_netcdf(
    dataset: Dataset,
    path_or_file: str | os.PathLike,
    mode: Literal["w", "a"] = "w",
    format: T_NetcdfTypes | None = None,
    group: str | None = None,
    engine: T_NetcdfEngine | None = None,
    encoding: Mapping[Hashable, Mapping[str, Any]] | None = None,
    unlimited_dims: Iterable[Hashable] | None = None,
    *,
    compute: Literal[False],
    multifile: Literal[False] = False,
    invalid_netcdf: bool = False,
) -> Delayed: ...


# default return None
@overload
def to_netcdf(
    dataset: Dataset,
    path_or_file: str | os.PathLike,
    mode: Literal["w", "a"] = "w",
    format: T_NetcdfTypes | None = None,
    group: str | None = None,
    engine: T_NetcdfEngine | None = None,
    encoding: Mapping[Hashable, Mapping[str, Any]] | None = None,
    unlimited_dims: Iterable[Hashable] | None = None,
    compute: Literal[True] = True,
    multifile: Literal[False] = False,
    invalid_netcdf: bool = False,
) -> None: ...


# if compute cannot be evaluated at type check time
# we may get back either Delayed or None
@overload
def to_netcdf(
    dataset: Dataset,
    path_or_file: str | os.PathLike,
    mode: Literal["w", "a"] = "w",
    format: T_NetcdfTypes | None = None,
    group: str | None = None,
    engine: T_NetcdfEngine | None = None,
    encoding: Mapping[Hashable, Mapping[str, Any]] | None = None,
    unlimited_dims: Iterable[Hashable] | None = None,
    compute: bool = False,
    multifile: Literal[False] = False,
    invalid_netcdf: bool = False,
) -> Delayed | None: ...


# if multifile cannot be evaluated at type check time
# we may get back either writer and datastore or Delayed or None
@overload
def to_netcdf(
    dataset: Dataset,
    path_or_file: str | os.PathLike,
    mode: Literal["w", "a"] = "w",
    format: T_NetcdfTypes | None = None,
    group: str | None = None,
    engine: T_NetcdfEngine | None = None,
    encoding: Mapping[Hashable, Mapping[str, Any]] | None = None,
    unlimited_dims: Iterable[Hashable] | None = None,
    compute: bool = False,
    multifile: bool = False,
    invalid_netcdf: bool = False,
) -> tuple[ArrayWriter, AbstractDataStore] | Delayed | None: ...


# Any
@overload
def to_netcdf(
    dataset: Dataset,
    path_or_file: str | os.PathLike | None,
    mode: Literal["w", "a"] = "w",
    format: T_NetcdfTypes | None = None,
    group: str | None = None,
    engine: T_NetcdfEngine | None = None,
    encoding: Mapping[Hashable, Mapping[str, Any]] | None = None,
    unlimited_dims: Iterable[Hashable] | None = None,
    compute: bool = False,
    multifile: bool = False,
    invalid_netcdf: bool = False,
) -> tuple[ArrayWriter, AbstractDataStore] | bytes | Delayed | None: ...


def to_netcdf(
    dataset: Dataset,
    path_or_file: str | os.PathLike | None = None,
    mode: Literal["w", "a"] = "w",
    format: T_NetcdfTypes | None = None,
    group: str | None = None,
    engine: T_NetcdfEngine | None = None,
    encoding: Mapping[Hashable, Mapping[str, Any]] | None = None,
    unlimited_dims: Iterable[Hashable] | None = None,
    compute: bool = True,
    multifile: bool = False,
    invalid_netcdf: bool = False,
) -> tuple[ArrayWriter, AbstractDataStore] | bytes | Delayed | None:
    """This function creates an appropriate datastore for writing a dataset to
    disk as a netCDF file

    See `Dataset.to_netcdf` for full API docs.

    The ``multifile`` argument is only for the private use of save_mfdataset.
    """
    if isinstance(path_or_file, os.PathLike):
        path_or_file = os.fspath(path_or_file)

    if encoding is None:
        encoding = {}

    if path_or_file is None:
        if engine is None:
            engine = "scipy"
        elif engine != "scipy":
            raise ValueError(
                "invalid engine for creating bytes with "
                f"to_netcdf: {engine!r}. Only the default engine "
                "or engine='scipy' is supported"
            )
        if not compute:
            raise NotImplementedError(
                "to_netcdf() with compute=False is not yet implemented when "
                "returning bytes"
            )
    elif isinstance(path_or_file, str):
        if engine is None:
            engine = _get_default_engine(path_or_file)
        path_or_file = _normalize_path(path_or_file)
    else:  # file-like object
        engine = "scipy"

    # validate Dataset keys, DataArray names, and attr keys/values
    _validate_dataset_names(dataset)
    _validate_attrs(dataset, invalid_netcdf=invalid_netcdf and engine == "h5netcdf")

    try:
        store_open = WRITEABLE_STORES[engine]
    except KeyError:
        raise ValueError(f"unrecognized engine for to_netcdf: {engine!r}")

    if format is not None:
        format = format.upper()  # type: ignore[assignment]

    # handle scheduler specific logic
    scheduler = _get_scheduler()
    have_chunks = any(v.chunks is not None for v in dataset.variables.values())

    autoclose = have_chunks and scheduler in ["distributed", "multiprocessing"]
    if autoclose and engine == "scipy":
        raise NotImplementedError(
            f"Writing netCDF files with the {engine} backend "
            f"is not currently supported with dask's {scheduler} scheduler"
        )

    target = path_or_file if path_or_file is not None else BytesIO()
    kwargs = dict(autoclose=True) if autoclose else {}
    if invalid_netcdf:
        if engine == "h5netcdf":
            kwargs["invalid_netcdf"] = invalid_netcdf
        else:
            raise ValueError(
                f"unrecognized option 'invalid_netcdf' for engine {engine}"
            )
    store = store_open(target, mode, format, group, **kwargs)

    if unlimited_dims is None:
        unlimited_dims = dataset.encoding.get("unlimited_dims", None)
    if unlimited_dims is not None:
        if isinstance(unlimited_dims, str) or not isinstance(unlimited_dims, Iterable):
            unlimited_dims = [unlimited_dims]
        else:
            unlimited_dims = list(unlimited_dims)

    writer = ArrayWriter()

    # TODO: figure out how to refactor this logic (here and in save_mfdataset)
    # to avoid this mess of conditionals
    try:
        # TODO: allow this work (setting up the file for writing array data)
        # to be parallelized with dask
        dump_to_store(
            dataset, store, writer, encoding=encoding, unlimited_dims=unlimited_dims
        )
        if autoclose:
            store.close()

        if multifile:
            return writer, store

        writes = writer.sync(compute=compute)

        if isinstance(target, BytesIO):
            store.sync()
            return target.getvalue()
    finally:
        if not multifile and compute:
            store.close()

    if not compute:
        import dask

        return dask.delayed(_finalize_store)(writes, store)
    return None


def dump_to_store(
    dataset, store, writer=None, encoder=None, encoding=None, unlimited_dims=None
):
    """Store dataset contents to a backends.*DataStore object."""
    if writer is None:
        writer = ArrayWriter()

    if encoding is None:
        encoding = {}

    variables, attrs = conventions.encode_dataset_coordinates(dataset)

    check_encoding = set()
    for k, enc in encoding.items():
        # no need to shallow copy the variable again; that already happened
        # in encode_dataset_coordinates
        variables[k].encoding = enc
        check_encoding.add(k)

    if encoder:
        variables, attrs = encoder(variables, attrs)

    store.store(variables, attrs, check_encoding, writer, unlimited_dims=unlimited_dims)


def save_mfdataset(
    datasets,
    paths,
    mode="w",
    format=None,
    groups=None,
    engine=None,
    compute=True,
    **kwargs,
):
    """Write multiple datasets to disk as netCDF files simultaneously.

    This function is intended for use with datasets consisting of dask.array
    objects, in which case it can write the multiple datasets to disk
    simultaneously using a shared thread pool.

    When not using dask, it is no different than calling ``to_netcdf``
    repeatedly.

    Parameters
    ----------
    datasets : list of Dataset
        List of datasets to save.
    paths : list of str or list of path-like objects
        List of paths to which to save each corresponding dataset.
    mode : {"w", "a"}, optional
        Write ("w") or append ("a") mode. If mode="w", any existing file at
        these locations will be overwritten.
    format : {"NETCDF4", "NETCDF4_CLASSIC", "NETCDF3_64BIT", \
              "NETCDF3_CLASSIC"}, optional
    **kwargs : additional arguments are passed along to ``to_netcdf``

        File format for the resulting netCDF file:

        * NETCDF4: Data is stored in an HDF5 file, using netCDF4 API
          features.
        * NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only
          netCDF 3 compatible API features.
        * NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format,
          which fully supports 2+ GB files, but is only compatible with
          clients linked against netCDF version 3.6.0 or later.
        * NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not
          handle 2+ GB files very well.

        All formats are supported by the netCDF4-python library.
        scipy.io.netcdf only supports the last two formats.

        The default format is NETCDF4 if you are saving a file to disk and
        have the netCDF4-python library available. Otherwise, xarray falls
        back to using scipy to write netCDF files and defaults to the
        NETCDF3_64BIT format (scipy does not support netCDF4).
    groups : list of str, optional
        Paths to the netCDF4 group in each corresponding file to which to save
        datasets (only works for format="NETCDF4"). The groups will be created
        if necessary.
    engine : {"netcdf4", "scipy", "h5netcdf"}, optional
        Engine to use when writing netCDF files. If not provided, the
        default engine is chosen based on available dependencies, with a
        preference for "netcdf4" if writing to a file on disk.
        See `Dataset.to_netcdf` for additional information.
    compute : bool
        If true compute immediately, otherwise return a
        ``dask.delayed.Delayed`` object that can be computed later.

    Examples
    --------

    Save a dataset into one netCDF per year of data:

    >>> ds = xr.Dataset(
    ...     {"a": ("time", np.linspace(0, 1, 48))},
    ...     coords={"time": pd.date_range("2010-01-01", freq="ME", periods=48)},
    ... )
    >>> ds
    <xarray.Dataset> Size: 768B
    Dimensions:  (time: 48)
    Coordinates:
      * time     (time) datetime64[ns] 384B 2010-01-31 2010-02-28 ... 2013-12-31
    Data variables:
        a        (time) float64 384B 0.0 0.02128 0.04255 ... 0.9574 0.9787 1.0
    >>> years, datasets = zip(*ds.groupby("time.year"))
    >>> paths = [f"{y}.nc" for y in years]
    >>> xr.save_mfdataset(datasets, paths)
    """
    if mode == "w" and len(set(paths)) < len(paths):
        raise ValueError(
            "cannot use mode='w' when writing multiple datasets to the same path"
        )

    for obj in datasets:
        if not isinstance(obj, Dataset):
            raise TypeError(
                "save_mfdataset only supports writing Dataset "
                f"objects, received type {type(obj)}"
            )

    if groups is None:
        groups = [None] * len(datasets)

    if len({len(datasets), len(paths), len(groups)}) > 1:
        raise ValueError(
            "must supply lists of the same length for the "
            "datasets, paths and groups arguments to "
            "save_mfdataset"
        )

    writers, stores = zip(
        *[
            to_netcdf(
                ds,
                path,
                mode,
                format,
                group,
                engine,
                compute=compute,
                multifile=True,
                **kwargs,
            )
            for ds, path, group in zip(datasets, paths, groups)
        ]
    )

    try:
        writes = [w.sync(compute=compute) for w in writers]
    finally:
        if compute:
            for store in stores:
                store.close()

    if not compute:
        import dask

        return dask.delayed(
            [dask.delayed(_finalize_store)(w, s) for w, s in zip(writes, stores)]
        )


def _auto_detect_region(ds_new, ds_orig, dim):
    # Create a mapping array of coordinates to indices on the original array
    coord = ds_orig[dim]
    da_map = DataArray(np.arange(coord.size), coords={dim: coord})

    try:
        da_idxs = da_map.sel({dim: ds_new[dim]})
    except KeyError as e:
        if "not all values found" in str(e):
            raise KeyError(
                f"Not all values of coordinate '{dim}' in the new array were"
                " found in the original store. Writing to a zarr region slice"
                " requires that no dimensions or metadata are changed by the write."
            )
        else:
            raise e

    if (da_idxs.diff(dim) != 1).any():
        raise ValueError(
            f"The auto-detected region of coordinate '{dim}' for writing new data"
            " to the original store had non-contiguous indices. Writing to a zarr"
            " region slice requires that the new data constitute a contiguous subset"
            " of the original store."
        )

    dim_slice = slice(da_idxs.values[0], da_idxs.values[-1] + 1)

    return dim_slice


def _auto_detect_regions(ds, region, open_kwargs):
    ds_original = open_zarr(**open_kwargs)
    for key, val in region.items():
        if val == "auto":
            region[key] = _auto_detect_region(ds, ds_original, key)
    return region


def _validate_and_autodetect_region(ds, region, mode, open_kwargs) -> dict[str, slice]:
    if region == "auto":
        region = {dim: "auto" for dim in ds.dims}

    if not isinstance(region, dict):
        raise TypeError(f"``region`` must be a dict, got {type(region)}")

    if any(v == "auto" for v in region.values()):
        if mode != "r+":
            raise ValueError(
                f"``mode`` must be 'r+' when using ``region='auto'``, got {mode}"
            )
        region = _auto_detect_regions(ds, region, open_kwargs)

    for k, v in region.items():
        if k not in ds.dims:
            raise ValueError(
                f"all keys in ``region`` are not in Dataset dimensions, got "
                f"{list(region)} and {list(ds.dims)}"
            )
        if not isinstance(v, slice):
            raise TypeError(
                "all values in ``region`` must be slice objects, got "
                f"region={region}"
            )
        if v.step not in {1, None}:
            raise ValueError(
                "step on all slices in ``region`` must be 1 or None, got "
                f"region={region}"
            )

    non_matching_vars = [
        k for k, v in ds.variables.items() if not set(region).intersection(v.dims)
    ]
    if non_matching_vars:
        raise ValueError(
            f"when setting `region` explicitly in to_zarr(), all "
            f"variables in the dataset to write must have at least "
            f"one dimension in common with the region's dimensions "
            f"{list(region.keys())}, but that is not "
            f"the case for some variables here. To drop these variables "
            f"from this dataset before exporting to zarr, write: "
            f".drop_vars({non_matching_vars!r})"
        )

    return region


def _validate_datatypes_for_zarr_append(zstore, dataset):
    """If variable exists in the store, confirm dtype of the data to append is compatible with
    existing dtype.
    """

    existing_vars = zstore.get_variables()

    def check_dtype(vname, var):
        if (
            vname not in existing_vars
            or np.issubdtype(var.dtype, np.number)
            or np.issubdtype(var.dtype, np.datetime64)
            or np.issubdtype(var.dtype, np.bool_)
            or var.dtype == object
        ):
            # We can skip dtype equality checks under two conditions: (1) if the var to append is
            # new to the dataset, because in this case there is no existing var to compare it to;
            # or (2) if var to append's dtype is known to be easy-to-append, because in this case
            # we can be confident appending won't cause problems. Examples of dtypes which are not
            # easy-to-append include length-specified strings of type `|S*` or `<U*` (where * is a
            # positive integer character length). For these dtypes, appending dissimilar lengths
            # can result in truncation of appended data. Therefore, variables which already exist
            # in the dataset, and with dtypes which are not known to be easy-to-append, necessitate
            # exact dtype equality, as checked below.
            pass
        elif not var.dtype == existing_vars[vname].dtype:
            raise ValueError(
                f"Mismatched dtypes for variable {vname} between Zarr store on disk "
                f"and dataset to append. Store has dtype {existing_vars[vname].dtype} but "
                f"dataset to append has dtype {var.dtype}."
            )

    for vname, var in dataset.data_vars.items():
        check_dtype(vname, var)


# compute=True returns ZarrStore
@overload
def to_zarr(
    dataset: Dataset,
    store: MutableMapping | str | os.PathLike[str] | None = None,
    chunk_store: MutableMapping | str | os.PathLike | None = None,
    mode: ZarrWriteModes | None = None,
    synchronizer=None,
    group: str | None = None,
    encoding: Mapping | None = None,
    *,
    compute: Literal[True] = True,
    consolidated: bool | None = None,
    append_dim: Hashable | None = None,
    region: Mapping[str, slice | Literal["auto"]] | Literal["auto"] | None = None,
    safe_chunks: bool = True,
    storage_options: dict[str, str] | None = None,
    zarr_version: int | None = None,
    write_empty_chunks: bool | None = None,
    chunkmanager_store_kwargs: dict[str, Any] | None = None,
) -> backends.ZarrStore: ...


# compute=False returns dask.Delayed
@overload
def to_zarr(
    dataset: Dataset,
    store: MutableMapping | str | os.PathLike[str] | None = None,
    chunk_store: MutableMapping | str | os.PathLike | None = None,
    mode: ZarrWriteModes | None = None,
    synchronizer=None,
    group: str | None = None,
    encoding: Mapping | None = None,
    *,
    compute: Literal[False],
    consolidated: bool | None = None,
    append_dim: Hashable | None = None,
    region: Mapping[str, slice | Literal["auto"]] | Literal["auto"] | None = None,
    safe_chunks: bool = True,
    storage_options: dict[str, str] | None = None,
    zarr_version: int | None = None,
    write_empty_chunks: bool | None = None,
    chunkmanager_store_kwargs: dict[str, Any] | None = None,
) -> Delayed: ...


def to_zarr(
    dataset: Dataset,
    store: MutableMapping | str | os.PathLike[str] | None = None,
    chunk_store: MutableMapping | str | os.PathLike | None = None,
    mode: ZarrWriteModes | None = None,
    synchronizer=None,
    group: str | None = None,
    encoding: Mapping | None = None,
    *,
    compute: bool = True,
    consolidated: bool | None = None,
    append_dim: Hashable | None = None,
    region: Mapping[str, slice | Literal["auto"]] | Literal["auto"] | None = None,
    safe_chunks: bool = True,
    storage_options: dict[str, str] | None = None,
    zarr_version: int | None = None,
    write_empty_chunks: bool | None = None,
    chunkmanager_store_kwargs: dict[str, Any] | None = None,
) -> backends.ZarrStore | Delayed:
    """This function creates an appropriate datastore for writing a dataset to
    a zarr ztore

    See `Dataset.to_zarr` for full API docs.
    """

    # Load empty arrays to avoid bug saving zero length dimensions (Issue #5741)
    for v in dataset.variables.values():
        if v.size == 0:
            v.load()

    # expand str and path-like arguments
    store = _normalize_path(store)
    chunk_store = _normalize_path(chunk_store)

    if storage_options is None:
        mapper = store
        chunk_mapper = chunk_store
    else:
        from fsspec import get_mapper

        if not isinstance(store, str):
            raise ValueError(
                f"store must be a string to use storage_options. Got {type(store)}"
            )
        mapper = get_mapper(store, **storage_options)
        if chunk_store is not None:
            chunk_mapper = get_mapper(chunk_store, **storage_options)
        else:
            chunk_mapper = chunk_store

    if encoding is None:
        encoding = {}

    if mode is None:
        if append_dim is not None:
            mode = "a"
        elif region is not None:
            mode = "r+"
        else:
            mode = "w-"

    if mode not in ["a", "a-"] and append_dim is not None:
        raise ValueError("cannot set append_dim unless mode='a' or mode=None")

    if mode not in ["a", "a-", "r+"] and region is not None:
        raise ValueError(
            "cannot set region unless mode='a', mode='a-', mode='r+' or mode=None"
        )

    if mode not in ["w", "w-", "a", "a-", "r+"]:
        raise ValueError(
            "The only supported options for mode are 'w', "
            f"'w-', 'a', 'a-', and 'r+', but mode={mode!r}"
        )

    # validate Dataset keys, DataArray names
    _validate_dataset_names(dataset)

    if region is not None:
        open_kwargs = dict(
            store=store,
            synchronizer=synchronizer,
            group=group,
            consolidated=consolidated,
            storage_options=storage_options,
            zarr_version=zarr_version,
        )
        region = _validate_and_autodetect_region(dataset, region, mode, open_kwargs)
        # can't modify indexed with region writes
        dataset = dataset.drop_vars(dataset.indexes)
        if append_dim is not None and append_dim in region:
            raise ValueError(
                f"cannot list the same dimension in both ``append_dim`` and "
                f"``region`` with to_zarr(), got {append_dim} in both"
            )

    if zarr_version is None:
        # default to 2 if store doesn't specify it's version (e.g. a path)
        zarr_version = int(getattr(store, "_store_version", 2))

    if consolidated is None and zarr_version > 2:
        consolidated = False

    if mode == "r+":
        already_consolidated = consolidated
        consolidate_on_close = False
    else:
        already_consolidated = False
        consolidate_on_close = consolidated or consolidated is None
    zstore = backends.ZarrStore.open_group(
        store=mapper,
        mode=mode,
        synchronizer=synchronizer,
        group=group,
        consolidated=already_consolidated,
        consolidate_on_close=consolidate_on_close,
        chunk_store=chunk_mapper,
        append_dim=append_dim,
        write_region=region,
        safe_chunks=safe_chunks,
        stacklevel=4,  # for Dataset.to_zarr()
        zarr_version=zarr_version,
        write_empty=write_empty_chunks,
    )

    if mode in ["a", "a-", "r+"]:
        _validate_datatypes_for_zarr_append(zstore, dataset)
        if append_dim is not None:
            existing_dims = zstore.get_dimensions()
            if append_dim not in existing_dims:
                raise ValueError(
                    f"append_dim={append_dim!r} does not match any existing "
                    f"dataset dimensions {existing_dims}"
                )
        existing_var_names = set(zstore.zarr_group.array_keys())
        for var_name in existing_var_names:
            if var_name in encoding.keys():
                raise ValueError(
                    f"variable {var_name!r} already exists, but encoding was provided"
                )
        if mode == "r+":
            new_names = [k for k in dataset.variables if k not in existing_var_names]
            if new_names:
                raise ValueError(
                    f"dataset contains non-pre-existing variables {new_names}, "
                    "which is not allowed in ``xarray.Dataset.to_zarr()`` with "
                    "mode='r+'. To allow writing new variables, set mode='a'."
                )

    writer = ArrayWriter()
    # TODO: figure out how to properly handle unlimited_dims
    dump_to_store(dataset, zstore, writer, encoding=encoding)
    writes = writer.sync(
        compute=compute, chunkmanager_store_kwargs=chunkmanager_store_kwargs
    )

    if compute:
        _finalize_store(writes, zstore)
    else:
        import dask

        return dask.delayed(_finalize_store)(writes, zstore)

    return zstore