dataarray.py

from __future__ import annotations

import datetime
import warnings
from os import PathLike
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Hashable,
    Iterable,
    Literal,
    Mapping,
    NoReturn,
    Sequence,
    cast,
    overload,
)

import numpy as np
import pandas as pd

from ..coding.calendar_ops import convert_calendar, interp_calendar
from ..coding.cftimeindex import CFTimeIndex
from ..plot.plot import _PlotMethods
from ..plot.utils import _get_units_from_attrs
from . import alignment, computation, dtypes, indexing, ops, utils
from ._reductions import DataArrayReductions
from .accessor_dt import CombinedDatetimelikeAccessor
from .accessor_str import StringAccessor
from .alignment import _broadcast_helper, _get_broadcast_dims_map_common_coords, align
from .arithmetic import DataArrayArithmetic
from .common import AbstractArray, DataWithCoords, get_chunksizes
from .computation import unify_chunks
from .coordinates import DataArrayCoordinates, assert_coordinate_consistent
from .dataset import Dataset
from .formatting import format_item
from .indexes import (
    Index,
    Indexes,
    PandasMultiIndex,
    filter_indexes_from_coords,
    isel_indexes,
)
from .indexing import is_fancy_indexer, map_index_queries
from .merge import PANDAS_TYPES, MergeError, _create_indexes_from_coords
from .npcompat import QUANTILE_METHODS, ArrayLike
from .options import OPTIONS, _get_keep_attrs
from .utils import (
    Default,
    HybridMappingProxy,
    ReprObject,
    _default,
    either_dict_or_kwargs,
)
from .variable import IndexVariable, Variable, as_compatible_data, as_variable

if TYPE_CHECKING:
    from typing import TypeVar, Union

    try:
        from dask.delayed import Delayed
    except ImportError:
        Delayed = None  # type: ignore
    try:
        from cdms2 import Variable as cdms2_Variable
    except ImportError:
        cdms2_Variable = None
    try:
        from iris.cube import Cube as iris_Cube
    except ImportError:
        iris_Cube = None

    from ..backends.api import T_NetcdfEngine, T_NetcdfTypes
    from .groupby import DataArrayGroupBy
    from .resample import DataArrayResample
    from .rolling import DataArrayCoarsen, DataArrayRolling
    from .types import (
        CoarsenBoundaryOptions,
        DatetimeUnitOptions,
        ErrorOptions,
        ErrorOptionsWithWarn,
        InterpOptions,
        PadModeOptions,
        PadReflectOptions,
        QueryEngineOptions,
        QueryParserOptions,
        ReindexMethodOptions,
        SideOptions,
        T_DataArray,
        T_Xarray,
    )
    from .weighted import DataArrayWeighted

    T_XarrayOther = TypeVar("T_XarrayOther", bound=Union["DataArray", Dataset])


def _infer_coords_and_dims(
    shape, coords, dims
) -> tuple[dict[Hashable, Variable], tuple[Hashable, ...]]:
    """All the logic for creating a new DataArray"""

    if (
        coords is not None
        and not utils.is_dict_like(coords)
        and len(coords) != len(shape)
    ):
        raise ValueError(
            f"coords is not dict-like, but it has {len(coords)} items, "
            f"which does not match the {len(shape)} dimensions of the "
            "data"
        )

    if isinstance(dims, str):
        dims = (dims,)

    if dims is None:
        dims = [f"dim_{n}" for n in range(len(shape))]
        if coords is not None and len(coords) == len(shape):
            # try to infer dimensions from coords
            if utils.is_dict_like(coords):
                dims = list(coords.keys())
            else:
                for n, (dim, coord) in enumerate(zip(dims, coords)):
                    coord = as_variable(coord, name=dims[n]).to_index_variable()
                    dims[n] = coord.name
        dims = tuple(dims)
    elif len(dims) != len(shape):
        raise ValueError(
            "different number of dimensions on data "
            f"and dims: {len(shape)} vs {len(dims)}"
        )
    else:
        for d in dims:
            if not isinstance(d, str):
                raise TypeError(f"dimension {d} is not a string")

    new_coords: dict[Hashable, Variable] = {}

    if utils.is_dict_like(coords):
        for k, v in coords.items():
            new_coords[k] = as_variable(v, name=k)
    elif coords is not None:
        for dim, coord in zip(dims, coords):
            var = as_variable(coord, name=dim)
            var.dims = (dim,)
            new_coords[dim] = var.to_index_variable()

    sizes = dict(zip(dims, shape))
    for k, v in new_coords.items():
        if any(d not in dims for d in v.dims):
            raise ValueError(
                f"coordinate {k} has dimensions {v.dims}, but these "
                "are not a subset of the DataArray "
                f"dimensions {dims}"
            )

        for d, s in zip(v.dims, v.shape):
            if s != sizes[d]:
                raise ValueError(
                    f"conflicting sizes for dimension {d!r}: "
                    f"length {sizes[d]} on the data but length {s} on "
                    f"coordinate {k!r}"
                )

        if k in sizes and v.shape != (sizes[k],):
            raise ValueError(
                f"coordinate {k!r} is a DataArray dimension, but "
                f"it has shape {v.shape!r} rather than expected shape {sizes[k]!r} "
                "matching the dimension size"
            )

    return new_coords, dims


def _check_data_shape(data, coords, dims):
    if data is dtypes.NA:
        data = np.nan
    if coords is not None and utils.is_scalar(data, include_0d=False):
        if utils.is_dict_like(coords):
            if dims is None:
                return data
            else:
                data_shape = tuple(
                    as_variable(coords[k], k).size if k in coords.keys() else 1
                    for k in dims
                )
        else:
            data_shape = tuple(as_variable(coord, "foo").size for coord in coords)
        data = np.full(data_shape, data)
    return data


class _LocIndexer:
    __slots__ = ("data_array",)

    def __init__(self, data_array: DataArray):
        self.data_array = data_array

    def __getitem__(self, key) -> DataArray:
        if not utils.is_dict_like(key):
            # expand the indexer so we can handle Ellipsis
            labels = indexing.expanded_indexer(key, self.data_array.ndim)
            key = dict(zip(self.data_array.dims, labels))
        return self.data_array.sel(key)

    def __setitem__(self, key, value) -> None:
        if not utils.is_dict_like(key):
            # expand the indexer so we can handle Ellipsis
            labels = indexing.expanded_indexer(key, self.data_array.ndim)
            key = dict(zip(self.data_array.dims, labels))

        dim_indexers = map_index_queries(self.data_array, key).dim_indexers
        self.data_array[dim_indexers] = value


# Used as the key corresponding to a DataArray's variable when converting
# arbitrary DataArray objects to datasets
_THIS_ARRAY = ReprObject("<this-array>")


class DataArray(
    AbstractArray, DataWithCoords, DataArrayArithmetic, DataArrayReductions
):
    """N-dimensional array with labeled coordinates and dimensions.

    DataArray provides a wrapper around numpy ndarrays that uses
    labeled dimensions and coordinates to support metadata aware
    operations. The API is similar to that for the pandas Series or
    DataFrame, but DataArray objects can have any number of dimensions,
    and their contents have fixed data types.

    Additional features over raw numpy arrays:

    - Apply operations over dimensions by name: ``x.sum('time')``.
    - Select or assign values by integer location (like numpy):
      ``x[:10]`` or by label (like pandas): ``x.loc['2014-01-01']`` or
      ``x.sel(time='2014-01-01')``.
    - Mathematical operations (e.g., ``x - y``) vectorize across
      multiple dimensions (known in numpy as "broadcasting") based on
      dimension names, regardless of their original order.
    - Keep track of arbitrary metadata in the form of a Python
      dictionary: ``x.attrs``
    - Convert to a pandas Series: ``x.to_series()``.

    Getting items from or doing mathematical operations with a
    DataArray always returns another DataArray.

    Parameters
    ----------
    data : array_like
        Values for this array. Must be an ``numpy.ndarray``, ndarray
        like, or castable to an ``ndarray``. If a self-described xarray
        or pandas object, attempts are made to use this array's
        metadata to fill in other unspecified arguments. A view of the
        array's data is used instead of a copy if possible.
    coords : sequence or dict of array_like, optional
        Coordinates (tick labels) to use for indexing along each
        dimension. The following notations are accepted:

        - mapping {dimension name: array-like}
        - sequence of tuples that are valid arguments for
          ``xarray.Variable()``
          - (dims, data)
          - (dims, data, attrs)
          - (dims, data, attrs, encoding)

        Additionally, it is possible to define a coord whose name
        does not match the dimension name, or a coord based on multiple
        dimensions, with one of the following notations:

        - mapping {coord name: DataArray}
        - mapping {coord name: Variable}
        - mapping {coord name: (dimension name, array-like)}
        - mapping {coord name: (tuple of dimension names, array-like)}

    dims : Hashable or sequence of Hashable, optional
        Name(s) of the data dimension(s). Must be either a Hashable
        (only for 1D data) or a sequence of Hashables with length equal
        to the number of dimensions. If this argument is omitted,
        dimension names are taken from ``coords`` (if possible) and
        otherwise default to ``['dim_0', ... 'dim_n']``.
    name : str or None, optional
        Name of this array.
    attrs : dict_like or None, optional
        Attributes to assign to the new instance. By default, an empty
        attribute dictionary is initialized.

    Examples
    --------
    Create data:

    >>> np.random.seed(0)
    >>> temperature = 15 + 8 * np.random.randn(2, 2, 3)
    >>> lon = [[-99.83, -99.32], [-99.79, -99.23]]
    >>> lat = [[42.25, 42.21], [42.63, 42.59]]
    >>> time = pd.date_range("2014-09-06", periods=3)
    >>> reference_time = pd.Timestamp("2014-09-05")

    Initialize a dataarray with multiple dimensions:

    >>> da = xr.DataArray(
    ...     data=temperature,
    ...     dims=["x", "y", "time"],
    ...     coords=dict(
    ...         lon=(["x", "y"], lon),
    ...         lat=(["x", "y"], lat),
    ...         time=time,
    ...         reference_time=reference_time,
    ...     ),
    ...     attrs=dict(
    ...         description="Ambient temperature.",
    ...         units="degC",
    ...     ),
    ... )
    >>> da
    <xarray.DataArray (x: 2, y: 2, time: 3)>
    array([[[29.11241877, 18.20125767, 22.82990387],
            [32.92714559, 29.94046392,  7.18177696]],
    <BLANKLINE>
           [[22.60070734, 13.78914233, 14.17424919],
            [18.28478802, 16.15234857, 26.63418806]]])
    Coordinates:
        lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
        lat             (x, y) float64 42.25 42.21 42.63 42.59
      * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
        reference_time  datetime64[ns] 2014-09-05
    Dimensions without coordinates: x, y
    Attributes:
        description:  Ambient temperature.
        units:        degC

    Find out where the coldest temperature was:

    >>> da.isel(da.argmin(...))
    <xarray.DataArray ()>
    array(7.18177696)
    Coordinates:
        lon             float64 -99.32
        lat             float64 42.21
        time            datetime64[ns] 2014-09-08
        reference_time  datetime64[ns] 2014-09-05
    Attributes:
        description:  Ambient temperature.
        units:        degC
    """

    _cache: dict[str, Any]
    _coords: dict[Any, Variable]
    _close: Callable[[], None] | None
    _indexes: dict[Hashable, Index]
    _name: Hashable | None
    _variable: Variable

    __slots__ = (
        "_cache",
        "_coords",
        "_close",
        "_indexes",
        "_name",
        "_variable",
        "__weakref__",
    )

    dt = utils.UncachedAccessor(CombinedDatetimelikeAccessor["DataArray"])

    def __init__(
        self,
        data: Any = dtypes.NA,
        coords: Sequence[Sequence[Any] | pd.Index | DataArray]
        | Mapping[Any, Any]
        | None = None,
        dims: Hashable | Sequence[Hashable] | None = None,
        name: Hashable = None,
        attrs: Mapping = None,
        # internal parameters
        indexes: dict[Hashable, Index] = None,
        fastpath: bool = False,
    ) -> None:
        if fastpath:
            variable = data
            assert dims is None
            assert attrs is None
            assert indexes is not None
        else:
            # TODO: (benbovy - explicit indexes) remove
            # once it becomes part of the public interface
            if indexes is not None:
                raise ValueError("Providing explicit indexes is not supported yet")

            # try to fill in arguments from data if they weren't supplied
            if coords is None:

                if isinstance(data, DataArray):
                    coords = data.coords
                elif isinstance(data, pd.Series):
                    coords = [data.index]
                elif isinstance(data, pd.DataFrame):
                    coords = [data.index, data.columns]
                elif isinstance(data, (pd.Index, IndexVariable)):
                    coords = [data]

            if dims is None:
                dims = getattr(data, "dims", getattr(coords, "dims", None))
            if name is None:
                name = getattr(data, "name", None)
            if attrs is None and not isinstance(data, PANDAS_TYPES):
                attrs = getattr(data, "attrs", None)

            data = _check_data_shape(data, coords, dims)
            data = as_compatible_data(data)
            coords, dims = _infer_coords_and_dims(data.shape, coords, dims)
            variable = Variable(dims, data, attrs, fastpath=True)
            indexes, coords = _create_indexes_from_coords(coords)

        # These fully describe a DataArray
        self._variable = variable
        assert isinstance(coords, dict)
        self._coords = coords
        self._name = name

        # TODO(shoyer): document this argument, once it becomes part of the
        # public interface.
        self._indexes = indexes  # type: ignore[assignment]

        self._close = None

    @classmethod
    def _construct_direct(
        cls: type[T_DataArray],
        variable: Variable,
        coords: dict[Any, Variable],
        name: Hashable,
        indexes: dict[Hashable, Index],
    ) -> T_DataArray:
        """Shortcut around __init__ for internal use when we want to skip
        costly validation
        """
        obj = object.__new__(cls)
        obj._variable = variable
        obj._coords = coords
        obj._name = name
        obj._indexes = indexes
        obj._close = None
        return obj

    def _replace(
        self: T_DataArray,
        variable: Variable = None,
        coords=None,
        name: Hashable | None | Default = _default,
        indexes=None,
    ) -> T_DataArray:
        if variable is None:
            variable = self.variable
        if coords is None:
            coords = self._coords
        if indexes is None:
            indexes = self._indexes
        if name is _default:
            name = self.name
        return type(self)(variable, coords, name=name, indexes=indexes, fastpath=True)

    def _replace_maybe_drop_dims(
        self: T_DataArray,
        variable: Variable,
        name: Hashable | None | Default = _default,
    ) -> T_DataArray:
        if variable.dims == self.dims and variable.shape == self.shape:
            coords = self._coords.copy()
            indexes = self._indexes
        elif variable.dims == self.dims:
            # Shape has changed (e.g. from reduce(..., keepdims=True)
            new_sizes = dict(zip(self.dims, variable.shape))
            coords = {
                k: v
                for k, v in self._coords.items()
                if v.shape == tuple(new_sizes[d] for d in v.dims)
            }
            indexes = filter_indexes_from_coords(self._indexes, set(coords))
        else:
            allowed_dims = set(variable.dims)
            coords = {
                k: v for k, v in self._coords.items() if set(v.dims) <= allowed_dims
            }
            indexes = filter_indexes_from_coords(self._indexes, set(coords))
        return self._replace(variable, coords, name, indexes=indexes)

    def _overwrite_indexes(
        self: T_DataArray,
        indexes: Mapping[Any, Index],
        coords: Mapping[Any, Variable] = None,
        drop_coords: list[Hashable] = None,
        rename_dims: Mapping[Any, Any] = None,
    ) -> T_DataArray:
        """Maybe replace indexes and their corresponding coordinates."""
        if not indexes:
            return self

        if coords is None:
            coords = {}
        if drop_coords is None:
            drop_coords = []

        new_variable = self.variable.copy()
        new_coords = self._coords.copy()
        new_indexes = dict(self._indexes)

        for name in indexes:
            new_coords[name] = coords[name]
            new_indexes[name] = indexes[name]

        for name in drop_coords:
            new_coords.pop(name)
            new_indexes.pop(name)

        if rename_dims:
            new_variable.dims = [rename_dims.get(d, d) for d in new_variable.dims]

        return self._replace(
            variable=new_variable, coords=new_coords, indexes=new_indexes
        )

    def _to_temp_dataset(self) -> Dataset:
        return self._to_dataset_whole(name=_THIS_ARRAY, shallow_copy=False)

    def _from_temp_dataset(
        self: T_DataArray, dataset: Dataset, name: Hashable | None | Default = _default
    ) -> T_DataArray:
        variable = dataset._variables.pop(_THIS_ARRAY)
        coords = dataset._variables
        indexes = dataset._indexes
        return self._replace(variable, coords, name, indexes=indexes)

    def _to_dataset_split(self, dim: Hashable) -> Dataset:
        """splits dataarray along dimension 'dim'"""

        def subset(dim, label):
            array = self.loc[{dim: label}]
            array.attrs = {}
            return as_variable(array)

        variables = {label: subset(dim, label) for label in self.get_index(dim)}
        variables.update({k: v for k, v in self._coords.items() if k != dim})
        coord_names = set(self._coords) - {dim}
        indexes = filter_indexes_from_coords(self._indexes, coord_names)
        dataset = Dataset._construct_direct(
            variables, coord_names, indexes=indexes, attrs=self.attrs
        )
        return dataset

    def _to_dataset_whole(
        self, name: Hashable = None, shallow_copy: bool = True
    ) -> Dataset:
        if name is None:
            name = self.name
        if name is None:
            raise ValueError(
                "unable to convert unnamed DataArray to a "
                "Dataset without providing an explicit name"
            )
        if name in self.coords:
            raise ValueError(
                "cannot create a Dataset from a DataArray with "
                "the same name as one of its coordinates"
            )
        # use private APIs for speed: this is called by _to_temp_dataset(),
        # which is used in the guts of a lot of operations (e.g., reindex)
        variables = self._coords.copy()
        variables[name] = self.variable
        if shallow_copy:
            for k in variables:
                variables[k] = variables[k].copy(deep=False)
        indexes = self._indexes

        coord_names = set(self._coords)
        return Dataset._construct_direct(variables, coord_names, indexes=indexes)

    def to_dataset(
        self,
        dim: Hashable = None,
        *,
        name: Hashable = None,
        promote_attrs: bool = False,
    ) -> Dataset:
        """Convert a DataArray to a Dataset.

        Parameters
        ----------
        dim : Hashable, optional
            Name of the dimension on this array along which to split this array
            into separate variables. If not provided, this array is converted
            into a Dataset of one variable.
        name : Hashable, optional
            Name to substitute for this array's name. Only valid if ``dim`` is
            not provided.
        promote_attrs : bool, default: False
            Set to True to shallow copy attrs of DataArray to returned Dataset.

        Returns
        -------
        dataset : Dataset
        """
        if dim is not None and dim not in self.dims:
            raise TypeError(
                f"{dim} is not a dim. If supplying a ``name``, pass as a kwarg."
            )

        if dim is not None:
            if name is not None:
                raise TypeError("cannot supply both dim and name arguments")
            result = self._to_dataset_split(dim)
        else:
            result = self._to_dataset_whole(name)

        if promote_attrs:
            result.attrs = dict(self.attrs)

        return result

    @property
    def name(self) -> Hashable | None:
        """The name of this array."""
        return self._name

    @name.setter
    def name(self, value: Hashable | None) -> None:
        self._name = value

    @property
    def variable(self) -> Variable:
        """Low level interface to the Variable object for this DataArray."""
        return self._variable

    @property
    def dtype(self) -> np.dtype:
        return self.variable.dtype

    @property
    def shape(self) -> tuple[int, ...]:
        return self.variable.shape

    @property
    def size(self) -> int:
        return self.variable.size

    @property
    def nbytes(self) -> int:
        return self.variable.nbytes

    @property
    def ndim(self) -> int:
        return self.variable.ndim

    def __len__(self) -> int:
        return len(self.variable)

    @property
    def data(self) -> Any:
        """
        The DataArray's data as an array. The underlying array type
        (e.g. dask, sparse, pint) is preserved.

        See Also
        --------
        DataArray.to_numpy
        DataArray.as_numpy
        DataArray.values
        """
        return self.variable.data

    @data.setter
    def data(self, value: Any) -> None:
        self.variable.data = value

    @property
    def values(self) -> np.ndarray:
        """
        The array's data as a numpy.ndarray.

        If the array's data is not a numpy.ndarray this will attempt to convert
        it naively using np.array(), which will raise an error if the array
        type does not support coercion like this (e.g. cupy).
        """
        return self.variable.values

    @values.setter
    def values(self, value: Any) -> None:
        self.variable.values = value

    def to_numpy(self) -> np.ndarray:
        """
        Coerces wrapped data to numpy and returns a numpy.ndarray.

        See Also
        --------
        DataArray.as_numpy : Same but returns the surrounding DataArray instead.
        Dataset.as_numpy
        DataArray.values
        DataArray.data
        """
        return self.variable.to_numpy()

    def as_numpy(self: T_DataArray) -> T_DataArray:
        """
        Coerces wrapped data and coordinates into numpy arrays, returning a DataArray.

        See Also
        --------
        DataArray.to_numpy : Same but returns only the data as a numpy.ndarray object.
        Dataset.as_numpy : Converts all variables in a Dataset.
        DataArray.values
        DataArray.data
        """
        coords = {k: v.as_numpy() for k, v in self._coords.items()}
        return self._replace(self.variable.as_numpy(), coords, indexes=self._indexes)

    @property
    def _in_memory(self) -> bool:
        return self.variable._in_memory

    def to_index(self) -> pd.Index:
        """Convert this variable to a pandas.Index. Only possible for 1D
        arrays.
        """
        return self.variable.to_index()

    @property
    def dims(self) -> tuple[Hashable, ...]:
        """Tuple of dimension names associated with this array.

        Note that the type of this property is inconsistent with
        `Dataset.dims`.  See `Dataset.sizes` and `DataArray.sizes` for
        consistently named properties.

        See Also
        --------
        DataArray.sizes
        Dataset.dims
        """
        return self.variable.dims

    @dims.setter
    def dims(self, value: Any) -> NoReturn:
        raise AttributeError(
            "you cannot assign dims on a DataArray. Use "
            ".rename() or .swap_dims() instead."
        )

    def _item_key_to_dict(self, key: Any) -> Mapping[Hashable, Any]:
        if utils.is_dict_like(key):
            return key
        key = indexing.expanded_indexer(key, self.ndim)
        return dict(zip(self.dims, key))

    def _getitem_coord(self: T_DataArray, key: Any) -> T_DataArray:
        from .dataset import _get_virtual_variable

        try:
            var = self._coords[key]
        except KeyError:
            dim_sizes = dict(zip(self.dims, self.shape))
            _, key, var = _get_virtual_variable(self._coords, key, dim_sizes)

        return self._replace_maybe_drop_dims(var, name=key)

    def __getitem__(self: T_DataArray, key: Any) -> T_DataArray:
        if isinstance(key, str):
            return self._getitem_coord(key)
        else:
            # xarray-style array indexing
            return self.isel(indexers=self._item_key_to_dict(key))

    def __setitem__(self, key: Any, value: Any) -> None:
        if isinstance(key, str):
            self.coords[key] = value
        else:
            # Coordinates in key, value and self[key] should be consistent.
            # TODO Coordinate consistency in key is checked here, but it
            # causes unnecessary indexing. It should be optimized.
            obj = self[key]
            if isinstance(value, DataArray):
                assert_coordinate_consistent(value, obj.coords.variables)
            # DataArray key -> Variable key
            key = {
                k: v.variable if isinstance(v, DataArray) else v
                for k, v in self._item_key_to_dict(key).items()
            }
            self.variable[key] = value

    def __delitem__(self, key: Any) -> None:
        del self.coords[key]

    @property
    def _attr_sources(self) -> Iterable[Mapping[Hashable, Any]]:
        """Places to look-up items for attribute-style access"""
        yield from self._item_sources
        yield self.attrs

    @property
    def _item_sources(self) -> Iterable[Mapping[Hashable, Any]]:
        """Places to look-up items for key-completion"""
        yield HybridMappingProxy(keys=self._coords, mapping=self.coords)

        # virtual coordinates
        # uses empty dict -- everything here can already be found in self.coords.
        yield HybridMappingProxy(keys=self.dims, mapping={})

    def __contains__(self, key: Any) -> bool:
        return key in self.data

    @property
    def loc(self) -> _LocIndexer:
        """Attribute for location based indexing like pandas."""
        return _LocIndexer(self)

    @property
    # Key type needs to be `Any` because of mypy#4167
    def attrs(self) -> dict[Any, Any]:
        """Dictionary storing arbitrary metadata with this array."""
        return self.variable.attrs

    @attrs.setter
    def attrs(self, value: Mapping[Any, Any]) -> None:
        # Disable type checking to work around mypy bug - see mypy#4167
        self.variable.attrs = value  # type: ignore[assignment]

    @property
    def encoding(self) -> dict[Hashable, Any]:
        """Dictionary of format-specific settings for how this array should be
        serialized."""
        return self.variable.encoding

    @encoding.setter
    def encoding(self, value: Mapping[Any, Any]) -> None:
        self.variable.encoding = value

    @property
    def indexes(self) -> Indexes:
        """Mapping of pandas.Index objects used for label based indexing.

        Raises an error if this Dataset has indexes that cannot be coerced
        to pandas.Index objects.

        See Also
        --------
        DataArray.xindexes

        """
        return self.xindexes.to_pandas_indexes()

    @property
    def xindexes(self) -> Indexes:
        """Mapping of xarray Index objects used for label based indexing."""
        return Indexes(self._indexes, {k: self._coords[k] for k in self._indexes})

    @property
    def coords(self) -> DataArrayCoordinates:
        """Dictionary-like container of coordinate arrays."""
        return DataArrayCoordinates(self)

    @overload
    def reset_coords(
        self: T_DataArray,
        names: Hashable | Iterable[Hashable] | None = None,
        drop: Literal[False] = False,
    ) -> Dataset:
        ...

    @overload
    def reset_coords(
        self: T_DataArray,
        names: Hashable | Iterable[Hashable] | None = None,
        *,
        drop: Literal[True],
    ) -> T_DataArray:
        ...

    def reset_coords(
        self: T_DataArray,
        names: Hashable | Iterable[Hashable] | None = None,
        drop: bool = False,
    ) -> T_DataArray | Dataset:
        """Given names of coordinates, reset them to become variables.

        Parameters
        ----------
        names : Hashable or iterable of Hashable, optional
            Name(s) of non-index coordinates in this dataset to reset into
            variables. By default, all non-index coordinates are reset.
        drop : bool, default: False
            If True, remove coordinates instead of converting them into
            variables.

        Returns
        -------
        Dataset, or DataArray if ``drop == True``
        """
        if names is None:
            names = set(self.coords) - set(self._indexes)
        dataset = self.coords.to_dataset().reset_coords(names, drop)
        if drop:
            return self._replace(coords=dataset._variables)
        if self.name is None:
            raise ValueError(
                "cannot reset_coords with drop=False on an unnamed DataArrray"
            )
        dataset[self.name] = self.variable
        return dataset

    def __dask_tokenize__(self):
        from dask.base import normalize_token

        return normalize_token((type(self), self._variable, self._coords, self._name))

    def __dask_graph__(self):
        return self._to_temp_dataset().__dask_graph__()

    def __dask_keys__(self):
        return self._to_temp_dataset().__dask_keys__()

    def __dask_layers__(self):
        return self._to_temp_dataset().__dask_layers__()

    @property
    def __dask_optimize__(self):
        return self._to_temp_dataset().__dask_optimize__

    @property
    def __dask_scheduler__(self):
        return self._to_temp_dataset().__dask_scheduler__

    def __dask_postcompute__(self):
        func, args = self._to_temp_dataset().__dask_postcompute__()
        return self._dask_finalize, (self.name, func) + args

    def __dask_postpersist__(self):
        func, args = self._to_temp_dataset().__dask_postpersist__()
        return self._dask_finalize, (self.name, func) + args

    @staticmethod
    def _dask_finalize(results, name, func, *args, **kwargs) -> DataArray:
        ds = func(results, *args, **kwargs)
        variable = ds._variables.pop(_THIS_ARRAY)
        coords = ds._variables
        indexes = ds._indexes
        return DataArray(variable, coords, name=name, indexes=indexes, fastpath=True)

    def load(self: T_DataArray, **kwargs) -> T_DataArray:
        """Manually trigger loading of this array's data from disk or a
        remote source into memory and return this array.

        Normally, it should not be necessary to call this method in user code,
        because all xarray functions should either work on deferred data or
        load data automatically. However, this method can be necessary when
        working with many file objects on disk.

        Parameters
        ----------
        **kwargs : dict
            Additional keyword arguments passed on to ``dask.compute``.

        See Also
        --------
        dask.compute
        """
        ds = self._to_temp_dataset().load(**kwargs)
        new = self._from_temp_dataset(ds)
        self._variable = new._variable
        self._coords = new._coords
        return self

    def compute(self: T_DataArray, **kwargs) -> T_DataArray:
        """Manually trigger loading of this array's data from disk or a
        remote source into memory and return a new array. The original is
        left unaltered.

        Normally, it should not be necessary to call this method in user code,
        because all xarray functions should either work on deferred data or
        load data automatically. However, this method can be necessary when
        working with many file objects on disk.

        Parameters
        ----------
        **kwargs : dict
            Additional keyword arguments passed on to ``dask.compute``.

        See Also
        --------
        dask.compute
        """
        new = self.copy(deep=False)
        return new.load(**kwargs)

    def persist(self: T_DataArray, **kwargs) -> T_DataArray:
        """Trigger computation in constituent dask arrays

        This keeps them as dask arrays but encourages them to keep data in
        memory.  This is particularly useful when on a distributed machine.
        When on a single machine consider using ``.compute()`` instead.

        Parameters
        ----------
        **kwargs : dict
            Additional keyword arguments passed on to ``dask.persist``.

        See Also
        --------
        dask.persist
        """
        ds = self._to_temp_dataset().persist(**kwargs)
        return self._from_temp_dataset(ds)

    def copy(self: T_DataArray, deep: bool = True, data: Any = None) -> T_DataArray:
        """Returns a copy of this array.

        If `deep=True`, a deep copy is made of the data array.
        Otherwise, a shallow copy is made, and the returned data array's
        values are a new view of this data array's values.

        Use `data` to create a new object with the same structure as
        original but entirely new data.

        Parameters
        ----------
        deep : bool, optional
            Whether the data array and its coordinates are loaded into memory
            and copied onto the new object. Default is True.
        data : array_like, optional
            Data to use in the new object. Must have same shape as original.
            When `data` is used, `deep` is ignored for all data variables,
            and only used for coords.

        Returns
        -------
        copy : DataArray
            New object with dimensions, attributes, coordinates, name,
            encoding, and optionally data copied from original.

        Examples
        --------
        Shallow versus deep copy

        >>> array = xr.DataArray([1, 2, 3], dims="x", coords={"x": ["a", "b", "c"]})
        >>> array.copy()
        <xarray.DataArray (x: 3)>
        array([1, 2, 3])
        Coordinates:
          * x        (x) <U1 'a' 'b' 'c'
        >>> array_0 = array.copy(deep=False)
        >>> array_0[0] = 7
        >>> array_0
        <xarray.DataArray (x: 3)>
        array([7, 2, 3])
        Coordinates:
          * x        (x) <U1 'a' 'b' 'c'
        >>> array
        <xarray.DataArray (x: 3)>
        array([7, 2, 3])
        Coordinates:
          * x        (x) <U1 'a' 'b' 'c'

        Changing the data using the ``data`` argument maintains the
        structure of the original object, but with the new data. Original
        object is unaffected.

        >>> array.copy(data=[0.1, 0.2, 0.3])
        <xarray.DataArray (x: 3)>
        array([0.1, 0.2, 0.3])
        Coordinates:
          * x        (x) <U1 'a' 'b' 'c'
        >>> array
        <xarray.DataArray (x: 3)>
        array([7, 2, 3])
        Coordinates:
          * x        (x) <U1 'a' 'b' 'c'

        See Also
        --------
        pandas.DataFrame.copy
        """
        variable = self.variable.copy(deep=deep, data=data)
        indexes, index_vars = self.xindexes.copy_indexes(deep=deep)

        coords = {}
        for k, v in self._coords.items():
            if k in index_vars:
                coords[k] = index_vars[k]
            else:
                coords[k] = v.copy(deep=deep)

        return self._replace(variable, coords, indexes=indexes)

    def __copy__(self: T_DataArray) -> T_DataArray:
        return self.copy(deep=False)

    def __deepcopy__(self: T_DataArray, memo=None) -> T_DataArray:
        # memo does nothing but is required for compatibility with
        # copy.deepcopy
        return self.copy(deep=True)

    # mutable objects should not be Hashable
    # https://github.com/python/mypy/issues/4266
    __hash__ = None  # type: ignore[assignment]

    @property
    def chunks(self) -> tuple[tuple[int, ...], ...] | None:
        """
        Tuple of block lengths for this dataarray's data, in order of dimensions, or None if
        the underlying data is not a dask array.

        See Also
        --------
        DataArray.chunk
        DataArray.chunksizes
        xarray.unify_chunks
        """
        return self.variable.chunks

    @property
    def chunksizes(self) -> Mapping[Any, tuple[int, ...]]:
        """
        Mapping from dimension names to block lengths for this dataarray's data, or None if
        the underlying data is not a dask array.
        Cannot be modified directly, but can be modified by calling .chunk().

        Differs from DataArray.chunks because it returns a mapping of dimensions to chunk shapes
        instead of a tuple of chunk shapes.

        See Also
        --------
        DataArray.chunk
        DataArray.chunks
        xarray.unify_chunks
        """
        all_variables = [self.variable] + [c.variable for c in self.coords.values()]
        return get_chunksizes(all_variables)

    def chunk(
        self: T_DataArray,
        chunks: (
            int
            | Literal["auto"]
            | tuple[int, ...]
            | tuple[tuple[int, ...], ...]
            | Mapping[Any, None | int | tuple[int, ...]]
        ) = {},  # {} even though it's technically unsafe, is being used intentionally here (#4667)
        name_prefix: str = "xarray-",
        token: str | None = None,
        lock: bool = False,
        inline_array: bool = False,
        **chunks_kwargs: Any,
    ) -> T_DataArray:
        """Coerce this array's data into a dask arrays with the given chunks.

        If this variable is a non-dask array, it will be converted to dask
        array. If it's a dask array, it will be rechunked to the given chunk
        sizes.

        If neither chunks is not provided for one or more dimensions, chunk
        sizes along that dimension will not be updated; non-dask arrays will be
        converted into dask arrays with a single block.

        Parameters
        ----------
        chunks : int, "auto", tuple of int or mapping of Hashable to int, optional
            Chunk sizes along each dimension, e.g., ``5``, ``"auto"``, ``(5, 5)`` or
            ``{"x": 5, "y": 5}``.
        name_prefix : str, optional
            Prefix for the name of the new dask array.
        token : str, optional
            Token uniquely identifying this array.
        lock : optional
            Passed on to :py:func:`dask.array.from_array`, if the array is not
            already as dask array.
        inline_array: optional
            Passed on to :py:func:`dask.array.from_array`, if the array is not
            already as dask array.
        **chunks_kwargs : {dim: chunks, ...}, optional
            The keyword arguments form of ``chunks``.
            One of chunks or chunks_kwargs must be provided.

        Returns
        -------
        chunked : xarray.DataArray

        See Also
        --------
        DataArray.chunks
        DataArray.chunksizes
        xarray.unify_chunks
        dask.array.from_array
        """
        if chunks is None:
            warnings.warn(
                "None value for 'chunks' is deprecated. "
                "It will raise an error in the future. Use instead '{}'",
                category=FutureWarning,
            )
            chunks = {}

        if isinstance(chunks, (float, str, int)):
            # ignoring type; unclear why it won't accept a Literal into the value.
            chunks = dict.fromkeys(self.dims, chunks)  # type: ignore
        elif isinstance(chunks, (tuple, list)):
            chunks = dict(zip(self.dims, chunks))
        else:
            chunks = either_dict_or_kwargs(chunks, chunks_kwargs, "chunk")

        ds = self._to_temp_dataset().chunk(
            chunks,
            name_prefix=name_prefix,
            token=token,
            lock=lock,
            inline_array=inline_array,
        )
        return self._from_temp_dataset(ds)

    def isel(
        self: T_DataArray,
        indexers: Mapping[Any, Any] | None = None,
        drop: bool = False,
        missing_dims: ErrorOptionsWithWarn = "raise",
        **indexers_kwargs: Any,
    ) -> T_DataArray:
        """Return a new DataArray whose data is given by selecting indexes
        along the specified dimension(s).

        Parameters
        ----------
        indexers : dict, optional
            A dict with keys matching dimensions and values given
            by integers, slice objects or arrays.
            indexer can be a integer, slice, array-like or DataArray.
            If DataArrays are passed as indexers, xarray-style indexing will be
            carried out. See :ref:`indexing` for the details.
            One of indexers or indexers_kwargs must be provided.
        drop : bool, default: False
            If ``drop=True``, drop coordinates variables indexed by integers
            instead of making them scalar.
        missing_dims : {"raise", "warn", "ignore"}, default: "raise"
            What to do if dimensions that should be selected from are not present in the
            DataArray:
            - "raise": raise an exception
            - "warn": raise a warning, and ignore the missing dimensions
            - "ignore": ignore the missing dimensions
        **indexers_kwargs : {dim: indexer, ...}, optional
            The keyword arguments form of ``indexers``.

        Returns
        -------
        indexed : xarray.DataArray

        See Also
        --------
        Dataset.isel
        DataArray.sel

        Examples
        --------
        >>> da = xr.DataArray(np.arange(25).reshape(5, 5), dims=("x", "y"))
        >>> da
        <xarray.DataArray (x: 5, y: 5)>
        array([[ 0,  1,  2,  3,  4],
               [ 5,  6,  7,  8,  9],
               [10, 11, 12, 13, 14],
               [15, 16, 17, 18, 19],
               [20, 21, 22, 23, 24]])
        Dimensions without coordinates: x, y

        >>> tgt_x = xr.DataArray(np.arange(0, 5), dims="points")
        >>> tgt_y = xr.DataArray(np.arange(0, 5), dims="points")
        >>> da = da.isel(x=tgt_x, y=tgt_y)
        >>> da
        <xarray.DataArray (points: 5)>
        array([ 0,  6, 12, 18, 24])
        Dimensions without coordinates: points
        """

        indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "isel")

        if any(is_fancy_indexer(idx) for idx in indexers.values()):
            ds = self._to_temp_dataset()._isel_fancy(
                indexers, drop=drop, missing_dims=missing_dims
            )
            return self._from_temp_dataset(ds)

        # Much faster algorithm for when all indexers are ints, slices, one-dimensional
        # lists, or zero or one-dimensional np.ndarray's

        variable = self._variable.isel(indexers, missing_dims=missing_dims)
        indexes, index_variables = isel_indexes(self.xindexes, indexers)

        coords = {}
        for coord_name, coord_value in self._coords.items():
            if coord_name in index_variables:
                coord_value = index_variables[coord_name]
            else:
                coord_indexers = {
                    k: v for k, v in indexers.items() if k in coord_value.dims
                }
                if coord_indexers:
                    coord_value = coord_value.isel(coord_indexers)
                    if drop and coord_value.ndim == 0:
                        continue
            coords[coord_name] = coord_value

        return self._replace(variable=variable, coords=coords, indexes=indexes)

    def sel(
        self: T_DataArray,
        indexers: Mapping[Any, Any] = None,
        method: str = None,
        tolerance=None,
        drop: bool = False,
        **indexers_kwargs: Any,
    ) -> T_DataArray:
        """Return a new DataArray whose data is given by selecting index
        labels along the specified dimension(s).

        In contrast to `DataArray.isel`, indexers for this method should use
        labels instead of integers.

        Under the hood, this method is powered by using pandas's powerful Index
        objects. This makes label based indexing essentially just as fast as
        using integer indexing.

        It also means this method uses pandas's (well documented) logic for
        indexing. This means you can use string shortcuts for datetime indexes
        (e.g., '2000-01' to select all values in January 2000). It also means
        that slices are treated as inclusive of both the start and stop values,
        unlike normal Python indexing.

        .. warning::

          Do not try to assign values when using any of the indexing methods
          ``isel`` or ``sel``::

            da = xr.DataArray([0, 1, 2, 3], dims=['x'])
            # DO NOT do this
            da.isel(x=[0, 1, 2])[1] = -1

          Assigning values with the chained indexing using ``.sel`` or
          ``.isel`` fails silently.

        Parameters
        ----------
        indexers : dict, optional
            A dict with keys matching dimensions and values given
            by scalars, slices or arrays of tick labels. For dimensions with
            multi-index, the indexer may also be a dict-like object with keys
            matching index level names.
            If DataArrays are passed as indexers, xarray-style indexing will be
            carried out. See :ref:`indexing` for the details.
            One of indexers or indexers_kwargs must be provided.
        method : {None, "nearest", "pad", "ffill", "backfill", "bfill"}, optional
            Method to use for inexact matches:

            - None (default): only exact matches
            - pad / ffill: propagate last valid index value forward
            - backfill / bfill: propagate next valid index value backward
            - nearest: use nearest valid index value

        tolerance : optional
            Maximum distance between original and new labels for inexact
            matches. The values of the index at the matching locations must
            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
        drop : bool, optional
            If ``drop=True``, drop coordinates variables in `indexers` instead
            of making them scalar.
        **indexers_kwargs : {dim: indexer, ...}, optional
            The keyword arguments form of ``indexers``.
            One of indexers or indexers_kwargs must be provided.

        Returns
        -------
        obj : DataArray
            A new DataArray with the same contents as this DataArray, except the
            data and each dimension is indexed by the appropriate indexers.
            If indexer DataArrays have coordinates that do not conflict with
            this object, then these coordinates will be attached.
            In general, each array's data will be a view of the array's data
            in this DataArray, unless vectorized indexing was triggered by using
            an array indexer, in which case the data will be a copy.

        See Also
        --------
        Dataset.sel
        DataArray.isel

        Examples
        --------
        >>> da = xr.DataArray(
        ...     np.arange(25).reshape(5, 5),
        ...     coords={"x": np.arange(5), "y": np.arange(5)},
        ...     dims=("x", "y"),
        ... )
        >>> da
        <xarray.DataArray (x: 5, y: 5)>
        array([[ 0,  1,  2,  3,  4],
               [ 5,  6,  7,  8,  9],
               [10, 11, 12, 13, 14],
               [15, 16, 17, 18, 19],
               [20, 21, 22, 23, 24]])
        Coordinates:
          * x        (x) int64 0 1 2 3 4
          * y        (y) int64 0 1 2 3 4

        >>> tgt_x = xr.DataArray(np.linspace(0, 4, num=5), dims="points")
        >>> tgt_y = xr.DataArray(np.linspace(0, 4, num=5), dims="points")
        >>> da = da.sel(x=tgt_x, y=tgt_y, method="nearest")
        >>> da
        <xarray.DataArray (points: 5)>
        array([ 0,  6, 12, 18, 24])
        Coordinates:
            x        (points) int64 0 1 2 3 4
            y        (points) int64 0 1 2 3 4
        Dimensions without coordinates: points
        """
        ds = self._to_temp_dataset().sel(
            indexers=indexers,
            drop=drop,
            method=method,
            tolerance=tolerance,
            **indexers_kwargs,
        )
        return self._from_temp_dataset(ds)

    def head(
        self: T_DataArray,
        indexers: Mapping[Any, int] | int | None = None,
        **indexers_kwargs: Any,
    ) -> T_DataArray:
        """Return a new DataArray whose data is given by the the first `n`
        values along the specified dimension(s). Default `n` = 5

        See Also
        --------
        Dataset.head
        DataArray.tail
        DataArray.thin
        """
        ds = self._to_temp_dataset().head(indexers, **indexers_kwargs)
        return self._from_temp_dataset(ds)

    def tail(
        self: T_DataArray,
        indexers: Mapping[Any, int] | int | None = None,
        **indexers_kwargs: Any,
    ) -> T_DataArray:
        """Return a new DataArray whose data is given by the the last `n`
        values along the specified dimension(s). Default `n` = 5

        See Also
        --------
        Dataset.tail
        DataArray.head
        DataArray.thin
        """
        ds = self._to_temp_dataset().tail(indexers, **indexers_kwargs)
        return self._from_temp_dataset(ds)

    def thin(
        self: T_DataArray,
        indexers: Mapping[Any, int] | int | None = None,
        **indexers_kwargs: Any,
    ) -> T_DataArray:
        """Return a new DataArray whose data is given by each `n` value
        along the specified dimension(s).

        Examples
        --------
        >>> x_arr = np.arange(0, 26)
        >>> x_arr
        array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
               17, 18, 19, 20, 21, 22, 23, 24, 25])
        >>> x = xr.DataArray(
        ...     np.reshape(x_arr, (2, 13)),
        ...     dims=("x", "y"),
        ...     coords={"x": [0, 1], "y": np.arange(0, 13)},
        ... )
        >>> x
        <xarray.DataArray (x: 2, y: 13)>
        array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12],
               [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]])
        Coordinates:
          * x        (x) int64 0 1
          * y        (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12

        >>>
        >>> x.thin(3)
        <xarray.DataArray (x: 1, y: 5)>
        array([[ 0,  3,  6,  9, 12]])
        Coordinates:
          * x        (x) int64 0
          * y        (y) int64 0 3 6 9 12
        >>> x.thin({"x": 2, "y": 5})
        <xarray.DataArray (x: 1, y: 3)>
        array([[ 0,  5, 10]])
        Coordinates:
          * x        (x) int64 0
          * y        (y) int64 0 5 10

        See Also
        --------
        Dataset.thin
        DataArray.head
        DataArray.tail
        """
        ds = self._to_temp_dataset().thin(indexers, **indexers_kwargs)
        return self._from_temp_dataset(ds)

    def broadcast_like(
        self: T_DataArray,
        other: DataArray | Dataset,
        exclude: Iterable[Hashable] | None = None,
    ) -> T_DataArray:
        """Broadcast this DataArray against another Dataset or DataArray.

        This is equivalent to xr.broadcast(other, self)[1]

        xarray objects are broadcast against each other in arithmetic
        operations, so this method is not be necessary for most uses.

        If no change is needed, the input data is returned to the output
        without being copied.

        If new coords are added by the broadcast, their values are
        NaN filled.

        Parameters
        ----------
        other : Dataset or DataArray
            Object against which to broadcast this array.
        exclude : iterable of Hashable, optional
            Dimensions that must not be broadcasted

        Returns
        -------
        new_da : DataArray
            The caller broadcasted against ``other``.

        Examples
        --------
        >>> arr1 = xr.DataArray(
        ...     np.random.randn(2, 3),
        ...     dims=("x", "y"),
        ...     coords={"x": ["a", "b"], "y": ["a", "b", "c"]},
        ... )
        >>> arr2 = xr.DataArray(
        ...     np.random.randn(3, 2),
        ...     dims=("x", "y"),
        ...     coords={"x": ["a", "b", "c"], "y": ["a", "b"]},
        ... )
        >>> arr1
        <xarray.DataArray (x: 2, y: 3)>
        array([[ 1.76405235,  0.40015721,  0.97873798],
               [ 2.2408932 ,  1.86755799, -0.97727788]])
        Coordinates:
          * x        (x) <U1 'a' 'b'
          * y        (y) <U1 'a' 'b' 'c'
        >>> arr2
        <xarray.DataArray (x: 3, y: 2)>
        array([[ 0.95008842, -0.15135721],
               [-0.10321885,  0.4105985 ],
               [ 0.14404357,  1.45427351]])
        Coordinates:
          * x        (x) <U1 'a' 'b' 'c'
          * y        (y) <U1 'a' 'b'
        >>> arr1.broadcast_like(arr2)
        <xarray.DataArray (x: 3, y: 3)>
        array([[ 1.76405235,  0.40015721,  0.97873798],
               [ 2.2408932 ,  1.86755799, -0.97727788],
               [        nan,         nan,         nan]])
        Coordinates:
          * x        (x) <U1 'a' 'b' 'c'
          * y        (y) <U1 'a' 'b' 'c'
        """
        if exclude is None:
            exclude = set()
        else:
            exclude = set(exclude)
        args = align(other, self, join="outer", copy=False, exclude=exclude)

        dims_map, common_coords = _get_broadcast_dims_map_common_coords(args, exclude)

        return _broadcast_helper(
            cast("T_DataArray", args[1]), exclude, dims_map, common_coords
        )

    def _reindex_callback(
        self: T_DataArray,
        aligner: alignment.Aligner,
        dim_pos_indexers: dict[Hashable, Any],
        variables: dict[Hashable, Variable],
        indexes: dict[Hashable, Index],
        fill_value: Any,
        exclude_dims: frozenset[Hashable],
        exclude_vars: frozenset[Hashable],
    ) -> T_DataArray:
        """Callback called from ``Aligner`` to create a new reindexed DataArray."""

        if isinstance(fill_value, dict):
            fill_value = fill_value.copy()
            sentinel = object()
            value = fill_value.pop(self.name, sentinel)
            if value is not sentinel:
                fill_value[_THIS_ARRAY] = value

        ds = self._to_temp_dataset()
        reindexed = ds._reindex_callback(
            aligner,
            dim_pos_indexers,
            variables,
            indexes,
            fill_value,
            exclude_dims,
            exclude_vars,
        )
        return self._from_temp_dataset(reindexed)

    def reindex_like(
        self: T_DataArray,
        other: DataArray | Dataset,
        method: ReindexMethodOptions = None,
        tolerance: int | float | Iterable[int | float] | None = None,
        copy: bool = True,
        fill_value=dtypes.NA,
    ) -> T_DataArray:
        """Conform this object onto the indexes of another object, filling in
        missing values with ``fill_value``. The default fill value is NaN.

        Parameters
        ----------
        other : Dataset or DataArray
            Object with an 'indexes' attribute giving a mapping from dimension
            names to pandas.Index objects, which provides coordinates upon
            which to index the variables in this dataset. The indexes on this
            other object need not be the same as the indexes on this
            dataset. Any mis-matched index values will be filled in with
            NaN, and any mis-matched dimension names will simply be ignored.
        method : {None, "nearest", "pad", "ffill", "backfill", "bfill"}, optional
            Method to use for filling index values from other not found on this
            data array:

            - None (default): don't fill gaps
            - pad / ffill: propagate last valid index value forward
            - backfill / bfill: propagate next valid index value backward
            - nearest: use nearest valid index value

        tolerance : optional
            Maximum distance between original and new labels for inexact
            matches. The values of the index at the matching locations must
            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
            Tolerance may be a scalar value, which applies the same tolerance
            to all values, or list-like, which applies variable tolerance per
            element. List-like must be the same size as the index and its dtype
            must exactly match the index’s type.
        copy : bool, default: True
            If ``copy=True``, data in the return value is always copied. If
            ``copy=False`` and reindexing is unnecessary, or can be performed
            with only slice operations, then the output may share memory with
            the input. In either case, a new xarray object is always returned.
        fill_value : scalar or dict-like, optional
            Value to use for newly missing values. If a dict-like, maps
            variable names (including coordinates) to fill values. Use this
            data array's name to refer to the data array's values.

        Returns
        -------
        reindexed : DataArray
            Another dataset array, with this array's data but coordinates from
            the other object.

        See Also
        --------
        DataArray.reindex
        align
        """
        return alignment.reindex_like(
            self,
            other=other,
            method=method,
            tolerance=tolerance,
            copy=copy,
            fill_value=fill_value,
        )

    def reindex(
        self: T_DataArray,
        indexers: Mapping[Any, Any] = None,
        method: ReindexMethodOptions = None,
        tolerance: float | Iterable[float] | None = None,
        copy: bool = True,
        fill_value=dtypes.NA,
        **indexers_kwargs: Any,
    ) -> T_DataArray:
        """Conform this object onto the indexes of another object, filling in
        missing values with ``fill_value``. The default fill value is NaN.

        Parameters
        ----------
        indexers : dict, optional
            Dictionary with keys given by dimension names and values given by
            arrays of coordinates tick labels. Any mis-matched coordinate
            values will be filled in with NaN, and any mis-matched dimension
            names will simply be ignored.
            One of indexers or indexers_kwargs must be provided.
        copy : bool, optional
            If ``copy=True``, data in the return value is always copied. If
            ``copy=False`` and reindexing is unnecessary, or can be performed
            with only slice operations, then the output may share memory with
            the input. In either case, a new xarray object is always returned.
        method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional
            Method to use for filling index values in ``indexers`` not found on
            this data array:

            - None (default): don't fill gaps
            - pad / ffill: propagate last valid index value forward
            - backfill / bfill: propagate next valid index value backward
            - nearest: use nearest valid index value

        tolerance : float | Iterable[float] | None, default: None
            Maximum distance between original and new labels for inexact
            matches. The values of the index at the matching locations must
            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
            Tolerance may be a scalar value, which applies the same tolerance
            to all values, or list-like, which applies variable tolerance per
            element. List-like must be the same size as the index and its dtype
            must exactly match the index’s type.
        fill_value : scalar or dict-like, optional
            Value to use for newly missing values. If a dict-like, maps
            variable names (including coordinates) to fill values. Use this
            data array's name to refer to the data array's values.
        **indexers_kwargs : {dim: indexer, ...}, optional
            The keyword arguments form of ``indexers``.
            One of indexers or indexers_kwargs must be provided.

        Returns
        -------
        reindexed : DataArray
            Another dataset array, with this array's data but replaced
            coordinates.

        Examples
        --------
        Reverse latitude:

        >>> da = xr.DataArray(
        ...     np.arange(4),
        ...     coords=[np.array([90, 89, 88, 87])],
        ...     dims="lat",
        ... )
        >>> da
        <xarray.DataArray (lat: 4)>
        array([0, 1, 2, 3])
        Coordinates:
          * lat      (lat) int64 90 89 88 87
        >>> da.reindex(lat=da.lat[::-1])
        <xarray.DataArray (lat: 4)>
        array([3, 2, 1, 0])
        Coordinates:
          * lat      (lat) int64 87 88 89 90

        See Also
        --------
        DataArray.reindex_like
        align
        """
        indexers = utils.either_dict_or_kwargs(indexers, indexers_kwargs, "reindex")
        return alignment.reindex(
            self,
            indexers=indexers,
            method=method,
            tolerance=tolerance,
            copy=copy,
            fill_value=fill_value,
        )

    def interp(
        self: T_DataArray,
        coords: Mapping[Any, Any] | None = None,
        method: InterpOptions = "linear",
        assume_sorted: bool = False,
        kwargs: Mapping[str, Any] | None = None,
        **coords_kwargs: Any,
    ) -> T_DataArray:
        """Interpolate a DataArray onto new coordinates

        Performs univariate or multivariate interpolation of a DataArray onto
        new coordinates using scipy's interpolation routines. If interpolating
        along an existing dimension, :py:class:`scipy.interpolate.interp1d` is
        called. When interpolating along multiple existing dimensions, an
        attempt is made to decompose the interpolation into multiple
        1-dimensional interpolations. If this is possible,
        :py:class:`scipy.interpolate.interp1d` is called. Otherwise,
        :py:func:`scipy.interpolate.interpn` is called.

        Parameters
        ----------
        coords : dict, optional
            Mapping from dimension names to the new coordinates.
            New coordinate can be a scalar, array-like or DataArray.
            If DataArrays are passed as new coordinates, their dimensions are
            used for the broadcasting. Missing values are skipped.
        method : {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial"}, default: "linear"
            The method used to interpolate. The method should be supported by
            the scipy interpolator:

            - ``interp1d``: {"linear", "nearest", "zero", "slinear",
              "quadratic", "cubic", "polynomial"}
            - ``interpn``: {"linear", "nearest"}

            If ``"polynomial"`` is passed, the ``order`` keyword argument must
            also be provided.
        assume_sorted : bool, default: False
            If False, values of x can be in any order and they are sorted
            first. If True, x has to be an array of monotonically increasing
            values.
        kwargs : dict-like or None, default: None
            Additional keyword arguments passed to scipy's interpolator. Valid
            options and their behavior depend whether ``interp1d`` or
            ``interpn`` is used.
        **coords_kwargs : {dim: coordinate, ...}, optional
            The keyword arguments form of ``coords``.
            One of coords or coords_kwargs must be provided.

        Returns
        -------
        interpolated : DataArray
            New dataarray on the new coordinates.

        Notes
        -----
        scipy is required.

        See Also
        --------
        scipy.interpolate.interp1d
        scipy.interpolate.interpn

        Examples
        --------
        >>> da = xr.DataArray(
        ...     data=[[1, 4, 2, 9], [2, 7, 6, np.nan], [6, np.nan, 5, 8]],
        ...     dims=("x", "y"),
        ...     coords={"x": [0, 1, 2], "y": [10, 12, 14, 16]},
        ... )
        >>> da
        <xarray.DataArray (x: 3, y: 4)>
        array([[ 1.,  4.,  2.,  9.],
               [ 2.,  7.,  6., nan],
               [ 6., nan,  5.,  8.]])
        Coordinates:
          * x        (x) int64 0 1 2
          * y        (y) int64 10 12 14 16

        1D linear interpolation (the default):

        >>> da.interp(x=[0, 0.75, 1.25, 1.75])
        <xarray.DataArray (x: 4, y: 4)>
        array([[1.  , 4.  , 2.  ,  nan],
               [1.75, 6.25, 5.  ,  nan],
               [3.  ,  nan, 5.75,  nan],
               [5.  ,  nan, 5.25,  nan]])
        Coordinates:
          * y        (y) int64 10 12 14 16
          * x        (x) float64 0.0 0.75 1.25 1.75

        1D nearest interpolation:

        >>> da.interp(x=[0, 0.75, 1.25, 1.75], method="nearest")
        <xarray.DataArray (x: 4, y: 4)>
        array([[ 1.,  4.,  2.,  9.],
               [ 2.,  7.,  6., nan],
               [ 2.,  7.,  6., nan],
               [ 6., nan,  5.,  8.]])
        Coordinates:
          * y        (y) int64 10 12 14 16
          * x        (x) float64 0.0 0.75 1.25 1.75

        1D linear extrapolation:

        >>> da.interp(
        ...     x=[1, 1.5, 2.5, 3.5],
        ...     method="linear",
        ...     kwargs={"fill_value": "extrapolate"},
        ... )
        <xarray.DataArray (x: 4, y: 4)>
        array([[ 2. ,  7. ,  6. ,  nan],
               [ 4. ,  nan,  5.5,  nan],
               [ 8. ,  nan,  4.5,  nan],
               [12. ,  nan,  3.5,  nan]])
        Coordinates:
          * y        (y) int64 10 12 14 16
          * x        (x) float64 1.0 1.5 2.5 3.5

        2D linear interpolation:

        >>> da.interp(x=[0, 0.75, 1.25, 1.75], y=[11, 13, 15], method="linear")
        <xarray.DataArray (x: 4, y: 3)>
        array([[2.5  , 3.   ,   nan],
               [4.   , 5.625,   nan],
               [  nan,   nan,   nan],
               [  nan,   nan,   nan]])
        Coordinates:
          * x        (x) float64 0.0 0.75 1.25 1.75
          * y        (y) int64 11 13 15
        """
        if self.dtype.kind not in "uifc":
            raise TypeError(
                "interp only works for a numeric type array. "
                "Given {}.".format(self.dtype)
            )
        ds = self._to_temp_dataset().interp(
            coords,
            method=method,
            kwargs=kwargs,
            assume_sorted=assume_sorted,
            **coords_kwargs,
        )
        return self._from_temp_dataset(ds)

    def interp_like(
        self: T_DataArray,
        other: DataArray | Dataset,
        method: InterpOptions = "linear",
        assume_sorted: bool = False,
        kwargs: Mapping[str, Any] | None = None,
    ) -> T_DataArray:
        """Interpolate this object onto the coordinates of another object,
        filling out of range values with NaN.

        If interpolating along a single existing dimension,
        :py:class:`scipy.interpolate.interp1d` is called. When interpolating
        along multiple existing dimensions, an attempt is made to decompose the
        interpolation into multiple 1-dimensional interpolations. If this is
        possible, :py:class:`scipy.interpolate.interp1d` is called. Otherwise,
        :py:func:`scipy.interpolate.interpn` is called.

        Parameters
        ----------
        other : Dataset or DataArray
            Object with an 'indexes' attribute giving a mapping from dimension
            names to an 1d array-like, which provides coordinates upon
            which to index the variables in this dataset. Missing values are skipped.
        method : {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial"}, default: "linear"
            The method used to interpolate. The method should be supported by
            the scipy interpolator:

            - {"linear", "nearest", "zero", "slinear", "quadratic", "cubic",
              "polynomial"} when ``interp1d`` is called.
            - {"linear", "nearest"} when ``interpn`` is called.

            If ``"polynomial"`` is passed, the ``order`` keyword argument must
            also be provided.
        assume_sorted : bool, default: False
            If False, values of coordinates that are interpolated over can be
            in any order and they are sorted first. If True, interpolated
            coordinates are assumed to be an array of monotonically increasing
            values.
        kwargs : dict, optional
            Additional keyword passed to scipy's interpolator.

        Returns
        -------
        interpolated : DataArray
            Another dataarray by interpolating this dataarray's data along the
            coordinates of the other object.

        Notes
        -----
        scipy is required.
        If the dataarray has object-type coordinates, reindex is used for these
        coordinates instead of the interpolation.

        See Also
        --------
        DataArray.interp
        DataArray.reindex_like
        """
        if self.dtype.kind not in "uifc":
            raise TypeError(
                "interp only works for a numeric type array. "
                "Given {}.".format(self.dtype)
            )
        ds = self._to_temp_dataset().interp_like(
            other, method=method, kwargs=kwargs, assume_sorted=assume_sorted
        )
        return self._from_temp_dataset(ds)

    # change type of self and return to T_DataArray once
    # https://github.com/python/mypy/issues/12846 is resolved
    def rename(
        self,
        new_name_or_name_dict: Hashable | Mapping[Any, Hashable] | None = None,
        **names: Hashable,
    ) -> DataArray:
        """Returns a new DataArray with renamed coordinates or a new name.

        Parameters
        ----------
        new_name_or_name_dict : str or dict-like, optional
            If the argument is dict-like, it used as a mapping from old
            names to new names for coordinates. Otherwise, use the argument
            as the new name for this array.
        **names : Hashable, optional
            The keyword arguments form of a mapping from old names to
            new names for coordinates.
            One of new_name_or_name_dict or names must be provided.

        Returns
        -------
        renamed : DataArray
            Renamed array or array with renamed coordinates.

        See Also
        --------
        Dataset.rename
        DataArray.swap_dims
        """
        if names or utils.is_dict_like(new_name_or_name_dict):
            new_name_or_name_dict = cast(
                Mapping[Hashable, Hashable], new_name_or_name_dict
            )
            name_dict = either_dict_or_kwargs(new_name_or_name_dict, names, "rename")
            dataset = self._to_temp_dataset().rename(name_dict)
            return self._from_temp_dataset(dataset)
        else:
            new_name_or_name_dict = cast(Hashable, new_name_or_name_dict)
            return self._replace(name=new_name_or_name_dict)

    def swap_dims(
        self: T_DataArray,
        dims_dict: Mapping[Any, Hashable] | None = None,
        **dims_kwargs,
    ) -> T_DataArray:
        """Returns a new DataArray with swapped dimensions.

        Parameters
        ----------
        dims_dict : dict-like
            Dictionary whose keys are current dimension names and whose values
            are new names.
        **dims_kwargs : {existing_dim: new_dim, ...}, optional
            The keyword arguments form of ``dims_dict``.
            One of dims_dict or dims_kwargs must be provided.

        Returns
        -------
        swapped : DataArray
            DataArray with swapped dimensions.

        Examples
        --------
        >>> arr = xr.DataArray(
        ...     data=[0, 1],
        ...     dims="x",
        ...     coords={"x": ["a", "b"], "y": ("x", [0, 1])},
        ... )
        >>> arr
        <xarray.DataArray (x: 2)>
        array([0, 1])
        Coordinates:
          * x        (x) <U1 'a' 'b'
            y        (x) int64 0 1

        >>> arr.swap_dims({"x": "y"})
        <xarray.DataArray (y: 2)>
        array([0, 1])
        Coordinates:
            x        (y) <U1 'a' 'b'
          * y        (y) int64 0 1

        >>> arr.swap_dims({"x": "z"})
        <xarray.DataArray (z: 2)>
        array([0, 1])
        Coordinates:
            x        (z) <U1 'a' 'b'
            y        (z) int64 0 1
        Dimensions without coordinates: z

        See Also
        --------
        DataArray.rename
        Dataset.swap_dims
        """
        dims_dict = either_dict_or_kwargs(dims_dict, dims_kwargs, "swap_dims")
        ds = self._to_temp_dataset().swap_dims(dims_dict)
        return self._from_temp_dataset(ds)

    # change type of self and return to T_DataArray once
    # https://github.com/python/mypy/issues/12846 is resolved
    def expand_dims(
        self,
        dim: None | Hashable | Sequence[Hashable] | Mapping[Any, Any] = None,
        axis: None | int | Sequence[int] = None,
        **dim_kwargs: Any,
    ) -> DataArray:
        """Return a new object with an additional axis (or axes) inserted at
        the corresponding position in the array shape. The new object is a
        view into the underlying array, not a copy.

        If dim is already a scalar coordinate, it will be promoted to a 1D
        coordinate consisting of a single value.

        Parameters
        ----------
        dim : Hashable, sequence of Hashable, dict, or None, optional
            Dimensions to include on the new variable.
            If provided as str or sequence of str, then dimensions are inserted
            with length 1. If provided as a dict, then the keys are the new
            dimensions and the values are either integers (giving the length of
            the new dimensions) or sequence/ndarray (giving the coordinates of
            the new dimensions).
        axis : int, sequence of int, or None, default: None
            Axis position(s) where new axis is to be inserted (position(s) on
            the result array). If a sequence of integers is passed,
            multiple axes are inserted. In this case, dim arguments should be
            same length list. If axis=None is passed, all the axes will be
            inserted to the start of the result array.
        **dim_kwargs : int or sequence or ndarray
            The keywords are arbitrary dimensions being inserted and the values
            are either the lengths of the new dims (if int is given), or their
            coordinates. Note, this is an alternative to passing a dict to the
            dim kwarg and will only be used if dim is None.

        Returns
        -------
        expanded : DataArray
            This object, but with additional dimension(s).
        """
        if isinstance(dim, int):
            raise TypeError("dim should be Hashable or sequence/mapping of Hashables")
        elif isinstance(dim, Sequence) and not isinstance(dim, str):
            if len(dim) != len(set(dim)):
                raise ValueError("dims should not contain duplicate values.")
            dim = dict.fromkeys(dim, 1)
        elif dim is not None and not isinstance(dim, Mapping):
            dim = {cast(Hashable, dim): 1}

        dim = either_dict_or_kwargs(dim, dim_kwargs, "expand_dims")
        ds = self._to_temp_dataset().expand_dims(dim, axis)
        return self._from_temp_dataset(ds)

    # change type of self and return to T_DataArray once
    # https://github.com/python/mypy/issues/12846 is resolved
    def set_index(
        self,
        indexes: Mapping[Any, Hashable | Sequence[Hashable]] = None,
        append: bool = False,
        **indexes_kwargs: Hashable | Sequence[Hashable],
    ) -> DataArray:
        """Set DataArray (multi-)indexes using one or more existing
        coordinates.

        Parameters
        ----------
        indexes : {dim: index, ...}
            Mapping from names matching dimensions and values given
            by (lists of) the names of existing coordinates or variables to set
            as new (multi-)index.
        append : bool, default: False
            If True, append the supplied index(es) to the existing index(es).
            Otherwise replace the existing index(es).
        **indexes_kwargs : optional
            The keyword arguments form of ``indexes``.
            One of indexes or indexes_kwargs must be provided.

        Returns
        -------
        obj : DataArray
            Another DataArray, with this data but replaced coordinates.

        Examples
        --------
        >>> arr = xr.DataArray(
        ...     data=np.ones((2, 3)),
        ...     dims=["x", "y"],
        ...     coords={"x": range(2), "y": range(3), "a": ("x", [3, 4])},
        ... )
        >>> arr
        <xarray.DataArray (x: 2, y: 3)>
        array([[1., 1., 1.],
               [1., 1., 1.]])
        Coordinates:
          * x        (x) int64 0 1
          * y        (y) int64 0 1 2
            a        (x) int64 3 4
        >>> arr.set_index(x="a")
        <xarray.DataArray (x: 2, y: 3)>
        array([[1., 1., 1.],
               [1., 1., 1.]])
        Coordinates:
          * x        (x) int64 3 4
          * y        (y) int64 0 1 2

        See Also
        --------
        DataArray.reset_index
        """
        ds = self._to_temp_dataset().set_index(indexes, append=append, **indexes_kwargs)
        return self._from_temp_dataset(ds)

    # change type of self and return to T_DataArray once
    # https://github.com/python/mypy/issues/12846 is resolved
    def reset_index(
        self,
        dims_or_levels: Hashable | Sequence[Hashable],
        drop: bool = False,
    ) -> DataArray:
        """Reset the specified index(es) or multi-index level(s).

        Parameters
        ----------
        dims_or_levels : Hashable or sequence of Hashable
            Name(s) of the dimension(s) and/or multi-index level(s) that will
            be reset.
        drop : bool, default: False
            If True, remove the specified indexes and/or multi-index levels
            instead of extracting them as new coordinates (default: False).

        Returns
        -------
        obj : DataArray
            Another dataarray, with this dataarray's data but replaced
            coordinates.

        See Also
        --------
        DataArray.set_index
        """
        ds = self._to_temp_dataset().reset_index(dims_or_levels, drop=drop)
        return self._from_temp_dataset(ds)

    def reorder_levels(
        self: T_DataArray,
        dim_order: Mapping[Any, Sequence[int | Hashable]] | None = None,
        **dim_order_kwargs: Sequence[int | Hashable],
    ) -> T_DataArray:
        """Rearrange index levels using input order.

        Parameters
        ----------
        dim_order dict-like of Hashable to int or Hashable: optional
            Mapping from names matching dimensions and values given
            by lists representing new level orders. Every given dimension
            must have a multi-index.
        **dim_order_kwargs : optional
            The keyword arguments form of ``dim_order``.
            One of dim_order or dim_order_kwargs must be provided.

        Returns
        -------
        obj : DataArray
            Another dataarray, with this dataarray's data but replaced
            coordinates.
        """
        ds = self._to_temp_dataset().reorder_levels(dim_order, **dim_order_kwargs)
        return self._from_temp_dataset(ds)

    def stack(
        self: T_DataArray,
        dimensions: Mapping[Any, Sequence[Hashable]] | None = None,
        create_index: bool | None = True,
        index_cls: type[Index] = PandasMultiIndex,
        **dimensions_kwargs: Sequence[Hashable],
    ) -> T_DataArray:
        """
        Stack any number of existing dimensions into a single new dimension.

        New dimensions will be added at the end, and the corresponding
        coordinate variables will be combined into a MultiIndex.

        Parameters
        ----------
        dimensions : mapping of Hashable to sequence of Hashable
            Mapping of the form `new_name=(dim1, dim2, ...)`.
            Names of new dimensions, and the existing dimensions that they
            replace. An ellipsis (`...`) will be replaced by all unlisted dimensions.
            Passing a list containing an ellipsis (`stacked_dim=[...]`) will stack over
            all dimensions.
        create_index : bool or None, default: True
            If True, create a multi-index for each of the stacked dimensions.
            If False, don't create any index.
            If None, create a multi-index only if exactly one single (1-d) coordinate
            index is found for every dimension to stack.
        index_cls: class, optional
            Can be used to pass a custom multi-index type. Must be an Xarray index that
            implements `.stack()`. By default, a pandas multi-index wrapper is used.
        **dimensions_kwargs
            The keyword arguments form of ``dimensions``.
            One of dimensions or dimensions_kwargs must be provided.

        Returns
        -------
        stacked : DataArray
            DataArray with stacked data.

        Examples
        --------
        >>> arr = xr.DataArray(
        ...     np.arange(6).reshape(2, 3),
        ...     coords=[("x", ["a", "b"]), ("y", [0, 1, 2])],
        ... )
        >>> arr
        <xarray.DataArray (x: 2, y: 3)>
        array([[0, 1, 2],
               [3, 4, 5]])
        Coordinates:
          * x        (x) <U1 'a' 'b'
          * y        (y) int64 0 1 2
        >>> stacked = arr.stack(z=("x", "y"))
        >>> stacked.indexes["z"]
        MultiIndex([('a', 0),
                    ('a', 1),
                    ('a', 2),
                    ('b', 0),
                    ('b', 1),
                    ('b', 2)],
                   name='z')

        See Also
        --------
        DataArray.unstack
        """
        ds = self._to_temp_dataset().stack(
            dimensions,
            create_index=create_index,
            index_cls=index_cls,
            **dimensions_kwargs,
        )
        return self._from_temp_dataset(ds)

    # change type of self and return to T_DataArray once
    # https://github.com/python/mypy/issues/12846 is resolved
    def unstack(
        self,
        dim: Hashable | Sequence[Hashable] | None = None,
        fill_value: Any = dtypes.NA,
        sparse: bool = False,
    ) -> DataArray:
        """
        Unstack existing dimensions corresponding to MultiIndexes into
        multiple new dimensions.

        New dimensions will be added at the end.

        Parameters
        ----------
        dim : Hashable or sequence of Hashable, optional
            Dimension(s) over which to unstack. By default unstacks all
            MultiIndexes.
        fill_value : scalar or dict-like, default: nan
            Value to be filled. If a dict-like, maps variable names to
            fill values. Use the data array's name to refer to its
            name. If not provided or if the dict-like does not contain
            all variables, the dtype's NA value will be used.
        sparse : bool, default: False
            Use sparse-array if True

        Returns
        -------
        unstacked : DataArray
            Array with unstacked data.

        Examples
        --------
        >>> arr = xr.DataArray(
        ...     np.arange(6).reshape(2, 3),
        ...     coords=[("x", ["a", "b"]), ("y", [0, 1, 2])],
        ... )
        >>> arr
        <xarray.DataArray (x: 2, y: 3)>
        array([[0, 1, 2],
               [3, 4, 5]])
        Coordinates:
          * x        (x) <U1 'a' 'b'
          * y        (y) int64 0 1 2
        >>> stacked = arr.stack(z=("x", "y"))
        >>> stacked.indexes["z"]
        MultiIndex([('a', 0),
                    ('a', 1),
                    ('a', 2),
                    ('b', 0),
                    ('b', 1),
                    ('b', 2)],
                   name='z')
        >>> roundtripped = stacked.unstack()
        >>> arr.identical(roundtripped)
        True

        See Also
        --------
        DataArray.stack
        """
        ds = self._to_temp_dataset().unstack(dim, fill_value, sparse)
        return self._from_temp_dataset(ds)

    def to_unstacked_dataset(self, dim: Hashable, level: int | Hashable = 0) -> Dataset:
        """Unstack DataArray expanding to Dataset along a given level of a
        stacked coordinate.

        This is the inverse operation of Dataset.to_stacked_array.

        Parameters
        ----------
        dim : Hashable
            Name of existing dimension to unstack
        level : int or Hashable, default: 0
            The MultiIndex level to expand to a dataset along. Can either be
            the integer index of the level or its name.

        Returns
        -------
        unstacked: Dataset

        Examples
        --------
        >>> arr = xr.DataArray(
        ...     np.arange(6).reshape(2, 3),
        ...     coords=[("x", ["a", "b"]), ("y", [0, 1, 2])],
        ... )
        >>> data = xr.Dataset({"a": arr, "b": arr.isel(y=0)})
        >>> data
        <xarray.Dataset>
        Dimensions:  (x: 2, y: 3)
        Coordinates:
          * x        (x) <U1 'a' 'b'
          * y        (y) int64 0 1 2
        Data variables:
            a        (x, y) int64 0 1 2 3 4 5
            b        (x) int64 0 3
        >>> stacked = data.to_stacked_array("z", ["x"])
        >>> stacked.indexes["z"]
        MultiIndex([('a', 0.0),
                    ('a', 1.0),
                    ('a', 2.0),
                    ('b', nan)],
                   name='z')
        >>> roundtripped = stacked.to_unstacked_dataset(dim="z")
        >>> data.identical(roundtripped)
        True

        See Also
        --------
        Dataset.to_stacked_array
        """
        idx = self._indexes[dim].to_pandas_index()
        if not isinstance(idx, pd.MultiIndex):
            raise ValueError(f"'{dim}' is not a stacked coordinate")

        level_number = idx._get_level_number(level)
        variables = idx.levels[level_number]
        variable_dim = idx.names[level_number]

        # pull variables out of datarray
        data_dict = {}
        for k in variables:
            data_dict[k] = self.sel({variable_dim: k}, drop=True).squeeze(drop=True)

        # unstacked dataset
        return Dataset(data_dict)

    def transpose(
        self: T_DataArray,
        *dims: Hashable,
        transpose_coords: bool = True,
        missing_dims: ErrorOptionsWithWarn = "raise",
    ) -> T_DataArray:
        """Return a new DataArray object with transposed dimensions.

        Parameters
        ----------
        *dims : Hashable, optional
            By default, reverse the dimensions. Otherwise, reorder the
            dimensions to this order.
        transpose_coords : bool, default: True
            If True, also transpose the coordinates of this DataArray.
        missing_dims : {"raise", "warn", "ignore"}, default: "raise"
            What to do if dimensions that should be selected from are not present in the
            DataArray:
            - "raise": raise an exception
            - "warn": raise a warning, and ignore the missing dimensions
            - "ignore": ignore the missing dimensions

        Returns
        -------
        transposed : DataArray
            The returned DataArray's array is transposed.

        Notes
        -----
        This operation returns a view of this array's data. It is
        lazy for dask-backed DataArrays but not for numpy-backed DataArrays
        -- the data will be fully loaded.

        See Also
        --------
        numpy.transpose
        Dataset.transpose
        """
        if dims:
            dims = tuple(utils.infix_dims(dims, self.dims, missing_dims))
        variable = self.variable.transpose(*dims)
        if transpose_coords:
            coords: dict[Hashable, Variable] = {}
            for name, coord in self.coords.items():
                coord_dims = tuple(dim for dim in dims if dim in coord.dims)
                coords[name] = coord.variable.transpose(*coord_dims)
            return self._replace(variable, coords)
        else:
            return self._replace(variable)

    @property
    def T(self: T_DataArray) -> T_DataArray:
        return self.transpose()

    # change type of self and return to T_DataArray once
    # https://github.com/python/mypy/issues/12846 is resolved
    def drop_vars(
        self,
        names: Hashable | Iterable[Hashable],
        *,
        errors: ErrorOptions = "raise",
    ) -> DataArray:
        """Returns an array with dropped variables.

        Parameters
        ----------
        names : Hashable or iterable of Hashable
            Name(s) of variables to drop.
        errors : {"raise", "ignore"}, default: "raise"
            If 'raise', raises a ValueError error if any of the variable
            passed are not in the dataset. If 'ignore', any given names that are in the
            DataArray are dropped and no error is raised.

        Returns
        -------
        dropped : Dataset
            New Dataset copied from `self` with variables removed.
        """
        ds = self._to_temp_dataset().drop_vars(names, errors=errors)
        return self._from_temp_dataset(ds)

    def drop(
        self: T_DataArray,
        labels: Mapping[Any, Any] | None = None,
        dim: Hashable | None = None,
        *,
        errors: ErrorOptions = "raise",
        **labels_kwargs,
    ) -> T_DataArray:
        """Backward compatible method based on `drop_vars` and `drop_sel`

        Using either `drop_vars` or `drop_sel` is encouraged

        See Also
        --------
        DataArray.drop_vars
        DataArray.drop_sel
        """
        ds = self._to_temp_dataset().drop(labels, dim, errors=errors, **labels_kwargs)
        return self._from_temp_dataset(ds)

    def drop_sel(
        self: T_DataArray,
        labels: Mapping[Any, Any] | None = None,
        *,
        errors: ErrorOptions = "raise",
        **labels_kwargs,
    ) -> T_DataArray:
        """Drop index labels from this DataArray.

        Parameters
        ----------
        labels : mapping of Hashable to Any
            Index labels to drop
        errors : {"raise", "ignore"}, default: "raise"
            If 'raise', raises a ValueError error if
            any of the index labels passed are not
            in the dataset. If 'ignore', any given labels that are in the
            dataset are dropped and no error is raised.
        **labels_kwargs : {dim: label, ...}, optional
            The keyword arguments form of ``dim`` and ``labels``

        Returns
        -------
        dropped : DataArray
        """
        if labels_kwargs or isinstance(labels, dict):
            labels = either_dict_or_kwargs(labels, labels_kwargs, "drop")

        ds = self._to_temp_dataset().drop_sel(labels, errors=errors)
        return self._from_temp_dataset(ds)

    def drop_isel(
        self: T_DataArray, indexers: Mapping[Any, Any] | None = None, **indexers_kwargs
    ) -> T_DataArray:
        """Drop index positions from this DataArray.

        Parameters
        ----------
        indexers : mapping of Hashable to Any or None, default: None
            Index locations to drop
        **indexers_kwargs : {dim: position, ...}, optional
            The keyword arguments form of ``dim`` and ``positions``

        Returns
        -------
        dropped : DataArray

        Raises
        ------
        IndexError
        """
        dataset = self._to_temp_dataset()
        dataset = dataset.drop_isel(indexers=indexers, **indexers_kwargs)
        return self._from_temp_dataset(dataset)

    def dropna(
        self: T_DataArray,
        dim: Hashable,
        how: Literal["any", "all"] = "any",
        thresh: int | None = None,
    ) -> T_DataArray:
        """Returns a new array with dropped labels for missing values along
        the provided dimension.

        Parameters
        ----------
        dim : Hashable
            Dimension along which to drop missing values. Dropping along
            multiple dimensions simultaneously is not yet supported.
        how : {"any", "all"}, default: "any"
            - any : if any NA values are present, drop that label
            - all : if all values are NA, drop that label

        thresh : int or None, default: None
            If supplied, require this many non-NA values.

        Returns
        -------
        dropped : DataArray
        """
        ds = self._to_temp_dataset().dropna(dim, how=how, thresh=thresh)
        return self._from_temp_dataset(ds)

    def fillna(self: T_DataArray, value: Any) -> T_DataArray:
        """Fill missing values in this object.

        This operation follows the normal broadcasting and alignment rules that
        xarray uses for binary arithmetic, except the result is aligned to this
        object (``join='left'``) instead of aligned to the intersection of
        index coordinates (``join='inner'``).

        Parameters
        ----------
        value : scalar, ndarray or DataArray
            Used to fill all matching missing values in this array. If the
            argument is a DataArray, it is first aligned with (reindexed to)
            this array.

        Returns
        -------
        filled : DataArray
        """
        if utils.is_dict_like(value):
            raise TypeError(
                "cannot provide fill value as a dictionary with "
                "fillna on a DataArray"
            )
        out = ops.fillna(self, value)
        return out

    def interpolate_na(
        self: T_DataArray,
        dim: Hashable | None = None,
        method: InterpOptions = "linear",
        limit: int | None = None,
        use_coordinate: bool | str = True,
        max_gap: (
            None
            | int
            | float
            | str
            | pd.Timedelta
            | np.timedelta64
            | datetime.timedelta
        ) = None,
        keep_attrs: bool | None = None,
        **kwargs: Any,
    ) -> T_DataArray:
        """Fill in NaNs by interpolating according to different methods.

        Parameters
        ----------
        dim : Hashable or None, optional
            Specifies the dimension along which to interpolate.
        method : {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial", \
            "barycentric", "krog", "pchip", "spline", "akima"}, default: "linear"
            String indicating which method to use for interpolation:

            - 'linear': linear interpolation. Additional keyword
              arguments are passed to :py:func:`numpy.interp`
            - 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'polynomial':
              are passed to :py:func:`scipy.interpolate.interp1d`. If
              ``method='polynomial'``, the ``order`` keyword argument must also be
              provided.
            - 'barycentric', 'krog', 'pchip', 'spline', 'akima': use their
              respective :py:class:`scipy.interpolate` classes.

        use_coordinate : bool or str, default: True
            Specifies which index to use as the x values in the interpolation
            formulated as `y = f(x)`. If False, values are treated as if
            eqaully-spaced along ``dim``. If True, the IndexVariable `dim` is
            used. If ``use_coordinate`` is a string, it specifies the name of a
            coordinate variariable to use as the index.
        limit : int or None, default: None
            Maximum number of consecutive NaNs to fill. Must be greater than 0
            or None for no limit. This filling is done regardless of the size of
            the gap in the data. To only interpolate over gaps less than a given length,
            see ``max_gap``.
        max_gap : int, float, str, pandas.Timedelta, numpy.timedelta64, datetime.timedelta, default: None
            Maximum size of gap, a continuous sequence of NaNs, that will be filled.
            Use None for no limit. When interpolating along a datetime64 dimension
            and ``use_coordinate=True``, ``max_gap`` can be one of the following:

            - a string that is valid input for pandas.to_timedelta
            - a :py:class:`numpy.timedelta64` object
            - a :py:class:`pandas.Timedelta` object
            - a :py:class:`datetime.timedelta` object

            Otherwise, ``max_gap`` must be an int or a float. Use of ``max_gap`` with unlabeled
            dimensions has not been implemented yet. Gap length is defined as the difference
            between coordinate values at the first data point after a gap and the last value
            before a gap. For gaps at the beginning (end), gap length is defined as the difference
            between coordinate values at the first (last) valid data point and the first (last) NaN.
            For example, consider::

                <xarray.DataArray (x: 9)>
                array([nan, nan, nan,  1., nan, nan,  4., nan, nan])
                Coordinates:
                  * x        (x) int64 0 1 2 3 4 5 6 7 8

            The gap lengths are 3-0 = 3; 6-3 = 3; and 8-6 = 2 respectively
        keep_attrs : bool or None, default: None
            If True, the dataarray's attributes (`attrs`) will be copied from
            the original object to the new one.  If False, the new
            object will be returned without attributes.
        **kwargs : dict, optional
            parameters passed verbatim to the underlying interpolation function

        Returns
        -------
        interpolated: DataArray
            Filled in DataArray.

        See Also
        --------
        numpy.interp
        scipy.interpolate

        Examples
        --------
        >>> da = xr.DataArray(
        ...     [np.nan, 2, 3, np.nan, 0], dims="x", coords={"x": [0, 1, 2, 3, 4]}
        ... )
        >>> da
        <xarray.DataArray (x: 5)>
        array([nan,  2.,  3., nan,  0.])
        Coordinates:
          * x        (x) int64 0 1 2 3 4

        >>> da.interpolate_na(dim="x", method="linear")
        <xarray.DataArray (x: 5)>
        array([nan, 2. , 3. , 1.5, 0. ])
        Coordinates:
          * x        (x) int64 0 1 2 3 4

        >>> da.interpolate_na(dim="x", method="linear", fill_value="extrapolate")
        <xarray.DataArray (x: 5)>
        array([1. , 2. , 3. , 1.5, 0. ])
        Coordinates:
          * x        (x) int64 0 1 2 3 4
        """
        from .missing import interp_na

        return interp_na(
            self,
            dim=dim,
            method=method,
            limit=limit,
            use_coordinate=use_coordinate,
            max_gap=max_gap,
            keep_attrs=keep_attrs,
            **kwargs,
        )

    def ffill(
        self: T_DataArray, dim: Hashable, limit: int | None = None
    ) -> T_DataArray:
        """Fill NaN values by propagating values forward

        *Requires bottleneck.*

        Parameters
        ----------
        dim : Hashable
            Specifies the dimension along which to propagate values when
            filling.
        limit : int or None, default: None
            The maximum number of consecutive NaN values to forward fill. In
            other words, if there is a gap with more than this number of
            consecutive NaNs, it will only be partially filled. Must be greater
            than 0 or None for no limit. Must be None or greater than or equal
            to axis length if filling along chunked axes (dimensions).

        Returns
        -------
        filled : DataArray
        """
        from .missing import ffill

        return ffill(self, dim, limit=limit)

    def bfill(
        self: T_DataArray, dim: Hashable, limit: int | None = None
    ) -> T_DataArray:
        """Fill NaN values by propagating values backward

        *Requires bottleneck.*

        Parameters
        ----------
        dim : str
            Specifies the dimension along which to propagate values when
            filling.
        limit : int or None, default: None
            The maximum number of consecutive NaN values to backward fill. In
            other words, if there is a gap with more than this number of
            consecutive NaNs, it will only be partially filled. Must be greater
            than 0 or None for no limit. Must be None or greater than or equal
            to axis length if filling along chunked axes (dimensions).

        Returns
        -------
        filled : DataArray
        """
        from .missing import bfill

        return bfill(self, dim, limit=limit)

    def combine_first(self: T_DataArray, other: T_DataArray) -> T_DataArray:
        """Combine two DataArray objects, with union of coordinates.

        This operation follows the normal broadcasting and alignment rules of
        ``join='outer'``.  Default to non-null values of array calling the
        method.  Use np.nan to fill in vacant cells after alignment.

        Parameters
        ----------
        other : DataArray
            Used to fill all matching missing values in this array.

        Returns
        -------
        DataArray
        """
        return ops.fillna(self, other, join="outer")

    def reduce(
        self: T_DataArray,
        func: Callable[..., Any],
        dim: None | Hashable | Iterable[Hashable] = None,
        *,
        axis: None | int | Sequence[int] = None,
        keep_attrs: bool | None = None,
        keepdims: bool = False,
        **kwargs: Any,
    ) -> T_DataArray:
        """Reduce this array by applying `func` along some dimension(s).

        Parameters
        ----------
        func : callable
            Function which can be called in the form
            `f(x, axis=axis, **kwargs)` to return the result of reducing an
            np.ndarray over an integer valued axis.
        dim : Hashable or Iterable of Hashable, optional
            Dimension(s) over which to apply `func`.
        axis : int or sequence of int, optional
            Axis(es) over which to repeatedly apply `func`. Only one of the
            'dim' and 'axis' arguments can be supplied. If neither are
            supplied, then the reduction is calculated over the flattened array
            (by calling `f(x)` without an axis argument).
        keep_attrs : bool or None, optional
            If True, the variable's attributes (`attrs`) will be copied from
            the original object to the new one.  If False (default), the new
            object will be returned without attributes.
        keepdims : bool, default: False
            If True, the dimensions which are reduced are left in the result
            as dimensions of size one. Coordinates that use these dimensions
            are removed.
        **kwargs : dict
            Additional keyword arguments passed on to `func`.

        Returns
        -------
        reduced : DataArray
            DataArray with this object's array replaced with an array with
            summarized data and the indicated dimension(s) removed.
        """

        var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
        return self._replace_maybe_drop_dims(var)

    def to_pandas(self) -> DataArray | pd.Series | pd.DataFrame:
        """Convert this array into a pandas object with the same shape.

        The type of the returned object depends on the number of DataArray
        dimensions:

        * 0D -> `xarray.DataArray`
        * 1D -> `pandas.Series`
        * 2D -> `pandas.DataFrame`

        Only works for arrays with 2 or fewer dimensions.

        The DataArray constructor performs the inverse transformation.

        Returns
        -------
        result : DataArray | Series | DataFrame
            DataArray, pandas Series or pandas DataFrame.
        """
        # TODO: consolidate the info about pandas constructors and the
        # attributes that correspond to their indexes into a separate module?
        constructors = {0: lambda x: x, 1: pd.Series, 2: pd.DataFrame}
        try:
            constructor = constructors[self.ndim]
        except KeyError:
            raise ValueError(
                f"Cannot convert arrays with {self.ndim} dimensions into "
                "pandas objects. Requires 2 or fewer dimensions."
            )
        indexes = [self.get_index(dim) for dim in self.dims]
        return constructor(self.values, *indexes)

    def to_dataframe(
        self, name: Hashable | None = None, dim_order: Sequence[Hashable] | None = None
    ) -> pd.DataFrame:
        """Convert this array and its coordinates into a tidy pandas.DataFrame.

        The DataFrame is indexed by the Cartesian product of index coordinates
        (in the form of a :py:class:`pandas.MultiIndex`). Other coordinates are
        included as columns in the DataFrame.

        For 1D and 2D DataArrays, see also :py:func:`DataArray.to_pandas` which
        doesn't rely on a MultiIndex to build the DataFrame.

        Parameters
        ----------
        name: Hashable or None, optional
            Name to give to this array (required if unnamed).
        dim_order: Sequence of Hashable or None, optional
            Hierarchical dimension order for the resulting dataframe.
            Array content is transposed to this order and then written out as flat
            vectors in contiguous order, so the last dimension in this list
            will be contiguous in the resulting DataFrame. This has a major
            influence on which operations are efficient on the resulting
            dataframe.

            If provided, must include all dimensions of this DataArray. By default,
            dimensions are sorted according to the DataArray dimensions order.

        Returns
        -------
        result: DataFrame
            DataArray as a pandas DataFrame.

        See also
        --------
        DataArray.to_pandas
        DataArray.to_series
        """
        if name is None:
            name = self.name
        if name is None:
            raise ValueError(
                "cannot convert an unnamed DataArray to a "
                "DataFrame: use the ``name`` parameter"
            )
        if self.ndim == 0:
            raise ValueError("cannot convert a scalar to a DataFrame")

        # By using a unique name, we can convert a DataArray into a DataFrame
        # even if it shares a name with one of its coordinates.
        # I would normally use unique_name = object() but that results in a
        # dataframe with columns in the wrong order, for reasons I have not
        # been able to debug (possibly a pandas bug?).
        unique_name = "__unique_name_identifier_z98xfz98xugfg73ho__"
        ds = self._to_dataset_whole(name=unique_name)

        if dim_order is None:
            ordered_dims = dict(zip(self.dims, self.shape))
        else:
            ordered_dims = ds._normalize_dim_order(dim_order=dim_order)

        df = ds._to_dataframe(ordered_dims)
        df.columns = [name if c == unique_name else c for c in df.columns]
        return df

    def to_series(self) -> pd.Series:
        """Convert this array into a pandas.Series.

        The Series is indexed by the Cartesian product of index coordinates
        (in the form of a :py:class:`pandas.MultiIndex`).

        Returns
        -------
        result : Series
            DataArray as a pandas Series.

        See also
        --------
        DataArray.to_pandas
        DataArray.to_dataframe
        """
        index = self.coords.to_index()
        return pd.Series(self.values.reshape(-1), index=index, name=self.name)

    def to_masked_array(self, copy: bool = True) -> np.ma.MaskedArray:
        """Convert this array into a numpy.ma.MaskedArray

        Parameters
        ----------
        copy : bool, default: True
            If True make a copy of the array in the result. If False,
            a MaskedArray view of DataArray.values is returned.

        Returns
        -------
        result : MaskedArray
            Masked where invalid values (nan or inf) occur.
        """
        values = self.to_numpy()  # only compute lazy arrays once
        isnull = pd.isnull(values)
        return np.ma.MaskedArray(data=values, mask=isnull, copy=copy)

    # path=None writes to bytes
    @overload
    def to_netcdf(
        self,
        path: 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,
        invalid_netcdf: bool = False,
    ) -> bytes:
        ...

    # default return None
    @overload
    def to_netcdf(
        self,
        path: str | 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,
        invalid_netcdf: bool = False,
    ) -> None:
        ...

    # compute=False returns dask.Delayed
    @overload
    def to_netcdf(
        self,
        path: str | 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],
        invalid_netcdf: bool = False,
    ) -> Delayed:
        ...

    def to_netcdf(
        self,
        path: str | 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,
        invalid_netcdf: bool = False,
    ) -> bytes | Delayed | None:
        """Write dataset contents to a netCDF file.

        Parameters
        ----------
        path : str, path-like or None, optional
            Path to which to save this dataset. File-like objects are only
            supported by the scipy engine. If no path is provided, this
            function returns the resulting netCDF file as bytes; in this case,
            we need to use scipy, which does not support netCDF version 4 (the
            default format becomes NETCDF3_64BIT).
        mode : {"w", "a"}, default: "w"
            Write ('w') or append ('a') mode. If mode='w', any existing file at
            this location will be overwritten. If mode='a', existing variables
            will be overwritten.
        format : {"NETCDF4", "NETCDF4_CLASSIC", "NETCDF3_64BIT", \
                  "NETCDF3_CLASSIC"}, optional
            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).
        group : str, optional
            Path to the netCDF4 group in the given file to open (only works for
            format='NETCDF4'). The group(s) 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.
        encoding : dict, optional
            Nested dictionary with variable names as keys and dictionaries of
            variable specific encodings as values, e.g.,
            ``{"my_variable": {"dtype": "int16", "scale_factor": 0.1,
            "zlib": True}, ...}``

            The `h5netcdf` engine supports both the NetCDF4-style compression
            encoding parameters ``{"zlib": True, "complevel": 9}`` and the h5py
            ones ``{"compression": "gzip", "compression_opts": 9}``.
            This allows using any compression plugin installed in the HDF5
            library, e.g. LZF.

        unlimited_dims : iterable of Hashable, optional
            Dimension(s) that should be serialized as unlimited dimensions.
            By default, no dimensions are treated as unlimited dimensions.
            Note that unlimited_dims may also be set via
            ``dataset.encoding["unlimited_dims"]``.
        compute: bool, default: True
            If true compute immediately, otherwise return a
            ``dask.delayed.Delayed`` object that can be computed later.
        invalid_netcdf: bool, default: False
            Only valid along with ``engine="h5netcdf"``. If True, allow writing
            hdf5 files which are invalid netcdf as described in
            https://github.com/h5netcdf/h5netcdf.

        Returns
        -------
        store: bytes or Delayed or None
            * ``bytes`` if path is None
            * ``dask.delayed.Delayed`` if compute is False
            * None otherwise

        Notes
        -----
        Only xarray.Dataset objects can be written to netCDF files, so
        the xarray.DataArray is converted to a xarray.Dataset object
        containing a single variable. If the DataArray has no name, or if the
        name is the same as a coordinate name, then it is given the name
        ``"__xarray_dataarray_variable__"``.

        See Also
        --------
        Dataset.to_netcdf
        """
        from ..backends.api import DATAARRAY_NAME, DATAARRAY_VARIABLE, to_netcdf

        if self.name is None:
            # If no name is set then use a generic xarray name
            dataset = self.to_dataset(name=DATAARRAY_VARIABLE)
        elif self.name in self.coords or self.name in self.dims:
            # The name is the same as one of the coords names, which netCDF
            # doesn't support, so rename it but keep track of the old name
            dataset = self.to_dataset(name=DATAARRAY_VARIABLE)
            dataset.attrs[DATAARRAY_NAME] = self.name
        else:
            # No problems with the name - so we're fine!
            dataset = self.to_dataset()

        return to_netcdf(  # type: ignore  # mypy cannot resolve the overloads:(
            dataset,
            path,
            mode=mode,
            format=format,
            group=group,
            engine=engine,
            encoding=encoding,
            unlimited_dims=unlimited_dims,
            compute=compute,
            multifile=False,
            invalid_netcdf=invalid_netcdf,
        )

    def to_dict(self, data: bool = True, encoding: bool = False) -> dict[str, Any]:
        """
        Convert this xarray.DataArray into a dictionary following xarray
        naming conventions.

        Converts all variables and attributes to native Python objects.
        Useful for converting to json. To avoid datetime incompatibility
        use decode_times=False kwarg in xarray.open_dataset.

        Parameters
        ----------
        data : bool, default: True
            Whether to include the actual data in the dictionary. When set to
            False, returns just the schema.
        encoding : bool, default: False
            Whether to include the Dataset's encoding in the dictionary.

        Returns
        -------
        dict: dict

        See Also
        --------
        DataArray.from_dict
        Dataset.to_dict
        """
        d = self.variable.to_dict(data=data)
        d.update({"coords": {}, "name": self.name})
        for k in self.coords:
            d["coords"][k] = self.coords[k].variable.to_dict(data=data)
        if encoding:
            d["encoding"] = dict(self.encoding)
        return d

    @classmethod
    def from_dict(cls: type[T_DataArray], d: Mapping[str, Any]) -> T_DataArray:
        """Convert a dictionary into an xarray.DataArray

        Parameters
        ----------
        d : dict
            Mapping with a minimum structure of {"dims": [...], "data": [...]}

        Returns
        -------
        obj : xarray.DataArray

        See Also
        --------
        DataArray.to_dict
        Dataset.from_dict

        Examples
        --------
        >>> d = {"dims": "t", "data": [1, 2, 3]}
        >>> da = xr.DataArray.from_dict(d)
        >>> da
        <xarray.DataArray (t: 3)>
        array([1, 2, 3])
        Dimensions without coordinates: t

        >>> d = {
        ...     "coords": {
        ...         "t": {"dims": "t", "data": [0, 1, 2], "attrs": {"units": "s"}}
        ...     },
        ...     "attrs": {"title": "air temperature"},
        ...     "dims": "t",
        ...     "data": [10, 20, 30],
        ...     "name": "a",
        ... }
        >>> da = xr.DataArray.from_dict(d)
        >>> da
        <xarray.DataArray 'a' (t: 3)>
        array([10, 20, 30])
        Coordinates:
          * t        (t) int64 0 1 2
        Attributes:
            title:    air temperature
        """
        coords = None
        if "coords" in d:
            try:
                coords = {
                    k: (v["dims"], v["data"], v.get("attrs"))
                    for k, v in d["coords"].items()
                }
            except KeyError as e:
                raise ValueError(
                    "cannot convert dict when coords are missing the key "
                    "'{dims_data}'".format(dims_data=str(e.args[0]))
                )
        try:
            data = d["data"]
        except KeyError:
            raise ValueError("cannot convert dict without the key 'data''")
        else:
            obj = cls(data, coords, d.get("dims"), d.get("name"), d.get("attrs"))

        obj.encoding.update(d.get("encoding", {}))

        return obj

    @classmethod
    def from_series(cls, series: pd.Series, sparse: bool = False) -> DataArray:
        """Convert a pandas.Series into an xarray.DataArray.

        If the series's index is a MultiIndex, it will be expanded into a
        tensor product of one-dimensional coordinates (filling in missing
        values with NaN). Thus this operation should be the inverse of the
        `to_series` method.

        Parameters
        ----------
        series : Series
            Pandas Series object to convert.
        sparse : bool, default: False
            If sparse=True, creates a sparse array instead of a dense NumPy array.
            Requires the pydata/sparse package.

        See Also
        --------
        DataArray.to_series
        Dataset.from_dataframe
        """
        temp_name = "__temporary_name"
        df = pd.DataFrame({temp_name: series})
        ds = Dataset.from_dataframe(df, sparse=sparse)
        result = cast(DataArray, ds[temp_name])
        result.name = series.name
        return result

    def to_cdms2(self) -> cdms2_Variable:
        """Convert this array into a cdms2.Variable"""
        from ..convert import to_cdms2

        return to_cdms2(self)

    @classmethod
    def from_cdms2(cls, variable: cdms2_Variable) -> DataArray:
        """Convert a cdms2.Variable into an xarray.DataArray"""
        from ..convert import from_cdms2

        return from_cdms2(variable)

    def to_iris(self) -> iris_Cube:
        """Convert this array into a iris.cube.Cube"""
        from ..convert import to_iris

        return to_iris(self)

    @classmethod
    def from_iris(cls, cube: iris_Cube) -> DataArray:
        """Convert a iris.cube.Cube into an xarray.DataArray"""
        from ..convert import from_iris

        return from_iris(cube)

    def _all_compat(self: T_DataArray, other: T_DataArray, compat_str: str) -> bool:
        """Helper function for equals, broadcast_equals, and identical"""

        def compat(x, y):
            return getattr(x.variable, compat_str)(y.variable)

        return utils.dict_equiv(self.coords, other.coords, compat=compat) and compat(
            self, other
        )

    def broadcast_equals(self: T_DataArray, other: T_DataArray) -> bool:
        """Two DataArrays are broadcast equal if they are equal after
        broadcasting them against each other such that they have the same
        dimensions.

        Parameters
        ----------
        other : DataArray
            DataArray to compare to.

        Returns
        ----------
        equal : bool
            True if the two DataArrays are broadcast equal.

        See Also
        --------
        DataArray.equals
        DataArray.identical
        """
        try:
            return self._all_compat(other, "broadcast_equals")
        except (TypeError, AttributeError):
            return False

    def equals(self: T_DataArray, other: T_DataArray) -> bool:
        """True if two DataArrays have the same dimensions, coordinates and
        values; otherwise False.

        DataArrays can still be equal (like pandas objects) if they have NaN
        values in the same locations.

        This method is necessary because `v1 == v2` for ``DataArray``
        does element-wise comparisons (like numpy.ndarrays).

        Parameters
        ----------
        other : DataArray
            DataArray to compare to.

        Returns
        ----------
        equal : bool
            True if the two DataArrays are equal.

        See Also
        --------
        DataArray.broadcast_equals
        DataArray.identical
        """
        try:
            return self._all_compat(other, "equals")
        except (TypeError, AttributeError):
            return False

    def identical(self: T_DataArray, other: T_DataArray) -> bool:
        """Like equals, but also checks the array name and attributes, and
        attributes on all coordinates.

        Parameters
        ----------
        other : DataArray
            DataArray to compare to.

        Returns
        ----------
        equal : bool
            True if the two DataArrays are identical.

        See Also
        --------
        DataArray.broadcast_equals
        DataArray.equals
        """
        try:
            return self.name == other.name and self._all_compat(other, "identical")
        except (TypeError, AttributeError):
            return False

    def _result_name(self, other: Any = None) -> Hashable | None:
        # use the same naming heuristics as pandas:
        # https://github.com/ContinuumIO/blaze/issues/458#issuecomment-51936356
        other_name = getattr(other, "name", _default)
        if other_name is _default or other_name == self.name:
            return self.name
        else:
            return None

    def __array_wrap__(self: T_DataArray, obj, context=None) -> T_DataArray:
        new_var = self.variable.__array_wrap__(obj, context)
        return self._replace(new_var)

    def __matmul__(self: T_DataArray, obj: T_DataArray) -> T_DataArray:
        return self.dot(obj)

    def __rmatmul__(self: T_DataArray, other: T_DataArray) -> T_DataArray:
        # currently somewhat duplicative, as only other DataArrays are
        # compatible with matmul
        return computation.dot(other, self)

    def _unary_op(self: T_DataArray, f: Callable, *args, **kwargs) -> T_DataArray:
        keep_attrs = kwargs.pop("keep_attrs", None)
        if keep_attrs is None:
            keep_attrs = _get_keep_attrs(default=True)
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", r"All-NaN (slice|axis) encountered")
            warnings.filterwarnings(
                "ignore", r"Mean of empty slice", category=RuntimeWarning
            )
            with np.errstate(all="ignore"):
                da = self.__array_wrap__(f(self.variable.data, *args, **kwargs))
            if keep_attrs:
                da.attrs = self.attrs
            return da

    def _binary_op(
        self: T_DataArray,
        other: Any,
        f: Callable,
        reflexive: bool = False,
    ) -> T_DataArray:
        from .groupby import GroupBy

        if isinstance(other, (Dataset, GroupBy)):
            return NotImplemented
        if isinstance(other, DataArray):
            align_type = OPTIONS["arithmetic_join"]
            self, other = align(self, other, join=align_type, copy=False)  # type: ignore
        other_variable = getattr(other, "variable", other)
        other_coords = getattr(other, "coords", None)

        variable = (
            f(self.variable, other_variable)
            if not reflexive
            else f(other_variable, self.variable)
        )
        coords, indexes = self.coords._merge_raw(other_coords, reflexive)
        name = self._result_name(other)

        return self._replace(variable, coords, name, indexes=indexes)

    def _inplace_binary_op(self: T_DataArray, other: Any, f: Callable) -> T_DataArray:
        from .groupby import GroupBy

        if isinstance(other, GroupBy):
            raise TypeError(
                "in-place operations between a DataArray and "
                "a grouped object are not permitted"
            )
        # n.b. we can't align other to self (with other.reindex_like(self))
        # because `other` may be converted into floats, which would cause
        # in-place arithmetic to fail unpredictably. Instead, we simply
        # don't support automatic alignment with in-place arithmetic.
        other_coords = getattr(other, "coords", None)
        other_variable = getattr(other, "variable", other)
        try:
            with self.coords._merge_inplace(other_coords):
                f(self.variable, other_variable)
        except MergeError as exc:
            raise MergeError(
                "Automatic alignment is not supported for in-place operations.\n"
                "Consider aligning the indices manually or using a not-in-place operation.\n"
                "See https://github.com/pydata/xarray/issues/3910 for more explanations."
            ) from exc
        return self

    def _copy_attrs_from(self, other: DataArray | Dataset | Variable) -> None:
        self.attrs = other.attrs

    plot = utils.UncachedAccessor(_PlotMethods)

    def _title_for_slice(self, truncate: int = 50) -> str:
        """
        If the dataarray has 1 dimensional coordinates or comes from a slice
        we can show that info in the title

        Parameters
        ----------
        truncate : int, default: 50
            maximum number of characters for title

        Returns
        -------
        title : string
            Can be used for plot titles

        """
        one_dims = []
        for dim, coord in self.coords.items():
            if coord.size == 1:
                one_dims.append(
                    "{dim} = {v}{unit}".format(
                        dim=dim,
                        v=format_item(coord.values),
                        unit=_get_units_from_attrs(coord),
                    )
                )

        title = ", ".join(one_dims)
        if len(title) > truncate:
            title = title[: (truncate - 3)] + "..."

        return title

    def diff(
        self: T_DataArray,
        dim: Hashable,
        n: int = 1,
        label: Literal["upper", "lower"] = "upper",
    ) -> T_DataArray:
        """Calculate the n-th order discrete difference along given axis.

        Parameters
        ----------
        dim : Hashable
            Dimension over which to calculate the finite difference.
        n : int, default: 1
            The number of times values are differenced.
        label : {"upper", "lower"}, default: "upper"
            The new coordinate in dimension ``dim`` will have the
            values of either the minuend's or subtrahend's coordinate
            for values 'upper' and 'lower', respectively.

        Returns
        -------
        difference : DataArray
            The n-th order finite difference of this object.

        Notes
        -----
        `n` matches numpy's behavior and is different from pandas' first argument named
        `periods`.

        Examples
        --------
        >>> arr = xr.DataArray([5, 5, 6, 6], [[1, 2, 3, 4]], ["x"])
        >>> arr.diff("x")
        <xarray.DataArray (x: 3)>
        array([0, 1, 0])
        Coordinates:
          * x        (x) int64 2 3 4
        >>> arr.diff("x", 2)
        <xarray.DataArray (x: 2)>
        array([ 1, -1])
        Coordinates:
          * x        (x) int64 3 4

        See Also
        --------
        DataArray.differentiate
        """
        ds = self._to_temp_dataset().diff(n=n, dim=dim, label=label)
        return self._from_temp_dataset(ds)

    def shift(
        self: T_DataArray,
        shifts: Mapping[Any, int] | None = None,
        fill_value: Any = dtypes.NA,
        **shifts_kwargs: int,
    ) -> T_DataArray:
        """Shift this DataArray by an offset along one or more dimensions.

        Only the data is moved; coordinates stay in place. This is consistent
        with the behavior of ``shift`` in pandas.

        Values shifted from beyond array bounds will appear at one end of
        each dimension, which are filled according to `fill_value`. For periodic
        offsets instead see `roll`.

        Parameters
        ----------
        shifts : mapping of Hashable to int or None, optional
            Integer offset to shift along each of the given dimensions.
            Positive offsets shift to the right; negative offsets shift to the
            left.
        fill_value : scalar, optional
            Value to use for newly missing values
        **shifts_kwargs
            The keyword arguments form of ``shifts``.
            One of shifts or shifts_kwargs must be provided.

        Returns
        -------
        shifted : DataArray
            DataArray with the same coordinates and attributes but shifted
            data.

        See Also
        --------
        roll

        Examples
        --------
        >>> arr = xr.DataArray([5, 6, 7], dims="x")
        >>> arr.shift(x=1)
        <xarray.DataArray (x: 3)>
        array([nan,  5.,  6.])
        Dimensions without coordinates: x
        """
        variable = self.variable.shift(
            shifts=shifts, fill_value=fill_value, **shifts_kwargs
        )
        return self._replace(variable=variable)

    def roll(
        self: T_DataArray,
        shifts: Mapping[Hashable, int] | None = None,
        roll_coords: bool = False,
        **shifts_kwargs: int,
    ) -> T_DataArray:
        """Roll this array by an offset along one or more dimensions.

        Unlike shift, roll treats the given dimensions as periodic, so will not
        create any missing values to be filled.

        Unlike shift, roll may rotate all variables, including coordinates
        if specified. The direction of rotation is consistent with
        :py:func:`numpy.roll`.

        Parameters
        ----------
        shifts : mapping of Hashable to int, optional
            Integer offset to rotate each of the given dimensions.
            Positive offsets roll to the right; negative offsets roll to the
            left.
        roll_coords : bool, default: False
            Indicates whether to roll the coordinates by the offset too.
        **shifts_kwargs : {dim: offset, ...}, optional
            The keyword arguments form of ``shifts``.
            One of shifts or shifts_kwargs must be provided.

        Returns
        -------
        rolled : DataArray
            DataArray with the same attributes but rolled data and coordinates.

        See Also
        --------
        shift

        Examples
        --------
        >>> arr = xr.DataArray([5, 6, 7], dims="x")
        >>> arr.roll(x=1)
        <xarray.DataArray (x: 3)>
        array([7, 5, 6])
        Dimensions without coordinates: x
        """
        ds = self._to_temp_dataset().roll(
            shifts=shifts, roll_coords=roll_coords, **shifts_kwargs
        )
        return self._from_temp_dataset(ds)

    @property
    def real(self: T_DataArray) -> T_DataArray:
        return self._replace(self.variable.real)

    @property
    def imag(self: T_DataArray) -> T_DataArray:
        return self._replace(self.variable.imag)

    def dot(
        self: T_DataArray,
        other: T_DataArray,
        dims: Hashable | Sequence[Hashable] | None = None,
    ) -> T_DataArray:
        """Perform dot product of two DataArrays along their shared dims.

        Equivalent to taking taking tensordot over all shared dims.

        Parameters
        ----------
        other : DataArray
            The other array with which the dot product is performed.
        dims : ..., Hashable or sequence of Hashable, optional
            Which dimensions to sum over. Ellipsis (`...`) sums over all dimensions.
            If not specified, then all the common dimensions are summed over.

        Returns
        -------
        result : DataArray
            Array resulting from the dot product over all shared dimensions.

        See Also
        --------
        dot
        numpy.tensordot

        Examples
        --------
        >>> da_vals = np.arange(6 * 5 * 4).reshape((6, 5, 4))
        >>> da = xr.DataArray(da_vals, dims=["x", "y", "z"])
        >>> dm_vals = np.arange(4)
        >>> dm = xr.DataArray(dm_vals, dims=["z"])

        >>> dm.dims
        ('z',)

        >>> da.dims
        ('x', 'y', 'z')

        >>> dot_result = da.dot(dm)
        >>> dot_result.dims
        ('x', 'y')

        """
        if isinstance(other, Dataset):
            raise NotImplementedError(
                "dot products are not yet supported with Dataset objects."
            )
        if not isinstance(other, DataArray):
            raise TypeError("dot only operates on DataArrays.")

        return computation.dot(self, other, dims=dims)

    # change type of self and return to T_DataArray once
    # https://github.com/python/mypy/issues/12846 is resolved
    def sortby(
        self,
        variables: Hashable | DataArray | Sequence[Hashable | DataArray],
        ascending: bool = True,
    ) -> DataArray:
        """Sort object by labels or values (along an axis).

        Sorts the dataarray, either along specified dimensions,
        or according to values of 1-D dataarrays that share dimension
        with calling object.

        If the input variables are dataarrays, then the dataarrays are aligned
        (via left-join) to the calling object prior to sorting by cell values.
        NaNs are sorted to the end, following Numpy convention.

        If multiple sorts along the same dimension is
        given, numpy's lexsort is performed along that dimension:
        https://numpy.org/doc/stable/reference/generated/numpy.lexsort.html
        and the FIRST key in the sequence is used as the primary sort key,
        followed by the 2nd key, etc.

        Parameters
        ----------
        variables : Hashable, DataArray, or sequence of Hashable or DataArray
            1D DataArray objects or name(s) of 1D variable(s) in
            coords whose values are used to sort this array.
        ascending : bool, default: True
            Whether to sort by ascending or descending order.

        Returns
        -------
        sorted : DataArray
            A new dataarray where all the specified dims are sorted by dim
            labels.

        See Also
        --------
        Dataset.sortby
        numpy.sort
        pandas.sort_values
        pandas.sort_index

        Examples
        --------
        >>> da = xr.DataArray(
        ...     np.random.rand(5),
        ...     coords=[pd.date_range("1/1/2000", periods=5)],
        ...     dims="time",
        ... )
        >>> da
        <xarray.DataArray (time: 5)>
        array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ])
        Coordinates:
          * time     (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05

        >>> da.sortby(da)
        <xarray.DataArray (time: 5)>
        array([0.4236548 , 0.54488318, 0.5488135 , 0.60276338, 0.71518937])
        Coordinates:
          * time     (time) datetime64[ns] 2000-01-05 2000-01-04 ... 2000-01-02
        """
        ds = self._to_temp_dataset().sortby(variables, ascending=ascending)
        return self._from_temp_dataset(ds)

    def quantile(
        self: T_DataArray,
        q: ArrayLike,
        dim: str | Iterable[Hashable] | None = None,
        method: QUANTILE_METHODS = "linear",
        keep_attrs: bool | None = None,
        skipna: bool | None = None,
        interpolation: QUANTILE_METHODS = None,
    ) -> T_DataArray:
        """Compute the qth quantile of the data along the specified dimension.

        Returns the qth quantiles(s) of the array elements.

        Parameters
        ----------
        q : float or array-like of float
            Quantile to compute, which must be between 0 and 1 inclusive.
        dim : str or Iterable of Hashable, optional
            Dimension(s) over which to apply quantile.
        method : str, default: "linear"
            This optional parameter specifies the interpolation method to use when the
            desired quantile lies between two data points. The options sorted by their R
            type as summarized in the H&F paper [1]_ are:

                1. "inverted_cdf" (*)
                2. "averaged_inverted_cdf" (*)
                3. "closest_observation" (*)
                4. "interpolated_inverted_cdf" (*)
                5. "hazen" (*)
                6. "weibull" (*)
                7. "linear"  (default)
                8. "median_unbiased" (*)
                9. "normal_unbiased" (*)

            The first three methods are discontiuous.  The following discontinuous
            variations of the default "linear" (7.) option are also available:

                * "lower"
                * "higher"
                * "midpoint"
                * "nearest"

            See :py:func:`numpy.quantile` or [1]_ for details. Methods marked with
            an asterix require numpy version 1.22 or newer. The "method" argument was
            previously called "interpolation", renamed in accordance with numpy
            version 1.22.0.

        keep_attrs : bool or None, optional
            If True, the dataset's attributes (`attrs`) will be copied from
            the original object to the new one.  If False (default), the new
            object will be returned without attributes.
        skipna : bool or None, optional
            If True, skip missing values (as marked by NaN). By default, only
            skips missing values for float dtypes; other dtypes either do not
            have a sentinel missing value (int) or skipna=True has not been
            implemented (object, datetime64 or timedelta64).

        Returns
        -------
        quantiles : DataArray
            If `q` is a single quantile, then the result
            is a scalar. If multiple percentiles are given, first axis of
            the result corresponds to the quantile and a quantile dimension
            is added to the return array. The other dimensions are the
            dimensions that remain after the reduction of the array.

        See Also
        --------
        numpy.nanquantile, numpy.quantile, pandas.Series.quantile, Dataset.quantile

        Examples
        --------
        >>> da = xr.DataArray(
        ...     data=[[0.7, 4.2, 9.4, 1.5], [6.5, 7.3, 2.6, 1.9]],
        ...     coords={"x": [7, 9], "y": [1, 1.5, 2, 2.5]},
        ...     dims=("x", "y"),
        ... )
        >>> da.quantile(0)  # or da.quantile(0, dim=...)
        <xarray.DataArray ()>
        array(0.7)
        Coordinates:
            quantile  float64 0.0
        >>> da.quantile(0, dim="x")
        <xarray.DataArray (y: 4)>
        array([0.7, 4.2, 2.6, 1.5])
        Coordinates:
          * y         (y) float64 1.0 1.5 2.0 2.5
            quantile  float64 0.0
        >>> da.quantile([0, 0.5, 1])
        <xarray.DataArray (quantile: 3)>
        array([0.7, 3.4, 9.4])
        Coordinates:
          * quantile  (quantile) float64 0.0 0.5 1.0
        >>> da.quantile([0, 0.5, 1], dim="x")
        <xarray.DataArray (quantile: 3, y: 4)>
        array([[0.7 , 4.2 , 2.6 , 1.5 ],
               [3.6 , 5.75, 6.  , 1.7 ],
               [6.5 , 7.3 , 9.4 , 1.9 ]])
        Coordinates:
          * y         (y) float64 1.0 1.5 2.0 2.5
          * quantile  (quantile) float64 0.0 0.5 1.0

        References
        ----------
        .. [1] R. J. Hyndman and Y. Fan,
           "Sample quantiles in statistical packages,"
           The American Statistician, 50(4), pp. 361-365, 1996
        """

        ds = self._to_temp_dataset().quantile(
            q,
            dim=dim,
            keep_attrs=keep_attrs,
            method=method,
            skipna=skipna,
            interpolation=interpolation,
        )
        return self._from_temp_dataset(ds)

    def rank(
        self: T_DataArray,
        dim: Hashable,
        pct: bool = False,
        keep_attrs: bool | None = None,
    ) -> T_DataArray:
        """Ranks the data.

        Equal values are assigned a rank that is the average of the ranks that
        would have been otherwise assigned to all of the values within that
        set.  Ranks begin at 1, not 0. If pct, computes percentage ranks.

        NaNs in the input array are returned as NaNs.

        The `bottleneck` library is required.

        Parameters
        ----------
        dim : Hashable
            Dimension over which to compute rank.
        pct : bool, default: False
            If True, compute percentage ranks, otherwise compute integer ranks.
        keep_attrs : bool or None, optional
            If True, the dataset's attributes (`attrs`) will be copied from
            the original object to the new one.  If False (default), the new
            object will be returned without attributes.

        Returns
        -------
        ranked : DataArray
            DataArray with the same coordinates and dtype 'float64'.

        Examples
        --------
        >>> arr = xr.DataArray([5, 6, 7], dims="x")
        >>> arr.rank("x")
        <xarray.DataArray (x: 3)>
        array([1., 2., 3.])
        Dimensions without coordinates: x
        """

        ds = self._to_temp_dataset().rank(dim, pct=pct, keep_attrs=keep_attrs)
        return self._from_temp_dataset(ds)

    def differentiate(
        self: T_DataArray,
        coord: Hashable,
        edge_order: Literal[1, 2] = 1,
        datetime_unit: DatetimeUnitOptions = None,
    ) -> T_DataArray:
        """ Differentiate the array with the second order accurate central
        differences.

        .. note::
            This feature is limited to simple cartesian geometry, i.e. coord
            must be one dimensional.

        Parameters
        ----------
        coord : Hashable
            The coordinate to be used to compute the gradient.
        edge_order : {1, 2}, default: 1
            N-th order accurate differences at the boundaries.
        datetime_unit : {"Y", "M", "W", "D", "h", "m", "s", "ms", \
                         "us", "ns", "ps", "fs", "as", None}, optional
            Unit to compute gradient. Only valid for datetime coordinate.

        Returns
        -------
        differentiated: DataArray

        See also
        --------
        numpy.gradient: corresponding numpy function

        Examples
        --------

        >>> da = xr.DataArray(
        ...     np.arange(12).reshape(4, 3),
        ...     dims=["x", "y"],
        ...     coords={"x": [0, 0.1, 1.1, 1.2]},
        ... )
        >>> da
        <xarray.DataArray (x: 4, y: 3)>
        array([[ 0,  1,  2],
               [ 3,  4,  5],
               [ 6,  7,  8],
               [ 9, 10, 11]])
        Coordinates:
          * x        (x) float64 0.0 0.1 1.1 1.2
        Dimensions without coordinates: y
        >>>
        >>> da.differentiate("x")
        <xarray.DataArray (x: 4, y: 3)>
        array([[30.        , 30.        , 30.        ],
               [27.54545455, 27.54545455, 27.54545455],
               [27.54545455, 27.54545455, 27.54545455],
               [30.        , 30.        , 30.        ]])
        Coordinates:
          * x        (x) float64 0.0 0.1 1.1 1.2
        Dimensions without coordinates: y
        """
        ds = self._to_temp_dataset().differentiate(coord, edge_order, datetime_unit)
        return self._from_temp_dataset(ds)

    # change type of self and return to T_DataArray once
    # https://github.com/python/mypy/issues/12846 is resolved
    def integrate(
        self,
        coord: Hashable | Sequence[Hashable] = None,
        datetime_unit: DatetimeUnitOptions = None,
    ) -> DataArray:
        """Integrate along the given coordinate using the trapezoidal rule.

        .. note::
            This feature is limited to simple cartesian geometry, i.e. coord
            must be one dimensional.

        Parameters
        ----------
        coord : Hashable, or sequence of Hashable
            Coordinate(s) used for the integration.
        datetime_unit : {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', \
                        'ps', 'fs', 'as', None}, optional
            Specify the unit if a datetime coordinate is used.

        Returns
        -------
        integrated : DataArray

        See also
        --------
        Dataset.integrate
        numpy.trapz : corresponding numpy function

        Examples
        --------

        >>> da = xr.DataArray(
        ...     np.arange(12).reshape(4, 3),
        ...     dims=["x", "y"],
        ...     coords={"x": [0, 0.1, 1.1, 1.2]},
        ... )
        >>> da
        <xarray.DataArray (x: 4, y: 3)>
        array([[ 0,  1,  2],
               [ 3,  4,  5],
               [ 6,  7,  8],
               [ 9, 10, 11]])
        Coordinates:
          * x        (x) float64 0.0 0.1 1.1 1.2
        Dimensions without coordinates: y
        >>>
        >>> da.integrate("x")
        <xarray.DataArray (y: 3)>
        array([5.4, 6.6, 7.8])
        Dimensions without coordinates: y
        """
        ds = self._to_temp_dataset().integrate(coord, datetime_unit)
        return self._from_temp_dataset(ds)

    # change type of self and return to T_DataArray once
    # https://github.com/python/mypy/issues/12846 is resolved
    def cumulative_integrate(
        self,
        coord: Hashable | Sequence[Hashable] = None,
        datetime_unit: DatetimeUnitOptions = None,
    ) -> DataArray:
        """Integrate cumulatively along the given coordinate using the trapezoidal rule.

        .. note::
            This feature is limited to simple cartesian geometry, i.e. coord
            must be one dimensional.

            The first entry of the cumulative integral is always 0, in order to keep the
            length of the dimension unchanged between input and output.

        Parameters
        ----------
        coord : Hashable, or sequence of Hashable
            Coordinate(s) used for the integration.
        datetime_unit : {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', \
                        'ps', 'fs', 'as', None}, optional
            Specify the unit if a datetime coordinate is used.

        Returns
        -------
        integrated : DataArray

        See also
        --------
        Dataset.cumulative_integrate
        scipy.integrate.cumulative_trapezoid : corresponding scipy function

        Examples
        --------

        >>> da = xr.DataArray(
        ...     np.arange(12).reshape(4, 3),
        ...     dims=["x", "y"],
        ...     coords={"x": [0, 0.1, 1.1, 1.2]},
        ... )
        >>> da
        <xarray.DataArray (x: 4, y: 3)>
        array([[ 0,  1,  2],
               [ 3,  4,  5],
               [ 6,  7,  8],
               [ 9, 10, 11]])
        Coordinates:
          * x        (x) float64 0.0 0.1 1.1 1.2
        Dimensions without coordinates: y
        >>>
        >>> da.cumulative_integrate("x")
        <xarray.DataArray (x: 4, y: 3)>
        array([[0.  , 0.  , 0.  ],
               [0.15, 0.25, 0.35],
               [4.65, 5.75, 6.85],
               [5.4 , 6.6 , 7.8 ]])
        Coordinates:
          * x        (x) float64 0.0 0.1 1.1 1.2
        Dimensions without coordinates: y
        """
        ds = self._to_temp_dataset().cumulative_integrate(coord, datetime_unit)
        return self._from_temp_dataset(ds)

    def unify_chunks(self) -> DataArray:
        """Unify chunk size along all chunked dimensions of this DataArray.

        Returns
        -------
        DataArray with consistent chunk sizes for all dask-array variables

        See Also
        --------
        dask.array.core.unify_chunks
        """

        return unify_chunks(self)[0]

    def map_blocks(
        self,
        func: Callable[..., T_Xarray],
        args: Sequence[Any] = (),
        kwargs: Mapping[str, Any] | None = None,
        template: DataArray | Dataset | None = None,
    ) -> T_Xarray:
        """
        Apply a function to each block of this DataArray.

        .. warning::
            This method is experimental and its signature may change.

        Parameters
        ----------
        func : callable
            User-provided function that accepts a DataArray as its first
            parameter. The function will receive a subset or 'block' of this DataArray (see below),
            corresponding to one chunk along each chunked dimension. ``func`` will be
            executed as ``func(subset_dataarray, *subset_args, **kwargs)``.

            This function must return either a single DataArray or a single Dataset.

            This function cannot add a new chunked dimension.
        args : sequence
            Passed to func after unpacking and subsetting any xarray objects by blocks.
            xarray objects in args must be aligned with this object, otherwise an error is raised.
        kwargs : mapping
            Passed verbatim to func after unpacking. xarray objects, if any, will not be
            subset to blocks. Passing dask collections in kwargs is not allowed.
        template : DataArray or Dataset, optional
            xarray object representing the final result after compute is called. If not provided,
            the function will be first run on mocked-up data, that looks like this object but
            has sizes 0, to determine properties of the returned object such as dtype,
            variable names, attributes, new dimensions and new indexes (if any).
            ``template`` must be provided if the function changes the size of existing dimensions.
            When provided, ``attrs`` on variables in `template` are copied over to the result. Any
            ``attrs`` set by ``func`` will be ignored.

        Returns
        -------
        A single DataArray or Dataset with dask backend, reassembled from the outputs of the
        function.

        Notes
        -----
        This function is designed for when ``func`` needs to manipulate a whole xarray object
        subset to each block. Each block is loaded into memory. In the more common case where
        ``func`` can work on numpy arrays, it is recommended to use ``apply_ufunc``.

        If none of the variables in this object is backed by dask arrays, calling this function is
        equivalent to calling ``func(obj, *args, **kwargs)``.

        See Also
        --------
        dask.array.map_blocks, xarray.apply_ufunc, xarray.Dataset.map_blocks
        xarray.DataArray.map_blocks

        Examples
        --------
        Calculate an anomaly from climatology using ``.groupby()``. Using
        ``xr.map_blocks()`` allows for parallel operations with knowledge of ``xarray``,
        its indices, and its methods like ``.groupby()``.

        >>> def calculate_anomaly(da, groupby_type="time.month"):
        ...     gb = da.groupby(groupby_type)
        ...     clim = gb.mean(dim="time")
        ...     return gb - clim
        ...
        >>> time = xr.cftime_range("1990-01", "1992-01", freq="M")
        >>> month = xr.DataArray(time.month, coords={"time": time}, dims=["time"])
        >>> np.random.seed(123)
        >>> array = xr.DataArray(
        ...     np.random.rand(len(time)),
        ...     dims=["time"],
        ...     coords={"time": time, "month": month},
        ... ).chunk()
        >>> array.map_blocks(calculate_anomaly, template=array).compute()
        <xarray.DataArray (time: 24)>
        array([ 0.12894847,  0.11323072, -0.0855964 , -0.09334032,  0.26848862,
                0.12382735,  0.22460641,  0.07650108, -0.07673453, -0.22865714,
               -0.19063865,  0.0590131 , -0.12894847, -0.11323072,  0.0855964 ,
                0.09334032, -0.26848862, -0.12382735, -0.22460641, -0.07650108,
                0.07673453,  0.22865714,  0.19063865, -0.0590131 ])
        Coordinates:
          * time     (time) object 1990-01-31 00:00:00 ... 1991-12-31 00:00:00
            month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

        Note that one must explicitly use ``args=[]`` and ``kwargs={}`` to pass arguments
        to the function being applied in ``xr.map_blocks()``:

        >>> array.map_blocks(
        ...     calculate_anomaly, kwargs={"groupby_type": "time.year"}, template=array
        ... )  # doctest: +ELLIPSIS
        <xarray.DataArray (time: 24)>
        dask.array<<this-array>-calculate_anomaly, shape=(24,), dtype=float64, chunksize=(24,), chunktype=numpy.ndarray>
        Coordinates:
          * time     (time) object 1990-01-31 00:00:00 ... 1991-12-31 00:00:00
            month    (time) int64 dask.array<chunksize=(24,), meta=np.ndarray>
        """
        from .parallel import map_blocks

        return map_blocks(func, self, args, kwargs, template)

    def polyfit(
        self,
        dim: Hashable,
        deg: int,
        skipna: bool | None = None,
        rcond: float | None = None,
        w: Hashable | Any | None = None,
        full: bool = False,
        cov: bool | Literal["unscaled"] = False,
    ) -> Dataset:
        """
        Least squares polynomial fit.

        This replicates the behaviour of `numpy.polyfit` but differs by skipping
        invalid values when `skipna = True`.

        Parameters
        ----------
        dim : Hashable
            Coordinate along which to fit the polynomials.
        deg : int
            Degree of the fitting polynomial.
        skipna : bool or None, optional
            If True, removes all invalid values before fitting each 1D slices of the array.
            Default is True if data is stored in a dask.array or if there is any
            invalid values, False otherwise.
        rcond : float or None, optional
            Relative condition number to the fit.
        w : Hashable, array-like or None, optional
            Weights to apply to the y-coordinate of the sample points.
            Can be an array-like object or the name of a coordinate in the dataset.
        full : bool, default: False
            Whether to return the residuals, matrix rank and singular values in addition
            to the coefficients.
        cov : bool or "unscaled", default: False
            Whether to return to the covariance matrix in addition to the coefficients.
            The matrix is not scaled if `cov='unscaled'`.

        Returns
        -------
        polyfit_results : Dataset
            A single dataset which contains:

            polyfit_coefficients
                The coefficients of the best fit.
            polyfit_residuals
                The residuals of the least-square computation (only included if `full=True`).
                When the matrix rank is deficient, np.nan is returned.
            [dim]_matrix_rank
                The effective rank of the scaled Vandermonde coefficient matrix (only included if `full=True`)
            [dim]_singular_value
                The singular values of the scaled Vandermonde coefficient matrix (only included if `full=True`)
            polyfit_covariance
                The covariance matrix of the polynomial coefficient estimates (only included if `full=False` and `cov=True`)

        See Also
        --------
        numpy.polyfit
        numpy.polyval
        xarray.polyval
        """
        return self._to_temp_dataset().polyfit(
            dim, deg, skipna=skipna, rcond=rcond, w=w, full=full, cov=cov
        )

    def pad(
        self: T_DataArray,
        pad_width: Mapping[Any, int | tuple[int, int]] | None = None,
        mode: PadModeOptions = "constant",
        stat_length: int
        | tuple[int, int]
        | Mapping[Any, tuple[int, int]]
        | None = None,
        constant_values: float
        | tuple[float, float]
        | Mapping[Any, tuple[float, float]]
        | None = None,
        end_values: int | tuple[int, int] | Mapping[Any, tuple[int, int]] | None = None,
        reflect_type: PadReflectOptions = None,
        **pad_width_kwargs: Any,
    ) -> T_DataArray:
        """Pad this array along one or more dimensions.

        .. warning::
            This function is experimental and its behaviour is likely to change
            especially regarding padding of dimension coordinates (or IndexVariables).

        When using one of the modes ("edge", "reflect", "symmetric", "wrap"),
        coordinates will be padded with the same mode, otherwise coordinates
        are padded using the "constant" mode with fill_value dtypes.NA.

        Parameters
        ----------
        pad_width : mapping of Hashable to tuple of int
            Mapping with the form of {dim: (pad_before, pad_after)}
            describing the number of values padded along each dimension.
            {dim: pad} is a shortcut for pad_before = pad_after = pad
        mode : {"constant", "edge", "linear_ramp", "maximum", "mean", "median", \
            "minimum", "reflect", "symmetric", "wrap"}, default: "constant"
            How to pad the DataArray (taken from numpy docs):

            - "constant": Pads with a constant value.
            - "edge": Pads with the edge values of array.
            - "linear_ramp": Pads with the linear ramp between end_value and the
              array edge value.
            - "maximum": Pads with the maximum value of all or part of the
              vector along each axis.
            - "mean": Pads with the mean value of all or part of the
              vector along each axis.
            - "median": Pads with the median value of all or part of the
              vector along each axis.
            - "minimum": Pads with the minimum value of all or part of the
              vector along each axis.
            - "reflect": Pads with the reflection of the vector mirrored on
              the first and last values of the vector along each axis.
            - "symmetric": Pads with the reflection of the vector mirrored
              along the edge of the array.
            - "wrap": Pads with the wrap of the vector along the axis.
              The first values are used to pad the end and the
              end values are used to pad the beginning.

        stat_length : int, tuple or mapping of Hashable to tuple, default: None
            Used in 'maximum', 'mean', 'median', and 'minimum'.  Number of
            values at edge of each axis used to calculate the statistic value.
            {dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)} unique
            statistic lengths along each dimension.
            ((before, after),) yields same before and after statistic lengths
            for each dimension.
            (stat_length,) or int is a shortcut for before = after = statistic
            length for all axes.
            Default is ``None``, to use the entire axis.
        constant_values : scalar, tuple or mapping of Hashable to tuple, default: 0
            Used in 'constant'.  The values to set the padded values for each
            axis.
            ``{dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)}`` unique
            pad constants along each dimension.
            ``((before, after),)`` yields same before and after constants for each
            dimension.
            ``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
            all dimensions.
            Default is 0.
        end_values : scalar, tuple or mapping of Hashable to tuple, default: 0
            Used in 'linear_ramp'.  The values used for the ending value of the
            linear_ramp and that will form the edge of the padded array.
            ``{dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)}`` unique
            end values along each dimension.
            ``((before, after),)`` yields same before and after end values for each
            axis.
            ``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
            all axes.
            Default is 0.
        reflect_type : {"even", "odd", None}, optional
            Used in "reflect", and "symmetric". The "even" style is the
            default with an unaltered reflection around the edge value. For
            the "odd" style, the extended part of the array is created by
            subtracting the reflected values from two times the edge value.
        **pad_width_kwargs
            The keyword arguments form of ``pad_width``.
            One of ``pad_width`` or ``pad_width_kwargs`` must be provided.

        Returns
        -------
        padded : DataArray
            DataArray with the padded coordinates and data.

        See Also
        --------
        DataArray.shift, DataArray.roll, DataArray.bfill, DataArray.ffill, numpy.pad, dask.array.pad

        Notes
        -----
        For ``mode="constant"`` and ``constant_values=None``, integer types will be
        promoted to ``float`` and padded with ``np.nan``.

        Padding coordinates will drop their corresponding index (if any) and will reset default
        indexes for dimension coordinates.

        Examples
        --------
        >>> arr = xr.DataArray([5, 6, 7], coords=[("x", [0, 1, 2])])
        >>> arr.pad(x=(1, 2), constant_values=0)
        <xarray.DataArray (x: 6)>
        array([0, 5, 6, 7, 0, 0])
        Coordinates:
          * x        (x) float64 nan 0.0 1.0 2.0 nan nan

        >>> da = xr.DataArray(
        ...     [[0, 1, 2, 3], [10, 11, 12, 13]],
        ...     dims=["x", "y"],
        ...     coords={"x": [0, 1], "y": [10, 20, 30, 40], "z": ("x", [100, 200])},
        ... )
        >>> da.pad(x=1)
        <xarray.DataArray (x: 4, y: 4)>
        array([[nan, nan, nan, nan],
               [ 0.,  1.,  2.,  3.],
               [10., 11., 12., 13.],
               [nan, nan, nan, nan]])
        Coordinates:
          * x        (x) float64 nan 0.0 1.0 nan
          * y        (y) int64 10 20 30 40
            z        (x) float64 nan 100.0 200.0 nan

        Careful, ``constant_values`` are coerced to the data type of the array which may
        lead to a loss of precision:

        >>> da.pad(x=1, constant_values=1.23456789)
        <xarray.DataArray (x: 4, y: 4)>
        array([[ 1,  1,  1,  1],
               [ 0,  1,  2,  3],
               [10, 11, 12, 13],
               [ 1,  1,  1,  1]])
        Coordinates:
          * x        (x) float64 nan 0.0 1.0 nan
          * y        (y) int64 10 20 30 40
            z        (x) float64 nan 100.0 200.0 nan
        """
        ds = self._to_temp_dataset().pad(
            pad_width=pad_width,
            mode=mode,
            stat_length=stat_length,
            constant_values=constant_values,
            end_values=end_values,
            reflect_type=reflect_type,
            **pad_width_kwargs,
        )
        return self._from_temp_dataset(ds)

    def idxmin(
        self,
        dim: Hashable | None = None,
        skipna: bool | None = None,
        fill_value: Any = dtypes.NA,
        keep_attrs: bool | None = None,
    ) -> DataArray:
        """Return the coordinate label of the minimum value along a dimension.

        Returns a new `DataArray` named after the dimension with the values of
        the coordinate labels along that dimension corresponding to minimum
        values along that dimension.

        In comparison to :py:meth:`~DataArray.argmin`, this returns the
        coordinate label while :py:meth:`~DataArray.argmin` returns the index.

        Parameters
        ----------
        dim : str, optional
            Dimension over which to apply `idxmin`.  This is optional for 1D
            arrays, but required for arrays with 2 or more dimensions.
        skipna : bool or None, default: None
            If True, skip missing values (as marked by NaN). By default, only
            skips missing values for ``float``, ``complex``, and ``object``
            dtypes; other dtypes either do not have a sentinel missing value
            (``int``) or ``skipna=True`` has not been implemented
            (``datetime64`` or ``timedelta64``).
        fill_value : Any, default: NaN
            Value to be filled in case all of the values along a dimension are
            null.  By default this is NaN.  The fill value and result are
            automatically converted to a compatible dtype if possible.
            Ignored if ``skipna`` is False.
        keep_attrs : bool or None, optional
            If True, the attributes (``attrs``) will be copied from the
            original object to the new one. If False, the new object
            will be returned without attributes.

        Returns
        -------
        reduced : DataArray
            New `DataArray` object with `idxmin` applied to its data and the
            indicated dimension removed.

        See Also
        --------
        Dataset.idxmin, DataArray.idxmax, DataArray.min, DataArray.argmin

        Examples
        --------
        >>> array = xr.DataArray(
        ...     [0, 2, 1, 0, -2], dims="x", coords={"x": ["a", "b", "c", "d", "e"]}
        ... )
        >>> array.min()
        <xarray.DataArray ()>
        array(-2)
        >>> array.argmin()
        <xarray.DataArray ()>
        array(4)
        >>> array.idxmin()
        <xarray.DataArray 'x' ()>
        array('e', dtype='<U1')

        >>> array = xr.DataArray(
        ...     [
        ...         [2.0, 1.0, 2.0, 0.0, -2.0],
        ...         [-4.0, np.NaN, 2.0, np.NaN, -2.0],
        ...         [np.NaN, np.NaN, 1.0, np.NaN, np.NaN],
        ...     ],
        ...     dims=["y", "x"],
        ...     coords={"y": [-1, 0, 1], "x": np.arange(5.0) ** 2},
        ... )
        >>> array.min(dim="x")
        <xarray.DataArray (y: 3)>
        array([-2., -4.,  1.])
        Coordinates:
          * y        (y) int64 -1 0 1
        >>> array.argmin(dim="x")
        <xarray.DataArray (y: 3)>
        array([4, 0, 2])
        Coordinates:
          * y        (y) int64 -1 0 1
        >>> array.idxmin(dim="x")
        <xarray.DataArray 'x' (y: 3)>
        array([16.,  0.,  4.])
        Coordinates:
          * y        (y) int64 -1 0 1
        """
        return computation._calc_idxminmax(
            array=self,
            func=lambda x, *args, **kwargs: x.argmin(*args, **kwargs),
            dim=dim,
            skipna=skipna,
            fill_value=fill_value,
            keep_attrs=keep_attrs,
        )

    def idxmax(
        self,
        dim: Hashable = None,
        skipna: bool | None = None,
        fill_value: Any = dtypes.NA,
        keep_attrs: bool | None = None,
    ) -> DataArray:
        """Return the coordinate label of the maximum value along a dimension.

        Returns a new `DataArray` named after the dimension with the values of
        the coordinate labels along that dimension corresponding to maximum
        values along that dimension.

        In comparison to :py:meth:`~DataArray.argmax`, this returns the
        coordinate label while :py:meth:`~DataArray.argmax` returns the index.

        Parameters
        ----------
        dim : Hashable, optional
            Dimension over which to apply `idxmax`.  This is optional for 1D
            arrays, but required for arrays with 2 or more dimensions.
        skipna : bool or None, default: None
            If True, skip missing values (as marked by NaN). By default, only
            skips missing values for ``float``, ``complex``, and ``object``
            dtypes; other dtypes either do not have a sentinel missing value
            (``int``) or ``skipna=True`` has not been implemented
            (``datetime64`` or ``timedelta64``).
        fill_value : Any, default: NaN
            Value to be filled in case all of the values along a dimension are
            null.  By default this is NaN.  The fill value and result are
            automatically converted to a compatible dtype if possible.
            Ignored if ``skipna`` is False.
        keep_attrs : bool or None, optional
            If True, the attributes (``attrs``) will be copied from the
            original object to the new one. If False, the new object
            will be returned without attributes.

        Returns
        -------
        reduced : DataArray
            New `DataArray` object with `idxmax` applied to its data and the
            indicated dimension removed.

        See Also
        --------
        Dataset.idxmax, DataArray.idxmin, DataArray.max, DataArray.argmax

        Examples
        --------
        >>> array = xr.DataArray(
        ...     [0, 2, 1, 0, -2], dims="x", coords={"x": ["a", "b", "c", "d", "e"]}
        ... )
        >>> array.max()
        <xarray.DataArray ()>
        array(2)
        >>> array.argmax()
        <xarray.DataArray ()>
        array(1)
        >>> array.idxmax()
        <xarray.DataArray 'x' ()>
        array('b', dtype='<U1')

        >>> array = xr.DataArray(
        ...     [
        ...         [2.0, 1.0, 2.0, 0.0, -2.0],
        ...         [-4.0, np.NaN, 2.0, np.NaN, -2.0],
        ...         [np.NaN, np.NaN, 1.0, np.NaN, np.NaN],
        ...     ],
        ...     dims=["y", "x"],
        ...     coords={"y": [-1, 0, 1], "x": np.arange(5.0) ** 2},
        ... )
        >>> array.max(dim="x")
        <xarray.DataArray (y: 3)>
        array([2., 2., 1.])
        Coordinates:
          * y        (y) int64 -1 0 1
        >>> array.argmax(dim="x")
        <xarray.DataArray (y: 3)>
        array([0, 2, 2])
        Coordinates:
          * y        (y) int64 -1 0 1
        >>> array.idxmax(dim="x")
        <xarray.DataArray 'x' (y: 3)>
        array([0., 4., 4.])
        Coordinates:
          * y        (y) int64 -1 0 1
        """
        return computation._calc_idxminmax(
            array=self,
            func=lambda x, *args, **kwargs: x.argmax(*args, **kwargs),
            dim=dim,
            skipna=skipna,
            fill_value=fill_value,
            keep_attrs=keep_attrs,
        )

    # change type of self and return to T_DataArray once
    # https://github.com/python/mypy/issues/12846 is resolved
    def argmin(
        self,
        dim: Hashable | Sequence[Hashable] | None = None,
        axis: int | None = None,
        keep_attrs: bool | None = None,
        skipna: bool | None = None,
    ) -> DataArray | dict[Hashable, DataArray]:
        """Index or indices of the minimum of the DataArray over one or more dimensions.

        If a sequence is passed to 'dim', then result returned as dict of DataArrays,
        which can be passed directly to isel(). If a single str is passed to 'dim' then
        returns a DataArray with dtype int.

        If there are multiple minima, the indices of the first one found will be
        returned.

        Parameters
        ----------
        dim : Hashable, sequence of Hashable, None or ..., optional
            The dimensions over which to find the minimum. By default, finds minimum over
            all dimensions - for now returning an int for backward compatibility, but
            this is deprecated, in future will return a dict with indices for all
            dimensions; to return a dict with all dimensions now, pass '...'.
        axis : int or None, optional
            Axis over which to apply `argmin`. Only one of the 'dim' and 'axis' arguments
            can be supplied.
        keep_attrs : bool or None, optional
            If True, the attributes (`attrs`) will be copied from the original
            object to the new one. If False, the new object will be
            returned without attributes.
        skipna : bool or None, optional
            If True, skip missing values (as marked by NaN). By default, only
            skips missing values for float dtypes; other dtypes either do not
            have a sentinel missing value (int) or skipna=True has not been
            implemented (object, datetime64 or timedelta64).

        Returns
        -------
        result : DataArray or dict of DataArray

        See Also
        --------
        Variable.argmin, DataArray.idxmin

        Examples
        --------
        >>> array = xr.DataArray([0, 2, -1, 3], dims="x")
        >>> array.min()
        <xarray.DataArray ()>
        array(-1)
        >>> array.argmin()
        <xarray.DataArray ()>
        array(2)
        >>> array.argmin(...)
        {'x': <xarray.DataArray ()>
        array(2)}
        >>> array.isel(array.argmin(...))
        <xarray.DataArray ()>
        array(-1)

        >>> array = xr.DataArray(
        ...     [[[3, 2, 1], [3, 1, 2], [2, 1, 3]], [[1, 3, 2], [2, -5, 1], [2, 3, 1]]],
        ...     dims=("x", "y", "z"),
        ... )
        >>> array.min(dim="x")
        <xarray.DataArray (y: 3, z: 3)>
        array([[ 1,  2,  1],
               [ 2, -5,  1],
               [ 2,  1,  1]])
        Dimensions without coordinates: y, z
        >>> array.argmin(dim="x")
        <xarray.DataArray (y: 3, z: 3)>
        array([[1, 0, 0],
               [1, 1, 1],
               [0, 0, 1]])
        Dimensions without coordinates: y, z
        >>> array.argmin(dim=["x"])
        {'x': <xarray.DataArray (y: 3, z: 3)>
        array([[1, 0, 0],
               [1, 1, 1],
               [0, 0, 1]])
        Dimensions without coordinates: y, z}
        >>> array.min(dim=("x", "z"))
        <xarray.DataArray (y: 3)>
        array([ 1, -5,  1])
        Dimensions without coordinates: y
        >>> array.argmin(dim=["x", "z"])
        {'x': <xarray.DataArray (y: 3)>
        array([0, 1, 0])
        Dimensions without coordinates: y, 'z': <xarray.DataArray (y: 3)>
        array([2, 1, 1])
        Dimensions without coordinates: y}
        >>> array.isel(array.argmin(dim=["x", "z"]))
        <xarray.DataArray (y: 3)>
        array([ 1, -5,  1])
        Dimensions without coordinates: y
        """
        result = self.variable.argmin(dim, axis, keep_attrs, skipna)
        if isinstance(result, dict):
            return {k: self._replace_maybe_drop_dims(v) for k, v in result.items()}
        else:
            return self._replace_maybe_drop_dims(result)

    # change type of self and return to T_DataArray once
    # https://github.com/python/mypy/issues/12846 is resolved
    def argmax(
        self,
        dim: Hashable | Sequence[Hashable] = None,
        axis: int | None = None,
        keep_attrs: bool | None = None,
        skipna: bool | None = None,
    ) -> DataArray | dict[Hashable, DataArray]:
        """Index or indices of the maximum of the DataArray over one or more dimensions.

        If a sequence is passed to 'dim', then result returned as dict of DataArrays,
        which can be passed directly to isel(). If a single str is passed to 'dim' then
        returns a DataArray with dtype int.

        If there are multiple maxima, the indices of the first one found will be
        returned.

        Parameters
        ----------
        dim : Hashable, sequence of Hashable, None or ..., optional
            The dimensions over which to find the maximum. By default, finds maximum over
            all dimensions - for now returning an int for backward compatibility, but
            this is deprecated, in future will return a dict with indices for all
            dimensions; to return a dict with all dimensions now, pass '...'.
        axis : int or None, optional
            Axis over which to apply `argmax`. Only one of the 'dim' and 'axis' arguments
            can be supplied.
        keep_attrs : bool or None, optional
            If True, the attributes (`attrs`) will be copied from the original
            object to the new one. If False, the new object will be
            returned without attributes.
        skipna : bool or None, optional
            If True, skip missing values (as marked by NaN). By default, only
            skips missing values for float dtypes; other dtypes either do not
            have a sentinel missing value (int) or skipna=True has not been
            implemented (object, datetime64 or timedelta64).

        Returns
        -------
        result : DataArray or dict of DataArray

        See Also
        --------
        Variable.argmax, DataArray.idxmax

        Examples
        --------
        >>> array = xr.DataArray([0, 2, -1, 3], dims="x")
        >>> array.max()
        <xarray.DataArray ()>
        array(3)
        >>> array.argmax()
        <xarray.DataArray ()>
        array(3)
        >>> array.argmax(...)
        {'x': <xarray.DataArray ()>
        array(3)}
        >>> array.isel(array.argmax(...))
        <xarray.DataArray ()>
        array(3)

        >>> array = xr.DataArray(
        ...     [[[3, 2, 1], [3, 1, 2], [2, 1, 3]], [[1, 3, 2], [2, 5, 1], [2, 3, 1]]],
        ...     dims=("x", "y", "z"),
        ... )
        >>> array.max(dim="x")
        <xarray.DataArray (y: 3, z: 3)>
        array([[3, 3, 2],
               [3, 5, 2],
               [2, 3, 3]])
        Dimensions without coordinates: y, z
        >>> array.argmax(dim="x")
        <xarray.DataArray (y: 3, z: 3)>
        array([[0, 1, 1],
               [0, 1, 0],
               [0, 1, 0]])
        Dimensions without coordinates: y, z
        >>> array.argmax(dim=["x"])
        {'x': <xarray.DataArray (y: 3, z: 3)>
        array([[0, 1, 1],
               [0, 1, 0],
               [0, 1, 0]])
        Dimensions without coordinates: y, z}
        >>> array.max(dim=("x", "z"))
        <xarray.DataArray (y: 3)>
        array([3, 5, 3])
        Dimensions without coordinates: y
        >>> array.argmax(dim=["x", "z"])
        {'x': <xarray.DataArray (y: 3)>
        array([0, 1, 0])
        Dimensions without coordinates: y, 'z': <xarray.DataArray (y: 3)>
        array([0, 1, 2])
        Dimensions without coordinates: y}
        >>> array.isel(array.argmax(dim=["x", "z"]))
        <xarray.DataArray (y: 3)>
        array([3, 5, 3])
        Dimensions without coordinates: y
        """
        result = self.variable.argmax(dim, axis, keep_attrs, skipna)
        if isinstance(result, dict):
            return {k: self._replace_maybe_drop_dims(v) for k, v in result.items()}
        else:
            return self._replace_maybe_drop_dims(result)

    def query(
        self,
        queries: Mapping[Any, Any] | None = None,
        parser: QueryParserOptions = "pandas",
        engine: QueryEngineOptions = None,
        missing_dims: ErrorOptionsWithWarn = "raise",
        **queries_kwargs: Any,
    ) -> DataArray:
        """Return a new data array indexed along the specified
        dimension(s), where the indexers are given as strings containing
        Python expressions to be evaluated against the values in the array.

        Parameters
        ----------
        queries : dict-like or None, optional
            A dict-like with keys matching dimensions and values given by strings
            containing Python expressions to be evaluated against the data variables
            in the dataset. The expressions will be evaluated using the pandas
            eval() function, and can contain any valid Python expressions but cannot
            contain any Python statements.
        parser : {"pandas", "python"}, default: "pandas"
            The parser to use to construct the syntax tree from the expression.
            The default of 'pandas' parses code slightly different than standard
            Python. Alternatively, you can parse an expression using the 'python'
            parser to retain strict Python semantics.
        engine : {"python", "numexpr", None}, default: None
            The engine used to evaluate the expression. Supported engines are:

            - None: tries to use numexpr, falls back to python
            - "numexpr": evaluates expressions using numexpr
            - "python": performs operations as if you had eval’d in top level python

        missing_dims : {"raise", "warn", "ignore"}, default: "raise"
            What to do if dimensions that should be selected from are not present in the
            DataArray:

            - "raise": raise an exception
            - "warn": raise a warning, and ignore the missing dimensions
            - "ignore": ignore the missing dimensions

        **queries_kwargs : {dim: query, ...}, optional
            The keyword arguments form of ``queries``.
            One of queries or queries_kwargs must be provided.

        Returns
        -------
        obj : DataArray
            A new DataArray with the same contents as this dataset, indexed by
            the results of the appropriate queries.

        See Also
        --------
        DataArray.isel
        Dataset.query
        pandas.eval

        Examples
        --------
        >>> da = xr.DataArray(np.arange(0, 5, 1), dims="x", name="a")
        >>> da
        <xarray.DataArray 'a' (x: 5)>
        array([0, 1, 2, 3, 4])
        Dimensions without coordinates: x
        >>> da.query(x="a > 2")
        <xarray.DataArray 'a' (x: 2)>
        array([3, 4])
        Dimensions without coordinates: x
        """

        ds = self._to_dataset_whole(shallow_copy=True)
        ds = ds.query(
            queries=queries,
            parser=parser,
            engine=engine,
            missing_dims=missing_dims,
            **queries_kwargs,
        )
        return ds[self.name]

    def curvefit(
        self,
        coords: str | DataArray | Iterable[str | DataArray],
        func: Callable[..., Any],
        reduce_dims: Hashable | Iterable[Hashable] | None = None,
        skipna: bool = True,
        p0: dict[str, Any] | None = None,
        bounds: dict[str, Any] | None = None,
        param_names: Sequence[str] | None = None,
        kwargs: dict[str, Any] | None = None,
    ) -> Dataset:
        """
        Curve fitting optimization for arbitrary functions.

        Wraps `scipy.optimize.curve_fit` with `apply_ufunc`.

        Parameters
        ----------
        coords : Hashable, DataArray, or sequence of DataArray or Hashable
            Independent coordinate(s) over which to perform the curve fitting. Must share
            at least one dimension with the calling object. When fitting multi-dimensional
            functions, supply `coords` as a sequence in the same order as arguments in
            `func`. To fit along existing dimensions of the calling object, `coords` can
            also be specified as a str or sequence of strs.
        func : callable
            User specified function in the form `f(x, *params)` which returns a numpy
            array of length `len(x)`. `params` are the fittable parameters which are optimized
            by scipy curve_fit. `x` can also be specified as a sequence containing multiple
            coordinates, e.g. `f((x0, x1), *params)`.
        reduce_dims : Hashable or sequence of Hashable
            Additional dimension(s) over which to aggregate while fitting. For example,
            calling `ds.curvefit(coords='time', reduce_dims=['lat', 'lon'], ...)` will
            aggregate all lat and lon points and fit the specified function along the
            time dimension.
        skipna : bool, default: True
            Whether to skip missing values when fitting. Default is True.
        p0 : dict-like or None, optional
            Optional dictionary of parameter names to initial guesses passed to the
            `curve_fit` `p0` arg. If none or only some parameters are passed, the rest will
            be assigned initial values following the default scipy behavior.
        bounds : dict-like or None, optional
            Optional dictionary of parameter names to bounding values passed to the
            `curve_fit` `bounds` arg. If none or only some parameters are passed, the rest
            will be unbounded following the default scipy behavior.
        param_names : sequence of Hashable or None, optional
            Sequence of names for the fittable parameters of `func`. If not supplied,
            this will be automatically determined by arguments of `func`. `param_names`
            should be manually supplied when fitting a function that takes a variable
            number of parameters.
        **kwargs : optional
            Additional keyword arguments to passed to scipy curve_fit.

        Returns
        -------
        curvefit_results : Dataset
            A single dataset which contains:

            [var]_curvefit_coefficients
                The coefficients of the best fit.
            [var]_curvefit_covariance
                The covariance matrix of the coefficient estimates.

        See Also
        --------
        DataArray.polyfit
        scipy.optimize.curve_fit
        """
        return self._to_temp_dataset().curvefit(
            coords,
            func,
            reduce_dims=reduce_dims,
            skipna=skipna,
            p0=p0,
            bounds=bounds,
            param_names=param_names,
            kwargs=kwargs,
        )

    def drop_duplicates(
        self: T_DataArray,
        dim: Hashable | Iterable[Hashable],
        keep: Literal["first", "last", False] = "first",
    ) -> T_DataArray:
        """Returns a new DataArray with duplicate dimension values removed.

        Parameters
        ----------
        dim : dimension label or labels
            Pass `...` to drop duplicates along all dimensions.
        keep : {"first", "last", False}, default: "first"
            Determines which duplicates (if any) to keep.

            - ``"first"`` : Drop duplicates except for the first occurrence.
            - ``"last"`` : Drop duplicates except for the last occurrence.
            - False : Drop all duplicates.

        Returns
        -------
        DataArray

        See Also
        --------
        Dataset.drop_duplicates
        """
        deduplicated = self._to_temp_dataset().drop_duplicates(dim, keep=keep)
        return self._from_temp_dataset(deduplicated)

    def convert_calendar(
        self,
        calendar: str,
        dim: str = "time",
        align_on: str | None = None,
        missing: Any | None = None,
        use_cftime: bool | None = None,
    ) -> DataArray:
        """Convert the DataArray to another calendar.

        Only converts the individual timestamps, does not modify any data except
        in dropping invalid/surplus dates or inserting missing dates.

        If the source and target calendars are either no_leap, all_leap or a
        standard type, only the type of the time array is modified.
        When converting to a leap year from a non-leap year, the 29th of February
        is removed from the array. In the other direction the 29th of February
        will be missing in the output, unless `missing` is specified,
        in which case that value is inserted.

        For conversions involving `360_day` calendars, see Notes.

        This method is safe to use with sub-daily data as it doesn't touch the
        time part of the timestamps.

        Parameters
        ---------
        calendar : str
            The target calendar name.
        dim : str
            Name of the time coordinate.
        align_on : {None, 'date', 'year'}
            Must be specified when either source or target is a `360_day` calendar,
           ignored otherwise. See Notes.
        missing : Optional[any]
            By default, i.e. if the value is None, this method will simply attempt
            to convert the dates in the source calendar to the same dates in the
            target calendar, and drop any of those that are not possible to
            represent.  If a value is provided, a new time coordinate will be
            created in the target calendar with the same frequency as the original
            time coordinate; for any dates that are not present in the source, the
            data will be filled with this value.  Note that using this mode requires
            that the source data have an inferable frequency; for more information
            see :py:func:`xarray.infer_freq`.  For certain frequency, source, and
            target calendar combinations, this could result in many missing values, see notes.
        use_cftime : boolean, optional
            Whether to use cftime objects in the output, only used if `calendar`
            is one of {"proleptic_gregorian", "gregorian" or "standard"}.
            If True, the new time axis uses cftime objects.
            If None (default), it uses :py:class:`numpy.datetime64` values if the
            date range permits it, and :py:class:`cftime.datetime` objects if not.
            If False, it uses :py:class:`numpy.datetime64`  or fails.

        Returns
        -------
        DataArray
            Copy of the dataarray with the time coordinate converted to the
            target calendar. If 'missing' was None (default), invalid dates in
            the new calendar are dropped, but missing dates are not inserted.
            If `missing` was given, the new data is reindexed to have a time axis
            with the same frequency as the source, but in the new calendar; any
            missing datapoints are filled with `missing`.

        Notes
        -----
        Passing a value to `missing` is only usable if the source's time coordinate as an
        inferable frequencies (see :py:func:`~xarray.infer_freq`) and is only appropriate
        if the target coordinate, generated from this frequency, has dates equivalent to the
        source. It is usually **not** appropriate to use this mode with:

        - Period-end frequencies : 'A', 'Y', 'Q' or 'M', in opposition to 'AS' 'YS', 'QS' and 'MS'
        - Sub-monthly frequencies that do not divide a day evenly : 'W', 'nD' where `N != 1`
            or 'mH' where 24 % m != 0).

        If one of the source or target calendars is `"360_day"`, `align_on` must
        be specified and two options are offered.

        - "year"
            The dates are translated according to their relative position in the year,
            ignoring their original month and day information, meaning that the
            missing/surplus days are added/removed at regular intervals.

            From a `360_day` to a standard calendar, the output will be missing the
            following dates (day of year in parentheses):

            To a leap year:
                January 31st (31), March 31st (91), June 1st (153), July 31st (213),
                September 31st (275) and November 30th (335).
            To a non-leap year:
                February 6th (36), April 19th (109), July 2nd (183),
                September 12th (255), November 25th (329).

            From a standard calendar to a `"360_day"`, the following dates in the
            source array will be dropped:

            From a leap year:
                January 31st (31), April 1st (92), June 1st (153), August 1st (214),
                September 31st (275), December 1st (336)
            From a non-leap year:
                February 6th (37), April 20th (110), July 2nd (183),
                September 13th (256), November 25th (329)

            This option is best used on daily and subdaily data.

        - "date"
            The month/day information is conserved and invalid dates are dropped
            from the output. This means that when converting from a `"360_day"` to a
            standard calendar, all 31st (Jan, March, May, July, August, October and
            December) will be missing as there is no equivalent dates in the
            `"360_day"` calendar and the 29th (on non-leap years) and 30th of February
            will be dropped as there are no equivalent dates in a standard calendar.

            This option is best used with data on a frequency coarser than daily.
        """
        return convert_calendar(
            self,
            calendar,
            dim=dim,
            align_on=align_on,
            missing=missing,
            use_cftime=use_cftime,
        )

    def interp_calendar(
        self,
        target: pd.DatetimeIndex | CFTimeIndex | DataArray,
        dim: str = "time",
    ) -> DataArray:
        """Interpolates the DataArray to another calendar based on decimal year measure.

        Each timestamp in `source` and `target` are first converted to their decimal
        year equivalent then `source` is interpolated on the target coordinate.
        The decimal year of a timestamp is its year plus its sub-year component
        converted to the fraction of its year. For example "2000-03-01 12:00" is
        2000.1653 in a standard calendar or 2000.16301 in a `"noleap"` calendar.

        This method should only be used when the time (HH:MM:SS) information of
        time coordinate is not important.

        Parameters
        ----------
        target: DataArray or DatetimeIndex or CFTimeIndex
            The target time coordinate of a valid dtype
            (np.datetime64 or cftime objects)
        dim : str
            The time coordinate name.

        Return
        ------
        DataArray
            The source interpolated on the decimal years of target,
        """
        return interp_calendar(self, target, dim=dim)

    def groupby(
        self,
        group: Hashable | DataArray | IndexVariable,
        squeeze: bool = True,
        restore_coord_dims: bool = False,
    ) -> DataArrayGroupBy:
        """Returns a DataArrayGroupBy object for performing grouped operations.

        Parameters
        ----------
        group : Hashable, DataArray or IndexVariable
            Array whose unique values should be used to group this array. If a
            Hashable, must be the name of a coordinate contained in this dataarray.
        squeeze : bool, default: True
            If "group" is a dimension of any arrays in this dataset, `squeeze`
            controls whether the subarrays have a dimension of length 1 along
            that dimension or if the dimension is squeezed out.
        restore_coord_dims : bool, default: False
            If True, also restore the dimension order of multi-dimensional
            coordinates.

        Returns
        -------
        grouped : DataArrayGroupBy
            A `DataArrayGroupBy` object patterned after `pandas.GroupBy` that can be
            iterated over in the form of `(unique_value, grouped_array)` pairs.

        Examples
        --------
        Calculate daily anomalies for daily data:

        >>> da = xr.DataArray(
        ...     np.linspace(0, 1826, num=1827),
        ...     coords=[pd.date_range("1/1/2000", "31/12/2004", freq="D")],
        ...     dims="time",
        ... )
        >>> da
        <xarray.DataArray (time: 1827)>
        array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03,
               1.826e+03])
        Coordinates:
          * time     (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31
        >>> da.groupby("time.dayofyear") - da.groupby("time.dayofyear").mean("time")
        <xarray.DataArray (time: 1827)>
        array([-730.8, -730.8, -730.8, ...,  730.2,  730.2,  730.5])
        Coordinates:
          * time       (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31
            dayofyear  (time) int64 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365 366

        See Also
        --------
        DataArray.groupby_bins
        Dataset.groupby
        core.groupby.DataArrayGroupBy
        pandas.DataFrame.groupby
        """
        from .groupby import DataArrayGroupBy

        # While we don't generally check the type of every arg, passing
        # multiple dimensions as multiple arguments is common enough, and the
        # consequences hidden enough (strings evaluate as true) to warrant
        # checking here.
        # A future version could make squeeze kwarg only, but would face
        # backward-compat issues.
        if not isinstance(squeeze, bool):
            raise TypeError(
                f"`squeeze` must be True or False, but {squeeze} was supplied"
            )

        return DataArrayGroupBy(
            self, group, squeeze=squeeze, restore_coord_dims=restore_coord_dims
        )

    def groupby_bins(
        self,
        group: Hashable | DataArray | IndexVariable,
        bins: ArrayLike,
        right: bool = True,
        labels: ArrayLike | Literal[False] | None = None,
        precision: int = 3,
        include_lowest: bool = False,
        squeeze: bool = True,
        restore_coord_dims: bool = False,
    ) -> DataArrayGroupBy:
        """Returns a DataArrayGroupBy object for performing grouped operations.

        Rather than using all unique values of `group`, the values are discretized
        first by applying `pandas.cut` [1]_ to `group`.

        Parameters
        ----------
        group : Hashable, DataArray or IndexVariable
            Array whose binned values should be used to group this array. If a
            Hashable, must be the name of a coordinate contained in this dataarray.
        bins : int or array-like
            If bins is an int, it defines the number of equal-width bins in the
            range of x. However, in this case, the range of x is extended by .1%
            on each side to include the min or max values of x. If bins is a
            sequence it defines the bin edges allowing for non-uniform bin
            width. No extension of the range of x is done in this case.
        right : bool, default: True
            Indicates whether the bins include the rightmost edge or not. If
            right == True (the default), then the bins [1,2,3,4] indicate
            (1,2], (2,3], (3,4].
        labels : array-like, False or None, default: None
            Used as labels for the resulting bins. Must be of the same length as
            the resulting bins. If False, string bin labels are assigned by
            `pandas.cut`.
        precision : int, default: 3
            The precision at which to store and display the bins labels.
        include_lowest : bool, default: False
            Whether the first interval should be left-inclusive or not.
        squeeze : bool, default: True
            If "group" is a dimension of any arrays in this dataset, `squeeze`
            controls whether the subarrays have a dimension of length 1 along
            that dimension or if the dimension is squeezed out.
        restore_coord_dims : bool, default: False
            If True, also restore the dimension order of multi-dimensional
            coordinates.

        Returns
        -------
        grouped : DataArrayGroupBy
            A `DataArrayGroupBy` object patterned after `pandas.GroupBy` that can be
            iterated over in the form of `(unique_value, grouped_array)` pairs.
            The name of the group has the added suffix `_bins` in order to
            distinguish it from the original variable.

        See Also
        --------
        DataArray.groupby
        Dataset.groupby_bins
        core.groupby.DataArrayGroupBy
        pandas.DataFrame.groupby

        References
        ----------
        .. [1] http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html
        """
        from .groupby import DataArrayGroupBy

        return DataArrayGroupBy(
            self,
            group,
            squeeze=squeeze,
            bins=bins,
            restore_coord_dims=restore_coord_dims,
            cut_kwargs={
                "right": right,
                "labels": labels,
                "precision": precision,
                "include_lowest": include_lowest,
            },
        )

    def weighted(self, weights: DataArray) -> DataArrayWeighted:
        """
        Weighted DataArray operations.

        Parameters
        ----------
        weights : DataArray
            An array of weights associated with the values in this Dataset.
            Each value in the data contributes to the reduction operation
            according to its associated weight.

        Notes
        -----
        ``weights`` must be a DataArray and cannot contain missing values.
        Missing values can be replaced by ``weights.fillna(0)``.

        Returns
        -------
        core.weighted.DataArrayWeighted

        See Also
        --------
        Dataset.weighted
        """
        from .weighted import DataArrayWeighted

        return DataArrayWeighted(self, weights)

    def rolling(
        self,
        dim: Mapping[Any, int] | None = None,
        min_periods: int | None = None,
        center: bool | Mapping[Any, bool] = False,
        **window_kwargs: int,
    ) -> DataArrayRolling:
        """
        Rolling window object for DataArrays.

        Parameters
        ----------
        dim : dict, optional
            Mapping from the dimension name to create the rolling iterator
            along (e.g. `time`) to its moving window size.
        min_periods : int or None, default: None
            Minimum number of observations in window required to have a value
            (otherwise result is NA). The default, None, is equivalent to
            setting min_periods equal to the size of the window.
        center : bool or Mapping to int, default: False
            Set the labels at the center of the window.
        **window_kwargs : optional
            The keyword arguments form of ``dim``.
            One of dim or window_kwargs must be provided.

        Returns
        -------
        core.rolling.DataArrayRolling

        Examples
        --------
        Create rolling seasonal average of monthly data e.g. DJF, JFM, ..., SON:

        >>> da = xr.DataArray(
        ...     np.linspace(0, 11, num=12),
        ...     coords=[
        ...         pd.date_range(
        ...             "1999-12-15",
        ...             periods=12,
        ...             freq=pd.DateOffset(months=1),
        ...         )
        ...     ],
        ...     dims="time",
        ... )
        >>> da
        <xarray.DataArray (time: 12)>
        array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 2000-01-15 ... 2000-11-15
        >>> da.rolling(time=3, center=True).mean()
        <xarray.DataArray (time: 12)>
        array([nan,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., nan])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 2000-01-15 ... 2000-11-15

        Remove the NaNs using ``dropna()``:

        >>> da.rolling(time=3, center=True).mean().dropna("time")
        <xarray.DataArray (time: 10)>
        array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])
        Coordinates:
          * time     (time) datetime64[ns] 2000-01-15 2000-02-15 ... 2000-10-15

        See Also
        --------
        core.rolling.DataArrayRolling
        Dataset.rolling
        """
        from .rolling import DataArrayRolling

        dim = either_dict_or_kwargs(dim, window_kwargs, "rolling")
        return DataArrayRolling(self, dim, min_periods=min_periods, center=center)

    def coarsen(
        self,
        dim: Mapping[Any, int] | None = None,
        boundary: CoarsenBoundaryOptions = "exact",
        side: SideOptions | Mapping[Any, SideOptions] = "left",
        coord_func: str | Callable | Mapping[Any, str | Callable] = "mean",
        **window_kwargs: int,
    ) -> DataArrayCoarsen:
        """
        Coarsen object for DataArrays.

        Parameters
        ----------
        dim : mapping of hashable to int, optional
            Mapping from the dimension name to the window size.
        boundary : {"exact", "trim", "pad"}, default: "exact"
            If 'exact', a ValueError will be raised if dimension size is not a
            multiple of the window size. If 'trim', the excess entries are
            dropped. If 'pad', NA will be padded.
        side : {"left", "right"} or mapping of str to {"left", "right"}, default: "left"
        coord_func : str or mapping of hashable to str, default: "mean"
            function (name) that is applied to the coordinates,
            or a mapping from coordinate name to function (name).

        Returns
        -------
        core.rolling.DataArrayCoarsen

        Examples
        --------
        Coarsen the long time series by averaging over every four days.

        >>> da = xr.DataArray(
        ...     np.linspace(0, 364, num=364),
        ...     dims="time",
        ...     coords={"time": pd.date_range("1999-12-15", periods=364)},
        ... )
        >>> da  # +doctest: ELLIPSIS
        <xarray.DataArray (time: 364)>
        array([  0.        ,   1.00275482,   2.00550964,   3.00826446,
                 4.01101928,   5.0137741 ,   6.01652893,   7.01928375,
                 8.02203857,   9.02479339,  10.02754821,  11.03030303,
        ...
               356.98071625, 357.98347107, 358.9862259 , 359.98898072,
               360.99173554, 361.99449036, 362.99724518, 364.        ])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-12-12
        >>> da.coarsen(time=3, boundary="trim").mean()  # +doctest: ELLIPSIS
        <xarray.DataArray (time: 121)>
        array([  1.00275482,   4.01101928,   7.01928375,  10.02754821,
                13.03581267,  16.04407713,  19.0523416 ,  22.06060606,
                25.06887052,  28.07713499,  31.08539945,  34.09366391,
        ...
               349.96143251, 352.96969697, 355.97796143, 358.9862259 ,
               361.99449036])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-16 1999-12-19 ... 2000-12-10
        >>>

        See Also
        --------
        core.rolling.DataArrayCoarsen
        Dataset.coarsen
        """
        from .rolling import DataArrayCoarsen

        dim = either_dict_or_kwargs(dim, window_kwargs, "coarsen")
        return DataArrayCoarsen(
            self,
            dim,
            boundary=boundary,
            side=side,
            coord_func=coord_func,
        )

    def resample(
        self,
        indexer: Mapping[Any, str] | None = None,
        skipna: bool | None = None,
        closed: SideOptions | None = None,
        label: SideOptions | None = None,
        base: int = 0,
        keep_attrs: bool | None = None,
        loffset: datetime.timedelta | str | None = None,
        restore_coord_dims: bool | None = None,
        **indexer_kwargs: str,
    ) -> DataArrayResample:
        """Returns a Resample object for performing resampling operations.

        Handles both downsampling and upsampling. The resampled
        dimension must be a datetime-like coordinate. If any intervals
        contain no values from the original object, they will be given
        the value ``NaN``.

        Parameters
        ----------
        indexer : Mapping of Hashable to str, optional
            Mapping from the dimension name to resample frequency [1]_. The
            dimension must be datetime-like.
        skipna : bool, optional
            Whether to skip missing values when aggregating in downsampling.
        closed : {"left", "right"}, optional
            Side of each interval to treat as closed.
        label : {"left", "right"}, optional
            Side of each interval to use for labeling.
        base : int, default = 0
            For frequencies that evenly subdivide 1 day, the "origin" of the
            aggregated intervals. For example, for "24H" frequency, base could
            range from 0 through 23.
        loffset : timedelta or str, optional
            Offset used to adjust the resampled time labels. Some pandas date
            offset strings are supported.
        restore_coord_dims : bool, optional
            If True, also restore the dimension order of multi-dimensional
            coordinates.
        **indexer_kwargs : str
            The keyword arguments form of ``indexer``.
            One of indexer or indexer_kwargs must be provided.

        Returns
        -------
        resampled : core.resample.DataArrayResample
            This object resampled.

        Examples
        --------
        Downsample monthly time-series data to seasonal data:

        >>> da = xr.DataArray(
        ...     np.linspace(0, 11, num=12),
        ...     coords=[
        ...         pd.date_range(
        ...             "1999-12-15",
        ...             periods=12,
        ...             freq=pd.DateOffset(months=1),
        ...         )
        ...     ],
        ...     dims="time",
        ... )
        >>> da
        <xarray.DataArray (time: 12)>
        array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 2000-01-15 ... 2000-11-15
        >>> da.resample(time="QS-DEC").mean()
        <xarray.DataArray (time: 4)>
        array([ 1.,  4.,  7., 10.])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-01 2000-03-01 2000-06-01 2000-09-01

        Upsample monthly time-series data to daily data:

        >>> da.resample(time="1D").interpolate("linear")  # +doctest: ELLIPSIS
        <xarray.DataArray (time: 337)>
        array([ 0.        ,  0.03225806,  0.06451613,  0.09677419,  0.12903226,
                0.16129032,  0.19354839,  0.22580645,  0.25806452,  0.29032258,
                0.32258065,  0.35483871,  0.38709677,  0.41935484,  0.4516129 ,
        ...
               10.80645161, 10.83870968, 10.87096774, 10.90322581, 10.93548387,
               10.96774194, 11.        ])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15

        Limit scope of upsampling method

        >>> da.resample(time="1D").nearest(tolerance="1D")
        <xarray.DataArray (time: 337)>
        array([ 0.,  0., nan, ..., nan, 11., 11.])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15

        See Also
        --------
        Dataset.resample
        pandas.Series.resample
        pandas.DataFrame.resample

        References
        ----------
        .. [1] http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
        """
        from .resample import DataArrayResample

        return self._resample(
            resample_cls=DataArrayResample,
            indexer=indexer,
            skipna=skipna,
            closed=closed,
            label=label,
            base=base,
            keep_attrs=keep_attrs,
            loffset=loffset,
            restore_coord_dims=restore_coord_dims,
            **indexer_kwargs,
        )

    # this needs to be at the end, or mypy will confuse with `str`
    # https://mypy.readthedocs.io/en/latest/common_issues.html#dealing-with-conflicting-names
    str = utils.UncachedAccessor(StringAccessor["DataArray"])