text: import datetime
  import functools
  import warnings
  from numbers import Number
  from typing import (
      TYPE_CHECKING,
      Any,
      Callable,
      Dict,
      Hashable,
      Iterable,
      List,
      Mapping,
      Optional,
      Sequence,
      Tuple,
      TypeVar,
      Union,
      cast,
  )
  
  import numpy as np
  import pandas as pd
  
  from ..plot.plot import _PlotMethods
  from . import (
      computation,
      dtypes,
      groupby,
      indexing,
      ops,
      pdcompat,
      resample,
      rolling,
      utils,
      weighted,
  )
  from .accessor_dt import CombinedDatetimelikeAccessor
  from .accessor_str import StringAccessor
  from .alignment import (
      _broadcast_helper,
      _get_broadcast_dims_map_common_coords,
      align,
      reindex_like_indexers,
  )
  from .common import AbstractArray, DataWithCoords
  from .coordinates import (
      DataArrayCoordinates,
      assert_coordinate_consistent,
      remap_label_indexers,
  )
  from .dataset import Dataset, split_indexes
  from .formatting import format_item
  from .indexes import Indexes, default_indexes, propagate_indexes
  from .indexing import is_fancy_indexer
  from .merge import PANDAS_TYPES, MergeError, _extract_indexes_from_coords
  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,
      assert_unique_multiindex_level_names,
  )
  
  if TYPE_CHECKING:
      T_DSorDA = TypeVar("T_DSorDA", "DataArray", Dataset)
  
      try:
          from dask.delayed import Delayed
      except ImportError:
          Delayed = None
      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
  
  
  def _infer_coords_and_dims(
      shape, coords, dims
  ) -> "Tuple[Dict[Any, 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(
              "coords is not dict-like, but it has %s items, "
              "which does not match the %s dimensions of the "
              "data" % (len(coords), len(shape))
          )
  
      if isinstance(dims, str):
          dims = (dims,)
  
      if dims is None:
          dims = ["dim_%s" % 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):
                  # deprecated in GH993, removed in GH1539
                  raise ValueError(
                      "inferring DataArray dimensions from "
                      "dictionary like ``coords`` is no longer "
                      "supported. Use an explicit list of "
                      "``dims`` instead."
                  )
              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 "
              "and dims: %s vs %s" % (len(shape), len(dims))
          )
      else:
          for d in dims:
              if not isinstance(d, str):
                  raise TypeError("dimension %s is not a string" % d)
  
      new_coords: Dict[Any, 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(
                  "coordinate %s has dimensions %s, but these "
                  "are not a subset of the DataArray "
                  "dimensions %s" % (k, v.dims, dims)
              )
  
          for d, s in zip(v.dims, v.shape):
              if s != sizes[d]:
                  raise ValueError(
                      "conflicting sizes for dimension %r: "
                      "length %s on the data but length %s on "
                      "coordinate %r" % (d, sizes[d], s, k)
                  )
  
          if k in sizes and v.shape != (sizes[k],):
              raise ValueError(
                  "coordinate %r is a DataArray dimension, but "
                  "it has shape %r rather than expected shape %r "
                  "matching the dimension size" % (k, v.shape, (sizes[k],))
              )
  
      assert_unique_multiindex_level_names(new_coords)
  
      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))
  
          pos_indexers, _ = remap_label_indexers(self.data_array, key)
          self.data_array[pos_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):
      """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 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)
      >>> precipitation = 10 * np.random.rand(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: Optional[Callable[[], None]]
      _indexes: Optional[Dict[Hashable, pd.Index]]
      _name: Optional[Hashable]
      _variable: Variable
  
      __slots__ = (
          "_cache",
          "_coords",
          "_close",
          "_indexes",
          "_name",
          "_variable",
          "__weakref__",
      )
  
      _groupby_cls = groupby.DataArrayGroupBy
      _rolling_cls = rolling.DataArrayRolling
      _coarsen_cls = rolling.DataArrayCoarsen
      _resample_cls = resample.DataArrayResample
      _weighted_cls = weighted.DataArrayWeighted
  
      dt = utils.UncachedAccessor(CombinedDatetimelikeAccessor)
  
      def __init__(
          self,
          data: Any = dtypes.NA,
          coords: Union[Sequence[Tuple], Mapping[Hashable, Any], None] = None,
          dims: Union[Hashable, Sequence[Hashable], None] = None,
          name: Hashable = None,
          attrs: Mapping = None,
          # internal parameters
          indexes: Dict[Hashable, pd.Index] = None,
          fastpath: bool = False,
      ):
          if fastpath:
              variable = data
              assert dims is None
              assert attrs is None
          else:
              # 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]
                  elif isinstance(data, pdcompat.Panel):
                      coords = [data.items, data.major_axis, data.minor_axis]
  
              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 = dict(
                  _extract_indexes_from_coords(coords)
              )  # needed for to_dataset
  
          # 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
  
          self._close = None
  
      def _replace(
          self,
          variable: Variable = None,
          coords=None,
          name: Union[Hashable, None, Default] = _default,
          indexes=None,
      ) -> "DataArray":
          if variable is None:
              variable = self.variable
          if coords is None:
              coords = self._coords
          if name is _default:
              name = self.name
          return type(self)(variable, coords, name=name, fastpath=True, indexes=indexes)
  
      def _replace_maybe_drop_dims(
          self, variable: Variable, name: Union[Hashable, None, Default] = _default
      ) -> "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)
              }
              changed_dims = [
                  k for k in variable.dims if variable.sizes[k] != self.sizes[k]
              ]
              indexes = propagate_indexes(self._indexes, exclude=changed_dims)
          else:
              allowed_dims = set(variable.dims)
              coords = {
                  k: v for k, v in self._coords.items() if set(v.dims) <= allowed_dims
              }
              indexes = propagate_indexes(
                  self._indexes, exclude=(set(self.dims) - allowed_dims)
              )
          return self._replace(variable, coords, name, indexes=indexes)
  
      def _overwrite_indexes(self, indexes: Mapping[Hashable, Any]) -> "DataArray":
          if not len(indexes):
              return self
          coords = self._coords.copy()
          for name, idx in indexes.items():
              coords[name] = IndexVariable(name, idx)
          obj = self._replace(coords=coords)
  
          # switch from dimension to level names, if necessary
          dim_names: Dict[Any, str] = {}
          for dim, idx in indexes.items():
              if not isinstance(idx, pd.MultiIndex) and idx.name != dim:
                  dim_names[dim] = idx.name
          if dim_names:
              obj = obj.rename(dim_names)
          return obj
  
      def _to_temp_dataset(self) -> Dataset:
          return self._to_dataset_whole(name=_THIS_ARRAY, shallow_copy=False)
  
      def _from_temp_dataset(
          self, dataset: Dataset, name: Union[Hashable, None, Default] = _default
      ) -> "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})
          indexes = propagate_indexes(self._indexes, exclude=dim)
          coord_names = set(self._coords) - {dim}
          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)
          dataset = Dataset._construct_direct(variables, coord_names, indexes=indexes)
          return dataset
  
      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) -> Optional[Hashable]:
          """The name of this array."""
          return self._name
  
      @name.setter
      def name(self, value: Optional[Hashable]) -> 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 array's data as a dask or numpy array"""
          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"""
          return self.variable.values
  
      @values.setter
      def values(self, value: Any) -> None:
          self.variable.values = value
  
      @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.
          """
          return self.variable.dims
  
      @dims.setter
      def dims(self, value):
          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
          else:
              key = indexing.expanded_indexer(key, self.ndim)
              return dict(zip(self.dims, key))
  
      @property
      def _level_coords(self) -> Dict[Hashable, Hashable]:
          """Return a mapping of all MultiIndex levels and their corresponding
          coordinate name.
          """
          level_coords: Dict[Hashable, Hashable] = {}
  
          for cname, var in self._coords.items():
              if var.ndim == 1 and isinstance(var, IndexVariable):
                  level_names = var.level_names
                  if level_names is not None:
                      (dim,) = var.dims
                      level_coords.update({lname: dim for lname in level_names})
          return level_coords
  
      def _getitem_coord(self, key):
          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, self._level_coords, dim_sizes
              )
  
          return self._replace_maybe_drop_dims(var, name=key)
  
      def __getitem__(self, key: Any) -> "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={})
          yield HybridMappingProxy(keys=self._level_coords, 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
      def attrs(self) -> Dict[Hashable, Any]:
          """Dictionary storing arbitrary metadata with this array."""
          return self.variable.attrs
  
      @attrs.setter
      def attrs(self, value: Mapping[Hashable, 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[Hashable, Any]) -> None:
          self.variable.encoding = value
  
      @property
      def indexes(self) -> Indexes:
          """Mapping of pandas.Index objects used for label based indexing"""
          if self._indexes is None:
              self._indexes = default_indexes(self._coords, self.dims)
          return Indexes(self._indexes)
  
      @property
      def coords(self) -> DataArrayCoordinates:
          """Dictionary-like container of coordinate arrays."""
          return DataArrayCoordinates(self)
  
      def reset_coords(
          self,
          names: Union[Iterable[Hashable], Hashable, None] = None,
          drop: bool = False,
      ) -> Union[None, "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, optional
              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.dims)
          dataset = self.coords.to_dataset().reset_coords(names, drop)
          if drop:
              return self._replace(coords=dataset._variables)
          else:
              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):
          ds = func(results, *args, **kwargs)
          variable = ds._variables.pop(_THIS_ARRAY)
          coords = ds._variables
          return DataArray(variable, coords, name=name, fastpath=True)
  
      def load(self, **kwargs) -> "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, **kwargs) -> "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, **kwargs) -> "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, deep: bool = True, data: Any = None) -> "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
          -------
          object : 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)
          coords = {k: v.copy(deep=deep) for k, v in self._coords.items()}
          if self._indexes is None:
              indexes = self._indexes
          else:
              indexes = {k: v.copy(deep=deep) for k, v in self._indexes.items()}
          return self._replace(variable, coords, indexes=indexes)
  
      def __copy__(self) -> "DataArray":
          return self.copy(deep=False)
  
      def __deepcopy__(self, memo=None) -> "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) -> Optional[Tuple[Tuple[int, ...], ...]]:
          """Block dimensions for this array's data or None if it's not a dask
          array.
          """
          return self.variable.chunks
  
      def chunk(
          self,
          chunks: Union[
              Number,
              Tuple[Number, ...],
              Tuple[Tuple[Number, ...], ...],
              Mapping[Hashable, Union[None, Number, Tuple[Number, ...]]],
          ] = {},  # {} even though it's technically unsafe, is being used intentionally here (#4667)
          name_prefix: str = "xarray-",
          token: str = None,
          lock: bool = False,
      ) -> "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, tuple of int or mapping of hashable to int, optional
              Chunk sizes along each dimension, e.g., ``5``, ``(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.
  
          Returns
          -------
          chunked : xarray.DataArray
          """
          if isinstance(chunks, (tuple, list)):
              chunks = dict(zip(self.dims, chunks))
  
          ds = self._to_temp_dataset().chunk(
              chunks, name_prefix=name_prefix, token=token, lock=lock
          )
          return self._from_temp_dataset(ds)
  
      def isel(
          self,
          indexers: Mapping[Hashable, Any] = None,
          drop: bool = False,
          missing_dims: str = "raise",
          **indexers_kwargs: Any,
      ) -> "DataArray":
          """Return a new DataArray whose data is given by integer indexing
          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, optional
              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
              - "warning": raise a warning, and ignore the missing dimensions
              - "ignore": ignore the missing dimensions
          **indexers_kwargs : {dim: indexer, ...}, optional
              The keyword arguments form of ``indexers``.
  
          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)
  
          coords = {}
          for coord_name, coord_value in self._coords.items():
              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)
  
      def sel(
          self,
          indexers: Mapping[Hashable, Any] = None,
          method: str = None,
          tolerance=None,
          drop: bool = False,
          **indexers_kwargs: Any,
      ) -> "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,
          indexers: Union[Mapping[Hashable, int], int] = None,
          **indexers_kwargs: Any,
      ) -> "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,
          indexers: Union[Mapping[Hashable, int], int] = None,
          **indexers_kwargs: Any,
      ) -> "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,
          indexers: Union[Mapping[Hashable, int], int] = None,
          **indexers_kwargs: Any,
      ) -> "DataArray":
          """Return a new DataArray whose data is given by each `n` value
          along the specified dimension(s).
  
          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, other: Union["DataArray", Dataset], exclude: Iterable[Hashable] = None
      ) -> "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(args[1], exclude, dims_map, common_coords)
  
      def reindex_like(
          self,
          other: Union["DataArray", Dataset],
          method: str = None,
          tolerance=None,
          copy: bool = True,
          fill_value=dtypes.NA,
      ) -> "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``.
          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.
          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
          """
          indexers = reindex_like_indexers(self, other)
          return self.reindex(
              indexers=indexers,
              method=method,
              tolerance=tolerance,
              copy=copy,
              fill_value=fill_value,
          )
  
      def reindex(
          self,
          indexers: Mapping[Hashable, Any] = None,
          method: str = None,
          tolerance=None,
          copy: bool = True,
          fill_value=dtypes.NA,
          **indexers_kwargs: Any,
      ) -> "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 : 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``.
          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 = either_dict_or_kwargs(indexers, indexers_kwargs, "reindex")
          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().reindex(
              indexers=indexers,
              method=method,
              tolerance=tolerance,
              copy=copy,
              fill_value=fill_value,
          )
          return self._from_temp_dataset(ds)
  
      def interp(
          self,
          coords: Mapping[Hashable, Any] = None,
          method: str = "linear",
          assume_sorted: bool = False,
          kwargs: Mapping[str, Any] = None,
          **coords_kwargs: Any,
      ) -> "DataArray":
          """Multidimensional interpolation of variables.
  
          Parameters
          ----------
          coords : dict, optional
              Mapping from dimension names to the new coordinates.
              New coordinate can be an scalar, array-like or DataArray.
              If DataArrays are passed as new coordinates, their dimensions are
              used for the broadcasting. Missing values are skipped.
          method : str, default: "linear"
              The method used to interpolate. Choose from
  
              - {"linear", "nearest"} for multidimensional array,
              - {"linear", "nearest", "zero", "slinear", "quadratic", "cubic"} for 1-dimensional array.
          assume_sorted : bool, optional
              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
              Additional keyword arguments passed to scipy's interpolator. Valid
              options and their behavior depend on if 1-dimensional or
              multi-dimensional interpolation 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,
          other: Union["DataArray", Dataset],
          method: str = "linear",
          assume_sorted: bool = False,
          kwargs: Mapping[str, Any] = None,
      ) -> "DataArray":
          """Interpolate this object onto the coordinates of another object,
          filling out of range values with NaN.
  
          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 : str, default: "linear"
              The method used to interpolate. Choose from
  
              - {"linear", "nearest"} for multidimensional array,
              - {"linear", "nearest", "zero", "slinear", "quadratic", "cubic"} for 1-dimensional array.
          assume_sorted : bool, optional
              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)
  
      def rename(
          self,
          new_name_or_name_dict: Union[Hashable, Mapping[Hashable, Hashable]] = 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, dims_dict: Mapping[Hashable, Hashable] = None, **dims_kwargs
      ) -> "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)
  
      def expand_dims(
          self,
          dim: Union[None, Hashable, Sequence[Hashable], Mapping[Hashable, Any]] = None,
          axis=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, list of int or tuple of int, or None, default: None
              Axis position(s) where new axis is to be inserted (position(s) on
              the result array). If a list (or tuple) 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 : same type as caller
              This object, but with an 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)
  
      def set_index(
          self,
          indexes: Mapping[Hashable, Union[Hashable, Sequence[Hashable]]] = None,
          append: bool = False,
          **indexes_kwargs: Union[Hashable, Sequence[Hashable]],
      ) -> Optional["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, optional
              If True, append the supplied index(es) to the existing index(es).
              Otherwise replace the existing index(es) (default).
          **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)
  
      def reset_index(
          self,
          dims_or_levels: Union[Hashable, Sequence[Hashable]],
          drop: bool = False,
      ) -> Optional["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, optional
              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
          """
          coords, _ = split_indexes(
              dims_or_levels, self._coords, set(), self._level_coords, drop=drop
          )
          return self._replace(coords=coords)
  
      def reorder_levels(
          self,
          dim_order: Mapping[Hashable, Sequence[int]] = None,
          **dim_order_kwargs: Sequence[int],
      ) -> "DataArray":
          """Rearrange index levels using input order.
  
          Parameters
          ----------
          dim_order : 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.
          """
          dim_order = either_dict_or_kwargs(dim_order, dim_order_kwargs, "reorder_levels")
          replace_coords = {}
          for dim, order in dim_order.items():
              coord = self._coords[dim]
              index = coord.to_index()
              if not isinstance(index, pd.MultiIndex):
                  raise ValueError("coordinate %r has no MultiIndex" % dim)
              replace_coords[dim] = IndexVariable(coord.dims, index.reorder_levels(order))
          coords = self._coords.copy()
          coords.update(replace_coords)
          return self._replace(coords=coords)
  
      def stack(
          self,
          dimensions: Mapping[Hashable, Sequence[Hashable]] = None,
          **dimensions_kwargs: Sequence[Hashable],
      ) -> "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.
          **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)],
                     names=['x', 'y'])
  
          See Also
          --------
          DataArray.unstack
          """
          ds = self._to_temp_dataset().stack(dimensions, **dimensions_kwargs)
          return self._from_temp_dataset(ds)
  
      def unstack(
          self,
          dim: Union[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)],
                     names=['x', 'y'])
          >>> 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, level=0):
          """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 : str
              Name of existing dimension to unstack
          level : int or str
              The MultiIndex level to expand to a dataset along. Can either be
              the integer index of the level or its name.
          label : int, default: 0
              Label of the level to expand dataset along. Overrides the label
              argument if given.
  
          Returns
          -------
          unstacked: Dataset
  
          Examples
          --------
          >>> import xarray as xr
          >>> 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)],
                     names=['variable', 'y'])
          >>> roundtripped = stacked.to_unstacked_dataset(dim="z")
          >>> data.identical(roundtripped)
          True
  
          See Also
          --------
          Dataset.to_stacked_array
          """
  
          idx = self.indexes[dim]
          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,
          *dims: Hashable,
          transpose_coords: bool = True,
          missing_dims: str = "raise",
      ) -> "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
              - "warning": 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) -> "DataArray":
          return self.transpose()
  
      def drop_vars(
          self, names: Union[Hashable, Iterable[Hashable]], *, errors: str = "raise"
      ) -> "DataArray":
          """Returns an array with dropped variables.
  
          Parameters
          ----------
          names : hashable or iterable of hashable
              Name(s) of variables to drop.
          errors : {"raise", "ignore"}, optional
              If 'raise' (default), 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,
          labels: Mapping = None,
          dim: Hashable = None,
          *,
          errors: str = "raise",
          **labels_kwargs,
      ) -> "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)
          return self._from_temp_dataset(ds)
  
      def drop_sel(
          self,
          labels: Mapping[Hashable, Any] = None,
          *,
          errors: str = "raise",
          **labels_kwargs,
      ) -> "DataArray":
          """Drop index labels from this DataArray.
  
          Parameters
          ----------
          labels : mapping of hashable to Any
              Index labels to drop
          errors : {"raise", "ignore"}, optional
              If 'raise' (default), 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, indexers=None, **indexers_kwargs):
          """Drop index positions from this DataArray.
  
          Parameters
          ----------
          indexers : mapping of hashable to Any
              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, dim: Hashable, how: str = "any", thresh: int = None
      ) -> "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"}, optional
              * any : if any NA values are present, drop that label
              * all : if all values are NA, drop that label
          thresh : int, default: None
              If supplied, require this many non-NA values.
  
          Returns
          -------
          DataArray
          """
          ds = self._to_temp_dataset().dropna(dim, how=how, thresh=thresh)
          return self._from_temp_dataset(ds)
  
      def fillna(self, value: Any) -> "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
          -------
          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,
          dim: Hashable = None,
          method: str = "linear",
          limit: int = None,
          use_coordinate: Union[bool, str] = True,
          max_gap: Union[
              int, float, str, pd.Timedelta, np.timedelta64, datetime.timedelta
          ] = None,
          keep_attrs: bool = None,
          **kwargs: Any,
      ) -> "DataArray":
          """Fill in NaNs by interpolating according to different methods.
  
          Parameters
          ----------
          dim : str
              Specifies the dimension along which to interpolate.
          method : str, optional
              String indicating which method to use for interpolation:
  
              - 'linear': linear interpolation (Default). 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, 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, default: True
              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, dim: Hashable, limit: int = None) -> "DataArray":
          """Fill NaN values by propogating values forward
  
          *Requires bottleneck.*
  
          Parameters
          ----------
          dim : hashable
              Specifies the dimension along which to propagate values when
              filling.
          limit : int, 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.
  
          Returns
          -------
          DataArray
          """
          from .missing import ffill
  
          return ffill(self, dim, limit=limit)
  
      def bfill(self, dim: Hashable, limit: int = None) -> "DataArray":
          """Fill NaN values by propogating values backward
  
          *Requires bottleneck.*
  
          Parameters
          ----------
          dim : str
              Specifies the dimension along which to propagate values when
              filling.
          limit : int, 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.
  
          Returns
          -------
          DataArray
          """
          from .missing import bfill
  
          return bfill(self, dim, limit=limit)
  
      def combine_first(self, other: "DataArray") -> "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,
          func: Callable[..., Any],
          dim: Union[None, Hashable, Sequence[Hashable]] = None,
          axis: Union[None, int, Sequence[int]] = None,
          keep_attrs: bool = None,
          keepdims: bool = False,
          **kwargs: Any,
      ) -> "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 sequence 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, 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) -> Union["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.
          """
          # 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(
                  "cannot convert arrays with %s dimensions into "
                  "pandas objects" % self.ndim
              )
          indexes = [self.get_index(dim) for dim in self.dims]
          return constructor(self.values, *indexes)
  
      def to_dataframe(
          self, name: Hashable = None, dim_order: List[Hashable] = 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.
  
          Parameters
          ----------
          name
              Name to give to this array (required if unnamed).
          dim_order
              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
              DataArray as a pandas DataFrame.
  
          """
          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`).
          """
          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.values  # only compute lazy arrays once
          isnull = pd.isnull(values)
          return np.ma.MaskedArray(data=values, mask=isnull, copy=copy)
  
      def to_netcdf(self, *args, **kwargs) -> Union[bytes, "Delayed", None]:
          """Write DataArray contents to a netCDF file.
  
          All parameters are passed directly to :py:meth:`xarray.Dataset.to_netcdf`.
  
          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
  
          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 dataset.to_netcdf(*args, **kwargs)
  
      def to_dict(self, data: bool = True) -> dict:
          """
          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, optional
              Whether to include the actual data in the dictionary. When set to
              False, returns just the schema.
  
          See Also
          --------
          DataArray.from_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)
          return d
  
      @classmethod
      def from_dict(cls, d: dict) -> "DataArray":
          """
          Convert a dictionary into an xarray.DataArray
  
          Input dict can take several forms:
  
          .. code:: python
  
              d = {"dims": ("t"), "data": x}
  
              d = {
                  "coords": {"t": {"dims": "t", "data": t, "attrs": {"units": "s"}}},
                  "attrs": {"title": "air temperature"},
                  "dims": "t",
                  "data": x,
                  "name": "a",
              }
  
          where "t" is the name of the dimesion, "a" is the name of the array,
          and x and t are lists, numpy.arrays, or pandas objects.
  
          Parameters
          ----------
          d : dict
              Mapping with a minimum structure of {"dims": [...], "data": [...]}
  
          Returns
          -------
          obj : xarray.DataArray
  
          See Also
          --------
          DataArray.to_dict
          Dataset.from_dict
          """
          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"))
          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.
  
          If sparse=True, creates a sparse array instead of a dense NumPy array.
          Requires the pydata/sparse package.
  
          See Also
          --------
          xarray.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, other: "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, other: "DataArray") -> bool:
          """Two DataArrays are broadcast equal if they are equal after
          broadcasting them against each other such that they have the same
          dimensions.
  
          See Also
          --------
          DataArray.equals
          DataArray.identical
          """
          try:
              return self._all_compat(other, "broadcast_equals")
          except (TypeError, AttributeError):
              return False
  
      def equals(self, other: "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).
  
          See Also
          --------
          DataArray.broadcast_equals
          DataArray.identical
          """
          try:
              return self._all_compat(other, "equals")
          except (TypeError, AttributeError):
              return False
  
      def identical(self, other: "DataArray") -> bool:
          """Like equals, but also checks the array name and attributes, and
          attributes on all coordinates.
  
          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) -> Optional[Hashable]:
          # 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, obj, context=None) -> "DataArray":
          new_var = self.variable.__array_wrap__(obj, context)
          return self._replace(new_var)
  
      def __matmul__(self, obj):
          return self.dot(obj)
  
      def __rmatmul__(self, other):
          # currently somewhat duplicative, as only other DataArrays are
          # compatible with matmul
          return computation.dot(other, self)
  
      @staticmethod
      def _unary_op(f: Callable[..., Any]) -> Callable[..., "DataArray"]:
          @functools.wraps(f)
          def func(self, *args, **kwargs):
              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
  
          return func
  
      @staticmethod
      def _binary_op(
          f: Callable[..., Any],
          reflexive: bool = False,
          join: str = None,  # see xarray.align
          **ignored_kwargs,
      ) -> Callable[..., "DataArray"]:
          @functools.wraps(f)
          def func(self, other):
              if isinstance(other, (Dataset, groupby.GroupBy)):
                  return NotImplemented
              if isinstance(other, DataArray):
                  align_type = OPTIONS["arithmetic_join"] if join is None else join
                  self, other = align(self, other, join=align_type, copy=False)
              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)
              name = self._result_name(other)
  
              return self._replace(variable, coords, name, indexes=indexes)
  
          return func
  
      @staticmethod
      def _inplace_binary_op(f: Callable) -> Callable[..., "DataArray"]:
          @functools.wraps(f)
          def func(self, other):
              if isinstance(other, groupby.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
  
          return func
  
      def _copy_attrs_from(self, other: Union["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}".format(dim=dim, v=format_item(coord.values))
                  )
  
          title = ", ".join(one_dims)
          if len(title) > truncate:
              title = title[: (truncate - 3)] + "..."
  
          return title
  
      def diff(self, dim: Hashable, n: int = 1, label: Hashable = "upper") -> "DataArray":
          """Calculate the n-th order discrete difference along given axis.
  
          Parameters
          ----------
          dim : hashable
              Dimension over which to calculate the finite difference.
          n : int, optional
              The number of times values are differenced.
          label : hashable, optional
              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.  Other
              values are not supported.
  
          Returns
          -------
          difference : same type as caller
              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,
          shifts: Mapping[Hashable, int] = None,
          fill_value: Any = dtypes.NA,
          **shifts_kwargs: int,
      ) -> "DataArray":
          """Shift this array by an offset along one or more dimensions.
  
          Only the data is moved; coordinates stay in place. Values shifted from
          beyond array bounds are replaced by NaN. This is consistent with the
          behavior of ``shift`` in pandas.
  
          Parameters
          ----------
          shifts : mapping of hashable to int, 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,
          shifts: Mapping[Hashable, int] = None,
          roll_coords: bool = None,
          **shifts_kwargs: int,
      ) -> "DataArray":
          """Roll this array by an offset along one or more dimensions.
  
          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
              Indicates whether to roll the coordinates by the offset
              The current default of roll_coords (None, equivalent to True) is
              deprecated and will change to False in a future version.
              Explicitly pass roll_coords to silence the warning.
          **shifts_kwargs
              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) -> "DataArray":
          return self._replace(self.variable.real)
  
      @property
      def imag(self) -> "DataArray":
          return self._replace(self.variable.imag)
  
      def dot(
          self, other: "DataArray", dims: Union[Hashable, Sequence[Hashable], None] = None
      ) -> "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)
  
      def sortby(
          self,
          variables: Union[Hashable, "DataArray", Sequence[Union[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://docs.scipy.org/doc/numpy/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, optional
              Whether to sort by ascending or descending order.
  
          Returns
          -------
          sorted : DataArray
              A new dataarray where all the specified dims are sorted by dim
              labels.
  
          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,
          q: Any,
          dim: Union[Hashable, Sequence[Hashable], None] = None,
          interpolation: str = "linear",
          keep_attrs: bool = None,
          skipna: bool = True,
      ) -> "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 : hashable or sequence of hashable, optional
              Dimension(s) over which to apply quantile.
          interpolation : {"linear", "lower", "higher", "midpoint", "nearest"}, default: "linear"
              This optional parameter specifies the interpolation method to
              use when the desired quantile lies between two data points
              ``i < j``:
  
                  - linear: ``i + (j - i) * fraction``, where ``fraction`` is
                    the fractional part of the index surrounded by ``i`` and
                    ``j``.
                  - lower: ``i``.
                  - higher: ``j``.
                  - nearest: ``i`` or ``j``, whichever is nearest.
                  - midpoint: ``(i + j) / 2``.
          keep_attrs : bool, 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, optional
              Whether to skip missing values when aggregating.
  
          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
          """
  
          ds = self._to_temp_dataset().quantile(
              q,
              dim=dim,
              keep_attrs=keep_attrs,
              interpolation=interpolation,
              skipna=skipna,
          )
          return self._from_temp_dataset(ds)
  
      def rank(
          self, dim: Hashable, pct: bool = False, keep_attrs: bool = None
      ) -> "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, optional
              If True, compute percentage ranks, otherwise compute integer ranks.
          keep_attrs : bool, 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, coord: Hashable, edge_order: int = 1, datetime_unit: str = None
      ) -> "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"} or 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)
  
      def integrate(
          self,
          coord: Union[Hashable, Sequence[Hashable]] = None,
          datetime_unit: str = None,
          *,
          dim: Union[Hashable, Sequence[Hashable]] = 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.
          dim : 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'}, 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
          """
          if dim is not None and coord is not None:
              raise ValueError(
                  "Cannot pass both 'dim' and 'coord'. Please pass only 'coord' instead."
              )
  
          if dim is not None and coord is None:
              coord = dim
              msg = (
                  "The `dim` keyword argument to `DataArray.integrate` is "
                  "being replaced with `coord`, for consistency with "
                  "`Dataset.integrate`. Please pass `coord` instead."
                  " `dim` will be removed in version 0.19.0."
              )
              warnings.warn(msg, FutureWarning, stacklevel=2)
  
          ds = self._to_temp_dataset().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
          """
          ds = self._to_temp_dataset().unify_chunks()
          return self._from_temp_dataset(ds)
  
      def map_blocks(
          self,
          func: "Callable[..., T_DSorDA]",
          args: Sequence[Any] = (),
          kwargs: Mapping[str, Any] = None,
          template: Union["DataArray", "Dataset"] = None,
      ) -> "T_DSorDA":
          """
          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,
          rcond: float = None,
          w: Union[Hashable, Any] = None,
          full: bool = False,
          cov: bool = False,
      ):
          """
          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, 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, optional
              Relative condition number to the fit.
          w : hashable or array-like, 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, optional
              Whether to return the residuals, matrix rank and singular values in addition
              to the coefficients.
          cov : bool or str, optional
              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,
          pad_width: Mapping[Hashable, Union[int, Tuple[int, int]]] = None,
          mode: str = "constant",
          stat_length: Union[
              int, Tuple[int, int], Mapping[Hashable, Tuple[int, int]]
          ] = None,
          constant_values: Union[
              int, Tuple[int, int], Mapping[Hashable, Tuple[int, int]]
          ] = None,
          end_values: Union[
              int, Tuple[int, int], Mapping[Hashable, Tuple[int, int]]
          ] = None,
          reflect_type: str = None,
          **pad_width_kwargs: Any,
      ) -> "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 : str, default: "constant"
              One of the following string values (taken from numpy docs)
  
              'constant' (default)
                  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"}, 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``.
  
          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,
          skipna: bool = None,
          fill_value: Any = dtypes.NA,
          keep_attrs: bool = 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, default: False
              If True, the 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
          -------
          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,
          fill_value: Any = dtypes.NA,
          keep_attrs: bool = 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, default: False
              If True, the 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
          -------
          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,
          )
  
      def argmin(
          self,
          dim: Union[Hashable, Sequence[Hashable]] = None,
          axis: int = None,
          keep_attrs: bool = None,
          skipna: bool = None,
      ) -> Union["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 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, optional
              Axis over which to apply `argmin`. Only one of the 'dim' and 'axis' arguments
              can be supplied.
          keep_attrs : bool, optional
              If True, the 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, 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)
  
      def argmax(
          self,
          dim: Union[Hashable, Sequence[Hashable]] = None,
          axis: int = None,
          keep_attrs: bool = None,
          skipna: bool = None,
      ) -> Union["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 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, optional
              Axis over which to apply `argmax`. Only one of the 'dim' and 'axis' arguments
              can be supplied.
          keep_attrs : bool, optional
              If True, the 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, 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[Hashable, Any] = None,
          parser: str = "pandas",
          engine: str = None,
          missing_dims: str = "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, optional
              A dict 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
              Dataset:
              - "raise": raise an exception
              - "warning": 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
  
          """
  
          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: Union[Union[str, "DataArray"], Iterable[Union[str, "DataArray"]]],
          func: Callable[..., Any],
          reduce_dims: Union[Hashable, Iterable[Hashable]] = None,
          skipna: bool = True,
          p0: Dict[str, Any] = None,
          bounds: Dict[str, Any] = None,
          param_names: Sequence[str] = None,
          kwargs: Dict[str, Any] = None,
      ):
          """
          Curve fitting optimization for arbitrary functions.
  
          Wraps `scipy.optimize.curve_fit` with `apply_ufunc`.
  
          Parameters
          ----------
          coords : DataArray, str or sequence of DataArray, str
              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 : str or sequence of str
              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, optional
              Whether to skip missing values when fitting. Default is True.
          p0 : dictionary, 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 : dictionary, 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: seq, 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 : dictionary
              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,
          )
  
      # 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)
  
  
  # priority most be higher than Variable to properly work with binary ufuncs
  ops.inject_all_ops_and_reduce_methods(DataArray, priority=60)
  