code: import warnings
  from contextlib import suppress
  from html import escape
  from textwrap import dedent
  from typing import (
      Any,
      Callable,
      Dict,
      Hashable,
      Iterable,
      Iterator,
      List,
      Mapping,
      Tuple,
      TypeVar,
      Union,
  )
  
  import numpy as np
  import pandas as pd
  
  from . import dtypes, duck_array_ops, formatting, formatting_html, ops
  from .arithmetic import SupportsArithmetic
  from .npcompat import DTypeLike
  from .options import OPTIONS, _get_keep_attrs
  from .pycompat import dask_array_type
  from .rolling_exp import RollingExp
  from .utils import Frozen, either_dict_or_kwargs, is_scalar
  
  # Used as a sentinel value to indicate a all dimensions
  ALL_DIMS = ...
  
  
  C = TypeVar("C")
  T = TypeVar("T")
  
  
  class ImplementsArrayReduce:
      __slots__ = ()
  
      @classmethod
      def _reduce_method(cls, func: Callable, include_skipna: bool, numeric_only: bool):
          if include_skipna:
  
              def wrapped_func(self, dim=None, axis=None, skipna=None, **kwargs):
                  return self.reduce(func, dim, axis, skipna=skipna, **kwargs)
  
          else:
  
              def wrapped_func(self, dim=None, axis=None, **kwargs):  # type: ignore
                  return self.reduce(func, dim, axis, **kwargs)
  
          return wrapped_func
  
      _reduce_extra_args_docstring = dedent(
          """\
          dim : str or sequence of str, optional
              Dimension(s) over which to apply `{name}`.
          axis : int or sequence of int, optional
              Axis(es) over which to apply `{name}`. Only one of the 'dim'
              and 'axis' arguments can be supplied. If neither are supplied, then
              `{name}` is calculated over axes."""
      )
  
      _cum_extra_args_docstring = dedent(
          """\
          dim : str or sequence of str, optional
              Dimension over which to apply `{name}`.
          axis : int or sequence of int, optional
              Axis over which to apply `{name}`. Only one of the 'dim'
              and 'axis' arguments can be supplied."""
      )
  
  
  class ImplementsDatasetReduce:
      __slots__ = ()
  
      @classmethod
      def _reduce_method(cls, func: Callable, include_skipna: bool, numeric_only: bool):
          if include_skipna:
  
              def wrapped_func(self, dim=None, skipna=None, **kwargs):
                  return self.reduce(
                      func, dim, skipna=skipna, numeric_only=numeric_only, **kwargs
                  )
  
          else:
  
              def wrapped_func(self, dim=None, **kwargs):  # type: ignore
                  return self.reduce(func, dim, numeric_only=numeric_only, **kwargs)
  
          return wrapped_func
  
      _reduce_extra_args_docstring = dedent(
          """
          dim : str or sequence of str, optional
              Dimension(s) over which to apply `{name}`.  By default `{name}` is
              applied over all dimensions.
          """
      ).strip()
  
      _cum_extra_args_docstring = dedent(
          """
          dim : str or sequence of str, optional
              Dimension over which to apply `{name}`.
          axis : int or sequence of int, optional
              Axis over which to apply `{name}`. Only one of the 'dim'
              and 'axis' arguments can be supplied.
          """
      ).strip()
  
  
  class AbstractArray(ImplementsArrayReduce):
      """Shared base class for DataArray and Variable.
      """
  
      __slots__ = ()
  
      def __bool__(self: Any) -> bool:
          return bool(self.values)
  
      def __float__(self: Any) -> float:
          return float(self.values)
  
      def __int__(self: Any) -> int:
          return int(self.values)
  
      def __complex__(self: Any) -> complex:
          return complex(self.values)
  
      def __array__(self: Any, dtype: DTypeLike = None) -> np.ndarray:
          return np.asarray(self.values, dtype=dtype)
  
      def __repr__(self) -> str:
          return formatting.array_repr(self)
  
      def _repr_html_(self):
          if OPTIONS["display_style"] == "text":
              return f"<pre>{escape(repr(self))}</pre>"
          return formatting_html.array_repr(self)
  
      def _iter(self: Any) -> Iterator[Any]:
          for n in range(len(self)):
              yield self[n]
  
      def __iter__(self: Any) -> Iterator[Any]:
          if self.ndim == 0:
              raise TypeError("iteration over a 0-d array")
          return self._iter()
  
      def get_axis_num(
          self, dim: Union[Hashable, Iterable[Hashable]]
      ) -> Union[int, Tuple[int, ...]]:
          """Return axis number(s) corresponding to dimension(s) in this array.
  
          Parameters
          ----------
          dim : str or iterable of str
              Dimension name(s) for which to lookup axes.
  
          Returns
          -------
          int or tuple of int
              Axis number or numbers corresponding to the given dimensions.
          """
          if isinstance(dim, Iterable) and not isinstance(dim, str):
              return tuple(self._get_axis_num(d) for d in dim)
          else:
              return self._get_axis_num(dim)
  
      def _get_axis_num(self: Any, dim: Hashable) -> int:
          try:
              return self.dims.index(dim)
          except ValueError:
              raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
  
      @property
      def sizes(self: Any) -> Mapping[Hashable, int]:
          """Ordered mapping from dimension names to lengths.
  
          Immutable.
  
          See also
          --------
          Dataset.sizes
          """
          return Frozen(dict(zip(self.dims, self.shape)))
  
  
  class AttrAccessMixin:
      """Mixin class that allows getting keys with attribute access
      """
  
      __slots__ = ()
  
      def __init_subclass__(cls):
          """Verify that all subclasses explicitly define ``__slots__``. If they don't,
          raise error in the core xarray module and a FutureWarning in third-party
          extensions.
          """
          if not hasattr(object.__new__(cls), "__dict__"):
              pass
          elif cls.__module__.startswith("xarray."):
              raise AttributeError("%s must explicitly define __slots__" % cls.__name__)
          else:
              cls.__setattr__ = cls._setattr_dict
              warnings.warn(
                  "xarray subclass %s should explicitly define __slots__" % cls.__name__,
                  FutureWarning,
                  stacklevel=2,
              )
  
      @property
      def _attr_sources(self) -> List[Mapping[Hashable, Any]]:
          """List of places to look-up items for attribute-style access
          """
          return []
  
      @property
      def _item_sources(self) -> List[Mapping[Hashable, Any]]:
          """List of places to look-up items for key-autocompletion
          """
          return []
  
      def __getattr__(self, name: str) -> Any:
          if name not in {"__dict__", "__setstate__"}:
              # this avoids an infinite loop when pickle looks for the
              # __setstate__ attribute before the xarray object is initialized
              for source in self._attr_sources:
                  with suppress(KeyError):
                      return source[name]
          raise AttributeError(
              "{!r} object has no attribute {!r}".format(type(self).__name__, name)
          )
  
      # This complicated two-method design boosts overall performance of simple operations
      # - particularly DataArray methods that perform a _to_temp_dataset() round-trip - by
      # a whopping 8% compared to a single method that checks hasattr(self, "__dict__") at
      # runtime before every single assignment. All of this is just temporary until the
      # FutureWarning can be changed into a hard crash.
      def _setattr_dict(self, name: str, value: Any) -> None:
          """Deprecated third party subclass (see ``__init_subclass__`` above)
          """
          object.__setattr__(self, name, value)
          if name in self.__dict__:
              # Custom, non-slotted attr, or improperly assigned variable?
              warnings.warn(
                  "Setting attribute %r on a %r object. Explicitly define __slots__ "
                  "to suppress this warning for legitimate custom attributes and "
                  "raise an error when attempting variables assignments."
                  % (name, type(self).__name__),
                  FutureWarning,
                  stacklevel=2,
              )
  
      def __setattr__(self, name: str, value: Any) -> None:
          """Objects with ``__slots__`` raise AttributeError if you try setting an
          undeclared attribute. This is desirable, but the error message could use some
          improvement.
          """
          try:
              object.__setattr__(self, name, value)
          except AttributeError as e:
              # Don't accidentally shadow custom AttributeErrors, e.g.
              # DataArray.dims.setter
              if str(e) != "{!r} object has no attribute {!r}".format(
                  type(self).__name__, name
              ):
                  raise
              raise AttributeError(
                  "cannot set attribute %r on a %r object. Use __setitem__ style"
                  "assignment (e.g., `ds['name'] = ...`) instead of assigning variables."
                  % (name, type(self).__name__)
              ) from e
  
      def __dir__(self) -> List[str]:
          """Provide method name lookup and completion. Only provide 'public'
          methods.
          """
          extra_attrs = [
              item
              for sublist in self._attr_sources
              for item in sublist
              if isinstance(item, str)
          ]
          return sorted(set(dir(type(self)) + extra_attrs))
  
      def _ipython_key_completions_(self) -> List[str]:
          """Provide method for the key-autocompletions in IPython.
          See http://ipython.readthedocs.io/en/stable/config/integrating.html#tab-completion
          For the details.
          """
          item_lists = [
              item
              for sublist in self._item_sources
              for item in sublist
              if isinstance(item, str)
          ]
          return list(set(item_lists))
  
  
  def get_squeeze_dims(
      xarray_obj,
      dim: Union[Hashable, Iterable[Hashable], None] = None,
      axis: Union[int, Iterable[int], None] = None,
  ) -> List[Hashable]:
      """Get a list of dimensions to squeeze out.
      """
      if dim is not None and axis is not None:
          raise ValueError("cannot use both parameters `axis` and `dim`")
      if dim is None and axis is None:
          return [d for d, s in xarray_obj.sizes.items() if s == 1]
  
      if isinstance(dim, Iterable) and not isinstance(dim, str):
          dim = list(dim)
      elif dim is not None:
          dim = [dim]
      else:
          assert axis is not None
          if isinstance(axis, int):
              axis = [axis]
          axis = list(axis)
          if any(not isinstance(a, int) for a in axis):
              raise TypeError("parameter `axis` must be int or iterable of int.")
          alldims = list(xarray_obj.sizes.keys())
          dim = [alldims[a] for a in axis]
  
      if any(xarray_obj.sizes[k] > 1 for k in dim):
          raise ValueError(
              "cannot select a dimension to squeeze out "
              "which has length greater than one"
          )
      return dim
  
  
  class DataWithCoords(SupportsArithmetic, AttrAccessMixin):
      """Shared base class for Dataset and DataArray."""
  
      __slots__ = ()
  
      _rolling_exp_cls = RollingExp
  
      def squeeze(
          self,
          dim: Union[Hashable, Iterable[Hashable], None] = None,
          drop: bool = False,
          axis: Union[int, Iterable[int], None] = None,
      ):
          """Return a new object with squeezed data.
  
          Parameters
          ----------
          dim : None or Hashable or iterable of Hashable, optional
              Selects a subset of the length one dimensions. If a dimension is
              selected with length greater than one, an error is raised. If
              None, all length one dimensions are squeezed.
          drop : bool, optional
              If ``drop=True``, drop squeezed coordinates instead of making them
              scalar.
          axis : None or int or iterable of int, optional
              Like dim, but positional.
  
          Returns
          -------
          squeezed : same type as caller
              This object, but with with all or a subset of the dimensions of
              length 1 removed.
  
          See Also
          --------
          numpy.squeeze
          """
          dims = get_squeeze_dims(self, dim, axis)
          return self.isel(drop=drop, **{d: 0 for d in dims})
  
      def get_index(self, key: Hashable) -> pd.Index:
          """Get an index for a dimension, with fall-back to a default RangeIndex
          """
          if key not in self.dims:
              raise KeyError(key)
  
          try:
              return self.indexes[key]
          except KeyError:
              # need to ensure dtype=int64 in case range is empty on Python 2
              return pd.Index(range(self.sizes[key]), name=key, dtype=np.int64)
  
      def _calc_assign_results(
          self: C, kwargs: Mapping[Hashable, Union[T, Callable[[C], T]]]
      ) -> Dict[Hashable, T]:
          return {k: v(self) if callable(v) else v for k, v in kwargs.items()}
  
      def assign_coords(self, coords=None, **coords_kwargs):
          """Assign new coordinates to this object.
  
          Returns a new object with all the original data in addition to the new
          coordinates.
  
          Parameters
          ----------
          coords : dict, optional
              A dict where the keys are the names of the coordinates
              with the new values to assign. If the values are callable, they are
              computed on this object and assigned to new coordinate variables.
              If the values are not callable, (e.g. a ``DataArray``, scalar, or
              array), they are simply assigned. A new coordinate can also be
              defined and attached to an existing dimension using a tuple with
              the first element the dimension name and the second element the
              values for this new coordinate.
  
          **coords_kwargs : keyword, value pairs, optional
              The keyword arguments form of ``coords``.
              One of ``coords`` or ``coords_kwargs`` must be provided.
  
          Returns
          -------
          assigned : same type as caller
              A new object with the new coordinates in addition to the existing
              data.
  
          Examples
          --------
          Convert longitude coordinates from 0-359 to -180-179:
  
          >>> da = xr.DataArray(
          ...     np.random.rand(4), coords=[np.array([358, 359, 0, 1])], dims="lon",
          ... )
          >>> da
          <xarray.DataArray (lon: 4)>
          array([0.28298 , 0.667347, 0.657938, 0.177683])
          Coordinates:
            * lon      (lon) int64 358 359 0 1
          >>> da.assign_coords(lon=(((da.lon + 180) % 360) - 180))
          <xarray.DataArray (lon: 4)>
          array([0.28298 , 0.667347, 0.657938, 0.177683])
          Coordinates:
            * lon      (lon) int64 -2 -1 0 1
  
          The function also accepts dictionary arguments:
  
          >>> da.assign_coords({"lon": (((da.lon + 180) % 360) - 180)})
          <xarray.DataArray (lon: 4)>
          array([0.28298 , 0.667347, 0.657938, 0.177683])
          Coordinates:
            * lon      (lon) int64 -2 -1 0 1
  
          New coordinate can also be attached to an existing dimension:
  
          >>> lon_2 = np.array([300, 289, 0, 1])
          >>> da.assign_coords(lon_2=("lon", lon_2))
          <xarray.DataArray (lon: 4)>
          array([0.28298 , 0.667347, 0.657938, 0.177683])
          Coordinates:
            * lon      (lon) int64 358 359 0 1
              lon_2    (lon) int64 300 289 0 1
  
          Note that the same result can also be obtained with a dict e.g.
  
          >>> _ = da.assign_coords({"lon_2": ("lon", lon_2)})
  
          Notes
          -----
          Since ``coords_kwargs`` is a dictionary, the order of your arguments
          may not be preserved, and so the order of the new variables is not well
          defined. Assigning multiple variables within the same ``assign_coords``
          is possible, but you cannot reference other variables created within
          the same ``assign_coords`` call.
  
          See also
          --------
          Dataset.assign
          Dataset.swap_dims
          """
          coords_kwargs = either_dict_or_kwargs(coords, coords_kwargs, "assign_coords")
          data = self.copy(deep=False)
          results = self._calc_assign_results(coords_kwargs)
          data.coords.update(results)
          return data
  
      def assign_attrs(self, *args, **kwargs):
          """Assign new attrs to this object.
  
          Returns a new object equivalent to ``self.attrs.update(*args, **kwargs)``.
  
          Parameters
          ----------
          args : positional arguments passed into ``attrs.update``.
          kwargs : keyword arguments passed into ``attrs.update``.
  
          Returns
          -------
          assigned : same type as caller
              A new object with the new attrs in addition to the existing data.
  
          See also
          --------
          Dataset.assign
          """
          out = self.copy(deep=False)
          out.attrs.update(*args, **kwargs)
          return out
  
      def pipe(
          self,
          func: Union[Callable[..., T], Tuple[Callable[..., T], str]],
          *args,
          **kwargs,
      ) -> T:
          """
          Apply ``func(self, *args, **kwargs)``
  
          This method replicates the pandas method of the same name.
  
          Parameters
          ----------
          func : function
              function to apply to this xarray object (Dataset/DataArray).
              ``args``, and ``kwargs`` are passed into ``func``.
              Alternatively a ``(callable, data_keyword)`` tuple where
              ``data_keyword`` is a string indicating the keyword of
              ``callable`` that expects the xarray object.
          args : positional arguments passed into ``func``.
          kwargs : a dictionary of keyword arguments passed into ``func``.
  
          Returns
          -------
          object : the return type of ``func``.
  
          Notes
          -----
  
          Use ``.pipe`` when chaining together functions that expect
          xarray or pandas objects, e.g., instead of writing
  
          >>> f(g(h(ds), arg1=a), arg2=b, arg3=c)
  
          You can write
  
          >>> (ds.pipe(h).pipe(g, arg1=a).pipe(f, arg2=b, arg3=c))
  
          If you have a function that takes the data as (say) the second
          argument, pass a tuple indicating which keyword expects the
          data. For example, suppose ``f`` takes its data as ``arg2``:
  
          >>> (ds.pipe(h).pipe(g, arg1=a).pipe((f, "arg2"), arg1=a, arg3=c))
  
          Examples
          --------
  
          >>> import numpy as np
          >>> import xarray as xr
          >>> x = xr.Dataset(
          ...     {
          ...         "temperature_c": (
          ...             ("lat", "lon"),
          ...             20 * np.random.rand(4).reshape(2, 2),
          ...         ),
          ...         "precipitation": (("lat", "lon"), np.random.rand(4).reshape(2, 2)),
          ...     },
          ...     coords={"lat": [10, 20], "lon": [150, 160]},
          ... )
          >>> x
          <xarray.Dataset>
          Dimensions:        (lat: 2, lon: 2)
          Coordinates:
          * lat            (lat) int64 10 20
          * lon            (lon) int64 150 160
          Data variables:
              temperature_c  (lat, lon) float64 14.53 11.85 19.27 16.37
              precipitation  (lat, lon) float64 0.7315 0.7189 0.8481 0.4671
  
          >>> def adder(data, arg):
          ...     return data + arg
          ...
          >>> def div(data, arg):
          ...     return data / arg
          ...
          >>> def sub_mult(data, sub_arg, mult_arg):
          ...     return (data * mult_arg) - sub_arg
          ...
          >>> x.pipe(adder, 2)
          <xarray.Dataset>
          Dimensions:        (lat: 2, lon: 2)
          Coordinates:
          * lon            (lon) int64 150 160
          * lat            (lat) int64 10 20
          Data variables:
              temperature_c  (lat, lon) float64 16.53 13.85 21.27 18.37
              precipitation  (lat, lon) float64 2.731 2.719 2.848 2.467
  
          >>> x.pipe(adder, arg=2)
          <xarray.Dataset>
          Dimensions:        (lat: 2, lon: 2)
          Coordinates:
          * lon            (lon) int64 150 160
          * lat            (lat) int64 10 20
          Data variables:
              temperature_c  (lat, lon) float64 16.53 13.85 21.27 18.37
              precipitation  (lat, lon) float64 2.731 2.719 2.848 2.467
  
          >>> (
          ...     x.pipe(adder, arg=2)
          ...     .pipe(div, arg=2)
          ...     .pipe(sub_mult, sub_arg=2, mult_arg=2)
          ... )
          <xarray.Dataset>
          Dimensions:        (lat: 2, lon: 2)
          Coordinates:
          * lon            (lon) int64 150 160
          * lat            (lat) int64 10 20
          Data variables:
              temperature_c  (lat, lon) float64 14.53 11.85 19.27 16.37
              precipitation  (lat, lon) float64 0.7315 0.7189 0.8481 0.4671
  
          See Also
          --------
          pandas.DataFrame.pipe
          """
          if isinstance(func, tuple):
              func, target = func
              if target in kwargs:
                  raise ValueError(
                      "%s is both the pipe target and a keyword " "argument" % target
                  )
              kwargs[target] = self
              return func(*args, **kwargs)
          else:
              return func(self, *args, **kwargs)
  
      def groupby(self, group, squeeze: bool = True, restore_coord_dims: bool = None):
          """Returns a GroupBy object for performing grouped operations.
  
          Parameters
          ----------
          group : str, DataArray or IndexVariable
              Array whose unique values should be used to group this array. If a
              string, must be the name of a variable contained in this dataset.
          squeeze : boolean, optional
              If "group" is a dimension of any arrays in this dataset, `squeeze`
              controls whether the subarrays have a dimension of length 1 along
              that dimension or if the dimension is squeezed out.
          restore_coord_dims : bool, optional
              If True, also restore the dimension order of multi-dimensional
              coordinates.
  
          Returns
          -------
          grouped : GroupBy
              A `GroupBy` object patterned after `pandas.GroupBy` that can be
              iterated over in the form of `(unique_value, grouped_array)` pairs.
  
          Examples
          --------
          Calculate daily anomalies for daily data:
  
          >>> da = xr.DataArray(
          ...     np.linspace(0, 1826, num=1827),
          ...     coords=[pd.date_range("1/1/2000", "31/12/2004", freq="D")],
          ...     dims="time",
          ... )
          >>> da
          <xarray.DataArray (time: 1827)>
          array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03, 1.826e+03])
          Coordinates:
            * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ...
          >>> da.groupby("time.dayofyear") - da.groupby("time.dayofyear").mean("time")
          <xarray.DataArray (time: 1827)>
          array([-730.8, -730.8, -730.8, ...,  730.2,  730.2,  730.5])
          Coordinates:
            * time       (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ...
              dayofyear  (time) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
  
          See Also
          --------
          core.groupby.DataArrayGroupBy
          core.groupby.DatasetGroupBy
          """
          # While we don't generally check the type of every arg, passing
          # multiple dimensions as multiple arguments is common enough, and the
          # consequences hidden enough (strings evaluate as true) to warrant
          # checking here.
          # A future version could make squeeze kwarg only, but would face
          # backward-compat issues.
          if not isinstance(squeeze, bool):
              raise TypeError(
                  f"`squeeze` must be True or False, but {squeeze} was supplied"
              )
  
          return self._groupby_cls(
              self, group, squeeze=squeeze, restore_coord_dims=restore_coord_dims
          )
  
      def groupby_bins(
          self,
          group,
          bins,
          right: bool = True,
          labels=None,
          precision: int = 3,
          include_lowest: bool = False,
          squeeze: bool = True,
          restore_coord_dims: bool = None,
      ):
          """Returns a GroupBy object for performing grouped operations.
  
          Rather than using all unique values of `group`, the values are discretized
          first by applying `pandas.cut` [1]_ to `group`.
  
          Parameters
          ----------
          group : str, DataArray or IndexVariable
              Array whose binned values should be used to group this array. If a
              string, must be the name of a variable contained in this dataset.
          bins : int or array of scalars
              If bins is an int, it defines the number of equal-width bins in the
              range of x. However, in this case, the range of x is extended by .1%
              on each side to include the min or max values of x. If bins is a
              sequence it defines the bin edges allowing for non-uniform bin
              width. No extension of the range of x is done in this case.
          right : boolean, optional
              Indicates whether the bins include the rightmost edge or not. If
              right == True (the default), then the bins [1,2,3,4] indicate
              (1,2], (2,3], (3,4].
          labels : array or boolean, default None
              Used as labels for the resulting bins. Must be of the same length as
              the resulting bins. If False, string bin labels are assigned by
              `pandas.cut`.
          precision : int
              The precision at which to store and display the bins labels.
          include_lowest : bool
              Whether the first interval should be left-inclusive or not.
          squeeze : boolean, optional
              If "group" is a dimension of any arrays in this dataset, `squeeze`
              controls whether the subarrays have a dimension of length 1 along
              that dimension or if the dimension is squeezed out.
          restore_coord_dims : bool, optional
              If True, also restore the dimension order of multi-dimensional
              coordinates.
  
          Returns
          -------
          grouped : GroupBy
              A `GroupBy` object patterned after `pandas.GroupBy` that can be
              iterated over in the form of `(unique_value, grouped_array)` pairs.
              The name of the group has the added suffix `_bins` in order to
              distinguish it from the original variable.
  
          References
          ----------
          .. [1] http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html
          """
          return self._groupby_cls(
              self,
              group,
              squeeze=squeeze,
              bins=bins,
              restore_coord_dims=restore_coord_dims,
              cut_kwargs={
                  "right": right,
                  "labels": labels,
                  "precision": precision,
                  "include_lowest": include_lowest,
              },
          )
  
      def weighted(self, weights):
          """
          Weighted operations.
  
          Parameters
          ----------
          weights : DataArray
              An array of weights associated with the values in this Dataset.
              Each value in the data contributes to the reduction operation
              according to its associated weight.
  
          Notes
          -----
          ``weights`` must be a DataArray and cannot contain missing values.
          Missing values can be replaced by ``weights.fillna(0)``.
          """
  
          return self._weighted_cls(self, weights)
  
      def rolling(
          self,
          dim: Mapping[Hashable, int] = None,
          min_periods: int = None,
          center: bool = False,
          keep_attrs: bool = None,
          **window_kwargs: int,
      ):
          """
          Rolling window object.
  
          Parameters
          ----------
          dim: dict, optional
              Mapping from the dimension name to create the rolling iterator
              along (e.g. `time`) to its moving window size.
          min_periods : int, default None
              Minimum number of observations in window required to have a value
              (otherwise result is NA). The default, None, is equivalent to
              setting min_periods equal to the size of the window.
          center : boolean, default False
              Set the labels at the center of the window.
          keep_attrs : bool, optional
              If True, the object'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.
          **window_kwargs : optional
              The keyword arguments form of ``dim``.
              One of dim or window_kwargs must be provided.
  
          Returns
          -------
          Rolling object (core.rolling.DataArrayRolling for DataArray,
          core.rolling.DatasetRolling for Dataset.)
  
          Examples
          --------
          Create rolling seasonal average of monthly data e.g. DJF, JFM, ..., SON:
  
          >>> da = xr.DataArray(
          ...     np.linspace(0, 11, num=12),
          ...     coords=[
          ...         pd.date_range(
          ...             "15/12/1999", periods=12, freq=pd.DateOffset(months=1),
          ...         )
          ...     ],
          ...     dims="time",
          ... )
          >>> da
          <xarray.DataArray (time: 12)>
          array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7., 8.,   9.,  10.,  11.])
          Coordinates:
            * time     (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ...
          >>> da.rolling(time=3, center=True).mean()
          <xarray.DataArray (time: 12)>
          array([nan,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., nan])
          Coordinates:
            * time     (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ...
  
          Remove the NaNs using ``dropna()``:
  
          >>> da.rolling(time=3, center=True).mean().dropna("time")
          <xarray.DataArray (time: 10)>
          array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])
          Coordinates:
            * time     (time) datetime64[ns] 2000-01-15 2000-02-15 2000-03-15 ...
  
          See Also
          --------
          core.rolling.DataArrayRolling
          core.rolling.DatasetRolling
          """
          if keep_attrs is None:
              keep_attrs = _get_keep_attrs(default=False)
  
          dim = either_dict_or_kwargs(dim, window_kwargs, "rolling")
          return self._rolling_cls(
              self, dim, min_periods=min_periods, center=center, keep_attrs=keep_attrs
          )
  
      def rolling_exp(
          self,
          window: Mapping[Hashable, int] = None,
          window_type: str = "span",
          **window_kwargs,
      ):
          """
          Exponentially-weighted moving window.
          Similar to EWM in pandas
  
          Requires the optional Numbagg dependency.
  
          Parameters
          ----------
          window : A single mapping from a dimension name to window value,
                   optional
  
              dim : str
                  Name of the dimension to create the rolling exponential window
                  along (e.g., `time`).
              window : int
                  Size of the moving window. The type of this is specified in
                  `window_type`
          window_type : str, one of ['span', 'com', 'halflife', 'alpha'],
                        default 'span'
              The format of the previously supplied window. Each is a simple
              numerical transformation of the others. Described in detail:
              https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.ewm.html
          **window_kwargs : optional
              The keyword arguments form of ``window``.
              One of window or window_kwargs must be provided.
  
          See Also
          --------
          core.rolling_exp.RollingExp
          """
          window = either_dict_or_kwargs(window, window_kwargs, "rolling_exp")
  
          return self._rolling_exp_cls(self, window, window_type)
  
      def coarsen(
          self,
          dim: Mapping[Hashable, int] = None,
          boundary: str = "exact",
          side: Union[str, Mapping[Hashable, str]] = "left",
          coord_func: str = "mean",
          keep_attrs: bool = None,
          **window_kwargs: int,
      ):
          """
          Coarsen object.
  
          Parameters
          ----------
          dim: dict, optional
              Mapping from the dimension name to the window size.
  
              dim : str
                  Name of the dimension to create the rolling iterator
                  along (e.g., `time`).
              window : int
                  Size of the moving window.
          boundary : 'exact' | 'trim' | 'pad'
              If 'exact', a ValueError will be raised if dimension size is not a
              multiple of the window size. If 'trim', the excess entries are
              dropped. If 'pad', NA will be padded.
          side : 'left' or 'right' or mapping from dimension to 'left' or 'right'
          coord_func : function (name) that is applied to the coordinates,
              or a mapping from coordinate name to function (name).
          keep_attrs : bool, optional
              If True, the object'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
          -------
          Coarsen object (core.rolling.DataArrayCoarsen for DataArray,
          core.rolling.DatasetCoarsen for Dataset.)
  
          Examples
          --------
          Coarsen the long time series by averaging over every four days.
  
          >>> da = xr.DataArray(
          ...     np.linspace(0, 364, num=364),
          ...     dims="time",
          ...     coords={"time": pd.date_range("15/12/1999", periods=364)},
          ... )
          >>> da
          <xarray.DataArray (time: 364)>
          array([  0.      ,   1.002755,   2.00551 , ..., 361.99449 , 362.997245,
                 364.      ])
          Coordinates:
            * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-12-12
          >>>
          >>> da.coarsen(time=3, boundary="trim").mean()
          <xarray.DataArray (time: 121)>
          array([  1.002755,   4.011019,   7.019284,  ...,  358.986226,
                 361.99449 ])
          Coordinates:
            * time     (time) datetime64[ns] 1999-12-16 1999-12-19 ... 2000-12-10
          >>>
  
          See Also
          --------
          core.rolling.DataArrayCoarsen
          core.rolling.DatasetCoarsen
          """
          if keep_attrs is None:
              keep_attrs = _get_keep_attrs(default=False)
  
          dim = either_dict_or_kwargs(dim, window_kwargs, "coarsen")
          return self._coarsen_cls(
              self,
              dim,
              boundary=boundary,
              side=side,
              coord_func=coord_func,
              keep_attrs=keep_attrs,
          )
  
      def resample(
          self,
          indexer: Mapping[Hashable, str] = None,
          skipna=None,
          closed: str = None,
          label: str = None,
          base: int = 0,
          keep_attrs: bool = None,
          loffset=None,
          restore_coord_dims: bool = None,
          **indexer_kwargs: str,
      ):
          """Returns a Resample object for performing resampling operations.
  
          Handles both downsampling and upsampling. The resampled
          dimension must be a datetime-like coordinate. If any intervals
          contain no values from the original object, they will be given
          the value ``NaN``.
  
          Parameters
          ----------
          indexer : {dim: freq}, optional
              Mapping from the dimension name to resample frequency [1]_. The
              dimension must be datetime-like.
          skipna : bool, optional
              Whether to skip missing values when aggregating in downsampling.
          closed : 'left' or 'right', optional
              Side of each interval to treat as closed.
          label : 'left or 'right', optional
              Side of each interval to use for labeling.
          base : int, optional
              For frequencies that evenly subdivide 1 day, the "origin" of the
              aggregated intervals. For example, for '24H' frequency, base could
              range from 0 through 23.
          loffset : timedelta or str, optional
              Offset used to adjust the resampled time labels. Some pandas date
              offset strings are supported.
          keep_attrs : bool, optional
              If True, the object'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.
          restore_coord_dims : bool, optional
              If True, also restore the dimension order of multi-dimensional
              coordinates.
          **indexer_kwargs : {dim: freq}
              The keyword arguments form of ``indexer``.
              One of indexer or indexer_kwargs must be provided.
  
          Returns
          -------
          resampled : same type as caller
              This object resampled.
  
          Examples
          --------
          Downsample monthly time-series data to seasonal data:
  
          >>> da = xr.DataArray(
          ...     np.linspace(0, 11, num=12),
          ...     coords=[
          ...         pd.date_range(
          ...             "15/12/1999", periods=12, freq=pd.DateOffset(months=1),
          ...         )
          ...     ],
          ...     dims="time",
          ... )
          >>> da
          <xarray.DataArray (time: 12)>
          array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7., 8.,   9.,  10.,  11.])
          Coordinates:
            * time     (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ...
          >>> da.resample(time="QS-DEC").mean()
          <xarray.DataArray (time: 4)>
          array([ 1.,  4.,  7., 10.])
          Coordinates:
            * time     (time) datetime64[ns] 1999-12-01 2000-03-01 2000-06-01 2000-09-01
  
          Upsample monthly time-series data to daily data:
  
          >>> da.resample(time="1D").interpolate("linear")
          <xarray.DataArray (time: 337)>
          array([ 0.      ,  0.032258,  0.064516, ..., 10.935484, 10.967742, 11.      ])
          Coordinates:
            * time     (time) datetime64[ns] 1999-12-15 1999-12-16 1999-12-17 ...
  
          Limit scope of upsampling method
  
          >>> da.resample(time="1D").nearest(tolerance="1D")
          <xarray.DataArray (time: 337)>
          array([ 0.,  0., nan, ..., nan, 11., 11.])
          Coordinates:
            * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15
  
          See Also
          --------
          pandas.Series.resample
          pandas.DataFrame.resample
  
          References
          ----------
  
          .. [1] http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
          """
          # TODO support non-string indexer after removing the old API.
  
          from .dataarray import DataArray
          from .resample import RESAMPLE_DIM
          from ..coding.cftimeindex import CFTimeIndex
  
          if keep_attrs is None:
              keep_attrs = _get_keep_attrs(default=False)
  
          # note: the second argument (now 'skipna') use to be 'dim'
          if (
              (skipna is not None and not isinstance(skipna, bool))
              or ("how" in indexer_kwargs and "how" not in self.dims)
              or ("dim" in indexer_kwargs and "dim" not in self.dims)
          ):
              raise TypeError(
                  "resample() no longer supports the `how` or "
                  "`dim` arguments. Instead call methods on resample "
                  "objects, e.g., data.resample(time='1D').mean()"
              )
  
          indexer = either_dict_or_kwargs(indexer, indexer_kwargs, "resample")
          if len(indexer) != 1:
              raise ValueError("Resampling only supported along single dimensions.")
          dim, freq = next(iter(indexer.items()))
  
          dim_name = dim
          dim_coord = self[dim]
  
          if isinstance(self.indexes[dim_name], CFTimeIndex):
              from .resample_cftime import CFTimeGrouper
  
              grouper = CFTimeGrouper(freq, closed, label, base, loffset)
          else:
              grouper = pd.Grouper(
                  freq=freq, closed=closed, label=label, base=base, loffset=loffset
              )
          group = DataArray(
              dim_coord, coords=dim_coord.coords, dims=dim_coord.dims, name=RESAMPLE_DIM
          )
          resampler = self._resample_cls(
              self,
              group=group,
              dim=dim_name,
              grouper=grouper,
              resample_dim=RESAMPLE_DIM,
              restore_coord_dims=restore_coord_dims,
          )
  
          return resampler
  
      def where(self, cond, other=dtypes.NA, drop: bool = False):
          """Filter elements from this object according to a condition.
  
          This operation follows the normal broadcasting and alignment rules that
          xarray uses for binary arithmetic.
  
          Parameters
          ----------
          cond : DataArray or Dataset with boolean dtype
              Locations at which to preserve this object's values.
          other : scalar, DataArray or Dataset, optional
              Value to use for locations in this object where ``cond`` is False.
              By default, these locations filled with NA.
          drop : boolean, optional
              If True, coordinate labels that only correspond to False values of
              the condition are dropped from the result. Mutually exclusive with
              ``other``.
  
          Returns
          -------
          Same xarray type as caller, with dtype float64.
  
          Examples
          --------
  
          >>> import numpy as np
          >>> a = xr.DataArray(np.arange(25).reshape(5, 5), dims=("x", "y"))
          >>> a
          <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
  
          >>> a.where(a.x + a.y < 4)
          <xarray.DataArray (x: 5, y: 5)>
          array([[  0.,   1.,   2.,   3.,  nan],
                 [  5.,   6.,   7.,  nan,  nan],
                 [ 10.,  11.,  nan,  nan,  nan],
                 [ 15.,  nan,  nan,  nan,  nan],
                 [ nan,  nan,  nan,  nan,  nan]])
          Dimensions without coordinates: x, y
  
          >>> a.where(a.x + a.y < 5, -1)
          <xarray.DataArray (x: 5, y: 5)>
          array([[ 0,  1,  2,  3,  4],
                 [ 5,  6,  7,  8, -1],
                 [10, 11, 12, -1, -1],
                 [15, 16, -1, -1, -1],
                 [20, -1, -1, -1, -1]])
          Dimensions without coordinates: x, y
  
          >>> a.where(a.x + a.y < 4, drop=True)
          <xarray.DataArray (x: 4, y: 4)>
          array([[  0.,   1.,   2.,   3.],
                 [  5.,   6.,   7.,  nan],
                 [ 10.,  11.,  nan,  nan],
                 [ 15.,  nan,  nan,  nan]])
          Dimensions without coordinates: x, y
  
          >>> a.where(lambda x: x.x + x.y < 4, drop=True)
          <xarray.DataArray (x: 4, y: 4)>
          array([[  0.,   1.,   2.,   3.],
                 [  5.,   6.,   7.,  nan],
                 [ 10.,  11.,  nan,  nan],
                 [ 15.,  nan,  nan,  nan]])
          Dimensions without coordinates: x, y
  
          See also
          --------
          numpy.where : corresponding numpy function
          where : equivalent function
          """
          from .alignment import align
          from .dataarray import DataArray
          from .dataset import Dataset
  
          if callable(cond):
              cond = cond(self)
  
          if drop:
              if other is not dtypes.NA:
                  raise ValueError("cannot set `other` if drop=True")
  
              if not isinstance(cond, (Dataset, DataArray)):
                  raise TypeError(
                      "cond argument is %r but must be a %r or %r"
                      % (cond, Dataset, DataArray)
                  )
  
              # align so we can use integer indexing
              self, cond = align(self, cond)
  
              # get cond with the minimal size needed for the Dataset
              if isinstance(cond, Dataset):
                  clipcond = cond.to_array().any("variable")
              else:
                  clipcond = cond
  
              # clip the data corresponding to coordinate dims that are not used
              nonzeros = zip(clipcond.dims, np.nonzero(clipcond.values))
              indexers = {k: np.unique(v) for k, v in nonzeros}
  
              self = self.isel(**indexers)
              cond = cond.isel(**indexers)
  
          return ops.where_method(self, cond, other)
  
      def close(self: Any) -> None:
          """Close any files linked to this object
          """
          if self._file_obj is not None:
              self._file_obj.close()
          self._file_obj = None
  
      def isin(self, test_elements):
          """Tests each value in the array for whether it is in test elements.
  
          Parameters
          ----------
          test_elements : array_like
              The values against which to test each value of `element`.
              This argument is flattened if an array or array_like.
              See numpy notes for behavior with non-array-like parameters.
  
          Returns
          -------
          isin : same as object, bool
              Has the same shape as this object.
  
          Examples
          --------
  
          >>> array = xr.DataArray([1, 2, 3], dims="x")
          >>> array.isin([1, 3])
          <xarray.DataArray (x: 3)>
          array([ True, False,  True])
          Dimensions without coordinates: x
  
          See also
          --------
          numpy.isin
          """
          from .computation import apply_ufunc
          from .dataset import Dataset
          from .dataarray import DataArray
          from .variable import Variable
  
          if isinstance(test_elements, Dataset):
              raise TypeError(
                  "isin() argument must be convertible to an array: {}".format(
                      test_elements
                  )
              )
          elif isinstance(test_elements, (Variable, DataArray)):
              # need to explicitly pull out data to support dask arrays as the
              # second argument
              test_elements = test_elements.data
  
          return apply_ufunc(
              duck_array_ops.isin,
              self,
              kwargs=dict(test_elements=test_elements),
              dask="allowed",
          )
  
      def __enter__(self: T) -> T:
          return self
  
      def __exit__(self, exc_type, exc_value, traceback) -> None:
          self.close()
  
      def __getitem__(self, value):
          # implementations of this class should implement this method
          raise NotImplementedError()
  
  
  def full_like(other, fill_value, dtype: DTypeLike = None):
      """Return a new object with the same shape and type as a given object.
  
      Parameters
      ----------
      other : DataArray, Dataset, or Variable
          The reference object in input
      fill_value : scalar
          Value to fill the new object with before returning it.
      dtype : dtype, optional
          dtype of the new array. If omitted, it defaults to other.dtype.
  
      Returns
      -------
      out : same as object
          New object with the same shape and type as other, with the data
          filled with fill_value. Coords will be copied from other.
          If other is based on dask, the new one will be as well, and will be
          split in the same chunks.
  
      Examples
      --------
  
      >>> import numpy as np
      >>> import xarray as xr
      >>> x = xr.DataArray(
      ...     np.arange(6).reshape(2, 3),
      ...     dims=["lat", "lon"],
      ...     coords={"lat": [1, 2], "lon": [0, 1, 2]},
      ... )
      >>> x
      <xarray.DataArray (lat: 2, lon: 3)>
      array([[0, 1, 2],
             [3, 4, 5]])
      Coordinates:
      * lat      (lat) int64 1 2
      * lon      (lon) int64 0 1 2
  
      >>> xr.full_like(x, 1)
      <xarray.DataArray (lat: 2, lon: 3)>
      array([[1, 1, 1],
             [1, 1, 1]])
      Coordinates:
      * lat      (lat) int64 1 2
      * lon      (lon) int64 0 1 2
  
      >>> xr.full_like(x, 0.5)
      <xarray.DataArray (lat: 2, lon: 3)>
      array([[0, 0, 0],
             [0, 0, 0]])
      Coordinates:
      * lat      (lat) int64 1 2
      * lon      (lon) int64 0 1 2
  
      >>> xr.full_like(x, 0.5, dtype=np.double)
      <xarray.DataArray (lat: 2, lon: 3)>
      array([[0.5, 0.5, 0.5],
             [0.5, 0.5, 0.5]])
      Coordinates:
      * lat      (lat) int64 1 2
      * lon      (lon) int64 0 1 2
  
      >>> xr.full_like(x, np.nan, dtype=np.double)
      <xarray.DataArray (lat: 2, lon: 3)>
      array([[nan, nan, nan],
             [nan, nan, nan]])
      Coordinates:
      * lat      (lat) int64 1 2
      * lon      (lon) int64 0 1 2
  
      See also
      --------
  
      zeros_like
      ones_like
  
      """
      from .dataarray import DataArray
      from .dataset import Dataset
      from .variable import Variable
  
      if not is_scalar(fill_value):
          raise ValueError(f"fill_value must be scalar. Received {fill_value} instead.")
  
      if isinstance(other, Dataset):
          data_vars = {
              k: _full_like_variable(v, fill_value, dtype)
              for k, v in other.data_vars.items()
          }
          return Dataset(data_vars, coords=other.coords, attrs=other.attrs)
      elif isinstance(other, DataArray):
          return DataArray(
              _full_like_variable(other.variable, fill_value, dtype),
              dims=other.dims,
              coords=other.coords,
              attrs=other.attrs,
              name=other.name,
          )
      elif isinstance(other, Variable):
          return _full_like_variable(other, fill_value, dtype)
      else:
          raise TypeError("Expected DataArray, Dataset, or Variable")
  
  
  def _full_like_variable(other, fill_value, dtype: DTypeLike = None):
      """Inner function of full_like, where other must be a variable
      """
      from .variable import Variable
  
      if isinstance(other.data, dask_array_type):
          import dask.array
  
          if dtype is None:
              dtype = other.dtype
          data = dask.array.full(
              other.shape, fill_value, dtype=dtype, chunks=other.data.chunks
          )
      else:
          data = np.full_like(other, fill_value, dtype=dtype)
  
      return Variable(dims=other.dims, data=data, attrs=other.attrs)
  
  
  def zeros_like(other, dtype: DTypeLike = None):
      """Return a new object of zeros with the same shape and
      type as a given dataarray or dataset.
  
      Parameters
      ----------
      other : DataArray, Dataset, or Variable
          The reference object. The output will have the same dimensions and coordinates as this object.
      dtype : dtype, optional
          dtype of the new array. If omitted, it defaults to other.dtype.
  
      Returns
      -------
      out : same as object
          New object of zeros with the same shape and type as other.
  
      Examples
      --------
  
      >>> import numpy as np
      >>> import xarray as xr
      >>> x = xr.DataArray(
      ...     np.arange(6).reshape(2, 3),
      ...     dims=["lat", "lon"],
      ...     coords={"lat": [1, 2], "lon": [0, 1, 2]},
      ... )
      >>> x
      <xarray.DataArray (lat: 2, lon: 3)>
      array([[0, 1, 2],
             [3, 4, 5]])
      Coordinates:
      * lat      (lat) int64 1 2
      * lon      (lon) int64 0 1 2
  
      >>> xr.zeros_like(x)
      <xarray.DataArray (lat: 2, lon: 3)>
      array([[0, 0, 0],
             [0, 0, 0]])
      Coordinates:
      * lat      (lat) int64 1 2
      * lon      (lon) int64 0 1 2
  
      >>> xr.zeros_like(x, dtype=float)
      <xarray.DataArray (lat: 2, lon: 3)>
      array([[0., 0., 0.],
             [0., 0., 0.]])
      Coordinates:
      * lat      (lat) int64 1 2
      * lon      (lon) int64 0 1 2
  
      See also
      --------
  
      ones_like
      full_like
  
      """
      return full_like(other, 0, dtype)
  
  
  def ones_like(other, dtype: DTypeLike = None):
      """Return a new object of ones with the same shape and
      type as a given dataarray or dataset.
  
      Parameters
      ----------
      other : DataArray, Dataset, or Variable
          The reference object. The output will have the same dimensions and coordinates as this object.
      dtype : dtype, optional
          dtype of the new array. If omitted, it defaults to other.dtype.
  
      Returns
      -------
      out : same as object
          New object of ones with the same shape and type as other.
  
      Examples
      --------
  
      >>> import numpy as np
      >>> import xarray as xr
      >>> x = xr.DataArray(
      ...     np.arange(6).reshape(2, 3),
      ...     dims=["lat", "lon"],
      ...     coords={"lat": [1, 2], "lon": [0, 1, 2]},
      ... )
      >>> x
      <xarray.DataArray (lat: 2, lon: 3)>
      array([[0, 1, 2],
             [3, 4, 5]])
      Coordinates:
      * lat      (lat) int64 1 2
      * lon      (lon) int64 0 1 2
  
      >>> xr.ones_like(x)
      <xarray.DataArray (lat: 2, lon: 3)>
      array([[1, 1, 1],
             [1, 1, 1]])
      Coordinates:
      * lat      (lat) int64 1 2
      * lon      (lon) int64 0 1 2
  
      See also
      --------
  
      zeros_like
      full_like
  
      """
      return full_like(other, 1, dtype)
  
  
  def is_np_datetime_like(dtype: DTypeLike) -> bool:
      """Check if a dtype is a subclass of the numpy datetime types
      """
      return np.issubdtype(dtype, np.datetime64) or np.issubdtype(dtype, np.timedelta64)
  
  
  def is_np_timedelta_like(dtype: DTypeLike) -> bool:
      """Check whether dtype is of the timedelta64 dtype.
      """
      return np.issubdtype(dtype, np.timedelta64)
  
  
  def _contains_cftime_datetimes(array) -> bool:
      """Check if an array contains cftime.datetime objects
      """
      try:
          from cftime import datetime as cftime_datetime
      except ImportError:
          return False
      else:
          if array.dtype == np.dtype("O") and array.size > 0:
              sample = array.ravel()[0]
              if isinstance(sample, dask_array_type):
                  sample = sample.compute()
                  if isinstance(sample, np.ndarray):
                      sample = sample.item()
              return isinstance(sample, cftime_datetime)
          else:
              return False
  
  
  def contains_cftime_datetimes(var) -> bool:
      """Check if an xarray.Variable contains cftime.datetime objects
      """
      return _contains_cftime_datetimes(var.data)
  
  
  def _contains_datetime_like_objects(var) -> bool:
      """Check if a variable contains datetime like objects (either
      np.datetime64, np.timedelta64, or cftime.datetime)
      """
      return is_np_datetime_like(var.dtype) or contains_cftime_datetimes(var)
  
apis: numpy.full_like numpy.dtype numpy.issubdtype numpy.asarray pandas.Grouper numpy.nonzero numpy.unique