text: import pickle
  import sys
  import warnings
  from copy import deepcopy
  from textwrap import dedent
  
  import numpy as np
  import pandas as pd
  import pytest
  from packaging.version import Version
  from pandas.core.computation.ops import UndefinedVariableError
  
  import xarray as xr
  from xarray import (
      DataArray,
      Dataset,
      IndexVariable,
      Variable,
      align,
      broadcast,
      set_options,
  )
  from xarray.coding.times import CFDatetimeCoder
  from xarray.convert import from_cdms2
  from xarray.core import dtypes
  from xarray.core.common import full_like
  from xarray.core.indexes import Index, PandasIndex, propagate_indexes
  from xarray.core.utils import is_scalar
  from xarray.tests import (
      ReturnItem,
      assert_allclose,
      assert_array_equal,
      assert_equal,
      assert_identical,
      has_dask,
      raise_if_dask_computes,
      requires_bottleneck,
      requires_cupy,
      requires_dask,
      requires_iris,
      requires_numbagg,
      requires_numexpr,
      requires_pint,
      requires_scipy,
      requires_sparse,
      source_ndarray,
  )
  
  pytestmark = [
      pytest.mark.filterwarnings("error:Mean of empty slice"),
      pytest.mark.filterwarnings("error:All-NaN (slice|axis) encountered"),
  ]
  
  
  class TestDataArray:
      @pytest.fixture(autouse=True)
      def setup(self):
          self.attrs = {"attr1": "value1", "attr2": 2929}
          self.x = np.random.random((10, 20))
          self.v = Variable(["x", "y"], self.x)
          self.va = Variable(["x", "y"], self.x, self.attrs)
          self.ds = Dataset({"foo": self.v})
          self.dv = self.ds["foo"]
  
          self.mindex = pd.MultiIndex.from_product(
              [["a", "b"], [1, 2]], names=("level_1", "level_2")
          )
          self.mda = DataArray([0, 1, 2, 3], coords={"x": self.mindex}, dims="x")
  
      def test_repr(self):
          v = Variable(["time", "x"], [[1, 2, 3], [4, 5, 6]], {"foo": "bar"})
          coords = {"x": np.arange(3, dtype=np.int64), "other": np.int64(0)}
          data_array = DataArray(v, coords, name="my_variable")
          expected = dedent(
              """\
              <xarray.DataArray 'my_variable' (time: 2, x: 3)>
              array([[1, 2, 3],
                     [4, 5, 6]])
              Coordinates:
                * x        (x) int64 0 1 2
                  other    int64 0
              Dimensions without coordinates: time
              Attributes:
                  foo:      bar"""
          )
          assert expected == repr(data_array)
  
      def test_repr_multiindex(self):
          expected = dedent(
              """\
              <xarray.DataArray (x: 4)>
              array([0, 1, 2, 3])
              Coordinates:
                * x        (x) MultiIndex
                - level_1  (x) object 'a' 'a' 'b' 'b'
                - level_2  (x) int64 1 2 1 2"""
          )
          assert expected == repr(self.mda)
  
      def test_repr_multiindex_long(self):
          mindex_long = pd.MultiIndex.from_product(
              [["a", "b", "c", "d"], [1, 2, 3, 4, 5, 6, 7, 8]],
              names=("level_1", "level_2"),
          )
          mda_long = DataArray(list(range(32)), coords={"x": mindex_long}, dims="x")
          expected = dedent(
              """\
              <xarray.DataArray (x: 32)>
              array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
                     17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31])
              Coordinates:
                * x        (x) MultiIndex
                - level_1  (x) object 'a' 'a' 'a' 'a' 'a' 'a' 'a' ... 'd' 'd' 'd' 'd' 'd' 'd'
                - level_2  (x) int64 1 2 3 4 5 6 7 8 1 2 3 4 5 6 ... 4 5 6 7 8 1 2 3 4 5 6 7 8"""
          )
          assert expected == repr(mda_long)
  
      def test_properties(self):
          assert_equal(self.dv.variable, self.v)
          assert_array_equal(self.dv.values, self.v.values)
          for attr in ["dims", "dtype", "shape", "size", "nbytes", "ndim", "attrs"]:
              assert getattr(self.dv, attr) == getattr(self.v, attr)
          assert len(self.dv) == len(self.v)
          assert_equal(self.dv.variable, self.v)
          assert set(self.dv.coords) == set(self.ds.coords)
          for k, v in self.dv.coords.items():
              assert_array_equal(v, self.ds.coords[k])
          with pytest.raises(AttributeError):
              self.dv.dataset
          assert isinstance(self.ds["x"].to_index(), pd.Index)
          with pytest.raises(ValueError, match=r"must be 1-dimensional"):
              self.ds["foo"].to_index()
          with pytest.raises(AttributeError):
              self.dv.variable = self.v
  
      def test_data_property(self):
          array = DataArray(np.zeros((3, 4)))
          actual = array.copy()
          actual.values = np.ones((3, 4))
          assert_array_equal(np.ones((3, 4)), actual.values)
          actual.data = 2 * np.ones((3, 4))
          assert_array_equal(2 * np.ones((3, 4)), actual.data)
          assert_array_equal(actual.data, actual.values)
  
      def test_indexes(self):
          array = DataArray(np.zeros((2, 3)), [("x", [0, 1]), ("y", ["a", "b", "c"])])
          expected_indexes = {"x": pd.Index([0, 1]), "y": pd.Index(["a", "b", "c"])}
          expected_xindexes = {
              k: PandasIndex(idx, k) for k, idx in expected_indexes.items()
          }
          assert array.xindexes.keys() == expected_xindexes.keys()
          assert array.indexes.keys() == expected_indexes.keys()
          assert all([isinstance(idx, pd.Index) for idx in array.indexes.values()])
          assert all([isinstance(idx, Index) for idx in array.xindexes.values()])
          for k in expected_indexes:
              assert array.xindexes[k].equals(expected_xindexes[k])
              assert array.indexes[k].equals(expected_indexes[k])
  
      def test_get_index(self):
          array = DataArray(np.zeros((2, 3)), coords={"x": ["a", "b"]}, dims=["x", "y"])
          assert array.get_index("x").equals(pd.Index(["a", "b"]))
          assert array.get_index("y").equals(pd.Index([0, 1, 2]))
          with pytest.raises(KeyError):
              array.get_index("z")
  
      def test_get_index_size_zero(self):
          array = DataArray(np.zeros((0,)), dims=["x"])
          actual = array.get_index("x")
          expected = pd.Index([], dtype=np.int64)
          assert actual.equals(expected)
          assert actual.dtype == expected.dtype
  
      def test_struct_array_dims(self):
          """
          This test checks subraction of two DataArrays for the case
          when dimension is a structured array.
          """
          # GH837, GH861
          # checking array subtraction when dims are the same
          p_data = np.array(
              [("Abe", 180), ("Stacy", 150), ("Dick", 200)],
              dtype=[("name", "|S256"), ("height", object)],
          )
          weights_0 = DataArray(
              [80, 56, 120], dims=["participant"], coords={"participant": p_data}
          )
          weights_1 = DataArray(
              [81, 52, 115], dims=["participant"], coords={"participant": p_data}
          )
          actual = weights_1 - weights_0
  
          expected = DataArray(
              [1, -4, -5], dims=["participant"], coords={"participant": p_data}
          )
  
          assert_identical(actual, expected)
  
          # checking array subraction when dims are not the same
          p_data_alt = np.array(
              [("Abe", 180), ("Stacy", 151), ("Dick", 200)],
              dtype=[("name", "|S256"), ("height", object)],
          )
          weights_1 = DataArray(
              [81, 52, 115], dims=["participant"], coords={"participant": p_data_alt}
          )
          actual = weights_1 - weights_0
  
          expected = DataArray(
              [1, -5], dims=["participant"], coords={"participant": p_data[[0, 2]]}
          )
  
          assert_identical(actual, expected)
  
          # checking array subraction when dims are not the same and one
          # is np.nan
          p_data_nan = np.array(
              [("Abe", 180), ("Stacy", np.nan), ("Dick", 200)],
              dtype=[("name", "|S256"), ("height", object)],
          )
          weights_1 = DataArray(
              [81, 52, 115], dims=["participant"], coords={"participant": p_data_nan}
          )
          actual = weights_1 - weights_0
  
          expected = DataArray(
              [1, -5], dims=["participant"], coords={"participant": p_data[[0, 2]]}
          )
  
          assert_identical(actual, expected)
  
      def test_name(self):
          arr = self.dv
          assert arr.name == "foo"
  
          copied = arr.copy()
          arr.name = "bar"
          assert arr.name == "bar"
          assert_equal(copied, arr)
  
          actual = DataArray(IndexVariable("x", [3]))
          actual.name = "y"
          expected = DataArray([3], [("x", [3])], name="y")
          assert_identical(actual, expected)
  
      def test_dims(self):
          arr = self.dv
          assert arr.dims == ("x", "y")
  
          with pytest.raises(AttributeError, match=r"you cannot assign"):
              arr.dims = ("w", "z")
  
      def test_sizes(self):
          array = DataArray(np.zeros((3, 4)), dims=["x", "y"])
          assert array.sizes == {"x": 3, "y": 4}
          assert tuple(array.sizes) == array.dims
          with pytest.raises(TypeError):
              array.sizes["foo"] = 5
  
      def test_encoding(self):
          expected = {"foo": "bar"}
          self.dv.encoding["foo"] = "bar"
          assert expected == self.dv.encoding
  
          expected = {"baz": 0}
          self.dv.encoding = expected
  
          assert expected is not self.dv.encoding
  
      def test_constructor(self):
          data = np.random.random((2, 3))
  
          actual = DataArray(data)
          expected = Dataset({None: (["dim_0", "dim_1"], data)})[None]
          assert_identical(expected, actual)
  
          actual = DataArray(data, [["a", "b"], [-1, -2, -3]])
          expected = Dataset(
              {
                  None: (["dim_0", "dim_1"], data),
                  "dim_0": ("dim_0", ["a", "b"]),
                  "dim_1": ("dim_1", [-1, -2, -3]),
              }
          )[None]
          assert_identical(expected, actual)
  
          actual = DataArray(
              data, [pd.Index(["a", "b"], name="x"), pd.Index([-1, -2, -3], name="y")]
          )
          expected = Dataset(
              {None: (["x", "y"], data), "x": ("x", ["a", "b"]), "y": ("y", [-1, -2, -3])}
          )[None]
          assert_identical(expected, actual)
  
          coords = [["a", "b"], [-1, -2, -3]]
          actual = DataArray(data, coords, ["x", "y"])
          assert_identical(expected, actual)
  
          coords = [pd.Index(["a", "b"], name="A"), pd.Index([-1, -2, -3], name="B")]
          actual = DataArray(data, coords, ["x", "y"])
          assert_identical(expected, actual)
  
          coords = {"x": ["a", "b"], "y": [-1, -2, -3]}
          actual = DataArray(data, coords, ["x", "y"])
          assert_identical(expected, actual)
  
          actual = DataArray(data, coords)
          assert_identical(expected, actual)
  
          coords = [("x", ["a", "b"]), ("y", [-1, -2, -3])]
          actual = DataArray(data, coords)
          assert_identical(expected, actual)
  
          expected = Dataset({None: (["x", "y"], data), "x": ("x", ["a", "b"])})[None]
          actual = DataArray(data, {"x": ["a", "b"]}, ["x", "y"])
          assert_identical(expected, actual)
  
          actual = DataArray(data, dims=["x", "y"])
          expected = Dataset({None: (["x", "y"], data)})[None]
          assert_identical(expected, actual)
  
          actual = DataArray(data, dims=["x", "y"], name="foo")
          expected = Dataset({"foo": (["x", "y"], data)})["foo"]
          assert_identical(expected, actual)
  
          actual = DataArray(data, name="foo")
          expected = Dataset({"foo": (["dim_0", "dim_1"], data)})["foo"]
          assert_identical(expected, actual)
  
          actual = DataArray(data, dims=["x", "y"], attrs={"bar": 2})
          expected = Dataset({None: (["x", "y"], data, {"bar": 2})})[None]
          assert_identical(expected, actual)
  
          actual = DataArray(data, dims=["x", "y"])
          expected = Dataset({None: (["x", "y"], data, {}, {"bar": 2})})[None]
          assert_identical(expected, actual)
  
          actual = DataArray([1, 2, 3], coords={"x": [0, 1, 2]})
          expected = DataArray([1, 2, 3], coords=[("x", [0, 1, 2])])
          assert_identical(expected, actual)
  
      def test_constructor_invalid(self):
          data = np.random.randn(3, 2)
  
          with pytest.raises(ValueError, match=r"coords is not dict-like"):
              DataArray(data, [[0, 1, 2]], ["x", "y"])
  
          with pytest.raises(ValueError, match=r"not a subset of the .* dim"):
              DataArray(data, {"x": [0, 1, 2]}, ["a", "b"])
          with pytest.raises(ValueError, match=r"not a subset of the .* dim"):
              DataArray(data, {"x": [0, 1, 2]})
  
          with pytest.raises(TypeError, match=r"is not a string"):
              DataArray(data, dims=["x", None])
  
          with pytest.raises(ValueError, match=r"conflicting sizes for dim"):
              DataArray([1, 2, 3], coords=[("x", [0, 1])])
          with pytest.raises(ValueError, match=r"conflicting sizes for dim"):
              DataArray([1, 2], coords={"x": [0, 1], "y": ("x", [1])}, dims="x")
  
          with pytest.raises(ValueError, match=r"conflicting MultiIndex"):
              DataArray(np.random.rand(4, 4), [("x", self.mindex), ("y", self.mindex)])
          with pytest.raises(ValueError, match=r"conflicting MultiIndex"):
              DataArray(np.random.rand(4, 4), [("x", self.mindex), ("level_1", range(4))])
  
          with pytest.raises(ValueError, match=r"matching the dimension size"):
              DataArray(data, coords={"x": 0}, dims=["x", "y"])
  
      def test_constructor_from_self_described(self):
          data = [[-0.1, 21], [0, 2]]
          expected = DataArray(
              data,
              coords={"x": ["a", "b"], "y": [-1, -2]},
              dims=["x", "y"],
              name="foobar",
              attrs={"bar": 2},
          )
          actual = DataArray(expected)
          assert_identical(expected, actual)
  
          actual = DataArray(expected.values, actual.coords)
          assert_equal(expected, actual)
  
          frame = pd.DataFrame(
              data,
              index=pd.Index(["a", "b"], name="x"),
              columns=pd.Index([-1, -2], name="y"),
          )
          actual = DataArray(frame)
          assert_equal(expected, actual)
  
          series = pd.Series(data[0], index=pd.Index([-1, -2], name="y"))
          actual = DataArray(series)
          assert_equal(expected[0].reset_coords("x", drop=True), actual)
  
          expected = DataArray(
              data,
              coords={"x": ["a", "b"], "y": [-1, -2], "a": 0, "z": ("x", [-0.5, 0.5])},
              dims=["x", "y"],
          )
          actual = DataArray(expected)
          assert_identical(expected, actual)
  
          actual = DataArray(expected.values, expected.coords)
          assert_identical(expected, actual)
  
          expected = Dataset({"foo": ("foo", ["a", "b"])})["foo"]
          actual = DataArray(pd.Index(["a", "b"], name="foo"))
          assert_identical(expected, actual)
  
          actual = DataArray(IndexVariable("foo", ["a", "b"]))
          assert_identical(expected, actual)
  
      def test_constructor_from_0d(self):
          expected = Dataset({None: ([], 0)})[None]
          actual = DataArray(0)
          assert_identical(expected, actual)
  
      @requires_dask
      def test_constructor_dask_coords(self):
          # regression test for GH1684
          import dask.array as da
  
          coord = da.arange(8, chunks=(4,))
          data = da.random.random((8, 8), chunks=(4, 4)) + 1
          actual = DataArray(data, coords={"x": coord, "y": coord}, dims=["x", "y"])
  
          ecoord = np.arange(8)
          expected = DataArray(data, coords={"x": ecoord, "y": ecoord}, dims=["x", "y"])
          assert_equal(actual, expected)
  
      def test_equals_and_identical(self):
          orig = DataArray(np.arange(5.0), {"a": 42}, dims="x")
  
          expected = orig
          actual = orig.copy()
          assert expected.equals(actual)
          assert expected.identical(actual)
  
          actual = expected.rename("baz")
          assert expected.equals(actual)
          assert not expected.identical(actual)
  
          actual = expected.rename({"x": "xxx"})
          assert not expected.equals(actual)
          assert not expected.identical(actual)
  
          actual = expected.copy()
          actual.attrs["foo"] = "bar"
          assert expected.equals(actual)
          assert not expected.identical(actual)
  
          actual = expected.copy()
          actual["x"] = ("x", -np.arange(5))
          assert not expected.equals(actual)
          assert not expected.identical(actual)
  
          actual = expected.reset_coords(drop=True)
          assert not expected.equals(actual)
          assert not expected.identical(actual)
  
          actual = orig.copy()
          actual[0] = np.nan
          expected = actual.copy()
          assert expected.equals(actual)
          assert expected.identical(actual)
  
          actual[:] = np.nan
          assert not expected.equals(actual)
          assert not expected.identical(actual)
  
          actual = expected.copy()
          actual["a"] = 100000
          assert not expected.equals(actual)
          assert not expected.identical(actual)
  
      def test_equals_failures(self):
          orig = DataArray(np.arange(5.0), {"a": 42}, dims="x")
          assert not orig.equals(np.arange(5))
          assert not orig.identical(123)
          assert not orig.broadcast_equals({1: 2})
  
      def test_broadcast_equals(self):
          a = DataArray([0, 0], {"y": 0}, dims="x")
          b = DataArray([0, 0], {"y": ("x", [0, 0])}, dims="x")
          assert a.broadcast_equals(b)
          assert b.broadcast_equals(a)
          assert not a.equals(b)
          assert not a.identical(b)
  
          c = DataArray([0], coords={"x": 0}, dims="y")
          assert not a.broadcast_equals(c)
          assert not c.broadcast_equals(a)
  
      def test_getitem(self):
          # strings pull out dataarrays
          assert_identical(self.dv, self.ds["foo"])
          x = self.dv["x"]
          y = self.dv["y"]
          assert_identical(self.ds["x"], x)
          assert_identical(self.ds["y"], y)
  
          arr = ReturnItem()
          for i in [
              arr[:],
              arr[...],
              arr[x.values],
              arr[x.variable],
              arr[x],
              arr[x, y],
              arr[x.values > -1],
              arr[x.variable > -1],
              arr[x > -1],
              arr[x > -1, y > -1],
          ]:
              assert_equal(self.dv, self.dv[i])
          for i in [
              arr[0],
              arr[:, 0],
              arr[:3, :2],
              arr[x.values[:3]],
              arr[x.variable[:3]],
              arr[x[:3]],
              arr[x[:3], y[:4]],
              arr[x.values > 3],
              arr[x.variable > 3],
              arr[x > 3],
              arr[x > 3, y > 3],
          ]:
              assert_array_equal(self.v[i], self.dv[i])
  
      def test_getitem_dict(self):
          actual = self.dv[{"x": slice(3), "y": 0}]
          expected = self.dv.isel(x=slice(3), y=0)
          assert_identical(expected, actual)
  
      def test_getitem_coords(self):
          orig = DataArray(
              [[10], [20]],
              {
                  "x": [1, 2],
                  "y": [3],
                  "z": 4,
                  "x2": ("x", ["a", "b"]),
                  "y2": ("y", ["c"]),
                  "xy": (["y", "x"], [["d", "e"]]),
              },
              dims=["x", "y"],
          )
  
          assert_identical(orig, orig[:])
          assert_identical(orig, orig[:, :])
          assert_identical(orig, orig[...])
          assert_identical(orig, orig[:2, :1])
          assert_identical(orig, orig[[0, 1], [0]])
  
          actual = orig[0, 0]
          expected = DataArray(
              10, {"x": 1, "y": 3, "z": 4, "x2": "a", "y2": "c", "xy": "d"}
          )
          assert_identical(expected, actual)
  
          actual = orig[0, :]
          expected = DataArray(
              [10],
              {
                  "x": 1,
                  "y": [3],
                  "z": 4,
                  "x2": "a",
                  "y2": ("y", ["c"]),
                  "xy": ("y", ["d"]),
              },
              dims="y",
          )
          assert_identical(expected, actual)
  
          actual = orig[:, 0]
          expected = DataArray(
              [10, 20],
              {
                  "x": [1, 2],
                  "y": 3,
                  "z": 4,
                  "x2": ("x", ["a", "b"]),
                  "y2": "c",
                  "xy": ("x", ["d", "e"]),
              },
              dims="x",
          )
          assert_identical(expected, actual)
  
      def test_getitem_dataarray(self):
          # It should not conflict
          da = DataArray(np.arange(12).reshape((3, 4)), dims=["x", "y"])
          ind = DataArray([[0, 1], [0, 1]], dims=["x", "z"])
          actual = da[ind]
          assert_array_equal(actual, da.values[[[0, 1], [0, 1]], :])
  
          da = DataArray(
              np.arange(12).reshape((3, 4)),
              dims=["x", "y"],
              coords={"x": [0, 1, 2], "y": ["a", "b", "c", "d"]},
          )
          ind = xr.DataArray([[0, 1], [0, 1]], dims=["X", "Y"])
          actual = da[ind]
          expected = da.values[[[0, 1], [0, 1]], :]
          assert_array_equal(actual, expected)
          assert actual.dims == ("X", "Y", "y")
  
          # boolean indexing
          ind = xr.DataArray([True, True, False], dims=["x"])
          assert_equal(da[ind], da[[0, 1], :])
          assert_equal(da[ind], da[[0, 1]])
          assert_equal(da[ind], da[ind.values])
  
      def test_getitem_empty_index(self):
          da = DataArray(np.arange(12).reshape((3, 4)), dims=["x", "y"])
          assert_identical(da[{"x": []}], DataArray(np.zeros((0, 4)), dims=["x", "y"]))
          assert_identical(
              da.loc[{"y": []}], DataArray(np.zeros((3, 0)), dims=["x", "y"])
          )
          assert_identical(da[[]], DataArray(np.zeros((0, 4)), dims=["x", "y"]))
  
      def test_setitem(self):
          # basic indexing should work as numpy's indexing
          tuples = [
              (0, 0),
              (0, slice(None, None)),
              (slice(None, None), slice(None, None)),
              (slice(None, None), 0),
              ([1, 0], slice(None, None)),
              (slice(None, None), [1, 0]),
          ]
          for t in tuples:
              expected = np.arange(6).reshape(3, 2)
              orig = DataArray(
                  np.arange(6).reshape(3, 2),
                  {
                      "x": [1, 2, 3],
                      "y": ["a", "b"],
                      "z": 4,
                      "x2": ("x", ["a", "b", "c"]),
                      "y2": ("y", ["d", "e"]),
                  },
                  dims=["x", "y"],
              )
              orig[t] = 1
              expected[t] = 1
              assert_array_equal(orig.values, expected)
  
      def test_setitem_fancy(self):
          # vectorized indexing
          da = DataArray(np.ones((3, 2)), dims=["x", "y"])
          ind = Variable(["a"], [0, 1])
          da[dict(x=ind, y=ind)] = 0
          expected = DataArray([[0, 1], [1, 0], [1, 1]], dims=["x", "y"])
          assert_identical(expected, da)
          # assign another 0d-variable
          da[dict(x=ind, y=ind)] = Variable((), 0)
          expected = DataArray([[0, 1], [1, 0], [1, 1]], dims=["x", "y"])
          assert_identical(expected, da)
          # assign another 1d-variable
          da[dict(x=ind, y=ind)] = Variable(["a"], [2, 3])
          expected = DataArray([[2, 1], [1, 3], [1, 1]], dims=["x", "y"])
          assert_identical(expected, da)
  
          # 2d-vectorized indexing
          da = DataArray(np.ones((3, 2)), dims=["x", "y"])
          ind_x = DataArray([[0, 1]], dims=["a", "b"])
          ind_y = DataArray([[1, 0]], dims=["a", "b"])
          da[dict(x=ind_x, y=ind_y)] = 0
          expected = DataArray([[1, 0], [0, 1], [1, 1]], dims=["x", "y"])
          assert_identical(expected, da)
  
          da = DataArray(np.ones((3, 2)), dims=["x", "y"])
          ind = Variable(["a"], [0, 1])
          da[ind] = 0
          expected = DataArray([[0, 0], [0, 0], [1, 1]], dims=["x", "y"])
          assert_identical(expected, da)
  
      def test_setitem_dataarray(self):
          def get_data():
              return DataArray(
                  np.ones((4, 3, 2)),
                  dims=["x", "y", "z"],
                  coords={
                      "x": np.arange(4),
                      "y": ["a", "b", "c"],
                      "non-dim": ("x", [1, 3, 4, 2]),
                  },
              )
  
          da = get_data()
          # indexer with inconsistent coordinates.
          ind = DataArray(np.arange(1, 4), dims=["x"], coords={"x": np.random.randn(3)})
          with pytest.raises(IndexError, match=r"dimension coordinate 'x'"):
              da[dict(x=ind)] = 0
  
          # indexer with consistent coordinates.
          ind = DataArray(np.arange(1, 4), dims=["x"], coords={"x": np.arange(1, 4)})
          da[dict(x=ind)] = 0  # should not raise
          assert np.allclose(da[dict(x=ind)].values, 0)
          assert_identical(da["x"], get_data()["x"])
          assert_identical(da["non-dim"], get_data()["non-dim"])
  
          da = get_data()
          # conflict in the assigning values
          value = xr.DataArray(
              np.zeros((3, 3, 2)),
              dims=["x", "y", "z"],
              coords={"x": [0, 1, 2], "non-dim": ("x", [0, 2, 4])},
          )
          with pytest.raises(IndexError, match=r"dimension coordinate 'x'"):
              da[dict(x=ind)] = value
  
          # consistent coordinate in the assigning values
          value = xr.DataArray(
              np.zeros((3, 3, 2)),
              dims=["x", "y", "z"],
              coords={"x": [1, 2, 3], "non-dim": ("x", [0, 2, 4])},
          )
          da[dict(x=ind)] = value
          assert np.allclose(da[dict(x=ind)].values, 0)
          assert_identical(da["x"], get_data()["x"])
          assert_identical(da["non-dim"], get_data()["non-dim"])
  
          # Conflict in the non-dimension coordinate
          value = xr.DataArray(
              np.zeros((3, 3, 2)),
              dims=["x", "y", "z"],
              coords={"x": [1, 2, 3], "non-dim": ("x", [0, 2, 4])},
          )
          da[dict(x=ind)] = value  # should not raise
  
          # conflict in the assigning values
          value = xr.DataArray(
              np.zeros((3, 3, 2)),
              dims=["x", "y", "z"],
              coords={"x": [0, 1, 2], "non-dim": ("x", [0, 2, 4])},
          )
          with pytest.raises(IndexError, match=r"dimension coordinate 'x'"):
              da[dict(x=ind)] = value
  
          # consistent coordinate in the assigning values
          value = xr.DataArray(
              np.zeros((3, 3, 2)),
              dims=["x", "y", "z"],
              coords={"x": [1, 2, 3], "non-dim": ("x", [0, 2, 4])},
          )
          da[dict(x=ind)] = value  # should not raise
  
      def test_contains(self):
          data_array = DataArray([1, 2])
          assert 1 in data_array
          assert 3 not in data_array
  
      def test_attr_sources_multiindex(self):
          # make sure attr-style access for multi-index levels
          # returns DataArray objects
          assert isinstance(self.mda.level_1, DataArray)
  
      def test_pickle(self):
          data = DataArray(np.random.random((3, 3)), dims=("id", "time"))
          roundtripped = pickle.loads(pickle.dumps(data))
          assert_identical(data, roundtripped)
  
      @requires_dask
      def test_chunk(self):
          unblocked = DataArray(np.ones((3, 4)))
          assert unblocked.chunks is None
  
          blocked = unblocked.chunk()
          assert blocked.chunks == ((3,), (4,))
          first_dask_name = blocked.data.name
  
          blocked = unblocked.chunk(chunks=((2, 1), (2, 2)))
          assert blocked.chunks == ((2, 1), (2, 2))
          assert blocked.data.name != first_dask_name
  
          blocked = unblocked.chunk(chunks=(3, 3))
          assert blocked.chunks == ((3,), (3, 1))
          assert blocked.data.name != first_dask_name
  
          # name doesn't change when rechunking by same amount
          # this fails if ReprObject doesn't have __dask_tokenize__ defined
          assert unblocked.chunk(2).data.name == unblocked.chunk(2).data.name
  
          assert blocked.load().chunks is None
  
          # Check that kwargs are passed
          import dask.array as da
  
          blocked = unblocked.chunk(name_prefix="testname_")
          assert isinstance(blocked.data, da.Array)
          assert "testname_" in blocked.data.name
  
      def test_isel(self):
          assert_identical(self.dv[0], self.dv.isel(x=0))
          assert_identical(self.dv, self.dv.isel(x=slice(None)))
          assert_identical(self.dv[:3], self.dv.isel(x=slice(3)))
          assert_identical(self.dv[:3, :5], self.dv.isel(x=slice(3), y=slice(5)))
          with pytest.raises(
              ValueError,
              match=r"Dimensions {'not_a_dim'} do not exist. Expected "
              r"one or more of \('x', 'y'\)",
          ):
              self.dv.isel(not_a_dim=0)
          with pytest.warns(
              UserWarning,
              match=r"Dimensions {'not_a_dim'} do not exist. "
              r"Expected one or more of \('x', 'y'\)",
          ):
              self.dv.isel(not_a_dim=0, missing_dims="warn")
          assert_identical(self.dv, self.dv.isel(not_a_dim=0, missing_dims="ignore"))
  
      def test_isel_types(self):
          # regression test for #1405
          da = DataArray([1, 2, 3], dims="x")
          # uint64
          assert_identical(
              da.isel(x=np.array([0], dtype="uint64")), da.isel(x=np.array([0]))
          )
          # uint32
          assert_identical(
              da.isel(x=np.array([0], dtype="uint32")), da.isel(x=np.array([0]))
          )
          # int64
          assert_identical(
              da.isel(x=np.array([0], dtype="int64")), da.isel(x=np.array([0]))
          )
  
      @pytest.mark.filterwarnings("ignore::DeprecationWarning")
      def test_isel_fancy(self):
          shape = (10, 7, 6)
          np_array = np.random.random(shape)
          da = DataArray(
              np_array, dims=["time", "y", "x"], coords={"time": np.arange(0, 100, 10)}
          )
          y = [1, 3]
          x = [3, 0]
  
          expected = da.values[:, y, x]
  
          actual = da.isel(y=(("test_coord",), y), x=(("test_coord",), x))
          assert actual.coords["test_coord"].shape == (len(y),)
          assert list(actual.coords) == ["time"]
          assert actual.dims == ("time", "test_coord")
  
          np.testing.assert_equal(actual, expected)
  
          # a few corner cases
          da.isel(
              time=(("points",), [1, 2]), x=(("points",), [2, 2]), y=(("points",), [3, 4])
          )
          np.testing.assert_allclose(
              da.isel(
                  time=(("p",), [1]), x=(("p",), [2]), y=(("p",), [4])
              ).values.squeeze(),
              np_array[1, 4, 2].squeeze(),
          )
          da.isel(time=(("points",), [1, 2]))
          y = [-1, 0]
          x = [-2, 2]
          expected = da.values[:, y, x]
          actual = da.isel(x=(("points",), x), y=(("points",), y)).values
          np.testing.assert_equal(actual, expected)
  
          # test that the order of the indexers doesn't matter
          assert_identical(
              da.isel(y=(("points",), y), x=(("points",), x)),
              da.isel(x=(("points",), x), y=(("points",), y)),
          )
  
          # make sure we're raising errors in the right places
          with pytest.raises(IndexError, match=r"Dimensions of indexers mismatch"):
              da.isel(y=(("points",), [1, 2]), x=(("points",), [1, 2, 3]))
  
          # tests using index or DataArray as indexers
          stations = Dataset()
          stations["station"] = (("station",), ["A", "B", "C"])
          stations["dim1s"] = (("station",), [1, 2, 3])
          stations["dim2s"] = (("station",), [4, 5, 1])
  
          actual = da.isel(x=stations["dim1s"], y=stations["dim2s"])
          assert "station" in actual.coords
          assert "station" in actual.dims
          assert_identical(actual["station"], stations["station"])
  
          with pytest.raises(ValueError, match=r"conflicting values for "):
              da.isel(
                  x=DataArray([0, 1, 2], dims="station", coords={"station": [0, 1, 2]}),
                  y=DataArray([0, 1, 2], dims="station", coords={"station": [0, 1, 3]}),
              )
  
          # multi-dimensional selection
          stations = Dataset()
          stations["a"] = (("a",), ["A", "B", "C"])
          stations["b"] = (("b",), [0, 1])
          stations["dim1s"] = (("a", "b"), [[1, 2], [2, 3], [3, 4]])
          stations["dim2s"] = (("a",), [4, 5, 1])
  
          actual = da.isel(x=stations["dim1s"], y=stations["dim2s"])
          assert "a" in actual.coords
          assert "a" in actual.dims
          assert "b" in actual.coords
          assert "b" in actual.dims
          assert_identical(actual["a"], stations["a"])
          assert_identical(actual["b"], stations["b"])
          expected = da.variable[
              :, stations["dim2s"].variable, stations["dim1s"].variable
          ]
          assert_array_equal(actual, expected)
  
      def test_sel(self):
          self.ds["x"] = ("x", np.array(list("abcdefghij")))
          da = self.ds["foo"]
          assert_identical(da, da.sel(x=slice(None)))
          assert_identical(da[1], da.sel(x="b"))
          assert_identical(da[:3], da.sel(x=slice("c")))
          assert_identical(da[:3], da.sel(x=["a", "b", "c"]))
          assert_identical(da[:, :4], da.sel(y=(self.ds["y"] < 4)))
          # verify that indexing with a dataarray works
          b = DataArray("b")
          assert_identical(da[1], da.sel(x=b))
          assert_identical(da[[1]], da.sel(x=slice(b, b)))
  
      def test_sel_dataarray(self):
          # indexing with DataArray
          self.ds["x"] = ("x", np.array(list("abcdefghij")))
          da = self.ds["foo"]
  
          ind = DataArray(["a", "b", "c"], dims=["x"])
          actual = da.sel(x=ind)
          assert_identical(actual, da.isel(x=[0, 1, 2]))
  
          # along new dimension
          ind = DataArray(["a", "b", "c"], dims=["new_dim"])
          actual = da.sel(x=ind)
          assert_array_equal(actual, da.isel(x=[0, 1, 2]))
          assert "new_dim" in actual.dims
  
          # with coordinate
          ind = DataArray(
              ["a", "b", "c"], dims=["new_dim"], coords={"new_dim": [0, 1, 2]}
          )
          actual = da.sel(x=ind)
          assert_array_equal(actual, da.isel(x=[0, 1, 2]))
          assert "new_dim" in actual.dims
          assert "new_dim" in actual.coords
          assert_equal(actual["new_dim"].drop_vars("x"), ind["new_dim"])
  
      def test_sel_invalid_slice(self):
          array = DataArray(np.arange(10), [("x", np.arange(10))])
          with pytest.raises(ValueError, match=r"cannot use non-scalar arrays"):
              array.sel(x=slice(array.x))
  
      def test_sel_dataarray_datetime_slice(self):
          # regression test for GH1240
          times = pd.date_range("2000-01-01", freq="D", periods=365)
          array = DataArray(np.arange(365), [("time", times)])
          result = array.sel(time=slice(array.time[0], array.time[-1]))
          assert_equal(result, array)
  
          array = DataArray(np.arange(365), [("delta", times - times[0])])
          result = array.sel(delta=slice(array.delta[0], array.delta[-1]))
          assert_equal(result, array)
  
      def test_sel_float(self):
          data_values = np.arange(4)
  
          # case coords are float32 and label is list of floats
          float_values = [0.0, 0.111, 0.222, 0.333]
          coord_values = np.asarray(float_values, dtype="float32")
          array = DataArray(data_values, [("float32_coord", coord_values)])
          expected = DataArray(data_values[1:3], [("float32_coord", coord_values[1:3])])
          actual = array.sel(float32_coord=float_values[1:3])
          # case coords are float16 and label is list of floats
          coord_values_16 = np.asarray(float_values, dtype="float16")
          expected_16 = DataArray(
              data_values[1:3], [("float16_coord", coord_values_16[1:3])]
          )
          array_16 = DataArray(data_values, [("float16_coord", coord_values_16)])
          actual_16 = array_16.sel(float16_coord=float_values[1:3])
  
          # case coord, label are scalars
          expected_scalar = DataArray(
              data_values[2], coords={"float32_coord": coord_values[2]}
          )
          actual_scalar = array.sel(float32_coord=float_values[2])
  
          assert_equal(expected, actual)
          assert_equal(expected_scalar, actual_scalar)
          assert_equal(expected_16, actual_16)
  
      def test_sel_no_index(self):
          array = DataArray(np.arange(10), dims="x")
          assert_identical(array[0], array.sel(x=0))
          assert_identical(array[:5], array.sel(x=slice(5)))
          assert_identical(array[[0, -1]], array.sel(x=[0, -1]))
          assert_identical(array[array < 5], array.sel(x=(array < 5)))
  
      def test_sel_method(self):
          data = DataArray(np.random.randn(3, 4), [("x", [0, 1, 2]), ("y", list("abcd"))])
  
          expected = data.sel(y=["a", "b"])
          actual = data.sel(y=["ab", "ba"], method="pad")
          assert_identical(expected, actual)
  
          expected = data.sel(x=[1, 2])
          actual = data.sel(x=[0.9, 1.9], method="backfill", tolerance=1)
          assert_identical(expected, actual)
  
      def test_sel_drop(self):
          data = DataArray([1, 2, 3], [("x", [0, 1, 2])])
          expected = DataArray(1)
          selected = data.sel(x=0, drop=True)
          assert_identical(expected, selected)
  
          expected = DataArray(1, {"x": 0})
          selected = data.sel(x=0, drop=False)
          assert_identical(expected, selected)
  
          data = DataArray([1, 2, 3], dims=["x"])
          expected = DataArray(1)
          selected = data.sel(x=0, drop=True)
          assert_identical(expected, selected)
  
      def test_isel_drop(self):
          data = DataArray([1, 2, 3], [("x", [0, 1, 2])])
          expected = DataArray(1)
          selected = data.isel(x=0, drop=True)
          assert_identical(expected, selected)
  
          expected = DataArray(1, {"x": 0})
          selected = data.isel(x=0, drop=False)
          assert_identical(expected, selected)
  
      def test_head(self):
          assert_equal(self.dv.isel(x=slice(5)), self.dv.head(x=5))
          assert_equal(self.dv.isel(x=slice(0)), self.dv.head(x=0))
          assert_equal(
              self.dv.isel({dim: slice(6) for dim in self.dv.dims}), self.dv.head(6)
          )
          assert_equal(
              self.dv.isel({dim: slice(5) for dim in self.dv.dims}), self.dv.head()
          )
          with pytest.raises(TypeError, match=r"either dict-like or a single int"):
              self.dv.head([3])
          with pytest.raises(TypeError, match=r"expected integer type"):
              self.dv.head(x=3.1)
          with pytest.raises(ValueError, match=r"expected positive int"):
              self.dv.head(-3)
  
      def test_tail(self):
          assert_equal(self.dv.isel(x=slice(-5, None)), self.dv.tail(x=5))
          assert_equal(self.dv.isel(x=slice(0)), self.dv.tail(x=0))
          assert_equal(
              self.dv.isel({dim: slice(-6, None) for dim in self.dv.dims}),
              self.dv.tail(6),
          )
          assert_equal(
              self.dv.isel({dim: slice(-5, None) for dim in self.dv.dims}), self.dv.tail()
          )
          with pytest.raises(TypeError, match=r"either dict-like or a single int"):
              self.dv.tail([3])
          with pytest.raises(TypeError, match=r"expected integer type"):
              self.dv.tail(x=3.1)
          with pytest.raises(ValueError, match=r"expected positive int"):
              self.dv.tail(-3)
  
      def test_thin(self):
          assert_equal(self.dv.isel(x=slice(None, None, 5)), self.dv.thin(x=5))
          assert_equal(
              self.dv.isel({dim: slice(None, None, 6) for dim in self.dv.dims}),
              self.dv.thin(6),
          )
          with pytest.raises(TypeError, match=r"either dict-like or a single int"):
              self.dv.thin([3])
          with pytest.raises(TypeError, match=r"expected integer type"):
              self.dv.thin(x=3.1)
          with pytest.raises(ValueError, match=r"expected positive int"):
              self.dv.thin(-3)
          with pytest.raises(ValueError, match=r"cannot be zero"):
              self.dv.thin(time=0)
  
      def test_loc(self):
          self.ds["x"] = ("x", np.array(list("abcdefghij")))
          da = self.ds["foo"]
          assert_identical(da[:3], da.loc[:"c"])
          assert_identical(da[1], da.loc["b"])
          assert_identical(da[1], da.loc[{"x": "b"}])
          assert_identical(da[1], da.loc["b", ...])
          assert_identical(da[:3], da.loc[["a", "b", "c"]])
          assert_identical(da[:3, :4], da.loc[["a", "b", "c"], np.arange(4)])
          assert_identical(da[:, :4], da.loc[:, self.ds["y"] < 4])
  
      def test_loc_datetime64_value(self):
          # regression test for https://github.com/pydata/xarray/issues/4283
          t = np.array(["2017-09-05T12", "2017-09-05T15"], dtype="datetime64[ns]")
          array = DataArray(np.ones(t.shape), dims=("time",), coords=(t,))
          assert_identical(array.loc[{"time": t[0]}], array[0])
  
      def test_loc_assign(self):
          self.ds["x"] = ("x", np.array(list("abcdefghij")))
          da = self.ds["foo"]
          # assignment
          da.loc["a":"j"] = 0
          assert np.all(da.values == 0)
          da.loc[{"x": slice("a", "j")}] = 2
          assert np.all(da.values == 2)
  
          da.loc[{"x": slice("a", "j")}] = 2
          assert np.all(da.values == 2)
  
          # Multi dimensional case
          da = DataArray(np.arange(12).reshape(3, 4), dims=["x", "y"])
          da.loc[0, 0] = 0
          assert da.values[0, 0] == 0
          assert da.values[0, 1] != 0
  
          da = DataArray(np.arange(12).reshape(3, 4), dims=["x", "y"])
          da.loc[0] = 0
          assert np.all(da.values[0] == np.zeros(4))
          assert da.values[1, 0] != 0
  
      def test_loc_assign_dataarray(self):
          def get_data():
              return DataArray(
                  np.ones((4, 3, 2)),
                  dims=["x", "y", "z"],
                  coords={
                      "x": np.arange(4),
                      "y": ["a", "b", "c"],
                      "non-dim": ("x", [1, 3, 4, 2]),
                  },
              )
  
          da = get_data()
          # indexer with inconsistent coordinates.
          ind = DataArray(np.arange(1, 4), dims=["y"], coords={"y": np.random.randn(3)})
          with pytest.raises(IndexError, match=r"dimension coordinate 'y'"):
              da.loc[dict(x=ind)] = 0
  
          # indexer with consistent coordinates.
          ind = DataArray(np.arange(1, 4), dims=["x"], coords={"x": np.arange(1, 4)})
          da.loc[dict(x=ind)] = 0  # should not raise
          assert np.allclose(da[dict(x=ind)].values, 0)
          assert_identical(da["x"], get_data()["x"])
          assert_identical(da["non-dim"], get_data()["non-dim"])
  
          da = get_data()
          # conflict in the assigning values
          value = xr.DataArray(
              np.zeros((3, 3, 2)),
              dims=["x", "y", "z"],
              coords={"x": [0, 1, 2], "non-dim": ("x", [0, 2, 4])},
          )
          with pytest.raises(IndexError, match=r"dimension coordinate 'x'"):
              da.loc[dict(x=ind)] = value
  
          # consistent coordinate in the assigning values
          value = xr.DataArray(
              np.zeros((3, 3, 2)),
              dims=["x", "y", "z"],
              coords={"x": [1, 2, 3], "non-dim": ("x", [0, 2, 4])},
          )
          da.loc[dict(x=ind)] = value
          assert np.allclose(da[dict(x=ind)].values, 0)
          assert_identical(da["x"], get_data()["x"])
          assert_identical(da["non-dim"], get_data()["non-dim"])
  
      def test_loc_single_boolean(self):
          data = DataArray([0, 1], coords=[[True, False]])
          assert data.loc[True] == 0
          assert data.loc[False] == 1
  
      def test_loc_dim_name_collision_with_sel_params(self):
          da = xr.DataArray(
              [[0, 0], [1, 1]],
              dims=["dim1", "method"],
              coords={"dim1": ["x", "y"], "method": ["a", "b"]},
          )
          np.testing.assert_array_equal(
              da.loc[dict(dim1=["x", "y"], method=["a"])], [[0], [1]]
          )
  
      def test_selection_multiindex(self):
          mindex = pd.MultiIndex.from_product(
              [["a", "b"], [1, 2], [-1, -2]], names=("one", "two", "three")
          )
          mdata = DataArray(range(8), [("x", mindex)])
  
          def test_sel(lab_indexer, pos_indexer, replaced_idx=False, renamed_dim=None):
              da = mdata.sel(x=lab_indexer)
              expected_da = mdata.isel(x=pos_indexer)
              if not replaced_idx:
                  assert_identical(da, expected_da)
              else:
                  if renamed_dim:
                      assert da.dims[0] == renamed_dim
                      da = da.rename({renamed_dim: "x"})
                  assert_identical(da.variable, expected_da.variable)
                  assert not da["x"].equals(expected_da["x"])
  
          test_sel(("a", 1, -1), 0)
          test_sel(("b", 2, -2), -1)
          test_sel(("a", 1), [0, 1], replaced_idx=True, renamed_dim="three")
          test_sel(("a",), range(4), replaced_idx=True)
          test_sel("a", range(4), replaced_idx=True)
          test_sel([("a", 1, -1), ("b", 2, -2)], [0, 7])
          test_sel(slice("a", "b"), range(8))
          test_sel(slice(("a", 1), ("b", 1)), range(6))
          test_sel({"one": "a", "two": 1, "three": -1}, 0)
          test_sel({"one": "a", "two": 1}, [0, 1], replaced_idx=True, renamed_dim="three")
          test_sel({"one": "a"}, range(4), replaced_idx=True)
  
          assert_identical(mdata.loc["a"], mdata.sel(x="a"))
          assert_identical(mdata.loc[("a", 1), ...], mdata.sel(x=("a", 1)))
          assert_identical(mdata.loc[{"one": "a"}, ...], mdata.sel(x={"one": "a"}))
          with pytest.raises(IndexError):
              mdata.loc[("a", 1)]
  
          assert_identical(mdata.sel(x={"one": "a", "two": 1}), mdata.sel(one="a", two=1))
  
      def test_selection_multiindex_remove_unused(self):
          # GH2619. For MultiIndex, we need to call remove_unused.
          ds = xr.DataArray(
              np.arange(40).reshape(8, 5),
              dims=["x", "y"],
              coords={"x": np.arange(8), "y": np.arange(5)},
          )
          ds = ds.stack(xy=["x", "y"])
          ds_isel = ds.isel(xy=ds["x"] < 4)
          with pytest.raises(KeyError):
              ds_isel.sel(x=5)
  
          actual = ds_isel.unstack()
          expected = ds.reset_index("xy").isel(xy=ds["x"] < 4)
          expected = expected.set_index(xy=["x", "y"]).unstack()
          assert_identical(expected, actual)
  
      def test_selection_multiindex_from_level(self):
          # GH: 3512
          da = DataArray([0, 1], dims=["x"], coords={"x": [0, 1], "y": "a"})
          db = DataArray([2, 3], dims=["x"], coords={"x": [0, 1], "y": "b"})
          data = xr.concat([da, db], dim="x").set_index(xy=["x", "y"])
          assert data.dims == ("xy",)
          actual = data.sel(y="a")
          expected = data.isel(xy=[0, 1]).unstack("xy").squeeze("y").drop_vars("y")
          assert_equal(actual, expected)
  
      def test_virtual_default_coords(self):
          array = DataArray(np.zeros((5,)), dims="x")
          expected = DataArray(range(5), dims="x", name="x")
          assert_identical(expected, array["x"])
          assert_identical(expected, array.coords["x"])
  
      def test_virtual_time_components(self):
          dates = pd.date_range("2000-01-01", periods=10)
          da = DataArray(np.arange(1, 11), [("time", dates)])
  
          assert_array_equal(da["time.dayofyear"], da.values)
          assert_array_equal(da.coords["time.dayofyear"], da.values)
  
      def test_coords(self):
          # use int64 to ensure repr() consistency on windows
          coords = [
              IndexVariable("x", np.array([-1, -2], "int64")),
              IndexVariable("y", np.array([0, 1, 2], "int64")),
          ]
          da = DataArray(np.random.randn(2, 3), coords, name="foo")
  
          assert 2 == len(da.coords)
  
          assert ["x", "y"] == list(da.coords)
  
          assert coords[0].identical(da.coords["x"])
          assert coords[1].identical(da.coords["y"])
  
          assert "x" in da.coords
          assert 0 not in da.coords
          assert "foo" not in da.coords
  
          with pytest.raises(KeyError):
              da.coords[0]
          with pytest.raises(KeyError):
              da.coords["foo"]
  
          expected = dedent(
              """\
          Coordinates:
            * x        (x) int64 -1 -2
            * y        (y) int64 0 1 2"""
          )
          actual = repr(da.coords)
          assert expected == actual
  
          del da.coords["x"]
          da._indexes = propagate_indexes(da._indexes, exclude="x")
          expected = DataArray(da.values, {"y": [0, 1, 2]}, dims=["x", "y"], name="foo")
          assert_identical(da, expected)
  
          with pytest.raises(ValueError, match=r"conflicting MultiIndex"):
              self.mda["level_1"] = np.arange(4)
              self.mda.coords["level_1"] = np.arange(4)
  
      def test_coords_to_index(self):
          da = DataArray(np.zeros((2, 3)), [("x", [1, 2]), ("y", list("abc"))])
  
          with pytest.raises(ValueError, match=r"no valid index"):
              da[0, 0].coords.to_index()
  
          expected = pd.Index(["a", "b", "c"], name="y")
          actual = da[0].coords.to_index()
          assert expected.equals(actual)
  
          expected = pd.MultiIndex.from_product(
              [[1, 2], ["a", "b", "c"]], names=["x", "y"]
          )
          actual = da.coords.to_index()
          assert expected.equals(actual)
  
          expected = pd.MultiIndex.from_product(
              [["a", "b", "c"], [1, 2]], names=["y", "x"]
          )
          actual = da.coords.to_index(["y", "x"])
          assert expected.equals(actual)
  
          with pytest.raises(ValueError, match=r"ordered_dims must match"):
              da.coords.to_index(["x"])
  
      def test_coord_coords(self):
          orig = DataArray(
              [10, 20], {"x": [1, 2], "x2": ("x", ["a", "b"]), "z": 4}, dims="x"
          )
  
          actual = orig.coords["x"]
          expected = DataArray(
              [1, 2], {"z": 4, "x2": ("x", ["a", "b"]), "x": [1, 2]}, dims="x", name="x"
          )
          assert_identical(expected, actual)
  
          del actual.coords["x2"]
          assert_identical(expected.reset_coords("x2", drop=True), actual)
  
          actual.coords["x3"] = ("x", ["a", "b"])
          expected = DataArray(
              [1, 2], {"z": 4, "x3": ("x", ["a", "b"]), "x": [1, 2]}, dims="x", name="x"
          )
          assert_identical(expected, actual)
  
      def test_reset_coords(self):
          data = DataArray(
              np.zeros((3, 4)),
              {"bar": ("x", ["a", "b", "c"]), "baz": ("y", range(4)), "y": range(4)},
              dims=["x", "y"],
              name="foo",
          )
  
          actual = data.reset_coords()
          expected = Dataset(
              {
                  "foo": (["x", "y"], np.zeros((3, 4))),
                  "bar": ("x", ["a", "b", "c"]),
                  "baz": ("y", range(4)),
                  "y": range(4),
              }
          )
          assert_identical(actual, expected)
  
          actual = data.reset_coords(["bar", "baz"])
          assert_identical(actual, expected)
  
          actual = data.reset_coords("bar")
          expected = Dataset(
              {"foo": (["x", "y"], np.zeros((3, 4))), "bar": ("x", ["a", "b", "c"])},
              {"baz": ("y", range(4)), "y": range(4)},
          )
          assert_identical(actual, expected)
  
          actual = data.reset_coords(["bar"])
          assert_identical(actual, expected)
  
          actual = data.reset_coords(drop=True)
          expected = DataArray(
              np.zeros((3, 4)), coords={"y": range(4)}, dims=["x", "y"], name="foo"
          )
          assert_identical(actual, expected)
  
          actual = data.copy()
          actual = actual.reset_coords(drop=True)
          assert_identical(actual, expected)
  
          actual = data.reset_coords("bar", drop=True)
          expected = DataArray(
              np.zeros((3, 4)),
              {"baz": ("y", range(4)), "y": range(4)},
              dims=["x", "y"],
              name="foo",
          )
          assert_identical(actual, expected)
  
          with pytest.raises(ValueError, match=r"cannot be found"):
              data.reset_coords("foo", drop=True)
          with pytest.raises(ValueError, match=r"cannot be found"):
              data.reset_coords("not_found")
          with pytest.raises(ValueError, match=r"cannot remove index"):
              data.reset_coords("y")
  
      def test_assign_coords(self):
          array = DataArray(10)
          actual = array.assign_coords(c=42)
          expected = DataArray(10, {"c": 42})
          assert_identical(actual, expected)
  
          with pytest.raises(ValueError, match=r"conflicting MultiIndex"):
              self.mda.assign_coords(level_1=range(4))
  
          # GH: 2112
          da = xr.DataArray([0, 1, 2], dims="x")
          with pytest.raises(ValueError):
              da["x"] = [0, 1, 2, 3]  # size conflict
          with pytest.raises(ValueError):
              da.coords["x"] = [0, 1, 2, 3]  # size conflict
  
      def test_coords_alignment(self):
          lhs = DataArray([1, 2, 3], [("x", [0, 1, 2])])
          rhs = DataArray([2, 3, 4], [("x", [1, 2, 3])])
          lhs.coords["rhs"] = rhs
  
          expected = DataArray(
              [1, 2, 3], coords={"rhs": ("x", [np.nan, 2, 3]), "x": [0, 1, 2]}, dims="x"
          )
          assert_identical(lhs, expected)
  
      def test_set_coords_update_index(self):
          actual = DataArray([1, 2, 3], [("x", [1, 2, 3])])
          actual.coords["x"] = ["a", "b", "c"]
          assert actual.xindexes["x"].to_pandas_index().equals(pd.Index(["a", "b", "c"]))
  
      def test_coords_replacement_alignment(self):
          # regression test for GH725
          arr = DataArray([0, 1, 2], dims=["abc"])
          new_coord = DataArray([1, 2, 3], dims=["abc"], coords=[[1, 2, 3]])
          arr["abc"] = new_coord
          expected = DataArray([0, 1, 2], coords=[("abc", [1, 2, 3])])
          assert_identical(arr, expected)
  
      def test_coords_non_string(self):
          arr = DataArray(0, coords={1: 2})
          actual = arr.coords[1]
          expected = DataArray(2, coords={1: 2}, name=1)
          assert_identical(actual, expected)
  
      def test_coords_delitem_delete_indexes(self):
          # regression test for GH3746
          arr = DataArray(np.ones((2,)), dims="x", coords={"x": [0, 1]})
          del arr.coords["x"]
          assert "x" not in arr.xindexes
  
      def test_broadcast_like(self):
          arr1 = DataArray(
              np.ones((2, 3)),
              dims=["x", "y"],
              coords={"x": ["a", "b"], "y": ["a", "b", "c"]},
          )
          arr2 = DataArray(
              np.ones((3, 2)),
              dims=["x", "y"],
              coords={"x": ["a", "b", "c"], "y": ["a", "b"]},
          )
          orig1, orig2 = broadcast(arr1, arr2)
          new1 = arr1.broadcast_like(arr2)
          new2 = arr2.broadcast_like(arr1)
  
          assert_identical(orig1, new1)
          assert_identical(orig2, new2)
  
          orig3 = DataArray(np.random.randn(5), [("x", range(5))])
          orig4 = DataArray(np.random.randn(6), [("y", range(6))])
          new3, new4 = broadcast(orig3, orig4)
  
          assert_identical(orig3.broadcast_like(orig4), new3.transpose("y", "x"))
          assert_identical(orig4.broadcast_like(orig3), new4)
  
      def test_reindex_like(self):
          foo = DataArray(np.random.randn(5, 6), [("x", range(5)), ("y", range(6))])
          bar = foo[:2, :2]
          assert_identical(foo.reindex_like(bar), bar)
  
          expected = foo.copy()
          expected[:] = np.nan
          expected[:2, :2] = bar
          assert_identical(bar.reindex_like(foo), expected)
  
      def test_reindex_like_no_index(self):
          foo = DataArray(np.random.randn(5, 6), dims=["x", "y"])
          assert_identical(foo, foo.reindex_like(foo))
  
          bar = foo[:4]
          with pytest.raises(ValueError, match=r"different size for unlabeled"):
              foo.reindex_like(bar)
  
      def test_reindex_regressions(self):
          da = DataArray(np.random.randn(5), coords=[("time", range(5))])
          time2 = DataArray(np.arange(5), dims="time2")
          with pytest.raises(ValueError):
              da.reindex(time=time2)
  
          # regression test for #736, reindex can not change complex nums dtype
          x = np.array([1, 2, 3], dtype=complex)
          x = DataArray(x, coords=[[0.1, 0.2, 0.3]])
          y = DataArray([2, 5, 6, 7, 8], coords=[[-1.1, 0.21, 0.31, 0.41, 0.51]])
          re_dtype = x.reindex_like(y, method="pad").dtype
          assert x.dtype == re_dtype
  
      def test_reindex_method(self):
          x = DataArray([10, 20], dims="y", coords={"y": [0, 1]})
          y = [-0.1, 0.5, 1.1]
          actual = x.reindex(y=y, method="backfill", tolerance=0.2)
          expected = DataArray([10, np.nan, np.nan], coords=[("y", y)])
          assert_identical(expected, actual)
  
          alt = Dataset({"y": y})
          actual = x.reindex_like(alt, method="backfill")
          expected = DataArray([10, 20, np.nan], coords=[("y", y)])
          assert_identical(expected, actual)
  
      @pytest.mark.parametrize("fill_value", [dtypes.NA, 2, 2.0, {None: 2, "u": 1}])
      def test_reindex_fill_value(self, fill_value):
          x = DataArray([10, 20], dims="y", coords={"y": [0, 1], "u": ("y", [1, 2])})
          y = [0, 1, 2]
          if fill_value == dtypes.NA:
              # if we supply the default, we expect the missing value for a
              # float array
              fill_value_var = fill_value_u = np.nan
          elif isinstance(fill_value, dict):
              fill_value_var = fill_value[None]
              fill_value_u = fill_value["u"]
          else:
              fill_value_var = fill_value_u = fill_value
          actual = x.reindex(y=y, fill_value=fill_value)
          expected = DataArray(
              [10, 20, fill_value_var],
              dims="y",
              coords={"y": y, "u": ("y", [1, 2, fill_value_u])},
          )
          assert_identical(expected, actual)
  
      @pytest.mark.parametrize("dtype", [str, bytes])
      def test_reindex_str_dtype(self, dtype):
  
          data = DataArray(
              [1, 2], dims="x", coords={"x": np.array(["a", "b"], dtype=dtype)}
          )
  
          actual = data.reindex(x=data.x)
          expected = data
  
          assert_identical(expected, actual)
          assert actual.dtype == expected.dtype
  
      def test_rename(self):
          renamed = self.dv.rename("bar")
          assert_identical(renamed.to_dataset(), self.ds.rename({"foo": "bar"}))
          assert renamed.name == "bar"
  
          renamed = self.dv.x.rename({"x": "z"}).rename("z")
          assert_identical(renamed, self.ds.rename({"x": "z"}).z)
          assert renamed.name == "z"
          assert renamed.dims == ("z",)
  
          renamed_kwargs = self.dv.x.rename(x="z").rename("z")
          assert_identical(renamed, renamed_kwargs)
  
      def test_init_value(self):
          expected = DataArray(
              np.full((3, 4), 3), dims=["x", "y"], coords=[range(3), range(4)]
          )
          actual = DataArray(3, dims=["x", "y"], coords=[range(3), range(4)])
          assert_identical(expected, actual)
  
          expected = DataArray(
              np.full((1, 10, 2), 0),
              dims=["w", "x", "y"],
              coords={"x": np.arange(10), "y": ["north", "south"]},
          )
          actual = DataArray(0, dims=expected.dims, coords=expected.coords)
          assert_identical(expected, actual)
  
          expected = DataArray(
              np.full((10, 2), np.nan), coords=[("x", np.arange(10)), ("y", ["a", "b"])]
          )
          actual = DataArray(coords=[("x", np.arange(10)), ("y", ["a", "b"])])
          assert_identical(expected, actual)
  
          with pytest.raises(ValueError, match=r"different number of dim"):
              DataArray(np.array(1), coords={"x": np.arange(10)}, dims=["x"])
          with pytest.raises(ValueError, match=r"does not match the 0 dim"):
              DataArray(np.array(1), coords=[("x", np.arange(10))])
  
      def test_swap_dims(self):
          array = DataArray(np.random.randn(3), {"x": list("abc")}, "x")
          expected = DataArray(array.values, {"x": ("y", list("abc"))}, dims="y")
          actual = array.swap_dims({"x": "y"})
          assert_identical(expected, actual)
          for dim_name in set().union(expected.xindexes.keys(), actual.xindexes.keys()):
              pd.testing.assert_index_equal(
                  expected.xindexes[dim_name].to_pandas_index(),
                  actual.xindexes[dim_name].to_pandas_index(),
              )
  
          # as kwargs
          array = DataArray(np.random.randn(3), {"x": list("abc")}, "x")
          expected = DataArray(array.values, {"x": ("y", list("abc"))}, dims="y")
          actual = array.swap_dims(x="y")
          assert_identical(expected, actual)
          for dim_name in set().union(expected.xindexes.keys(), actual.xindexes.keys()):
              pd.testing.assert_index_equal(
                  expected.xindexes[dim_name].to_pandas_index(),
                  actual.xindexes[dim_name].to_pandas_index(),
              )
  
          # multiindex case
          idx = pd.MultiIndex.from_arrays([list("aab"), list("yzz")], names=["y1", "y2"])
          array = DataArray(np.random.randn(3), {"y": ("x", idx)}, "x")
          expected = DataArray(array.values, {"y": idx}, "y")
          actual = array.swap_dims({"x": "y"})
          assert_identical(expected, actual)
          for dim_name in set().union(expected.xindexes.keys(), actual.xindexes.keys()):
              pd.testing.assert_index_equal(
                  expected.xindexes[dim_name].to_pandas_index(),
                  actual.xindexes[dim_name].to_pandas_index(),
              )
  
      def test_expand_dims_error(self):
          array = DataArray(
              np.random.randn(3, 4),
              dims=["x", "dim_0"],
              coords={"x": np.linspace(0.0, 1.0, 3)},
              attrs={"key": "entry"},
          )
  
          with pytest.raises(TypeError, match=r"dim should be hashable or"):
              array.expand_dims(0)
          with pytest.raises(ValueError, match=r"lengths of dim and axis"):
              # dims and axis argument should be the same length
              array.expand_dims(dim=["a", "b"], axis=[1, 2, 3])
          with pytest.raises(ValueError, match=r"Dimension x already"):
              # Should not pass the already existing dimension.
              array.expand_dims(dim=["x"])
          # raise if duplicate
          with pytest.raises(ValueError, match=r"duplicate values"):
              array.expand_dims(dim=["y", "y"])
          with pytest.raises(ValueError, match=r"duplicate values"):
              array.expand_dims(dim=["y", "z"], axis=[1, 1])
          with pytest.raises(ValueError, match=r"duplicate values"):
              array.expand_dims(dim=["y", "z"], axis=[2, -2])
  
          # out of bounds error, axis must be in [-4, 3]
          with pytest.raises(IndexError):
              array.expand_dims(dim=["y", "z"], axis=[2, 4])
          with pytest.raises(IndexError):
              array.expand_dims(dim=["y", "z"], axis=[2, -5])
          # Does not raise an IndexError
          array.expand_dims(dim=["y", "z"], axis=[2, -4])
          array.expand_dims(dim=["y", "z"], axis=[2, 3])
  
          array = DataArray(
              np.random.randn(3, 4),
              dims=["x", "dim_0"],
              coords={"x": np.linspace(0.0, 1.0, 3)},
              attrs={"key": "entry"},
          )
          with pytest.raises(TypeError):
              array.expand_dims({"new_dim": 3.2})
  
          # Attempt to use both dim and kwargs
          with pytest.raises(ValueError):
              array.expand_dims({"d": 4}, e=4)
  
      def test_expand_dims(self):
          array = DataArray(
              np.random.randn(3, 4),
              dims=["x", "dim_0"],
              coords={"x": np.linspace(0.0, 1.0, 3)},
              attrs={"key": "entry"},
          )
          # pass only dim label
          actual = array.expand_dims(dim="y")
          expected = DataArray(
              np.expand_dims(array.values, 0),
              dims=["y", "x", "dim_0"],
              coords={"x": np.linspace(0.0, 1.0, 3)},
              attrs={"key": "entry"},
          )
          assert_identical(expected, actual)
          roundtripped = actual.squeeze("y", drop=True)
          assert_identical(array, roundtripped)
  
          # pass multiple dims
          actual = array.expand_dims(dim=["y", "z"])
          expected = DataArray(
              np.expand_dims(np.expand_dims(array.values, 0), 0),
              dims=["y", "z", "x", "dim_0"],
              coords={"x": np.linspace(0.0, 1.0, 3)},
              attrs={"key": "entry"},
          )
          assert_identical(expected, actual)
          roundtripped = actual.squeeze(["y", "z"], drop=True)
          assert_identical(array, roundtripped)
  
          # pass multiple dims and axis. Axis is out of order
          actual = array.expand_dims(dim=["z", "y"], axis=[2, 1])
          expected = DataArray(
              np.expand_dims(np.expand_dims(array.values, 1), 2),
              dims=["x", "y", "z", "dim_0"],
              coords={"x": np.linspace(0.0, 1.0, 3)},
              attrs={"key": "entry"},
          )
          assert_identical(expected, actual)
          # make sure the attrs are tracked
          assert actual.attrs["key"] == "entry"
          roundtripped = actual.squeeze(["z", "y"], drop=True)
          assert_identical(array, roundtripped)
  
          # Negative axis and they are out of order
          actual = array.expand_dims(dim=["y", "z"], axis=[-1, -2])
          expected = DataArray(
              np.expand_dims(np.expand_dims(array.values, -1), -1),
              dims=["x", "dim_0", "z", "y"],
              coords={"x": np.linspace(0.0, 1.0, 3)},
              attrs={"key": "entry"},
          )
          assert_identical(expected, actual)
          assert actual.attrs["key"] == "entry"
          roundtripped = actual.squeeze(["y", "z"], drop=True)
          assert_identical(array, roundtripped)
  
      def test_expand_dims_with_scalar_coordinate(self):
          array = DataArray(
              np.random.randn(3, 4),
              dims=["x", "dim_0"],
              coords={"x": np.linspace(0.0, 1.0, 3), "z": 1.0},
              attrs={"key": "entry"},
          )
          actual = array.expand_dims(dim="z")
          expected = DataArray(
              np.expand_dims(array.values, 0),
              dims=["z", "x", "dim_0"],
              coords={"x": np.linspace(0.0, 1.0, 3), "z": np.ones(1)},
              attrs={"key": "entry"},
          )
          assert_identical(expected, actual)
          roundtripped = actual.squeeze(["z"], drop=False)
          assert_identical(array, roundtripped)
  
      def test_expand_dims_with_greater_dim_size(self):
          array = DataArray(
              np.random.randn(3, 4),
              dims=["x", "dim_0"],
              coords={"x": np.linspace(0.0, 1.0, 3), "z": 1.0},
              attrs={"key": "entry"},
          )
          actual = array.expand_dims({"y": 2, "z": 1, "dim_1": ["a", "b", "c"]})
  
          expected_coords = {
              "y": [0, 1],
              "z": [1.0],
              "dim_1": ["a", "b", "c"],
              "x": np.linspace(0, 1, 3),
              "dim_0": range(4),
          }
          expected = DataArray(
              array.values * np.ones([2, 1, 3, 3, 4]),
              coords=expected_coords,
              dims=list(expected_coords.keys()),
              attrs={"key": "entry"},
          ).drop_vars(["y", "dim_0"])
          assert_identical(expected, actual)
  
          # Test with kwargs instead of passing dict to dim arg.
  
          other_way = array.expand_dims(dim_1=["a", "b", "c"])
  
          other_way_expected = DataArray(
              array.values * np.ones([3, 3, 4]),
              coords={
                  "dim_1": ["a", "b", "c"],
                  "x": np.linspace(0, 1, 3),
                  "dim_0": range(4),
                  "z": 1.0,
              },
              dims=["dim_1", "x", "dim_0"],
              attrs={"key": "entry"},
          ).drop_vars("dim_0")
          assert_identical(other_way_expected, other_way)
  
      def test_set_index(self):
          indexes = [self.mindex.get_level_values(n) for n in self.mindex.names]
          coords = {idx.name: ("x", idx) for idx in indexes}
          array = DataArray(self.mda.values, coords=coords, dims="x")
          expected = self.mda.copy()
          level_3 = ("x", [1, 2, 3, 4])
          array["level_3"] = level_3
          expected["level_3"] = level_3
  
          obj = array.set_index(x=self.mindex.names)
          assert_identical(obj, expected)
  
          obj = obj.set_index(x="level_3", append=True)
          expected = array.set_index(x=["level_1", "level_2", "level_3"])
          assert_identical(obj, expected)
  
          array = array.set_index(x=["level_1", "level_2", "level_3"])
          assert_identical(array, expected)
  
          array2d = DataArray(
              np.random.rand(2, 2),
              coords={"x": ("x", [0, 1]), "level": ("y", [1, 2])},
              dims=("x", "y"),
          )
          with pytest.raises(ValueError, match=r"dimension mismatch"):
              array2d.set_index(x="level")
  
          # Issue 3176: Ensure clear error message on key error.
          with pytest.raises(ValueError) as excinfo:
              obj.set_index(x="level_4")
          assert str(excinfo.value) == "level_4 is not the name of an existing variable."
  
      def test_reset_index(self):
          indexes = [self.mindex.get_level_values(n) for n in self.mindex.names]
          coords = {idx.name: ("x", idx) for idx in indexes}
          expected = DataArray(self.mda.values, coords=coords, dims="x")
  
          obj = self.mda.reset_index("x")
          assert_identical(obj, expected)
          obj = self.mda.reset_index(self.mindex.names)
          assert_identical(obj, expected)
          obj = self.mda.reset_index(["x", "level_1"])
          assert_identical(obj, expected)
  
          coords = {
              "x": ("x", self.mindex.droplevel("level_1")),
              "level_1": ("x", self.mindex.get_level_values("level_1")),
          }
          expected = DataArray(self.mda.values, coords=coords, dims="x")
          obj = self.mda.reset_index(["level_1"])
          assert_identical(obj, expected)
  
          expected = DataArray(self.mda.values, dims="x")
          obj = self.mda.reset_index("x", drop=True)
          assert_identical(obj, expected)
  
          array = self.mda.copy()
          array = array.reset_index(["x"], drop=True)
          assert_identical(array, expected)
  
          # single index
          array = DataArray([1, 2], coords={"x": ["a", "b"]}, dims="x")
          expected = DataArray([1, 2], coords={"x_": ("x", ["a", "b"])}, dims="x")
          assert_identical(array.reset_index("x"), expected)
  
      def test_reset_index_keep_attrs(self):
          coord_1 = DataArray([1, 2], dims=["coord_1"], attrs={"attrs": True})
          da = DataArray([1, 0], [coord_1])
          expected = DataArray([1, 0], {"coord_1_": coord_1}, dims=["coord_1"])
          obj = da.reset_index("coord_1")
          assert_identical(expected, obj)
  
      def test_reorder_levels(self):
          midx = self.mindex.reorder_levels(["level_2", "level_1"])
          expected = DataArray(self.mda.values, coords={"x": midx}, dims="x")
  
          obj = self.mda.reorder_levels(x=["level_2", "level_1"])
          assert_identical(obj, expected)
  
          array = DataArray([1, 2], dims="x")
          with pytest.raises(KeyError):
              array.reorder_levels(x=["level_1", "level_2"])
  
          array["x"] = [0, 1]
          with pytest.raises(ValueError, match=r"has no MultiIndex"):
              array.reorder_levels(x=["level_1", "level_2"])
  
      def test_dataset_getitem(self):
          dv = self.ds["foo"]
          assert_identical(dv, self.dv)
  
      def test_array_interface(self):
          assert_array_equal(np.asarray(self.dv), self.x)
          # test patched in methods
          assert_array_equal(self.dv.astype(float), self.v.astype(float))
          assert_array_equal(self.dv.argsort(), self.v.argsort())
          assert_array_equal(self.dv.clip(2, 3), self.v.clip(2, 3))
          # test ufuncs
          expected = deepcopy(self.ds)
          expected["foo"][:] = np.sin(self.x)
          assert_equal(expected["foo"], np.sin(self.dv))
          assert_array_equal(self.dv, np.maximum(self.v, self.dv))
          bar = Variable(["x", "y"], np.zeros((10, 20)))
          assert_equal(self.dv, np.maximum(self.dv, bar))
  
      def test_astype_attrs(self):
          for v in [self.va.copy(), self.mda.copy(), self.ds.copy()]:
              v.attrs["foo"] = "bar"
              assert v.attrs == v.astype(float).attrs
              assert not v.astype(float, keep_attrs=False).attrs
  
      def test_astype_dtype(self):
          original = DataArray([-1, 1, 2, 3, 1000])
          converted = original.astype(float)
          assert_array_equal(original, converted)
          assert np.issubdtype(original.dtype, np.integer)
          assert np.issubdtype(converted.dtype, np.floating)
  
      def test_astype_order(self):
          original = DataArray([[1, 2], [3, 4]])
          converted = original.astype("d", order="F")
          assert_equal(original, converted)
          assert original.values.flags["C_CONTIGUOUS"]
          assert converted.values.flags["F_CONTIGUOUS"]
  
      def test_astype_subok(self):
          class NdArraySubclass(np.ndarray):
              pass
  
          original = DataArray(NdArraySubclass(np.arange(3)))
          converted_not_subok = original.astype("d", subok=False)
          converted_subok = original.astype("d", subok=True)
          if not isinstance(original.data, NdArraySubclass):
              pytest.xfail("DataArray cannot be backed yet by a subclasses of np.ndarray")
          assert isinstance(converted_not_subok.data, np.ndarray)
          assert not isinstance(converted_not_subok.data, NdArraySubclass)
          assert isinstance(converted_subok.data, NdArraySubclass)
  
      def test_is_null(self):
          x = np.random.RandomState(42).randn(5, 6)
          x[x < 0] = np.nan
          original = DataArray(x, [-np.arange(5), np.arange(6)], ["x", "y"])
          expected = DataArray(pd.isnull(x), [-np.arange(5), np.arange(6)], ["x", "y"])
          assert_identical(expected, original.isnull())
          assert_identical(~expected, original.notnull())
  
      def test_math(self):
          x = self.x
          v = self.v
          a = self.dv
          # variable math was already tested extensively, so let's just make sure
          # that all types are properly converted here
          assert_equal(a, +a)
          assert_equal(a, a + 0)
          assert_equal(a, 0 + a)
          assert_equal(a, a + 0 * v)
          assert_equal(a, 0 * v + a)
          assert_equal(a, a + 0 * x)
          assert_equal(a, 0 * x + a)
          assert_equal(a, a + 0 * a)
          assert_equal(a, 0 * a + a)
  
      def test_math_automatic_alignment(self):
          a = DataArray(range(5), [("x", range(5))])
          b = DataArray(range(5), [("x", range(1, 6))])
          expected = DataArray(np.ones(4), [("x", [1, 2, 3, 4])])
          assert_identical(a - b, expected)
  
      def test_non_overlapping_dataarrays_return_empty_result(self):
  
          a = DataArray(range(5), [("x", range(5))])
          result = a.isel(x=slice(2)) + a.isel(x=slice(2, None))
          assert len(result["x"]) == 0
  
      def test_empty_dataarrays_return_empty_result(self):
  
          a = DataArray(data=[])
          result = a * a
          assert len(result["dim_0"]) == 0
  
      def test_inplace_math_basics(self):
          x = self.x
          a = self.dv
          v = a.variable
          b = a
          b += 1
          assert b is a
          assert b.variable is v
          assert_array_equal(b.values, x)
          assert source_ndarray(b.values) is x
  
      def test_inplace_math_error(self):
          data = np.random.rand(4)
          times = np.arange(4)
          foo = DataArray(data, coords=[times], dims=["time"])
          b = times.copy()
          with pytest.raises(
              TypeError, match=r"Values of an IndexVariable are immutable"
          ):
              foo.coords["time"] += 1
          # Check error throwing prevented inplace operation
          assert_array_equal(foo.coords["time"], b)
  
      def test_inplace_math_automatic_alignment(self):
          a = DataArray(range(5), [("x", range(5))])
          b = DataArray(range(1, 6), [("x", range(1, 6))])
          with pytest.raises(xr.MergeError, match="Automatic alignment is not supported"):
              a += b
          with pytest.raises(xr.MergeError, match="Automatic alignment is not supported"):
              b += a
  
      def test_math_name(self):
          # Verify that name is preserved only when it can be done unambiguously.
          # The rule (copied from pandas.Series) is keep the current name only if
          # the other object has the same name or no name attribute and this
          # object isn't a coordinate; otherwise reset to None.
          a = self.dv
          assert (+a).name == "foo"
          assert (a + 0).name == "foo"
          assert (a + a.rename(None)).name is None
          assert (a + a.rename("bar")).name is None
          assert (a + a).name == "foo"
          assert (+a["x"]).name == "x"
          assert (a["x"] + 0).name == "x"
          assert (a + a["x"]).name is None
  
      def test_math_with_coords(self):
          coords = {
              "x": [-1, -2],
              "y": ["ab", "cd", "ef"],
              "lat": (["x", "y"], [[1, 2, 3], [-1, -2, -3]]),
              "c": -999,
          }
          orig = DataArray(np.random.randn(2, 3), coords, dims=["x", "y"])
  
          actual = orig + 1
          expected = DataArray(orig.values + 1, orig.coords)
          assert_identical(expected, actual)
  
          actual = 1 + orig
          assert_identical(expected, actual)
  
          actual = orig + orig[0, 0]
          exp_coords = {k: v for k, v in coords.items() if k != "lat"}
          expected = DataArray(
              orig.values + orig.values[0, 0], exp_coords, dims=["x", "y"]
          )
          assert_identical(expected, actual)
  
          actual = orig[0, 0] + orig
          assert_identical(expected, actual)
  
          actual = orig[0, 0] + orig[-1, -1]
          expected = DataArray(orig.values[0, 0] + orig.values[-1, -1], {"c": -999})
          assert_identical(expected, actual)
  
          actual = orig[:, 0] + orig[0, :]
          exp_values = orig[:, 0].values[:, None] + orig[0, :].values[None, :]
          expected = DataArray(exp_values, exp_coords, dims=["x", "y"])
          assert_identical(expected, actual)
  
          actual = orig[0, :] + orig[:, 0]
          assert_identical(expected.transpose(transpose_coords=True), actual)
  
          actual = orig - orig.transpose(transpose_coords=True)
          expected = DataArray(np.zeros((2, 3)), orig.coords)
          assert_identical(expected, actual)
  
          actual = orig.transpose(transpose_coords=True) - orig
          assert_identical(expected.transpose(transpose_coords=True), actual)
  
          alt = DataArray([1, 1], {"x": [-1, -2], "c": "foo", "d": 555}, "x")
          actual = orig + alt
          expected = orig + 1
          expected.coords["d"] = 555
          del expected.coords["c"]
          assert_identical(expected, actual)
  
          actual = alt + orig
          assert_identical(expected, actual)
  
      def test_index_math(self):
          orig = DataArray(range(3), dims="x", name="x")
          actual = orig + 1
          expected = DataArray(1 + np.arange(3), dims="x", name="x")
          assert_identical(expected, actual)
  
          # regression tests for #254
          actual = orig[0] < orig
          expected = DataArray([False, True, True], dims="x", name="x")
          assert_identical(expected, actual)
  
          actual = orig > orig[0]
          assert_identical(expected, actual)
  
      def test_dataset_math(self):
          # more comprehensive tests with multiple dataset variables
          obs = Dataset(
              {"tmin": ("x", np.arange(5)), "tmax": ("x", 10 + np.arange(5))},
              {"x": ("x", 0.5 * np.arange(5)), "loc": ("x", range(-2, 3))},
          )
  
          actual = 2 * obs["tmax"]
          expected = DataArray(2 * (10 + np.arange(5)), obs.coords, name="tmax")
          assert_identical(actual, expected)
  
          actual = obs["tmax"] - obs["tmin"]
          expected = DataArray(10 * np.ones(5), obs.coords)
          assert_identical(actual, expected)
  
          sim = Dataset(
              {
                  "tmin": ("x", 1 + np.arange(5)),
                  "tmax": ("x", 11 + np.arange(5)),
                  # does *not* include 'loc' as a coordinate
                  "x": ("x", 0.5 * np.arange(5)),
              }
          )
  
          actual = sim["tmin"] - obs["tmin"]
          expected = DataArray(np.ones(5), obs.coords, name="tmin")
          assert_identical(actual, expected)
  
          actual = -obs["tmin"] + sim["tmin"]
          assert_identical(actual, expected)
  
          actual = sim["tmin"].copy()
          actual -= obs["tmin"]
          assert_identical(actual, expected)
  
          actual = sim.copy()
          actual["tmin"] = sim["tmin"] - obs["tmin"]
          expected = Dataset(
              {"tmin": ("x", np.ones(5)), "tmax": ("x", sim["tmax"].values)}, obs.coords
          )
          assert_identical(actual, expected)
  
          actual = sim.copy()
          actual["tmin"] -= obs["tmin"]
          assert_identical(actual, expected)
  
      def test_stack_unstack(self):
          orig = DataArray([[0, 1], [2, 3]], dims=["x", "y"], attrs={"foo": 2})
          assert_identical(orig, orig.unstack())
  
          # test GH3000
          a = orig[:0, :1].stack(dim=("x", "y")).dim.to_index()
          if pd.__version__ < "0.24.0":
              b = pd.MultiIndex(
                  levels=[pd.Int64Index([]), pd.Int64Index([0])],
                  labels=[[], []],
                  names=["x", "y"],
              )
          else:
              b = pd.MultiIndex(
                  levels=[pd.Int64Index([]), pd.Int64Index([0])],
                  codes=[[], []],
                  names=["x", "y"],
              )
          pd.testing.assert_index_equal(a, b)
  
          actual = orig.stack(z=["x", "y"]).unstack("z").drop_vars(["x", "y"])
          assert_identical(orig, actual)
  
          actual = orig.stack(z=[...]).unstack("z").drop_vars(["x", "y"])
          assert_identical(orig, actual)
  
          dims = ["a", "b", "c", "d", "e"]
          orig = xr.DataArray(np.random.rand(1, 2, 3, 2, 1), dims=dims)
          stacked = orig.stack(ab=["a", "b"], cd=["c", "d"])
  
          unstacked = stacked.unstack(["ab", "cd"])
          roundtripped = unstacked.drop_vars(["a", "b", "c", "d"]).transpose(*dims)
          assert_identical(orig, roundtripped)
  
          unstacked = stacked.unstack()
          roundtripped = unstacked.drop_vars(["a", "b", "c", "d"]).transpose(*dims)
          assert_identical(orig, roundtripped)
  
      def test_stack_unstack_decreasing_coordinate(self):
          # regression test for GH980
          orig = DataArray(
              np.random.rand(3, 4),
              dims=("y", "x"),
              coords={"x": np.arange(4), "y": np.arange(3, 0, -1)},
          )
          stacked = orig.stack(allpoints=["y", "x"])
          actual = stacked.unstack("allpoints")
          assert_identical(orig, actual)
  
      def test_unstack_pandas_consistency(self):
          df = pd.DataFrame({"foo": range(3), "x": ["a", "b", "b"], "y": [0, 0, 1]})
          s = df.set_index(["x", "y"])["foo"]
          expected = DataArray(s.unstack(), name="foo")
          actual = DataArray(s, dims="z").unstack("z")
          assert_identical(expected, actual)
  
      def test_stack_nonunique_consistency(self, da):
          da = da.isel(time=0, drop=True)  # 2D
          actual = da.stack(z=["a", "x"])
          expected = DataArray(da.to_pandas().stack(), dims="z")
          assert_identical(expected, actual)
  
      def test_to_unstacked_dataset_raises_value_error(self):
          data = DataArray([0, 1], dims="x", coords={"x": [0, 1]})
          with pytest.raises(ValueError, match="'x' is not a stacked coordinate"):
              data.to_unstacked_dataset("x", 0)
  
      def test_transpose(self):
          da = DataArray(
              np.random.randn(3, 4, 5),
              dims=("x", "y", "z"),
              coords={
                  "x": range(3),
                  "y": range(4),
                  "z": range(5),
                  "xy": (("x", "y"), np.random.randn(3, 4)),
              },
          )
  
          actual = da.transpose(transpose_coords=False)
          expected = DataArray(da.values.T, dims=("z", "y", "x"), coords=da.coords)
          assert_equal(expected, actual)
  
          actual = da.transpose("z", "y", "x", transpose_coords=True)
          expected = DataArray(
              da.values.T,
              dims=("z", "y", "x"),
              coords={
                  "x": da.x.values,
                  "y": da.y.values,
                  "z": da.z.values,
                  "xy": (("y", "x"), da.xy.values.T),
              },
          )
          assert_equal(expected, actual)
  
          # same as previous but with ellipsis
          actual = da.transpose("z", ..., "x", transpose_coords=True)
          assert_equal(expected, actual)
  
          # same as previous but with a missing dimension
          actual = da.transpose(
              "z", "y", "x", "not_a_dim", transpose_coords=True, missing_dims="ignore"
          )
          assert_equal(expected, actual)
  
          with pytest.raises(ValueError):
              da.transpose("x", "y")
  
          with pytest.raises(ValueError):
              da.transpose("not_a_dim", "z", "x", ...)
  
          with pytest.warns(UserWarning):
              da.transpose("not_a_dim", "y", "x", ..., missing_dims="warn")
  
      def test_squeeze(self):
          assert_equal(self.dv.variable.squeeze(), self.dv.squeeze().variable)
  
      def test_squeeze_drop(self):
          array = DataArray([1], [("x", [0])])
          expected = DataArray(1)
          actual = array.squeeze(drop=True)
          assert_identical(expected, actual)
  
          expected = DataArray(1, {"x": 0})
          actual = array.squeeze(drop=False)
          assert_identical(expected, actual)
  
          array = DataArray([[[0.0, 1.0]]], dims=["dim_0", "dim_1", "dim_2"])
          expected = DataArray([[0.0, 1.0]], dims=["dim_1", "dim_2"])
          actual = array.squeeze(axis=0)
          assert_identical(expected, actual)
  
          array = DataArray([[[[0.0, 1.0]]]], dims=["dim_0", "dim_1", "dim_2", "dim_3"])
          expected = DataArray([[0.0, 1.0]], dims=["dim_1", "dim_3"])
          actual = array.squeeze(axis=(0, 2))
          assert_identical(expected, actual)
  
          array = DataArray([[[0.0, 1.0]]], dims=["dim_0", "dim_1", "dim_2"])
          with pytest.raises(ValueError):
              array.squeeze(axis=0, dim="dim_1")
  
      def test_drop_coordinates(self):
          expected = DataArray(np.random.randn(2, 3), dims=["x", "y"])
          arr = expected.copy()
          arr.coords["z"] = 2
          actual = arr.drop_vars("z")
          assert_identical(expected, actual)
  
          with pytest.raises(ValueError):
              arr.drop_vars("not found")
  
          actual = expected.drop_vars("not found", errors="ignore")
          assert_identical(actual, expected)
  
          with pytest.raises(ValueError, match=r"cannot be found"):
              arr.drop_vars("w")
  
          actual = expected.drop_vars("w", errors="ignore")
          assert_identical(actual, expected)
  
          renamed = arr.rename("foo")
          with pytest.raises(ValueError, match=r"cannot be found"):
              renamed.drop_vars("foo")
  
          actual = renamed.drop_vars("foo", errors="ignore")
          assert_identical(actual, renamed)
  
      def test_drop_index_labels(self):
          arr = DataArray(np.random.randn(2, 3), coords={"y": [0, 1, 2]}, dims=["x", "y"])
          actual = arr.drop_sel(y=[0, 1])
          expected = arr[:, 2:]
          assert_identical(actual, expected)
  
          with pytest.raises((KeyError, ValueError), match=r"not .* in axis"):
              actual = arr.drop_sel(y=[0, 1, 3])
  
          actual = arr.drop_sel(y=[0, 1, 3], errors="ignore")
          assert_identical(actual, expected)
  
          with pytest.warns(DeprecationWarning):
              arr.drop([0, 1, 3], dim="y", errors="ignore")
  
      def test_drop_index_positions(self):
          arr = DataArray(np.random.randn(2, 3), dims=["x", "y"])
          actual = arr.drop_isel(y=[0, 1])
          expected = arr[:, 2:]
          assert_identical(actual, expected)
  
      def test_dropna(self):
          x = np.random.randn(4, 4)
          x[::2, 0] = np.nan
          arr = DataArray(x, dims=["a", "b"])
  
          actual = arr.dropna("a")
          expected = arr[1::2]
          assert_identical(actual, expected)
  
          actual = arr.dropna("b", how="all")
          assert_identical(actual, arr)
  
          actual = arr.dropna("a", thresh=1)
          assert_identical(actual, arr)
  
          actual = arr.dropna("b", thresh=3)
          expected = arr[:, 1:]
          assert_identical(actual, expected)
  
      def test_where(self):
          arr = DataArray(np.arange(4), dims="x")
          expected = arr.sel(x=slice(2))
          actual = arr.where(arr.x < 2, drop=True)
          assert_identical(actual, expected)
  
      def test_where_lambda(self):
          arr = DataArray(np.arange(4), dims="y")
          expected = arr.sel(y=slice(2))
          actual = arr.where(lambda x: x.y < 2, drop=True)
          assert_identical(actual, expected)
  
      def test_where_string(self):
          array = DataArray(["a", "b"])
          expected = DataArray(np.array(["a", np.nan], dtype=object))
          actual = array.where([True, False])
          assert_identical(actual, expected)
  
      def test_cumops(self):
          coords = {
              "x": [-1, -2],
              "y": ["ab", "cd", "ef"],
              "lat": (["x", "y"], [[1, 2, 3], [-1, -2, -3]]),
              "c": -999,
          }
          orig = DataArray([[-1, 0, 1], [-3, 0, 3]], coords, dims=["x", "y"])
  
          actual = orig.cumsum()
          expected = DataArray([[-1, -1, 0], [-4, -4, 0]], coords, dims=["x", "y"])
          assert_identical(expected, actual)
  
          actual = orig.cumsum("x")
          expected = DataArray([[-1, 0, 1], [-4, 0, 4]], coords, dims=["x", "y"])
          assert_identical(expected, actual)
  
          actual = orig.cumsum("y")
          expected = DataArray([[-1, -1, 0], [-3, -3, 0]], coords, dims=["x", "y"])
          assert_identical(expected, actual)
  
          actual = orig.cumprod("x")
          expected = DataArray([[-1, 0, 1], [3, 0, 3]], coords, dims=["x", "y"])
          assert_identical(expected, actual)
  
          actual = orig.cumprod("y")
          expected = DataArray([[-1, 0, 0], [-3, 0, 0]], coords, dims=["x", "y"])
          assert_identical(expected, actual)
  
      def test_reduce(self):
          coords = {
              "x": [-1, -2],
              "y": ["ab", "cd", "ef"],
              "lat": (["x", "y"], [[1, 2, 3], [-1, -2, -3]]),
              "c": -999,
          }
          orig = DataArray([[-1, 0, 1], [-3, 0, 3]], coords, dims=["x", "y"])
  
          actual = orig.mean()
          expected = DataArray(0, {"c": -999})
          assert_identical(expected, actual)
  
          actual = orig.mean(["x", "y"])
          assert_identical(expected, actual)
  
          actual = orig.mean("x")
          expected = DataArray([-2, 0, 2], {"y": coords["y"], "c": -999}, "y")
          assert_identical(expected, actual)
  
          actual = orig.mean(["x"])
          assert_identical(expected, actual)
  
          actual = orig.mean("y")
          expected = DataArray([0, 0], {"x": coords["x"], "c": -999}, "x")
          assert_identical(expected, actual)
  
          assert_equal(self.dv.reduce(np.mean, "x").variable, self.v.reduce(np.mean, "x"))
  
          orig = DataArray([[1, 0, np.nan], [3, 0, 3]], coords, dims=["x", "y"])
          actual = orig.count()
          expected = DataArray(5, {"c": -999})
          assert_identical(expected, actual)
  
          # uint support
          orig = DataArray(np.arange(6).reshape(3, 2).astype("uint"), dims=["x", "y"])
          assert orig.dtype.kind == "u"
          actual = orig.mean(dim="x", skipna=True)
          expected = DataArray(orig.values.astype(int), dims=["x", "y"]).mean("x")
          assert_equal(actual, expected)
  
      def test_reduce_keepdims(self):
          coords = {
              "x": [-1, -2],
              "y": ["ab", "cd", "ef"],
              "lat": (["x", "y"], [[1, 2, 3], [-1, -2, -3]]),
              "c": -999,
          }
          orig = DataArray([[-1, 0, 1], [-3, 0, 3]], coords, dims=["x", "y"])
  
          # Mean on all axes loses non-constant coordinates
          actual = orig.mean(keepdims=True)
          expected = DataArray(
              orig.data.mean(keepdims=True),
              dims=orig.dims,
              coords={k: v for k, v in coords.items() if k in ["c"]},
          )
          assert_equal(actual, expected)
  
          assert actual.sizes["x"] == 1
          assert actual.sizes["y"] == 1
  
          # Mean on specific axes loses coordinates not involving that axis
          actual = orig.mean("y", keepdims=True)
          expected = DataArray(
              orig.data.mean(axis=1, keepdims=True),
              dims=orig.dims,
              coords={k: v for k, v in coords.items() if k not in ["y", "lat"]},
          )
          assert_equal(actual, expected)
  
      @requires_bottleneck
      def test_reduce_keepdims_bottleneck(self):
          import bottleneck
  
          coords = {
              "x": [-1, -2],
              "y": ["ab", "cd", "ef"],
              "lat": (["x", "y"], [[1, 2, 3], [-1, -2, -3]]),
              "c": -999,
          }
          orig = DataArray([[-1, 0, 1], [-3, 0, 3]], coords, dims=["x", "y"])
  
          # Bottleneck does not have its own keepdims implementation
          actual = orig.reduce(bottleneck.nanmean, keepdims=True)
          expected = orig.mean(keepdims=True)
          assert_equal(actual, expected)
  
      def test_reduce_dtype(self):
          coords = {
              "x": [-1, -2],
              "y": ["ab", "cd", "ef"],
              "lat": (["x", "y"], [[1, 2, 3], [-1, -2, -3]]),
              "c": -999,
          }
          orig = DataArray([[-1, 0, 1], [-3, 0, 3]], coords, dims=["x", "y"])
  
          for dtype in [np.float16, np.float32, np.float64]:
              assert orig.astype(float).mean(dtype=dtype).dtype == dtype
  
      def test_reduce_out(self):
          coords = {
              "x": [-1, -2],
              "y": ["ab", "cd", "ef"],
              "lat": (["x", "y"], [[1, 2, 3], [-1, -2, -3]]),
              "c": -999,
          }
          orig = DataArray([[-1, 0, 1], [-3, 0, 3]], coords, dims=["x", "y"])
  
          with pytest.raises(TypeError):
              orig.mean(out=np.ones(orig.shape))
  
      @pytest.mark.parametrize("skipna", [True, False])
      @pytest.mark.parametrize("q", [0.25, [0.50], [0.25, 0.75]])
      @pytest.mark.parametrize(
          "axis, dim", zip([None, 0, [0], [0, 1]], [None, "x", ["x"], ["x", "y"]])
      )
      def test_quantile(self, q, axis, dim, skipna):
          actual = DataArray(self.va).quantile(q, dim=dim, keep_attrs=True, skipna=skipna)
          _percentile_func = np.nanpercentile if skipna else np.percentile
          expected = _percentile_func(self.dv.values, np.array(q) * 100, axis=axis)
          np.testing.assert_allclose(actual.values, expected)
          if is_scalar(q):
              assert "quantile" not in actual.dims
          else:
              assert "quantile" in actual.dims
  
          assert actual.attrs == self.attrs
  
      def test_reduce_keep_attrs(self):
          # Test dropped attrs
          vm = self.va.mean()
          assert len(vm.attrs) == 0
          assert vm.attrs == {}
  
          # Test kept attrs
          vm = self.va.mean(keep_attrs=True)
          assert len(vm.attrs) == len(self.attrs)
          assert vm.attrs == self.attrs
  
      def test_assign_attrs(self):
          expected = DataArray([], attrs=dict(a=1, b=2))
          expected.attrs["a"] = 1
          expected.attrs["b"] = 2
          new = DataArray([])
          actual = DataArray([]).assign_attrs(a=1, b=2)
          assert_identical(actual, expected)
          assert new.attrs == {}
  
          expected.attrs["c"] = 3
          new_actual = actual.assign_attrs({"c": 3})
          assert_identical(new_actual, expected)
          assert actual.attrs == {"a": 1, "b": 2}
  
      @pytest.mark.parametrize(
          "func", [lambda x: x.clip(0, 1), lambda x: np.float64(1.0) * x, np.abs, abs]
      )
      def test_propagate_attrs(self, func):
          da = DataArray(self.va)
  
          # test defaults
          assert func(da).attrs == da.attrs
  
          with set_options(keep_attrs=False):
              assert func(da).attrs == {}
  
          with set_options(keep_attrs=True):
              assert func(da).attrs == da.attrs
  
      def test_fillna(self):
          a = DataArray([np.nan, 1, np.nan, 3], coords={"x": range(4)}, dims="x")
          actual = a.fillna(-1)
          expected = DataArray([-1, 1, -1, 3], coords={"x": range(4)}, dims="x")
          assert_identical(expected, actual)
  
          b = DataArray(range(4), coords={"x": range(4)}, dims="x")
          actual = a.fillna(b)
          expected = b.copy()
          assert_identical(expected, actual)
  
          actual = a.fillna(range(4))
          assert_identical(expected, actual)
  
          actual = a.fillna(b[:3])
          assert_identical(expected, actual)
  
          actual = a.fillna(b[:0])
          assert_identical(a, actual)
  
          with pytest.raises(TypeError, match=r"fillna on a DataArray"):
              a.fillna({0: 0})
  
          with pytest.raises(ValueError, match=r"broadcast"):
              a.fillna([1, 2])
  
      def test_align(self):
          array = DataArray(
              np.random.random((6, 8)), coords={"x": list("abcdef")}, dims=["x", "y"]
          )
          array1, array2 = align(array, array[:5], join="inner")
          assert_identical(array1, array[:5])
          assert_identical(array2, array[:5])
  
      def test_align_dtype(self):
          # regression test for #264
          x1 = np.arange(30)
          x2 = np.arange(5, 35)
          a = DataArray(np.random.random((30,)).astype(np.float32), [("x", x1)])
          b = DataArray(np.random.random((30,)).astype(np.float32), [("x", x2)])
          c, d = align(a, b, join="outer")
          assert c.dtype == np.float32
  
      def test_align_copy(self):
          x = DataArray([1, 2, 3], coords=[("a", [1, 2, 3])])
          y = DataArray([1, 2], coords=[("a", [3, 1])])
  
          expected_x2 = x
          expected_y2 = DataArray([2, np.nan, 1], coords=[("a", [1, 2, 3])])
  
          x2, y2 = align(x, y, join="outer", copy=False)
          assert_identical(expected_x2, x2)
          assert_identical(expected_y2, y2)
          assert source_ndarray(x2.data) is source_ndarray(x.data)
  
          x2, y2 = align(x, y, join="outer", copy=True)
          assert_identical(expected_x2, x2)
          assert_identical(expected_y2, y2)
          assert source_ndarray(x2.data) is not source_ndarray(x.data)
  
          # Trivial align - 1 element
          x = DataArray([1, 2, 3], coords=[("a", [1, 2, 3])])
          (x2,) = align(x, copy=False)
          assert_identical(x, x2)
          assert source_ndarray(x2.data) is source_ndarray(x.data)
  
          (x2,) = align(x, copy=True)
          assert_identical(x, x2)
          assert source_ndarray(x2.data) is not source_ndarray(x.data)
  
      def test_align_override(self):
          left = DataArray([1, 2, 3], dims="x", coords={"x": [0, 1, 2]})
          right = DataArray(
              np.arange(9).reshape((3, 3)),
              dims=["x", "y"],
              coords={"x": [0.1, 1.1, 2.1], "y": [1, 2, 3]},
          )
  
          expected_right = DataArray(
              np.arange(9).reshape(3, 3),
              dims=["x", "y"],
              coords={"x": [0, 1, 2], "y": [1, 2, 3]},
          )
  
          new_left, new_right = align(left, right, join="override")
          assert_identical(left, new_left)
          assert_identical(new_right, expected_right)
  
          new_left, new_right = align(left, right, exclude="x", join="override")
          assert_identical(left, new_left)
          assert_identical(right, new_right)
  
          new_left, new_right = xr.align(
              left.isel(x=0, drop=True), right, exclude="x", join="override"
          )
          assert_identical(left.isel(x=0, drop=True), new_left)
          assert_identical(right, new_right)
  
          with pytest.raises(ValueError, match=r"Indexes along dimension 'x' don't have"):
              align(left.isel(x=0).expand_dims("x"), right, join="override")
  
      @pytest.mark.parametrize(
          "darrays",
          [
              [
                  DataArray(0),
                  DataArray([1], [("x", [1])]),
                  DataArray([2, 3], [("x", [2, 3])]),
              ],
              [
                  DataArray([2, 3], [("x", [2, 3])]),
                  DataArray([1], [("x", [1])]),
                  DataArray(0),
              ],
          ],
      )
      def test_align_override_error(self, darrays):
          with pytest.raises(ValueError, match=r"Indexes along dimension 'x' don't have"):
              xr.align(*darrays, join="override")
  
      def test_align_exclude(self):
          x = DataArray([[1, 2], [3, 4]], coords=[("a", [-1, -2]), ("b", [3, 4])])
          y = DataArray([[1, 2], [3, 4]], coords=[("a", [-1, 20]), ("b", [5, 6])])
          z = DataArray([1], dims=["a"], coords={"a": [20], "b": 7})
  
          x2, y2, z2 = align(x, y, z, join="outer", exclude=["b"])
          expected_x2 = DataArray(
              [[3, 4], [1, 2], [np.nan, np.nan]],
              coords=[("a", [-2, -1, 20]), ("b", [3, 4])],
          )
          expected_y2 = DataArray(
              [[np.nan, np.nan], [1, 2], [3, 4]],
              coords=[("a", [-2, -1, 20]), ("b", [5, 6])],
          )
          expected_z2 = DataArray(
              [np.nan, np.nan, 1], dims=["a"], coords={"a": [-2, -1, 20], "b": 7}
          )
          assert_identical(expected_x2, x2)
          assert_identical(expected_y2, y2)
          assert_identical(expected_z2, z2)
  
      def test_align_indexes(self):
          x = DataArray([1, 2, 3], coords=[("a", [-1, 10, -2])])
          y = DataArray([1, 2], coords=[("a", [-2, -1])])
  
          x2, y2 = align(x, y, join="outer", indexes={"a": [10, -1, -2]})
          expected_x2 = DataArray([2, 1, 3], coords=[("a", [10, -1, -2])])
          expected_y2 = DataArray([np.nan, 2, 1], coords=[("a", [10, -1, -2])])
          assert_identical(expected_x2, x2)
          assert_identical(expected_y2, y2)
  
          (x2,) = align(x, join="outer", indexes={"a": [-2, 7, 10, -1]})
          expected_x2 = DataArray([3, np.nan, 2, 1], coords=[("a", [-2, 7, 10, -1])])
          assert_identical(expected_x2, x2)
  
      def test_align_without_indexes_exclude(self):
          arrays = [DataArray([1, 2, 3], dims=["x"]), DataArray([1, 2], dims=["x"])]
          result0, result1 = align(*arrays, exclude=["x"])
          assert_identical(result0, arrays[0])
          assert_identical(result1, arrays[1])
  
      def test_align_mixed_indexes(self):
          array_no_coord = DataArray([1, 2], dims=["x"])
          array_with_coord = DataArray([1, 2], coords=[("x", ["a", "b"])])
          result0, result1 = align(array_no_coord, array_with_coord)
          assert_identical(result0, array_with_coord)
          assert_identical(result1, array_with_coord)
  
          result0, result1 = align(array_no_coord, array_with_coord, exclude=["x"])
          assert_identical(result0, array_no_coord)
          assert_identical(result1, array_with_coord)
  
      def test_align_without_indexes_errors(self):
          with pytest.raises(ValueError, match=r"cannot be aligned"):
              align(DataArray([1, 2, 3], dims=["x"]), DataArray([1, 2], dims=["x"]))
  
          with pytest.raises(ValueError, match=r"cannot be aligned"):
              align(
                  DataArray([1, 2, 3], dims=["x"]),
                  DataArray([1, 2], coords=[("x", [0, 1])]),
              )
  
      def test_align_str_dtype(self):
  
          a = DataArray([0, 1], dims=["x"], coords={"x": ["a", "b"]})
          b = DataArray([1, 2], dims=["x"], coords={"x": ["b", "c"]})
  
          expected_a = DataArray(
              [0, 1, np.NaN], dims=["x"], coords={"x": ["a", "b", "c"]}
          )
          expected_b = DataArray(
              [np.NaN, 1, 2], dims=["x"], coords={"x": ["a", "b", "c"]}
          )
  
          actual_a, actual_b = xr.align(a, b, join="outer")
  
          assert_identical(expected_a, actual_a)
          assert expected_a.x.dtype == actual_a.x.dtype
  
          assert_identical(expected_b, actual_b)
          assert expected_b.x.dtype == actual_b.x.dtype
  
      def test_broadcast_arrays(self):
          x = DataArray([1, 2], coords=[("a", [-1, -2])], name="x")
          y = DataArray([1, 2], coords=[("b", [3, 4])], name="y")
          x2, y2 = broadcast(x, y)
          expected_coords = [("a", [-1, -2]), ("b", [3, 4])]
          expected_x2 = DataArray([[1, 1], [2, 2]], expected_coords, name="x")
          expected_y2 = DataArray([[1, 2], [1, 2]], expected_coords, name="y")
          assert_identical(expected_x2, x2)
          assert_identical(expected_y2, y2)
  
          x = DataArray(np.random.randn(2, 3), dims=["a", "b"])
          y = DataArray(np.random.randn(3, 2), dims=["b", "a"])
          x2, y2 = broadcast(x, y)
          expected_x2 = x
          expected_y2 = y.T
          assert_identical(expected_x2, x2)
          assert_identical(expected_y2, y2)
  
      def test_broadcast_arrays_misaligned(self):
          # broadcast on misaligned coords must auto-align
          x = DataArray([[1, 2], [3, 4]], coords=[("a", [-1, -2]), ("b", [3, 4])])
          y = DataArray([1, 2], coords=[("a", [-1, 20])])
          expected_x2 = DataArray(
              [[3, 4], [1, 2], [np.nan, np.nan]],
              coords=[("a", [-2, -1, 20]), ("b", [3, 4])],
          )
          expected_y2 = DataArray(
              [[np.nan, np.nan], [1, 1], [2, 2]],
              coords=[("a", [-2, -1, 20]), ("b", [3, 4])],
          )
          x2, y2 = broadcast(x, y)
          assert_identical(expected_x2, x2)
          assert_identical(expected_y2, y2)
  
      def test_broadcast_arrays_nocopy(self):
          # Test that input data is not copied over in case
          # no alteration is needed
          x = DataArray([1, 2], coords=[("a", [-1, -2])], name="x")
          y = DataArray(3, name="y")
          expected_x2 = DataArray([1, 2], coords=[("a", [-1, -2])], name="x")
          expected_y2 = DataArray([3, 3], coords=[("a", [-1, -2])], name="y")
  
          x2, y2 = broadcast(x, y)
          assert_identical(expected_x2, x2)
          assert_identical(expected_y2, y2)
          assert source_ndarray(x2.data) is source_ndarray(x.data)
  
          # single-element broadcast (trivial case)
          (x2,) = broadcast(x)
          assert_identical(x, x2)
          assert source_ndarray(x2.data) is source_ndarray(x.data)
  
      def test_broadcast_arrays_exclude(self):
          x = DataArray([[1, 2], [3, 4]], coords=[("a", [-1, -2]), ("b", [3, 4])])
          y = DataArray([1, 2], coords=[("a", [-1, 20])])
          z = DataArray(5, coords={"b": 5})
  
          x2, y2, z2 = broadcast(x, y, z, exclude=["b"])
          expected_x2 = DataArray(
              [[3, 4], [1, 2], [np.nan, np.nan]],
              coords=[("a", [-2, -1, 20]), ("b", [3, 4])],
          )
          expected_y2 = DataArray([np.nan, 1, 2], coords=[("a", [-2, -1, 20])])
          expected_z2 = DataArray(
              [5, 5, 5], dims=["a"], coords={"a": [-2, -1, 20], "b": 5}
          )
          assert_identical(expected_x2, x2)
          assert_identical(expected_y2, y2)
          assert_identical(expected_z2, z2)
  
      def test_broadcast_coordinates(self):
          # regression test for GH649
          ds = Dataset({"a": (["x", "y"], np.ones((5, 6)))})
          x_bc, y_bc, a_bc = broadcast(ds.x, ds.y, ds.a)
          assert_identical(ds.a, a_bc)
  
          X, Y = np.meshgrid(np.arange(5), np.arange(6), indexing="ij")
          exp_x = DataArray(X, dims=["x", "y"], name="x")
          exp_y = DataArray(Y, dims=["x", "y"], name="y")
          assert_identical(exp_x, x_bc)
          assert_identical(exp_y, y_bc)
  
      def test_to_pandas(self):
          # 0d
          actual = DataArray(42).to_pandas()
          expected = np.array(42)
          assert_array_equal(actual, expected)
  
          # 1d
          values = np.random.randn(3)
          index = pd.Index(["a", "b", "c"], name="x")
          da = DataArray(values, coords=[index])
          actual = da.to_pandas()
          assert_array_equal(actual.values, values)
          assert_array_equal(actual.index, index)
          assert_array_equal(actual.index.name, "x")
  
          # 2d
          values = np.random.randn(3, 2)
          da = DataArray(
              values, coords=[("x", ["a", "b", "c"]), ("y", [0, 1])], name="foo"
          )
          actual = da.to_pandas()
          assert_array_equal(actual.values, values)
          assert_array_equal(actual.index, ["a", "b", "c"])
          assert_array_equal(actual.columns, [0, 1])
  
          # roundtrips
          for shape in [(3,), (3, 4)]:
              dims = list("abc")[: len(shape)]
              da = DataArray(np.random.randn(*shape), dims=dims)
              roundtripped = DataArray(da.to_pandas()).drop_vars(dims)
              assert_identical(da, roundtripped)
  
          with pytest.raises(ValueError, match=r"cannot convert"):
              DataArray(np.random.randn(1, 2, 3, 4, 5)).to_pandas()
  
      def test_to_dataframe(self):
          # regression test for #260
          arr_np = np.random.randn(3, 4)
  
          arr = DataArray(arr_np, [("B", [1, 2, 3]), ("A", list("cdef"))], name="foo")
          expected = arr.to_series()
          actual = arr.to_dataframe()["foo"]
          assert_array_equal(expected.values, actual.values)
          assert_array_equal(expected.name, actual.name)
          assert_array_equal(expected.index.values, actual.index.values)
  
          actual = arr.to_dataframe(dim_order=["A", "B"])["foo"]
          assert_array_equal(arr_np.transpose().reshape(-1), actual.values)
  
          # regression test for coords with different dimensions
          arr.coords["C"] = ("B", [-1, -2, -3])
          expected = arr.to_series().to_frame()
          expected["C"] = [-1] * 4 + [-2] * 4 + [-3] * 4
          expected = expected[["C", "foo"]]
          actual = arr.to_dataframe()
          assert_array_equal(expected.values, actual.values)
          assert_array_equal(expected.columns.values, actual.columns.values)
          assert_array_equal(expected.index.values, actual.index.values)
  
          with pytest.raises(ValueError, match="does not match the set of dimensions"):
              arr.to_dataframe(dim_order=["B", "A", "C"])
  
          with pytest.raises(ValueError, match=r"cannot convert a scalar"):
              arr.sel(A="c", B=2).to_dataframe()
  
          arr.name = None  # unnamed
          with pytest.raises(ValueError, match=r"unnamed"):
              arr.to_dataframe()
  
      def test_to_dataframe_multiindex(self):
          # regression test for #3008
          arr_np = np.random.randn(4, 3)
  
          mindex = pd.MultiIndex.from_product([[1, 2], list("ab")], names=["A", "B"])
  
          arr = DataArray(arr_np, [("MI", mindex), ("C", [5, 6, 7])], name="foo")
  
          actual = arr.to_dataframe()
          assert_array_equal(actual["foo"].values, arr_np.flatten())
          assert_array_equal(actual.index.names, list("ABC"))
          assert_array_equal(actual.index.levels[0], [1, 2])
          assert_array_equal(actual.index.levels[1], ["a", "b"])
          assert_array_equal(actual.index.levels[2], [5, 6, 7])
  
      def test_to_dataframe_0length(self):
          # regression test for #3008
          arr_np = np.random.randn(4, 0)
  
          mindex = pd.MultiIndex.from_product([[1, 2], list("ab")], names=["A", "B"])
  
          arr = DataArray(arr_np, [("MI", mindex), ("C", [])], name="foo")
  
          actual = arr.to_dataframe()
          assert len(actual) == 0
          assert_array_equal(actual.index.names, list("ABC"))
  
      def test_to_pandas_name_matches_coordinate(self):
          # coordinate with same name as array
          arr = DataArray([1, 2, 3], dims="x", name="x")
          series = arr.to_series()
          assert_array_equal([1, 2, 3], series.values)
          assert_array_equal([0, 1, 2], series.index.values)
          assert "x" == series.name
          assert "x" == series.index.name
  
          frame = arr.to_dataframe()
          expected = series.to_frame()
          assert expected.equals(frame)
  
      def test_to_and_from_series(self):
          expected = self.dv.to_dataframe()["foo"]
          actual = self.dv.to_series()
          assert_array_equal(expected.values, actual.values)
          assert_array_equal(expected.index.values, actual.index.values)
          assert "foo" == actual.name
          # test roundtrip
          assert_identical(self.dv, DataArray.from_series(actual).drop_vars(["x", "y"]))
          # test name is None
          actual.name = None
          expected_da = self.dv.rename(None)
          assert_identical(
              expected_da, DataArray.from_series(actual).drop_vars(["x", "y"])
          )
  
      def test_from_series_multiindex(self):
          # GH:3951
          df = pd.DataFrame({"B": [1, 2, 3], "A": [4, 5, 6]})
          df = df.rename_axis("num").rename_axis("alpha", axis=1)
          actual = df.stack("alpha").to_xarray()
          assert (actual.sel(alpha="B") == [1, 2, 3]).all()
          assert (actual.sel(alpha="A") == [4, 5, 6]).all()
  
      @requires_sparse
      def test_from_series_sparse(self):
          import sparse
  
          series = pd.Series([1, 2], index=[("a", 1), ("b", 2)])
  
          actual_sparse = DataArray.from_series(series, sparse=True)
          actual_dense = DataArray.from_series(series, sparse=False)
  
          assert isinstance(actual_sparse.data, sparse.COO)
          actual_sparse.data = actual_sparse.data.todense()
          assert_identical(actual_sparse, actual_dense)
  
      @requires_sparse
      def test_from_multiindex_series_sparse(self):
          # regression test for GH4019
          import sparse
  
          idx = pd.MultiIndex.from_product([np.arange(3), np.arange(5)], names=["a", "b"])
          series = pd.Series(np.random.RandomState(0).random(len(idx)), index=idx).sample(
              n=5, random_state=3
          )
  
          dense = DataArray.from_series(series, sparse=False)
          expected_coords = sparse.COO.from_numpy(dense.data, np.nan).coords
  
          actual_sparse = xr.DataArray.from_series(series, sparse=True)
          actual_coords = actual_sparse.data.coords
  
          np.testing.assert_equal(actual_coords, expected_coords)
  
      def test_to_and_from_empty_series(self):
          # GH697
          expected = pd.Series([], dtype=np.float64)
          da = DataArray.from_series(expected)
          assert len(da) == 0
          actual = da.to_series()
          assert len(actual) == 0
          assert expected.equals(actual)
  
      def test_series_categorical_index(self):
          # regression test for GH700
          if not hasattr(pd, "CategoricalIndex"):
              pytest.skip("requires pandas with CategoricalIndex")
  
          s = pd.Series(np.arange(5), index=pd.CategoricalIndex(list("aabbc")))
          arr = DataArray(s)
          assert "'a'" in repr(arr)  # should not error
  
      def test_to_and_from_dict(self):
          array = DataArray(
              np.random.randn(2, 3), {"x": ["a", "b"]}, ["x", "y"], name="foo"
          )
          expected = {
              "name": "foo",
              "dims": ("x", "y"),
              "data": array.values.tolist(),
              "attrs": {},
              "coords": {"x": {"dims": ("x",), "data": ["a", "b"], "attrs": {}}},
          }
          actual = array.to_dict()
  
          # check that they are identical
          assert expected == actual
  
          # check roundtrip
          assert_identical(array, DataArray.from_dict(actual))
  
          # a more bare bones representation still roundtrips
          d = {
              "name": "foo",
              "dims": ("x", "y"),
              "data": array.values.tolist(),
              "coords": {"x": {"dims": "x", "data": ["a", "b"]}},
          }
          assert_identical(array, DataArray.from_dict(d))
  
          # and the most bare bones representation still roundtrips
          d = {"name": "foo", "dims": ("x", "y"), "data": array.values}
          assert_identical(array.drop_vars("x"), DataArray.from_dict(d))
  
          # missing a dims in the coords
          d = {
              "dims": ("x", "y"),
              "data": array.values,
              "coords": {"x": {"data": ["a", "b"]}},
          }
          with pytest.raises(
              ValueError,
              match=r"cannot convert dict when coords are missing the key 'dims'",
          ):
              DataArray.from_dict(d)
  
          # this one is missing some necessary information
          d = {"dims": ("t")}
          with pytest.raises(
              ValueError, match=r"cannot convert dict without the key 'data'"
          ):
              DataArray.from_dict(d)
  
          # check the data=False option
          expected_no_data = expected.copy()
          del expected_no_data["data"]
          del expected_no_data["coords"]["x"]["data"]
          endiantype = "<U1" if sys.byteorder == "little" else ">U1"
          expected_no_data["coords"]["x"].update({"dtype": endiantype, "shape": (2,)})
          expected_no_data.update({"dtype": "float64", "shape": (2, 3)})
          actual_no_data = array.to_dict(data=False)
          assert expected_no_data == actual_no_data
  
      def test_to_and_from_dict_with_time_dim(self):
          x = np.random.randn(10, 3)
          t = pd.date_range("20130101", periods=10)
          lat = [77.7, 83.2, 76]
          da = DataArray(x, {"t": t, "lat": lat}, dims=["t", "lat"])
          roundtripped = DataArray.from_dict(da.to_dict())
          assert_identical(da, roundtripped)
  
      def test_to_and_from_dict_with_nan_nat(self):
          y = np.random.randn(10, 3)
          y[2] = np.nan
          t = pd.Series(pd.date_range("20130101", periods=10))
          t[2] = np.nan
          lat = [77.7, 83.2, 76]
          da = DataArray(y, {"t": t, "lat": lat}, dims=["t", "lat"])
          roundtripped = DataArray.from_dict(da.to_dict())
          assert_identical(da, roundtripped)
  
      def test_to_dict_with_numpy_attrs(self):
          # this doesn't need to roundtrip
          x = np.random.randn(10, 3)
          t = list("abcdefghij")
          lat = [77.7, 83.2, 76]
          attrs = {
              "created": np.float64(1998),
              "coords": np.array([37, -110.1, 100]),
              "maintainer": "bar",
          }
          da = DataArray(x, {"t": t, "lat": lat}, dims=["t", "lat"], attrs=attrs)
          expected_attrs = {
              "created": attrs["created"].item(),
              "coords": attrs["coords"].tolist(),
              "maintainer": "bar",
          }
          actual = da.to_dict()
  
          # check that they are identical
          assert expected_attrs == actual["attrs"]
  
      def test_to_masked_array(self):
          rs = np.random.RandomState(44)
          x = rs.random_sample(size=(10, 20))
          x_masked = np.ma.masked_where(x < 0.5, x)
          da = DataArray(x_masked)
  
          # Test round trip
          x_masked_2 = da.to_masked_array()
          da_2 = DataArray(x_masked_2)
          assert_array_equal(x_masked, x_masked_2)
          assert_equal(da, da_2)
  
          da_masked_array = da.to_masked_array(copy=True)
          assert isinstance(da_masked_array, np.ma.MaskedArray)
          # Test masks
          assert_array_equal(da_masked_array.mask, x_masked.mask)
          # Test that mask is unpacked correctly
          assert_array_equal(da.values, x_masked.filled(np.nan))
          # Test that the underlying data (including nans) hasn't changed
          assert_array_equal(da_masked_array, x_masked.filled(np.nan))
  
          # Test that copy=False gives access to values
          masked_array = da.to_masked_array(copy=False)
          masked_array[0, 0] = 10.0
          assert masked_array[0, 0] == 10.0
          assert da[0, 0].values == 10.0
          assert masked_array.base is da.values
          assert isinstance(masked_array, np.ma.MaskedArray)
  
          # Test with some odd arrays
          for v in [4, np.nan, True, "4", "four"]:
              da = DataArray(v)
              ma = da.to_masked_array()
              assert isinstance(ma, np.ma.MaskedArray)
  
          # Fix GH issue 684 - masked arrays mask should be an array not a scalar
          N = 4
          v = range(N)
          da = DataArray(v)
          ma = da.to_masked_array()
          assert len(ma.mask) == N
  
      def test_to_and_from_cdms2_classic(self):
          """Classic with 1D axes"""
          pytest.importorskip("cdms2")
  
          original = DataArray(
              np.arange(6).reshape(2, 3),
              [
                  ("distance", [-2, 2], {"units": "meters"}),
                  ("time", pd.date_range("2000-01-01", periods=3)),
              ],
              name="foo",
              attrs={"baz": 123},
          )
          expected_coords = [
              IndexVariable("distance", [-2, 2]),
              IndexVariable("time", [0, 1, 2]),
          ]
          actual = original.to_cdms2()
          assert_array_equal(actual.asma(), original)
          assert actual.id == original.name
          assert tuple(actual.getAxisIds()) == original.dims
          for axis, coord in zip(actual.getAxisList(), expected_coords):
              assert axis.id == coord.name
              assert_array_equal(axis, coord.values)
          assert actual.baz == original.attrs["baz"]
  
          component_times = actual.getAxis(1).asComponentTime()
          assert len(component_times) == 3
          assert str(component_times[0]) == "2000-1-1 0:0:0.0"
  
          roundtripped = DataArray.from_cdms2(actual)
          assert_identical(original, roundtripped)
  
          back = from_cdms2(actual)
          assert original.dims == back.dims
          assert original.coords.keys() == back.coords.keys()
          for coord_name in original.coords.keys():
              assert_array_equal(original.coords[coord_name], back.coords[coord_name])
  
      def test_to_and_from_cdms2_sgrid(self):
          """Curvilinear (structured) grid
  
          The rectangular grid case is covered by the classic case
          """
          pytest.importorskip("cdms2")
  
          lonlat = np.mgrid[:3, :4]
          lon = DataArray(lonlat[1], dims=["y", "x"], name="lon")
          lat = DataArray(lonlat[0], dims=["y", "x"], name="lat")
          x = DataArray(np.arange(lon.shape[1]), dims=["x"], name="x")
          y = DataArray(np.arange(lon.shape[0]), dims=["y"], name="y")
          original = DataArray(
              lonlat.sum(axis=0),
              dims=["y", "x"],
              coords=dict(x=x, y=y, lon=lon, lat=lat),
              name="sst",
          )
          actual = original.to_cdms2()
          assert tuple(actual.getAxisIds()) == original.dims
          assert_array_equal(original.coords["lon"], actual.getLongitude().asma())
          assert_array_equal(original.coords["lat"], actual.getLatitude().asma())
  
          back = from_cdms2(actual)
          assert original.dims == back.dims
          assert set(original.coords.keys()) == set(back.coords.keys())
          assert_array_equal(original.coords["lat"], back.coords["lat"])
          assert_array_equal(original.coords["lon"], back.coords["lon"])
  
      def test_to_and_from_cdms2_ugrid(self):
          """Unstructured grid"""
          pytest.importorskip("cdms2")
  
          lon = DataArray(np.random.uniform(size=5), dims=["cell"], name="lon")
          lat = DataArray(np.random.uniform(size=5), dims=["cell"], name="lat")
          cell = DataArray(np.arange(5), dims=["cell"], name="cell")
          original = DataArray(
              np.arange(5), dims=["cell"], coords={"lon": lon, "lat": lat, "cell": cell}
          )
          actual = original.to_cdms2()
          assert tuple(actual.getAxisIds()) == original.dims
          assert_array_equal(original.coords["lon"], actual.getLongitude().getValue())
          assert_array_equal(original.coords["lat"], actual.getLatitude().getValue())
  
          back = from_cdms2(actual)
          assert set(original.dims) == set(back.dims)
          assert set(original.coords.keys()) == set(back.coords.keys())
          assert_array_equal(original.coords["lat"], back.coords["lat"])
          assert_array_equal(original.coords["lon"], back.coords["lon"])
  
      def test_to_dataset_whole(self):
          unnamed = DataArray([1, 2], dims="x")
          with pytest.raises(ValueError, match=r"unable to convert unnamed"):
              unnamed.to_dataset()
  
          actual = unnamed.to_dataset(name="foo")
          expected = Dataset({"foo": ("x", [1, 2])})
          assert_identical(expected, actual)
  
          named = DataArray([1, 2], dims="x", name="foo", attrs={"y": "testattr"})
          actual = named.to_dataset()
          expected = Dataset({"foo": ("x", [1, 2], {"y": "testattr"})})
          assert_identical(expected, actual)
  
          # Test promoting attrs
          actual = named.to_dataset(promote_attrs=True)
          expected = Dataset(
              {"foo": ("x", [1, 2], {"y": "testattr"})}, attrs={"y": "testattr"}
          )
          assert_identical(expected, actual)
  
          with pytest.raises(TypeError):
              actual = named.to_dataset("bar")
  
      def test_to_dataset_split(self):
          array = DataArray([1, 2, 3], coords=[("x", list("abc"))], attrs={"a": 1})
          expected = Dataset({"a": 1, "b": 2, "c": 3}, attrs={"a": 1})
          actual = array.to_dataset("x")
          assert_identical(expected, actual)
  
          with pytest.raises(TypeError):
              array.to_dataset("x", name="foo")
  
          roundtripped = actual.to_array(dim="x")
          assert_identical(array, roundtripped)
  
          array = DataArray([1, 2, 3], dims="x")
          expected = Dataset({0: 1, 1: 2, 2: 3})
          actual = array.to_dataset("x")
          assert_identical(expected, actual)
  
      def test_to_dataset_retains_keys(self):
  
          # use dates as convenient non-str objects. Not a specific date test
          import datetime
  
          dates = [datetime.date(2000, 1, d) for d in range(1, 4)]
  
          array = DataArray([1, 2, 3], coords=[("x", dates)], attrs={"a": 1})
  
          # convert to dateset and back again
          result = array.to_dataset("x").to_array(dim="x")
  
          assert_equal(array, result)
  
      def test__title_for_slice(self):
          array = DataArray(
              np.ones((4, 3, 2)),
              dims=["a", "b", "c"],
              coords={"a": range(4), "b": range(3), "c": range(2)},
          )
          assert "" == array._title_for_slice()
          assert "c = 0" == array.isel(c=0)._title_for_slice()
          title = array.isel(b=1, c=0)._title_for_slice()
          assert "b = 1, c = 0" == title or "c = 0, b = 1" == title
  
          a2 = DataArray(np.ones((4, 1)), dims=["a", "b"])
          assert "" == a2._title_for_slice()
  
      def test__title_for_slice_truncate(self):
          array = DataArray(np.ones(4))
          array.coords["a"] = "a" * 100
          array.coords["b"] = "b" * 100
  
          nchar = 80
          title = array._title_for_slice(truncate=nchar)
  
          assert nchar == len(title)
          assert title.endswith("...")
  
      def test_dataarray_diff_n1(self):
          da = DataArray(np.random.randn(3, 4), dims=["x", "y"])
          actual = da.diff("y")
          expected = DataArray(np.diff(da.values, axis=1), dims=["x", "y"])
          assert_equal(expected, actual)
  
      def test_coordinate_diff(self):
          # regression test for GH634
          arr = DataArray(range(0, 20, 2), dims=["lon"], coords=[range(10)])
          lon = arr.coords["lon"]
          expected = DataArray([1] * 9, dims=["lon"], coords=[range(1, 10)], name="lon")
          actual = lon.diff("lon")
          assert_equal(expected, actual)
  
      @pytest.mark.parametrize("offset", [-5, 0, 1, 2])
      @pytest.mark.parametrize("fill_value, dtype", [(2, int), (dtypes.NA, float)])
      def test_shift(self, offset, fill_value, dtype):
          arr = DataArray([1, 2, 3], dims="x")
          actual = arr.shift(x=1, fill_value=fill_value)
          if fill_value == dtypes.NA:
              # if we supply the default, we expect the missing value for a
              # float array
              fill_value = np.nan
          expected = DataArray([fill_value, 1, 2], dims="x")
          assert_identical(expected, actual)
          assert actual.dtype == dtype
  
          arr = DataArray([1, 2, 3], [("x", ["a", "b", "c"])])
          expected = DataArray(arr.to_pandas().shift(offset))
          actual = arr.shift(x=offset)
          assert_identical(expected, actual)
  
      def test_roll_coords(self):
          arr = DataArray([1, 2, 3], coords={"x": range(3)}, dims="x")
          actual = arr.roll(x=1, roll_coords=True)
          expected = DataArray([3, 1, 2], coords=[("x", [2, 0, 1])])
          assert_identical(expected, actual)
  
      def test_roll_no_coords(self):
          arr = DataArray([1, 2, 3], coords={"x": range(3)}, dims="x")
          actual = arr.roll(x=1)
          expected = DataArray([3, 1, 2], coords=[("x", [0, 1, 2])])
          assert_identical(expected, actual)
  
      def test_copy_with_data(self):
          orig = DataArray(
              np.random.random(size=(2, 2)),
              dims=("x", "y"),
              attrs={"attr1": "value1"},
              coords={"x": [4, 3]},
              name="helloworld",
          )
          new_data = np.arange(4).reshape(2, 2)
          actual = orig.copy(data=new_data)
          expected = orig.copy()
          expected.data = new_data
          assert_identical(expected, actual)
  
      @pytest.mark.xfail(raises=AssertionError)
      @pytest.mark.parametrize(
          "deep, expected_orig",
          [
              [
                  True,
                  xr.DataArray(
                      xr.IndexVariable("a", np.array([1, 2])),
                      coords={"a": [1, 2]},
                      dims=["a"],
                  ),
              ],
              [
                  False,
                  xr.DataArray(
                      xr.IndexVariable("a", np.array([999, 2])),
                      coords={"a": [999, 2]},
                      dims=["a"],
                  ),
              ],
          ],
      )
      def test_copy_coords(self, deep, expected_orig):
          """The test fails for the shallow copy, and apparently only on Windows
          for some reason. In windows coords seem to be immutable unless it's one
          dataarray deep copied from another."""
          da = xr.DataArray(
              np.ones([2, 2, 2]),
              coords={"a": [1, 2], "b": ["x", "y"], "c": [0, 1]},
              dims=["a", "b", "c"],
          )
          da_cp = da.copy(deep)
          da_cp["a"].data[0] = 999
  
          expected_cp = xr.DataArray(
              xr.IndexVariable("a", np.array([999, 2])),
              coords={"a": [999, 2]},
              dims=["a"],
          )
          assert_identical(da_cp["a"], expected_cp)
  
          assert_identical(da["a"], expected_orig)
  
      def test_real_and_imag(self):
          array = DataArray(1 + 2j)
          assert_identical(array.real, DataArray(1))
          assert_identical(array.imag, DataArray(2))
  
      def test_setattr_raises(self):
          array = DataArray(0, coords={"scalar": 1}, attrs={"foo": "bar"})
          with pytest.raises(AttributeError, match=r"cannot set attr"):
              array.scalar = 2
          with pytest.raises(AttributeError, match=r"cannot set attr"):
              array.foo = 2
          with pytest.raises(AttributeError, match=r"cannot set attr"):
              array.other = 2
  
      def test_full_like(self):
          # For more thorough tests, see test_variable.py
          da = DataArray(
              np.random.random(size=(2, 2)),
              dims=("x", "y"),
              attrs={"attr1": "value1"},
              coords={"x": [4, 3]},
              name="helloworld",
          )
  
          actual = full_like(da, 2)
          expect = da.copy(deep=True)
          expect.values = [[2.0, 2.0], [2.0, 2.0]]
          assert_identical(expect, actual)
  
          # override dtype
          actual = full_like(da, fill_value=True, dtype=bool)
          expect.values = [[True, True], [True, True]]
          assert expect.dtype == bool
          assert_identical(expect, actual)
  
          with pytest.raises(ValueError, match="'dtype' cannot be dict-like"):
              full_like(da, fill_value=True, dtype={"x": bool})
  
      def test_dot(self):
          x = np.linspace(-3, 3, 6)
          y = np.linspace(-3, 3, 5)
          z = range(4)
          da_vals = np.arange(6 * 5 * 4).reshape((6, 5, 4))
          da = DataArray(da_vals, coords=[x, y, z], dims=["x", "y", "z"])
  
          dm_vals = range(4)
          dm = DataArray(dm_vals, coords=[z], dims=["z"])
  
          # nd dot 1d
          actual = da.dot(dm)
          expected_vals = np.tensordot(da_vals, dm_vals, [2, 0])
          expected = DataArray(expected_vals, coords=[x, y], dims=["x", "y"])
          assert_equal(expected, actual)
  
          # all shared dims
          actual = da.dot(da)
          expected_vals = np.tensordot(da_vals, da_vals, axes=([0, 1, 2], [0, 1, 2]))
          expected = DataArray(expected_vals)
          assert_equal(expected, actual)
  
          # multiple shared dims
          dm_vals = np.arange(20 * 5 * 4).reshape((20, 5, 4))
          j = np.linspace(-3, 3, 20)
          dm = DataArray(dm_vals, coords=[j, y, z], dims=["j", "y", "z"])
          actual = da.dot(dm)
          expected_vals = np.tensordot(da_vals, dm_vals, axes=([1, 2], [1, 2]))
          expected = DataArray(expected_vals, coords=[x, j], dims=["x", "j"])
          assert_equal(expected, actual)
  
          # Ellipsis: all dims are shared
          actual = da.dot(da, dims=...)
          expected = da.dot(da)
          assert_equal(expected, actual)
  
          # Ellipsis: not all dims are shared
          actual = da.dot(dm, dims=...)
          expected = da.dot(dm, dims=("j", "x", "y", "z"))
          assert_equal(expected, actual)
  
          with pytest.raises(NotImplementedError):
              da.dot(dm.to_dataset(name="dm"))
          with pytest.raises(TypeError):
              da.dot(dm.values)
  
      def test_dot_align_coords(self):
          # GH 3694
  
          x = np.linspace(-3, 3, 6)
          y = np.linspace(-3, 3, 5)
          z_a = range(4)
          da_vals = np.arange(6 * 5 * 4).reshape((6, 5, 4))
          da = DataArray(da_vals, coords=[x, y, z_a], dims=["x", "y", "z"])
  
          z_m = range(2, 6)
          dm_vals = range(4)
          dm = DataArray(dm_vals, coords=[z_m], dims=["z"])
  
          with xr.set_options(arithmetic_join="exact"):
              with pytest.raises(ValueError, match=r"indexes along dimension"):
                  da.dot(dm)
  
          da_aligned, dm_aligned = xr.align(da, dm, join="inner")
  
          # nd dot 1d
          actual = da.dot(dm)
          expected_vals = np.tensordot(da_aligned.values, dm_aligned.values, [2, 0])
          expected = DataArray(expected_vals, coords=[x, da_aligned.y], dims=["x", "y"])
          assert_equal(expected, actual)
  
          # multiple shared dims
          dm_vals = np.arange(20 * 5 * 4).reshape((20, 5, 4))
          j = np.linspace(-3, 3, 20)
          dm = DataArray(dm_vals, coords=[j, y, z_m], dims=["j", "y", "z"])
          da_aligned, dm_aligned = xr.align(da, dm, join="inner")
          actual = da.dot(dm)
          expected_vals = np.tensordot(
              da_aligned.values, dm_aligned.values, axes=([1, 2], [1, 2])
          )
          expected = DataArray(expected_vals, coords=[x, j], dims=["x", "j"])
          assert_equal(expected, actual)
  
      def test_matmul(self):
  
          # copied from above (could make a fixture)
          x = np.linspace(-3, 3, 6)
          y = np.linspace(-3, 3, 5)
          z = range(4)
          da_vals = np.arange(6 * 5 * 4).reshape((6, 5, 4))
          da = DataArray(da_vals, coords=[x, y, z], dims=["x", "y", "z"])
  
          result = da @ da
          expected = da.dot(da)
          assert_identical(result, expected)
  
      def test_matmul_align_coords(self):
          # GH 3694
  
          x_a = np.arange(6)
          x_b = np.arange(2, 8)
          da_vals = np.arange(6)
          da_a = DataArray(da_vals, coords=[x_a], dims=["x"])
          da_b = DataArray(da_vals, coords=[x_b], dims=["x"])
  
          # only test arithmetic_join="inner" (=default)
          result = da_a @ da_b
          expected = da_a.dot(da_b)
          assert_identical(result, expected)
  
          with xr.set_options(arithmetic_join="exact"):
              with pytest.raises(ValueError, match=r"indexes along dimension"):
                  da_a @ da_b
  
      def test_binary_op_propagate_indexes(self):
          # regression test for GH2227
          self.dv["x"] = np.arange(self.dv.sizes["x"])
          expected = self.dv.xindexes["x"]
  
          actual = (self.dv * 10).xindexes["x"]
          assert expected is actual
  
          actual = (self.dv > 10).xindexes["x"]
          assert expected is actual
  
      def test_binary_op_join_setting(self):
          dim = "x"
          align_type = "outer"
          coords_l, coords_r = [0, 1, 2], [1, 2, 3]
          missing_3 = xr.DataArray(coords_l, [(dim, coords_l)])
          missing_0 = xr.DataArray(coords_r, [(dim, coords_r)])
          with xr.set_options(arithmetic_join=align_type):
              actual = missing_0 + missing_3
          missing_0_aligned, missing_3_aligned = xr.align(
              missing_0, missing_3, join=align_type
          )
          expected = xr.DataArray([np.nan, 2, 4, np.nan], [(dim, [0, 1, 2, 3])])
          assert_equal(actual, expected)
  
      def test_combine_first(self):
          ar0 = DataArray([[0, 0], [0, 0]], [("x", ["a", "b"]), ("y", [-1, 0])])
          ar1 = DataArray([[1, 1], [1, 1]], [("x", ["b", "c"]), ("y", [0, 1])])
          ar2 = DataArray([2], [("x", ["d"])])
  
          actual = ar0.combine_first(ar1)
          expected = DataArray(
              [[0, 0, np.nan], [0, 0, 1], [np.nan, 1, 1]],
              [("x", ["a", "b", "c"]), ("y", [-1, 0, 1])],
          )
          assert_equal(actual, expected)
  
          actual = ar1.combine_first(ar0)
          expected = DataArray(
              [[0, 0, np.nan], [0, 1, 1], [np.nan, 1, 1]],
              [("x", ["a", "b", "c"]), ("y", [-1, 0, 1])],
          )
          assert_equal(actual, expected)
  
          actual = ar0.combine_first(ar2)
          expected = DataArray(
              [[0, 0], [0, 0], [2, 2]], [("x", ["a", "b", "d"]), ("y", [-1, 0])]
          )
          assert_equal(actual, expected)
  
      def test_sortby(self):
          da = DataArray(
              [[1, 2], [3, 4], [5, 6]], [("x", ["c", "b", "a"]), ("y", [1, 0])]
          )
  
          sorted1d = DataArray(
              [[5, 6], [3, 4], [1, 2]], [("x", ["a", "b", "c"]), ("y", [1, 0])]
          )
  
          sorted2d = DataArray(
              [[6, 5], [4, 3], [2, 1]], [("x", ["a", "b", "c"]), ("y", [0, 1])]
          )
  
          expected = sorted1d
          dax = DataArray([100, 99, 98], [("x", ["c", "b", "a"])])
          actual = da.sortby(dax)
          assert_equal(actual, expected)
  
          # test descending order sort
          actual = da.sortby(dax, ascending=False)
          assert_equal(actual, da)
  
          # test alignment (fills in nan for 'c')
          dax_short = DataArray([98, 97], [("x", ["b", "a"])])
          actual = da.sortby(dax_short)
          assert_equal(actual, expected)
  
          # test multi-dim sort by 1D dataarray values
          expected = sorted2d
          dax = DataArray([100, 99, 98], [("x", ["c", "b", "a"])])
          day = DataArray([90, 80], [("y", [1, 0])])
          actual = da.sortby([day, dax])
          assert_equal(actual, expected)
  
          expected = sorted1d
          actual = da.sortby("x")
          assert_equal(actual, expected)
  
          expected = sorted2d
          actual = da.sortby(["x", "y"])
          assert_equal(actual, expected)
  
      @requires_bottleneck
      def test_rank(self):
          # floats
          ar = DataArray([[3, 4, np.nan, 1]])
          expect_0 = DataArray([[1, 1, np.nan, 1]])
          expect_1 = DataArray([[2, 3, np.nan, 1]])
          assert_equal(ar.rank("dim_0"), expect_0)
          assert_equal(ar.rank("dim_1"), expect_1)
          # int
          x = DataArray([3, 2, 1])
          assert_equal(x.rank("dim_0"), x)
          # str
          y = DataArray(["c", "b", "a"])
          assert_equal(y.rank("dim_0"), x)
  
          x = DataArray([3.0, 1.0, np.nan, 2.0, 4.0], dims=("z",))
          y = DataArray([0.75, 0.25, np.nan, 0.5, 1.0], dims=("z",))
          assert_equal(y.rank("z", pct=True), y)
  
      @pytest.mark.parametrize("use_dask", [True, False])
      @pytest.mark.parametrize("use_datetime", [True, False])
      @pytest.mark.filterwarnings("ignore:overflow encountered in multiply")
      def test_polyfit(self, use_dask, use_datetime):
          if use_dask and not has_dask:
              pytest.skip("requires dask")
          xcoord = xr.DataArray(
              pd.date_range("1970-01-01", freq="D", periods=10), dims=("x",), name="x"
          )
          x = xr.core.missing.get_clean_interp_index(xcoord, "x")
          if not use_datetime:
              xcoord = x
  
          da_raw = DataArray(
              np.stack(
                  (10 + 1e-15 * x + 2e-28 * x ** 2, 30 + 2e-14 * x + 1e-29 * x ** 2)
              ),
              dims=("d", "x"),
              coords={"x": xcoord, "d": [0, 1]},
          )
  
          if use_dask:
              da = da_raw.chunk({"d": 1})
          else:
              da = da_raw
  
          out = da.polyfit("x", 2)
          expected = DataArray(
              [[2e-28, 1e-15, 10], [1e-29, 2e-14, 30]],
              dims=("d", "degree"),
              coords={"degree": [2, 1, 0], "d": [0, 1]},
          ).T
          assert_allclose(out.polyfit_coefficients, expected, rtol=1e-3)
  
          # Full output and deficient rank
          with warnings.catch_warnings():
              warnings.simplefilter("ignore", np.RankWarning)
              out = da.polyfit("x", 12, full=True)
              assert out.polyfit_residuals.isnull().all()
  
          # With NaN
          da_raw[0, 1:3] = np.nan
          if use_dask:
              da = da_raw.chunk({"d": 1})
          else:
              da = da_raw
          out = da.polyfit("x", 2, skipna=True, cov=True)
          assert_allclose(out.polyfit_coefficients, expected, rtol=1e-3)
          assert "polyfit_covariance" in out
  
          # Skipna + Full output
          out = da.polyfit("x", 2, skipna=True, full=True)
          assert_allclose(out.polyfit_coefficients, expected, rtol=1e-3)
          assert out.x_matrix_rank == 3
          np.testing.assert_almost_equal(out.polyfit_residuals, [0, 0])
  
          with warnings.catch_warnings():
              warnings.simplefilter("ignore", np.RankWarning)
              out = da.polyfit("x", 8, full=True)
              np.testing.assert_array_equal(out.polyfit_residuals.isnull(), [True, False])
  
      def test_pad_constant(self):
          ar = DataArray(np.arange(3 * 4 * 5).reshape(3, 4, 5))
          actual = ar.pad(dim_0=(1, 3))
          expected = DataArray(
              np.pad(
                  np.arange(3 * 4 * 5).reshape(3, 4, 5).astype(np.float32),
                  mode="constant",
                  pad_width=((1, 3), (0, 0), (0, 0)),
                  constant_values=np.nan,
              )
          )
          assert actual.shape == (7, 4, 5)
          assert_identical(actual, expected)
  
          ar = xr.DataArray([9], dims="x")
  
          actual = ar.pad(x=1)
          expected = xr.DataArray([np.NaN, 9, np.NaN], dims="x")
          assert_identical(actual, expected)
  
          actual = ar.pad(x=1, constant_values=1.23456)
          expected = xr.DataArray([1, 9, 1], dims="x")
          assert_identical(actual, expected)
  
          if Version(np.__version__) >= Version("1.20"):
              with pytest.raises(ValueError, match="cannot convert float NaN to integer"):
                  ar.pad(x=1, constant_values=np.NaN)
          else:
              actual = ar.pad(x=1, constant_values=np.NaN)
              expected = xr.DataArray(
                  [-9223372036854775808, 9, -9223372036854775808], dims="x"
              )
              assert_identical(actual, expected)
  
      def test_pad_coords(self):
          ar = DataArray(
              np.arange(3 * 4 * 5).reshape(3, 4, 5),
              [("x", np.arange(3)), ("y", np.arange(4)), ("z", np.arange(5))],
          )
          actual = ar.pad(x=(1, 3), constant_values=1)
          expected = DataArray(
              np.pad(
                  np.arange(3 * 4 * 5).reshape(3, 4, 5),
                  mode="constant",
                  pad_width=((1, 3), (0, 0), (0, 0)),
                  constant_values=1,
              ),
              [
                  (
                      "x",
                      np.pad(
                          np.arange(3).astype(np.float32),
                          mode="constant",
                          pad_width=(1, 3),
                          constant_values=np.nan,
                      ),
                  ),
                  ("y", np.arange(4)),
                  ("z", np.arange(5)),
              ],
          )
          assert_identical(actual, expected)
  
      @pytest.mark.parametrize("mode", ("minimum", "maximum", "mean", "median"))
      @pytest.mark.parametrize(
          "stat_length", (None, 3, (1, 3), {"dim_0": (2, 1), "dim_2": (4, 2)})
      )
      def test_pad_stat_length(self, mode, stat_length):
          ar = DataArray(np.arange(3 * 4 * 5).reshape(3, 4, 5))
          actual = ar.pad(dim_0=(1, 3), dim_2=(2, 2), mode=mode, stat_length=stat_length)
          if isinstance(stat_length, dict):
              stat_length = (stat_length["dim_0"], (4, 4), stat_length["dim_2"])
          expected = DataArray(
              np.pad(
                  np.arange(3 * 4 * 5).reshape(3, 4, 5),
                  pad_width=((1, 3), (0, 0), (2, 2)),
                  mode=mode,
                  stat_length=stat_length,
              )
          )
          assert actual.shape == (7, 4, 9)
          assert_identical(actual, expected)
  
      @pytest.mark.parametrize(
          "end_values", (None, 3, (3, 5), {"dim_0": (2, 1), "dim_2": (4, 2)})
      )
      def test_pad_linear_ramp(self, end_values):
          ar = DataArray(np.arange(3 * 4 * 5).reshape(3, 4, 5))
          actual = ar.pad(
              dim_0=(1, 3), dim_2=(2, 2), mode="linear_ramp", end_values=end_values
          )
          if end_values is None:
              end_values = 0
          elif isinstance(end_values, dict):
              end_values = (end_values["dim_0"], (4, 4), end_values["dim_2"])
          expected = DataArray(
              np.pad(
                  np.arange(3 * 4 * 5).reshape(3, 4, 5),
                  pad_width=((1, 3), (0, 0), (2, 2)),
                  mode="linear_ramp",
                  end_values=end_values,
              )
          )
          assert actual.shape == (7, 4, 9)
          assert_identical(actual, expected)
  
      @pytest.mark.parametrize("mode", ("reflect", "symmetric"))
      @pytest.mark.parametrize("reflect_type", (None, "even", "odd"))
      def test_pad_reflect(self, mode, reflect_type):
  
          ar = DataArray(np.arange(3 * 4 * 5).reshape(3, 4, 5))
          actual = ar.pad(
              dim_0=(1, 3), dim_2=(2, 2), mode=mode, reflect_type=reflect_type
          )
          np_kwargs = {
              "array": np.arange(3 * 4 * 5).reshape(3, 4, 5),
              "pad_width": ((1, 3), (0, 0), (2, 2)),
              "mode": mode,
          }
          # numpy does not support reflect_type=None
          if reflect_type is not None:
              np_kwargs["reflect_type"] = reflect_type
          expected = DataArray(np.pad(**np_kwargs))
  
          assert actual.shape == (7, 4, 9)
          assert_identical(actual, expected)
  
      @pytest.mark.parametrize("parser", ["pandas", "python"])
      @pytest.mark.parametrize(
          "engine", ["python", None, pytest.param("numexpr", marks=[requires_numexpr])]
      )
      @pytest.mark.parametrize(
          "backend", ["numpy", pytest.param("dask", marks=[requires_dask])]
      )
      def test_query(self, backend, engine, parser):
          """Test querying a dataset."""
  
          # setup test data
          np.random.seed(42)
          a = np.arange(0, 10, 1)
          b = np.random.randint(0, 100, size=10)
          c = np.linspace(0, 1, 20)
          d = np.random.choice(["foo", "bar", "baz"], size=30, replace=True).astype(
              object
          )
          if backend == "numpy":
              aa = DataArray(data=a, dims=["x"], name="a")
              bb = DataArray(data=b, dims=["x"], name="b")
              cc = DataArray(data=c, dims=["y"], name="c")
              dd = DataArray(data=d, dims=["z"], name="d")
  
          elif backend == "dask":
              import dask.array as da
  
              aa = DataArray(data=da.from_array(a, chunks=3), dims=["x"], name="a")
              bb = DataArray(data=da.from_array(b, chunks=3), dims=["x"], name="b")
              cc = DataArray(data=da.from_array(c, chunks=7), dims=["y"], name="c")
              dd = DataArray(data=da.from_array(d, chunks=12), dims=["z"], name="d")
  
          # query single dim, single variable
          actual = aa.query(x="a > 5", engine=engine, parser=parser)
          expect = aa.isel(x=(a > 5))
          assert_identical(expect, actual)
  
          # query single dim, single variable, via dict
          actual = aa.query(dict(x="a > 5"), engine=engine, parser=parser)
          expect = aa.isel(dict(x=(a > 5)))
          assert_identical(expect, actual)
  
          # query single dim, single variable
          actual = bb.query(x="b > 50", engine=engine, parser=parser)
          expect = bb.isel(x=(b > 50))
          assert_identical(expect, actual)
  
          # query single dim, single variable
          actual = cc.query(y="c < .5", engine=engine, parser=parser)
          expect = cc.isel(y=(c < 0.5))
          assert_identical(expect, actual)
  
          # query single dim, single string variable
          if parser == "pandas":
              # N.B., this query currently only works with the pandas parser
              # xref https://github.com/pandas-dev/pandas/issues/40436
              actual = dd.query(z='d == "bar"', engine=engine, parser=parser)
              expect = dd.isel(z=(d == "bar"))
              assert_identical(expect, actual)
  
          # test error handling
          with pytest.raises(ValueError):
              aa.query("a > 5")  # must be dict or kwargs
          with pytest.raises(ValueError):
              aa.query(x=(a > 5))  # must be query string
          with pytest.raises(UndefinedVariableError):
              aa.query(x="spam > 50")  # name not present
  
      @requires_scipy
      @pytest.mark.parametrize("use_dask", [True, False])
      def test_curvefit(self, use_dask):
          if use_dask and not has_dask:
              pytest.skip("requires dask")
  
          def exp_decay(t, n0, tau=1):
              return n0 * np.exp(-t / tau)
  
          t = np.arange(0, 5, 0.5)
          da = DataArray(
              np.stack([exp_decay(t, 3, 3), exp_decay(t, 5, 4), np.nan * t], axis=-1),
              dims=("t", "x"),
              coords={"t": t, "x": [0, 1, 2]},
          )
          da[0, 0] = np.nan
  
          expected = DataArray(
              [[3, 3], [5, 4], [np.nan, np.nan]],
              dims=("x", "param"),
              coords={"x": [0, 1, 2], "param": ["n0", "tau"]},
          )
  
          if use_dask:
              da = da.chunk({"x": 1})
  
          fit = da.curvefit(
              coords=[da.t], func=exp_decay, p0={"n0": 4}, bounds={"tau": [2, 6]}
          )
          assert_allclose(fit.curvefit_coefficients, expected, rtol=1e-3)
  
          da = da.compute()
          fit = da.curvefit(coords="t", func=np.power, reduce_dims="x", param_names=["a"])
          assert "a" in fit.param
          assert "x" not in fit.dims
  
      def test_curvefit_helpers(self):
          def exp_decay(t, n0, tau=1):
              return n0 * np.exp(-t / tau)
  
          params, func_args = xr.core.dataset._get_func_args(exp_decay, [])
          assert params == ["n0", "tau"]
          param_defaults, bounds_defaults = xr.core.dataset._initialize_curvefit_params(
              params, {"n0": 4}, {"tau": [5, np.inf]}, func_args
          )
          assert param_defaults == {"n0": 4, "tau": 6}
          assert bounds_defaults == {"n0": (-np.inf, np.inf), "tau": (5, np.inf)}
  
          param_names = ["a"]
          params, func_args = xr.core.dataset._get_func_args(np.power, param_names)
          assert params == param_names
          with pytest.raises(ValueError):
              xr.core.dataset._get_func_args(np.power, [])
  
  
  class TestReduce:
      @pytest.fixture(autouse=True)
      def setup(self):
          self.attrs = {"attr1": "value1", "attr2": 2929}
  
  
  @pytest.mark.parametrize(
      "x, minindex, maxindex, nanindex",
      [
          (np.array([0, 1, 2, 0, -2, -4, 2]), 5, 2, None),
          (np.array([0.0, 1.0, 2.0, 0.0, -2.0, -4.0, 2.0]), 5, 2, None),
          (np.array([1.0, np.NaN, 2.0, np.NaN, -2.0, -4.0, 2.0]), 5, 2, 1),
          (
              np.array([1.0, np.NaN, 2.0, np.NaN, -2.0, -4.0, 2.0]).astype("object"),
              5,
              2,
              1,
          ),
          (np.array([np.NaN, np.NaN]), np.NaN, np.NaN, 0),
          (
              np.array(
                  ["2015-12-31", "2020-01-02", "2020-01-01", "2016-01-01"],
                  dtype="datetime64[ns]",
              ),
              0,
              1,
              None,
          ),
      ],
  )
  class TestReduce1D(TestReduce):
      def test_min(self, x, minindex, maxindex, nanindex):
          ar = xr.DataArray(
              x, dims=["x"], coords={"x": np.arange(x.size) * 4}, attrs=self.attrs
          )
  
          if np.isnan(minindex):
              minindex = 0
  
          expected0 = ar.isel(x=minindex, drop=True)
          result0 = ar.min(keep_attrs=True)
          assert_identical(result0, expected0)
  
          result1 = ar.min()
          expected1 = expected0.copy()
          expected1.attrs = {}
          assert_identical(result1, expected1)
  
          result2 = ar.min(skipna=False)
          if nanindex is not None and ar.dtype.kind != "O":
              expected2 = ar.isel(x=nanindex, drop=True)
              expected2.attrs = {}
          else:
              expected2 = expected1
  
          assert_identical(result2, expected2)
  
      def test_max(self, x, minindex, maxindex, nanindex):
          ar = xr.DataArray(
              x, dims=["x"], coords={"x": np.arange(x.size) * 4}, attrs=self.attrs
          )
  
          if np.isnan(minindex):
              maxindex = 0
  
          expected0 = ar.isel(x=maxindex, drop=True)
          result0 = ar.max(keep_attrs=True)
          assert_identical(result0, expected0)
  
          result1 = ar.max()
          expected1 = expected0.copy()
          expected1.attrs = {}
          assert_identical(result1, expected1)
  
          result2 = ar.max(skipna=False)
          if nanindex is not None and ar.dtype.kind != "O":
              expected2 = ar.isel(x=nanindex, drop=True)
              expected2.attrs = {}
          else:
              expected2 = expected1
  
          assert_identical(result2, expected2)
  
      @pytest.mark.filterwarnings(
          "ignore:Behaviour of argmin/argmax with neither dim nor :DeprecationWarning"
      )
      def test_argmin(self, x, minindex, maxindex, nanindex):
          ar = xr.DataArray(
              x, dims=["x"], coords={"x": np.arange(x.size) * 4}, attrs=self.attrs
          )
          indarr = xr.DataArray(np.arange(x.size, dtype=np.intp), dims=["x"])
  
          if np.isnan(minindex):
              with pytest.raises(ValueError):
                  ar.argmin()
              return
  
          expected0 = indarr[minindex]
          result0 = ar.argmin()
          assert_identical(result0, expected0)
  
          result1 = ar.argmin(keep_attrs=True)
          expected1 = expected0.copy()
          expected1.attrs = self.attrs
          assert_identical(result1, expected1)
  
          result2 = ar.argmin(skipna=False)
          if nanindex is not None and ar.dtype.kind != "O":
              expected2 = indarr.isel(x=nanindex, drop=True)
              expected2.attrs = {}
          else:
              expected2 = expected0
  
          assert_identical(result2, expected2)
  
      @pytest.mark.filterwarnings(
          "ignore:Behaviour of argmin/argmax with neither dim nor :DeprecationWarning"
      )
      def test_argmax(self, x, minindex, maxindex, nanindex):
          ar = xr.DataArray(
              x, dims=["x"], coords={"x": np.arange(x.size) * 4}, attrs=self.attrs
          )
          indarr = xr.DataArray(np.arange(x.size, dtype=np.intp), dims=["x"])
  
          if np.isnan(maxindex):
              with pytest.raises(ValueError):
                  ar.argmax()
              return
  
          expected0 = indarr[maxindex]
          result0 = ar.argmax()
          assert_identical(result0, expected0)
  
          result1 = ar.argmax(keep_attrs=True)
          expected1 = expected0.copy()
          expected1.attrs = self.attrs
          assert_identical(result1, expected1)
  
          result2 = ar.argmax(skipna=False)
          if nanindex is not None and ar.dtype.kind != "O":
              expected2 = indarr.isel(x=nanindex, drop=True)
              expected2.attrs = {}
          else:
              expected2 = expected0
  
          assert_identical(result2, expected2)
  
      @pytest.mark.parametrize("use_dask", [True, False])
      def test_idxmin(self, x, minindex, maxindex, nanindex, use_dask):
          if use_dask and not has_dask:
              pytest.skip("requires dask")
          if use_dask and x.dtype.kind == "M":
              pytest.xfail("dask operation 'argmin' breaks when dtype is datetime64 (M)")
          ar0_raw = xr.DataArray(
              x, dims=["x"], coords={"x": np.arange(x.size) * 4}, attrs=self.attrs
          )
  
          if use_dask:
              ar0 = ar0_raw.chunk({})
          else:
              ar0 = ar0_raw
  
          # dim doesn't exist
          with pytest.raises(KeyError):
              ar0.idxmin(dim="spam")
  
          # Scalar Dataarray
          with pytest.raises(ValueError):
              xr.DataArray(5).idxmin()
  
          coordarr0 = xr.DataArray(ar0.coords["x"], dims=["x"])
          coordarr1 = coordarr0.copy()
  
          hasna = np.isnan(minindex)
          if np.isnan(minindex):
              minindex = 0
  
          if hasna:
              coordarr1[...] = 1
              fill_value_0 = np.NaN
          else:
              fill_value_0 = 1
  
          expected0 = (
              (coordarr1 * fill_value_0).isel(x=minindex, drop=True).astype("float")
          )
          expected0.name = "x"
  
          # Default fill value (NaN)
          result0 = ar0.idxmin()
          assert_identical(result0, expected0)
  
          # Manually specify NaN fill_value
          result1 = ar0.idxmin(fill_value=np.NaN)
          assert_identical(result1, expected0)
  
          # keep_attrs
          result2 = ar0.idxmin(keep_attrs=True)
          expected2 = expected0.copy()
          expected2.attrs = self.attrs
          assert_identical(result2, expected2)
  
          # skipna=False
          if nanindex is not None and ar0.dtype.kind != "O":
              expected3 = coordarr0.isel(x=nanindex, drop=True).astype("float")
              expected3.name = "x"
              expected3.attrs = {}
          else:
              expected3 = expected0.copy()
  
          result3 = ar0.idxmin(skipna=False)
          assert_identical(result3, expected3)
  
          # fill_value should be ignored with skipna=False
          result4 = ar0.idxmin(skipna=False, fill_value=-100j)
          assert_identical(result4, expected3)
  
          # Float fill_value
          if hasna:
              fill_value_5 = -1.1
          else:
              fill_value_5 = 1
  
          expected5 = (coordarr1 * fill_value_5).isel(x=minindex, drop=True)
          expected5.name = "x"
  
          result5 = ar0.idxmin(fill_value=-1.1)
          assert_identical(result5, expected5)
  
          # Integer fill_value
          if hasna:
              fill_value_6 = -1
          else:
              fill_value_6 = 1
  
          expected6 = (coordarr1 * fill_value_6).isel(x=minindex, drop=True)
          expected6.name = "x"
  
          result6 = ar0.idxmin(fill_value=-1)
          assert_identical(result6, expected6)
  
          # Complex fill_value
          if hasna:
              fill_value_7 = -1j
          else:
              fill_value_7 = 1
  
          expected7 = (coordarr1 * fill_value_7).isel(x=minindex, drop=True)
          expected7.name = "x"
  
          result7 = ar0.idxmin(fill_value=-1j)
          assert_identical(result7, expected7)
  
      @pytest.mark.parametrize("use_dask", [True, False])
      def test_idxmax(self, x, minindex, maxindex, nanindex, use_dask):
          if use_dask and not has_dask:
              pytest.skip("requires dask")
          if use_dask and x.dtype.kind == "M":
              pytest.xfail("dask operation 'argmax' breaks when dtype is datetime64 (M)")
          ar0_raw = xr.DataArray(
              x, dims=["x"], coords={"x": np.arange(x.size) * 4}, attrs=self.attrs
          )
  
          if use_dask:
              ar0 = ar0_raw.chunk({})
          else:
              ar0 = ar0_raw
  
          # dim doesn't exist
          with pytest.raises(KeyError):
              ar0.idxmax(dim="spam")
  
          # Scalar Dataarray
          with pytest.raises(ValueError):
              xr.DataArray(5).idxmax()
  
          coordarr0 = xr.DataArray(ar0.coords["x"], dims=["x"])
          coordarr1 = coordarr0.copy()
  
          hasna = np.isnan(maxindex)
          if np.isnan(maxindex):
              maxindex = 0
  
          if hasna:
              coordarr1[...] = 1
              fill_value_0 = np.NaN
          else:
              fill_value_0 = 1
  
          expected0 = (
              (coordarr1 * fill_value_0).isel(x=maxindex, drop=True).astype("float")
          )
          expected0.name = "x"
  
          # Default fill value (NaN)
          result0 = ar0.idxmax()
          assert_identical(result0, expected0)
  
          # Manually specify NaN fill_value
          result1 = ar0.idxmax(fill_value=np.NaN)
          assert_identical(result1, expected0)
  
          # keep_attrs
          result2 = ar0.idxmax(keep_attrs=True)
          expected2 = expected0.copy()
          expected2.attrs = self.attrs
          assert_identical(result2, expected2)
  
          # skipna=False
          if nanindex is not None and ar0.dtype.kind != "O":
              expected3 = coordarr0.isel(x=nanindex, drop=True).astype("float")
              expected3.name = "x"
              expected3.attrs = {}
          else:
              expected3 = expected0.copy()
  
          result3 = ar0.idxmax(skipna=False)
          assert_identical(result3, expected3)
  
          # fill_value should be ignored with skipna=False
          result4 = ar0.idxmax(skipna=False, fill_value=-100j)
          assert_identical(result4, expected3)
  
          # Float fill_value
          if hasna:
              fill_value_5 = -1.1
          else:
              fill_value_5 = 1
  
          expected5 = (coordarr1 * fill_value_5).isel(x=maxindex, drop=True)
          expected5.name = "x"
  
          result5 = ar0.idxmax(fill_value=-1.1)
          assert_identical(result5, expected5)
  
          # Integer fill_value
          if hasna:
              fill_value_6 = -1
          else:
              fill_value_6 = 1
  
          expected6 = (coordarr1 * fill_value_6).isel(x=maxindex, drop=True)
          expected6.name = "x"
  
          result6 = ar0.idxmax(fill_value=-1)
          assert_identical(result6, expected6)
  
          # Complex fill_value
          if hasna:
              fill_value_7 = -1j
          else:
              fill_value_7 = 1
  
          expected7 = (coordarr1 * fill_value_7).isel(x=maxindex, drop=True)
          expected7.name = "x"
  
          result7 = ar0.idxmax(fill_value=-1j)
          assert_identical(result7, expected7)
  
      @pytest.mark.filterwarnings(
          "ignore:Behaviour of argmin/argmax with neither dim nor :DeprecationWarning"
      )
      def test_argmin_dim(self, x, minindex, maxindex, nanindex):
          ar = xr.DataArray(
              x, dims=["x"], coords={"x": np.arange(x.size) * 4}, attrs=self.attrs
          )
          indarr = xr.DataArray(np.arange(x.size, dtype=np.intp), dims=["x"])
  
          if np.isnan(minindex):
              with pytest.raises(ValueError):
                  ar.argmin()
              return
  
          expected0 = {"x": indarr[minindex]}
          result0 = ar.argmin(...)
          for key in expected0:
              assert_identical(result0[key], expected0[key])
  
          result1 = ar.argmin(..., keep_attrs=True)
          expected1 = deepcopy(expected0)
          for da in expected1.values():
              da.attrs = self.attrs
          for key in expected1:
              assert_identical(result1[key], expected1[key])
  
          result2 = ar.argmin(..., skipna=False)
          if nanindex is not None and ar.dtype.kind != "O":
              expected2 = {"x": indarr.isel(x=nanindex, drop=True)}
              expected2["x"].attrs = {}
          else:
              expected2 = expected0
  
          for key in expected2:
              assert_identical(result2[key], expected2[key])
  
      @pytest.mark.filterwarnings(
          "ignore:Behaviour of argmin/argmax with neither dim nor :DeprecationWarning"
      )
      def test_argmax_dim(self, x, minindex, maxindex, nanindex):
          ar = xr.DataArray(
              x, dims=["x"], coords={"x": np.arange(x.size) * 4}, attrs=self.attrs
          )
          indarr = xr.DataArray(np.arange(x.size, dtype=np.intp), dims=["x"])
  
          if np.isnan(maxindex):
              with pytest.raises(ValueError):
                  ar.argmax()
              return
  
          expected0 = {"x": indarr[maxindex]}
          result0 = ar.argmax(...)
          for key in expected0:
              assert_identical(result0[key], expected0[key])
  
          result1 = ar.argmax(..., keep_attrs=True)
          expected1 = deepcopy(expected0)
          for da in expected1.values():
              da.attrs = self.attrs
          for key in expected1:
              assert_identical(result1[key], expected1[key])
  
          result2 = ar.argmax(..., skipna=False)
          if nanindex is not None and ar.dtype.kind != "O":
              expected2 = {"x": indarr.isel(x=nanindex, drop=True)}
              expected2["x"].attrs = {}
          else:
              expected2 = expected0
  
          for key in expected2:
              assert_identical(result2[key], expected2[key])
  
  
  @pytest.mark.parametrize(
      "x, minindex, maxindex, nanindex",
      [
          (
              np.array(
                  [
                      [0, 1, 2, 0, -2, -4, 2],
                      [1, 1, 1, 1, 1, 1, 1],
                      [0, 0, -10, 5, 20, 0, 0],
                  ]
              ),
              [5, 0, 2],
              [2, 0, 4],
              [None, None, None],
          ),
          (
              np.array(
                  [
                      [2.0, 1.0, 2.0, 0.0, -2.0, -4.0, 2.0],
                      [-4.0, np.NaN, 2.0, np.NaN, -2.0, -4.0, 2.0],
                      [np.NaN] * 7,
                  ]
              ),
              [5, 0, np.NaN],
              [0, 2, np.NaN],
              [None, 1, 0],
          ),
          (
              np.array(
                  [
                      [2.0, 1.0, 2.0, 0.0, -2.0, -4.0, 2.0],
                      [-4.0, np.NaN, 2.0, np.NaN, -2.0, -4.0, 2.0],
                      [np.NaN] * 7,
                  ]
              ).astype("object"),
              [5, 0, np.NaN],
              [0, 2, np.NaN],
              [None, 1, 0],
          ),
          (
              np.array(
                  [
                      ["2015-12-31", "2020-01-02", "2020-01-01", "2016-01-01"],
                      ["2020-01-02", "2020-01-02", "2020-01-02", "2020-01-02"],
                      ["1900-01-01", "1-02-03", "1900-01-02", "1-02-03"],
                  ],
                  dtype="datetime64[ns]",
              ),
              [0, 0, 1],
              [1, 0, 2],
              [None, None, None],
          ),
      ],
  )
  class TestReduce2D(TestReduce):
      def test_min(self, x, minindex, maxindex, nanindex):
          ar = xr.DataArray(
              x,
              dims=["y", "x"],
              coords={"x": np.arange(x.shape[1]) * 4, "y": 1 - np.arange(x.shape[0])},
              attrs=self.attrs,
          )
  
          minindex = [x if not np.isnan(x) else 0 for x in minindex]
          expected0 = [
              ar.isel(y=yi).isel(x=indi, drop=True) for yi, indi in enumerate(minindex)
          ]
          expected0 = xr.concat(expected0, dim="y")
  
          result0 = ar.min(dim="x", keep_attrs=True)
          assert_identical(result0, expected0)
  
          result1 = ar.min(dim="x")
          expected1 = expected0
          expected1.attrs = {}
          assert_identical(result1, expected1)
  
          result2 = ar.min(axis=1)
          assert_identical(result2, expected1)
  
          minindex = [
              x if y is None or ar.dtype.kind == "O" else y
              for x, y in zip(minindex, nanindex)
          ]
          expected2 = [
              ar.isel(y=yi).isel(x=indi, drop=True) for yi, indi in enumerate(minindex)
          ]
          expected2 = xr.concat(expected2, dim="y")
          expected2.attrs = {}
  
          result3 = ar.min(dim="x", skipna=False)
  
          assert_identical(result3, expected2)
  
      def test_max(self, x, minindex, maxindex, nanindex):
          ar = xr.DataArray(
              x,
              dims=["y", "x"],
              coords={"x": np.arange(x.shape[1]) * 4, "y": 1 - np.arange(x.shape[0])},
              attrs=self.attrs,
          )
  
          maxindex = [x if not np.isnan(x) else 0 for x in maxindex]
          expected0 = [
              ar.isel(y=yi).isel(x=indi, drop=True) for yi, indi in enumerate(maxindex)
          ]
          expected0 = xr.concat(expected0, dim="y")
  
          result0 = ar.max(dim="x", keep_attrs=True)
          assert_identical(result0, expected0)
  
          result1 = ar.max(dim="x")
          expected1 = expected0.copy()
          expected1.attrs = {}
          assert_identical(result1, expected1)
  
          result2 = ar.max(axis=1)
          assert_identical(result2, expected1)
  
          maxindex = [
              x if y is None or ar.dtype.kind == "O" else y
              for x, y in zip(maxindex, nanindex)
          ]
          expected2 = [
              ar.isel(y=yi).isel(x=indi, drop=True) for yi, indi in enumerate(maxindex)
          ]
          expected2 = xr.concat(expected2, dim="y")
          expected2.attrs = {}
  
          result3 = ar.max(dim="x", skipna=False)
  
          assert_identical(result3, expected2)
  
      def test_argmin(self, x, minindex, maxindex, nanindex):
          ar = xr.DataArray(
              x,
              dims=["y", "x"],
              coords={"x": np.arange(x.shape[1]) * 4, "y": 1 - np.arange(x.shape[0])},
              attrs=self.attrs,
          )
          indarr = np.tile(np.arange(x.shape[1], dtype=np.intp), [x.shape[0], 1])
          indarr = xr.DataArray(indarr, dims=ar.dims, coords=ar.coords)
  
          if np.isnan(minindex).any():
              with pytest.raises(ValueError):
                  ar.argmin(dim="x")
              return
  
          expected0 = [
              indarr.isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(minindex)
          ]
          expected0 = xr.concat(expected0, dim="y")
  
          result0 = ar.argmin(dim="x")
          assert_identical(result0, expected0)
  
          result1 = ar.argmin(axis=1)
          assert_identical(result1, expected0)
  
          result2 = ar.argmin(dim="x", keep_attrs=True)
          expected1 = expected0.copy()
          expected1.attrs = self.attrs
          assert_identical(result2, expected1)
  
          minindex = [
              x if y is None or ar.dtype.kind == "O" else y
              for x, y in zip(minindex, nanindex)
          ]
          expected2 = [
              indarr.isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(minindex)
          ]
          expected2 = xr.concat(expected2, dim="y")
          expected2.attrs = {}
  
          result3 = ar.argmin(dim="x", skipna=False)
  
          assert_identical(result3, expected2)
  
      def test_argmax(self, x, minindex, maxindex, nanindex):
          ar = xr.DataArray(
              x,
              dims=["y", "x"],
              coords={"x": np.arange(x.shape[1]) * 4, "y": 1 - np.arange(x.shape[0])},
              attrs=self.attrs,
          )
          indarr = np.tile(np.arange(x.shape[1], dtype=np.intp), [x.shape[0], 1])
          indarr = xr.DataArray(indarr, dims=ar.dims, coords=ar.coords)
  
          if np.isnan(maxindex).any():
              with pytest.raises(ValueError):
                  ar.argmax(dim="x")
              return
  
          expected0 = [
              indarr.isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(maxindex)
          ]
          expected0 = xr.concat(expected0, dim="y")
  
          result0 = ar.argmax(dim="x")
          assert_identical(result0, expected0)
  
          result1 = ar.argmax(axis=1)
          assert_identical(result1, expected0)
  
          result2 = ar.argmax(dim="x", keep_attrs=True)
          expected1 = expected0.copy()
          expected1.attrs = self.attrs
          assert_identical(result2, expected1)
  
          maxindex = [
              x if y is None or ar.dtype.kind == "O" else y
              for x, y in zip(maxindex, nanindex)
          ]
          expected2 = [
              indarr.isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(maxindex)
          ]
          expected2 = xr.concat(expected2, dim="y")
          expected2.attrs = {}
  
          result3 = ar.argmax(dim="x", skipna=False)
  
          assert_identical(result3, expected2)
  
      @pytest.mark.parametrize("use_dask", [True, False])
      def test_idxmin(self, x, minindex, maxindex, nanindex, use_dask):
          if use_dask and not has_dask:
              pytest.skip("requires dask")
          if use_dask and x.dtype.kind == "M":
              pytest.xfail("dask operation 'argmin' breaks when dtype is datetime64 (M)")
  
          if x.dtype.kind == "O":
              # TODO: nanops._nan_argminmax_object computes once to check for all-NaN slices.
              max_computes = 1
          else:
              max_computes = 0
  
          ar0_raw = xr.DataArray(
              x,
              dims=["y", "x"],
              coords={"x": np.arange(x.shape[1]) * 4, "y": 1 - np.arange(x.shape[0])},
              attrs=self.attrs,
          )
  
          if use_dask:
              ar0 = ar0_raw.chunk({})
          else:
              ar0 = ar0_raw
  
          assert_identical(ar0, ar0)
  
          # No dimension specified
          with pytest.raises(ValueError):
              ar0.idxmin()
  
          # dim doesn't exist
          with pytest.raises(KeyError):
              ar0.idxmin(dim="Y")
  
          assert_identical(ar0, ar0)
  
          coordarr0 = xr.DataArray(
              np.tile(ar0.coords["x"], [x.shape[0], 1]), dims=ar0.dims, coords=ar0.coords
          )
  
          hasna = [np.isnan(x) for x in minindex]
          coordarr1 = coordarr0.copy()
          coordarr1[hasna, :] = 1
          minindex0 = [x if not np.isnan(x) else 0 for x in minindex]
  
          nan_mult_0 = np.array([np.NaN if x else 1 for x in hasna])[:, None]
          expected0 = [
              (coordarr1 * nan_mult_0).isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(minindex0)
          ]
          expected0 = xr.concat(expected0, dim="y")
          expected0.name = "x"
  
          # Default fill value (NaN)
          with raise_if_dask_computes(max_computes=max_computes):
              result0 = ar0.idxmin(dim="x")
          assert_identical(result0, expected0)
  
          # Manually specify NaN fill_value
          with raise_if_dask_computes(max_computes=max_computes):
              result1 = ar0.idxmin(dim="x", fill_value=np.NaN)
          assert_identical(result1, expected0)
  
          # keep_attrs
          with raise_if_dask_computes(max_computes=max_computes):
              result2 = ar0.idxmin(dim="x", keep_attrs=True)
          expected2 = expected0.copy()
          expected2.attrs = self.attrs
          assert_identical(result2, expected2)
  
          # skipna=False
          minindex3 = [
              x if y is None or ar0.dtype.kind == "O" else y
              for x, y in zip(minindex0, nanindex)
          ]
          expected3 = [
              coordarr0.isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(minindex3)
          ]
          expected3 = xr.concat(expected3, dim="y")
          expected3.name = "x"
          expected3.attrs = {}
  
          with raise_if_dask_computes(max_computes=max_computes):
              result3 = ar0.idxmin(dim="x", skipna=False)
          assert_identical(result3, expected3)
  
          # fill_value should be ignored with skipna=False
          with raise_if_dask_computes(max_computes=max_computes):
              result4 = ar0.idxmin(dim="x", skipna=False, fill_value=-100j)
          assert_identical(result4, expected3)
  
          # Float fill_value
          nan_mult_5 = np.array([-1.1 if x else 1 for x in hasna])[:, None]
          expected5 = [
              (coordarr1 * nan_mult_5).isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(minindex0)
          ]
          expected5 = xr.concat(expected5, dim="y")
          expected5.name = "x"
  
          with raise_if_dask_computes(max_computes=max_computes):
              result5 = ar0.idxmin(dim="x", fill_value=-1.1)
          assert_identical(result5, expected5)
  
          # Integer fill_value
          nan_mult_6 = np.array([-1 if x else 1 for x in hasna])[:, None]
          expected6 = [
              (coordarr1 * nan_mult_6).isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(minindex0)
          ]
          expected6 = xr.concat(expected6, dim="y")
          expected6.name = "x"
  
          with raise_if_dask_computes(max_computes=max_computes):
              result6 = ar0.idxmin(dim="x", fill_value=-1)
          assert_identical(result6, expected6)
  
          # Complex fill_value
          nan_mult_7 = np.array([-5j if x else 1 for x in hasna])[:, None]
          expected7 = [
              (coordarr1 * nan_mult_7).isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(minindex0)
          ]
          expected7 = xr.concat(expected7, dim="y")
          expected7.name = "x"
  
          with raise_if_dask_computes(max_computes=max_computes):
              result7 = ar0.idxmin(dim="x", fill_value=-5j)
          assert_identical(result7, expected7)
  
      @pytest.mark.parametrize("use_dask", [True, False])
      def test_idxmax(self, x, minindex, maxindex, nanindex, use_dask):
          if use_dask and not has_dask:
              pytest.skip("requires dask")
          if use_dask and x.dtype.kind == "M":
              pytest.xfail("dask operation 'argmax' breaks when dtype is datetime64 (M)")
  
          if x.dtype.kind == "O":
              # TODO: nanops._nan_argminmax_object computes once to check for all-NaN slices.
              max_computes = 1
          else:
              max_computes = 0
  
          ar0_raw = xr.DataArray(
              x,
              dims=["y", "x"],
              coords={"x": np.arange(x.shape[1]) * 4, "y": 1 - np.arange(x.shape[0])},
              attrs=self.attrs,
          )
  
          if use_dask:
              ar0 = ar0_raw.chunk({})
          else:
              ar0 = ar0_raw
  
          # No dimension specified
          with pytest.raises(ValueError):
              ar0.idxmax()
  
          # dim doesn't exist
          with pytest.raises(KeyError):
              ar0.idxmax(dim="Y")
  
          ar1 = ar0.copy()
          del ar1.coords["y"]
          with pytest.raises(KeyError):
              ar1.idxmax(dim="y")
  
          coordarr0 = xr.DataArray(
              np.tile(ar0.coords["x"], [x.shape[0], 1]), dims=ar0.dims, coords=ar0.coords
          )
  
          hasna = [np.isnan(x) for x in maxindex]
          coordarr1 = coordarr0.copy()
          coordarr1[hasna, :] = 1
          maxindex0 = [x if not np.isnan(x) else 0 for x in maxindex]
  
          nan_mult_0 = np.array([np.NaN if x else 1 for x in hasna])[:, None]
          expected0 = [
              (coordarr1 * nan_mult_0).isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(maxindex0)
          ]
          expected0 = xr.concat(expected0, dim="y")
          expected0.name = "x"
  
          # Default fill value (NaN)
          with raise_if_dask_computes(max_computes=max_computes):
              result0 = ar0.idxmax(dim="x")
          assert_identical(result0, expected0)
  
          # Manually specify NaN fill_value
          with raise_if_dask_computes(max_computes=max_computes):
              result1 = ar0.idxmax(dim="x", fill_value=np.NaN)
          assert_identical(result1, expected0)
  
          # keep_attrs
          with raise_if_dask_computes(max_computes=max_computes):
              result2 = ar0.idxmax(dim="x", keep_attrs=True)
          expected2 = expected0.copy()
          expected2.attrs = self.attrs
          assert_identical(result2, expected2)
  
          # skipna=False
          maxindex3 = [
              x if y is None or ar0.dtype.kind == "O" else y
              for x, y in zip(maxindex0, nanindex)
          ]
          expected3 = [
              coordarr0.isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(maxindex3)
          ]
          expected3 = xr.concat(expected3, dim="y")
          expected3.name = "x"
          expected3.attrs = {}
  
          with raise_if_dask_computes(max_computes=max_computes):
              result3 = ar0.idxmax(dim="x", skipna=False)
          assert_identical(result3, expected3)
  
          # fill_value should be ignored with skipna=False
          with raise_if_dask_computes(max_computes=max_computes):
              result4 = ar0.idxmax(dim="x", skipna=False, fill_value=-100j)
          assert_identical(result4, expected3)
  
          # Float fill_value
          nan_mult_5 = np.array([-1.1 if x else 1 for x in hasna])[:, None]
          expected5 = [
              (coordarr1 * nan_mult_5).isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(maxindex0)
          ]
          expected5 = xr.concat(expected5, dim="y")
          expected5.name = "x"
  
          with raise_if_dask_computes(max_computes=max_computes):
              result5 = ar0.idxmax(dim="x", fill_value=-1.1)
          assert_identical(result5, expected5)
  
          # Integer fill_value
          nan_mult_6 = np.array([-1 if x else 1 for x in hasna])[:, None]
          expected6 = [
              (coordarr1 * nan_mult_6).isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(maxindex0)
          ]
          expected6 = xr.concat(expected6, dim="y")
          expected6.name = "x"
  
          with raise_if_dask_computes(max_computes=max_computes):
              result6 = ar0.idxmax(dim="x", fill_value=-1)
          assert_identical(result6, expected6)
  
          # Complex fill_value
          nan_mult_7 = np.array([-5j if x else 1 for x in hasna])[:, None]
          expected7 = [
              (coordarr1 * nan_mult_7).isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(maxindex0)
          ]
          expected7 = xr.concat(expected7, dim="y")
          expected7.name = "x"
  
          with raise_if_dask_computes(max_computes=max_computes):
              result7 = ar0.idxmax(dim="x", fill_value=-5j)
          assert_identical(result7, expected7)
  
      @pytest.mark.filterwarnings(
          "ignore:Behaviour of argmin/argmax with neither dim nor :DeprecationWarning"
      )
      def test_argmin_dim(self, x, minindex, maxindex, nanindex):
          ar = xr.DataArray(
              x,
              dims=["y", "x"],
              coords={"x": np.arange(x.shape[1]) * 4, "y": 1 - np.arange(x.shape[0])},
              attrs=self.attrs,
          )
          indarr = np.tile(np.arange(x.shape[1], dtype=np.intp), [x.shape[0], 1])
          indarr = xr.DataArray(indarr, dims=ar.dims, coords=ar.coords)
  
          if np.isnan(minindex).any():
              with pytest.raises(ValueError):
                  ar.argmin(dim="x")
              return
  
          expected0 = [
              indarr.isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(minindex)
          ]
          expected0 = {"x": xr.concat(expected0, dim="y")}
  
          result0 = ar.argmin(dim=["x"])
          for key in expected0:
              assert_identical(result0[key], expected0[key])
  
          result1 = ar.argmin(dim=["x"], keep_attrs=True)
          expected1 = deepcopy(expected0)
          expected1["x"].attrs = self.attrs
          for key in expected1:
              assert_identical(result1[key], expected1[key])
  
          minindex = [
              x if y is None or ar.dtype.kind == "O" else y
              for x, y in zip(minindex, nanindex)
          ]
          expected2 = [
              indarr.isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(minindex)
          ]
          expected2 = {"x": xr.concat(expected2, dim="y")}
          expected2["x"].attrs = {}
  
          result2 = ar.argmin(dim=["x"], skipna=False)
  
          for key in expected2:
              assert_identical(result2[key], expected2[key])
  
          result3 = ar.argmin(...)
          min_xind = ar.isel(expected0).argmin()
          expected3 = {
              "y": DataArray(min_xind),
              "x": DataArray(minindex[min_xind.item()]),
          }
  
          for key in expected3:
              assert_identical(result3[key], expected3[key])
  
      @pytest.mark.filterwarnings(
          "ignore:Behaviour of argmin/argmax with neither dim nor :DeprecationWarning"
      )
      def test_argmax_dim(self, x, minindex, maxindex, nanindex):
          ar = xr.DataArray(
              x,
              dims=["y", "x"],
              coords={"x": np.arange(x.shape[1]) * 4, "y": 1 - np.arange(x.shape[0])},
              attrs=self.attrs,
          )
          indarr = np.tile(np.arange(x.shape[1], dtype=np.intp), [x.shape[0], 1])
          indarr = xr.DataArray(indarr, dims=ar.dims, coords=ar.coords)
  
          if np.isnan(maxindex).any():
              with pytest.raises(ValueError):
                  ar.argmax(dim="x")
              return
  
          expected0 = [
              indarr.isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(maxindex)
          ]
          expected0 = {"x": xr.concat(expected0, dim="y")}
  
          result0 = ar.argmax(dim=["x"])
          for key in expected0:
              assert_identical(result0[key], expected0[key])
  
          result1 = ar.argmax(dim=["x"], keep_attrs=True)
          expected1 = deepcopy(expected0)
          expected1["x"].attrs = self.attrs
          for key in expected1:
              assert_identical(result1[key], expected1[key])
  
          maxindex = [
              x if y is None or ar.dtype.kind == "O" else y
              for x, y in zip(maxindex, nanindex)
          ]
          expected2 = [
              indarr.isel(y=yi).isel(x=indi, drop=True)
              for yi, indi in enumerate(maxindex)
          ]
          expected2 = {"x": xr.concat(expected2, dim="y")}
          expected2["x"].attrs = {}
  
          result2 = ar.argmax(dim=["x"], skipna=False)
  
          for key in expected2:
              assert_identical(result2[key], expected2[key])
  
          result3 = ar.argmax(...)
          max_xind = ar.isel(expected0).argmax()
          expected3 = {
              "y": DataArray(max_xind),
              "x": DataArray(maxindex[max_xind.item()]),
          }
  
          for key in expected3:
              assert_identical(result3[key], expected3[key])
  
  
  @pytest.mark.parametrize(
      "x, minindices_x, minindices_y, minindices_z, minindices_xy, "
      "minindices_xz, minindices_yz, minindices_xyz, maxindices_x, "
      "maxindices_y, maxindices_z, maxindices_xy, maxindices_xz, maxindices_yz, "
      "maxindices_xyz, nanindices_x, nanindices_y, nanindices_z, nanindices_xy, "
      "nanindices_xz, nanindices_yz, nanindices_xyz",
      [
          (
              np.array(
                  [
                      [[0, 1, 2, 0], [-2, -4, 2, 0]],
                      [[1, 1, 1, 1], [1, 1, 1, 1]],
                      [[0, 0, -10, 5], [20, 0, 0, 0]],
                  ]
              ),
              {"x": np.array([[0, 2, 2, 0], [0, 0, 2, 0]])},
              {"y": np.array([[1, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]])},
              {"z": np.array([[0, 1], [0, 0], [2, 1]])},
              {"x": np.array([0, 0, 2, 0]), "y": np.array([1, 1, 0, 0])},
              {"x": np.array([2, 0]), "z": np.array([2, 1])},
              {"y": np.array([1, 0, 0]), "z": np.array([1, 0, 2])},
              {"x": np.array(2), "y": np.array(0), "z": np.array(2)},
              {"x": np.array([[1, 0, 0, 2], [2, 1, 0, 1]])},
              {"y": np.array([[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 1, 0]])},
              {"z": np.array([[2, 2], [0, 0], [3, 0]])},
              {"x": np.array([2, 0, 0, 2]), "y": np.array([1, 0, 0, 0])},
              {"x": np.array([2, 2]), "z": np.array([3, 0])},
              {"y": np.array([0, 0, 1]), "z": np.array([2, 0, 0])},
              {"x": np.array(2), "y": np.array(1), "z": np.array(0)},
              {"x": np.array([[None, None, None, None], [None, None, None, None]])},
              {
                  "y": np.array(
                      [
                          [None, None, None, None],
                          [None, None, None, None],
                          [None, None, None, None],
                      ]
                  )
              },
              {"z": np.array([[None, None], [None, None], [None, None]])},
              {
                  "x": np.array([None, None, None, None]),
                  "y": np.array([None, None, None, None]),
              },
              {"x": np.array([None, None]), "z": np.array([None, None])},
              {"y": np.array([None, None, None]), "z": np.array([None, None, None])},
              {"x": np.array(None), "y": np.array(None), "z": np.array(None)},
          ),
          (
              np.array(
                  [
                      [[2.0, 1.0, 2.0, 0.0], [-2.0, -4.0, 2.0, 0.0]],
                      [[-4.0, np.NaN, 2.0, np.NaN], [-2.0, -4.0, 2.0, 0.0]],
                      [[np.NaN] * 4, [np.NaN] * 4],
                  ]
              ),
              {"x": np.array([[1, 0, 0, 0], [0, 0, 0, 0]])},
              {
                  "y": np.array(
                      [[1, 1, 0, 0], [0, 1, 0, 1], [np.NaN, np.NaN, np.NaN, np.NaN]]
                  )
              },
              {"z": np.array([[3, 1], [0, 1], [np.NaN, np.NaN]])},
              {"x": np.array([1, 0, 0, 0]), "y": np.array([0, 1, 0, 0])},
              {"x": np.array([1, 0]), "z": np.array([0, 1])},
              {"y": np.array([1, 0, np.NaN]), "z": np.array([1, 0, np.NaN])},
              {"x": np.array(0), "y": np.array(1), "z": np.array(1)},
              {"x": np.array([[0, 0, 0, 0], [0, 0, 0, 0]])},
              {
                  "y": np.array(
                      [[0, 0, 0, 0], [1, 1, 0, 1], [np.NaN, np.NaN, np.NaN, np.NaN]]
                  )
              },
              {"z": np.array([[0, 2], [2, 2], [np.NaN, np.NaN]])},
              {"x": np.array([0, 0, 0, 0]), "y": np.array([0, 0, 0, 0])},
              {"x": np.array([0, 0]), "z": np.array([2, 2])},
              {"y": np.array([0, 0, np.NaN]), "z": np.array([0, 2, np.NaN])},
              {"x": np.array(0), "y": np.array(0), "z": np.array(0)},
              {"x": np.array([[2, 1, 2, 1], [2, 2, 2, 2]])},
              {
                  "y": np.array(
                      [[None, None, None, None], [None, 0, None, 0], [0, 0, 0, 0]]
                  )
              },
              {"z": np.array([[None, None], [1, None], [0, 0]])},
              {"x": np.array([2, 1, 2, 1]), "y": np.array([0, 0, 0, 0])},
              {"x": np.array([1, 2]), "z": np.array([1, 0])},
              {"y": np.array([None, 0, 0]), "z": np.array([None, 1, 0])},
              {"x": np.array(1), "y": np.array(0), "z": np.array(1)},
          ),
          (
              np.array(
                  [
                      [[2.0, 1.0, 2.0, 0.0], [-2.0, -4.0, 2.0, 0.0]],
                      [[-4.0, np.NaN, 2.0, np.NaN], [-2.0, -4.0, 2.0, 0.0]],
                      [[np.NaN] * 4, [np.NaN] * 4],
                  ]
              ).astype("object"),
              {"x": np.array([[1, 0, 0, 0], [0, 0, 0, 0]])},
              {
                  "y": np.array(
                      [[1, 1, 0, 0], [0, 1, 0, 1], [np.NaN, np.NaN, np.NaN, np.NaN]]
                  )
              },
              {"z": np.array([[3, 1], [0, 1], [np.NaN, np.NaN]])},
              {"x": np.array([1, 0, 0, 0]), "y": np.array([0, 1, 0, 0])},
              {"x": np.array([1, 0]), "z": np.array([0, 1])},
              {"y": np.array([1, 0, np.NaN]), "z": np.array([1, 0, np.NaN])},
              {"x": np.array(0), "y": np.array(1), "z": np.array(1)},
              {"x": np.array([[0, 0, 0, 0], [0, 0, 0, 0]])},
              {
                  "y": np.array(
                      [[0, 0, 0, 0], [1, 1, 0, 1], [np.NaN, np.NaN, np.NaN, np.NaN]]
                  )
              },
              {"z": np.array([[0, 2], [2, 2], [np.NaN, np.NaN]])},
              {"x": np.array([0, 0, 0, 0]), "y": np.array([0, 0, 0, 0])},
              {"x": np.array([0, 0]), "z": np.array([2, 2])},
              {"y": np.array([0, 0, np.NaN]), "z": np.array([0, 2, np.NaN])},
              {"x": np.array(0), "y": np.array(0), "z": np.array(0)},
              {"x": np.array([[2, 1, 2, 1], [2, 2, 2, 2]])},
              {
                  "y": np.array(
                      [[None, None, None, None], [None, 0, None, 0], [0, 0, 0, 0]]
                  )
              },
              {"z": np.array([[None, None], [1, None], [0, 0]])},
              {"x": np.array([2, 1, 2, 1]), "y": np.array([0, 0, 0, 0])},
              {"x": np.array([1, 2]), "z": np.array([1, 0])},
              {"y": np.array([None, 0, 0]), "z": np.array([None, 1, 0])},
              {"x": np.array(1), "y": np.array(0), "z": np.array(1)},
          ),
          (
              np.array(
                  [
                      [["2015-12-31", "2020-01-02"], ["2020-01-01", "2016-01-01"]],
                      [["2020-01-02", "2020-01-02"], ["2020-01-02", "2020-01-02"]],
                      [["1900-01-01", "1-02-03"], ["1900-01-02", "1-02-03"]],
                  ],
                  dtype="datetime64[ns]",
              ),
              {"x": np.array([[2, 2], [2, 2]])},
              {"y": np.array([[0, 1], [0, 0], [0, 0]])},
              {"z": np.array([[0, 1], [0, 0], [1, 1]])},
              {"x": np.array([2, 2]), "y": np.array([0, 0])},
              {"x": np.array([2, 2]), "z": np.array([1, 1])},
              {"y": np.array([0, 0, 0]), "z": np.array([0, 0, 1])},
              {"x": np.array(2), "y": np.array(0), "z": np.array(1)},
              {"x": np.array([[1, 0], [1, 1]])},
              {"y": np.array([[1, 0], [0, 0], [1, 0]])},
              {"z": np.array([[1, 0], [0, 0], [0, 0]])},
              {"x": np.array([1, 0]), "y": np.array([0, 0])},
              {"x": np.array([0, 1]), "z": np.array([1, 0])},
              {"y": np.array([0, 0, 1]), "z": np.array([1, 0, 0])},
              {"x": np.array(0), "y": np.array(0), "z": np.array(1)},
              {"x": np.array([[None, None], [None, None]])},
              {"y": np.array([[None, None], [None, None], [None, None]])},
              {"z": np.array([[None, None], [None, None], [None, None]])},
              {"x": np.array([None, None]), "y": np.array([None, None])},
              {"x": np.array([None, None]), "z": np.array([None, None])},
              {"y": np.array([None, None, None]), "z": np.array([None, None, None])},
              {"x": np.array(None), "y": np.array(None), "z": np.array(None)},
          ),
      ],
  )
  class TestReduce3D(TestReduce):
      def test_argmin_dim(
          self,
          x,
          minindices_x,
          minindices_y,
          minindices_z,
          minindices_xy,
          minindices_xz,
          minindices_yz,
          minindices_xyz,
          maxindices_x,
          maxindices_y,
          maxindices_z,
          maxindices_xy,
          maxindices_xz,
          maxindices_yz,
          maxindices_xyz,
          nanindices_x,
          nanindices_y,
          nanindices_z,
          nanindices_xy,
          nanindices_xz,
          nanindices_yz,
          nanindices_xyz,
      ):
  
          ar = xr.DataArray(
              x,
              dims=["x", "y", "z"],
              coords={
                  "x": np.arange(x.shape[0]) * 4,
                  "y": 1 - np.arange(x.shape[1]),
                  "z": 2 + 3 * np.arange(x.shape[2]),
              },
              attrs=self.attrs,
          )
          xindarr = np.tile(
              np.arange(x.shape[0], dtype=np.intp)[:, np.newaxis, np.newaxis],
              [1, x.shape[1], x.shape[2]],
          )
          xindarr = xr.DataArray(xindarr, dims=ar.dims, coords=ar.coords)
          yindarr = np.tile(
              np.arange(x.shape[1], dtype=np.intp)[np.newaxis, :, np.newaxis],
              [x.shape[0], 1, x.shape[2]],
          )
          yindarr = xr.DataArray(yindarr, dims=ar.dims, coords=ar.coords)
          zindarr = np.tile(
              np.arange(x.shape[2], dtype=np.intp)[np.newaxis, np.newaxis, :],
              [x.shape[0], x.shape[1], 1],
          )
          zindarr = xr.DataArray(zindarr, dims=ar.dims, coords=ar.coords)
  
          for inds in [
              minindices_x,
              minindices_y,
              minindices_z,
              minindices_xy,
              minindices_xz,
              minindices_yz,
              minindices_xyz,
          ]:
              if np.array([np.isnan(i) for i in inds.values()]).any():
                  with pytest.raises(ValueError):
                      ar.argmin(dim=[d for d in inds])
                  return
  
          result0 = ar.argmin(dim=["x"])
          expected0 = {
              key: xr.DataArray(value, dims=("y", "z"))
              for key, value in minindices_x.items()
          }
          for key in expected0:
              assert_identical(result0[key].drop_vars(["y", "z"]), expected0[key])
  
          result1 = ar.argmin(dim=["y"])
          expected1 = {
              key: xr.DataArray(value, dims=("x", "z"))
              for key, value in minindices_y.items()
          }
          for key in expected1:
              assert_identical(result1[key].drop_vars(["x", "z"]), expected1[key])
  
          result2 = ar.argmin(dim=["z"])
          expected2 = {
              key: xr.DataArray(value, dims=("x", "y"))
              for key, value in minindices_z.items()
          }
          for key in expected2:
              assert_identical(result2[key].drop_vars(["x", "y"]), expected2[key])
  
          result3 = ar.argmin(dim=("x", "y"))
          expected3 = {
              key: xr.DataArray(value, dims=("z")) for key, value in minindices_xy.items()
          }
          for key in expected3:
              assert_identical(result3[key].drop_vars("z"), expected3[key])
  
          result4 = ar.argmin(dim=("x", "z"))
          expected4 = {
              key: xr.DataArray(value, dims=("y")) for key, value in minindices_xz.items()
          }
          for key in expected4:
              assert_identical(result4[key].drop_vars("y"), expected4[key])
  
          result5 = ar.argmin(dim=("y", "z"))
          expected5 = {
              key: xr.DataArray(value, dims=("x")) for key, value in minindices_yz.items()
          }
          for key in expected5:
              assert_identical(result5[key].drop_vars("x"), expected5[key])
  
          result6 = ar.argmin(...)
          expected6 = {key: xr.DataArray(value) for key, value in minindices_xyz.items()}
          for key in expected6:
              assert_identical(result6[key], expected6[key])
  
          minindices_x = {
              key: xr.where(
                  nanindices_x[key] == None,  # noqa: E711
                  minindices_x[key],
                  nanindices_x[key],
              )
              for key in minindices_x
          }
          expected7 = {
              key: xr.DataArray(value, dims=("y", "z"))
              for key, value in minindices_x.items()
          }
  
          result7 = ar.argmin(dim=["x"], skipna=False)
          for key in expected7:
              assert_identical(result7[key].drop_vars(["y", "z"]), expected7[key])
  
          minindices_y = {
              key: xr.where(
                  nanindices_y[key] == None,  # noqa: E711
                  minindices_y[key],
                  nanindices_y[key],
              )
              for key in minindices_y
          }
          expected8 = {
              key: xr.DataArray(value, dims=("x", "z"))
              for key, value in minindices_y.items()
          }
  
          result8 = ar.argmin(dim=["y"], skipna=False)
          for key in expected8:
              assert_identical(result8[key].drop_vars(["x", "z"]), expected8[key])
  
          minindices_z = {
              key: xr.where(
                  nanindices_z[key] == None,  # noqa: E711
                  minindices_z[key],
                  nanindices_z[key],
              )
              for key in minindices_z
          }
          expected9 = {
              key: xr.DataArray(value, dims=("x", "y"))
              for key, value in minindices_z.items()
          }
  
          result9 = ar.argmin(dim=["z"], skipna=False)
          for key in expected9:
              assert_identical(result9[key].drop_vars(["x", "y"]), expected9[key])
  
          minindices_xy = {
              key: xr.where(
                  nanindices_xy[key] == None,  # noqa: E711
                  minindices_xy[key],
                  nanindices_xy[key],
              )
              for key in minindices_xy
          }
          expected10 = {
              key: xr.DataArray(value, dims="z") for key, value in minindices_xy.items()
          }
  
          result10 = ar.argmin(dim=("x", "y"), skipna=False)
          for key in expected10:
              assert_identical(result10[key].drop_vars("z"), expected10[key])
  
          minindices_xz = {
              key: xr.where(
                  nanindices_xz[key] == None,  # noqa: E711
                  minindices_xz[key],
                  nanindices_xz[key],
              )
              for key in minindices_xz
          }
          expected11 = {
              key: xr.DataArray(value, dims="y") for key, value in minindices_xz.items()
          }
  
          result11 = ar.argmin(dim=("x", "z"), skipna=False)
          for key in expected11:
              assert_identical(result11[key].drop_vars("y"), expected11[key])
  
          minindices_yz = {
              key: xr.where(
                  nanindices_yz[key] == None,  # noqa: E711
                  minindices_yz[key],
                  nanindices_yz[key],
              )
              for key in minindices_yz
          }
          expected12 = {
              key: xr.DataArray(value, dims="x") for key, value in minindices_yz.items()
          }
  
          result12 = ar.argmin(dim=("y", "z"), skipna=False)
          for key in expected12:
              assert_identical(result12[key].drop_vars("x"), expected12[key])
  
          minindices_xyz = {
              key: xr.where(
                  nanindices_xyz[key] == None,  # noqa: E711
                  minindices_xyz[key],
                  nanindices_xyz[key],
              )
              for key in minindices_xyz
          }
          expected13 = {key: xr.DataArray(value) for key, value in minindices_xyz.items()}
  
          result13 = ar.argmin(..., skipna=False)
          for key in expected13:
              assert_identical(result13[key], expected13[key])
  
      def test_argmax_dim(
          self,
          x,
          minindices_x,
          minindices_y,
          minindices_z,
          minindices_xy,
          minindices_xz,
          minindices_yz,
          minindices_xyz,
          maxindices_x,
          maxindices_y,
          maxindices_z,
          maxindices_xy,
          maxindices_xz,
          maxindices_yz,
          maxindices_xyz,
          nanindices_x,
          nanindices_y,
          nanindices_z,
          nanindices_xy,
          nanindices_xz,
          nanindices_yz,
          nanindices_xyz,
      ):
  
          ar = xr.DataArray(
              x,
              dims=["x", "y", "z"],
              coords={
                  "x": np.arange(x.shape[0]) * 4,
                  "y": 1 - np.arange(x.shape[1]),
                  "z": 2 + 3 * np.arange(x.shape[2]),
              },
              attrs=self.attrs,
          )
          xindarr = np.tile(
              np.arange(x.shape[0], dtype=np.intp)[:, np.newaxis, np.newaxis],
              [1, x.shape[1], x.shape[2]],
          )
          xindarr = xr.DataArray(xindarr, dims=ar.dims, coords=ar.coords)
          yindarr = np.tile(
              np.arange(x.shape[1], dtype=np.intp)[np.newaxis, :, np.newaxis],
              [x.shape[0], 1, x.shape[2]],
          )
          yindarr = xr.DataArray(yindarr, dims=ar.dims, coords=ar.coords)
          zindarr = np.tile(
              np.arange(x.shape[2], dtype=np.intp)[np.newaxis, np.newaxis, :],
              [x.shape[0], x.shape[1], 1],
          )
          zindarr = xr.DataArray(zindarr, dims=ar.dims, coords=ar.coords)
  
          for inds in [
              maxindices_x,
              maxindices_y,
              maxindices_z,
              maxindices_xy,
              maxindices_xz,
              maxindices_yz,
              maxindices_xyz,
          ]:
              if np.array([np.isnan(i) for i in inds.values()]).any():
                  with pytest.raises(ValueError):
                      ar.argmax(dim=[d for d in inds])
                  return
  
          result0 = ar.argmax(dim=["x"])
          expected0 = {
              key: xr.DataArray(value, dims=("y", "z"))
              for key, value in maxindices_x.items()
          }
          for key in expected0:
              assert_identical(result0[key].drop_vars(["y", "z"]), expected0[key])
  
          result1 = ar.argmax(dim=["y"])
          expected1 = {
              key: xr.DataArray(value, dims=("x", "z"))
              for key, value in maxindices_y.items()
          }
          for key in expected1:
              assert_identical(result1[key].drop_vars(["x", "z"]), expected1[key])
  
          result2 = ar.argmax(dim=["z"])
          expected2 = {
              key: xr.DataArray(value, dims=("x", "y"))
              for key, value in maxindices_z.items()
          }
          for key in expected2:
              assert_identical(result2[key].drop_vars(["x", "y"]), expected2[key])
  
          result3 = ar.argmax(dim=("x", "y"))
          expected3 = {
              key: xr.DataArray(value, dims=("z")) for key, value in maxindices_xy.items()
          }
          for key in expected3:
              assert_identical(result3[key].drop_vars("z"), expected3[key])
  
          result4 = ar.argmax(dim=("x", "z"))
          expected4 = {
              key: xr.DataArray(value, dims=("y")) for key, value in maxindices_xz.items()
          }
          for key in expected4:
              assert_identical(result4[key].drop_vars("y"), expected4[key])
  
          result5 = ar.argmax(dim=("y", "z"))
          expected5 = {
              key: xr.DataArray(value, dims=("x")) for key, value in maxindices_yz.items()
          }
          for key in expected5:
              assert_identical(result5[key].drop_vars("x"), expected5[key])
  
          result6 = ar.argmax(...)
          expected6 = {key: xr.DataArray(value) for key, value in maxindices_xyz.items()}
          for key in expected6:
              assert_identical(result6[key], expected6[key])
  
          maxindices_x = {
              key: xr.where(
                  nanindices_x[key] == None,  # noqa: E711
                  maxindices_x[key],
                  nanindices_x[key],
              )
              for key in maxindices_x
          }
          expected7 = {
              key: xr.DataArray(value, dims=("y", "z"))
              for key, value in maxindices_x.items()
          }
  
          result7 = ar.argmax(dim=["x"], skipna=False)
          for key in expected7:
              assert_identical(result7[key].drop_vars(["y", "z"]), expected7[key])
  
          maxindices_y = {
              key: xr.where(
                  nanindices_y[key] == None,  # noqa: E711
                  maxindices_y[key],
                  nanindices_y[key],
              )
              for key in maxindices_y
          }
          expected8 = {
              key: xr.DataArray(value, dims=("x", "z"))
              for key, value in maxindices_y.items()
          }
  
          result8 = ar.argmax(dim=["y"], skipna=False)
          for key in expected8:
              assert_identical(result8[key].drop_vars(["x", "z"]), expected8[key])
  
          maxindices_z = {
              key: xr.where(
                  nanindices_z[key] == None,  # noqa: E711
                  maxindices_z[key],
                  nanindices_z[key],
              )
              for key in maxindices_z
          }
          expected9 = {
              key: xr.DataArray(value, dims=("x", "y"))
              for key, value in maxindices_z.items()
          }
  
          result9 = ar.argmax(dim=["z"], skipna=False)
          for key in expected9:
              assert_identical(result9[key].drop_vars(["x", "y"]), expected9[key])
  
          maxindices_xy = {
              key: xr.where(
                  nanindices_xy[key] == None,  # noqa: E711
                  maxindices_xy[key],
                  nanindices_xy[key],
              )
              for key in maxindices_xy
          }
          expected10 = {
              key: xr.DataArray(value, dims="z") for key, value in maxindices_xy.items()
          }
  
          result10 = ar.argmax(dim=("x", "y"), skipna=False)
          for key in expected10:
              assert_identical(result10[key].drop_vars("z"), expected10[key])
  
          maxindices_xz = {
              key: xr.where(
                  nanindices_xz[key] == None,  # noqa: E711
                  maxindices_xz[key],
                  nanindices_xz[key],
              )
              for key in maxindices_xz
          }
          expected11 = {
              key: xr.DataArray(value, dims="y") for key, value in maxindices_xz.items()
          }
  
          result11 = ar.argmax(dim=("x", "z"), skipna=False)
          for key in expected11:
              assert_identical(result11[key].drop_vars("y"), expected11[key])
  
          maxindices_yz = {
              key: xr.where(
                  nanindices_yz[key] == None,  # noqa: E711
                  maxindices_yz[key],
                  nanindices_yz[key],
              )
              for key in maxindices_yz
          }
          expected12 = {
              key: xr.DataArray(value, dims="x") for key, value in maxindices_yz.items()
          }
  
          result12 = ar.argmax(dim=("y", "z"), skipna=False)
          for key in expected12:
              assert_identical(result12[key].drop_vars("x"), expected12[key])
  
          maxindices_xyz = {
              key: xr.where(
                  nanindices_xyz[key] == None,  # noqa: E711
                  maxindices_xyz[key],
                  nanindices_xyz[key],
              )
              for key in maxindices_xyz
          }
          expected13 = {key: xr.DataArray(value) for key, value in maxindices_xyz.items()}
  
          result13 = ar.argmax(..., skipna=False)
          for key in expected13:
              assert_identical(result13[key], expected13[key])
  
  
  class TestReduceND(TestReduce):
      @pytest.mark.parametrize("op", ["idxmin", "idxmax"])
      @pytest.mark.parametrize("ndim", [3, 5])
      def test_idxminmax_dask(self, op, ndim):
          if not has_dask:
              pytest.skip("requires dask")
  
          ar0_raw = xr.DataArray(
              np.random.random_sample(size=[10] * ndim),
              dims=[i for i in "abcdefghij"[: ndim - 1]] + ["x"],
              coords={"x": np.arange(10)},
              attrs=self.attrs,
          )
  
          ar0_dsk = ar0_raw.chunk({})
          # Assert idx is the same with dask and without
          assert_equal(getattr(ar0_dsk, op)(dim="x"), getattr(ar0_raw, op)(dim="x"))
  
  
  @pytest.fixture(params=[1])
  def da(request, backend):
      if request.param == 1:
          times = pd.date_range("2000-01-01", freq="1D", periods=21)
          da = DataArray(
              np.random.random((3, 21, 4)),
              dims=("a", "time", "x"),
              coords=dict(time=times),
          )
  
      if request.param == 2:
          da = DataArray([0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time")
  
      if request.param == "repeating_ints":
          da = DataArray(
              np.tile(np.arange(12), 5).reshape(5, 4, 3),
              coords={"x": list("abc"), "y": list("defg")},
              dims=list("zyx"),
          )
  
      if backend == "dask":
          return da.chunk()
      elif backend == "numpy":
          return da
      else:
          raise ValueError
  
  
  @pytest.mark.parametrize("da", ("repeating_ints",), indirect=True)
  def test_isin(da):
      expected = DataArray(
          np.asarray([[0, 0, 0], [1, 0, 0]]),
          dims=list("yx"),
          coords={"x": list("abc"), "y": list("de")},
      ).astype("bool")
  
      result = da.isin([3]).sel(y=list("de"), z=0)
      assert_equal(result, expected)
  
      expected = DataArray(
          np.asarray([[0, 0, 1], [1, 0, 0]]),
          dims=list("yx"),
          coords={"x": list("abc"), "y": list("de")},
      ).astype("bool")
      result = da.isin([2, 3]).sel(y=list("de"), z=0)
      assert_equal(result, expected)
  
  
  @pytest.mark.parametrize("da", (1, 2), indirect=True)
  def test_rolling_iter(da):
      rolling_obj = da.rolling(time=7)
      rolling_obj_mean = rolling_obj.mean()
  
      assert len(rolling_obj.window_labels) == len(da["time"])
      assert_identical(rolling_obj.window_labels, da["time"])
  
      for i, (label, window_da) in enumerate(rolling_obj):
          assert label == da["time"].isel(time=i)
  
          actual = rolling_obj_mean.isel(time=i)
          expected = window_da.mean("time")
  
          # TODO add assert_allclose_with_nan, which compares nan position
          # as well as the closeness of the values.
          assert_array_equal(actual.isnull(), expected.isnull())
          if (~actual.isnull()).sum() > 0:
              np.allclose(
                  actual.values[actual.values.nonzero()],
                  expected.values[expected.values.nonzero()],
              )
  
  
  @pytest.mark.parametrize("da", (1,), indirect=True)
  def test_rolling_repr(da):
      rolling_obj = da.rolling(time=7)
      assert repr(rolling_obj) == "DataArrayRolling [time->7]"
      rolling_obj = da.rolling(time=7, center=True)
      assert repr(rolling_obj) == "DataArrayRolling [time->7(center)]"
      rolling_obj = da.rolling(time=7, x=3, center=True)
      assert repr(rolling_obj) == "DataArrayRolling [time->7(center),x->3(center)]"
  
  
  @requires_dask
  def test_repeated_rolling_rechunks():
  
      # regression test for GH3277, GH2514
      dat = DataArray(np.random.rand(7653, 300), dims=("day", "item"))
      dat_chunk = dat.chunk({"item": 20})
      dat_chunk.rolling(day=10).mean().rolling(day=250).std()
  
  
  def test_rolling_doc(da):
      rolling_obj = da.rolling(time=7)
  
      # argument substitution worked
      assert "`mean`" in rolling_obj.mean.__doc__
  
  
  def test_rolling_properties(da):
      rolling_obj = da.rolling(time=4)
  
      assert rolling_obj.obj.get_axis_num("time") == 1
  
      # catching invalid args
      with pytest.raises(ValueError, match="window must be > 0"):
          da.rolling(time=-2)
  
      with pytest.raises(ValueError, match="min_periods must be greater than zero"):
          da.rolling(time=2, min_periods=0)
  
  
  @pytest.mark.parametrize("name", ("sum", "mean", "std", "min", "max", "median"))
  @pytest.mark.parametrize("center", (True, False, None))
  @pytest.mark.parametrize("min_periods", (1, None))
  @pytest.mark.parametrize("backend", ["numpy"], indirect=True)
  def test_rolling_wrapped_bottleneck(da, name, center, min_periods):
      bn = pytest.importorskip("bottleneck", minversion="1.1")
  
      # Test all bottleneck functions
      rolling_obj = da.rolling(time=7, min_periods=min_periods)
  
      func_name = f"move_{name}"
      actual = getattr(rolling_obj, name)()
      expected = getattr(bn, func_name)(
          da.values, window=7, axis=1, min_count=min_periods
      )
      assert_array_equal(actual.values, expected)
  
      with pytest.warns(DeprecationWarning, match="Reductions are applied"):
          getattr(rolling_obj, name)(dim="time")
  
      # Test center
      rolling_obj = da.rolling(time=7, center=center)
      actual = getattr(rolling_obj, name)()["time"]
      assert_equal(actual, da["time"])
  
  
  @requires_dask
  @pytest.mark.parametrize("name", ("mean", "count"))
  @pytest.mark.parametrize("center", (True, False, None))
  @pytest.mark.parametrize("min_periods", (1, None))
  @pytest.mark.parametrize("window", (7, 8))
  @pytest.mark.parametrize("backend", ["dask"], indirect=True)
  def test_rolling_wrapped_dask(da, name, center, min_periods, window):
      # dask version
      rolling_obj = da.rolling(time=window, min_periods=min_periods, center=center)
      actual = getattr(rolling_obj, name)().load()
      if name != "count":
          with pytest.warns(DeprecationWarning, match="Reductions are applied"):
              getattr(rolling_obj, name)(dim="time")
      # numpy version
      rolling_obj = da.load().rolling(time=window, min_periods=min_periods, center=center)
      expected = getattr(rolling_obj, name)()
  
      # using all-close because rolling over ghost cells introduces some
      # precision errors
      assert_allclose(actual, expected)
  
      # with zero chunked array GH:2113
      rolling_obj = da.chunk().rolling(
          time=window, min_periods=min_periods, center=center
      )
      actual = getattr(rolling_obj, name)().load()
      assert_allclose(actual, expected)
  
  
  @pytest.mark.parametrize("center", (True, None))
  def test_rolling_wrapped_dask_nochunk(center):
      # GH:2113
      pytest.importorskip("dask.array")
  
      da_day_clim = xr.DataArray(
          np.arange(1, 367), coords=[np.arange(1, 367)], dims="dayofyear"
      )
      expected = da_day_clim.rolling(dayofyear=31, center=center).mean()
      actual = da_day_clim.chunk().rolling(dayofyear=31, center=center).mean()
      assert_allclose(actual, expected)
  
  
  @pytest.mark.parametrize("center", (True, False))
  @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
  @pytest.mark.parametrize("window", (1, 2, 3, 4))
  def test_rolling_pandas_compat(center, window, min_periods):
      s = pd.Series(np.arange(10))
      da = DataArray.from_series(s)
  
      if min_periods is not None and window < min_periods:
          min_periods = window
  
      s_rolling = s.rolling(window, center=center, min_periods=min_periods).mean()
      da_rolling = da.rolling(index=window, center=center, min_periods=min_periods).mean()
      da_rolling_np = da.rolling(
          index=window, center=center, min_periods=min_periods
      ).reduce(np.nanmean)
  
      np.testing.assert_allclose(s_rolling.values, da_rolling.values)
      np.testing.assert_allclose(s_rolling.index, da_rolling["index"])
      np.testing.assert_allclose(s_rolling.values, da_rolling_np.values)
      np.testing.assert_allclose(s_rolling.index, da_rolling_np["index"])
  
  
  @pytest.mark.parametrize("center", (True, False))
  @pytest.mark.parametrize("window", (1, 2, 3, 4))
  def test_rolling_construct(center, window):
      s = pd.Series(np.arange(10))
      da = DataArray.from_series(s)
  
      s_rolling = s.rolling(window, center=center, min_periods=1).mean()
      da_rolling = da.rolling(index=window, center=center, min_periods=1)
  
      da_rolling_mean = da_rolling.construct("window").mean("window")
      np.testing.assert_allclose(s_rolling.values, da_rolling_mean.values)
      np.testing.assert_allclose(s_rolling.index, da_rolling_mean["index"])
  
      # with stride
      da_rolling_mean = da_rolling.construct("window", stride=2).mean("window")
      np.testing.assert_allclose(s_rolling.values[::2], da_rolling_mean.values)
      np.testing.assert_allclose(s_rolling.index[::2], da_rolling_mean["index"])
  
      # with fill_value
      da_rolling_mean = da_rolling.construct("window", stride=2, fill_value=0.0).mean(
          "window"
      )
      assert da_rolling_mean.isnull().sum() == 0
      assert (da_rolling_mean == 0.0).sum() >= 0
  
  
  @pytest.mark.parametrize("da", (1, 2), indirect=True)
  @pytest.mark.parametrize("center", (True, False))
  @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
  @pytest.mark.parametrize("window", (1, 2, 3, 4))
  @pytest.mark.parametrize("name", ("sum", "mean", "std", "max"))
  def test_rolling_reduce(da, center, min_periods, window, name):
      if min_periods is not None and window < min_periods:
          min_periods = window
  
      if da.isnull().sum() > 1 and window == 1:
          # this causes all nan slices
          window = 2
  
      rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
  
      # add nan prefix to numpy methods to get similar # behavior as bottleneck
      actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
      expected = getattr(rolling_obj, name)()
      assert_allclose(actual, expected)
      assert actual.dims == expected.dims
  
  
  @pytest.mark.parametrize("center", (True, False))
  @pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
  @pytest.mark.parametrize("window", (1, 2, 3, 4))
  @pytest.mark.parametrize("name", ("sum", "max"))
  def test_rolling_reduce_nonnumeric(center, min_periods, window, name):
      da = DataArray(
          [0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
      ).isnull()
  
      if min_periods is not None and window < min_periods:
          min_periods = window
  
      rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
  
      # add nan prefix to numpy methods to get similar behavior as bottleneck
      actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
      expected = getattr(rolling_obj, name)()
      assert_allclose(actual, expected)
      assert actual.dims == expected.dims
  
  
  def test_rolling_count_correct():
      da = DataArray([0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time")
  
      kwargs = [
          {"time": 11, "min_periods": 1},
          {"time": 11, "min_periods": None},
          {"time": 7, "min_periods": 2},
      ]
      expecteds = [
          DataArray([1, 1, 2, 3, 3, 4, 5, 6, 6, 7, 8], dims="time"),
          DataArray(
              [
                  np.nan,
                  np.nan,
                  np.nan,
                  np.nan,
                  np.nan,
                  np.nan,
                  np.nan,
                  np.nan,
                  np.nan,
                  np.nan,
                  np.nan,
              ],
              dims="time",
          ),
          DataArray([np.nan, np.nan, 2, 3, 3, 4, 5, 5, 5, 5, 5], dims="time"),
      ]
  
      for kwarg, expected in zip(kwargs, expecteds):
          result = da.rolling(**kwarg).count()
          assert_equal(result, expected)
  
          result = da.to_dataset(name="var1").rolling(**kwarg).count()["var1"]
          assert_equal(result, expected)
  
  
  @pytest.mark.parametrize("da", (1,), indirect=True)
  @pytest.mark.parametrize("center", (True, False))
  @pytest.mark.parametrize("min_periods", (None, 1))
  @pytest.mark.parametrize("name", ("sum", "mean", "max"))
  def test_ndrolling_reduce(da, center, min_periods, name):
      rolling_obj = da.rolling(time=3, x=2, center=center, min_periods=min_periods)
  
      actual = getattr(rolling_obj, name)()
      expected = getattr(
          getattr(
              da.rolling(time=3, center=center, min_periods=min_periods), name
          )().rolling(x=2, center=center, min_periods=min_periods),
          name,
      )()
  
      assert_allclose(actual, expected)
      assert actual.dims == expected.dims
  
      if name in ["mean"]:
          # test our reimplementation of nanmean using np.nanmean
          expected = getattr(rolling_obj.construct({"time": "tw", "x": "xw"}), name)(
              ["tw", "xw"]
          )
          count = rolling_obj.count()
          if min_periods is None:
              min_periods = 1
          assert_allclose(actual, expected.where(count >= min_periods))
  
  
  @pytest.mark.parametrize("center", (True, False, (True, False)))
  @pytest.mark.parametrize("fill_value", (np.nan, 0.0))
  def test_ndrolling_construct(center, fill_value):
      da = DataArray(
          np.arange(5 * 6 * 7).reshape(5, 6, 7).astype(float),
          dims=["x", "y", "z"],
          coords={"x": ["a", "b", "c", "d", "e"], "y": np.arange(6)},
      )
      actual = da.rolling(x=3, z=2, center=center).construct(
          x="x1", z="z1", fill_value=fill_value
      )
      if not isinstance(center, tuple):
          center = (center, center)
      expected = (
          da.rolling(x=3, center=center[0])
          .construct(x="x1", fill_value=fill_value)
          .rolling(z=2, center=center[1])
          .construct(z="z1", fill_value=fill_value)
      )
      assert_allclose(actual, expected)
  
  
  @pytest.mark.parametrize(
      "funcname, argument",
      [
          ("reduce", (np.mean,)),
          ("mean", ()),
          ("construct", ("window_dim",)),
          ("count", ()),
      ],
  )
  def test_rolling_keep_attrs(funcname, argument):
      attrs_da = {"da_attr": "test"}
  
      data = np.linspace(10, 15, 100)
      coords = np.linspace(1, 10, 100)
  
      da = DataArray(
          data, dims=("coord"), coords={"coord": coords}, attrs=attrs_da, name="name"
      )
  
      # attrs are now kept per default
      func = getattr(da.rolling(dim={"coord": 5}), funcname)
      result = func(*argument)
      assert result.attrs == attrs_da
      assert result.name == "name"
  
      # discard attrs
      func = getattr(da.rolling(dim={"coord": 5}), funcname)
      result = func(*argument, keep_attrs=False)
      assert result.attrs == {}
      assert result.name == "name"
  
      # test discard attrs using global option
      func = getattr(da.rolling(dim={"coord": 5}), funcname)
      with set_options(keep_attrs=False):
          result = func(*argument)
      assert result.attrs == {}
      assert result.name == "name"
  
      # keyword takes precedence over global option
      func = getattr(da.rolling(dim={"coord": 5}), funcname)
      with set_options(keep_attrs=False):
          result = func(*argument, keep_attrs=True)
      assert result.attrs == attrs_da
      assert result.name == "name"
  
      func = getattr(da.rolling(dim={"coord": 5}), funcname)
      with set_options(keep_attrs=True):
          result = func(*argument, keep_attrs=False)
      assert result.attrs == {}
      assert result.name == "name"
  
  
  def test_raise_no_warning_for_nan_in_binary_ops():
      with pytest.warns(None) as record:
          xr.DataArray([1, 2, np.NaN]) > 0
      assert len(record) == 0
  
  
  @pytest.mark.filterwarnings("error")
  def test_no_warning_for_all_nan():
      _ = xr.DataArray([np.NaN, np.NaN]).mean()
  
  
  def test_name_in_masking():
      name = "RingoStarr"
      da = xr.DataArray(range(10), coords=[("x", range(10))], name=name)
      assert da.where(da > 5).name == name
      assert da.where((da > 5).rename("YokoOno")).name == name
      assert da.where(da > 5, drop=True).name == name
      assert da.where((da > 5).rename("YokoOno"), drop=True).name == name
  
  
  class TestIrisConversion:
      @requires_iris
      def test_to_and_from_iris(self):
          import cf_units  # iris requirement
          import iris
  
          # to iris
          coord_dict = {}
          coord_dict["distance"] = ("distance", [-2, 2], {"units": "meters"})
          coord_dict["time"] = ("time", pd.date_range("2000-01-01", periods=3))
          coord_dict["height"] = 10
          coord_dict["distance2"] = ("distance", [0, 1], {"foo": "bar"})
          coord_dict["time2"] = (("distance", "time"), [[0, 1, 2], [2, 3, 4]])
  
          original = DataArray(
              np.arange(6, dtype="float").reshape(2, 3),
              coord_dict,
              name="Temperature",
              attrs={
                  "baz": 123,
                  "units": "Kelvin",
                  "standard_name": "fire_temperature",
                  "long_name": "Fire Temperature",
              },
              dims=("distance", "time"),
          )
  
          # Set a bad value to test the masking logic
          original.data[0, 2] = np.NaN
  
          original.attrs["cell_methods"] = "height: mean (comment: A cell method)"
          actual = original.to_iris()
          assert_array_equal(actual.data, original.data)
          assert actual.var_name == original.name
          assert tuple(d.var_name for d in actual.dim_coords) == original.dims
          assert actual.cell_methods == (
              iris.coords.CellMethod(
                  method="mean",
                  coords=("height",),
                  intervals=(),
                  comments=("A cell method",),
              ),
          )
  
          for coord, orginal_key in zip((actual.coords()), original.coords):
              original_coord = original.coords[orginal_key]
              assert coord.var_name == original_coord.name
              assert_array_equal(
                  coord.points, CFDatetimeCoder().encode(original_coord).values
              )
              assert actual.coord_dims(coord) == original.get_axis_num(
                  original.coords[coord.var_name].dims
              )
  
          assert (
              actual.coord("distance2").attributes["foo"]
              == original.coords["distance2"].attrs["foo"]
          )
          assert actual.coord("distance").units == cf_units.Unit(
              original.coords["distance"].units
          )
          assert actual.attributes["baz"] == original.attrs["baz"]
          assert actual.standard_name == original.attrs["standard_name"]
  
          roundtripped = DataArray.from_iris(actual)
          assert_identical(original, roundtripped)
  
          actual.remove_coord("time")
          auto_time_dimension = DataArray.from_iris(actual)
          assert auto_time_dimension.dims == ("distance", "dim_1")
  
      @requires_iris
      @requires_dask
      def test_to_and_from_iris_dask(self):
          import cf_units  # iris requirement
          import dask.array as da
          import iris
  
          coord_dict = {}
          coord_dict["distance"] = ("distance", [-2, 2], {"units": "meters"})
          coord_dict["time"] = ("time", pd.date_range("2000-01-01", periods=3))
          coord_dict["height"] = 10
          coord_dict["distance2"] = ("distance", [0, 1], {"foo": "bar"})
          coord_dict["time2"] = (("distance", "time"), [[0, 1, 2], [2, 3, 4]])
  
          original = DataArray(
              da.from_array(np.arange(-1, 5, dtype="float").reshape(2, 3), 3),
              coord_dict,
              name="Temperature",
              attrs=dict(
                  baz=123,
                  units="Kelvin",
                  standard_name="fire_temperature",
                  long_name="Fire Temperature",
              ),
              dims=("distance", "time"),
          )
  
          # Set a bad value to test the masking logic
          original.data = da.ma.masked_less(original.data, 0)
  
          original.attrs["cell_methods"] = "height: mean (comment: A cell method)"
          actual = original.to_iris()
  
          # Be careful not to trigger the loading of the iris data
          actual_data = (
              actual.core_data() if hasattr(actual, "core_data") else actual.data
          )
          assert_array_equal(actual_data, original.data)
          assert actual.var_name == original.name
          assert tuple(d.var_name for d in actual.dim_coords) == original.dims
          assert actual.cell_methods == (
              iris.coords.CellMethod(
                  method="mean",
                  coords=("height",),
                  intervals=(),
                  comments=("A cell method",),
              ),
          )
  
          for coord, orginal_key in zip((actual.coords()), original.coords):
              original_coord = original.coords[orginal_key]
              assert coord.var_name == original_coord.name
              assert_array_equal(
                  coord.points, CFDatetimeCoder().encode(original_coord).values
              )
              assert actual.coord_dims(coord) == original.get_axis_num(
                  original.coords[coord.var_name].dims
              )
  
          assert (
              actual.coord("distance2").attributes["foo"]
              == original.coords["distance2"].attrs["foo"]
          )
          assert actual.coord("distance").units == cf_units.Unit(
              original.coords["distance"].units
          )
          assert actual.attributes["baz"] == original.attrs["baz"]
          assert actual.standard_name == original.attrs["standard_name"]
  
          roundtripped = DataArray.from_iris(actual)
          assert_identical(original, roundtripped)
  
          # If the Iris version supports it then we should have a dask array
          # at each stage of the conversion
          if hasattr(actual, "core_data"):
              assert isinstance(original.data, type(actual.core_data()))
              assert isinstance(original.data, type(roundtripped.data))
  
          actual.remove_coord("time")
          auto_time_dimension = DataArray.from_iris(actual)
          assert auto_time_dimension.dims == ("distance", "dim_1")
  
      @requires_iris
      @pytest.mark.parametrize(
          "var_name, std_name, long_name, name, attrs",
          [
              (
                  "var_name",
                  "height",
                  "Height",
                  "var_name",
                  {"standard_name": "height", "long_name": "Height"},
              ),
              (
                  None,
                  "height",
                  "Height",
                  "height",
                  {"standard_name": "height", "long_name": "Height"},
              ),
              (None, None, "Height", "Height", {"long_name": "Height"}),
              (None, None, None, None, {}),
          ],
      )
      def test_da_name_from_cube(self, std_name, long_name, var_name, name, attrs):
          from iris.cube import Cube
  
          data = []
          cube = Cube(
              data, var_name=var_name, standard_name=std_name, long_name=long_name
          )
          result = xr.DataArray.from_iris(cube)
          expected = xr.DataArray(data, name=name, attrs=attrs)
          xr.testing.assert_identical(result, expected)
  
      @requires_iris
      @pytest.mark.parametrize(
          "var_name, std_name, long_name, name, attrs",
          [
              (
                  "var_name",
                  "height",
                  "Height",
                  "var_name",
                  {"standard_name": "height", "long_name": "Height"},
              ),
              (
                  None,
                  "height",
                  "Height",
                  "height",
                  {"standard_name": "height", "long_name": "Height"},
              ),
              (None, None, "Height", "Height", {"long_name": "Height"}),
              (None, None, None, "unknown", {}),
          ],
      )
      def test_da_coord_name_from_cube(self, std_name, long_name, var_name, name, attrs):
          from iris.coords import DimCoord
          from iris.cube import Cube
  
          latitude = DimCoord(
              [-90, 0, 90], standard_name=std_name, var_name=var_name, long_name=long_name
          )
          data = [0, 0, 0]
          cube = Cube(data, dim_coords_and_dims=[(latitude, 0)])
          result = xr.DataArray.from_iris(cube)
          expected = xr.DataArray(data, coords=[(name, [-90, 0, 90], attrs)])
          xr.testing.assert_identical(result, expected)
  
      @requires_iris
      def test_prevent_duplicate_coord_names(self):
          from iris.coords import DimCoord
          from iris.cube import Cube
  
          # Iris enforces unique coordinate names. Because we use a different
          # name resolution order a valid iris Cube with coords that have the
          # same var_name would lead to duplicate dimension names in the
          # DataArray
          longitude = DimCoord([0, 360], standard_name="longitude", var_name="duplicate")
          latitude = DimCoord(
              [-90, 0, 90], standard_name="latitude", var_name="duplicate"
          )
          data = [[0, 0, 0], [0, 0, 0]]
          cube = Cube(data, dim_coords_and_dims=[(longitude, 0), (latitude, 1)])
          with pytest.raises(ValueError):
              xr.DataArray.from_iris(cube)
  
      @requires_iris
      @pytest.mark.parametrize(
          "coord_values",
          [["IA", "IL", "IN"], [0, 2, 1]],  # non-numeric values  # non-monotonic values
      )
      def test_fallback_to_iris_AuxCoord(self, coord_values):
          from iris.coords import AuxCoord
          from iris.cube import Cube
  
          data = [0, 0, 0]
          da = xr.DataArray(data, coords=[coord_values], dims=["space"])
          result = xr.DataArray.to_iris(da)
          expected = Cube(
              data, aux_coords_and_dims=[(AuxCoord(coord_values, var_name="space"), 0)]
          )
          assert result == expected
  
  
  @requires_numbagg
  @pytest.mark.parametrize("dim", ["time", "x"])
  @pytest.mark.parametrize(
      "window_type, window", [["span", 5], ["alpha", 0.5], ["com", 0.5], ["halflife", 5]]
  )
  @pytest.mark.parametrize("backend", ["numpy"], indirect=True)
  @pytest.mark.parametrize("func", ["mean", "sum"])
  def test_rolling_exp_runs(da, dim, window_type, window, func):
      import numbagg
  
      if (
          Version(getattr(numbagg, "__version__", "0.1.0")) < Version("0.2.1")
          and func == "sum"
      ):
          pytest.skip("rolling_exp.sum requires numbagg 0.2.1")
  
      da = da.where(da > 0.2)
  
      rolling_exp = da.rolling_exp(window_type=window_type, **{dim: window})
      result = getattr(rolling_exp, func)()
      assert isinstance(result, DataArray)
  
  
  @requires_numbagg
  @pytest.mark.parametrize("dim", ["time", "x"])
  @pytest.mark.parametrize(
      "window_type, window", [["span", 5], ["alpha", 0.5], ["com", 0.5], ["halflife", 5]]
  )
  @pytest.mark.parametrize("backend", ["numpy"], indirect=True)
  def test_rolling_exp_mean_pandas(da, dim, window_type, window):
      da = da.isel(a=0).where(lambda x: x > 0.2)
  
      result = da.rolling_exp(window_type=window_type, **{dim: window}).mean()
      assert isinstance(result, DataArray)
  
      pandas_array = da.to_pandas()
      assert pandas_array.index.name == "time"
      if dim == "x":
          pandas_array = pandas_array.T
      expected = xr.DataArray(pandas_array.ewm(**{window_type: window}).mean()).transpose(
          *da.dims
      )
  
      assert_allclose(expected.variable, result.variable)
  
  
  @requires_numbagg
  @pytest.mark.parametrize("backend", ["numpy"], indirect=True)
  @pytest.mark.parametrize("func", ["mean", "sum"])
  def test_rolling_exp_keep_attrs(da, func):
      import numbagg
  
      if (
          Version(getattr(numbagg, "__version__", "0.1.0")) < Version("0.2.1")
          and func == "sum"
      ):
          pytest.skip("rolling_exp.sum requires numbagg 0.2.1")
  
      attrs = {"attrs": "da"}
      da.attrs = attrs
  
      # Equivalent of `da.rolling_exp(time=10).mean`
      rolling_exp_func = getattr(da.rolling_exp(time=10), func)
  
      # attrs are kept per default
      result = rolling_exp_func()
      assert result.attrs == attrs
  
      # discard attrs
      result = rolling_exp_func(keep_attrs=False)
      assert result.attrs == {}
  
      # test discard attrs using global option
      with set_options(keep_attrs=False):
          result = rolling_exp_func()
      assert result.attrs == {}
  
      # keyword takes precedence over global option
      with set_options(keep_attrs=False):
          result = rolling_exp_func(keep_attrs=True)
      assert result.attrs == attrs
  
      with set_options(keep_attrs=True):
          result = rolling_exp_func(keep_attrs=False)
      assert result.attrs == {}
  
      with pytest.warns(
          UserWarning, match="Passing ``keep_attrs`` to ``rolling_exp`` has no effect."
      ):
          da.rolling_exp(time=10, keep_attrs=True)
  
  
  def test_no_dict():
      d = DataArray()
      with pytest.raises(AttributeError):
          d.__dict__
  
  
  def test_subclass_slots():
      """Test that DataArray subclasses must explicitly define ``__slots__``.
  
      .. note::
         As of 0.13.0, this is actually mitigated into a FutureWarning for any class
         defined outside of the xarray package.
      """
      with pytest.raises(AttributeError) as e:
  
          class MyArray(DataArray):
              pass
  
      assert str(e.value) == "MyArray must explicitly define __slots__"
  
  
  def test_weakref():
      """Classes with __slots__ are incompatible with the weakref module unless they
      explicitly state __weakref__ among their slots
      """
      from weakref import ref
  
      a = DataArray(1)
      r = ref(a)
      assert r() is a
  
  
  def test_delete_coords():
      """Make sure that deleting a coordinate doesn't corrupt the DataArray.
      See issue #3899.
  
      Also test that deleting succeeds and produces the expected output.
      """
      a0 = DataArray(
          np.array([[1, 2, 3], [4, 5, 6]]),
          dims=["y", "x"],
          coords={"x": ["a", "b", "c"], "y": [-1, 1]},
      )
      assert_identical(a0, a0)
  
      a1 = a0.copy()
      del a1.coords["y"]
  
      # This test will detect certain sorts of corruption in the DataArray
      assert_identical(a0, a0)
  
      assert a0.dims == ("y", "x")
      assert a1.dims == ("y", "x")
      assert set(a0.coords.keys()) == {"x", "y"}
      assert set(a1.coords.keys()) == {"x"}
  
  
  def test_deepcopy_obj_array():
      x0 = DataArray(np.array([object()]))
      x1 = deepcopy(x0)
      assert x0.values[0] is not x1.values[0]
  
  
  def test_clip(da):
      with raise_if_dask_computes():
          result = da.clip(min=0.5)
      assert result.min(...) >= 0.5
  
      result = da.clip(max=0.5)
      assert result.max(...) <= 0.5
  
      result = da.clip(min=0.25, max=0.75)
      assert result.min(...) >= 0.25
      assert result.max(...) <= 0.75
  
      with raise_if_dask_computes():
          result = da.clip(min=da.mean("x"), max=da.mean("a"))
      assert result.dims == da.dims
      assert_array_equal(
          result.data,
          np.clip(da.data, da.mean("x").data[:, :, np.newaxis], da.mean("a").data),
      )
  
      with_nans = da.isel(time=[0, 1]).reindex_like(da)
      with raise_if_dask_computes():
          result = da.clip(min=da.mean("x"), max=da.mean("a"))
      result = da.clip(with_nans)
      # The values should be the same where there were NaNs.
      assert_array_equal(result.isel(time=[0, 1]), with_nans.isel(time=[0, 1]))
  
      # Unclear whether we want this work, OK to adjust the test when we have decided.
      with pytest.raises(ValueError, match="arguments without labels along dimension"):
          result = da.clip(min=da.mean("x"), max=da.mean("a").isel(x=[0, 1]))
  
  
  @pytest.mark.parametrize("keep", ["first", "last", False])
  def test_drop_duplicates(keep):
      ds = xr.DataArray(
          [0, 5, 6, 7], dims="time", coords={"time": [0, 0, 1, 2]}, name="test"
      )
  
      if keep == "first":
          data = [0, 6, 7]
          time = [0, 1, 2]
      elif keep == "last":
          data = [5, 6, 7]
          time = [0, 1, 2]
      else:
          data = [6, 7]
          time = [1, 2]
  
      expected = xr.DataArray(data, dims="time", coords={"time": time}, name="test")
      result = ds.drop_duplicates("time", keep=keep)
      assert_equal(expected, result)
  
  
  class TestNumpyCoercion:
      # TODO once flexible indexes refactor complete also test coercion of dimension coords
      def test_from_numpy(self):
          da = xr.DataArray([1, 2, 3], dims="x", coords={"lat": ("x", [4, 5, 6])})
  
          assert_identical(da.as_numpy(), da)
          np.testing.assert_equal(da.to_numpy(), np.array([1, 2, 3]))
          np.testing.assert_equal(da["lat"].to_numpy(), np.array([4, 5, 6]))
  
      @requires_dask
      def test_from_dask(self):
          da = xr.DataArray([1, 2, 3], dims="x", coords={"lat": ("x", [4, 5, 6])})
          da_chunked = da.chunk(1)
  
          assert_identical(da_chunked.as_numpy(), da.compute())
          np.testing.assert_equal(da.to_numpy(), np.array([1, 2, 3]))
          np.testing.assert_equal(da["lat"].to_numpy(), np.array([4, 5, 6]))
  
      @requires_pint
      def test_from_pint(self):
          from pint import Quantity
  
          arr = np.array([1, 2, 3])
          da = xr.DataArray(
              Quantity(arr, units="Pa"),
              dims="x",
              coords={"lat": ("x", Quantity(arr + 3, units="m"))},
          )
  
          expected = xr.DataArray(arr, dims="x", coords={"lat": ("x", arr + 3)})
          assert_identical(da.as_numpy(), expected)
          np.testing.assert_equal(da.to_numpy(), arr)
          np.testing.assert_equal(da["lat"].to_numpy(), arr + 3)
  
      @requires_sparse
      def test_from_sparse(self):
          import sparse
  
          arr = np.diagflat([1, 2, 3])
          sparr = sparse.COO.from_numpy(arr)
          da = xr.DataArray(
              sparr, dims=["x", "y"], coords={"elev": (("x", "y"), sparr + 3)}
          )
  
          expected = xr.DataArray(
              arr, dims=["x", "y"], coords={"elev": (("x", "y"), arr + 3)}
          )
          assert_identical(da.as_numpy(), expected)
          np.testing.assert_equal(da.to_numpy(), arr)
  
      @requires_cupy
      def test_from_cupy(self):
          import cupy as cp
  
          arr = np.array([1, 2, 3])
          da = xr.DataArray(
              cp.array(arr), dims="x", coords={"lat": ("x", cp.array(arr + 3))}
          )
  
          expected = xr.DataArray(arr, dims="x", coords={"lat": ("x", arr + 3)})
          assert_identical(da.as_numpy(), expected)
          np.testing.assert_equal(da.to_numpy(), arr)
  
      @requires_dask
      @requires_pint
      def test_from_pint_wrapping_dask(self):
          import dask
          from pint import Quantity
  
          arr = np.array([1, 2, 3])
          d = dask.array.from_array(arr)
          da = xr.DataArray(
              Quantity(d, units="Pa"),
              dims="x",
              coords={"lat": ("x", Quantity(d, units="m") * 2)},
          )
  
          result = da.as_numpy()
          result.name = None  # remove dask-assigned name
          expected = xr.DataArray(arr, dims="x", coords={"lat": ("x", arr * 2)})
          assert_identical(result, expected)
          np.testing.assert_equal(da.to_numpy(), arr)
  