ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 3, dim = 'time', ddof = 0
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [3, 4, 5, 6, 7, 8])
    @pytest.mark.parametrize("dim", [None, "x", "time"])
    @requires_dask
    def test_lazy_corrcov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        # GH 5284
        from dask import is_dask_collection
    
        da_a, da_b = array_tuples[n]
    
        with raise_if_dask_computes():
>           cov = xr.cov(da_a.chunk(), da_b.chunk(), dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1600: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 3, dim = 'time', ddof = 1
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [3, 4, 5, 6, 7, 8])
    @pytest.mark.parametrize("dim", [None, "x", "time"])
    @requires_dask
    def test_lazy_corrcov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        # GH 5284
        from dask import is_dask_collection
    
        da_a, da_b = array_tuples[n]
    
        with raise_if_dask_computes():
>           cov = xr.cov(da_a.chunk(), da_b.chunk(), dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1600: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 4, dim = 'time', ddof = 0
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [3, 4, 5, 6, 7, 8])
    @pytest.mark.parametrize("dim", [None, "x", "time"])
    @requires_dask
    def test_lazy_corrcov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        # GH 5284
        from dask import is_dask_collection
    
        da_a, da_b = array_tuples[n]
    
        with raise_if_dask_computes():
>           cov = xr.cov(da_a.chunk(), da_b.chunk(), dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1600: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 4, dim = 'time', ddof = 1
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [3, 4, 5, 6, 7, 8])
    @pytest.mark.parametrize("dim", [None, "x", "time"])
    @requires_dask
    def test_lazy_corrcov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        # GH 5284
        from dask import is_dask_collection
    
        da_a, da_b = array_tuples[n]
    
        with raise_if_dask_computes():
>           cov = xr.cov(da_a.chunk(), da_b.chunk(), dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1600: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 5, dim = 'time', ddof = 0
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [3, 4, 5, 6, 7, 8])
    @pytest.mark.parametrize("dim", [None, "x", "time"])
    @requires_dask
    def test_lazy_corrcov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        # GH 5284
        from dask import is_dask_collection
    
        da_a, da_b = array_tuples[n]
    
        with raise_if_dask_computes():
>           cov = xr.cov(da_a.chunk(), da_b.chunk(), dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1600: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 5, dim = 'time', ddof = 1
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [3, 4, 5, 6, 7, 8])
    @pytest.mark.parametrize("dim", [None, "x", "time"])
    @requires_dask
    def test_lazy_corrcov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        # GH 5284
        from dask import is_dask_collection
    
        da_a, da_b = array_tuples[n]
    
        with raise_if_dask_computes():
>           cov = xr.cov(da_a.chunk(), da_b.chunk(), dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1600: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 6, dim = 'time', ddof = 0
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [3, 4, 5, 6, 7, 8])
    @pytest.mark.parametrize("dim", [None, "x", "time"])
    @requires_dask
    def test_lazy_corrcov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        # GH 5284
        from dask import is_dask_collection
    
        da_a, da_b = array_tuples[n]
    
        with raise_if_dask_computes():
>           cov = xr.cov(da_a.chunk(), da_b.chunk(), dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1600: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 6, dim = 'time', ddof = 1
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [3, 4, 5, 6, 7, 8])
    @pytest.mark.parametrize("dim", [None, "x", "time"])
    @requires_dask
    def test_lazy_corrcov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        # GH 5284
        from dask import is_dask_collection
    
        da_a, da_b = array_tuples[n]
    
        with raise_if_dask_computes():
>           cov = xr.cov(da_a.chunk(), da_b.chunk(), dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1600: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 7, dim = 'time', ddof = 0
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [3, 4, 5, 6, 7, 8])
    @pytest.mark.parametrize("dim", [None, "x", "time"])
    @requires_dask
    def test_lazy_corrcov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        # GH 5284
        from dask import is_dask_collection
    
        da_a, da_b = array_tuples[n]
    
        with raise_if_dask_computes():
>           cov = xr.cov(da_a.chunk(), da_b.chunk(), dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1600: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 7, dim = 'time', ddof = 1
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [3, 4, 5, 6, 7, 8])
    @pytest.mark.parametrize("dim", [None, "x", "time"])
    @requires_dask
    def test_lazy_corrcov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        # GH 5284
        from dask import is_dask_collection
    
        da_a, da_b = array_tuples[n]
    
        with raise_if_dask_computes():
>           cov = xr.cov(da_a.chunk(), da_b.chunk(), dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1600: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 8, dim = 'time', ddof = 0
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [3, 4, 5, 6, 7, 8])
    @pytest.mark.parametrize("dim", [None, "x", "time"])
    @requires_dask
    def test_lazy_corrcov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        # GH 5284
        from dask import is_dask_collection
    
        da_a, da_b = array_tuples[n]
    
        with raise_if_dask_computes():
>           cov = xr.cov(da_a.chunk(), da_b.chunk(), dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1600: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 8, dim = 'time', ddof = 1
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [3, 4, 5, 6, 7, 8])
    @pytest.mark.parametrize("dim", [None, "x", "time"])
    @requires_dask
    def test_lazy_corrcov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        # GH 5284
        from dask import is_dask_collection
    
        da_a, da_b = array_tuples[n]
    
        with raise_if_dask_computes():
>           cov = xr.cov(da_a.chunk(), da_b.chunk(), dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1600: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
dask.array<and_, shape=(2, 2), dtype=bool, chunksize=(2, 2), chunktype=numpy.ndarray>
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('a', 'time', 'x')
self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[ True,  True,  True,  True],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 0, dim = 'time', ddof = 0
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [0, 1, 2])
    @pytest.mark.parametrize("dim", [None, "time"])
    def test_cov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
    
        if dim is not None:
    
            def np_cov_ind(ts1, ts2, a, x):
                # Ensure the ts are aligned and missing values ignored
                ts1, ts2 = broadcast(ts1, ts2)
                valid_values = ts1.notnull() & ts2.notnull()
    
                # While dropping isn't ideal here, numpy will return nan
                # if any segment contains a NaN.
                ts1 = ts1.where(valid_values)
                ts2 = ts2.where(valid_values)
    
                return np.ma.cov(
                    np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()),
                    np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()),
                    ddof=ddof,
                )[0, 1]
    
            expected = np.zeros((3, 4))
            for a in [0, 1, 2]:
                for x in [0, 1, 2, 3]:
                    expected[a, x] = np_cov_ind(da_a, da_b, a=a, x=x)
>           actual = xr.cov(da_a, da_b, dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1637: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[ True,  True,  True,  True],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('a', 'time', 'x')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('a', 'time', 'x')
self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[ True,  True,  True,  True],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 0, dim = 'time', ddof = 1
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [0, 1, 2])
    @pytest.mark.parametrize("dim", [None, "time"])
    def test_cov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
    
        if dim is not None:
    
            def np_cov_ind(ts1, ts2, a, x):
                # Ensure the ts are aligned and missing values ignored
                ts1, ts2 = broadcast(ts1, ts2)
                valid_values = ts1.notnull() & ts2.notnull()
    
                # While dropping isn't ideal here, numpy will return nan
                # if any segment contains a NaN.
                ts1 = ts1.where(valid_values)
                ts2 = ts2.where(valid_values)
    
                return np.ma.cov(
                    np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()),
                    np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()),
                    ddof=ddof,
                )[0, 1]
    
            expected = np.zeros((3, 4))
            for a in [0, 1, 2]:
                for x in [0, 1, 2, 3]:
                    expected[a, x] = np_cov_ind(da_a, da_b, a=a, x=x)
>           actual = xr.cov(da_a, da_b, dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1637: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[ True,  True,  True,  True],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('a', 'time', 'x')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('a', 'time', 'x')
self = <xarray.Variable (a: 3, time: 16, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 1, dim = 'time', ddof = 0
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [0, 1, 2])
    @pytest.mark.parametrize("dim", [None, "time"])
    def test_cov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
    
        if dim is not None:
    
            def np_cov_ind(ts1, ts2, a, x):
                # Ensure the ts are aligned and missing values ignored
                ts1, ts2 = broadcast(ts1, ts2)
                valid_values = ts1.notnull() & ts2.notnull()
    
                # While dropping isn't ideal here, numpy will return nan
                # if any segment contains a NaN.
                ts1 = ts1.where(valid_values)
                ts2 = ts2.where(valid_values)
    
                return np.ma.cov(
                    np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()),
                    np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()),
                    ddof=ddof,
                )[0, 1]
    
            expected = np.zeros((3, 4))
            for a in [0, 1, 2]:
                for x in [0, 1, 2, 3]:
                    expected[a, x] = np_cov_ind(da_a, da_b, a=a, x=x)
>           actual = xr.cov(da_a, da_b, dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1637: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (a: 3, time: 16, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('a', 'time', 'x')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('a', 'time', 'x')
self = <xarray.Variable (a: 3, time: 16, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 1, dim = 'time', ddof = 1
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [0, 1, 2])
    @pytest.mark.parametrize("dim", [None, "time"])
    def test_cov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
    
        if dim is not None:
    
            def np_cov_ind(ts1, ts2, a, x):
                # Ensure the ts are aligned and missing values ignored
                ts1, ts2 = broadcast(ts1, ts2)
                valid_values = ts1.notnull() & ts2.notnull()
    
                # While dropping isn't ideal here, numpy will return nan
                # if any segment contains a NaN.
                ts1 = ts1.where(valid_values)
                ts2 = ts2.where(valid_values)
    
                return np.ma.cov(
                    np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()),
                    np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()),
                    ddof=ddof,
                )[0, 1]
    
            expected = np.zeros((3, 4))
            for a in [0, 1, 2]:
                for x in [0, 1, 2, 3]:
                    expected[a, x] = np_cov_ind(da_a, da_b, a=a, x=x)
>           actual = xr.cov(da_a, da_b, dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1637: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (a: 3, time: 16, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('a', 'time', 'x')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('a', 'time', 'x')
self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [False, False, False, False]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 2, dim = 'time', ddof = 0
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [0, 1, 2])
    @pytest.mark.parametrize("dim", [None, "time"])
    def test_cov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
    
        if dim is not None:
    
            def np_cov_ind(ts1, ts2, a, x):
                # Ensure the ts are aligned and missing values ignored
                ts1, ts2 = broadcast(ts1, ts2)
                valid_values = ts1.notnull() & ts2.notnull()
    
                # While dropping isn't ideal here, numpy will return nan
                # if any segment contains a NaN.
                ts1 = ts1.where(valid_values)
                ts2 = ts2.where(valid_values)
    
                return np.ma.cov(
                    np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()),
                    np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()),
                    ddof=ddof,
                )[0, 1]
    
            expected = np.zeros((3, 4))
            for a in [0, 1, 2]:
                for x in [0, 1, 2, 3]:
                    expected[a, x] = np_cov_ind(da_a, da_b, a=a, x=x)
>           actual = xr.cov(da_a, da_b, dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1637: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [False, False, False, False]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('a', 'time', 'x')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('a', 'time', 'x')
self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [False, False, False, False]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 2, dim = 'time', ddof = 1
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("ddof", [0, 1])
    @pytest.mark.parametrize("n", [0, 1, 2])
    @pytest.mark.parametrize("dim", [None, "time"])
    def test_cov(
        n: int, dim: str | None, ddof: int, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
    
        if dim is not None:
    
            def np_cov_ind(ts1, ts2, a, x):
                # Ensure the ts are aligned and missing values ignored
                ts1, ts2 = broadcast(ts1, ts2)
                valid_values = ts1.notnull() & ts2.notnull()
    
                # While dropping isn't ideal here, numpy will return nan
                # if any segment contains a NaN.
                ts1 = ts1.where(valid_values)
                ts2 = ts2.where(valid_values)
    
                return np.ma.cov(
                    np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()),
                    np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()),
                    ddof=ddof,
                )[0, 1]
    
            expected = np.zeros((3, 4))
            for a in [0, 1, 2]:
                for x in [0, 1, 2, 3]:
                    expected[a, x] = np_cov_ind(da_a, da_b, a=a, x=x)
>           actual = xr.cov(da_a, da_b, dim=dim, ddof=ddof)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1637: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [False, False, False, False]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('a', 'time', 'x')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('a', 'time', 'x')
self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[ True,  True,  True,  True],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 0, dim = 'time'
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("n", [0, 1, 2])
    @pytest.mark.parametrize("dim", [None, "time"])
    def test_corr(
        n: int, dim: str | None, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
    
        if dim is not None:
    
            def np_corr_ind(ts1, ts2, a, x):
                # Ensure the ts are aligned and missing values ignored
                ts1, ts2 = broadcast(ts1, ts2)
                valid_values = ts1.notnull() & ts2.notnull()
    
                ts1 = ts1.where(valid_values)
                ts2 = ts2.where(valid_values)
    
                return np.ma.corrcoef(
                    np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()),
                    np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()),
                )[0, 1]
    
            expected = np.zeros((3, 4))
            for a in [0, 1, 2]:
                for x in [0, 1, 2, 3]:
                    expected[a, x] = np_corr_ind(da_a, da_b, a=a, x=x)
>           actual = xr.corr(da_a, da_b, dim)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1687: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1435: in corr
    return _cov_corr(da_a, da_b, dim=dim, method="corr")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[ True,  True,  True,  True],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('a', 'time', 'x')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('a', 'time', 'x')
self = <xarray.Variable (a: 3, time: 16, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 1, dim = 'time'
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("n", [0, 1, 2])
    @pytest.mark.parametrize("dim", [None, "time"])
    def test_corr(
        n: int, dim: str | None, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
    
        if dim is not None:
    
            def np_corr_ind(ts1, ts2, a, x):
                # Ensure the ts are aligned and missing values ignored
                ts1, ts2 = broadcast(ts1, ts2)
                valid_values = ts1.notnull() & ts2.notnull()
    
                ts1 = ts1.where(valid_values)
                ts2 = ts2.where(valid_values)
    
                return np.ma.corrcoef(
                    np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()),
                    np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()),
                )[0, 1]
    
            expected = np.zeros((3, 4))
            for a in [0, 1, 2]:
                for x in [0, 1, 2, 3]:
                    expected[a, x] = np_corr_ind(da_a, da_b, a=a, x=x)
>           actual = xr.corr(da_a, da_b, dim)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1687: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1435: in corr
    return _cov_corr(da_a, da_b, dim=dim, method="corr")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (a: 3, time: 16, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('a', 'time', 'x')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('a', 'time', 'x')
self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [False, False, False, False]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 2, dim = 'time'
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("n", [0, 1, 2])
    @pytest.mark.parametrize("dim", [None, "time"])
    def test_corr(
        n: int, dim: str | None, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
    
        if dim is not None:
    
            def np_corr_ind(ts1, ts2, a, x):
                # Ensure the ts are aligned and missing values ignored
                ts1, ts2 = broadcast(ts1, ts2)
                valid_values = ts1.notnull() & ts2.notnull()
    
                ts1 = ts1.where(valid_values)
                ts2 = ts2.where(valid_values)
    
                return np.ma.corrcoef(
                    np.ma.masked_invalid(ts1.sel(a=a, x=x).data.flatten()),
                    np.ma.masked_invalid(ts2.sel(a=a, x=x).data.flatten()),
                )[0, 1]
    
            expected = np.zeros((3, 4))
            for a in [0, 1, 2]:
                for x in [0, 1, 2, 3]:
                    expected[a, x] = np_corr_ind(da_a, da_b, a=a, x=x)
>           actual = xr.corr(da_a, da_b, dim)

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1687: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1435: in corr
    return _cov_corr(da_a, da_b, dim=dim, method="corr")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [False, False, False, False]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('a', 'time', 'x')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('a', 'time', 'x')
self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[ True,  True,  True,  True],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 0, dim = 'time'
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("n", range(9))
    @pytest.mark.parametrize("dim", [None, "time", "x"])
    def test_covcorr_consistency(
        n: int, dim: str | None, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
        # Testing that xr.corr and xr.cov are consistent with each other
        # 1. Broadcast the two arrays
        da_a, da_b = broadcast(da_a, da_b)
        # 2. Ignore the nans
        valid_values = da_a.notnull() & da_b.notnull()
        da_a = da_a.where(valid_values)
        da_b = da_b.where(valid_values)
    
>       expected = xr.cov(da_a, da_b, dim=dim, ddof=0) / (
            da_a.std(dim=dim) * da_b.std(dim=dim)
        )

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1724: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[ True,  True,  True,  True],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('a', 'time', 'x')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('a', 'time', 'x')
self = <xarray.Variable (a: 3, time: 20, x: 4)>
array([[[False, False, False, False],
        [False, False, False, False],
 ...e],
        [ True,  True,  True,  True],
        [False, False, False, False],
        [False, False, False, False]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 1, dim = 'time'
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("n", range(9))
    @pytest.mark.parametrize("dim", [None, "time", "x"])
    def test_covcorr_consistency(
        n: int, dim: str | None, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
        # Testing that xr.corr and xr.cov are consistent with each other
        # 1. Broadcast the two arrays
        da_a, da_b = broadcast(da_a, da_b)
        # 2. Ignore the nans
        valid_values = da_a.notnull() & da_b.notnull()
        da_a = da_a.where(valid_values)
        da_b = da_b.where(valid_values)
    
>       expected = xr.cov(da_a, da_b, dim=dim, ddof=0) / (
            da_a.std(dim=dim) * da_b.std(dim=dim)
        )

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1724: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (a: 3, time: 20, x: 4)>
array([[[False, False, False, False],
        [False, False, False, False],
 ...e],
        [ True,  True,  True,  True],
        [False, False, False, False],
        [False, False, False, False]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('a', 'time', 'x')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('a', 'time', 'x')
self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [False, False, False, False]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 2, dim = 'time'
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("n", range(9))
    @pytest.mark.parametrize("dim", [None, "time", "x"])
    def test_covcorr_consistency(
        n: int, dim: str | None, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
        # Testing that xr.corr and xr.cov are consistent with each other
        # 1. Broadcast the two arrays
        da_a, da_b = broadcast(da_a, da_b)
        # 2. Ignore the nans
        valid_values = da_a.notnull() & da_b.notnull()
        da_a = da_a.where(valid_values)
        da_b = da_b.where(valid_values)
    
>       expected = xr.cov(da_a, da_b, dim=dim, ddof=0) / (
            da_a.std(dim=dim) * da_b.std(dim=dim)
        )

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1724: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (a: 3, time: 18, x: 4)>
array([[[False, False, False, False],
        [ True,  True,  True,  True],
 ...e],
        [ True,  True,  True,  True],
        [ True,  True,  True,  True],
        [False, False, False, False]]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('a', 'time', 'x')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError

ValueError: 't' not found in array dimensions ('x', 'time')
self = <xarray.Variable (x: 2, time: 2)>
array([[ True,  True],
       [ True, False]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
>           out: int = self.dims.index(dim)
E           ValueError: tuple.index(x): x not in tuple

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:617: ValueError

During handling of the above exception, another exception occurred:

n = 3, dim = 'time'
array_tuples = [(<xarray.DataArray (a: 3, time: 18, x: 4)>
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        [0.42365... time, <xarray.DataArray (x: 2, time: 2)>
array([[1, 2],
       [2, 1]])
Dimensions without coordinates: x, time), ...]

    @pytest.mark.parametrize("n", range(9))
    @pytest.mark.parametrize("dim", [None, "time", "x"])
    def test_covcorr_consistency(
        n: int, dim: str | None, array_tuples: tuple[xr.DataArray, xr.DataArray]
    ) -> None:
        da_a, da_b = array_tuples[n]
        # Testing that xr.corr and xr.cov are consistent with each other
        # 1. Broadcast the two arrays
        da_a, da_b = broadcast(da_a, da_b)
        # 2. Ignore the nans
        valid_values = da_a.notnull() & da_b.notnull()
        da_a = da_a.where(valid_values)
        da_b = da_b.where(valid_values)
    
>       expected = xr.cov(da_a, da_b, dim=dim, ddof=0) / (
            da_a.std(dim=dim) * da_b.std(dim=dim)
        )

/Users/runner/work/xarray/xarray/xarray/tests/test_computation.py:1724: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1357: in cov
    return _cov_corr(da_a, da_b, dim=dim, ddof=ddof, method="cov")
/Users/runner/work/xarray/xarray/xarray/core/computation.py:1456: in _cov_corr
    valid_count = valid_values.sum(dim) - ddof
/Users/runner/work/xarray/xarray/xarray/core/_aggregations.py:1856: in sum
    return self.reduce(
/Users/runner/work/xarray/xarray/xarray/core/dataarray.py:3715: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
/Users/runner/work/xarray/xarray/xarray/core/variable.py:1776: in reduce
    result = super().reduce(
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:711: in reduce
    axis = self._get_axis_nums(dim)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in _get_axis_nums
    return tuple(self._get_axis_num(d) for d in dims)
/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:613: in <genexpr>
    return tuple(self._get_axis_num(d) for d in dims)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 2, time: 2)>
array([[ True,  True],
       [ True, False]])
dim = 't'

    def _get_axis_num(self: Any, dim: _Dim) -> int:
        try:
            out: int = self.dims.index(dim)
            return out
        except ValueError:
>           raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
E           ValueError: 't' not found in array dimensions ('x', 'time')

/Users/runner/work/xarray/xarray/xarray/namedarray/core.py:620: ValueError