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Add support for Array API in NamedArray #3

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Add support for Array API in NamedArray

Add support for Array API in NamedArray #3

GitHub Actions / Test Results failed Aug 18, 2024 in 0s

66 errors, 3 142 fail, 7 888 skipped, 2 524 pass in 7m 33s

     2 files  ±0      2 suites  ±0   7m 33s ⏱️ +9s
13 620 tests ±0  2 524 ✅ ±0  7 888 💤 ±0  3 142 ❌ ±0  66 🔥 ±0 
13 634 runs  ±0  2 524 ✅ ±0  7 899 💤 ±0  3 142 ❌ ±0  69 🔥 ±0 

Results for commit 9bf9fe8. ± Comparison against earlier commit 192b1b5.

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1 out of 2 runs with error: xarray.tests.test_dask 

artifacts/Test results for Linux-3.12 flaky/pytest.xml [took 0s]
Raw output 
collection failure
collection failure
collection failure
collection failure
#x1B[1m#x1B[31mxarray/tests/test_dask.py#x1B[0m:1127: in <module>
    @pytest.mark.parametrize("obj", [make_ds(), make_da()])
#x1B[1m#x1B[31mxarray/tests/test_dask.py#x1B[0m:1067: in make_ds
    map_ds["a"] = make_da()
#x1B[1m#x1B[31mxarray/tests/test_dask.py#x1B[0m:1060: in make_da
    da.coords["cxy"] = (da.x * da.y).chunk({"x": 4, "y": 5})
#x1B[1m#x1B[31mxarray/core/_typed_ops.py#x1B[0m:253: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mxarray/core/dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mxarray/core/_typed_ops.py#x1B[0m:483: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2318: in _binary_op
    self_data, other_data, dims = _broadcast_compat_data(self, other)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2928: in _broadcast_compat_data
    new_self, new_other = _broadcast_compat_variables(self, other)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2895: in _broadcast_compat_variables
    return tuple(var.set_dims(dims) if var.dims != dims else var for var in variables)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2895: in <genexpr>
    return tuple(var.set_dims(dims) if var.dims != dims else var for var in variables)
#x1B[1m#x1B[31mxarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:1389: in set_dims
    return expanded_var.transpose(*dim)
#x1B[1m#x1B[31mxarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:1332: in transpose
    axes = self.get_axis_num(dim)
#x1B[1m#x1B[31mxarray/namedarray/core.py#x1B[0m:799: in get_axis_num
    if dims is _default:
#x1B[1m#x1B[31mE   NameError: name 'dims' is not defined#x1B[0m
#x1B[1m#x1B[31mxarray/tests/test_dask.py#x1B[0m:1127: in <module>
    @pytest.mark.parametrize("obj", [make_ds(), make_da()])
#x1B[1m#x1B[31mxarray/tests/test_dask.py#x1B[0m:1067: in make_ds
    map_ds["a"] = make_da()
#x1B[1m#x1B[31mxarray/tests/test_dask.py#x1B[0m:1060: in make_da
    da.coords["cxy"] = (da.x * da.y).chunk({"x": 4, "y": 5})
#x1B[1m#x1B[31mxarray/core/_typed_ops.py#x1B[0m:253: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mxarray/core/dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mxarray/core/_typed_ops.py#x1B[0m:483: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2318: in _binary_op
    self_data, other_data, dims = _broadcast_compat_data(self, other)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2928: in _broadcast_compat_data
    new_self, new_other = _broadcast_compat_variables(self, other)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2895: in _broadcast_compat_variables
    return tuple(var.set_dims(dims) if var.dims != dims else var for var in variables)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2895: in <genexpr>
    return tuple(var.set_dims(dims) if var.dims != dims else var for var in variables)
#x1B[1m#x1B[31mxarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:1389: in set_dims
    return expanded_var.transpose(*dim)
#x1B[1m#x1B[31mxarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:1332: in transpose
    axes = self.get_axis_num(dim)
#x1B[1m#x1B[31mxarray/namedarray/core.py#x1B[0m:799: in get_axis_num
    if dims is _default:
#x1B[1m#x1B[31mE   NameError: name 'dims' is not defined#x1B[0m
#x1B[1m#x1B[31mxarray/tests/test_dask.py#x1B[0m:1127: in <module>
    @pytest.mark.parametrize("obj", [make_ds(), make_da()])
#x1B[1m#x1B[31mxarray/tests/test_dask.py#x1B[0m:1067: in make_ds
    map_ds["a"] = make_da()
#x1B[1m#x1B[31mxarray/tests/test_dask.py#x1B[0m:1060: in make_da
    da.coords["cxy"] = (da.x * da.y).chunk({"x": 4, "y": 5})
#x1B[1m#x1B[31mxarray/core/_typed_ops.py#x1B[0m:253: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mxarray/core/dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mxarray/core/_typed_ops.py#x1B[0m:483: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2318: in _binary_op
    self_data, other_data, dims = _broadcast_compat_data(self, other)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2928: in _broadcast_compat_data
    new_self, new_other = _broadcast_compat_variables(self, other)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2895: in _broadcast_compat_variables
    return tuple(var.set_dims(dims) if var.dims != dims else var for var in variables)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2895: in <genexpr>
    return tuple(var.set_dims(dims) if var.dims != dims else var for var in variables)
#x1B[1m#x1B[31mxarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:1389: in set_dims
    return expanded_var.transpose(*dim)
#x1B[1m#x1B[31mxarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:1332: in transpose
    axes = self.get_axis_num(dim)
#x1B[1m#x1B[31mxarray/namedarray/core.py#x1B[0m:799: in get_axis_num
    if dims is _default:
#x1B[1m#x1B[31mE   NameError: name 'dims' is not defined#x1B[0m
#x1B[1m#x1B[31mxarray/tests/test_dask.py#x1B[0m:1127: in <module>
    @pytest.mark.parametrize("obj", [make_ds(), make_da()])
#x1B[1m#x1B[31mxarray/tests/test_dask.py#x1B[0m:1067: in make_ds
    map_ds["a"] = make_da()
#x1B[1m#x1B[31mxarray/tests/test_dask.py#x1B[0m:1060: in make_da
    da.coords["cxy"] = (da.x * da.y).chunk({"x": 4, "y": 5})
#x1B[1m#x1B[31mxarray/core/_typed_ops.py#x1B[0m:253: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mxarray/core/dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mxarray/core/_typed_ops.py#x1B[0m:483: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2318: in _binary_op
    self_data, other_data, dims = _broadcast_compat_data(self, other)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2928: in _broadcast_compat_data
    new_self, new_other = _broadcast_compat_variables(self, other)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2895: in _broadcast_compat_variables
    return tuple(var.set_dims(dims) if var.dims != dims else var for var in variables)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:2895: in <genexpr>
    return tuple(var.set_dims(dims) if var.dims != dims else var for var in variables)
#x1B[1m#x1B[31mxarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:1389: in set_dims
    return expanded_var.transpose(*dim)
#x1B[1m#x1B[31mxarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31mxarray/core/variable.py#x1B[0m:1332: in transpose
    axes = self.get_axis_num(dim)
#x1B[1m#x1B[31mxarray/namedarray/core.py#x1B[0m:799: in get_axis_num
    if dims is _default:
#x1B[1m#x1B[31mE   NameError: name 'dims' is not defined#x1B[0m

Check warning on line 0 in xarray.tests.test_backends_api

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test_multiindex (xarray.tests.test_backends_api) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
NameError: name 'dims' is not defined
def test_multiindex() -> None:
        # GH7139
        # Check that we properly handle backends that change index variables
        dataset = xr.Dataset(coords={"coord1": ["A", "B"], "coord2": [1, 2]})
>       dataset = dataset.stack(z=["coord1", "coord2"])

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_backends_api.py#x1B[0m:54: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataset.py#x1B[0m:5411: in stack
    result = result._stack_once(dims, new_dim, index_cls, create_index)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataset.py#x1B[0m:5325: in _stack_once
    exp_var = var.set_dims(vdims, shape)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1389: in set_dims
    return expanded_var.transpose(*dim)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1332: in transpose
    axes = self.get_axis_num(dim)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (coord2: 2, coord1: 2)> Size: 16B
array([['A', 'B'],
       ['A', 'B']], dtype='<U1')
dim = ('coord1', 'coord2')

    def get_axis_num(self, dim: Hashable | Iterable[Hashable]) -> int | tuple[int, ...]:
        """Return axis number(s) corresponding to dimension(s) in this array.
    
        Parameters
        ----------
        dim : str or iterable of str
            Dimension name(s) for which to lookup axes.
    
        Returns
        -------
        int or tuple of int
            Axis number or numbers corresponding to the given dimensions.
        """
>       if dims is _default:
#x1B[1m#x1B[31mE       NameError: name 'dims' is not defined#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:799: NameError

Check warning on line 0 in xarray.tests.test_accessor_str

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test_contains_broadcast[str] (xarray.tests.test_accessor_str) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
dtype = <class 'numpy.str_'>

    def test_contains_broadcast(dtype) -> None:
        values = xr.DataArray(["Foo", "xYz", "fOOomMm__fOo", "MMM_"], dims="X").astype(
            dtype
        )
        pat_str = xr.DataArray(["FOO|mmm", "Foo", "MMM"], dims="Y").astype(dtype)
        pat_re = xr.DataArray([re.compile(x) for x in pat_str.data], dims="Y")
    
        # case insensitive using regex
        result = values.str.contains(pat_str, case=False)
        expected = xr.DataArray(
            [
                [True, True, False],
                [False, False, False],
                [True, True, True],
                [True, False, True],
            ],
            dims=["X", "Y"],
        )
        assert result.dtype == expected.dtype
        assert_equal(result, expected)
    
        # case sensitive using regex
        result = values.str.contains(pat_str)
        expected = xr.DataArray(
            [
                [False, True, False],
                [False, False, False],
                [False, False, False],
                [False, False, True],
            ],
            dims=["X", "Y"],
        )
        assert result.dtype == expected.dtype
        assert_equal(result, expected)
>       result = values.str.contains(pat_re)

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_accessor_str.py#x1B[0m:181: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:1491: in contains
    is_compiled_re = _contains_compiled_re(pat)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:104: in _contains_compiled_re
    return _contains_obj_type(pat=pat, checker=re.Pattern)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:88: in _contains_obj_type
    return _apply_str_ufunc(func=checker, obj=pat).all()
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1358: in all
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (Y: 3)> Size: 24B
array([True, True, True], dtype=object)
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_accessor_str

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test_contains_broadcast[bytes] (xarray.tests.test_accessor_str) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
dtype = <class 'numpy.bytes_'>

    def test_contains_broadcast(dtype) -> None:
        values = xr.DataArray(["Foo", "xYz", "fOOomMm__fOo", "MMM_"], dims="X").astype(
            dtype
        )
        pat_str = xr.DataArray(["FOO|mmm", "Foo", "MMM"], dims="Y").astype(dtype)
        pat_re = xr.DataArray([re.compile(x) for x in pat_str.data], dims="Y")
    
        # case insensitive using regex
        result = values.str.contains(pat_str, case=False)
        expected = xr.DataArray(
            [
                [True, True, False],
                [False, False, False],
                [True, True, True],
                [True, False, True],
            ],
            dims=["X", "Y"],
        )
        assert result.dtype == expected.dtype
        assert_equal(result, expected)
    
        # case sensitive using regex
        result = values.str.contains(pat_str)
        expected = xr.DataArray(
            [
                [False, True, False],
                [False, False, False],
                [False, False, False],
                [False, False, True],
            ],
            dims=["X", "Y"],
        )
        assert result.dtype == expected.dtype
        assert_equal(result, expected)
>       result = values.str.contains(pat_re)

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_accessor_str.py#x1B[0m:181: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:1491: in contains
    is_compiled_re = _contains_compiled_re(pat)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:104: in _contains_compiled_re
    return _contains_obj_type(pat=pat, checker=re.Pattern)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:88: in _contains_obj_type
    return _apply_str_ufunc(func=checker, obj=pat).all()
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1358: in all
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (Y: 3)> Size: 24B
array([True, True, True], dtype=object)
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_accessor_str

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test_replace_callable (xarray.tests.test_accessor_str) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
def test_replace_callable() -> None:
        values = xr.DataArray(["fooBAD__barBAD"])
    
        # test with callable
        repl = lambda m: m.group(0).swapcase()
        result = values.str.replace("[a-z][A-Z]{2}", repl, n=2)
        exp = xr.DataArray(["foObaD__baRbaD"])
        assert result.dtype == exp.dtype
        assert_equal(result, exp)
    
        # test regex named groups
        values = xr.DataArray(["Foo Bar Baz"])
        pat = r"(?P<first>\w+) (?P<middle>\w+) (?P<last>\w+)"
        repl = lambda m: m.group("middle").swapcase()
        result = values.str.replace(pat, repl)
        exp = xr.DataArray(["bAR"])
        assert result.dtype == exp.dtype
        assert_equal(result, exp)
    
        # test broadcast
        values = xr.DataArray(["Foo Bar Baz"], dims=["x"])
        pat = r"(?P<first>\w+) (?P<middle>\w+) (?P<last>\w+)"
        repl2 = xr.DataArray(
            [
                lambda m: m.group("first").swapcase(),
                lambda m: m.group("middle").swapcase(),
                lambda m: m.group("last").swapcase(),
            ],
            dims=["Y"],
        )
>       result = values.str.replace(pat, repl2)

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_accessor_str.py#x1B[0m:417: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:1931: in replace
    elif not _contains_callable(repl):  # pragma: no cover
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:109: in _contains_callable
    return _contains_obj_type(pat=pat, checker=callable)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:88: in _contains_obj_type
    return _apply_str_ufunc(func=checker, obj=pat).all()
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1358: in all
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (Y: 3)> Size: 24B
array([True, True, True], dtype=object)
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_accessor_str

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test_replace_unicode (xarray.tests.test_accessor_str) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
def test_replace_unicode() -> None:
        # flags + unicode
        values = xr.DataArray([b"abcd,\xc3\xa0".decode("utf-8")])
        expected = xr.DataArray([b"abcd, \xc3\xa0".decode("utf-8")])
        pat = re.compile(r"(?<=\w),(?=\w)", flags=re.UNICODE)
        result = values.str.replace(pat, ", ")
        assert result.dtype == expected.dtype
        assert_equal(result, expected)
    
        # broadcast version
        values = xr.DataArray([b"abcd,\xc3\xa0".decode("utf-8")], dims=["X"])
        expected = xr.DataArray(
            [[b"abcd, \xc3\xa0".decode("utf-8"), b"BAcd,\xc3\xa0".decode("utf-8")]],
            dims=["X", "Y"],
        )
        pat2 = xr.DataArray(
            [re.compile(r"(?<=\w),(?=\w)", flags=re.UNICODE), r"ab"], dims=["Y"]
        )
        repl = xr.DataArray([", ", "BA"], dims=["Y"])
>       result = values.str.replace(pat2, repl)

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_accessor_str.py#x1B[0m:442: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:1934: in replace
    is_compiled_re = _contains_compiled_re(pat)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:104: in _contains_compiled_re
    return _contains_obj_type(pat=pat, checker=re.Pattern)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:88: in _contains_obj_type
    return _apply_str_ufunc(func=checker, obj=pat).all()
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1358: in all
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (Y: 2)> Size: 16B
array([True, False], dtype=object)
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_coarsen

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test_coarsen_da_reduce[numpy-sum-1-1] (xarray.tests.test_coarsen) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
da = <xarray.DataArray (a: 3, time: 21, x: 4)> Size: 2kB
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        ...ates:
  * time     (time) datetime64[ns] 168B 2000-01-01 2000-01-02 ... 2000-01-21
Dimensions without coordinates: a, x
window = 1, name = 'sum'

    @pytest.mark.parametrize("da", (1, 2), indirect=True)
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "mean", "std", "max"))
    def test_coarsen_da_reduce(da, window, name) -> None:
>       if da.isnull().sum() > 1 and window == 1:

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_coarsen.py#x1B[0m:244: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1857: in sum
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (a: 3, time: 21, x: 4)> Size: 252B
array([[[False, False, False, False],
        [False, False, False...e],
        [False, False, False, False],
        [False, False, False, False],
        [False, False, False, False]]])
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_dataset.TestDataset

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test_selection_multiindex (xarray.tests.test_dataset.TestDataset) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
NameError: name 'dims' is not defined
self = <xarray.tests.test_dataset.TestDataset object at 0x7f9c21d18c40>

    def test_selection_multiindex(self) -> None:
        midx = pd.MultiIndex.from_product(
            [["a", "b"], [1, 2], [-1, -2]], names=("one", "two", "three")
        )
        midx_coords = Coordinates.from_pandas_multiindex(midx, "x")
        mdata = Dataset(data_vars={"var": ("x", range(8))}, coords=midx_coords)
    
        def test_sel(
            lab_indexer, pos_indexer, replaced_idx=False, renamed_dim=None
        ) -> None:
            ds = mdata.sel(x=lab_indexer)
            expected_ds = mdata.isel(x=pos_indexer)
            if not replaced_idx:
                assert_identical(ds, expected_ds)
            else:
                if renamed_dim:
                    assert ds["var"].dims[0] == renamed_dim
                    ds = ds.rename({renamed_dim: "x"})
                assert_identical(ds["var"].variable, expected_ds["var"].variable)
                assert not ds["x"].equals(expected_ds["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")

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_dataset.py#x1B[0m:2067: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_dataset.py#x1B[0m:2054: in test_sel
    ds = mdata.sel(x=lab_indexer)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataset.py#x1B[0m:3192: in sel
    result = self.isel(indexers=query_results.dim_indexers, drop=drop)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataset.py#x1B[0m:3044: in isel
    var = var.isel(var_indexers)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1034: in isel
    return self[key]
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:798: in __getitem__
    dims, indexer, new_order = self._broadcast_indexes(key)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:638: in _broadcast_indexes
    self._validate_indexers(key)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:679: in _validate_indexers
    if self.shape[self.get_axis_num(dim)] != len(k):
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (x: 8)> Size: 64B
array([0, 1, 2, 3, 4, 5, 6, 7])
dim = 'x'

    def get_axis_num(self, dim: Hashable | Iterable[Hashable]) -> int | tuple[int, ...]:
        """Return axis number(s) corresponding to dimension(s) in this array.
    
        Parameters
        ----------
        dim : str or iterable of str
            Dimension name(s) for which to lookup axes.
    
        Returns
        -------
        int or tuple of int
            Axis number or numbers corresponding to the given dimensions.
        """
>       if dims is _default:
#x1B[1m#x1B[31mE       NameError: name 'dims' is not defined#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:799: NameError

Check warning on line 0 in xarray.tests.test_accessor_str

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test_replace_compiled_regex[str] (xarray.tests.test_accessor_str) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
dtype = <class 'numpy.str_'>

    def test_replace_compiled_regex(dtype) -> None:
        values = xr.DataArray(["fooBAD__barBAD"], dims=["x"]).astype(dtype)
    
        # test with compiled regex
        pat = re.compile(dtype("BAD[_]*"))
        result = values.str.replace(pat, "")
        expected = xr.DataArray(["foobar"], dims=["x"]).astype(dtype)
        assert result.dtype == expected.dtype
        assert_equal(result, expected)
    
        result = values.str.replace(pat, "", n=1)
        expected = xr.DataArray(["foobarBAD"], dims=["x"]).astype(dtype)
        assert result.dtype == expected.dtype
        assert_equal(result, expected)
    
        # broadcast
        pat2 = xr.DataArray(
            [re.compile(dtype("BAD[_]*")), re.compile(dtype("AD[_]*"))], dims=["y"]
        )
>       result = values.str.replace(pat2, "")

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_accessor_str.py#x1B[0m:466: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:1934: in replace
    is_compiled_re = _contains_compiled_re(pat)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:104: in _contains_compiled_re
    return _contains_obj_type(pat=pat, checker=re.Pattern)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:88: in _contains_obj_type
    return _apply_str_ufunc(func=checker, obj=pat).all()
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1358: in all
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (y: 2)> Size: 16B
array([True, True], dtype=object)
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_coarsen

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test_coarsen_da_reduce[numpy-sum-1-2] (xarray.tests.test_coarsen) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
da = <xarray.DataArray (time: 11)> Size: 88B
array([ 0., nan,  1.,  2., nan,  3.,  4.,  5., nan,  6.,  7.])
Dimensions without coordinates: time
window = 1, name = 'sum'

    @pytest.mark.parametrize("da", (1, 2), indirect=True)
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "mean", "std", "max"))
    def test_coarsen_da_reduce(da, window, name) -> None:
>       if da.isnull().sum() > 1 and window == 1:

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_coarsen.py#x1B[0m:244: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1857: in sum
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (time: 11)> Size: 11B
array([False,  True, False, False,  True, False, False, False,  True,
       False, False])
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_coarsen

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test_coarsen_da_reduce[numpy-sum-2-1] (xarray.tests.test_coarsen) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
da = <xarray.DataArray (a: 3, time: 21, x: 4)> Size: 2kB
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        ...ates:
  * time     (time) datetime64[ns] 168B 2000-01-01 2000-01-02 ... 2000-01-21
Dimensions without coordinates: a, x
window = 2, name = 'sum'

    @pytest.mark.parametrize("da", (1, 2), indirect=True)
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "mean", "std", "max"))
    def test_coarsen_da_reduce(da, window, name) -> None:
>       if da.isnull().sum() > 1 and window == 1:

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_coarsen.py#x1B[0m:244: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1857: in sum
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (a: 3, time: 21, x: 4)> Size: 252B
array([[[False, False, False, False],
        [False, False, False...e],
        [False, False, False, False],
        [False, False, False, False],
        [False, False, False, False]]])
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_accessor_str

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test_replace_compiled_regex[bytes] (xarray.tests.test_accessor_str) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
dtype = <class 'numpy.bytes_'>

    def test_replace_compiled_regex(dtype) -> None:
        values = xr.DataArray(["fooBAD__barBAD"], dims=["x"]).astype(dtype)
    
        # test with compiled regex
        pat = re.compile(dtype("BAD[_]*"))
        result = values.str.replace(pat, "")
        expected = xr.DataArray(["foobar"], dims=["x"]).astype(dtype)
        assert result.dtype == expected.dtype
        assert_equal(result, expected)
    
        result = values.str.replace(pat, "", n=1)
        expected = xr.DataArray(["foobarBAD"], dims=["x"]).astype(dtype)
        assert result.dtype == expected.dtype
        assert_equal(result, expected)
    
        # broadcast
        pat2 = xr.DataArray(
            [re.compile(dtype("BAD[_]*")), re.compile(dtype("AD[_]*"))], dims=["y"]
        )
>       result = values.str.replace(pat2, "")

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_accessor_str.py#x1B[0m:466: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:1934: in replace
    is_compiled_re = _contains_compiled_re(pat)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:104: in _contains_compiled_re
    return _contains_obj_type(pat=pat, checker=re.Pattern)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:88: in _contains_obj_type
    return _apply_str_ufunc(func=checker, obj=pat).all()
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1358: in all
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (y: 2)> Size: 16B
array([True, True], dtype=object)
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_coarsen

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test_coarsen_da_reduce[numpy-sum-2-2] (xarray.tests.test_coarsen) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
da = <xarray.DataArray (time: 11)> Size: 88B
array([ 0., nan,  1.,  2., nan,  3.,  4.,  5., nan,  6.,  7.])
Dimensions without coordinates: time
window = 2, name = 'sum'

    @pytest.mark.parametrize("da", (1, 2), indirect=True)
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "mean", "std", "max"))
    def test_coarsen_da_reduce(da, window, name) -> None:
>       if da.isnull().sum() > 1 and window == 1:

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_coarsen.py#x1B[0m:244: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1857: in sum
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (time: 11)> Size: 11B
array([False,  True, False, False,  True, False, False, False,  True,
       False, False])
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_coarsen

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test_coarsen_da_reduce[numpy-sum-3-1] (xarray.tests.test_coarsen) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
da = <xarray.DataArray (a: 3, time: 21, x: 4)> Size: 2kB
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        ...ates:
  * time     (time) datetime64[ns] 168B 2000-01-01 2000-01-02 ... 2000-01-21
Dimensions without coordinates: a, x
window = 3, name = 'sum'

    @pytest.mark.parametrize("da", (1, 2), indirect=True)
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "mean", "std", "max"))
    def test_coarsen_da_reduce(da, window, name) -> None:
>       if da.isnull().sum() > 1 and window == 1:

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_coarsen.py#x1B[0m:244: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1857: in sum
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (a: 3, time: 21, x: 4)> Size: 252B
array([[[False, False, False, False],
        [False, False, False...e],
        [False, False, False, False],
        [False, False, False, False],
        [False, False, False, False]]])
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_dataset.TestDataset

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test_broadcast_like (xarray.tests.test_dataset.TestDataset) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
NameError: name 'dims' is not defined
self = <xarray.tests.test_dataset.TestDataset object at 0x7f9c21d189d0>

    def test_broadcast_like(self) -> None:
        original1 = DataArray(
            np.random.randn(5), [("x", range(5))], name="a"
        ).to_dataset()
    
        original2 = DataArray(np.random.randn(6), [("y", range(6))], name="b")
    
>       expected1, expected2 = broadcast(original1, original2)

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_dataset.py#x1B[0m:2091: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/alignment.py#x1B[0m:1215: in broadcast
    result = [_broadcast_helper(arg, exclude, dims_map, common_coords) for arg in args]
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/alignment.py#x1B[0m:1215: in <listcomp>
    result = [_broadcast_helper(arg, exclude, dims_map, common_coords) for arg in args]
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/alignment.py#x1B[0m:1085: in _broadcast_helper
    return cast(T_Alignable, _broadcast_dataset(arg))
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/alignment.py#x1B[0m:1076: in _broadcast_dataset
    data_vars = {k: _set_dims(ds.variables[k]) for k in ds.data_vars}
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/alignment.py#x1B[0m:1076: in <dictcomp>
    data_vars = {k: _set_dims(ds.variables[k]) for k in ds.data_vars}
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/alignment.py#x1B[0m:1065: in _set_dims
    return var.set_dims(var_dims_map)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1389: in set_dims
    return expanded_var.transpose(*dim)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/util/deprecation_helpers.py#x1B[0m:141: in wrapper
    return func(*args, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1332: in transpose
    axes = self.get_axis_num(dim)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (y: 6, x: 5)> Size: 240B
array([[1.76405235, 0.40015721, 0.97873798, 2.2408932 , 1.86755799],
       ... 0.40015721, 0.97873798, 2.2408932 , 1.86755799],
       [1.76405235, 0.40015721, 0.97873798, 2.2408932 , 1.86755799]])
dim = ('x', 'y')

    def get_axis_num(self, dim: Hashable | Iterable[Hashable]) -> int | tuple[int, ...]:
        """Return axis number(s) corresponding to dimension(s) in this array.
    
        Parameters
        ----------
        dim : str or iterable of str
            Dimension name(s) for which to lookup axes.
    
        Returns
        -------
        int or tuple of int
            Axis number or numbers corresponding to the given dimensions.
        """
>       if dims is _default:
#x1B[1m#x1B[31mE       NameError: name 'dims' is not defined#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:799: NameError

Check warning on line 0 in xarray.tests.test_accessor_str

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test_extract_extractall_findall_empty_raises[str] (xarray.tests.test_accessor_str) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
dtype = <class 'numpy.str_'>

    def test_extract_extractall_findall_empty_raises(dtype) -> None:
        pat_str = dtype(r".*")
        pat_re = re.compile(pat_str)
    
        value = xr.DataArray([["a"]], dims=["X", "Y"]).astype(dtype)
    
        with pytest.raises(ValueError, match="No capture groups found in pattern."):
            value.str.extract(pat=pat_str, dim="ZZ")
    
        with pytest.raises(ValueError, match="No capture groups found in pattern."):
            value.str.extract(pat=pat_re, dim="ZZ")
    
        with pytest.raises(ValueError, match="No capture groups found in pattern."):
>           value.str.extractall(pat=pat_str, group_dim="XX", match_dim="YY")

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_accessor_str.py#x1B[0m:565: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:2236: in extractall
    .max()
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1502: in max
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (X: 1, Y: 1)> Size: 8B
array([[2]])
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_coarsen

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test_coarsen_da_reduce[numpy-sum-3-2] (xarray.tests.test_coarsen) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
da = <xarray.DataArray (time: 11)> Size: 88B
array([ 0., nan,  1.,  2., nan,  3.,  4.,  5., nan,  6.,  7.])
Dimensions without coordinates: time
window = 3, name = 'sum'

    @pytest.mark.parametrize("da", (1, 2), indirect=True)
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "mean", "std", "max"))
    def test_coarsen_da_reduce(da, window, name) -> None:
>       if da.isnull().sum() > 1 and window == 1:

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_coarsen.py#x1B[0m:244: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1857: in sum
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (time: 11)> Size: 11B
array([False,  True, False, False,  True, False, False, False,  True,
       False, False])
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_coarsen

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test_coarsen_da_reduce[numpy-sum-4-1] (xarray.tests.test_coarsen) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
da = <xarray.DataArray (a: 3, time: 21, x: 4)> Size: 2kB
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        ...ates:
  * time     (time) datetime64[ns] 168B 2000-01-01 2000-01-02 ... 2000-01-21
Dimensions without coordinates: a, x
window = 4, name = 'sum'

    @pytest.mark.parametrize("da", (1, 2), indirect=True)
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "mean", "std", "max"))
    def test_coarsen_da_reduce(da, window, name) -> None:
>       if da.isnull().sum() > 1 and window == 1:

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_coarsen.py#x1B[0m:244: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1857: in sum
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (a: 3, time: 21, x: 4)> Size: 252B
array([[[False, False, False, False],
        [False, False, False...e],
        [False, False, False, False],
        [False, False, False, False],
        [False, False, False, False]]])
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_accessor_str

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test_extract_extractall_findall_empty_raises[bytes] (xarray.tests.test_accessor_str) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
dtype = <class 'numpy.bytes_'>

    def test_extract_extractall_findall_empty_raises(dtype) -> None:
        pat_str = dtype(r".*")
        pat_re = re.compile(pat_str)
    
        value = xr.DataArray([["a"]], dims=["X", "Y"]).astype(dtype)
    
        with pytest.raises(ValueError, match="No capture groups found in pattern."):
            value.str.extract(pat=pat_str, dim="ZZ")
    
        with pytest.raises(ValueError, match="No capture groups found in pattern."):
            value.str.extract(pat=pat_re, dim="ZZ")
    
        with pytest.raises(ValueError, match="No capture groups found in pattern."):
>           value.str.extractall(pat=pat_str, group_dim="XX", match_dim="YY")

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_accessor_str.py#x1B[0m:565: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:2236: in extractall
    .max()
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1502: in max
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (X: 1, Y: 1)> Size: 8B
array([[2]])
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_combine.TestCombineND

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test_concat_once[dim1] (xarray.tests.test_combine.TestCombineND) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
NameError: name 'dims' is not defined
self = <xarray.tests.test_combine.TestCombineND object at 0x7f0c276cc580>
create_combined_ids = <function _create_combined_ids at 0x7f0c27bd00d0>
concat_dim = 'dim1'

    @pytest.mark.parametrize("concat_dim", ["dim1", "new_dim"])
    def test_concat_once(self, create_combined_ids, concat_dim):
        shape = (2,)
        combined_ids = create_combined_ids(shape)
        ds = create_test_data
>       result = _combine_all_along_first_dim(
            combined_ids,
            dim=concat_dim,
            data_vars="all",
            coords="different",
            compat="no_conflicts",
        )

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_combine.py#x1B[0m:284: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/combine.py#x1B[0m:269: in _combine_all_along_first_dim
    new_combined_ids[new_id] = _combine_1d(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/combine.py#x1B[0m:292: in _combine_1d
    combined = concat(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/concat.py#x1B[0m:277: in concat
    return _dataset_concat(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/concat.py#x1B[0m:667: in _dataset_concat
    combined_var = concat_vars(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:2992: in concat
    return Variable.concat(variables, dim, positions, shortcut, combine_attrs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1741: in concat
    axis = first_var.get_axis_num(dim)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (dim1: 8, dim2: 9)> Size: 576B
array([[ 1.76405235,  0.40015721,  0.97873798,  2.2408932 ,  1.8675579...63019835,  0.46278226,
        -0.90729836,  0.0519454 ,  0.72909056,  0.12898291]])
Attributes:
    foo:      variable
dim = 'dim1'

    def get_axis_num(self, dim: Hashable | Iterable[Hashable]) -> int | tuple[int, ...]:
        """Return axis number(s) corresponding to dimension(s) in this array.
    
        Parameters
        ----------
        dim : str or iterable of str
            Dimension name(s) for which to lookup axes.
    
        Returns
        -------
        int or tuple of int
            Axis number or numbers corresponding to the given dimensions.
        """
>       if dims is _default:
#x1B[1m#x1B[31mE       NameError: name 'dims' is not defined#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:799: NameError

Check warning on line 0 in xarray.tests.test_coarsen

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test_coarsen_da_reduce[numpy-sum-4-2] (xarray.tests.test_coarsen) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
da = <xarray.DataArray (time: 11)> Size: 88B
array([ 0., nan,  1.,  2., nan,  3.,  4.,  5., nan,  6.,  7.])
Dimensions without coordinates: time
window = 4, name = 'sum'

    @pytest.mark.parametrize("da", (1, 2), indirect=True)
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "mean", "std", "max"))
    def test_coarsen_da_reduce(da, window, name) -> None:
>       if da.isnull().sum() > 1 and window == 1:

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_coarsen.py#x1B[0m:244: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1857: in sum
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (time: 11)> Size: 11B
array([False,  True, False, False,  True, False, False, False,  True,
       False, False])
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_dataset.TestDataset

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test_align (xarray.tests.test_dataset.TestDataset) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
self = <xarray.tests.test_dataset.TestDataset object at 0x7f9c21d044c0>

    def test_align(self) -> None:
        left = create_test_data()
        right = left.copy(deep=True)
        right["dim3"] = ("dim3", list("cdefghijkl"))
        right["var3"][:-2] = right["var3"][2:].values
        right["var3"][-2:] = np.random.randn(*right["var3"][-2:].shape)
        right["numbers"][:-2] = right["numbers"][2:].values
        right["numbers"][-2:] = -10
    
        intersection = list("cdefghij")
        union = list("abcdefghijkl")
    
        left2, right2 = align(left, right, join="inner")
        assert_array_equal(left2["dim3"], intersection)
        assert_identical(left2, right2)
    
        left2, right2 = align(left, right, join="outer")
    
        assert_array_equal(left2["dim3"], union)
        assert_equal(left2["dim3"].variable, right2["dim3"].variable)
    
        assert_identical(left2.sel(dim3=intersection), right2.sel(dim3=intersection))
>       assert np.isnan(left2["var3"][-2:]).all()

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_dataset.py#x1B[0m:2390: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1358: in all
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (dim3: 2, dim1: 8)> Size: 16B
array([[ True,  True,  True,  True,  True,  True,  True,  True],
       [ True,  True,  True,  True,  True,  True,  True,  True]])
Attributes:
    foo:      variable
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_combine.TestCombineND

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test_concat_once[new_dim] (xarray.tests.test_combine.TestCombineND) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
NameError: name 'dims' is not defined
self = <xarray.tests.test_combine.TestCombineND object at 0x7f0c276cc850>
create_combined_ids = <function _create_combined_ids at 0x7f0c27bd00d0>
concat_dim = 'new_dim'

    @pytest.mark.parametrize("concat_dim", ["dim1", "new_dim"])
    def test_concat_once(self, create_combined_ids, concat_dim):
        shape = (2,)
        combined_ids = create_combined_ids(shape)
        ds = create_test_data
>       result = _combine_all_along_first_dim(
            combined_ids,
            dim=concat_dim,
            data_vars="all",
            coords="different",
            compat="no_conflicts",
        )

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_combine.py#x1B[0m:284: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/combine.py#x1B[0m:269: in _combine_all_along_first_dim
    new_combined_ids[new_id] = _combine_1d(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/combine.py#x1B[0m:292: in _combine_1d
    combined = concat(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/concat.py#x1B[0m:277: in concat
    return _dataset_concat(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/concat.py#x1B[0m:667: in _dataset_concat
    combined_var = concat_vars(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:2992: in concat
    return Variable.concat(variables, dim, positions, shortcut, combine_attrs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1741: in concat
    axis = first_var.get_axis_num(dim)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.Variable (new_dim: 1, dim1: 8, dim2: 9)> Size: 576B
array([[[ 1.76405235,  0.40015721,  0.97873798,  2.2408932...         0.46278226, -0.90729836,  0.0519454 ,  0.72909056,
          0.12898291]]])
Attributes:
    foo:      variable
dim = 'new_dim'

    def get_axis_num(self, dim: Hashable | Iterable[Hashable]) -> int | tuple[int, ...]:
        """Return axis number(s) corresponding to dimension(s) in this array.
    
        Parameters
        ----------
        dim : str or iterable of str
            Dimension name(s) for which to lookup axes.
    
        Returns
        -------
        int or tuple of int
            Axis number or numbers corresponding to the given dimensions.
        """
>       if dims is _default:
#x1B[1m#x1B[31mE       NameError: name 'dims' is not defined#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:799: NameError

Check warning on line 0 in xarray.tests.test_coarsen

See this annotation in the file changed.

@github-actions github-actions / Test Results

test_coarsen_da_reduce[numpy-mean-1-1] (xarray.tests.test_coarsen) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
da = <xarray.DataArray (a: 3, time: 21, x: 4)> Size: 2kB
array([[[0.5488135 , 0.71518937, 0.60276338, 0.54488318],
        ...ates:
  * time     (time) datetime64[ns] 168B 2000-01-01 2000-01-02 ... 2000-01-21
Dimensions without coordinates: a, x
window = 1, name = 'mean'

    @pytest.mark.parametrize("da", (1, 2), indirect=True)
    @pytest.mark.parametrize("window", (1, 2, 3, 4))
    @pytest.mark.parametrize("name", ("sum", "mean", "std", "max"))
    def test_coarsen_da_reduce(da, window, name) -> None:
>       if da.isnull().sum() > 1 and window == 1:

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_coarsen.py#x1B[0m:244: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1857: in sum
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (a: 3, time: 21, x: 4)> Size: 252B
array([[[False, False, False, False],
        [False, False, False...e],
        [False, False, False, False],
        [False, False, False, False],
        [False, False, False, False]]])
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError

Check warning on line 0 in xarray.tests.test_accessor_str

See this annotation in the file changed.

@github-actions github-actions / Test Results

test_extract_broadcast[str] (xarray.tests.test_accessor_str) failed 

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s]
Raw output 
AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?
dtype = <class 'numpy.str_'>

    def test_extract_broadcast(dtype) -> None:
        value = xr.DataArray(
            ["a_Xy_0", "ab_xY_10", "abc_Xy_01"],
            dims=["X"],
        ).astype(dtype)
    
        pat_str = xr.DataArray(
            [r"(\w+)_Xy_(\d*)", r"(\w+)_xY_(\d*)"],
            dims=["Y"],
        ).astype(dtype)
        pat_compiled = value.str._re_compile(pat=pat_str)
    
        expected_list = [
            [["a", "0"], ["", ""]],
            [["", ""], ["ab", "10"]],
            [["abc", "01"], ["", ""]],
        ]
        expected = xr.DataArray(expected_list, dims=["X", "Y", "Zz"]).astype(dtype)
    
>       res_str = value.str.extract(pat=pat_str, dim="Zz")

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_accessor_str.py#x1B[0m:862: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/accessor_str.py#x1B[0m:2052: in extract
    .max()
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_aggregations.py#x1B[0m:1502: in max
    return self.reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:3831: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/variable.py#x1B[0m:1668: in reduce
    result = super().reduce(
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:1018: in reduce
    axis_ = _dims_to_axis(self, d, axislike)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

x = <xarray.Variable (Y: 2)> Size: 16B
array([2, 2])
dims = (<Default.token: 0>,), axis = None

    def _dims_to_axis(
        x: NamedArray[Any, Any], dims: _Dim | _Dims | Default, axis: _AxisLike | None
    ) -> _AxisLike | None:
        """
        Convert dims to axis indices.
    
        Examples
        --------
        >>> narr = NamedArray(("x", "y"), np.array([[1, 2, 3], [5, 6, 7]]))
        >>> _dims_to_axis(narr, ("y",), None)
        (1,)
        >>> _dims_to_axis(narr, None, 0)
        (0,)
        >>> _dims_to_axis(narr, None, None)
        """
        _assert_either_dim_or_axis(dims, axis)
    
        if dims is not _default:
>           return x._dims_to_axes(dims)
#x1B[1m#x1B[31mE           AttributeError: 'Variable' object has no attribute '_dims_to_axes'. Did you mean: '_dim_to_axis'?#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:150: AttributeError
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