TypeError: asarray() got an unexpected keyword argument 'device'
self = <xarray.tests.test_backends.TestZarrDirectoryStore object at 0x7f24db2edfc0>

    def test_write_read_select_write(self) -> None:
        # Test for https://github.com/pydata/xarray/issues/4084
        ds = create_test_data()
    
        # NOTE: using self.roundtrip, which uses open_dataset, will not trigger the bug.
        with self.create_zarr_target() as initial_store:
            ds.to_zarr(initial_store, mode="w", **self.version_kwargs)
            ds1 = xr.open_zarr(initial_store, **self.version_kwargs)
    
            # Combination of where+squeeze triggers error on write.
>           ds_sel = ds1.where(ds1.coords["dim3"] == "a", drop=True).squeeze("dim3")

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_backends.py#x1B[0m:3070: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_typed_ops.py#x1B[0m:295: in __eq__
    return self._binary_op(other, nputils.array_eq)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/nputils.py#x1B[0m:113: in array_eq
    return _ensure_bool_is_ndarray(self == other, self, other)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:469: in __eq__
    return equal(self, asarray(other))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

obj = 'a'

    def asarray(
        obj: duckarray[_ShapeType, _DType] | _ArrayLike,
        /,
        *,
        dtype: _DType | None = None,
        device: _Device | None = None,
        copy: bool | None = None,
        dims: _DimsLike = _default,
    ) -> NamedArray[_ShapeType, _DType] | NamedArray[Any, Any]:
        """
        Create a Named array from an array-like object.
    
        Parameters
        ----------
        dims : str or iterable of str
            Name(s) of the dimension(s).
        data : T_DuckArray or ArrayLike
            The actual data that populates the array. Should match the
            shape specified by `dims`.
        attrs : dict, optional
            A dictionary containing any additional information or
            attributes you want to store with the array.
            Default is None, meaning no attributes will be stored.
        """
        data = obj
        if isinstance(data, NamedArray):
            if copy:
                return data.copy()
            else:
                return data
    
        # TODO: dask.array.ma.MaskedArray also exists, better way?
        if isinstance(data, np.ma.MaskedArray):
            mask = np.ma.getmaskarray(data)  # type: ignore[no-untyped-call]
            if mask.any():
                # TODO: requires refactoring/vendoring xarray.core.dtypes and
                # xarray.core.duck_array_ops
                raise NotImplementedError("MaskedArray is not supported yet")
    
            _dims = _infer_dims(data.shape, dims)
            return NamedArray(_dims, data)
    
        if isinstance(data, _arrayfunction_or_api):
            _dims = _infer_dims(data.shape, dims)
            return NamedArray(_dims, data)
    
        if isinstance(data, tuple):
            _data = to_0d_object_array(data)
            _dims = _infer_dims(_data.shape, dims)
            return NamedArray(_dims, _data)
    
        # validate whether the data is valid data types.
>       _data = np.asarray(data, dtype=dtype, device=device, copy=copy)
#x1B[1m#x1B[31mE       TypeError: asarray() got an unexpected keyword argument 'device'#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/_array_api/_creation_functions.py#x1B[0m:144: TypeError

TypeError: asarray() got an unexpected keyword argument 'device'
self = <xarray.tests.test_backends.TestZarrWriteEmpty object at 0x7f24d98f55a0>

    def test_isel_dataarray(self) -> None:
        # Make sure isel works lazily. GH:issue:1688
        in_memory = create_test_data()
        with self.roundtrip(in_memory) as on_disk:
>           expected = in_memory.isel(dim2=in_memory["dim2"] < 3)

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_backends.py#x1B[0m:800: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_typed_ops.py#x1B[0m:283: in __lt__
    return self._binary_op(other, operator.lt)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:530: in __lt__
    return less(self, asarray(other))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

obj = 3

    def asarray(
        obj: duckarray[_ShapeType, _DType] | _ArrayLike,
        /,
        *,
        dtype: _DType | None = None,
        device: _Device | None = None,
        copy: bool | None = None,
        dims: _DimsLike = _default,
    ) -> NamedArray[_ShapeType, _DType] | NamedArray[Any, Any]:
        """
        Create a Named array from an array-like object.
    
        Parameters
        ----------
        dims : str or iterable of str
            Name(s) of the dimension(s).
        data : T_DuckArray or ArrayLike
            The actual data that populates the array. Should match the
            shape specified by `dims`.
        attrs : dict, optional
            A dictionary containing any additional information or
            attributes you want to store with the array.
            Default is None, meaning no attributes will be stored.
        """
        data = obj
        if isinstance(data, NamedArray):
            if copy:
                return data.copy()
            else:
                return data
    
        # TODO: dask.array.ma.MaskedArray also exists, better way?
        if isinstance(data, np.ma.MaskedArray):
            mask = np.ma.getmaskarray(data)  # type: ignore[no-untyped-call]
            if mask.any():
                # TODO: requires refactoring/vendoring xarray.core.dtypes and
                # xarray.core.duck_array_ops
                raise NotImplementedError("MaskedArray is not supported yet")
    
            _dims = _infer_dims(data.shape, dims)
            return NamedArray(_dims, data)
    
        if isinstance(data, _arrayfunction_or_api):
            _dims = _infer_dims(data.shape, dims)
            return NamedArray(_dims, data)
    
        if isinstance(data, tuple):
            _data = to_0d_object_array(data)
            _dims = _infer_dims(_data.shape, dims)
            return NamedArray(_dims, _data)
    
        # validate whether the data is valid data types.
>       _data = np.asarray(data, dtype=dtype, device=device, copy=copy)
#x1B[1m#x1B[31mE       TypeError: asarray() got an unexpected keyword argument 'device'#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/_array_api/_creation_functions.py#x1B[0m:144: TypeError

TypeError: asarray() got an unexpected keyword argument 'device'
self = <xarray.tests.test_backends.TestZarrWriteEmpty object at 0x7f24d98f6a40>

    def test_append_overwrite_values(self) -> None:
        # regression for GH1215
        data = create_test_data()
        with create_tmp_file(allow_cleanup_failure=False) as tmp_file:
            self.save(data, tmp_file, mode="w")
            data["var2"][:] = -999
>           data["var9"] = data["var2"] * 3

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_backends.py#x1B[0m:1346: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_typed_ops.py#x1B[0m:253: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:545: in __mul__
    return multiply(self, asarray(other))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

obj = 3

    def asarray(
        obj: duckarray[_ShapeType, _DType] | _ArrayLike,
        /,
        *,
        dtype: _DType | None = None,
        device: _Device | None = None,
        copy: bool | None = None,
        dims: _DimsLike = _default,
    ) -> NamedArray[_ShapeType, _DType] | NamedArray[Any, Any]:
        """
        Create a Named array from an array-like object.
    
        Parameters
        ----------
        dims : str or iterable of str
            Name(s) of the dimension(s).
        data : T_DuckArray or ArrayLike
            The actual data that populates the array. Should match the
            shape specified by `dims`.
        attrs : dict, optional
            A dictionary containing any additional information or
            attributes you want to store with the array.
            Default is None, meaning no attributes will be stored.
        """
        data = obj
        if isinstance(data, NamedArray):
            if copy:
                return data.copy()
            else:
                return data
    
        # TODO: dask.array.ma.MaskedArray also exists, better way?
        if isinstance(data, np.ma.MaskedArray):
            mask = np.ma.getmaskarray(data)  # type: ignore[no-untyped-call]
            if mask.any():
                # TODO: requires refactoring/vendoring xarray.core.dtypes and
                # xarray.core.duck_array_ops
                raise NotImplementedError("MaskedArray is not supported yet")
    
            _dims = _infer_dims(data.shape, dims)
            return NamedArray(_dims, data)
    
        if isinstance(data, _arrayfunction_or_api):
            _dims = _infer_dims(data.shape, dims)
            return NamedArray(_dims, data)
    
        if isinstance(data, tuple):
            _data = to_0d_object_array(data)
            _dims = _infer_dims(_data.shape, dims)
            return NamedArray(_dims, _data)
    
        # validate whether the data is valid data types.
>       _data = np.asarray(data, dtype=dtype, device=device, copy=copy)
#x1B[1m#x1B[31mE       TypeError: asarray() got an unexpected keyword argument 'device'#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/_array_api/_creation_functions.py#x1B[0m:144: TypeError

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_accessor_dt.TestDatetimeAccessor object at 0x00000187DEBD8E60>

    def test_total_seconds(self) -> None:
        # Subtract a value in the middle of the range to ensure that some values
        # are negative
>       delta = self.data.time - np.datetime64("2000-01-03")

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_accessor_dt.py#x1B[0m:107: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:250: in __sub__
    return self._binary_op(other, operator.sub)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:590: in __sub__
    from xarray.namedarray._array_api import asarray, subtract
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

TypeError: asarray() got an unexpected keyword argument 'device'
self = <xarray.tests.test_backends.TestZarrWriteEmpty object at 0x7fbe3eaf5ba0>

    def test_write_read_select_write(self) -> None:
        # Test for https://github.com/pydata/xarray/issues/4084
        ds = create_test_data()
    
        # NOTE: using self.roundtrip, which uses open_dataset, will not trigger the bug.
        with self.create_zarr_target() as initial_store:
            ds.to_zarr(initial_store, mode="w", **self.version_kwargs)
            ds1 = xr.open_zarr(initial_store, **self.version_kwargs)
    
            # Combination of where+squeeze triggers error on write.
>           ds_sel = ds1.where(ds1.coords["dim3"] == "a", drop=True).squeeze("dim3")

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/tests/test_backends.py#x1B[0m:3070: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/_typed_ops.py#x1B[0m:295: in __eq__
    return self._binary_op(other, nputils.array_eq)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/core/nputils.py#x1B[0m:113: in array_eq
    return _ensure_bool_is_ndarray(self == other, self, other)
#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/core.py#x1B[0m:469: in __eq__
    return equal(self, asarray(other))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

obj = 'a'

    def asarray(
        obj: duckarray[_ShapeType, _DType] | _ArrayLike,
        /,
        *,
        dtype: _DType | None = None,
        device: _Device | None = None,
        copy: bool | None = None,
        dims: _DimsLike = _default,
    ) -> NamedArray[_ShapeType, _DType] | NamedArray[Any, Any]:
        """
        Create a Named array from an array-like object.
    
        Parameters
        ----------
        dims : str or iterable of str
            Name(s) of the dimension(s).
        data : T_DuckArray or ArrayLike
            The actual data that populates the array. Should match the
            shape specified by `dims`.
        attrs : dict, optional
            A dictionary containing any additional information or
            attributes you want to store with the array.
            Default is None, meaning no attributes will be stored.
        """
        data = obj
        if isinstance(data, NamedArray):
            if copy:
                return data.copy()
            else:
                return data
    
        # TODO: dask.array.ma.MaskedArray also exists, better way?
        if isinstance(data, np.ma.MaskedArray):
            mask = np.ma.getmaskarray(data)  # type: ignore[no-untyped-call]
            if mask.any():
                # TODO: requires refactoring/vendoring xarray.core.dtypes and
                # xarray.core.duck_array_ops
                raise NotImplementedError("MaskedArray is not supported yet")
    
            _dims = _infer_dims(data.shape, dims)
            return NamedArray(_dims, data)
    
        if isinstance(data, _arrayfunction_or_api):
            _dims = _infer_dims(data.shape, dims)
            return NamedArray(_dims, data)
    
        if isinstance(data, tuple):
            _data = to_0d_object_array(data)
            _dims = _infer_dims(_data.shape, dims)
            return NamedArray(_dims, _data)
    
        # validate whether the data is valid data types.
>       _data = np.asarray(data, dtype=dtype, device=device, copy=copy)
#x1B[1m#x1B[31mE       TypeError: asarray() got an unexpected keyword argument 'device'#x1B[0m

#x1B[1m#x1B[31m/home/runner/work/xarray/xarray/xarray/namedarray/_array_api/_creation_functions.py#x1B[0m:144: TypeError

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_accessor_dt.TestDatetimeAccessor object at 0x00000187DEBDB380>

    def test_strftime(self) -> None:
>       assert (
            "2000-01-01 01:00:00" == self.data.time.dt.strftime("%Y-%m-%d %H:%M:%S")[1]
        )

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_accessor_dt.py#x1B[0m:140: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:295: in __eq__
    return self._binary_op(other, nputils.array_eq)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\nputils.py#x1B[0m:113: in array_eq
    return _ensure_bool_is_ndarray(self == other, self, other)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:467: in __eq__
    from xarray.namedarray._array_api import asarray, equal
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestZarrKVStoreV3 object at 0x000001FE11C9C8C0>

    def test_isel_dataarray(self) -> None:
        # Make sure isel works lazily. GH:issue:1688
        in_memory = create_test_data()
        with self.roundtrip(in_memory) as on_disk:
>           expected = in_memory.isel(dim2=in_memory["dim2"] < 3)

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:800: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:283: in __lt__
    return self._binary_op(other, operator.lt)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:528: in __lt__
    from xarray.namedarray._array_api import asarray, less
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestH5NetCDFData object at 0x000001254F141D60>

    def test_isel_dataarray(self) -> None:
        # Make sure isel works lazily. GH:issue:1688
        in_memory = create_test_data()
        with self.roundtrip(in_memory) as on_disk:
>           expected = in_memory.isel(dim2=in_memory["dim2"] < 3)

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:800: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:283: in __lt__
    return self._binary_op(other, operator.lt)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:528: in __lt__
    from xarray.namedarray._array_api import asarray, less
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestZarrKVStoreV3 object at 0x000001FE11CD4F50>

    def test_append_overwrite_values(self) -> None:
        # regression for GH1215
        data = create_test_data()
        with create_tmp_file(allow_cleanup_failure=False) as tmp_file:
            self.save(data, tmp_file, mode="w")
            data["var2"][:] = -999
>           data["var9"] = data["var2"] * 3

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:1346: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:253: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:543: in __mul__
    from xarray.namedarray._array_api import asarray, multiply
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestH5NetCDFData object at 0x000001254F122750>

    def test_append_overwrite_values(self) -> None:
        # regression for GH1215
        data = create_test_data()
        with create_tmp_file(allow_cleanup_failure=False) as tmp_file:
            self.save(data, tmp_file, mode="w")
            data["var2"][:] = -999
>           data["var9"] = data["var2"] * 3

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:1346: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:253: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:543: in __mul__
    from xarray.namedarray._array_api import asarray, multiply
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestH5NetCDFData object at 0x000001254F1229C0>

    def test_append_with_invalid_dim_raises(self) -> None:
        data = create_test_data()
        with create_tmp_file(allow_cleanup_failure=False) as tmp_file:
            self.save(data, tmp_file, mode="w")
>           data["var9"] = data["var2"] * 3

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:1355: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:253: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:543: in __mul__
    from xarray.namedarray._array_api import asarray, multiply
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
arrays = (<xarray.DataArray (x: 2, y: 3)> Size: 48B
array([[ 1.,  2.,  3.],
       [ 4.,  5., nan]])
Coordinates:
  * x        ... nan]], dtype=array_api_strict.float64)
Coordinates:
  * x        (x) int32 8B 10 20
Dimensions without coordinates: y)

    def test_arithmetic(arrays: tuple[xr.DataArray, xr.DataArray]) -> None:
        np_arr, xp_arr = arrays
>       expected = np_arr + 7

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_array_api.py#x1B[0m:32: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:247: in __add__
    return self._binary_op(other, operator.add)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:424: in __add__
    from xarray.namedarray._array_api import add, asarray
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
arrays = (<xarray.DataArray (x: 2, y: 3)> Size: 48B
array([[ 1.,  2.,  3.],
       [ 4.,  5., nan]])
Coordinates:
  * x        ... nan]], dtype=array_api_strict.float64)
Coordinates:
  * x        (x) int32 8B 10 20
Dimensions without coordinates: y)

    def test_broadcast_during_arithmetic(arrays: tuple[xr.DataArray, xr.DataArray]) -> None:
        np_arr, xp_arr = arrays
        np_arr2 = xr.DataArray(np.array([1.0, 2.0]), dims="x")
        xp_arr2 = xr.DataArray(xp.asarray([1.0, 2.0]), dims="x")
    
>       expected = np_arr * np_arr2

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_array_api.py#x1B[0m:81: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:253: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:543: in __mul__
    from xarray.namedarray._array_api import asarray, multiply
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestH5NetCDFData object at 0x000001254F0E7E90>

    def test_write_groups(self) -> None:
        data1 = create_test_data()
>       data2 = data1 * 2

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:1493: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:41: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataset.py#x1B[0m:7778: in _binary_op
    ds = self._calculate_binary_op(g, other, join=align_type)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataset.py#x1B[0m:7847: in _calculate_binary_op
    new_vars = {k: f(self.variables[k], other_variable) for k in self.data_vars}
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:543: in __mul__
    from xarray.namedarray._array_api import asarray, multiply
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

AttributeError: module 'numpy' has no attribute 'bool'.
`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations. Did you mean: 'bool_'?
func = 'assert_duckarray_equal'

    @pytest.mark.parametrize(
        "func",
        [
            "assert_equal",
            "assert_identical",
            "assert_allclose",
            "assert_duckarray_equal",
            "assert_duckarray_allclose",
        ],
    )
    def test_ensure_warnings_not_elevated(func) -> None:
        # make sure warnings are not elevated to errors in the assertion functions
        # e.g. by @pytest.mark.filterwarnings("error")
        # see https://github.com/pydata/xarray/pull/4760#issuecomment-774101639
    
        # define a custom Variable class that raises a warning in assert_*
        class WarningVariable(xr.Variable):
            @property  # type: ignore[misc]
            def dims(self):
                warnings.warn("warning in test")
                return super().dims
    
            def __array__(self, dtype=None, copy=None):
                warnings.warn("warning in test")
                return super().__array__()
    
        a = WarningVariable("x", [1])
        b = WarningVariable("x", [2])
    
        with warnings.catch_warnings(record=True) as w:
            # elevate warnings to errors
            warnings.filterwarnings("error")
            with pytest.raises(AssertionError):
>               getattr(xr.testing, func)(a, b)

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_assertions.py#x1B[0m:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\duck_array_ops.py#x1B[0m:314: in array_equiv
    flag_array = (arr1 == arr2) | (isnull(arr1) & isnull(arr2))
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:467: in __eq__
    from xarray.namedarray._array_api import asarray, equal
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
#x1B[1m#x1B[31mE           AttributeError: module 'numpy' has no attribute 'bool'.#x1B[0m
#x1B[1m#x1B[31mE           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.#x1B[0m
#x1B[1m#x1B[31mE           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:#x1B[0m
#x1B[1m#x1B[31mE               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations. Did you mean: 'bool_'?#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:324: AttributeError

AttributeError: module 'numpy' has no attribute 'bool'.
`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations. Did you mean: 'bool_'?
func = 'assert_duckarray_allclose'

    @pytest.mark.parametrize(
        "func",
        [
            "assert_equal",
            "assert_identical",
            "assert_allclose",
            "assert_duckarray_equal",
            "assert_duckarray_allclose",
        ],
    )
    def test_ensure_warnings_not_elevated(func) -> None:
        # make sure warnings are not elevated to errors in the assertion functions
        # e.g. by @pytest.mark.filterwarnings("error")
        # see https://github.com/pydata/xarray/pull/4760#issuecomment-774101639
    
        # define a custom Variable class that raises a warning in assert_*
        class WarningVariable(xr.Variable):
            @property  # type: ignore[misc]
            def dims(self):
                warnings.warn("warning in test")
                return super().dims
    
            def __array__(self, dtype=None, copy=None):
                warnings.warn("warning in test")
                return super().__array__()
    
        a = WarningVariable("x", [1])
        b = WarningVariable("x", [2])
    
        with warnings.catch_warnings(record=True) as w:
            # elevate warnings to errors
            warnings.filterwarnings("error")
            with pytest.raises(AssertionError):
>               getattr(xr.testing, func)(a, b)

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_assertions.py#x1B[0m:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\testing\assertions.py#x1B[0m:294: in _format_message
    diff = x - y
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:590: in __sub__
    from xarray.namedarray._array_api import asarray, subtract
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
            warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    
        if attr in __former_attrs__:
>           raise AttributeError(__former_attrs__[attr])
#x1B[1m#x1B[31mE           AttributeError: module 'numpy' has no attribute 'bool'.#x1B[0m
#x1B[1m#x1B[31mE           `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.#x1B[0m
#x1B[1m#x1B[31mE           The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:#x1B[0m
#x1B[1m#x1B[31mE               https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations. Did you mean: 'bool_'?#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:324: AttributeError

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestNetCDF4Data object at 0x00000187DEDC6390>

    def test_isel_dataarray(self) -> None:
        # Make sure isel works lazily. GH:issue:1688
        in_memory = create_test_data()
        with self.roundtrip(in_memory) as on_disk:
>           expected = in_memory.isel(dim2=in_memory["dim2"] < 3)

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:800: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:283: in __lt__
    return self._binary_op(other, operator.lt)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:528: in __lt__
    from xarray.namedarray._array_api import asarray, less
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestH5NetCDFFileObject object at 0x000001254F06E7B0>

    def test_isel_dataarray(self) -> None:
        # Make sure isel works lazily. GH:issue:1688
        in_memory = create_test_data()
        with self.roundtrip(in_memory) as on_disk:
>           expected = in_memory.isel(dim2=in_memory["dim2"] < 3)

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:800: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:283: in __lt__
    return self._binary_op(other, operator.lt)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:528: in __lt__
    from xarray.namedarray._array_api import asarray, less
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestZarrKVStoreV3 object at 0x000001FE11CDDB50>

    def test_write_read_select_write(self) -> None:
        # Test for https://github.com/pydata/xarray/issues/4084
        ds = create_test_data()
    
        # NOTE: using self.roundtrip, which uses open_dataset, will not trigger the bug.
        with self.create_zarr_target() as initial_store:
            ds.to_zarr(initial_store, mode="w", **self.version_kwargs)
            ds1 = xr.open_zarr(initial_store, **self.version_kwargs)
    
            # Combination of where+squeeze triggers error on write.
>           ds_sel = ds1.where(ds1.coords["dim3"] == "a", drop=True).squeeze("dim3")

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:3070: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:295: in __eq__
    return self._binary_op(other, nputils.array_eq)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\nputils.py#x1B[0m:113: in array_eq
    return _ensure_bool_is_ndarray(self == other, self, other)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:467: in __eq__
    from xarray.namedarray._array_api import asarray, equal
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestH5NetCDFFileObject object at 0x000001254F12B5C0>

    def test_append_overwrite_values(self) -> None:
        # regression for GH1215
        data = create_test_data()
        with create_tmp_file(allow_cleanup_failure=False) as tmp_file:
            self.save(data, tmp_file, mode="w")
            data["var2"][:] = -999
>           data["var9"] = data["var2"] * 3

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:1346: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:253: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:543: in __mul__
    from xarray.namedarray._array_api import asarray, multiply
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestH5NetCDFFileObject object at 0x000001254F12A930>

    def test_append_with_invalid_dim_raises(self) -> None:
        data = create_test_data()
        with create_tmp_file(allow_cleanup_failure=False) as tmp_file:
            self.save(data, tmp_file, mode="w")
>           data["var9"] = data["var2"] * 3

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:1355: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:253: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:543: in __mul__
    from xarray.namedarray._array_api import asarray, multiply
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestNetCDF4Data object at 0x00000187DEDDA0C0>

    def test_append_overwrite_values(self) -> None:
        # regression for GH1215
        data = create_test_data()
        with create_tmp_file(allow_cleanup_failure=False) as tmp_file:
            self.save(data, tmp_file, mode="w")
            data["var2"][:] = -999
>           data["var9"] = data["var2"] * 3

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:1346: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:253: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataarray.py#x1B[0m:4749: in _binary_op
    f(self.variable, other_variable_or_arraylike)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:543: in __mul__
    from xarray.namedarray._array_api import asarray, multiply
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning

FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
self = <xarray.tests.test_backends.TestH5NetCDFFileObject object at 0x000001254F124860>

    def test_write_groups(self) -> None:
        data1 = create_test_data()
>       data2 = data1 * 2

#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\tests\test_backends.py#x1B[0m:1493: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\_typed_ops.py#x1B[0m:41: in __mul__
    return self._binary_op(other, operator.mul)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataset.py#x1B[0m:7778: in _binary_op
    ds = self._calculate_binary_op(g, other, join=align_type)
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\core\dataset.py#x1B[0m:7847: in _calculate_binary_op
    new_vars = {k: f(self.variables[k], other_variable) for k in self.data_vars}
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\core.py#x1B[0m:543: in __mul__
    from xarray.namedarray._array_api import asarray, multiply
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\__init__.py#x1B[0m:69: in <module>
    from xarray.namedarray._array_api._dtypes import (
#x1B[1m#x1B[31mD:\a\xarray\xarray\xarray\namedarray\_array_api\_dtypes.py#x1B[0m:18: in <module>
    bool = _xp.bool
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

attr = 'bool'

    def __getattr__(attr):
        # Warn for expired attributes, and return a dummy function
        # that always raises an exception.
        import warnings
        import math
        try:
            msg = __expired_functions__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
    
            def _expired(*args, **kwds):
                raise RuntimeError(msg)
    
            return _expired
    
        # Emit warnings for deprecated attributes
        try:
            val, msg = __deprecated_attrs__[attr]
        except KeyError:
            pass
        else:
            warnings.warn(msg, DeprecationWarning, stacklevel=2)
            return val
    
        if attr in __future_scalars__:
            # And future warnings for those that will change, but also give
            # the AttributeError
>           warnings.warn(
                f"In the future `np.{attr}` will be defined as the "
                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
#x1B[1m#x1B[31mE           FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.#x1B[0m

#x1B[1m#x1B[31mC:\Users\runneradmin\micromamba\envs\xarray-tests\Lib\site-packages\numpy\__init__.py#x1B[0m:319: FutureWarning