Method test_broadcast_like

xarray/tests/test_units.py:3102–3140

(self, variant, unit, dtype)

        ),
    )
    def test_broadcast_like(self, variant, unit, dtype):
        original_unit = unit_registry.m

        variants = {
            "data": ((original_unit, unit), (1, 1), (1, 1)),
            "dims": ((1, 1), (original_unit, unit), (1, 1)),
            "coords": ((1, 1), (1, 1), (original_unit, unit)),
        }
        (
            (data_unit1, data_unit2),
            (dim_unit1, dim_unit2),
            (coord_unit1, coord_unit2),
        ) = variants[variant]

        array1 = np.linspace(1, 2, 2 * 1).reshape(2, 1).astype(dtype) * data_unit1
        array2 = np.linspace(0, 1, 2 * 3).reshape(2, 3).astype(dtype) * data_unit2

        x1 = np.arange(2) * dim_unit1
        x2 = np.arange(2) * dim_unit2
        y1 = np.array([0]) * dim_unit1
        y2 = np.arange(3) * dim_unit2

        u1 = np.linspace(0, 1, 2) * coord_unit1
        u2 = np.linspace(0, 1, 2) * coord_unit2

        arr1 = xr.DataArray(
            data=array1, coords={"x": x1, "y": y1, "u": ("x", u1)}, dims=("x", "y")
        )
        arr2 = xr.DataArray(
            data=array2, coords={"x": x2, "y": y2, "u": ("x", u2)}, dims=("x", "y")
        )

        expected = attach_units(
            strip_units(arr1).broadcast_like(strip_units(arr2)), extract_units(arr1)
        )
        actual = arr1.broadcast_like(arr2)

        assert_units_equal(expected, actual)
        assert_identical(expected, actual)

    @pytest.mark.parametrize(
        "unit",