Function test_combine_nested

xarray/tests/test_units.py:828–908

(variant, unit, error, dtype)

    ),
)
def test_combine_nested(variant, unit, error, 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.zeros(shape=(2, 3), dtype=dtype) * data_unit1
    array2 = np.zeros(shape=(2, 3), dtype=dtype) * data_unit1

    x = np.arange(1, 4) * 10 * dim_unit1
    y = np.arange(2) * dim_unit1
    z = np.arange(3) * coord_unit1

    ds1 = xr.Dataset(
        data_vars={"a": (("y", "x"), array1), "b": (("y", "x"), array2)},
        coords={"x": x, "y": y, "z": ("x", z)},
    )
    ds2 = xr.Dataset(
        data_vars={
            "a": (("y", "x"), np.ones_like(array1) * data_unit2),
            "b": (("y", "x"), np.ones_like(array2) * data_unit2),
        },
        coords={
            "x": np.arange(3) * dim_unit2,
            "y": np.arange(2, 4) * dim_unit2,
            "z": ("x", np.arange(-3, 0) * coord_unit2),
        },
    )
    ds3 = xr.Dataset(
        data_vars={
            "a": (("y", "x"), np.full_like(array1, fill_value=np.nan) * data_unit2),
            "b": (("y", "x"), np.full_like(array2, fill_value=np.nan) * data_unit2),
        },
        coords={
            "x": np.arange(3, 6) * dim_unit2,
            "y": np.arange(4, 6) * dim_unit2,
            "z": ("x", np.arange(3, 6) * coord_unit2),
        },
    )
    ds4 = xr.Dataset(
        data_vars={
            "a": (("y", "x"), -1 * np.ones_like(array1) * data_unit2),
            "b": (("y", "x"), -1 * np.ones_like(array2) * data_unit2),
        },
        coords={
            "x": np.arange(6, 9) * dim_unit2,
            "y": np.arange(6, 8) * dim_unit2,
            "z": ("x", np.arange(6, 9) * coord_unit2),
        },
    )