Function test_concat_dataarray

xarray/tests/test_units.py:934–976

(variant, unit, error, dtype)

    ),
)
def test_concat_dataarray(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.linspace(0, 5, 10).astype(dtype) * data_unit1
    array2 = np.linspace(-5, 0, 5).astype(dtype) * data_unit2

    x1 = np.arange(5, 15) * dim_unit1
    x2 = np.arange(5) * dim_unit2

    u1 = np.linspace(1, 2, 10).astype(dtype) * coord_unit1
    u2 = np.linspace(0, 1, 5).astype(dtype) * coord_unit2

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

    if error is not None:
        with pytest.raises(error):
            xr.concat([arr1, arr2], dim="x")

        return

    units = extract_units(arr1)
    expected = attach_units(
        xr.concat(
            [strip_units(arr1), strip_units(convert_units(arr2, units))], dim="x"
        ),
        units,
    )
    actual = xr.concat([arr1, arr2], dim="x")

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


@pytest.mark.parametrize(