Function test_interpolate_pd_compat_non_uniform_index

xarray/tests/test_missing.py:156–181

()

@requires_scipy
def test_interpolate_pd_compat_non_uniform_index():
    shapes = [(8, 8), (1, 20), (20, 1), (100, 100)]
    frac_nans = [0, 0.5, 1]
    methods = ["time", "index", "values"]

    for shape, frac_nan, method in itertools.product(shapes, frac_nans, methods):
        da, df = make_interpolate_example_data(shape, frac_nan, non_uniform=True)
        for dim in ["time", "x"]:
            if method == "time" and dim != "time":
                continue
            actual = da.interpolate_na(
                method="linear", dim=dim, use_coordinate=True, fill_value=np.nan
            )
            expected = df.interpolate(
                method=method,
                axis=da.get_axis_num(dim),
            )

            # Note, Pandas does some odd things with the left/right fill_value
            # for the linear methods. This next line inforces the xarray
            # fill_value convention on the pandas output. Therefore, this test
            # only checks that interpolated values are the same (not nans)
            expected_values = expected.values.copy()
            expected_values[pd.isnull(actual.values)] = np.nan

            np.testing.assert_allclose(actual.values, expected_values)


@requires_scipy