Function test_multiple_groupers_mixed

xarray/tests/test_groupby.py:3094–3135

(use_flox: bool, shuffle: bool)

@pytest.mark.parametrize("use_flox", [True, False])
@pytest.mark.parametrize("shuffle", [True, False])
def test_multiple_groupers_mixed(use_flox: bool, shuffle: bool) -> None:
    # This groupby has missing groups
    ds = xr.Dataset(
        {"foo": (("x", "y"), np.arange(12).reshape((4, 3)))},
        coords={"x": [10, 20, 30, 40], "letters": ("x", list("abba"))},
    )
    groupers: dict[str, Grouper] = dict(
        x=BinGrouper(bins=[5, 15, 25]), letters=UniqueGrouper()
    )
    gb = ds.groupby(groupers)
    if shuffle:
        gb = gb.shuffle_to_chunks().groupby(groupers)
    expected_data = np.array(
        [
            [[0.0, np.nan], [np.nan, 3.0]],
            [[1.0, np.nan], [np.nan, 4.0]],
            [[2.0, np.nan], [np.nan, 5.0]],
        ]
    )
    expected = xr.Dataset(
        {"foo": (("y", "x_bins", "letters"), expected_data)},
        coords={
            "x_bins": (
                "x_bins",
                pd.IntervalIndex.from_breaks([5, 15, 25], closed="right"),
            ),
            "letters": ("letters", np.array(["a", "b"], dtype=object)),
        },
    )
    with xr.set_options(use_flox=use_flox):
        actual = gb.sum()
    assert_identical(actual, expected)

    # assert_identical(
    #     b.groupby(['x', 'y']).apply(lambda x: x - x.mean()),
    #     b - b.mean("z"),
    # )

    # gb = square.groupby(x=UniqueGrouper(), y=UniqueGrouper())
    # gb - gb.mean()

    # ------


@requires_flox_0_9_12