Method test_groupby_bins

xarray/tests/test_groupby.py:1711–1748

(
        self,
        coords: np.typing.ArrayLike,
        use_flox: bool,
        cut_kwargs: dict,
    )

        ),
    )
    def test_groupby_bins(
        self,
        coords: np.typing.ArrayLike,
        use_flox: bool,
        cut_kwargs: dict,
    ) -> None:
        array = DataArray(
            np.arange(4), dims="dim_0", coords={"dim_0": coords}, name="a"
        )
        # the first value should not be part of any group ("right" binning)
        array[0] = 99
        # bins follow conventions for pandas.cut
        # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html
        bins = [0, 1.5, 5]

        df = array.to_dataframe()
        df["dim_0_bins"] = pd.cut(array["dim_0"], bins, **cut_kwargs)  # type: ignore[call-overload]

        expected_df = df.groupby("dim_0_bins", observed=True).sum()
        expected = expected_df.to_xarray().assign_coords(
            dim_0_bins=cast(pd.CategoricalIndex, expected_df.index).categories
        )["a"]

        with xr.set_options(use_flox=use_flox):
            gb = array.groupby_bins("dim_0", bins=bins, **cut_kwargs)
            shuffled = gb.shuffle_to_chunks().groupby_bins(
                "dim_0", bins=bins, **cut_kwargs
            )
            actual = gb.sum()
            assert_identical(expected, actual)
            assert_identical(expected, shuffled.sum())

            actual = gb.map(lambda x: x.sum())
            assert_identical(expected, actual)
            assert_identical(expected, shuffled.map(lambda x: x.sum()))

            # make sure original array dims are unchanged
            assert len(array.dim_0) == 4

    def test_groupby_bins_ellipsis(self) -> None:
        da = xr.DataArray(np.ones((2, 3, 4)))