Method test_argmax_dim

xarray/tests/test_dataarray.py:5582–5623

(
        self,
        x: np.ndarray,
        minindex: int | float,
        maxindex: int | float,
        nanindex: int | None,
    )

        "ignore:Behaviour of argmin/argmax with neither dim nor :FutureWarning"
    )
    def test_argmax_dim(
        self,
        x: np.ndarray,
        minindex: int | float,
        maxindex: int | float,
        nanindex: int | None,
    ) -> None:
        ar = xr.DataArray(
            x, dims=["x"], coords={"x": np.arange(x.size) * 4}, attrs=self.attrs
        )
        indarr = xr.DataArray(np.arange(x.size, dtype=np.intp), dims=["x"])

        if np.isnan(maxindex):
            with pytest.raises(ValueError):
                ar.argmax()
            return

        expected0 = {"x": indarr[maxindex]}
        for da in expected0.values():
            da.attrs = self.attrs  # Default keeps attrs for reduction operations
        result0 = ar.argmax(...)
        for key in expected0:
            assert_identical(result0[key], expected0[key])

        result1 = ar.argmax(..., keep_attrs=True)
        expected1 = deepcopy(expected0)
        for da in expected1.values():
            da.attrs = self.attrs
        for key in expected1:
            assert_identical(result1[key], expected1[key])

        result2 = ar.argmax(..., skipna=False)
        if nanindex is not None and ar.dtype.kind != "O":
            expected2 = {"x": indarr.isel(x=nanindex, drop=True)}
            expected2[
                "x"
            ].attrs = self.attrs  # Default keeps attrs for reduction operations
        else:
            expected2 = expected0

        for key in expected2:
            assert_identical(result2[key], expected2[key])


@pytest.mark.parametrize(