Method test_reindex

xarray/tests/test_dataset.py:2319–2396

(self)

        assert_identical(actual, expected)

    def test_reindex(self) -> None:
        data = create_test_data()
        assert_identical(data, data.reindex())

        expected = data.assign_coords(dim1=data["dim1"])
        actual = data.reindex(dim1=data["dim1"])
        assert_identical(actual, expected)

        actual = data.reindex(dim1=data["dim1"].values)
        assert_identical(actual, expected)

        actual = data.reindex(dim1=data["dim1"].to_index())
        assert_identical(actual, expected)

        with pytest.raises(
            ValueError, match=r"cannot reindex or align along dimension"
        ):
            data.reindex(dim1=data["dim1"][:5])

        expected = data.isel(dim2=slice(5))
        actual = data.reindex(dim2=data["dim2"][:5])
        assert_identical(actual, expected)

        # test dict-like argument
        actual = data.reindex({"dim2": data["dim2"]})
        expected = data
        assert_identical(actual, expected)
        with pytest.raises(ValueError, match=r"cannot specify both"):
            data.reindex({"x": 0}, x=0)
        with pytest.raises(ValueError, match=r"dictionary"):
            data.reindex("foo")  # type: ignore[arg-type]

        # invalid dimension
        # TODO: (benbovy - explicit indexes): uncomment?
        # --> from reindex docstrings: "any mismatched dimension is simply ignored"
        # with pytest.raises(ValueError, match=r"indexer keys.*not correspond.*"):
        #     data.reindex(invalid=0)

        # out of order
        expected = data.sel(dim2=data["dim2"][:5:-1])
        actual = data.reindex(dim2=data["dim2"][:5:-1])
        assert_identical(actual, expected)

        # multiple fill values
        expected = data.reindex(dim2=[0.1, 2.1, 3.1, 4.1]).assign(
            var1=lambda ds: ds.var1.copy(data=[[-10, -10, -10, -10]] * len(ds.dim1)),
            var2=lambda ds: ds.var2.copy(data=[[-20, -20, -20, -20]] * len(ds.dim1)),
        )
        actual = data.reindex(
            dim2=[0.1, 2.1, 3.1, 4.1], fill_value={"var1": -10, "var2": -20}
        )
        assert_identical(actual, expected)
        # use the default value
        expected = data.reindex(dim2=[0.1, 2.1, 3.1, 4.1]).assign(
            var1=lambda ds: ds.var1.copy(data=[[-10, -10, -10, -10]] * len(ds.dim1)),
            var2=lambda ds: ds.var2.copy(
                data=[[np.nan, np.nan, np.nan, np.nan]] * len(ds.dim1)
            ),