Method test_manual_chunk

xarray/tests/test_backends.py:2785–2825

(self)

    @requires_dask
    @pytest.mark.filterwarnings("ignore:The specified chunks separate:UserWarning")
    def test_manual_chunk(self) -> None:
        original = create_test_data().chunk({"dim1": 3, "dim2": 4, "dim3": 3})

        # Using chunks = None should return non-chunked arrays
        open_kwargs: dict[str, Any] = {"chunks": None}
        with self.roundtrip(original, open_kwargs=open_kwargs) as actual:
            for k, v in actual.variables.items():
                # only index variables should be in memory
                assert v._in_memory == (k in actual.dims)
                # there should be no chunks
                assert v.chunks is None

        # uniform arrays
        for i in range(2, 6):
            rechunked = original.chunk(chunks=i)
            open_kwargs = {"chunks": i}
            with self.roundtrip(original, open_kwargs=open_kwargs) as actual:
                for k, v in actual.variables.items():
                    # only index variables should be in memory
                    assert v._in_memory == (k in actual.dims)
                    # chunk size should be the same as rechunked
                    assert v.chunks == rechunked[k].chunks

        chunks = {"dim1": 2, "dim2": 3, "dim3": 5}
        rechunked = original.chunk(chunks=chunks)

        open_kwargs = {
            "chunks": chunks,
            "backend_kwargs": {"overwrite_encoded_chunks": True},
        }
        with self.roundtrip(original, open_kwargs=open_kwargs) as actual:
            for k, v in actual.variables.items():
                assert v.chunks == rechunked[k].chunks

            with self.roundtrip(actual) as auto:
                # encoding should have changed
                for k, v in actual.variables.items():
                    assert v.chunks == rechunked[k].chunks

                assert_identical(actual, auto)
                assert_identical(actual.load(), auto.load())

    def test_unlimited_dims_encoding_is_ignored(self) -> None:
        ds = Dataset({"x": np.arange(10)})