def test_deepcopy_subset(adata, spmat: type):
    adata.obsp["arr"] = np.zeros((adata.n_obs, adata.n_obs))
    adata.obsp["spmat"] = spmat((adata.n_obs, adata.n_obs))

    adata = deepcopy(adata[:10].copy())

    assert isinstance(adata.obsp["arr"], np.ndarray)
    assert not isinstance(adata.obsp["arr"], ArrayView)
    np.testing.assert_array_equal(adata.obsp["arr"].shape, (10, 10))

    assert isinstance(adata.obsp["spmat"], spmat)
    assert not isinstance(
        adata.obsp["spmat"],
        SparseCSRView if spmat is sparse.csr_matrix else SparseCSCView,
    )
    np.testing.assert_array_equal(adata.obsp["spmat"].shape, (10, 10))
def __init__(self, root, split="train", transforms=None):
        super().__init__(root=root, transforms=transforms)

        verify_str_arg(split, "split", valid_values=("train", "test"))

        root = Path(root) / "KittiFlow" / (split + "ing")
        images1 = sorted(glob(str(root / "image_2" / "*_10.png")))
        images2 = sorted(glob(str(root / "image_2" / "*_11.png")))

        if not images1 or not images2:
            raise FileNotFoundError(
                "Could not find the Kitti flow images. Please make sure the directory structure is correct."
            )

        for img1, img2 in zip(images1, images2):
            self._image_list += [[img1, img2]]

        if split == "train":
            self._flow_list = sorted(glob(str(root / "flow_occ" / "*_10.png")))