def test_view_mixin_copies_data(adata, array_type: type, attr):
    N = 100
    adata = ad.AnnData(
        obs=pd.DataFrame(index=np.arange(N)), var=pd.DataFrame(index=np.arange(N))
    )

    X = array_type(sparse.eye(N, N).multiply(np.arange(1, N + 1)))
    if attr == "X":
        adata.X = X
    else:
        getattr(adata, attr)["arr"] = X

    view = adata[:50]

    if attr == "X":
        arr_view = view.X
    else:
        arr_view = getattr(view, attr)["arr"]

    arr_view_copy = arr_view.copy()

    if sparse.issparse(X):
        assert not np.shares_memory(arr_view.indices, arr_view_copy.indices)
        assert not np.shares_memory(arr_view.indptr, arr_view_copy.indptr)
        assert not np.shares_memory(arr_view.data, arr_view_copy.data)

        arr_view_copy.data[0] = -5
        assert not np.array_equal(arr_view_copy.data, arr_view.data)
    else:
        assert not np.shares_memory(arr_view, arr_view_copy)

        arr_view_copy[0, 0] = -5
        assert not np.array_equal(arr_view_copy, arr_view)
def __init__(self, root, split="train", transforms=None):
        super().__init__(root=root, transforms=transforms)

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

        root = Path(root) / "FlyingChairs"
        images = sorted(glob(str(root / "data" / "*.ppm")))
        flows = sorted(glob(str(root / "data" / "*.flo")))

        split_file_name = "FlyingChairs_train_val.txt"

        if not os.path.exists(root / split_file_name):
            raise FileNotFoundError(
                "The FlyingChairs_train_val.txt file was not found - please download it from the dataset page (see docstring)."
            )

        split_list = np.loadtxt(str(root / split_file_name), dtype=np.int32)
        for i in range(len(flows)):
            split_id = split_list[i]
            if (split == "train" and split_id == 1) or (split == "val" and split_id == 2):
                self._flow_list += [flows[i]]
                self._image_list += [[images[2 * i], images[2 * i + 1]]]