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 normalize(patterns, default, root, cwd, auditor, warn):
            kp = _donormalize(patterns, default, root, cwd, auditor, warn)
            kindpats = []
            for kind, pats, source in kp:
                if kind not in ("re", "relre"):  # regex can't be normalized
                    p = pats
                    pats = dsnormalize(pats)

                    # Preserve the original to handle a case only rename.
                    if p != pats and p in dirstate:
                        kindpats.append((kind, p, source))

                kindpats.append((kind, pats, source))
            return kindpats