def test_negative_scalar_index(adata, index: int, obs: bool):
    pos_index = index + (adata.n_obs if obs else adata.n_vars)

    if obs:
        adata_pos_subset = adata[pos_index]
        adata_neg_subset = adata[index]
    else:
        adata_pos_subset = adata[:, pos_index]
        adata_neg_subset = adata[:, index]

    np.testing.assert_array_equal(
        adata_pos_subset.obs_names, adata_neg_subset.obs_names
    )
    np.testing.assert_array_equal(
        adata_pos_subset.var_names, adata_neg_subset.var_names
    )
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