def test_set_scalar_subset_X(matrix_type, subset_func):
    adata = ad.AnnData(matrix_type(np.zeros((10, 10))))
    orig_X_val = adata.X.copy()
    subset_idx = slice_subset(adata.obs_names)

    adata_subset = adata[subset_idx, :]

    adata_subset.X = 1

    assert adata_subset.is_view
    assert np.all(asarray(adata[subset_idx, :].X) == 1)

    assert asarray((orig_X_val != adata.X)).sum() == mul(*adata_subset.shape)
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