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 test_set_subset_obsm(adata, subset_func):
init_hash = joblib.hash(adata)
orig_obsm_val = adata.obsm["o"].copy()
while True:
subset_idx = slice_subset(adata.obs_names)
if len(adata[subset_idx, :]) > 2:
break
subset = adata[subset_idx, :]
internal_idx = subset_func(np.arange(subset.obsm["o"].shape[0]))
assert subset.is_view
subset.obsm["o"][internal_idx] = 1
assert not subset.is_view
assert np.all(adata.obsm["o"] == orig_obsm_val)
assert init_hash == joblib.hash(adata)
def test_set_subset_varm(adata, subset_func):
init_hash = joblib.hash(adata)
orig_varm_val = adata.varm["o"].copy()
while True:
subset_idx = slice_subset(adata.var_names)
if (adata[:, subset_idx]).shape[1] > 2:
break
subset = adata[:, subset_idx]
internal_idx = subset_func(np.arange(subset.varm["o"].shape[0]))
assert subset.is_view
subset.varm["o"][internal_idx] = 1
assert not subset.is_view
assert np.all(adata.varm["o"] == orig_varm_val)
assert init_hash == joblib.hash(adata)
def test_not_set_subset_X(matrix_type, subset_func):
adata = ad.AnnData(matrix_type(asarray(sparse.random(20, 20))))
init_hash = joblib.hash(adata)
orig_X_val = adata.X.copy()
while True:
subset_idx = slice_subset(adata.obs_names)
if len(adata[subset_idx, :]) > 2:
break
subset = adata[subset_idx, :]
subset = adata[:, subset_idx]
internal_idx = subset_func(np.arange(subset.X.shape[1]))
assert subset.is_view
subset.X[:, internal_idx] = 1
assert not subset.is_view
assert not np.any(asarray(adata.X != orig_X_val))
assert init_hash == joblib.hash(adata)
