def test_combine_batches():
X = data.load_10X()
Y = pd.concat([X, scprep.utils.select_rows(X, np.arange(X.shape[0] // 2))])
Y2, sample_labels = scprep.utils.combine_batches(
[X, scprep.utils.select_rows(X, np.arange(X.shape[0] // 2))],
batch_labels=[0, 1])
assert utils.assert_matrix_class_equivalent(Y, Y2)
utils.assert_all_equal(Y, Y2)
assert np.all(Y.index == Y2.index)
assert np.all(sample_labels == np.concatenate(
[np.repeat(0, X.shape[0]),
np.repeat(1, X.shape[0] // 2)]))
Y2, sample_labels = scprep.utils.combine_batches(
[X, scprep.utils.select_rows(X, np.arange(X.shape[0] // 2))],
batch_labels=[0, 1],
append_to_cell_names=True)
assert np.all(Y.index == np.array([i[:-2] for i in Y2.index]))
assert np.all(
np.core.defchararray.add("_", sample_labels.astype(str)) == np.array(
[i[-2:] for i in Y2.index], dtype=str))
transform = lambda X: scprep.utils.combine_batches(
[X, scprep.utils.select_rows(X, np.arange(X.shape[0] // 2))],
batch_labels=[0, 1])[0]
matrix.test_matrix_types(X,
utils.assert_transform_equals,
matrix._indexable_matrix_types,
Y=Y,
transform=transform,
check=utils.assert_all_equal)
def test_singular_values_dense(self):
utils.assert_all_equal(
self.S_random,
scprep.reduce.pca(self.X,
n_components=100,
seed=42,
return_singular_values=True)[1],
)
def test_setup(self):
assert self.Y.shape[0] == self.X_dense.shape[0]
utils.assert_all_equal(
self.Y, scprep.select.select_cols(self.X_dense, idx="Arl8b")
)
def test_combine_batches():
X = data.load_10X()
Y = pd.concat(
[X, scprep.select.select_rows(X, idx=np.arange(X.shape[0] // 2))],
axis=0,
sort=True,
)
Y2, sample_labels = scprep.utils.combine_batches(
[X, scprep.select.select_rows(X, idx=np.arange(X.shape[0] // 2))],
batch_labels=[0, 1],
append_to_cell_names=False,
)
assert utils.assert_matrix_class_equivalent(Y, Y2)
utils.assert_all_equal(Y, Y2)
assert np.all(Y.index == Y2.index)
assert np.all(
sample_labels
== np.concatenate([np.repeat(0, X.shape[0]), np.repeat(1, X.shape[0] // 2)])
)
assert np.all(sample_labels.index == Y2.index)
assert sample_labels.name == "sample_labels"
Y2, sample_labels = scprep.utils.combine_batches(
[X, scprep.select.select_rows(X, idx=np.arange(X.shape[0] // 2))],
batch_labels=[0, 1],
append_to_cell_names=True,
)
assert np.all(Y.index == np.array([i[:-2] for i in Y2.index]))
assert np.all(
np.core.defchararray.add("_", sample_labels.astype(str))
== np.array([i[-2:] for i in Y2.index], dtype=str)
)
assert np.all(sample_labels.index == Y2.index)
assert sample_labels.name == "sample_labels"
def transform(X):
return scprep.utils.combine_batches(
[X, scprep.select.select_rows(X, idx=np.arange(X.shape[0] // 2))],
batch_labels=[0, 1],
)[0]
matrix.test_matrix_types(
X,
utils.assert_transform_equals,
matrix._pandas_matrix_types,
Y=Y,
transform=transform,
check=utils.assert_all_equal,
)
# don't sort for non pandas
Y = pd.concat(
[X, scprep.select.select_rows(X, idx=np.arange(X.shape[0] // 2))],
axis=0,
sort=False,
)
matrix.test_matrix_types(
X,
utils.assert_transform_equals,
matrix._scipy_indexable_matrix_types + matrix._numpy_matrix_types,
Y=Y,
transform=transform,
check=utils.assert_all_equal,
)
def test_fun(X):
Y, sample_labels = scprep.utils.combine_batches(
[X, scprep.select.select_rows(X, idx=np.arange(X.shape[0] // 2))],
batch_labels=[0, 1],
)
assert np.all(sample_labels.index == Y.index)
assert sample_labels.name == "sample_labels"
matrix.test_pandas_matrix_types(X, test_fun)