def test_neighbors_small():
matrix = pd.DataFrame(
index=['INS', 'GCG', 'PPY'],
columns=['cell1', 'cell2', 'cell3'],
data=[[2302, 123, 0], [0, 5034, 6453], [0, 0, 1]],
)
n_pcs = 2
k = 1
threshold = 0.8
sa = Averages('Enge_2017',
n_neighbors=k,
threshold_neighborhood=threshold,
n_pcs=n_pcs,
distance_metric='correlation',
n_cells_per_type=20,
n_neighbors_out_of_atlas=1)
sa.new_data = matrix
sa._check_init_arguments()
sa.fetch_atlas_if_needed()
sa.compute_feature_intersection()
sa._check_feature_intersection()
sa.prepare_feature_selection()
sa.select_features()
sa._check_feature_selection()
sa.merge_atlas_newdata()
sa.compute_pca()
sa.compute_similarity_graph()
neis = sa.neighbors
assert (isinstance(neis, list))
assert (len(neis) == int(np.sum(sa.sizes)))
for nei in neis:
assert (isinstance(nei, list))
def test_merge_small():
matrix = pd.DataFrame(
index=['INS', 'GCG', 'PPY'],
columns=['cell1', 'cell2', 'cell3'],
data=[[2302, 123, 0], [0, 5034, 6453], [0, 0, 1]],
)
n_pcs = 2
k = 1
threshold = 0.8
sa = Averages('Enge_2017',
n_neighbors=k,
threshold_neighborhood=threshold,
n_pcs=n_pcs,
distance_metric='correlation',
n_neighbors_out_of_atlas=1)
sa.new_data = matrix
sa._check_init_arguments()
sa.fetch_atlas_if_needed()
sa.compute_feature_intersection()
sa._check_feature_intersection()
sa.prepare_feature_selection()
sa.select_features()
sa._check_feature_selection()
sa.merge_atlas_newdata()
assert (sa is not None)