reformatted

test/data_tests/data_utilities_test.py

@@ -85,7 +85,7 @@ def test_bootstrap_character_matrices_no_priors(self):
 
         self.assertEqual(len(bootstrap_samples), 10)
 
-        for (bootstrap_matrix, bootstrap_priors) in bootstrap_samples:
+        for bootstrap_matrix, bootstrap_priors in bootstrap_samples:
             self.assertCountEqual(
                 self.character_matrix.index, bootstrap_matrix.index
             )
@@ -113,7 +113,7 @@ def test_bootstrap_character_matrices_with_priors(self):
 
         self.assertEqual(len(bootstrap_samples), 10)
 
-        for (bootstrap_matrix, bootstrap_priors) in bootstrap_samples:
+        for bootstrap_matrix, bootstrap_priors in bootstrap_samples:
             self.assertCountEqual(
                 self.character_matrix.index, bootstrap_matrix.index
             )
@@ -317,11 +317,31 @@ def test_lca_characters_ambiguous(self):
         self.assertEqual(ret_vec, [1, 2, 3, 0, 5])
 
     def test_lca_characters_ambiguous2(self):
-
-        s1 = [(4, 62), (3, 10), (3, 10, 16), (0, 3), (0, 2, 3), (0, 2, 3), (0, 4, 7), (0, 2, 23), (0, 1, 4, 44)]
+
+        s1 = [
+            (4, 62),
+            (3, 10),
+            (3, 10, 16),
+            (0, 3),
+            (0, 2, 3),
+            (0, 2, 3),
+            (0, 4, 7),
+            (0, 2, 23),
+            (0, 1, 4, 44),
+        ]
         s2 = [4, 3, -1, 0, 0, 0, (0, 7), (0, 2), (0, 4)]
 
-        expected_reconstruction = [4, 3, (3, 10, 16), 0, 0, 0, (0, 7), (0, 2), (0, 4)]
+        expected_reconstruction = [
+            4,
+            3,
+            (3, 10, 16),
+            0,
+            0,
+            0,
+            (0, 7),
+            (0, 2),
+            (0, 4),
+        ]
         ret_vec = data_utilities.get_lca_characters(
             [s1, s2], missing_state_indicator=-1
         )
@@ -336,7 +356,7 @@ def test_lca_characters_ambiguous_and_missing(self):
         ret_vec = data_utilities.get_lca_characters(
             vecs, missing_state_indicator=-1
         )
-        self.assertEqual(ret_vec, [1, (0,2), (3,0), 0, 5])
+        self.assertEqual(ret_vec, [1, (0, 2), (3, 0), 0, 5])
 
     def test_resolve_most_abundant(self):
         state = (1, 2, 3, 3)
@@ -463,8 +483,10 @@ def test_inter_cluster_distance_basic(self):
 
         tree = CassiopeiaTree(tree=tree, cell_meta=meta_data)
 
-        inter_cluster_distances = data_utilities.compute_inter_cluster_distances(
-            tree, meta_item="CellType"
+        inter_cluster_distances = (
+            data_utilities.compute_inter_cluster_distances(
+                tree, meta_item="CellType"
+            )
         )
 
         expected_distances = pd.DataFrame.from_dict(
@@ -518,10 +540,12 @@ def test_inter_cluster_distance_custom_input(self):
 
         tree = CassiopeiaTree(tree=tree)
 
-        inter_cluster_distances = data_utilities.compute_inter_cluster_distances(
-            tree,
-            meta_data=meta_data["CellType"],
-            dissimilarity_map=weight_matrix,
+        inter_cluster_distances = (
+            data_utilities.compute_inter_cluster_distances(
+                tree,
+                meta_data=meta_data["CellType"],
+                dissimilarity_map=weight_matrix,
+            )
         )
 
         expected_distances = pd.DataFrame.from_dict(