formatting

cassiopeia/solver/missing_data_methods.py

@@ -1,4 +1,5 @@
 """This file contains included missing data imputation methods."""
+
 from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -59,33 +60,52 @@ def assign_missing_average(
     def score_side(subset_character_states, query_states, weights):
         score = 0
         for char in range(len(subset_character_states)):
-
-            query_state = [q for q in query_states[char] if q != 0 and q != missing_state_indicator]
+
+            query_state = [
+                q
+                for q in query_states[char]
+                if q != 0 and q != missing_state_indicator
+            ]
             all_states = np.array(subset_character_states[char])
             for q in query_state:
                 if weights:
-                    score += (
-                        weights[char][q]
-                        * np.count_nonzero(all_states == q)
+                    score += weights[char][q] * np.count_nonzero(
+                        all_states == q
                     )
                 else:
                     score += np.count_nonzero(all_states == q)
 
-        return score                                                
-            
+        return score
+
     subset_character_array_left = character_array[left_indices, :]
     subset_character_array_right = character_array[right_indices, :]
 
-    all_left_states = [unravel_ambiguous_states(subset_character_array_left[:,char]) for char in range(subset_character_array_left.shape[1])]
-    all_right_states = [unravel_ambiguous_states(subset_character_array_right[:,char]) for char in range(subset_character_array_right.shape[1])]
+    all_left_states = [
+        unravel_ambiguous_states(subset_character_array_left[:, char])
+        for char in range(subset_character_array_left.shape[1])
+    ]
+    all_right_states = [
+        unravel_ambiguous_states(subset_character_array_right[:, char])
+        for char in range(subset_character_array_right.shape[1])
+    ]
 
-
     for sample_index in missing_indices:
 
-        all_states_for_sample = [unravel_ambiguous_states([character_array[sample_index, char]]) for char in range(character_array.shape[1])]
-
-        left_score = score_side(np.array(all_left_states, dtype=object), np.array(all_states_for_sample, dtype=object), weights)
-        right_score = score_side(np.array(all_right_states, dtype=object), np.array(all_states_for_sample, dtype=object), weights)
+        all_states_for_sample = [
+            unravel_ambiguous_states([character_array[sample_index, char]])
+            for char in range(character_array.shape[1])
+        ]
+
+        left_score = score_side(
+            np.array(all_left_states, dtype=object),
+            np.array(all_states_for_sample, dtype=object),
+            weights,
+        )
+        right_score = score_side(
+            np.array(all_right_states, dtype=object),
+            np.array(all_states_for_sample, dtype=object),
+            weights,
+        )
 
         if (left_score / len(left_set)) > (right_score / len(right_set)):
             left_set.append(sample_names[sample_index])