modfiy format

src/signature_mahalanobis_knn/sig_mahal_knn.py

@@ -206,8 +206,8 @@ def conformance(
         kth: int | 1, optional
             The distance to the kth nearest neighbor to be returned, 1 is nearest
         n_neighbors: int | 20, optional
-            The neighborhood to look for the kth nearest neighbor. 
-            
+            The neighborhood to look for the kth nearest neighbor.
+
         return_indices : bool, optional
             Whether to return the indices of the nearest neighbors,
             by default False.
@@ -256,11 +256,11 @@ def conformance(
             )
 
         # post-process the candidate distances (in the original space)
-        # create (n_test, n_neighbours) array with each column as [0, 1, ..., n_test-1] 
+        # create (n_test, n_neighbours) array with each column as [0, 1, ..., n_test-1]
         test_indices = np.tile(
             np.arange(train_indices.shape[0]), (train_indices.shape[1], 1)
         ).T
-        
+
         # differences has shape (n_test x n_neighbors x sig_dim)
         differences = (
             self.signatures_train[train_indices] - signatures_test[test_indices]
@@ -285,6 +285,9 @@ def conformance(
 
         # compute the kth closest point of the candidate distances for each data point
         if return_indices:
-            return np.partition(candidate_distances, kth-1, axis=-1)[:, kth-1], train_indices
+            return (
+                np.partition(candidate_distances, kth - 1, axis=-1)[:, kth - 1],
+                train_indices,
+            )
 
-        return np.partition(candidate_distances, kth-1, axis=-1)[:, kth-1]
+        return np.partition(candidate_distances, kth - 1, axis=-1)[:, kth - 1]