diff --git a/viscy/representation/evaluation.py b/viscy/representation/evaluation.py
index 812e186e..622f532e 100644
--- a/viscy/representation/evaluation.py
+++ b/viscy/representation/evaluation.py
@@ -602,6 +602,7 @@ def compute_displacement(
 
     return displacement_per_tau
 
+
 def calculate_normalized_euclidean_distance_cell(embedding_dataset, fov_name, track_id):
     filtered_data = embedding_dataset.where(
         (embedding_dataset["fov_name"] == fov_name)
@@ -619,31 +620,35 @@ def calculate_normalized_euclidean_distance_cell(embedding_dataset, fov_name, tr
 
     euclidean_distances = []
     for i in range(len(time_points)):
-        distance = np.linalg.norm(first_time_point_embedding - normalized_features[i].reshape(1, -1))
+        distance = np.linalg.norm(
+            first_time_point_embedding - normalized_features[i].reshape(1, -1)
+        )
         euclidean_distances.append(distance)
 
     return time_points, euclidean_distances
 
-def compute_displacement_mean_std_full(
-    embedding_dataset, max_tau=10
-):
+
+def compute_displacement_mean_std_full(embedding_dataset, max_tau=10):
     fov_names = embedding_dataset["fov_name"].values
     track_ids = embedding_dataset["track_id"].values
     timepoints = embedding_dataset["t"].values
     embeddings = embedding_dataset["features"].values
 
-    cell_identifiers = np.array(list(zip(fov_names, track_ids)), dtype=[('fov_name', 'O'), ('track_id', 'int64')])
+    cell_identifiers = np.array(
+        list(zip(fov_names, track_ids)),
+        dtype=[("fov_name", "O"), ("track_id", "int64")],
+    )
 
     unique_cells = np.unique(cell_identifiers)
 
     displacement_per_tau = defaultdict(list)
 
     for cell in unique_cells:
-        fov_name = cell['fov_name']
-        track_id = cell['track_id']
+        fov_name = cell["fov_name"]
+        track_id = cell["track_id"]
 
         indices = np.where((fov_names == fov_name) & (track_ids == track_id))[0]
-        
+
         cell_timepoints = timepoints[indices]
         cell_embeddings = embeddings[indices]
 
@@ -664,10 +669,12 @@ def compute_displacement_mean_std_full(
 
                 if len(future_index) >= 1:
                     future_embedding = cell_embeddings[future_index[0]]
-                    future_embedding = future_embedding / np.linalg.norm(future_embedding)
+                    future_embedding = future_embedding / np.linalg.norm(
+                        future_embedding
+                    )
 
                     distance = np.linalg.norm(current_embedding - future_embedding)
-                    
+
                     displacement_per_tau[tau].append(distance)
 
     mean_displacement_per_tau = {
@@ -680,4 +687,3 @@ def compute_displacement_mean_std_full(
     }
 
     return mean_displacement_per_tau, std_displacement_per_tau
-