diff --git a/src/spikeinterface/sortingcomponents/clustering/random_projections.py b/src/spikeinterface/sortingcomponents/clustering/random_projections.py
index df9290a1f5..864548e7d4 100644
--- a/src/spikeinterface/sortingcomponents/clustering/random_projections.py
+++ b/src/spikeinterface/sortingcomponents/clustering/random_projections.py
@@ -127,7 +127,9 @@ def sigmoid(x, L, x0, k, b):
 
         pipeline_nodes = [node0, node1, node2, node3]
 
-        hdbscan_data = run_node_pipeline(recording, pipeline_nodes, params["job_kwargs"], job_name="extracting features")
+        hdbscan_data = run_node_pipeline(
+            recording, pipeline_nodes, params["job_kwargs"], job_name="extracting features"
+        )
 
         import sklearn
 
diff --git a/src/spikeinterface/sortingcomponents/matching/circus.py b/src/spikeinterface/sortingcomponents/matching/circus.py
index b963447ba2..358691cd25 100644
--- a/src/spikeinterface/sortingcomponents/matching/circus.py
+++ b/src/spikeinterface/sortingcomponents/matching/circus.py
@@ -687,16 +687,18 @@ def main_function(cls, traces, d):
         # Filter using overlap-and-add convolution
         if len(ignored_ids) > 0:
             mask = ~np.isin(np.arange(num_templates), ignored_ids)
-            spatially_filtered_data =  np.matmul(d["spatial"][:, mask, :], traces.T[np.newaxis, :, :])
+            spatially_filtered_data = np.matmul(d["spatial"][:, mask, :], traces.T[np.newaxis, :, :])
             scaled_filtered_data = spatially_filtered_data * d["singular"][:, mask, :]
-            objective_by_rank = scipy.signal.oaconvolve(scaled_filtered_data, d["temporal"][:, mask, :], axes=2, mode="valid")
+            objective_by_rank = scipy.signal.oaconvolve(
+                scaled_filtered_data, d["temporal"][:, mask, :], axes=2, mode="valid"
+            )
             scalar_products[mask] += np.sum(objective_by_rank, axis=0)
             scalar_products[ignored_ids] = -np.inf
         else:
             spatially_filtered_data = np.matmul(d["spatial"], traces.T[np.newaxis, :, :])
             scaled_filtered_data = spatially_filtered_data * d["singular"]
             objective_by_rank = scipy.signal.oaconvolve(scaled_filtered_data, d["temporal"], axes=2, mode="valid")
-            scalar_products += np.sum(objective_by_rank, axis=0)        
+            scalar_products += np.sum(objective_by_rank, axis=0)
 
         num_spikes = 0