diff --git a/src/spikeinterface/sortingcomponents/clustering/circus.py b/src/spikeinterface/sortingcomponents/clustering/circus.py
index 1569914562..1cbe848a18 100644
--- a/src/spikeinterface/sortingcomponents/clustering/circus.py
+++ b/src/spikeinterface/sortingcomponents/clustering/circus.py
@@ -40,11 +40,7 @@ class CircusClustering:
     """
 
     _default_params = {
-        "hdbscan_kwargs": {
-            "min_cluster_size": 10,
-            "allow_single_cluster": True,
-            "min_samples" : 5
-        },
+        "hdbscan_kwargs": {"min_cluster_size": 10, "allow_single_cluster": True, "min_samples": 5},
         "cleaning_kwargs": {},
         "waveforms": {"ms_before": 2, "ms_after": 2},
         "sparsity": {"method": "snr", "amplitude_mode": "peak_to_peak", "threshold": 0.25},
@@ -202,7 +198,7 @@ def main_function(cls, recording, peaks, params, job_kwargs=dict()):
             original_labels = peaks["channel_index"]
             from spikeinterface.sortingcomponents.clustering.split import split_clusters
 
-            min_size = 2*params["hdbscan_kwargs"].get("min_cluster_size", 10)
+            min_size = 2 * params["hdbscan_kwargs"].get("min_cluster_size", 10)
 
             peak_labels, _ = split_clusters(
                 original_labels,
@@ -216,7 +212,7 @@ def main_function(cls, recording, peaks, params, job_kwargs=dict()):
                     waveforms_sparse_mask=sparse_mask,
                     min_size_split=min_size,
                     clusterer_kwargs=d["hdbscan_kwargs"],
-                    n_pca_features=[2, 4, 6, 8, 10]
+                    n_pca_features=[2, 4, 6, 8, 10],
                 ),
                 **params["recursive_kwargs"],
                 **job_kwargs,
diff --git a/src/spikeinterface/sortingcomponents/clustering/split.py b/src/spikeinterface/sortingcomponents/clustering/split.py
index b880660d20..570a393c23 100644
--- a/src/spikeinterface/sortingcomponents/clustering/split.py
+++ b/src/spikeinterface/sortingcomponents/clustering/split.py
@@ -220,7 +220,7 @@ def split(
 
         if not isinstance(n_pca_features, np.ndarray):
             n_pca_features = np.array([n_pca_features])
-        
+
         n_pca_features = n_pca_features[n_pca_features <= aligned_wfs.shape[1]]
         flatten_features = aligned_wfs.reshape(aligned_wfs.shape[0], -1)
 
@@ -230,6 +230,7 @@ def split(
 
             if flatten_features.shape[1] > n_pca:
                 from sklearn.decomposition import PCA
+
                 # from sklearn.decomposition import TruncatedSVD
                 # tsvd = TruncatedSVD(n_pca_features)
                 tsvd = PCA(n_pca, whiten=True)
@@ -258,7 +259,7 @@ def split(
             else:
                 raise ValueError(f"wrong clusterer {clusterer}")
 
-            #DEBUG = True
+            # DEBUG = True
             DEBUG = False
             if DEBUG:
                 import matplotlib.pyplot as plt
@@ -279,14 +280,15 @@ def split(
 
                     ax = axs[1]
                     ax.plot(flatten_wfs[mask][sl].T, color=colors[k], alpha=0.5)
-                    ax.set_xlabel('PCA features')
+                    ax.set_xlabel("PCA features")
 
                 axs[0].set_title(f"{clusterer} {is_split} {peak_indices[0]} {n_pca}, recursion_level={recursion_level}")
                 import time
-                plt.savefig(f'split_{recursion_level}_{time.time()}.png')
+
+                plt.savefig(f"split_{recursion_level}_{time.time()}.png")
                 plt.close()
-                #plt.show()
-            
+                # plt.show()
+
             if is_split:
                 break