diff --git a/src/spikeinterface/core/sortinganalyzer.py b/src/spikeinterface/core/sortinganalyzer.py
index d3514a7d21..fc47955814 100644
--- a/src/spikeinterface/core/sortinganalyzer.py
+++ b/src/spikeinterface/core/sortinganalyzer.py
@@ -650,7 +650,7 @@ def _save_or_select_or_merge(
             from spikeinterface.core.sorting_tools import get_ids_after_merging
 
             new_unit_ids = get_ids_after_merging(self.sorting, units_to_merge, new_unit_ids=unit_ids)
-        
+
         if self.has_recording():
             recording = self._recording
         elif self.has_temporary_recording():
diff --git a/src/spikeinterface/postprocessing/template_similarity.py b/src/spikeinterface/postprocessing/template_similarity.py
index 2ace0945d5..469e76fadc 100644
--- a/src/spikeinterface/postprocessing/template_similarity.py
+++ b/src/spikeinterface/postprocessing/template_similarity.py
@@ -68,7 +68,9 @@ def _merge_extension_data(
         self, units_to_merge, new_unit_ids, new_sorting_analyzer, kept_indices=None, verbose=False, **job_kwargs
     ):
         num_shifts = int(self.params["max_lag_ms"] * self.sorting_analyzer.sampling_frequency / 1000)
-        templates_array = get_dense_templates_array(new_sorting_analyzer, return_scaled=self.sorting_analyzer.return_scaled)
+        templates_array = get_dense_templates_array(
+            new_sorting_analyzer, return_scaled=self.sorting_analyzer.return_scaled
+        )
         arr = self.data["similarity"]
         sparsity = new_sorting_analyzer.sparsity
         all_new_unit_ids = new_sorting_analyzer.unit_ids
@@ -213,7 +215,7 @@ def compute_similarity_with_templates_array(
                     distances[count, i, j] /= norm_i + norm_j
                 else:
                     distances[count, i, j] = sklearn.metrics.pairwise.pairwise_distances(src, tgt, metric="cosine")
-                
+
                 distances[count, j, i] = distances[count, i, j]
 
         if num_shifts != 0: