[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

src/spikeinterface/sorters/internal/spyking_circus2.py

@@ -194,13 +194,10 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         max_n_peaks = selection_params["n_peaks_per_channel"] * num_channels
         n_peaks = max(selection_params["min_n_peaks"], max_n_peaks)
 
-        if params["matched_filtering"]:            
-            prototype = get_prototype(recording_w, 
-                                      n_peaks=5000, 
-                                      ms_before=ms_before, 
-                                      ms_after=ms_after, 
-                                      **detection_params, 
-                                      **job_kwargs)
+        if params["matched_filtering"]:
+            prototype = get_prototype(
+                recording_w, n_peaks=5000, ms_before=ms_before, ms_after=ms_after, **detection_params, **job_kwargs
+            )
             detection_params["prototype"] = prototype
             detection_params["ms_before"] = ms_before
             if skip_peaks:
@@ -431,26 +428,24 @@ def final_cleaning_circus(recording, sorting, templates, **merging_kwargs):
 def get_prototype(recording, n_peaks, ms_before, ms_after, **all_kwargs):
     from spikeinterface.sortingcomponents.peak_detection import detect_peaks
     from spikeinterface.core.node_pipeline import ExtractSparseWaveforms
-    
+
     detection_kwargs, job_kwargs = split_job_kwargs(all_kwargs)
-    
+
     node = ExtractSparseWaveforms(
-            recording,
-            parents=None,
-            return_output=True,
-            ms_before=ms_before,
-            ms_after=ms_after,
-            radius_um=0,
-        )
+        recording,
+        parents=None,
+        return_output=True,
+        ms_before=ms_before,
+        ms_after=ms_after,
+        radius_um=0,
+    )
 
     nbefore = int(ms_before * recording.sampling_frequency / 1000.0)
     pipeline_nodes = [node]
-    res = detect_peaks(recording, 
-                       pipeline_nodes=pipeline_nodes,
-                       skip_after_n_peaks=n_peaks, 
-                       **detection_kwargs, 
-                       **job_kwargs)
+    res = detect_peaks(
+        recording, pipeline_nodes=pipeline_nodes, skip_after_n_peaks=n_peaks, **detection_kwargs, **job_kwargs
+    )
     waveforms = res[1]
     with np.errstate(divide="ignore", invalid="ignore"):
         prototype = np.nanmedian(waveforms[:, :, 0] / (np.abs(waveforms[:, nbefore, 0][:, np.newaxis])), axis=0)
-    return prototype
+    return prototype