WIP

src/spikeinterface/sorters/internal/spyking_circus2.py

@@ -204,7 +204,7 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
             detection_params["prototype"] = prototype
             detection_params["ms_before"] = ms_before
             if params["debug"]:
-                np.save(clustering_folder / 'prototype.npy', prototype)
+                np.save(clustering_folder / "prototype.npy", prototype)
             if skip_peaks:
                 detection_params["skip_after_n_peaks"] = n_peaks
             peaks = detect_peaks(recording_w, "matched_filtering", **detection_params, **job_kwargs)
@@ -450,4 +450,4 @@ def get_prototype(recording, n_peaks, ms_before, ms_after, **all_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

src/spikeinterface/sortingcomponents/clustering/circus.py

@@ -241,10 +241,10 @@ def main_function(cls, recording, peaks, params, job_kwargs=dict()):
             job_name=None,
             **job_kwargs,
         )
-    
-        peak_snrs = np.abs(templates_array[:, nbefore, :])/templates_array_std[:, nbefore, :]
+
+        peak_snrs = np.abs(templates_array[:, nbefore, :]) / templates_array_std[:, nbefore, :]
         best_channels = np.argmax(np.abs(templates_array[:, nbefore, :]), axis=1)
-        best_snrs_ratio = (peak_snrs/params["noise_levels"])[np.arange(len(peak_snrs)), best_channels]
+        best_snrs_ratio = (peak_snrs / params["noise_levels"])[np.arange(len(peak_snrs)), best_channels]
         valid_templates = best_snrs_ratio > params["noise_threshold"]
 
         if d["rank"] is not None:

src/spikeinterface/sortingcomponents/clustering/random_projections.py

@@ -144,10 +144,10 @@ def main_function(cls, recording, peaks, params, job_kwargs=dict()):
             job_name=None,
             **job_kwargs,
         )
-            
-        peak_snrs = np.abs(templates_array[:, nbefore, :])/templates_array_std[:, nbefore, :]
+
+        peak_snrs = np.abs(templates_array[:, nbefore, :]) / templates_array_std[:, nbefore, :]
         best_channels = np.argmax(np.abs(templates_array[:, nbefore, :]), axis=1)
-        best_snrs_ratio = (peak_snrs/params["noise_levels"])[np.arange(len(peak_snrs)), best_channels]
+        best_snrs_ratio = (peak_snrs / params["noise_levels"])[np.arange(len(peak_snrs)), best_channels]
         valid_templates = best_snrs_ratio > params["noise_threshold"]
 
         templates = Templates(

src/spikeinterface/sortingcomponents/peak_detection.py

@@ -727,7 +727,7 @@ def compute(self, traces, start_frame, end_frame, segment_index, max_margin):
 
         peak_sample_ind += self.exclude_sweep_size + self.conv_margin + self.nbefore
         peak_amplitude = traces[peak_sample_ind, peak_chan_ind]
-        
+
         local_peaks = np.zeros(peak_sample_ind.size, dtype=self._dtype)
         local_peaks["sample_index"] = peak_sample_ind
         local_peaks["channel_index"] = peak_chan_ind