Docstrings

src/spikeinterface/sorters/internal/spyking_circus2.py

@@ -11,7 +11,11 @@
 from spikeinterface.core.template import Templates
 from spikeinterface.core.waveform_tools import estimate_templates
 from spikeinterface.preprocessing import common_reference, whiten, bandpass_filter, correct_motion
-from spikeinterface.sortingcomponents.tools import cache_preprocessing, get_prototype_and_waveforms, get_shuffled_recording_slices
+from spikeinterface.sortingcomponents.tools import (
+    cache_preprocessing,
+    get_prototype_and_waveforms,
+    get_shuffled_recording_slices,
+)
 from spikeinterface.core.basesorting import minimum_spike_dtype
 from spikeinterface.core.sparsity import compute_sparsity
 from spikeinterface.core.sortinganalyzer import create_sorting_analyzer
@@ -208,8 +212,8 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
                 ms_after=ms_after, 
                 seed=params["seed"],
                 return_waveforms=True,
-                **detection_params, 
-                **job_kwargs
+                **detection_params,
+                **job_kwargs,
             )
             detection_params["prototype"] = prototype
             detection_params["ms_before"] = ms_before

src/spikeinterface/sortingcomponents/tests/test_peak_detection.py

@@ -314,7 +314,9 @@ def test_detect_peaks_locally_exclusive_matched_filtering(recording, job_kwargs)
 
     ms_before = 1.0
     ms_after = 1.0
-    prototype = get_prototype_and_waveforms(recording, peaks=peaks_by_channel_np, ms_before=ms_before, ms_after=ms_after, **job_kwargs)
+    prototype = get_prototype_and_waveforms(
+        recording, peaks=peaks_by_channel_np, ms_before=ms_before, ms_after=ms_after, **job_kwargs
+    )
 
     peaks_local_mf_filtering = detect_peaks(
         recording,

src/spikeinterface/sortingcomponents/tools.py

@@ -68,7 +68,10 @@ def extract_waveform_at_max_channel(rec, peaks, ms_before=0.5, ms_after=1.5, **j
 
     return all_wfs
 
-def get_prototype_and_waveforms(recording, n_peaks=5000, peaks=None, ms_before=0.5, ms_after=0.5, seed=None, return_waveforms=False, **all_kwargs):
+
+def get_prototype_and_waveforms(
+    recording, n_peaks=5000, peaks=None, ms_before=0.5, ms_after=0.5, seed=None, return_waveforms=False, **all_kwargs
+):
     """
     Function to extract a prototype waveform from a peak list or from a peak detection. Note that in case
     of a peak detection, the detection stops as soon as n_peaks are detected.
@@ -99,7 +102,7 @@ def get_prototype_and_waveforms(recording, n_peaks=5000, peaks=None, ms_before=0
     waveforms : numpy.array, optional
         The extracted waveforms, returned if return_waveforms is True.
     """
-    
+
     seed = seed if seed else None
     rng = np.random.default_rng(seed=seed)
 
@@ -110,6 +113,7 @@ def get_prototype_and_waveforms(recording, n_peaks=5000, peaks=None, ms_before=0
     if peaks is None:
         from spikeinterface.sortingcomponents.peak_detection import detect_peaks
         from spikeinterface.core.node_pipeline import ExtractSparseWaveforms
+
         node = ExtractSparseWaveforms(
             recording,
             parents=None,
@@ -123,16 +127,20 @@ def get_prototype_and_waveforms(recording, n_peaks=5000, peaks=None, ms_before=0
         recording_slices = get_shuffled_recording_slices(recording, seed=seed, **job_kwargs)
 
         res = detect_peaks(
-            recording, pipeline_nodes=pipeline_nodes, 
-            skip_after_n_peaks=n_peaks, 
-            recording_slices=recording_slices, 
-            **detection_kwargs, 
-            **job_kwargs, 
+            recording,
+            pipeline_nodes=pipeline_nodes,
+            skip_after_n_peaks=n_peaks,
+            recording_slices=recording_slices,
+            **detection_kwargs,
+            **job_kwargs,
         )
         waveforms = res[1]
     else:
         from spikeinterface.sortingcomponents.peak_selection import select_peaks
-        few_peaks = select_peaks(peaks, recording=recording, method="uniform", n_peaks=n_peaks, margin=(nbefore, nafter), seed=seed)
+
+        few_peaks = select_peaks(
+            peaks, recording=recording, method="uniform", n_peaks=n_peaks, margin=(nbefore, nafter), seed=seed
+        )
         waveforms = extract_waveform_at_max_channel(
             recording, few_peaks, ms_before=ms_before, ms_after=ms_after, **job_kwargs
         )
@@ -229,4 +237,4 @@ def get_shuffled_recording_slices(recording, seed=None, **job_kwargs):
         rng = np.random.RandomState(seed)
         recording_slices = rng.permutation(recording_slices)
 
-    return recording_slices
+    return recording_slices