Docstrings

src/spikeinterface/sorters/internal/spyking_circus2.py

@@ -206,12 +206,11 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
             np.save(clustering_folder / "noise_levels.npy", noise_levels)
 
         if params["matched_filtering"]:
-            prototype, waveforms = get_prototype_and_waveforms_from_recording(
+            prototype, waveforms, _ = get_prototype_and_waveforms_from_recording(
                 recording_w,
                 ms_before=ms_before,
                 ms_after=ms_after,
                 seed=params["seed"],
-                return_waveforms=True,
                 **detection_params,
                 **job_kwargs,
             )

src/spikeinterface/sortingcomponents/peak_localization.py

@@ -73,7 +73,7 @@ def get_localization_pipeline_nodes(
             assert isinstance(peak_source, (PeakRetriever, SpikeRetriever))
             # extract prototypes silently
             job_kwargs["progress_bar"] = False
-            method_kwargs["prototype"] = get_prototype_and_waveforms_from_peaks(
+            method_kwargs["prototype"], _, _ = get_prototype_and_waveforms_from_peaks(
                 recording, peaks=peak_source.peaks, ms_before=ms_before, ms_after=ms_after, **job_kwargs
             )
         extract_dense_waveforms = ExtractDenseWaveforms(

src/spikeinterface/sortingcomponents/tests/test_peak_detection.py

@@ -314,7 +314,7 @@ def test_detect_peaks_locally_exclusive_matched_filtering(recording, job_kwargs)
 
     ms_before = 1.0
     ms_after = 1.0
-    prototype = get_prototype_and_waveforms_from_peaks(
+    prototype, _, _ = get_prototype_and_waveforms_from_peaks(
         recording, peaks=peaks_by_channel_np, ms_before=ms_before, ms_after=ms_after, **job_kwargs
     )
 

src/spikeinterface/sortingcomponents/tools.py

@@ -206,17 +206,17 @@ def get_prototype_and_waveforms(
         Time in milliseconds after the peak to extract the waveform, by default 0.5.
     seed : int or None, optional
         Seed for random number generator, by default None.
-    return_waveforms : bool, optional
-        Whether to return the waveforms along with the prototype, by default False.
     **all_kwargs : dict
         Additional keyword arguments for peak detection and job kwargs.
 
     Returns
     -------
     prototype : numpy.array
         The prototype waveform.
-    waveforms : numpy.array, optional
-        The extracted waveforms, returned if return_waveforms is True.
+    waveforms : numpy.array
+        The extracted waveforms for the selected peaks.
+    peaks : numpy.array
+        The selected peaks used to extract waveforms.
     """
     if peaks is None:
        return get_prototype_and_waveforms_from_peaks(recording, n_peaks, ms_before=ms_before, ms_after=ms_after, seed=seed, **all_kwargs)