feat: replace output_folder with folder when calling run_sorter, use default value for peak_sign

Author: ttngu207

element_array_ephys/ephys_no_curation.py

@@ -1045,10 +1045,13 @@ def make(self, key):
             # Find representative channel for each unit
             unit_peak_channel: dict[int, np.ndarray] = (
                 si.ChannelSparsity.from_best_channels(
-                    sorting_analyzer, 1, peak_sign="both"
+                    sorting_analyzer,
+                    1,
                 ).unit_id_to_channel_indices
             )
-            unit_peak_channel: dict[int, int] = {u: chn[0] for u, chn in unit_peak_channel.items()}
+            unit_peak_channel: dict[int, int] = {
+                u: chn[0] for u, chn in unit_peak_channel.items()
+            }
 
             spike_count_dict: dict[int, int] = si_sorting.count_num_spikes_per_unit()
             # {unit: spike_count}
@@ -1084,7 +1087,9 @@ def make(self, key):
                 )
                 unit_spikes_loc = spike_locations.get_data()[unit_spikes_df.index]
                 _, spike_depths = zip(*unit_spikes_loc)  # x-coordinates, y-coordinates
-                spike_times = si_sorting.get_unit_spike_train(unit_id, return_times=True)
+                spike_times = si_sorting.get_unit_spike_train(
+                    unit_id, return_times=True
+                )
 
                 assert len(spike_times) == len(spike_sites) == len(spike_depths)
 
@@ -1243,13 +1248,13 @@ def make(self, key):
         si_sorting_analyzer_dir = output_dir / sorter_name / "sorting_analyzer"
         if si_sorting_analyzer_dir.exists():  # read from spikeinterface outputs
             import spikeinterface as si
-            
+
             sorting_analyzer = si.load_sorting_analyzer(folder=si_sorting_analyzer_dir)
 
             # Find representative channel for each unit
             unit_peak_channel: dict[int, np.ndarray] = (
                 si.ChannelSparsity.from_best_channels(
-                    sorting_analyzer, 1, peak_sign="both"
+                    sorting_analyzer, 1
                 ).unit_id_to_channel_indices
             )  # {unit: peak_channel_index}
             unit_peak_channel = {u: chn[0] for u, chn in unit_peak_channel.items()}
@@ -1272,7 +1277,9 @@ def yield_unit_waveforms():
                     )
                     unit_peak_waveform = {
                         **unit,
-                        "peak_electrode_waveform": unit_waveforms[:, unit_peak_channel[unit["unit"]]],
+                        "peak_electrode_waveform": unit_waveforms[
+                            :, unit_peak_channel[unit["unit"]]
+                        ],
                     }
 
                     unit_electrode_waveforms = [
@@ -1495,7 +1502,7 @@ def make(self, key):
         si_sorting_analyzer_dir = output_dir / sorter_name / "sorting_analyzer"
         if si_sorting_analyzer_dir.exists():  # read from spikeinterface outputs
             import spikeinterface as si
-            
+
             sorting_analyzer = si.load_sorting_analyzer(folder=si_sorting_analyzer_dir)
             qc_metrics = sorting_analyzer.get_extension("quality_metrics").get_data()
             template_metrics = sorting_analyzer.get_extension(

element_array_ephys/spike_sorting/si_spike_sorting.py

@@ -205,7 +205,7 @@ def _run_sorter():
             si_sorting: si.sorters.BaseSorter = si.sorters.run_sorter(
                 sorter_name=sorter_name,
                 recording=si_recording,
-                output_folder=sorting_output_dir,
+                folder=sorting_output_dir,
                 remove_existing_folder=True,
                 verbose=True,
                 docker_image=sorter_name not in si.sorters.installed_sorters(),