clean

SpikeInterface · May 22, 2024 · ef0bba9 · ef0bba9
1 parent 9a03744
commit ef0bba9
Showing 1 changed file with 6 additions and 39 deletions.
diff --git a/src/spikeinterface/sorters/internal/tridesclous2.py b/src/spikeinterface/sorters/internal/tridesclous2.py
@@ -117,8 +117,6 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         num_chans = recording_raw.get_num_channels()
         sampling_frequency = recording_raw.get_sampling_frequency()
 
-
-
         # preprocessing
         if params["apply_preprocessing"]:
             if params["apply_motion_correction"]:
@@ -183,25 +181,24 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         clustering_kwargs["waveforms"] = params["waveforms"].copy()
         clustering_kwargs["clustering"] = params["clustering"].copy()
 
-        labels_set, post_clean_label, extra_out = find_cluster_from_peaks(
+        labels_set, clustering_label, extra_out = find_cluster_from_peaks(
             recording, peaks, method="tdc_clustering", method_kwargs=clustering_kwargs, extra_outputs=True, **job_kwargs
         )
         peak_shifts = extra_out["peak_shifts"]
         new_peaks = peaks.copy()
         new_peaks["sample_index"] -= peak_shifts
 
-        mask = post_clean_label >= 0
+        mask = clustering_label >= 0
         sorting_pre_peeler = NumpySorting.from_times_labels(
             new_peaks["sample_index"][mask],
-            post_clean_label[mask],
+            clustering_label[mask],
             sampling_frequency,
             unit_ids=labels_set,
         )
 
         if verbose:
             print(f"find_cluster_from_peaks(): {sorting_pre_peeler.unit_ids.size} cluster found")
 
-        # recording_w = whiten(recording, mode="local", radius_um=100.0)
         recording_for_peeler = recording
 
         nbefore = int(params["templates"]["ms_before"] * sampling_frequency / 1000.0)
@@ -232,40 +229,11 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         templates = templates_dense.to_sparse(sparsity)
         templates = remove_empty_templates(templates)
 
-        # snrs = compute_snrs(we, peak_sign=params["detection"]["peak_sign"], peak_mode="extremum")
-        # print(snrs)
-
-        # matching_params = params["matching"].copy()
-        # matching_params["noise_levels"] = noise_levels
-        # matching_params["peak_sign"] = params["detection"]["peak_sign"]
-        # matching_params["detect_threshold"] = params["detection"]["detect_threshold"]
-        # matching_params["radius_um"] = params["detection"]["radius_um"]
-
-        # spikes = find_spikes_from_templates(
-        #     recording, method="tridesclous", method_kwargs=matching_params, **job_kwargs
-        # )
-
+        ## peeler
         matching_method = params["matching"]["method"]
         matching_params = params["matching"]["method_kwargs"].copy()
-
         matching_params["templates"] = templates
         matching_params["noise_levels"] = noise_levels
-        # matching_params["peak_sign"] = params["detection"]["peak_sign"]
-        # matching_params["detect_threshold"] = params["detection"]["detect_threshold"]
-        # matching_params["radius_um"] = params["detection"]["radius_um"]
-
-        # spikes = find_spikes_from_templates(
-        #     recording, method="tridesclous", method_kwargs=matching_params, **job_kwargs
-        # )
-        # )
-
-        # if matching_method == "circus-omp-svd":
-        #     job_kwargs = job_kwargs.copy()
-        #     for value in ["chunk_size", "chunk_memory", "total_memory", "chunk_duration"]:
-        #         if value in job_kwargs:
-        #             job_kwargs.pop(value)
-        #     job_kwargs["chunk_duration"] = "100ms"
-
         spikes = find_spikes_from_templates(
             recording_for_peeler, method=matching_method, method_kwargs=matching_params, **job_kwargs
         )
@@ -275,9 +243,8 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
 
             np.save(sorter_output_folder / "noise_levels.npy", noise_levels)
             np.save(sorter_output_folder / "all_peaks.npy", all_peaks)
-            # np.save(sorter_output_folder / "post_split_label.npy", post_split_label)
-            # np.save(sorter_output_folder / "split_count.npy", split_count)
-            # np.save(sorter_output_folder / "post_merge_label.npy", post_merge_label)
+            np.save(sorter_output_folder / "peaks.npy", peaks)
+            np.save(sorter_output_folder / "clustering_label.npy", clustering_label)
             np.save(sorter_output_folder / "spikes.npy", spikes)
 
         final_spikes = np.zeros(spikes.size, dtype=minimum_spike_dtype)