Merge pull request #2205 from yger/legacy_mode

Legacy mode for circus 2 to ease comparison with circus 1

src/spikeinterface/comparison/groundtruthstudy.py

@@ -21,7 +21,7 @@
 
 
 # This is to separate names when the key are tuples when saving folders
-_key_separator = " ## "
+_key_separator = "_##_"
 
 
 class GroundTruthStudy:

src/spikeinterface/sorters/internal/spyking_circus2.py

@@ -21,12 +21,17 @@ class Spykingcircus2Sorter(ComponentsBasedSorter):
 
     _default_params = {
         "general": {"ms_before": 2, "ms_after": 2, "radius_um": 100},
-        "waveforms": {"max_spikes_per_unit": 200, "overwrite": True, "sparse": True, "method": "ptp", "threshold": 1},
+        "waveforms": {
+            "max_spikes_per_unit": 200,
+            "overwrite": True,
+            "sparse": True,
+            "method": "energy",
+            "threshold": 0.25,
+        },
         "filtering": {"freq_min": 150, "dtype": "float32"},
-        "detection": {"peak_sign": "neg", "detect_threshold": 5},
+        "detection": {"peak_sign": "neg", "detect_threshold": 4},
         "selection": {"n_peaks_per_channel": 5000, "min_n_peaks": 20000},
-        "localization": {},
-        "clustering": {},
+        "clustering": {"legacy": False},
         "matching": {},
         "apply_preprocessing": True,
         "shared_memory": True,
@@ -66,6 +71,7 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
             recording_f = common_reference(recording_f)
         else:
             recording_f = recording
+            recording_f.annotate(is_filtered=True)
 
         # recording_f = whiten(recording_f, dtype="float32")
         recording_f = zscore(recording_f, dtype="float32")
@@ -111,8 +117,18 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         clustering_params["tmp_folder"] = sorter_output_folder / "clustering"
         clustering_params.update({"noise_levels": noise_levels})
 
+        if "legacy" in clustering_params:
+            legacy = clustering_params.pop("legacy")
+        else:
+            legacy = False
+
+        if legacy:
+            clustering_method = "circus"
+        else:
+            clustering_method = "random_projections"
+
         labels, peak_labels = find_cluster_from_peaks(
-            recording_f, selected_peaks, method="random_projections", method_kwargs=clustering_params
+            recording_f, selected_peaks, method=clustering_method, method_kwargs=clustering_params
         )
 
         ## We get the labels for our peaks
@@ -140,13 +156,18 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
             waveforms_folder = sorter_output_folder / "waveforms"
 
         we = extract_waveforms(
-            recording_f, sorting, waveforms_folder, mode=mode, **waveforms_params, return_scaled=False
+            recording_f,
+            sorting,
+            waveforms_folder,
+            return_scaled=False,
+            precompute_template=["median"],
+            mode=mode,
+            **waveforms_params,
         )
 
         ## We launch a OMP matching pursuit by full convolution of the templates and the raw traces
         matching_params = params["matching"].copy()
         matching_params["waveform_extractor"] = we
-        matching_params.update({"noise_levels": noise_levels})
 
         matching_job_params = job_kwargs.copy()
         for value in ["chunk_size", "chunk_memory", "total_memory", "chunk_duration"]:

src/spikeinterface/sorters/internal/tridesclous2.py

@@ -9,12 +9,14 @@
     NumpySorting,
     get_channel_distances,
 )
-from spikeinterface.core.waveform_tools import extract_waveforms_to_single_buffer
+
 from spikeinterface.core.job_tools import fix_job_kwargs
 
 from spikeinterface.preprocessing import bandpass_filter, common_reference, zscore
 from spikeinterface.core.basesorting import minimum_spike_dtype
 
+from spikeinterface.sortingcomponents.tools import extract_waveform_at_max_channel
+
 import numpy as np
 
 import pickle
@@ -115,9 +117,14 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         if verbose:
             print("We kept %d peaks for clustering" % len(peaks))
 
+        ms_before = params["waveforms"]["ms_before"]
+        ms_after = params["waveforms"]["ms_after"]
+
         # SVD for time compression
         few_peaks = select_peaks(peaks, method="uniform", n_peaks=5000)
-        few_wfs = extract_waveform_at_max_channel(recording, few_peaks, **job_kwargs)
+        few_wfs = extract_waveform_at_max_channel(
+            recording, few_peaks, ms_before=ms_before, ms_after=ms_after, **job_kwargs
+        )
 
         wfs = few_wfs[:, :, 0]
         tsvd = TruncatedSVD(params["svd"]["n_components"])
@@ -129,8 +136,6 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         with open(model_folder / "pca_model.pkl", "wb") as f:
             pickle.dump(tsvd, f)
 
-        ms_before = params["waveforms"]["ms_before"]
-        ms_after = params["waveforms"]["ms_after"]
         model_params = {
             "ms_before": ms_before,
             "ms_after": ms_after,
@@ -319,39 +324,3 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         sorting = sorting.save(folder=sorter_output_folder / "sorting")
 
         return sorting
-
-
-def extract_waveform_at_max_channel(rec, peaks, ms_before=0.5, ms_after=1.5, **job_kwargs):
-    """
-    Helper function to extractor waveforms at max channel from a peak list
-
-
-    """
-    n = rec.get_num_channels()
-    unit_ids = np.arange(n, dtype="int64")
-    sparsity_mask = np.eye(n, dtype="bool")
-
-    spikes = np.zeros(
-        peaks.size, dtype=[("sample_index", "int64"), ("unit_index", "int64"), ("segment_index", "int64")]
-    )
-    spikes["sample_index"] = peaks["sample_index"]
-    spikes["unit_index"] = peaks["channel_index"]
-    spikes["segment_index"] = peaks["segment_index"]
-
-    nbefore = int(ms_before * rec.sampling_frequency / 1000.0)
-    nafter = int(ms_after * rec.sampling_frequency / 1000.0)
-
-    all_wfs = extract_waveforms_to_single_buffer(
-        rec,
-        spikes,
-        unit_ids,
-        nbefore,
-        nafter,
-        mode="shared_memory",
-        return_scaled=False,
-        sparsity_mask=sparsity_mask,
-        copy=True,
-        **job_kwargs,
-    )
-
-    return all_wfs