Update on tridesclous2

src/spikeinterface/sorters/internal/tridesclous2.py

@@ -29,10 +29,12 @@ class Tridesclous2Sorter(ComponentsBasedSorter):
         "waveforms": {
             "ms_before": 0.5,
             "ms_after": 1.5,
+            "radius_um": 120.0,
         },
-        "filtering": {"freq_min": 300.0, "freq_max": 8000.0},
+        "filtering": {"freq_min": 300.0, "freq_max": 12000.0},
         "detection": {"peak_sign": "neg", "detect_threshold": 5, "exclude_sweep_ms": 1.5, "radius_um": 150.0},
         "selection": {"n_peaks_per_channel": 5000, "min_n_peaks": 20000},
+        "features": {},
         "svd": {"n_components": 6},
         "clustering": {
             "split_radius_um": 40.0,
@@ -154,13 +156,14 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         #                             upsampling_um=5.0,
         #                             )
 
+        radius_um = params["waveforms"]["radius_um"]
         node3 = ExtractSparseWaveforms(
             recording,
             parents=[node0],
             return_output=True,
-            ms_before=0.5,
-            ms_after=1.5,
-            radius_um=100.0,
+            ms_before=ms_before,
+            ms_after=ms_after,
+            radius_um=radius_um,
         )
 
         model_folder_path = sorter_output_folder / "tsvd_model"
@@ -193,6 +196,8 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
 
         original_labels = peaks["channel_index"]
 
+        min_cluster_size = 50
+
         post_split_label, split_count = split_clusters(
             original_labels,
             recording,
@@ -205,8 +210,8 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
                 # feature_name="sparse_wfs",
                 neighbours_mask=neighbours_mask,
                 waveforms_sparse_mask=sparse_mask,
-                min_size_split=50,
-                min_cluster_size=50,
+                min_size_split=min_cluster_size,
+                min_cluster_size=min_cluster_size,
                 min_samples=50,
                 n_pca_features=3,
             ),
@@ -237,9 +242,10 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
                 # criteria="percentile",
                 # threshold_percentile=80.,
                 criteria="distrib_overlap",
-                threshold_overlap=0.4,
+                threshold_overlap=0.3,
+                min_cluster_size=min_cluster_size + 1,
                 # num_shift=0
-                num_shift=2,
+                num_shift=5,
             ),
             **job_kwargs,
         )

src/spikeinterface/sortingcomponents/clustering/merge.py

@@ -398,6 +398,7 @@ def merge(
         threshold_diptest=0.5,
         threshold_percentile=80.0,
         threshold_overlap=0.4,
+        min_cluster_size=50,
         num_shift=2,
     ):
         if num_shift > 0:
@@ -414,7 +415,7 @@ def merge(
         chans1 = np.unique(peaks["channel_index"][inds1])
         target_chans1 = np.flatnonzero(np.all(waveforms_sparse_mask[chans1, :], axis=0))
 
-        if inds0.size < 40 or inds1.size < 40:
+        if inds0.size < min_cluster_size or inds1.size < min_cluster_size:
             is_merge = False
             merge_value = 0
             final_shift = 0
@@ -525,7 +526,9 @@ def merge(
         # DEBUG = True
         DEBUG = False
 
-        if DEBUG and is_merge:
+        # if DEBUG and is_merge:
+        # if DEBUG and (overlap > 0.1 and overlap <0.3):
+        if DEBUG:
             # if DEBUG and not is_merge:
             # if DEBUG and (overlap > 0.05 and overlap <0.25):
             # if label0 == 49 and label1== 65:

src/spikeinterface/sortingcomponents/clustering/split.py

@@ -192,6 +192,7 @@ def split(
 
         # target channel subset is done intersect local channels + neighbours
         local_chans = np.unique(peaks["channel_index"][peak_indices])
+
         target_channels = np.flatnonzero(np.all(neighbours_mask[local_chans, :], axis=0))
 
         # TODO fix this a better way, this when cluster have too few overlapping channels
@@ -204,6 +205,7 @@ def split(
 
         local_labels[dont_have_channels] = -2
         kept = np.flatnonzero(~dont_have_channels)
+
         if kept.size < min_size_split:
             return False, None