Merge pull request #3213 from yger/patch_release

Patch for SC2 after release AND bugs in auto merge

src/spikeinterface/curation/auto_merge.py

@@ -243,14 +243,14 @@ def get_potential_auto_merge(
         assert step in all_steps, f"{step} is not a valid step"
 
         # STEP : remove units with too few spikes
-        if step == "min_spikes":
+        if step == "num_spikes":
             num_spikes = sorting.count_num_spikes_per_unit(outputs="array")
             to_remove = num_spikes < min_spikes
             pair_mask[to_remove, :] = False
             pair_mask[:, to_remove] = False
 
         # STEP : remove units with too small SNR
-        elif step == "min_snr":
+        elif step == "snr":
             qm_ext = sorting_analyzer.get_extension("quality_metrics")
             if qm_ext is None:
                 sorting_analyzer.compute("noise_levels")

src/spikeinterface/postprocessing/tests/common_extension_tests.py

@@ -135,7 +135,7 @@ def _check_one(self, sorting_analyzer, extension_class, params):
 
         some_merges = [sorting_analyzer.unit_ids[:2].tolist()]
         num_units_after_merge = len(sorting_analyzer.unit_ids) - 1
-        merged = sorting_analyzer.merge_units(some_merges, format="memory", mode="soft", sparsity_overlap=0.0)
+        merged = sorting_analyzer.merge_units(some_merges, format="memory", merging_mode="soft", sparsity_overlap=0.0)
         assert len(merged.unit_ids) == num_units_after_merge
 
     def run_extension_tests(self, extension_class, params):

src/spikeinterface/sorters/internal/spyking_circus2.py

@@ -17,6 +17,7 @@
 from spikeinterface.core.sortinganalyzer import create_sorting_analyzer
 from spikeinterface.curation.auto_merge import get_potential_auto_merge
 from spikeinterface.core.analyzer_extension_core import ComputeTemplates
+from spikeinterface.core.sparsity import ChannelSparsity
 
 
 class Spykingcircus2Sorter(ComponentsBasedSorter):
@@ -41,8 +42,7 @@ class Spykingcircus2Sorter(ComponentsBasedSorter):
             "correlograms_kwargs": {},
             "auto_merge": {
                 "min_spikes": 10,
-                "corr_diff_thresh": 0.5,
-                "censor_correlograms_ms": 0.4,
+                "corr_diff_thresh": 0.25,
             },
         },
         "clustering": {"legacy": True},
@@ -357,27 +357,40 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         return sorting
 
 
-def final_cleaning_circus(recording, sorting, templates, **merging_kwargs):
-
-    from spikeinterface.core.sorting_tools import apply_merges_to_sorting
-    from spikeinterface.curation.curation_tools import resolve_merging_graph
-
+def create_sorting_analyzer_with_templates(sorting, recording, templates, remove_empty=True):
     sparsity = templates.sparsity
     templates_array = templates.get_dense_templates().copy()
 
-    sa = create_sorting_analyzer(sorting, recording, format="memory", sparsity=sparsity)
-
+    if remove_empty:
+        non_empty_unit_ids = sorting.get_non_empty_unit_ids()
+        non_empty_sorting = sorting.remove_empty_units()
+        non_empty_unit_indices = sorting.ids_to_indices(non_empty_unit_ids)
+        templates_array = templates_array[non_empty_unit_indices]
+        sparsity_mask = sparsity.mask[non_empty_unit_indices, :]
+        sparsity = ChannelSparsity(sparsity_mask, non_empty_unit_ids, sparsity.channel_ids)
+    else:
+        non_empty_sorting = sorting
+
+    sa = create_sorting_analyzer(non_empty_sorting, recording, format="memory", sparsity=sparsity)
     sa.extensions["templates"] = ComputeTemplates(sa)
     sa.extensions["templates"].params = {"ms_before": templates.ms_before, "ms_after": templates.ms_after}
     sa.extensions["templates"].data["average"] = templates_array
+    return sa
+
+
+def final_cleaning_circus(recording, sorting, templates, **merging_kwargs):
+
+    from spikeinterface.core.sorting_tools import apply_merges_to_sorting
+
+    sa = create_sorting_analyzer_with_templates(sorting, recording, templates)
+
     sa.compute("unit_locations", method="monopolar_triangulation")
     similarity_kwargs = merging_kwargs.pop("similarity_kwargs", {})
     sa.compute("template_similarity", **similarity_kwargs)
     correlograms_kwargs = merging_kwargs.pop("correlograms_kwargs", {})
     sa.compute("correlograms", **correlograms_kwargs)
     auto_merge_kwargs = merging_kwargs.pop("auto_merge", {})
-    merges = get_potential_auto_merge(sa, **auto_merge_kwargs)
-    merges = resolve_merging_graph(sorting, merges)
-    sorting = apply_merges_to_sorting(sorting, merges)
+    merges = get_potential_auto_merge(sa, resolve_graph=True, **auto_merge_kwargs)
+    sorting = apply_merges_to_sorting(sa.sorting, merges)
 
     return sorting