Update TDC2 and SC2 with various improvement in sorting components (clustering + matching)

pyproject.toml

@@ -161,10 +161,14 @@ test = [
     # streaming templates
     "s3fs",
 
-    # tridesclous
+    # tridesclous2
     "numba<0.61.0;python_version<'3.13'",
     "numba>=0.61.0;python_version>='3.13'",
     "hdbscan>=0.8.33",  # Previous version had a broken wheel
+
+    # isosplit is needed for trideclous2 noramaly but isosplit is only build until python3.11
+    # so lets wait a new build of isosplit6
+    # "isosplit6",
 
     # for sortingview backend
     "sortingview>=0.12.0",

src/spikeinterface/benchmark/benchmark_base.py

@@ -9,6 +9,7 @@
 import time
 
 
+from spikeinterface.core import SortingAnalyzer, ChannelSparsity, NumpySorting
 from spikeinterface.core import SortingAnalyzer, NumpySorting
 from spikeinterface.core.job_tools import fix_job_kwargs, split_job_kwargs
 from spikeinterface import load, create_sorting_analyzer, load_sorting_analyzer
@@ -46,6 +47,7 @@ def __init__(self, study_folder):
         self.levels = None
         self.colors_by_case = None
         self.colors_by_levels = {}
+        self.labels_by_levels = {}
         self.scan_folder()
 
     @classmethod
@@ -120,8 +122,29 @@ def create(cls, study_folder, datasets={}, cases={}, levels=None):
                 rec, gt_sorting = data
 
                 if gt_sorting is not None:
+                    if "gt_unit_locations" in gt_sorting.get_property_keys():
+                        # if real units locations is present then use it for a better sparsity
+                        # then the real max channel is used
+                        radius_um = 100.
+                        channel_ids = rec.channel_ids
+                        unit_ids = gt_sorting.unit_ids
+                        gt_unit_locations = gt_sorting.get_property("gt_unit_locations")
+                        channel_locations = rec.get_channel_locations()
+                        max_channel_indices = np.argmin(np.linalg.norm(gt_unit_locations[:, np.newaxis, :2] - channel_locations[np.newaxis, :], axis=2), axis=1)
+                        mask = np.zeros((unit_ids.size, channel_ids.size), dtype="bool")
+                        distances = np.linalg.norm(channel_locations[:, np.newaxis] - channel_locations[np.newaxis, :], axis=2)
+                        for unit_ind, unit_id in enumerate(unit_ids):
+                            chan_ind = max_channel_indices[unit_ind]
+                            (chan_inds,) = np.nonzero(distances[chan_ind, :] <= radius_um)
+                            mask[unit_ind, chan_inds] = True
+                        sparsity = ChannelSparsity(mask, unit_ids, channel_ids)
+                        sparse =False
+                    else:
+                        sparse = True
+                        sparsity = None
+
                     analyzer = create_sorting_analyzer(
-                        gt_sorting, rec, sparse=True, format="binary_folder", folder=local_analyzer_folder
+                        gt_sorting, rec, sparse=sparse, sparsity=sparsity, format="binary_folder", folder=local_analyzer_folder
                     )
                     analyzer.compute("random_spikes")
                     analyzer.compute("templates")
@@ -135,6 +158,7 @@ def create(cls, study_folder, datasets={}, cases={}, levels=None):
                     analyzer = create_sorting_analyzer(
                         gt_sorting, rec, sparse=False, format="binary_folder", folder=local_analyzer_folder
                     )
+
             else:
                 # new case : analzyer
                 assert isinstance(data, SortingAnalyzer)
@@ -254,6 +278,8 @@ def run(self, case_keys=None, keep=True, verbose=False, **job_kwargs):
 
         for key in job_keys:
             benchmark = self.create_benchmark(key)
+            if verbose:
+                print("### Run benchmark", key, "###")
             t0 = time.perf_counter()
             benchmark.run(**job_kwargs)
             t1 = time.perf_counter()
@@ -324,14 +350,16 @@ def get_run_times(self, case_keys=None):
             df.index.names = self.levels
         return df
 
-    def get_grouped_keys_mapping(self, levels_to_group_by=None):
+    def get_grouped_keys_mapping(self, levels_to_group_by=None, case_keys=None):
         """
         Return a dictionary of grouped keys.
 
         Parameters
         ----------
         levels_to_group_by : list
             A list of levels to group by.
+        case_keys : list
+            Optionaly a sub list of case_keys to consider
 
         Returns
         -------
@@ -341,18 +369,19 @@ def get_grouped_keys_mapping(self, levels_to_group_by=None):
         labels : dict
             A dictionary of labels, with the new keys as keys and the labels as values.
         """
-        cases = list(self.cases.keys())
+        if case_keys is None:
+            case_keys = list(self.cases.keys())
         if levels_to_group_by is None or self.levels is None:
-            keys_mapping = {key: [key] for key in cases}
+            keys_mapping = {key: [key] for key in case_keys}
         elif len(self.levels) == 1:
-            keys_mapping = {key: [key] for key in cases}
+            keys_mapping = {key: [key] for key in case_keys}
         else:
             study_levels = self.levels
             assert np.all(
                 [l in study_levels for l in levels_to_group_by]
             ), f"levels_to_group_by must be in {study_levels}, got {levels_to_group_by}"
             keys_mapping = {}
-            for key in cases:
+            for key in case_keys:
                 new_key = tuple(key[list(study_levels).index(level)] for level in levels_to_group_by)
                 if len(new_key) == 1:
                     new_key = new_key[0]
@@ -361,13 +390,17 @@ def get_grouped_keys_mapping(self, levels_to_group_by=None):
                 keys_mapping[new_key].append(key)
 
         if levels_to_group_by is None:
-            labels = {key: self.cases[key]["label"] for key in cases}
+            labels = {key: self.cases[key]["label"] for key in case_keys}
         else:
-            key0 = list(keys_mapping.keys())[0]
-            if isinstance(key0, tuple):
-                labels = {key: "-".join(key) for key in keys_mapping}
+            level_key = tuple(levels_to_group_by) if len(levels_to_group_by) > 1 else levels_to_group_by[0]
+            if level_key in self.labels_by_levels:
+                labels = self.labels_by_levels[level_key]
             else:
-                labels = {key: key for key in keys_mapping}
+                key0 = list(keys_mapping.keys())[0]
+                if isinstance(key0, tuple):
+                    labels = {key: "-".join(key) for key in keys_mapping}
+                else:
+                    labels = {key: key for key in keys_mapping}
 
         return keys_mapping, labels
 
@@ -383,6 +416,8 @@ def compute_results(self, case_keys=None, verbose=False, **result_params):
 
         job_keys = []
         for key in case_keys:
+            if verbose:
+                print("### Compute result", key, "###")
             benchmark = self.benchmarks[key]
             assert benchmark is not None
             benchmark.compute_result(**result_params)
@@ -438,9 +473,9 @@ def get_gt_unit_locations(self, case_key):
             unit_locations_ext = sorting_analyzer.get_extension("unit_locations")
             return unit_locations_ext.get_data()
 
-    def get_templates(self, key, operator="average"):
+    def get_templates(self, key, operator="average", outputs='numpy'):
         sorting_analyzer = self.get_sorting_analyzer(case_key=key)
-        templates = sorting_analyzer.get_extenson("templates").get_data(operator=operator)
+        templates = sorting_analyzer.get_extension("templates").get_data(operator=operator, outputs=outputs)
         return templates
 
     def compute_metrics(self, case_keys=None, metric_names=["snr", "firing_rate"], force=False, **job_kwargs):
@@ -668,15 +703,15 @@ def get_count_units(self, case_keys=None, well_detected_score=None, redundant_sc
             gt_sorting = comp.sorting1
             sorting = comp.sorting2
 
-            count_units.loc[key, "num_gt"] = len(gt_sorting.get_unit_ids())
-            count_units.loc[key, "num_sorter"] = len(sorting.get_unit_ids())
-            count_units.loc[key, "num_well_detected"] = comp.count_well_detected_units(well_detected_score)
+            count_units.at[key, "num_gt"] = len(gt_sorting.get_unit_ids())
+            count_units.at[key, "num_sorter"] = len(sorting.get_unit_ids())
+            count_units.at[key, "num_well_detected"] = comp.count_well_detected_units(well_detected_score)
 
             if comp.exhaustive_gt:
-                count_units.loc[key, "num_redundant"] = comp.count_redundant_units(redundant_score)
-                count_units.loc[key, "num_overmerged"] = comp.count_overmerged_units(overmerged_score)
-                count_units.loc[key, "num_false_positive"] = comp.count_false_positive_units(redundant_score)
-                count_units.loc[key, "num_bad"] = comp.count_bad_units()
+                count_units.at[key, "num_redundant"] = comp.count_redundant_units(redundant_score)
+                count_units.at[key, "num_overmerged"] = comp.count_overmerged_units(overmerged_score)
+                count_units.at[key, "num_false_positive"] = comp.count_false_positive_units(redundant_score)
+                count_units.at[key, "num_bad"] = comp.count_bad_units()
 
         return count_units
 

src/spikeinterface/benchmark/benchmark_clustering.py

@@ -35,8 +35,8 @@ def run(self, **job_kwargs):
         )
         self.result["peak_labels"] = peak_labels
 
-    def compute_result(self, **result_params):
-        result_params, job_kwargs = split_job_kwargs(result_params)
+    def compute_result(self, with_template=False, **job_kwargs):
+        # result_params, job_kwargs = split_job_kwargs(result_params)
         job_kwargs = fix_job_kwargs(job_kwargs)
         self.noise = self.result["peak_labels"] < 0
         spikes = self.gt_sorting.to_spike_vector()
@@ -68,19 +68,20 @@ def compute_result(self, **result_params):
             self.result["sliced_gt_sorting"], self.result["clustering"], exhaustive_gt=self.exhaustive_gt
         )
 
-        sorting_analyzer = create_sorting_analyzer(
-            self.result["sliced_gt_sorting"], self.recording, format="memory", sparse=False, **job_kwargs
-        )
-        sorting_analyzer.compute("random_spikes")
-        ext = sorting_analyzer.compute("templates", **job_kwargs)
-        self.result["sliced_gt_templates"] = ext.get_data(outputs="Templates")
+        if with_template:
+            sorting_analyzer = create_sorting_analyzer(
+                self.result["sliced_gt_sorting"], self.recording, format="memory", sparse=False, **job_kwargs
+            )
+            sorting_analyzer.compute("random_spikes")
+            ext = sorting_analyzer.compute("templates", **job_kwargs)
+            self.result["sliced_gt_templates"] = ext.get_data(outputs="Templates")
 
-        sorting_analyzer = create_sorting_analyzer(
-            self.result["clustering"], self.recording, format="memory", sparse=False, **job_kwargs
-        )
-        sorting_analyzer.compute("random_spikes")
-        ext = sorting_analyzer.compute("templates", **job_kwargs)
-        self.result["clustering_templates"] = ext.get_data(outputs="Templates")
+            sorting_analyzer = create_sorting_analyzer(
+                self.result["clustering"], self.recording, format="memory", sparse=False, **job_kwargs
+            )
+            sorting_analyzer.compute("random_spikes")
+            ext = sorting_analyzer.compute("templates", **job_kwargs)
+            self.result["clustering_templates"] = ext.get_data(outputs="Templates")
 
     _run_key_saved = [("peak_labels", "npy")]
 

src/spikeinterface/benchmark/benchmark_matching.py

@@ -91,23 +91,35 @@ def plot_performances_ordered(self, *args, **kwargs):
 
         return plot_performances_ordered(self, *args, **kwargs)
 
-    def plot_collisions(self, case_keys=None, figsize=None):
+    def plot_collisions(self, case_keys=None, metric="l2", mode="lines", show_legend=True, axs=None, figsize=None):
         if case_keys is None:
             case_keys = list(self.cases.keys())
         import matplotlib.pyplot as plt
 
-        fig, axs = plt.subplots(ncols=len(case_keys), nrows=1, figsize=figsize, squeeze=False)
+        if axs is None:
+            fig, axs = plt.subplots(ncols=len(case_keys), nrows=1, figsize=figsize, squeeze=False)
+            axs = axs[0, :]
+        else:
+            fig = axs[0].figure
+
 
         for count, key in enumerate(case_keys):
-            templates_array = self.get_result(key)["templates"].templates_array
+            label = self.cases[key]["label"]
+            templates_array = self.get_sorting_analyzer(key).get_extension("templates").get_templates(outputs="numpy")
+            ax = axs[count]
             plot_comparison_collision_by_similarity(
                 self.get_result(key)["gt_collision"],
                 templates_array,
-                ax=axs[0, count],
-                show_legend=True,
-                mode="lines",
-                good_only=False,
+                metric=metric,
+                ax=ax,
+                show_legend=show_legend,
+                mode=mode,
+                # good_only=False,
+                # good_only=False,
+                good_only=True,
             )
+
+            ax.set_title(label)
 
         return fig