Bombcell integration

doc/how_to/import_kilosort_data.rst

@@ -49,7 +49,7 @@ If you'd like to store the information you've computed, you can save the analyze
     )
 
 You now have a fully functional ``SortingAnalyzer`` - congrats! You can now use `spikeinterface-gui <https://github.com/SpikeInterface/spikeinterface-gui/>`__. to view the results
-interactively, or start manually labelling your units to `create an automated curation model <https://spikeinterface.readthedocs.io/en/stable/tutorials_custom_index.html#automated-curation-tutorials>`__.
+interactively, or start manually labeling your units to `create an automated curation model <https://spikeinterface.readthedocs.io/en/stable/tutorials_custom_index.html#automated-curation-tutorials>`__.
 
 Note that if you have access to the raw recording, you can attach it to the analyzer, and re-compute extensions from the raw data. E.g.
 

src/spikeinterface/comparison/comparisontools.py

@@ -569,7 +569,7 @@ def make_hungarian_match(agreement_scores, min_score):
 
 def do_score_labels(sorting1, sorting2, delta_frames, unit_map12, label_misclassification=False):
     """
-    Makes the labelling at spike level for each spike train:
+    Makes the labeling at spike level for each spike train:
       * TP: true positive
       * CL: classification error
       * FN: False negative

src/spikeinterface/core/analyzer_extension_core.py

@@ -997,6 +997,38 @@ def get_optional_dependencies(cls, **params):
         depend_on = list(cls.depend_on) + list(metric_depend_on)
         return depend_on
 
+    def get_computed_metric_names(self):
+        """
+        Get the list of already computed metric names.
+
+        Returns
+        -------
+        computed_metric_names : list[str]
+            List of computed metric names.
+        """
+        if self.data is None or len(self.data) == 0:
+            return []
+        else:
+            computed_metric_columns = self.data["metrics"].columns.tolist()
+            computed_metric_names = []
+            for m in self.metric_list:
+                if all(col in computed_metric_columns for col in m.metric_columns.keys()):
+                    computed_metric_names.append(m.metric_name)
+            return computed_metric_names
+
+    def _cast_metrics(self, metrics_df):
+        metric_dtypes = {}
+        for m in self.metric_list:
+            metric_dtypes.update(m.metric_columns)
+
+        for col in metrics_df.columns:
+            if col in metric_dtypes:
+                try:
+                    metrics_df[col] = metrics_df[col].astype(metric_dtypes[col])
+                except Exception as e:
+                    print(f"Error casting column {col}: {e}")
+        return metrics_df
+
     def _set_params(
         self,
         metric_names: list[str] | None = None,
@@ -1155,6 +1187,13 @@ def _compute_metrics(
             metric = [m for m in self.metric_list if m.metric_name == metric_name][0]
             column_names_dtypes.update(metric.metric_columns)
 
+        # drop metric that don't map to any metric names
+        possible_metric_names = [m.metric_name for m in self.metric_list]
+        wrong_metric_names = [m for m in metric_names if m not in possible_metric_names]
+        if len(wrong_metric_names) > 0:
+            warnings.warn(f"The following metric names are not recognized and will be ignored: {wrong_metric_names}")
+            metric_names = [m for m in metric_names if m in possible_metric_names]
+
         metrics = pd.DataFrame(index=unit_ids, columns=list(column_names_dtypes.keys()))
 
         run_times = {}

src/spikeinterface/core/tests/test_channelsaggregationrecording.py

@@ -189,7 +189,7 @@ def test_aggregation_labeling_for_lists():
     assert np.all(user_group_property == [6, 6, 7, 7])
 
 
-def test_aggretion_labelling_for_dicts():
+def test_aggretion_labeling_for_dicts():
     """Aggregated dicts of recordings get different labels depending on their underlying `property`s"""
 
     recording1 = generate_recording(num_channels=4, durations=[20], set_probe=False)

src/spikeinterface/curation/__init__.py

@@ -20,5 +20,12 @@
 from .sortingview_curation import apply_sortingview_curation
 
 # automated curation
+from .bombcell_curation import (
+    bombcell_get_default_thresholds,
+    bombcell_label_units,
+    get_bombcell_labeling_summary,
+    save_bombcell_results,
+)
+
 from .model_based_curation import auto_label_units, load_model
 from .train_manual_curation import train_model, get_default_classifier_search_spaces