fix synchrony

src/spikeinterface/qualitymetrics/quality_metric_calculator.py

@@ -230,7 +230,13 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
         # we do this because the convert_dtypes infers the wrong types sometimes.
         # the actual types for columns can be found in column_name_to_column_dtype dictionary.
         for column in metrics.columns:
-            metrics[column] = metrics[column].astype(column_name_to_column_dtype[column])
+            # we have one issue where the name of the columns for synchrony are named based on
+            # what the user has input as arguments so we need a way to handle this separately
+            # everything else should be handled with the column name.
+            if "sync" in column:
+                metrics[column] = metrics[column].astype(column_name_to_column_dtype["sync"])
+            else:
+                metrics[column] = metrics[column].astype(column_name_to_column_dtype[column])
 
         return metrics
 

src/spikeinterface/qualitymetrics/quality_metric_list.py

@@ -84,6 +84,7 @@
     "silhouette_full": ["silhouette_full"],
 }
 
+# this dict allows us to ensure the appropriate dtype of metrics rather than allow Pandas to infer them
 column_name_to_column_dtype = {
     "num_spikes": int,
     "firing_rate": float,
@@ -98,7 +99,7 @@
     "amplitude_median": float,
     "amplitude_cv_median": float,
     "amplitude_cv_range": float,
-    "synch": float,
+    "sync": float,
     "firing_range": float,
     "drift_ptp": float,
     "drift_std": float,