Merge pull request #3417 from alejoe91/fixes-convert-dtypes

Fix metrics widgets for convert_dtypes

Author: alejoe91

src/spikeinterface/widgets/metrics.py

@@ -235,6 +235,9 @@ def plot_sortingview(self, data_plot, **backend_kwargs):
             values = check_json(metrics.loc[unit_id].to_dict())
             values_skip_nans = {}
             for k, v in values.items():
+                # convert_dypes returns NaN as None or np.nan (for float)
+                if v is None:
+                    continue
                 if np.isnan(v):
                     continue
                 values_skip_nans[k] = v

src/spikeinterface/widgets/tests/test_widgets.py

@@ -73,7 +73,7 @@ def setUpClass(cls):
             spike_amplitudes=dict(),
             unit_locations=dict(),
             spike_locations=dict(),
-            quality_metrics=dict(metric_names=["snr", "isi_violation", "num_spikes"]),
+            quality_metrics=dict(metric_names=["snr", "isi_violation", "num_spikes", "amplitude_cutoff"]),
             template_metrics=dict(),
             correlograms=dict(),
             template_similarity=dict(),

src/spikeinterface/widgets/utils_sortingview.py

@@ -106,9 +106,9 @@ def generate_unit_table_view(
             if prop_name in sorting_props:
                 property_values = sorting.get_property(prop_name)
             elif prop_name in qm_props:
-                property_values = qm_data[prop_name].values
+                property_values = qm_data[prop_name].to_numpy()
             elif prop_name in tm_props:
-                property_values = tm_data[prop_name].values
+                property_values = tm_data[prop_name].to_numpy()
             else:
                 warn(f"Property '{prop_name}' not found in sorting, quality_metrics, or template_metrics")
                 continue
@@ -137,9 +137,9 @@ def generate_unit_table_view(
                 if prop_name in sorting_props:
                     property_values = sorting.get_property(prop_name)
                 elif prop_name in qm_props:
-                    property_values = qm_data[prop_name].values
+                    property_values = qm_data[prop_name].to_numpy()
                 elif prop_name in tm_props:
-                    property_values = tm_data[prop_name].values
+                    property_values = tm_data[prop_name].to_numpy()
 
                 # Check for NaN values and round floats
                 val0 = np.array(property_values[0])