Fix dtype of quality metrics before and after merging

src/spikeinterface/qualitymetrics/quality_metric_calculator.py

@@ -16,7 +16,8 @@
     compute_pc_metrics,
     _misc_metric_name_to_func,
     _possible_pc_metric_names,
-    qm_compute_name_to_column_names,
+    compute_name_to_column_names,
+    column_name_to_column_dtype,
 )
 from .misc_metrics import _default_params as misc_metrics_params
 from .pca_metrics import _default_params as pca_metrics_params
@@ -140,13 +141,20 @@ def _merge_extension_data(
         all_unit_ids = new_sorting_analyzer.unit_ids
         not_new_ids = all_unit_ids[~np.isin(all_unit_ids, new_unit_ids)]
 
+        # this creates a new metrics dictionary, but the dtype for everything will be
+        # object. So we will need to fix this later after computing metrics
         metrics = pd.DataFrame(index=all_unit_ids, columns=old_metrics.columns)
-
         metrics.loc[not_new_ids, :] = old_metrics.loc[not_new_ids, :]
         metrics.loc[new_unit_ids, :] = self._compute_metrics(
             new_sorting_analyzer, new_unit_ids, verbose, metric_names, **job_kwargs
         )
 
+        # we need to fix the dtypes after we compute everything because we have nans
+        # we can iterate through the columns and convert them back to the dtype
+        # of the original quality dataframe.
+        for column in old_metrics.columns:
+            metrics[column] = metrics[column].astype(old_metrics[column].dtype)
+
         new_data = dict(metrics=metrics)
         return new_data
 
@@ -229,10 +237,25 @@ def _compute_metrics(self, sorting_analyzer, unit_ids=None, verbose=False, metri
         # add NaN for empty units
         if len(empty_unit_ids) > 0:
             metrics.loc[empty_unit_ids] = np.nan
+            # num_spikes is an int and should be 0
+            if "num_spikes" in metrics.columns:
+                metrics.loc[empty_unit_ids, ["num_spikes"]] = 0
 
         # we use the convert_dtypes to convert the columns to the most appropriate dtype and avoid object columns
         # (in case of NaN values)
         metrics = metrics.convert_dtypes()
+
+        # 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:
+            # 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
 
     def _run(self, verbose=False, **job_kwargs):

src/spikeinterface/qualitymetrics/quality_metric_list.py

@@ -66,7 +66,11 @@
     "amplitude_cutoff": ["amplitude_cutoff"],
     "amplitude_median": ["amplitude_median"],
     "amplitude_cv": ["amplitude_cv_median", "amplitude_cv_range"],
-    "synchrony": ["sync_spike_2", "sync_spike_4", "sync_spike_8"],
+    "synchrony": [
+        "sync_spike_2",
+        "sync_spike_4",
+        "sync_spike_8",
+    ],  # we probably shouldn't hard code this. This is determined by the arguments in the function...
     "firing_range": ["firing_range"],
     "drift": ["drift_ptp", "drift_std", "drift_mad"],
     "sd_ratio": ["sd_ratio"],
@@ -79,3 +83,36 @@
     "silhouette": ["silhouette"],
     "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,
+    "presence_ratio": float,
+    "snr": float,
+    "isi_violations_ratio": float,
+    "isi_violations_count": float,
+    "rp_violations": float,
+    "rp_contamination": float,
+    "sliding_rp_violation": float,
+    "amplitude_cutoff": float,
+    "amplitude_median": float,
+    "amplitude_cv_median": float,
+    "amplitude_cv_range": float,
+    "sync": float,
+    "firing_range": float,
+    "drift_ptp": float,
+    "drift_std": float,
+    "drift_mad": float,
+    "sd_ratio": float,
+    "isolation_distance": float,
+    "l_ratio": float,
+    "d_prime": float,
+    "nn_hit_rate": float,
+    "nn_miss_rate": float,
+    "nn_isolation": float,
+    "nn_unit_id": float,
+    "nn_noise_overlap": float,
+    "silhouette": float,
+    "silhouette_full": float,
+}

src/spikeinterface/qualitymetrics/tests/test_quality_metric_calculator.py

@@ -48,6 +48,33 @@ def test_compute_quality_metrics(sorting_analyzer_simple):
     assert "isolation_distance" in metrics.columns
 
 
+def test_merging_quality_metrics(sorting_analyzer_simple):
+
+    sorting_analyzer = sorting_analyzer_simple
+
+    metrics = compute_quality_metrics(
+        sorting_analyzer,
+        metric_names=None,
+        qm_params=dict(isi_violation=dict(isi_threshold_ms=2)),
+        skip_pc_metrics=False,
+        seed=2205,
+    )
+
+    # sorting_analyzer_simple has ten units
+    new_sorting_analyzer = sorting_analyzer.merge_units([[0, 1]])
+
+    new_metrics = new_sorting_analyzer.get_extension("quality_metrics").get_data()
+
+    # we should copy over the metrics after merge
+    for column in metrics.columns:
+        assert column in new_metrics.columns
+        # should copy dtype too
+        assert metrics[column].dtype == new_metrics[column].dtype
+
+    # 10 units vs 9 units
+    assert len(metrics.index) > len(new_metrics.index)
+
+
 def test_compute_quality_metrics_recordingless(sorting_analyzer_simple):
 
     sorting_analyzer = sorting_analyzer_simple
@@ -106,10 +133,15 @@ def test_empty_units(sorting_analyzer_simple):
         seed=2205,
     )
 
-    for empty_unit_id in sorting_empty.get_empty_unit_ids():
+    # num_spikes are ints not nans so we confirm empty units are nans for everything except
+    # num_spikes which should be 0
+    nan_containing_columns = [column for column in metrics_empty.columns if column != "num_spikes"]
+    for empty_unit_ids in sorting_empty.get_empty_unit_ids():
         from pandas import isnull
 
-        assert np.all(isnull(metrics_empty.loc[empty_unit_id].values))
+        assert np.all(isnull(metrics_empty.loc[empty_unit_ids, nan_containing_columns].values))
+        if "num_spikes" in metrics_empty.columns:
+            assert sum(metrics_empty.loc[empty_unit_ids, ["num_spikes"]]) == 0
 
 
 # TODO @alessio all theses old test should be moved in test_metric_functions.py or test_pca_metrics()