Only load template_metrics on compute if propogating some metrics

Author: chrishalcrow

src/spikeinterface/core/sortinganalyzer.py

@@ -2033,19 +2033,18 @@ def load(cls, sorting_analyzer):
         return None
 
     def load_run_info(self):
+        run_info = None
         if self.format == "binary_folder":
             extension_folder = self._get_binary_extension_folder()
             run_info_file = extension_folder / "run_info.json"
             if run_info_file.is_file():
                 with open(str(run_info_file), "r") as f:
                     run_info = json.load(f)
-            else:
-                warnings.warn(f"Found no run_info file for {self.extension_name}, extension should be re-computed.")
-                run_info = None
 
         elif self.format == "zarr":
             extension_group = self._get_zarr_extension_group(mode="r")
             run_info = extension_group.attrs.get("run_info", None)
+
         if run_info is None:
             warnings.warn(f"Found no run_info file for {self.extension_name}, extension should be re-computed.")
         self.run_info = run_info

src/spikeinterface/postprocessing/template_metrics.py

@@ -335,13 +335,18 @@ def _run(self, verbose=False):
         )
 
         existing_metrics = []
-        tm_extension = self.sorting_analyzer.get_extension("template_metrics")
-        if (
-            delete_existing_metrics is False
-            and tm_extension is not None
-            and tm_extension.data.get("metrics") is not None
-        ):
-            existing_metrics = tm_extension.params["metric_names"]
+
+        # Check if we need to propogate any old metrics. If so, we'll do that.
+        # Otherwise, we'll avoid attempting to load an empty template_metrics.
+        if set(self.params["metrics_to_compute"]) != set(self.params["metric_names"]):
+
+            tm_extension = self.sorting_analyzer.get_extension("template_metrics")
+            if (
+                delete_existing_metrics is False
+                and tm_extension is not None
+                and tm_extension.data.get("metrics") is not None
+            ):
+                existing_metrics = tm_extension.params["metric_names"]
 
         # append the metrics which were previously computed
         for metric_name in set(existing_metrics).difference(metrics_to_compute):