type, lint

src/allencell_ml_segmenter/main/main_model.py

@@ -85,4 +85,3 @@ def set_selected_channels(
 
     def training_complete(self) -> None:
         self.dispatch(Event.PROCESS_TRAINING_COMPLETE)
-

src/allencell_ml_segmenter/main/main_widget.py

@@ -148,9 +148,7 @@ def __init__(
 
         # events for auto window switching
         self._model.subscribe(
-            Event.PROCESS_TRAINING_COMPLETE,
-            self,
-            self._handle_existing_model
+            Event.PROCESS_TRAINING_COMPLETE, self, self._handle_existing_model
         )
 
     def _handle_experiment_applied(self, _: Event) -> None:

src/allencell_ml_segmenter/training/view.py

@@ -298,17 +298,26 @@ def getTypeOfWork(self) -> str:
 
     def showResults(self) -> None:
         # double check to see if a ckpt was generated
+        exp_path: Optional[Path] = self._experiments_model.get_user_experiments_path()
+        if exp_path is None:
+            raise ValueError("Experiments path should not be None after training complete.")
+        exp_name: Optional[str] = self._experiments_model.get_experiment_name()
+        if exp_name is None:
+            raise ValueError("Experiment name should not be None after training complete.")
         ckpt_generated: Optional[Path] = ExperimentUtils.get_best_ckpt(
-            self._experiments_model.get_user_experiments_path(),
-            self._experiments_model.get_experiment_name())
+            exp_path,
+            exp_name,
+        )
         if ckpt_generated is not None:
             # if model was successfully trained, get metrics to display
             csv_path: Optional[Path] = (
                 self._experiments_model.get_latest_metrics_csv_path()
             )
             if csv_path is None:
                 raise RuntimeError("Cannot get min loss from undefined csv")
-            min_loss: Optional[float] = FileUtils.get_min_loss_from_csv(csv_path)
+            min_loss: Optional[float] = FileUtils.get_min_loss_from_csv(
+                csv_path
+            )
             if min_loss is None:
                 raise RuntimeError("Cannot compute min loss")
 
@@ -323,7 +332,6 @@ def showResults(self) -> None:
             )
             dialog_box.exec()
 
-
     def _num_epochs_field_handler(self, num_epochs: str) -> None:
         self._training_model.set_num_epochs(int(num_epochs))