use dictreader csv

src/allencell_ml_segmenter/main/i_viewer.py

@@ -32,4 +32,3 @@ def get_paths_of_image_layers(self):
     @abstractmethod
     def subscribe_layers_change_event(self, function):
         pass
-

src/allencell_ml_segmenter/main/viewer.py

@@ -41,4 +41,4 @@ def subscribe_layers_change_event(self, function: Callable):
 
     def get_image_dims(self) -> Tuple:
         # just return x_y dims
-        return self.viewer.layers[0].data.shape
+        return self.viewer.layers[0].data.shape

src/allencell_ml_segmenter/prediction/file_input_widget.py

@@ -223,7 +223,8 @@ def _set_selected_image_paths_from_napari(
         if self._model.is_prediction_running():
             selected_indices: List[int] = self._image_list.get_checked_rows()
             selected_paths: List[Path] = [
-                self._viewer.get_layers()[i].source.path for i in selected_indices
+                self._viewer.get_layers()[i].source.path
+                for i in selected_indices
             ]
             self._model.set_selected_paths(selected_paths)
 

src/allencell_ml_segmenter/prediction/model.py

@@ -186,4 +186,3 @@ def set_max_channels(self, max: int) -> None:
 
     def get_max_channels(self) -> int:
         return self._max_channels
-

src/allencell_ml_segmenter/prediction/service.py

@@ -60,11 +60,11 @@ def extract_num_channels_in_folder(self, path: Path) -> int:
 
     def extract_num_channels_from_csv(self, path: Path):
         with open(path) as file:
-            reader: csv.reader = csv.reader(file)
+            reader: csv.reader = csv.DictReader(file)
             # skip heading
             next(reader)
             # first column contrains files of interest (zeroth column is index)
-            line_data_path: str = next(reader)[1]
+            line_data_path: str = next(reader)["raw"]
             return extract_num_channels_from_image(line_data_path)
 
     def _determine_input_selection_type(self, path: Path):

src/allencell_ml_segmenter/services/prediction_service.py

@@ -43,7 +43,10 @@ def _predict_model(self, _: Event) -> None:
         """
         Predict segmentations using model according to spec
         """
-        if self._prediction_model.is_prediction_running() and self._able_to_continue_prediction():
+        if (
+            self._prediction_model.is_prediction_running()
+            and self._able_to_continue_prediction()
+        ):
             cyto_api: CytoDLModel = CytoDLModel()
             cyto_api.load_config_from_file(
                 self._experiments_model.get_train_config_path
@@ -182,4 +185,3 @@ def _setup_inputs_from_napari(self) -> bool:
                 self._prediction_model.get_selected_paths()
             )
             return True
-