lint, mypy, and fixing tests

src/allencell_ml_segmenter/core/file_input_widget.py

@@ -214,14 +214,18 @@ def _from_directory_slot(self) -> None:
         self._model.set_input_mode(InputMode.FROM_PATH)
 
     def _update_layer_list(self, event: Optional[NapariEvent] = None) -> None:
-        existing_selection = [
-            self._image_list.item(i).text()
-            for i in self._image_list.get_checked_rows()
-        ]
+        existing_selection: List[str] = []
+        for i in self._image_list.get_checked_rows():
+            item: Optional[QListWidgetItem] = self._image_list.item(i)
+            if item is not None:
+                existing_selection.append(item.text())
+
         self._image_list.clear()
         self._reset_channel_combobox()
         for layer in self._viewer.get_layers():
-            path_of_layer_image: str = self._viewer.get_source_path(layer)
+            path_of_layer_image: Optional[Path] = self._viewer.get_source_path(
+                layer
+            )
             if path_of_layer_image:
                 self._image_list.add_item(
                     layer.name, set_checked=layer.name in existing_selection
@@ -230,12 +234,13 @@ def _update_layer_list(self, event: Optional[NapariEvent] = None) -> None:
     def _process_checked_signal(self, row: int, state: Qt.CheckState) -> None:
         if self._model.get_input_mode() == InputMode.FROM_NAPARI_LAYERS:
             selected_indices: List[int] = self._image_list.get_checked_rows()
-            selected_paths: List[Path] = [
-                self._viewer.get_source_path(
+            selected_paths: List[Path] = []
+            for i in selected_indices:
+                source_path: Optional[Path] = self._viewer.get_source_path(
                     self._viewer.get_layers_nonthreshold()[i]
                 )
-                for i in selected_indices
-            ]
+                if source_path is not None:
+                    selected_paths.append(source_path)
 
             # this will preserve the invariant: the options in the dropdown will be equal
             # to the number of channels in at least one of the selected images (or empty if no images selected)

src/allencell_ml_segmenter/curation/main_view.py

@@ -295,22 +295,23 @@ def add_curr_images_to_widget(self) -> None:
         )
         if raw_img_data is not None and raw_img_data.np_data is not None:
             self._viewer.add_image(
-                raw_img_data.np_data, f"[raw] {raw_img_data.path.name}"
+                raw_img_data.np_data, name=f"[raw] {raw_img_data.path.name}"
             )
         seg1_img_data: Optional[ImageData] = (
             self._curation_model.get_curr_image_data(ImageType.SEG1)
         )
         if seg1_img_data is not None and seg1_img_data.np_data is not None:
             self._viewer.add_labels(
-                seg1_img_data.np_data, f"[seg1] {seg1_img_data.path.name}"
+                seg1_img_data.np_data, name=f"[seg1] {seg1_img_data.path.name}"
             )
         if self._curation_model.has_seg2_data():
             seg2_img_data: Optional[ImageData] = (
                 self._curation_model.get_curr_image_data(ImageType.SEG2)
             )
             if seg2_img_data is not None and seg2_img_data.np_data is not None:
                 self._viewer.add_labels(
-                    seg2_img_data.np_data, f"[seg2] {seg2_img_data.path.name}"
+                    seg2_img_data.np_data,
+                    name=f"[seg2] {seg2_img_data.path.name}",
                 )
 
         self.enable_valid_masks()

src/allencell_ml_segmenter/main/i_viewer.py

@@ -1,6 +1,6 @@
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Optional, Callable
+from typing import Optional, Callable, Any
 from allencell_ml_segmenter.main.segmenter_layer import (
     ShapesLayer,
     ImageLayer,
@@ -16,7 +16,7 @@ def __init__(self) -> None:
         super().__init__()
 
     @abstractmethod
-    def add_image(self, image: np.ndarray, **kwargs) -> None:
+    def add_image(self, image: np.ndarray, **kwargs: Any) -> None:
         pass
 
     @abstractmethod
@@ -40,7 +40,7 @@ def get_all_shapes(self) -> list[ShapesLayer]:
         pass
 
     @abstractmethod
-    def add_labels(self, data: np.ndarray, **kwargs) -> None:
+    def add_labels(self, data: np.ndarray, **kwargs: Any) -> None:
         pass
 
     @abstractmethod

src/allencell_ml_segmenter/main/viewer.py

@@ -11,7 +11,7 @@
     LabelsLayer,
 )
 import napari  # type: ignore
-from typing import Callable, Optional
+from typing import Callable, Optional, Any
 import numpy as np
 
 
@@ -23,7 +23,7 @@ def __init__(
         super().__init__()
         self.viewer: napari.Viewer = viewer
 
-    def add_image(self, image: np.ndarray, **kwargs) -> None:
+    def add_image(self, image: np.ndarray, **kwargs: Any) -> None:
         self.viewer.add_image(image, **kwargs)
 
     def get_image(self, name: str) -> Optional[ImageLayer]:
@@ -61,7 +61,7 @@ def get_all_shapes(self) -> list[ShapesLayer]:
             if isinstance(l, Shapes)
         ]
 
-    def add_labels(self, data: np.ndarray, **kwargs) -> None:
+    def add_labels(self, data: np.ndarray, **kwargs: Any) -> None:
         self.viewer.add_labels(data, **kwargs)
 
     def get_labels(self, name: str) -> Optional[LabelsLayer]:

src/allencell_ml_segmenter/prediction/view.py

@@ -192,13 +192,13 @@ def showResults(self) -> None:
                         self._viewer.add_image(
                             raw_np_data,
                             name=f"[raw] {data['raw'].name}",
-                            metadata={"source_path": data["raw"]},
+                            metadata={"source_path": str(data["raw"])},
                         )
                     if seg_np_data is not None:
                         self._viewer.add_labels(
                             seg_np_data,
                             name=f"[seg] {data['seg'].name}",
-                            metadata={"source_path": data["seg"]},
+                            metadata={"source_path": str(data["seg"])},
                         )
 
                     self._main_model.set_predictions_in_viewer(True)

src/allencell_ml_segmenter/thresholding/thresholding_service.py

@@ -167,11 +167,10 @@ def _update_original_layers(self, _: Event) -> None:
         )
         new_layers_added: list[Layer] = current_layers
         if original_layers is not None:
-            new_layers_added: list[Layer] = [
+            new_layers_added = [
                 layer
                 for layer in current_layers
-                if layer.name
-                not in self._thresholding_model.get_original_layers()
+                if layer.name not in original_layers
             ]
 
         # refresh layers only if the new layers are not threshold layers (we dont want to track this in original layers state)