Linting

Author: schilling40

flamingo_tools/segmentation/postprocessing.py

@@ -717,7 +717,7 @@ def filter_cochlea_volume(
     resolution: float = 0.38,
     dilation_iterations: int = 12,
     padding: int = 1200,
-    dilation_method = "individual",
+    dilation_method: str = "individual",
 ) -> np.ndarray:
     """Filter cochlea volume with SGN and IHC segmentation.
     Centroids contained in the segmentation tables are used to create down-scaled binary arrays.

scripts/export_lower_resolution.py

@@ -127,18 +127,18 @@ def upscale_volume(
 def export_lower_resolution(args):
     # calculate single filter mask for all lower resolutions
     if args.filter_cochlea_channels is not None:
-            ds_factor = 48
-            filter_volume = filter_cochlea(args.cochlea, args.filter_cochlea_channels,
-                                           sgn_components=args.filter_sgn_components,
-                                           ihc_components=args.filter_ihc_components,
-                                           dilation_iterations=args.filter_dilation_iterations, ds_factor=ds_factor)
-            filter_volume = np.transpose(filter_volume, (2,1,0))
+        ds_factor = 48
+        filter_volume = filter_cochlea(args.cochlea, args.filter_cochlea_channels,
+                                       sgn_components=args.filter_sgn_components,
+                                       ihc_components=args.filter_ihc_components,
+                                       dilation_iterations=args.filter_dilation_iterations, ds_factor=ds_factor)
+        filter_volume = np.transpose(filter_volume, (2, 1, 0))
 
     # iterate through exporting lower resolutions
     for scale in args.scale:
         if args.filter_cochlea_channels is not None:
             output_folder = os.path.join(args.output_folder, args.cochlea,
-                                        f"scale{scale}_dilation{args.filter_dilation_iterations}")
+                                         f"scale{scale}_dilation{args.filter_dilation_iterations}")
         else:
             output_folder = os.path.join(args.output_folder, args.cochlea, f"scale{scale}")
         os.makedirs(output_folder, exist_ok=True)

scripts/training/sgn_domain_adaptation_gerbil_CR.py

@@ -20,7 +20,9 @@ def get_paths():
 def run_training(name):
     patch_shape = (64, 128, 128)
     batch_size = 8
-    source_checkpoint = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/trained_models/SGN/v2_cochlea_distance_unet_SGN_supervised_2025-05-27"
+    source_checkpoint = os.path.join("/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet",
+                                     "trained_models/SGN",
+                                     "v2_cochlea_distance_unet_SGN_supervised_2025-05-27")
 
     train_paths, val_paths = get_paths()
     mean_teacher_training(

scripts/training/sgn_domain_adaptation_gerbil_PV.py

@@ -20,7 +20,9 @@ def get_paths():
 def run_training(name):
     patch_shape = (64, 128, 128)
     batch_size = 8
-    source_checkpoint = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/trained_models/SGN/v2_cochlea_distance_unet_SGN_supervised_2025-05-27"
+    source_checkpoint = os.path.join("/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet",
+                                     "trained_models/SGN",
+                                     "v2_cochlea_distance_unet_SGN_supervised_2025-05-27")
 
     train_paths, val_paths = get_paths()
     mean_teacher_training(