ruff

Author: Hendrik-code

example.py

@@ -69,7 +69,9 @@ def run_example(
     print("Extracted features:")
     for fk, fv in features.items():
         if isinstance(fv, list):
-            fv = f"{round(np.mean(fv), 3)} +- {round(np.std(fv), 3)}"
+            fvv = f"{round(np.mean(fv), 3)} +- {round(np.std(fv), 3)}"
         elif isinstance(fv, float):
-            fv = round(fv, 3)
-        print(f"{fk}: {fv}")
+            fvv = round(fv, 3)
+        else:
+            fvv = fv
+        print(f"{fk}: {fvv}")

src/calc_label_thickness.py

@@ -69,7 +69,7 @@ def compute_boundary_normals(seg, label1, label2):
     kernel[0, 1, 1] = kernel[2, 1, 1] = 1
 
     # Convolve class 1 with kernel to count neighbors
-    neighbors_of_class1 = convolve(class1.astype(int), kernel, mode="constant")
+    # neighbors_of_class1 = convolve(class1.astype(int), kernel, mode="constant")
 
     # Find class 1 voxels that are adjacent to class 2
     touching_mask = np.logical_and(class1, convolve(class2.astype(int), kernel, mode="constant") > 0)
@@ -137,7 +137,7 @@ def raymarch_until_background(
     #    mask_value = interpolator(coords)
     #    return mask_value > 0.5
 
-    for step in range(max_steps):
+    for _step in range(max_steps):
         if max_distance is not None:
             dist = np.linalg.norm(pos - start_coord)
             if dist >= max_distance:

src/inference_function.py

@@ -1,3 +1,5 @@
+from pathlib import Path
+
 import numpy as np
 from spineps.seg_enums import OutputType
 from spineps.seg_model import Segmentation_Model

src/load_model.py

@@ -27,9 +27,9 @@ def load_model(model: str | Path | int) -> Segmentation_Model:
         raise TypeError("Model must be a string, Path, or integer.")
 
 
-def load_model_by_path(dir: str | Path) -> Segmentation_Model:
+def load_model_by_path(path_dir: str | Path) -> Segmentation_Model:
     """Load a model from a specified directory."""
-    return get_actual_model(in_config=dir).load()
+    return get_actual_model(in_config=path_dir).load()
 
 
 def load_model_by_version(version: int) -> Segmentation_Model: