[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

tiatoolbox/utils/misc.py

@@ -983,7 +983,7 @@ def select_cv2_interpolation(scale_factor: float | npt.NDArray[np.float64]) -> s
             interpolation type
 
     """
-    if np.any(scale_factor > 1.0):  # noqa: PLR2004
+    if np.any(scale_factor > 1.0):
         return "cubic"
     return "area"
 

tiatoolbox/utils/transforms.py

@@ -141,7 +141,7 @@ def imresize(
         scale_factor_array = img.shape[:2][::-1] / np.array(output_size_array)
 
     # Return original if scale factor is 1
-    if np.all(scale_factor_array == 1.0):  # noqa: PLR2004
+    if np.all(scale_factor_array == 1.0):
         return img
 
     # Get appropriate cv2 interpolation enum

tiatoolbox/utils/visualization.py

@@ -119,7 +119,7 @@ def overlay_prediction_mask(
             msg,
         )
     if np.issubdtype(img.dtype, np.floating):
-        if not (img.max() <= 1.0 and img.min() >= 0):  # noqa: PLR2004
+        if not (img.max() <= 1.0 and img.min() >= 0):
             msg = "Not support float `img` outside [0, 1]."
             raise ValueError(msg)
         img = np.array(img * 255, dtype=np.uint8)
@@ -157,7 +157,7 @@ def overlay_prediction_mask(
     cv2.addWeighted(rgb_prediction, alpha, overlay, 1 - alpha, 0, overlay)
     overlay = overlay.astype(np.uint8)
 
-    if min_val > 0.0:  # noqa: PLR2004
+    if min_val > 0.0:
         overlay[~prediction_sel] = img[~prediction_sel]
 
     if ax is None and not return_ax:
@@ -310,7 +310,7 @@ def overlay_probability_map(
     overlay[overlay > 255.0] = 255.0  # noqa: PLR2004
     overlay = overlay.astype(np.uint8)
 
-    if min_val > 0.0:  # noqa: PLR2004
+    if min_val > 0.0:
         overlay[~prediction_sel] = img[~prediction_sel]
 
     if ax is None and not return_ax:
@@ -374,23 +374,23 @@ def _validate_overlay_probability_map(
             msg,
         )
 
-    if prediction.max() > 1.0:  # noqa: PLR2004
+    if prediction.max() > 1.0:
         msg = "Not support float `prediction` outside [0, 1]."
         raise ValueError(msg)
     if prediction.min() < 0:
         msg = "Not support float `prediction` outside [0, 1]."
         raise ValueError(msg)
 
     # if `min_val` is defined, only display the overlay for areas with prob > min_val
-    if min_val < 0.0:  # noqa: PLR2004
+    if min_val < 0.0:
         msg = f"`min_val={min_val}` is not between [0, 1]."
         raise ValueError(msg)
-    if min_val > 1.0:  # noqa: PLR2004
+    if min_val > 1.0:
         msg = f"`min_val={min_val}` is not between [0, 1]."
         raise ValueError(msg)
 
     if np.issubdtype(img.dtype, np.floating):
-        if img.max() > 1.0:  # noqa: PLR2004
+        if img.max() > 1.0:
             msg = "Not support float `img` outside [0, 1]."
             raise ValueError(msg)
         if img.min() < 0:

tiatoolbox/wsicore/wsireader.py

@@ -5519,7 +5519,7 @@ def read_rect(
                 utils.transforms.background_composite(base_region, alpha=True),
             )
             im_region = Image.fromarray(im_region)
-            if self.alpha < 1.0:  # noqa: PLR2004
+            if self.alpha < 1.0:
                 im_region.putalpha(
                     im_region.getchannel("A").point(lambda i: i * self.alpha),
                 )
@@ -5712,7 +5712,7 @@ class docstrings for more information.
                 utils.transforms.background_composite(base_region, alpha=True),
             )
             im_region = Image.fromarray(im_region)
-            if self.alpha < 1.0:  # noqa: PLR2004
+            if self.alpha < 1.0:
                 im_region.putalpha(
                     im_region.getchannel("A").point(lambda i: i * self.alpha),
                 )