[pre-commit.ci] pre-commit autoupdate (#788)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.2.1 → v0.2.2](astral-sh/ruff-pre-commit@v0.2.1...v0.2.2)

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

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

* 🐛 Fix NPY002 Replace legacy `np.random.sample` call with `np.random.Generator`

* 📌 Pin dependency

* 🐛 Fix deepsource errors

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Shan E Ahmed Raza <[email protected]>

.github/workflows/python-package.yml

@@ -30,7 +30,7 @@ jobs:
         sudo apt update
         sudo apt-get install -y libopenslide-dev openslide-tools libopenjp2-7 libopenjp2-tools
         python -m pip install --upgrade pip
-        python -m pip install ruff==0.2.1 pytest pytest-cov pytest-runner
+        python -m pip install ruff==0.2.2 pytest pytest-cov pytest-runner
         pip install -r requirements/requirements.txt
     - name: Cache tiatoolbox static assets
       uses: actions/cache@v3

.pre-commit-config.yaml

@@ -68,7 +68,7 @@ repos:
         language: python
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.2.1
+    rev: v0.2.2
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]

requirements/requirements_dev.txt

@@ -12,7 +12,7 @@ pytest>=7.2.0
 pytest-cov>=4.0.0
 pytest-runner>=6.0
 pytest-xdist[psutil]
-ruff==0.2.1  # This will be updated by pre-commit bot to latest version
+ruff==0.2.2  # This will be updated by pre-commit bot to latest version
 toml>=0.10.2
 twine>=4.0.1
 wheel>=0.37.1

tests/test_graph.py

@@ -87,7 +87,7 @@ def test_affinity_to_edge_index_fuzz_output_shape() -> None:
     for _ in range(1000):
         # Generate some random square inputs
         input_shape = [rng.integers(2, 10)] * 2
-        affinity_matrix = np.random.sample(input_shape)
+        affinity_matrix = rng.random(input_shape)
         threshold = rng.random()
         # Convert to torch randomly
         if rng.random() > 0.5:
@@ -108,7 +108,7 @@ def test_affinity_to_edge_index_invalid_fuzz_input_shape() -> None:
     for _ in range(100):
         input_shape = [rng.integers(2, 10)] * 2
         input_shape[1] -= 1
-        affinity_matrix = np.random.sample(input_shape)
+        affinity_matrix = rng.random(input_shape)
         threshold = rng.random()
         # Convert to torch randomly
         if rng.random() > 0.5:

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

@@ -99,8 +99,8 @@ def is_zarr(path: Path) -> bool:
         _ = zarr.open(str(path), mode="r")
     except Exception:  # skipcq: PYL-W0703  # noqa: BLE001
         return False
-    else:
-        return True
+
+    return True
 
 
 def is_ngff(  # noqa: PLR0911
@@ -1578,7 +1578,7 @@ def save_tiles(
 
             # Rescale to the correct objective value
             if rescale != 1:
-                im = utils.transforms.imresize(img=im, scale_factor=(1 / rescale))
+                im = utils.transforms.imresize(img=im, scale_factor=1 / rescale)
 
             img_save_name = (
                 "_".join(
@@ -5520,7 +5520,7 @@ 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),
                 )
@@ -5713,7 +5713,7 @@ 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),
                 )