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

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

Author: pre-commit-ci[bot]

.gitignore

@@ -166,4 +166,4 @@ runs
 
 *zarr/*
 
-monai-dev/
+monai-dev/

monai/transforms/intensity/array.py

@@ -173,7 +173,7 @@ def __init__(
         prob: float = 0.1,
         mean: Sequence[float] | float = 0.0,
         std: Sequence[float] | float = 1.0,
-        degrees_of_freedom: int = 64, #64 default because typical modern brain MRI is 32 quadrature coils 
+        degrees_of_freedom: int = 64, #64 default because typical modern brain MRI is 32 quadrature coils
         channel_wise: bool = False,
         relative: bool = False,
         sample_std: bool = True,
@@ -242,9 +242,9 @@ def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTen
                 raise RuntimeError(f"std must be a float or int number, got {type(std)}.")
             img = self._add_noise(img, mean=self.mean, std=std, k=self.degrees_of_freedom)
         return img
-    
-    
-    
+
+
+
 class RandRicianNoise(RandomizableTransform):
     """
     Add Rician noise to image.

monai/transforms/intensity/dictionary.py

@@ -236,8 +236,8 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, N
         for key in self.key_iterator(d):
             d[key] = self.rand_gaussian_noise(img=d[key], randomize=False)
         return d
-    
-    
+
+
 class RandNonCentralChiNoised(RandomizableTransform, MapTransform):
     """
     Dictionary-based version :py:class:`monai.transforms.RandNonCentralChiNoise`.
@@ -283,7 +283,7 @@ def __init__(
         MapTransform.__init__(self, keys, allow_missing_keys)
         RandomizableTransform.__init__(self, prob)
         self.rand_non_central_chi_noise = RandNonCentralChiNoise(
-            prob=1.0,  
+            prob=1.0,
             mean=mean,
             std=std,
             degrees_of_freedom=degrees_of_freedom,

tests/transforms/test_rand_noncentralchi_noise.py

@@ -40,17 +40,17 @@ def test_correct_results(self, _, in_type, mean, std):
         np.random.seed(seed)
         np.random.random()
         _std = np.random.uniform(0, std)
-        
+
         noise_shape = (degrees_of_freedom, *self.imt.shape)
         all_noises = np.random.normal(mean, _std, size=noise_shape).astype(np.float32)
         all_noises[0] += self.imt
         sum_sq = np.sum(all_noises**2, axis=0)
         expected = np.sqrt(sum_sq)
-        
+
         if isinstance(noised, torch.Tensor):
             noised = noised.cpu()
         np.testing.assert_allclose(expected, noised, atol=1e-5)
-        
+
     @parameterized.expand(TESTS)
     def test_correct_results_dof2(self, _, in_type, mean, std):
         """

tests/transforms/test_rand_noncentralchi_noised.py

@@ -48,7 +48,7 @@ def test_correct_results(self, _, in_type, keys, mean, std):
             if isinstance(noised[k], torch.Tensor):
                 noised[k] = noised[k].cpu()
             np.testing.assert_allclose(expected, noised[k], atol=1e-5, rtol=1e-5)
-            
+
     @parameterized.expand(TESTS)
     def test_correct_results_k2(self, _, in_type, keys, mean, std):
         degrees_of_freedom = 2
@@ -61,7 +61,7 @@ def test_correct_results_k2(self, _, in_type, keys, mean, std):
         for k in keys:
             np.random.random()
             _std = np.random.uniform(0, std)
-            
+
             noise_shape = (degrees_of_freedom, *self.imt.shape)
             all_noises = np.random.normal(mean, _std, size=noise_shape).astype(np.float32)
             all_noises[0] += self.imt