Fix inconsistent alpha parameter/docs for RandGibbsNoise/RandGibbsNoi…

…sed (#7584)

Fixes inconsistent alpha parameter/docs for
RandGibbsNoise/RandGibbsNoised

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [x] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [x] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: John Zielke <j.l.zielke@gmail.com>
Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>

monai/transforms/intensity/array.py

@@ -1840,15 +1840,19 @@ class RandGibbsNoise(RandomizableTransform):
 
     Args:
         prob (float): probability of applying the transform.
-        alpha (Sequence(float)): Parametrizes the intensity of the Gibbs noise filter applied. Takes
+        alpha (float, Sequence(float)): Parametrizes the intensity of the Gibbs noise filter applied. Takes
             values in the interval [0,1] with alpha = 0 acting as the identity mapping.
             If a length-2 list is given as [a,b] then the value of alpha will be
             sampled uniformly from the interval [a,b]. 0 <= a <= b <= 1.
+            If a float is given, then the value of alpha will be sampled uniformly from the interval [0, alpha].
     """
 
     backend = GibbsNoise.backend
 
-    def __init__(self, prob: float = 0.1, alpha: Sequence[float] = (0.0, 1.0)) -> None:
+    def __init__(self, prob: float = 0.1, alpha: float | Sequence[float] = (0.0, 1.0)) -> None:
+        if isinstance(alpha, float):
+            alpha = (0, alpha)
+        alpha = ensure_tuple(alpha)
         if len(alpha) != 2:
             raise ValueError("alpha length must be 2.")
         if alpha[1] > 1 or alpha[0] < 0:

monai/transforms/intensity/dictionary.py

@@ -1423,10 +1423,11 @@ class RandGibbsNoised(RandomizableTransform, MapTransform):
         keys: 'image', 'label', or ['image', 'label'] depending on which data
                 you need to transform.
         prob (float): probability of applying the transform.
-        alpha (float, List[float]): Parametrizes the intensity of the Gibbs noise filter applied. Takes
+        alpha (float, Sequence[float]): Parametrizes the intensity of the Gibbs noise filter applied. Takes
             values in the interval [0,1] with alpha = 0 acting as the identity mapping.
             If a length-2 list is given as [a,b] then the value of alpha will be sampled
             uniformly from the interval [a,b].
+            If a float is given, then the value of alpha will be sampled uniformly from the interval [0, alpha].
         allow_missing_keys: do not raise exception if key is missing.
     """
 
@@ -1436,7 +1437,7 @@ def __init__(
         self,
         keys: KeysCollection,
         prob: float = 0.1,
-        alpha: Sequence[float] = (0.0, 1.0),
+        alpha: float | Sequence[float] = (0.0, 1.0),
         allow_missing_keys: bool = False,
     ) -> None:
         MapTransform.__init__(self, keys, allow_missing_keys)

tests/test_rand_gibbs_noise.py

@@ -90,6 +90,15 @@ def test_alpha(self, im_shape, input_type):
         self.assertGreaterEqual(t.sampled_alpha, 0.5)
         self.assertLessEqual(t.sampled_alpha, 0.51)
 
+    @parameterized.expand(TEST_CASES)
+    def test_alpha_single_value(self, im_shape, input_type):
+        im = self.get_data(im_shape, input_type)
+        alpha = 0.01
+        t = RandGibbsNoise(1.0, alpha)
+        _ = t(deepcopy(im))
+        self.assertGreaterEqual(t.sampled_alpha, 0)
+        self.assertLessEqual(t.sampled_alpha, 0.01)
+
 
 if __name__ == "__main__":
     unittest.main()

tests/test_rand_gibbs_noised.py

@@ -105,6 +105,14 @@ def test_alpha(self, im_shape, input_type):
         _ = t(deepcopy(data))
         self.assertTrue(0.5 <= t.rand_gibbs_noise.sampled_alpha <= 0.51)
 
+    @parameterized.expand(TEST_CASES)
+    def test_alpha_single_value(self, im_shape, input_type):
+        data = self.get_data(im_shape, input_type)
+        alpha = 0.01
+        t = RandGibbsNoised(KEYS, 1.0, alpha)
+        _ = t(deepcopy(data))
+        self.assertTrue(0 <= t.rand_gibbs_noise.sampled_alpha <= 0.01)
+
 
 if __name__ == "__main__":
     unittest.main()