Remove useless multichannel=True kwarg from tests

scikit-image · Feb 5, 2016 · 3265e1b · 3265e1b
1 parent b40d028
commit 3265e1b
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Showing 3 changed files with 7 additions and 13 deletions.
diff --git a/doc/examples/filters/plot_denoise.py b/doc/examples/filters/plot_denoise.py
@@ -49,16 +49,14 @@
 ax[0, 1].imshow(denoise_tv_chambolle(noisy, weight=0.1, multichannel=True))
 ax[0, 1].axis('off')
 ax[0, 1].set_title('TV')
-ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.05, sigma_spatial=15,
-                multichannel=True))
+ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.05, sigma_spatial=15))
 ax[0, 2].axis('off')
 ax[0, 2].set_title('Bilateral')
 
 ax[1, 0].imshow(denoise_tv_chambolle(noisy, weight=0.2, multichannel=True))
 ax[1, 0].axis('off')
 ax[1, 0].set_title('(more) TV')
-ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.1, sigma_spatial=15,
-                multichannel=True))
+ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.1, sigma_spatial=15))
 ax[1, 1].axis('off')
 ax[1, 1].set_title('(more) Bilateral')
 ax[1, 2].imshow(astro)

diff --git a/skimage/restoration/_denoise.py b/skimage/restoration/_denoise.py
@@ -66,8 +66,7 @@ def denoise_bilateral(image, win_size=5, sigma_range=None, sigma_spatial=1,
     >>> astro = astro[220:300, 220:320]
     >>> noisy = astro + 0.6 * astro.std() * np.random.random(astro.shape)
     >>> noisy = np.clip(noisy, 0, 1)
-    >>> denoised = denoise_bilateral(noisy, sigma_range=0.05,
-    ...                              sigma_spatial=15, multichannel=True)
+    >>> denoised = denoise_bilateral(noisy, sigma_range=0.05, sigma_spatial=15)
     """
     if multichannel:
         if image.ndim != 3:

diff --git a/skimage/restoration/tests/test_denoise.py b/skimage/restoration/tests/test_denoise.py
@@ -175,10 +175,8 @@ def test_denoise_bilateral_color():
     img += 0.5 * img.std() * np.random.rand(*img.shape)
     img = np.clip(img, 0, 1)
 
-    out1 = restoration.denoise_bilateral(img, sigma_range=0.1,
-                                         sigma_spatial=20, multichannel=True)
-    out2 = restoration.denoise_bilateral(img, sigma_range=0.2,
-                                         sigma_spatial=30, multichannel=True)
+    out1 = restoration.denoise_bilateral(img, sigma_range=0.1, sigma_spatial=20)
+    out2 = restoration.denoise_bilateral(img, sigma_range=0.2, sigma_spatial=30)
 
     # make sure noise is reduced in the checkerboard cells
     assert img[30:45, 5:15].std() > out1[30:45, 5:15].std()
@@ -194,7 +192,7 @@ def test_denoise_bilateral_3d_grayscale():
 def test_denoise_bilateral_3d_multichannel():
     img = np.ones((50, 50, 50))
     with expected_warnings(["grayscale"]):
-        result = restoration.denoise_bilateral(img, multichannel=True)
+        result = restoration.denoise_bilateral(img)
 
     expected = np.empty_like(img)
     expected.fill(np.nan)
@@ -204,8 +202,7 @@ def test_denoise_bilateral_3d_multichannel():
 
 def test_denoise_bilateral_multidimensional():
     img = np.ones((10, 10, 10, 10))
-    assert_raises(ValueError, restoration.denoise_bilateral, img,
-                  multichannel=True)
+    assert_raises(ValueError, restoration.denoise_bilateral, img)
 
 
 def test_denoise_bilateral_nan():