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Reduce runtime of long doctests

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Johannes Schönberger
2013-11-18 12:54:42 +01:00
parent 2bc4cf6074
commit df4ca92794
2 changed files with 3 additions and 18 deletions
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skimage/filter/_denoise.py
+2 -17
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@@ -30,14 +30,6 @@ def _denoise_tv_chambolle_3d(im, weight=100, eps=2.e-4, n_iter_max=200):
-----
Rudin, Osher and Fatemi algorithm.
Examples
--------
>>> x, y, z = np.ogrid[0:40, 0:40, 0:40]
>>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
>>> mask = mask.astype(np.float)
>>> mask += 0.2 * np.random.randn(*mask.shape)
>>> res = denoise_tv_chambolle(mask, weight=100)
"""
px = np.zeros_like(im)
@@ -121,13 +113,6 @@ def _denoise_tv_chambolle_2d(im, weight=50, eps=2.e-4, n_iter_max=200):
applications, Journal of Mathematical Imaging and Vision,
Springer, 2004, 20, 89-97.
Examples
--------
>>> from skimage import color, data
>>> lena = color.rgb2gray(data.lena())
>>> lena += 0.5 * lena.std() * np.random.randn(*lena.shape)
>>> denoised_lena = denoise_tv_chambolle(lena, weight=60)
"""
px = np.zeros_like(im)
@@ -224,13 +209,13 @@ def denoise_tv_chambolle(im, weight=50, eps=2.e-4, n_iter_max=200,
2D example on Lena image:
>>> from skimage import color, data
>>> lena = color.rgb2gray(data.lena())
>>> lena = color.rgb2gray(data.lena())[:50, :50]
>>> lena += 0.5 * lena.std() * np.random.randn(*lena.shape)
>>> denoised_lena = denoise_tv_chambolle(lena, weight=60)
3D example on synthetic data:
>>> x, y, z = np.ogrid[0:40, 0:40, 0:40]
>>> x, y, z = np.ogrid[0:20, 0:20, 0:20]
>>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
>>> mask = mask.astype(np.float)
>>> mask += 0.2*np.random.randn(*mask.shape)
skimage/filter/thresholding.py
+1 -1
View File
@@ -57,7 +57,7 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
Examples
--------
>>> from skimage.data import camera
>>> image = camera()
>>> image = camera()[:50, :50]
>>> binary_image1 = threshold_adaptive(image, 15, 'mean')
>>> func = lambda arr: arr.mean()
>>> binary_image2 = threshold_adaptive(image, 15, 'generic', param=func)