diff --git a/doc/examples/plot_entropy.py b/doc/examples/plot_entropy.py
index f140b116..12c6777a 100644
--- a/doc/examples/plot_entropy.py
+++ b/doc/examples/plot_entropy.py
@@ -3,20 +3,24 @@
 Entropy
 =======
 
-In information theory, information entropy is the log-base-2 of the number of possible outcomes
-for a message.
+In information theory, information entropy is the log-base-2 of the number of
+possible outcomes for a message.
 
-For an image, local entropy is related to the complexity contained in a given neighborhood, typically defined by a
-structuring element. A large number of various gray levels has a higher entropy than an homogeneous neighborhood.
+For an image, local entropy is related to the complexity contained in a given
+neighborhood, typically defined by a structuring element. A large number of
+various gray levels has a higher entropy than an homogeneous neighborhood.
 
-Entropy filter can detect subtle variations of local gray level distribution, in the example, the
-image is composed of two surfaces with two slightly different distributions.
+Entropy filter can detect subtle variations of local gray level distribution,
+in the example, the image is composed of two surfaces with two slightly
+different distributions.
 
-Image center has a random distribution in the range [-14,+14] centered on 128, while the borders has a
-random distribution in the range [-15,+15] centered on 128.
+Image center has a random distribution in the range [-14,+14] centered on 128,
+while the borders has a random distribution in the range [-15,+15] centered
+on 128.
 
-We apply the local entropy measure using a circular structuring element of radius 10. As a result, one can
-detect the central square. Radius should be big enough to efficiently sample the local gray level distribution.
+We apply the local entropy measure using a circular structuring element of
+radius 10. As a result, one can detect the central square. Radius should be big
+enough to efficiently sample the local gray level distribution.
 
 In the second example, the local entropy is used to detect image texture.
 
@@ -29,10 +33,11 @@ from skimage.util import img_as_ubyte
 from skimage.filters.rank import entropy
 from skimage.morphology import disk
 
-noise_mask = 28*np.ones((128, 128), dtype=np.uint8)
+noise_mask = 28 * np.ones((128, 128), dtype=np.uint8)
 noise_mask[32:-32, 32:-32] = 30
 
-noise = (noise_mask*np.random.random(noise_mask.shape)-.5*noise_mask).astype(np.uint8)
+noise = (noise_mask * np.random.random(noise_mask.shape) - .5 *
+         noise_mask).astype(np.uint8)
 img = noise + 128
 
 radius = 10
@@ -52,7 +57,7 @@ plt.imshow(e)
 plt.xlabel('image local entropy ($r=%d$)' % radius)
 plt.colorbar()
 
-#second example: texture detection
+# second example: texture detection
 
 image = img_as_ubyte(data.camera())