Rename mean filter example and improve description

doc/examples/applications/plot_morphology.py

@@ -178,7 +178,7 @@ element, but the thicker region at the top disappears.
 Black tophat
 ============
 
-The ``black_tophat`` of an image is defined as its morphological **closing 
+The ``black_tophat`` of an image is defined as its morphological **closing
 minus the original image**. This operation returns the *dark spots of the
 image that are smaller than the structuring element*.
 """
@@ -264,8 +264,8 @@ Additional Resources
 ====================
 
 1. `MathWorks tutorial on morphological processing
-<http://www.mathworks.com/help/images/morphology-fundamentals-dilation-and-erosion.html>`_ 
-2. `Auckland university's tutorial on Morphological Image Processing 
+<http://www.mathworks.com/help/images/morphology-fundamentals-dilation-and-erosion.html>`_
+2. `Auckland university's tutorial on Morphological Image Processing
 <http://www.cs.auckland.ac.nz/courses/compsci773s1c/lectures/ImageProcessing-html/topic4.htm>`_
 3. http://en.wikipedia.org/wiki/Mathematical_morphology
 

doc/examples/plot_16bitbilateral.py

@@ -1,47 +0,0 @@
-"""
-==============================
-Bilateral mean
-==============================
-This example compares
-
-* local mean
-* percentile mean
-* bilateral mean
-
-build on the local histogram distribution
-local mean uses all pixels belonging to the structuring element to compute average gray level,
-percentile mean uses only values between percentiles p0 and p1 (here 10% and 90%),
-whereas bilateral mean uses only pixels of the structuring element having a gray level situated inside
-g-s0 and g+s1 (here g-500 and g+500).
-The filters are applied on a 16 bit image (actual bitdepth is 12bit).
-
-Percentile and usual mean give here similar results, these filters smooth the complete image (background and details).
-Bilateral mean exhibits a high filtering rate for continuous area (i.e. background) while image higher frequencies
-remains untouched.
-
-"""
-import numpy as np
-import matplotlib.pyplot as plt
-
-from skimage import data
-from skimage.morphology import disk
-import skimage.filter.rank as rank
-
-a16 = (data.coins()).astype('uint16') * 16
-selem = disk(20)
-
-f1 = rank.percentile_mean(a16, selem=selem, p0=.1, p1=.9)
-f2 = rank.bilateral_mean(a16, selem=selem, s0=500, s1=500)
-f3 = rank.mean(a16, selem=selem)
-
-# display results
-fig, axes = plt.subplots(nrows=3, figsize=(15, 10))
-ax0, ax1, ax2 = axes
-
-ax0.imshow(np.hstack((a16, f1)))
-ax0.set_title('percentile mean')
-ax1.imshow(np.hstack((a16, f2)))
-ax1.set_title('bilateral mean')
-ax2.imshow(np.hstack((a16, f3)))
-ax2.set_title('local mean')
-plt.show()

doc/examples/plot_hough_transform.py

@@ -1,4 +1,4 @@
-r'''
+'''
 ===============
 Hough transform
 ===============

doc/examples/plot_rank_mean.py

@@ -0,0 +1,45 @@
+"""
+============
+Mean filters
+============
+
+This example compares the following mean filters of the rank filter package:
+
+ * **local mean**: all pixels belonging to the structuring element to compute
+   average gray level
+ * **percentile mean**: only use values between percentiles p0 and p1
+   (here 10% and 90%)
+ * **bilateral mean**: only use pixels of the structuring element having a gray
+   level situated inside g-s0 and g+s1 (here g-500 and g+500)
+
+Percentile and usual mean give here similar results, these filters smooth the
+complete image (background and details). Bilateral mean exhibits a high
+filtering rate for continuous area (i.e. background) while higher image
+frequencies remain untouched.
+
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+from skimage import data
+from skimage.morphology import disk
+import skimage.filter.rank as rank
+
+a16 = (data.coins()).astype('uint16') * 16
+selem = disk(20)
+
+f1 = rank.percentile_mean(a16, selem=selem, p0=.1, p1=.9)
+f2 = rank.bilateral_mean(a16, selem=selem, s0=500, s1=500)
+f3 = rank.mean(a16, selem=selem)
+
+# display results
+fig, axes = plt.subplots(nrows=3, figsize=(15, 10))
+ax0, ax1, ax2 = axes
+
+ax0.imshow(np.hstack((a16, f1)))
+ax0.set_title('percentile mean')
+ax1.imshow(np.hstack((a16, f2)))
+ax1.set_title('bilateral mean')
+ax2.imshow(np.hstack((a16, f3)))
+ax2.set_title('local mean')
+plt.show()

doc/examples/plot_swirl.py

@@ -1,4 +1,4 @@
-r"""
+"""
 =====
 Swirl
 =====