old_contents: """
  ============
  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
  from skimage.filters import rank
  
  
  image = (data.coins()).astype(np.uint16) * 16
  selem = disk(20)
  
  percentile_result = rank.mean_percentile(image, selem=selem, p0=.1, p1=.9)
  bilateral_result = rank.mean_bilateral(image, selem=selem, s0=500, s1=500)
  normal_result = rank.mean(image, selem=selem)
  
  
  fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 10),
                           sharex=True, sharey=True)
  ax = axes.ravel()
  
  titles = ['Original', 'Percentile mean', 'Bilateral mean', 'Local mean']
  imgs = [image, percentile_result, bilateral_result, normal_result]
  for n in range(0, len(imgs)):
      ax[n].imshow(imgs[n])
      ax[n].set_title(titles[n])
      ax[n].set_adjustable('box-forced')
      ax[n].axis('off')
  
  
  plt.show()
  
new_contents: """
  ============
  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
  from skimage.filters import rank
  
  
  image = data.coins()
  selem = disk(20)
  
  percentile_result = rank.mean_percentile(image, selem=selem, p0=.1, p1=.9)
  bilateral_result = rank.mean_bilateral(image, selem=selem, s0=500, s1=500)
  normal_result = rank.mean(image, selem=selem)
  
  
  fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 10),
                           sharex=True, sharey=True)
  ax = axes.ravel()
  
  titles = ['Original', 'Percentile mean', 'Bilateral mean', 'Local mean']
  imgs = [image, percentile_result, bilateral_result, normal_result]
  for n in range(0, len(imgs)):
      ax[n].imshow(imgs[n])
      ax[n].set_title(titles[n])
      ax[n].set_adjustable('box-forced')
      ax[n].axis('off')
  
  plt.show()
  
subject: Fix warning in plot_rank_mean