content: #!/usr/bin/env python
  '''
  Make a set of images with a single colormap, norm, and colorbar.
  
  It also illustrates colorbar tick labelling with a multiplier.
  '''
  
  from matplotlib.pyplot import figure, show, sci
  from matplotlib import cm, colors
  from matplotlib.font_manager import FontProperties
  from numpy import amin, amax, ravel
  from numpy.random import rand
  
  Nr = 3
  Nc = 2
  
  fig = figure()
  cmap = cm.cool
  
  figtitle = 'Multiple images'
  t = fig.text(0.5, 0.95, figtitle,
                 horizontalalignment='center',
                 fontproperties=FontProperties(size=16))
  
  cax = fig.add_axes([0.2, 0.08, 0.6, 0.04])
  
  w = 0.4
  h = 0.22
  ax = []
  images = []
  vmin = 1e40
  vmax = -1e40
  for i in range(Nr):
      for j in range(Nc):
          pos = [0.075 + j*1.1*w, 0.18 + i*1.2*h, w, h]
          a = fig.add_axes(pos)
          if i > 0:
              a.set_xticklabels([])
          # Make some fake data with a range that varies
          # somewhat from one plot to the next.
          data =((1+i+j)/10.0)*rand(10,20)*1e-6
          dd = ravel(data)
          # Manually find the min and max of all colors for
          # use in setting the color scale.
          vmin = min(vmin, amin(dd))
          vmax = max(vmax, amax(dd))
          images.append(a.imshow(data, cmap=cmap))
  
          ax.append(a)
  
  # Set the first image as the master, with all the others
  # observing it for changes in cmap or norm.
  norm = colors.Normalize(vmin=vmin, vmax=vmax)
  for i, im in enumerate(images):
      im.set_norm(norm)
      if i > 0:
          images[0].add_observer(im)
  
  # The colorbar is also based on this master image.
  fig.colorbar(images[0], cax, orientation='horizontal')
  
  # We need the following only if we want to run this
  # script interactively and be able to change the colormap.
  
  sci(images[0])
  
  show()
  
  
  
  
  
  
  