old_contents: import matplotlib.image as mpimg
  import matplotlib.pylab as plb
  import numpy
  import sys
  from scipy import stats
  import glob
  import os.path
  import warnings
  import time
  
  def compare_entropy(name_img1,name_img2,method="rms"):
       '''Compare two images by the Kullback-Leibler divergence
  
       Parameters
       ----------
       name_img1 : string
         filename of image 1 (png format)
  
       name_img2 : string
         filename of image 2 (png format)
  
       Returns
       -------
       S : float
          Kullback-Leibler divergence S = sum(pk * log(pk / qk), axis=0)
  
       Note
       ----
       See http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.entropy.html
       '''
       img1 = mpimg.imread(name_img1)
       img2 = mpimg.imread(name_img2)
       fimg1 = img1.flatten()
       fimg2 = img2.flatten()
       if method == "KL-div":
            eps = 0.0001
            S = stats.entropy(fimg2+eps,fimg1+eps)
            S = numpy.log10(S)
       elif method == "rms":
            fdiff=fimg1-fimg2
            fdiff_sqr = fdiff**4
            S = (fdiff_sqr.sum())**(1./4)
  
       return S,fimg1, fimg2
  
  
  def compare_images(path = '.'):
       S_limit = 1.
       file_list = glob.glob(os.path.join(path, 'Abu*'))
       file_list_master = glob.glob(os.path.join(path, 'MasterAbu*'))
       print(file_list, file_list_master, path)
       S=[]
       print("Identifying images with modified log KL-divergence > "+'%3.1f'%S_limit)
       ierr_count = 0
       for i in range(len(file_list)):
           this_S,fimg1,fimg2 = compare_entropy(file_list[i],file_list_master[i])
           if this_S > S_limit:
                warnings.warn(file_list[i-1]+" and "+file_list[i]+" differ by "+'%6.3f'%this_S)
                ierr_count += 1
                S.append(this_S)
       if ierr_count > 0:
            sys.exit(1)
       #print ("S: ",S)
       #plb.plot(S,'o')
       #plb.xlabel("image number")
       #plb.ylabel("modified log KL-divergence to previous image")
       #plb.show()
  
  
  if __name__ == "__main__":
       compare_images()
  
new_contents: import matplotlib.image as mpimg
  import matplotlib.pylab as plb
  import numpy
  import sys
  from scipy import stats
  import glob
  import os.path
  import warnings
  import time
  
  def compare_entropy(name_img1,name_img2,method="rms"):
       '''Compare two images by the Kullback-Leibler divergence
  
       Parameters
       ----------
       name_img1 : string
         filename of image 1 (png format)
  
       name_img2 : string
         filename of image 2 (png format)
  
       Returns
       -------
       S : float
          Kullback-Leibler divergence S = sum(pk * log(pk / qk), axis=0)
  
       Note
       ----
       See http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.entropy.html
       '''
       img1 = mpimg.imread(name_img1)
       img2 = mpimg.imread(name_img2)
       fimg1 = img1.flatten()
       fimg2 = img2.flatten()
       if method == "KL-div":
            eps = 0.0001
            S = stats.entropy(fimg2+eps,fimg1+eps)
            S = numpy.log10(S)
       elif method == "rms":
            fdiff=fimg1-fimg2
            fdiff_sqr = fdiff**4
            S = (fdiff_sqr.sum())**(1./4)
  
       return S,fimg1, fimg2
  
  
  def compare_images(path = '.'):
       S_limit = 1.
       file_list = glob.glob(os.path.join(path, 'Abu*'))
       file_list_master = glob.glob(os.path.join(path, 'MasterAbu*'))
       print(file_list, file_list_master, path)
       S=[]
       print("Identifying images with modified log KL-divergence > "+'%3.1f'%S_limit)
       ierr_count = 0
       for i in range(len(file_list)):
           this_S,fimg1,fimg2 = compare_entropy(file_list[i],file_list_master[i])
           if this_S > S_limit:
                warnings.warn(file_list[i-1]+" and "+file_list[i]+" differ by "+'%6.3f'%this_S)
                ierr_count += 1
                S.append(this_S)
       if ierr_count > 0:
            print("Error: at least one image differs by more than S_limit"
            sys.exit(1)
       #print ("S: ",S)
       #plb.plot(S,'o')
       #plb.xlabel("image number")
       #plb.ylabel("modified log KL-divergence to previous image")
       #plb.show()
  
  
  if __name__ == "__main__":
       compare_images()
  
subject: add error statement