code: def populationStability(vectors, numSamples=None):
    
  
    # ----------------------------------------------------------------------
    # Calculate the stability
    numVectors = len(vectors)
  
    if numSamples is None:
      numSamples = numVectors-1
      countOn = range(numVectors-1)
    else:
      countOn = numpy.random.randint(0, numVectors-1, numSamples)
  
  
    sigmap = 0.0
    for i in countOn:
      match = checkMatch(vectors[i], vectors[i+1], sparse=False)
      # Ignore reset vectors (all 0's)
      if match[1] != 0:
        sigmap += float(match[0])/match[1]
  
    return sigmap / numSamples
docs: Returns the stability for the population averaged over multiple time steps
  
    Parameters:
    -----------------------------------------------
    vectors:          the vectors for which the stability is calculated
    numSamples        the number of time steps where stability is counted
  
    At each time step, count the fraction of the active elements which are stable
    from the previous step
    Average all the fraction