new_contents: import numpy as np
  import csv
  import sys
  import os
  from math import exp
  
  import mete
  
  if len(sys.argv) > 1:
      S0 = int(sys.argv[1])
      N0 = int(sys.argv[2])
  
  if os.path.exists('../demo') is False:
      os.mkdir('../demo')
  
  beta = mete.get_beta(S0, N0)
  
  n0 = mete.trunc_logser_rvs(exp(-beta), N0, S0)
  n0 = list(n0)
  n0 = [int(x) for x in n0]
  n0.sort(reverse=True)
  
  rad = mete.get_mete_rad(S0, N0)[0]
  
  Amax = 4
  Amin = 1
  
  recur = mete.downscale_sar(Amax, S0, N0, Amin)
  recur_obsSAD = mete.downscale_sar_fixed_abu(Amax, n0, Amin)
  
  Avals = recur_obsSAD[0][ : ]
  
  nonrecur = mete.sar_noniterative(Avals, Amax, S0, N0, 'precise')
  nonrecur_obsSAD = mete.sar_noniterative_fixed_abu(Avals, Amax, n0)
  
  sad_out = np.empty((S0, 2)) 
  
  sad_out[ : , 0] = n0
  sad_out[ : , 1] = rad
  
  filename = '../demo/' + 'abu_sar_demo.txt'
  
  writer = open(filename, 'wb') 
  datawriter = csv.writer(writer)
  datawriter.writerow(['n0', 'sad'])
  for i in range(0, np.shape(sad_out)[0]):
      datawriter.writerow(sad_out[i, ])
  
  writer.close()
  
  sar_out = np.empty((3, 4))
  
  sar_out[ : , 0] = recur[1] + [S0]
  sar_out[ : , 1] = recur_obsSAD[1]
  sar_out[ : , 2] = nonrecur[1]
  sar_out[ : , 3] = nonrecur_obsSAD[1]
  
  filename = '../demo/' + 'rich_sar_demo.txt'
  
  writer = open(filename, 'wb') 
  datawriter = csv.writer(writer)
  datawriter.writerow(['recur', 'recur_obsSAD', 'nonrecur', 'nonrecur_obsSAD'])
  for i in range(0, np.shape(sar_out)[0]):
      datawriter.writerow(sar_out[i, ])
  
  writer.close()
subject: Add script to compute sad and sar for conceptual sar figure