text: import matplotlib.pyplot as plt
  import numpy as np
  from _schon import DiscreteSIS
  
  #structure : all individuals belong to two groups
  N = 500
  edge_list = []
  for node in range(N):
      edge_list.append((node,0))
      edge_list.append((node,1))
  
  #infection parameter
  recovery_probability = 0.05
  scale_infection = 0.06*10**(-3)
  infection_probability = [[]]*500+[[scale_infection*i for i in range(501)]]
  initial_infected_fraction = 0.05
  seed = 42
  nb_history = 50
  
  cont = DiscreteSIS(edge_list,recovery_probability,infection_probability)
  cont.infect_fraction(initial_infected_fraction)
  # cont.seed(seed) #optional
  cont.initialize_history(nb_history)
  
  #define some measures
  cont.measure_prevalence()
  cont.measure_marginal_infection_probability()
  
  #evolve in the quasistationary state without measuring (burn-in)
  dt = 10000
  dec_dt = 100
  cont.evolve(dt,dec_dt,measure=False,quasistationary=True)
  
  #evolve and measure
  dt = 10000
  dec_dt = 10
  cont.evolve(dt,dec_dt,measure=True,quasistationary=True)
  
  #print the result measure
  for measure in cont.get_measure_vector():
      name = measure.get_name()
      if name == "prevalence":
          print("----------------")
          print(name)
          print("----------------")
          print(np.mean(measure.get_result()))
      elif name == "marginal_infection_probability":
          plt.hist(measure.get_result())
          plt.show()
  
  
  