def MakePoissonPmf(lam, high, step=1):
 pmf = Pmf()
 for k in range(0, (high + 1), step):
  p = EvalPoissonPmf(k, lam)
  pmf.Set(k, p)
 pmf.Normalize()
 return pmf

def MakePoissonPmf ( lam , high , step = 1 ) : pmf = Pmf ( ) for k in range ( 0 , ( high + 1 ) , step ) : p = EvalPoissonPmf ( k , lam ) pmf . Set ( k , p ) pmf . Normalize ( ) return pmf
makes a pmf discrete approx to a poisson distribution .