def MakeNormalPmf(mu, sigma, num_sigmas, n=201):
 pmf = Pmf()
 low = (mu - (num_sigmas * sigma))
 high = (mu + (num_sigmas * sigma))
 for x in np.linspace(low, high, n):
  p = EvalNormalPdf(x, mu, sigma)
  pmf.Set(x, p)
 pmf.Normalize()
 return pmf

def MakeNormalPmf ( mu , sigma , num_sigmas , n = 201 ) : pmf = Pmf ( ) low = ( mu - ( num_sigmas * sigma ) ) high = ( mu + ( num_sigmas * sigma ) ) for x in np . linspace ( low , high , n ) : p = EvalNormalPdf ( x , mu , sigma ) pmf . Set ( x , p ) pmf . Normalize ( ) return pmf
makes a pmf discrete approx to a normal distribution .