pi  = [0.1, 0.9] #probabaility
mu = [-1, 1] #means
sigma = [0.1 , 0.2] #spreads.
Ns = 100
choices = random.choices( np.arange( len(pi) ), pi, k=Ns)

def onenumber(i, mu=mu,sigma=sigma):
	return float (np.random.normal(mu[i], sigma[i] , 1))

S = [onenumber(i) for i in choices]