import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import random as rnd

theta = np.array([[3.91973221e-05, 2.59889568e-04], [5.32160367e-06, 4.99763548e-06],[6.65158426e-01, 3.34841574e-01]])

n = 100000
number_of_distributions = 2
mu = theta[0]
sigma = theta[1]
weights = theta[2]
samples = []

for i in range(n):
       population = [rnd.gauss(mu[i], np.sqrt(sigma[i])) for i in range(number_of_distributions)]
       samples.append(rnd.choices(population, weights=weights))
sns.distplot(samples)
plt.show()