# Draw samples from the distribution:

mu, sigma = 0, 0.1 # mean and standard deviation
s = np.random.normal(mu, sigma, 1000)

# Verify the mean and the variance:

abs(mu - np.mean(s)) < 0.01
# True

abs(sigma - np.std(s, ddof=1)) < 0.01
# True

# Display the histogram of the samples, along with
# the probability density function:

import matplotlib.pyplot as plt
count, bins, ignored = plt.hist(s, 30, normed=True)
plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
               np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
         linewidth=2, color='r')
plt.show()