adding multiple lines to plot without matplotlib (seaborn)

Question

I am working on the following code from this link . I have a question: in the mentioned link the following code lead to a plot. But running this code does not plot any thing. what should be added?

import numpy as np
import scipy.stats as st
import seaborn as sns


mus = np.array([5, 5])
sigmas = np.array([[1, .9], [.9, 1]])


def circle(x, y):
    return (x-1)**2 + (y-2)**2 - 3**2


def pgauss(x, y):
    return st.multivariate_normal.pdf([x, y], mean=mus, cov=sigmas)


def metropolis_hastings(p, iter=1000):
    x, y = 0., 0.
    samples = np.zeros((iter, 2))

    for i in range(iter):
        x_star, y_star = np.array([x, y]) + np.random.normal(size=2)
        if np.random.rand() < p(x_star, y_star) / p(x, y):
            x, y = x_star, y_star
        samples[i] = np.array([x, y])

    return samples


if __name__ == '__main__':
    samples = metropolis_hastings(circle, iter=10000)
    sns.jointplot(samples[:, 0], samples[:, 1])

    samples = metropolis_hastings(pgauss, iter=10000)
    sns.jointplot(samples[:, 0], samples[:, 1])

Thank you for your help.

Answer 1

As reported by @Mr. T and also in Seaborn plots not showing up , you should add plt.show() . plt has to be imported from matplotlib.

So your code should read:

import numpy as np
import scipy.stats as st
import seaborn as sns
import matplotlib.pyplot as plt

mus = np.array([5, 5])
sigmas = np.array([[1, .9], [.9, 1]])


def circle(x, y):
    return (x-1)**2 + (y-2)**2 - 3**2


def pgauss(x, y):
    return st.multivariate_normal.pdf([x, y], mean=mus, cov=sigmas)


def metropolis_hastings(p, iter=1000):
    x, y = 0., 0.
    samples = np.zeros((iter, 2))

    for i in range(iter):
        x_star, y_star = np.array([x, y]) + np.random.normal(size=2)
        if np.random.rand() < p(x_star, y_star) / p(x, y):
            x, y = x_star, y_star
        samples[i] = np.array([x, y])

    return samples


if __name__ == '__main__':
    samples = metropolis_hastings(circle, iter=10000)
    sns.jointplot(samples[:, 0], samples[:, 1])

    samples = metropolis_hastings(pgauss, iter=10000)
    sns.jointplot(samples[:, 0], samples[:, 1])
    plt.show()