Draw histogram on Python

draw_histogram.py

import h5py
import os
import numpy as np
import cv2
import matplotlib.pyplot as plt
from scipy import ndimage, misc
from scipy.stats import norm

def draw_histogram(x, n_bins, name):
    n, bins, patches = plt.hist(x, bins=n_bins, color=(0.0, 0.0, 0.5), density=True)

    plt.cla()
    fig = plt.figure()

    weights = np.ones_like(x)/float(len(x))
    plt.hist(x, bins=n_bins, color=(0.0, 0.0, 0.5), weights=weights)

    ''' For gaussian fitting'''
    xt = plt.xticks()[0]
    xmin, xmax = min(xt), max(xt)
    lnspc = np.linspace(xmin, xmax, len(x))

    # lets try the normal distribution first
    m, s = norm.fit(x)  # get mean and standard deviation
    pdf_g = norm.pdf(lnspc, m, s)  # now get theoretical values in our interval
    pdf_g = pdf_g/sum(n)
    plt.plot(lnspc, pdf_g, label="Norm", color='r', linestyle='--')  # plot it
    print(m, s)
    # plt.title(r'$ \mu=100,\ \sigma=15$')
    plt.title(r'$ \mu={},\ \sigma={}$'.format(round(m, 3), round(s, 3)))
    # plt.title("Mean: " + str(round(m, 2)) + "Std: " + str(round(s,2)))
    plt.xlabel("Measured Y [m]")
    plt.ylabel("# of points")

    # (mu, sigma) = norm.fit(x)
    # gaussian_line = mlab.normpdf(n_bins, mu, sigma)
    # plt.plot(n_bins, gaussian_line, 'r', linewidth=2)

    fig = plt.gcf()
    fig.savefig("histo_{}.png".format(name))