plot_normal_area.py

# Modified from https://www.science-emergence.com/Articles/How-to-plot-a-normal-distribution-with-matplotlib-in-python-/
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt


def plot_area(mean, std, pt1, pt2, fill):
  plt.plot([pt1, pt1], [0.0, scipy.stats.norm.pdf(pt1, mean, std)], color='black')
  plt.plot([pt2, pt2], [0.0, scipy.stats.norm.pdf(pt2, mean, std)], color='black')

  ptx = np.linspace(pt1, pt2, 10)
  pty = scipy.stats.norm.pdf(ptx, mean, std)

  plt.fill_between(ptx, pty, color=fill, alpha='1.0')


def plot_normal(mean=0, std=1, xmin=-5, xmax=5, breaks=np.array([1, 2, 3, 10]), 
                fills=['#0b559f', '#2b7bba', '#539ecd', '#89bedc']):
  x = np.linspace(xmin, xmax, 100)
  y = scipy.stats.norm.pdf(x, mean, std)

  plt.plot(x, y, color='black')

  pt1 = mean + breaks[0] * std
  pt2 = mean - breaks[0] * std
  plot_area(mean, std, pt1, pt2, fill=fills[0])

  for i in range(breaks.size - 1):
    pt1 = mean + breaks[i] * std
    pt2 = mean + breaks[i + 1] * std
    plot_area(mean, std, pt1, pt2, fill=fills[i + 1])

    pt1 = mean - breaks[i] * std
    pt2 = mean - breaks[i + 1] * std
    plot_area(mean, std, pt1, pt2, fill=fills[i + 1])

  plt.grid()

  plt.xlim(xmin, xmax)

  plt.title('Normal Distribution', fontsize=10)
  plt.xlabel('x')
  plt.ylabel('p(x)')

  plt.show()


plot_normal(8, 3, -3, 19)
plot_normal()