ec_animation1.py

# Modified from Coursera

# how to do an animation in matplotlib:

# step 1: import the animation module
import matplotlib.animation as animation
import numpy as np
import matplotlib.pyplot as plt
# another way to import:
# from matplotlib animation import FuncAnimation


# step 2: define the number of frames. 
n = 100

# step 3: prepare the data to plot 
# draw sample from standardized normal distribution
x = np.random.randn(n) 


# step 4: prep the figure
fig = plt.figure()


# step 5: prep the callable function that will do the plotting
# the argument must be the next value in frames
def update(curr):  #? how the info about the curr can be passed? 
    # check if animation is at the last frame, and if so, stop the animation a. 
    if curr == n:
        a.event_source.stop() 
    plt.cla()  # each iteration will draw a new axes (frame), thus we have to clear the prev frame first
    bins = np.arange(-4, 4, 0.5)  # the bins edge range from -4 to 4 with a step of 0.5
    plt.hist(x[:curr], bins=bins) # each iteration will plot the 0th - the(curr-1)th elements of the list x. 
                                  # thus, data plotted in current iteration = data plotted in prev iteration + 1
    plt.axis([-4, 4, 0, 30]) # set the lim for x & y axis
    plt.gca().set_title('Sampling the Normal Distribution') # plot title
    plt.gca().set_ylabel('Frequency') # y axis title
    plt.gca().set_xlabel('Value') # x axis title
    plt.annotate('n = {}'.format(curr), [3, 27]) # give annotation to the plot. --> similar to plt.text. 

# step 6: create the animation by calling the FuncAnimation
a = animation.FuncAnimation(fig, update, interval=100)