How to wait until matplotlib animation ends?

Question

Consider the following code directly taken from the Matplotlib documentation:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import time # optional for testing only
import cv2 # optional for testing only

fig = plt.figure()   

def f(x, y):
    return np.sin(x) + np.cos(y)

x = np.linspace(0, 2 * np.pi, 120)
y = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)

im = plt.imshow(f(x, y), animated=True)    

def updatefig(*args):
    global x, y
    x += np.pi / 15.
    y += np.pi / 20.
    im.set_array(f(x, y))
    return im,

ani = animation.FuncAnimation(fig, updatefig, interval=50, blit=True)
plt.show()

This work fine on my system. Now, try to append the following piece of code to the above code:

while True: 
  #I have tried any of these 3 commands, without success:  
    pass
    #time.sleep(1)
    #cv2.waitKey(10)

What happens is that the program freezes. Apparently, the "Animation" class of Matplotlib runs the animation in a separate thread. So I have the 2 following questions:

1) If the process runs in a separate thread, why is it disturbed by the subsequent loop ?

2) How to say to python to wait until the animation has ended ?

Answer 1

For me, copying into ipython works as expected (animation plays first then the infinite loop) but when running the script it freezes.

1) I'm not sure exactly how cpython handles multi-threading, especially when combined with a particular matplotlib backend but it seems to be failing here. One possibility is to be explicit about how you want to use threads, by using

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import multiprocessing as mp


fig = plt.figure()   

def f(x, y):
    return np.sin(x) + np.cos(y)

x = np.linspace(0, 2 * np.pi, 120)
y = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)

im = plt.imshow(f(x, y), animated=True)    

def updatefig(*args):
    global x, y
    x += np.pi / 15.
    y += np.pi / 20.
    im.set_array(f(x, y))
    return im,

#A function to set thread number 0 to animate and the rest to loop
def worker(num):
    if num == 0:
        ani = animation.FuncAnimation(fig, updatefig, interval=50, blit=True)
        plt.show()
    else:
        while True: 
            print("in loop")
            pass

    return


# Create two threads
jobs = []
for i in range(2):
    p = mp.Process(target=worker, args=(i,))
    jobs.append(p)
    p.start()

Which defines two threads and sets one to work on animation, one to loop.

2) To fix this, as suggested by @Mitesh Shah, you can use plt.show(block=True). For me, the script then behaves as expected with animation and then loop. Full code:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

fig = plt.figure()   

def f(x, y):
    return np.sin(x) + np.cos(y)

x = np.linspace(0, 2 * np.pi, 120)
y = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)

im = plt.imshow(f(x, y), animated=True)    

def updatefig(*args):
    global x, y
    x += np.pi / 15.
    y += np.pi / 20.
    im.set_array(f(x, y))
    return im,

ani = animation.FuncAnimation(fig, updatefig, interval=50, blit=True)
plt.show(block=True)

while True: 
    print("in loop")
    pass

UPDATE: Alternative is to simply use interactive mode,

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

fig = plt.figure()   
plt.ion()
plt.show()

def f(x, y):
    return np.sin(x) + np.cos(y)

def updatefig(*args):
    global x, y


x = np.linspace(0, 2 * np.pi, 120)
y = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)
im = plt.imshow(f(x, y))    

for i in range(500):

    x += np.pi / 15.
    y += np.pi / 20.
    im.set_array(f(x, y))
    plt.draw()
    plt.pause(0.0000001)