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)
global x, y
x += np.pi / 15.
y += np.pi / 20.
ani = animation.FuncAnimation(fig, updatefig, interval=50, blit=True)
#I have tried any of these 3 commands, without success:
Thanks to the help of Ed Smith and MiteshNinja, I have finally succeeded in finding a robust method that works not only with the Ipython console, but also with the Python console and the command line. Furthermore, it allows total control on the animation process. Code is self explanatory.
import numpy as np import matplotlib import matplotlib.pyplot as plt from multiprocessing import Process import time # optional for testing only import matplotlib.animation as animation # A. First we define some useful tools: def wait_fig(): # Block the execution of the code until the figure is closed. # This works even with multiprocessing. if matplotlib.pyplot.isinteractive(): matplotlib.pyplot.ioff() # this is necessary in mutliprocessing matplotlib.pyplot.show(block=True) matplotlib.pyplot.ion() # restitute the interractive state else: matplotlib.pyplot.show(block=True) return def wait_anim(anim_flag, refresh_rate = 0.1): #This will be used in synergy with the animation class in the example #below, whenever the user want the figure to close automatically just #after the animation has ended. #Note: this function uses the controversial event_loop of Matplotlib, but #I see no other way to obtain the desired result. while anim_flag: #next code extracted from plt.pause(...) backend = plt.rcParams['backend'] if backend in plt._interactive_bk: figManager = plt._pylab_helpers.Gcf.get_active() if figManager is not None: figManager.canvas.start_event_loop(refresh_rate) def draw_fig(fig = None): #Draw the artists of a figure immediately. #Note: if you are using this function inside a loop, it should be less time #consuming to set the interactive mode "on" using matplotlib.pyplot.ion() #before the loop, event if restituting the previous state after the loop. if matplotlib.pyplot.isinteractive(): if fig is None: matplotlib.pyplot.draw() else: fig.canvas.draw() else: matplotlib.pyplot.ion() if fig is None: matplotlib.pyplot.draw() else: fig.canvas.draw() matplotlib.pyplot.ioff() # restitute the interactive state matplotlib.pyplot.show(block=False) return def pause_anim(t): #This is taken from plt.pause(...), but without unnecessary #stuff. Note that the time module should be previously imported. #Again, this use the controversial event_loop of Matplotlib. backend = matplotlib.pyplot.rcParams['backend'] if backend in matplotlib.pyplot._interactive_bk: figManager = matplotlib.pyplot._pylab_helpers.Gcf.get_active() if figManager is not None: figManager.canvas.start_event_loop(t) return else: time.sleep(t) #-------------------------- # B. Now come the particular functions that will do the job. def f(x, y): return np.sin(x) + np.cos(y) def plot_graph(): fig = plt.figure() x = np.linspace(0, 2 * np.pi, 120) y = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1) im = fig.gca().imshow(f(x, y)) draw_fig(fig) n_frames = 50 #============================================== #First method - direct animation: This use the start_event_loop, so is #somewhat controversial according to the Matplotlib doc. #Uncomment and put the "Second method" below into comments to test. '''for i in range(n_frames): # n_frames iterations x += np.pi / 15. y += np.pi / 20. im.set_array(f(x, y)) draw_fig(fig) pause_anim(0.015) # plt.pause(0.015) can also be used, but is slower wait_fig() # simply suppress this command if you want the figure to close # automatically just after the animation has ended ''' #================================================ #Second method: this uses the Matplotlib prefered animation class. #Put the "first method" above in comments to test it. def updatefig(i, fig, im, x, y, anim_flag, n_frames): x = x + i * np.pi / 15. y = y + i * np.pi / 20. im.set_array(f(x, y)) if i == n_frames-1: anim_flag = False anim_flag = [True] animation.FuncAnimation(fig, updatefig, repeat = False, frames = n_frames, interval=50, fargs = (fig, im, x, y, anim_flag, n_frames), blit=False) #Unfortunately, blit=True seems to causes problems wait_fig() #wait_anim(anim_flag) #replace the previous command by this one if you want the #figure to close automatically just after the animation #has ended #================================================ return #-------------------------- # C. Using multiprocessing to obtain the desired effects. I believe this # method also works with the "threading" module, but I haven't test that. def main(): # it is important that ALL the code be typed inside # this function, otherwise the program will do weird # things with the Ipython or even the Python console. # Outside of this condition, type nothing but import # clauses and function/class definitions. if __name__ != '__main__': return p = Process(target=plot_graph) p.start() print('hello', flush = True) #just to have something printed here p.join() # suppress this command if you want the animation be executed in # parallel with the subsequent code for i in range(3): # This allows to see if execution takes place after the #process above, as should be the case because of p.join(). print('world', flush = True) time.sleep(1) main()