Fast image sequences / animation in Jupyter Notebook with matplotlib

Question

I can't seem to find a simple and fast way of plotting image sequences with plain matplotlib in a Jupyter Notebook. I've tried FuncAnimation, fig.canvas.draw(), blitting, as well as just the standard imshow-pause combo; without success or with very slow refresh rate. I don't need the images to be interactive - they just need to be shown sequentially and can't pop up a new figure window for each image. I've seen many solutions here, with none seeming to work the way I want.

My general pipeline does significant processing, with each image generated and plotted within a while or for loop. FuncAnimation is not desirable since it requires passing a function handle and my use case involves many arguments and state variables that make it difficult to use.

The best I've got is the working example below using fig.canvas.draw() - showing that drawing time increases linearly per iteration, where I need it to remain constant!

import numpy as np
import matplotlib.pyplot as plt
from timeit import default_timer as timer
%matplotlib notebook

num_iters = 50
im = np.arange(60).reshape((15,4))

fig, ax = plt.subplots(1,1)
fig.show()
fig.canvas.draw()

iter_times = np.zeros(num_iters)
for i in range(num_iters):
    im = np.roll( a=im, shift=1, axis=0 )

    t0 = timer()
    ax.imshow(im.T, vmin=im.min(), vmax=im.max())
    ax.set_title('Iter # {}/{}'.format(i+1, num_iters))
    fig.canvas.draw()

    iter_times[i] = timer()-t0

plt.figure(figsize=(6,3))
plt.plot(np.arange(num_iters)+1, iter_times)
plt.title('Imshow/drawing time per iteration')
plt.xlabel('Iteration number')
plt.ylabel('Time (seconds)')
plt.tight_layout()
plt.show()

Answer 1

I think the problem is that the plots are 'building up', so every one is being plotted every time. If you add ax.clear() right before the imshow(), you'll get linear plot times.