Saving multiple matplotlib figures with multiprocessing

Question

I have a code which reads data from multiple files named 001.txt, 002.txt, ... , 411.txt. I would like to read the data from each file, plot them, and save as 001.jpg, 002.jpg, ... , 411.jpg.

I can do this by looping through the files, but I would like to use the multiprocess module to speed things up.
However, when I use the code below, the computer hangs- I can't click on anything, but the mouse moves, and the sound continues. I then have to power down the computer.

I'm obviously misusing the multiprocess module with matplotlib. I have used something very similar to the below code to actually generate the data, and save to text files with no problems. What am I missing?

    import multiprocessing

    def do_plot(number):
        fig = figure(number)

        a, b = random.sample(range(1,9999),1000), random.sample(range(1,9999),1000)
        # generate random data
        scatter(a, b)

        savefig("%03d" % (number,) + ".jpg")
        print "Done ", number
        close()



    for i in (0, 1, 2, 3):
        jobs = []
    #    for j in chunk:
        p = multiprocessing.Process(target = do_plot, args = (i,))
        jobs.append(p)
        p.start()
    p.join()

Answer 1

The most important thing in using multiprocessing is to run the main code of the module only for the main process. This can be achieved by testing if __name__ == '__main__' as shown below:

import matplotlib.pyplot as plt
import numpy.random as random
from multiprocessing import Pool


def do_plot(number):
    fig = plt.figure(number)

    a = random.sample(1000)
    b = random.sample(1000)

    # generate random data
    plt.scatter(a, b)

    plt.savefig("%03d.jpg" % (number,))
    plt.close()

    print("Done ", number)


if __name__ == '__main__':
    pool = Pool()
    pool.map(do_plot, range(4))

Note also that I replaced the creation of the separate processes by a process pool (which scales better to many pictures since it only uses as many process as you have cores available).