通过多重处理保存多个matplotlib图形 [英] Saving multiple matplotlib figures with multiprocessing

问题描述

我有一个代码，可以从名为001.txt，002.txt，...，411.txt的多个文件中读取数据.我想从每个文件中读取数据，对其进行绘制，然后另存为001.jpg，002.jpg，...，411.jpg.

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.

我可以通过遍历文件来做到这一点，但是我想使用多进程模块来加快处理速度.
但是，当我使用下面的代码时，计算机挂起-我无法单击任何东西，但是鼠标移动了，声音仍在继续.然后，我必须关闭计算机电源.

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.

我显然在将多进程模块与matplotlib混用.我使用了与下面的代码非常相似的东西来实际生成数据，并毫无问题地保存到文本文件中.我想念什么?

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()

推荐答案

使用multiprocessing的最重要的事情是仅对主进程运行模块的主代码.这可以通过测试if __name__ == '__main__'来实现，如下所示:

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))

还请注意，我用进程池代替了单独进程的创建(进程池可更好地缩放到许多图片，因为它只使用与可用内核一样多的进程).

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).

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