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Multiprocess

Demo of using multiprocessing for generating data in one process and plotting in another.

Written by Robert Cimrman

from __future__ import print_function

import time
import numpy as np

from multiprocessing import Process, Pipe

# This example will likely not work with the native OSX backend.
# Uncomment the following lines to use the qt5 backend instead.
#
# import matplotlib
# matplotlib.use('qt5Agg')

import matplotlib.pyplot as plt

# Fixing random state for reproducibility
np.random.seed(19680801)

Processing Class

This class plots data it recieves from a pipe.

class ProcessPlotter(object):
    def __init__(self):
        self.x = []
        self.y = []

    def terminate(self):
        plt.close('all')

    def poll_draw(self):

        def call_back():
            while self.pipe.poll():
                command = self.pipe.recv()
                if command is None:
                    self.terminate()
                    return False
                else:
                    self.x.append(command[0])
                    self.y.append(command[1])
                    self.ax.plot(self.x, self.y, 'ro')
            self.fig.canvas.draw()
            return True

        return call_back

    def __call__(self, pipe):
        print('starting plotter...')

        self.pipe = pipe
        self.fig, self.ax = plt.subplots()
        timer = self.fig.canvas.new_timer(interval=1000)
        timer.add_callback(self.poll_draw())
        timer.start()

        print('...done')
        plt.show()

Plotting class

This class uses multiprocessing to spawn a process to run code from the class above. When initialized, it creates a pipe and an instance of ProcessPlotter which will be run in a separate process.

When run from the command line, the parent process sends data to the spawned process which is then plotted via the callback function specified in ProcessPlotter:__call__.

class NBPlot(object):
    def __init__(self):
        self.plot_pipe, plotter_pipe = Pipe()
        self.plotter = ProcessPlotter()
        self.plot_process = Process(
            target=self.plotter,
            args=(plotter_pipe,)
        )
        self.plot_process.daemon = True
        self.plot_process.start()

    def plot(self, finished=False):
        send = self.plot_pipe.send
        if finished:
            send(None)
        else:
            data = np.random.random(2)
            send(data)


def main():
    pl = NBPlot()
    for ii in range(10):
        pl.plot()
        time.sleep(0.5)
    pl.plot(finished=True)


if __name__ == '__main__':
    main()

Total running time of the script: ( 0 minutes 0.000 seconds)

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