.. _sphx_glr_gallery_misc_multiprocess_sgskip.py:


============
Multiprocess
============

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

Written by Robert Cimrman



.. code-block:: python

    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.




.. code-block:: python



    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__``.




.. code-block:: python



    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)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: multiprocess_sgskip.py <multiprocess_sgskip.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: multiprocess_sgskip.ipynb <multiprocess_sgskip.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_