Asynchronous Plotting in Matplotlib: rather than call savefig directly, add plots to an asynchronous queue to avoid holding up the main program. Makes use of multiple processes to speed up the writing out. Suggestions welcome!

async_plotting.py

import time
import multiprocessing as mp

import numpy as np

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt


class AsyncPlotter():

    def __init__(self, processes=mp.cpu_count()):

        self.manager = mp.Manager()
        self.nc = self.manager.Value('i', 0)
        self.pids = []
        self.processes = processes

    def async_plotter(self, nc, fig, filename, processes):
        while nc.value >= processes:
            time.sleep(0.1)
        nc.value += 1
        print "Plotting " + filename
        fig.savefig(filename)
        plt.close(fig)
        nc.value -= 1

    def save(self, fig, filename):
        p = mp.Process(target=self.async_plotter,
                       args=(self.nc, fig, filename, self.processes))
        p.start()
        self.pids.append(p)

    def join(self):
        for p in self.pids:
            p.join()

# Create instance of Asynchronous plotter
a = AsyncPlotter()

for i in range(10):

    print 'Preparing %04i.png' % i

    # Generate random points
    x = np.random.random(10000)
    y = np.random.random(10000)

    # Generate figure
    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    ax.set_xscale('log')
    ax.set_yscale('log')
    ax.scatter(x, y)

    # Add figure to queue
    a.save(fig, '%04i.png' % i)

# Wait for all plots to finish
a.join()

thanks guys v helpful