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@astrofrog /async_plotting.py
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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!
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()
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