I'm using matplotlib to generate many plots of the results of a numerical simulation. The plots are used as frames in a video, and so I'm generating many of them by repeatedly calling a function similar to this one:
from pylab import * def plot_density(filename,i,t,psi_Na): figure(figsize=(8,6)) imshow(abs(psi_Na)**2,origin = 'lower') savefig(filename + '_%04d.png'%i) clf()
The problem is that the memory usage of the python process grows by a couple of megabytes with every call to this function. For example if I call it with this loop:
if __name__ == "__main__": x = linspace(-6e-6,6e-6,128,endpoint=False) y = linspace(-6e-6,6e-6,128,endpoint=False) X,Y = meshgrid(x,y) k = 1000000 omega = 200 times = linspace(0,100e-3,100,endpoint=False) for i,t in enumerate(times): psi_Na = sin(k*X-omega*t) plot_density('wavefunction',i,t,psi_Na) print i
then the ram usage grows with time to 600MB. If however I comment out the line
figure(figsize=(8,6)) in the function definition, then the ram usage stays steady at 52MB.
(8,6) is the default figure size and so identical images are produced in both cases. I'd like to make different sized plots from my numerical data without running out of ram. How might I force python to free up this memory?
gc.collect() each loop to force garbage collection, and I've tried
f = gcf() to get the current figure and then
del f to delete it, but to no avail.
I'm running CPython 2.6.5 on 64 bit Ubuntu 10.04.
From the docstring for
In : pylab.figure?
If you are creating many figures, make sure you explicitly call "close" on the figures you are not using, because this will enable pylab to properly clean up the memory.
So perhaps try:
pylab.close() # closes the current figure
Closing a figure is definitely an option, however, repeated many times, this is time consuming. What I suggest is to have a single persistent figure object (via static function variable, or as additional function argument). If that object is
fig, the function will then call
fig.clf() before each plotting cycle.
from matplotlib import pylab as pl import numpy as np TIMES = 10 x = np.linspace(-10, 10, 100) y = np.sin(x) def withClose(): def plotStuff(i): fig = pl.figure() pl.plot(x, y + x * i, '-k') pl.savefig('withClose_%03d.png'%i) pl.close(fig) for i in range(TIMES): plotStuff(i) def withCLF(): def plotStuff(i): if plotStuff.fig is None: plotStuff.fig = pl.figure() pl.clf() pl.plot(x, y + x * i, '-') pl.savefig('withCLF_%03d.png'%i) plotStuff.fig = None for i in range(TIMES): plotStuff(i)
Here is the timing values
In : %timeit withClose() 1 loops, best of 3: 3.05 s per loop In : %timeit withCLF() 1 loops, best of 3: 2.24 s per loop