Tom, I just went through this, though with version 1.01 of mpl, so it may be different. You can read the very long thread at:
http://www.mail-archive.com/matplotlib-users@lists.sourceforge.net/msg20031.html Those who maintain mpl don't think there is a memory leak. What I found was that imshow() does consume a lot of memory (now fixed in the development version) and that the first 2 or so uses build on each other, but after that it levels off giving back memory after close(). There is a discrepancy between what python reports it's using and what the OS reports (I had 500MB from the OS, but only 150MB from python). There is a chance that ipython is caching your results (try ipython -pylab -cs 0), but when I ran without ipython, python still had a large portion of memory. -robert On 2/9/2011 3:52 PM, Tom Dimiduk wrote: > I am using matplotlib pylab in association with ipython -pylab to show > many large (~2000x2000 or larger) images. Each time I show another > image it consumes more memory until eventually exhausting all system > memory and making my whole system unresponsive. > > The easiest way to replicate this behaviour is with > a = ones((3333,3333)) > imshow(a) > > optionally > > close() > > and then > > imshow(a) > > again. I am using ipython .10.1 and matplotlib 0.99.3. Is there > something I should be doing differently to avoid this problem? Is it > fixed in a later version? > > Thanks, > Tom > > ------------------------------------------------------------------------------ > The ultimate all-in-one performance toolkit: Intel(R) Parallel Studio XE: > Pinpoint memory and threading errors before they happen. > Find and fix more than 250 security defects in the development cycle. > Locate bottlenecks in serial and parallel code that limit performance. > http://p.sf.net/sfu/intel-dev2devfeb > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users ------------------------------------------------------------------------------ The ultimate all-in-one performance toolkit: Intel(R) Parallel Studio XE: Pinpoint memory and threading errors before they happen. Find and fix more than 250 security defects in the development cycle. Locate bottlenecks in serial and parallel code that limit performance. http://p.sf.net/sfu/intel-dev2devfeb _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
