On Fri, May 28, 2010 at 3:18 AM, Pearu Peterson <pe...@cens.ioc.ee> wrote: > > Hi, > > In an application that updates a plot with > new experimental data, say, every second and the experiment > can last hours, I have tried two approaches: > 1) clear axes and plot new experimental data - this is > slow and takes too much cpu resources. > 2) remove lines and plot new experimental data - this is > fast enough but unfortunately there seems to be a memory > leakage, the application runs out of memory. > > Here follows a simple script that demonstrates the > leakage problem: > > # > import numpy > from numpy.testing.utils import memusage > import matplotlib.pyplot as plt > x = range (1000) > axes1 = plt.figure().add_subplot( 111 ) > y = numpy.random.rand (len (x)) > while 1: > if 1: > # leakage > for line in axes1.lines: > if line.get_label ()=='data': > line.remove() > else: > # no leak, but slow > axes1.clear() > axes1.plot(x, y, 'b', label='data') > print memusage (), len (axes1.lines) > #eof > > When running the script, the memory usage > is increasing by 132 kbytes per iteration, that is, > with an hour this example application will consume > 464MB RAM while no new data has been generated. In real > application this effect will be even worse. > > So, I am looking for an advice how to avoid > this memory leakage without clearing axes.
Hey Pearu -- thanks for the report. We'll try and track down and fix this leak. In the interim, would an acceptable work around for you be to *reuse* an existing line by calling set_data on it. That way you wouldn't have to do the add/remove that is causing your leak. Have you confirmed this leak on various backends (eg Agg, PDF, PS)? ------------------------------------------------------------------------------ _______________________________________________ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
