On 3/27/07, Eric Firing <[EMAIL PROTECTED]> wrote:
> I can add a couple of things to item (1) below. First, the problem
> occurs only with toolbar2, not with classic or None. Second, a script
> that illustrates it is attached.
I defintely agree that this is important -- and it is a big help to
have a script and the info that you narrowed the problem down to the
presence of the toolbar. report_memory is platform dependent since ps
is. I added a report_memory function to cbook so we could have some
common functionality to rely on. So far it checks for linux or sunos5
and we should add to this and fix it up as necessary. I also stripped
the script down to the bare essentials (the memory report) and added
it to unit/memleak_gui.py so others can use it for testing.
It turns out if you add a savefig call, TkAgg is terribly (1MB per
figure) and I can reproduce the smaller toolbar induced leak on my
platform with TkAgg and GTKAgg. I tried adding some code to
figure.clf to help, but it didn't. I also spent some time trying to
figure out what was going wrong with TkAgg but unfortunately did not
succeed. I don't know anything about Tk, really. One interesting
thing: the enormous filesave leak in TkAgg also only occurs if the
toolbar is present. w/o the toolbar, neither gtkagg nor tkagg leak w/
or w/o the filesave. With the toolbar, both leak a 20-80k w/o the
file save.
Developers: if you know something about a particular GUI, try this
script with -dYourGUIBackend and see if you can isolate the problem!
JDH
# in svn as unit/memleak_gui.py
#!/usr/bin/env python
'''
This illustrates a leak that occurs with any interactive backend.
Run with
> python memleak_gui.py -dGTKAgg # or TkAgg, etc..
You may need to edit cbook.report_memory to support your platform
'''
import os, sys, time
import gc
import matplotlib
#matplotlib.use('TkAgg') # or TkAgg or WxAgg or QtAgg or Gtk
matplotlib.rcParams['toolbar'] = 'toolbar2' # None, classic, toolbar2
#matplotlib.rcParams['toolbar'] = None # None, classic, toolbar2
import pylab
from matplotlib import _pylab_helpers as ph
import matplotlib.cbook as cbook
indStart, indEnd = 30, 50
for i in range(indEnd):
fig = pylab.figure()
fig.savefig('test')
fig.clf()
pylab.close(fig)
val = cbook.report_memory(i)
print i, val
gc.collect()
if i==indStart: start = val # wait a few cycles for memory usage
to stabilize
gc.collect()
print
print 'uncollectable list:', gc.garbage
print
end = val
if i > indStart:
print 'Average memory consumed per loop: %1.4fk bytes\n' %
((end-start)/float(indEnd-indStart))
-------------------------------------------------------------------------
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