matplotlib-devel Mailing List for matplotlib

Hi,

thank you very much! The memory leak disappeared after I updated
pygobject from 2.12 to 2.13.2.

matplotlib SVN trunk appears to be running backend_driver.py fine under 
Python-2.6 with Numpy SVN and TkAgg backend.

I had to make a handful of changes to remove deprecation warnings, none 
of which changes our "still supporting Python 2.4" policy.  One change 
was required to pytz, which I've communicated upstream to its author.

In case anyone is wondering, there don't seem to be any measurable 
performance increases... :(

Cheers,
Mike

-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

Agreed.  I'm not sure how this bug could be resolved only on the 
matplotlib side.  See if you can make a standalone (pygtk-only) example 
that demonstrates this bug and send it to their mailing list.  Also, if 
it's possible, try updating pygtk.  There was a very similar bug in an 
earlier version (sorry, I can't find the reference), that was resolved.

Cheers,
Mike

Eric Firing wrote:
> Mátyás János wrote:
>   
>> Hi,
>>
>> I'm looking for memory leaks in a python application and found leaks in
>> matplotlib. The application is graphic intensive. Each time it updates
>> the screen, matplotlib allocates another 5-10 megabytes memory for the
>> new gtk.gdk.Pixbuf and gtk.gdk.GCX11 while does not free up the buffers
>> allocated for the previous content.
>>
>>     
>
> This sounds to me like a pygtk bug, not a matplotlib bug; a gtk object 
> is hanging around after its reference has been deleted. What versions of 
> gtk and pygtk are you using?  This sounds dimly familiar.  I don't know 
> enough about gtk and pygtk to help, but I am sure the people who do know 
> more will want to know the version.
>
> Eric
>
>   
>> I switched on garbage collection debugging with:
>>
>> import gc
>> gc.enable()
>> gc.set_debug(gc.DEBUG_LEAK)
>>
>> and tried to delete the leaking objects:
>>
>> --- ./lib/matplotlib/backends/backend_gtkagg.py 2008-06-23
>> 04:09:29.000000000 +0200 ++
>> + /usr/lib/python2.4/site-packages/matplotlib-0.91.4-py2.4-linux-i686.egg/matplotlib/backends/backend_gtkagg.py
>> 2008-10-02 00:05:32.000000000 +0200 @@ -3,6 +3,7 @@ """
>>  from __future__ import division
>>  import os
>> +import gc
>>
>>  import matplotlib
>>  from matplotlib.figure import Figure
>> @@ -82,8 +83,17 @@
>>          h = int(ren.height)
>>          pixbuf = gtk.gdk.pixbuf_new_from_data(
>>              buf, gtk.gdk.COLORSPACE_RGB,  True, 8, w, h, w*4)
>> -        pixmap.draw_pixbuf(pixmap.new_gc(), pixbuf, 0, 0, 0, 0, w, h,
>> +       g = pixmap.new_gc()
>> +        pixmap.draw_pixbuf(g, pixbuf, 0, 0, 0, 0, w, h,
>>                             gtk.gdk.RGB_DITHER_NONE, 0, 0)
>> +
>> +       print "XXXXXXXXXX", pixbuf,
>> g,pixbuf.__grefcount__,g.__grefcount__
>> +       print gc.get_referrers(pixbuf)
>> +       print gc.get_referrers(g)
>> +       del pixbuf
>> +       del g
>> +       gc.collect()
>> +
>>          if DEBUG: print 'FigureCanvasGTKAgg.render_figure done'
>>
>>      def blit(self, bbox=None):
>>
>>
>> The __grefcount__ values are 1 but the memory is not freed up even after
>> gc.collect().
>>
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-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

Hi Eric,

Sorry for the late reply.

On Sep 27, 2008, at 8:56 PM, Eric Firing wrote:

> Actually, I think the most logical thing would be to let the default  
> None give the old behavior, and require precision=0 to get the new  
> behavior.  What do you think?  Is it OK if I make this change?  It  
> is more consistent with the old behavior.

I'm ambivalent about this change. On one hand, I think it makes a lot  
more sense to have None give the old behavior and precision=0 to  
ignore zero values in the sparse array (then precision would be  
consistent for finite values and for zero).

On the other hand, I think ignoring zero values should be the default  
behavior for sparse arrays (although, I definitely agree there should  
be the option to plot all assigned values).

Would it be possible to make the change you suggest and also change  
the default precision value to 0? (see diff below) This change would  
also allow you to remove a lot of the special handling for  
precision=None, since precision=0 gives the same result (I didn't go  
this far in the diff below).

> I also changed the behavior so that if a sparse array is input, with  
> no marker specifications, it simply makes a default marker plot  
> instead of raising an exception.


Excellent idea. That behavior is much more user-friendly.

Thanks,

-Tony

PS. Any comments on the small changes to the examples. Both changes  
are necessary for those examples to work on my computer (the shebang  
line throws an error when I run the code from my text editor).


Index: matplotlib/lib/matplotlib/axes.py
===================================================================
--- matplotlib/lib/matplotlib/axes.py	(revision 6141)
+++ matplotlib/lib/matplotlib/axes.py	(working copy)
@@ -6648,7 +6648,7 @@

          return Pxx, freqs, bins, im

-    def spy(self, Z, precision=None, marker=None, markersize=None,
+    def spy(self, Z, precision=0., marker=None, markersize=None,
              aspect='equal',  **kwargs):
          """
          call signature::
@@ -6731,14 +6731,11 @@
          else:
              if hasattr(Z, 'tocoo'):
                  c = Z.tocoo()
-                if precision == 0:
+                if precision is None:
                      y = c.row
                      x = c.col
                  else:
-                    if precision is None:
-                        nonzero = c.data != 0.
-                    else:
-                        nonzero = np.absolute(c.data) > precision
+                    nonzero = np.absolute(c.data) > precision
                      y = c.row[nonzero]
                      x = c.col[nonzero]
              else:
Index: matplotlib/examples/pylab_examples/masked_demo.py
===================================================================
--- matplotlib/examples/pylab_examples/masked_demo.py	(revision 6141)
+++ matplotlib/examples/pylab_examples/masked_demo.py	(working copy)
@@ -1,4 +1,4 @@
-#!/bin/env python
+#!/usr/bin/env python
  '''
  Plot lines with points masked out.

Index: matplotlib/examples/misc/rec_groupby_demo.py
===================================================================
--- matplotlib/examples/misc/rec_groupby_demo.py	(revision 6141)
+++ matplotlib/examples/misc/rec_groupby_demo.py	(working copy)
@@ -2,7 +2,7 @@
  import matplotlib.mlab as mlab


-r = mlab.csv2rec('data/aapl.csv')
+r = mlab.csv2rec('../data/aapl.csv')
  r.sort()

  def daily_return(prices):