There is a fix in r8341. It passes the regression tests, and all of the
event handling examples I tried seem to still work.
It seems that many places in matplotlib were never disconnecting
callbacks, and these callbacks keep references to the destination
objects alive.
Unfortunately, it's not quite obvious where the "disconnect" calls
should be added -- the lifetime of objects isn't very symmetrical. For
example, the "units" callback is set up by Lines2D inside of its
"set_axes" method, but there is no "remove_axes" method in which to put
the disconnect. Tracking down all of the ways in which a line could be
removed from an axes seems daunting.
Instead, my solution is to store weak references to the methods stored
in the CallbackRegistry -- that way the CallbackRegistry won't leak
references like it does now. Since the Python stdlib weakref module
doesn't directly support weak references to bound methods, the whole
thing is a bit hairy -- but I think it's a more permanent solution than
trying to ensure that all callbacks get explicitly disconnected.
As this change is rather fundamental and may have unintended
consequences, please play with it in your contexts and let me know if
you see anything strange.
Mike
On 05/28/2010 10:47 AM, Michael Droettboom wrote:
> I'm on to something -- some callbacks are being created that are never
> disconnected.
>
> In Line2D.set_axes:
>
> self._xcid = ax.xaxis.callbacks.connect('units', self.recache_always)
>
> gets called twice. This is problematic because the id of the first
> connection is simply lost. Also, there doesn't seem to be any code to
> attempt to remove either of them.
>
> I'm looking into it further -- forcibly deleting these callbacks reduces
> the reference count on the line object, but doesn't seem to completely
> eliminate the leak.
>
> Mike
>
> On 05/28/2010 10:12 AM, John Hunter wrote:
>
>> 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)?
>>
>> ------------------------------------------------------------------------------
>>
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>> Matplotlib-devel@lists.sourceforge.net
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>>
>>
>
>
--
Michael Droettboom
Science Software Branch
Space Telescope Science Institute
Baltimore, Maryland, USA
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