Re: [Visualpython-users] Possible memory leak

Hi Bruce,

I think the difference here is that I am changing the pos (in fact only the
y part of pos)
each cycle.  You are deleting the curve and creating a new one.

This is an important clue as it means it is the part of code that alters the
curve that has a problem, not the act of
creating, drawing and distruction.

Try the same loop, but don't re-create c each pass. Create it outside the
loop and update it's y by some factor.

Try this and take a look at task manager:

from visual import *
import random
c=curve(pos=[[0,0,0],[1,1,1]])
while True:
  c.pos=[[random.random() for x in ones(3)],[random.random() for x in
ones(3)]]


For me this loop causes pythonw.exe to consume about 8Mbyte a second.
I also note that when only the original static line is showing pythonw still
consumes about 10k a second - which is odd.

j.



-----Original Message-----
From: Bruce Sherwood [mailto:bas...@nc...]
Sent: 29 October 2010 18:33
To: vpusers
Subject: Re: [Visualpython-users] Possible memory leak


Following your suggestion, I tried the following in VPython 5.4 on
Python 2.7 in Windows 7

from visual import *
N = 1000000
pts = ones(N*3).reshape(N,3)
while True:
    c = curve(pos=pts)
    c.visible = False
    del c

Is this something like what you did? I watch the Windows Task Manager
and cannot see any change in the memory usage.

Bruce Sherwood

On Fri, Oct 29, 2010 at 8:38 AM, Beracah Yankama <be...@mi...> wrote:
> hah!  now i'm not the only one who noticed this!  i was graphing a lot
> of data too with lots of curves (a graph cluster growing to thousands of
> nodes and edges, which are dynamically positioned), but after like 200
> edges and being repositioned, an out of memory error gets thrown.  I had
> tracked it down to vpython.
>
> I had written (in case it is helpful to you):
> """
> I made a test script, that only
> creates objects in the window, then deletes them over and over, and I
> found the long-term memory use/growth to be related linearly to the .pos
> attribute.  For instance, a curve with 20 points leads to unreleased
> memory that grows 10x as fast as curves with only 2 points.  Similarly,
> if I assign the .pos attribute as a numpy ndarray directly, the leak
> grows twice as fast as if assign the individual xyz values.
> """
>
> B
>

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