Memory error

Comments

• Josh

  February 2016 edited 4:00AM

  Hi Michel,

  If your canvas has the same name in every trial, it's just a single object and should therefore not increase the memory load. The cause of your memory problem depends on the design of your experiment. What kind of data do you log, and how do you create your stimuli?

  Cheers,

  Josh
• Michif

  February 2016 edited February 2016

  Hm... my experiment is actually very simple.

  I only log integers, strings, a single list of 10 tuples (size 2) and a dictionary with 4-5 lists (all no larger than size 10). I cannot imagine that this is the issue.
  I create my stimuli using largely in-build functions (canvas.lines, canvas.circle, etc.). There is no other excessive use of other functions.
  The fact that the program is rather simple makes me think it may have something to do with overwriting (instead of deleting or canvas.clear - ing my canvas) rather than something about my stimuli.

  I'd love to switch to pygaze for more stability but since it is an EEG experiment I'd prefer to stick to psychopy backend.
• Josh

  February 2016 edited 4:00AM

  Hi Michel,

  With overwriting, do you mean that you keep on adding shapes to the same canvas without clearing previous shapes between trials? If so, then I think that may be the problem. Is it possible to add a simple canvas.clear() at the end of your trial?

  Cheers
• Michif

  February 2016 edited February 2016

  I create the canvas in the prepare phase (self.offline) under a certain reference (exp.canvasA) on Trial A. Then I fill that canvas with certain stimuli (line and circle elements) and present it in the run phase. Then I do exactly the same in the next Trial B. That is, the canvas from Trial A is not deleted or cleared. I create a new canvas on Trial B with the same reference (exp.canvasA) that I used in Trial A. So I wondered, whether in this case, the canvas from Trial A has a separate memory trace than the canvas created in Trial B and I just use the same variable name (i.e. reference -- exp.canvasA) for a different memory trace or whether I actually overwrite the existing one (i.e. use the same memory trace). I suspect the former, which would explain why there is at some point just a memory overload.

  I could indeed just create the canvas (self.offline) outside the trial loop and clear the canvas at the end of each trial and fill it again on the next. Or create a new canvas on each trial like I do right now, but delete the existing one after I presented it. I suspect that both approaches would solve the problem, if I create separate memory traces for each created canvas instead of overwriting them.
• Michif

  August 2016

  Just to update this post:
  I fixed the issue but clearing the canvas and creating as few canvases/vars as possible outside the loop. Since then I do not have this issue anymore.
• Daniel

  August 2016

  Hi Michel. This is a known problem with the OS + psychopy combination (ask Berno or Jarik about it). Apparently it can be solved by doing some explicit garbage collection once in a while:

  import gc
  ... 
  gc.collect()
  

  Note that this operation can take up to a few seconds, so better not to do it during time critical phases (but rather before the breaks etc). Hope this helps. And even though you said you didn't want to upgrade, I think this problem is fixed from v3.1 on.

  
• sebastiaan

  August 2016

  And even though you said you didn't want to upgrade, I think this problem is fixed from v3.1 on.

  That's right, there is now an experimental variable called disable_garbage_collection which, when set to 'yes' (the default), causes OpenSesame to disable automatic garbage collector during the experiment, and explicitly do garbage collection at the end of every run phase of a sequence.

  
  
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