Using Python's gc module ( gc.collect() ) I seem to manage a forced
collection of the R objects accumulating mysteriously otherwise.
I am now reaching iteration 4594 of your example, with a memory
footprint still oscillating between 40 and 60 Mb.
Laurent
laurent oget wrote:
> I have been a
When rebuilding rpy2 after commenting out line 106 in setup.py:
define_macros.append(('RPY_DEBUG_PRESERVE', 1))
I can see that it seems that R objects are
preserved but not released until the termination of the python process.
In fact during each iteration I have 6 R objects preserved, but only 4
I have been able to improve the leak considerably with your suggestion
and also attaching the dataFrame instead of passing it as an argument
of lm every time. As an added benefit this reduces the number of copy,
which increases performance. It is still leaking, though
Laurent
2008/11/21 Laure
laurent oget wrote:
> is there something i can do to prevent the following script from leaking
> memory?
There seems to be something happening.
(more below)
> Laurent
>
> from rpy2.robjects import r,RVector,RDataFrame
> from rpy2.rlike.container import TaggedList
> from math import sin
> impor
Use
fun = r(" function(x) { x+1 }")
-G
- Original Message -
From: Antonio Garcia-Martinez <[EMAIL PROTECTED]>
To: rpy-list@lists.sourceforge.net
Sent: Thu Nov 20 23:35:25 2008
Subject: [Rpy] uniroot, and r.function()
I'm trying to use R's uniroot function from within python. The first
