Travis Oliphant wrote:
As far as numpy knows this is all it's supposed to do. This seems to
indicate that something is going on inside numarray.array(a)
because once you had that line to the loop, memory consumption shows up.
In fact, you can just add the line
a =
I agree with all of Travis' comments below and committed the suggested
changes to numarray CVS. I found one other numarray change needed
for Francesc's examples to run (apparently) leak-free:
Py_INCREF(obj)
Py_XDECREF(a-base)
a-base = obj
Py_DECREF(cobj)
Thanks Travis!
Regards,
Todd
A Dissabte 12 Agost 2006 14:37, Todd Miller va escriure:
I agree with all of Travis' comments below and committed the suggested
changes to numarray CVS. I found one other numarray change needed
for Francesc's examples to run (apparently) leak-free:
Py_INCREF(obj)
Py_XDECREF(a-base)
Hi,
I was tracking down a memory leak in PyTables and it boiled down to a problem
in the array protocol. The issue is easily exposed by:
for i in range(100):
numarray.array(numpy.zeros(dtype=numpy.float64, shape=3))
and looking at the memory consumption of the process. The same happens
Francesc Altet wrote:
Hi,
I was tracking down a memory leak in PyTables and it boiled down to a problem
in the array protocol. The issue is easily exposed by:
for i in range(100):
numarray.array(numpy.zeros(dtype=numpy.float64, shape=3))
and looking at the memory consumption of
Todd Miller wrote:
I looked at this a little with a debug python and figure it's a bug in
numpy.zeros():
Hmmm. I thought of that, but could not get any memory leak by just
creating zeros in a four loop.
In other words:
for i in xrange(1000):
Francesc Altet wrote:
Hi,
I was tracking down a memory leak in PyTables and it boiled down to a problem
in the array protocol. The issue is easily exposed by:
for i in range(100):
numarray.array(numpy.zeros(dtype=numpy.float64, shape=3))
More data:
The following code does not
Francesc Altet wrote:
Hi,
I was tracking down a memory leak in PyTables and it boiled down to a problem
in the array protocol. The issue is easily exposed by:
for i in range(100):
numarray.array(numpy.zeros(dtype=numpy.float64, shape=3))
and looking at the memory consumption of
[EMAIL PROTECTED] wrote:
Hi Travis
Not sure if you've had a chance to look at the previous code I sent or not,
but I was able to reduce the code (see below) to its smallest size and still
have the problem, albeit at a slower rate. The problem appears to come from
changing values in the
