On Mon, Apr 11, 2011 at 7:05 AM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
Hey Sturla,
It's really great that you are still working on that. I'll test the code
under Linux.
The scipy community has moved to github. If I create a repository under
github and put the code on it, would
On 04/11/2011 09:21 AM, Sebastian Haase wrote:
I have a non python question:
for Java there seems to exist a module/package/class called nio
http://download.oracle.com/javase/1.4.2/docs/api/java/nio/MappedByteBuffer.html
public abstract class MappedByteBufferextends ByteBuffer
A
On 04/11/2011 05:40 AM, David Crisp wrote:
On Mon, Apr 11, 2011 at 1:17 PM, David Crisp david.cr...@gmail.com wrote:
On Mon, Apr 11, 2011 at 11:00 AM, Sturla Molden stu...@molden.no wrote:
Den 11.04.2011 02:01, skrev David Crisp:
Can anybody guide me through this problem?
I dont know how
Hi everyone,
I was looking up the options that are available for shared memory arrays
and this thread came up at the right time. The doc says that multiprocessing
.Array(...) gives a shared memory array. But from the code it seems to me
that it is actually using a mmap. Is that correct a
Apologies for adding to my own post. multiprocessing.Array(...) uses an
anonymous mmapped file. I am not sure if that means it is resident on RAM or
the swap device. But my original question remains, what are the pros and
cons of using it versus numpy mmapped arrays. If multiprocessing.Array is
Shared memory is memory mapping from the paging file (i.e. RAM), not a
file on disk. They can have a name or be anonymous. I have explained why
we need named shared memory before. If you didn't understand it, try to
pass an instance of |multiprocessing.Array over | |multiprocessing.Queue.
Den 11.04.2011 14:58, skrev Zbigniew Jędrzejewski-Szmek:
Hi,
it could, but you'd have to do the parsing of data yourself. So nothing
fancy unless you want to reimplement numpy in Java :)
Not really. Only the data buffer is stored in shared memory. If you can
pass the required fields to Java
All,
We noticed some failing tests for statsmodels between numpy 1.5.1 and
numpy = 1.6.0. These are the versions where I noticed the change. It
seems that when you divide a float array and multiply by a boolean
array the answers are different (unless the others are also off by
some floating
Got you and thanks a lot for the explanation. I am not using Queues so I
think I am safe for the time being. Given that you have worked a lot on
these issues, would you recommend plain mmapped numpy arrays over
multiprocessing.Array
Thanks again
-- srean
On Mon, Apr 11, 2011 at 1:36 PM, Sturla
Den 11.04.2011 01:20, skrev Sturla Molden:
Changes:
- 64-bit support.
- Memory leak on Linux/Unix should be gone (monkey patch for os._exit).
- Added a global lock as there are callbacks to Python (the GIL is not
sufficient serialization).
I will also add a barrier synchronization
Den 11.04.2011 21:15, skrev srean:
Got you and thanks a lot for the explanation. I am not using Queues so
I think I am safe for the time being. Given that you have worked a
lot on these issues, would you recommend plain mmapped numpy arrays
over |multiprocessing.Array|
|
With multiprocessing
On Mon, Apr 11, 2011 at 13:54, Skipper Seabold jsseab...@gmail.com wrote:
All,
We noticed some failing tests for statsmodels between numpy 1.5.1 and
numpy = 1.6.0. These are the versions where I noticed the change. It
seems that when you divide a float array and multiply by a boolean
array
On Mon, Apr 11, 2011 at 12:54 PM, Skipper Seabold jsseab...@gmail.comwrote:
All,
We noticed some failing tests for statsmodels between numpy 1.5.1 and
numpy = 1.6.0. These are the versions where I noticed the change. It
seems that when you divide a float array and multiply by a boolean
On Mon, Apr 11, 2011 at 12:37 PM, Robert Kern robert.k...@gmail.com wrote:
On Mon, Apr 11, 2011 at 13:54, Skipper Seabold jsseab...@gmail.com
wrote:
All,
We noticed some failing tests for statsmodels between numpy 1.5.1 and
numpy = 1.6.0. These are the versions where I noticed the
On Mon, Apr 11, 2011 at 2:31 PM, Mark Wiebe mwwi...@gmail.com wrote:
On Mon, Apr 11, 2011 at 12:37 PM, Robert Kern robert.k...@gmail.comwrote:
On Mon, Apr 11, 2011 at 13:54, Skipper Seabold jsseab...@gmail.com
wrote:
All,
We noticed some failing tests for statsmodels between numpy 1.5.1
Hi list.
For mi application, I would like to implement some new statistics
functions over numpy arrays, such as truncated mean. Ideally this new
function should have the same arguments
than numpy.mean: axis, dtype and out. Is there a way of writing this
function that doesn't imply writing it in C
What I have is some C++ functions that implement statistic functions.
What I need is some kind of ufunc where I can plug my functions. But
I doesn't seem to exist an ufunc that operates on a N-d array and
turns it into a number.
2011/4/12 Keith Goodman kwgood...@gmail.com:
On Mon, Apr 11, 2011
On Apr 11, 2011, at 3:55 PM, Charles R Harris wrote:
On Mon, Apr 11, 2011 at 2:31 PM, Mark Wiebe mwwi...@gmail.com wrote:
On Mon, Apr 11, 2011 at 12:37 PM, Robert Kern robert.k...@gmail.com wrote:
On Mon, Apr 11, 2011 at 13:54, Skipper Seabold jsseab...@gmail.com wrote:
All,
We
On Mon, Apr 11, 2011 at 8:48 PM, Travis Oliphant oliph...@enthought.comwrote:
On Apr 11, 2011, at 3:55 PM, Charles R Harris wrote:
snip
I agree with Charles. Let's take the needed time and work this through.
This is the sort of thing I was a bit nervous about with the changes made to
the
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