On Tue, Mar 11, 2008 at 12:11 AM, Vladislav Petyuk <[EMAIL PROTECTED]> wrote:
> I have Memory Error if I try to create numpy arrays or large size like
> 100-500 Mb (e.g. 3 x 3000 'float32' array)
> My computer has 3 Gb of RAM, which is well enough to handle these arrays.
> And there is definet
On Tue, Mar 11, 2008 at 12:10 AM, Jarrod Millman <[EMAIL PROTECTED]>
wrote:
> On Wed, Mar 5, 2008 at 10:44 PM, Charles R Harris
> <[EMAIL PROTECTED]> wrote:
> > Hmm. Well, it's in now. I have a 32 bit xeon at work and numpy fails one
> > test and warns on another, so that might be a related proble
Vladislav Petyuk wrote:
> I have Memory Error if I try to create numpy arrays or large size like
> 100-500 Mb (e.g. 3 x 3000 'float32' array)
> My computer has 3 Gb of RAM, which is well enough to handle these
> arrays. And there is definetely memory available.
> Nevertheless, the program cru
I have Memory Error if I try to create numpy arrays or large size like
100-500 Mb (e.g. 3 x 3000 'float32' array)
My computer has 3 Gb of RAM, which is well enough to handle these arrays.
And there is definetely memory available.
Nevertheless, the program crushes with "Potential Memory Error".
On Wed, Mar 5, 2008 at 10:44 PM, Charles R Harris
<[EMAIL PROTECTED]> wrote:
> Hmm. Well, it's in now. I have a 32 bit xeon at work and numpy fails one
> test and warns on another, so that might be a related problem. I'll give
> things a try and see what happens. I would think things should fail ra
The steps you describe here are correct. I am putting together an
open source computer vision library based on numpy/scipy. It will
include an automatic PCA algorithm with face detection, eye detection,
PCA dimensionally reduction, and distance measurement. If you are
interested let me k
On Mon, Mar 10, 2008 at 11:50 AM, Francesc Altet <[EMAIL PROTECTED]> wrote:
> A Monday 10 March 2008, Charles R Harris escrigué:
> > On Mon, Mar 10, 2008 at 11:08 AM, Francesc Altet <[EMAIL PROTECTED]>
> wrote:
> > > Hi,
> > >
> > > In order to allow in-kernel queries in PyTables (www.pytables.org
A Monday 10 March 2008, Charles R Harris escrigué:
> On Mon, Mar 10, 2008 at 11:08 AM, Francesc Altet <[EMAIL PROTECTED]>
wrote:
> > Hi,
> >
> > In order to allow in-kernel queries in PyTables (www.pytables.org)
> > work with unsigned 64-bit integers, we would like to see uint64
> > support in Num
On Mon, Mar 10, 2008 at 11:08 AM, Francesc Altet <[EMAIL PROTECTED]> wrote:
> Hi,
>
> In order to allow in-kernel queries in PyTables (www.pytables.org) work
> with unsigned 64-bit integers, we would like to see uint64 support in
> Numexpr (http://code.google.com/p/numexpr/).
>
> To do this, we ha
Hi,
In order to allow in-kernel queries in PyTables (www.pytables.org) work
with unsigned 64-bit integers, we would like to see uint64 support in
Numexpr (http://code.google.com/p/numexpr/).
To do this, we have to decide first how uint64 interacts with other
types. For example, which should b
Hi Fernando,
> I hope this (Travis' ideas teaser and all :) provides some better
> perspective on the recent enthusiasm regarding cython, as a tool
> complementary to ctypes that could greatly benefit numpy and scipy.
> If it doesn't it just means I did a poor job of communicating,
Nope, you did
On Mon, Mar 10, 2008 at 1:17 AM, royG <[EMAIL PROTECTED]> wrote:
> hi
> can numpy.dot() be used instead of tensordot()? is there any
> performance difference? I am talking about multipln btw numpy arrays
> of dimensions 50 X 20,000 where elements are of float type.
>
Dot is the usual matrix mult
On Mon, Mar 10, 2008 at 3:45 AM, David Cournapeau
<[EMAIL PROTECTED]> wrote:
> On Mon, 2008-03-10 at 02:11 -0500, Robert Kern wrote:
>
> >
> > Yes, but it's probably going to be easier to wrap whatever by hand
> > than try to ensure that f2py bootstraps correctly, scons or no scons.
> >
>
> Ok
On Mon, 2008-03-10 at 02:11 -0500, Robert Kern wrote:
>
> Yes, but it's probably going to be easier to wrap whatever by hand
> than try to ensure that f2py bootstraps correctly, scons or no scons.
>
Ok, thanks. Some last questions regarding f2py:
- does it make any difference to use it
hi
can numpy.dot() be used instead of tensordot()? is there any
performance difference? I am talking about multipln btw numpy arrays
of dimensions 50 X 20,000 where elements are of float type.
RG
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On Mon, Mar 10, 2008 at 1:49 AM, David Cournapeau
<[EMAIL PROTECTED]> wrote:
> On Sun, 2008-03-09 at 23:11 -0500, Robert Kern wrote:
> >
> > Almost certainly f2py will never be used to build any part of numpy
> > itself because we will not include something that requires a FORTRAN
> > compiler
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