On 28-Apr-09, at 10:56 AM, Dan Goodman wrote:
> Can anyone explain the results below? It seems that for small matrices
> dot(x,y) is outperforming dgemm(1,x,y,0,y,overwrite_c=1), but for
> larger
> matrices the latter is winning. In principle it seems like I ought
> to be
> able to always do b
Stéfan van der Walt wrote:
> 2009/4/28 David Cournapeau :
>
>> the header file is not very reliable. More generally, I think we
>> should have a way to track C API and ABI relatively to the number
>> version, as well as automatically generate the related documentation.
>>
>
> We can already
Thanks. My mistake.
The os is 32-bit. I am doing a network-simulation for my teacher. The average
degree of the network topology is about 6.0. So I think it is sparse.
The paper needs the eigen values and the eigen vectors which are necessary for
the further simulation. I use the following pro
Zhenxin Zhan wrote:
> Thanks for your reply.
> My os is Windows XP SP3. I tried to use array(ojb, dtype=float), but
> it didn't work. And I tried 'float32' as you told me. And here is the
> error message:
> File "C:\Python26\Lib\site-packages\numpy\linalg\linalg.py", line 791,
> in eig
> a, t, resu
2009/4/28 Zhenxin Zhan
> Thanks for your reply.
>
> My os is Windows XP SP3. I tried to use array(ojb, dtype=float), but it
> didn't work. And I tried 'float32' as you told me. And here is the error
> message:
>
>
> File "C:\Python26\Lib\site-packages\numpy\linalg\linalg.py", line 791, in
> e
Thanks for your reply.
My os is Windows XP SP3. I tried to use array(ojb, dtype=float), but it didn't
work. And I tried 'float32' as you told me. And here is the error message:
File "C:\Python26\Lib\site-packages\numpy\linalg\linalg.py", line 791, in eig
a, t, result_t = _convertarray(a) #
Zhenxin Zhan wrote:
> Hello,
>
> I am a new learner of Numpy. From 'numpybook', I use
> numpy.linalg.eig(A) to calculate a matrix 2,000*2,000 and it works well.
>
> But, when I calculate eigen vector for 10,000*10,000 matrix, there is
> 'MemoryError' error message in statement numpy.array(...)
Hello,
I am a new learner of Numpy. From 'numpybook', I use numpy.linalg.eig(A) to
calculate a matrix 2,000*2,000 and it works well.
But, when I calculate eigen vector for 10,000*10,000 matrix, there is
'MemoryError' error message in statement numpy.array(...). My laptop has 4GB
memory.
How
Hi all,
I have a slightly strange idea for something I would like to do with
numpy which I guess probably doesn't exist, but I may be wrong. What I
want to do, essentially, is to have two arrays of equal size, say X and
Y, of pointers to doubles say. Then I want to do (in C notation) *x +=
*y
2009/4/29 Charles R Harris :
> 1.4 should probably run under with 1.3. Or we could just bite the bullet and
> make folks upgrade their numpy installations again.
I don't want to bite any bullets :) I'd opt for a workaround on our
side, as not to inconvenience users.
Stéfan
__
2009/4/28 Stéfan van der Walt
> 2009/4/28 Charles R Harris :
> >> IIRC, we can expose NPY_FEATURE_VERSION as part of the API without
> >> breaking ABI compatibility, as long as we add it at the end of the API
> >> functions list. As a hack, we can then check the length of the API
> >> functions
2009/4/28 Charles R Harris :
>> IIRC, we can expose NPY_FEATURE_VERSION as part of the API without
>> breaking ABI compatibility, as long as we add it at the end of the API
>> functions list. As a hack, we can then check the length of the API
>> functions list to make sure it is available before w
2009/4/28 Stéfan van der Walt
> 2009/4/28 Charles R Harris :
> > It is needed when the extension is loaded, that is why the very first
> > function in the API returns it. Otherwise it is impossible to check if
> > extensions compiled against one version of numpy can be loaded when
> another
> > v
2009/4/28 Charles R Harris :
> It is needed when the extension is loaded, that is why the very first
> function in the API returns it. Otherwise it is impossible to check if
> extensions compiled against one version of numpy can be loaded when another
> version of numpy is present. You need to know
2009/4/28 Stéfan van der Walt
> 2009/4/28 Charles R Harris :
> > 2009/4/28 Stéfan van der Walt
> >> We can already do this: simply choose a convention for NPY_VERSION and
> >> NPY_FEATURE_VERSION so that they are related.
> >
> > NPY_VERSION has been 0x0109 since 1.0. NPY_FEATURE_VERSION was
2009/4/28 Charles R Harris :
> 2009/4/28 Stéfan van der Walt
>> We can already do this: simply choose a convention for NPY_VERSION and
>> NPY_FEATURE_VERSION so that they are related.
>
> NPY_VERSION has been 0x0109 since 1.0. NPY_FEATURE_VERSION was added for
> 1.2, but is useless since it ca
2009/4/28 Stéfan van der Walt
> 2009/4/28 David Cournapeau :
> > the header file is not very reliable. More generally, I think we
> > should have a way to track C API and ABI relatively to the number
> > version, as well as automatically generate the related documentation.
>
> We can already do t
I'm not sure this is the right forum for my question; it's quite possible
that this is a Boost question.
VPython (vpython.org) is the name of Python plus the 3D Visual module, which
is mostly written in C++ and connected to Python using the Boost libraries.
Visual imports numpy.
When I build Visu
> > To further complicate issues, I do not have sysadmin rights on the
> > machine in question, and I'm not entirely confident that
> Python itself
> > was built properly (I've had to sort out some other issues as well).
> > The version in use is:
> > Python 2.5.2 (r252:60911, Sep 17 2008, 13:24
2009/4/28 David Cournapeau :
> the header file is not very reliable. More generally, I think we
> should have a way to track C API and ABI relatively to the number
> version, as well as automatically generate the related documentation.
We can already do this: simply choose a convention for NPY_VER
On Tue, Apr 28, 2009 at 9:57 AM, David Cournapeau wrote:
> On Wed, Apr 29, 2009 at 12:45 AM, Charles R Harris
> wrote:
> >
> >
> > On Tue, Apr 28, 2009 at 9:41 AM, David Cournapeau
> > wrote:
> >>
> >> On Tue, Apr 28, 2009 at 11:58 PM, Travis Oliphant
> >> wrote:
> >> >
> >> > On Apr 27, 2009,
On Wed, Apr 29, 2009 at 12:45 AM, Charles R Harris
wrote:
>
>
> On Tue, Apr 28, 2009 at 9:41 AM, David Cournapeau
> wrote:
>>
>> On Tue, Apr 28, 2009 at 11:58 PM, Travis Oliphant
>> wrote:
>> >
>> > On Apr 27, 2009, at 4:05 PM, Wes McKinney wrote:
>> >
>> >> Hello,
>> >>
>> >> I am wondering if
On Tue, Apr 28, 2009 at 9:41 AM, David Cournapeau wrote:
> On Tue, Apr 28, 2009 at 11:58 PM, Travis Oliphant
> wrote:
> >
> > On Apr 27, 2009, at 4:05 PM, Wes McKinney wrote:
> >
> >> Hello,
> >>
> >> I am wondering if anyone can offer some suggestions on this problem.
> >> Over the last year or
On Tue, Apr 28, 2009 at 11:58 PM, Travis Oliphant
wrote:
>
> On Apr 27, 2009, at 4:05 PM, Wes McKinney wrote:
>
>> Hello,
>>
>> I am wondering if anyone can offer some suggestions on this problem.
>> Over the last year or so I have been building a number of libraries
>> on top of NumPy + SciPy + m
Can anyone explain the results below? It seems that for small matrices
dot(x,y) is outperforming dgemm(1,x,y,0,y,overwrite_c=1), but for larger
matrices the latter is winning. In principle it seems like I ought to be
able to always do better with inplace rather than making copies?
From looking
On Apr 27, 2009, at 4:05 PM, Wes McKinney wrote:
> Hello,
>
> I am wondering if anyone can offer some suggestions on this problem.
> Over the last year or so I have been building a number of libraries
> on top of NumPy + SciPy + matplotlib and other libraries which are
> being used for inve
Hi Johannes,
According to http://www.pygtk.org/pygtk2reference/class-
gdkpixbuf.html , the pixels_array is a numeric python array (a
predecessor to numpy). The upshot is that perhaps the nice
broadcasting machinery will work fine:
pb_pixels[...] = fits_pixels[..., numpy.newaxis]
This might
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