On Fri, Jun 22, 2012 at 2:42 PM, Travis Oliphant wrote:
>
> The usual practice is to announce a schedule first.
>
>
> I just did announce the schedule.
>
>
What has been done in the past is that an intent to fork is announced some
two weeks in advance so that people can weigh in on what needs to b
Hi folks,
sorry for the cross-post, but I expect all replies to this to happen off-list.
I'm in the process of writing an NSF grant that will partly include
IPython support, and along with Brian we will soon be doing more of
the same. In the past we haven't had the best of luck with the NFS,
hop
It you link dynamically to MKL (vs statically to ATLAS),
the easiest way is to look at the size of the C extension
numpy/linalg/lapack_lite.pyd
I have (on 32-bit Windows) 24KB when linking to MKL,
as supposed to 2.3MB when linking statically to ATLAS,
where all lapack symbols are linked in.
- Ilan
>
> The usual practice is to announce a schedule first.
I just did announce the schedule.
>
> There is time before the first Release candidate to make changes on the 1.7.x
> branch. If you want to make the changes on master, and just indicate the
> Pull requests, Ondrej can make sure th
I found the problem. It was a missing /manifest option in the distutils
bundled with Python 2.6 (how this ever worked without my patch I'll never
understand). Anyway, I now have numpy built with MKL 10.3, MSVC 2010, and
Intel Fortran 11. What I need to know is how to verify that the linear a
On Fri, Jun 22, 2012 at 9:49 AM, Thouis (Ray) Jones wrote:
> On Mon, Jun 4, 2012 at 7:43 PM, Travis Oliphant
> wrote:
> > I have turned on issue tracking and started a few labels. Feel free to
> add
> > more / adjust the names as appropriate. I am trying to find someone
> who
> > can help m
Hi,
On Fri, Jun 22, 2012 at 6:05 PM, Benjamin Root wrote:
>
>
> On Fri, Jun 22, 2012 at 10:25 AM, Travis Oliphant wrote:
>
>> Accessing individual elements of NumPy arrays is slower than accessing
>> individual elements of lists --- around 2.5x-3x slower.NumPy has to do
>> more work to figur
Ooops. The message about the Fortran compiler version comes from building numpy
1.6.1. The problem I am seeing with numpy 1.4.1 follows.
No module named msvccompiler in numpy.distutils; trying from distutils
customize IntelFCompiler
Found executable c:\Program Files (x86)\Intel\Composer XE\bin\i
I have reason to build Python 2.6.8 and numpy 1.4.1 with MSVC 2010 and Intel
Fortran 10.1 (and higher). I also am building with MKL 10.3.
So far, I am able to get the setup to recognize the MKL libraries:
C:\Users\tky\Python\numpy-1.6.2>python setup.py build --compiler=msvc
--fcompiler=int
>
>
> -Travis
>
>
> However, what is the timing/memory cost of converting a large numpy array
> that already exists into python list of lists? If all my processing before
> the munkres step is using NumPy, converting it into python lists has a cost.
> Also, your timings indicate only ~2x s
On Fri, Jun 22, 2012 at 10:25 AM, Travis Oliphant wrote:
> Accessing individual elements of NumPy arrays is slower than accessing
> individual elements of lists --- around 2.5x-3x slower.NumPy has to do
> more work to figure out what kind of indexing you are trying to do because
> of its flexi
Accessing individual elements of NumPy arrays is slower than accessing
individual elements of lists --- around 2.5x-3x slower.NumPy has to do more
work to figure out what kind of indexing you are trying to do because of its
flexibility. It also has to create the Python object to return.
On Fri, Jun 22, 2012 at 9:42 AM, eat wrote:
> Hi,
>
> On Fri, Jun 22, 2012 at 7:51 AM, Gael Varoquaux <
> gael.varoqu...@normalesup.org> wrote:
>
>> On Thu, Jun 21, 2012 at 08:59:09PM -0400, Benjamin Root wrote:
>> > > munkres seems to be a pure python implementation ;-).
>>
>> > Oops!
On Mon, Jun 4, 2012 at 7:43 PM, Travis Oliphant wrote:
> I have turned on issue tracking and started a few labels. Feel free to add
> more / adjust the names as appropriate. I am trying to find someone who
> can help manage the migration from Trac.
Are the github issues set up sufficiently
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