Hi all,
I have been investigation the limitation of the choose() method (and
function) to 32 elements. This is a regression in recent versions of NumPy.
I have tested choose() in the following NumPy versions:
1.0.4: fine
1.1.1: bug
1.2.1: fine
1.3.0: bug
1.4.x: bug
1.5.x: bug
1.6.x: bug
Numeric 2
Den 15.06.2011 23:22, skrev Christopher Barker:
>
> I think the issue got confused -- the OP was not looking to speed up a
> matrix multiply, but rather to speed up a whole bunch of independent
> matrix multiplies.
I would do it like this:
1. Write a Fortran function that make multiple calls DGEM
Den 15.06.2011 23:22, skrev Christopher Barker:
>
> It would also would be great if someone that actually understands this
> stuff could look at his code and explain why the slowdown occurs (hint,
> hint!)
>
Not sure I qualify, but I think I notice several potential problems in
the OP's multiproc
Ok. I suspected it was something of that sort and that certainly
makes sense to have this ability. Can we perhaps add this to the
documentation/specification so that we can more confident that this
behavior remains into the future?
Thanks,
Jonathan.
On Wed, Jun 15, 2011 at 11:46 AM, Charles R H
On 15.06.2011, at 1:34AM, Mark Wiebe wrote:
> These functions are now fully implemented and documented. As always, code
> reviews are welcome here:
>
> https://github.com/numpy/numpy/pull/87
>
> and for those that don't want to dig into review C code, the commit for the
> documentation is here
On Tue, Jun 7, 2011 at 2:08 PM, Ralf Gommers wrote:
> On Tue, Jun 7, 2011 at 8:59 PM, Mark Wiebe wrote:
>
>> On Tue, Jun 7, 2011 at 1:41 PM, Ralf Gommers > > wrote:
>>
>>> On Mon, Jun 6, 2011 at 6:56 PM, Mark Wiebe wrote:
>>>
On Mon, Jun 6, 2011 at 10:30 AM, Mark Wiebe wrote:
> On
Sturla Molden wrote:
> IMHO, trying to beat Intel or AMD performance library developers with
> Python, NumPy and multiprocessing is just silly. Nothing we do with
> array operator * and np.sum is ever going to compare with BLAS functions
> from these libraries.
I think the issue got confused --
Towards a reasonable behavior with regard to local times, I've made the
default repr for datetimes use the C standard library to print them in a
local ISO format. Combined with the ISO8601-prescribed behavior of
interpreting datetime strings with no timezone specifier to be in local
times, this all
Hello,
The following snippet works fine for a regular string and prints out
the string without a problem.
python
Python 2.7 (r27:82500, Sep 16 2010, 18:02:00)
[GCC 4.5.1 20100907 (Red Hat 4.5.1-3)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> mystr = u"öööğ
Perhaps it is time to write somthing in the SciPy cookbook about
parallel computing with NumPy? It seems to be certain problems that are
discussed again and again. These are some issues that come to mind (I'm
sure there is more):
- The difference between I/O bound, memory bound, and CPU bound w
Den 13.06.2011 19:51, skrev srean:
> If you are on an intel machine and you have MKL libraries around I
> would strongly recommend that you use the matrix multiplication
> routine if possible. MKL will do the parallelization for you. Well,
> any good BLAS implementation would do the same, you dont
Using savetxt with delimiter=',' should do the trick.
If you want a more advanced csv interface to e.g. save more than a numpy
array into a single csv, you can probably look into the python csv module.
-=- Olivier
2011/6/15 Chao YUE
> Dear all pythoners,
>
> what do you use python module to ha
Dear all pythoners,
what do you use python module to handle csv file (for reading we can use
numpy.genfromtxt)? is there anyone we can do with csv file very convinient
as that in R?
can numpy.genfromtxt be used as writing? (I didn't try this yet because on
our server we have only numpy 1.0.1...).
On Wed, Jun 15, 2011 at 9:34 AM, Olivier Delalleau wrote:
> I don't really understand this behavior either, but juste note that
> according to
> http://docs.scipy.org/doc/numpy/user/c-info.beyond-basics.html
> "This attribute can also be defined by objects that are not sub-types of
> the ndarray"
I don't really understand this behavior either, but juste note that
according to
http://docs.scipy.org/doc/numpy/user/c-info.beyond-basics.html
"This attribute can also be defined by objects that are not sub-types of the
ndarray"
-=- Olivier
2011/6/15 Jonathan Taylor
> Hi,
>
> I would like to h
Hi,
I would like to have objects that I can mix with ndarrays in
arithmetic expressions but I need my object to have control of the
operation even when it is on the right hand side of the equation. I
realize from the documentation that the way to do this is to actually
subclass ndarray but this i
On Tue, Jun 14, 2011 at 6:30 PM, Gael Varoquaux
wrote:
> On Tue, Jun 14, 2011 at 10:29:38AM -0300, Thiago Franco Moraes wrote:
>> I don't know if I understand. The idea is to use _make_edges_3d to
>> give me the connectivity, isn't it? Like for example, a 3x3 image:
>
>> 0, 1, 2
>> 3, 4, 5
>> 6, 7
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