[Numpy-discussion] ANN: SfePy 2015.1

2015-02-26 Thread Robert Cimrman
I am pleased to announce release 2015.1 of SfePy.

Description
---

SfePy (simple finite elements in Python) is a software for solving systems of
coupled partial differential equations by the finite element method or by the
isogeometric analysis (preliminary support). It is distributed under the new
BSD license.

Home page: http://sfepy.org
Mailing list: http://groups.google.com/group/sfepy-devel
Git (source) repository, issue tracker, wiki: http://github.com/sfepy

Highlights of this release
--

- support for multiple fields in isogeometric analysis
- redesigned handling of solver parameters
- new modal analysis example

For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1
(rather long and technical).

Best regards,
Robert Cimrman and Contributors (*)

(*) Contributors to this release (alphabetical order):

Lubos Kejzlar, Vladimir Lukes
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Re: [Numpy-discussion] GSoC'15 - mentors & ideas

2015-02-26 Thread Sebastian Berg
On Do, 2015-02-26 at 07:09 -0800, Jaime Fernández del Río wrote:

> 
> 
> To add one of my own: the old iterator is still being used in many,
> many places throughout the numpy code base. Wouldn't it make sense to
> port those to the new one? In doing so, it would probably lead to
> producing simplified interfaces to the new iterator, e.g. reproducing
> the old PyIter_AllButAxis is infinitely more verbose with the new
> iterator.
> 
> 

Might be a bit off topic. But I used to wonder if it could make sense to
create a Cython code generation support for nditer? NDiter is pretty
powerful, but we often have things like the contiguous special case,
buffering, etc. that is always identical code but without having
something ready in cython nobody will ever use nditer from cython, even
though for some things it might make a lot of sense.

- Sebastian


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Re: [Numpy-discussion] GSoC'15 - mentors & ideas

2015-02-26 Thread Jaime Fernández del Río
On Thu, Feb 26, 2015 at 2:54 AM, Todd  wrote:

> I am not able to mentor, but I have some ideas about easier projects.
> These may be too easy, too hard, or not even desirable so take them or
> leave them as you please.
>
> scipy:
>
> Implement a set of circular statistics functions comparable to those in R
> or MATLAB circular statistics toolbox.
>
> Either implement some window functions that only apply to the beginning
> and end of an array, or implement a wrapper that takes a window function
> and some parameters and creates a new window that only applies to the
> beginning and end of an array.
>
> numpy:
>
> Integrate the bottleneck project optimizations into numpy proper.
>
> Not sure how much of the bottleneck optimizations can be fitted into the
ufunc machinery. But I'd be more than happy to mentor or co-mentor an
implementation in numpy of the moving window functions. I have already
contributed some work on some of those in scipy.ndimage and pandas, and
find the subject fascinating.

> Integrate as much as possible the matplotlib.mlab functionality into numpy
> (and, optionally, also scipy).
>
> In many places different approaches to the same task have substantially
> different performance (such as indexing vs. take) and check for one
> approach being substantially slower.  If it is, fix the performance problem
> if possible (perhaps by using the same implementation), and if not document
> the difference.
>
The take performance advantage is no longer there since seberg's rewrite of
indexing. Are there any other obvious examples?

> Modify ufuncs so their documentation appears in help() in addition to
> numpy.info().
>
To add one of my own: the old iterator is still being used in many, many
places throughout the numpy code base. Wouldn't it make sense to port those
to the new one? In doing so, it would probably lead to producing simplified
interfaces to the new iterator, e.g. reproducing the old PyIter_AllButAxis
is infinitely more verbose with the new iterator.



> Hi all,
>
>
> On Fri, Feb 20, 2015 at 10:05 AM, Ralf Gommers 
> wrote:
>
>> Hi all,
>>
>> It's time to start preparing for this year's Google Summer of Code. There
>> is actually one urgent thing to be done (before 19.00 UTC today), which is
>> to get our ideas page in decent shape. It doesn't have to be final, but
>> there has to be enough on there for the organizers to judge it. This page
>> is here: https://github.com/scipy/scipy/wiki/GSoC-project-ideas. I'll be
>> reworking it and linking it from the PSF page today, but if you already
>> have new ideas please add them there. See
>> https://wiki.python.org/moin/SummerOfCode/OrgIdeasPageTemplate for this
>> year's template for adding a new idea.
>>
>
> The ideas page is now in pretty good shape. More ideas are very welcome
> though, especially easy or easy/intermediate ideas. Numpy right now has
> zero easy ones and Scipy only one and a half.
>
> What we also need is mentors. All ideas already have a potential mentor
> listed, however some ideas are from last year and I'm not sure that all
> those mentors really are available this year. And more than one potential
> mentor per idea is always good. So can everyone please add/remove his or
> her name on that page?
>
> I'm happy to take care of most of the organizational aspects this year,
> however I'll be offline for two weeks in July and from the end of August
> onwards, so I'll some help in those periods. Any volunteers?
>
> Thanks,
> Ralf
>
>
>
>
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Re: [Numpy-discussion] GSoC'15 - mentors & ideas

2015-02-26 Thread Todd
I am not able to mentor, but I have some ideas about easier projects.
These may be too easy, too hard, or not even desirable so take them or
leave them as you please.

scipy:

Implement a set of circular statistics functions comparable to those in R
or MATLAB circular statistics toolbox.

Either implement some window functions that only apply to the beginning and
end of an array, or implement a wrapper that takes a window function and
some parameters and creates a new window that only applies to the beginning
and end of an array.

numpy:

Integrate the bottleneck project optimizations into numpy proper.

Integrate as much as possible the matplotlib.mlab functionality into numpy
(and, optionally, also scipy).

In many places different approaches to the same task have substantially
different performance (such as indexing vs. take) and check for one
approach being substantially slower.  If it is, fix the performance problem
if possible (perhaps by using the same implementation), and if not document
the difference.

Modify ufuncs so their documentation appears in help() in addition to
numpy.info().


 Hi all,


On Fri, Feb 20, 2015 at 10:05 AM, Ralf Gommers 
wrote:

> Hi all,
>
> It's time to start preparing for this year's Google Summer of Code. There
> is actually one urgent thing to be done (before 19.00 UTC today), which is
> to get our ideas page in decent shape. It doesn't have to be final, but
> there has to be enough on there for the organizers to judge it. This page
> is here: https://github.com/scipy/scipy/wiki/GSoC-project-ideas. I'll be
> reworking it and linking it from the PSF page today, but if you already
> have new ideas please add them there. See
> https://wiki.python.org/moin/SummerOfCode/OrgIdeasPageTemplate for this
> year's template for adding a new idea.
>

The ideas page is now in pretty good shape. More ideas are very welcome
though, especially easy or easy/intermediate ideas. Numpy right now has
zero easy ones and Scipy only one and a half.

What we also need is mentors. All ideas already have a potential mentor
listed, however some ideas are from last year and I'm not sure that all
those mentors really are available this year. And more than one potential
mentor per idea is always good. So can everyone please add/remove his or
her name on that page?

I'm happy to take care of most of the organizational aspects this year,
however I'll be offline for two weeks in July and from the end of August
onwards, so I'll some help in those periods. Any volunteers?

Thanks,
Ralf




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