[Numpy-discussion] (no subject)

Ralf Gommers ralf.gommers at gmail.com
Wed Jan 22 16:56:26 EST 2014


On Tue, Jan 21, 2014 at 5:46 PM, Charles R Harris <charlesr.harris at gmail.com
> wrote:

>
>
>
> On Tue, Jan 21, 2014 at 9:26 AM, jennifer stone <jenny.stone125 at gmail.com>wrote:
>
>>
>> >What are your interests and experience? If you use numpy, are there
>>> things
>>> >you would like to fix, or enhancements you would like to see?
>>>
>>> Chuck
>>>
>>>
>>  I am an undergraduate student with CS as major and have interest in Math
>> and Physics. This has led me to use NumPy and SciPy to work on innumerable
>> cases involving special polynomial functions and polynomials like Legendre
>> polynomials, Bessel Functions and so on. So, The packages are closer known
>> to me from this point of view. I have a* few proposals* in mind. But I
>> don't have any idea if they are acceptable within the scope of GSoC
>> 1. Many special functions and polynomials are neither included in NumPy
>> nor on SciPy.. These include Ellipsoidal Harmonic Functions (lames
>> function), Cylindrical Harmonic function. Scipy at present supports only
>> spherical Harmonic function.
>>
>

> Further, why cant we extend SciPy  to incorporate* Inverse Laplace
>> Transforms*? At present Matlab has this amazing function *ilaplace* and
>> SymPy does have *Inverse_Laplace_transform* but it would be better to
>> incorporate all in one package. I mean SciPy does have function to evaluate
>> laplace transform
>>
>
Scipy doesn't have a function for the Laplace transform, it has only a
Laplace distribution in scipy.stats and a Laplace filter in scipy.ndimage.
An inverse Laplace transform would be very welcome I'd think - it has real
world applications, and there's no good implementation in any open source
library as far as I can tell. It's probably doable, but not the easiest
topic for a GSoC I think. From what I can find, the paper "Numerical
Transform Inversion Using Gaussian Quadrature" from den Iseger contains
what's considered the current state of the art algorithm. Browsing that
gives a reasonable idea of the difficulty of implementing `ilaplace`.


> After having written this, I feel that this post should have been sent to
>> SciPy
>> but as a majority of contributors are the same I proceed.
>> Please suggest any other possible projects,
>>
>
You can have a look at https://github.com/scipy/scipy/pull/2908/files for
ideas. Most of the things that need improving or we really think we should
have in Scipy are listed there. Possible topics are not restricted to that
list though - it's more important that you pick something you're interested
in and have the required background and coding skills for.

Cheers,
Ralf


as I would like to continue with SciPy or NumPy, preferably NumPy as I have
>> been fiddling with its source code for a month now and so am pretty
>> comfortable with it.
>>
>> As for my experience, I have known C for past 4 years and have been a
>> python lover for past 1 year. I am pretty new to open source communities,
>> started before a manth and a half.
>>
>>
> It does sound like scipy might be a better match, I don't think anyone
> would complain if you cross posted. Both scipy and numpy require GSOC
> candidates to have a pull request accepted as part of the application
> process. I'd suggest implementing a function not currently in scipy that
> you think would be useful. That would also help in finding a mentor for the
> summer. I'd also suggest getting familiar with cython.
>
> Chuck
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20140122/f0286008/attachment.html>


More information about the NumPy-Discussion mailing list