<div dir="ltr"><br><div class="gmail_extra"><br><br><div class="gmail_quote">On Tue, Jan 21, 2014 at 5:46 PM, Charles R Harris <span dir="ltr"><<a href="mailto:charlesr.harris@gmail.com" target="_blank">charlesr.harris@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><br><div class="gmail_extra"><br><br><div class="gmail_quote"><div><div class="h5">On Tue, Jan 21, 2014 at 9:26 AM, jennifer stone <span dir="ltr"><<a href="mailto:jenny.stone125@gmail.com" target="_blank">jenny.stone125@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><br><div class="gmail_extra"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
>What are your interests and experience? If you use numpy, are there things<br>
>you would like to fix, or enhancements you would like to see?<br>
<br>
Chuck<br>
<br></blockquote><div> 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<b> few proposals</b> in mind. But I don't have any idea if they are acceptable within the scope of GSoC<br>
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.<br></div></div></div></div></blockquote></div></div></div></div></div></blockquote><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
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<div class="gmail_extra"><div class="gmail_quote"><div></div><div>Further, why cant we extend SciPy to incorporate<b> Inverse Laplace Transforms</b>? At present Matlab has this amazing function <b>ilaplace</b> and SymPy does have <b>Inverse_Laplace_transform</b> but it would be better to incorporate all in one package. I mean SciPy does have function to evaluate laplace transform<br>
</div></div></div></div></blockquote></div></div></div></div></div></blockquote><div><br></div><div>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`.<br>
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</div><div>After having written this, I feel that this post should have been sent to SciPy<br></div><div>but as a majority of contributors are the same I proceed.<br></div><div>Please suggest any other possible projects,</div>
</div></div></div></blockquote></div></div></div></div></div></blockquote><div><br></div><div>You can have a look at <a href="https://github.com/scipy/scipy/pull/2908/files">https://github.com/scipy/scipy/pull/2908/files</a> 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.<br>
<br>Cheers,<br>Ralf<br><br><br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div class="gmail_extra"><div class="gmail_quote">
<div><div class="h5"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div class="gmail_extra"><div class="gmail_quote"><div> 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.<br>
<br></div><div>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.<br><br></div></div></div>
</div></blockquote><div><br></div></div></div><div>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.<br>
<br></div><div>Chuck <br></div></div></div></div>
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