<div dir="ltr">Hi Abraham,<br><br><div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Mar 4, 2015 at 8:08 PM, Abraham Escalante <span dir="ltr"><<a href="mailto:aeklant@gmail.com" target="_blank">aeklant@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"><div>Hello,</div><div><br></div><div>My name is Abraham Escalante. I would like to make a proposal for the "scipy.stats improvements" project for the Google Summer of Code. I am new to the Open Source community (although I do have experience with git and github) and this seems to me like a perfect place to start contributing.</div></div></blockquote><div><br></div><div>Welcome!<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>I forked the scipy/scipy project and I've been perusing some of the StatisticsCleanup issues since I would like to make my first contribution before I actually make my formal proposal (and I know it would be a great way for me to become acquainted with the code, guidelines, tests and the like).</div></div></blockquote><div><br></div><div>That's definitely a good idea (and actually it's required). <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><br></div><div>I have a few questions that I would like to trouble you with:</div><div><br></div><div>1) Most of the StatisticsCleanup open issues mention a "need for review" and also "StatisticsReview guidelines". <b>Could you refer me to the StatisticsReview guidelines?</b> (I have been looking but I have not been able to find it in the forked project nor the scipy documentation). <b>What does it mean to have an issue flagged as "review"?</b> </div><div>see <a href="https://github.com/scipy/scipy/issues/693" target="_blank">https://github.com/scipy/scipy/issues/693</a> for an example of what I mean.</div></div></blockquote><div><br></div><div>Ah, this was a pre-Github wiki page that has disappeared after Trac was disabled. I can't find the original anymore; I'll rewrite those guidelines on the Github scipy wiki. Basically it comes down to checking (and fixing/implementing if needed) the following: <br></div><div>- is the implementation correct?<br></div><div> - needs checking against another implementation (R/Matlab) and/or a reliable reference<br></div><div> - this includes handling of small or empty arrays, and array_like (list, tuple) inputs<br></div><div>- is the docstring complete?<br></div><div> - at a minimum should include a good summary line, parameters, returns section and needed details to understand the algorithm<br></div><div> - preferably also References and Examples sections<br></div><div>- is the test coverage OK?<br><br><br></div><div>For some functions that have StatisticsReview issues it's a matter of checking and making a few tweaks, for others it may be a complete rewrite (see <a href="https://github.com/scipy/scipy/pull/4563">https://github.com/scipy/scipy/pull/4563</a> for a recent example).<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><b><br></b></div><div>2) I am currently going through the code (using the StatisticsCleanup issues as a guide) and starting to read the SciPy statistics tutorial. <b>Do you have any suggested reading</b> to get more familiarised with SciPy (the statistics part in particular), Numpy or to brush up on my statistics knowledge? (pretty much anything to get me up the learning curve would be useful).</div></div></blockquote><div><br></div><div>The tutorial you started on is good, for a broad intro to numpy/scipy this is also a quite good tutorial: <a href="http://scipy-lectures.github.io/">http://scipy-lectures.github.io/</a>. Regarding books on statistics, there's an almost infinite choice, I'm not going to try to make recommendation. Maybe the real statisticians on this list will give you their favorites:)<br><br></div><div>When starting to work on scipy, reading the developer guidelines at <a href="http://docs.scipy.org/doc/numpy-dev/dev/">http://docs.scipy.org/doc/numpy-dev/dev/</a> is also a good idea.<br></div><div><br></div><div>Cheers,<br></div><div>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><br></div><div>Thanks in advance,</div><div>Abraham Escalante.</div><div><br></div></div>
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