[SciPy-Dev] scipy.stats improvements

[Abraham Escalante]

Hello again,

Since the last message I have been busy getting more intimate with SciPy
and I'm glad to say that thanks to the members of the community I have
started making some contributions and getting acquainted with the
`scipy.stats` module, its functions, implementation and the test coverage.

I have also spent some time making my first scipy.stats improvements
proposal draft for GSoC 2015. I would like to request the input of anyone
who is interested and willing to provide it. I added a few things (as
optional deliverables) to the ideas originally proposed by the
organisation, things I expect to do before the official coding period
starts to help me get myself up the learning curve in the next couple of
months. Please feel free to suggest additions, amendments, removals or
anything that you may think is relevant. Any and all input is appreciated.

Kind regards,
Abraham.

2015-03-06 10:52 GMT-06:00 Ralf Gommers <ralf.gommers at gmail.com>:

> Hi Abraham,
>
>
> On Wed, Mar 4, 2015 at 8:08 PM, Abraham Escalante <aeklant at gmail.com>
> wrote:
>
>> Hello,
>>
>> 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.
>>
>
> Welcome!
>
>
>> 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).
>>
>
> That's definitely a good idea (and actually it's required).
>
>
>> I have a few questions that I would like to trouble you with:
>>
>> 1) Most of the StatisticsCleanup open issues mention a "need for review"
>> and also "StatisticsReview guidelines". *Could you refer me to the
>> StatisticsReview guidelines?* (I have been looking but I have not been
>> able to find it in the forked project nor the scipy documentation). *What
>> does it mean to have an issue flagged as "review"?*
>> see https://github.com/scipy/scipy/issues/693 for an example of what I
>> mean.
>>
>
> 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:
> - is the implementation correct?
>   - needs checking against another implementation (R/Matlab) and/or a
> reliable reference
>   - this includes handling of small or empty arrays, and array_like (list,
> tuple) inputs
> - is the docstring complete?
>   - at a minimum should include a good summary line, parameters, returns
> section and needed details to understand the algorithm
>   - preferably also References and Examples sections
> - is the test coverage OK?
>
>
> 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 https://github.com/scipy/scipy/pull/4563 for a recent example).
>
>
>> 2) I am currently going through the code (using the StatisticsCleanup
>> issues as a guide) and starting to read the SciPy statistics tutorial. *Do
>> you have any suggested reading* 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).
>>
>
> The tutorial you started on is good, for a broad intro to numpy/scipy this
> is also a quite good tutorial: http://scipy-lectures.github.io/.
> 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:)
>
> When starting to work on scipy, reading the developer guidelines at
> http://docs.scipy.org/doc/numpy-dev/dev/ is also a good idea.
>
> Cheers,
> Ralf
>
>
>
>> Thanks in advance,
>> Abraham Escalante.
>>
>>
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>>
>>
>
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