[SciPy-Dev] scikits contribution?

Jonathan Stickel jjstickel@vcn....
Wed Jul 14 12:36:57 CDT 2010

On 7/8/10 07:05 , scipy-dev-request@scipy.org wrote:
> Date: Thu, 8 Jul 2010 08:24:25 -0400
> From:josef.pktd@gmail.com
> Subject: Re: [SciPy-Dev] scikits contribution?
> To: SciPy Developers List<scipy-dev@scipy.org>
> Message-ID:
> 	<AANLkTimQBH1NSjDfovoZFu8gmVFVmpGIlBiB5tneQET7@mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
> On Wed, Jul 7, 2010 at 4:42 PM, Jonathan Stickel<jjstickel@vcn.com>  wrote:
>> >  Some time ago, I offered to contribute (on the scipy-user list) some
>> >  code to smooth 1-D data by regularization:
>> >
>> >  http://mail.scipy.org/pipermail/scipy-user/2010-February/024351.html
>> >
>> >  Someone suggested that scikits might be the right place:
>> >
>> >  http://mail.scipy.org/pipermail/scipy-user/2010-February/024408.html
>> >
>> >  So I am finally looking into scikits, and I am not sure how to proceed.
>> >  ?My code consists of several functions in a single .py file. ?It seems
>> >  overkill to create a new scikit for just one file, but I do not see an
>> >  existing scikit that matches. ?'Optimization' would be the closest; in
>> >  core scipy I would put it in 'interpolate'.
>> >
>> >  So, what is the minimum that I need to do to create a scikit and upload
>> >  my code? ?Any suggestions for the name of the scikit (interpolate,
>> >  data_smoothing)?
> The easiest to get started is to copy the setup structure from another scikit.
> I think the template scikit in scikits svn is a bit out of date, the
> last time I looked.
> If you think your model could form the basis for enhancing the
> smoother or noisy interpolation category in scipy, then a scikits
> would be the best way, as we discussed.
> If you want to add it to an existing scikits, then statsmodels would
> be a possibility.
> Although statsmodels is more oriented towards multivariate approaches,
> I think a smoother category, together with some non-parametric
> methods, e.g. the existing kernel regression, would be an appropriate
> fit. There is a need for smoothers in gam, Generalized Additive
> Models, but that one is not cleaned up yet.
> And I think there will be more applications where it would be useful
> to share the cross-validation code as far as possible.
> Josef
>> >
>> >  Please know that I am just starting to learn python, being a convert
>> >  from matlab/octave. ?Although I have become fairly proficient using
>> >  numpy/scipy in ipython, I do not know much about python internals,
>> >  setuptools, etc.
>> >

OK, I created a scikit named "datasmooth" and included my current code. 
  It seems to install OK with "python setup install" and import 
correctly.  However, I am not able to commit to the svn repository.  I 
registered on the scikits wiki, but I guess there is something else I 
need to do?


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