[SciPy-Dev] GSoC'15 Idea: Approximation with Parametric Splines

Thu Mar 12 14:47:18 EDT 2015

Hello!

 Thanks for expanded and kind reply!

 Especially thanks for the link to bezierbuilder! It opened my eyes on what
can be done with the matplotlib. I guess now I’ll abandon my efforts to
make the implementation with Qt and will start again with only the
matplotlib. Anyway, this can wait for some time, and when it's done I'll
definitely share the link to repo with you.

 Regarding to the optimization I wrote about before:

Initially I was thinking about the precise positioning of control points
while dragging them on the screen in order to get best fit. It is obvious,
that manual positioning of control points can give a good visual result.
Following automatic variation in some boundaries can provide strict control
points positions and numerically best fitting result.

By now I’m thinking about the possibility to implement the request for some
additional parameters from the user for approximating spline functions.
Actually, this can be user-provided n-order derivatives in some points (for
example endpoints to get good extrapolation results). Maybe this will
require implementation of a new class like *DerivativeControlledSpline* or
something familiar.

Another issue of optimization is the construction of non-uniform knot vector
s. Just as an example, I think in some cases non-uniform knot vector can be
constructed using information about the data points’ density along x and y
axes. If these thoughts make any sense please, let me know and I’ll try to
expand them to some proposal-like state.

 Regarding to alternative tasks:

The list of your alternative tasks pushed me to reread the 7th chapter of
the book on spline methods, what made me feel excited about tensor product
spline surfaces. Current module fitpack2 has a big set of classes
representing bivariate splines. Aren’t they tensor product splines? Or the
idea is to move away from FITPACK wrapping? Anyway I feel some interest to
the issue and I would be grateful if you describe the problem more specific
so I can estimate the effort and the milestones.

Implementation of Cardinal B-splines seems to be of the less effort, but
not less interest :)

 In addition, I would like to know what you are thinking about *expo-rational
B-splines <http://gtwavelet.bme.gatech.edu/wp/ExpoSplines.pdf>*. If their
implementation in SciPy is welcome, I can think about the appropriate
proposal.

 So by now I have 4 ways to go:

   1.

   Tensor product spline surfaces;
   2.

   Cardinal B-splines;
   3.

   Expo-rational B-splines;
   4.

   Optimization methods for spline functions.

  If it is possible, please provide the information on their importance to
the SciPy project so I can choose 1 or 2 of them to make the GSoC
proposal(s).

 Thanks a lot and best regards,

Anastasiia

 PS

While discovering fitpack2 module I guess I found some copy-paste bug in
docstring on *LSQBivariateSpline*. It seems that the class doesn’t require
smoothing parameter on initialization but the docstring about it somehow
migrated from another class. Should I write about it on IRC channel or
somewhere else, or maybe do it by myself?

2015-03-09 23:48 GMT+02:00 Ralf Gommers <ralf.gommers at gmail.com>:

> Hi Anastasiia, welcome!
>
>
> On Sun, Mar 8, 2015 at 10:25 AM, Anastasiia Tsyplia <
> anastasiyatsyplya at gmail.com> wrote:
>
>> Hello,
>>
>> My name is Anastasiia Tsyplia. I am a 5th-yaer student of National Mining
>> University of Ukraine.
>>
>> I am keen on interpolation/approximation with splines and it was a nice
>> surprise to find out that there is a demand in interpolation improvements
>> amongst the Scipy's ideas for GSoC'15. However, I've spend some time on
>> working out the idea
>> <https://drive.google.com/file/d/0BzveGSDwNVtBanU1THpFTXNKZHc/view?usp=sharing>
>> of my own.
>>
>> Recently I've made a post <http://tsplb.blogspot.com/> dedicated to
>> description of the parametric spline curves construction process and
>> approaches to approximate engineering data by spline functions and
>> parametric spline curves with SciPy.
>>
>
> Nice blog post!
> I'll leave the commenting on technical details you have in your draft
> proposal to Evgeni and others, just want to say you've made a pretty good
> start so far.
>
>>  It seems that using parametric spline curves in approximation can be
>> extremely useful and time-saving approach. That's why I would like to share
>> my project idea and hope to hear some feedback as I am about to make a
>> proposal for the Google Summer of Code.
>>
>> I have a 2-year experience in programming with Python, PyOpengl, PyQt,
>> Matplotlib, Numpy & SciPy. Some time I spent to dive into ctypes and
>> scratched the surface of C. Now my priority is Cython. I've read the book
>> on the spline methods recommended on SciPy's idea page, so I feel myself
>> competent in spline methods. I feel free with recursions: the last
>> challenge I faced was implementation of binary space partitioning algorithm
>> in python as I was writing my own ray-tracer.
>>
>> I would like to contribute to SciPy by any means, so I'm ready to receive
>> instructions on my next move. And, certainly I'm looking forward to start
>> dealing with B-Splines in Cython as it is also a part of my project idea.
>>
>
> What I recommend to all newcomers is to start by reading
> https://github.com/scipy/scipy/blob/master/HACKING.rst.txt and then first
> tackly an issue labeled "easy-fix", just to get a feel for the
> development/PR process.
>
> I've checked open issues for Cyhon code, there aren't that many at the
> moment. Maybe something fun could be to take some code now using np.ndarray
> and change it to use memoryviews (suggestion by @jakevdp that in
> scipy.sparse.csgraph this could help). And include a benchmark to show that
> it does speed things up (seehttps://
> github.com/scipy/scipy/tree/master/benchmarks for details).
>
> Regarding B-splines there's https://github.com/scipy/scipy/issues/3423,
> but I don't recommend tackling that now - that'll be a significant amount
> of work + discussion.
>
> Cheers,
> Ralf
>
>
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