[SciPy-Dev] Lomb-Scargle Periodogram: Press & Rybicki Algorithm

[Jacob Vanderplas]

Hi,
We have a cython version of this Lomb-Scargle algorithm in astroML [1], as
well as a generalized version that does not depend on the sample mean being
a good approximation of the true mean.  We've not yet implemented the FFT
trick shown in Press & Rybicki, but it's fairly fast as-is for problems of
reasonable size.

For some examples of it in use on astronomical data, see [2-3] below (the
examples are figures from our upcoming astro/statistics textbook). This
code is BSD-licensed, so if it seems generally useful enough to include in
scipy, it would be no problem to port it.

Also, if you have implemented an FFT-based version, it would get a fair bit
of use in the astronomy community if you were willing to contribute it to
astroML.

Thanks,
   Jake

[1]
https://github.com/astroML/astroML/blob/master/astroML_addons/periodogram.pyx
[2] http://astroml.github.com/book_figures/chapter10/fig_LS_example.html
[3]
http://astroml.github.com/book_figures/chapter10/fig_LS_sg_comparison.html


On Fri, Mar 8, 2013 at 5:26 PM, Christian Geier <geier at lostpackets.de>wrote:

> Hello everyone!
>
> Would you in general be considering to include the Lombscargle
> Periodogram by Press & Rybicki [1] into scipy in addition to the already
> present one? I find the included algorithm by Townend rather slow and
> had recently some "interesting" results returned by it.
>
> I've recently translated the original FORTRAN code (which is actually
> the description of the algorithm [1]) to (pure) python [2] and would like
> to know what the legal situation is in this case: can I release this
> translated code under the BSD license?
>
> In this case I would translate the code further to cython and supply
> tests and more documentation.
>
> Greetings
>
> Christian Geier
>
> [1] http://adsabs.harvard.edu/full/1989ApJ...338..277P
> [2]
> https://github.com/geier/scipy/commit/710bf4ca514d223df39891eb20401aba2edd8cdb
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