[SciPy-Dev] Lomb-Scargle Periodogram: Press & Rybicki Algorithm
Mon Mar 18 10:19:22 CDT 2013
On Fri, Mar 08, 2013 at 05:18:56PM -0800, Jacob Vanderplas wrote:
> We have a cython version of this Lomb-Scargle algorithm in astroML , 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
>  http://astroml.github.com/book_figures/chapter10/fig_LS_example.html
> On Fri, Mar 8, 2013 at 5:26 PM, Christian Geier <firstname.lastname@example.org>wrote:
> > Hello everyone!
> > Would you in general be considering to include the Lombscargle
> > Periodogram by Press & Rybicki  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 ) to (pure) python  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
> >  http://adsabs.harvard.edu/full/1989ApJ...338..277P
> > 
> > https://github.com/geier/scipy/commit/710bf4ca514d223df39891eb20401aba2edd8cdb
thanks for your answer. When time allows for it (hopefully next week)
I will improve the code some more and then contact astroML.
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