Thank you Mose for this package! I've had the need for this functionality before but back then not the time to implement it. When the need arises again I now know where to go :)
On Monday, 18 July 2016 10:16:28 UTC+2, Mosè Giordano wrote: > > Dear all, > > I'm pleased to announce the first release of LombScargle.jl > <https://github.com/giordano/LombScargle.jl>, a package to compute the > Lomb-Scargle periodogram > <https://en.wikipedia.org/wiki/The_Lomb%E2%80%93Scargle_periodogram>. > Differently from standard FFT, this can be used to find periodicities in > unevenly sampled data, which is a fairly common case in astronomy, a field > where this periodogram is widely used. > > The README.md has some examples of use, in addition a manual is available > at http://lombscarglejl.readthedocs.io/ > > The package implements the standard Lomb-Scargle periodogram that doesn't > take into account a non-null mean of the signal (but it is possible to > automatically subtract the average of the signal from the signal itself, > and this is the default), and the generalised Lomb-Scargle algorithm which > instead can deal with a non-null mean. > > Relevant papers on this topic are: > > - Townsend, R. H. D. 2010, ApJS, 191, 247 (URL: > http://dx.doi.org/10.1088/0067-0049/191/2/247, Bibcode: > http://adsabs.harvard.edu/abs/2010ApJS..191..247T) > - Zechmeister, M., Kürster, M. 2009, A&A, 496, 577 (URL: > http://dx.doi.org/10.1051/0004-6361:200811296, Bibcode: > http://adsabs.harvard.edu/abs/2009A%26A...496..577Z) > > In the future I may implement another much-faster Lomb-Scargle algorithm > by Press & Rybicki (1989, ApJ, 338, 277), which however requires the data > to be equally sampled (but in this case also the FFT can be used). > > In order to test and benchmark the results of LombScargle.jl I compared > the result with those of equivalent methods > <http://astropy.readthedocs.io/en/latest/api/astropy.stats.LombScargle.html#astropy.stats.LombScargle> > > provided by Astropy package. Running Julia 0.5 I found that the standard > Lomb-Scargle periodogram as implemented in LombScargle.jl is ~40% and ~65 > faster than the "scipy" and "cython" methods of Astropy, respectively > (they're both in Cython, not pure Python). Instead, the generalised > Lomb-Scargle periodogram in LombScargle.jl is ~25% faster than the "cython" > method in Astropy. > > The LombScargle.jl package is licensed under the MIT “Expat” License. > > Bye, > Mosè >
