Hi all, an update about the situation of LombScargle.jl <https://github.com/giordano/LombScargle.jl>
2016-07-18 10:15 GMT+02:00 Mosè Giordano: > 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). > I managed to implement this method, so now users can choose between three different implementations of the Lomb-Scargle periodogram: - the original algorithm, that doesn't deal with uncertainties in the data nor for a non-null mean of the signal (based on Townsend, 2010, ApJS, 191, 247) - the so called generalised Lomb-Scargle algorithm, that takes into account uncertainties and a non-zero mean of the signal (based on Zechmeister, Kürster, 2009, A&A, 496, 577) - an approximation of the generalised Lomb-Scargle algorithm, made very fast (and with lower computational complexity than the true Lomb-Scargle algorithm) by some tricks suggested by Press & Rybicki, 1989, ApJ, 338, 277. The only downside is that this method requires the data to be equally sampled. In the package, this is controlled by the type of the time vector: if it's a Range, this fast method is used (but can be opted-out with a keyword) Other notable features introduced since the first release: - you can choose between 7 different normalizations - functions to compute the false-alarm probability - a function, findmaxpower, to quickly find the maximum power value of the periodogram - in findmaxfreq it is now possible to restrict the search for the frequency with the highest power to a specific range, instead of using the whole periodogram - a new function, LombScargle.model, to get the Lomb-Scargle model that best fits your data at a given frequency (without performing an actual periodogram, because this is done just for one frequency) The complete manual, with some examples complemented with pictures, is available at http://lombscarglejl.readthedocs.io/ (here <http://readthedocs.org/projects/lombscarglejl/downloads/pdf/latest/> the PDF version). The latest version of LombScargle.jl <https://github.com/giordano/LombScargle.jl> is v0.1.1, be sure to update your package list with Pkg.update() before installing it, if you want to try it out. Cheers, Mosè
