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History of LombScargle.jl

v0.4.0 (2018-08-23)

Breaking Changes

  • Support for Julia 0.6 was dropped.

v0.3.1 (2018-07-28)

v0.3.0 (2017-04-29)

New Features

  • You can pre-plan a periodogram before actually performing it using LombScargle.plan function. This computes some quantities needed afterwards and pre-allocate the memory for the actual computation of the periodogram. The speed-up is particularly relevant for the fast method.
  • Add flags and timelimit optional keywords to lombscargle function, to set the FFTW planner flags and the time limit.
  • Package license changed to BSD 3-clause "New" or "Revised".

Breaking Changes

  • Support for Julia 0.4 and 0.5 was dropped.
  • The normalization keyword of lombscargle function now must be a Symbol (instead of AbstractString), the default being :standard (instead of "standard"). The same normalizations as before are supported, the names kept the same capitalization.

Improvements

This version faced several performance improvements, in particular to lombscargle and LombScargle.model functions, besides the pre-planning feature.

  • The fast method of lombscarlge now is faster, the larger the size of input array, the larger the improvement. In addition, the fast Fourier transform computed internally with FFTW library can take advantage of multi-threading (call FFTW.set_num_threads(n) to use n threads) in order to speed-up computation. However, please note that the running time will not scale as n because computation of the FFT is only a part of the algorithm. The memory footprint of this function is also considerably lower. To give you an idea of the improvement, for an input of 100000 datapoints, a pre-planned periodogram is 70% faster than a (non-planned) periodogram in previous version and requires almost 90% less memory. With 4 FFTW threads the speed-up is of 80%.
  • The two non-fast methods now are about 20%-30% faster, thanks to the use of sincos function from math library. These methods now also support Julia’s native multi-threading. Run Julia with n threads (e.g., JULIA_NUM_THREADS=4 julia for 4 threads) in order to gain an n-fold scaling. These functions also eat considerably less memory: if the periodogram is pre-planned, all operations are then performed in-place, so memory usage of the periodogram only is independent of input size.
  • The LombScargle.model function is now a bit faster and less memory-greedy than before.

Bug Fixes

  • PSD normalization with heteroskedastic errors has been fixed.

v0.2.0 (2016-12-07)

Breaking Changes

  • The fast method is now used when the frequency grid is evenly spaced (a Range object), no matter what the times vector is. The previous behavior was due to a wrong interpretation of the applicability of the method.

Bug Fixes

  • Periodogram type now has 4 parameters, one for the type of each field. Now power, freq, and times fields need not to have all the same floating point type.
  • In the non-fast variant of the Generalised Lomb–Scargle method, when fit_mean and/or center_data are true, pre-center the data by subtracting from the signal the weighted average of the signal itself, instead of the arithmetic mean.

v0.1.2 (2016-10-17)

New Features

  • New function for performing bootstrap resampling: LombScargle.bootstrap. The fap and fapinv functions have now new methods to estimate false-alarm probability and its inverse from a bootstrap sample.
  • New utilities: period, periodpower, findmaxperiod.

Bug Fixes

  • Fix power in the standard (i.e., fast = false variant) generalised Lomb–Scargle algorithm with fit_mean = true. You will find different results than before, but for the better, previous results were slightly wrong.

v0.1.1 (2016-08-20)

New Features

  • New function: LombScargl.model. It gives the best fitting Lomb–Scargle model at a given frequency.
  • findmaxfreq function now can take an optional argument (interval) to limit the search for the maximum frequency to a certain frequency range.

v0.1.0 (2016-08-18)

New Features

  • The fast, but approximate, method by Press & Rybicki (1989) has been implemented. This has complexity O[N log(N)], to be compared with the O[N^2] of the true Lomb–Scargle periodogram. The implementation in this package is based on the one in Astropy. Related to this method, three new keywords for lombscargle function has been added: fast, oversampling, Mfft.
  • The generalised Lomb–Scargle algorithm by Zechmeister & Kürster is used also with fit_mean=false, when the user provided the uncertainties.

v0.0.2 (2016-08-05)

New Features

  • New functions: findmaxpower, prob, probinv, fap, fapinv.
  • New optional keyword for lombscargle function: noise_level.

v0.0.1 (2016-07-16)

Initial release.