lombscargle_fast

lombscargle.implementations.lombscargle_fast(t, y, dy, f0, df, Nf, center_data=True, fit_bias=True, normalization='normalized', use_fft=True, trig_sum_kwds=None)

Fast Lomb-Scargle Periodogram

This implements the Press & Rybicki method [R4] for fast O[N log(N)] Lomb-Scargle periodograms.

Parameters:

t, y, dy : array_like

times, values, and errors of the data points. These should be broadcastable to the same shape.

f0, df, Nf : (float, float, int)

parameters describing the frequency grid, f = f0 + df * arange(Nf).

center_data : bool (default=True)

Specify whether to subtract the mean of the data before the fit

fit_bias : bool (default=True)

If True, then compute the floating-mean periodogram; i.e. let the mean vary with the fit.

normalization : string (optional, default=’normalized’)

Normalization to use for the periodogram TODO: figure out what options to use

use_fft : bool (default=True)

If True, then use the Press & Rybicki O[NlogN] algorithm to compute the result. Otherwise, use a slower O[N^2] algorithm

trig_sum_kwds : dict or None (optional)

extra keyword arguments to pass to the trig_sum utility. Options are oversampling and Mfft. See documentation of trig_sum for details.

Returns:

power : ndarray

Lomb-Scargle power associated with each frequency. Units of the result depend on the normalization.

Notes

Note that the use_fft=True algorithm is an approximation to the true Lomb-Scargle periodogram, and as the number of points grows this approximation improves. On the other hand, for very small datasets (<~50 points or so) this approximation may not be useful.

References

[R4]	(1, 2) Press W.H. and Rybicki, G.B, “Fast algorithm for spectral analysis of unevenly sampled data”. ApJ 1:338, p277, 1989

[R5]	Zechmeister and M. Kurster, A&A 496, 577-584 (2009)

[R6]	Press et al, Numerical Recipies in C (2002)