lombscargle_fastchi2

lombscargle.implementations.lombscargle_fastchi2(t, y, dy, f0, df, Nf, normalization='normalized', fit_bias=True, center_data=True, nterms=1, use_fft=True, trig_sum_kwds=None)

Lomb-Scargle Periodogram

This implements a fast chi-squared periodogram using the algorithm outlined in [R10]. The result is identical to the stantard Lomb-Scargle periodogram. The advantage of this algorithm is the ability to compute multiterm periodograms relatively quickly.

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).

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

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

fit_bias : bool (optional, default=True)

if True, include a constant offet as part of the model at each frequency. This can lead to more accurate results, especially in then case of incomplete phase coverage.

center_data : bool (optional, default=True)

if True, pre-center the data by subtracting the weighted mean of the input data. This is especially important if fit_bias = False

nterms : int (optional, default=1)

Number of Fourier terms in the fit

Returns:

power : array_like

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

References

[R7]	Zechmeister and M. Kurster, A&A 496, 577-584 (2009)

[R8]	Press et al, Numerical Recipies in C (2002)

[R9]	Scargle, J.D. ApJ 263:835-853 (1982)

[R10]

(1, 2) Palmer, J. ApJ 695:496-502 (2009)