lombscargle_scipy

lombscargle.implementations.lombscargle_scipy(t, y, frequency, normalization='normalized', center_data=True)

Lomb-Scargle Periodogram

This is a wrapper of scipy.signal.lombscargle for computation of the Lomb-Scargle periodogram. This is a relatively fast version of the naive O[N^2] algorithm, but cannot handle heteroskedastic errors.

Parameters:

t, y: array_like

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

frequency : array_like

frequencies (not angular frequencies) at which to calculate periodogram

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

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

center_data : bool (optional, default=True)

if True, pre-center the data by subtracting the weighted mean of the input data.

Returns:

power : array_like

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

References

[R11]

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

[R12]

Press et al, Numerical Recipies in C (2002)

[R13]

Scargle, J.D. 1982, ApJ 263:835-853