lombscargle_slow

lombscargle.implementations.lombscargle_slow(t, y, dy, frequency, normalization='normalized', fit_bias=True, center_data=True) [edit on github][source]

Lomb-Scargle Periodogram

This is a pure-python implemnetation of the original Lomb-Scargle formalism.

Parameters:

t, y, dy : 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

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
Returns:

power : array_like

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

References

[R14]
  1. Zechmeister and M. Kurster, A&A 496, 577-584 (2009)
[R15]
  1. Press et al, Numerical Recipies in C (2002)
[R16]Scargle, J.D. 1982, ApJ 263:835-853