This documentation is for astroML version 0.2

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If you use the software, please consider citing astroML. astroML.time_series.search_frequencies

astroML.time_series.search_frequencies(t, y, dy, LS_func=<built-in function lomb_scargle>, LS_kwargs=None, initial_guess=25, limit_fractions=[0.04, 0.3, 0.9, 0.99], n_eval=10000, n_retry=5, n_save=50)

Utility Routine to find the best frequencies

To find the best frequency with a Lomb-Scargle periodogram requires searching a large range of frequencies at a very fine resolution. This is an iterative routine that searches progressively finer grids to narrow-in on the best result.

Parameters :

t: array_like :

observed times

y: array_like :

observed fluxes or magnitudes

dy: array_like :

observed errors on y

Returns :

omega_top, power_top: ndarrays :

The saved values of omega and power. These will have size 1 + n_save * (1 + n_retry * len(limit_fractions)) as long as n_save > n_retry