This documentation is for astroML version 0.2

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

astroML.time_series.lomb_scargle_bootstrap(t, y, dy, omega, generalized=True, subtract_mean=True, N_bootstraps=100, random_state=None)

Use a bootstrap analysis to compute Lomb-Scargle significance

Parameters :

The first set of parameters are passed to the lomb_scargle algorithm :

t : array_like

sequence of times

y : array_like

sequence of observations

dy : array_like

sequence of observational errors

omega : array_like

frequencies at which to evaluate p(omega)

generalized : bool

if True (default) use generalized lomb-scargle method otherwise, use classic lomb-scargle.

subtract_mean : bool

if True (default) subtract the sample mean from the data before computing the periodogram. Only referenced if generalized is False

Remaining parameters control the bootstrap :

N_bootstraps : int

number of bootstraps

random_state : None, int, or RandomState object

random seed, or random number generator

Returns :

D : ndarray

distribution of the height of the highest peak