lombscargle

lombscargle.implementations.lombscargle(t, y, dy=None, frequency=None, method='auto', assume_regular_frequency=False, normalization='normalized', fit_bias=True, center_data=True, method_kwds=None, nterms=1) [edit on github][source]

Compute the Lomb-scargle Periodogram with a given method.

Parameters:

t : array_like

sequence of observation times

y : array_like

sequence of observations associated with times t

dy : float or array_like (optional)

error or sequence of observational errors associated with times t

frequency : array_like

frequencies (not angular frequencies) at which to evaluate the periodogram. If not specified, optimal frequencies will be chosen using a heuristic which will attempt to provide sufficient frequency range and sampling so that peaks will not be missed. Note that in order to use method=’fast’, frequencies must be regularly spaced.

method : string (optional)

specify the lomb scargle implementation to use. Options are:

  • ‘auto’: choose the best method based on the input
  • ‘fast’: use the O[N log N] fast method. Note that this requires evenly-spaced frequencies: by default this will be checked unless assume_regular_frequency is set to True.
  • slow: use the O[N^2] pure-python implementation
  • chi2: use the O[N^2] chi2/linear-fitting implementation
  • fastchi2: use the O[N log N] chi2 implementation. Note that this requires evenly-spaced frequencies: by default this will be checked unless assume_regular_frequency is set to True.
  • scipy: use scipy.signal.lombscargle, which is an O[N^2] implementation written in C. Note that this does not support heteroskedastic errors.

assume_regular_frequency : bool (optional)

if True, assume that the input frequency is of the form freq = f0 + df * np.arange(N). Only referenced if method is ‘auto’ or ‘fast’.

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

Normalization to use for the periodogram. Options are ‘normalized’ or ‘unnormalized’.

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

method_kwds : dict (optional)

additional keywords to pass to the lomb-scargle method

nterms : int (default=1)

number of Fourier terms to use in the periodogram. Not supported with every method.
Returns:

PLS : array_like

Lomb-Scargle power associated with each frequency omega