'Compute Lomb-Scargle power at automatically-determined frequencies
Parameters
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
- \'cython\': use the O[N^2] cython implementation. This is slightly
faster than method=\'slow\', but much more memory efficient.
- \'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.
method_kwds : dict (optional)
additional keywords to pass to the lomb-scargle method
normalization : string (optional, default=\'standard\')
Normalization to use for the periodogram.
Options are \'standard\', \'model\', or \'psd\'.
samples_per_peak : float (optional, default=5)
The approximate number of desired samples across the typical peak
nyquist_factor : float (optional, default=5)
The multiple of the average nyquist frequency used to choose the
maximum frequency if maximum_frequency is not provided.
minimum_frequency : float (optional)
If specified, then use this minimum frequency rather than one
chosen based on the size of the baseline.
maximum_frequency : float (optional)
If specified, then use this maximum frequency rather than one
chosen based on the average nyquist frequency.
Returns
frequency, power : ndarrays
The frequency and Lomb-Scargle power'

def autopower(self, method='auto', method_kwds=None, normalization='standard', samples_per_peak=5, nyquist_factor=5, minimum_frequency=None, maximum_frequency=None):

    frequency = self.autofrequency(samples_per_peak=samples_per_peak, nyquist_factor=nyquist_factor, minimum_frequency=minimum_frequency, maximum_frequency=maximum_frequency)
    power = self.power(frequency, normalization=normalization, method=method, method_kwds=method_kwds, assume_regular_frequency=True)
    return (frequency, power)
