def autocorrelationfn(time, relFlux, relFluxErr):
acf = xo.autocorr_estimator(time.values,
relFlux.values,
yerr=relFluxErr.values,
min_period=0.05,
max_period=27,
max_peaks=10)
period = acf['autocorr'][0]
power = acf['autocorr'][1]
acfPowerPd = pd.DataFrame(power)
acfLocalMaxima = acfPowerPd[(acfPowerPd.shift(1) < acfPowerPd)
& (acfPowerPd.shift(-1) < acfPowerPd)]
maxPower = np.max(acfLocalMaxima).values
bestPeriod = period[np.where(power == maxPower)[0]][0]
peaks = acf['peaks'][0]['period']
if len(acf['peaks']) > 0:
window = int(peaks / np.abs(np.nanmedian(np.diff(time))) / k)
else:
window = 128
return period, power, bestPeriod, maxPower, window
def autocorr(t, y, max_peaks=1, min_period=0.5):
results = xo.autocorr_estimator(t, y, min_period=min_period)
return results["autocorr"]
def autocor(self, max_peaks=1, min_period=0.5):
results = xo.autocorr_estimator(self.t,
self.flux,
max_peaks=max_peaks,
min_period=min_period)
self.lags, self.power = results["autocorr"]
