##############################
plt.errorbar(time,rv,yerr=erv,fmt='.')
plt.show()
####EXTRACT PERIOD ESTIMATE##############
N = 10000
periods=np.linspace(0.1,2*max(time),N)
fmin = 1. / periods.max()
fmax = 1. / periods.min()
df = (fmax - fmin) / N
model = LombScargle().fit(time, rv, erv)
power = model.score_frequency_grid(fmin, df, N)
freqs = fmin + df * np.arange(N)
periods=1./freqs
# plot the results
plt.plot(1. / freqs, power)
plt.show()
print('guess p: ', periods[power==max(power)])
print('guess sys v: ',np.mean(rv))
print('guess K: ',np.std(rv-np.mean(rv)))
#rvsys, K, w, ecc, T0, period
labels=['rvsys', 'K', 'w', 'ecc', 'T0', 'period','sig2']
initial=[0,40,90,0.01,15,6.25,20]
