# Plot the function, the prediction and the 95% confidence interval based on
# the MSE
############# george part #########
#print flux, dyf, ph400
Yg = np.array(flux)
Y_err = np.array(dyfg)
Xg = np.array(ph400)
norm = Yg.max()
Yg /= norm
Y_err /= norm
gp = george.GP(Matern32Kernel(500)) # + WhiteKernel(0.001))
gp.compute(Xg, Y_err)
p0 = gp.get_parameter_vector()
def ll(p):
gp.set_parameter_vector(p)
return -gp.lnlikelihood(Yg, quiet=True)
def grad_ll(p):
gp.set_parameter_vector(p)
return -gp.grad_lnlikelihood(Yg, quiet=True)
results = opt.minimize(ll, p0, jac=grad_ll)
#print(np.exp(gp.kernel[:]))
#print results
