def predict(self, xpred):
""" Realize the GP using the current values of the hyperparameters at values x=xpred.
Used for making GP plots. Wrapper around `celerite.GP.predict()`.
Args:
xpred (np.array): numpy array of x values for realizing the GP
Returns:
tuple: tuple containing:
np.array: numpy array of predictive means \n
np.array: numpy array of predictive standard deviations
"""
self.update_kernel_params()
# build celerite kernel with current values of hparams
kernel = celerite.terms.JitterTerm(
log_sigma=np.log(self.params[self.jit_param].value))
for i in np.arange(self.kernel.num_terms):
kernel = kernel + celerite.terms.ComplexTerm(
log_a=self.kernel.hparams[i, 0],
log_b=self.kernel.hparams[i, 1],
log_c=self.kernel.hparams[i, 2],
log_d=self.kernel.hparams[i, 3])
gp = celerite.GP(kernel)
gp.compute(self.x, self.yerr)
mu, var = gp.predict(self.y - self.params[self.gamma_param].value,
xpred,
return_var=True)
stdev = np.sqrt(var)
return mu, stdev
def predict(self,xpred):
""" Realize the GP using the current values of the hyperparameters at values x=xpred.
Used for making GP plots. Wrapper for `celerite.GP.predict()`.
Args:
xpred (np.array): numpy array of x values for realizing the GP
Returns:
tuple: tuple containing:
np.array: numpy array of predictive means \n
np.array: numpy array of predictive standard deviations
"""
self.update_kernel_params()
B = self.kernel.hparams['gp_B'].value
C = self.kernel.hparams['gp_C'].value
L = self.kernel.hparams['gp_L'].value
Prot = self.kernel.hparams['gp_Prot'].value
# build celerite kernel with current values of hparams
kernel = celerite.terms.JitterTerm(
log_sigma = np.log(self.params[self.jit_param].value)
)
kernel += celerite.terms.RealTerm(
log_a=np.log(B*(1+C)/(2+C)),
log_c=np.log(1/L)
)
kernel += celerite.terms.ComplexTerm(
log_a=np.log(B/(2+C)),
log_b=-np.inf,
log_c=np.log(1/L),
log_d=np.log(2*np.pi/Prot)
)
gp = celerite.GP(kernel)
gp.compute(self.x, self.yerr)
# mu, var = gp.predict(self.y-self.params[self.gamma_param].value, xpred, return_var=True)
mu, var = gp.predict(self._resids(), xpred, return_var=True)
stdev = np.sqrt(var)
return mu, stdev
