def learn_gps(self):
for lcid in tqdm(self.lcids, desc='Learning GPs', leave=False):
kernel = Matern32Term(log(self.lpf.fluxes[lcid].std()), log(0.1))
gp = GP(kernel, mean=0.0)
gp.freeze_parameter('kernel:log_sigma')
def nll(x):
gp.set_parameter_vector(x)
gp.compute(self.lpf.times[lcid], self.lpf.wn[lcid])
return -gp.log_likelihood(self.lpf.fluxes[lcid] - 1.0)
res = minimize(nll, [log(1.)])
gp.set_parameter_vector(res.x)
gp.compute(self.lpf.times[lcid], self.lpf.wn[lcid])
self.gps.append(gp)
def __call__(self, period: Optional[float] = None):
logger.info("Running Celerite")
assert hasattr(
self.ts, 'ls'), "Celerite step requires a prior Lomb-Scargle step"
self.period = period if period is not None else self.ts.ls.period
gp = GP(SHOTerm(log(self.ts.flux.var()), log(10),
log(2 * pi / self.period)),
mean=1.)
gp.freeze_parameter('kernel:log_omega0')
gp.compute(self.ts.time, yerr=self.ts.ferr)
def minfun(pv):
gp.set_parameter_vector(pv)
gp.compute(self.ts.time, yerr=self.ts.ferr)
return -gp.log_likelihood(self.ts.flux)
res = minimize(minfun,
gp.get_parameter_vector(),
jac=False,
method='powell')
self.result = res
self.parameters = res.x
self.prediction = gp.predict(self.ts.flux, return_cov=False)
