signal_base.py 文件源码

def __call__(self, xs, phiinv_method='partition'):
        # map parameter vector if needed
        params = xs if isinstance(xs,dict) else self.pta.map_params(xs)

        # phiinvs will be a list or may be a big matrix if spatially
        # correlated signals
        TNrs = self.pta.get_TNr(params)
        TNTs = self.pta.get_TNT(params)
        phiinvs = self.pta.get_phiinv(params, logdet=True,
                                      method=phiinv_method)

        # get -0.5 * (rNr + logdet_N) piece of likelihood
        loglike = -0.5 * np.sum([l for l in self.pta.get_rNr_logdet(params)])

        # red noise piece
        if self.pta._commonsignals:
            phiinv, logdet_phi = phiinvs

            Sigma = self._make_sigma(TNTs, phiinv)
            TNr = np.concatenate(TNrs)

            cf = cholesky(Sigma)
            expval = cf(TNr)

            logdet_sigma = cf.logdet()

            loglike += 0.5*(np.dot(TNr, expval) - logdet_sigma - logdet_phi)
        else:
            for TNr, TNT, (phiinv, logdet_phi) in zip(TNrs, TNTs, phiinvs):
                Sigma = TNT + (np.diag(phiinv) if phiinv.ndim == 1 else phiinv)

                try:
                    cf = sl.cho_factor(Sigma)
                    expval = sl.cho_solve(cf, TNr)
                except:
                    return -np.inf

                logdet_sigma = np.sum(2 * np.log(np.diag(cf[0])))

                loglike += 0.5*(np.dot(TNr, expval) -
                                logdet_sigma - logdet_phi)

        return loglike