code: def logLikelihood(self, D_d, D_delta_t):
          
          if self._kde_type == 'scipy_gaussian':
              density = self._PDF_kernel([D_d, D_delta_t])
              logL = np.log(density)
          else:
              x = np.array([[D_d], [D_delta_t]])
              logL = self._kde.score_samples(x.T)
          return logL
docs: likelihood of the data (represented in the distribution of this class) given a model with predicted angular
          diameter distances.
  
          :param D_d: model predicted angular diameter distance
          :param D_delta_t: model predicted time-delay distance
          :return: loglikelihood (log of KDE value)