code: def model_stats(self):
          
          # likelihood has shape ntemps x nwalkers x niterations
          logl = self._sampler.lnlikelihood
          # get prior from posterior
          logp = self._sampler.lnprobability - logl
          logjacobian = numpy.zeros(logp.shape)
          return {'loglikelihood': logl, 'logprior': logp,
                  'logjacobian': logjacobian}
docs: Returns the log likelihood ratio and log prior as a dict of arrays.
  
          The returned array has shape ntemps x nwalkers x niterations.
  
          Unfortunately, because ``emcee_pt`` does not have blob support, this
          will only return the loglikelihood and logprior (with the logjacobian
          set to zero) regardless of what stats the model can return.
  
  
          .. warning::
              Since the `logjacobian` is not saved by `emcee_pt`, the `logprior`
              returned here is the log of the prior pdf in the sampling
              coordinate frame rather than the variable params frame. This
              differs from the variable params frame by the log of the Jacobian
              of the transform from one frame to the other. If no sampling
              transforms were used, then the `logprior` is the same.