code: def calculate_logevidence(cls, filename, thin_start=None, thin_end=None,
                                thin_interval=None):
          
          with cls._io(filename, 'r') as fp:
              logls = fp.read_raw_samples(['loglikelihood'],
                                          thin_start=thin_start,
                                          thin_interval=thin_interval,
                                          thin_end=thin_end,
                                          temps='all', flatten=False)
              logls = logls['loglikelihood']
              # we need the betas that were used
              betas = fp.betas
              # annoyingly, theromdynaimc integration in PTSampler is an instance
              # method, so we'll implement a dummy one
              ntemps = fp.ntemps
              nwalkers = fp.nwalkers
              ndim = len(fp.variable_params)
          dummy_sampler = emcee.PTSampler(ntemps, nwalkers, ndim, None,
                                          None, betas=betas)
          return dummy_sampler.thermodynamic_integration_log_evidence(
              logls=logls, fburnin=0.)
docs: Calculates the log evidence from the given file using ``emcee_pt``'s
          thermodynamic integration.
  
          Parameters
          ----------
          filename : str
              Name of the file to read the samples from. Should be an
              ``EmceePTFile``.
          thin_start : int
              Index of the sample to begin returning stats. Default is to read
              stats after burn in. To start from the beginning set thin_start
              to 0.
          thin_interval : int
              Interval to accept every i-th sample. Default is to use the
              `fp.acl`. If `fp.acl` is not set, then use all stats
              (set thin_interval to 1).
          thin_end : int
              Index of the last sample to read. If not given then
              `fp.niterations` is used.
  
          Returns
          -------
          lnZ : float
              The estimate of log of the evidence.
          dlnZ : float
              The error on the estimate.