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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def calculate_logevidence(cls, filename, thin_start=None, thin_end=None, thin_interval=None):
 """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. """

        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.)