def compute_acf(cls, filename, start_index=None, end_index=None,
                    per_walker=False, walkers=None, parameters=None,
                    temps=None):
        """Computes the autocorrleation function of the model params in the
        given file.

        By default, parameter values are averaged over all walkers at each
        iteration. The ACF is then calculated over the averaged chain for each
        temperature. An ACF per-walker will be returned instead if
        ``per_walker=True``.

        Parameters
        -----------
        filename : str
            Name of a samples file to compute ACFs for.
        start_index : {None, int}
            The start index to compute the acl from. If None, will try to use
            the number of burn-in iterations in the file; otherwise, will start
            at the first sample.
        end_index : {None, int}
            The end index to compute the acl to. If None, will go to the end
            of the current iteration.
        per_walker : optional, bool
            Return the ACF for each walker separately. Default is False.
        walkers : optional, int or array
            Calculate the ACF using only the given walkers. If None (the
            default) all walkers will be used.
        parameters : optional, str or array
            Calculate the ACF for only the given parameters. If None (the
            default) will calculate the ACF for all of the model params.
        temps : optional, (list of) int or 'all'
            The temperature index (or list of indices) to retrieve. If None
            (the default), the ACF will only be computed for the coldest (= 0)
            temperature chain. To compute an ACF for all temperates pass 'all',
            or a list of all of the temperatures.

        Returns
        -------
        dict :
            Dictionary of arrays giving the ACFs for each parameter. If
            ``per-walker`` is True, the arrays will have shape
            ``ntemps x nwalkers x niterations``. Otherwise, the returned array
            will have shape ``ntemps x niterations``.
        """
        acfs = {}
        with cls._io(filename, 'r') as fp:
            if parameters is None:
                parameters = fp.variable_params
            if isinstance(parameters, str) or isinstance(parameters, unicode):
                parameters = [parameters]
            if isinstance(temps, int):
                temps = [temps]
            elif temps == 'all':
                temps = numpy.arange(fp.ntemps)
            elif temps is None:
                temps = [0]
            for param in parameters:
                subacfs = []
                for tk in temps:
                    if per_walker:
                        # just call myself with a single walker
                        if walkers is None:
                            walkers = numpy.arange(fp.nwalkers)
                        arrays = [cls.compute_acfs(filename,
                                                   start_index=start_index,
                                                   end_index=end_index,
                                                   per_walker=False,
                                                   walkers=ii,
                                                   parameters=param,
                                                   temps=tk)[param][0, :]
                                  for ii in walkers]
                        # we'll stack all of the walker arrays to make a single
                        # nwalkers x niterations array; when these are stacked
                        # below, we'll get a ntemps x nwalkers x niterations
                        # array
                        subacfs.append(numpy.vstack(arrays))
                    else:
                        samples = fp.read_raw_samples(
                            param, thin_start=start_index,
                            thin_interval=1, thin_end=end_index,
                            walkers=walkers, temps=tk, flatten=False)[param]
                        # contract the walker dimension using the mean, and
                        # flatten the (length 1) temp dimension
                        samples = samples.mean(axis=1)[0, :]
                        thisacf = autocorrelation.calculate_acf(
                            samples).numpy()
                        subacfs.append(thisacf)
                # stack the temperatures
                acfs[param] = numpy.stack(subacfs)
        return acfs
Computes the autocorrleation function of the model params in the
        given file.

        By default, parameter values are averaged over all walkers at each
        iteration. The ACF is then calculated over the averaged chain for each
        temperature. An ACF per-walker will be returned instead if
        ``per_walker=True``.

        Parameters
        -----------
        filename : str
            Name of a samples file to compute ACFs for.
        start_index : {None, int}
            The start index to compute the acl from. If None, will try to use
            the number of burn-in iterations in the file; otherwise, will start
            at the first sample.
        end_index : {None, int}
            The end index to compute the acl to. If None, will go to the end
            of the current iteration.
        per_walker : optional, bool
            Return the ACF for each walker separately. Default is False.
        walkers : optional, int or array
            Calculate the ACF using only the given walkers. If None (the
            default) all walkers will be used.
        parameters : optional, str or array
            Calculate the ACF for only the given parameters. If None (the
            default) will calculate the ACF for all of the model params.
        temps : optional, (list of) int or 'all'
            The temperature index (or list of indices) to retrieve. If None
            (the default), the ACF will only be computed for the coldest (= 0)
            temperature chain. To compute an ACF for all temperates pass 'all',
            or a list of all of the temperatures.

        Returns
        -------
        dict :
            Dictionary of arrays giving the ACFs for each parameter. If
            ``per-walker`` is True, the arrays will have shape
            ``ntemps x nwalkers x niterations``. Otherwise, the returned array
            will have shape ``ntemps x niterations``.