code: def compute_acl(cls, filename, start_index=None, end_index=None,
                      min_nsamples=10):
          
          acls = {}
          with cls._io(filename, 'r') as fp:
              for param in fp.variable_params:
                  samples = fp.read_raw_samples(
                      param, thin_start=start_index, thin_interval=1,
                      thin_end=end_index, flatten=False)[param]
                  samples = samples.mean(axis=0)
                  # if < min number of samples, just set to inf
                  if samples.size < min_nsamples:
                      acl = numpy.inf
                  else:
                      acl = autocorrelation.calculate_acl(samples)
                  if acl <= 0:
                      acl = numpy.inf
                  acls[param] = acl
          return acls
docs: Computes the autocorrleation length for all model params in the
          given file.
  
          Parameter values are averaged over all walkers at each iteration.
          The ACL is then calculated over the averaged chain. If an ACL cannot
          be calculated because there are not enough samples, it will be set
          to ``inf``.
  
          Parameters
          -----------
          filename : str
              Name of a samples file to compute ACLs for.
          start_index : int, optional
              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 : int, optional
              The end index to compute the acl to. If None, will go to the end
              of the current iteration.
          min_nsamples : int, optional
              Require a minimum number of samples to compute an ACL. If the
              number of samples per walker is less than this, will just set to
              ``inf``. Default is 10.
  
          Returns
          -------
          dict
              A dictionary giving the ACL for each parameter.