code: def prop_samples(self,prop,return_values=True,conf=0.683):
          
          samples = self.samples[prop].values
          
          if return_values:
              sorted = np.sort(samples)
              med = np.median(samples)
              n = len(samples)
              lo_ind = int(n*(0.5 - conf/2))
              hi_ind = int(n*(0.5 + conf/2))
              lo = med - sorted[lo_ind]
              hi = sorted[hi_ind] - med
              return samples, (med,lo,hi)
          else:
              return samples
docs: Returns samples of given property, based on MCMC sampling
  
          :param prop:
              Name of desired property.  Must be column of ``self.samples``.
  
          :param return_values: (optional)
              If ``True`` (default), then also return (median, lo_err, hi_err)
              corresponding to desired credible interval.
  
          :param conf: (optional)
              Desired quantile for credible interval.  Default = 0.683.
  
          :return:
              :class:`np.ndarray` of desired samples
  
          :return: 
              Optionally also return summary statistics (median, lo_err, hi_err),
              if ``returns_values == True`` (this is default behavior)