code: def profile_bins(self):
          
          log_mean = np.log10(self.mean())
          log_half_width = max(5. * self.sigma(), 3.)
          # Default is to profile over +-5 sigma,
          # centered on mean, using 100 bins
          return np.logspace(log_mean - log_half_width,
                             log_mean + log_half_width, 101)/self._j_ref
docs: The binning to use to do the profile fitting