def profile_bins(self):
        """ The binning to use to do the profile fitting
        """
        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
The binning to use to do the profile fitting