code: def constrain_norms(self, srcNames, cov_scale=1.0):
          
  
          # Get the covariance matrix
  
          for name in srcNames:
              par = self.like.normPar(name)
  
              err = par.error()
              val = par.getValue()
  
              if par.error() == 0.0 or not par.isFree():
                  continue
  
              self.add_gauss_prior(name, par.getName(),
                                   val, err * cov_scale)
docs: Constrain the normalizations of one or more sources by
          adding gaussian priors with sigma equal to the parameter
          error times a scaling factor.