code: def fitNorm_v2(self, specVals):
          
          from scipy.optimize import fmin
  
          def fToMin(x): return self.__call__(specVals * x)
          result = fmin(fToMin, 0., disp=False, xtol=1e-6)
          return result
docs: Fit the normalization given a set of spectral values 
          that define a spectral shape.
  
          This version uses `scipy.optimize.fmin`.
  
          Parameters
          ----------
          specVals :  an array of (nebin values that define a spectral shape
          xlims    :  fit limits     
  
          Returns
          -------
          norm : float
              Best-fit normalization value