How to use the continuum.NORM_single function in continuum

final[2,orre,:] = obj_S[orre,2,:][::-1]
				tck = scipy.interpolate.splrep(blaze[0,orre,:],blaze[1,orre,:],k=3,s=0)
				blin = scipy.interpolate.splev(final[0,orre,:],tck,der = 0)
				final[3,orre,:] = final[1,orre,:]/blin
				final[4,orre,:] = final[2,orre,:]*(blin**2)
				
				X = final[0,orre,:]
				Z = final[3,orre,:]
				Z[:150]  = 0.0
				Z[-150:] = 0.0

				I    = np.isnan(Z)
				Z[I] = 0.0
				I    = np.where(Z!=0)[0]
				if len(I)>0:			
					cp    = continuum.NORM_single(X,Z,orden=1)
					ratio = np.polyval(cp,X)
				else:
					ratio = np.ones(len(X))

				ratio = np.polyval(cp,X)
				final[5,orre,:] = final[3,orre,:]/ratio
				Inan = np.where( np.isnan(final[1,orre,:]) == True )[0]
				final[5,orre,Inan] = 1.
				final[6,orre,:] = final[4,orre,:]*(ratio**2)
				final[7,orre,:] = ratio
				final[8,orre,:] = ratio*blin / np.sqrt( ratio *blin / GAIN + (RON/GAIN)**2 )
				
				spl        = scipy.interpolate.splrep(np.arange(len(final[0,orre,:])),final[0,orre,:] ,k=3)
				dlambda_dx = scipy.interpolate.splev(np.arange(len(final[0,orre,:])), spl, der=1)
				NN         = np.average(dlambda_dx)
				dlambda_dx /= NN