Uses 2 term single trait model to get covar params for initialization
        
        Args:
            termx:      non-noise term terms that is used for initialization

def _getScalesDiag(self,termx=0):
        """
        Uses 2 term single trait model to get covar params for initialization
        
        Args:
            termx:      non-noise term terms that is used for initialization 
        """
        assert self.P>1, 'CVarianceDecomposition:: diagonal init_method allowed only for multi trait models' 
        assert self.noisPos!=None, 'CVarianceDecomposition:: noise term has to be set'
        assert termx<self.n_terms-1, 'CVarianceDecomposition:: termx>=n_terms-1'
        assert self.covar_type[self.noisPos] not in ['lowrank','block','fixed'], 'CVarianceDecimposition:: diagonal initializaiton not posible for such a parametrization'
        assert self.covar_type[termx] not in ['lowrank','block','fixed'], 'CVarianceDecimposition:: diagonal initializaiton not posible for such a parametrization'
        scales = []
        res = self.estimateHeritabilities(self.vd.getTerm(termx).getK())
        scaleg = SP.sqrt(res['varg'].mean())
        scalen = SP.sqrt(res['varn'].mean())
        for term_i in range(self.n_terms):
            if term_i==termx:
                _scales = scaleg*self.diag[term_i]
            elif term_i==self.noisPos:
                _scales = scalen*self.diag[term_i]
            else:
                _scales = 0.*self.diag[term_i]
            if self.offset[term_i]>0:
                _scales = SP.concatenate((_scales,SP.array([SP.sqrt(self.offset[term_i])])))
            scales.append(_scales)
        return SP.concatenate(scales)