Returns standard errors on trait covariances from term_i (for the covariance estimate \see getTraitCovar)

        Args:
            term_i:     index of the term we are interested in

def getTraitCovarStdErrors(self,term_i):
        """
        Returns standard errors on trait covariances from term_i (for the covariance estimate \see getTraitCovar)

        Args:
            term_i:     index of the term we are interested in
        """
        assert self.init,        'GP not initialised'
        assert self.fast==False, 'Not supported for fast implementation'

        if self.P==1:
            out = (2*self.getScales()[term_i])**2*self._getLaplaceCovar()[term_i,term_i]
        else:
            C = self.vd.getTerm(term_i).getTraitCovar()
            n_params = C.getNumberParams()
            par_index = 0
            for term in range(term_i-1):
                par_index += self.vd.getTerm(term_i).getNumberScales()
            Sigma1 = self._getLaplaceCovar()[par_index:(par_index+n_params),:][:,par_index:(par_index+n_params)]
            out = sp.zeros((self.P,self.P))
            for param_i in range(n_params):
                out += C.Kgrad_param(param_i)**2*Sigma1[param_i,param_i]
                for param_j in range(param_i):
                    out += 2*abs(C.Kgrad_param(param_i)*C.Kgrad_param(param_j))*Sigma1[param_i,param_j]
        out = sp.sqrt(out)
        return out