def geterrors(self, params):

        #start = self._get_predict_start(start) # will be an index of a date
        #end, out_of_sample = self._get_predict_end(end)
        params = np.asarray(params)
        k_ar, k_ma = self.k_ar, self.k_ma
        k = self.k_exog + self.k_trend

        method = getattr(self, 'method', 'mle')
        if 'mle' in method:  # use KalmanFilter to get errors
            (y, k, nobs, k_ar, k_ma, k_lags, newparams, Z_mat, m, R_mat,
             T_mat, paramsdtype) = KalmanFilter._init_kalman_state(params,
                                                                   self)

            errors = KalmanFilter.geterrors(y, k, k_ar, k_ma, k_lags, nobs,
                                            Z_mat, m, R_mat, T_mat,
                                            paramsdtype)
            if isinstance(errors, tuple):
                errors = errors[0]  # non-cython version returns a tuple
        else:  # use scipy.signal.lfilter
            y = self.endog.copy()
            k = self.k_exog + self.k_trend
            if k > 0:
                y -= dot(self.exog, params[:k])

            k_ar = self.k_ar
            k_ma = self.k_ma

            (trendparams, exparams,
             arparams, maparams) = _unpack_params(params, (k_ar, k_ma),
                                                  self.k_trend, self.k_exog,
                                                  reverse=False)
            b, a = np.r_[1, -arparams], np.r_[1, maparams]
            zi = zeros((max(k_ar, k_ma)))
            for i in range(k_ar):
                zi[i] = sum(-b[:i+1][::-1]*y[:i+1])
            e = lfilter(b, a, y, zi=zi)
            errors = e[0][k_ar:]
        return errors.squeeze()
Return a list of all users
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/arima_model.py/ARMA.geterrors--crossover with--dataset/ETHPy150Open saltstack/salt/salt/modules/pw_user.py/list_users