code: def __init__(self, method="mle"):
      if method == "mle":
          self.params = [4.856475759430, 0.664363281011, 0.407547531124]
          self.aic = 737.922644877973
          self.bic = 752.008488549422
          self.arroots = [1.5052 + 0j]
          self.maroots = [-2.4537 + 0j]
          self.bse = [0.273164176960, 0.055495689209, 0.068249092654]
          self.cov_params = [
              [0.074619, -0.00012834, 1.5413e-05],
              [-0.00012834, 0.0030798, -0.0020242],
              [1.5413e-05, -0.0020242, 0.0046579],
          ]
          self.hqic = 743.591784752421
          self.llf = -364.961322438987
          self.resid = residsc_mle[:, 0]
          self.fittedvalues = yhatc_mle[:, 0]
          self.pvalues = [1.04e-70, 5.02e-33, 2.35e-9]
          self.tvalues = [17.78, 11.97, 5.971]
          self.sigma2 = 1.039168068701**2
          self.forecast = forecast_results["fc11c"]
          self.forecasterr = forecast_results["fe11c"]
      elif method == "css":
          # NOTE: params, bse, cov_params, tvalues taken from R;
          #   commented-out versions below are from gretl
          # NOTE: gretl gives the intercept not the mean, x-12-arima
          #   and R agree with us
          # self.params = [1.625462134333, 0.666386002049, 0.409512270580]
          self.params = [4.872477127267, 0.666395534262, 0.409517026658]
          self.aic = 734.613526514951
          self.bic = 748.683338100810
          self.arroots = [1.5006 + 0.0000j]
          self.maroots = [-2.4419 + 0.0000]
          # self.bse = [0.294788633992, 0.057503298669, 0.063059352497]
          self.bse = [0.2777238133284, 0.0557583459688, 0.0681432545482]
          # self.cov_params = [
          #   [0.086900, -0.016074, 0.010536],
          #   [-0.016074, 0.0033066, -0.0021977],
          #   [0.010536, -0.0021977, 0.0039765]
          # ]
          self.cov_params = [
              [7.71305164897e-02, 5.65375305967e-06, 1.29481824075e-06],
              [
                  5.65375305967e-06,
                  3.10899314518e-03,
                  -2.02754322743e-03,
              ],
              [
                  1.29481824075e-06,
                  -2.02754322743e-03,
                  4.64350314042e-03,
              ],
          ]
          self.hqic = 740.276857090925
          self.llf = -363.306763257476
          self.resid = residsc_css[1:, 0]
          self.fittedvalues = yhatc_css[1:, 0]
          self.pvalues = [3.51e-08, 4.70e-31, 8.35e-11]
          # self.tvalues = [5.514, 11.59, 6.494]
          self.tvalues = [17.544326, 11.951494, 6.009649]
          self.sigma2 = 1.040940645447**2
  
imports: import os
  from numpy import genfromtxt
label_desc: Use image node_id 1 to create a new Y_arma11c object  with example: obj = Y_arma11c(method)