code: def __init__(self, method="mle"):
      if method == "mle":
          self.params = [
              0.859167822255,
              -0.445990454620,
              -0.094364739597,
              0.633504596270,
              0.039251240870,
          ]
          self.aic = 680.801215465509
          self.bic = 701.929980972682
          self.arroots = [
              1.0209 - 0j,
              0.2966 - 0.9835j,
              0.2966 + 0.9835j,
              -1.4652 + 0.0000j,
          ]
          self.maroots = [-25.4769 + 0.0000]
          self.bse = [
              0.097363938243,
              0.136020728785,
              0.128467873077,
              0.081059611396,
              0.138536155409,
          ]
          self.cov_params = [
              [0.0094797, -0.012908, 0.011870, -0.0073247, -0.011669],
              [-0.012908, 0.018502, -0.017103, 0.010456, 0.015892],
              [0.011870, -0.017103, 0.016504, -0.010091, -0.014626],
              [-0.0073247, 0.010456, -0.010091, 0.0065707, 0.0089767],
              [-0.011669, 0.015892, -0.014626, 0.0089767, 0.019192],
          ]
          self.hqic = 689.304925277181
          self.llf = -334.400607732754
          self.resid = resids_mle[:, 2]
          self.fittedvalues = yhat_mle[:, 2]
          self.pvalues = [1.10e-18, 0.0010, 0.4626, 5.48e-15, 0.7769]
          self.tvalues = [8.824, -3.279, -0.7345, 7.815, 0.2833]
          self.sigma2 = 0.911409665692**2
          self.forecast = forecast_results["fc41"]
          self.forecasterr = forecast_results["fe41"]
      elif method == "css":
          self.params = [
              0.868370308475,
              -0.459433478113,
              -0.086098063077,
              0.635050245511,
              0.033645204508,
          ]
          self.aic = 666.171731561927
          self.bic = 687.203720777521
          self.arroots = [
              1.0184 + 0.0000j,
              0.2960 - 0.9803j,
              0.2960 + 0.9803j,
              -1.4747 + 0.0000j,
          ]
          self.maroots = [-29.7219 + 0.0000j]
          # NOTE: bse, cov_params, tvalues taken from R; commented-out
          #   versions below are from [TODO: finish this sentence]
          # self.bse = [0.077822066628, 0.112199961491, 0.104986211369,
          #             0.068394652456, 0.113996438269]
          self.bse = [
              0.09554032,
              0.13387533,
              0.12691479,
              0.08045129,
              0.13456419,
          ]
          # self.cov_params = [
          #   [0.0060563, -0.0083712, 0.0076270, -0.0047067, -0.0070610],
          #   [-0.0083712, 0.012589, -0.011391, 0.0069576, 0.0098601],
          #   [0.0076270, -0.011391, 0.011022, -0.0067771, -0.0089971],
          #   [-0.0047067, 0.0069576, -0.0067771, 0.0046778, 0.0054205],
          #   [-0.0070610, 0.0098601, -0.0089971, 0.0054205, 0.012995]
          # ]
          self.cov_params = [
              [
                  0.009127952,
                  -0.01243259,
                  0.011488329,
                  -0.007070855,
                  -0.011031907,
              ],
              [
                  -0.012432590,
                  0.01792260,
                  -0.016597806,
                  0.010136298,
                  0.015053122,
              ],
              [
                  0.011488329,
                  -0.01659781,
                  0.016107364,
                  -0.009851695,
                  -0.013923062,
              ],
              [
                  -0.007070855,
                  0.01013630,
                  -0.009851695,
                  0.006472410,
                  0.008562476,
              ],
              [
                  -0.011031907,
                  0.01505312,
                  -0.013923062,
                  0.008562476,
                  0.018107521,
              ],
          ]
          self.hqic = 674.640335476392
          self.llf = -327.085865780964
          self.resid = resids_css[4:, 2]
          self.fittedvalues = yhat_css[4:, 2]
          self.pvalues = [6.51e-29, 4.23e-05, 0.4122, 1.62e-20, 0.7679]
          # self.tvalues = [11.16, -4.095, -0.8201, 9.285, 0.2951]
          self.tvalues = [
              9.0887381,
              -3.4315100,
              -0.6786792,
              7.8938778,
              0.2503143,
          ]
          self.sigma2 = 0.914551777765**2
  
imports: import os
  from numpy import genfromtxt
label_desc: Use image node_id 1 to create a new Y_arma41 object  with example: obj = Y_arma41(method)