text: import sys
  import matplotlib.pyplot as plt
  import os
  root_path = os.path.dirname(os.path.abspath('__file__'))
  sys.path.append(root_path)
  from tools.models import one_step_esvr, one_step_esvr_multi_seed
  from Zhangjiashan_ssa.projects.variables import variables
  
  if __name__ == '__main__':
  
      # one_step_esvr_multi_seed(
      #     root_path=root_path,
      #     station='Zhangjiashan',
      #     decomposer='ssa',
      #     predict_pattern='one_step_1_ahead_forecast_pacf_train_val',# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle
      #     n_calls=100,
      # )
      # one_step_esvr_multi_seed(
      #     root_path=root_path,
      #     station='Zhangjiashan',
      #     decomposer='ssa',
      #     predict_pattern='one_step_1_ahead_forecast_pacf_traindev_test',# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle
      #     n_calls=100,
      # )
      # one_step_esvr_multi_seed(
      #     root_path=root_path,
      #     station='Zhangjiashan',
      #     decomposer='ssa',
      #     predict_pattern='one_step_1_ahead_forecast_pacf_traindev_append',# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle
      #     n_calls=100,
      # )
      # for leading_time in [1,3,5,7,9]:
      #     one_step_esvr_multi_seed(
      #         root_path=root_path,
      #         station='Zhangjiashan',
      #         decomposer='ssa',
      #         predict_pattern='one_step_'+str(leading_time)+'_ahead_forecast_pacf',# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle
      #         n_calls=100,
      #     )
  
      for leading_time in [1,3,5,7,9]:
          one_step_esvr_multi_seed(
              root_path=root_path,
              station='Zhangjiashan',
              decomposer='ssa',
              predict_pattern='one_step_'+str(leading_time)+'_ahead_forecast_pcc_local',# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle
              n_calls=100,
          )
      # one_step_esvr_multi_seed(
      #         root_path=root_path,
      #         station='Zhangjiashan',
      #         decomposer='ssa',
      #         predict_pattern='one_step_1_ahead_forecast_pacf_pca18',#+str(i),# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle
      #         n_calls=100,
      #     )
      # one_step_esvr_multi_seed(
      #         root_path=root_path,
      #         station='Zhangjiashan',
      #         decomposer='ssa',
      #         predict_pattern='one_step_1_ahead_forecast_pacf_pcamle',#+str(i),# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle
      #         n_calls=100,
      #     )
      # num_in_one = sum(variables['lags_dict'].values())
      # for n_components in range(num_in_one-16,num_in_one+1):
      #     one_step_esvr_multi_seed(
      #         root_path=root_path,
      #         station='Zhangjiashan',
      #         decomposer='ssa',
      #         predict_pattern='one_step_1_ahead_forecast_pacf_pca'+str(n_components),# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle
      #         n_calls=100,
      #     )
  