text: import pandas as pd
  import logging
  logging.basicConfig(level=logging.DEBUG,format='[%(asctime)s] %(message)s', datefmt='%d-%b-%y %H:%M:%S')
  logger = logging.getLogger(__name__)
  
  import os
  root_path = os.path.dirname(os.path.abspath('__file__'))
  import json
  with open(root_path+'/config/config.json') as handle:
      dictdump = json.loads(handle.read())
  data_part=dictdump['data_part']
  
  pacf_data = pd.read_csv(root_path+'/Zhangjiashan_ssa/data/PACF.csv')
  up_bounds=pacf_data['UP']
  lo_bounds=pacf_data['LOW']
  subsignals_pacf = pacf_data.drop(['ORIG','UP','LOW'],axis=1)
  lags_dict={}
  for signal in subsignals_pacf.columns.tolist():
      # print(subsignals_pacf[signal])
      lag=0
      for i in range(subsignals_pacf[signal].shape[0]):
          if abs(subsignals_pacf[signal][i])>0.5 and abs(subsignals_pacf[signal][i])>up_bounds[0]:
              lag=i
      lags_dict[signal]=lag  
  
  variables={
      'lags_dict':{
          'Trend':14,
          'Periodic1':4,
          'Periodic2':15,
          'Periodic3':17,
          'Periodic4':15,
          'Periodic5':17,
          'Periodic6':20,
          'Periodic7':19,
          'Periodic8':19,
          'Periodic9':18,
          'Periodic10':18,
          'Noise':18,
      },
      'full_len' :data_part['full_len'],
      'train_len' :data_part['train_len'],
      'dev_len' : data_part['dev_len'],
      'test_len' : data_part['test_len'],
  }
  logger.debug('variables:{}'.format(variables))
  
  # one_step_lags = [
  #     3,#Trend
  #     4,#Periodic1
  #     3,#Periodic2
  #     4,#Periodic3
  #     5,#Periodic4
  #     4,#Periodic5
  #     5,#Periodic6
  #     4,#Periodic7
  #     5,#Periodic8
  #     4,#Periodic9
  #     4,#Periodic10
  #     5,#Noise
  # ]
  
  # multi_step_lags = [
  #     3,#Trend
  #     4,#Periodic1
  #     3,#Periodic2
  #     4,#Periodic3
  #     5,#Periodic4
  #     4,#Periodic5
  #     5,#Periodic6
  #     4,#Periodic7
  #     5,#Periodic8
  #     4,#Periodic9
  #     4,#Periodic10
  #     5,#Noise
  # ]
  