fmda/data/dict_funcs.py

   1 def set_time_params(dict, h2, hours=None):
   2     # Given FMDA dictionary and time params,
   3     # You CAN use this function just to change h2 (training period hours)
   4     # NOTE: this only performs a shallow copy and once you filter you can't filter original data to make larger dataset
   5     # Inputs:
   6     # dict: fmda dictionary
   7     # h2: (int) length of training period
   8     # hours: (int) total number of hours (train + test)
   9
  10     return_dict = dict.copy()
  11
  12     if not(hours is None):
  13         for case in return_dict:
  14             if len(return_dict[case]['fm']) < hours:
  15                 raise ValueError(f"Number of hours ({hours}) is greater than length of fm data ({len(return_dict[case]['fm'])})")
  16             return_dict[case]['hours']=hours
  17             # Filter relevant fields (compatible with synthetic data)
  18             return_dict[case]['rain']=return_dict[case]['rain'][0:hours]
  19             return_dict[case]['Ed']=return_dict[case]['Ed'][0:hours]
  20             return_dict[case]['Ew']=return_dict[case]['Ew'][0:hours]
  21             return_dict[case]['fm']=return_dict[case]['fm'][0:hours]
  22
  23             # Filter fields not in synthetic data
  24             if not('synthetic' not in return_dict[case]['title'].lower() or 'synthetic' not in return_dict[case]['descr'].lower()):
  25                 return_dict[case]['solar']=return_dict[case]['solar'][0:hours]
  26                 return_dict[case]['temp']=return_dict[case]['temp'][0:hours]
  27                 return_dict[case]['rh']=return_dict[case]['rh'][0:hours]
  28                 return_dict[case]['wind_speed']=return_dict[case]['wind_speed'][0:hours]
  29
  30
  31
  32     # Loop through cases and change training period
  33     for case in return_dict:
  34         return_dict[case]['h2']=h2
  35
  36     return return_dict