@shuffle

pro separate_training

   train_frac = 0.75
   input_1 = 'ecm2_training_viirs_training_nowobble.sav'
   input_2 = 'ecm2_training_viirs_training_nowobble.sav'

   output_full = 'ecm_training_myd02ssh.sav'
   output_train = 'ecm_training_viirs_train.sav'
   output_test = 'ecm_training_viirs_test.sav'

   restore, input_1

;  restore, input_1
;  f = training_data
;  restore, input_2
;  push, training_data, f
;  f = !null
;  save, file = output_full, training_data, TRAINING_DATA_CODE_ID_STRING, TRAINING_DATA_CREATION_TIME_STRING


   ;-----------------------------------
   ; sort data
   ;-----------------------------------
print, 'starting shuffle'
   n_full = n_elements(training_data.temp_11_0um_nom)
   idx = lindgen(n_full)
   idx_sorted = shuffle(idx,n_full)
   training_data_full = training_data[idx_sorted]
print, 'ending shuffle'

   n_train = n_full * train_frac
   training_data = training_data_full[0:n_train-1]
   save, file = output_train, training_data, TRAINING_DATA_CODE_ID_STRING, TRAINING_DATA_CREATION_TIME_STRING
    
   training_data = training_data_full[n_train:n_full-1]
   save, file = output_test, training_data, TRAINING_DATA_CODE_ID_STRING, TRAINING_DATA_CREATION_TIME_STRING

stop

end