def TMrunModel(train_list, test_list, plot=False):

  for file1 in train_list: #train_list #was --> file_list
    subj_str = file1.split('_')[0]
    subj_test_list = [f for f in test_files if f.split('_')[0] == subj_str]
    save = False
    file_name = os.path.splitext(file1)[0]
    #path = '/home/sheiser1/Desktop/zaychik_proj1-master/Dr_Zaychik_Data/selectedData/newData/ready$' + file_name
    #path = '/home/nupic/examples/opf/clients/hotgym/anomaly/one_gym$' + file_name
    path = '/home/sheiser1/nupic-master/examples/opf/clients/hotgym/anomaly/one_gym/MCYT_100/models$' + file_name

    model = None
    if os.path.exists(path):
      model = Model.load(path)
    else:
      model = createModel(getModelParamsFromName(GYM_NAME))
      save = True

    print ("Creating model from %s..." % file_name)
    inputData = "%s/%s.csv" % (train_path, file_name.replace(" ", "_"))

    runIoThroughNupic(inputData, model, file_name, plot)
    #print 'Ran ' + file1 + ' thru Nupic'

    if save:
      model.save(path)

    for file2 in subj_test_list: #was --> file_list
      file2_name = os.path.splitext(file2)[0]
      model.disableLearning()
      #print ("Running model: " + file_name + ' on: ' + file2_name)
      inputData = "%s/%s.csv" % (test_path, file2_name.replace(" ", "_"))
      runIoThroughNupic(inputData, model, file2_name, plot)
      os.rename(file2_name + '_out.csv', file_name + '_' + file2_name + '_out.csv')