docs: def display_data(raw_data_string, raw_data_id, model_folder, show_raw):
      """Print ``raw_data_id`` with the content ``raw_data_string`` after
         applying the preprocessing of ``model_folder`` to it."""
      print("## Raw Data (ID: %i)" % raw_data_id)
      print("```")
      print(raw_data_string)
      print("```")
  
      preprocessing_desc, feature_desc, _ = _get_system(model_folder)
  
      # Print model
      print("## Model")
      print("%s\n" % model_folder)
  
      # Get the preprocessing queue
      tmp = preprocessing_desc['queue']
      preprocessing_queue = preprocessing.get_preprocessing_queue(tmp)
  
      # Get feature values as list of floats, rounded to 3 decimal places
      tmp = feature_desc['features']
      feature_list = features.get_features(tmp)
  
      # Print preprocessing queue
      preprocessing.print_preprocessing_list(preprocessing_queue)
      features.print_featurelist(feature_list)
  
      # Get Handwriting
      recording = handwritten_data.HandwrittenData(raw_data_string,
                                                   raw_data_id=raw_data_id)
      if show_raw:
          recording.show()
  
      recording.preprocessing(preprocessing_queue)
  
      feature_values = recording.feature_extraction(feature_list)
      feature_values = [round(el, 3) for el in feature_values]
      print("Features:")
      print(feature_values)
  
      # Get the list of data multiplication algorithms
      mult_queue = data_multiplication.get_data_multiplication_queue(
          feature_desc['data-multiplication'])
  
      # Multiply traing_set
      training_set = [{'id': 42,
                       'formula_id': 42,
                       'formula_in_latex': 'None',
                       'handwriting': recording}]
      training_set = create_ffiles.training_set_multiplication(training_set,
                                                               mult_queue)
  
      # Display it
      logging.info("Show %i recordings...", len(training_set))
      for recording in training_set:
          recording['handwriting'].show()