docs: def load_data(path):
          """
          loads the data that has been save with Script.save.
          Args:
              path: path to folder saved by Script.save or raw_data folder within
          Returns:
              a dictionary with the data of form
              data = {param_1_name: param_1_data, ...}
          """
  
  
          # check that path exists
          if not os.path.exists(path):
              print(path)
              raise AttributeError('Path given does not exist!')
  
          # windows can't deal with long filenames (>260 chars) so we have to use the prefix '\\\\?\\'
          # if len(path.split('\\\\?\\')) == 1:
          #     path = '\\\\?\\' + os.path.abspath(path)
  
  
          # if raw_data folder exists, get a list of directories from within it; otherwise, get names of all .csv files in
          # current directory
          data = {}
          # if self.RAW_DATA_DIR in os.listdir(path): #8/26/16 AK: self not defined in static context
          #     data_files = os.listdir(os.path.join(path, self.RAW_DATA_DIR + '/'))
          #     path = os.path.join(path, self.RAW_DATA_DIR + '/')
          #
          # else:
          if 'raw_data' in os.listdir(path):  #temporarily hardcoded
              data_files = os.listdir(os.path.join(path, 'raw_data' + '/'))
              path = os.path.join(path, 'raw_data' + '/')
  
          else:
              data_files = glob.glob(os.path.join(path, '*.csv'))
  
          # If no data files were found, raise error
          if not data_files:
              raise AttributeError('Could not find data files in {:s}'.format(path))
  
          # import data from each csv
          for data_file in data_files:
              # get data name, read the data from the csv, and save it to dictionary
              data_name = data_file.split('-')[-1][0:-4] # JG: why do we strip of the date?
              imported_data_df = pd.read_csv(os.path.join(path, data_file))
  
              # check if there are real headers, if the headers are digits than we ignore them because then they are just indecies
              # real headers are strings (however, the digits are also of type str! that why we use the isdigit method)
              column_headers = list(imported_data_df.columns.values)
              if sum([int(x.isdigit()) for x in column_headers]) != len(column_headers):
                  data[data_name] = {h: imported_data_df[h].as_matrix() for h in column_headers}
              else:
                  # note, np.squeeze removes extraneous length-1 dimensions from the returned 'matrix' from the dataframe
                  data[data_name] = np.squeeze(imported_data_df.as_matrix())
  
          return data