code: def load_text_data(dataset, directory, centre=True):
      
  
      points, point_names = parse_text(os.path.join(directory, dataset + '.txt'))[0:2]
      # Remove markers where there is a NaN
      present_index = [i for i in range(points[0].shape[1]) if not (np.any(np.isnan(points[0][:, i])) or np.any(np.isnan(points[0][:, i])) or np.any(np.isnan(points[0][:, i])))]
  
      point_names = point_names[present_index]
      for i in range(3):
          points[i] = points[i][:, present_index]
          if centre:
              points[i] = (points[i].T - points[i].mean(axis=1)).T 
  
      # Concatanate the X, Y and Z markers together
      Y = np.concatenate((points[0], points[1], points[2]), axis=1)
      Y = Y/400.
      connect = read_connections(os.path.join(directory, 'connections.txt'), point_names)
      return Y, connect
docs: Load in a data set of marker points from the Ohio State University C3D motion capture files (http://accad.osu.edu/research/mocap/mocap_data.htm).