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
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).
dataset/ETHPy150Open SheffieldML/GPy/GPy/util/mocap.py/load_text_data