def compute_splines()

in long_term/locomotion_utils.py [0:0]

• 17 lines of code
• 3 McCabe index (conditional complexity)

def compute_splines(dataset):
    """
    For each animation in the dataset, this method computes its equal-segment-length spline,
    along with its interpolated tracks (e.g. local speed, footstep frequency).
    """
    for subject in dataset.subjects():
        for action in dataset[subject].keys():
            xy = dataset[subject][action]['positions_world'][:, 0, [0, 2]]
            spline = Spline(xy, closed=False)

            # Add extra tracks (facing direction, phase/amplitude)
            xy_orientation = dataset[subject][action]['rotations_euler'][:, 0, 1]
            y_rot = np.array([np.sin(xy_orientation), np.cos(xy_orientation)]).T
            spline.add_track('direction', y_rot, interp_mode='circular')
            phase = dataset[subject][action]['extra_features'][:, [2, 3]]
            amplitude, _, frequency = phase_to_features(np.expand_dims(phase, 0))

            spline.add_track('amplitude', amplitude[0], interp_mode='linear')
            spline.add_track('frequency', frequency[0], interp_mode='linear')

            spline = spline.reparameterize(5, smoothing_factor=1)
            avg_speed_track = spline.get_track('amplitude')
            avg_speed_track[:] = np.mean(avg_speed_track)
            spline.add_track('average_speed', avg_speed_track, interp_mode='linear')
            
            dataset[subject][action]['spline'] = spline