def _original_label(original_data, threshold, drift_score, window_size, step_size):
    """
    To obtain a original drift label of time series.

    Args:
        original_data(numpy.ndarray): The input data.
        threshold(float): The drift threshold.
        drift_score(numpy.ndarray): The drift score of the input data.
        window_size(int): Size of a concept window.
            Usually 3 periods of the input data if it is periodic.
        step_size(int): The jump length of the sliding window.

    Returns:
        - list, the drift label of input data.
            0 means no drift, and 1 means drift happens.
        - list, the locations of drifts(x-axis).
    """
    label = []
    label_location = []
    # Label: label=0, no drifts; label=1, drift happens.
    for i in range(0, len(original_data) - 2*window_size, step_size):
        label_location.append(i + window_size)
        if drift_score[i + window_size] >= threshold:
            label.append(1)
        else:
            label.append(0)
    return label, label_location