def train_instance_plot(temp_data_X, temp_data_Y, save_name):
    plt.figure(figsize=(12, 8))
    plot_dx = 0
    for chdx in range(3):
        plt.plot(((temp_data_X[:, chdx] / np.max(temp_data_X[:, chdx])) + (plot_dx * 2)), color='k')
        plot_dx += 1
    plt.plot((temp_data_Y[:, 0] - 2), color='b')
    plt.plot((temp_data_Y[:, 1] - 3), color='r')
    plt.plot((temp_data_Y[:, 2] - 4), color='g')
    plt.yticks([])
    plt.tight_layout()
    plt.savefig(save_name, dpi=300)
    plt.close()
    return
def train_instance_plot(temp_data_X, temp_data_Y, save_name):
    plt.figure(figsize=(12, 8))
    plot_dx = 0
    for chdx in range(3):
        plt.plot(((temp_data_X[:, chdx] / np.max(temp_data_X[:, chdx])) + (plot_dx * 2)), color='k')
        plot_dx += 1
    plt.plot((temp_data_Y[:, 0] - 2), color='b')
    plt.plot((temp_data_Y[:, 1] - 3), color='r')
    plt.plot((temp_data_Y[:, 2] - 4), color='g')
    plt.yticks([])
    plt.tight_layout()
    plt.savefig(save_name, dpi=300)
    plt.close()
    return