bbr_plot.py 文件源码

def make_figure_8_plot(logfile):
    """Generate high quality plot of data to reproduce figure 8.

    The logfile is a CSV of the format [congestion_control, loss_rate, goodput, rtt, capacity, specified_bw]
    """
    results = {}
    plt.figure()
    cubic = {"loss": [], "goodput": []}
    bbr = {"loss": [], "goodput": []}

    # For available options on plot() method, see: https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot
    # We prefer to use explicit keyword syntax to help code readability.

    # Create a figure.
    fig_width = 8
    fig_height = 5
    fig, axes = plt.subplots(figsize=(fig_width, fig_height))

    results = parse_results_csv(logfile)
    xmark_ticks = get_loss_percent_xmark_ticks(results)
    cubic = results['cubic']
    bbr = results['bbr']
    debug_print_verbose("CUBIC: %s" % cubic)
    debug_print_verbose("BBR: %s" % bbr)

    matplotlib.rcParams.update({'figure.autolayout': True})

    plt.plot(cubic['loss'], cubic['goodput'], color='blue', linestyle='solid', marker='o',
             markersize=7, label='CUBIC')

    plt.plot(bbr['loss'], bbr['goodput'], color='red', linestyle='solid', marker='x',
             markersize=7, label='BBR')

    # Plot ideal line of (1-lossRate * BW)
    ideal = {}
    ideal['loss'] = cubic['loss']
    ideal['goodput'] = [(1 - (x / 100.0)) * 100 for x in ideal['loss']]

    plt.plot(ideal['loss'], ideal['goodput'], color='black', linestyle='dotted', label='ideal')

    plt.xscale('log')

    plot_titles(plt, xaxis="Loss Rate (%) - Log Scale", yaxis="Goodput (Mbps)")

    apply_axes_formatting(axes, deduplicate_xmark_ticks(xmark_ticks))
    plot_legend(plt, axes, ncol=3)

    save_figure(plt, name="figures/figure8.png")