def plot_mean_and_std(x, curves, title="",
        colors=['b', 'g', 'r', 'c', 'm', 'y', 'k'],
        log_scale_y=False, log_scale_x=False, legend=True,
        x_title="X Label", y_title="Y Label", std_scale=1):
    # Set Appearance properties using Seaborn
    sns.set(style="white", color_codes=True, font_scale=1.2)
    sns.set_style("ticks", {"xtick.major.size": 3, "ytick.major.size": 2})
    # - - - - - - - - - - - - - - - - - - -
    plt.figure()

    for i, j in enumerate(x):
        plt.errorbar(j,
                     curves[i][0],
                     yerr=curves[i][1],
                     fmt='-',
                     marker="o",
                     label='curve' + str(i + 1),
                     color=colors[i])
    
    plt.ylabel(y_title, fontsize = 14)
    plt.xlabel(x_title, fontsize=13)    
    plt.title(title)
    plt.grid()
    if log_scale_x:
        plt.xscale('log')
    if log_scale_y:
        plt.yscale('log')
    if legend:
        plt.legend(loc=0)

    # Adjust Margins
    plot_margin = 0.25
    x0, x1, y0, y1 = plt.axis()
    plt.axis((x0 - plot_margin,
              x1 + plot_margin,
              y0 - plot_margin,
              y1 + plot_margin))
    return plt
Serialize representation of the table for local caching.
dataset/ETHPy150Open automl/RoBO/robo/visualization/evaluation.py/plot_mean_and_std--crossover with--dataset/ETHPy150Open yhat/db.py/db/db.py/Table.to_dict