plot.py 文件源码

def plot_one_dir(args, directory):
    """ The actual plotting code.

    Assumes that we'll be plotting from one directory, which usually means
    considering one random seed only, however it's better to have multiple
    random seeds so this code generalizes. For ES, we should store the output at
    *every* timestep, so A['TotalIterations'] should be like np.arange(...), but
    this generalizes in case Ray can help me run for many more iterations.
    """
    print("Now plotting based on directory {} ...".format(directory))

    ### Figure 1: The log.txt file.
    num = len(ATTRIBUTES)
    fig, axes = subplots(num, figsize=(12,3*num))
    for (dd, cc) in zip(directory, COLORS):
        A = np.genfromtxt(join(args.expdir, dd, 'log.txt'),
                          delimiter='\t', dtype=None, names=True)
        x = A['TotalIterations']
        for (i,attr) in enumerate(ATTRIBUTES):
            axes[i].plot(x, A[attr], '-', lw=lw, color=cc, label=dd)
            axes[i].set_ylabel(attr, fontsize=ysize)
            axes[i].tick_params(axis='x', labelsize=tick_size)
            axes[i].tick_params(axis='y', labelsize=tick_size)
            axes[i].legend(loc='best', ncol=1, prop={'size':legend_size})
    plt.tight_layout()
    plt.savefig(args.out+'_log.png')

    ### Figure 2: Error regions.
    num = len(directory)
    if num == 1: 
        num+= 1
    fig, axes = subplots(1,num, figsize=(12*num,10))
    for (i, (dd, cc)) in enumerate(zip(directory, COLORS)):
        A = np.genfromtxt(join(args.expdir, dd, 'log.txt'),
                          delimiter='\t', dtype=None, names=True)
        axes[i].plot(A['TotalIterations'], A["FinalAvgReturns"], 
                     color=cc, marker='x', ms=ms, lw=lw)
        axes[i].fill_between(A['TotalIterations'],
                             A["FinalAvgReturns"] - A["FinalStdReturns"],
                             A["FinalAvgReturns"] + A["FinalStdReturns"],
                             alpha = error_region_alpha,
                             facecolor='y')
        axes[i].set_ylim(ENV_TO_YLABELS[args.envname])
        axes[i].tick_params(axis='x', labelsize=tick_size)
        axes[i].tick_params(axis='y', labelsize=tick_size)
        axes[i].set_title("Mean Episode Rewards ({})".format(dd), fontsize=title_size)
        axes[i].set_xlabel("ES Iterations", fontsize=xsize)
        axes[i].set_ylabel("Rewards", fontsize=ysize)
    plt.tight_layout()
    plt.savefig(args.out+'_rewards_std.png')