plot.py

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

def smooth(data, weight=0.9):
    '''用于平滑曲线，类似于Tensorboard中的smooth

    Args:
        data (List):输入数据
        weight (Float): 平滑权重，处于0-1之间，数值越高说明越平滑，一般取0.9

    Returns:
        smoothed (List): 平滑后的数据
    '''
    last = data[0]  # First value in the plot (first timestep)
    smoothed = list()
    for point in data:
        smoothed_val = last * weight + (1 - weight) * point  # 计算平滑值
        smoothed.append(smoothed_val)
        last = smoothed_val
    return smoothed


def plot_rewards(rewards, cfg, path=None, tag='train'):
    sns.set()
    plt.figure()  # 创建一个图形实例，方便同时多画几个图
    plt.title(
        f"{tag}ing curve on {cfg['device']} of {cfg['algo_name']} for {cfg['env_name']}")
    plt.xlabel('epsiodes')
    plt.plot(rewards, label='rewards')
    plt.plot(smooth(rewards), label='smoothed')
    plt.legend()


def plot_energy(cfg):
    seed = cfg['seed']
    np.random.seed(seed)
    plt.figure()
    plt.title('Energy Accumulation Testing Result')
    plt.xlabel('time[s]')
    plt.ylabel('energy[kwh]')
    y = np.random.randn()