plotDRL.py

import pickle
from re import escape
import matplotlib.pyplot as plt
import seaborn as sns 
import numpy as np 
import pandas as pd 
import os
from tools import Arguments
import matplotlib
matplotlib.rc('text', usetex=False) # 在绘图时是否使用 LaTeX 渲染文本
pd.options.display.notebook_repr_html=False # 在 Jupyter Notebook 中显示数据帧时，将使用普通的文本格式，而不是 HTML

def plot_evaluation_information(datasource,directory):
    sns.set_theme(style='whitegrid') # 设置图表主题

    with open(datasource,'rb') as tf:
        test_data=pickle.load(tf)
    # I need to plot unbalance, and reward of each step by bar figures
    plt.rcParams["figure.figsize"] = (16,9) # 设置图表的大小为16x9英寸
    fig,axs=plt.subplots(2,2)

    plt.subplots_adjust(wspace=0.9,hspace=0.3) # 调整子图之间的间距
    plt.autoscale(tight=True)
    # fig.tight_layout()
    #prepare data for evaluation the environment here 
    eval_data=pd.DataFrame(test_data['information'])
    eval_data.columns=['time_step','price','netload','action','real_action','soc','battery','gen1','gen2','gen3','unbalance','operation_cost']

    #plot unbalance in axs[0]   
    axs[0,0].cla()
    axs[0,0].set_ylabel('Unbalance of Generation and Load')
    axs[0,0].bar(eval_data['time_step'],eval_data['unbalance'],label='Exchange with Grid',width=0.4)
    axs[0,0].bar(eval_data['time_step']+0.4,eval_data['netload'],label='Netload',width=0.4)
    axs[0,0].legend(loc='upper left',fontsize=12, frameon=False,labelspacing=0.5)
    # axs[0,0].set_xticks([i for i in range(24)],[i for i in range(1,25)])
    # plot reward in axs[1]
    axs[1,1].cla()
    axs[1,1].set_ylabel('Costs')
    axs[1,1].bar(eval_data['time_step'],eval_data['operation_cost'])

    #plot generation and netload in ax[2]
    axs[1,0].cla()
    axs[1,0].set_ylabel('Outputs of Units and Netload (kWh)')
    # axs[1,0].set_xticks([i for i in range(24)], [i for i in range(1, 25)])
    # x=eval_data['time_step']
    battery_positive=np.array(eval_data['battery'])
    battery_negative=np.array(eval_data['battery'])

    battery_negative=np.minimum(battery_negative,0)#  discharge 
    battery_positive=np.maximum(battery_positive,0)# charge 
    # deal with power exchange within the figure
    imported_from_grid=np.minimum(np.array(eval_data['unbalance']),0)
    exported_2_grid=np.maximum(np.array(eval_data['unbalance']),0)
    # print(battery_negative)
    # print(battery_positive)
    axs[1,0].bar(eval_data['time_step'],eval_data['gen1'],label='gen1')
    axs[1,0].bar(eval_data['time_step'],eval_data['gen2'],label='gen2',bottom=eval_data['gen1'])
    axs[1,0].bar(eval_data['time_step'],eval_data['gen3'],label='gen3',bottom=eval_data['gen1']+eval_data['gen2'])
    axs[1,0].bar(eval_data['time_step'],-battery_positive,color='blue',hatch='/',label='battery charge')
    axs[1,0].bar(eval_data['time_step'],-battery_negative,label='battery discharge',hatch='/',bottom=eval_data['gen1']+eval_data['gen2']+eval_data['gen3'])
    axs[1,0].bar(eval_data['time_step'],-imported_from_grid,label='imported from grid',bottom=eval_data['gen1']+eval_data['gen2']+eval_data['gen3']-battery_negative)
    axs[1,0].bar(eval_data['time_step'],-exported_2_grid,label='exported to grid',bottom=-battery_positive)
    
    axs[1,0].plot(eval_data['time_step'],eval_data['netload'],drawstyle='steps-mid',label='netload')
    axs[1,0].legend(fontsize=12, frameon=False,labelspacing=0.3,loc=(1.05, 0.5))#loc='upper left',
    #plot energy charge/discharge with price in ax[3].
    
    axs[0,1].cla()
    axs[0,1].set_ylabel('Price')
    axs[0,1].set_xlabel('Time Steps')
    
    axs[0,1].plot(eval_data['time_step'],eval_data['price'],drawstyle='steps-mid',label='Price',color='pink')
    axs[0,1].legend(fontsize=12, frameon=False,labelspacing=0.3,loc=(0.2, 1.05))
    axs[0,1]=axs[0,1].twinx()
    axs[0,1].set_ylabel('SOC')
    # axs[0,1].set_xticks([i for i in range(24)], [i for i in range(1, 25)])
    axs[0,1].plot(eval_data['time_step'],eval_data['soc'],drawstyle='steps-mid',label='SOC',color='grey')
    axs[0,1].legend(fontsize=12, frameon=False, labelspacing=0.3, loc=(0.5, 1.05))
    # axs[0,1].legend(fontsize=12, frameon=False,labelspacing=0.3,loc=(0.5, 1.05))
    plt.show()
    # plt.close()
    fig.savefig(f"{directory}/Evoluation Information.svg", format='svg', dpi=600, bbox_inches='tight')
    print('the evaluation figure have been plot and saved')
def make_dir(directory,feature_change):
    cwd=f'{directory}/DRL_{feature_change}_plots'
    os.makedirs(cwd,exist_ok=True)
    return cwd
class PlotArgs():
    def __init__(self) -> None:
        self.cwd=None
        self.feature_change=None 
        self.plot_on=None
def plot_optimization_result(datasource, directory):  # data source is dataframe
    sns.set_theme(style='whitegrid')
    plt.rcParams["figure.figsize"] = (9,16)
    fig, axs = plt.subplots(3, 1)
    # fig.tight_layout()
    plt.subplots_adjust(wspace=0.9,hspace=0.3)
    plt.autoscale(tight=True)
    # plt.subplots_adjust(wspace=0.7, hspace=0.3)
    T = np.array([i for i in range(24)])
    # plot step cost
    axs[0].cla()
    axs[0].set_ylabel('Costs(D)')
    axs[0].set_xlabel('Time(h)')

    axs[0].bar(T, datasource['step_cost'])
    # axs[0,0].set_xticks([i for i in range(24)],[i for i in range(1,25)])

    # plot soc and price at first
    axs[1].cla()
    axs[1].set_ylabel('Price(D)')
    axs[1].set_xlabel('Time(h)')

    axs[1].plot(T, datasource['price'], drawstyle='steps-mid', label='Price',  color='pink')
    axs[1].legend(fontsize=12, frameon=False, labelspacing=0.3, loc=(0.2, 1.05))
    axs[1] = axs[1].twinx()

    axs[1].set_ylabel('SOC')
    axs[1].plot(T, datasource['soc'], drawstyle='steps-mid',  label='SOC', color='grey')
    # axs[0,1].set_xticks([i for i in range(24)],[i for i in range(1,25)])

    axs[1].legend(fontsize=12, frameon=False, labelspacing=0.3, loc=(0.5, 1.05))
    # plot accumulated generation and consumption here
    axs[2].cla()
    axs[2].set_ylabel('Outputs of DGs and Battery(kW)')
    axs[2].set_xlabel('Time(h)')
    battery_positive = np.array(datasource['battery_energy_change'])
    battery_negative = np.array(datasource['battery_energy_change'])
    battery_negative = np.minimum(battery_negative, 0)  # discharge
    battery_positive = np.maximum(battery_positive, 0)  # charge
    # deal with power exchange within the figure
    # imported_from_grid = np.maximum(np.array(datasource['grid_import']), 0)
    # exported_2_grid = np.minimum(np.array(datasource['grid_import']), 0)
    imported_from_grid=np.array(datasource['grid_import'])
    exported_2_grid=np.array(datasource['grid_export'])
    axs[2].bar(T, datasource['gen1'], label='gen1')
    axs[2].bar(T, datasource['gen2'], label='gen2', bottom=datasource['gen1'])
    axs[2].bar(T, datasource['gen3'], label='gen3', bottom=datasource['gen2'] + datasource['gen1'])
    axs[2].bar(T, -battery_positive, color='blue', hatch='/', label='battery charge')
    axs[2].bar(T, -battery_negative, hatch='/', label='battery discharge',
                  bottom=datasource['gen3'] + datasource['gen2'] + datasource['gen1'])
    # import as generate
    axs[2].bar(T, imported_from_grid, label='import from grid',
                  bottom=-battery_negative + datasource['gen3'] + datasource['gen2'] + datasource['gen1'])
    # export as load
    axs[2].bar(T, -exported_2_grid, label='export to grid', bottom=-battery_positive)
    axs[2].plot(T, datasource['netload'], label='netload',  drawstyle='steps-mid', alpha=0.7)
    axs[2].legend(fontsize=12, frameon=False, labelspacing=0.3, loc=(1.05, 0.5))
    # axs[1,0].set_xticks([i for i in range(24)],[i for i in range(1,25)])
    # plt.show()
    # plt.close()
    fig.savefig(f"{directory}/optimization_information.svg", format='svg', dpi=600, bbox_inches='tight')
    print('optimization result has been ploted')
def smooth(data, sm=5):
        if sm > 1:
            smooth_data = []
            for n, d in enumerate(data):
                if n - sm + 1 >= 0:
                    y = np.mean(data[n - sm + 1: n])
                else:
                    y = np.mean(data[: n])
                smooth_data.append(y)
        return smooth_data

def plot_training_rewardinfo(name,datasource,color):
    sns.set_theme(style='whitegrid')

    with open(datasource,'rb') as tf:
        train_data=pickle.load(tf)
    #plot reward
    random_seed_list = [1234, 2234, 3234, 4234, 5234]
    all_train_reward = []
    for seed in random_seed_list:
        all_train_reward.append(train_data[seed]['mean_episode_reward'])
    all_train_reward = np.array(all_train_reward)
    mean_rewards = np.mean(all_train_reward, axis=0)
    sem_rewards = np.std(all_train_reward, axis=0) / np.sqrt(all_train_reward.shape[0])
    episodes = []
    for i in range(400):
        episodes.append(i)
    plt.rcParams["figure.figsize"] = (16,9) # 设置图表的大小为16x9英寸
    plt.autoscale(tight=True)
    critical_value = 1.96  # 对应于95%置信水平
    margin_error = critical_value * sem_rewards
    # 绘制均值曲线
    plt.plot(episodes, mean_rewards, label=name, color=color)
    # 绘制95%置信区间
    plt.fill_between(episodes, mean_rewards - margin_error, mean_rewards + margin_error, color=f'light{color}', alpha=0.3)
    # 添加图例

def plot_cost_rewardinfo(name,datasource,color):
    sns.set_theme(style='whitegrid')

    with open(datasource,'rb') as tf:
        train_data=pickle.load(tf)
    #plot reward
    random_seed_list = [1234, 2234, 3234, 4234, 5234]
    all_train_cost = []
    for seed in random_seed_list:
        all_train_cost.append(train_data[seed]['episode_operation_cost'])
    all_train_cost = np.array(all_train_cost)
    mean_rewards = np.mean(all_train_cost, axis=0)
    sem_rewards = np.std(all_train_cost, axis=0) / np.sqrt(all_train_cost.shape[0])
    episodes = []
    for i in range(400):
        episodes.append(i)
    plt.rcParams["figure.figsize"] = (16,9) # 设置图表的大小为16x9英寸
    plt.autoscale(tight=True)
    critical_value = 1.96  # 对应于95%置信水平
    margin_error = critical_value * sem_rewards
    # 绘制均值曲线
    plt.plot(episodes, mean_rewards, label=name, color=color)
    # 绘制95%置信区间
    plt.fill_between(episodes, mean_rewards - margin_error, mean_rewards + margin_error, color=f'light{color}', alpha=0.3)
    # 添加图例

def plot_cost_rewardinfo_MIP(name,datasource,color):
    sns.set_theme(style='whitegrid')

    with open(datasource,'rb') as tf:
        train_data=pickle.load(tf)
    #plot reward
    random_seed_list = [1234, 2234, 3234, 4234, 5234]
    all_train_cost = []
    for seed in random_seed_list:
        all_train_cost.append(train_data[seed]['episode_operation_cost'])
    all_train_cost = np.array(all_train_cost)
    mean_rewards = np.mean(all_train_cost, axis=0)
    sem_rewards = np.std(all_train_cost, axis=0) / np.sqrt(all_train_cost.shape[0])
    episodes = []
    for i in range(400):
        episodes.append(i)
    plt.rcParams["figure.figsize"] = (16,9) # 设置图表的大小为16x9英寸
    plt.autoscale(tight=True)
    critical_value = 1.96  # 对应于95%置信水平
    margin_error = critical_value * sem_rewards
    # 绘制均值曲线
    plt.plot(episodes, mean_rewards, label=name, color=color)
    # 绘制95%置信区间
    plt.fill_between(episodes, mean_rewards - margin_error, mean_rewards + margin_error, color=f'light{color}', alpha=0.3)
    # 添加图例

def plot_training_unbalanceinfo(name, datasource, color):
    sns.set_theme(style='whitegrid')

    with open(datasource, 'rb') as tf:
        train_data = pickle.load(tf)
    # plot reward
    random_seed_list = [1234, 2234, 3234, 4234, 5234]
    all_train_unbalance = []
    for seed in random_seed_list:
        all_train_unbalance.append(train_data[seed]['unbalance'])
    all_train_unbalance = np.array(all_train_unbalance)
    mean_rewards = np.mean(all_train_unbalance, axis=0)
    sem_rewards = np.std(all_train_unbalance, axis=0) / np.sqrt(all_train_unbalance.shape[0])
    episodes = []
    for i in range(50):
        episodes.append(i)
    plt.rcParams["figure.figsize"] = (16, 9)  # 设置图表的大小为16x9英寸
    plt.autoscale(tight=True)
    critical_value = 1.96  # 对应于95%置信水平
    margin_error = critical_value * sem_rewards
    # 绘制均值曲线
    plt.plot(episodes, mean_rewards, label=name, color=color)
    # 绘制95%置信区间
    plt.fill_between(episodes, mean_rewards - margin_error, mean_rewards + margin_error, color=f'light{color}',
                     alpha=0.3)
    # 添加图例

if __name__=='__main__':
    print('test dir and plot shadow loss with sub figure ')
    plot_training_rewardinfo('D:\桌面\待实现\代码\DRL-for-Energy-Systems-Optimal-Scheduling\AgentDDPG\\reward_data.pkl')