#!/usr/bin/env python
# coding=utf-8
'''
@Author: John
@Email: johnjim0816@gmail.com
@Date: 2020-06-11 16:30:09
@LastEditor: John
@LastEditTime: 2020-06-12 11:34:52
@Discription: 
@Environment: python 3.7.7
'''
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns; sns.set()
import numpy as np
import os 

# def plot(item,ylabel='rewards'):
#     plt.figure()
#     plt.plot(np.arange(len(item)), item)
#     plt.title(ylabel+' of DDPG') 
#     plt.ylabel(ylabel)
#     plt.xlabel('episodes')
#     plt.savefig(os.path.dirname(__file__)+"/result/"+ylabel+".png")
#     plt.show()

def plot(item,ylabel='rewards'):
    df = pd.DataFrame(dict(time=np.arange(500),
                       value=np.random.randn(500).cumsum()))
    g = sns.relplot(x="time", y="value", kind="line", data=df)
    g.fig.autofmt_xdate()
    # time = range(len(item))
    # sns.set(style="darkgrid", font_scale=1.5)
    # sns.lineplot(time=time, data=item, color="r", condition="behavior_cloning")
    # # sns.tsplot(time=time, data=x2, color="b", condition="dagger")
    # plt.ylabel("Reward")
    # plt.xlabel("Iteration Number")
    # plt.title("Imitation Learning")

    plt.show()
if __name__ == "__main__":

    output_path = os.path.dirname(__file__)+"/result/"
    rewards=np.load(output_path+"rewards.npy", )
    moving_average_rewards=np.load(output_path+"moving_average_rewards.npy",)
    plot(rewards)
    plot(moving_average_rewards,ylabel='moving_average_rewards')