# -*- encoding:utf-8 -*-

'''
@Author : dingjiawen
@Date : 2023/6/14 15:28
@Usage : 
@Desc :  一些画图方法
'''
from sklearn.metrics import mean_absolute_error, mean_squared_error
from pylab import *

# 图像上显示中文
mpl.rcParams['font.sans-serif'] = ['SimHei']
# 调整使图像支持负号
mpl.rcParams["axes.unicode_minus"] = False

font1 = {
    'family': 'Times New Roman',
    'weight': 'normal',
    'size': 12,
}

font2 = {
    'family': 'Times New Roman',
    'weight': 'normal',
    'size': 15,
}


def calScore(y_test, pred):
    # TODO 打印误差
    test_mse = round(mean_squared_error(y_test, pred), 4)
    test_rmse = round(math.sqrt(mean_squared_error(y_test, pred)), 4)
    # mape 暂时这样
    test_mape = round(mean_absolute_error(pred, y_test) * 100, 4)
    test_mae = round(mean_absolute_error(pred, y_test), 4)
    # TODO 计算得分
    result = list(np.squeeze(pred, 1))
    exceed = list(filter(lambda res: res >= y_test[-1], result))
    print("len(exceed)", len(exceed))
    if len(exceed) > 0:
        exceed_index = result.index(exceed[0])
        print("len(result)", len(result))
        # Eri = round((((2750 - (len(result) - exceed_index)) - 2750) / 2750), 4)
        Eri = round((exceed_index / len(result)), 4)
        print("RUL", 100 - (len(result) - exceed_index))
        print("Eri", Eri)

        if Eri <= 0:
            score = round(math.exp(-math.log(0.5, e) * (Eri / 5)), 4)
        else:
            score = round(math.exp(math.log(0.5, e) * (Eri / 20)), 4)
            print("score1", score)
            score = exceed_index / len(result)
    else:
        Eri = nan
        score = nan

    print('MSE_testScore: %.4f MSE' % test_mse)
    print('RMSE_testScore: %.4f RMSE' % test_rmse)
    print('MAE_testScore: %.4f MAE' % test_mae)
    print('MAPE_testScore: %.4f MAPE' % test_mape)
    print("score: %.4f score" % score)
    pass


# 画图
def getPlot(data, feature, time_step, x_train, x_test, pred, truePred, train_pred,
            saveName="../store/test"):
    train_pred = np.squeeze(train_pred, 1)
    print("train_pred", train_pred)

    # TODO 实际值
    # 设置xtick和ytick的方向：in、out、inout
    plt.rcParams['xtick.direction'] = 'in'
    plt.rcParams['ytick.direction'] = 'in'
    plt.plot(list(range(data.shape[0])), data)
    # 画出 y=1 这条水平线
    plt.axhline(data[-1], c='green')
    plt.grid()

    plt.ylim(0, 1)
    plt.xlim(-50, 1300)

    # TODO 真实预测散点图

    # TODO 图2
    plt.figure(2)
    point_len = x_train.shape[0] + feature + time_step - 1

    # plt.figure(2, figsize=(12, 4))
    # 设置xtick和ytick的方向：in、out、inout
    plt.rcParams['xtick.direction'] = 'in'
    plt.rcParams['ytick.direction'] = 'in'

    print("pred", pred[:, -1])
    print("truePred", truePred[:, -1])
    figname2 = saveName + "single.png"
    plt.scatter(list(range(data.shape[0])), data, c='blue', s=12, label='Actual value')
    # # TODO 这里要改成Training value  10(重叠丢失) + 9(转置)  +1141(训练数据已知)   + 9(转置) = 1169      + 81 (预测数据) =1250
    # # 训练数据传入模型预测一次即为训练数据
    plt.plot(list(range(time_step + feature - 1, point_len)), train_pred, linewidth=2, color='red',
             label='Training value')

    plt.scatter(list(range(point_len, point_len + x_test.shape[0])), pred, c='black', s=15,
                label='Predictive value')
    # 画出 y=1 这条水平线
    plt.axhline(data[-1], linewidth=2, c='green', label='Failure threshold')
    plt.ylim(-0.2, 0.95)
    plt.xlim(-50, 1300)
    plt.xlabel("Serial number of the fusion feature point", font=font2)
    plt.ylabel("Virtual health indicator", font=font2)
    plt.legend(loc='upper left', prop=font1)
    plt.savefig(figname2, )