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

'''
@Author : dingjiawen
@Date : 2023/10/12 16:14
@Usage : 
@Desc :
'''

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


def getThreshold(data, cigma_num=1.5):
    dims, = data.shape

    mean = np.mean(data)
    std = np.sqrt(np.var(data))
    max = mean + cigma_num * std
    min = mean - cigma_num * std
    max = np.broadcast_to(max, shape=[dims, ])
    min = np.broadcast_to(min, shape=[dims, ])
    mean = np.broadcast_to(mean, shape=[dims, ])

    # plt.plot(max)
    # plt.plot(data)
    # # plt.plot(mean)
    # plt.plot(min)
    # plt.show()
    #
    #
    return max, min
    # pass


def EWMA(data):
    data1 = pd.DataFrame(data).ewm(span=5).mean()

    # plt.plot(data)
    # plt.plot(data1, color='blue')
    # getThreshold(data)
    # plt.show()

    return data1


if __name__ == '__main__':
    data = np.load("E:\self_example\TensorFlow_eaxmple\Model_train_test/2012轴承数据集预测挑战\data\HI_DATA\Bearing1_1.npy")
    a, b = data.shape

    minlist = np.array([])
    maxlist = np.array([])

    for d in data:
        max, min = getThreshold(d)
        minlist = np.concatenate([minlist, min], axis=0)
        maxlist = np.concatenate([maxlist, max], axis=0)

    data = data.reshape([a * b, 1])

    origin_data = data
    # data = np.array([0.5, 5, 0.8, 4.0, 10.0, -0.1, -0.3, 0, 0.5, 6.5])
    data = EWMA(data)

    data = np.squeeze(data.values)

    count = 0
    origin_count = 0

    for a, b, c, d in zip(data, maxlist, minlist, origin_data):
        if c > a or a > b:
            count += 1
        if c > d or d > b:
            origin_count += 1

    print("原始劣质率:", origin_count / len(data) * 100, "%")
    print("修复后劣质率:", count / len(data) * 100, "%")
    plt.plot(origin_data, color='blue', label='Original data')
    plt.plot(data, color='green', label='After data repair')
    plt.plot(maxlist, color='red', label='upper Threshold')
    plt.plot(minlist, color='red', label='lower Threshold')

    plt.show()

    # getThreshold(data)