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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from scipy.fftpack import fft,ifft
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import math
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import pywt
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import csv
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from pandas import DataFrame;
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data = pd.read_csv('E:/python/shujuwajue/kddcup.data_10_percent/kddcup_data_10_percent_corrected.csv',encoding="gbk")
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print(data.columns)
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y_values = np.array(data['0.1'])
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x_values = np.array(data['0'])
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plt.plot(x_values,y_values)
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plt.show()
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yy=fft(y_values) #快速傅里叶变换
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yreal = yy.real # 获取实数部分
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yimag = yy.imag # 获取虚数部分
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yf=abs(fft(y_values)) # 取模
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yf1=abs(fft(y_values))/((len(x_values)/2)) #归一化处理
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yf2 = yf1[range(int(len(x_values)/2))] #由于对称性，只取一半区间
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xf = np.arange(len(y_values)) # 取频率
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xf1 = xf
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xf2 = xf[range(int(len(x_values)/2))] #取一半区间
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#plt.plot(xf,yf,'r') #显示原始信号的FFT模值
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plt.subplot(2, 1, 1)
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plt.plot(xf2,yf2,'g')
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#plt.show()
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#-----------------------分界线-------------------------------------------
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w = pywt.Wavelet('sym8')#选用sym8小波
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maxlev = pywt.dwt_max_level(len(data), w)#最大分解级别，返回max_level。db.dec_lenx为小波的长度
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coeffs = pywt.wavedec(y_values, w, mode='constant',level=maxlev)#分解波，constant是边缘都填充0，不管tensor的内容
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threshold = 0.4
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#plt.figure()
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for i in range(1, len(coeffs)):
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coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i])) # 将噪声滤波
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datarec = pywt.waverec(coeffs, 'sym8') # 将信号进行小波重构
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yy1=fft(datarec)
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yreal = yy1.real # 获取实数部分
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yimag = yy1.imag # 获取虚数部分
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yf_1 = abs(fft(datarec)) # 取模
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yf1_1 = abs(fft(datarec))/((len(x_values)/2)) #归一化处理
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yf2_1 = yf1_1[range(int(len(x_values)/2))] #由于对称性，只取一半区间
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xf_1 = np.arange(len(datarec)) # 频率
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xf1_1 = xf_1
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xf2_1 = xf_1[range(int(len(x_values)/2))] #取一半区间
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plt.subplot(2, 1, 2)
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plt.plot(xf2_1,yf2_1)
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#plt.plot(datarec,'g')
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plt.show()
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#d = DataFrame(datarec)
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#d.to_csv('C:/Users/ASUS/Desktop/data.csv')
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df1 = pd.DataFrame(datarec)
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df1.to_excel("E:/python/shujuwajue/kddcup.data_10_percent/output.xlsx")
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