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xiaobo.py

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