import math
import matplotlib.pyplot as plt
import numpy as np
# from scipy.fftpack import fft,ifft
# from scipy import signal
# import seaborn
# tables=[]
# results=[]
# with open('C:\\Users\Administrator\Desktop\happy.txt','r') as f:
# for line in f:
# tables.append(list(line.strip('\n').split('\t')))
# f.close()
# for i in range(1,len(tables)):#输入数据
# array = {'Time':'','Sensor1':'','Sensor2':'','Sensor3':'','Sensor4':'','Sensor5':'','Sensor6':'','Sensor7':'','Sensor8':'','Sensor9':'','Sensor10':'','Sensor11':'','Sensor12':'','Sensor13':'','Sensor14':'','Sensor15':'','Sensor16':'','Sensor17':'','Sensor18':'','Sensor19':'','Sensor20':'','Sensor21':'','Sensor22':'','Sensor23':'','Sensor24':'','Sensor25':'','Sensor26':'','Sensor27':'','Sensor28':'','Sensor29':'','Sensor30':''}
# array['Time'] = float(tables[i][0])
# array['Sensor1'] = float(tables[i][1])
# array['Sensor2'] = float(tables[i][2])
# array['Sensor3'] = float(tables[i][3])
# array['Sensor4'] = float(tables[i][4])
# array['Sensor5'] = float(tables[i][5])
# array['Sensor6'] = float(tables[i][6])
# array['Sensor7'] = float(tables[i][7])
# array['Sensor8'] = float(tables[i][8])
# array['Sensor9'] = float(tables[i][9])
# array['Sensor10'] = float(tables[i][10])
# array['Sensor11'] = float(tables[i][11])
# array['Sensor12'] = float(tables[i][12])
# array['Sensor13'] = float(tables[i][13])
# array['Sensor14'] = float(tables[i][14])
# array['Sensor15'] = float(tables[i][15])
# array['Sensor16'] = float(tables[i][16])
# array['Sensor17'] = float(tables[i][17])
# array['Sensor18'] = float(tables[i][18])
# array['Sensor19'] = float(tables[i][19])
# array['Sensor20'] = float(tables[i][20])
# array['Sensor21'] = float(tables[i][21])
# array['Sensor22'] = float(tables[i][22])
# array['Sensor23'] = float(tables[i][23])
# array['Sensor24'] = float(tables[i][24])
# array['Sensor25'] = float(tables[i][25])
# array['Sensor26'] = float(tables[i][26])
# array['Sensor27'] = float(tables[i][27])
# array['Sensor28'] = float(tables[i][28])
# array['Sensor29'] = float(tables[i][29])
# array['Sensor30'] = float(tables[i][30])
# results.append(array)
# sig=[[]for i in range(len(tables)-1)]
# time=[]
# S1=[]
# S2=[]
# S3=[]
# S4=[]
# S5=[]
# S6=[]
# S7=[]
# S8=[]
# S9=[]
# S10=[]
# S11=[]
# S12=[]
# S13=[]
# S14=[]
# S15=[]
# S16=[]
# S17=[]
# S18=[]
# S19=[]
# S20=[]
# S21=[]
# S22=[]
# S23=[]
# S24=[]
# S25=[]
# S26=[]
# S27=[]
# S28=[]
# S29=[]
# S30=[]
# #x=np.linspace(0,len(results),len(results))
# for i in range(len(results)):
# S1.append(results[i]['Sensor1'])
# S2.append(results[i]['Sensor2'])
# S3.append(results[i]['Sensor3'])
# S4.append(results[i]['Sensor4'])
# S5.append(results[i]['Sensor5'])
# S6.append(results[i]['Sensor6'])
# S7.append(results[i]['Sensor7'])
# S8.append(results[i]['Sensor8'])
# S9.append(results[i]['Sensor9'])
# S10.append(results[i]['Sensor10'])
# S11.append(results[i]['Sensor11'])
# S12.append(results[i]['Sensor12'])
# S13.append(results[i]['Sensor13'])
# S14.append(results[i]['Sensor14'])
# S15.append(results[i]['Sensor15'])
# S16.append(results[i]['Sensor16'])
# S17.append(results[i]['Sensor17'])
# S18.append(results[i]['Sensor18'])
# S19.append(results[i]['Sensor19'])
# S20.append(results[i]['Sensor20'])
# S21.append(results[i]['Sensor21'])
# S22.append(results[i]['Sensor22'])
# S23.append(results[i]['Sensor23'])
# S24.append(results[i]['Sensor24'])
# S25.append(results[i]['Sensor25'])
# S26.append(results[i]['Sensor26'])
# S27.append(results[i]['Sensor27'])
# S28.append(results[i]['Sensor28'])
# S29.append(results[i]['Sensor29'])
# S30.append(results[i]['Sensor30'])
# time.append(results[i]['Time'])
# sig[0]=S1[:]
# sig[1]=S2[:]
# sig[2]=S3[:]
# sig[3]=S4[:]
# sig[4]=S5[:]
# sig[5]=S6[:]
# sig[6]=S7[:]
# sig[7]=S8[:]
# sig[8]=S9[:]
# sig[9]=S10[:]
# sig[10]=S11[:]
# sig[11]=S12[:]
# sig[12]=S13[:]
# sig[13]=S14[:]
# sig[14]=S15[:]
# sig[15]=S16[:]
# sig[16]=S17[:]
# sig[17]=S18[:]
# sig[18]=S19[:]
# sig[19]=S20[:]
# sig[20]=S21[:]
# sig[21]=S22[:]
# sig[22]=S23[:]
# sig[23]=S24[:]
# sig[24]=S25[:]
# sig[25]=S26[:]
# sig[26]=S27[:]
# sig[27]=S28[:]
# sig[28]=S29[:]
# sig[29]=S30[:]
# plt.rcParams['font.sans-serif']=['SimHei'] #显示中文标签
# plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
# for i in range (30):
# data=sig[i]
#
# Fs=256#采样频率256hz
# freqs=np.fft.fftfreq(len(time),time[1]-time[0])
# L = len (data) # 信号长度
# def FFT (Fs,data):
# L = len (data) # 信号长度
# N =np.power(2,np.ceil(np.log2(L))) # 下一个最近二次幂
# FFT_y = ((np.fft.fft(data))/L) # L点FFT,但除以实际信号长度 L
# FFT_y1=np.abs(FFT_y) # 取绝对值
# Fre = np.arange(int(L/2))*Fs/L # 频率坐标
# FFT_y2 = FFT_y1[range(int(L/2))] # 取一半
# FFT_y3 = FFT_y[range(int(L/2))] #为傅里叶反变换提供数据
# return Fre, FFT_y2,FFT_y3
# Fre,FFT_data,FFT_data_complex=FFT(Fs,data)
#
#
# for j in range(len(Fre)):
# if Fre[j]>15.6 or Fre[j]<7.8:#手动提取7.8-15.6hz
# FFT_data_complex[j]=0
#
# iFFT_data=np.fft.ifft(FFT_data_complex*L)
#
#
# filename = 'S'+str(i+1)+'alpha.txt'
# with open(filename,'w') as f:
# np.savetxt(filename,iFFT_data.real,fmt='%f',delimiter=',')
import math
import matplotlib.pyplot as plt
import numpy as np
import scipy
from scipy.fftpack import fft, ifft
from scipy import signal
import seaborn
tables = []
results = []
with open('C:\\Users\Administrator\Desktop\happy.txt', 'r') as f:
for line in f:
tables.append(list(line.strip('\n').split(', ')))
f.close()
for i in range(1, len(tables)): # 输入数据
array = {
'Time': float(tables[i][0]),
'Sensor1': float(tables[i][1]),
'Sensor2': float(tables[i][2]),
'Sensor3': float(tables[i][3]), 'Sensor4': float(tables[i][4]), 'Sensor5': float(tables[i][5]),
'Sensor6': float(tables[i][6]), 'Sensor7': float(tables[i][7]), 'Sensor8': float(tables[i][8]),
# 'Sensor9': float(tables[i][9]), 'Sensor10': float(tables[i][10]), 'Sensor11': float(tables[i][11]),
# 'Sensor12': float(tables[i][12]), 'Sensor13': float(tables[i][13]), 'Sensor14': float(tables[i][14]),
# 'Sensor15': float(tables[i][15]), 'Sensor16': float(tables[i][16]), 'Sensor17': float(tables[i][17]),
# 'Sensor18': float(tables[i][18]), 'Sensor19': float(tables[i][19]), 'Sensor20': float(tables[i][20]),
# 'Sensor21': float(tables[i][21]), 'Sensor22': float(tables[i][22]), 'Sensor23': float(tables[i][23]),
# 'Sensor24': float(tables[i][24]), 'Sensor25': float(tables[i][25]), 'Sensor26': float(tables[i][26]),
# 'Sensor27': float(tables[i][27]), 'Sensor28': float(tables[i][28]), 'Sensor29': float(tables[i][29]),
# 'Sensor30': float(tables[i][30])
}
results.append(array)
sig = [[] for i in range(len(tables) - 1)]
time = []
S1 = []
S2 = []
S3 = []
S4 = []
S5 = []
S6 = []
S7 = []
S8 = []
# S9 = []
# S10 = []
# S11 = []
# S12 = []
# S13 = []
# S14 = []
# S15 = []
# S16 = []
# S17 = []
# S18 = []
# S19 = []
# S20 = []
# S21 = []
# S22 = []
# S23 = []
# S24 = []
# S25 = []
# S26 = []
# S27 = []
# S28 = []
# S29 = []
# S30 = []
# x=np.linspace(0,len(results),len(results))
for i in range(len(results)):
S1.append(results[i]['Sensor1'])
S2.append(results[i]['Sensor2'])
S3.append(results[i]['Sensor3'])
S4.append(results[i]['Sensor4'])
S5.append(results[i]['Sensor5'])
S6.append(results[i]['Se
提取信号alpha_脑电信号处理_
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2021-09-29
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