使用python接受tgam的脑波数据实例

# -*- coding: utf-8 -*-
import serial 
filename='yjy.txt' 
t = serial.Serial('COM5',57600)
b=t.read(3)
vaul=[]
i=0
y=0
p=0
while b[0]!=170 or b[1]!=170 or b[2]!=4:
b=t.read(3)
print(b)
if b[0]==b[1]==170 and b[2]==4:
a=b+t.read(5)
print(a)
if a[0] == 170 and a[1]==170 and a[2]==4 and a[3]==128 and a[4]==2: 
while 1:
i=i+1
#  print(i)
a=t.read(8)
#  print(a)
sum=((0x80+0x02+a[5]+a[6])^0xffffffff)&0xff
if a[0]==a[1]==170 and a[2]==32:
y=1
else:
y=0
if a[0] == 170 and a[1]==170 and a[2]==4 and a[3]==128 and a[4]==2:
p=1
else:
p=0
if sum!=a[7] and y!=1 and p!=1:
print("wrroy1")
b=t.read(3)
c=b[0]
d=b[1]
e=b[2]
print(b)
while c!=170 or d!=170 or e!=4:
c=d
d=e
e=t.read()
print("c:")
print(c)
print("d:")
print(d)
print("e:")
print(e)
if c==(b'\xaa'or 170) and d==(b'\xaa'or 170) and e==b'\x04':
g=t.read(5)
print(g)
if c == b'\xaa' and d==b'\xaa' and e==b'\x04' and g[0]==128 and g[1]==2: 
a=t.read(8)
print(a)
break
#  if a[0]==a[1]==170 and a[2]==4:
# print(type(a))
if a[0] == 170 and a[1]==170 and a[2]==4 and a[3]==128 and a[4]==2:
high=a[5]
low=a[6]
#  print(a)
rawdata=(high<<8)|low 
if rawdata 32768:
rawdata=rawdata-65536
#  vaul.append(rawdata)
sum=((0x80+0x02+high+low)^0xffffffff)&0xff
if sum==a[7]:
vaul.append(rawdata)
if sum!=a[7]:
print("wrroy2")
b=t.read(3)
c=b[0]
d=b[1]
e=b[2]
#   print(b)
while c!=170 or d!=170 or e!=4:
c=d
d=e
e=t.read()
if c==b'\xaa' and d==b'\xaa' and e==b'\x04':
g=t.read(5)
print(g)
if c == b'\xaa' and d==b'\xaa' and e==b'\x04' and g[0]==128 and g[1]==2: 
a=t.read(8)
print(a)
break
if a[0]==a[1]==170 and a[2]==32:
c=a+t.read(28)
print(vaul)
print(len(vaul))
for v in vaul:
w=0
if v<=102:
w+=v
q=w/len(vaul)
q=str(q)
with open(filename,'a') as file_object:
file_object.write(q)
file_object.write("\n")
if 102<v<=204:
w+=v
q=w/len(vaul)
q=str(q)
with open(filename,'a') as file_object:
file_object.write(q)
file_object.write("\n")
if 204<v<=306:
w+=v
q=w/len(vaul)
q=str(q)
with open(filename,'a') as file_object:
file_object.write(q)
file_object.write("\n")
if 306<v<=408:
w+=v
q=w/len(vaul)
q=str(q)
with open(filename,'a') as file_object:
file_object.write(q)
file_object.write("\n")
if 408<v<=510:
w+=v
q=w/len(vaul)
q=str(q)
with open(filename,'a') as file_object:
file_object.write(q)
file_object.write("\n")
#  print(c)
vaul=[]
#  if i==250:
#  break
#  with open(filename,'a') as file_object:
#   file_object.write(q)
#   file_object.write("\n")

#!-coding:UTF-8-
from numpy import *
import numpy as np
def loadDataSet(fileName, delim='\t'):
fr = open(fileName)
stringArr = [line.strip().split(delim) for line in fr.readlines()]
datArr = [map(float,line) for line in stringArr]
return mat(datArr)
def percentage2n(eigVals,percentage):
sortArray=np.sort(eigVals) #升序
sortArray=sortArray[-1::-1] #逆转，即降序
arraySum=sum(sortArray)
tmpSum=0
num=0
for i in sortArray:
tmpSum+=i
num+=1
if tmpSum =arraySum*percentage:
return num
def pca(dataMat, topNfeat=9999999):
meanVals = mean(dataMat, axis=0)
meanRemoved = dataMat - meanVals #remove mean
covMat = cov(meanRemoved, rowvar=0)
eigVals,eigVects = linalg.eig(mat(covMat))
eigValInd = argsort(eigVals)  #sort, sort goes smallest to largest
eigValInd = eigValInd[:-(topNfeat+1):-1] #cut off unwanted dimensions
redEigVects = eigVects[:,eigValInd] #reorganize eig vects largest to smallest
lowData_N = meanRemoved * redEigVects#transform data into new dimensions
reconMat_N = (lowData_N * redEigVects.T) + meanVals
return lowData_N,reconMat_N
def pcaPerc(dataMat, percentage=1):
meanVals = mean(dataMat, axis=0)
meanRemoved = dataMat - meanVals #remove mean
covMat = cov(meanRemoved, rowvar=0)
eigVals,eigVects = linalg.eig(mat(covMat))
eigValInd = argsort(eigVals)  #sort, sort goes smallest to largest
n=percentage2n(eigVals,percentage)
n_eigValIndice=eigValInd[-1:-(n+1):-1]
n_eigVect=eigVects[:,n_eigValIndice]
lowData_P=meanRemoved*n_eigVect
reconMat_P = (lowData_P * n_eigVect.T) + meanVals
return lowData_P,reconMat_P

import matplotlib.pyplot as plt
from pylab import *
import numpy as np
import scipy.io as sio
def loadData(filename,mName):
load_fn = filename
load_data = sio.loadmat(load_fn)
load_matrix = load_data[mName]
#load_matrix_row = load_matrix[0]
#figure(mName)
#plot(load_matrix,'r-')
#show()
#print type(load_data)
#print type(load_matrix)
#print load_matrix_row
return load_matrix

#!-coding:UTF-8
import matplotlib.pyplot as plt
from pylab import *
import numpy as np
import scipy.io as sio
import pca
from numpy import mat,matrix
import scipy as sp
import readData
import pca
if __name__ == '__main__':
A1=readData.loadData('6electrodes.mat','A1')
lowData_N, reconMat_N= pca.pca(A1,30)
lowData_P, reconMat_P = pca.pcaPerc(A1,0.95)
#print lowDMat
#print reconMat
print shape(lowData_N)
print shape(reconMat_N)
print shape(lowData_P)
print shape(reconMat_P)