from mff import read_mff_header, read_mff_data #, getEpochInfos, mff_getSummaryInfo
filePath ='/Users/jesong1126/Work/Data/VGT/VGT_105_fil_segA_mff_32_100_32_tp_bcr_blc.mff'
hdr = read_mff_header.read_mff_header(filePath)
nC = hdr['nChans']
nSamples = hdr['nSamples']
nSamplesPre = hdr['nSamplesPre']
nTrials = hdr['nTrials']
srate = hdr['Fs']
summaryInfo = hdr['orig']
trialsName = summaryInfo['epochLabels']
categoryName = list(set(trialsName))
nCategory = len(categoryName)
data = read_mff_data.read_mff_data(filePath, 'epoch', 1, hdr['nTrials'], hdr)
len(hdr['orig']['epochLabels'])
#epochLabels = hdr['orig']['epochLabels']
#[epochLabel.encode('utf-8') for epochLabel in epochLabels]
SegStatus = hdr['orig']['epochSegStatus']
GoodSeg = []
epochLabels = []
for i in range(len(SegStatus)):
if SegStatus[i] == 'good' :
GoodSeg.append(i)
epochLabels.append(trialsName[i])
#GoodSeg = np.array(GoodSeg, 'i4')
data = data[:,:,GoodSeg]
nTrials = len(GoodSeg)
filePath = '/Users/jesong1126/Work/Data/VGT/VGT_105_fil_segA_mff_32_100_32_tp_bcr_blc.mff'
hdr = read_mff_header.read_mff_header(filePath)
nC = hdr['nChans']
nE = nC - 1
nSamples = hdr['nSamples']
nSamplesPre = hdr['nSamplesPre']
nTrials = hdr['nTrials']
srate = hdr['Fs']
summaryInfo = hdr['orig']
trialsName = summaryInfo['epochLabels']
categoryName = list(set(trialsName))
nCategory = len(categoryName)
data = read_mff_data.read_mff_data(filePath, 'epoch', 1, hdr['nTrials'], hdr)
len(hdr['orig']['epochLabels'])
#epochLabels = hdr['orig']['epochLabels']
#[epochLabel.encode('utf-8') for epochLabel in epochLabels]
SegStatus = hdr['orig']['epochSegStatus']
GoodSeg = []
epochLabels = []
for i in range(len(SegStatus)):
if SegStatus[i] == 'good':
GoodSeg.append(i)
epochLabels.append(trialsName[i])
#GoodSeg = np.array(GoodSeg, 'i4')
data = data[:nE, :, GoodSeg]
nTrials = len(GoodSeg)
###----------------------------------------------------------------------------- # aaa = getSignalNbin(filePath, 'signal1.bin') # getEpochInfos(filePath, 500) # SignalBlocks = getSignalBlocks(filePath) # summaryInfo = mff_getSummaryInfo(filePath) # filePath = '/Users/jesong1126/Python27/Data/mffdata_test/MEP_107_LT.mff' hdr = read_mff_header(filePath) hdr.keys() summaryInfo = hdr['orig'] #event1 = read_mff_event(filePath, hdr) hdr['nTrials'] dataEpoch = read_mff_data(filePath, 'epoch', 1, hdr['nTrials'], hdr) print(dataEpoch.shape) ts = (np.arange(0, hdr['nSamples']) - hdr['nSamplesPre']) * (1000 / hdr['Fs']) plt.figure(1) plt.plot(ts, dataEpoch[:,:,0].T, ':') plt.xlim((ts[0], ts[-1])) hdr['nSamples'] startInd = 4001; lastInd = 40000 ; dataSample = read_mff_data(filePath, 'sample', startInd, lastInd, hdr) print dataSample.shape ts = (np.arange(0, hdr['nSamples']) - hdr['nSamplesPre']) * (1000 / hdr['Fs'])
def pbMFF_Callback(self):
global s, nC, nSamples, nSamplesPre, nTrials, srate, trialsName
global categoryName, nCategory
import config
from mff import read_mff_header, read_mff_data
import numpy as np
options = QtGui.QFileDialog.Options()
mfffilenames = QtGui.QFileDialog.getOpenFileNames(self.pbMFF, "Select a mff file", "", "mff files (*.mff)", options= options)
filePath = mfffilenames[0] #[0]
print('%s' %filePath)
hdr = read_mff_header.read_mff_header(filePath)
nC = hdr['nChans'] ; config.nC = nC
nSamples = hdr['nSamples'] ; config.nSamples = nSamples
nSamplesPre = hdr['nSamplesPre'] ; config.nSamplesPre = nSamplesPre
nTrials = hdr['nTrials'] ; config.nTrials = nTrials
srate = hdr['Fs'] ; config.srate = srate
baseline = (nSamplesPre * 1000 / srate) ; config.baseline = baseline
msSamples = np.arange(0, nSamples,1) * 1000/srate - baseline
config.msSamples = msSamples
summaryInfo = hdr['orig'] ; # config.nC = nC
trialsName = summaryInfo['epochLabels'] ; config.trialsName = trialsName
categoryName = list(set(trialsName)) ; config.categoryName = categoryName
nCategory = len(categoryName) ; config.nCategory = nCategory
for i in range(nCategory):
self.cbOneTCat.addItem(categoryName[i])
self.cbTwoTCat1.addItem(categoryName[i])
self.cbTwoTCat2.addItem(categoryName[i])
self.cbPairTCat1.addItem(categoryName[i])
self.cbPairTCat2.addItem(categoryName[i])
data = read_mff_data.read_mff_data(filePath, 'epoch', 1, hdr['nTrials'], hdr)
SegStatus = hdr['orig']['epochSegStatus']
GoodSeg = []; BadSeg = []; #epochLabels = [];
for i in range(len(SegStatus)):
if SegStatus[i] == 'bad' :
BadSeg.append(i)
else :
GoodSeg.append(i)
nTrials = len(GoodSeg)
## average reference
H = np.identity(nC) - np.ones((nC, nC))/nC
s = np.zeros(data.shape)
if nTrials > 1 :
for i in range(nTrials):
s[:,:,i] = np.dot(H, data[:,:, GoodSeg[i]])
else :
s = np.dot(H, data)
whichCat = np.zeros((nTrials, nCategory))
for j in range(nCategory):
for i in range(nTrials):
if (trialsName[i] == categoryName[j]):
whichCat[i,j] = 1
whichCat = np.array(whichCat, dtype='i')
config.s = s
config.whichCat = whichCat
config.categoryName = categoryName
print('nC:%d' %nC)
print('nSamples:%d' %nSamples)
print('nTrials:%d' %nTrials)
print('nCategory:%d' %nCategory)
for i in range(nCategory):
print('categoryName:%s' %categoryName[i])
print("EEG data is loaded.")
return s
def pbMFF_Callback(self):
global s, nC, nSamples, nSamplesPre, nTrials, srate, trialsName
global categoryName, nCategory
import config
from mff import read_mff_header, read_mff_data
import numpy as np
options = QtGui.QFileDialog.Options()
mfffilenames = QtGui.QFileDialog.getOpenFileNames(self.pbMFF,
"Select a mff file",
"",
"mff files (*.mff)",
options=options)
filePath = mfffilenames[0] #[0]
print('%s' % filePath)
hdr = read_mff_header.read_mff_header(filePath)
nC = hdr['nChans']
config.nC = nC
nSamples = hdr['nSamples']
config.nSamples = nSamples
nSamplesPre = hdr['nSamplesPre']
config.nSamplesPre = nSamplesPre
nTrials = hdr['nTrials']
config.nTrials = nTrials
srate = hdr['Fs']
config.srate = srate
baseline = (nSamplesPre * 1000 / srate)
config.baseline = baseline
msSamples = np.arange(0, nSamples, 1) * 1000 / srate - baseline
config.msSamples = msSamples
summaryInfo = hdr['orig']
# config.nC = nC
trialsName = summaryInfo['epochLabels']
config.trialsName = trialsName
categoryName = list(set(trialsName))
config.categoryName = categoryName
nCategory = len(categoryName)
config.nCategory = nCategory
for i in range(nCategory):
self.cbOneTCat.addItem(categoryName[i])
self.cbTwoTCat1.addItem(categoryName[i])
self.cbTwoTCat2.addItem(categoryName[i])
self.cbPairTCat1.addItem(categoryName[i])
self.cbPairTCat2.addItem(categoryName[i])
data = read_mff_data.read_mff_data(filePath, 'epoch', 1,
hdr['nTrials'], hdr)
SegStatus = hdr['orig']['epochSegStatus']
GoodSeg = []
BadSeg = []
#epochLabels = [];
for i in range(len(SegStatus)):
if SegStatus[i] == 'bad':
BadSeg.append(i)
else:
GoodSeg.append(i)
nTrials = len(GoodSeg)
## average reference
H = np.identity(nC) - np.ones((nC, nC)) / nC
s = np.zeros(data.shape)
if nTrials > 1:
for i in range(nTrials):
s[:, :, i] = np.dot(H, data[:, :, GoodSeg[i]])
else:
s = np.dot(H, data)
whichCat = np.zeros((nTrials, nCategory))
for j in range(nCategory):
for i in range(nTrials):
if (trialsName[i] == categoryName[j]):
whichCat[i, j] = 1
whichCat = np.array(whichCat, dtype='i')
config.s = s
config.whichCat = whichCat
config.categoryName = categoryName
print('nC:%d' % nC)
print('nSamples:%d' % nSamples)
print('nTrials:%d' % nTrials)
print('nCategory:%d' % nCategory)
for i in range(nCategory):
print('categoryName:%s' % categoryName[i])
print("EEG data is loaded.")
return s
def pbEEG_Callback(self):
global s, nC, nSamples, nSamplesPre, nTrials, srate, trialsName
global categoryName, nCategory
import config
from mff import read_mff_header, read_mff_data
import matplotlib.pyplot as plt
import numpy as np
options = QtWidgets.QFileDialog.Options()
mfffilename, _ = QtWidgets.QFileDialog.getOpenFileNames(self.pbEEG, "Select a mff file", "", "mff files (*.mff)", options= options)
filePath = mfffilename[0]
hdr = read_mff_header.read_mff_header(filePath)
nC = hdr['nChans'] ; config.nC = nC
nSamples = hdr['nSamples'] ; config.nSamples = nSamples
nSamplesPre = hdr['nSamplesPre'] ; config.nSamplesPre = nSamplesPre
nTrials = hdr['nTrials'] ; config.nTrials = nTrials
srate = hdr['Fs'] ; config.srate = srate
summaryInfo = hdr['orig'] ; # config.nC = nC
trialsName = summaryInfo['epochLabels'] ; config.trialsName = trialsName
categoryName = list(set(trialsName)) ; config.categoryName = categoryName
nCategory = len(categoryName) ; config.nCategory = nCategory
for i in range(nTrials):
self.cbTrial.addItem(trialsName[i])
data = read_mff_data.read_mff_data(filePath, 'epoch', 1, hdr['nTrials'], hdr)
SegStatus = hdr['orig']['epochSegStatus']
GoodSeg = []; BadSeg = []; #epochLabels = [];
for i in range(len(SegStatus)):
if SegStatus[i] == 'bad' :
BadSeg.append(i)
else :
GoodSeg.append(i)
nTrials = len(GoodSeg)
print('nC:%d' %nC)
print('nSamples:%d' %nSamples)
print('nTrials:%d' %nTrials)
print('nCategory:%d' %nCategory)
print('categoryName:%s' %categoryName)
# print('categoryName:%s' %trialsName)
## average reference
H = np.identity(nC) - np.ones((nC, nC))/nC
s = np.zeros((nC, nSamples, nTrials))
sGFP = np.zeros((nSamples, nTrials))
if nTrials > 1:
for i in range(nTrials):
s[:,:,i] = np.dot(H, data[:,:,GoodSeg[i]])
sGFP[:,i] = np.std(s[:,:,i], axis=0)
# s2 = s[:,:,i] * s[:,:,i]
# gfp = s2.mean(axis=0)
# sGFP = np.sqrt(gfp)
else :
s = np.dot(H, data)
sGFP = np.std(s, axis=0)
# s2 = s * s
# gfp = s2.mean(axis=0)
# sGFP = np.sqrt(gfp)
baseline = (nSamplesPre * 1000 / srate) ; config.baseline = baseline
msSamples = np.arange(0, nSamples,1) * 1000/srate - baseline
config.msSamples = msSamples
# global f1
if nTrials == nCategory :
# f1 = plt.figure
plt.plot(msSamples, sGFP)
plt.title('gfp')
plt.xlabel('ms')
plt.legend(trialsName, loc='best')
plt.show()
print('EEG is ERP.')
#eventsName = trialsName
else :
# f1 = plt.figure
plt.plot(msSamples, sGFP)
plt.title('gfp')
plt.xlabel('ms')
plt.show()
print('EEG is Single trial. There are',nTrials,'trials. ')
#eventsName = categoryName
config.nSamples = nSamples
config.msSamples = msSamples
config.nTrials = nTrials
config.trialsName = trialsName
config.s = s
config.sGFP = sGFP
print("EEG data is loaded.")
# self.pbEEG.configure(background = "red")
return s
###----------------------------------------------------------------------------- # aaa = getSignalNbin(filePath, 'signal1.bin') # getEpochInfos(filePath, 500) # SignalBlocks = getSignalBlocks(filePath) # summaryInfo = mff_getSummaryInfo(filePath) # filePath = '/Users/jesong1126/Python27/Data/mffdata_test/MEP_107_LT.mff' hdr = read_mff_header(filePath) hdr.keys() summaryInfo = hdr['orig'] #event1 = read_mff_event(filePath, hdr) hdr['nTrials'] dataEpoch = read_mff_data(filePath, 'epoch', 1, hdr['nTrials'], hdr) print(dataEpoch.shape) ts = (np.arange(0, hdr['nSamples']) - hdr['nSamplesPre']) * (1000 / hdr['Fs']) plt.figure(1) plt.plot(ts, dataEpoch[:, :, 0].T, ':') plt.xlim((ts[0], ts[-1])) hdr['nSamples'] startInd = 4001 lastInd = 40000 dataSample = read_mff_data(filePath, 'sample', startInd, lastInd, hdr) print dataSample.shape ts = (np.arange(0, hdr['nSamples']) - hdr['nSamplesPre']) * (1000 / hdr['Fs'])
def pbEEG_Callback(self):
global s, nC, nSamples, nSamplesPre, nTrials, srate, trialsName
global categoryName, nCategory
import config
from mff import read_mff_header, read_mff_data
import matplotlib.pyplot as plt
import numpy as np
options = QtWidgets.QFileDialog.Options()
mfffilename, _ = QtWidgets.QFileDialog.getOpenFileNames(
self.pbEEG,
"Select a mff file",
"",
"mff files (*.mff)",
options=options)
filePath = mfffilename[0]
hdr = read_mff_header.read_mff_header(filePath)
nC = hdr['nChans']
config.nC = nC
nSamples = hdr['nSamples']
config.nSamples = nSamples
nSamplesPre = hdr['nSamplesPre']
config.nSamplesPre = nSamplesPre
nTrials = hdr['nTrials']
config.nTrials = nTrials
srate = hdr['Fs']
config.srate = srate
summaryInfo = hdr['orig']
# config.nC = nC
trialsName = summaryInfo['epochLabels']
config.trialsName = trialsName
categoryName = list(set(trialsName))
config.categoryName = categoryName
nCategory = len(categoryName)
config.nCategory = nCategory
for i in range(nTrials):
self.cbTrial.addItem(trialsName[i])
data = read_mff_data.read_mff_data(filePath, 'epoch', 1,
hdr['nTrials'], hdr)
SegStatus = hdr['orig']['epochSegStatus']
GoodSeg = []
BadSeg = []
#epochLabels = [];
for i in range(len(SegStatus)):
if SegStatus[i] == 'bad':
BadSeg.append(i)
else:
GoodSeg.append(i)
nTrials = len(GoodSeg)
print('nC:%d' % nC)
print('nSamples:%d' % nSamples)
print('nTrials:%d' % nTrials)
print('nCategory:%d' % nCategory)
print('categoryName:%s' % categoryName)
# print('categoryName:%s' %trialsName)
## average reference
H = np.identity(nC) - np.ones((nC, nC)) / nC
s = np.zeros((nC, nSamples, nTrials))
sGFP = np.zeros((nSamples, nTrials))
if nTrials > 1:
for i in range(nTrials):
s[:, :, i] = np.dot(H, data[:, :, GoodSeg[i]])
sGFP[:, i] = np.std(s[:, :, i], axis=0)
# s2 = s[:,:,i] * s[:,:,i]
# gfp = s2.mean(axis=0)
# sGFP = np.sqrt(gfp)
else:
s = np.dot(H, data)
sGFP = np.std(s, axis=0)
# s2 = s * s
# gfp = s2.mean(axis=0)
# sGFP = np.sqrt(gfp)
baseline = (nSamplesPre * 1000 / srate)
config.baseline = baseline
msSamples = np.arange(0, nSamples, 1) * 1000 / srate - baseline
config.msSamples = msSamples
# global f1
if nTrials == nCategory:
# f1 = plt.figure
plt.plot(msSamples, sGFP)
plt.title('gfp')
plt.xlabel('ms')
plt.legend(trialsName, loc='best')
plt.show()
print('EEG is ERP.')
#eventsName = trialsName
else:
# f1 = plt.figure
plt.plot(msSamples, sGFP)
plt.title('gfp')
plt.xlabel('ms')
plt.show()
print('EEG is Single trial. There are', nTrials, 'trials. ')
#eventsName = categoryName
config.nSamples = nSamples
config.msSamples = msSamples
config.nTrials = nTrials
config.trialsName = trialsName
config.s = s
config.sGFP = sGFP
print("EEG data is loaded.")
# self.pbEEG.configure(background = "red")
return s