def simulation_onedata(num_activ,fname,TimeShift,WidthShift,IntensityRange,mitc_,outputdir,minic=2,plotall = True):
# ActivChan = range(int(num_activ))
# Read the resting state data and compute the concentration matrix
data = sio.loadmat(fname,appendmat = False)
data = nirs(data)
data.CalcOD()
data.MBLL(6)
data = nirs.PruneChan(data,8)
# Pick (num_activ) random channels to simulate the activation
num_activ = int(num_activ * len(data.Chan)/100)
data.ActivChan = random.sample(data.usefulChan,num_activ)
# Add signals to the concentration matrix
data = GenerateSignals(data,data.ActivChan,[5,20],TimeShift,WidthShift,IntensityRange)
temp = deepcopy(data)
data.conc[:,:,0] = BPFilter(data.conc[:,:,0],data,[0.005,1.4])
# data = nirs.Normalize(data)
# Compute the block average HRF for each channel
temp.conc[:,:,0] = BPFilter(temp.conc[:,:,0],data,[0.005,0.5])
temp = HRFAvg(temp,5,20)
data.HRF = temp.HRF
data.tHRF = temp.tHRF
# Compute the mean block average HRF for the activated channels
data.mHRF = np.mean(data.HRF[:,data.ActivChan,:],1)
#Now for the ICA procedure int(0.1*np.size(data.Chan))
data.ICA = icastream(data,minic,'Sim',[0.7/20, 1.3/20, 1.4],[5,20],[2.5,mitc_],\
outputdir,plotall = False,dump = False,plotResults = False)
# with open(outputdir+'/icastream.dump', 'wb') as output:
# cPickle.dump(data, output, cPickle.HIGHEST_PROTOCOL)
# try:
# ManuscriptPlotSim(data,dirname = outputdir)
# except:
# print 'Deu ruim no plot final'
if hasattr(data,'A_'):
if plotall == True:
PlotICSpatial(data,data.ICA,dirname=outputdir)
return data.ICA
nchan = len(data_aux.Chan)
del temp
#Now for the ICA procedure
for mitcidx in MITC_:
criteria = [2.5, mitcidx]
outputdir = direc+'/Results/MITC_'+str(mitcidx)+'/'+\
os.path.split(files[file_num])[1].split('.')[0]
if not os.path.exists(outputdir):
os.makedirs(outputdir)
for chromo in ['Phantom']:
print chromo
worked = 0
try:#int(max(3,0.15*nchan))
setattr(data_aux,'ICA',icastream(data_aux,2,chromo,\
[0.7/posttime,1.3/posttime,1.4], [pretime,posttime],criteria, outputdir,plotall = True,dump=True,plotResults=False))
worked = 1
except:
print 'ERROR: icastream failed. This is likely due to minic being larger than the number of channels.'
# PlotICSpatial(data_aux,getattr(data_aux,chromo),dirname=outputdir)
if worked == 1:
if not os.path.exists(direc+'/Results/MITC_'+str(mitcidx)+'/Dump') :
os.makedirs(direc+'/Results/MITC_'+str(mitcidx)+'/Dump')
with open(direc+'/Results/MITC_'+str(mitcidx)+'/Dump/'+chromo+'_'+os.path.split(files[file_num])[1].split('.')[0]+'.dump', 'wb') as output:
cPickle.dump(getattr(data_aux,'ICA'), output, cPickle.HIGHEST_PROTOCOL)
ManuscriptPlotSim(data_aux,dirname=direc+'/Results/MITC_'+str(mitcidx)+\
'/'+os.path.split(files[file_num])[1].split('.')[0],res=0.1)
## Collect all data together
RealDataStats(direc,MITC_)
# Compute the mean block average HRF for the activated channels
if hasattr(data_aux,'ActivChan'):
data_aux.mHRF = np.mean(temp.HRF[:,data_aux.ActivChan,:],1)
else:
if hasattr(data_aux,'usefulChan'):
try:
data_aux.mHRF = np.mean(temp.HRF[:,data_aux.usefulChan,:],1)
except:
data_aux.mHRF = np.mean(temp.HRF[:,:,:],1)
else:
data_aux.mHRF = np.mean(temp.HRF[:,:,:],1)
del temp
#Now for the ICA procedure
for MinIC in [4,5,6,7,12]:
outputdir = direc+'/Results/'+\
os.path.split(files[file_num])[1].split('.')[0]+'/'+str(MinIC)
if not os.path.exists(outputdir):
os.makedirs(outputdir)
# def icastream(data,minic,chromo,rfracRng,blockAvgTime,dirname):
# try:
for chromo in ('HbO','HbR','HbT'):
print chromo
setattr(data_aux,chromo,icastream(data_aux,MinIC,chromo,\
[0.7/posttime,1.3/posttime,1.4], [pretime,posttime], outputdir,plotall = False))
PlotICSpatial(data_aux,getattr(data_aux,chromo),dirname=outputdir)
#Store everything back to the data list
data.append(data_aux)
nchan = len(data_aux.Chan)
del temp
#Now for the ICA procedure
for mitcidx in MITC_:
criteria = [3.5, mitcidx]
outputdir = direc+'/Results/MITC_'+str(mitcidx)+'/'+\
os.path.split(files[file_num])[1].split('.')[0]
if not os.path.exists(outputdir):
os.makedirs(outputdir)
for chromo in ('HbO','HbR'):
print chromo
worked = 0
try:
setattr(data_aux,chromo,icastream(data_aux,int(max(5,0.15*nchan)),chromo,\
[0.7/posttime,1.3/posttime,1.4], [pretime,posttime],criteria, outputdir,plotall = True,dump=True))
worked = 1
except:
print 'ERROR: icastream failed. This is likely due to minic being larger than the number of channels.'
# PlotICSpatial(data_aux,getattr(data_aux,chromo),dirname=outputdir)
if worked == 1:
if not os.path.exists(direc+'/Results/MITC_'+str(mitcidx)+'/Dump') :
os.makedirs(direc+'/Results/MITC_'+str(mitcidx)+'/Dump')
with open(direc+'/Results/MITC_'+str(mitcidx)+'/Dump/'+chromo+'_'+os.path.split(files[file_num])[1].split('.')[0]+'.dump', 'wb') as output:
cPickle.dump(getattr(data_aux,chromo), output, cPickle.HIGHEST_PROTOCOL)
if data_aux.HbO.ICAFailed == False and data_aux.HbR.ICAFailed == False:
ManuscriptPlotVisual(data_aux,dirname=direc_root,\
fname = os.path.split(files[file_num])[1].split('.')[0],res=0.1)
## Collect all data together
RealDataStats(direc,MITC_)
else:
if hasattr(data_aux,'usefulChan'):
try:
data_aux.mHRF = np.mean(temp.HRF[:,data_aux.usefulChan,0],1)
except:
data_aux.mHRF = np.mean(temp.HRF[:,:,0],1)
else:
data_aux.mHRF = np.mean(temp.HRF[:,:,0],1)
del temp
#Now for the ICA procedure
for MinIC in [2,3,4,5,6]:
outputdir = direc+'/Results/'+\
os.path.split(files[file_num])[1].split('.')[0]+'/'+str(MinIC)+'/HbO'
if not os.path.exists(outputdir):
os.makedirs(outputdir)
# def icastream(data,minic,chromo,rfracRng,blockAvgTime,dirname):
try:
data_aux = icastream(data_aux,MinIC,0,[0.7/posttime,1.3/posttime,1.4], [pretime,posttime],\
outputdir)
except:
print 'ERROR: icastream failed. This is probably due to bad data or if MinIC is too large.'
try:
PlotICSpatial(data_aux,dirname=outputdir)
except:
print 'ERROR: PlotICSpatial has failed. Will resume the analysis for the next dataset.'
#Store everything back to the data list
data.append(data_aux)
