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
#files = os.path.split(fname)[1]
if files == []:
raise NameError('No .nirs file was loaded.')
# Read all the .nirs an d extract the useful info out of them inside a hmr2 class
pretime = 2
posttime = 10
data = []
MITC_ = np.array([0.5])#np.arange(0.45,0.55,0.02)
for file_num in range(np.size(files)):
# Read the resting state data and compute the concentration matrix
print files[file_num]
data_aux = sio.loadmat(files[file_num],appendmat = False)
data_aux = nirs(data_aux)
# Channels where the actvation pattern was seen
# data_aux.ActivChan = np.array([0,1,4,5,7,38])
data_aux.stimDur = 8
data_aux.CalcOD()
data_aux.BPFilter([0.005,1.4])
dod = (data_aux.dod[:,0:50]+data_aux.dod[:,50::])/2
del data_aux.dod
data_aux.dod = dod
# data_aux.PruneChan(0,2.5,[0])
# data_aux.Normalize(chromo = 'Phantom')
# Compute the block average HRF for each channel
