def processBrainAreasBOLDForEpsilon(brainAreaSignal, areas):
global neuro_act
simulateActivitySubject(C, N, we)
epsilons = np.arange(0,2.1,0.1)
results = [] #np.zeros([len(epsilons), len(areas)])
minEpsilon=0
minValue=1e99
for epsilon in epsilons:
print("Epsilon:", epsilon, end=' ')
BOLDHemModel_Stephan2008.epsilon = epsilon
bds = simulateFCD.computeSubjectBOLD(neuro_act, N, areasToSimulate=areas)
bdsT = bds.T
#for area, areaID in enumerate(areas):
norm_bdsT = normalizeSignal(brainAreaSignal, bdsT)
#print("sim: ", np.average(norm_bdsT), "+/-", np.sqrt(np.var(norm_bdsT)))
#print("fMRFI:", np.average(signal), "+/-", np.sqrt(np.var(signal)))
error = errorMetrics.l2(brainAreaSignal, norm_bdsT)
if error < minValue:
minEpsilon = epsilon
minValue = error
results.append([error])
print("Error:", error)
print("Minimum:", minValue, "at:", minEpsilon,)
plt.plot(epsilons, results, 'r-')
plt.show()
BOLDHemModel_Stephan2008.epsilon = minEpsilon
bds = simulateFCD.computeSubjectBOLD(neuro_act, N, areasToSimulate=[0])
bdsT = bds.T
norm_bdsT = normalizeSignal(brainAreaSignal, bdsT)
interval=range(len(brainAreaSignal))
plt.plot(interval,brainAreaSignal.flatten(), 'b-', interval,norm_bdsT.flatten(), 'r-')
plt.suptitle('Area:'+str(areas[0]))
plt.show()
def testSingleSubjectMultipleTimes(signal, times):
print("Testing single subject, multiple {} times...".format(times))
results = []
for t in range(times):
neuro_act = simulateFCD.computeSubjectSimulation(C, N)
bds = simulateFCD.computeSubjectBOLD(neuro_act)
bdsT = bds.T
sim_filt = BOLDFilters.filterBrainArea(bdsT, 0)
sim_filt /= np.std(sim_filt)
error = errorMetrics.l2(signal, sim_filt)
print("Trial: {} ({}) of {}".format(t+1, t, times), "Error:", error)
results.append([error])
avg = np.average(results)
std = np.std(results)
print("Average:", avg, "std:", std)
# the histogram of the data
n, bins, patches = plt.hist(results/avg*10, bins=10, facecolor='g', alpha=0.75)
plt.xlabel('error')
plt.ylabel('Probability')
plt.title('Histogram of errors')
plt.text(60, .025, '$\mu$={}, $\sigma$={}'.format(avg, std))
#plt.xlim(40, 160)
#plt.ylim(0, 0.03)
#plt.grid(True)
plt.show()
def simBrainAreaForOptimVars(N, area, keysAndValues):
global neuro_act
simulateActivitySubject(C, N, we)
for key, value in keysAndValues.items():
exec('BOLDHemModel2.'+key+' = '+str(value))
bds = simulateFCD.computeSubjectBOLD(neuro_act, areasToSimulate=[area])
bdsT = bds.T
sim_filt = BOLDFilters.filterBrainArea(bdsT, 0)
sim_filt /= np.std(sim_filt)
return sim_filt
def pltSubjectSimulatedBOLD(BOLDSignal):
global neuro_act
simulateActivitySubject(C, N, we)
bds = simulateFCD.computeSubjectBOLD(neuro_act, N)
bdsT = bds.T
norm_bdsT = normalizeSignal(BOLDSignal, bdsT)
interval=range(Tmax)
for area in range(N):
plt.plot(interval,norm_bdsT[area].flatten())
plt.suptitle('BOLD simulation for single subject')
plt.show()
def errorFunc(neuro_act, epsilon, alpha, tau, gamma, kappa):
if Verbose:
global evalCounter
evalCounter += 1
print("Test:", evalCounter)
BOLDModel.epsilon = epsilon
BOLDModel.alpha = alpha
BOLDModel.tau = tau
BOLDModel.gamma = gamma
BOLDModel.kappa = kappa
bds = simulateFCD.computeSubjectBOLD(neuro_act, areasToSimulate=[area])
bdsT = bds.T
sim_filt = BOLDFilters.filterBrainArea(bdsT, 0)
sim_filt /= np.std(sim_filt)
return sim_filt
def pltSubjectBOLDForEpsilon(signal):
global neuro_act
simulateActivitySubject(C, N, we)
results = []
epsilons = np.arange(0,2.1,0.2)
for epsilon in epsilons:
print("Epsilon:", epsilon, end=' ')
BOLDHemModel_Stephan2008.epsilon = epsilon
bds = simulateFCD.computeSubjectBOLD(neuro_act, N)
bdsT = bds.T
norm_bdsT = normalizeSignal(signal, bdsT)
#print("sim: ", np.average(norm_bdsT), "+/-", np.sqrt(np.var(norm_bdsT)))
#print("fMRFI:", np.average(signal), "+/-", np.sqrt(np.var(signal)))
error = errorMetrics.l2(signal, norm_bdsT)
results = np.append(results, [error])
print("Error:", error)
plt.plot(epsilons, results, 'r-')
plt.show()
def computeValues(
NumSimSubjects,
SCnorm, # fullBOLDSeries,
processedBOLDemp,
distanceSettings,
parmPos,
value): # analyzeSignals = False):
print(
f"========== computeValues for ad[{parmPos}]={value} (with {NumSimSubjects} subjects)"
)
N = SCnorm.shape[0]
neuronalModel.ad = np.ones(N)
neuronalModel.ad[parmPos] = value
integrator.recompileSignatures()
print(" --- BEGIN TIME ---")
simulatedBOLDs = {}
start_time = time.clock()
for nsub in range(NumSimSubjects): # trials. Originally it was 20.
print(" Simulating subject {}/{}!!!".format(nsub, NumSimSubjects))
# bds = simulateFCD.simulateSingleSubject(AvgHC, warmup=False).T
# --- Simple test to check the max rate at the nodes during simulation... ---
neuro_act = simulateFCD.computeSubjectSimulation(SCnorm,
N,
warmup=False)
bds = simulateFCD.computeSubjectBOLD(neuro_act)
# if analyzeSignals: analyzeSignals(neuro_act.T, bds.T, fullBOLDSeries)
simulatedBOLDs[nsub] = bds
simMeasures = processBOLDSignals(simulatedBOLDs, distanceSettings)
print(" --- TOTAL TIME: {} seconds ---".format(time.clock() -
start_time))
# ---- and now compute the final FC, FCD, ... distances !!! ----
fitting = {}
for ds in distanceSettings:
fitting[ds] = distanceSettings[ds][0].distance(simMeasures[ds],
processedBOLDemp[ds])
return fitting
def processSubjectForEpsilon(signal):
global neuro_act
if doIndividualSim: # each subject goes with his/her own sim...
simulateActivitySubject(C, N, we)
results = []
epsilons = np.arange(0,1.5,0.1)
for epsilon in epsilons:
print("Epsilon:", epsilon, end=' ')
BOLDHemModel_Stephan2008.epsilon = epsilon
bds = simulateFCD.computeSubjectBOLD(neuro_act)
bdsT = bds.T
norm_bdsT = normalizeSignal(signal, bdsT)
#print("sim: ", np.average(norm_bdsT), "+/-", np.sqrt(np.var(norm_bdsT)))
#print("fMRFI:", np.average(signal), "+/-", np.sqrt(np.var(signal)))
error = errorMetrics.l2(signal, norm_bdsT)
np.append(results, [error])
print("Error:", error)
plt.plot(epsilons, results, 'r-')
plt.show()