if x > xeff: return None
xsmear = gRandom.Gaus(-2.5, 0.2)
# bias and smear
return xt + xsmear
# ==============================================================================
# Example Unfolding
# ==============================================================================
print "==================================== TRAIN ===================================="
response = RooUnfoldResponse(40, -10.0, 10.0)
# Train with a Breit-Wigner, mean 0.3 and width 2.5.
for i in xrange(100000):
xt = gRandom.BreitWigner(0.3, 2.5)
x = smear(xt)
if x != None:
response.Fill(x, xt)
else:
response.Miss(xt)
print "==================================== TEST ====================================="
hTrue = TH1D("true", "Test Truth", 40, -10.0, 10.0)
hMeas = TH1D("meas", "Test Measured", 40, -10.0, 10.0)
# Test with a Gaussian, mean 0 and width 2.
for i in xrange(10000):
xt = gRandom.Gaus(0.0, 2.0)
x = smear(xt)
hTrue.Fill(xt)
if x != None: hMeas.Fill(x)
def main(optunf="Bayes"):
optunfs = ["Bayes", "SVD", "TUnfold", "Invert", "Reverse"]
if not optunf in optunfs:
txt = "Unfolding option " + optunf + " not recognised"
raise ValueError(txt)
global hReco, hMeas, hTrue
print "==================================== TRAIN ===================================="
# Create response matrix object for 40 measured and 20
# unfolded bins:
response = RooUnfoldResponse(40, -10.0, 10.0, 20, -10.0, 10.0)
# Train with a Breit-Wigner, mean 0.3 and width 2.5.
for i in xrange(100000):
# xt= gRandom.BreitWigner( 0.3, 2.5 )
xt = gRandom.Gaus(0.0, 5.0)
x = smear(xt)
if x != None:
response.Fill(x, xt)
else:
response.Miss(xt)
print "==================================== TEST ====================================="
hTrue = TH1D("true", "Test Truth", 20, -10.0, 10.0)
hMeas = TH1D("meas", "Test Measured", 40, -10.0, 10.0)
# Test with a Gaussian, mean 0 and width 2.
for i in xrange(10000):
# xt= gRandom.Gaus( 0.0, 2.0 )
xt = gRandom.BreitWigner(0.3, 2.5)
x = smear(xt)
hTrue.Fill(xt)
if x != None:
hMeas.Fill(x)
print "==================================== UNFOLD ==================================="
print "Unfolding method:", optunf
if "Bayes" in optunf:
# Bayes unfoldung with 4 iterations
# unfold= RooUnfoldBayes( response, hMeas, 4 )
unfold = RooUnfoldBayes(response, hMeas, 10, False, True)
elif "SVD" in optunf:
# SVD unfoding with free regularisation
# unfold= RooUnfoldSvd( response, hMeas, 20 )
unfold = RooUnfoldSvd(response, hMeas)
elif "TUnfold" in optunf:
# TUnfold with fixed regularisation tau=0.002
# unfold= RooUnfoldTUnfold( response, hMeas )
unfold = RooUnfoldTUnfold(response, hMeas, 0.002)
elif "Invert" in optunf:
unfold = RooUnfoldInvert(response, hMeas)
elif "Reverse" in optunf:
unfold = RooUnfoldBayes(response, hMeas, 1)
hReco = unfold.Hreco()
# unfold.PrintTable( cout, hTrue )
unfold.PrintTable(cout, hTrue, 2)
hReco.Draw()
hMeas.Draw("SAME")
hTrue.SetLineColor(8)
hTrue.Draw("SAME")
return