def train(self, measurement, loufl=False):
print "============================ TRAIN ============================="
txt = "Smear mu, s.d.: " + str(measurement.getMean())
txt += ", " + str(measurement.getSigma()) + ", eff.: "
txt += str(measurement.getLeff())
txt += ", o/u-flow: " + str(loufl) + ", function: " + self.opttfun
print txt
# Create response matrix object:
response = RooUnfoldResponse(
self.bininfo["mbins"], self.bininfo["mlo"], self.bininfo["mhi"],
self.bininfo["tbins"] * self.bininfo["nrebin"],
self.bininfo["tlo"], self.bininfo["thi"])
response.UseOverflow(loufl)
for i in xrange(100000):
xt, x = measurement.generate(self.trainfun)
if x != None:
response.Fill(x, xt)
else:
response.Miss(xt)
return response
class unfoldHandle(object):
def __init__(self, genHist, recHist, hist2d, name):
self.genHist = genHist.Clone()
self.recHist = recHist.Clone()
self.hist2d = hist2d.Clone()
self.response = RooUnfoldResponse(recHist, genHist, hist2d,
"response matrix", "response matrix")
self.response.UseOverflow()
self.name = name
hx = self.hist2d.ProjectionX()
hy = self.hist2d.ProjectionY()
vaild = 0.0001
test1 = 0
test2 = 0
for i in range(hx.GetNbinsX() + 2):
if recHist.GetBinContent(i) != 0:
val = abs(hx.GetBinContent(i) / recHist.GetBinContent(i) - 1)
else:
val = 0
if val > test1:
test1 = val
for i in range(hx.GetNbinsX() + 2):
if genHist.GetBinContent(i) != 0:
val = abs(hy.GetBinContent(i) / genHist.GetBinContent(i) - 1)
else:
val = 0
if val > test2:
test2 = val
if test1 < vaild and test2 < vaild:
print "response matrix is correct"
else:
print "response matrix is wrong"
print test1
print test2
def getConditionN(self):
m = self.response.Mresponse()
SVD = ROOT.TDecompSVD(m)
return SVD.Condition()
def doUnfold(self, data, back=0, regdiff=0, iteration=0):
self.data = data.Clone()
if back != 0:
self.data.Sumw2()
back.Sumw2()
self.data.Add(back, -1.)
self.back = back.Clone()
for i in range(self.data.GetNbinsX() + 2):
if self.data.GetBinContent(i) < 0:
self.data.SetBinContent(i, 0)
if regdiff != 0:
Unfold = RooUnfoldBayes(self.response, self.data, 1, False,
self.name + "_reg", self.name + "_reg")
h2 = self.data.Clone()
for i in range(9999):
h1 = Unfold.Hreco().Clone()
vaild = 0
val = 0
for j in range(1, h2.GetNbinsX() + 1):
if h2.GetBinContent(j) > 0:
val = h1.GetBinContent(j) / h2.GetBinContent(j)
val = abs(val - 1)
if vaild < val:
vaild = val
print vaild
if vaild < regdiff:
print "%d times iteration" % i
self.UnfoldedData = h1
return h1
else:
h2 = h1.Clone()
Unfold.Reset()
Unfold = RooUnfoldBayes(self.response, self.data, i + 2,
False, self.name + "_reg",
self.name + "_reg")
elif iteration != 0:
s = "iter_%d" % iteration
Unfold = RooUnfoldBayes(self.response, self.data, iteration, False,
self.name + s, self.name + s)
h1 = Unfold.Hreco().Clone()
return h1
else:
Unfold = RooUnfoldInvert(self.response, self.data,
self.name + "_invert",
self.name + "_invert")
Unfold.SetMeasured(self.data)
self.Unfold = Unfold
h1 = Unfold.Hreco().Clone()
self.UnfoldedData = h1
return h1
