def addEntry(self, mass, title, evaluationRange, hSignal, hQCD, hEmbedded,
hEWKfake):
# Obtain event counts
mySignal = self._evaluate(evaluationRange, hSignal)
myQCD = self._evaluate(evaluationRange, hQCD)
myEmbedded = self._evaluate(evaluationRange, hEmbedded)
myEWKfake = self._evaluate(evaluationRange, hEWKfake)
# Produce output
myOutput = "%s, mass=%d, range=%d-%d\n" % (
title, mass, evaluationRange[0], evaluationRange[1])
myOutput += " signal: %f +- %f\n" % (mySignal.value(),
mySignal.uncertainty())
myOutput += " QCD: %f +- %f\n" % (myQCD.value(), myQCD.uncertainty())
myOutput += " EWKtau: %f +- %f\n" % (myEmbedded.value(),
myEmbedded.uncertainty())
myOutput += " EWKfake: %f +- %f\n" % (myEWKfake.value(),
myEWKfake.uncertainty())
myExpected = Count(0.0, 0.0)
myExpected.add(myQCD)
myExpected.add(myEmbedded)
myExpected.add(myEWKfake)
myOutput += " Total expected: %f +- %f\n" % (myExpected.value(),
myExpected.uncertainty())
mySignal.divide(myExpected)
myOutput += " signal/expected: %f +- %f\n" % (mySignal.value(),
mySignal.uncertainty())
myOutput += "\n"
self._output += myOutput
def getMinimumPurity(self):
'''
Return the minimum QCD purity as a Count object
'''
myMinPurity = 0.0
myMinPurityUncert = 0.0
nData = 0.0
nEwk = 0.0
# For-loop: All data
for i in range(0, len(self._dataList)):
# For-loop: All bins
for j in range(0, self._dataList[i].GetNbinsX() + 2):
nData += self._dataList[i].GetBinContent(j)
nEwk += self._ewkList[i].GetBinContent(j)
myPurity = 0.0
myUncert = 0.0
if (nData > 0.0):
# Assume binomial error
myPurity = (nData - nEwk) / nData
myUncert = sqrt(myPurity * (1.0 - myPurity) / nData)
if myPurity < myMinPurity:
myMinPurity = myPurity
myMinPurityUncert = myUncert
return Count(myMinPurity, myMinPurityUncert)
def _evaluate(self,evaluationRange,h):
myResult = 0.0
myError = 0.0
for i in range(1,h.GetNbinsX()+1):
if (h.GetXaxis().GetBinLowEdge(i) >= evaluationRange[0] and
h.GetXaxis().GetBinUpEdge(i) <= evaluationRange[1]):
myResult += h.GetBinContent(i)
myError += pow(h.GetBinError(i),2)
return Count(myResult,sqrt(myError))
def getIntegratedPurity(self):
nData = 0.0
nEwk = 0.0
for i in range(0, len(self._dataList)):
for j in range(0, self._dataList[i].GetNbinsX()+2):
nData += self._dataList[i].GetBinContent(j)
nEwk += self._ewkList[i].GetBinContent(j)
myPurity = 0.0
myUncert = 0.0
if (nData > 0.0):
myPurity = (nData - nEwk) / nData
myUncert = sqrt(myPurity * (1.0-myPurity) / nData) # Assume binomial error
return Count(myPurity, myUncert)
def getShapeByUnfoldedBin(self, unfoldedBinIndex, h):
self._initialize(h)
myResult = []
myMin = 0
myMax = h.GetNbinsX()
if h.GetNbinsX() != 1:
# Include under- and overflow bin
myMin -= 1
myMax += 1
for i in range(myMin, myMax):
myValue = h.GetBinContent(i + 1, unfoldedBinIndex + 1)
myUncertainty = h.GetBinError(i + 1, unfoldedBinIndex + 1)
myResult.append(Count(myValue, myUncertainty))
return myResult
def addEntry(self,mass,title,evaluationRange,hSignal,hQCD,hEmbedded,hEWKfake):
# Obtain event counts
mySignal = self._evaluate(evaluationRange,hSignal)
myQCD = self._evaluate(evaluationRange,hQCD)
myEmbedded = self._evaluate(evaluationRange,hEmbedded)
myEWKfake = self._evaluate(evaluationRange,hEWKfake)
# Produce output
myOutput = "%s, mass=%d, range=%d-%d\n"%(title,mass,evaluationRange[0],evaluationRange[1])
myOutput += " signal: %f +- %f\n"%(mySignal.value(),mySignal.uncertainty())
myOutput += " QCD: %f +- %f\n"%(myQCD.value(),myQCD.uncertainty())
myOutput += " EWKtau: %f +- %f\n"%(myEmbedded.value(),myEmbedded.uncertainty())
myOutput += " EWKfake: %f +- %f\n"%(myEWKfake.value(),myEWKfake.uncertainty())
myExpected = Count(0.0, 0.0)
myExpected.add(myQCD)
myExpected.add(myEmbedded)
myExpected.add(myEWKfake)
myOutput += " Total expected: %f +- %f\n"%(myExpected.value(),myExpected.uncertainty())
mySignal.divide(myExpected)
myOutput += " signal/expected: %f +- %f\n"%(mySignal.value(),mySignal.uncertainty())
myOutput += "\n"
self._output += myOutput
def getContractedShapeForBin(self, factorisationAxisToKeep,
factorisationBin, h):
self._initialize(h)
myResult = []
myMin = 0
myMax = h.GetNbinsX()
if h.GetNbinsX() != 1:
# Include under- and overflow bin
myMin -= 1
myMax += 1
for i in range(myMin, myMax):
myValue = self._contractionRecursionForBin([],
factorisationAxisToKeep,
factorisationBin, h, i)
myUncertainty = sqrt(
self._contractionRecursionUncertaintyForBin(
[], factorisationAxisToKeep, factorisationBin, h, i))
myResult.append(Count(myValue, myUncertainty))
return myResult
def getShapeForBin(self, factorisationBinIndexList, h):
self._initialize(h)
# Check that binning dimension is correct
if len(self._binCount) != len(factorisationBinIndexList):
raise Exception(
"Error in UnfoldedHistogramReader::getEventCountForBin(): You asked for %d dimensions, but the histogram has %d dimensions (the dimension needs to be the same)!"
% (len(factorisationBinIndexList), len(self._binCount)))
myResult = []
myMin = 0
myMax = h.GetNbinsX()
if h.GetNbinsX() != 1:
# Include under- and overflow bin
myMin -= 1
myMax += 1
myUnfoldedBin = self._convertBinIndexListToUnfoldedIndex(
factorisationBinIndexList) + 1
for i in range(myMin, myMax):
myValue = h.GetBinContent(i + 1, myUnfoldedBin)
myUncertainty = h.GetBinError(i + 1, myUnfoldedBin)
myResult.append(Count(myValue, myUncertainty))
return myResult
