def createBinnedFitUncertaintyHistograms(hRate, hUp, hDown, applyFrom, opts):
hupList = []
hDownList = []
# find bin
myFirstBin = hRate.GetXaxis().FindBin(applyFrom + 0.0001)
for i in range(myFirstBin, hRate.GetNbinsX() + 1):
hup = aux.Clone(
hRate,
"%s_%s_fitBinByBin%dUp" % (hRate.GetTitle(), hRate.GetTitle(), i))
hup.SetTitle(hup.GetName())
hdown = aux.Clone(
hRate, "%s_%s_fitBinByBin%dDown" %
(hRate.GetTitle(), hRate.GetTitle(), i))
hup.SetTitle(hdown.GetName())
for j in range(1, hRate.GetNbinsX() + 1):
hup.SetBinError(j, 0.0)
hdown.SetBinError(j, 0.0)
if opts.doubleFitUncert:
hup.SetBinContent(
i, (hUp.GetBinContent(i) - hRate.GetBinContent(i)) * 2.0 +
hRate.GetBinContent(i))
hdown.SetBinContent(
i, (hDown.GetBinContent(i) - hRate.GetBinContent(i)) * 2.0 +
hRate.GetBinContent(i))
else:
hup.SetBinContent(i, hUp.GetBinContent(i))
hdown.SetBinContent(i, hDown.GetBinContent(i))
if hDown.GetBinContent(i) < 0.0:
hdown.SetBinContent(i, 0.0)
hupList.append(hup)
hDownList.append(hdown)
# Return histogram lists
return (hupList, hDownList)
def getDataDrivenQCDHistoForSplittedBin(self, binIndex):
'''
Return the sum of data-ewk in a given phase space split bin
at bin-index 0 you have the inclusive histogram!
'''
if binIndex >= len(self._dataList):
# FIXME: New addition to fix "optionUseInclusiveNorm" functionality (24-Mar-2018). Should not affect binned result!
if self._optionUseInclusiveNorm: #new
h = aux.Clone(self._dataList[0]) #new
newName = h.GetName() + "_DataDriven" #new
h.SetName(newName) #new
return h #new
msg = "Requested bin index %d out of range (0-%d)!" % (
binIndex, len(self._dataList))
raise Exception(ShellStyles.ErrorStyle() + msg,
ShellStyles.NormalStyle())
# Clone the Data histos
h = aux.Clone(self._dataList[binIndex])
newName = h.GetName() + "_DataDriven"
h.SetName(newName)
self.Verbose(
"Cloned histo %s from histo %s" %
(h.GetName(), self._dataList[0].GetName()), True)
# Subtract the EWK histos
self.Verbose(
"Subtracting histo %s from histo %s (bin=%i)" %
(self._ewkList[binIndex].GetName(), h.GetName(), binIndex), False)
h.Add(self._ewkList[binIndex], -1.0)
return h
def calculateTotalVariationHistograms(self, hRate, hup, hdown):
# Create empty histogram templates
hFitUncertaintyUpTotal = aux.Clone(hup[0], self._label + "_TailFitUp")
hFitUncertaintyDownTotal = aux.Clone(hup[0],
self._label + "_TailFitDown")
hFitUncertaintyUpTotal.Reset() # clean bin contents and errors
hFitUncertaintyDownTotal.Reset() # clean bin contents and errors
for i in range(1, hup[0].GetNbinsX() + 1):
myPedestal = hRate.GetBinContent(i)
myVarianceUp = 0.0
myVarianceDown = 0.0
for j in range(0, len(hup)):
a = 0.0
b = 0.0
if hup[j].GetBinContent(i) > 0.0:
a = hup[j].GetBinContent(i) - myPedestal
else:
a = -myPedestal
if hdown[j].GetBinContent(i) > 0.0:
b = hdown[j].GetBinContent(i) - myPedestal
else:
b = -myPedestal
if abs(a) != float('Inf') and not math.isnan(
a) and abs(b) != float('Inf') and not math.isnan(b):
(varA,
varB) = aux.getProperAdditivesForVariationUncertainties(
a, b) # essentially squaring a and b
myVarianceUp += varA
myVarianceDown += varB
hFitUncertaintyUpTotal.SetBinContent(
i, myPedestal + math.sqrt(myVarianceUp))
hFitUncertaintyDownTotal.SetBinContent(
i, myPedestal - math.sqrt(myVarianceDown))
return (hFitUncertaintyUpTotal, hFitUncertaintyDownTotal)
def _obtainShapeHistograms(self, i, dataPath, ewkPath, dsetMgr, plotName, luminosity, normFactors):
self.Verbose("_obtainShapeHistograms()", True)
if self._verbose:
self.PrintShapeInputSummary(dataPath, ewkPath, dsetMgr, plotName, luminosity, normFactors)
self.Verbose("Obtain the (pre-normFactor) shape %s as \"DataDrivenQCDShape\" type object" % (plotName), True)
myShape = dataDrivenQCDCount.DataDrivenQCDShape(dsetMgr, "Data", "EWK", plotName, dataPath, ewkPath, luminosity, self._useInclusiveNorm, self._verbose)
if self._verbose:
msg = "Printing TH1s (before NormFactors) of \"Data\", \"Data-Driven QCD\", \"EWK\", and \"Bin-by\Bin Purity\" and \"Integrated Purity\""
self.Print(msg, True)
PrintTH1Info(myShape.getIntegratedDataHisto())
PrintTH1Info(myShape.getIntegratedDataDrivenQCDHisto()) #Data-EWK. NormFactor not applied
PrintTH1Info(myShape.getIntegratedEwkHisto())
PrintTH1Info(myShape.getPurityHisto()) # Splitted-bin (disabled for Inclusive mode)
PrintTH1Info(myShape.getIntegratedPurityForShapeHisto())
self.PrintShapePuritySummary(myShape)
self.Verbose("Obtain the (post-normFactor) shape %s as \"QCDInvertedShape\" type object (Takes \"DataDrivenQCDShape\" type object as argument)" % (plotName), True)
moduleInfo = self._moduleInfoString + "_" + plotName
myPlot= QCDInvertedShape(myShape, moduleInfo, normFactors, optionUseInclusiveNorm=self._useInclusiveNorm)
myPlot.PrintSettings(printHistos=self._verbose, verbose=self._verbose)
# Define histogram name as will be written in the ROOT file
self.Verbose("%sDefining the name of histogram objects, as they will appear in the ROOT file%s" % (ShellStyles.WarningStyle(), ShellStyles.NormalStyle()), True)
hName = plotName + "%d" %i
hTitle = plotName.replace("CRSelections", "AllSelections").replace("Baseline_", "").replace("Inverted_", "")#FIXME: not elegant!
# DataDriven
myShape.delete()
myPlotHisto = aux.Clone(myPlot.getResultShape(), "ctrlPlotShapeInManager")
myPlot.delete()
myPlotHisto.SetName(hName)
myPlotHisto.SetTitle(hTitle)
self._shapePlots.append(myPlotHisto)
self._shapePlotLabels.append(myPlotHisto.GetTitle())# this is the name the object will have in the ROOT file
# MC EWK
hName = plotName + "%d_MCEWK" %i
myPlotMCEWKHisto = aux.Clone(myPlot.getResultMCEWK(), "ctrlPlotMCEWKInManager")
myPlotMCEWKHisto.SetName(hName)
myPlotMCEWKHisto.SetTitle(hTitle + "_MCEWK")
self._shapePlots.append(myPlotMCEWKHisto)
self._shapePlotLabels.append(myPlotMCEWKHisto.GetTitle())# this is the name the object will have in the ROOT file
# Purity
hName = plotName + "%d_Purity" %i
#self.Print("myPlot.getResultPurity().GetName() = %s" % myPlot.getResultPurity().GetName(), True)
#self.Print("myPlot.getResultPurity().GetIntegral() = %s" % myPlot.getResultPurity().Integral(), True)
myPlotPurityHisto = aux.Clone(myPlot.getResultPurity(), "ctrlPlotPurityInManager")
myPlotPurityHisto.SetName(hName)
myPlotPurityHisto.SetTitle(hTitle + "_Purity")
self._shapePlots.append(myPlotPurityHisto)
self._shapePlotLabels.append(myPlotPurityHisto.GetTitle())# this is the name the object will have in the ROOT file
return myPlotHisto
def _createHistograms(self,data,expectedList):
for e in expectedList:
self._expectedList.append(aux.Clone(self._hFrame))
self._expectedList[len(self._expectedList)-1].Reset()
self._expectedListSystUp.append(aux.Clone(self._hFrame))
self._expectedListSystUp[len(self._expectedListSystUp)-1].Reset()
self._expectedListSystDown.append(aux.Clone(self._hFrame))
self._expectedListSystDown[len(self._expectedListSystDown)-1].Reset()
self._expectedLabelList.append(e.name)
self._data = aux.Clone(self._hFrame)
self._data.Reset()
def _obtainQCDNormalizationSystHistograms(self, shapeHisto, dsetMgr,
plotName, luminosity,
normDataSrc, normEWKSrc):
print ShellStyles.HighlightStyle(
) + "...Obtaining region transition systematics" + ShellStyles.NormalStyle(
)
myPlotSignalRegionShape = dataDrivenQCDCount.DataDrivenQCDShape(
dsetMgr=dsetMgr,
dsetLabelData="Data",
dsetLabelEwk="EWK",
histoName=plotName,
dataPath=normDataSrc + "QCDNormalizationSignal",
ewkPath=normEWKSrc + "QCDNormalizationSignal",
luminosity=luminosity)
myPlotControlRegionShape = dataDrivenQCDCount.DataDrivenQCDShape(
dsetMgr=dsetMgr,
dsetLabelData="Data",
dsetLabelEwk="EWK",
histoName=plotName,
dataPath=normDataSrc + "QCDNormalizationControl",
ewkPath=normEWKSrc + "QCDNormalizationControl",
luminosity=luminosity)
myPlotRegionTransitionSyst = metSyst.SystematicsForMetShapeDifference(
myPlotSignalRegionShape,
myPlotControlRegionShape,
shapeHisto,
moduleInfoString=self._moduleInfoString,
quietMode=True)
myPlotSignalRegionShape.delete()
myPlotControlRegionShape.delete()
# Store up and down variations
#hUp = aux.Clone(myPlotRegionTransitionSyst.getUpHistogram(), "QCDfactMgrSystQCDSystUp%d"%i)
#hUp.SetTitle(plotName+"systQCDUp")
#self._QCDNormalizationSystPlots.append(hUp)
#self._QCDNormalizationSystPlotLabels.append(hUp.GetTitle())
#hDown = aux.Clone(myPlotRegionTransitionSyst.getDownHistogram(), "QCDfactMgrSystQCDSystDown%d"%i)
#hDown.SetTitle(plotName+"systQCDDown")
#self._QCDNormalizationSystPlots.append(hDown)
#self._QCDNormalizationSystPlotLabels.append(hDown.GetTitle())
# Store source histograms
hNum = aux.Clone(
myPlotRegionTransitionSyst.getCombinedSignalRegionHistogram(),
"QCDfactMgrSystQCDSystNumerator")
hNum.SetTitle(plotName + "systQCDNumerator")
self._QCDNormalizationSystPlots.append(hNum)
self._QCDNormalizationSystPlotLabels.append(hNum.GetTitle())
hDenom = aux.Clone(
myPlotRegionTransitionSyst.getCombinedCtrlRegionHistogram(),
"QCDfactMgrSystQCDSystDenominator")
hDenom.SetTitle(plotName + "systQCDDenominator")
self._QCDNormalizationSystPlots.append(hDenom)
self._QCDNormalizationSystPlotLabels.append(hDenom.GetTitle())
# Free memory
myPlotRegionTransitionSyst.delete()
def getIntegratedDataDrivenQCDHisto(self):
'''
Return the sum of data-ewk integrated over the phase space splitted bins
'''
# 1) Do the "Inclusive" histogram (here I assume that the first in the list is the inclusive)
h = aux.Clone(self._dataList[0])
histoName = "_".join(h.GetName().split(
"_", 2)[:2]) + "_DataMinusEWK_Integrated"
# histoName = h.GetName()+"Integrated" #original code
h.SetName(histoName)
# Subtract the EWK histo from Data histo (QCD=Data-EWK)
h.Add(self._ewkList[0], -1.0)
# 2) Do the bin-by-bin histograms
# For-loop: All data histos
for i in range(1,
len(self._dataList)): # starts from bin #1 (not zero)
# Bin-by-bin mode has not been validated by me yet
print "FIXME " * 20
h.Add(self._dataList[i])
# Subtract the EWK histo from Data histo (QCD=Data-EWK)
h.Add(self._ewkList[i], -1.0)
return h
def getDataDrivenQCDHistoForSplittedBin(self, binIndex):
if binIndex >= len(self._dataList):
raise Exception(ShellStyles.ErrorLabel()+"DataDrivenQCDShape::getDataDrivenQCDForSplittedBin: requested bin index %d out of range (0-%d)!"%(binIndex,len(self._dataList)))
h = aux.Clone(self._dataList[binIndex])
h.SetName(h.GetName()+"dataDriven")
h.Add(self._ewkList[binIndex], -1.0)
return h
def _obtainShapeHistograms(self, i, dataPath, ewkPath, dsetMgr, plotName, luminosity, normFactors):
myShape = dataDrivenQCDCount.DataDrivenQCDShape(dsetMgr=dsetMgr,
dsetLabelData="Data",
dsetLabelEwk="EWK",
histoName=plotName,
dataPath=dataPath,
ewkPath=ewkPath,
luminosity=luminosity)
myPlot = QCDInvertedShape(myShape,
self._moduleInfoString+"_"+plotName,
normFactors,
optionUseInclusiveNorm=self._useInclusiveNorm)
myShape.delete()
myPlotHisto = aux.Clone(myPlot.getResultShape(), "ctrlPlotShapeInManager")
myPlot.delete()
myPlotHisto.SetName(plotName+"%d"%i)
myPlotHisto.SetTitle(plotName)
self._shapePlots.append(myPlotHisto)
self._shapePlotLabels.append(plotName)
# MC EWK and purity
#myPlotMCEWKHisto = aux.Clone(myPlot.getResultMCEWK(), "ctrlPlotMCEWKInManager")
#myPlotMCEWKHisto.SetName(plotName+"%d_MCEWK"%i)
#myPlotMCEWKHisto.SetTitle(plotName+"_MCEWK")
#self._shapePlots.append(myPlotMCEWKHisto)
#self._shapePlotLabels.append(myPlotMCEWKHisto.GetTitle())
#myPlotPurityHisto = aux.Clone(myPlot.getResultPurity(), "ctrlPlotPurityInManager")
#myPlotPurityHisto.SetName(plotName+"%d_Purity"%i)
#myPlotPurityHisto.SetTitle(plotName+"_Purity")
#self._shapePlots.append(myPlotPurityHisto)
#self._shapePlotLabels.append(myPlotPurityHisto.GetTitle())
return myPlotHisto
def getIntegratedDataDrivenQCDHisto(self):
'''
Return the sum of data-ewk integrated over the phase space splitted bins
'''
# Do the "Inclusive" histogram (here I assume that the first in the list is the inclusive)
h = aux.Clone(self._dataList[0])
histoName = "_".join(h.GetName().split(
"_", 2)[:2]) + "_DataDriven_Integrated"
h.SetName(histoName)
self.Verbose(
"Cloned histo %s from histo %s" %
(h.GetName(), self._dataList[0].GetName()), True)
# Subtract the EWK histo from Data histo (QCD=Data-EWK)
self.Verbose(
"Subtracting histo %s from histo %s" %
(self._ewkList[0].GetName(), h.GetName()), False)
h.Add(self._ewkList[0], -1.0)
# For-loop: All data histos [N.B. Starts from bin 1 (not 0=inclusive) ]
for i in range(1, len(self._dataList)):
msg = "Splitted-bin mode has not been validated yet"
#raise Exception(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle())
# Accumulate given bin-histo from Data
h.Add(self._dataList[i])
# Subtract given bin-histo from EWK
h.Add(self._ewkList[i], -1.0)
return h
def _obtainHistograms(self, i, dsetMgr, plotName, luminosity):
self.Verbose(
"Obtain histogram %s as \"NewShape\" type object" % (plotName),
True)
myShape = NewShape(dsetMgr, opts.newDsetName, opts.dsetsToMerge,
self._moduleInfoString, plotName, luminosity,
self._verbose)
myPlot = myShape.getIntegratedNewDsetHisto(
) #myPlot = myShape.getIntegratedEwkHisto()
if self._verbose:
aux.PrintTH1Info(myPlot)
self.Verbose(
"Cloning histogram %s, settting name and title" % (plotName), True)
saveName = "%s_%d" % (plotName, i)
myPlotHisto = aux.Clone(myPlot)
myPlotHisto.SetName(saveName)
myPlotHisto.SetTitle(plotName)
# Append the plot to the list. The plot title is the name the object will saved as in the ROOT file
saveName = myPlotHisto.GetTitle()
self.Verbose(
"Saving histogram %s for dataset %s" %
(saveName, opts.newDsetName), i == 1)
if self._verbose:
aux.PrintTH1Info(myPlotHisto)
self._myPlots.append(myPlotHisto)
self._myPlotLabels.append(saveName)
# Delete objects from memory
# myPlot.delete()
myShape.delete()
return myPlotHisto
def _obtainShapeHistograms(self, i, dataPath, ewkPath, dsetMgr, plotName, luminosity, normFactors):
Verbose("_obtainShapeHistograms()", True)
if self._verbose:
self.PrintShapeInputSummary(dataPath, ewkPath, dsetMgr, plotName, luminosity, normFactors)
Verbose("Obtain the (pre-normFactor) shape %s as \"DataDrivenQCDShape\" type object" % (plotName), True)
myShape = dataDrivenQCDCount.DataDrivenQCDShape(dsetMgr, "Data", "EWK", plotName, dataPath, ewkPath, luminosity, self._useInclusiveNorm) #pre-normFactor
if self._verbose:
msg = "Printing TH1s (before NormFactors) of \"Data\", \"Data-Driven QCD\", \"EWK\", and \"Bin-by\Bin Purity\" and \"Integrated Purity\""
Print(msg, True)
PrintTH1Info(myShape.getIntegratedDataHisto())
PrintTH1Info(myShape.getIntegratedDataDrivenQCDHisto()) #Data-EWK. NormFactor not applied
PrintTH1Info(myShape.getIntegratedEwkHisto())
PrintTH1Info(myShape.getPurityHisto())
PrintTH1Info(myShape.getIntegratedPurityForShapeHisto())
if self._verbose:
self.PrintShapePuritySummary(myShape)
Verbose("Obtain the (post-normFactor) shape %s as \"QCDInvertedShape\" type object (Takes \"DataDrivenQCDShape\" type object as argument)" % (plotName), True)
moduleInfo = self._moduleInfoString + "_" + plotName
myPlot = QCDInvertedShape(myShape, moduleInfo, normFactors, optionUseInclusiveNorm=self._useInclusiveNorm)
myPlot.PrintSettings(printHistos=self._verbose, verbose=self._verbose)
myShape.delete()
myPlotHisto = aux.Clone(myPlot.getResultShape(), "ctrlPlotShapeInManager")
myPlot.delete()
myPlotHisto.SetName(plotName+"%d"%i)
myPlotHisto.SetTitle(plotName)
self._shapePlots.append(myPlotHisto)
self._shapePlotLabels.append(plotName)
# MC EWK and purity
myPlotMCEWKHisto = aux.Clone(myPlot.getResultMCEWK(), "ctrlPlotMCEWKInManager")
myPlotMCEWKHisto.SetName(plotName+"%d_MCEWK"%i)
myPlotMCEWKHisto.SetTitle(plotName+"_MCEWK")
self._shapePlots.append(myPlotMCEWKHisto)
self._shapePlotLabels.append(myPlotMCEWKHisto.GetTitle())
myPlotPurityHisto = aux.Clone(myPlot.getResultPurity(), "ctrlPlotPurityInManager")
myPlotPurityHisto.SetName(plotName+"%d_Purity"%i)
myPlotPurityHisto.SetTitle(plotName+"_Purity")
self._shapePlots.append(myPlotPurityHisto)
self._shapePlotLabels.append(myPlotPurityHisto.GetTitle())
return myPlotHisto
def getIntegratedDataDrivenQCDHisto(self):
h = aux.Clone(self._dataList[0])
h.SetName(h.GetName()+"Integrated")
h.Add(self._ewkList[0],-1.0)
for i in range(1, len(self._dataList)):
h.Add(self._dataList[i])
h.Add(self._ewkList[i],-1.0)
return h
def getDataHistoForSplittedBin(self, binIndex):
'''
Return the data in a given phase space split bin
'''
if binIndex >= len(self._dataList):
# FIXME: New addition to fix "optionUseInclusiveNorm" functionality (24-Mar-2018). Should not affect binned result!
if self._optionUseInclusiveNorm: #new
h = aux.Clone(self._dataList[0]) #new
h.SetName(h.GetName() + "_") #new
return h #new
msg = "Requested bin index %d out of range (0-%d)!" % (
binIndex, len(self._dataList))
raise Exception(ShellStyles.ErrorLabel() + msg +
ShellStyles.NormalStyle())
h = aux.Clone(self._dataList[binIndex])
h.SetName(h.GetName() + "_")
return h
def getIntegratedEwkHisto(self):
'''
Return the sum of ewk integrated over the phase space splitted bins
'''
h = aux.Clone(self._histoList[0])
h.SetName("_".join(h.GetName().split("_", 2)[:2]) + "_EWK_Integrated")
for i in range(1, len(self._histoList)):
h.Add(self._histoList[i])
return h
def getIntegratedDataHisto(self):
'''
Return the sum of data integrated over the phase space splitted bins
'''
h = aux.Clone(self._dataList[0])
#h.SetName(h.GetName()+"Integrated") #original code
h.SetName("_".join(h.GetName().split("_", 2)[:2]) + "_Data_Integrated")
for i in range(1, len(self._dataList)):
h.Add(self._dataList[i])
return h
def getEwkHistoForSplittedBin(self, binIndex):
'''
Return the EWK MC in a given phase space split bin
'''
if binIndex >= len(self._dataList):
raise Exception(
ShellStyles.ErrorLabel() +
"DataDrivenQCDShape::getEwkHistoForSplittedBin: requested bin index %d out of range (0-%d)!"
% (binIndex, len(self._ewkList)))
h = aux.Clone(self._ewkList[binIndex])
h.SetName(h.GetName() + "_")
return h
def calculateTotalVariationHistograms(self, hRate, hup, hdown):
# Calculate total uncertainty (only for reference, note that can give also negative results)
hFitUncertaintyUpTotal = aux.Clone(hup[0], self._label + "_TailFitUp")
hFitUncertaintyUpTotal.Reset()
hFitUncertaintyDownTotal = aux.Clone(hup[0],
self._label + "_TailFitDown")
hFitUncertaintyDownTotal.Reset()
for i in range(1, hup[0].GetNbinsX() + 1):
myPedestal = hRate.GetBinContent(i)
myVarianceUp = 0.0
myVarianceDown = 0.0
for j in range(0, len(hup)):
a = 0.0
b = 0.0
#if hup[j].GetBinContent(i) > 1e-10:
#a = hup[j].GetBinContent(i) - myPedestal
#if hdown[j].GetBinContent(i) > 1e-10:
#b = hdown[j].GetBinContent(i) - myPedestal
if hup[j].GetBinContent(i) > 0.0:
a = hup[j].GetBinContent(i) - myPedestal
else:
a = -myPedestal
if hdown[j].GetBinContent(i) > 0.0:
b = hdown[j].GetBinContent(i) - myPedestal
else:
b = -myPedestal
if abs(a) != float('Inf') and not math.isnan(
a) and abs(b) != float('Inf') and not math.isnan(b):
(varA,
varB) = aux.getProperAdditivesForVariationUncertainties(
a, b)
myVarianceUp += varA
myVarianceDown += varB
#print j,hup[j].GetBinContent(i),hdown[j].GetBinContent(i),a,b,varA,varB
#print self._hFitFineBinning.GetXaxis().GetBinLowEdge(i),":", myPedestal,math.sqrt(myVarianceUp), math.sqrt(myVarianceDown), myPedestal+math.sqrt(myVarianceUp), myPedestal-math.sqrt(myVarianceDown)
hFitUncertaintyUpTotal.SetBinContent(
i, myPedestal + math.sqrt(myVarianceUp))
hFitUncertaintyDownTotal.SetBinContent(
i, myPedestal - math.sqrt(myVarianceDown))
return (hFitUncertaintyUpTotal, hFitUncertaintyDownTotal)
def __init__(self,
h,
label,
fitFuncName,
fitmin,
fitmax,
applyFitFrom,
doPlots=False,
luminosity=None):
self._label = label
self._fittedRate = None
self._centralParams = None
self._eigenVectors = None
self._eigenValues = None
self._fitmin = fitmin
self._hRate = aux.Clone(h)
self._luminosity = luminosity
# Initialize style
myStyle = tdrstyle.TDRStyle()
myStyle.setOptStat(True)
myStyle.tdrStyle.SetOptFit(True)
# Find fit function class
scaleFactor = h.Integral(h.FindBin(fitmin), h.GetNbinsX() + 1) * 1.01
self._myFitFuncObject = self._findFitFunction(fitFuncName, scaleFactor)
# Obtain bin list for fine binning (compatibility with fine binning)
myBinList = []
for i in range(1, h.GetNbinsX() + 1):
myBinList.append(h.GetXaxis().GetBinLowEdge(i))
myBinList.append(h.GetXaxis().GetBinUpEdge(h.GetNbinsX()))
# Do fit
myFitResult = self._doFit(h, myBinList, fitFuncName, fitmin, fitmax)
# Calculate eigenvectors and values
self._calculateEigenVectorsAndValues(myFitResult, printStatus=True)
# Create histograms and control plots
if doPlots:
(hFitUncertaintyUp,
hFitUncertaintyDown) = self.calculateVariationHistograms(
myBinList, applyFitFrom)
self.makeVariationPlotDetailed("FineBinning", self._hRate,
self._hFitFineBinning,
hFitUncertaintyUp,
hFitUncertaintyDown)
(hupTotal, hdownTotal) = self.calculateTotalVariationHistograms(
self._hFitFineBinning, hFitUncertaintyUp, hFitUncertaintyDown)
self.makeVariationPlotSimple("FineBinning", self._hRate,
self._hFitFineBinning, hupTotal,
hdownTotal)
def getAndRebinQCDShapeNuisanceHistos(columnName, rootFile, hRate,
nuisanceInfo, binlist):
if not "QCD" in columnName:
return []
myHistograms = []
# Loop over nuisance info
for n in nuisanceInfo:
if n["distribution"] == "shape" and n[
columnName] == "1" and "QCD_metshape" in n["name"]:
# Obtain numerator and denominator
myNumName = "%s_QCD_metshapeSource_Numerator" % (columnName)
myDenomName = "%s_QCD_metshapeSource_Denominator" % (columnName)
hNum = rootFile.Get(myNumName)
if hNum == None:
raise Exception(ErrorLabel() +
"Cannot find histogram '%s'!" % myNumName)
hDenom = rootFile.Get(myDenomName)
if hDenom == None:
raise Exception(ErrorLabel() +
"Cannot find histogram '%s'!" % myDenomName)
myArray = array.array("d", binlist)
hDenomRebinned = hDenom.Rebin(len(myArray) - 1, "", myArray)
hNumRebinned = hNum.Rebin(len(myArray) - 1, "", myArray)
hDenom.Delete()
hNum.Delete()
# Create output histograms
hUp = aux.Clone(hRate, "%s_QCD_metshapeUp" % columnName)
hDown = aux.Clone(hRate, "%s_QCD_metshapeDown" % columnName)
hUp.Reset()
hDown.Reset()
# Calculate
systematicsForMetShapeDifference.createSystHistograms(
hRate, hUp, hDown, hNumRebinned, hDenomRebinned)
myHistograms.append(hUp)
myHistograms.append(hDown)
return myHistograms
def getIntegratedPurityForShapeHisto(self):
'''
Return the QCD purity in bins of the final shape
'''
hData = self.getIntegratedDataHisto()
hEwk = self.getIntegratedEwkHisto()
# newName = ("_".join(hData.GetName().split("_", 2)[:2]) + "_IntegratedPurity_" + str(self._uniqueN) )
newName = hData.GetName() + "_Purity"
h = aux.Clone(hData, newName)
nameList = self._dataList[0].GetName().split("_")
newTitle = "PurityByFinalShapeBin_%s" % nameList[0][:len(nameList[0]) -
1]
h.SetTitle(newTitle)
self._uniqueN += 1
# For-loop: All bins
for i in range(1, h.GetNbinsX() + 1):
myPurity = 0.0
myUncert = 0.0
nData = hData.GetBinContent(i)
nEWK = hEwk.GetBinContent(i)
# Calculate the purity
if (nData > 0.0):
myPurity = (nData - nEWK) / nData
# Sanity check
if myPurity < 0.0:
myPurity = 0.0
myUncert = 0.0
else:
# Assume binomial error
myUncertSq = myPurity * (1.0 - myPurity) / nData
if myUncertSq >= 0.0:
myUncert = sqrt(myUncertSq)
else:
msg = "Purity is greater than 1 (%.4f) in bin %i of histogram %s" % (
myPurity, i, h.GetName())
self.Verbose(ShellStyles.WarningLabel() + msg, True)
myUncert = 0.0
# Set the purity value for the given bin
h.SetBinContent(i, myPurity)
h.SetBinError(i, myUncert)
return h
def getIntegratedPurityForShapeHisto(self):
hData = self.getIntegratedDataHisto()
hEwk = self.getIntegratedEwkHisto()
h = aux.Clone(hData, "%s_purity_%d"%(hData,self._uniqueN))
myNameList = self._dataList[0].GetName().split("_")
h.SetTitle("PurityByFinalShapeBin_%s"%myNameList[0][:len(myNameList[0])-1])
self._uniqueN += 1
for i in range(1, h.GetNbinsX()+1):
myPurity = 0.0
myUncert = 0.0
if (hData.GetBinContent(i) > 0.0):
myPurity = (hData.GetBinContent(i) - hEwk.GetBinContent(i)) / hData.GetBinContent(i)
if myPurity < 0.0:
myPurity = 0.0
myUncert = 0.0
else:
myUncert = sqrt(myPurity * (1.0-myPurity) / hData.GetBinContent(i)) # Assume binomial error
h.SetBinContent(i, myPurity)
h.SetBinError(i, myUncert)
return h
def getIntegratedNewDsetHisto(self):
'''
Return the sum of all datasets to be merged over the entire phase space
'''
newName = "%s_%s" % (self._histoName, self._newDsetName)
histo = aux.Clone(self._histoList[0], newName)
histo.Reset()
# For-loop: All histograms in list
for i, h in enumerate(self._histoList, 0):
self.Verbose(
"self.histoList[%s] = %s " % (i, self._histoList[i].GetName()),
True)
histo.Add(self._histoList[i])
self.Verbose("histo.Integral() += %.2f" % (histo.Integral()),
False)
self.Verbose(
"Integral of histogram %s is %.3f (Entries = %d)" %
(histo.GetName(), histo.Integral(), histo.GetEntries()), True)
return histo
def getIntegratedPurityForShapeHisto(self):
'''
Return the QCD purity in bins of the final shape
'''
hData = self.getIntegratedDataHisto()
hEwk = self.getIntegratedEwkHisto()
#cloneName = "%s_purity_%d" % (hData, self._uniqueN) # original code
cloneName = ("_".join(hData.GetName().split("_", 2)[:2]) +
"_IntegratedPurity_" + str(self._uniqueN))
h = aux.Clone(hData, cloneName)
nameList = self._dataList[0].GetName().split("_")
h.SetTitle("PurityByFinalShapeBin_%s" %
nameList[0][:len(nameList[0]) - 1])
self._uniqueN += 1
# For-loop: All bins
for i in range(1, h.GetNbinsX() + 1):
myPurity = 0.0
myUncert = 0.0
if (hData.GetBinContent(i) > 0.0):
myPurity = (hData.GetBinContent(i) -
hEwk.GetBinContent(i)) / hData.GetBinContent(i)
if myPurity < 0.0:
myPurity = 0.0
myUncert = 0.0
else:
# Assume binomial error
myUncertSq = myPurity * (1.0 -
myPurity) / hData.GetBinContent(i)
if myUncertSq >= 0.0:
myUncert = sqrt(myUncertSq)
else:
msg = "Purity is greater than 1 (%.4f) in bin %i of histogram %s" % (
myPurity, i, h.GetName())
self.Verbose(ShellStyles.WarningLabel() + msg, True)
myUncert = 0.0
h.SetBinContent(i, myPurity)
h.SetBinError(i, myUncert)
return h
def _obtainScaleFactor(self, h, fitmin, fitmax):
raise Exception(
"There is something fishy in this function, validate before using")
minbin = h.GetXaxis().FindBin(fitmin)
maxbin = h.GetXaxis().FindBin(fitmax)
hh = aux.Clone(h)
preFactor = hh.Integral(minbin, hh.GetNbinsX() + 1)
hh.Scale(1.0 / preFactor)
myFitFunc = FitFuncPreFitForIntegral(self._fitmin)
myFittedRate = ROOT.TF1(self._label + "myFitForIntegral", myFitFunc,
fitmin, fitmax, myFitFunc.getNparam())
myFitResult = hh.Fit(myFittedRate, _fitOptions)
# Set fitted params to function
for i in range(0, myFitFunc.getNparam()):
myFittedRate.SetParameter(i, myFitResult.Parameter(i))
# Calculate integral from fitmin to infinity and divide by bin width to normalize
myBinWidth = (hh.GetXaxis().GetBinLowEdge(maxbin) -
hh.GetXaxis().GetBinLowEdge(minbin)) / (maxbin - minbin)
myScaleFactor = myFittedRate.Integral(minbin,
1e5) / myBinWidth * preFactor
print "Scale factor calc cross-check: integral from histogram = %f, integral from fit = %f" % (
preFactor, myScaleFactor)
print myFittedRate.Integral(minbin, 1e5), myBinWidth, preFactor
return myScaleFactor
def _doCalculate(self, shape, moduleInfoString, normFactors,
optionPrintPurityByBins, optionDoNQCDByBinHistograms):
# Calculate final shape in signal region (shape * w_QCD)
nSplitBins = shape.getNumberOfPhaseSpaceSplitBins()
# Initialize result containers
self._resultShape = aux.Clone(
shape.getDataDrivenQCDHistoForSplittedBin(0))
self._resultShape.Reset()
self._resultShape.SetTitle("NQCDFinal_Total_%s" % moduleInfoString)
self._resultShape.SetName("NQCDFinal_Total_%s" % moduleInfoString)
self._resultShapeEWK = aux.Clone(
shape.getDataDrivenQCDHistoForSplittedBin(0))
self._resultShapeEWK.Reset()
self._resultShapeEWK.SetTitle("NQCDFinal_EWK_%s" % moduleInfoString)
self._resultShapeEWK.SetName("NQCDFinal_EWK_%s" % moduleInfoString)
self._resultShapePurity = aux.Clone(
shape.getDataDrivenQCDHistoForSplittedBin(0))
self._resultShapePurity.Reset()
self._resultShapePurity.SetTitle("NQCDFinal_Purity_%s" %
moduleInfoString)
self._resultShapePurity.SetName("NQCDFinal_Purity_%s" %
moduleInfoString)
self._histogramsList = []
myUncertaintyLabels = ["statData", "statEWK"]
self._resultCountObject = extendedCount.ExtendedCount(
0.0, [0.0, 0.0], myUncertaintyLabels)
if optionDoNQCDByBinHistograms:
for i in range(0, nSplitBins):
hBin = aux.Clone(self._resultShape)
hBin.SetTitle(
"NQCDFinal_%s_%s" %
(shape.getPhaseSpaceBinFileFriendlyTitle(i).replace(
" ", ""), moduleInfoString))
hBin.SetName(
"NQCDFinal_%s_%s" %
(shape.getPhaseSpaceBinFileFriendlyTitle(i).replace(
" ", ""), moduleInfoString))
self._histogramsList.append(hBin)
if isinstance(self._resultShape, ROOT.TH2):
self._doCalculate2D(nSplitBins, shape, normFactors,
optionPrintPurityByBins,
optionDoNQCDByBinHistograms,
myUncertaintyLabels)
return
# Intialize counters for purity calculation in final shape binning
myShapeDataSum = []
myShapeDataSumUncert = []
myShapeEwkSum = []
myShapeEwkSumUncert = []
for j in range(1, self._resultShape.GetNbinsX() + 1):
myShapeDataSum.append(0.0)
myShapeDataSumUncert.append(0.0)
myShapeEwkSum.append(0.0)
myShapeEwkSumUncert.append(0.0)
# Calculate results separately for each phase space bin and then combine
for i in range(0, nSplitBins):
# Get data-driven QCD, data, and MC EWK shape histogram for the phase space bin
h = shape.getDataDrivenQCDHistoForSplittedBin(i)
hData = shape.getDataHistoForSplittedBin(i)
hEwk = shape.getEwkHistoForSplittedBin(i)
# Get normalization factor
wQCDLabel = shape.getPhaseSpaceBinFileFriendlyTitle(i)
if self._optionUseInclusiveNorm:
wQCDLabel = "Inclusive"
wQCD = 0.0
if not wQCDLabel in normFactors.keys():
print ShellStyles.WarningLabel(
) + "No normalization factors available for bin '%s' when accessing histogram %s! Ignoring this bin..." % (
wQCDLabel, shape.getHistoName())
else:
wQCD = normFactors[wQCDLabel]
# Loop over bins in the shape histogram
for j in range(1, h.GetNbinsX() + 1):
myResult = 0.0
myStatDataUncert = 0.0
myStatEwkUncert = 0.0
if abs(h.GetBinContent(j)) > 0.00001: # Ignore zero bins
# Calculate result
myResult = h.GetBinContent(j) * wQCD
# Calculate abs. stat. uncert. for data and for MC EWK
myStatDataUncert = hData.GetBinError(j) * wQCD
myStatEwkUncert = hEwk.GetBinError(j) * wQCD
#errorPropagation.errorPropagationForProduct(hLeg1.GetBinContent(j), hLeg1Data.GetBinError(j), myEffObject.value(), myEffObject.uncertainty("statData"))
# Do not calculate here MC EWK syst.
myCountObject = extendedCount.ExtendedCount(
myResult, [myStatDataUncert, myStatEwkUncert],
myUncertaintyLabels)
self._resultCountObject.add(myCountObject)
if optionDoNQCDByBinHistograms:
self._histogramsList[i].SetBinContent(
j, myCountObject.value())
self._histogramsList[i].SetBinError(
j, myCountObject.statUncertainty())
self._resultShape.SetBinContent(
j,
self._resultShape.GetBinContent(j) + myCountObject.value())
self._resultShape.SetBinError(
j,
self._resultShape.GetBinError(j) +
myCountObject.statUncertainty()**2) # Sum squared
# Sum items for purity calculation
myShapeDataSum[j - 1] += hData.GetBinContent(j) * wQCD
myShapeDataSumUncert[j - 1] += (hData.GetBinError(j) * wQCD)**2
myShapeEwkSum[j - 1] += hEwk.GetBinContent(j) * wQCD
myShapeEwkSumUncert[j - 1] += (hEwk.GetBinError(j) * wQCD)**2
h.Delete()
hData.Delete()
hEwk.Delete()
# Take square root of uncertainties
for j in range(1, self._resultShape.GetNbinsX() + 1):
self._resultShape.SetBinError(
j, math.sqrt(self._resultShape.GetBinError(j)))
# Print result
print "NQCD Integral(%s) = %s " % (
shape.getHistoName(),
self._resultCountObject.getResultStringFull("%.1f"))
# Print purity as function of final shape bins
if optionPrintPurityByBins:
print "Purity of shape %s" % shape.getHistoName()
print "shapeBin purity purityUncert"
for j in range(1, self._resultShape.GetNbinsX() + 1):
myPurity = 0.0
myPurityUncert = 0.0
if abs(myShapeDataSum[j - 1]) > 0.000001:
myPurity = 1.0 - myShapeEwkSum[j - 1] / myShapeDataSum[j - 1]
myPurityUncert = errorPropagation.errorPropagationForDivision(
myShapeEwkSum[j - 1],
math.sqrt(myShapeEwkSumUncert[j - 1]),
myShapeDataSum[j - 1],
math.sqrt(myShapeDataSumUncert[j - 1]))
# Store MC EWK content
self._resultShapeEWK.SetBinContent(j, myShapeEwkSum[j - 1])
self._resultShapeEWK.SetBinError(
j, math.sqrt(myShapeEwkSumUncert[j - 1]))
self._resultShapePurity.SetBinContent(j, myPurity)
self._resultShapePurity.SetBinError(j, myPurityUncert)
# Print purity info of final shape
if optionPrintPurityByBins:
myString = ""
if j < self._resultShape.GetNbinsX():
myString = "%d..%d" % (
self._resultShape.GetXaxis().GetBinLowEdge(j),
self._resultShape.GetXaxis().GetBinUpEdge(j))
else:
myString = ">%d" % (
self._resultShape.GetXaxis().GetBinLowEdge(j))
myString += " %.3f %.3f" % (myPurity, myPurityUncert)
print myString
def getIntegratedEwkHisto(self):
h = aux.Clone(self._ewkList[0])
h.SetName(h.GetName()+"Integrated")
for i in range(1, len(self._dataList)):
h.Add(self._ewkList[i])
return h
def printSummaryInfo(columnNames, myNuisanceInfo, cachedHistos, hObs, m,
luminosity, opts):
config = aux.load_module(opts.settings)
def addOrReplace(dictionary, key, newItem):
if not key in dictionary.keys():
dictionary[key] = newItem.Clone()
else:
dictionary[key].Add(newItem)
def getHisto(cachedHistos, name):
for h in cachedHistos:
if h.GetName() == name:
return h
raise Exception("Cannot find histogram '%s'!" % name)
# Create for each column a root histo with uncertainties
myDict = OrderedDict()
myTotal = None
# Loop over columns (datasets)
for c in columnNames:
# Bugfix to prevent crashing with blacklisted datasets
if c in config.Blacklist:
continue
hRate = aux.Clone(getHisto(cachedHistos, c))
myRHWU = RootHistoWithUncertainties(hRate)
for n in myNuisanceInfo:
# Add shape uncertainties
if n["name"] != "observation" and n[
"distribution"] == "shape" and n[
c] == "1" and not "statBin" in n["name"]:
hUp = aux.Clone(
getHisto(cachedHistos, "%s_%sUp" % (c, n["name"])))
hDown = aux.Clone(
getHisto(cachedHistos, "%s_%sDown" % (c, n["name"])))
myRHWU.addShapeUncertaintyFromVariation(n["name"], hUp, hDown)
# Add constant uncertainties
elif n["name"] != "observation" and n["name"] != "rate" and n[
"name"] != "process" and n[c] != "-" and n[
c] != "1" and not "statBin" in n[
"name"] and not "BinByBin" in n["name"]:
diffUp = 0.0
diffDown = 0.0
if "/" in n[c]:
mySplit = n[c].split("/")
diffDown = float(mySplit[0]) - 1.0
diffUp = float(mySplit[1]) - 1.0
else:
diffDown = float(n[c]) - 1.0
diffUp = float(n[c]) - 1.0
myRHWU.addNormalizationUncertaintyRelative(
n["name"], diffUp, diffDown)
# Store column info
_myBr = 0.01
myAddToTotalStatus = False
if c.startswith("HH") or c.startswith("CMS_Hptntj_HH"):
myRHWU.Scale(_myBr**2)
addOrReplace(myDict, "Hp", myRHWU)
elif c.startswith("HW") or c.startswith("CMS_Hptntj_HW"):
myRHWU.Scale(2.0 * _myBr * (1.0 - _myBr))
addOrReplace(myDict, "Hp", myRHWU)
elif c.startswith("Hp") or c.startswith("CMS_Hptntj_Hp"):
addOrReplace(myDict, "Hp", myRHWU)
elif c == "EWK_Tau" or c.startswith("CMS_Hptntj_EWK_Tau"):
addOrReplace(myDict, "EWKtau", myRHWU)
myAddToTotalStatus = True
elif c.endswith("genuinetau"):
addOrReplace(myDict, c.replace("CMS_Hptntj_", ""), myRHWU)
myAddToTotalStatus = True
elif c.endswith("faketau"):
addOrReplace(myDict, "EWKfakes", myRHWU)
myAddToTotalStatus = True
elif c.startswith("QCD") or c.startswith("CMS_Hptntj_QCD"):
addOrReplace(myDict, "QCD", myRHWU)
myAddToTotalStatus = True
else:
myDict[c] = myRHWU
myAddToTotalStatus = True
if myAddToTotalStatus:
if myTotal == None:
myTotal = myRHWU.Clone()
else:
myTotal.Add(myRHWU.Clone())
myDict["Totalbkg"] = myTotal
# Make table
print "\nEvent yields:"
myTotal = None
for item in myDict.keys():
if myDict[item] != None:
myDict[item].makeFlowBinsVisible()
rate = myDict[item].getRate()
stat = myDict[item].getRateStatUncertainty()
(systUp, systDown) = myDict[item].getRateSystUncertainty()
#myDict[item].Debug()
print "%11s: %.1f +- %.1f (stat.) + %.1f - %.1f (syst.)" % (
item, rate, stat, systUp, systDown)
print "Observation: %d\n\n" % hObs.Integral(0, hObs.GetNbinsX() + 2)
def setTailFitUncToStat(rhwu):
tailfitNames = filter(lambda n: "_TailFit_" in n,
rhwu.getShapeUncertaintyNames())
rhwu.setShapeUncertaintiesAsStatistical(tailfitNames)
#rhwu.printUncertainties()
#print rhwu.getShapeUncertaintiesAsStatistical()
return rhwu
myLogList = [False, True]
for l in myLogList:
# Create post fit shape
myStackList = []
if "QCD" in myDict.keys():
myHisto = histograms.Histo(
setTailFitUncToStat(myDict["QCD"].Clone()),
"QCD",
legendLabel=ControlPlotMaker._legendLabelQCD)
myHisto.setIsDataMC(isData=False, isMC=True)
myStackList.append(myHisto)
if "EWKtau" in myDict.keys():
myHisto = histograms.Histo(
setTailFitUncToStat(myDict["EWKtau"].Clone()),
"Embedding",
legendLabel=ControlPlotMaker._legendLabelEmbedding)
myHisto.setIsDataMC(isData=False, isMC=True)
myStackList.append(myHisto)
for c in myDict.keys():
if c.endswith("genuinetau"):
histoID = c.replace("CMS_Hptntj_",
"").replace("_genuinetau", "")
lookupTable = { # Map column name to style in plots.py
"tt": "TT",
"W": "WJets",
"t": "SingleTop",
"DY": "DYJetsToLL",
"VV": "Diboson",
"tt_and_singleTop": "TTandSingleTop",
"EWK": "EWK"
}
histoString = lookupTable[histoID]
myHisto = histograms.Histo(
setTailFitUncToStat(myDict[c].Clone()),
histoString,
legendLabel=c.replace("CMS_Hptntj_", ""))
myHisto.setIsDataMC(isData=False, isMC=True)
myStackList.append(myHisto)
if "EWKfakes" in myDict.keys() and myDict["EWKfakes"] != None:
myHisto = histograms.Histo(
myDict["EWKfakes"].Clone(),
"EWKfakes",
legendLabel=ControlPlotMaker._legendLabelEWKFakes)
myHisto.setIsDataMC(isData=False, isMC=True)
myStackList.append(myHisto)
myBlindedStatus = not opts.unblinded
myBlindingString = None
hObsLocal = aux.Clone(hObs)
if myBlindedStatus:
myBlindingString = "%d-%d GeV" % (hObs.GetXaxis().GetBinLowEdge(1),
hObs.GetXaxis().GetBinUpEdge(
hObs.GetNbinsX()))
for i in range(0, hObs.GetNbinsX()):
hObsLocal.SetBinContent(i, -1.0)
hObsLocal.SetBinError(i, 0.0)
# Add data
myDataHisto = histograms.Histo(hObsLocal, "Data")
myDataHisto.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, myDataHisto)
# Add signal
mySignalLabel = "TTToHplus_M%d" % float(m)
if float(m) > 179:
mySignalLabel = "HplusTB_M%d" % float(m)
myHisto = histograms.Histo(myDict["Hp"].Clone(), mySignalLabel)
myHisto.setIsDataMC(isData=False, isMC=True)
myStackList.insert(1, myHisto)
# Make plot
myStackPlot = plots.DataMCPlot2(myStackList)
myStackPlot.setLuminosity(luminosity)
#myStackPlot.setEnergy("%d"%self._config.OptionSqrtS)
myStackPlot.setDefaultStyles()
myParams = {}
if myBlindedStatus:
myParams["blindingRangeString"] = myBlindingString
myParams["cmsTextPosition"] = "right"
myParams["ratio"] = True
myParams["ratioType"] = "errorScale"
myParams["ratioYlabel"] = "Data/Bkg. "
myParams["stackMCHistograms"] = True
myParams["addMCUncertainty"] = True
myParams["addLuminosityText"] = True
myParams["moveLegend"] = {"dx": -0.14, "dy": -0.10}
myParams["ratioErrorOptions"] = {"numeratorStatSyst": False}
myParams["ratioCreateLegend"] = True
#myParams["ratioMoveLegend"] = {"dx": -0.51, "dy": 0.03}
myParams["ratioMoveLegend"] = {"dx": -0.06, "dy": -0.1}
myParams["opts2"] = {"ymin": 0.0, "ymax": 2.5}
myParams["xlabel"] = "m_{T} (GeV)"
#if l:
# myParams["ylabel"] = "< Events / bin >"
#else:
myParams["ylabel"] = "Events / 20 GeV"
a = hObsLocal.GetXaxis().GetBinWidth(1)
b = hObsLocal.GetXaxis().GetBinWidth(hObsLocal.GetNbinsX())
#if abs(a-b) < 0.0001:
#myParams["ylabel"] += "%d GeV"%a
#else:
#myParams["ylabel"] += "%d-%d GeV"%(a,b)
#myParams["divideByBinWidth"] = l
myParams["log"] = l
myPlotName = "PostTailFitShape_M%d" % float(m)
if l:
# scale ymin by 20 in order to compare the rebinned mT with same y-scale
# ymax(factor) takes care of max automatically
myParams["opts"] = {"ymin": 20 * 1e-5}
else:
myParams["opts"] = {"ymin": 0.0}
myPlotName += "_Linear"
plots.drawPlot(myStackPlot, myPlotName, **myParams)
def updateNuisanceTail(opts,
hOriginalShape,
hFittedShape,
rootFile,
histoName,
skipNotFoundTest=False):
myList = []
myPostfixes = ["Up", "Down"]
for postfix in myPostfixes:
# Obtain original nuisance histogram
hOriginalNuisance = rootFile.Get(histoName + postfix)
hOriginalNuisance.Scale(opts.lumiProjection * opts.bkgxsecProjection)
if hOriginalNuisance == None:
if skipNotFoundTest:
return []
else:
raise Exception(ErrorLabel() + "Cannot open histogram '%s'!" %
(histoName + postfix))
# Sanity check
if hOriginalShape.GetNbinsX() != hOriginalNuisance.GetNbinsX():
raise Exception("This should not happen")
# Prepare new histogram
hNewNuisance = aux.Clone(hFittedShape, histoName + postfix)
hNewNuisance.Reset()
hNewNuisance.SetMarkerSize(0)
# Loop over final histogram bins
for i in range(1, hFittedShape.GetNbinsX() + 1):
myOriginalBin = hOriginalShape.GetXaxis().FindBin(
hFittedShape.GetXaxis().GetBinLowEdge(i) + 0.0001)
myOriginalBinUpEdge = hOriginalShape.GetXaxis().FindBin(
hFittedShape.GetXaxis().GetBinUpEdge(i) - 0.0001)
if myOriginalBin != myOriginalBinUpEdge:
raise Exception(
ErrorLabel() +
"final rebinning boundaries at %d-%d do not match to original histogram (needed for shape nuisance scaling)!"
% (hFittedShape.GetXaxis().GetBinLowEdge(i),
hFittedShape.GetXaxis().GetBinUpEdge(i)))
# Original relative uncertainty (if ambiguous, leave as None, i.e. perform no scaling
myOriginalDelta = None
if abs(hOriginalNuisance.GetBinContent(myOriginalBin)) < 0.00001:
myOriginalDelta = None
elif abs(hOriginalShape.GetBinContent(myOriginalBin)) < 0.00001:
myOriginalDelta = None
else:
myOriginalDelta = hOriginalNuisance.GetBinContent(
myOriginalBin) / hOriginalShape.GetBinContent(
myOriginalBin)
#print i, myOriginalBin, myOriginalDelta, hOriginalNuisance.GetBinContent(myOriginalBin), hOriginalShape.GetBinContent(myOriginalBin)
# Calculate new variation counts
myNewVariation = None
if myOriginalDelta == None:
# scale by bin width difference
myNewVariation = hOriginalNuisance.GetBinContent(
myOriginalBin) * hFittedShape.GetXaxis().GetBinWidth(
i) / hOriginalNuisance.GetXaxis().GetBinWidth(
myOriginalBin)
else:
myNewVariation = myOriginalDelta * hFittedShape.GetBinContent(
i)
#print i, myNewVariation, hFittedShape.GetBinContent(i)
# Store
hNewNuisance.SetBinContent(i, myNewVariation)
hNewNuisance.SetBinError(i,
hOriginalShape.GetBinError(myOriginalBin))
# Finalize
hOriginalNuisance.Delete()
hNewNuisance.SetTitle(histoName + postfix)
myList.append(hNewNuisance)
return myList
def drawAllInOne(self, myAllShapeNuisances, luminosity):
myMaxSize = len(myAllShapeNuisances)
# Create pads
canvasHeight = _cHeaderHeight + _cBodyHeight * myMaxSize + _cFooterHeight
c = ROOT.TCanvas("","",600,canvasHeight)
c.Divide(1,myMaxSize)
myHeightBefore = canvasHeight
myHeightAfter = canvasHeight
for i in range(0,myMaxSize):
myHeightBefore = myHeightAfter
p = c.cd(i+1)
myTopMargin = 0.0
myBottomMargin = 0.0
if i == 0:
# Top histogram with header
myHeightAfter -= _cHeaderHeight + _cBodyHeight
myTopMargin = float(_cHeaderHeight) / float(_cHeaderHeight+_cBodyHeight)
elif i == myMaxSize-1:
# Bottom histogram with x axis label and title
myHeightAfter -= _cFooterHeight + _cBodyHeight
myBottomMargin = float(_cFooterHeight) / float(_cFooterHeight+_cBodyHeight)
else:
# Middle histogram, only body
myHeightAfter -= _cBodyHeight
(xlow, ylow, xup, yup) = [ROOT.Double(x) for x in [0.0]*4]
p.GetPadPar(xlow, ylow, xup, yup)
p.SetPad(xlow, float(myHeightAfter)/float(canvasHeight), xup, float(myHeightBefore)/float(canvasHeight))
p.SetBorderMode(0)
p.SetFillStyle(4000)
p.SetTopMargin(myTopMargin)
p.SetBottomMargin(myBottomMargin)
# Draw plots
if len(self._ratioPlotList) == 0:
print "No ratioplots in list! Cannot draw all-in-one plot!"
return
o = self._ratioPlotList[0].getFrame2()
myEmptyPlot = aux.Clone(o) # Keep the clone if it is needed to draw the x axis
for i in range(0,myMaxSize):
p = c.cd(i+1)
# Find plot
myPlotIndex = None
for j in range(0,len(self._systNameList)):
if myAllShapeNuisances[i] == self._systNameList[j]:
myPlotIndex = j
plot = None
if myPlotIndex != None:
plot = self._ratioPlotList[myPlotIndex].getFrame2()
else:
if i == myMaxSize-1:
plot = myEmptyPlot # Use this to draw the x axis
else:
plot = self._ratioPlotList[0].getFrame2() # Only the empty frame matters
# Draw plot
if i == myMaxSize-1:
# Bottom histogram
plot.GetXaxis().SetTitleOffset(0.6*myMaxSize+0.6) # 6.6/10, 3.6/5
else:
plot.GetXaxis().SetTitleSize(0)
plot.GetXaxis().SetLabelSize(0)
plot.GetYaxis().SetLabelSize(26)
plot.GetYaxis().SetTitleOffset(0.34*myMaxSize+0.1) # 3.5/10, 1.8/5
# plot.SetMinimum(0.001)
# plot.SetMaximum(1.999)
plot.SetMinimum(0.601)
plot.SetMaximum(1.399)
plot.Draw() # Plot frame for every nuisance
if myPlotIndex != None:
self._ratioPlotList[myPlotIndex].ratioHistoMgr.draw() # Plot content only if affected
self._ratioPlotList[0].ratioLine.Draw("L")
p.RedrawAxis()
# Labels for shape nuisance and dataset
myHeight = 0.82
if i == 0:
myHeight = myHeight*float(_cBodyHeight) / float(_cHeaderHeight+_cBodyHeight)
elif i == myMaxSize-1:
myHeight = (myHeight*float(_cBodyHeight)+float(_cFooterHeight)) / float(_cFooterHeight +_cBodyHeight)
histograms.addText(x=0.18, y=myHeight, text=myAllShapeNuisances[i], size=30)
myHeight = 0.08
if i == 0:
myHeight = myHeight*float(_cBodyHeight) / float(_cHeaderHeight+_cBodyHeight)
elif i == myMaxSize-1:
myHeight = (myHeight*float(_cBodyHeight)+float(_cFooterHeight)) / float(_cFooterHeight +_cBodyHeight)
histograms.addText(x=0.93, y=myHeight, text=self._dsetName, size=30, align="right")
# Header labels
if i == 0:
histograms.addStandardTexts(lumi=luminosity, cmsTextPosition="outframe")
# Labels for non-existing nuisances
if myPlotIndex == None:
myHeight = 0.44
if i == 0:
myHeight = myHeight*float(_cBodyHeight) / float(_cHeaderHeight+_cBodyHeight)
elif i == myMaxSize-1:
myHeight = (myHeight*float(_cBodyHeight)+float(_cFooterHeight)) / float(_cFooterHeight +_cBodyHeight)
histograms.addText(x=0.555, y=myHeight, text="Not affected", size=30, align="center")
# Save plot
myPlotName = "shapeSystRatioOnlyAll_%s"%(self._dsetName)
backup = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = ROOT.kError
for suffix in [".png",".C",".eps"]:
c.Print("%s/%s%s"%(_dirname,myPlotName,suffix))
ROOT.gErrorIgnoreLevel = backup