for bin in range(0,MinusNomRatioGeV.GetNbinsX()+1) :
if basicBkgFrom4ParamFit.GetBinContent(bin) == 0 :
MinusNomRatioGeV.SetBinContent(bin,0)
else :
MinusNomRatioGeV.SetBinContent(bin,(newNomMinus1.GetBinContent(bin)-newbasicBkgFrom4ParamFit.GetBinContent(bin))/newbasicBkgFrom4ParamFit.GetBinContent(bin))
# Used at some point 8,5E4???
myPainter.drawDataWithFitAsHistogramAndResidual(basicData,basicBkgFrom4ParamFit,luminosity,13,"m_{jj} [GeV]","Events",["Data","Fit","Statistical uncertainty on fit","Function choice"],"{0}/compareFitQualityAndFitChoice_Asymm_WithRatio".format(folderextension),True,[[nomPlus1,nomMinus1],[placeHolderNomGeV,valueNewFuncErrDirected]],[altFitRatioGeV,MinusNomRatioGeV,PlusNomRatioGeV],firstBin,lastBin-1,True,True,True,False,False,True,False,True,bumpHunterPVal,True,fitRange[0],fitRange[1],5E3,5E6) # changed from lastBin+2 to lastBin+15E3 to match FancyFigure
myPainter.drawDataWithFitAsHistogramAndResidual(newbasicdata,newbasicBkgFrom4ParamFit,luminosity,13,"m_{jj} [TeV]","Events",["Data","Fit","Statistical uncertainty on fit","Function choice"],"{0}/compareFitQualityAndFitChoice_Asymm_WithRatioTeV".format(folderextension),True,[[newNomPlus1,newNomMinus1],[placeHolderNom,newValueNewFuncErrDirected]],[altFitRatio,MinusNomRatio,PlusNomRatio],firstBin,lastBin-1,True,True,True,False,False,True,False,True,bumpHunterPVal,True,fitRange[0],fitRange[1],5E3,5E6) # changed from lastBin+2 to lastBin+15E3 to match FancyFigure
#drawing as hist not smooth curve so doesn't drop to zero
#myPainter.drawDataWithFitAsHistogramAndResidual(newbasicdata,newbasicBkgFrom4ParamFit,luminosity,13,"m_{jj} [TeV]","Events",["Data","Fit","Statistical uncertainty on fit","Function choice"],"{0}/compareFitQualityAndFitChoice_Asymm_WithRatio".format(folderextension),True,[[newNomPlus1,newNomMinus1],[placeHolderNom,newValueNewFuncErrDirected]],[altFitRatio,MinusNomRatio,PlusNomRatio],firstBin,lastBin-1,True,True,False,False,False,True,False,True,bumpHunterPVal,True,fitRange[0],fitRange[1]) # changed from lastBin+2 to lastBin+15E3 to match FancyFigure
myPainter.drawDataWithFitAsHistogramAndResidualPaper(newbasicdata,newbasicBkgFrom4ParamFit,luminosity,13,"m_{jj} [TeV]","Events",["Data","Fit","Statistical uncertainty on fit","Function choice"],"{0}/compareFitQualityAndFitChoice_Asymm_WithRatioPaper".format(folderextension),True,[[newNomPlus1,newNomMinus1],[placeHolderNom,newValueNewFuncErrDirected]],[altFitRatio,MinusNomRatio,PlusNomRatio],firstBin,lastBin-1,True,True,True,False,False) # changed from lastBin+2 to lastBin+15E3 to match FancyFigure
myPainter.drawMultipleFitsAndResiduals(newbasicdata,[newbasicBkgFrom4ParamFit,newValueNewFuncErrDirected],[altFitRatio],["Nominal fit","Func choice unc"],"m_{jj} [TeV]","Events",["(alt-nom)/nom"],"{0}/directedFuncChoiceVersusNominal_withRatio".format(folderextension),luminosity,13,firstBin,lastBin-1)
doStatSearch=False
if doStatSearch :
TomographyPlotWithStats = searchInputFile.Get("TomographyPlotWithStats")
bumpHunterStatsWSyst = searchInputFile.Get("bumpHunterStatsWSyst")
bumpPValStat = bumpHunterStatsWSyst(0)
bumpPValLow = bumpHunterStatsWSyst(1)
bumpPValHigh = bumpHunterStatsWSyst[2]
myPainter.drawPseudoExperimentsWithObservedStat(bumpHunterStatsWSyst,3.20359,0.7241,0,luminosity,13,\
'BumpHunter','Pseudo-experiments',"{0}/bumpHunterStatPlot_withUncertainties".format(folderextension))
myPainter.drawBumpHunterTomographyPlot(TomographyPlotWithStats,"{0}/bumpHunterTomographyPlot_withStats".format(folderextension))
print "with stats, bumpLowEdge, bumpHighEdge are",bumpPValLow,bumpPValHigh
print "BumpHunter pvalue is",bumpPValStat
while (testHist.GetBinContent(firstBin) == 0
and firstBin < testHist.GetNbinsX()):
firstBin += 1
while (testHist.GetBinContent(lastBin) == 0 and lastBin > 0):
lastBin -= 1
if (firstBin > lastBin):
firstBin = 1
lastBin = testHist.GetNbinsX()
print "First bin = ", firstBin, ": lower edge at", testHist.GetBinLowEdge(
firstBin)
print "Last bin = ", lastBin, ": upper edge at", testHist.GetBinLowEdge(
lastBin + 1)
myPainter.drawMultipleFitsAndResiduals(
dataHist, [testHist, validateHist], [ratio],
["New fit", "SWIFT fit"], "m_{jj} [TeV]", "Events",
["(new-SWIFT)/SWIFT"],
"{0}/compareOnData_{1}".format(folder, mystring), luminosity, 13,
firstBin, lastBin)
myPainter.drawSignificanceHistAlone(ratio,
"m_{jj} [TeV]",
"(new-SWIFT)/SWIFT",
"{0}/fitsRatio_{1}".format(
folder, mystring),
firstBin=firstBin,
lastBin=lastBin)
myPainter.drawSignificanceHistAlone(diff,
"m_{jj} [TeV]",
"(new-SWIFT)",
"{0}/fitsDifference_{1}".format(
def main():
# User controlled arguments
parser = argparse.ArgumentParser()
parser.add_argument("--inFileName", type=str, default="", help="The path to the input file from SearchPhase")
parser.add_argument("--outPath", type=str, default="./plotting/SearchPhase/plots/", help="The path prefix (directory) where you want the output plots")
parser.add_argument("--lumi", type=float, default=1, help="Luminosity in fb-1")
parser.add_argument("--doAlternate", action='store_true', help="Compare Nominal and Alternate Fit")
parser.add_argument("--overlaidSignal", action='store_true', help="Overlaid Signal on Background")
parser.add_argument("--signalFileName", type=str, default="", help="Signal histogram overlaid on background")
parser.add_argument("--drawMCComparison", action='store_true', help="Draw the comparison between data and MC")
parser.add_argument("--mcFileName", type=str, default="", help="MC File Name")
args = parser.parse_args()
inFileName = args.inFileName
outPath = args.outPath
luminosity = 1000*args.lumi
doAlternate = args.doAlternate
overlaidSignal = args.overlaidSignal
signalFileName = args.signalFileName
drawMCComparison = args.drawMCComparison
mcFileName = args.mcFileName
print "==================================="
print "Executing Run_SearchPhase.py with :"
print "inFileName : ",inFileName
print "outPath : ",outPath
print "Lumi : ", args.lumi
print "doAlternate: ", doAlternate
print "overlaidSignal: ", overlaidSignal
if overlaidSignal:
print " signalFileName: ", signalFileName
print "drawMCComparison: ", drawMCComparison
print "mcFileName: ", mcFileName
print "==================================="
# Get input (the rootfile name should match that specified in the SearchPhase.config file)
searchInputFile = ROOT.TFile(inFileName, "READ")
# make plots folder i.e. make folder extension
if not os.path.exists(outPath):
os.makedirs(outPath)
# Define necessary quantities.
Ecm = 13
# Get input
doStatSearch = False
#doAlternate = True # You can use this if you included the alternate fit function in the search phase
# Initialize painter
myPainter = Morisot()
myPainter.setColourPalette("Teals")
#myPainter.setEPS(True)
myPainter.setLabelType(2) # Sets label type i.e. Internal, Work in progress etc.
# See below for label explanation
# 0 Just ATLAS
# 1 "Preliminary"
# 2 "Internal"
# 3 "Simulation Preliminary"
# 4 "Simulation Internal"
# 5 "Simulation"
# 6 "Work in Progress"
# Retrieve search phase inputs
basicData = searchInputFile.Get("basicData")
basicBkg = searchInputFile.Get("basicBkgFrom4ParamFit")
#normalizedData = searchInputFile.Get("normalizedData")
#normalizedBkgFrom4ParamFit = searchInputFile.Get("normalizedBkgFrom4ParamFit")
residualHist = searchInputFile.Get("residualHist")
relativeDiffHist = searchInputFile.Get("relativeDiffHist")
sigOfDiffHist = searchInputFile.Get("sigOfDiffHist")
logLikelihoodPseudoStatHist = searchInputFile.Get("logLikelihoodStatHistNullCase")
chi2PseudoStatHist = searchInputFile.Get("chi2StatHistNullCase")
bumpHunterStatHist = searchInputFile.Get("bumpHunterStatHistNullCase")
#theFitFunction = searchInputFile.Get('theFitFunction')
bumpHunterTomographyPlot = searchInputFile.Get('bumpHunterTomographyFromPseudoexperiments')
bumpHunterStatOfFitToData = searchInputFile.Get('bumpHunterStatOfFitToData')
# nominal background +- statistical uncertainty(uncertainty from fitting parameters)
nomPlus1 = searchInputFile.Get("nominalBkgFromFit_plus1Sigma")
nomMinus1 = searchInputFile.Get("nominalBkgFromFit_minus1Sigma")
#nominal background +- uncertainties from fitting function choice
if doAlternate :
alternateBkg = searchInputFile.Get("alternateFitOnRealData")
nomWithNewFuncErrSymm = searchInputFile.Get("nomOnDataWithSymmetricRMSScaleFuncChoiceErr")
valueNewFuncErrDirected = searchInputFile.Get("nomOnDataWithDirectedRMSScaleFuncChoiceErr")
logLOfFitToDataVec = searchInputFile.Get('logLOfFitToData')
chi2OfFitToDataVec = searchInputFile.Get('chi2OfFitToData')
statOfFitToData = searchInputFile.Get('bumpHunterPLowHigh')
logLOfFitToData = logLOfFitToDataVec[0]
logLPVal = logLOfFitToDataVec[1]
chi2OfFitToData = chi2OfFitToDataVec[0]
chi2PVal = chi2OfFitToDataVec[1]
bumpHunterStatFitToData = statOfFitToData[0]
bumpHunterPVal = bumpHunterStatOfFitToData[1]
bumpLowEdge = statOfFitToData[1]
bumpHighEdge = statOfFitToData[2]
#NDF = searchInputFile.Get('NDF')[0]
#fitparams = searchInputFile.Get('fittedParameters')
print "logL of fit to data is",logLOfFitToData
print "logL pvalue is",logLPVal
print "chi2 of fit to data is",chi2OfFitToData
#print "NDF is",NDF
#print "chi2/NDF is",chi2OfFitToData/NDF
print "chi2 pvalue is",chi2PVal
print "bump hunter stat of fit to data is",bumpHunterStatFitToData
print "bumpLowEdge, bumpHighEdge are",bumpLowEdge,bumpHighEdge
print "BumpHunter pvalue is",bumpHunterPVal
print "which is Z value of",GetZVal(bumpHunterPVal,True)
#print "Fitted parameters were:",fitparams
# Find range
firstBin = 1000
lastBin = basicData.GetNbinsX()
print lastBin
while (basicData.GetBinContent(lastBin)==0 and lastBin > 0) :
lastBin = lastBin - 1
# Calculate from fit range
fitRange = searchInputFile.Get("FitRange")
firstBin = basicData.FindBin(fitRange[0])
lastBin = basicData.FindBin(fitRange[1])
print "New firstbin, lastbin",firstBin,lastBin
# Convert plots into desired final form
standardbins = basicData.GetXaxis().GetXbins()
newbins = []#ROOT.TArrayD(standardbins.GetSize())
for np in range(standardbins.GetSize()) :
newbins.append(standardbins[np]/1000)
# Make new versions of old plots
newbasicdata = ROOT.TH1D("basicData_TeV","basicData_TeV",len(newbins)-1,array('d',newbins))
newbasicBkg = ROOT.TH1D("basicBkg_TeV","basicBkg_TeV",len(newbins)-1,array('d',newbins))
newresidualHist = ROOT.TH1D("residualHist_TeV","residualHist_TeV",len(newbins)-1,array('d',newbins))
newrelativeDiffHist = ROOT.TH1D("relativeDiffHist_TeV","relativeDiffHist_TeV",len(newbins)-1,array('d',newbins))
newsigOfDiffHist = ROOT.TH1D("sigOfDiffHist_TeV","sigOfDiffHist_TeV",len(newbins)-1,array('d',newbins))
newNomPlus1= ROOT.TH1D("nomPlus1_TeV","nomPlus1_TeV",len(newbins)-1,array('d',newbins))
newNomMinus1= ROOT.TH1D("nomMinus1_TeV","nomMinus1_TeV",len(newbins)-1,array('d',newbins))
newAlternateBkg = ROOT.TH1D("alternateBkg_TeV","alternateBkg_TeV",len(newbins)-1,array('d',newbins))
newnomWithNewFuncErrSymm = ROOT.TH1D("nomWithNewFuncErrSymm_TeV","nomWithNewFuncErrSymm_TeV",len(newbins)-1,array('d',newbins))
newValueNewFuncErrDirected= ROOT.TH1D("nomWithNewFuncErrDirected_TeV","nomWithNewFuncErrDirected_TeV",len(newbins)-1,array('d',newbins))
for histnew,histold in [[newbasicdata,basicData],[newbasicBkg,basicBkg],\
[newNomPlus1,nomPlus1],[newNomMinus1,nomMinus1], \
[newresidualHist,residualHist],[newrelativeDiffHist,relativeDiffHist],\
[newsigOfDiffHist,sigOfDiffHist]] :
for bin in range(histnew.GetNbinsX()+2) :
histnew.SetBinContent(bin,histold.GetBinContent(bin))
histnew.SetBinError(bin,histold.GetBinError(bin))
if doAlternate:
icount=0
for histnew,histold in [[newAlternateBkg,alternateBkg], [newnomWithNewFuncErrSymm,nomWithNewFuncErrSymm],\
[newValueNewFuncErrDirected,valueNewFuncErrDirected]] :
print "Count : ",icount
print histnew.GetName()
print histold.GetName()
icount+=1
for bin in range(histnew.GetNbinsX()+2) :
histnew.SetBinContent(bin,histold.GetBinContent(bin))
histnew.SetBinError(bin,histold.GetBinError(bin))
# Significances for Todd
ToddSignificancesHist = ROOT.TH1D("ToddSignificancesHist","ToddSignificancesHist",100,-5,5)
for bin in range(0,newresidualHist.GetNbinsX()+1):
if bin < firstBin: continue
if bin > lastBin: continue
residualValue = newresidualHist.GetBinContent(bin)
ToddSignificancesHist.Fill(residualValue)
# Search phase plots
myPainter.drawDataAndFitOverSignificanceHist(newbasicdata,newbasicBkg,newresidualHist, 'm_{jj} [TeV]','Events','Significance','{0}/figure1'.format(outPath), luminosity,13,fitRange[0],fitRange[1], firstBin,lastBin,True,bumpLowEdge/1000.0,bumpHighEdge/1000.0,[],True,False,[],True,bumpHunterPVal)
myPainter.drawDataAndFitOverSignificanceHist(newbasicdata,newbasicBkg,newresidualHist, 'm_{jj} [TeV]','Events','Significance','{0}/figure1_nologx'.format(outPath), luminosity,13,fitRange[0],fitRange[1], firstBin,lastBin,True,bumpLowEdge/1000.0,bumpHighEdge/1000.0,[],False,False,[],True,bumpHunterPVal)
myPainter.drawDataAndFitOverSignificanceHist(newbasicdata,newbasicBkg,newresidualHist, 'm_{jj} [TeV]','Events','Significance','{0}/figure1_nobump'.format(outPath), luminosity,13,fitRange[0],fitRange[1],firstBin,lastBin+2,False,bumpLowEdge,bumpHighEdge,[],True,False,[],True,bumpHunterPVal)
myPainter.drawDataAndFitOverSignificanceHist(newbasicdata,newbasicBkg,newresidualHist, 'm_{jj} [TeV]','Events','Significance','{0}/figure1_nobump_nologx'.format(outPath), luminosity,13,fitRange[0],fitRange[1],firstBin,lastBin,False,bumpLowEdge,bumpHighEdge,[],False,False,[],True,bumpHunterPVal)
myPainter.drawPseudoExperimentsWithObservedStat(logLikelihoodPseudoStatHist,float(logLOfFitToData),logLPVal,0,luminosity,13, 'logL statistic','Pseudo-exeperiments',"{0}/logLStatPlot".format(outPath))
myPainter.drawPseudoExperimentsWithObservedStat(chi2PseudoStatHist,float(chi2OfFitToData),chi2PVal,0,luminosity,13, "#chi^{2}",'Pseudo-exeperiments',"{0}/chi2StatPlot".format(outPath))
myPainter.drawPseudoExperimentsWithObservedStat(bumpHunterStatHist,float(bumpHunterStatFitToData),bumpHunterPVal,0,luminosity,13, 'BumpHunter','Pseudo-exeperiments',"{0}/bumpHunterStatPlot".format(outPath))
myPainter.drawBumpHunterTomographyPlot(bumpHunterTomographyPlot,"{0}/bumpHunterTomographyPlot".format(outPath))
# Various significance plots
myPainter.drawBasicHistogram(ToddSignificancesHist,-1,-1,"Residuals","Entries","{0}/ToddSignificancesHist".format(outPath))
myPainter.drawSignificanceHistAlone(newrelativeDiffHist,"m_{jj} [TeV]","(D - B)/B","{0}/significanceonlyplot".format(outPath))
myPainter.drawSignificanceHistAlone(newsigOfDiffHist,"m_{jj} [TeV]","(D - B)/#sqrt{Derr^{2}+Berr^{2}}","{0}/sigofdiffonlyplot".format(outPath))
# Now to make the one comparing uncertainties
placeHolderNom = newbasicBkg.Clone()
placeHolderNom.SetName("placeHolderNom")
nomPlusSymmFuncErr = newbasicBkg.Clone()
nomPlusSymmFuncErr.SetName("nomPlusNewFuncErr")
nomMinusSymmFuncErr = newbasicBkg.Clone()
nomMinusSymmFuncErr.SetName("nomMinusNewFuncErr")
for bin in range(nomPlusSymmFuncErr.GetNbinsX()+2) :
nomPlusSymmFuncErr.SetBinContent(bin,newnomWithNewFuncErrSymm.GetBinContent(bin) + newnomWithNewFuncErrSymm.GetBinError(bin))
nomMinusSymmFuncErr.SetBinContent(bin,newnomWithNewFuncErrSymm.GetBinContent(bin) - newnomWithNewFuncErrSymm.GetBinError(bin))
if doAlternate:
myPainter.drawDataWithFitAsHistogram(newbasicdata,newbasicBkg,luminosity,13,"m_{jj} [TeV]","Events",["Data","Fit","Fit uncertainty","Function choice"],"{0}/compareFitQualityAndAlternateFit".format(outPath),True,[[newNomPlus1,newNomMinus1],[placeHolderNom,newAlternateBkg]],firstBin,lastBin,True,True,True,False,False)
myPainter.drawDataWithFitAsHistogram(newbasicdata,newbasicBkg,luminosity,13,"m_{jj} [TeV]","Events",["Data","Fit","Function choice","Alternate function"],"{0}/compareFitChoiceAndAlternateFit".format(outPath),True,[[nomPlusSymmFuncErr,nomMinusSymmFuncErr],[placeHolderNom,newAlternateBkg]],firstBin,lastBin,True,True,True,False,False)
myPainter.drawDataWithFitAsHistogram(newbasicdata,newbasicBkg,luminosity,13,"m_{jj} [TeV]","Events",["Data","Fit","Fit uncertainty","Function choice"],"{0}/compareFitQualtiyAndFitChoice".format(outPath),True,[[newNomPlus1,newNomMinus1],[nomPlusSymmFuncErr,nomMinusSymmFuncErr]],firstBin,lastBin,True,True,True,False,False)
myPainter.drawDataWithFitAsHistogram(newbasicdata,newbasicBkg,luminosity,13,"m_{jj} [TeV]","Events",["Data","Fit","Statistical fit uncertainty","Function choice"],"{0}/compareFitQualityAndFitChoice_Asymm".format(outPath),True,[[newNomPlus1,newNomMinus1],[placeHolderNom,newValueNewFuncErrDirected]],firstBin,lastBin,True,True,True,False,False)
# Overlay nominal and alternate fit functions
myPainter.drawDataWithFitAsHistogram(newbasicdata,newbasicBkg,luminosity,13,"m_{jj} [TeV]","Events",["Data","Fit","Alternate function"],"{0}/compareFitChoices".format(outPath),True,[[placeHolderNom,newAlternateBkg]],firstBin,lastBin,True,True,True,False,False)
# Make a ratio histogram for bottom plot.
altFitRatio = ROOT.TH1D("altFitRatio","altFitRatio",len(newbins)-1,array('d',newbins))
for bin in range(0,altFitRatio.GetNbinsX()+1) :
if newbasicBkg.GetBinContent(bin) == 0 :
altFitRatio.SetBinContent(bin,0)
else :
altFitRatio.SetBinContent(bin,(valueNewFuncErrDirected.GetBinContent(bin)-newbasicBkg.GetBinContent(bin))/newbasicBkg.GetBinContent(bin))
# Make a ratio histogram for paper plot.
PlusNomRatio = ROOT.TH1D("PlusNomRatio","PlusNomRatio",len(newbins)-1,array('d',newbins))
for bin in range(0,PlusNomRatio.GetNbinsX()+1) :
if newbasicBkg.GetBinContent(bin) == 0 :
PlusNomRatio.SetBinContent(bin,0)
else :
PlusNomRatio.SetBinContent(bin,(newNomPlus1.GetBinContent(bin)-newbasicBkg.GetBinContent(bin))/newbasicBkg.GetBinContent(bin))
MinusNomRatio = ROOT.TH1D("MinusNomRatio","MinusNomRatio",len(newbins)-1,array('d',newbins))
for bin in range(0,MinusNomRatio.GetNbinsX()+1) :
if newbasicBkg.GetBinContent(bin) == 0 :
MinusNomRatio.SetBinContent(bin,0)
else :
MinusNomRatio.SetBinContent(bin,(newNomMinus1.GetBinContent(bin)-newbasicBkg.GetBinContent(bin))/newbasicBkg.GetBinContent(bin))
myPainter.drawDataWithFitAsHistogramAndResidual(newbasicdata,newbasicBkg,luminosity,13,"m_{jj} [TeV]","Events",["Data","Fit","Statistical uncertainty on fit","Function choice"],"{0}/compareFitQualityAndFitChoice_Asymm_WithRatio".format(outPath),True,[[newNomPlus1,newNomMinus1],[placeHolderNom,newValueNewFuncErrDirected]],[altFitRatio,MinusNomRatio,PlusNomRatio],firstBin,lastBin,True,True,True,False,False,True,False,True,bumpHunterPVal,True,fitRange[0],fitRange[1])
myPainter.drawDataWithFitAsHistogramAndResidualPaper(newbasicdata,newbasicBkg,luminosity,13,"m_{jj} [TeV]","Events",["Data","Fit","Statistical uncertainty on fit","Function choice"],"{0}/compareFitQualityAndFitChoice_Asymm_WithRatioPaper".format(outPath),True,[[newNomPlus1,newNomMinus1],[placeHolderNom,newValueNewFuncErrDirected]],[altFitRatio,MinusNomRatio,PlusNomRatio],firstBin,lastBin,True,True,True,False,False)
myPainter.drawMultipleFitsAndResiduals(newbasicdata,[newbasicBkg,newValueNewFuncErrDirected],[altFitRatio],["Nominal fit","Function choice"],"m_{jj} [TeV]","Events",["(alt-nom)/nom"],"{0}/directedFuncChoiceVersusNominal_withRatio".format(outPath),luminosity,13,firstBin,lastBin)
####### Draw Signal Template overlaid on Background###########
if overlaidSignal:
signalFile = ROOT.TFile.Open(signalFileName, "read")
if not signalFile:
print signalFileName, " doesn't exist!!!!"
return
# setup signal information
#signalTitles = {"QStar": "#it{q}*"}
#signalTypes = ["QStar"]
#signalsMasses = {"QStar":[4000, 5000]}
#signalScalingFactors = {"QStar": 0.1}
#signalAxes = {"QStar": {"X" : "M_{#it{q}*} [GeV]", "Y": "#sigma #times #it{A} #times BR [pb]"} }
signalTitles = {"DMZprime": "DM #it{Z}'"}
signalTypes = ["DMZprime"]
signalsMasses = {"DMZprime":[4000, 5000]}
signalScalingFactors = {"DMZprime": 1000}
signalAxes = {"DMZprime": {"X" : "M_{#it{q}*} [GeV]", "Y": "#sigma #times #it{A} #times BR [pb]"} }
for signalType in signalTypes:
print "in signal",signalType
signalMasses = signalsMasses[signalType]
signalMassesTeV = signalsMasses[signalType][:]
for index in range(len(signalMasses)) :
signalMassesTeV[index] = signalMasses[index]/1000.0
print signalMassesTeV
signalPlotsTeV = []
legendlistTeV = []
for mass in signalMasses :
#sigplot = signalFile.Get("h_mjj_{0}".format(mass))
sigplot = signalFile.Get("{0}_{1}".format(signalType, mass))
sigplot.SetDirectory(0)
sigplottev = newbasicdata.Clone()
sigplottev.SetName("sigplot_{0}_{1}_TeV".format(signalType,mass))
for bins in range(sigplot.GetNbinsX()+2) :
for bin in range(sigplottev.GetNbinsX()+2) :
if sigplot.GetBinLowEdge(bins)/1000.==sigplottev.GetBinLowEdge(bin) :
sigplottev.SetBinContent(bin,sigplot.GetBinContent(bins))
sigplottev.SetBinError(bin,sigplot.GetBinError(bins))
sigplotforfitplusbkg = sigplottev.Clone()
sigplotforfitplusbkg.SetDirectory(0)
sigplotforfitplusbkg.SetName(sigplottev.GetName()+"_forfitplusbkg_TeV")
sigplotforfitplusbkg.Scale(signalScalingFactors[signalType])
signalPlotsTeV.append(sigplotforfitplusbkg)
thistitle = signalTitles[signalType] + ", {0}= {1} TeV".format(signalAxes[signalType]["X"].split("[GeV]")[0].replace("M","m"),mass/1000.0)
legendlistTeV.append(thistitle)
extLastBin = lastBin
for bin in range(sigplotforfitplusbkg.GetNbinsX()) :
if bin > extLastBin and sigplotforfitplusbkg.GetBinContent(bin) > 0.01 :
extLastBin = bin
if sigplotforfitplusbkg.GetBinLowEdge(bin) > 1.3*mass/1000.0 :
continue
if extLastBin < lastBin :
extLastBin = lastBin
UserScaleText = signalTitles[signalType]
if signalScalingFactors[signalType] == 1 :
UserScaleText = signalTitles[signalType]
else :
UserScaleText = UserScaleText+", #sigma #times "+str(signalScalingFactors[signalType])
outputName = outPath+"FancyFigure1_"+signalType
myPainter.drawDataAndFitWithSignalsOverSignificances(newbasicdata,newbasicBkg, None,\
newresidualHist,signalPlotsTeV, None, signalMassesTeV,legendlistTeV,\
"m_{jj} [TeV]","Events","","Significance ", outputName,luminosity,\
Ecm, firstBin,extLastBin,\
True, bumpLowEdge/1000,bumpHighEdge/1000,\
True,False,True,UserScaleText,True,bumpHunterPVal, True, \
fitRange[0], fitRange[1])
outputName = outPath+"FancyFigure1_"+signalType+"_nologx"
myPainter.drawDataAndFitWithSignalsOverSignificances(newbasicdata,newbasicBkg, None,\
newresidualHist,signalPlotsTeV, None, signalMassesTeV,legendlistTeV,\
"m_{jj} [TeV]","Events","","Significance ", outputName,luminosity,\
Ecm, firstBin,extLastBin,\
True, bumpLowEdge/1000,bumpHighEdge/1000,\
False,False,True,UserScaleText,True,bumpHunterPVal, True, \
fitRange[0], fitRange[1])
outputName = outPath+"FancyFigure1_"+signalType+"_noBump"
myPainter.drawDataAndFitWithSignalsOverSignificances(newbasicdata,newbasicBkg, None,\
newresidualHist,signalPlotsTeV, None, signalMassesTeV,legendlistTeV,\
"m_{jj} [TeV]","Events","","Significance ", outputName,luminosity,\
Ecm, firstBin,extLastBin,\
False, bumpLowEdge/1000,bumpHighEdge/1000,\
True,False,True,UserScaleText,True,bumpHunterPVal, True, \
fitRange[0], fitRange[1])
outputName = outPath+"FancyFigure1_"+signalType+"_noBump_nologx"
myPainter.drawDataAndFitWithSignalsOverSignificances(newbasicdata,newbasicBkg, None,\
newresidualHist,signalPlotsTeV, None, signalMassesTeV,legendlistTeV,\
"m_{jj} [TeV]","Events","","Significance ", outputName,luminosity,\
Ecm, firstBin,extLastBin,\
False, bumpLowEdge/1000,bumpHighEdge/1000,\
False,False,True,UserScaleText,True,bumpHunterPVal, True, \
fitRange[0], fitRange[1])
###############################
# Draw the comparison between data and MC in the bottom panel
if drawMCComparison:
mcFile = ROOT.TFile(mcFileName, "read") ;
if not mcFile:
print "Can not open: ", mcFileName
return
mchist_nominal = mcFile.Get("djet_mjj_nominal")
mchist_jesup = mcFile.Get("djet_mjj_JES_up")
mchist_jesdown = mcFile.Get("djet_mjj_JES_down")
newmchist_nominal=ROOT.TH1D("djet_mjj_nominal_TeV","djet_mjj_nominal_TeV",len(newbins)-1,array('d',newbins))
newmchist_jesup=ROOT.TH1D("djet_mjj_jesup_TeV","djet_mjj_jesup_TeV",len(newbins)-1,array('d',newbins))
newmchist_jesdown=ROOT.TH1D("djet_mjj_jesdown_TeV","djet_mjj_jesdown_TeV",len(newbins)-1,array('d',newbins))
for iBin1 in range(1, newmchist_nominal.GetNbinsX()+1):
for iBin2 in range(1, mchist_nominal.GetNbinsX()+1):
if newmchist_nominal.GetBinLowEdge(iBin1)*1000==mchist_nominal.GetBinLowEdge(iBin2):
newmchist_nominal.SetBinContent(iBin1, mchist_nominal.GetBinContent(iBin2))
newmchist_nominal.SetBinError(iBin1, mchist_nominal.GetBinError(iBin2))
continue
for iBin1 in range(1, newmchist_jesup.GetNbinsX()+1):
for iBin2 in range(1, mchist_jesup.GetNbinsX()+1):
if newmchist_jesup.GetBinLowEdge(iBin1)*1000==mchist_jesup.GetBinLowEdge(iBin2):
newmchist_jesup.SetBinContent(iBin1, mchist_jesup.GetBinContent(iBin2))
newmchist_jesup.SetBinError(iBin1, mchist_jesup.GetBinError(iBin2))
continue
for iBin1 in range(1, newmchist_jesdown.GetNbinsX()+1):
for iBin2 in range(1, mchist_jesdown.GetNbinsX()+1):
if newmchist_jesdown.GetBinLowEdge(iBin1)*1000==mchist_jesdown.GetBinLowEdge(iBin2):
newmchist_jesdown.SetBinContent(iBin1, mchist_jesdown.GetBinContent(iBin2))
newmchist_jesdown.SetBinError(iBin1, mchist_jesdown.GetBinError(iBin2))
continue
tmpRatioHist = newbasicdata.Clone()
tmpRatioHist.SetMarkerColor(ROOT.kBlack)
tmpRatioHist.Add(newmchist_nominal,-1)
tmpRatioHist.Divide(newmchist_nominal)
## If data is 0 then there should be no ratio drawn
for iBin in range(1, tmpRatioHist.GetNbinsX()+1):
if newbasicdata.GetBinContent(iBin) == 0:
tmpRatioHist.SetBinContent(iBin, 0)
tmpRatioHist.SetBinError(iBin, 0)
UpDownRatioHists = []
if mchist_jesup.GetEntries() >= 0:
tmpJESRatioHist = newmchist_jesup
tmpJESRatioHist.Add( newmchist_nominal, -1. )
tmpJESRatioHist.Divide( newmchist_nominal )
tmpJESRatioHist.SetMarkerColorAlpha( ROOT.kBlue,0.15)
tmpJESRatioHist.SetLineColorAlpha( ROOT.kBlue,0.15)
tmpJESRatioHist.SetFillColorAlpha( ROOT.kBlue, 0.15)
tmpJESRatioHist.SetFillStyle(1001)
UpDownRatioHists.append(tmpJESRatioHist)
if mchist_jesdown.GetEntries() >= 0:
tmpJESRatioHist = newmchist_jesdown
tmpJESRatioHist.Add( newmchist_nominal, -1. )
tmpJESRatioHist.Divide( newmchist_nominal )
tmpJESRatioHist.SetMarkerColorAlpha( ROOT.kBlue,0.15)
tmpJESRatioHist.SetLineColorAlpha( ROOT.kBlue,0.15)
tmpJESRatioHist.SetFillColorAlpha( ROOT.kBlue, 0.15)
tmpJESRatioHist.SetFillStyle(1001)
UpDownRatioHists.append(tmpJESRatioHist)
outputName = outPath+"FancyFigure1_"+signalType+"_WithMCRatio"
myPainter.drawDataAndFitWithSignalsOverSignificancesWithMCRatio(newbasicdata,newbasicBkg,None,\
newresidualHist, signalPlotsTeV, [], signalMassesTeV,legendlistTeV,\
"m_{jj} [TeV]","Events","#frac{Data-MC}{MC}","Significance",\
outputName,luminosity,Ecm,firstBin,lastBin,True,bumpLowEdge/1000.0,bumpHighEdge/1000.0,\
True,False,False, UserScaleText,True,bumpHunterPVal,True,fitRange[0],fitRange[1],\
newmchist_nominal,tmpRatioHist,UpDownRatioHists[0],UpDownRatioHists[1])
outputName = outPath+"FancyFigure1_"+signalType+"_WithMCRatio_nologx"
myPainter.drawDataAndFitWithSignalsOverSignificancesWithMCRatio(newbasicdata,newbasicBkg,None, \
newresidualHist, signalPlotsTeV, [],signalMassesTeV,legendlistTeV, \
"m_{jj} [TeV]","Events","#frac{Data-MC}{MC}","Significance",\
outputName,luminosity,Ecm,firstBin,lastBin,True,bumpLowEdge/1000.0,bumpHighEdge/1000.0,\
False,False,False, UserScaleText,True,bumpHunterPVal,True,fitRange[0],fitRange[1],\
newmchist_nominal,tmpRatioHist,UpDownRatioHists[0],UpDownRatioHists[1])
searchInputFile.Close()
del searchInputFile
print "Done."
residual2.SetDirectory(0)
newfit2 = ROOT.TH1D("fit2_TeV", "fit2_TeV",
len(newbins) - 1, array('d', newbins))
newresidual2 = ROOT.TH1D("residual2_TeV", "residual2_TeV",
len(newbins) - 1, array('d', newbins))
for histnew, histold in [[newfit2, fit2],
[newresidual2, residual2]]:
for bin in range(histnew.GetNbinsX() + 2):
histnew.SetBinContent(bin, histold.GetBinContent(bin))
histnew.SetBinError(bin, histold.GetBinError(bin))
outputName = folderextension + "NominalAndBestFits_{0}_m{1}TeV".format(
signal, mass) + plotextension
myPainter.drawMultipleFitsAndResiduals(newdatahist,[newfithist,newfit2],[newresidual,newresidual2],["Nominal fit","Best fit in all #theta"],\
"m_{jj} [TeV]","Events",["Nominal","Best Fit "],outputName,luminosity,Ecm,\
firstBin,lastBin+2)
# outputName = folderextension+"NominalAndBestFits_{0}_m{1}TeV_megazoom".format(signal,mass)+plotextension
# myPainter.drawMultipleFitsAndResiduals(newdatahist,[newfithist,newfit2],[newresidual,newresidual2],["Nominal fit","Best fit in all #theta"],\
# "m_{jj} [TeV]","Events",["Nominal","Best Fit "],outputName,luminosity,Ecm,\
# newdatahist.FindBin(1.55),newdatahist.FindBin(1.74),False,0,0,True,False,notLogY=True)
# Posteriors with CLs
signalpost = inputfile.Get("likelihoodFunction")
signalpost.SetDirectory(0)
signalCL = inputfile.Get("CLOfRealLikelihood")[0]
outputName = folderextension + "SignalPosteriorWith95CL_{0}_m{1}TeV".format(
signal, mass)
inlist = [signalpost, signalCL]
for histnew, histold in [[newfit2, fit2],
[newresidual2, residual2]]:
for bin in range(histnew.GetNbinsX() + 2):
histnew.SetBinContent(bin, histold.GetBinContent(bin))
histnew.SetBinError(bin, histold.GetBinError(bin))
residual2.GetYaxis().SetNdivisions(604)
outputName = folderextension + "NominalAndBestFits_{0}_m{1}TeV".format(
signal, mass) + plotextension
#myPainter.drawMultipleFitsAndResiduals(datahist,[fithist,fit2],[residual,residual2],["Nominal fit","Best fit in all #theta"],\
#"m_{jj} [GeV]","Events",[" Significance","Significance "],outputName,luminosity,Ecm,\
#firstBin,lastBin-1,False,0,0,True,False,False,11,-1,True,["Nominal","Best Fit"],5E3,5E6)
myPainter.drawMultipleFitsAndResiduals(datahist,[fithist,fit2],[residual,residual2],["Nominal fit","Best fit"],\
"m_{jj} [GeV]","Events",[" Significance","Significance "],outputName,luminosity,Ecm,\
firstBin,lastBin-1,False,0,0,True,False,False,11,-1,True,["Nominal","Best Fit"],5E3,5E6)
#outputName = folderextension+"NominalAndBestFits_{0}_m{1}TeV_megazoom".format(signal,mass)+plotextension
#myPainter.drawMultipleFitsAndResiduals(newdatahist,[newfithist,newfit2],[newresidual,newresidual2],["Nominal fit","Best fit in all #theta"],\
#"m_{jj} [TeV]","Events",["Nominal","Best Fit "],outputName,luminosity,Ecm,\
#newdatahist.FindBin(1.55),newdatahist.FindBin(1.74),False,0,0,True,False,notLogY=True)
# Posteriors with CLs
signalpost = inputfile.Get("likelihoodFunction")
signalpost.SetDirectory(0)
signalCL = inputfile.Get("CLOfRealLikelihood")[0]
outputName = folderextension + "SignalPosteriorWith95CL_{0}_m{1}TeV".format(
signal, mass)
inlist = [signalpost, signalCL]
myPainter.drawPosteriorsWithCLs([inlist], ["Signal posterior"],
