# Define cuts
configMgr.cutsDict["UserRegion"] = "1."
# Define weights
configMgr.weights = "1."
# Define samples
bkgSample = Sample("Bkg", kGreen - 9)
bkgSample.setStatConfig(False)
bkgSample.buildHisto([nbkg], "UserRegion", "cuts")
# bkgSample.buildStatErrors([nbkgErr],"UserRegion","cuts")
# bkgSample.addSystematic(corb)
bkgSample.addSystematic(ucb)
sigSample = Sample("Sig", kPink)
sigSample.setNormFactor("mu_Sig", 1.0, 0.0, 100.0)
sigSample.setStatConfig(False)
sigSample.setNormByTheory(False)
sigSample.buildHisto([nsig], "UserRegion", "cuts")
# sigSample.buildStatErrors([nsigErr],"UserRegion","cuts")
# sigSample.addSystematic(cors)
# sigSample.addSystematic(ucs)
dataSample = Sample("Data", kBlack)
dataSample.setData()
dataSample.buildHisto([ndata], "UserRegion", "cuts")
# Define top-level
ana = configMgr.addTopLevelXML("SPlusB")
ana.addSamples([bkgSample, sigSample, dataSample])
ana.setSignalSample(sigSample)
other_sample.setNormByTheory()
sample_list_bkg.append(other_sample)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# single top
single_top_sample = Sample("SingleTop", kGreen-1)
single_top_sample.setStatConfig(use_stat)
single_top_sample.setNormByTheory()
sample_list_bkg.append(single_top_sample)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Z/gamma*
z_sample = Sample("ZGamma", kRed+1 )
z_sample.setNormFactor("mu_z", 1, 0, 100)
z_sample.setStatConfig(use_stat)
sample_list_bkg.append(z_sample)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# ttbar
ttbar_sample = Sample("ttbar", kGreen+2)
ttbar_sample.setNormFactor("mu_ttbar", 1, 0, 100)
ttbar_sample.setStatConfig(use_stat)
sample_list_bkg.append(ttbar_sample)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# data
data_sample = Sample("data",kBlack)
data_sample.setData()
# Define weights
configMgr.weights = "1."
# Define samples
bkgSample = Sample("Bkg",kGreen-9)
bkgSample.setNormByTheory(True)
bkgSample.buildHisto(nBkgCR,"CR","cuts",0.5)
bkgSample.buildHisto(nBkgSR,"SR","cuts",0.5)
bkgSample.addSystematic(bg1xsec)
ddSample = Sample("DataDriven",kGreen+2)
ddSample.addShapeFactor("DDShape")
sigSample = Sample("Sig",kPink)
sigSample.setNormFactor("mu_Sig",1.,0.2,1.5)
sigSample.buildHisto(nSigSR,"SR","cuts",0.5)
sigSample.setNormByTheory(True)
sigSample.addSystematic(sigxsec)
dataSample = Sample("Data",kBlack)
dataSample.setData()
dataSample.buildHisto(nDataCR,"CR","cuts",0.5)
dataSample.buildHisto(nDataSR,"SR","cuts",0.5)
# Define top-level
ana = configMgr.addFitConfig("SPlusB")
ana.addSamples([bkgSample,ddSample,dataSample])
# Define measurement
meas = ana.addMeasurement(name="NormalMeasurement",lumi=1.0,lumiErr=0.039)
# NB: note that theoSys on diboson are applied on the level of the region definitions,
# since we have one for the SR and one for the CR
dibosonSample = Sample(zlFitterConfig.dibosonSampleName, kRed+3)
dibosonSample.setTreeName("Diboson_SRAll")
dibosonSample.setFileList(dibosonFiles)
dibosonSample.setStatConfig(zlFitterConfig.useStat)
#--------------------------
# QCD
#--------------------------
qcdSample = Sample(zlFitterConfig.qcdSampleName, kOrange+2)
if zlFitterConfig.useDDQCDsample:#normWeight is 0 => remove it
qcdSample.setTreeName("Data_SRAll")
else :
qcdSample.setTreeName("QCD_SRAll")
qcdSample.setNormFactor("mu_"+zlFitterConfig.qcdSampleName, 1., 0., 50000000.)
qcdSample.setFileList(qcdFiles)
qcdSample.setStatConfig(zlFitterConfig.useStat)
qcdWeight = 1
nJets = channel.nJets
if nJets > 0 and nJets < len(zlFitterConfig.qcdWeightList)+1:
qcdWeight = zlFitterConfig.qcdWeightList[nJets-1]/ (zlFitterConfig.luminosity)
if zlFitterConfig.useMCQCDsample:
qcdWeight = 1
qcdSample.addWeight(str(qcdWeight))
for w in configMgr.weights: #add all other weights but not normWeight
qcdSample.addWeight(w)
if zlFitterConfig.useDDQCDsample:#normWeight is 0 => remove it
qcdSample.removeWeight("normWeight")
topKtScale = Systematic("KtScaleTop",configMgr.weights,ktScaleTopHighWeights,ktScaleTopLowWeights,"weight","overallNormHistoSys")
wzKtScale = Systematic("KtScaleWZ",configMgr.weights,ktScaleWHighWeights,ktScaleWLowWeights,"weight","overallNormHistoSys")
# JES uncertainty as shapeSys - one systematic per region (combine WR and TR), merge samples
jes = Systematic("JES","_NoSys","_JESup","_JESdown","tree","overallNormHistoSys")
statWRwz = Systematic("SLWR_wz", "_NoSys","","","tree","shapeStat")
statWRtop = Systematic("SLWR_top","_NoSys","","","tree","shapeStat")
# name of nominal histogram for systematics
configMgr.nomName = "_NoSys"
# List of samples and their plotting colours
topSample = Sample("Top",kGreen-9)
topSample.setNormFactor("mu_Top",1.,0.,5.)
topSample.setStatConfig(useStat)
topSample.setNormRegions([("SLWR","nJet"),("SLTR","nJet")])
wzSample = Sample("WZ",kAzure+1)
wzSample.setNormFactor("mu_WZ",1.,0.,5.)
wzSample.setStatConfig(useStat)
wzSample.setNormRegions([("SLWR","nJet"),("SLTR","nJet")])
bgSample = Sample("BG",kYellow-3)
bgSample.setNormFactor("mu_BG",1.,0.,5.)
bgSample.setStatConfig(useStat)
bgSample.setNormRegions([("SLWR","nJet"),("SLTR","nJet")])
qcdSample = Sample("QCD",kGray+1)
qcdSample.setQCD(True,"histoSys")
qcdSample.setStatConfig(useStat)
dataSample = Sample("Data",kBlack)
dataSample.setData()
topKtScale = Systematic("KtScaleTop", configMgr.weights, ktScaleTopHighWeights,
ktScaleTopLowWeights, "weight", "normHistoSys")
wzKtScale = Systematic("KtScaleWZ", configMgr.weights, ktScaleWHighWeights,
ktScaleWLowWeights, "weight", "normHistoSys")
# JES uncertainty as shapeSys - one systematic per region (combine WR and TR), merge samples
jes = Systematic("JES", "_NoSys", "_JESup", "_JESdown", "tree", "normHistoSys")
mcstat = Systematic("mcstat", "_NoSys", "_NoSys", "_NoSys", "tree",
"shapeStat")
# name of nominal histogram for systematics
configMgr.nomName = "_NoSys"
# List of samples and their plotting colours
topSample = Sample("Top", kGreen - 9)
topSample.setNormFactor("mu_Top", 1., 0., 5.)
topSample.setStatConfig(useStat)
topSample.setNormRegions([("SLWR", "nJet"), ("SLTR", "nJet")])
wzSample = Sample("WZ", kAzure + 1)
wzSample.setNormFactor("mu_WZ", 1., 0., 5.)
wzSample.setStatConfig(useStat)
wzSample.setNormRegions([("SLWR", "nJet"), ("SLTR", "nJet")])
bgSample = Sample("BG", kYellow - 3)
bgSample.setNormFactor("mu_BG", 1., 0., 5.)
bgSample.setStatConfig(useStat)
bgSample.setNormRegions([("SLWR", "nJet"), ("SLTR", "nJet")])
qcdSample = Sample("QCD", kGray + 1)
qcdSample.setQCD(True, "histoSys")
qcdSample.setStatConfig(useStat)
dataSample = Sample("Data", kBlack)
dataSample.setData()
# Define cuts
configMgr.cutsDict["UserRegion"] = "1."
#Define weights
configMgr.weights = "1."
#Define samples
bkgSample = Sample("Bkg",kGreen-9)
bkgSample.setStatConfig(True)
bkgSample.setNormByTheory(False) #this has to be true for samples with normalisation taken from MC, it means include lumi error (set false if data driven)
bkgSample.buildHisto([nbkg],"UserRegion","cuts")
bkgSample.buildStatErrors([nbkgErr],"UserRegion","cuts")
bkgSample.addSystematic(ucb)
sigSample = Sample("Sig",kPink)
sigSample.setNormFactor("mu_Sig",1.0,0.,24645.6)
sigSample.setStatConfig(True)
sigSample.setNormByTheory(False) #this has to be false since xsec is scaled by mu
sigSample.buildHisto([nsig],"UserRegion","cuts")
sigSample.buildStatErrors([nsigErr],"UserRegion","cuts")
dataSample = Sample("Data",kBlack)
dataSample.setData()
dataSample.buildHisto([ndata],"UserRegion","cuts")
# Define top-level
ana = configMgr.addFitConfig("SPlusB")
ana.addSamples([bkgSample,sigSample,dataSample])
ana.setSignalSample(sigSample)
# Define measurement
# --------------------------
# Diboson
# --------------------------
# NB: note that theoSys on diboson are applied on the level of the region definitions,
# since we have one for the SR and one for the CR
dibosonSample = Sample(zlFitterConfig.dibosonSampleName, kRed + 3)
dibosonSample.setTreeName("Diboson_SRAll")
dibosonSample.setFileList(dibosonFiles)
dibosonSample.setStatConfig(zlFitterConfig.useStat)
# --------------------------
# QCD
# --------------------------
qcdSample = Sample(zlFitterConfig.qcdSampleName, kOrange + 2)
qcdSample.setTreeName("QCDdd_SRAll")
qcdSample.setNormFactor("mu_" + zlFitterConfig.qcdSampleName, 1.0, 0.0, 500.0)
qcdSample.setFileList(qcdFiles)
qcdSample.setStatConfig(zlFitterConfig.useStat)
qcdWeight = 1
nJets = channel.nJets
if nJets > 0 and nJets < len(zlFitterConfig.qcdWeightList):
qcdWeight = zlFitterConfig.qcdWeightList[nJets - 1] / (zlFitterConfig.luminosity * 1000)
qcdSample.addWeight(str(qcdWeight))
for w in configMgr.weights: # ATT: there is a bug in HistFitter, I have to add the other weight by hand
qcdSample.addWeight(w)
# Define samples
# FakePhotonSample = Sample("Bkg",kGreen-9)
# FakePhotonSample.setStatConfig(False)
for region in yields_dict :
configMgr.cutsDict[region] = "1."
# if "CR" in region :
# sample_for_cr = region.split("_")[1].lower()
# if sample.name.lower() == sample_for_cr :
# set_norm_by_theory = False
# sample.setNormFactor("mu_%s" % sample.name.lower(), 1.0, 0.0, 10.0)
# sample.setNormRegions([ (region, "cuts") ])
if set_norm_by_theory :
sample.setNormByTheory()
# add the samples
tlx.addSamples(all_samples)
# signal
if myFitType == FitType.Exclusion :
sample_sig.setStatConfig(True)
sample_sig.setNormFactor("mu_Test", 1.0, 0.0, 10.0)
sample_sig.setNormRegions( [ ("CR_BKG0", "cuts") ] )#, ("CR_BKG1", "cuts") ] )
sample_sig.setNormByTheory()
tlx.addSamples(sample_sig)
# tlx.setSignalSample(sample_sig)
# tlx.addSignalChannels(sr_channels)
if myFitType == FitType.Background :
#tlx.addSignalChannels(sr_channels)
tlx.addValidationChannels(sr_channels)
configMgr.cutsDict["UserRegion"] = "1."
# Define weights
configMgr.weights = "1."
# Define samples
bkgSample = Sample("Bkg",kGreen-9)
bkgSample.setStatConfig(True)
bkgSample.buildHisto([nbkg],"UserRegion","cuts")
bkgSample.buildStatErrors([nbkgErr],"UserRegion","cuts") ###
if(runMode=="exclusion"):
bkgSample.addSystematic(corb)
bkgSample.addSystematic(ucb)
sigSample = Sample("Sig",kPink)
sigSample.setNormFactor("mu_Sig",1.,normFactorMin,normFactorMax)
sigSample.setStatConfig(False)
sigSample.setNormByTheory(False)
sigSample.buildHisto([nsig],"UserRegion","cuts")
sigSample.buildStatErrors([nsigErr],"UserRegion","cuts") ###
sigSample.addSystematic(cors) ###
sigSample.addSystematic(ucs) ###
dataSample = Sample("Data",kBlack)
dataSample.setData()
dataSample.buildHisto([ndata],"UserRegion","cuts")
# Define top-level
ana = configMgr.addTopLevelXML("SPlusB")
if(runMode=="exclusion"):
ana.addSamples([bkgSample,sigSample,dataSample])
# Define cuts
configMgr.cutsDict["UserRegion"] = "1."
# Define weights
configMgr.weights = "1."
# Define samples
bkgSample = Sample("Bkg", kGreen - 9)
bkgSample.setStatConfig(True)
bkgSample.buildHisto([nbkg], "UserRegion", "cuts", 0.5)
bkgSample.addSystematic(ucb)
sigSample = Sample("Sig", kPink)
sigSample.setNormFactor("mu_SS", 1, 0, 40)
#sigSample.setStatConfig(True)
sigSample.setNormByTheory()
sigSample.buildHisto([nsig], "UserRegion", "cuts", 0.5)
dataSample = Sample("Data", kBlack)
dataSample.setData()
dataSample.buildHisto([ndata], "UserRegion", "cuts", 0.5)
# Define top-level
ana = configMgr.addFitConfig("SPlusB")
ana.addSamples([bkgSample, sigSample, dataSample])
ana.setSignalSample(sigSample)
# Define measurement
meas = ana.addMeasurement(name="NormalMeasurement",
# Define cuts
configMgr.cutsDict["UserRegion"] = "1."
# Define weights
configMgr.weights = "1."
# Define samples
bkgSample = Sample("Bkg",kGreen-9)
bkgSample.setStatConfig(True)
bkgSample.buildHisto([nbkg],"UserRegion","cuts",0.5)
bkgSample.addSystematic(ucb)
sigSample = Sample("Sig",kPink)
sigSample.setNormFactor("mu_SS",1.,0.,10.)
#sigSample.setStatConfig(True)
sigSample.setNormByTheory()
sigSample.buildHisto([nsig],"UserRegion","cuts",0.5)
dataSample = Sample("Data",kBlack)
dataSample.setData()
dataSample.buildHisto([ndata],"UserRegion","cuts",0.5)
# Define top-level
ana = configMgr.addFitConfig("SPlusB")
ana.addSamples([bkgSample,sigSample,dataSample])
ana.setSignalSample(sigSample)
zvSample.setNormRegions([("emCRZV14b","cuts")])
# Add Systematics
zvSample = addSys(zvSample, userOpts.doSimFit2LZV, sysObj)
# Additional ZV Specific systematics
if userOpts.doSimFit2LZV:
#zvSample.addSystematic(sysObj.AR_all_GENZV)
#zvSample.addSystematic(sysObj.AR_all_SCALEZV)
#zvSample.addSystematic(sysObj.AR_all_SHOWERZV)
#zvSample.addSystematic(sysObj.AR_all_PDFZV)
# Specify where to take the normalization if
# we are doing the simultaneous fit
if 'SR1' in SR:
zvSample.setNormFactor("mu_2LZV1",1.,0.,10.)
elif('SR2a' in SR or 'SR2b' in SR or 'SR4a' in SR or 'SR4b' in SR or 'SR4c' in SR):
zvSample.setNormFactor("mu_2LZV2a",1.,0.,10.)
elif userOpts.doSimFit2LZV and 'SR5a' in SR:
zvSample.setNormFactor("mu_2LZV5a",1.,0.,10.)
elif('Super0a' in SR or 'Super0b' in SR or 'Super0c' in SR):
zvSample.setNormFactor("mu_ZV14a",1.,0.,10.)
elif('Super1a' in SR or 'Super1b' in SR or 'Super1c' in SR):
zvSample.setNormFactor("mu_ZV14b",1.,0.,10.)
else:
zvSample.setNormByTheory()
#zvSample.addSystematic(sysObj.AR_all_GENZV)
#zvSample.addSystematic(sysObj.AR_all_SCALEZV)
#zvSample.addSystematic(sysObj.AR_all_SHOWERZV)
#zvSample.addSystematic(sysObj.AR_all_PDFZV)
# Define weights
configMgr.weights = "1."
# Define samples
bkgSample = Sample("Bkg",kGreen-9)
bkgSample.setNormByTheory(True)
bkgSample.buildHisto(nBkgCR,"CR","cuts",0.5)
bkgSample.buildHisto(nBkgSR,"SR","cuts",0.5)
bkgSample.addSystematic(bg1xsec)
ddSample = Sample("DataDriven",kGreen+2)
ddSample.addShapeFactor("DDShape")
sigSample = Sample("Sig",kPink)
sigSample.setNormFactor("mu_Sig",1.,0.2,1.5)
sigSample.buildHisto(nSigSR,"SR","cuts",0.5)
sigSample.setNormByTheory(True)
sigSample.addSystematic(sigxsec)
dataSample = Sample("Data",kBlack)
dataSample.setData()
dataSample.buildHisto(nDataCR,"CR","cuts",0.5)
dataSample.buildHisto(nDataSR,"SR","cuts",0.5)
# Define top-level
ana = configMgr.addFitConfig("SPlusB")
ana.addSamples([bkgSample,ddSample,dataSample])
# Define measurement
meas = ana.addMeasurement(name="NormalMeasurement",lumi=1.0,lumiErr=0.039)
# phoScaleMuWgamma = Systematic("phoScale",configMgr.weights, 1.018, 1-.018, "user","userOverallSys")
# phoScaleMuttgamma = Systematic("phoScale",configMgr.weights, 1.015,1-.015, "user","userOverallSys")
# phoScaleMuttbarDilep = Systematic("phoScale",configMgr.weights, 1.028, 1-.028, "user","userOverallSys")
# phoScaleMust = Systematic("phoScale",configMgr.weights, 1.023, 1-.023, "user","userOverallSys")
# phoScaleMudiboson = Systematic("phoScale",configMgr.weights, 1.040, 1-.040, "user","userOverallSys")
# phoScaleMuZgamma = Systematic("phoScale",configMgr.weights, 1.025, 1-.025, "user","userOverallSys")
## List of samples and their plotting colours. Associate dedicated systematics if applicable.
ttbargamma = Sample("ttbargamma",46) # brick
ttbargamma.setNormByTheory()
ttbargamma.setStatConfig(True)
ttbargamma.addSystematic(ttbargammaNorm)
Wgamma = Sample("Wgamma",7) # cyan
Wgamma.setNormFactor("mu_Wgamma",1.,0.,5.)
Wgamma.setNormRegions([("WCRhHTEl", "cuts")])
Wgamma.setStatConfig(True)
#Wgamma.addSystematic(WgammaNorm)
Zgamma = Sample("Zgamma",kViolet) # cyan
Zgamma.setNormByTheory()
Zgamma.setStatConfig(True)
Zgamma.addSystematic(ZgammaNorm)
Zjets = Sample("Zjets",kBlue) # cyan
Zjets.setNormByTheory()
Zjets.setStatConfig(True)
Zjets.addSystematic(ZjetsNorm)
Wjets = Sample("Wjets",3) # green
other_sample.setNormByTheory()
sample_list_bkg.append(other_sample)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# single top
single_top_sample = Sample("SingleTop", kGreen-1)
single_top_sample.setStatConfig(use_stat)
single_top_sample.setNormByTheory()
sample_list_bkg.append(single_top_sample)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Z/gamma*
z_sample = Sample("ZGamma", kRed+1 )
z_sample.setNormFactor("mu_z", 1, 0, 100)
z_sample.setStatConfig(use_stat)
sample_list_bkg.append(z_sample)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# ttbar
ttbar_sample = Sample("ttbar", kGreen+2)
ttbar_sample.setNormFactor("mu_ttbar", 1, 0, 100)
ttbar_sample.setStatConfig(use_stat)
sample_list_bkg.append(ttbar_sample)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# data
data_sample = Sample("data",kBlack)
data_sample.setData()
"SU_160_160_0_10", "SU_1000_280_0_10", "SU_1000_160_0_10",
"SU_400_100_0_10", "SU_760_190_0_10", "SU_680_160_0_10", "SU_840_220_0_10",
"SU_360_340_0_10", "SU_1080_220_0_10", "SU_360_250_0_10",
"SU_760_130_0_10", "SU_440_115_0_10", "SU_240_160_0_10", "SU_200_310_0_10",
"SU_200_145_0_10", "SU_600_220_0_10", "SU_280_130_0_10", "SU_520_220_0_10",
"SU_1080_160_0_10", "SU_40_190_0_10", "SU_1160_160_0_10",
"SU_280_310_0_10", "SU_920_160_0_10", "SU_80_190_0_10", "SU_40_310_0_10",
"SU_1160_130_0_10", "SU_40_250_0_10", "SU_40_100_0_10", "SU_400_220_0_10",
"SU_40_340_0_10", "SU_1000_100_0_10", "SU_120_175_0_10", "SU_280_220_0_10",
"SU_760_340_0_10", "SU_240_115_0_10", "SU_440_190_0_10",
"SU_1160_340_0_10", "SU_600_100_0_10", "SU_200_250_0_10",
"SU_280_145_0_10", "SU_200_190_0_10", "SU_200_205_0_10", "SU_760_250_0_10",
"SU_120_250_0_10", "SU_80_175_0_10", "SU_40_130_0_10", "SU_920_250_0_10",
"SU_80_160_0_10", "SU_240_175_0_10", "SU_280_100_0_10", "SU_1080_310_0_10",
"SU_920_340_0_10", "SU_120_115_0_10", "SU_1160_100_0_10",
"SU_280_340_0_10", "SU_1160_220_0_10", "SU_200_130_0_10",
"SU_160_175_0_10", "SU_360_220_0_10"
]
for sig in sigSamples:
myTopLvl = configMgr.addTopLevelXMLClone(bkgOnly, "SimpleChannel_%s" % sig)
#myTopLvl.removeSystematic(jes)
sigSample = Sample(sig, kBlue)
sigSample.setNormFactor("mu_SIG", 0.5, 0., 1.)
sigXSSyst = Systematic("SigXSec", None, None, None, "user", "overallSys")
sigSample.addSystematic(sigXSSyst)
#sigSample.addSystematic(jesSig)
sigSample.setNormByTheory()
myTopLvl.addSamples(sigSample)
myTopLvl.setSignalSample(sigSample)
# Samples
#-----------------
# W/Z + jets
wjets_sample = Sample('wjets', color("wjets"))
zjets_sample = Sample('zjets', color("zjets"))
wjets_sample.setNormByTheory()
zjets_sample.setNormByTheory()
# ttbar
ttbar_sample = Sample('ttbar', color("ttbar"))
ttbarg_sample = Sample('ttbarg', color("ttbarg"))
ttbar_sample.setNormByTheory()
ttbarg_sample.setNormFactor("mu_t", 1., 0., 2.)
# W/Z gamma
wgamma_sample = Sample('wgamma', color("wgamma"))
zllgamma_sample = Sample('zllgamma', color("zllgamma"))
znunugamma_sample = Sample('znunugamma', color("znunugamma"))
vqqgamma_sample = Sample("vqqgamma", color('vqqgamma'))
zllgamma_sample.setNormByTheory()
znunugamma_sample.setNormByTheory()
wgamma_sample.setNormFactor("mu_w", 1., 0., 2.)
# Fake met
photonjet_sample = Sample('photonjet', color("photonjet"))
multijet_sample = Sample('multijet', color("multijet"))
# Define cuts
configMgr.cutsDict["UserRegion"] = "1."
# Define weights
configMgr.weights = "1."
# Define samples
bkgSample = Sample("Bkg", kGreen - 9)
bkgSample.setStatConfig(True)
bkgSample.buildHisto([nbkg], "UserRegion", "cuts", 0.5)
bkgSample.buildStatErrors([nbkgErr], "UserRegion", "cuts")
bkgSample.addSystematic(corb)
bkgSample.addSystematic(ucb)
sigSample = Sample("Sig", kPink)
sigSample.setNormFactor("mu_Sig", 1., 0., 100.)
sigSample.setStatConfig(True)
sigSample.setNormByTheory()
sigSample.buildHisto([nsig], "UserRegion", "cuts", 0.5)
sigSample.buildStatErrors([nsigErr], "UserRegion", "cuts")
sigSample.addSystematic(cors)
sigSample.addSystematic(ucs)
dataSample = Sample("Data", kBlack)
dataSample.setData()
dataSample.buildHisto([ndata], "UserRegion", "cuts", 0.5)
# Define top-level
ana = configMgr.addFitConfig("SPlusB")
ana.addSamples([bkgSample, sigSample, dataSample])
ana.setSignalSample(sigSample)
gammaToZSyst = Systematic("gammaToZSyst", configMgr.weights, 1.25,0.75, "user","userOverallSys")
#-------------------------------------------
# List of samples and their plotting colours
#-------------------------------------------
dibosonSample = Sample("Diboson",kRed+3)
dibosonSample.setTreeName("Diboson_SRAll")
dibosonSample.setFileList(dibosonFiles)
dibosonSample.setStatConfig(useStat)
dibosonSample.addSystematic(theoSysDiboson)
topSample = Sample("Top",kGreen-9)
topSample.setTreeName("Top_SRAll")
topSample.setNormFactor("mu_Top",1.,0.,50000.)
topSample.setFileList(topFiles)
topSample.setStatConfig(useStat)
qcdSample = Sample("MCMultijet",kOrange+2)
qcdSample.setTreeName("QCD_SRAll")
qcdSample.setNormFactor("mu_MCMultijet",1.,0.,500.)
qcdSample.setFileList(qcdFiles)
qcdSample.setStatConfig(useStat)
wSample = Sample("W",kAzure+1)
wSample.setTreeName("W_SRAll")
wSample.setNormFactor("mu_W",1.,0.,500.)
wSample.setFileList(wFiles)
wSample.setStatConfig(useStat)
def common_setting(mass):
from configManager import configMgr
from ROOT import kBlack, kGray, kRed, kPink, kViolet, kBlue, kAzure, kGreen, \
kOrange
from configWriter import Sample
from systematic import Systematic
import os
color_dict = {
"Zbb": kAzure,
"Zbc": kAzure,
"Zbl": kAzure,
"Zcc": kAzure,
"Zcl": kBlue,
"Zl": kBlue,
"Wbb": kGreen,
"Wbc": kGreen,
"Wbl": kGreen,
"Wcc": kGreen,
"Wcl": kGreen,
"Wl": kGreen,
"ttbar": kOrange,
"stop": kOrange,
"stopWt": kOrange,
"ZZPw": kGray,
"WZPw": kGray,
"WWPw": kGray,
"fakes": kPink,
"Zjets": kAzure,
"Wjets": kGreen,
"top": kOrange,
"diboson": kGray,
"$Z\\tau\\tau$+HF": kAzure,
"$Z\\tau\\tau$+LF": kBlue,
"$W$+jets": kGreen,
"$Zee$": kViolet,
"Zhf": kAzure,
"Zlf": kBlue,
"Zee": kViolet,
"others": kViolet,
signal_prefix + "1000": kRed,
signal_prefix + "1100": kRed,
signal_prefix + "1200": kRed,
signal_prefix + "1400": kRed,
signal_prefix + "1600": kRed,
signal_prefix + "1800": kRed,
signal_prefix + "2000": kRed,
signal_prefix + "2500": kRed,
signal_prefix + "3000": kRed,
# Add your new processes here
"VH": kGray + 2,
"VHtautau": kGray + 2,
"ttH": kGray + 2,
}
##########################
# Setting the parameters of the hypothesis test
configMgr.doExclusion = True # True=exclusion, False=discovery
configMgr.nTOYs = 10000 # default=5000
configMgr.calculatorType = 0 # 2=asymptotic calculator, 0=frequentist calculator
configMgr.testStatType = 3 # 3=one-sided profile likelihood test statistic (LHC default)
configMgr.nPoints = 30 # number of values scanned of signal-strength for upper-limit determination of signal strength.
configMgr.writeXML = False
configMgr.seed = 40
configMgr.toySeedSet = True
configMgr.toySeed = 400
# Pruning
# - any overallSys systematic uncertainty if the difference of between the up variation and the nominal and between
# the down variation and the nominal is below a certain (user) given threshold
# - for histoSys types, the situation is more complex:
# - a first check is done if the integral of the up histogram - the integral of the nominal histogram is smaller
# than the integral of the nominal histogram and the same for the down histogram
# - then a second check is done if the shape of the up, down and nominal histograms is very similar Only when both
# conditions are fulfilled the systematics will be removed.
# default is False, so the pruning is normally not enabled
configMgr.prun = True
# The threshold to decide if an uncertainty is small or not is set by configMgr.prunThreshold = 0.005
# where the number gives the fraction of deviation with respect to the nominal histogram below which an uncertainty
# is considered to be small. The default is currently set to 0.01, corresponding to 1 % (This might be very aggressive
# for the one or the other analyses!)
configMgr.prunThreshold = 0.005
# method 1: a chi2 test (this is still a bit experimental, so watch out if this is working or not)
# method 2: checking for every bin of the histograms that the difference between up variation and nominal and down (default)
configMgr.prunMethod = 2
# variation and nominal is below a certain threshold.
# Smoothing: HistFitter does not provide any smoothing tools.
# More Details: https://twiki.cern.ch/twiki/bin/viewauth/AtlasProtected/HistFitterAdvancedTutorial#Pruning_in_HistFitter
##########################
# Keep SRs also in background fit confuguration
configMgr.keepSignalRegionType = True
configMgr.blindSR = BLIND
# Give the analysis a name
configMgr.analysisName = "bbtautau" + "X" + mass
configMgr.histCacheFile = "data/" + configMgr.analysisName + ".root"
configMgr.outputFileName = "results/" + configMgr.analysisName + "_Output.root"
# Define cuts
configMgr.cutsDict["SR"] = "1."
# Define weights
configMgr.weights = "1."
# Define samples
list_samples = []
yields_mass = yields[mass]
for process, yields_process in yields_mass.items():
if process == 'data' or signal_prefix in process: continue
# print("-> {} / Colour: {}".format(process, color_dict[process]))
bkg = Sample(str(process), color_dict[process])
bkg.setStatConfig(stat_config)
# OLD: add lumi uncertainty (bkg/sig correlated, not for data-driven fakes)
# NOW: add lumi by hand
bkg.setNormByTheory(False)
noms = yields_process["nEvents"]
errors = yields_process["nEventsErr"] if use_mcstat else [0.0]
# print(" nEvents (StatError): {} ({})".format(noms, errors))
bkg.buildHisto(noms, "SR", my_disc, 0.5)
bkg.buildStatErrors(errors, "SR", my_disc)
if not stat_only and not no_syst:
if process == 'fakes':
key_here = "ATLAS_FF_1BTAG_SIDEBAND_Syst_hadhad"
if not impact_check_continue(dict_syst_check, key_here):
bkg.addSystematic(
Systematic(key_here, configMgr.weights, 1.50, 0.50,
"user", syst_type))
else:
key_here = "ATLAS_Lumi_Run2_hadhad"
if not impact_check_continue(dict_syst_check, key_here):
bkg.addSystematic(
Systematic(key_here, configMgr.weights, 1.017, 0.983,
"user", syst_type))
for key, values in yields_process.items():
if 'ATLAS' not in key: continue
if impact_check_continue(dict_syst_check, key): continue
# this should not be applied on the Sherpa
if process == 'Zhf' and key == 'ATLAS_DiTauSF_ZMODEL_hadhad':
continue
if process == 'Zlf' and key == 'ATLAS_DiTauSF_ZMODEL_hadhad':
continue
ups = values[0]
downs = values[1]
systUpRatio = [
u / n if n != 0. else float(1.) for u, n in zip(ups, noms)
]
systDoRatio = [
d / n if n != 0. else float(1.)
for d, n in zip(downs, noms)
]
bkg.addSystematic(
Systematic(str(key), configMgr.weights, systUpRatio,
systDoRatio, "user", syst_type))
list_samples.append(bkg)
# FIXME: This is unusual!
top = Sample('top', kOrange)
top.setStatConfig(False) # No stat error
top.setNormByTheory(False) # consider lumi for it
top.buildHisto([0.00001], "SR", my_disc, 0.5) # small enough
# HistFitter can accept such large up ratio
# Systematic(name, weight, ratio_up, ratio_down, syst_type, syst_fistfactory_type)
if not stat_only and not no_syst:
key_here = 'ATLAS_TTBAR_YIELD_UPPER_hadhad'
if not impact_check_continue(dict_syst_check, key_here):
top.addSystematic(
Systematic(key_here, configMgr.weights, unc_ttbar[mass], 0.9,
"user", syst_type))
list_samples.append(top)
sigSample = Sample("Sig", kRed)
sigSample.setNormFactor("mu_Sig", 1., 0., 100.)
#sigSample.setStatConfig(stat_config)
sigSample.setStatConfig(False)
sigSample.setNormByTheory(False)
noms = yields_mass[signal_prefix + mass]["nEvents"]
errors = yields_mass[signal_prefix +
mass]["nEventsErr"] if use_mcstat else [0.0]
sigSample.buildHisto([n * MY_SIGNAL_NORM * 1e-3 for n in noms], "SR",
my_disc, 0.5)
#sigSample.buildStatErrors(errors, "SR", my_disc)
for key, values in yields_mass[signal_prefix + mass].items():
if 'ATLAS' not in key: continue
if impact_check_continue(dict_syst_check, key):
continue
ups = values[0]
downs = values[1]
systUpRatio = [
u / n if n != 0. else float(1.) for u, n in zip(ups, noms)
]
systDoRatio = [
d / n if n != 0. else float(1.) for d, n in zip(downs, noms)
]
if not stat_only and not no_syst:
sigSample.addSystematic(
Systematic(str(key), configMgr.weights, systUpRatio,
systDoRatio, "user", syst_type))
if not stat_only and not no_syst:
key_here = "ATLAS_SigAccUnc_hadhad"
if not impact_check_continue(dict_syst_check, key_here):
sigSample.addSystematic(
Systematic(key_here, configMgr.weights,
[1 + unc_sig_acc[mass] for i in range(my_nbins)],
[1 - unc_sig_acc[mass]
for i in range(my_nbins)], "user", syst_type))
key_here = "ATLAS_Lumi_Run2_hadhad"
if not impact_check_continue(dict_syst_check, key_here):
sigSample.addSystematic(
Systematic(key_here, configMgr.weights, 1.017, 0.983, "user",
syst_type))
list_samples.append(sigSample)
# Set observed and expected number of events in counting experiment
n_SPlusB = yields_mass[signal_prefix +
mass]["nEvents"][0] + sum_of_bkg(yields_mass)[0]
n_BOnly = sum_of_bkg(yields_mass)[0]
if BLIND:
# configMgr.useAsimovSet = True # Use the Asimov dataset
# configMgr.generateAsimovDataForObserved = True # Generate Asimov data as obsData for UL
# configMgr.useSignalInBlindedData = False
ndata = sum_of_bkg(yields_mass)
else:
try:
ndata = yields_mass["data"]["nEvents"]
except:
ndata = [0. for _ in range(my_nbins)]
lumiError = 0.017 # Relative luminosity uncertainty
dataSample = Sample("Data", kBlack)
dataSample.setData()
dataSample.buildHisto(ndata, "SR", my_disc, 0.5)
list_samples.append(dataSample)
# Define top-level
ana = configMgr.addFitConfig("SPlusB")
ana.addSamples(list_samples)
ana.setSignalSample(sigSample)
# Define measurement
meas = ana.addMeasurement(name="NormalMeasurement",
lumi=1.0,
lumiErr=lumiError / 100000.)
# make it very small so that pruned
# we use the one added by hand
meas.addPOI("mu_Sig")
#meas.statErrorType = "Poisson"
# Fix the luminosity in HistFactory to constant
meas.addParamSetting("Lumi", True, 1)
# Add the channel
chan = ana.addChannel(my_disc, ["SR"], my_nbins, my_xmin, my_xmax)
chan.blind = BLIND
#chan.statErrorType = "Poisson"
ana.addSignalChannels([chan])
# These lines are needed for the user analysis to run
# Make sure file is re-made when executing HistFactory
if configMgr.executeHistFactory:
if os.path.isfile("data/%s.root" % configMgr.analysisName):
os.remove("data/%s.root" % configMgr.analysisName)
# Define cuts
configMgr.cutsDict["UserRegion"] = "1."
# Define weights
configMgr.weights = "1."
# Define samples
bkgSample = Sample("Bkg",kGreen-9)
bkgSample.setStatConfig(True)
bkgSample.buildHisto([nbkg],"UserRegion","cuts",0.5)
bkgSample.buildStatErrors([nbkgErr],"UserRegion","cuts")
bkgSample.addSystematic(corb)
bkgSample.addSystematic(ucb)
sigSample = Sample("Sig",kPink)
sigSample.setNormFactor("mu_Sig",1.,0.,100.)
sigSample.setStatConfig(True)
sigSample.setNormByTheory()
sigSample.buildHisto([nsig],"UserRegion","cuts",0.5)
sigSample.buildStatErrors([nsigErr],"UserRegion","cuts")
sigSample.addSystematic(cors)
sigSample.addSystematic(ucs)
dataSample = Sample("Data",kBlack)
dataSample.setData()
dataSample.buildHisto([ndata],"UserRegion","cuts",0.5)
# Define top-level
ana = configMgr.addFitConfig("SPlusB")
ana.addSamples([bkgSample,sigSample,dataSample])
ana.setSignalSample(sigSample)
# Samples
#-----------------
# W/Z + jets
wjets_sample = Sample('wjets', color("wjets"))
zjets_sample = Sample('zjets', color("zjets"))
wjets_sample.setNormByTheory()
zjets_sample.setNormByTheory()
# ttbar
ttbar_sample = Sample('ttbar', color("ttbar"))
ttbarg_sample = Sample('ttgamma', color("ttbarg"))
ttbar_sample.setNormByTheory()
ttbarg_sample.setNormFactor("mu_t", 1., 0., 2.)
# W/Z gamma
wgamma_sample = Sample('wgamma', color("wgamma"))
zllgamma_sample = Sample('zllgamma', color("zllgamma"))
znunugamma_sample = Sample('znunugamma', color("znunugamma"))
vqqgamma_sample = Sample("vqqgamma", color('vqqgamma'))
zllgamma_sample.setNormByTheory()
znunugamma_sample.setNormByTheory()
wgamma_sample.setNormFactor("mu_w", 1., 0., 2.)
# Fake met
photonjet_sample = Sample('photonjet', color("photonjet"))
multijet_sample = Sample('multijet', color("multijet"))
# Determine the normalization region :
# --> If zero jet : pick "a" CR
# --> If one jet : pick "b" CR
if userOpts.doSimFit2LZV :
if('Super0a' in SR or 'Super0b' in SR or 'Super0c' in SR) :
zvSample.setNormRegions([("emCRZV14a", "cuts")])
userPrint(" >>> Normalization region for ZV : emCRZV14a")
elif('Super1a' in SR or 'Super1b' in SR or 'Super1c' in SR) :
zvSample.setNormRegions([("emCRZV14b", "cuts")])
userPrint(" >>> Normalization region for ZV : emCRZV14b")
# add systematics
zvSample = addSys(zvSample, userOpts.doSimFit2LZV, sysObj)
# set normalization factor
if userOpts.doSimFit2LZV :
if('Super0a' in SR or 'Super0b' in SR or 'Super0c' in SR) :
zvSample.setNormFactor("mu_ZV14a", 1.,0.,10.)
userPrint(" >>> Normalization factor for ZV : mu_ZV14a")
elif('Super1a' in SR or 'Super1b' in SR or 'Super1c' in SR) :
zvSample.setNormFactor("mu_ZV14b", 1., 0., 10.)
userPrint(" >>> Normalization factor for ZV : mu_ZV14b")
else :
zvSample.setNormByTheory()
# ----------------------------------------------------- #
# TOP #
# ----------------------------------------------------- #
topSample.setStatConfig(useStat)
if userOpts.splitMCSys :
topSample.addSystematic(sysObj.AR_mcstat_TOP)
## Discovery fit
#discovery = configMgr.adFitConfigClone(bkgOnly,"SimpleChannel_Discovery")
#discovery.clearSystematics()
#sigSample = Sample("discoveryMode",kBlue)
#sigSample.setNormFactor("mu_SIG",1.0, 0.0, 5.0)
#sigSample.setNormByTheory()
#discovery.addSamples(sigSample)
#discovery.setSignalSample(sigSample)
## Exclusion fits (MSUGRA grid)
#sigSamples=["SU_180_360_0_10","SU_580_240_0_10","SU_740_330_0_10","SU_900_420_0_10","SU_1300_210_0_10"]
sigSamples = ["SU_680_310_0_10","SU_440_145_0_10","SU_200_160_0_10","SU_440_340_0_10","SU_440_100_0_10","SU_120_130_0_10","SU_600_280_0_10","SU_320_115_0_10","SU_360_175_0_10","SU_920_310_0_10","SU_280_205_0_10","SU_1080_340_0_10","SU_40_280_0_10","SU_760_160_0_10","SU_200_115_0_10","SU_280_280_0_10","SU_40_160_0_10","SU_520_280_0_10","SU_120_220_0_10","SU_680_220_0_10","SU_40_115_0_10","SU_920_190_0_10","SU_320_130_0_10","SU_440_280_0_10","SU_360_100_0_10","SU_120_160_0_10","SU_1080_190_0_10","SU_840_250_0_10","SU_120_100_0_10","SU_120_340_0_10","SU_840_280_0_10","SU_80_115_0_10","SU_840_130_0_10","SU_320_175_0_10","SU_120_205_0_10","SU_520_100_0_10","SU_400_130_0_10","SU_360_310_0_10","SU_160_115_0_10","SU_1000_310_0_10","SU_40_220_0_10","SU_440_130_0_10","SU_1000_190_0_10","SU_80_220_0_10","SU_840_160_0_10","SU_120_145_0_10","SU_440_175_0_10","SU_360_280_0_10","SU_320_145_0_10","SU_400_160_0_10","SU_1000_340_0_10","SU_600_310_0_10","SU_320_190_0_10","SU_840_310_0_10","SU_200_220_0_10","SU_440_205_0_10","SU_360_205_0_10","SU_120_280_0_10","SU_1080_130_0_10","SU_160_145_0_10","SU_520_250_0_10","SU_840_100_0_10","SU_160_220_0_10","SU_120_190_0_10","SU_40_205_0_10","SU_280_250_0_10","SU_80_145_0_10","SU_200_175_0_10","SU_840_190_0_10","SU_240_145_0_10","SU_160_205_0_10","SU_400_115_0_10","SU_440_250_0_10","SU_600_340_0_10","SU_80_100_0_10","SU_520_190_0_10","SU_1160_190_0_10","SU_80_130_0_10","SU_400_190_0_10","SU_400_175_0_10","SU_600_130_0_10","SU_1080_100_0_10","SU_200_340_0_10","SU_1160_310_0_10","SU_440_160_0_10","SU_240_220_0_10","SU_200_100_0_10","SU_240_130_0_10","SU_360_130_0_10","SU_1000_250_0_10","SU_920_130_0_10","SU_240_190_0_10","SU_520_340_0_10","SU_40_175_0_10","SU_240_100_0_10","SU_400_145_0_10","SU_40_145_0_10","SU_240_205_0_10","SU_1080_280_0_10","SU_600_250_0_10","SU_360_145_0_10","SU_520_130_0_10","SU_1000_130_0_10","SU_440_310_0_10","SU_600_160_0_10","SU_920_280_0_10","SU_760_280_0_10","SU_280_190_0_10","SU_280_175_0_10","SU_120_310_0_10","SU_440_220_0_10","SU_1000_220_0_10","SU_1160_250_0_10","SU_400_205_0_10","SU_160_160_0_10","SU_1000_280_0_10","SU_1000_160_0_10","SU_400_100_0_10","SU_760_190_0_10","SU_680_160_0_10","SU_840_220_0_10","SU_360_340_0_10","SU_1080_220_0_10","SU_360_250_0_10","SU_760_130_0_10","SU_440_115_0_10","SU_240_160_0_10","SU_200_310_0_10","SU_200_145_0_10","SU_600_220_0_10","SU_280_130_0_10","SU_520_220_0_10","SU_1080_160_0_10","SU_40_190_0_10","SU_1160_160_0_10","SU_280_310_0_10","SU_920_160_0_10","SU_80_190_0_10","SU_40_310_0_10","SU_1160_130_0_10","SU_40_250_0_10","SU_40_100_0_10","SU_400_220_0_10","SU_40_340_0_10","SU_1000_100_0_10","SU_120_175_0_10","SU_280_220_0_10","SU_760_340_0_10","SU_240_115_0_10","SU_440_190_0_10","SU_1160_340_0_10","SU_600_100_0_10","SU_200_250_0_10","SU_280_145_0_10","SU_200_190_0_10","SU_200_205_0_10","SU_760_250_0_10","SU_120_250_0_10","SU_80_175_0_10","SU_40_130_0_10","SU_920_250_0_10","SU_80_160_0_10","SU_240_175_0_10","SU_280_100_0_10","SU_1080_310_0_10","SU_920_340_0_10","SU_120_115_0_10","SU_1160_100_0_10","SU_280_340_0_10","SU_1160_220_0_10","SU_200_130_0_10","SU_160_175_0_10","SU_360_220_0_10"]
#if not 'sigSamples' in dir():
# sigSamples=["SU_680_310_0_10"]
for sig in sigSamples:
myTopLvl = configMgr.addFitConfigClone(bkgOnly,"SimpleChannel_%s"%sig)
#myTopLvl.removeSystematic(jes)
sigSample = Sample(sig,kBlue)
sigSample.setNormFactor("mu_SIG", 1.0, 0., 5.0)
sigXSSyst = Systematic("SigXSec",configMgr.weights,1.1,0.9,"user","overallSys")
sigSample.addSystematic(sigXSSyst)
#sigSample.addSystematic(jesSig)
sigSample.setNormByTheory()
myTopLvl.addSamples(sigSample)
myTopLvl.setSignalSample(sigSample)
ch = myTopLvl.getChannel("cuts",cutsRegions)
myTopLvl.addSignalChannels(ch)
#photon systematics in SR for Z
gammaToZSyst = Systematic("gammaToZSyst", configMgr.weights, 1.25, 0.75,
"user", "userOverallSys")
#-------------------------------------------
# List of samples and their plotting colours
#-------------------------------------------
dibosonSample = Sample("Diboson", kRed + 3)
dibosonSample.setTreeName("Diboson_SRAll")
dibosonSample.setFileList(dibosonFiles)
dibosonSample.setStatConfig(useStat)
dibosonSample.addSystematic(theoSysDiboson)
topSample = Sample("Top", kGreen - 9)
topSample.setTreeName("Top_SRAll")
topSample.setNormFactor("mu_Top", 1., 0., 500.)
topSample.setFileList(topFiles)
topSample.setStatConfig(useStat)
qcdSample = Sample("MCMultijet", kOrange + 2)
qcdSample.setTreeName("QCD_SRAll")
qcdSample.setNormFactor("mu_MCMultijet", 1., 0., 500.)
qcdSample.setFileList(qcdFiles)
qcdSample.setStatConfig(useStat)
wSample = Sample("W", kAzure + 1)
wSample.setTreeName("W_SRAll")
wSample.setNormFactor("mu_W", 1., 0., 500.)
wSample.setFileList(wFiles)
wSample.setStatConfig(useStat)
bkgOnly.addSystematic(jes)
meas = bkgOnly.addMeasurement(measName,measLumi,measLumiError)
meas.addPOI("mu_SIG")
cutsChannel = bkgOnly.addChannel("cuts",cutsRegions,cutsNBins,cutsBinLow,cutsBinHigh)
## Discovery fit
#discovery = configMgr.addTopLevelXMLClone(bkgOnly,"SimpleChannel_Discovery")
#discovery.clearSystematics()
#sigSample = Sample("discoveryMode",kBlue)
#sigSample.setNormFactor("mu_SIG",0.5,0.,1.)
#sigSample.setNormByTheory()
#discovery.addSamples(sigSample)
#discovery.setSignalSample(sigSample)
## Exclusion fits (MSUGRA grid)
#sigSamples=["SU_180_360_0_10","SU_580_240_0_10","SU_740_330_0_10","SU_900_420_0_10","SU_1300_210_0_10"]
sigSamples = ["SU_680_310_0_10","SU_440_145_0_10","SU_200_160_0_10","SU_440_340_0_10","SU_440_100_0_10","SU_120_130_0_10","SU_600_280_0_10","SU_320_115_0_10","SU_360_175_0_10","SU_920_310_0_10","SU_280_205_0_10","SU_1080_340_0_10","SU_40_280_0_10","SU_760_160_0_10","SU_200_115_0_10","SU_280_280_0_10","SU_40_160_0_10","SU_520_280_0_10","SU_120_220_0_10","SU_680_220_0_10","SU_40_115_0_10","SU_920_190_0_10","SU_320_130_0_10","SU_440_280_0_10","SU_360_100_0_10","SU_120_160_0_10","SU_1080_190_0_10","SU_840_250_0_10","SU_120_100_0_10","SU_120_340_0_10","SU_840_280_0_10","SU_80_115_0_10","SU_840_130_0_10","SU_320_175_0_10","SU_120_205_0_10","SU_520_100_0_10","SU_400_130_0_10","SU_360_310_0_10","SU_160_115_0_10","SU_1000_310_0_10","SU_40_220_0_10","SU_440_130_0_10","SU_1000_190_0_10","SU_80_220_0_10","SU_840_160_0_10","SU_120_145_0_10","SU_440_175_0_10","SU_360_280_0_10","SU_320_145_0_10","SU_400_160_0_10","SU_1000_340_0_10","SU_600_310_0_10","SU_320_190_0_10","SU_840_310_0_10","SU_200_220_0_10","SU_440_205_0_10","SU_360_205_0_10","SU_120_280_0_10","SU_1080_130_0_10","SU_160_145_0_10","SU_520_250_0_10","SU_840_100_0_10","SU_160_220_0_10","SU_120_190_0_10","SU_40_205_0_10","SU_280_250_0_10","SU_80_145_0_10","SU_200_175_0_10","SU_840_190_0_10","SU_240_145_0_10","SU_160_205_0_10","SU_400_115_0_10","SU_440_250_0_10","SU_600_340_0_10","SU_80_100_0_10","SU_520_190_0_10","SU_1160_190_0_10","SU_80_130_0_10","SU_400_190_0_10","SU_400_175_0_10","SU_600_130_0_10","SU_1080_100_0_10","SU_200_340_0_10","SU_1160_310_0_10","SU_440_160_0_10","SU_240_220_0_10","SU_200_100_0_10","SU_240_130_0_10","SU_360_130_0_10","SU_1000_250_0_10","SU_920_130_0_10","SU_240_190_0_10","SU_520_340_0_10","SU_40_175_0_10","SU_240_100_0_10","SU_400_145_0_10","SU_40_145_0_10","SU_240_205_0_10","SU_1080_280_0_10","SU_600_250_0_10","SU_360_145_0_10","SU_520_130_0_10","SU_1000_130_0_10","SU_440_310_0_10","SU_600_160_0_10","SU_920_280_0_10","SU_760_280_0_10","SU_280_190_0_10","SU_280_175_0_10","SU_120_310_0_10","SU_440_220_0_10","SU_1000_220_0_10","SU_1160_250_0_10","SU_400_205_0_10","SU_160_160_0_10","SU_1000_280_0_10","SU_1000_160_0_10","SU_400_100_0_10","SU_760_190_0_10","SU_680_160_0_10","SU_840_220_0_10","SU_360_340_0_10","SU_1080_220_0_10","SU_360_250_0_10","SU_760_130_0_10","SU_440_115_0_10","SU_240_160_0_10","SU_200_310_0_10","SU_200_145_0_10","SU_600_220_0_10","SU_280_130_0_10","SU_520_220_0_10","SU_1080_160_0_10","SU_40_190_0_10","SU_1160_160_0_10","SU_280_310_0_10","SU_920_160_0_10","SU_80_190_0_10","SU_40_310_0_10","SU_1160_130_0_10","SU_40_250_0_10","SU_40_100_0_10","SU_400_220_0_10","SU_40_340_0_10","SU_1000_100_0_10","SU_120_175_0_10","SU_280_220_0_10","SU_760_340_0_10","SU_240_115_0_10","SU_440_190_0_10","SU_1160_340_0_10","SU_600_100_0_10","SU_200_250_0_10","SU_280_145_0_10","SU_200_190_0_10","SU_200_205_0_10","SU_760_250_0_10","SU_120_250_0_10","SU_80_175_0_10","SU_40_130_0_10","SU_920_250_0_10","SU_80_160_0_10","SU_240_175_0_10","SU_280_100_0_10","SU_1080_310_0_10","SU_920_340_0_10","SU_120_115_0_10","SU_1160_100_0_10","SU_280_340_0_10","SU_1160_220_0_10","SU_200_130_0_10","SU_160_175_0_10","SU_360_220_0_10"]
for sig in sigSamples:
myTopLvl = configMgr.addTopLevelXMLClone(bkgOnly,"SimpleChannel_%s"%sig)
#myTopLvl.removeSystematic(jes)
sigSample = Sample(sig,kBlue)
sigSample.setNormFactor("mu_SIG",0.5,0.,1.)
sigXSSyst = Systematic("SigXSec",None,None,None,"user","overallSys")
sigSample.addSystematic(sigXSSyst)
#sigSample.addSystematic(jesSig)
sigSample.setNormByTheory()
myTopLvl.addSamples(sigSample)
myTopLvl.setSignalSample(sigSample)
configMgr.outputFileName = "results/%s_Output.root" % configMgr.analysisName
# Define cuts
configMgr.cutsDict["SR"] = "1."
# Define weights
configMgr.weights = "1."
# Define samples
bkgSample = Sample("Bkg", ROOT.kGreen - 9)
bkgSample.setStatConfig(True)
bkgSample.buildHisto([nbkg], "SR", "cuts", 0.5)
bkgSample.addSystematic(ucb)
sigSample = Sample("GGM_GG_bhmix_%d_%d" % (args.m3, args.mu), ROOT.kOrange + 3)
sigSample.setNormFactor("mu_SIG", 1., 0., 10.)
#sigSample.setStatConfig(True)
sigSample.setNormByTheory()
sigSample.buildHisto([nsig], "SR", "cuts", 0.5)
dataSample = Sample("Data", ROOT.kBlack)
dataSample.setData()
dataSample.buildHisto([ndata], "SR", "cuts", 0.5)
# Define top-level
ana = configMgr.addFitConfig("Disc")
ana.addSamples([bkgSample, sigSample, dataSample])
ana.setSignalSample(sigSample)
# Define measurement
meas = ana.addMeasurement(name="NormalMeasurement",
zvSample.setStatConfig(useStat)
if userOpts.splitMCSys:
zvSample.addSystematic(sysObj.AR_mcstat_ZV)
# Determine the normalization region
# This should be done only if we fit
# Regardless of channel combined ee+mm
if userOpts.doSimFit2LZV:
zvSample.setNormRegions([("emCRZV14a","cuts"),("emCRZV14b","cuts")])
# Add Systematics
zvSample = addSys(zvSample, userOpts.doSimFit2LZV, sysObj)
# Additional ZV Specific systematics
if userOpts.doSimFit2LZV:
zvSample.setNormFactor("mu_ZV",1.,0.,10.)
else:
zvSample.setNormByTheory()
#------------------------------------------------#
# TOP #
#------------------------------------------------#
topSample.setStatConfig(useStat)
if userOpts.splitMCSys:
topSample.addSystematic(sysObj.AR_mcstat_TOP)
# Determine the normalization regions
# This should be done only if we fit
if userOpts.doSimFit2LTop :
topSample.setNormRegions([("emCRTop14a","cuts"), ("emCRTop14b","cuts")])
signal_sample = None
# prepare the fit configuration
ana = configMgr.addFitConfig("shape_fit")
meas = ana.addMeasurement(name="shape_fit", lumi=1.0, lumiErr=0.01)
# load all MC templates ...
for sample_name, template_name, template_color, is_floating, is_signal in zip(
sample_names, template_names, template_colors, normalization_floating,
signal_samples):
cur_sample = Sample(sample_name, template_color)
if is_floating:
normalization_name = "mu_" + sample_name
cur_sample.setNormFactor(normalization_name, 1, 0, 100)
if is_signal:
POIs.append(normalization_name)
signal_sample = cur_sample
# ... for all regions
for region_name, region_infile in zip(region_names, region_infiles):
binvals, edges = HistogramImporter.import_histogram(
os.path.join(indir, region_infile), template_name)
bin_width = edges[1] - edges[0]
cur_sample.buildHisto(binvals,
region_name,
"mBB",
binLow=edges[0],
# - List of systematics
# ---------------------
# generic systematic -- placeholder for now
gen_syst = Systematic( "gen_syst" , configMgr.weights , 1.0 + 0.30 , 1.0 - 0.30 , "user" , "userOverallSys" )
# JES uncertainty as shapeSys - one systematic per region (combine WR and TR), merge samples
# jes = Systematic("JES","_NoSys","_JESup","_JESdown","tree","overallNormHistoSys")
# --------------------------------------------
# - List of samples and their plotting colours
# --------------------------------------------
sample_list = []
# ttbar
ttbar_sample = Sample( "ttbar" , kGreen-2 )
ttbar_sample.setNormFactor("mu_ttbar",1.,0.,5.)
ttbar_sample.setStatConfig(use_stat)
ttbar_sample.setNormByTheory()
sample_list.append(ttbar_sample)
# single top
single_top_sample = Sample( "SingleTop" , kGreen-1 )
single_top_sample.setNormFactor("mu_st",1.,0.,5.)
single_top_sample.setStatConfig(use_stat)
single_top_sample.setNormByTheory()
sample_list.append(single_top_sample)
# Z/gamma*
z_sample = Sample( "Z" , kRed+1 )
z_sample.setNormFactor("mu_z",1.,0.,5.)
z_sample.setStatConfig(use_stat)
# **************
# Fit config instance
exclusionFitConfig = configMgr.addTopLevelXML("Exclusion")
meas = exclusionFitConfig.addMeasurement(name="NormalMeasurement", lumi=1.0, lumiErr=0.039)
meas.addPOI("mu_SIG")
# Samples
exclusionFitConfig.addSamples([topSample, wzSample, dataSample])
# Systematics
exclusionFitConfig.getSample("Top").addSystematic(topKtScale)
exclusionFitConfig.getSample("WZ").addSystematic(wzKtScale)
exclusionFitConfig.addSystematic(jes)
# Channel
srBin = exclusionFitConfig.addChannel("met/meff2Jet", ["SR"], 6, 0.1, 0.7)
srBin.useOverflowBin = True
srBin.useUnderflowBin = True
exclusionFitConfig.setSignalChannels([srBin])
sigSample = Sample("SM_GG_onestepCC_425_385_345", kPink)
sigSample.setFileList(["samples/tutorial/SusyFitterTree_p832_GG-One-Step_soft_v1.root"])
sigSample.setNormByTheory()
sigSample.setNormFactor("mu_SIG", 1.0, 0.0, 5.0)
exclusionFitConfig.addSamples(sigSample)
exclusionFitConfig.setSignalSample(sigSample)
# 2nd cloned-copy just to accomodate -l option...
exclusionFitClone = configMgr.addTopLevelXMLClone(exclusionFitConfig, "ExclusionFitClone")
exclusionFitConfig = configMgr.addFitConfig("Exclusion_"+sig)
meas=exclusionFitConfig.addMeasurement(name="NormalMeasurement",lumi=1.0,lumiErr=0.039)
meas.addPOI("mu_SIG")
# Samples
exclusionFitConfig.addSamples([topSample,wzSample,dataSample])
# Systematics
exclusionFitConfig.getSample("Top").addSystematic(topKtScale)
exclusionFitConfig.getSample("WZ").addSystematic(wzKtScale)
exclusionFitConfig.addSystematic(jes)
# Channel
srBin = exclusionFitConfig.addChannel("met/meff2Jet",["SR"],6,0.1,0.7)
srBin.useOverflowBin=True
srBin.useUnderflowBin=True
exclusionFitConfig.setSignalChannels([srBin])
sigSample = Sample(sig,kPink)
sigSample.setFileList(["samples/tutorial/SusyFitterTree_p832_GG-One-Step_soft_v1.root"])
sigSample.setNormByTheory()
sigSample.setNormFactor("mu_SIG",1.,0.,5.)
#sigSample.addSampleSpecificWeight("0.001")
exclusionFitConfig.addSamples(sigSample)
exclusionFitConfig.setSignalSample(sigSample)
# Cosmetics
srBin.minY = 0.0001
srBin.maxY = 80.
other_sample.setNormByTheory()
sample_list_bkg.append(other_sample)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# single top
single_top_sample = Sample("SingleTop", kGreen-1)
single_top_sample.setStatConfig(use_stat)
single_top_sample.setNormByTheory()
sample_list_bkg.append(single_top_sample)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Z/gamma*
z_sample = Sample("ZGamma", kRed+1 )
z_sample.setNormFactor("mu_z", 1, 0, 100)
z_sample.setStatConfig(use_stat)
sample_list_bkg.append(z_sample)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# ttbar
ttbar_sample = Sample("ttbar", kGreen+2)
ttbar_sample.setNormFactor("mu_ttbar", 1, 0, 100)
ttbar_sample.setStatConfig(use_stat)
sample_list_bkg.append(ttbar_sample)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# data
data_sample = Sample("data",kBlack)
data_sample.setData()
configMgr.cutsDict[region] = "1."
if "CR" in region and "BKG" in region and perform_simfit :
sample_for_cr = region.split("_")[1].lower()
if sample.name.lower() == sample_for_cr :
set_norm_by_theory = False
sample.setNormFactor("mu_%s" % sample.name.lower(), 1.0, 0.0, 10.0)
sample.setNormRegions([ (region, "cuts") ])
if set_norm_by_theory :
sample.setNormByTheory()
# signal
if myFitType == FitType.Exclusion :
sample_sig.setStatConfig(False)
sample_sig.setNormFactor("mu_SIG", 1.0, 0.0, 10.0)
sample_sig.setNormRegions( [ ("SR_BKG0", "cuts") ] )#, ("CR_BKG1", "cuts") ] )
sample_sig.setNormByTheory()
#tlx.addSamples(sample_sig)
tlx.setSignalSample(sample_sig)
tlx.addSignalChannels(sr_channels)
# add the samples
tlx.addSamples(all_samples)
#if myFitType == FitType.Background :
# tlx.addSignalChannels(cr_channels)
# #tlx.addSignalChannels(sr_channels)
# #tlx.addValidationChannels(sr_channels)
