configMgr.writeXML = True
##########################
# Give the analysis a name
configMgr.analysisName = "MyUserAnalysis"
configMgr.outputFileName = "results/%s_Output.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)
# KtScale uncertainty as histoSys - two-sided, no additional normalization
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)
]
qFlipSys = formTreeSys("CFLIP_SYS", "__1up", "__1down", "overallSys")
fLepSys = formTreeSys("FAKE_LEP_E_SYS", "__1up", "__1down", "overallSys")
fLepSys = formTreeSys("FAKE_LEP_U_SYS", "__1up", "__1down", "overallSys")
#the input Lumi for mc is 0.001 but data driven bkg is obtained from 3.248fb-1 data
#so need a scale factor to convert data driven bkg back to 0.001 inputLumi...
dataDrivenBkgScale = "%e" % (configMgr.inputLumi / options.bkgLumi)
configMgr.nomName = ""
#-------------------------------------------
# List of samples and their plotting colours
#-------------------------------------------
wgammaSample = Sample("wgamma", kGreen - 9)
dibosonSample = Sample("diboson", kAzure + 1)
tribosonSample = Sample("triboson", kAzure + 2)
topXSample = Sample("topX", kRed + 1)
higgsSample = Sample("higgs", kRed + 2)
qFlipSample = Sample("qFlip", kBlue)
fLepSample = Sample("fLep", kYellow)
#cflipSample.setNormFactor("mu_CFlip",1.,0.7,1.3,True) #no sys for CFlip yet, use 30% uncertain non-constant normFac
#fakelepSample.setNormFactor("mu_FakeLep",1.,0.7,1.3,True) #no sys for CFlip yet, use 30% uncertain non-constant normFac
dataSample = Sample("data", kBlack)
dataSample.setData()
#dataSample.buildHisto([1.0],"SR","cuts",0.5)
#dataSample.setStatConfig(False)
##########################
# Give the analysis a name
configMgr.analysisName = "MyUserAnalysis_ShapeFactor"
configMgr.outputFileName = "results/%s_Output.root"%configMgr.analysisName
# Define cuts
configMgr.cutsDict["CR"] = "1."
configMgr.cutsDict["SR"] = "1."
# 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)
_signalRegion, # Specify the signal region
_ch, # Lepton Channel
_sleptonHand, # Slepton Handedness to consider
bkgFile, # input bkg file
dataFile, # input data file // dantrim
signalFile, # input signal file
analysisName, # Analysis Name for saving
20.3, # Input Lumi units
20.3, # Ouput Lumi units
"fb-1" # Input Lumi units
)
# Specify the samples to consider and use the correct names
# corresponding to your input trees
zxSample = Sample("Zjets" , kGreen+2)
fakeSample = Sample("Fake" , kGray)
higgsSample = Sample("Higgs" , kYellow)
zvSample = Sample("ZV" , kGreen)
topSample = Sample("Top" , kViolet)
wwSample = Sample("WW" , kAzure-4)
dataSample = Sample("Data_CENTRAL", kBlack)
#mcSamples = [zxSample, higgsSample, zvSample, topSample, wwSample, dataSample]
##mcSamples = [higgsSample, zvSample, wwSample, dataSample]
#bkgFiles = []
#if userOpts.do2L:
# bkgFiles.append( userOpts.bkgFile )
#for sample in mcSamples :
# sample.setFileList(bkgFiles)
"bTagWeight2Jet")
ktScaleTopLowWeights = ("genWeight", "eventWeight", "ktfacDownWeightTop",
"bTagWeight2Jet")
#topKtScale = Systematic("KtScaleTop",configMgr.weights,ktScaleTopHighWeights,ktScaleTopLowWeights,"weight","overallSys")
topKtScale = Systematic("KtScaleTop", configMgr.weights, ktScaleTopHighWeights,
ktScaleTopLowWeights, "weight", "histoSys")
#topKtScale = Systematic("KtScaleTop",configMgr.weights,ktScaleTopHighWeights,ktScaleTopLowWeights,"weight","normHistoSys")
#JES (tree-based)
jes = Systematic("JES", "_NoSys", "_JESup", "_JESdown", "tree", "overallSys")
configMgr.nomName = "_NoSys"
#-------------------------------------------
# List of samples and their plotting colours
#-------------------------------------------
topSample = Sample("Top", kGreen - 9)
#topSample.setNormFactor("mu_Top",1.,0.,5.)
wzSample = Sample("WZ", kAzure + 1)
#wzSample.setNormFactor("mu_WZ",1.,0.,5.)
dataSample = Sample("Data", kBlack)
dataSample.setData()
dataSample.buildHisto([0., 1., 5., 15., 4., 0.], "SR", "metmeff2Jet", 0.1, 0.1)
#dataSample.buildStatErrors([1.,1.,2.4,3.9,2.,0.],"SR","metmeff2Jet")
#**************
# Exclusion fit
#**************
if myFitType == FitType.Exclusion:
# loop over all signal points
for sig in sigSamples:
configMgr.writeXML = True
##########################
# Give the analysis a name
configMgr.analysisName = "PhotonMetAnalysis_Simple"
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
configMgr.nPoints=20 # number of values scanned of signal-strength for upper-limit determination of signal strength.
##########################
# Give the analysis a name
configMgr.analysisName = "MyUserAnalysis"
configMgr.outputFileName = "results/%s_Output.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")
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) ###
True, # Blind CR
True, # Blind VR
_sigUncert, # Specify uncertainty
_grid, # Specify grid
_signalRegion, # Specify the signal region
_ch, # Lepton Channel
bkgFile, # input bkg file
signalFile, # input signal file
analysisName, # Analysis Name for saving
20.3, # Input Lumi units
20.3, # Ouput Lumi units
"fb-1", # Input Lumi units
)
# Define Data and BG samples
dataSample = Sample("Data_CENTRAL", ROOT.kBlack)
zjetsSample = Sample("Zjets", ROOT.kGreen + 2)
fakeSample = Sample("fake", ROOT.kOrange - 4)
higgsSample = Sample("Higgs", ROOT.kYellow)
wwSample = Sample("WW", ROOT.kAzure - 4)
wzSample = Sample("WZ", ROOT.kGreen)
zzSample = Sample("ZZ", ROOT.kGreen - 4)
tribosonSample = Sample("triboson", ROOT.kAzure)
ttbarVSample = Sample("ttbarV", ROOT.kViolet)
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
# USER SHOULD NOT HAVE TO EDIT BELOW HERE #
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#
# --------------------------------------------------------#
# Useful methods used below
configMgr.nPoints = 20 # number of values scanned of signal-strength for upper-limit determination of signal strength.
##########################
# Give the analysis a name
configMgr.analysisName = "SimpleUL_%s" % SR
configMgr.outputFileName = "results/%s_Output.root" % configMgr.analysisName
# 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)
dataSample = Sample("Data", kBlack)
dataSample.setData()
dataSample.buildHisto([ndata], "UserRegion", "cuts")
# Define top-level
ana = configMgr.addFitConfig("SPlusB")
ana.addSamples([bkgSample, dataSample])
#ana.setSignalSample(sigSample)
configMgr.setLumiUnits("fb-1")
configMgr.weights = ["1"]
configMgr.calculatorType = 2 # calculator type: 0= Frequentist, 1=Hybrid, 2=Aymptotic
configMgr.testStatType = 3 # # test stat type: 0=LEP, 1=Tevatron, 2=Profile Likelihood, 3=One-sided PLL
configMgr.nPoints = 20
configMgr.writeXML = True
configMgr.histCacheFile = "data/" + configMgr.analysisName + ".root"
configMgr.outputFileName = "results/" + configMgr.analysisName + "_Output.root"
print "is discovery ? %s" % (myFitType == FitType.Discovery)
sample_bkg0 = Sample("bkg0", ROOT.kBlue)
sample_bkg0.setStatConfig(True)
sample_bkg1 = Sample("bkg1", ROOT.kGreen)
sample_bkg1.setStatConfig(True)
sample_data = Sample("data", ROOT.kBlack)
sample_data.setData()
sample_sig = Sample("sig", ROOT.kRed)
sample_sig.setStatConfig(True)
all_samples = [sample_bkg0, sample_bkg1, sample_data]
# systematics
norm_syst_bkg0 = Systematic("Norm_Bkg0", configMgr.weights, 1.0 + 0.5, 1.0 - 0.5, "user", "userHistoSys")
vv_df_file = hft_dir + "HFT_VVDF_13TeV_Nov22.root"
vv_sf_file = hft_dir + "HFT_VVSF_13TeV_Nov22.root"
drellyan_file = hft_dir + "HFT_BG_13TeV_DrellYan.root"
fake_file = hft_dir + "HFT_Fakes_13TeV_Jul27.root" # scaling up the fakes from ICHEP
signal_file = ""
if gridname == "bWN":
signal_file = hft_dir + "HFT_bWN_13TeV_Nov22.root"
else:
userPrint('HFT not available for requested grid "%s"' % gridname)
userPrint(" --> Exitting.")
sys.exit()
## set the samples
# ttbar skip
ttbarSample = Sample("TTbar", ROOT.TColor.GetColor("#FC0D1B"))
vvDFSample = Sample("VVDF", ROOT.TColor.GetColor("#41C1FC"))
vvSFSample = Sample("VVSF", ROOT.TColor.GetColor("#41C1FC"))
# wwSample = Sample("WW", ROOT.TColor.GetColor("#41C1FC"))
stSample = Sample("ST", ROOT.TColor.GetColor("#DE080C"))
dysample = Sample("DrellYan", ROOT.kYellow)
ttvSample = Sample("TTV", ROOT.kCyan - 7) # ROOT.kRed)
fakeSample = Sample("Fakes", ROOT.kOrange + 7)
higgsSample = Sample("Higgs", ROOT.TColor.GetColor("#ddc29a"))
print 45 * "-"
print "REMOVING W+JETS SAMPLE"
print "REMOVING W+JETS SAMPLE"
print "REMOVING W+JETS SAMPLE"
print "REMOVING W+JETS SAMPLE"
print "REMOVING W+JETS SAMPLE"
print 45 * "-"
wjetsSample = Sample("Wjets", ROOT.TColor.GetColor("#5E9AD6"))
configMgr.writeXML = True
##########################
# Give the analysis a name
configMgr.analysisName = "MyUpperLimitAnalysis_SS"
configMgr.outputFileName = "results/%s_Output.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.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)
ktScaleTopHighWeights = ("genWeight","eventWeight","ktfacUpWeightTop","bTagWeight2Jet")
ktScaleTopLowWeights = ("genWeight","eventWeight","ktfacDownWeightTop","bTagWeight2Jet")
topKtScale = Systematic("KtScaleTop",configMgr.weights,ktScaleTopHighWeights,ktScaleTopLowWeights,"weight","overallSys")
#JES (tree-based)
jes = Systematic("JES","_NoSys","_JESup","_JESdown","tree","overallSys")
configMgr.nomName = "_NoSys"
#-------------------------------------------
# List of samples and their plotting colours
#-------------------------------------------
topSample = Sample("Top",kGreen-9)
#topSample.setNormFactor("mu_Top",1.,0.,5.)
wzSample = Sample("WZ",kAzure+1)
#wzSample.setNormFactor("mu_WZ",1.,0.,5.)
dataSample = Sample("Data",kBlack)
dataSample.setData()
dataSample.buildHisto([3.],"SR","cuts",0.5)
#**************
# Discovery fit
#**************
if myFitType==FitType.Discovery:
#Fit config instance
discoveryFitConfig = configMgr.addTopLevelXML("Discovery")
meas=discoveryFitConfig.addMeasurement(name="NormalMeasurement",lumi=1.0,lumiErr=0.039)
meas.addPOI("mu_Discovery")
#Samples
##########################
# Give the analysis a name
configMgr.analysisName = "MyUserAnalysis_ShapeFactor"
configMgr.outputFileName = "results/%s_Output.root"%configMgr.analysisName
# Define cuts
configMgr.cutsDict["CR"] = "1."
configMgr.cutsDict["SR"] = "1."
# 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)
configMgr.nPoints=50 # number of values scanned of signal-strength for upper-limit determination of signal strength.
##########################
# Give the analysis a name
configMgr.analysisName = "MI_SR2_95CL_incl"
configMgr.outputFileName = "results/%s_Output.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.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()
configMgr.outputLumi = 139 # fb-1
configMgr.setLumiUnits("fb-1")
configMgr.weights = ["1"]
configMgr.calculatorType = 2 # calculator type: 0= Frequentist, 1=Hybrid, 2=Aymptotic
configMgr.testStatType = 3 # # test stat type: 0=LEP, 1=Tevatron, 2=Profile Likelihood, 3=One-sided PLL
configMgr.nPoints = 20
configMgr.writeXML = True
configMgr.histCacheFile = "data/" + configMgr.analysisName + ".root"
configMgr.outputFileName = "results/" + configMgr.analysisName + "_Output.root"
print "is discovery ? %s" % (myFitType == FitType.Discovery)
sample_bkg0 = Sample("bkg0", ROOT.kBlue)
sample_bkg0.setStatConfig(True)
#sample_bkg1 = Sample("bkg1", ROOT.kGreen)
#sample_bkg1.setStatConfig(True)
#
#sample_bkg2 = Sample("bkg2", ROOT.kMagenta)
#sample_bkg2.setStatConfig(True)
sample_data = Sample("data", ROOT.kBlack)
sample_data.setData()
#sample_sig = Sample("sig", ROOT.kRed)
#sample_sig.setStatConfig(True)
all_samples = [sample_bkg0,
configMgr.nPoints=20 # number of values scanned of signal-strength for upper-limit determination of signal strength.
#configMgr.blindSR = True
##########################
# Give the analysis a name
configMgr.analysisName = "UpperLimitScharm%s" % os.environ['REGION']
configMgr.outputFileName = "results/%s_Output.root"%configMgr.analysisName
# Define cuts
configMgr.cutsDict["UserRegion"] = "1."
# Define weights
configMgr.weights = "1."
# Define samples
bkgSample = Sample("Bkg",0)
bkgSample.setStatConfig(True)
bkgSample.buildHisto([nbkg],"UserRegion","BDTG")
bkgSample.addSystematic(ucb)
sigSample = Sample("Sig",0)
sigSample.setNormFactor("mu_Sig",1.,0.,30.)
#sigSample.setStatConfig(True)
sigSample.setNormByTheory()
sigSample.buildHisto([nsig],"UserRegion","BDTG")
dataSample = Sample("Data",0)
dataSample.setData()
1. - theoSysTopNumber, "user", "userOverallSys")
theoSysW = Systematic("theoSysW", configMgr.weights, 1.0 + theoSysWNumber,
1.0 - theoSysWNumber, "user", "userOverallSys")
#diboson
theoSysDiboson = Systematic("theoSysDiboson", configMgr.weights, 1.5, 0.5,
"user", "userOverallSys")
#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)
) # number of values scanned of signal-strength for upper-limit determination of signal strength.
##########################
# Give the analysis a name
configMgr.analysisName = "SimpleUL_%s" % SR
configMgr.outputFileName = "results/%s_Output.root" % configMgr.analysisName
# 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)
dataSample = Sample("Data", kBlack)
dataSample.setData()
dataSample.buildHisto([ndata], "UserRegion", "cuts")
# Define top-level
ana = configMgr.addFitConfig("SPlusB")
ana.addSamples([bkgSample, dataSample])
# ana.setSignalSample(sigSample)
nominal = nominal_weight_bkg,
high = [nominal_weight_bkg, '(1+0.5*(ht_signal>500))'],
low = [nominal_weight_bkg, '(1-0.5*(ht_signal>500))'],
type = 'weight',
method = 'overallSys')
# --------------------------------------------
# - List of samples and their plotting colours
# --------------------------------------------
sample_list_bkg = []
sample_list_data = []
sample_list_sig = []
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Other
other_sample = Sample("Other", kAzure+8)
other_sample.setStatConfig(use_stat)
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 )
phoScaleElttgamma = Systematic("phoScale",configMgr.weights, 1.013,1-.013, "user","userOverallSys")
phoScaleElttbarDilep = Systematic("phoScale",configMgr.weights, 1.027, 1-.027, "user","userOverallSys")
phoScaleElst = Systematic("phoScale",configMgr.weights, 1.036, 1-.036, "user","userOverallSys")
phoScaleEldiboson = Systematic("phoScale",configMgr.weights, 1.029, 1-.029, "user","userOverallSys")
phoScaleElZgamma = Systematic("phoScale",configMgr.weights, 1.025, 1-.025, "user","userOverallSys")
# 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)
nominal = nominal_weight_bkg,
high = [nominal_weight_bkg, '(1+0.5*(ht_signal>500))'],
low = [nominal_weight_bkg, '(1-0.5*(ht_signal>500))'],
type = 'weight',
method = 'overallSys')
# --------------------------------------------
# - List of samples and their plotting colours
# --------------------------------------------
sample_list_bkg = []
sample_list_data = []
sample_list_sig = []
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Other
other_sample = Sample("Other", kAzure+8)
other_sample.setStatConfig(use_stat)
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 )
log.debug(pprint.pformat(configMgr.cutsDict, width=60))
log.info("Wait 3 seconds for you to panic if these settings are wrong")
wait(3)
log.info("No panicking detected, continuing...")
#######################################################################
# List of samples and their plotting colours
#######################################################################
#--------------------------
# 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("QCD_SRAll")
qcdSample.setNormFactor("mu_"+zlFitterConfig.qcdSampleName, 1., 0., 500.)
qcdSample.setFileList(qcdFiles)
qcdSample.setStatConfig(zlFitterConfig.useStat)
qcdWeight = 1
nJets = channel.nJets
mu1ScaleTFSysW = Systematic("mu1ScaleTFSys", configMgr.weights, 1.0+WMap['mu1ScaleWeightUp'][level][chn],1.0-WMap['mu1ScaleWeightDown'][level][chn], "user","userOverallSys")
mu2ScaleTFSysW = Systematic("mu2ScaleTFSys", configMgr.weights, 1.0+WMap['mu2ScaleWeightUp'][level][chn],1.0-WMap['mu2ScaleWeightDown'][level][chn], "user","userOverallSys")
matchScaleTFSysW = Systematic("matchScaleTFSys", configMgr.weights, 1.0+WMap['matchScaleWeightUp'][level][chn],1.0, "user","userOverallSys")
mu1ScaleTFSysZ = Systematic("mu1ScaleTFSys", configMgr.weights, 1.0+ZMap['mu1ScaleWeightUp'][level][chn],1.0-ZMap['mu1ScaleWeightDown'][level][chn], "user","userOverallSys")
mu2ScaleTFSysZ = Systematic("mu2ScaleTFSys", configMgr.weights, 1.0+ZMap['mu2ScaleWeightUp'][level][chn],1.0-ZMap['mu2ScaleWeightDown'][level][chn], "user","userOverallSys")
sherpaTFSysTop = Systematic("sherpaTFSys", configMgr.weights, 1.0+TopMap['topSherpa'][level][chn],1.0-TopMap['topSherpa'][level][chn], "user","userOverallSys")
#-------------------------------------------
# List of samples and their plotting colours
#-------------------------------------------
dibosonSample = Sample("Diboson",kRed+3)
dibosonSample.setTreeName("Diboson_SRAll")
dibosonSample.setFileList(dibosonFiles)
dibosonSample.setStatConfig(useStat)
if useSyst: dibosonSample.addSystematic(theoSysDiboson)
topSample = Sample("ttbar",kGreen-9)
topSample.setTreeName("Top_SRAll")
topSample.setNormFactor("mu_Top",1.,0.,500.)
topSample.setFileList(topFiles)
topSample.setStatConfig(useStat)
if useTheoSys:
if useSyst: topSample.addSystematic(theoSysTop)
####topSample.addSystematic(mu1ScaleSysTop)
####topSample.addSystematic(mu2ScaleSysTop)
configMgr.weights + ["matchScaleWeightDown"],
"weight", "overallNormHistoSys")
# QCD
theoSysQCD = Systematic("theoSysQCD", configMgr.weights, 1.0 + theoSysQCDNumber,1.0-theoSysQCDNumber, "user", "userOverallSys")
QCDGausSys = Systematic("QCDGausSys", "", "_ghi", "_glo", "tree", "overallNormHistoSys")
QCDTailSys = Systematic("QCDTailSys", "", "_thi", "_tlo", "tree", "overallNormHistoSys")
# Diboson
theoSysDiboson = Systematic("theoSysDiboson", configMgr.weights, 1.5, 0.5, "user", "userOverallSys")
#-------------------------------------------
# List of samples and their plotting colours
#-------------------------------------------
# Diboson
dibosonSample = Sample("Diboson", kRed+3)
dibosonSample.setTreeName("Diboson_SRAll")
dibosonSample.setFileList(dibosonFiles)
dibosonSample.setStatConfig(useStat)
# Top
topSample = Sample("ttbar", kGreen-9)
topSample.setTreeName("Top_SRAll")
topSample.setNormFactor("mu_Top", 1., 0., 500.)
topSample.setFileList(topFiles)
topSample.setStatConfig(useStat)
if useTheoSys:
topSample.addSystematic(theoSysTop)
if useSyst :
#MC theo systematics
theoSysTop = Systematic("theoSysTop", configMgr.weights, 1.+theoSysTopNumber,1.-theoSysTopNumber, "user","userOverallSys")
theoSysW = Systematic("theoSysW", configMgr.weights, 1.0+theoSysWNumber,1.0-theoSysWNumber, "user","userOverallSys")
#diboson
theoSysDiboson = Systematic("theoSysDiboson", configMgr.weights, 1.5,0.5, "user","userOverallSys")
#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.,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)
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)
# KtScale uncertainty as histoSys - two-sided, no additional normalization
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)
'VRL3',
'VRL4',
'VRE',
]
regions += srs
for r in regions:
configMgr.cutsDict[r] = '' # need by HF but not used anyway o.O
#-----------------
# 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"))
configMgr.outputLumi = 139 # fb-1
configMgr.setLumiUnits("fb-1")
configMgr.weights = ["1"]
configMgr.calculatorType = 2 # calculator type: 0= Frequentist, 1=Hybrid, 2=Aymptotic
configMgr.testStatType = 3 # # test stat type: 0=LEP, 1=Tevatron, 2=Profile Likelihood, 3=One-sided PLL
configMgr.nPoints = 20
configMgr.writeXML = True
configMgr.histCacheFile = "data/" + configMgr.analysisName + ".root"
configMgr.outputFileName = "results/" + configMgr.analysisName + "_Output.root"
print "is discovery ? %s" % (myFitType == FitType.Discovery)
sample_bkg0 = Sample("bkg0", ROOT.kBlue)
sample_bkg0.setStatConfig(True)
sample_bkg1 = Sample("bkg1", ROOT.kGreen)
sample_bkg1.setStatConfig(True)
sample_bkg2 = Sample("bkg2", ROOT.kMagenta)
sample_bkg2.setStatConfig(True)
sample_data = Sample("data", ROOT.kBlack)
sample_data.setData()
sample_sig = Sample("sig", ROOT.kRed)
sample_sig.setStatConfig(True)
all_samples = [sample_bkg0, sample_bkg1, sample_bkg2, sample_data]
configMgr.writeXML = True
##########################
# Give the analysis a name
configMgr.analysisName = "MyUpperLimitAnalysis_SS"
configMgr.outputFileName = "results/%s_Output.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.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)
_signalRegion, # Specify the signal region
_ch, # Lepton Channel
_sleptonHand, # slepton handedness
bkgFile, # input bkg file
dataFile, # input data file
signalFile, # input signal file
analysisName, # Analysis Name for saving
20.3, # Input Lumi units
20.3, # Ouput Lumi units
"fb-1" # Input Lumi units
)
##############################################################
## Define Data and BG samples ##
##############################################################
dataSample = Sample("Data_CENTRAL", ROOT.kBlack )
zjetsSample = Sample("Zjets" , ROOT.kGreen+2 )
higgsSample = Sample("Higgs" , ROOT.kYellow )
zvSample = Sample("ZV" , ROOT.kGreen )
wwSample = Sample("WW" , ROOT.kAzure-4 )
topSample = Sample("Top" , ROOT.kViolet )
fakeSample = Sample("Fake" , ROOT.kOrange-4 )
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= #
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= #
# USER SHOULD NOT HAVE TO EDIT BELOW HERE #
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= #
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= #
## set scan range for the upper limit
#configMgr.scanRange = (0., 1.)
## Suffix of nominal tree
configMgr.nomName = "_NoSys"
## Map regions to cut strings
configMgr.cutsDict = {"SR":"1.0"}
## Systematics to be applied
jes = Systematic("JES","_NoSys","_JESup","_JESdown","tree","overallSys")
#jes = Systematic("JES",None,1.2,0.8,"user","overallSys")
## List of samples and their plotting colours
allbkgSample = Sample("Bkg",kGreen)
#allbkgSample.setNormFactor("mu_AllBkg",1.,0.,5.)
#allbkgSample.addSystematic(jesWT)
dataSample = Sample("Data",kBlack)
dataSample.setData()
commonSamples = [allbkgSample,dataSample]
configMgr.plotColours = [kGreen,kBlack]
## Parameters of the Measurement
measName = "BasicMeasurement"
measLumi = 1.
measLumiError = 0.039
## Parameters of Channels
cutsRegions = ["SR"]
phoScaleElttgamma = Systematic("phoScale",configMgr.weights, 1.013,1-.013, "user","userOverallSys")
phoScaleElttbarDilep = Systematic("phoScale",configMgr.weights, 1.027, 1-.027, "user","userOverallSys")
phoScaleElst = Systematic("phoScale",configMgr.weights, 1.036, 1-.036, "user","userOverallSys")
phoScaleEldiboson = Systematic("phoScale",configMgr.weights, 1.029, 1-.029, "user","userOverallSys")
phoScaleElZgamma = Systematic("phoScale",configMgr.weights, 1.025, 1-.025, "user","userOverallSys")
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.setNormByTheory()
Wgamma.setStatConfig(True)
Wgamma.addSystematic(WgammaNorm)
Zgamma = Sample("Zgamma",7) # cyan
Zgamma.setNormByTheory()
Zgamma.setStatConfig(True)
Zgamma.addSystematic(ZgammaNorm)
Zleplep = Sample("Zleplep",7) # cyan
## Suffix of nominal tree
configMgr.nomName = "_NoSys"
## Map regions to cut strings
configMgr.cutsDict = {"SR":"1.0"}
## Systematics to be applied
jes = Systematic("JES","_NoSys","_JESup","_JESdown","tree","overallSys")
#jes = Systematic("JES",None,1.2,0.8,"user","overallSys")
## List of samples and their plotting colours
allbkgSample = Sample("Bkg",kGreen)
#allbkgSample.setNormFactor("mu_AllBkg",1.,0.,5.)
#allbkgSample.addSystematic(jesWT)
dataSample = Sample("Data",kBlack)
dataSample.setData()
commonSamples = [allbkgSample,dataSample]
configMgr.plotColours = [kGreen,kBlack]
## Parameters of the Measurement
measName = "BasicMeasurement"
measLumi = 1.
measLumiError = 0.039
## Parameters of Channels
cutsRegions = ["SR"]
cutsNBins = 1
cutsBinLow = 0.0
cutsBinHigh = 1.0
log.debug(pprint.pformat(configMgr.cutsDict, width=60))
log.info("Wait 3 seconds for you to panic if these settings are wrong")
wait(3)
log.info("No panicking detected, continuing...")
#######################################################################
# List of samples and their plotting colours
#######################################################################
#--------------------------
# 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)
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)
#--------------------
# KtScale uncertainty as histoSys - two-sided, no additional normalization
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)
configMgr.nPoints = 20 # number of values scanned of signal-strength for upper-limit determination of signal strength.
##########################
# Give the analysis a name
configMgr.analysisName = "MyUserAnalysis"
configMgr.outputFileName = "results/%s_Output.root" % configMgr.analysisName
# 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)
# ---------------------
# - 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.)
def create_samples(self):
print "total samples:",len(sample_names)
for index in range(len(sample_names)):
sample_name = sample_names[index]
sample = Sample(sample_name, sample_colors[index])
file_name = input_dir+sample_name+"_combined.root"
yields_dic = self.read_hist(file_name)
print "Sample:", sample_name+";",
if "data" in sample_name:
sample.setData()
self.data_sample = sample
for region in regions:
nevts, nerror = yields_dic[region]
sample.buildHisto([nevts], region, "cuts", 0.5)
print region,str(round(nevts,3))+";",
print
continue
sample.setNormByTheory()
for region in regions:
nevts, nerror = yields_dic[region]
nevts *= weight
nerror *= weight
if "Dijets" in sample_name and "SR" in region:
if self.cut == 10:
nevts = 4.07
nerror = math.sqrt(nevts)
if self.cut == 14:
nevts = 2.42
nerror = math.sqrt(nevts)
sample.buildHisto([nevts], region, "cuts", 0.5)
sample.buildStatErrors([nerror], region, "cuts")
print region,str(round(nevts,3))+";",
print
#sample.setStatConfig(True)
sample.setFileList([in_file_path])
## add systematic??
sample.addSystematic(Systematic(sample_name+"_stats",\
configMgr.weights, 1.2, 0.8, "user", "userOverallSys"))
#for systematic in self.sys_common:
#sample.addSystematic(systematic)
self.set_norm_factor(sample)
#topKtScale = Systematic("KtScaleTop",configMgr.weights,ktScaleTopHighWeights,ktScaleTopLowWeights,"weight","overallSys")
topKtScale = Systematic("KtScaleTop",configMgr.weights,ktScaleTopHighWeights,ktScaleTopLowWeights,"weight","histoSys")
#topKtScale = Systematic("KtScaleTop",configMgr.weights,ktScaleTopHighWeights,ktScaleTopLowWeights,"weight","normHistoSys")
#JES (tree-based)
jes = Systematic("JES","_NoSys","_JESup","_JESdown","tree","overallSys")
configMgr.nomName = "_NoSys"
#-------------------------------------------
# List of samples and their plotting colours
#-------------------------------------------
topSample = Sample("Top",kGreen-9)
#topSample.setNormFactor("mu_Top",1.,0.,5.)
wzSample = Sample("WZ",kAzure+1)
#wzSample.setNormFactor("mu_WZ",1.,0.,5.)
dataSample = Sample("Data",kBlack)
dataSample.setData()
dataSample.buildHisto([0.,1.,5.,15.,4.,0.],"SR","metmeff2Jet",0.1,0.1)
#dataSample.buildStatErrors([1.,1.,2.4,3.9,2.,0.],"SR","metmeff2Jet")
#**************
# Exclusion fit
#**************
if myFitType==FitType.Exclusion:
# loop over all signal points
for sig in sigSamples:
# Fit config instance
exclusionFitConfig = configMgr.addFitConfig("Exclusion_"+sig)
meas=exclusionFitConfig.addMeasurement(name="NormalMeasurement",lumi=1.0,lumiErr=0.039)
meas.addPOI("mu_SIG")
samples = []
channels = []
POIs = []
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]
'VRL1', 'VRL2', 'VRL3', 'VRL4', 'VRZ',
'VRLW1', 'VRLT1', 'VRLW3', 'VRLT3',
]
regions += srs
for r in regions:
configMgr.cutsDict[r] = '' # need by HF but not used anyway o.O
#-----------------
# 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"))
"KtScaleTop", configMgr.weights, ktScaleTopHighWeights, ktScaleTopLowWeights, "weight", "histoSys"
)
# topKtScale = Systematic("KtScaleTop",configMgr.weights,ktScaleTopHighWeights,ktScaleTopLowWeights,"weight","normHistoSys")
# JES (tree-based)
jes = Systematic("JES", "_NoSys", "_JESup", "_JESdown", "tree", "overallSys")
configMgr.nomName = "_NoSys"
# -------------------------------------------
# List of samples and their plotting colours
# -------------------------------------------
topSample = Sample("Top", kGreen - 9)
# topSample.setNormFactor("mu_Top",1.,0.,5.)
wzSample = Sample("WZ", kAzure + 1)
# wzSample.setNormFactor("mu_WZ",1.,0.,5.)
dataSample = Sample("Data", kBlack)
dataSample.setData()
# **************
# Exclusion fit
# **************
# 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])
ktScaleTopHighWeights = ("genWeight", "eventWeight", "ktfacUpWeightTop",
"bTagWeight2Jet")
ktScaleTopLowWeights = ("genWeight", "eventWeight", "ktfacDownWeightTop",
"bTagWeight2Jet")
topKtScale = Systematic("KtScaleTop", configMgr.weights, ktScaleTopHighWeights,
ktScaleTopLowWeights, "weight", "overallSys")
#JES (tree-based)
jes = Systematic("JES", "_NoSys", "_JESup", "_JESdown", "tree", "overallSys")
configMgr.nomName = "_NoSys"
#-------------------------------------------
# List of samples and their plotting colours
#-------------------------------------------
topSample = Sample("Top", kGreen - 9)
#topSample.setNormFactor("mu_Top",1.,0.,5.)
wzSample = Sample("WZ", kAzure + 1)
#wzSample.setNormFactor("mu_WZ",1.,0.,5.)
dataSample = Sample("Data", kBlack)
dataSample.setData()
dataSample.buildHisto([3.], "SR", "cuts", 0.5)
#**************
# Discovery fit
#**************
if myFitType == FitType.Discovery:
#Fit config instance
discoveryFitConfig = configMgr.addTopLevelXML("Discovery")
nominal = nominal_weight_bkg,
high = [nominal_weight_bkg, '(1+0.5*(ht_signal>500))'],
low = [nominal_weight_bkg, '(1-0.5*(ht_signal>500))'],
type = 'weight',
method = 'overallSys')
# --------------------------------------------
# - List of samples and their plotting colours
# --------------------------------------------
sample_list_bkg = []
sample_list_data = []
sample_list_sig = []
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Other
other_sample = Sample("Other", kAzure+8)
other_sample.setStatConfig(use_stat)
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 )
# Define top-level
ana = configMgr.addFitConfig("ABCD")
# Define measurement
meas = ana.addMeasurement(name="NormalMeasurement",
lumi=1.0,
lumiErr=lumiError)
meas.addPOI("mu_A")
"""
meas.addParamSetting("mu_dummy_D",True,1)
meas.addParamSetting("mu_dummy_B",True,1)
meas.addParamSetting("mu_dummy_C",True,1)
"""
#meas.addParamSetting("Lumi",True,1)
#create test data
dataSample = Sample("Data", kBlack)
dataSample.setData()
dataSample.buildHisto([ndataA], "A", "cuts", 0.5)
dataSample.buildHisto([ndataB], "B", "cuts", 0.5)
dataSample.buildHisto([ndataC], "C", "cuts", 0.5)
dataSample.buildHisto([ndataD], "D", "cuts", 0.5)
backgroundSample = Sample("NonQCDBackground", kBlack)
backgroundSample.buildHisto([nbkgA], "A", "cuts", 0.5)
backgroundSample.buildHisto([nbkgB], "B", "cuts", 0.5)
backgroundSample.buildHisto([nbkgC], "C", "cuts", 0.5)
backgroundSample.buildHisto([nbkgD], "D", "cuts", 0.5)
ana.addSamples([dataSample, backgroundSample])
#make dummy samples