# 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.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_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,
configMgr.writeXML = True
##########################
# Give the analysis a name
configMgr.analysisName = "MyCutOptAnalysis_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.buildStatErrors([nbkgErr],"UserRegion","cuts")
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)
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
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]
"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()
#**************
# Exclusion fit
#**************
#Fit config instance
exclusionFitConfig = configMgr.addTopLevelXML("Exclusion")
meas = exclusionFitConfig.addMeasurement(name="NormalMeasurement",
lumi=1.0,
lumiErr=0.039)
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)
## 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"]
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)
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:
topSample.addSystematic(theoSysTop)
####topSample.addSystematic(mu1ScaleSysTop)
####topSample.addSystematic(mu2ScaleSysTop)
####topSample.addSystematic(matchScaleSysTop)
#qcdMuNorm = Systematic("qcdMuNorm",configMgr.weights, 1.15, 0.85, "user","userOverallSys")
photon = Systematic("photon", configMgr.weights, 1.011, 1 - 0.011, "user",
"userOverallSys")
electron = Systematic("electron", configMgr.weights, 1.01, 0.99, "user",
"userOverallSys")
trig = Systematic("trig", configMgr.weights, 1.0001, 1 - 0.0158, "user",
"userOverallSys")
muon = Systematic("muon", configMgr.weights, 1.004, 0.996, "user",
"userOverallSys")
elToPhoton = Systematic("elToPhoton", configMgr.weights, 1.07, 1 - 0.07,
"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, 0., 5.)
#Wgamma.setNormRegions([("WCRhHT", "cuts")])
Wgamma.setStatConfig(True)
#Wgamma.addSystematic(WgammaNorm)
Zgamma = Sample("Zgamma", kViolet) # cyan
Zgamma.setNormByTheory()
Zgamma.setStatConfig(True)
Zgamma.addSystematic(ZgammaNorm)
# 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
