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)
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)
dataSample.setData()
dataSample.buildHisto(nDataCR,"CR","cuts",0.5)
dataSample.buildHisto(nDataSR,"SR","cuts",0.5)
# 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
ana = configMgr.addFitConfig("Disc")
ana.addSamples([bkgSample, sigSample, dataSample])
ana.setSignalSample(sigSample)
#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)
qcdSample.setStatConfig(useStat)
wSample = Sample("W", kAzure + 1)
wSample.setTreeName("W_SRAll")
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
Zleplep.setNormByTheory()
Zleplep.setStatConfig(True)
Zleplep.addSystematic(ZjetsNorm)
useStat = False
# If using stat set some limits
tlx.statErrThreshold = 0.001
# define quantities to make configuration below easier
SR = userOpts.signalRegion
lepChan = userOpts.leptonChannel
if userOpts.do2L :
# ----------------------------------------------------- #
# Zjets #
# ----------------------------------------------------- #
zjetsSample.setStatConfig(useStat)
if userOpts.splitMCSys :
zjetsSample.addSystematic(sysObj.AR_mcstat_ZX)
zjetsSample.setNormByTheory()
zjetsSample = addSys(zjetsSample, False, sysObj)
# ----------------------------------------------------- #
# Higgs #
# ----------------------------------------------------- #
higgsSample.setStatConfig(useStat)
if userOpts.splitMCSys :
higgsSample.addSystematic(sysObj.AR_mcstat_H)
higgsSample.setNormByTheory()
higgsSample = addSys(higgsSample, False, sysObj)
# ----------------------------------------------------- #
# ZV #
# ----------------------------------------------------- #
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]
# systematics
norm_syst_bkg1 = Systematic("Norm_Bkg1_0", configMgr.weights, 1.0 + 0.1,
1.0 - 0.1, "user", "userHistoSys")
norm_syst2_bkg1 = Systematic("Norm_Bkg1_1", configMgr.weights, 1.0 + 0.1,
1.0 - 0.1, "user", "userHistoSys")
norm_syst_bkg2 = Systematic("Norm_Bkg2", configMgr.weights, 1.0 + 0.10,
1.0 - 0.10, "user", "userHistoSys")
sample_bkg1.addSystematic(norm_syst_bkg1)
sample_bkg1.addSystematic(norm_syst2_bkg1)
sample_bkg2.addSystematic(norm_syst_bkg2)
#sample_bkg0.addSystematic(norm_syst_bkg0)
#cb1a = Systematic("Norm_Bkg1_A", configMgr.weights, 1.0 + 0.80, 1.0 - 0.80, "user", "userHistoSys")
#cb1b = Systematic("Norm_Bkg1_B", configMgr.weights, 1.0 + 0.1, 1.0 - 0.1, "user", "userHistoSys")
#cb1c = Systematic("Norm_Bkg1_C", configMgr.weights, 1.0 + 0.1, 1.0 - 0.1, "user", "userHistoSys")
#cb1d = Systematic("Norm_Bkg1_D", configMgr.weights, 1.0 + 0.1, 1.0 - 0.1, "user", "userHistoSys")
#sample_bkg1.addSystematic(cb1a)
#sample_bkg1.addSystematic(cb1b)
#sample_bkg1.addSystematic(cb1c)
#sample_bkg1.addSystematic(cb1d)
def sample_by_name(name):
topXSample.setFileList(topXFiles)
higgsSample.setFileList(higgsFiles)
qFlipSample.setFileList(dataFiles)
fLepSample.setFileList(dataFiles)
dataSample.setFileList(dataFiles)
for aSample in [
wgammaSample, dibosonSample, tribosonSample, topXSample, higgsSample
]:
aSample.setNormByTheory(True)
aSample.setStatConfig(True)
for aSys in reconSys:
aSample.addSystematic(aSys)
qFlipSample.addSystematic(qFlipSys)
qFlipWeights = ("ElSF", "MuSF", "BtagSF", "weight", "pwt", "qFwt",
dataDrivenBkgScale)
qFlipSample.setWeights(qFlipWeights) #overrides configMgr.weights
qFlipSample.setNormByTheory(False)
qFlipSample.setStatConfig(True)
fLepSample.addSystematic(fLepSys)
fLepWeights = ("ElSF", "MuSF", "BtagSF", "weight", "pwt", "fLwt",
dataDrivenBkgScale)
fLepSample.setWeights(fLepWeights) #overrides configMgr.weights
fLepSample.setNormByTheory(False)
fLepSample.setStatConfig(True)
dataWeights = ("((%s)&&(%s))" % (ssUtil.sigLepCut, ssUtil.ssCut),
dataDrivenBkgScale)
# 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)
dataSample = Sample("Data", kBlack)
dataSample.setData()
dataSample.buildHisto([ndata], "UserRegion", "cuts", 0.5)
#-------------------------------------------
# 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 :
topSample.addSystematic(pileup)
topSample.addSystematic(jes)
topSample.addSystematic(jer)
topSample.addSystematic(scalest)
topSample.addSystematic(resost)
if useSyst and useCRWTY:
#topSample.setNormRegions([("CRT","cuts"),("CRW","cuts")])
topSample.setNormRegions([("CRT", binVar)])
# QCD
qcdSample = Sample("Multijets", kOrange+2)
qcdSample.setTreeName("QCDdd")
"userOverallSys")
electron = Systematic("electron", configMgr.weights, 1.05, 0.95, "user",
"userOverallSys")
muon = Systematic("muon", configMgr.weights, 1.05, 0.95, "user",
"userOverallSys")
metMu = Systematic("metMu", configMgr.weights, 1.1, 0.9, "user",
"userOverallSys")
metEl = Systematic("metEl", configMgr.weights, 1.1, 0.9, "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
Zleplep.setNormByTheory()
Zleplep.setStatConfig(True)
Zleplep.addSystematic(ZjetsNorm)
topSample = Sample(zlFitterConfig.topSampleName, kGreen-9)
topSample.setTreeName("Top_SRAll")
topSample.setNormFactor("mu_"+zlFitterConfig.topSampleName, 1., 0., 500.)
topSample.setFileList(topFiles)
topSample.setStatConfig(zlFitterConfig.useStat)
if zlFitterConfig.doSetNormRegion:
if "CRT" in zlFitterConfig.constrainingRegionsList and "CRW" in zlFitterConfig.constrainingRegionsList:
topSample.setNormRegions([("CRT", zlFitterConfig.binVar),("CRW", zlFitterConfig.binVar)])
if "CRTZL" in zlFitterConfig.constrainingRegionsList and "CRW" in zlFitterConfig.constrainingRegionsList:
topSample.setNormRegions([("CRTZL", zlFitterConfig.binVar),("CRW", zlFitterConfig.binVar)])
#### LL
# if "CRT0L" in zlFitterConfig.constrainingRegionsList:
# topSample.setNormRegions( [ ("CRT0L",zlFitterConfig.binVar) ] )
if not zlFitterConfig.usePreComputedTopGeneratorSys:
topSample.addSystematic(Systematic("generatorTop",configMgr.weights , "_aMcAtNloHerwigpp", "_aMcAtNloHerwigpp", "tree", "overallNormHistoSysOneSideSym"))
if not zlFitterConfig.usePreComputedTopFragmentationSys:
topSample.addSystematic(Systematic("fragmentationTop",configMgr.weights , "_PowhegHerwigpp", "_PowhegHerwigpp", "tree", "overallNormHistoSysOneSideSym"))
#--------------------------
# W
#--------------------------
wSample = Sample(zlFitterConfig.wSampleName, kAzure+1)
wSample.setTreeName("W_SRAll")
wSample.setNormFactor("mu_"+zlFitterConfig.wSampleName, 1., 0., 500.)
wSample.setFileList(wFiles)
wSample.setStatConfig(zlFitterConfig.useStat)
if not zlFitterConfig.usePreComputedWGeneratorSys:
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)
Zjets = Sample("Zjets",kBlue) # cyan
Zjets.setNormByTheory()
Zjets.setStatConfig(True)
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",kViolet) # cyan
Zgamma.setNormByTheory()
Zgamma.setStatConfig(True)
Zgamma.addSystematic(ZgammaNorm)
Zjets = Sample("Zjets",kBlue) # cyan
Zjets.setNormByTheory()
Zjets.setStatConfig(True)
Zjets.addSystematic(ZjetsNorm)
useStat = False # This is added as systematics below
# Add systematics here
# Stat
zjetsSample.setStatConfig(useStat)
fakeSample.setStatConfig(useStat)
higgsSample.setStatConfig(useStat)
wwSample.setStatConfig(useStat)
wzSample.setStatConfig(useStat)
zzSample.setStatConfig(useStat)
tribosonSample.setStatConfig(useStat)
ttbarVSample.setStatConfig(useStat)
if not useStat:
zjetsSample.addSystematic(sysObj.AR_mcstat_Zjets)
fakeSample.addSystematic(sysObj.AR_mcstat_fake)
higgsSample.addSystematic(sysObj.AR_mcstat_Higgs)
wwSample.addSystematic(sysObj.AR_mcstat_WW)
wzSample.addSystematic(sysObj.AR_mcstat_WZ)
zzSample.addSystematic(sysObj.AR_mcstat_ZZ)
tribosonSample.addSystematic(sysObj.AR_mcstat_triboson)
ttbarVSample.addSystematic(sysObj.AR_mcstat_ttbarV)
else:
tlx.statErrThreshold = 0.001
# Rest should be implemented here
fakeSample.addSystematic(sysObj.AR_fake_eFr)
fakeSample.addSystematic(sysObj.AR_fake_mFr)
fakeSample.addSystematic(sysObj.AR_fake_eRe)
fakeSample.addSystematic(sysObj.AR_fake_mRe)
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")
sample_bkg0.addSystematic(norm_syst_bkg0)
cb1a = Systematic("Norm_Bkg1_A", configMgr.weights, 1.0 + 0.1, 1.0 - 0.1, "user", "userHistoSys")
cb1b = Systematic("Norm_Bkg1_B", configMgr.weights, 1.0 + 0.1, 1.0 - 0.1, "user", "userHistoSys")
cb1c = Systematic("Norm_Bkg1_C", configMgr.weights, 1.0 + 0.1, 1.0 - 0.1, "user", "userHistoSys")
cb1d = Systematic("Norm_Bkg1_D", configMgr.weights, 1.0 + 0.1, 1.0 - 0.1, "user", "userHistoSys")
sample_bkg1.addSystematic(cb1a)
sample_bkg1.addSystematic(cb1b)
#sample_bkg1.addSystematic(cb1c)
#sample_bkg1.addSystematic(cb1d)
def sample_by_name(name) :
global all_samples
for s in all_samples :
if s.name.lower() == name.lower() :
return s
#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)
qcdSample.setStatConfig(useStat)
wSample = Sample("W",kAzure+1)
wSample.setTreeName("W_SRAll")
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)
# 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)
dataSample = Sample("Data",kBlack)
dataSample.setData()
dataSample.buildHisto([ndata],"UserRegion","cuts",0.5)
# Add Sample Specific Systematics (apparently it's needed to add these systs to the samples *BEFORE* adding them to the FitConfig
if do_detector_syst:
for gsyst in syst_to_all:
for sample in bkg_samples:
if sample.name.startswith('efake') or sample.name.startswith(
'jfake'):
continue
if sample.name == 'photonjet':
continue
sample.addSystematic(gsyst)
if do_dd_syst:
efake_sample.addSystematic(syst_feg)
jfake_sample.addSystematic(syst_fjg)
efake_sample.addSystematic(syst_stat_feg)
jfake_sample.addSystematic(syst_stat_fjg)
if do_mc_syst:
photonjet_sample.addSystematic(syst_gamjet_theo_all)
# if not do_detector_syst and not do_dd_syst and not do_mc_syst:
# ucb = Systematic("uncorrl_bkg", None, 1+0.30, 1-0.30, "user", "userOverallSys") # 30% error up and down
# for sample in bkg_samples:
# sample.addSystematic(ucb)
#---------
# Fit
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)
dataSample.setData()
dataSample.buildHisto(nDataCR,"CR","cuts",0.5)
dataSample.buildHisto(nDataSR,"SR","cuts",0.5)
## 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)
sample_data = Sample("data", ROOT.kBlack)
sample_data.setData()
#sample_sig = Sample("sig", ROOT.kRed)
#sample_sig.setStatConfig(True)
all_samples = [sample_bkg0,
sample_data] #, sample_bkg1, sample_bkg2, sample_data]
# systematics
syst_bkg0_cr = Systematic("SYST_BKG_CR", configMgr.weights, 1.0 + 0.10,
1.0 - 0.10, "user", "userHistoSys")
syst_bkg0_sr = Systematic("SYST_BKG_SR", configMgr.weights, 1.0 + 0.05,
1.0 - 0.05, "user", "userHistoSys")
sample_bkg0.addSystematic(syst_bkg0_sr)
#norm_syst_bkg1 = Systematic("Norm_Bkg1_0", configMgr.weights, 1.0 + 0.1, 1.0 - 0.1, "user", "userHistoSys")
#norm_syst2_bkg1 = Systematic("Norm_Bkg1_1", configMgr.weights, 1.0 + 0.1, 1.0 - 0.1, "user", "userHistoSys")
#norm_syst_bkg2 = Systematic("Norm_Bkg2", configMgr.weights, 1.0 + 0.10, 1.0 - 0.10, "user", "userHistoSys")
#sample_bkg1.addSystematic(norm_syst_bkg1)
#sample_bkg1.addSystematic(norm_syst2_bkg1)
#sample_bkg2.addSystematic(norm_syst_bkg2)
def sample_by_name(name):
global all_samples
for s in all_samples:
if s.name.lower() == name.lower():
return s
print "sample_by_name failed"
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)
# Top
#--------------------------
topSample = Sample(zlFitterConfig.topSampleName, kGreen-9)
topSample.setTreeName("Top_SRAll")
topSample.setNormFactor("mu_"+zlFitterConfig.topSampleName, 1., 0., 500.)
topSample.setFileList(topFiles)
topSample.setStatConfig(zlFitterConfig.useStat)
if zlFitterConfig.doSetNormRegion:
if "CRT" in zlFitterConfig.constrainingRegionsList and "CRW" in zlFitterConfig.constrainingRegionsList:
topSample.setNormRegions([("CRT", zlFitterConfig.binVar),("CRW", zlFitterConfig.binVar)])
if "CRTZL" in zlFitterConfig.constrainingRegionsList and "CRW" in zlFitterConfig.constrainingRegionsList:
topSample.setNormRegions([("CRTZL", zlFitterConfig.binVar),("CRW", zlFitterConfig.binVar)])
if not zlFitterConfig.usePreComputedTopGeneratorSys:
topSample.addSystematic(Systematic("generatorTop", "", "_aMcAtNloHerwigpp", "", "tree", "overallNormHistoSysOneSide"))
if not zlFitterConfig.usePreComputedTopFragmentationSys:
topSample.addSystematic(Systematic("Pythia8Top", "" , "_PowhegPythia8", "" , "tree", "overallNormHistoSysOneSide"))
# topSample.addSystematic(Systematic("HerwigppTop", "", "_PowhegHerwigpp", "", "tree", "overallNormHistoSysOneSide"))
if not zlFitterConfig.usePreComputedTopRadiationSys:
topSample.addSystematic(Systematic("radiationTop", "", "_RadLo", "_RadHi", "tree", "overallNormHistoSys"))
# topSample.addSystematic(Systematic("radiationTop", "", "_RadHi", "", "tree", "overallNormHistoSysOneSideSym"))
#--------------------------
# W
#--------------------------
wSample = Sample(zlFitterConfig.wSampleName, kAzure+1)
wSample.setTreeName("W_SRAll")
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)
dibosonSample.addSystematic(theoSysDiboson)
topSample = Sample("ttbar",kGreen-9)
if useShape:
topSample.addShapeFactor("topShape")
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)
topSample.setNormRegions([("CRT","meffInc"),("CRW","meffInc")])
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)
dataSample = Sample("Data", kBlack)
dataSample.setData()
dataSample.buildHisto([ndata], "UserRegion", "cuts")
# Define top-level
sigma_ttgamma_crq = 0.177
sigma_ttgamma_crwt = 0.085
syst_ttgamma_theo_srl = Systematic("theoSysTG", 1, 1+sigma_ttgamma_srl, 1-sigma_ttgamma_srl, "user", "userOverallSys")
syst_ttgamma_theo_srh = Systematic("theoSysTG", 1, 1+sigma_ttgamma_srh, 1-sigma_ttgamma_srh, "user", "userOverallSys")
syst_ttgamma_theo_crq = Systematic("theoSysTG", 1, 1+sigma_ttgamma_crq, 1-sigma_ttgamma_crq, "user", "userOverallSys")
syst_ttgamma_theo_crwt = Systematic("theoSysTG", 1, 1+sigma_ttgamma_crwt, 1-sigma_ttgamma_crwt, "user", "userOverallSys")
## signal
sigXsec = Sys('SigXSec')
# Add Sample Specific Systematics (apparently it's needed to add these systs to the samples *BEFORE* adding them to the FitConfig
if do_theo_syst:
zllgamma_sample. addSystematic(syst_zgamma_theo)
znunugamma_sample.addSystematic(syst_zgamma_theo)
if do_syst:
efake_sample.addSystematic(syst_feg)
jfake_sample.addSystematic(syst_fjg)
#-- global systematics
for gsyst in syst_to_all:
for sample in bkg_samples:
if sample.name.startswith('efake') or sample.name.startswith('jfake'):
continue
sample.addSystematic(gsyst)
all_samples = [sample_bkg0, sample_bkg1,
sample_data] #, sample_bkg1, sample_bkg2, sample_data]
# systematics
syst_bkg0_cr = Systematic("SYST_BKG0_CR", configMgr.weights, 1.0 + 0.05,
1.0 - 0.05, "user", "userHistoSys")
syst_bkg0_sr = Systematic("SYST_BKG0_SR", configMgr.weights, 1.0 + 0.25,
1.0 - 0.25, "user", "userHistoSys")
syst_bkg1_cr = Systematic("SYST_BKG1_CR", configMgr.weights, 1.0 + 0.05,
1.0 - 0.05, "user", "userHistoSys")
syst_bkg1_sr = Systematic("SYST_BKG1_SR", configMgr.weights, 1.0 + 0.25,
1.0 - 0.25, "user", "userHistoSys")
sample_bkg0.addSystematic(syst_bkg0_cr)
sample_bkg1.addSystematic(syst_bkg1_cr)
#norm_syst_bkg1 = Systematic("Norm_Bkg1_0", configMgr.weights, 1.0 + 0.1, 1.0 - 0.1, "user", "userHistoSys")
#norm_syst2_bkg1 = Systematic("Norm_Bkg1_1", configMgr.weights, 1.0 + 0.1, 1.0 - 0.1, "user", "userHistoSys")
#norm_syst_bkg2 = Systematic("Norm_Bkg2", configMgr.weights, 1.0 + 0.10, 1.0 - 0.10, "user", "userHistoSys")
#sample_bkg1.addSystematic(norm_syst_bkg1)
#sample_bkg1.addSystematic(norm_syst2_bkg1)
#sample_bkg2.addSystematic(norm_syst_bkg2)
def sample_by_name(name):
global all_samples
for s in all_samples:
if s.name.lower() == name.lower():
return s
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)
####topSample.addSystematic(matchScaleSysTop)
topSample.setNormRegions([("CRT","meffInc"),("CRW","meffInc")])
addSystematic([sig_sample],
(btag_sf_uncert_sig_list +
jes_uncert_list +
[jer_uncert]))
# apply the correct cross section systematic to the signal
# sample if it is available
if sig in signal_xsec_rel_uncert:
this_signal_xsec_uncert = Systematic(
name='SigXSec',
nominal=nominal_weight_sig,
high=(1+signal_xsec_rel_uncert[sig]),
low=(1-signal_xsec_rel_uncert[sig]),
type='user',
method='overallSys')
sig_sample.addSystematic(this_signal_xsec_uncert)
# add samples to configuration
exclusion_sr_config.addSamples(sig_sample)
exclusion_sr_config.setSignalSample(sig_sample)
# ------------------------------------------------------------------------------
# Create TLegend for our plots
# TCanvas is needed for this, but it gets deleted afterwards
c = TCanvas()
compFillStyle = 1001 # see ROOT for Fill styles
leg = TLegend(0.6, 0.475, 0.9, 0.925, "")
leg.SetFillStyle(0)
leg.SetFillColor(0)
leg.SetBorderSize(0)
# if oksampk in s : use_this = True
# if not use_this : continue
# if "225" not in s : continue
# if "135" not in s : continue
# s_ = s.replace(".0", "")
s_ = s
extlx = configMgr.addFitConfigClone(tlx, "Sig_%s" % s_)
userPrint(" > Adding signal sample to exclusion fit config : %s" % s)
sigSample_ = Sample(s, kPink)
sigSample_.setFileList(signal_files)
sigSample_.setNormByTheory()
sigSample_.setStatConfig(not runOptions.doSplitMCsys())
if runOptions.doSplitMCsys():
sigSample_.addSystematic(sysObj.mcstat_SIG)
## set the signal weight to be the weight with no PUPW
sigSample_.weights = ["eventweightNOPUPW"]
if runOptions.doTheoryBand(): ### TODO check if we need the configMgr setRunOnlyNominalXSec
sigXSSyst = Systematic(
"SigXSec", ["eventweightNOPUPW"], 1.07, 0.93, "user", "overallSys"
) ### TODO add xsec util to grab the uncertainties on xsec (rather than storing in tree)
# sigXSSyst = Systematic("SigXSec", configMgr.weights, 1.07, 0.93, "user", "overallSys") ### TODO add xsec util to grab the uncertainties on xsec (rather than storing in tree)
sigSample_.addSystematic(sigXSSyst)
## add systematics
sigSample_ = addSys(sigSample_, False, sysObj, True)
## attach the signal strength
sigSample_.setNormFactor("mu_SIG", 1.0, 0.0, 5.0)
