##########################
# 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)
wzKtScale = Systematic("KtScaleWZ",configMgr.weights,ktScaleWHighWeights,ktScaleWLowWeights,"weight","overallNormHistoSys")
# JES uncertainty as shapeSys - one systematic per region (combine WR and TR), merge samples
jes = Systematic("JES","_NoSys","_JESup","_JESdown","tree","overallNormHistoSys")
statWRwz = Systematic("SLWR_wz", "_NoSys","","","tree","shapeStat")
statWRtop = Systematic("SLWR_top","_NoSys","","","tree","shapeStat")
# name of nominal histogram for systematics
configMgr.nomName = "_NoSys"
# List of samples and their plotting colours
topSample = Sample("Top",kGreen-9)
topSample.setNormFactor("mu_Top",1.,0.,5.)
topSample.setStatConfig(useStat)
topSample.setNormRegions([("SLWR","nJet"),("SLTR","nJet")])
wzSample = Sample("WZ",kAzure+1)
wzSample.setNormFactor("mu_WZ",1.,0.,5.)
wzSample.setStatConfig(useStat)
wzSample.setNormRegions([("SLWR","nJet"),("SLTR","nJet")])
bgSample = Sample("BG",kYellow-3)
bgSample.setNormFactor("mu_BG",1.,0.,5.)
bgSample.setStatConfig(useStat)
bgSample.setNormRegions([("SLWR","nJet"),("SLTR","nJet")])
qcdSample = Sample("QCD",kGray+1)
qcdSample.setQCD(True,"histoSys")
qcdSample.setStatConfig(useStat)
dataSample = Sample("Data",kBlack)
dataSample.setData()
#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)
##########################
# 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)
# Define top-level
if data_name == 'data15':
efake_sample = Sample("efake15", color("efake"))
jfake_sample = Sample("jfake15", color("jfake"))
elif data_name == 'data16':
efake_sample = Sample("efake16", color("efake"))
jfake_sample = Sample("jfake16", color("jfake"))
else: # should be 'data'
efake_sample = Sample("efake", color("efake"))
jfake_sample = Sample("jfake", color("jfake"))
# Data
data_sample = Sample(data_name, ROOT.kBlack)
data_sample.setData()
# stat uncertainty
wjets_sample.setStatConfig(useStat)
zjets_sample.setStatConfig(useStat)
wgamma_sample.setStatConfig(useStat)
zllgamma_sample.setStatConfig(useStat)
znunugamma_sample.setStatConfig(useStat)
ttbar_sample.setStatConfig(useStat)
ttbarg_sample.setStatConfig(useStat)
photonjet_sample.setStatConfig(useStat)
multijet_sample.setStatConfig(useStat)
diphoton_sample.setStatConfig(useStat)
vgammagamma_sample.setStatConfig(useStat)
vqqgamma_sample.setStatConfig(useStat)
if use_mc_bkgs:
bkg_samples = [
wgamma_sample,
##########################
# 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)
dataSample = Sample("Data", kBlack)
##########################
# 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)
# Define measurement
# 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 :
topSample.addSystematic(pileup)
topSample.addSystematic(jes)
topSample.addSystematic(jer)
ktScaleTopLowWeights, "weight", "normHistoSys")
wzKtScale = Systematic("KtScaleWZ", configMgr.weights, ktScaleWHighWeights,
ktScaleWLowWeights, "weight", "normHistoSys")
# JES uncertainty as shapeSys - one systematic per region (combine WR and TR), merge samples
jes = Systematic("JES", "_NoSys", "_JESup", "_JESdown", "tree", "normHistoSys")
mcstat = Systematic("mcstat", "_NoSys", "_NoSys", "_NoSys", "tree",
"shapeStat")
# name of nominal histogram for systematics
configMgr.nomName = "_NoSys"
# List of samples and their plotting colours
topSample = Sample("Top", kGreen - 9)
topSample.setNormFactor("mu_Top", 1., 0., 5.)
topSample.setStatConfig(useStat)
topSample.setNormRegions([("SLWR", "nJet"), ("SLTR", "nJet")])
wzSample = Sample("WZ", kAzure + 1)
wzSample.setNormFactor("mu_WZ", 1., 0., 5.)
wzSample.setStatConfig(useStat)
wzSample.setNormRegions([("SLWR", "nJet"), ("SLTR", "nJet")])
bgSample = Sample("BG", kYellow - 3)
bgSample.setNormFactor("mu_BG", 1., 0., 5.)
bgSample.setStatConfig(useStat)
bgSample.setNormRegions([("SLWR", "nJet"), ("SLTR", "nJet")])
qcdSample = Sample("QCD", kGray + 1)
qcdSample.setQCD(True, "histoSys")
qcdSample.setStatConfig(useStat)
dataSample = Sample("Data", kBlack)
dataSample.setData()
dataSample.buildHisto([86., 66., 62., 35., 11., 7., 2., 0.], "SLTR", "nJet", 2)
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)
Zjets = Sample("Zjets",kBlue) # cyan
Zjets.setNormByTheory()
photon = Systematic("photon", configMgr.weights, 1.05, 0.95, "user",
"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)
## Setting up Samples and normalization factors
################################################################################################
userPrint("Setting up samples, norm factors and systematics")
# Specify the top level XML and the paramater of interest
tlx = configMgr.addFitConfig("TopLvlXML")
meas = tlx.addMeasurement(name="NormalMeasurement", lumi=1.0, lumiErr=0.028) # fractional luminosity error
meas.addPOI("mu_SIG") ## EXCL:mu_SIG, upper limit table
# Determine if we should use stat
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)
##########################
# 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) ###
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")
sample_bkg0.addSystematic(norm_syst_bkg0)
useStat = True
if userOpts.splitMCSys :
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)
# ----------------------------------------------------- #
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)
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)
qcdWeight = 1
nJets = channel.nJets
# efake_sample = Sample("efake15", color("efake"))
# jfake_sample = Sample("jfake15", color("jfake"))
# elif data_name == 'data16':
# efake_sample = Sample("efake16", color("efake"))
# jfake_sample = Sample("jfake16", color("jfake"))
# else: # should be 'data'
efake_sample = Sample("efake", color("efake"))
jfake_sample = Sample("jfake", color("jfake"))
# Data
data_sample = Sample('data', ROOT.kBlack)
data_sample.setData()
# stat uncertainty
# data_sample.setStatConfig(useStat)
wjets_sample.setStatConfig(useStat)
zjets_sample.setStatConfig(useStat)
wgamma_sample.setStatConfig(useStat)
zllgamma_sample.setStatConfig(useStat)
znunugamma_sample.setStatConfig(useStat)
ttbar_sample.setStatConfig(useStat)
ttbarg_sample.setStatConfig(useStat)
photonjet_sample.setStatConfig(useStat)
multijet_sample.setStatConfig(useStat)
diphoton_sample.setStatConfig(useStat)
vqqgamma_sample.setStatConfig(useStat)
efake_sample.setStatConfig(False)
jfake_sample.setStatConfig(False)
data_samples = [
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 )
# use_this = False
# for oksampk in ok_samples :
# 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)
