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)
# 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)
dataSample.setData()
dataSample.buildHisto(nDataCR,"CR","cuts",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)
dataSample.buildHisto([1092., 426., 170., 65., 27., 9., 4., 1.], "SLWR",
"nJet", 2)
# set the file from which the samples should be taken
for sam in [topSample, wzSample, bgSample, qcdSample, dataSample]:
sam.setFileList(bgdFiles)
#Binnings
nJetBinLowHard = 3
nJetBinLowSoft = 2
nJetBinHighTR = 10
nJetBinHighWR = 10
nBJetBinLow = 0
nBJetBinHigh = 4
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
# ZX SAMPLE #
#------------------------------------------------#
zxSample.setStatConfig(useStat)
if userOpts.splitMCSys:
zxSample.addSystematic(sysObj.AR_mcstat_ZX)
zxSample.setNormByTheory()
zxSample = addSys(zxSample, False, sysObj)
#------------------------------------------------#
# FAKES #
#------------------------------------------------#
# As we don't have the final fake estimates, use the preliminary results per region
# and take negative yields to be 0.1 (we expect the fake to not contribute so we take
# this small value and use the relative uncertainty of stat+syst to cover our asses)
fakeSample.buildHisto([0.24], "eeSuper0a", "cuts")
fakeSample.buildHisto([0.1], "mmSuper0a", "cuts")
fakeSample.buildHisto([0.37], "emSuper0a", "cuts")
fakeSample.buildHisto([0.1], "eeSuper0b", "cuts")
fakeSample.buildHisto([0.1], "mmSuper0b", "cuts")
fakeSample.buildHisto([0.1], "emSuper0b", "cuts")
fakeSample.buildHisto([0.02], "eeSuper0c", "cuts")
fakeSample.buildHisto([0.1], "mmSuper0c", "cuts")
fakeSample.buildHisto([0.1], "emSuper0c", "cuts")
fakeSample.buildHisto([3.85], "eeSuper1a", "cuts")
fakeSample.buildHisto([8.09], "mmSuper1a", "cuts")
fakeSample.buildHisto([5.76], "emSuper1a", "cuts")
fakeSample.buildHisto([0.65], "eeSuper1b", "cuts")
fakeSample.buildHisto([0.09], "mmSuper1b", "cuts")
fakeSample.buildHisto([0.64], "emSuper1b", "cuts")
fakeSample.buildHisto([1.88], "eeSuper1c", "cuts")
# 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)
dataSample.setData()
dataSample.buildHisto(nDataCR,"CR","cuts",0.5)
##########################
# 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
ana = configMgr.addFitConfig("SPlusB")
##########################
# 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)
dataSample.setData()
gammajets = Sample("gammajets",28) # brown
gammajets.setStatConfig(True)
gammajets.setNormByTheory(False)
#gammajets.addSystematic(qcdElNorm)
#gammajets.setQCD()
data = Sample("data",kBlack)
data.setData()
commonSamples = [ttbargamma, Wgamma, Wjets, ttbarDilep, singletop, Zgamma, Zjets, diboson, gammajets, data]
for lepton in ['El']:
#for region in ("WCRlHT","WCRhHT", "HMEThHT","HMETmeff", "HMThHT","HMTmeff", "SRS", "SRW"):
for region in ("WCRlHT","WCRhHT", "HMEThHT", "HMThHT", "SRW"):
ttbargamma.buildHisto([backyields.GetYield(lepton, region, "ttbargamma")], region+lepton, "cuts")
ttbargamma.buildStatErrors([backyields.GetYieldUnc(lepton, region, "ttbargamma")], region+lepton, "cuts")
Wgamma.buildHisto([backyields.GetYield(lepton, region, "Wgamma")], region+lepton, "cuts")
Wgamma.buildStatErrors([backyields.GetYieldUnc(lepton, region, "Wgamma")], region+lepton, "cuts")
Wjets.buildHisto([backyields.GetYield(lepton, region, "Wjets")], region+lepton, "cuts")
Wjets.buildStatErrors([backyields.GetYieldUnc(lepton, region, "Wjets")], region+lepton, "cuts")
ttbarDilep.buildHisto([backyields.GetYield(lepton, region, "ttbarDilep")], region+lepton, "cuts")
ttbarDilep.buildStatErrors([backyields.GetYieldUnc(lepton, region, "ttbarDilep")], region+lepton, "cuts")
singletop.buildHisto([backyields.GetYield(lepton, region, "singletop")], region+lepton, "cuts")
singletop.buildStatErrors([backyields.GetYieldUnc(lepton, region, "singletop")], region+lepton, "cuts")
Zgamma.buildHisto([backyields.GetYield(lepton, region, "Zgamma")], region+lepton, "cuts")
Zgamma.buildStatErrors([backyields.GetYieldUnc(lepton, region, "Zgamma")], region+lepton, "cuts")
Zjets.buildHisto([backyields.GetYield(lepton, region, "Zjets")], region+lepton, "cuts")
Zjets.buildStatErrors([backyields.GetYieldUnc(lepton, region, "Zjets")], region+lepton, "cuts")
diboson.buildHisto([backyields.GetYield(lepton, region, "diboson")], region+lepton, "cuts")
diboson.buildStatErrors([backyields.GetYieldUnc(lepton, region, "diboson")], region+lepton, "cuts")
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)
singletop.setStatConfig(True)
singletop.addSystematic(singletopNorm)
gammajets = Sample("gammajets",28) # brown
gammajets.setStatConfig(True)
gammajets.addSystematic(qcdElNorm)
#gammajets.setQCD()
data = Sample("data",kBlack)
data.setData()
commonSamples = [ttbargamma, Wgamma, Wjets, ttbarDilep, singletop, Zgamma, Zjets, diboson, gammajets, data]
for lepton in ('El', 'Mu'):
for region in ("WCRlHT","WCRhHT", "HMEThHT","HMETmeff", "HMThHT","HMTmeff", "SRS", "SRW"):
ttbargamma.buildHisto([Tables.GetYield(lepton, region, "ttbargamma")], region+lepton, "cuts")
ttbargamma.buildStatErrors([Tables.GetYieldUnc(lepton, region, "ttbargamma")], region+lepton, "cuts")
Wgamma.buildHisto([Tables.GetYield(lepton, region, "Wgamma")], region+lepton, "cuts")
Wgamma.buildStatErrors([Tables.GetYieldUnc(lepton, region, "Wgamma")], region+lepton, "cuts")
Wjets.buildHisto([Tables.GetYield(lepton, region, "Wjets")], region+lepton, "cuts")
Wjets.buildStatErrors([Tables.GetYieldUnc(lepton, region, "Wjets")], region+lepton, "cuts")
ttbarDilep.buildHisto([Tables.GetYield(lepton, region, "ttbarDilep")], region+lepton, "cuts")
ttbarDilep.buildStatErrors([Tables.GetYieldUnc(lepton, region, "ttbarDilep")], region+lepton, "cuts")
singletop.buildHisto([Tables.GetYield(lepton, region, "singletop")], region+lepton, "cuts")
singletop.buildStatErrors([Tables.GetYieldUnc(lepton, region, "singletop")], region+lepton, "cuts")
Zgamma.buildHisto([Tables.GetYield(lepton, region, "Zgamma")], region+lepton, "cuts")
Zgamma.buildStatErrors([Tables.GetYieldUnc(lepton, region, "Zgamma")], region+lepton, "cuts")
Zjets.buildHisto([Tables.GetYield(lepton, region, "Zjets")], region+lepton, "cuts")
Zjets.buildStatErrors([Tables.GetYieldUnc(lepton, region, "Zjets")], region+lepton, "cuts")
diboson.buildHisto([Tables.GetYield(lepton, region, "diboson")], region+lepton, "cuts")
diboson.buildStatErrors([Tables.GetYieldUnc(lepton, region, "diboson")], region+lepton, "cuts")
##########################
# 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
meas = ana.addMeasurement(name="NormalMeasurement",
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
discoveryFitConfig.addSamples([topSample,wzSample,dataSample])
# ----------------------------------------------------- #
# Data #
# ----------------------------------------------------- #
dataSample.setData()
# ----------------------------------------------------- #
# Fakes #
# ----------------------------------------------------- #
# Set by hand the fake estimates per-region.
# The regions in which we expect a negative yield, set the
# yield to 0.1 but keep the overal relative uncertainty
fakeSample.buildHisto([0.1], "eeSuper0a", "cuts")
fakeSample.buildHisto([0.1], "mmSuper0a", "cuts")
fakeSample.buildHisto([0.1], "emSuper0a", "cuts")
fakeSample.buildHisto([0.1], "eeSuper0b", "cuts")
fakeSample.buildHisto([0.1], "mmSuper0b", "cuts")
fakeSample.buildHisto([0.1], "emSuper0b", "cuts")
fakeSample.buildHisto([0.02], "eeSuper0c", "cuts")
fakeSample.buildHisto([0.1], "mmSuper0c", "cuts")
fakeSample.buildHisto([0.1], "emSuper0c", "cuts")
fakeSample.buildHisto([3.46], "eeSuper1a", "cuts")
fakeSample.buildHisto([4.18], "mmSuper1a", "cuts")
fakeSample.buildHisto([3.62], "emSuper1a", "cuts")
fakeSample.buildHisto([0.55], "eeSuper1b", "cuts")
fakeSample.buildHisto([0.1], "mmSuper1b", "cuts")
fakeSample.buildHisto([0.57], "emSuper1b", "cuts")
fakeSample.buildHisto([1.70], "eeSuper1c", "cuts")
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)
dataSample.buildHisto([1092.,426.,170.,65.,27.,9.,4.,1.],"SLWR","nJet",2)
# set the file from which the samples should be taken
for sam in [topSample, wzSample, bgSample, qcdSample, dataSample]:
sam.setFileList(bgdFiles)
#Binnings
nJetBinLowHard = 3
nJetBinLowSoft = 2
nJetBinHighTR = 10
nJetBinHighWR = 10
nBJetBinLow = 0
nBJetBinHigh = 4
#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")
# ZX SAMPLE #
#------------------------------------------------#
zxSample.setStatConfig(useStat)
if userOpts.splitMCSys:
zxSample.addSystematic(sysObj.AR_mcstat_ZX)
zxSample.setNormByTheory()
zxSample = addSys(zxSample, False, sysObj)
#------------------------------------------------#
# FAKES #
#------------------------------------------------#
# As we don't have the final fake estimates, use the preliminary results per region
# and take negative yields to be 0.1 (we expect the fake to not contribute so we take
# this small value and use the relative uncertainty of stat+syst to cover our asses)
fakeSample.buildHisto([0.99], "eeSuper0a", "cuts")
fakeSample.buildHisto([0.1], "mmSuper0a", "cuts")
fakeSample.buildHisto([0.1], "emSuper0a", "cuts")
fakeSample.buildHisto([0.1], "eeSuper0b", "cuts")
fakeSample.buildHisto([0.1], "mmSuper0b", "cuts")
fakeSample.buildHisto([0.1], "emSuper0b", "cuts")
fakeSample.buildHisto([0.02], "eeSuper0c", "cuts")
fakeSample.buildHisto([0.1], "mmSuper0c", "cuts")
fakeSample.buildHisto([0.1], "emSuper0c", "cuts")
fakeSample.buildHisto([1.07], "eeSuper1a", "cuts")
fakeSample.buildHisto([2.12], "mmSuper1a", "cuts")
fakeSample.buildHisto([0.37], "emSuper1a", "cuts")
fakeSample.buildHisto([0.49], "eeSuper1b", "cuts")
fakeSample.buildHisto([0.1], "mmSuper1b", "cuts")
fakeSample.buildHisto([0.1], "emSuper1b", "cuts")
fakeSample.buildHisto([0.31], "eeSuper1c", "cuts")
##########################
# 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])
#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
normalization_name = "mu_" + sample_name
cur_sample.setNormFactor(normalization_name, 1, 0, 100)
if is_signal:
POIs.append(normalization_name)
signal_sample = cur_sample
# ... for all regions
for region_name, region_infile in zip(region_names, region_infiles):
binvals, edges = HistogramImporter.import_histogram(
os.path.join(indir, region_infile), template_name)
bin_width = edges[1] - edges[0]
cur_sample.buildHisto(binvals,
region_name,
"mBB",
binLow=edges[0],
binWidth=bin_width)
samples.append(cur_sample)
# also make the (Asimov) data sample
data_sample = Sample("data", ROOT.kBlack)
data_sample.setData()
# in each region, it holds the total event content
for region_name, region_infile in zip(region_names, region_infiles):
binvals = None
for sample_name, template_name in zip(sample_names, template_names):
sample_binvals, edges = HistogramImporter.import_histogram(
#---------
# Fit
#---------
# Background only fit
if myFitType == FitType.Background:
fitconfig = configMgr.addFitConfig('BkgOnlyFit')
# Discovery fit
elif myFitType == FitType.Discovery:
fitconfig = configMgr.addFitConfig('DiscoveryFit')
unitary_sample = Sample('Unitary', ROOT.kViolet+5)
unitary_sample.setNormFactor('mu_SIG', 1, 0, 10)
unitary_sample.buildHisto([1,], 'SR', '0.5')
fitconfig.addSamples(unitary_sample)
fitconfig.setSignalSample(unitary_sample)
# Exclusion fit
elif myFitType == FitType.Exclusion:
fitconfig = configMgr.addFitConfig('ExclusionFit')
fitconfig.addSamples(bkg_samples + data_samples)
# Measurement
measName = "BasicMeasurement"
measLumi = 1.0
measLumiError = 0.029 # Preliminar for ICHEP: 2.9% (3.7% for 2016 and 2.1% for 2015)
# ------------------------------------------------------------------------------
# Configure exclusion fits
print 'Setting up exclusion fit!'
exclusion_sr_config = configMgr.addFitConfigClone(background_config, "Sig_excl")
for region_name in configMgr.cutsDict.keys():
if 'SR' not in region_name:
continue
print 'this sr name: ', region_name
sig_sample = Sample('fake_signal', kViolet+5)
sig_sample.setNormFactor('mu_SIG', 1, 0, 10)
sig_sample.buildHisto([1], region_name, '0.5')
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)
# Data entry
# 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
bkgSampleA = Sample("dummy_BkgA", kBlue)
bkgSampleA.buildHisto([1], "A", "cuts", 0.5)
