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)
# 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)
# Define measurement
meas = ana.addMeasurement(name="NormalMeasurement",
lumi=1.0,
lumiErr=lumiError)
# 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
ana = configMgr.addTopLevelXML("SPlusB")
ana.addSamples([bkgSample, sigSample, dataSample])
ana.setSignalSample(sigSample)
# Define measurement
phoScaleElttbarDilep = Systematic("phoScale",configMgr.weights, 1.027, 1-.027, "user","userOverallSys")
phoScaleElst = Systematic("phoScale",configMgr.weights, 1.036, 1-.036, "user","userOverallSys")
phoScaleEldiboson = Systematic("phoScale",configMgr.weights, 1.029, 1-.029, "user","userOverallSys")
phoScaleElZgamma = Systematic("phoScale",configMgr.weights, 1.025, 1-.025, "user","userOverallSys")
phoScaleMuWgamma = Systematic("phoScale",configMgr.weights, 1.018, 1-.018, "user","userOverallSys")
phoScaleMuttgamma = Systematic("phoScale",configMgr.weights, 1.015,1-.015, "user","userOverallSys")
phoScaleMuttbarDilep = Systematic("phoScale",configMgr.weights, 1.028, 1-.028, "user","userOverallSys")
phoScaleMust = Systematic("phoScale",configMgr.weights, 1.023, 1-.023, "user","userOverallSys")
phoScaleMudiboson = Systematic("phoScale",configMgr.weights, 1.040, 1-.040, "user","userOverallSys")
phoScaleMuZgamma = Systematic("phoScale",configMgr.weights, 1.025, 1-.025, "user","userOverallSys")
## List of samples and their plotting colours. Associate dedicated systematics if applicable.
ttbargamma = Sample("ttbargamma",46) # brick
ttbargamma.setNormByTheory()
ttbargamma.setStatConfig(True)
ttbargamma.addSystematic(ttbargammaNorm)
Wgamma = Sample("Wgamma",7) # cyan
Wgamma.setNormByTheory()
Wgamma.setStatConfig(True)
Wgamma.addSystematic(WgammaNorm)
Zgamma = Sample("Zgamma",7) # cyan
Zgamma.setNormByTheory()
Zgamma.setStatConfig(True)
Zgamma.addSystematic(ZgammaNorm)
Zleplep = Sample("Zleplep",7) # cyan
Zleplep.setNormByTheory()
# 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 #
# ----------------------------------------------------- #
##########################
# 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()
# 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) ###
dataSample = Sample("Data",kBlack)
dataSample.setData()
dataSample.buildHisto([ndata],"UserRegion","cuts")
# Define top-level
ana = configMgr.addTopLevelXML("SPlusB")
if(runMode=="exclusion"):
ana.addSamples([bkgSample,sigSample,dataSample])
else:
ana.addSamples([bkgSample,dataSample])
# **************
# Fit config instance
exclusionFitConfig = configMgr.addTopLevelXML("Exclusion")
meas = exclusionFitConfig.addMeasurement(name="NormalMeasurement", lumi=1.0, lumiErr=0.039)
meas.addPOI("mu_SIG")
# Samples
exclusionFitConfig.addSamples([topSample, wzSample, dataSample])
# Systematics
exclusionFitConfig.getSample("Top").addSystematic(topKtScale)
exclusionFitConfig.getSample("WZ").addSystematic(wzKtScale)
exclusionFitConfig.addSystematic(jes)
# Channel
srBin = exclusionFitConfig.addChannel("met/meff2Jet", ["SR"], 6, 0.1, 0.7)
srBin.useOverflowBin = True
srBin.useUnderflowBin = True
exclusionFitConfig.setSignalChannels([srBin])
sigSample = Sample("SM_GG_onestepCC_425_385_345", kPink)
sigSample.setFileList(["samples/tutorial/SusyFitterTree_p832_GG-One-Step_soft_v1.root"])
sigSample.setNormByTheory()
sigSample.setNormFactor("mu_SIG", 1.0, 0.0, 5.0)
exclusionFitConfig.addSamples(sigSample)
exclusionFitConfig.setSignalSample(sigSample)
# 2nd cloned-copy just to accomodate -l option...
exclusionFitClone = configMgr.addTopLevelXMLClone(exclusionFitConfig, "ExclusionFitClone")
# generic systematic -- placeholder for now
gen_syst = Systematic( "gen_syst" , configMgr.weights , 1.0 + 0.30 , 1.0 - 0.30 , "user" , "userOverallSys" )
# JES uncertainty as shapeSys - one systematic per region (combine WR and TR), merge samples
# jes = Systematic("JES","_NoSys","_JESup","_JESdown","tree","overallNormHistoSys")
# --------------------------------------------
# - List of samples and their plotting colours
# --------------------------------------------
sample_list = []
# ttbar
ttbar_sample = Sample( "ttbar" , kGreen-2 )
ttbar_sample.setNormFactor("mu_ttbar",1.,0.,5.)
ttbar_sample.setStatConfig(use_stat)
ttbar_sample.setNormByTheory()
sample_list.append(ttbar_sample)
# single top
single_top_sample = Sample( "SingleTop" , kGreen-1 )
single_top_sample.setNormFactor("mu_st",1.,0.,5.)
single_top_sample.setStatConfig(use_stat)
single_top_sample.setNormByTheory()
sample_list.append(single_top_sample)
# Z/gamma*
z_sample = Sample( "Z" , kRed+1 )
z_sample.setNormFactor("mu_z",1.,0.,5.)
z_sample.setStatConfig(use_stat)
z_sample.setNormByTheory()
sample_list.append(z_sample)
]
regions += srs
for r in regions:
configMgr.cutsDict[r] = '' # need by HF but not used anyway o.O
#-----------------
# Samples
#-----------------
# W/Z + jets
wjets_sample = Sample('wjets', color("wjets"))
zjets_sample = Sample('zjets', color("zjets"))
wjets_sample.setNormByTheory()
zjets_sample.setNormByTheory()
# ttbar
ttbar_sample = Sample('ttbar', color("ttbar"))
ttbarg_sample = Sample('ttgamma', color("ttbarg"))
ttbar_sample.setNormByTheory()
ttbarg_sample.setNormFactor("mu_t", 1., 0., 2.)
# W/Z gamma
wgamma_sample = Sample('wgamma', color("wgamma"))
zllgamma_sample = Sample('zllgamma', color("zllgamma"))
znunugamma_sample = Sample('znunugamma', color("znunugamma"))
vqqgamma_sample = Sample("vqqgamma", color('vqqgamma'))
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)
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()
phoScaleElttbarDilep = Systematic("phoScale",configMgr.weights, 1.027, 1-.027, "user","userOverallSys")
phoScaleElst = Systematic("phoScale",configMgr.weights, 1.036, 1-.036, "user","userOverallSys")
phoScaleEldiboson = Systematic("phoScale",configMgr.weights, 1.029, 1-.029, "user","userOverallSys")
phoScaleElZgamma = Systematic("phoScale",configMgr.weights, 1.025, 1-.025, "user","userOverallSys")
# phoScaleMuWgamma = Systematic("phoScale",configMgr.weights, 1.018, 1-.018, "user","userOverallSys")
# phoScaleMuttgamma = Systematic("phoScale",configMgr.weights, 1.015,1-.015, "user","userOverallSys")
# phoScaleMuttbarDilep = Systematic("phoScale",configMgr.weights, 1.028, 1-.028, "user","userOverallSys")
# phoScaleMust = Systematic("phoScale",configMgr.weights, 1.023, 1-.023, "user","userOverallSys")
# phoScaleMudiboson = Systematic("phoScale",configMgr.weights, 1.040, 1-.040, "user","userOverallSys")
# phoScaleMuZgamma = Systematic("phoScale",configMgr.weights, 1.025, 1-.025, "user","userOverallSys")
## List of samples and their plotting colours. Associate dedicated systematics if applicable.
ttbargamma = Sample("ttbargamma",46) # brick
ttbargamma.setNormByTheory()
ttbargamma.setStatConfig(True)
ttbargamma.addSystematic(ttbargammaNorm)
Wgamma = Sample("Wgamma",7) # cyan
Wgamma.setNormFactor("mu_Wgamma",1.,0.,5.)
Wgamma.setNormRegions([("WCRhHTEl", "cuts")])
Wgamma.setStatConfig(True)
#Wgamma.addSystematic(WgammaNorm)
Zgamma = Sample("Zgamma",kViolet) # cyan
Zgamma.setNormByTheory()
Zgamma.setStatConfig(True)
Zgamma.addSystematic(ZgammaNorm)
Zjets = Sample("Zjets",kBlue) # cyan
##########################
# Give the analysis a name
configMgr.analysisName = "MI_SR2_95CL_incl"
configMgr.outputFileName = "results/%s_Output.root"%configMgr.analysisName
# Define cuts
configMgr.cutsDict["UserRegion"] = "1."
#Define weights
configMgr.weights = "1."
#Define samples
bkgSample = Sample("Bkg",kGreen-9)
bkgSample.setStatConfig(True)
bkgSample.setNormByTheory(False) #this has to be true for samples with normalisation taken from MC, it means include lumi error (set false if data driven)
bkgSample.buildHisto([nbkg],"UserRegion","cuts")
bkgSample.buildStatErrors([nbkgErr],"UserRegion","cuts")
bkgSample.addSystematic(ucb)
sigSample = Sample("Sig",kPink)
sigSample.setNormFactor("mu_Sig",1.0,0.,24645.6)
sigSample.setStatConfig(True)
sigSample.setNormByTheory(False) #this has to be false since xsec is scaled by mu
sigSample.buildHisto([nsig],"UserRegion","cuts")
sigSample.buildStatErrors([nsigErr],"UserRegion","cuts")
dataSample = Sample("Data",kBlack)
dataSample.setData()
dataSample.buildHisto([ndata],"UserRegion","cuts")
# ttbarVSample.addSystematic(AR_ttbarV_GEN)
# XS
# XS
# XS
zjetsSample.addSystematic(sysObj.AR_zjets_XS)
higgsSample.addSystematic(sysObj.AR_Higgs_XS)
wwSample.addSystematic(sysObj.AR_ww_XS)
# wzSample.addSystematic(sysObj.AR_wz_XS)
zzSample.addSystematic(sysObj.AR_zz_XS)
tribosonSample.addSystematic(sysObj.AR_triboson_XS)
ttbarVSample.addSystematic(sysObj.AR_ttbarV_XS)
# Set those that are MC-only so that luminosity uncertainty is passed on
zjetsSample.setNormByTheory()
higgsSample.setNormByTheory()
wwSample.setNormByTheory()
wzSample.setNormByTheory()
zzSample.setNormByTheory()
tribosonSample.setNormByTheory()
ttbarVSample.setNormByTheory()
# Set data
dataSample.setData()
# Set samples list
tlx.addSamples(
[wwSample, wzSample, zzSample, tribosonSample, ttbarVSample, zjetsSample, higgsSample, dataSample, fakeSample]
)
regions += srs
for r in regions:
configMgr.cutsDict[r] = '' # need by HF but not used anyway o.O
#-----------------
# Samples
#-----------------
# W/Z + jets
wjets_sample = Sample('wjets', color("wjets"))
zjets_sample = Sample('zjets', color("zjets"))
wjets_sample.setNormByTheory()
zjets_sample.setNormByTheory()
# ttbar
ttbar_sample = Sample('ttbar', color("ttbar"))
ttbarg_sample = Sample('ttbarg', color("ttbarg"))
ttbar_sample.setNormByTheory()
ttbarg_sample.setNormFactor("mu_t", 1., 0., 2.)
# W/Z gamma
wgamma_sample = Sample('wgamma', color("wgamma"))
zllgamma_sample = Sample('zllgamma', color("zllgamma"))
znunugamma_sample = Sample('znunugamma', color("znunugamma"))
vqqgamma_sample = Sample("vqqgamma", color('vqqgamma'))
##########################
# 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()
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)
dataSample.setWeights(
dataWeights
) #need to edit python/configManager.setWeightsCutsVariable() in HistFitter for this to take effect
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 )
z_sample.setNormFactor("mu_z", 1, 0, 100)
for region in yields_dict :
configMgr.cutsDict[region] = "1."
# if "CR" in region :
# sample_for_cr = region.split("_")[1].lower()
# if sample.name.lower() == sample_for_cr :
# set_norm_by_theory = False
# sample.setNormFactor("mu_%s" % sample.name.lower(), 1.0, 0.0, 10.0)
# sample.setNormRegions([ (region, "cuts") ])
if set_norm_by_theory :
sample.setNormByTheory()
# add the samples
tlx.addSamples(all_samples)
# signal
if myFitType == FitType.Exclusion :
sample_sig.setStatConfig(True)
sample_sig.setNormFactor("mu_Test", 1.0, 0.0, 10.0)
sample_sig.setNormRegions( [ ("CR_BKG0", "cuts") ] )#, ("CR_BKG1", "cuts") ] )
sample_sig.setNormByTheory()
tlx.addSamples(sample_sig)
# tlx.setSignalSample(sample_sig)
# tlx.addSignalChannels(sr_channels)
if myFitType == FitType.Background :
#tlx.addSignalChannels(sr_channels)
tlx.addValidationChannels(sr_channels)
