def plotFatjetMatching(cont, sel, sub1, sub2):
plots = []
plots.append(
Plot.make1D(f"fatjet{sub1}{sub2}_sub1_dE",
op.map(cont, lambda m: m[2].subJet1.p4.E() - m[0].p4.E()),
sel,
EquidistantBinning(1000, -1000., 1000.),
xTitle=f"subJet1 {sub1} E - gen {sub1} E"))
plots.append(
Plot.make2D(
f"fatjet{sub1}{sub2}_sub1_TF", [
op.map(cont, lambda m: m[0].p4.E()),
op.map(cont, lambda m: m[2].subJet1.p4.E() - m[0].p4.E())
],
sel, [
EquidistantBinning(3000, 0., 3000.),
EquidistantBinning(8000, -1000., 1000.)
],
xTitle="E^{{parton}}({sub1})",
yTitle=f"#Delta E = E^{{reco}}({sub1})-E^{{parton}}({sub1})"))
plots.append(
Plot.make1D(f"fatjet{sub1}{sub2}_sub2_dE",
op.map(cont, lambda m: m[2].subJet2.p4.E() - m[1].p4.E()),
sel,
EquidistantBinning(1000, -1000., 1000.),
xTitle=f"subJet2 {sub2} E - gen {sub2} E"))
plots.append(
Plot.make2D(
f"fatjet{sub1}{sub2}_sub2_TF", [
op.map(cont, lambda m: m[1].p4.E()),
op.map(cont, lambda m: m[2].subJet1.p4.E() - m[1].p4.E())
],
sel, [
EquidistantBinning(3000, 0., 3000.),
EquidistantBinning(8000, -1000., 1000.)
],
xTitle="E^{{parton}}({sub2})",
yTitle=f"#Delta E = E^{{reco}}({sub2})-E^{{parton}}({sub2})"))
plots.append(
Plot.make2D(f"fatjet{sub1}{sub2}_dE2D", [
op.map(cont, lambda m: m[2].subJet1.p4.E() - m[0].p4.E()),
op.map(cont, lambda m: m[2].subJet2.p4.E() - m[1].p4.E())
],
sel, [
EquidistantBinning(1000, -1000., 1000.),
EquidistantBinning(1000, -1000., 1000.)
],
xTitle=f"subJet1 {sub1} E - gen {sub1} E",
yTitle=f"subJet2 {sub2} E - gen {sub2} E"))
return plots
def plotMatching(contName, cont, sel, partName):
plots = []
# plots.append(Plot.make1D(contName+"_deltaR",
# op.map(cont,lambda m : op.deltaR(m[0].p4,m[1].p4)),
# sel,
# EquidistantBinning(1000,0.,0.2),
# xTitle="#Delta R(%s_{gen},%s_{reco})"%(partName,partName)))
# plots.append(Plot.make1D(contName+"_deltaPtRel",
# op.map(cont,lambda m : (m[1].pt-m[0].pt)/(m[0].pt+m[1].pt)),
# sel,
# EquidistantBinning(1000,-1.,1.),
# xTitle="P_{T}(%s_{reco})-P_{T}(%s_{gen})/P_{T}(%s_{reco})+P_{T}(%s_{gen})"%(partName,partName,partName,partName)))
# plots.append(Plot.make2D(contName+"_Pt2D",
# [op.map(cont,lambda m : m[0].pt),op.map(cont,lambda m : m[1].pt)],
# sel,
# [EquidistantBinning(1000,0,1000),EquidistantBinning(1000,0,1000)],
# xTitle="P_{T}(gen)",
# yTitle="P_{T}(reco)"))
plots.append(
Plot.make2D(contName + "_TF",
(op.map(cont, lambda m: m[0].p4.E()),
op.map(cont, lambda m: m[1].p4.E() - m[0].p4.E())),
sel, [
EquidistantBinning(3000, 0., 3000.),
EquidistantBinning(8000, -1000., 1000.)
],
xTitle="E^{parton}(e^{-})",
yTitle="#Delta E = E^{reco}(%s)-E^{parton}(%s)" %
(partName, partName)))
return plots
def MakeEllipsesPLots(self, sel, bjets, lepton, wp, uname, suffix):
plots = []
binScaling = 1
for key in sel.keys():
tagger = key.replace(wp, "")
bjets_ = safeget(bjets, tagger, wp)
plots.append(
Plot.make1D(
"jj_M_{0}_{1}_hZA_lljj_{2}_mll_and_met_cut".format(
suffix, uname, key),
op.invariant_mass(bjets_[0].p4, bjets_[1].p4),
sel.get(key),
EqB(60 // binScaling, 0., 1000.),
title="invariant mass of two b-tagged jets wrt {0} Discriminator"
.format(suffix, key),
xTitle="mjj {0} {1} [GeV]".format(suffix, key),
plotopts=utils.getOpts(uname, **{"log-y": False})))
plots.append(
Plot.make1D(
"lljj_M_{0}_{1}_hZA_lljj_{2}_mll_and_met_cut".format(
suffix, uname, key), (lepton[0].p4 + lepton[1].p4 +
bjets_[0].p4 + bjets_[1].p4).M(),
sel.get(key),
EqB(60 // binScaling, 0., 1000.),
title=
"invariant mass of 2 leptons two b-tagged jets wrt {0} Discriminator"
.format(suffix, key),
xTitle="mlljj {0} {1} [GeV]".format(suffix, key),
plotopts=utils.getOpts(uname, **{"log-y": False})))
plots.append(
Plot.make2D(
"Mjj_vs_Mlljj_{0}_{1}_hZA_lljj_{2}_mll_and_met_cut".format(
suffix, uname,
key), (op.invariant_mass(bjets_[0].p4, bjets_[1].p4),
(lepton[0].p4 + lepton[1].p4 + bjets_[0].p4 +
bjets_[1].p4).M()),
sel.get(key), (EqB(60 // binScaling, 0.,
1000.), EqB(60 // binScaling, 0., 1000.)),
title="mlljj vs mjj invariant mass {0} wrt {1} Discriminator".
format(suffix, key),
plotopts=utils.getOpts(uname, **{"log-y": False})))
plots.append(
Plot.make1D(
"ll_M_{0}_{1}_hZA_lljj_{2}_mll_and_met_cut".format(
suffix, uname, key),
op.invariant_mass(lepton[0].p4, lepton[1].p4),
sel.get(key),
EqB(60 // binScaling, 70., 110.),
title=" dilepton invariant mass {0} wrt {1} Discriminator".
format(suffix, key),
xTitle="mll {0} {1} [GeV]".format(suffix, key),
plotopts=utils.getOpts(uname)))
return plots
def fatjetPlots(sel, fatjets, name):
plots = []
# plots.append(Plot.make2D(name+'_Pt2D',
# [op.map(fatjets, lambda fatjet : fatjet.subJet1.pt),op.map(fatjets, lambda fatjet : fatjet.subJet2.pt)],
# sel,
# [EquidistantBinning(1000,0.,1000.),EquidistantBinning(1000,0.,1000.)],
# xTitle = "subjet 1 P_{T}",
# yTitle = "subjet 2 P_{T}"))
plots.append(
Plot.make2D(name + '_E2D', [
op.map(fatjets, lambda fatjet: fatjet.subJet1.p4.E()),
op.map(fatjets, lambda fatjet: fatjet.subJet2.p4.E())
],
sel, [
EquidistantBinning(1000, 0., 1000.),
EquidistantBinning(1000, 0., 1000.)
],
xTitle="subjet 1 E",
yTitle="subjet 2 E"))
# plots.append(Plot.make2D(name+'_partonflavour2D',
# [op.map(fatjets, lambda fatjet : fatjet.subJet1.partonFlavour),op.map(fatjets, lambda fatjet : fatjet.subJet2.partonFlavour)],
# sel,
# [EquidistantBinning(7,0.,6.),EquidistantBinning(7,0.,6.)],
# xTitle = "subjet 1 P_{T}",
# yTitle = "subjet 2 P_{T}"))
# plots.append(Plot.make2D(name+'_subjet1VSFatjet_E2D',
# [op.map(fatjets, lambda fatjet : fatjet.subJet1.p4.E()),op.map(fatjets, lambda fatjet : fatjet.p4.E())],
# sel,
# [EquidistantBinning(1000,0.,1000.),EquidistantBinning(1000,0.,1000.)],
# xTitle = "subjet 1 E",
# yTitle = "fatjet E"))
# plots.append(Plot.make2D(name+'_subjet2VSFatjet_E2D',
# [op.map(fatjets, lambda fatjet : fatjet.subJet2.p4.E()),op.map(fatjets, lambda fatjet : fatjet.p4.E())],
# sel,
# [EquidistantBinning(1000,0.,1000.),EquidistantBinning(1000,0.,1000.)],
# xTitle = "subjet 2 E",
# yTitle = "fatjet E"))
#
# plots.append(Plot.make2D(name+'_Eta2D',
# [op.map(fatjets, lambda fatjet : fatjet.subJet1.p4.eta()),op.map(fatjets, lambda fatjet : fatjet.subJet2.p4.eta())],
# sel,
# [EquidistantBinning(500,-2.5,2.5),EquidistantBinning(500,-2.5,2.5)],
# xTitle = "subjet 1 #eta",
# yTitle = "subjet 2 #eta"))
# plots.append(Plot.make2D(name+'_Phi2D',
# [op.map(fatjets, lambda fatjet : fatjet.subJet1.p4.phi()),op.map(fatjets, lambda fatjet : fatjet.subJet2.p4.phi())],
# sel,
# [EquidistantBinning(320,-3.2,3.2),EquidistantBinning(320,-3.2,3.2)],
# xTitle = "subjet 1 #phi",
# yTitle = "subjet 2 #phi"))
return plots
def makeControlPlotsForFinalSel(self, sel, bjets, dilepton, uname):
plots = []
binScaling = 1
for key in sel.keys():
tagger = key.replace(wp, "")
bjets_ = safeget(bjets, tagger, wp)
plots.append(
Plot.make1D("{0}_TwoBtaggedJets_pT_{1}".format(uname, key),
(bjets_[0].p4 + bjets_[1].p4).Pt(),
sel.get(key),
EqB(60, 170., 800.),
title="di-bjet P_{T} [GeV]",
plotopts=utils.getOpts(uname)))
plots.append(
Plot.make1D("{0}_mlljj_btagged_{1}".format(uname, key),
(lepton[0].p4 + lepton[1].p4 + bjets_[0].p4 +
bjets_[1].p4).M(),
sel.get(key),
EqB(60, 170., 1000.),
title="mlljj [GeV]",
plotopts=utils.getOpts(uname)))
plots.append(
Plot.make1D("{0}_mjj_btagged_{1}".format(uname, key),
op.invariant_mass(bjets_[0].p4 + bjets_[1].p4),
sel.get(key),
EqB(60, 170., 800.),
title="mjj [GeV]",
plotopts=utils.getOpts(uname)))
plots.append(
Plot.make2D("{0}_mlljjvsmjj_btagged_{1}".format(uname, key),
(op.invariant_mass(bjets_[0].p4 + bjets_[1].p4),
(lepton[0].p4 + lepton[1].p4 + bjets_[0].p4 +
bjets_[1].p4).M()),
sel.get(key),
(EqB(60, 170., 1000.), EqB(60, 170., 1000.)),
title="mlljj vs mjj invariant mass [GeV]",
plotopts=utils.getOpts(uname)))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
if 'type' not in sampleCfg.keys() or sampleCfg["type"] != "signal":
raise RuntimeError("Sample needs to be HH signal LO GGF sample")
era = sampleCfg.get("era") if sampleCfg else None
# Select gen level Higgs #
genh = op.select(
t.GenPart,
lambda g: op.AND(g.pdgId == 25, g.statusFlags & (0x1 << 13)))
HH_p4 = genh[0].p4 + genh[1].p4
cm = HH_p4.BoostToCM()
boosted_h = op.extMethod("ROOT::Math::VectorUtil::boost",
returnType=genh[0].p4._typeName)(genh[0].p4,
cm)
mHH = op.invariant_mass(genh[0].p4, genh[1].p4)
cosHH = op.abs(boosted_h.Pz() / boosted_h.P())
# Apply reweighting #
benchmarks = [
'BenchmarkSM',
'Benchmark1',
'Benchmark2',
'Benchmark3',
'Benchmark4',
'Benchmark5',
'Benchmark6',
'Benchmark7',
'Benchmark8',
'Benchmark8a',
'Benchmark9',
'Benchmark10',
'Benchmark11',
'Benchmark12',
'BenchmarkcHHH0',
'BenchmarkcHHH1',
'BenchmarkcHHH2p45',
'BenchmarkcHHH5',
'Benchmarkcluster1',
'Benchmarkcluster2',
'Benchmarkcluster3',
'Benchmarkcluster4',
'Benchmarkcluster5',
'Benchmarkcluster6',
'Benchmarkcluster7',
]
selections = {'': noSel}
reweights = {}
if self.args.reweighting:
for benchmark in benchmarks:
json_file = os.path.join(
os.path.abspath(os.path.dirname(__file__)), 'data',
'ScaleFactors_GGF_LO',
'{}_to_{}_{}.json'.format(sample, benchmark, era))
if os.path.exists(json_file):
print("Found file {}".format(json_file))
reweightLO = get_scalefactor("lepton",
json_file,
paramDefs={
'Eta': lambda x: mHH,
'Pt': lambda x: cosHH
})
selections[benchmark] = SelectionWithDataDriven.create(
parent=noSel,
name='noSel' + benchmark,
ddSuffix=benchmark,
cut=op.c_bool(True),
ddCut=op.c_bool(True),
weight=op.c_float(1.),
ddWeight=reweightLO(op.c_float(1.)),
enable=True)
reweights[benchmark] = reweightLO(op.c_float(1.))
else:
print("Could not find file {}".format(json_file))
# Plots #
plots = []
for name, reweight in reweights.items():
plots.append(
Plot.make1D("weight_{}".format(name),
reweight,
noSel,
EquidistantBinning(100, 0, 5.),
xTitle='weight'))
for selName, sel in selections.items():
plots.append(
Plot.make2D(
f"mHHvsCosThetaStar{selName}", [mHH, cosHH],
sel, [
VariableBinning([
250., 270., 290., 310., 330., 350., 370., 390.,
410., 430., 450., 470., 490., 510., 530., 550.,
570., 590., 610., 630., 650., 670., 700., 750.,
800., 850., 900., 950., 1000., 1100., 1200., 1300.,
1400., 1500., 1750., 2000., 5000.
]),
VariableBinning([0.0, 0.4, 0.6, 0.8, 1.0])
],
xTitle='m_{HH}',
yTitle='cos(#theta^{*})'))
plots.append(
Plot.make1D(f"mHH{selName}",
mHH,
sel,
VariableBinning([
250., 270., 290., 310., 330., 350., 370., 390.,
410., 430., 450., 470., 490., 510., 530., 550.,
570., 590., 610., 630., 650., 670., 700., 750.,
800., 850., 900., 950., 1000., 1100., 1200.,
1300., 1400., 1500., 1750., 2000., 5000.
]),
xTitle='m_{HH}'))
plots.append(
Plot.make1D(f"cosThetaStar{selName}",
cosHH,
sel,
VariableBinning([0.0, 0.4, 0.6, 0.8, 1.0]),
xTitle='cos(#theta^{*})'))
return plots
def makeControlPlotsForBasicSel(self, sel, jets, dilepton, uname):
binScaling = 1
plots = []
plots.append(
Plot.make1D("{0}_leadJetPT".format(uname),
jets[0].pt,
sel,
EqB(60, 0., 450.),
title="P_{T} (leading Jet) [GeV]",
plotopts=utils.getOpts(uname)))
plots.append(
Plot.make1D("{0}_subleadJetPT".format(uname),
jets[1].pt,
sel,
EqB(60, 0., 450.),
title="P_{T} (sub-leading Jet) [GeV]",
plotopts=utils.getOpts(uname)))
plots.append(
Plot.make1D("{0}_leadJetETA".format(uname),
jets[0].eta,
sel,
EqB(10, -2.4, 2.4),
title="Eta (leading Jet)",
plotopts=utils.getOpts(uname)))
plots.append(
Plot.make1D("{0}_subleadJetETA".format(uname),
jets[1].eta,
sel,
EqB(10, -2.4, 2.4),
title="Eta (sub-leading Jet)",
plotopts=utils.getOpts(uname)))
for i in range(2):
plots.append(
Plot.make1D(f"{uname}_jet{i+1}_phi",
jets[i].phi,
sel,
EqB(50 // binScaling, -3.1416, 3.1416),
title=f"{utils.getCounter(i+1)} jet phi",
plotopts=utils.getOpts(uname, **{"log-y": False})))
plots.append(
Plot.make1D("{0}_jjpT".format(uname), (jets[0].p4 + jets[1].p4).pt,
sel,
EqB(100, 0., 450.),
title="dijet P_{T} [GeV]",
plotopts=utils.getOpts(uname)))
plots.append(
Plot.make1D("{0}_jjPhi".format(uname), (jets[0].p4 + jets[1].p4).phi,
sel,
EqB(50 // binScaling, -3.1416, 3.1416),
title="dijet phi",
plotopts=utils.getOpts(uname)))
plots.append(
Plot.make1D("{0}_jjEta".format(uname), (jets[0].p4 + jets[1].p4).eta,
sel,
EqB(50 // binScaling, -2.4, 2.4),
title="dijet eta",
plotopts=utils.getOpts(uname)))
plots.append(
Plot.make1D("{0}_mjj".format(uname),
op.invariant_mass(jets[0].p4, jets[1].p4),
sel,
EqB(100, 0., 800.),
title="mjj [GeV]",
plotopts=utils.getOpts(uname)))
plots.append(
Plot.make1D("{0}_mlljj".format(uname),
(dilepton[0].p4 + dilepton[1].p4 + jets[0].p4 +
jets[1].p4).M(),
sel,
EqB(100, 0., 1000.),
title="mlljj [GeV]",
plotopts=utils.getOpts(uname)))
plots.append(
Plot.make2D(
"{0}_mlljjvsmjj".format(uname),
(op.invariant_mass(jets[0].p4, jets[1].p4),
(dilepton[0].p4 + dilepton[1].p4 + jets[0].p4 + jets[1].p4).M()),
sel, (EqB(1000, 0., 1000.), EqB(1000, 0., 1000.)),
title="mlljj vs mjj invariant mass [Gev]",
plotopts=utils.getOpts(uname)))
return plots
def plotMatching(contName, list_cont, list_sel, partName):
plots = []
dr_plots = []
dr2_plots = []
ptrel_plots = []
TF_plots = []
for idx, (cont, sel) in enumerate(zip(list_cont, list_sel)):
dr_plots.append(
Plot.make1D(contName + str(idx) + "_deltaR",
op.map(cont, lambda m: op.deltaR(m[0].p4, m[1].p4)),
sel,
EquidistantBinning(100, 0., 0.2),
xTitle="#Delta R(%s_{gen},%s_{reco})" %
(partName, partName)))
dr2_plots.append(
Plot.make1D(contName + str(idx) + "_deltaR2",
op.map(
cont,
lambda m: op.pow(op.deltaR(m[0].p4, m[1].p4), 2)),
sel,
EquidistantBinning(100, 0., 0.04),
xTitle="#Delta R^{2}(%s_{gen},%s_{reco})" %
(partName, partName)))
ptrel_plots.append(
Plot.make1D(
contName + str(idx) + "_deltaPtRel",
op.map(cont, lambda m: (m[1].pt - m[0].pt) / m[1].pt),
sel,
EquidistantBinning(100, -2., 2.),
xTitle="P_{T}(%s_{reco})-P_{T}(%s_{gen})/P_{T}(%s_{reco})" %
(partName, partName, partName)))
TF_plots.append(
Plot.make2D(contName + str(idx) + "_TF",
(op.map(cont, lambda m: m[0].p4.E()),
op.map(cont, lambda m: m[1].p4.E() - m[0].p4.E())),
sel, [
EquidistantBinning(100, 0., 500.),
EquidistantBinning(400, -200., 200.)
],
xTitle="E^{parton}(e^{-})",
yTitle="#Delta E = E^{reco}(%s)-E^{parton}(%s)" %
(partName, partName)))
plots.append(
SummedPlot(contName + "_deltaR",
dr_plots,
xTitle="#Delta R(%s_{gen},%s_{reco})" %
(partName, partName)))
plots.append(
SummedPlot(contName + "_deltaR2",
dr2_plots,
xTitle="#Delta R^{2}(%s_{gen},%s_{reco})" %
(partName, partName)))
plots.append(
SummedPlot(contName + "_deltaPtRel",
ptrel_plots,
xTitle="P_{T}(%s_{reco})-P_{T}(%s_{gen}/P_{T}(%s_{reco})" %
(partName, partName, partName)))
plots.append(
SummedPlot(contName + "_TF",
TF_plots,
xTitle="#Delta E = E^{reco}(%s)-E^{patron}(%s)" %
(partName, partName)))
return plots