def isGoodMuon(mu, ptCut=10.):
return op.AND(
mu.pt > ptCut,
op.abs(mu.eta) < 2.4,
mu.mediumPromptId,
op.abs(mu.sip3d) < 8.,
op.abs(mu.dxy) < .05,
op.abs(mu.dz ) < .1,
mu.miniPFRelIso_all < 0.325,
mu.mvaTTH > 0.55,
op.NOT(op.AND(mu.jet.isValid, op.OR(mu.jet.btagDeepB > .1522, mu.jet.btagDeepB <= -999.)))
)
def isGoodElectron(el, ptCut=10.):
return op.AND(
el.pt > ptCut,
op.abs(el.eta) < 2.5,
el.lostHits == 0, ## do you want this?
op.abs(el.sip3d) < 8.,
op.abs(el.dxy) < .05,
op.abs(el.dz ) < .1,
el.miniPFRelIso_all < 0.085,
el.mvaTTH > 0.125,
op.NOT(op.AND(el.jet.isValid, op.OR(el.jet.btagDeepB > .1522, el.jet.btagDeepB <= -999.)))
)
def evaluateJPA_Hbb1Wj(lepton, muons, electrons, fatJets, jets, bJetsL, bJetsM, met, model, HLL):
#invars = [op.c_float(0.)]*14
invars = [op.switch(fatJets[0].subJet1.btagDeepB > fatJets[0].subJet2.btagDeepB, # dEta_bjet1_lep
op.abs(lepton.eta-fatJets[0].subJet1.eta),
op.abs(lepton.eta-fatJets[0].subJet2.eta)),
bJetCorrPT(jets[0]), # wjet1_ptReg
jets[0].btagDeepB, # wjet1_btagCSV
jets[0].qgl, # wjet1_qgDiscr
op.deltaR(lepton.p4, jets[0].p4), # dR_wjet1_lep
(fatJets[0].subJet1.p4 + fatJets[0].subJet2.p4).Pt(), # Hbb_Pt
op.rng_len(bJetsM) # nBJetMedium
]
return model(*invars, defineOnFirstUse=False)[0]
def isGoodMuon(mu, ptCut=10.):
from bamboo import treefunctions as op
return op.AND(
mu.pt > ptCut,
op.abs(mu.eta) < 2.4,
## TODO add the other cuts
)
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
plots = []
verbose = False
trijets = op.combine(tree.Jet, N=3)
hadTop = op.rng_min_element_by(trijets,
fun=lambda comb: op.abs((comb[0].p4+comb[1].p4+comb[2].p4).M()-172.5))
hasTriJet = noSel.refine("hasTriJet", cut=(op.rng_len(trijets) > 0))
hadTop_p4 = op.defineOnFirstUse(hadTop[0].p4 + hadTop[1].p4 + hadTop[2].p4)
plots.append(Plot.make1D("trijet_topPt",
hadTop_p4.Pt(), hasTriJet,
EqBin(100, 15., 40.), title="Trijet p_{T} (GeV/c)"))
plots.append(Plot.make1D("trijet_maxbtag",
op.max(op.max(hadTop[0].btag, hadTop[1].btag), hadTop[2].btag), hasTriJet,
EqBin(100, 0., 1.), title="Trijet maximum b-tag"))
if verbose:
plots.append(Plot.make1D("njets",
op.rng_len(tree.Jet), noSel,
EqBin(20, 0., 20.), title="Number of jets"))
plots.append(Plot.make1D("ntrijets",
op.rng_len(trijets), noSel,
EqBin(100, 0., 1000.), title="Number of 3-jet combinations"))
plots.append(Plot.make1D("trijet_mass",
hadTop_p4.M(), hasTriJet,
EqBin(100, 0., 250.), title="Trijet mass (GeV/c^{2})"))
return plots
def isGoodMuon(mu, ptCut=10.):
from bamboo import treefunctions as op
return op.AND(
mu.pt > ptCut,
op.abs(mu.eta) < 2.4,
## TODO add the other cuts
mu.sip3d < 8,
op.abs(mu.dxy) < 0.05,
op.abs(mu.dz) < 0.1,
mu.miniPFRelIso_all < 0.085,
mu.mediumPromptId,
mu.mvaTTH > 0.55,
op.NOT(
op.AND(mu.jet.isValid,
op.OR(mu.jet.btagDeepB > .1522,
mu.jet.btagDeepB <= -999.))))
def evaluateJPA_1b1Wj(lepton, muons, electrons, ak4jets, jets, bJetsL, bJetsM, met, model, HLL):
#invars = [op.c_float(0.)]*12
invars = [bJetCorrPT(jets[0]), # bjet1_ptReg
jets[0].btagDeepFlavB, # bjet1_btagCSV
jets[0].qgl, # bjet1_qgDiscr
op.abs(lepton.eta - jets[0].eta), # dEta_bjet1_lep
bJetCorrPT(jets[1]), # wjet1_ptReg
jets[1].btagDeepFlavB, # wjet1_btagCSV
jets[1].qgl, # wjet1_qgDiscr
op.abs(lepton.eta - jets[1].eta), # dEta_wjet1_lep
op.rng_len(ak4jets), # nJets
op.rng_len(bJetsL), # nBJetLoose
op.rng_len(bJetsM), # nBJetMedium
op.rng_len(muons) + op.rng_len(electrons) # nLep
]
return model(*invars, defineOnFirstUse=False)[0]
def isGoodElectron(el, ptCut=10.):
from bamboo import treefunctions as op
return op.AND(
el.pt > ptCut,
op.abs(el.eta) < 2.5,
## TODO add the other cuts
el.sip3d < 8,
op.abs(el.dxy) < 0.05,
op.abs(el.dz) < 0.1,
el.miniPFRelIso_all < 0.085,
el.mvaTTH > 0.125,
el.lostHits == 0,
op.NOT(
op.AND(el.jet.isValid,
op.OR(el.jet.btagDeepB > .1522, el.jet.btagDeepB <= -999.)))
## ^^ this one is tricky, the rest are straightforward
)
def evaluateJPA_2b1Wj(lepton, muons, electrons, ak4jets, jets, bJetsL, bJetsM, met, model, HLL):
#invars = [op.c_float(0.)]*13
invars = [jets[0].btagDeepFlavB, # bjet1_btagCSV
bJetCorrPT(jets[1]), # bjet2_ptReg
jets[1].btagDeepFlavB, # bjet2_btagCSV
jets[1].qgl, # bjet2_qgDiscr
op.abs(lepton.eta - jets[1].eta), # dEta_bjet2_lep
bJetCorrPT(jets[2]), # wjet1_ptReg
jets[2].btagDeepFlavB, # wjet1_btagCSV
jets[2].qgl, # wjet1_qgDiscr
op.abs(lepton.eta - jets[2].eta), # dEta_wjet1_lep
op.deltaR(jets[0].p4, jets[1].p4), # dR_bjet1bjet2
op.rng_len(ak4jets), # nJets
op.rng_len(bJetsL), # nBJetLoose
op.rng_len(bJetsM) # nBJetMedium
]
return model(*invars, defineOnFirstUse=False)[0]
def isGoodElectron(el, ptCut=10.):
from bamboo import treefunctions as op
return op.AND(
el.pt > ptCut,
op.abs(el.eta) < 2.5,
## TODO add the other cuts
op.NOT(op.AND(el.jet.isValid, op.OR(el.jet.btagDeepB > .1522, el.jet.btagDeepB <= -999.)))
## ^^ this one is tricky, the rest are straightforward
)
def reco_4l(leptons, lName, baseSel):
## select events with four leptons, and find the best Z candidate
## shared between 4el and 4mu
has4l = baseSel.refine(f"has4{lName}", cut=[
op.rng_len(leptons) == 4,
op.rng_sum(leptons, lambda l : l.charge) == 0,
])
allZcand = op.combine(leptons, N=2, pred=lambda l1,l2 : l1.charge != l2.charge)
bestZ = op.rng_min_element_by(allZcand, lambda ll : op.abs(op.invariant_mass(ll[0].p4, ll[1].p4)-mZ))
otherLeptons = op.select(leptons, partial(lambda l,oz=None : op.AND(l.idx != oz[0].idx, l.idx != oz[1].idx), oz=bestZ))
return has4l, bestZ, otherLeptons
def evaluateJPA_2b0Wj(lepton, muons, electrons, ak4jets, jets, bJetsL, bJetsM, met, model, HLL):
#invars = [op.c_float(0.)]*9
invars = [bJetCorrPT(jets[0]), # bjet1_ptReg
jets[0].btagDeepFlavB, # bjet1_btagCSV
bJetCorrPT(jets[1]), # bjet2_ptReg
jets[1].btagDeepFlavB, # bjet2_btagCSV
jets[1].qgl, # bjet2_qgDiscr
op.abs(lepton.eta - jets[1].eta), # dEta_bjet2_lep
(HLL.bJetCorrP4(jets[0]) + HLL.bJetCorrP4(jets[1])).M(), # Hbb_massReg
op.rng_len(ak4jets), # nJets
op.rng_len(bJetsM) # nBJetMedium
]
return model(*invars, defineOnFirstUse=False)[0]
def comp_cosThetaSbetBeamAndHiggs(self, genColl):
genh = op.select(
genColl,
lambda g: op.AND(g.pdgId == 25, g.statusFlags & (0x1 << 13)))
HH_p4 = genh[0].p4 + genh[1].p4
cm = HH_p4.BoostToCM()
boosted_h1 = op.extMethod("ROOT::Math::VectorUtil::boost",
returnType=genh[0].p4._typeName)(genh[0].p4,
cm)
boosted_h2 = op.extMethod("ROOT::Math::VectorUtil::boost",
returnType=genh[1].p4._typeName)(genh[1].p4,
cm)
mHH = op.switch(
op.rng_len(genh) == 2, op.invariant_mass(genh[0].p4, genh[1].p4),
op.c_float(-9999))
cosTheta1 = op.switch(
op.rng_len(genh) == 2, op.abs(boosted_h1.Pz() / boosted_h1.P()),
op.c_float(-9999))
cosTheta2 = op.switch(
op.rng_len(genh) == 2, op.abs(boosted_h1.Pz() / boosted_h2.P()),
op.c_float(-9999))
return [mHH, cosTheta1, cosTheta2]
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, SummedPlot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
plots = []
# The plot is made for each of the different flavour categories (l+/- l-/+ l') and then summed,
# because concatenation of containers is not (yet) supported.
lepColl = {"El": tree.Electron, "Mu": tree.Muon}
mt3lPlots = []
for dlNm, dlCol in lepColl.items():
dilep = op.combine(
dlCol,
N=2,
pred=(lambda l1, l2: op.AND(l1.charge != l2.charge)))
hasDiLep = noSel.refine("hasDilep{0}{0}".format(dlNm),
cut=(op.rng_len(dilep) > 0))
dilepZ = op.rng_min_element_by(
dilep,
fun=lambda ll: op.abs(
op.invariant_mass(ll[0].p4, ll[1].p4) - 91.2))
for tlNm, tlCol in lepColl.items():
if tlCol == dlCol:
hasTriLep = hasDiLep.refine("hasTrilep{0}{0}{1}".format(
dlNm, tlNm),
cut=(op.rng_len(tlCol) > 2))
residLep = op.select(
tlCol, lambda l: op.AND(l.idx != dilepZ[0].idx, l.idx
!= dilepZ[1].idx))
l3 = op.rng_max_element_by(residLep, lambda l: l.pt)
else:
hasTriLep = hasDiLep.refine("hasTriLep{0}{0}{1}".format(
dlNm, tlNm),
cut=(op.rng_len(tlCol) > 0))
l3 = op.rng_max_element_by(tlCol, lambda l: l.pt)
mtPlot = Plot.make1D(
"3lMT_{0}{0}{1}".format(dlNm, tlNm),
op.sqrt(2 * l3.pt * tree.MET.pt *
(1 - op.cos(l3.phi - tree.MET.phi))),
hasTriLep,
EqBin(100, 15., 250.),
title="M_{T} (GeV/c^2)")
mt3lPlots.append(mtPlot)
plots.append(mtPlot)
plots.append(SummedPlot("3lMT", mt3lPlots))
return plots
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, SummedPlot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
plots = []
metSel = noSel.refine("MET", cut=(tree.met.et > 30000))
trigSel = metSel.refine("trig", cut=op.OR(tree.trigE, tree.trigM))
goodLeptons = op.select(
tree.lep, lambda l: op.AND(l.isTightID, l.pt > 35000., l.ptcone30 /
l.pt < 0.1, l.etcone20 / l.pt < 0.1))
oneLepSel = trigSel.refine("1goodlep",
cut=(op.rng_len(goodLeptons) == 1))
lep = goodLeptons[0]
signalSel = oneLepSel.refine(
"signalRegion",
cut=op.AND(
op.abs(lep.z0 * op.sin(lep.p4.theta())) < 0.5,
op.multiSwitch(
(lep.type == 11,
op.AND(
op.abs(lep.eta) < 2.46,
op.NOT(op.in_range(1.37, op.abs(lep.eta), 1.52)),
op.abs(lep.trackd0pvunbiased /
lep.tracksigd0pvunbiased) < 5)),
(lep.type == 13,
op.AND(
op.abs(lep.eta) < 2.5,
op.abs(lep.trackd0pvunbiased /
lep.tracksigd0pvunbiased) < 3)),
op.c_bool(False))))
metp4 = makePtEtaPhiEP4(tree.met.et, op.c_float(0.), tree.met.phi,
tree.met.et)
plots.append(
Plot.make1D("mt_w", (lep.p4 + metp4).Mt() / 1000.,
signalSel,
EqBin(40, 60., 180.),
title="m_{T}^{W#rightarrow l#nu} (GeV)",
plotopts={"log-y-axis-range": [25., 2.5e8]}))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
# Some imports #
from bamboo.analysisutils import forceDefine
era = sampleCfg['era']
# Get pile-up configs #
puWeightsFile = None
if era == "2016":
sfTag = "94X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data",
"puweights2016.json")
elif era == "2017":
sfTag = "94X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data",
"puweights2017.json")
elif era == "2018":
sfTag = "102X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data",
"puweights2018.json")
if self.isMC(sample) and puWeightsFile is not None:
from bamboo.analysisutils import makePileupWeight
noSel = noSel.refine("puWeight",
weight=makePileupWeight(puWeightsFile,
t.Pileup_nTrueInt,
systName="pileup"))
isMC = self.isMC(sample)
plots = []
forceDefine(t._Muon.calcProd, noSel)
#############################################################################
################################ Muons #####################################
#############################################################################
# Wp // 2016- 2017 -2018 : Muon_mediumId // https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideMuonIdRun2#Muon_Isolation
muonsByPt = op.sort(t.Muon, lambda mu: -mu.p4.Pt())
muons = op.select(
muonsByPt, lambda mu: op.AND(mu.p4.Pt() > 10.,
op.abs(mu.p4.Eta()) < 2.4, mu.tightId,
mu.pfRelIso04_all < 0.15))
# Scalefactors #
#if era=="2016":
# doubleMuTrigSF = get_scalefactor("dilepton", ("doubleMuLeg_HHMoriond17_2016"), systName="mumutrig")
# muMediumIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "id_medium"), combine="weight", systName="muid")
# muMediumISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "iso_tight_id_medium"), combine="weight", systName="muiso")
# TrkIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "highpt"), combine="weight")
# TrkISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "isotrk_loose_idtrk_tightidandipcut"), combine="weight")
#else:
# muMediumIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "id_medium"), systName="muid")
# muMediumISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "iso_tight_id_medium"), systName="muiso")
#############################################################################
############################# Electrons ###################################
#############################################################################
#Wp // 2016: Electron_cutBased_Sum16==3 -> medium // 2017 -2018 : Electron_cutBased ==3 --> medium ( Fall17_V2)
# asking for electrons to be in the Barrel region with dz<1mm & dxy< 0.5mm // Endcap region dz<2mm & dxy< 0.5mm
electronsByPt = op.sort(t.Electron, lambda ele: -ele.p4.Pt())
electrons = op.select(
electronsByPt,
lambda ele: op.AND(ele.p4.Pt() > 15.,
op.abs(ele.p4.Eta()) < 2.5, ele.cutBased >= 3)
) # //cut-based ID Fall17 V2 the recomended one from POG for the FullRunII
# Scalefactors #
#elMediumIDSF = get_scalefactor("lepton", ("electron_{0}_{1}".format(era,sfTag), "id_medium"), systName="elid")
#doubleEleTrigSF = get_scalefactor("dilepton", ("doubleEleLeg_HHMoriond17_2016"), systName="eleltrig")
#elemuTrigSF = get_scalefactor("dilepton", ("elemuLeg_HHMoriond17_2016"), systName="elmutrig")
#mueleTrigSF = get_scalefactor("dilepton", ("mueleLeg_HHMoriond17_2016"), systName="mueltrig")
OsElEl = op.combine(
electrons,
N=2,
pred=lambda el1, el2: op.AND(el1.charge != el2.charge,
el1.p4.Pt() > 25,
el2.p4.Pt() > 15))
OsMuMu = op.combine(
muons,
N=2,
pred=lambda mu1, mu2: op.AND(mu1.charge != mu2.charge,
mu1.p4.Pt() > 25,
mu2.p4.Pt() > 15))
OsElMu = op.combine((electrons, muons),
pred=lambda el, mu: op.AND(el.charge != mu.charge,
el.p4.Pt() > 25,
mu.p4.Pt() > 15))
OsMuEl = op.combine((electrons, muons),
pred=lambda el, mu: op.AND(el.charge != mu.charge,
el.p4.Pt() > 15,
mu.p4.Pt() > 25))
hasOsElEl = noSel.refine("hasOsElEl", cut=[op.rng_len(OsElEl) >= 1])
hasOsMuMu = noSel.refine("hasOsMuMu", cut=[op.rng_len(OsMuMu) >= 1])
hasOsElMu = noSel.refine("hasOsElMu", cut=[op.rng_len(OsElMu) >= 1])
hasOsMuEl = noSel.refine("hasOsMuEl", cut=[op.rng_len(OsMuEl) >= 1])
plots.append(
Plot.make1D("ElEl_channel",
op.rng_len(OsElEl),
noSel,
EquidistantBinning(10, 0, 10.),
title='Number of dilepton events in ElEl channel',
xTitle='N_{dilepton} (ElEl channel)'))
plots.append(
Plot.make1D("MuMu_channel",
op.rng_len(OsMuMu),
noSel,
EquidistantBinning(10, 0, 10.),
title='Number of dilepton events in MuMu channel',
xTitle='N_{dilepton} (MuMu channel)'))
plots.append(
Plot.make1D("ElMu_channel",
op.rng_len(OsElMu),
noSel,
EquidistantBinning(10, 0, 10.),
title='Number of dilepton events in ElMu channel',
xTitle='N_{dilepton} (ElMu channel)'))
plots.append(
Plot.make1D("MuEl_channel",
op.rng_len(OsMuEl),
noSel,
EquidistantBinning(10, 0, 10.),
title='Number of dilepton events in MuEl channel',
xTitle='N_{dilepton} (MuEl channel)'))
plots += makeDileptonPlots(self,
sel=hasOsElEl,
dilepton=OsElEl[0],
suffix='hasOsdilep',
channel='ElEl')
plots += makeDileptonPlots(self,
sel=hasOsMuMu,
dilepton=OsMuMu[0],
suffix='hasOsdilep',
channel='MuMu')
plots += makeDileptonPlots(self,
sel=hasOsElMu,
dilepton=OsElMu[0],
suffix='hasOsdilep',
channel='ElMu')
plots += makeDileptonPlots(self,
sel=hasOsMuEl,
dilepton=OsMuEl[0],
suffix='hasOsdilep',
channel='MuEl')
# Dilepton Z peak exclusion (charge already done in previous selection) #
lambda_mllLowerband = lambda dilep: op.in_range(
12., op.invariant_mass(dilep[0].p4, dilep[1].p4), 80.)
lambda_mllUpperband = lambda dilep: op.invariant_mass(
dilep[0].p4, dilep[1].p4) > 100.
lambda_mllCut = lambda dilep: op.OR(lambda_mllLowerband(dilep),
lambda_mllUpperband(dilep))
hasOsElElOutZ = hasOsElEl.refine("hasOsElElOutZ",
cut=[lambda_mllCut(OsElEl[0])])
hasOsMuMuOutZ = hasOsMuMu.refine("hasOsMuMuOutZ",
cut=[lambda_mllCut(OsMuMu[0])])
hasOsElMuOutZ = hasOsElMu.refine("hasOsElMuOutZ",
cut=[lambda_mllCut(OsElMu[0])])
hasOsMuElOutZ = hasOsMuEl.refine("hasOsMuElOutZ",
cut=[lambda_mllCut(OsMuEl[0])])
plots += makeDileptonPlots(self,
sel=hasOsElElOutZ,
dilepton=OsElEl[0],
suffix='hasOsdilep_OutZ',
channel='ElEl')
plots += makeDileptonPlots(self,
sel=hasOsMuMuOutZ,
dilepton=OsMuMu[0],
suffix='hasOsdilep_OutZ',
channel='MuMu')
plots += makeDileptonPlots(self,
sel=hasOsElMuOutZ,
dilepton=OsElMu[0],
suffix='hasOsdilep_OutZ',
channel='ElMu')
plots += makeDileptonPlots(self,
sel=hasOsMuElOutZ,
dilepton=OsMuEl[0],
suffix='hasOsdilep_OutZ',
channel='MuEl')
#############################################################################
################################ Jets #####################################
#############################################################################
# select jets // 2016 - 2017 - 2018 ( j.jetId &2) -> tight jet ID
jetsByPt = op.sort(t.Jet, lambda jet: -jet.p4.Pt())
jetsSel = op.select(jetsByPt,
lambda j: op.AND(j.p4.Pt() > 20.,
op.abs(j.p4.Eta()) < 2.4,
(j.jetId & 2))) # Jets = AK4 jets
fatjetsByPt = op.sort(t.FatJet, lambda fatjet: -fatjet.p4.Pt())
fatjetsSel = op.select(
fatjetsByPt, lambda j: op.AND(j.p4.Pt() > 20.,
op.abs(j.p4.Eta()) < 2.4,
(j.jetId & 2))) # FatJets = AK8 jets
# exclude from the jetsSel any jet that happens to include within its reconstruction cone a muon or an electron.
jets = op.select(
jetsSel, lambda j: op.AND(
op.NOT(
op.rng_any(electrons, lambda ele: op.deltaR(j.p4, ele.p4) <
0.3)),
op.NOT(
op.rng_any(muons, lambda mu: op.deltaR(j.p4, mu.p4) < 0.3))
))
fatjets = op.select(
fatjetsSel, lambda j: op.AND(
op.NOT(
op.rng_any(electrons, lambda ele: op.deltaR(j.p4, ele.p4) <
0.3)),
op.NOT(
op.rng_any(muons, lambda mu: op.deltaR(j.p4, mu.p4) < 0.3))
))
# Boosted and resolved jets categories #
if era == "2016": # Must check that subJet exists before looking at the btag
lambda_boosted = lambda fatjet: op.OR(
op.AND(fatjet.subJet1._idx.result != -1, fatjet.subJet1.
btagDeepB > 0.6321),
op.AND(fatjet.subJet2._idx.result != -1, fatjet.subJet2.
btagDeepB > 0.6321))
lambda_resolved = lambda jet: jet.btagDeepB > 0.6321
elif era == "2017":
lambda_boosted = lambda fatjet: op.OR(
op.AND(fatjet.subJet1._idx.result != -1, fatjet.subJet1.
btagDeepB > 0.4941),
op.AND(fatjet.subJet2._idx.result != -1, fatjet.subJet2.
btagDeepB > 0.4941))
lambda_resolved = lambda jet: jet.btagDeepB > 0.4941
elif era == "2018":
lambda_boosted = lambda fatjet: op.OR(
op.AND(fatjet.subJet1._idx.result != -1, fatjet.subJet1.
btagDeepB > 0.4184),
op.AND(fatjet.subJet2._idx.result != -1, fatjet.subJet2.
btagDeepB > 0.4184))
lambda_resolved = lambda jet: jet.btagDeepB > 0.4184
# Select the bjets we want #
bjetsResolved = op.select(jets, lambda_resolved)
bjetsBoosted = op.select(fatjets, lambda_boosted)
# Define the boosted and Resolved (+exclusive) selections #
hasBoostedJets = noSel.refine("hasBoostedJets",
cut=[op.rng_len(bjetsBoosted) >= 1])
hasNotBoostedJets = noSel.refine("hasNotBoostedJets",
cut=[op.rng_len(bjetsBoosted) == 0])
hasResolvedJets = noSel.refine(
"hasResolvedJets",
cut=[op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1])
hasNotResolvedJets = noSel.refine(
"hasNotResolvedJets",
cut=[op.OR(op.rng_len(jets) <= 1,
op.rng_len(bjetsResolved) == 0)])
hasBoostedAndResolvedJets = noSel.refine(
"hasBoostedAndResolvedJets",
cut=[
op.rng_len(bjetsBoosted) >= 1,
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1
])
hasNotBoostedAndResolvedJets = noSel.refine(
"hasNotBoostedAndResolvedJets",
cut=[
op.OR(
op.rng_len(bjetsBoosted) == 0,
op.rng_len(jets) <= 1,
op.rng_len(bjetsResolved) == 0)
])
hasExlusiveResolvedJets = noSel.refine(
"hasExlusiveResolved",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
hasNotExlusiveResolvedJets = noSel.refine(
"hasNotExlusiveResolved",
cut=[
op.OR(
op.OR(
op.rng_len(jets) <= 1,
op.rng_len(bjetsResolved) == 0),
op.AND(
op.rng_len(bjetsBoosted) >= 1,
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1))
])
hasExlusiveBoostedJets = noSel.refine(
"hasExlusiveBoostedJets",
cut=[
op.rng_len(bjetsBoosted) >= 1,
op.OR(op.rng_len(jets) <= 1,
op.rng_len(bjetsResolved) == 0)
])
hasNotExlusiveBoostedJets = noSel.refine(
"hasNotExlusiveBoostedJets",
cut=[
op.OR(
op.rng_len(bjetsBoosted) == 0,
op.AND(
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1))
])
# Counting events from different selections for debugging #
# Passing Boosted selection #
PassedBoosted = Plot.make1D("PassedBoosted",
op.c_int(1),
hasBoostedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Boosted',
xTitle='Passed Boosted')
FailedBoosted = Plot.make1D("FailedBoosted",
op.c_int(0),
hasNotBoostedJets,
EquidistantBinning(2, 0., 2.),
title='Failed Boosted',
xTitle='Failed Boosted')
plots.append(
SummedPlot("BoostedCase", [FailedBoosted, PassedBoosted],
xTitle="Boosted selection"))
# Passing Resolved selection #
PassedResolved = Plot.make1D("PassedResolved",
op.c_int(1),
hasResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Resolved',
xTitle='Passed Resolved')
FailedResolved = Plot.make1D("FailedResolved",
op.c_int(0),
hasNotResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Failed Resolved',
xTitle='Failed Resolved')
plots.append(
SummedPlot("ResolvedCase", [FailedResolved, PassedResolved],
xTitle="Resolved selection"))
# Passing Exclusive Resolved (Resolved AND NOT Boosted) #
PassedExclusiveResolved = Plot.make1D(
"PassedExclusiveResolved",
op.c_int(1),
hasExlusiveResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Exclusive Resolved',
xTitle='Passed Exclusive Resolved')
FailedExclusiveResolved = Plot.make1D(
"FailedExclusiveResolved",
op.c_int(0),
hasNotExlusiveResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Failed Exclusive Resolved',
xTitle='Failed Exclusive Resolved')
plots.append(
SummedPlot("ExclusiveResolvedCase",
[FailedExclusiveResolved, PassedExclusiveResolved],
xTitle="Exclusive Resolved selection"))
# Passing Exclusive Boosted (Boosted AND NOT Resolved) #
PassedExclusiveBoosted = Plot.make1D("PassedExclusiveBoosted",
op.c_int(1),
hasExlusiveBoostedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Exclusive Boosted',
xTitle='Passed Exclusive Boosted')
FailedExclusiveBoosted = Plot.make1D("FailedExclusiveBoosted",
op.c_int(0),
hasNotExlusiveBoostedJets,
EquidistantBinning(2, 0., 2.),
title='Failed Exclusive Boosted',
xTitle='Failed Exclusive Boosted')
plots.append(
SummedPlot("ExclusiveBoostedCase",
[FailedExclusiveBoosted, PassedExclusiveBoosted],
xTitle="Exclusive Boosted selection"))
# Passing Boosted AND Resolved #
PassedBoth = Plot.make1D("PassedBoth",
op.c_int(1),
hasBoostedAndResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Both Boosted and Resolved',
xTitle='Passed Boosted and Resolved')
FailedBoth = Plot.make1D(
"FailedBoth", # Means failed the (Boosted AND Resolved) = either one or the other
op.c_int(0),
hasNotBoostedAndResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Failed combination Boosted and Resolved',
xTitle='Failed combination')
plots.append(
SummedPlot("BoostedAndResolvedCase", [FailedBoth, PassedBoth],
xTitle="Boosted and Resolved selection"))
# Count number of boosted and resolved jets #
plots.append(
Plot.make1D("NBoostedJets",
op.rng_len(bjetsBoosted),
hasBoostedJets,
EquidistantBinning(5, 0., 5.),
title='Number of boosted jets in boosted case',
xTitle='N boosted bjets'))
plots.append(
Plot.make1D(
"NResolvedJets",
op.rng_len(bjetsResolved),
hasExlusiveResolvedJets,
EquidistantBinning(5, 0., 5.),
title='Number of resolved jets in exclusive resolved case',
xTitle='N resolved bjets'))
# Plot number of subjets in the boosted fatjets #
lambda_noSubjet = lambda fatjet: op.AND(
fatjet.subJet1._idx.result == -1,
op.AND(fatjet.subJet2._idx.result == -1))
lambda_oneSubjet = lambda fatjet: op.AND(
fatjet.subJet1._idx.result != -1,
op.AND(fatjet.subJet2._idx.result == -1))
lambda_twoSubjet = lambda fatjet: op.AND(
fatjet.subJet1._idx.result != -1,
op.AND(fatjet.subJet2._idx.result != -1))
hasNoSubjet = hasBoostedJets.refine(
"hasNoSubjet", cut=[lambda_noSubjet(bjetsBoosted[0])])
hasOneSubjet = hasBoostedJets.refine(
"hasOneSubjet", cut=[lambda_oneSubjet(bjetsBoosted[0])])
hasTwoSubjet = hasBoostedJets.refine(
"hasTwoSubjet", cut=[lambda_twoSubjet(bjetsBoosted[0])])
plot_hasNoSubjet = Plot.make1D(
"plot_hasNoSubjet", # Fill bin 0
op.c_int(0),
hasNoSubjet,
EquidistantBinning(3, 0., 3.),
title='Boosted jet without subjet')
plot_hasOneSubjet = Plot.make1D(
"plot_hasOneSubjet", # Fill bin 1
op.c_int(1),
hasOneSubjet,
EquidistantBinning(3, 0., 3.),
title='Boosted jet with one subjet')
plot_hasTwoSubjet = Plot.make1D(
"plot_hasTwoSubjet", # Fill bin 2
op.c_int(2),
hasTwoSubjet,
EquidistantBinning(3, 0., 3.),
title='Boosted jet with two subjets')
plots.append(
SummedPlot(
"NumberOfSubjets",
[plot_hasNoSubjet, plot_hasOneSubjet, plot_hasTwoSubjet],
xTitle="Number of subjets in boosted jet"))
# Plot jets quantities without the dilepton selections #
plots += makeFatJetPlots(self,
sel=hasBoostedJets,
fatjet=bjetsBoosted[0],
suffix="BoostedJets",
channel="NoChannel")
plots += makeJetsPlots(self,
sel=hasResolvedJets,
jets=bjetsResolved,
suffix="ResolvedJets",
channel="NoChannel")
#############################################################################
##################### Jets + Dilepton combination ###########################
#############################################################################
# Combine dilepton and Resolved (Exclusive = NOT Boosted) selections #
hasOsElElOutZResolvedJets = hasOsElElOutZ.refine(
"hasOsElElOutZResolvedJets",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
hasOsMuMuOutZResolvedJets = hasOsMuMuOutZ.refine(
"hasOsMuMuOutZResolvedJets",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
hasOsElMuOutZResolvedJets = hasOsElMuOutZ.refine(
"hasOsElMuOutZResolvedJets",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
hasOsMuElOutZResolvedJets = hasOsMuElOutZ.refine(
"hasOsMuElOutZResolvedJets",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
# Combine dilepton and Boosted selections #
hasOsElElOutZBoostedJets = hasOsElElOutZ.refine(
"hasOsElElOutZBoostedJets", cut=[op.rng_len(bjetsBoosted) >= 1])
hasOsMuMuOutZBoostedJets = hasOsMuMuOutZ.refine(
"hasOsMuMuOutZBoostedJets", cut=[op.rng_len(bjetsBoosted) >= 1])
hasOsElMuOutZBoostedJets = hasOsElMuOutZ.refine(
"hasOsElMuOutZBoostedJets", cut=[op.rng_len(bjetsBoosted) >= 1])
hasOsMuElOutZBoostedJets = hasOsMuElOutZ.refine(
"hasOsMuElOutZBoostedJets", cut=[op.rng_len(bjetsBoosted) >= 1])
# Plot dilepton with OS, Z peak and Resolved jets selections #
plots += makeDileptonPlots(self,
sel=hasOsElElOutZResolvedJets,
dilepton=OsElEl[0],
suffix='hasOsdilep_OutZ_ResolvedJets',
channel='ElEl')
plots += makeDileptonPlots(self,
sel=hasOsMuMuOutZResolvedJets,
dilepton=OsMuMu[0],
suffix='hasOsdilep_OutZ_ResolvedJets',
channel='MuMu')
plots += makeDileptonPlots(self,
sel=hasOsElMuOutZResolvedJets,
dilepton=OsElMu[0],
suffix='hasOsdilep_OutZ_ResolvedJets',
channel='ElMu')
plots += makeDileptonPlots(self,
sel=hasOsMuElOutZResolvedJets,
dilepton=OsMuEl[0],
suffix='hasOsdilep_OutZ_ResolvedJets',
channel='MuEl')
# Plot dilepton with OS dilepton, Z peak and Boosted jets selections #
plots += makeDileptonPlots(self,
sel=hasOsElElOutZBoostedJets,
dilepton=OsElEl[0],
suffix='hasOsdilep_OutZ_BoostedJets',
channel='ElEl')
plots += makeDileptonPlots(self,
sel=hasOsMuMuOutZBoostedJets,
dilepton=OsMuMu[0],
suffix='hasOsdilep_OutZ_BoostedJets',
channel='MuMu')
plots += makeDileptonPlots(self,
sel=hasOsElMuOutZBoostedJets,
dilepton=OsElMu[0],
suffix='hasOsdilep_OutZ_BoostedJets',
channel='ElMu')
plots += makeDileptonPlots(self,
sel=hasOsMuElOutZBoostedJets,
dilepton=OsMuEl[0],
suffix='hasOsdilep_OutZ_BoostedJets',
channel='MuEl')
# Plotting the fatjet for OS dilepton, Z peak and Boosted jets selections #
plots += makeFatJetPlots(self,
sel=hasOsElElOutZBoostedJets,
fatjet=bjetsBoosted[0],
suffix="hasOsdilep_OutZ_BoostedJets",
channel="ElEl")
plots += makeFatJetPlots(self,
sel=hasOsMuMuOutZBoostedJets,
fatjet=bjetsBoosted[0],
suffix="hasOsdilep_OutZ_BoostedJets",
channel="MuMu")
plots += makeFatJetPlots(self,
sel=hasOsElMuOutZBoostedJets,
fatjet=bjetsBoosted[0],
suffix="hasOsdilep_OutZ_BoostedJets",
channel="ElMu")
plots += makeFatJetPlots(self,
sel=hasOsMuElOutZBoostedJets,
fatjet=bjetsBoosted[0],
suffix="hasOsdilep_OutZ_BoostedJets",
channel="MuEl")
# Plotting the jets for OS dilepton, Z peak and Resolved jets selections #
plots += makeJetsPlots(self,
sel=hasOsElElOutZResolvedJets,
jets=bjetsResolved,
suffix="hasOsdilep_OutZ_ResolvedJets",
channel="ElEl")
plots += makeJetsPlots(self,
sel=hasOsMuMuOutZResolvedJets,
jets=bjetsResolved,
suffix="hasOsdilep_OutZ_ResolvedJets",
channel="MuMu")
plots += makeJetsPlots(self,
sel=hasOsElMuOutZResolvedJets,
jets=bjetsResolved,
suffix="hasOsdilep_OutZ_ResolvedJets",
channel="ElMu")
plots += makeJetsPlots(self,
sel=hasOsMuElOutZResolvedJets,
jets=bjetsResolved,
suffix="hasOsdilep_OutZ_ResolvedJets",
channel="MuEl")
## helper selection (OR) to make sure jet calculations are only done once
#hasOSLL = noSel.refine("hasOSLL", cut=op.OR(*( hasOSLL_cmbRng(rng) for rng in osLLRng.values())))
#forceDefine(t._Jet.calcProd, hasOSLL)
#for varNm in t._Jet.available:
# forceDefine(t._Jet[varNm], hasOSLL)
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, CutFlowReport, SummedPlot
from bamboo.plots import EquidistantBinning as EqB
from bamboo import treefunctions as op
isMC = self.isMC(sample)
trigCut, trigWeight = None, None
if isMC:
noSel = noSel.refine("mcWeight", weight=[ t.genWeight, t.puWeight, t.PrefireWeight ])
trigCut = op.OR(t.HLT.HIEle20_Ele12_CaloIdL_TrackIdL_IsoVL_DZ, t.HLT.HIL3DoubleMu0, t.HLT.HIL3Mu20, t.HLT.HIEle20_WPLoose_Gsf)
## TODO add a correction for prescaled triggers
else:
## suggested trigger order: dielectron, dimuon or single muon, single electron (to minimise loss due to prescales). Electron triggered-events should be taken from the HighEGJet primary datasets, muon-triggered events from the SingleMuon primary datset
pd = sample.split("_")[0]
if pd == "SingleMuon":
## TODO fill trigger cut
elif pd == "HighEGJet":
## TODO fill trigger cut
noSel = noSel.refine("trig", cut=trigCut, weight=trigWeight)
plots = []
goodLeptons = {
"el" : op.select(t.Electron, partial(isGoodElectron, ptCut=15.)),
"mu" : op.select(t.Muon, partial(isGoodMuon, ptCut=15.))
}
plots += [
Plot.make1D("trig_nLeptons15", op.rng_len(goodLeptons["el"])+op.rng_len(goodLeptons["mu"]), noSel, EqB(15, 0., 15.)),
Plot.make1D("trig_nEl15", op.rng_len(goodLeptons["el"]), noSel, EqB(15, 0., 15.)),
Plot.make1D("trig_nMu15", op.rng_len(goodLeptons["mu"]), noSel, EqB(15, 0., 15.))
]
from bamboo.scalefactors import get_scalefactor
sf_loose = {
"mu": get_scalefactor("lepton", "Muon_RecoToLoose", sfLib=scalefactors_lepMVA, paramDefs=binningVariables_nano_noScaleSyst, systName="muLoose"),
"el": get_scalefactor("lepton", "Electron_RecoToLoose", sfLib=scalefactors_lepMVA, paramDefs=binningVariables_nano_noScaleSyst, systName="elLoose")
}
sf_tight = {
"mu": get_scalefactor("lepton", "Muon_LooseToTight", sfLib=scalefactors_lepMVA, paramDefs=binningVariables_nano_noScaleSyst, systName="muTight"),
"el": get_scalefactor("lepton", "Electron_LooseToTight", sfLib=scalefactors_lepMVA, paramDefs=binningVariables_nano_noScaleSyst, systName="elTight")
}
nGoodLeptons = op.rng_len(goodLeptons["el"])+op.rng_len(goodLeptons["mu"])
hasTwoGoodLeptons = noSel.refine("has2Lep", cut=(nGoodLeptons > 1)) # avoid overlap with 1l
jets = op.sort(op.select(t.Jet, lambda j : op.AND(
j.pt > 25., ## you decide...
op.abs(j.eta) < 2.4,
j.jetId & 0x2, ## tight JetID
op.AND( ## lepton-jet cross-cleaning
op.NOT(op.rng_any(goodLeptons["el"], lambda l : op.deltaR(l.p4, j.p4) < 0.4)),
op.NOT(op.rng_any(goodLeptons["mu"], lambda l : op.deltaR(l.p4, j.p4) < 0.4)))
)), lambda j : -j.pt)
for fl1,fl2 in product(*repeat(goodLeptons.keys(), 2)):
dilepSel = lambda l1,l2 : op.AND(
l1.charge != l2.charge,
(l1.p4+l2.p4).M() > 12.
)
if fl1 == fl2:
lGood = op.sort(goodLeptons[fl1], lambda l : -l.pt)
dilep = op.combine(lGood, N=2, pred=dilepSel)
else:
l1Good = op.sort(goodLeptons[fl1], lambda l : -l.pt)
l2Good = op.sort(goodLeptons[fl2], lambda l : -l.pt)
dilep = op.combine((l1Good, l2Good), pred=dilepSel)
ll = dilep[0]
hasDilep = hasTwoGoodLeptons.refine(f"hasDilep{fl1}{fl2}", cut=(op.rng_len(dilep) > 0, ll[0].pt > 25.),
weight=([ sf_loose[fl1](ll[0]), sf_loose[fl2](ll[1]), sf_tight[fl1](ll[0]), sf_tight[fl2](ll[1]) ] if isMC else None))
plots += [
Plot.make1D(f"dilepton_{fl1}{fl2}_Mll", (ll[0].p4+ll[1].p4).M(), hasDilep, EqB(50, 70, 120.), title="Dilepton mass"),
]
for il,ifl in enumerate((fl1, fl2)):
plots += [
Plot.make1D(f"dilepton_{fl1}{fl2}_L{il:d}PT", ll[il].pt, hasDilep, EqB(50, 0., 100.), title=f"Lepton {il:d} PT"),
Plot.make1D(f"dilepton_{fl1}{fl2}_L{il:d}ETA", ll[il].eta, hasDilep, EqB(50, -2.5, 2.5), title=f"Lepton {il:d} ETA"),
]
plots += [
Plot.make1D(f"dilepton_{fl1}{fl2}_nJets", op.rng_len(jets), hasDilep, EqB(15, 0, 15.), title="Jet multiplicity"),
]
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 returnHighLevelMVAInputs(self,l1,l2,met,jets,bjets,electrons,muons,channel):
if channel == "ElEl":
cone_l1 = self.getElectronConeP4(l1)
cone_l2 = self.getElectronConeP4(l2)
elif channel == "MuMu":
cone_l1 = self.getMuonConeP4(l1)
cone_l2 = self.getMuonConeP4(l2)
elif channel == "ElMu":
cone_l1 = self.getElectronConeP4(l1)
cone_l2 = self.getMuonConeP4(l2)
else:
raise RuntimeError("Wrong channel")
dijets = op.combine(jets, N=2)
import bamboo.treeoperations as _to
def rng_min(rng, fun=(lambda x : x), typeName="float"):
return op._to.Reduce.fromRngFun(rng, op.c_float(float("+inf"), typeName), ( lambda fn : (
lambda res, elm : op.extMethod("std::min", returnType="Float_t")(res, fn(elm))
) )(fun) )
if self.args.Boosted0Btag or self.args.Boosted1Btag:
VBFJetPairs = self.VBFJetPairsBoosted
elif self.args.Resolved0Btag or self.args.Resolved1Btag or self.args.Resolved2Btag:
VBFJetPairs = self.VBFJetPairsResolved
else:
raise RuntimeError("Wrong selection to be used by the DNN")
return {
('m_bb_bregcorr', 'Di-bjet invariant mass (regcorr) [GeV]', (100,0.,1000.)) : op.multiSwitch((op.rng_len(bjets) == 0, op.c_float(0.)),
(op.rng_len(bjets) == 1, self.HLL.getCorrBp4(bjets[0]).M()),
op.invariant_mass(self.HLL.getCorrBp4(bjets[0]),self.HLL.getCorrBp4(bjets[1]))),
('ht', 'HT(jets) [GeV]', (100,0.,1000.)) : op.rng_sum(jets, lambda j : j.pt),
('min_dr_jets_lep1', 'Min(#Delta R(lead lepton,jets))', (25,0.,5.)) : op.switch(op.rng_len(jets) > 0,
op.switch(cone_l1.Pt() >= cone_l2.Pt(),
self.HLL.MinDR_part1_partCont(cone_l1,jets),
self.HLL.MinDR_part1_partCont(cone_l2,jets)),
op.c_float(0.)),
('min_dr_jets_lep2', 'Min(#Delta R(sublead lepton,jets))', (25,0.,5.)) : op.switch(op.rng_len(jets) > 0,
op.switch(cone_l1.Pt() >= cone_l2.Pt(),
self.HLL.MinDR_part1_partCont(cone_l2,jets),
self.HLL.MinDR_part1_partCont(cone_l1,jets)),
op.c_float(0.)),
('m_ll', 'Dilepton invariant mass [GeV]', (100,0.,1000.)) : op.invariant_mass(cone_l1,cone_l2),
('dr_ll', 'Dilepton #Delta R', (25,0.,5.)) : op.deltaR(cone_l1,cone_l2),
('min_dr_jet', 'Min(#Delta R(jets))', (25,0.,5.)) : op.switch(op.rng_len(dijets) > 0,
op.rng_min(dijets,lambda dijet : op.deltaR(dijet[0].p4,dijet[1].p4)),
op.c_float(0.)),
('min_dhi_jet', 'Min(#Delta #Phi(jets))', (16,0.,3.2)) : op.switch(op.rng_len(dijets) > 0,
rng_min(dijets,lambda dijet : op.abs(op.deltaPhi(dijet[0].p4,dijet[1].p4)),typeName='double'),
op.c_float(0.)),
('m_hh_simplemet_bregcorr','M_{HH} (simple MET) (regcorr) [GeV]', (100,0.,1000.)) : op.invariant_mass(op.rng_sum(bjets,
lambda bjet : self.HLL.getCorrBp4(bjet),
start=self.HLL.empty_p4),
cone_l1,
cone_l2,
met.p4),
('met_ld', 'MET_{LD}', (100,0.,1000.)) : self.HLL.MET_LD_DL(met,jets,electrons,muons),
('dr_bb', 'Di-bjet #Delta R', (25,0.,5.)) : op.switch(op.rng_len(bjets)>=2,
op.deltaR(bjets[0].p4,bjets[1].p4),
op.c_float(0.)),
('min_dr_leps_b1', 'Min(#Delta R(lead bjet,dilepton))', (25,0.,5.)) : op.switch(op.rng_len(bjets)>=1,
self.HLL.MinDR_part1_dipart(bjets[0].p4,[cone_l1,cone_l2]),
op.c_float(0.)),
('min_dr_leps_b2', 'Min(#Delta R(sublead bjet,dilepton))', (25,0.,5.)) : op.switch(op.rng_len(bjets)>=2,
self.HLL.MinDR_part1_dipart(bjets[1].p4,[cone_l1,cone_l2]),
op.c_float(0.)),
('lep1_conept', 'Lead lepton cone-P_T [GeV]', (40,0.,200.)) : op.switch(cone_l1.Pt() >= cone_l2.Pt() , cone_l1.Pt() , cone_l2.Pt()),
('lep2_conept', 'Sublead lepton cone-P_T [GeV]', (40,0.,200.)) : op.switch(cone_l1.Pt() >= cone_l2.Pt() , cone_l2.Pt() , cone_l1.Pt()),
('mww_simplemet', 'M_{WW} (simple MET) [GeV]', (100,0.,1000.)) : op.invariant_mass(cone_l1,cone_l2,met.p4),
('vbf_tag', 'VBF tag', (2,0.,2.)) : op.c_int(op.rng_len(VBFJetPairs)>0),
('boosted_tag', 'Boosted tag', (2,0.,2.)) : op.c_int(op.OR(op.rng_len(self.ak8BJets) > 0, # Boosted 1B
op.AND(op.rng_len(self.ak8BJets) == 0, # Boosted 0B
op.rng_len(self.ak8Jets) > 0,
op.rng_len(self.ak4BJets) == 0))),
('dphi_met_dilep', 'Dilepton-MET #Delta #Phi', (32,-3.2,3.2)) : op.abs(op.deltaPhi(met.p4,(cone_l1+cone_l2))),
('dphi_met_dibjet', 'Dibjet-MET #Delta #Phi', (32,-3.2,3.2)) : op.multiSwitch((op.rng_len(bjets) == 0, op.c_float(0.)),
(op.rng_len(bjets) == 1, op.abs(op.deltaPhi(met.p4,bjets[0].p4))),
op.abs(op.deltaPhi(met.p4,(bjets[0].p4+bjets[1].p4)))),
('dr_dilep_dijet', 'Dilepton-dijet #Delta R', (25,0.,5.)) : op.multiSwitch((op.rng_len(jets) == 0, op.c_float(0.)),
(op.rng_len(jets) == 1, op.deltaR((cone_l1+cone_l2),jets[0].p4)),
op.deltaR((cone_l1+cone_l2),(jets[0].p4+jets[1].p4))),
('dr_dilep_dibjet', 'Dilepton-dibjet #Delta R', (25,0.,5.)) : op.multiSwitch((op.rng_len(bjets) == 0, op.c_float(0.)),
(op.rng_len(bjets) == 1, op.deltaR((cone_l1+cone_l2),bjets[0].p4)),
op.deltaR((cone_l1+cone_l2),(bjets[0].p4+bjets[1].p4))),
('vbf_pair_mass', 'VBF pair M_{jj}', (100,0.,1000.)) : op.switch(op.rng_len(VBFJetPairs)>0,
op.invariant_mass(VBFJetPairs[0][0].p4,VBFJetPairs[0][1].p4),
op.c_float(0.)),
('vbf_pairs_absdeltaeta', 'VBF pair #Delta#eta', (25,0.,5.)) : op.switch(op.rng_len(VBFJetPairs)>0,
op.abs(VBFJetPairs[0][0].eta-VBFJetPairs[0][1].eta),
op.c_float(0.)),
('sphericity', 'None', (1,0.,1.)) : op.c_float(0.),
('sphericity_T', 'None', (1,0.,1.)) : op.c_float(0.),
('aplanarity', 'None', (1,0.,1.)) : op.c_float(0.),
('eventshape_C', 'None', (1,0.,1.)) : op.c_float(0.),
('eventshape_D', 'None', (1,0.,1.)) : op.c_float(0.),
('eventshape_Y', 'None', (1,0.,1.)) : op.c_float(0.),
('foxwolfram1', 'None', (1,0.,1.)) : op.c_float(0.),
('foxwolfram2', 'None', (1,0.,1.)) : op.c_float(0.),
('foxwolfram3', 'None', (1,0.,1.)) : op.c_float(0.),
('foxwolfram4', 'None', (1,0.,1.)) : op.c_float(0.),
('foxwolfram5', 'None', (1,0.,1.)) : op.c_float(0.),
('centrality', 'None', (1,0.,1.)) : op.c_float(0.),
('centrality_jets', 'None', (1,0.,1.)) : op.c_float(0.),
('eigenvalue1', 'None', (1,0.,1.)) : op.c_float(0.),
('eigenvalue2', 'None', (1,0.,1.)) : op.c_float(0.),
('eigenvalue3', 'None', (1,0.,1.)) : op.c_float(0.),
}
def defineSkimSelection(self, t, noSel, sample=None, sampleCfg=None):
noSel = super(SkimmerNanoHHtobbWWSL,
self).prepareObjects(t,
noSel,
sample,
sampleCfg,
"SL",
forSkimmer=True)
# For the Skimmer, SF must not use defineOnFirstUse -> segmentation fault
era = sampleCfg['era']
# Initialize varsToKeep dict #
varsToKeep = dict()
#---------------------------------------------------------------------------------------#
# Selections #
#---------------------------------------------------------------------------------------#
# keep the exact same order of nodes as mentioned in respective JPA model xml files
ResolvedJPANodeList = [
'2b2Wj', '2b1Wj', '1b2Wj', '2b0Wj', '1b1Wj', '1b0Wj', '0b'
]
BoostedJPANodeList = ['Hbb2Wj', 'Hbb1Wj', 'Hbb0Wj']
# JPA Models
basepath = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'MachineLearning', 'ml-models',
'JPA_Loose_ttH')
resolvedModelDict = getResolvedJpaModelDict(basepath,
ResolvedJPANodeList, era)
boostedModelDict = getBoostedJpaModelDict(basepath, BoostedJPANodeList,
era)
if not self.inclusive_sel:
#----- Check arguments -----#
jet_level = [
"Ak4", "Ak8", "Res2b2Wj", "Res2b1Wj", "Res2b0Wj", "Res1b2Wj",
"Res1b1Wj", "Res1b0Wj", "Res0b", "Hbb2Wj", "Hbb1Wj", "Hbb0Wj"
]
if [
boolean for (level, boolean) in self.args.__dict__.items()
if level in jet_level
].count(True) != 1:
raise RuntimeError(
"Only one of the jet arguments must be used, check --help")
if self.args.Channel not in ["El", "Mu"]:
raise RuntimeError("Channel must be either 'El' or 'Mu'")
#----- Lepton selection -----#
ElSelObj, MuSelObj = makeSingleLeptonSelection(
self, noSel, use_dd=False, fake_selection=self.args.FakeCR)
if self.args.Channel is None:
raise RuntimeError("You need to specify --Channel")
if self.args.Channel == "El":
selObj = ElSelObj
lep = self.electronsTightSel[0]
if self.args.Channel == "Mu":
selObj = MuSelObj
lep = self.muonsTightSel[0]
#----- Apply jet corrections -----#
ElSelObject.sel = self.beforeJetselection(ElSelObj.sel, 'El')
MuSelObject.sel = self.beforeJetselection(MuSelObj.sel, 'Mu')
#----- Jet selection -----#
if any([
self.args.__dict__[item] for item in [
"Ak4", "Res2b2Wj", "Res2b1Wj", "Res2b0Wj", "Res1b2Wj",
"Res1b1Wj", "Res1b0Wj", "Res0b"
]
]):
makeResolvedSelection(self, selObj)
if self.args.Channel == "El":
print('... Resolved :: El Channel')
L1out, L2out, selObjAndJetsPerJpaCatDict = findJPACategoryResolved(
self,
selObj,
lep,
self.muonsPreSel,
self.electronsPreSel,
self.ak4Jets,
self.ak4BJetsLoose,
self.ak4BJets,
self.corrMET,
resolvedModelDict,
t.event,
self.HLL,
ResolvedJPANodeList,
plot_yield=False)
if self.args.Channel == "Mu":
print('... Resolved :: Mu Channel')
L1out, L2out, selObjAndJetsPerJpaCatDict = findJPACategoryResolved(
self,
selObj,
lep,
self.muonsPreSel,
self.electronsPreSel,
self.ak4Jets,
self.ak4BJetsLoose,
self.ak4BJets,
self.corrMET,
resolvedModelDict,
t.event,
self.HLL,
ResolvedJPANodeList,
plot_yield=False)
if any([
self.args.__dict__[item]
for item in ["Ak8", "Hbb2Wj", "Hbb1Wj", "Hbb0Wj"]
]):
makeBoostedSelection(self, selObj)
if self.args.Channel == "El":
print('... Boosted :: El Channel')
L1out, L2out, selObjAndJetsPerJpaCatDict = findJPACategoryBoosted(
self,
selObj,
lep,
self.muonsPreSel,
self.electronsPreSel,
self.ak8BJets,
self.ak4JetsCleanedFromAk8b,
self.ak4BJetsLoose,
self.ak4BJets,
self.corrMET,
boostedModelDict,
t.event,
self.HLL,
BoostedJPANodeList,
plot_yield=False)
if self.args.Channel == "Mu":
print('... Boosted :: Mu Channel')
L1out, L2out, selObjAndJetsPerJpaCatDict = findJPACategoryBoosted(
self,
selObj,
lep,
self.muonsPreSel,
self.electronsPreSel,
self.ak8BJets,
self.ak4JetsCleanedFromAk8b,
self.ak4BJetsLoose,
self.ak4BJets,
self.corrMET,
boostedModelDict,
t.event,
self.HLL,
BoostedJPANodeList,
plot_yield=False)
if self.args.Res2b2Wj:
print("...... 2b2Wj")
selObj = selObjAndJetsPerJpaCatDict.get('2b2Wj')[0]
jpaJets = selObjAndJetsPerJpaCatDict.get('2b2Wj')[1]
jpaarg = "Res2b2Wj"
if self.args.Res2b1Wj:
print("...... 2b1Wj")
selObj = selObjAndJetsPerJpaCatDict.get('2b1Wj')[0]
jpaJets = selObjAndJetsPerJpaCatDict.get('2b1Wj')[1]
jpaarg = "Res2b1Wj"
if self.args.Res1b2Wj:
print("...... 1b2Wj")
selObj = selObjAndJetsPerJpaCatDict.get('1b2Wj')[0]
jpaJets = selObjAndJetsPerJpaCatDict.get('1b2Wj')[1]
jpaarg = "Res1b2Wj"
if self.args.Res2b0Wj:
print("...... 2b0Wj")
selObj = selObjAndJetsPerJpaCatDict.get('2b0Wj')[0]
jpaJets = selObjAndJetsPerJpaCatDict.get('2b0Wj')[1]
jpaarg = "Res2b0Wj"
if self.args.Res1b1Wj:
print("...... 1b1Wj")
selObj = selObjAndJetsPerJpaCatDict.get('1b1Wj')[0]
jpaJets = selObjAndJetsPerJpaCatDict.get('1b1Wj')[1]
jpaarg = "Res1b1Wj"
if self.args.Res1b0Wj:
print("...... 1b0Wj")
selObj = selObjAndJetsPerJpaCatDict.get('1b0Wj')[0]
jpaJets = selObjAndJetsPerJpaCatDict.get('1b0Wj')[1]
jpaarg = "Res1b0Wj"
if self.args.Res0b:
print("...... 0b")
selObj = selObjAndJetsPerJpaCatDict.get('0b')[0]
jpaJets = None
jpaarg = "Res0b"
#######################################
# Hbb2Wj : jet1 jet2 jet3 jet4
# Hbb1Wj : jet1 jet2 jet3 jet4=0
# Hbb0Wj : jet1 jet2 jet3=0 jet4=0
#######################################
if self.args.Hbb2Wj:
print("...... Hbb2Wj")
selObj = selObjAndJetsPerJpaCatDict.get('Hbb2Wj')[0]
jpaJets = selObjAndJetsPerJpaCatDict.get('Hbb2Wj')[1]
jpaarg = "Hbb2Wj"
if self.args.Hbb1Wj:
print("...... Hbb1Wj")
selObj = selObjAndJetsPerJpaCatDict.get('Hbb1Wj')[0]
jpaJets = selObjAndJetsPerJpaCatDict.get('Hbb1Wj')[1]
jpaarg = "Hbb1Wj"
if self.args.Hbb0Wj:
print("...... Hbb0Wj")
selObj = selObjAndJetsPerJpaCatDict.get('Hbb0Wj')[0]
jpaJets = None
jpaarg = "Hbb0Wj"
else:
noSel = self.beforeJetselection(noSel)
#---------------------------------------------------------------------------------------#
# Synchronization tree #
#---------------------------------------------------------------------------------------#
if self.args.Synchronization:
# Event variables #
varsToKeep["event"] = None # Already in tree
varsToKeep["run"] = None # Already in tree
varsToKeep["ls"] = t.luminosityBlock
varsToKeep["n_presel_mu"] = op.static_cast(
"UInt_t", op.rng_len(self.muonsPreSel))
varsToKeep["n_fakeablesel_mu"] = op.static_cast(
"UInt_t", op.rng_len(self.muonsFakeSel))
varsToKeep["n_mvasel_mu"] = op.static_cast(
"UInt_t", op.rng_len(self.muonsTightSel))
varsToKeep["n_presel_ele"] = op.static_cast(
"UInt_t", op.rng_len(self.electronsPreSel))
varsToKeep["n_fakeablesel_ele"] = op.static_cast(
"UInt_t", op.rng_len(self.electronsFakeSel))
varsToKeep["n_mvasel_ele"] = op.static_cast(
"UInt_t", op.rng_len(self.electronsTightSel))
varsToKeep["n_presel_ak4Jet"] = op.static_cast(
"UInt_t", op.rng_len(self.ak4Jets))
varsToKeep["n_presel_ak8Jet"] = op.static_cast(
"UInt_t", op.rng_len(self.ak8Jets))
varsToKeep["n_presel_ak8BJet"] = op.static_cast(
"UInt_t", op.rng_len(self.ak8BJets))
varsToKeep["n_loose_ak4BJet"] = op.static_cast(
"UInt_t", op.rng_len(self.ak4BJetsLoose))
varsToKeep["n_medium_ak4BJet"] = op.static_cast(
"UInt_t", op.rng_len(self.ak4BJets))
varsToKeep["n_ak4JetsCleanAk8b"] = op.static_cast(
"UInt_t", op.rng_len(self.ak4JetsCleanedFromAk8b))
varsToKeep["n_presel_ak4JetVBF"] = op.static_cast(
"UInt_t", op.rng_len(self.VBFJetsPreSel))
varsToKeep["is_SR"] = op.static_cast(
"UInt_t",
op.OR(
op.rng_len(self.electronsTightSel) == 1,
op.rng_len(self.muonsTightSel) == 1))
varsToKeep["is_e"] = op.c_float(
True) if self.args.Channel == 'El' else op.c_float(False)
varsToKeep["is_m"] = op.c_float(
False) if self.args.Channel == 'El' else op.c_float(True)
varsToKeep["is_resolved"] = op.switch(
op.AND(
op.rng_len(self.ak4Jets) >= 3,
op.rng_len(self.ak4BJets) >= 1,
op.rng_len(self.ak8BJets) == 0), op.c_bool(True),
op.c_bool(False))
varsToKeep["is_boosted"] = op.switch(
op.AND(
op.rng_len(self.ak8BJets) >= 1,
op.rng_len(self.ak4JetsCleanedFromAk8b) >= 1),
op.c_bool(True), op.c_bool(False))
varsToKeep["n_tau"] = op.static_cast("UInt_t",
op.rng_len(self.tauCleanSel))
varsToKeep['resolved_tag'] = op.static_cast(
"UInt_t",
op.AND(
op.rng_len(self.ak4Jets) >= 3,
op.rng_len(self.ak4BJets) >= 1,
op.rng_len(self.ak8BJets) == 0))
varsToKeep['boosted_tag'] = op.static_cast(
"UInt_t",
op.AND(
op.rng_len(self.ak8BJets) >= 1,
op.rng_len(self.ak4JetsCleanedFromAk8b) >= 1))
# Triggers #
'''
varsToKeep["triggers"] = self.triggers
varsToKeep["triggers_SingleElectron"] = op.OR(*self.triggersPerPrimaryDataset['SingleElectron'])
varsToKeep["triggers_SingleMuon"] = op.OR(*self.triggersPerPrimaryDataset['SingleMuon'])
varsToKeep["triggers_DoubleElectron"] = op.OR(*self.triggersPerPrimaryDataset['DoubleEGamma'])
varsToKeep["triggers_DoubleMuon"] = op.OR(*self.triggersPerPrimaryDataset['DoubleMuon'])
varsToKeep["triggers_MuonElectron"] = op.OR(*self.triggersPerPrimaryDataset['MuonEG'])
'''
# Muons #
for i in range(1, 3): # 2 leading muons
varsToKeep["mu{}_pt".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].pt, op.c_float(-9999., "float"))
varsToKeep["mu{}_eta".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].eta, op.c_float(-9999.))
varsToKeep["mu{}_phi".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].phi, op.c_float(-9999.))
varsToKeep["mu{}_E".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].p4.E(),
op.c_float(-9999., "float"))
varsToKeep["mu{}_charge".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].charge, op.c_int(-9999.))
varsToKeep["mu{}_conept".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muon_conept[self.muonsPreSel[i - 1].idx],
op.c_float(-9999.))
varsToKeep["mu{}_miniRelIso".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].miniPFRelIso_all,
op.c_float(-9999.))
varsToKeep["mu{}_PFRelIso04".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].pfRelIso04_all, op.c_float(-9999.))
varsToKeep["mu{}_jetNDauChargedMVASel".format(i)] = op.c_float(
-9999.)
varsToKeep["mu{}_jetPtRel".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].jetPtRelv2, op.c_float(-9999.))
varsToKeep["mu{}_jetRelIso".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].jetRelIso, op.c_float(-9999.))
varsToKeep["mu{}_jetDeepJet".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].jet.btagDeepFlavB,
op.c_float(-9999.))
varsToKeep["mu{}_sip3D".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].sip3d, op.c_float(-9999.))
varsToKeep["mu{}_dxy".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].dxy, op.c_float(-9999.))
varsToKeep["mu{}_dxyAbs".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
op.abs(self.muonsPreSel[i - 1].dxy), op.c_float(-9999.))
varsToKeep["mu{}_dz".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].dz, op.c_float(-9999.))
varsToKeep["mu{}_segmentCompatibility".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].segmentComp, op.c_float(-9999.))
varsToKeep["mu{}_leptonMVA".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].mvaTTH, op.c_float(-9999.))
varsToKeep["mu{}_mediumID".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].mediumId,
op.c_float(-9999., "Bool_t"))
varsToKeep["mu{}_dpt_div_pt".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].tunepRelPt,
op.c_float(-9999.)) # Not sure
varsToKeep["mu{}_isfakeablesel".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
op.switch(self.lambda_muonFakeSel(self.muonsPreSel[i - 1]),
op.c_int(1), op.c_int(0)), op.c_int(-9999))
varsToKeep["mu{}_ismvasel".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
op.switch(
op.AND(
self.lambda_muonTightSel(self.muonsPreSel[i - 1]),
self.lambda_muonFakeSel(self.muonsPreSel[i - 1])),
op.c_int(1), op.c_int(0)),
op.c_int(-9999)) # mvasel encompasses fakeablesel
varsToKeep["mu{}_isGenMatched".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
op.switch(self.lambda_is_matched(self.muonsPreSel[i - 1]),
op.c_int(1), op.c_int(0)), op.c_int(-9999))
varsToKeep["mu{}_genPartFlav".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.muonsPreSel[i - 1].genPartFlav, op.c_int(-9999))
varsToKeep["mu{}_FR".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.lambda_FR_mu(self.muonsPreSel[i - 1]),
op.c_int(-9999))
varsToKeep["mu{}_FRcorr".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.lambda_FRcorr_mu(self.muonsPreSel[i - 1]),
op.c_int(-9999))
varsToKeep["mu{}_FF".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
self.lambda_FF_mu(self.muonsPreSel[i - 1]),
op.c_int(-9999))
varsToKeep["mu{}_looseSF".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
reduce(mul,
self.lambda_MuonLooseSF(self.muonsPreSel[i - 1])),
op.c_int(-9999))
varsToKeep["mu{}_tightSF".format(i)] = op.switch(
op.rng_len(self.muonsPreSel) >= i,
reduce(mul,
self.lambda_MuonTightSF(self.muonsPreSel[i - 1])),
op.c_int(-9999))
# Electrons #
for i in range(1, 3): # 2 leading electrons
varsToKeep["ele{}_pt".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].pt, op.c_float(-9999.))
varsToKeep["ele{}_eta".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].eta, op.c_float(-9999.))
varsToKeep["ele{}_phi".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].phi, op.c_float(-9999.))
varsToKeep["ele{}_E".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].p4.E(), op.c_float(-9999., ))
varsToKeep["ele{}_charge".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].charge, op.c_int(-9999.))
varsToKeep["ele{}_conept".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electron_conept[self.electronsPreSel[i - 1].idx],
op.c_float(-9999.))
varsToKeep["ele{}_miniRelIso".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].miniPFRelIso_all,
op.c_float(-9999.))
varsToKeep["ele{}_PFRelIso03".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].pfRelIso03_all,
op.c_float(-9999.)) # Iso03, Iso04 not in NanoAOD
varsToKeep["ele{}_jetNDauChargedMVASel".format(
i)] = op.c_float(-9999.)
varsToKeep["ele{}_jetPtRel".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].jetPtRelv2, op.c_float(-9999.))
varsToKeep["ele{}_jetRelIso".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].jetRelIso, op.c_float(-9999.))
varsToKeep["ele{}_jetDeepJet".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].jet.btagDeepFlavB,
op.c_float(-9999.))
varsToKeep["ele{}_sip3D".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].sip3d, op.c_float(-9999.))
varsToKeep["ele{}_dxy".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].dxy, op.c_float(-9999.))
varsToKeep["ele{}_dxyAbs".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
op.abs(self.electronsPreSel[i - 1].dxy),
op.c_float(-9999.))
varsToKeep["ele{}_dz".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].dz, op.c_float(-9999.))
varsToKeep["ele{}_ntMVAeleID".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].mvaFall17V2noIso,
op.c_float(-9999.))
varsToKeep["ele{}_leptonMVA".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].mvaTTH, op.c_float(-9999.))
varsToKeep["ele{}_passesConversionVeto".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].convVeto,
op.c_float(-9999., "Bool_t"))
varsToKeep["ele{}_nMissingHits".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].lostHits,
op.c_float(-9999., "UChar_t"))
varsToKeep["ele{}_sigmaEtaEta".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].sieie, op.c_float(-9999.))
varsToKeep["ele{}_HoE".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].hoe, op.c_float(-9999.))
varsToKeep["ele{}_OoEminusOoP".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].eInvMinusPInv,
op.c_float(-9999.))
varsToKeep["ele{}_isfakeablesel".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
op.switch(
self.lambda_electronFakeSel(self.electronsPreSel[i -
1]),
op.c_int(1), op.c_int(0)), op.c_int(-9999))
varsToKeep["ele{}_ismvasel".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
op.switch(
op.AND(
self.lambda_electronTightSel(
self.electronsPreSel[i - 1]),
self.lambda_electronFakeSel(
self.electronsPreSel[i - 1])), op.c_int(1),
op.c_int(0)),
op.c_int(-9999)) # mvasel encompasses fakeablesel
varsToKeep["ele{}_isGenMatched".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
op.switch(
self.lambda_is_matched(self.electronsPreSel[i - 1]),
op.c_int(1), op.c_int(0)), op.c_int(-9999))
varsToKeep["ele{}_genPartFlav".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].genPartFlav, op.c_int(-9999))
varsToKeep["ele{}_deltaEtaSC".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.electronsPreSel[i - 1].deltaEtaSC, op.c_int(-9999))
varsToKeep["ele{}_FR".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.lambda_FR_el(self.electronsPreSel[i - 1]),
op.c_int(-9999))
varsToKeep["ele{}_FRcorr".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.lambda_FRcorr_el(self.electronsPreSel[i - 1]),
op.c_int(-9999))
varsToKeep["ele{}_FF".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
self.lambda_FF_el(self.electronsPreSel[i - 1]),
op.c_int(-9999))
varsToKeep["ele{}_looseSF".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
reduce(
mul,
self.lambda_ElectronLooseSF(self.electronsPreSel[i -
1])),
op.c_int(-9999))
varsToKeep["ele{}_tightSF".format(i)] = op.switch(
op.rng_len(self.electronsPreSel) >= i,
reduce(
mul,
self.lambda_ElectronTightSF(self.electronsPreSel[i -
1])),
op.c_int(-9999))
# AK4 Jets #
for i in range(1, 5): # 4 leading jets
varsToKeep["ak4Jet{}_pt".format(i)] = op.switch(
op.rng_len(self.ak4Jets) >= i, self.ak4Jets[i - 1].pt,
op.c_float(-9999.))
varsToKeep["ak4Jet{}_eta".format(i)] = op.switch(
op.rng_len(self.ak4Jets) >= i, self.ak4Jets[i - 1].eta,
op.c_float(-9999.))
varsToKeep["ak4Jet{}_phi".format(i)] = op.switch(
op.rng_len(self.ak4Jets) >= i, self.ak4Jets[i - 1].phi,
op.c_float(-9999.))
varsToKeep["ak4Jet{}_E".format(i)] = op.switch(
op.rng_len(self.ak4Jets) >= i, self.ak4Jets[i - 1].p4.E(),
op.c_float(-9999.))
varsToKeep["ak4Jet{}_CSV".format(i)] = op.switch(
op.rng_len(self.ak4Jets) >= i,
self.ak4Jets[i - 1].btagDeepFlavB, op.c_float(-9999.))
varsToKeep["ak4Jet{}_hadronFlavour".format(i)] = op.switch(
op.rng_len(self.ak4Jets) >= i,
self.ak4Jets[i - 1].hadronFlavour, op.c_float(-9999.))
varsToKeep["ak4Jet{}_btagSF".format(i)] = op.switch(
op.rng_len(self.ak4Jets) >= i,
self.DeepJetDiscReshapingSF(self.ak4Jets[i - 1]),
op.c_float(-9999.))
varsToKeep["ak4Jet{}_puid_eff".format(i)] = op.switch(
op.rng_len(self.ak4Jets) >= i,
self.jetpuid_mc_eff(self.ak4Jets[i - 1]),
op.c_float(-9999.))
varsToKeep["ak4Jet{}_puid_sfeff".format(i)] = op.switch(
op.rng_len(self.ak4Jets) >= i,
self.jetpuid_sf_eff(self.ak4Jets[i - 1]),
op.c_float(-9999.))
varsToKeep["ak4Jet{}_puid_mis".format(i)] = op.switch(
op.rng_len(self.ak4Jets) >= i,
self.jetpuid_mc_mis(self.ak4Jets[i - 1]),
op.c_float(-9999.))
varsToKeep["ak4Jet{}_puid_sfmis".format(i)] = op.switch(
op.rng_len(self.ak4Jets) >= i,
self.jetpuid_sf_mis(self.ak4Jets[i - 1]),
op.c_float(-9999.))
# VBF Jets #
for i in range(1, 6): # 5 leading jets
varsToKeep["ak4JetVBF{}_pt".format(i)] = op.switch(
op.rng_len(self.VBFJetsPreSel) >= i,
self.VBFJetsPreSel[i - 1].pt, op.c_float(-9999.))
varsToKeep["ak4JetVBF{}_eta".format(i)] = op.switch(
op.rng_len(self.VBFJetsPreSel) >= i,
self.VBFJetsPreSel[i - 1].eta, op.c_float(-9999.))
varsToKeep["ak4JetVBF{}_phi".format(i)] = op.switch(
op.rng_len(self.VBFJetsPreSel) >= i,
self.VBFJetsPreSel[i - 1].phi, op.c_float(-9999.))
varsToKeep["ak4JetVBF{}_E".format(i)] = op.switch(
op.rng_len(self.VBFJetsPreSel) >= i,
self.VBFJetsPreSel[i - 1].p4.E(), op.c_float(-9999.))
varsToKeep["ak4JetVBF{}_CSV".format(i)] = op.switch(
op.rng_len(self.VBFJetsPreSel) >= i,
self.VBFJetsPreSel[i - 1].btagDeepFlavB,
op.c_float(-9999.))
varsToKeep["ak4JetVBF{}_btagSF".format(i)] = op.switch(
op.rng_len(self.VBFJetsPreSel) >= i,
self.DeepJetDiscReshapingSF(self.VBFJetsPreSel[i - 1]),
op.c_float(-9999.))
if not self.inclusive_sel:
if not any([self.args.Res0b, self.args.Ak4, self.args.Ak8]):
varsToKeep["ak4JetVBFPair1_pt"] = op.switch(
op.rng_len(VBFJetPairsJPA) >= 1,
VBFJetPairsJPA[0][0].pt, op.c_float(-9999.))
varsToKeep["ak4JetVBFPair1_eta"] = op.switch(
op.rng_len(VBFJetPairsJPA) >= 1,
VBFJetPairsJPA[0][0].eta, op.c_float(-9999.))
varsToKeep["ak4JetVBFPair1_phi"] = op.switch(
op.rng_len(VBFJetPairsJPA) >= 1,
VBFJetPairsJPA[0][0].phi, op.c_float(-9999.))
varsToKeep["ak4JetVBFPair1_E"] = op.switch(
op.rng_len(VBFJetPairsJPA) >= 1,
VBFJetPairsJPA[0][0].p4.E(), op.c_float(-9999.))
varsToKeep["ak4JetVBFPair1_CSV"] = op.switch(
op.rng_len(VBFJetPairsJPA) >= 1,
VBFJetPairsJPA[0][0].btagDeepFlavB, op.c_float(-9999.))
varsToKeep["ak4JetVBFPair1_btagSF"] = op.switch(
op.rng_len(VBFJetPairsJPA) >= 1,
self.DeepJetDiscReshapingSF(VBFJetPairsJPA[0][0]),
op.c_float(-9999.))
varsToKeep["ak4JetVBFPair2_pt"] = op.switch(
op.rng_len(VBFJetPairsJPA) >= 1,
VBFJetPairsJPA[0][1].pt, op.c_float(-9999.))
varsToKeep["ak4JetVBFPair2_eta"] = op.switch(
op.rng_len(VBFJetPairsJPA) >= 1,
VBFJetPairsJPA[0][1].eta, op.c_float(-9999.))
varsToKeep["ak4JetVBFPair2_phi"] = op.switch(
op.rng_len(VBFJetPairsJPA) >= 1,
VBFJetPairsJPA[0][1].phi, op.c_float(-9999.))
varsToKeep["ak4JetVBFPair2_E"] = op.switch(
op.rng_len(VBFJetPairsJPA) >= 1,
VBFJetPairsJPA[0][1].p4.E(), op.c_float(-9999.))
varsToKeep["ak4JetVBFPair2_CSV"] = op.switch(
op.rng_len(VBFJetPairsJPA) >= 1,
VBFJetPairsJPA[0][1].btagDeepFlavB, op.c_float(-9999.))
varsToKeep["ak4JetVBFPair2_btagSF"] = op.switch(
op.rng_len(VBFJetPairsJPA) >= 1,
self.DeepJetDiscReshapingSF(VBFJetPairsJPA[0][1]),
op.c_float(-9999.))
else:
varsToKeep["ak4JetVBFPair1_pt"] = op.c_float(-9999.)
varsToKeep["ak4JetVBFPair1_eta"] = op.c_float(-9999.)
varsToKeep["ak4JetVBFPair1_phi"] = op.c_float(-9999.)
varsToKeep["ak4JetVBFPair1_E"] = op.c_float(-9999.)
varsToKeep["ak4JetVBFPair1_CSV"] = op.c_float(-9999.)
varsToKeep["ak4JetVBFPair1_btagSF"] = op.c_float(-9999.)
varsToKeep["ak4JetVBFPair2_pt"] = op.c_float(-9999.)
varsToKeep["ak4JetVBFPair2_eta"] = op.c_float(-9999.)
varsToKeep["ak4JetVBFPair2_phi"] = op.c_float(-9999.)
varsToKeep["ak4JetVBFPair2_E"] = op.c_float(-9999.)
varsToKeep["ak4JetVBFPair2_CSV"] = op.c_float(-9999.)
varsToKeep["ak4JetVBFPair2_btagSF"] = op.c_float(-9999.)
# AK8 Jets #
for i in range(1, 3): # 2 leading fatjets
varsToKeep["ak8Jet{}_pt".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i, self.ak8BJets[i - 1].pt,
op.c_float(-9999.))
varsToKeep["ak8Jet{}_eta".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i, self.ak8BJets[i - 1].eta,
op.c_float(-9999.))
varsToKeep["ak8Jet{}_phi".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i, self.ak8BJets[i - 1].phi,
op.c_float(-9999.))
varsToKeep["ak8Jet{}_E".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i,
self.ak8BJets[i - 1].p4.E(), op.c_float(-9999.))
varsToKeep["ak8Jet{}_msoftdrop".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i,
self.ak8BJets[i - 1].msoftdrop, op.c_float(-9999.))
varsToKeep["ak8Jet{}_tau1".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i, self.ak8BJets[i - 1].tau1,
op.c_float(-9999.))
varsToKeep["ak8Jet{}_tau2".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i, self.ak8BJets[i - 1].tau2,
op.c_float(-9999.))
varsToKeep["ak8Jet{}_subjet0_pt".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i,
self.ak8BJets[i - 1].subJet1.pt, op.c_float(-9999.))
varsToKeep["ak8Jet{}_subjet0_eta".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i,
self.ak8BJets[i - 1].subJet1.eta, op.c_float(-9999.))
varsToKeep["ak8Jet{}_subjet0_phi".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i,
self.ak8BJets[i - 1].subJet1.phi, op.c_float(-9999.))
varsToKeep["ak8Jet{}_subjet0_CSV".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i,
self.ak8BJets[i - 1].subJet1.btagDeepB, op.c_float(-9999.))
varsToKeep["ak8Jet{}_subjet1_pt".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i,
self.ak8BJets[i - 1].subJet2.pt, op.c_float(-9999.))
varsToKeep["ak8Jet{}_subjet1_eta".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i,
self.ak8BJets[i - 1].subJet2.eta, op.c_float(-9999.))
varsToKeep["ak8Jet{}_subjet1_phi".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i,
self.ak8BJets[i - 1].subJet2.phi, op.c_float(-9999.))
varsToKeep["ak8Jet{}_subjet1_CSV".format(i)] = op.switch(
op.rng_len(self.ak8BJets) >= i,
self.ak8BJets[i - 1].subJet2.btagDeepB, op.c_float(-9999.))
varsToKeep["PFMET"] = self.corrMET.pt
varsToKeep["PFMETphi"] = self.corrMET.phi
varsToKeep["met1_E"] = self.corrMET.p4.E()
varsToKeep["met1_pt"] = self.corrMET.pt
varsToKeep["met1_eta"] = self.corrMET.eta
varsToKeep["met1_phi"] = self.corrMET.phi
# SF #
electronMuon_cont = op.combine(
(self.electronsFakeSel, self.muonsFakeSel))
varsToKeep["trigger_SF"] = op.multiSwitch(
(op.AND(
op.rng_len(self.electronsTightSel) == 1,
op.rng_len(self.muonsTightSel) == 0),
self.ttH_singleElectron_trigSF(self.electronsTightSel[0])),
(op.AND(
op.rng_len(self.electronsTightSel) == 0,
op.rng_len(self.muonsTightSel)
== 1), self.ttH_singleMuon_trigSF(self.muonsTightSel[0])),
(op.AND(
op.rng_len(self.electronsTightSel) >= 2,
op.rng_len(self.muonsTightSel) == 0),
self.lambda_ttH_doubleElectron_trigSF(
self.electronsTightSel)),
(op.AND(
op.rng_len(self.electronsTightSel) == 0,
op.rng_len(self.muonsTightSel) >= 2),
self.lambda_ttH_doubleMuon_trigSF(self.muonsTightSel)),
(op.AND(
op.rng_len(self.electronsTightSel) >= 1,
op.rng_len(self.muonsTightSel) >= 1),
self.lambda_ttH_electronMuon_trigSF(electronMuon_cont[0])),
op.c_float(1.))
if not self.inclusive_sel:
varsToKeep["weight_trigger_el_sf"] = op.switch(
op.rng_len(self.electronsTightSel) > 0,
self.ttH_singleElectron_trigSF(lep), op.c_float(1.))
varsToKeep["weight_trigger_mu_sf"] = op.switch(
op.rng_len(self.muonsTightSel) > 0,
self.ttH_singleMuon_trigSF(lep), op.c_float(1.))
varsToKeep["lepton_IDSF"] = op.rng_product(self.electronsFakeSel, lambda el : reduce(mul,self.lambda_ElectronLooseSF(el)+self.lambda_ElectronTightSF(el))) * \
op.rng_product(self.muonsFakeSel, lambda mu : reduce(mul,self.lambda_MuonLooseSF(mu)+self.lambda_MuonTightSF(mu)))
varsToKeep["lepton_IDSF_recoToLoose"] = op.rng_product(self.electronsFakeSel, lambda el : reduce(mul,self.lambda_ElectronLooseSF(el))) * \
op.rng_product(self.muonsFakeSel, lambda mu : reduce(mul,self.lambda_MuonLooseSF(mu)))
varsToKeep["lepton_IDSF_looseToTight"] = op.rng_product(self.electronsFakeSel, lambda el : reduce(mul,self.lambda_ElectronTightSF(el))) * \
op.rng_product(self.muonsFakeSel, lambda mu : reduce(mul,self.lambda_MuonTightSF(mu)))
if era == "2016" or era == "2017":
if self.args.Channel == "El":
varsToKeep["weight_electron_reco_low"] = op.switch(
op.AND(self.lambda_is_matched(lep), lep.pt <= 20.),
self.elLooseRecoPtLt20(lep), op.c_float(1.))
varsToKeep["weight_electron_reco_high"] = op.switch(
op.AND(self.lambda_is_matched(lep), lep.pt > 20.),
self.elLooseRecoPtGt20(lep), op.c_float(1.))
varsToKeep["weight_muon_idiso_loose"] = op.c_float(1.)
varsToKeep["weight_electron_id_loose_01"] = op.switch(
self.lambda_is_matched(lep), self.elLooseEff(lep),
op.c_float(1.))
varsToKeep["weight_electron_id_loose_02"] = op.switch(
self.lambda_is_matched(lep), self.elLooseId(lep),
op.c_float(1.))
varsToKeep[
"weight_electron_tth_loose"] = self.lambda_ElectronTightSF(
lep)[0]
varsToKeep["weight_muon_tth_loose"] = op.c_float(1.)
if self.args.Channel == "Mu":
varsToKeep["weight_muon_idiso_loose"] = op.switch(
self.lambda_is_matched(lep), self.muLooseId(lep),
op.c_float(1.))
varsToKeep["weight_electron_reco_low"] = op.c_float(1.)
varsToKeep["weight_electron_reco_high"] = op.c_float(
1.)
varsToKeep["weight_electron_id_loose_01"] = op.c_float(
1.)
varsToKeep["weight_electron_id_loose_02"] = op.c_float(
1.)
varsToKeep["weight_electron_tth_loose"] = op.c_float(
1.)
varsToKeep[
"weight_muon_tth_loose"] = self.lambda_MuonTightSF(
lep)[0]
else:
raise NotImplementedError
# L1 Prefire #
if era in ["2016", "2017"]:
varsToKeep["L1prefire"] = self.L1Prefiring
varsToKeep["weight_l1_ecal_prefiring"] = self.L1Prefiring
else:
varsToKeep["L1prefire"] = op.c_float(-9999.)
varsToKeep["weight_l1_ecal_prefiring"] = op.c_float(-9999.)
# Fake rate #
if self.args.Channel == "El":
varsToKeep["fakeRate"] = op.switch(
self.lambda_electronTightSel(self.electronsFakeSel[0]),
self.ElFakeFactor(self.electronsFakeSel[0]),
op.c_float(1.))
varsToKeep["weight_fake_electrons"] = op.switch(
self.lambda_electronTightSel(self.electronsFakeSel[0]),
op.abs(self.ElFakeFactor(self.electronsFakeSel[0])),
op.c_float(1.))
varsToKeep["weight_fake_muons"] = op.c_float(1.)
varsToKeep["weight_fake_two_non_tight"] = op.c_float(999.0)
if self.args.Channel == "Mu":
varsToKeep["fakeRate"] = op.switch(
self.lambda_muonTightSel(self.muonsFakeSel[0]),
self.MuFakeFactor(self.muonsFakeSel[0]), op.c_float(1.))
varsToKeep["weight_fake_electrons"] = op.c_float(1.)
varsToKeep["weight_fake_muons"] = op.switch(
self.lambda_muonTightSel(self.muonsFakeSel[0]),
op.abs(self.MuFakeFactor(self.muonsFakeSel[0])),
op.c_float(1.))
varsToKeep["weight_fake_two_non_tight"] = op.c_float(999.0)
if self.is_MC:
varsToKeep["weight_fake_is_mc"] = op.c_float(-1.)
else:
varsToKeep["weight_fake_is_mc"] = op.c_float(1.)
# PU ID SF #
varsToKeep["PU_jetID_SF"] = self.puid_reweighting
varsToKeep[
"weight_jet_PUid_efficiency"] = self.puid_reweighting_efficiency
varsToKeep["weight_jet_PUid_mistag"] = self.puid_reweighting_mistag
# Btagging SF #
varsToKeep["btag_SF"] = self.btagAk4SF
varsToKeep["weight_btagWeight"] = self.btagAk4SF
if "BtagRatioWeight" in self.__dict__.keys():
varsToKeep["btag_ratio_SF"] = self.BtagRatioWeight
varsToKeep["weight_btagNorm"] = self.BtagRatioWeight
# PS weights #
varsToKeep["weight_PSWeight_ISR"] = self.psISRSyst
varsToKeep["weight_PSWeight_FSR"] = self.psFSRSyst
# ttbar PT reweighting #
if "group" in sampleCfg and sampleCfg["group"] == 'ttbar':
varsToKeep["topPt_wgt"] = self.ttbar_weight(
self.genTop[0], self.genAntitop[0])
# Event Weight #
if self.is_MC:
varsToKeep["MC_weight"] = t.genWeight
puWeightsFile = os.path.join(os.path.dirname(__file__), "data",
"pileup",
sample + '_%s.json' % era)
#puWeightsFile = os.path.join(os.path.dirname(__file__), "data" , "pileup", sampleCfg["pufile"])
varsToKeep["PU_weight"] = makePileupWeight(
puWeightsFile,
t.Pileup_nTrueInt,
nameHint=f"puweightFromFile{sample}".replace('-', '_'))
varsToKeep[
"eventWeight"] = noSel.weight if self.inclusive_sel else selObj.sel.weight
if self.inclusive_sel:
return noSel, varsToKeep
else:
return selObj.sel, varsToKeep
#---------------------------------------------------------------------------------------#
# Selection tree #
#---------------------------------------------------------------------------------------#
#----- EVT variables -----#
varsToKeep["event"] = None # Already in tree
varsToKeep["run"] = None # Already in tree
varsToKeep["ls"] = t.luminosityBlock
genVarsList = self.HLL.comp_cosThetaSbetBeamAndHiggs(t.GenPart)
varsToKeep['mHH_gen'] = genVarsList[0]
varsToKeep['consTheta1_gen'] = genVarsList[1]
varsToKeep['consTheta2_gen'] = genVarsList[2]
if any([
self.args.__dict__[item] for item in [
"Ak4", "Res2b2Wj", "Res2b1Wj", "Res2b0Wj", "Res1b2Wj",
"Res1b1Wj", "Res1b0Wj", "Res0b"
]
]):
commonInputs_Resolved = returnCommonInputs_Resolved(self=self)
classicInputs_Resolved = returnClassicInputs_Resolved(
self=self,
lepton=lep,
jpaSelectedJets=jpaJets,
L1out=L1out,
L2out=L2out,
jpaArg=jpaarg)
inputs_resolved = {
**commonInputs_Resolved,
**classicInputs_Resolved
}
for (varname, _, _), var in inputs_resolved.items():
varsToKeep[varname] = var
#----- Fatjet variables -----#
if any([
self.args.__dict__[item]
for item in ["Ak8", "Hbb2Wj", "Hbb1Wj", "Hbb0Wj"]
]):
commonInputs_Boosted = returnCommonInputs_Boosted(self=self)
classicInputs_Boosted = returnClassicInputs_Boosted(
self=self,
lepton=lep,
jpaSelectedJets=jpaJets,
L1out=L1out,
L2out=L2out,
jpaArg=jpaarg)
inputs_boosted = {**commonInputs_Boosted, **classicInputs_Boosted}
for (varname, _, _), var in inputs_boosted.items():
varsToKeep[varname] = var
#----- Additional variables -----#
varsToKeep["MC_weight"] = t.genWeight
varsToKeep['total_weight'] = selObj.sel.weight
#return leptonSel.sel, varsToKeep
return selObj.sel, varsToKeep
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.analysisutils import forceDefine
from bamboo.plots import Plot
from bamboo.plots import EquidistantBinning as EqB
from bamboo import treefunctions as op
era = sampleCfg.get("era") if sampleCfg else None
noSel = noSel.refine("passMETFlags", cut=METFilter(t.Flag, era) )
puWeightsFile = None
if era == "2016":
sfTag="94X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data/PileupFullRunII", "puweights2016.json")
elif era == "2017":
sfTag="94X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data/PileupFullRunII", "puweights2017.json")
elif era == "2018":
sfTag="102X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data/PileupFullRunII", "puweights2018.json")
if self.isMC(sample) and puWeightsFile is not None:
from bamboo.analysisutils import makePileupWeight
noSel = noSel.refine("puWeight", weight=makePileupWeight(puWeightsFile, t.Pileup_nTrueInt, systName="pileup"))
isMC = self.isMC(sample)
plots = []
forceDefine(t._Muon.calcProd, noSel)
# Wp // 2016- 2017 -2018 : Muon_mediumId // https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideMuonIdRun2#Muon_Isolation
#To suppress nonprompt lep-tons, the impact parameter in three dimensions of the lepton track, with respect to the primaryvertex, is required to be less than 4 times its uncertainty (|SIP3D|<4)
sorted_muons = op.sort(t.Muon, lambda mu : -mu.pt)
muons = op.select(sorted_muons, lambda mu : op.AND(mu.pt > 10., op.abs(mu.eta) < 2.4, mu.mediumId, mu.pfRelIso04_all<0.15, op.abs(mu.sip3d < 4.)))
# i pass 2016 seprate from 2017 &2018 because SFs need to be combined for BCDEF and GH eras !
if era=="2016":
doubleMuTrigSF = get_scalefactor("dilepton", ("doubleMuLeg_HHMoriond17_2016"), systName="mumutrig")
muMediumIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "id_medium"), combine="weight", systName="muid")
muMediumISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "iso_tight_id_medium"), combine="weight", systName="muiso")
else:
doubleMuTrigSF = get_scalefactor("dilepton", ("doubleMuLeg_HHMoriond17_2016"), systName="mumutrig")
muMediumIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "id_medium"), systName="muid")
muMediumISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "iso_tight_id_medium"), systName="muiso")
#Wp // 2016: Electron_cutBased_Sum16==3 -> medium // 2017 -2018 : Electron_cutBased ==3 --> medium ( Fall17_V2)
# asking for electrons to be in the Barrel region with dz<1mm & dxy< 0.5mm // Endcap region dz<2mm & dxy< 0.5mm
# cut-based ID Fall17 V2 the recomended one from POG for the FullRunII
sorted_electrons = op.sort(t.Electron, lambda ele : -ele.pt)
electrons = op.select(sorted_electrons, lambda ele : op.AND(ele.pt > 15., op.abs(ele.eta) < 2.5 , ele.cutBased>=3, op.abs(ele.sip3d)< 4., op.OR(op.AND(op.abs(ele.dxy) < 0.05, op.abs(ele.dz) < 0.1), op.AND(op.abs(ele.dxy) < 0.05, op.abs(ele.dz) < 0.2) )))
elMediumIDSF = get_scalefactor("lepton", ("electron_{0}_{1}".format(era,sfTag), "id_medium"), systName="elid")
doubleEleTrigSF = get_scalefactor("dilepton", ("doubleEleLeg_HHMoriond17_2016"), systName="eleltrig")
elemuTrigSF = get_scalefactor("dilepton", ("elemuLeg_HHMoriond17_2016"), systName="elmutrig")
mueleTrigSF = get_scalefactor("dilepton", ("mueleLeg_HHMoriond17_2016"), systName="mueltrig")
MET = t.MET if era != "2017" else t.METFixEE2017
corrMET=METcorrection(MET,t.PV,sample,era,self.isMC(sample))
####### select jets
##################################
#// 2016 - 2017 - 2018 ( j.jetId &2) -> tight jet ID
# For 2017 data, there is the option of "Tight" or "TightLepVeto", depending on how much you want to veto jets that overlap with/are faked by leptons
sorted_AK4jets=op.sort(t.Jet, lambda j : -j.pt)
AK4jetsSel = op.select(sorted_AK4jets, lambda j : op.AND(j.pt > 20., op.abs(j.eta)< 2.4, (j.jetId &2)))# j.jetId == 6))# oldcut: (j.jetId &2)))
# exclude from the jetsSel any jet that happens to include within its reconstruction cone a muon or an electron.
AK4jets= op.select(AK4jetsSel, lambda j : op.AND(op.NOT(op.rng_any(electrons, lambda ele : op.deltaR(j.p4, ele.p4) < 0.3 )), op.NOT(op.rng_any(muons, lambda mu : op.deltaR(j.p4, mu.p4) < 0.3 ))))
# order jets by *decreasing* deepFlavour
cleaned_AK4JetsByDeepFlav = op.sort(AK4jets, lambda j: -j.btagDeepFlavB)
cleaned_AK4JetsByDeepB = op.sort(AK4jets, lambda j: -j.btagDeepB)
# Boosted Region
sorted_AK8jets=op.sort(t.FatJet, lambda j : -j.pt)
AK8jetsSel = op.select(sorted_AK8jets, lambda j : op.AND(j.pt > 200., op.abs(j.eta)< 2.4, (j.jetId &2), j.subJet1._idx.result != -1, j.subJet2._idx.result != -1))
AK8jets= op.select(AK8jetsSel, lambda j : op.AND(op.NOT(op.rng_any(electrons, lambda ele : op.deltaR(j.p4, ele.p4) < 0.3 )), op.NOT(op.rng_any(muons, lambda mu : op.deltaR(j.p4, mu.p4) < 0.3 ))))
cleaned_AK8JetsByDeepB = op.sort(AK8jets, lambda j: -j.btagDeepB)
# Now, let's ask for the jets to be a b-jets
# DeepCSV or deepJet Medium b-tag working point
btagging = {
"DeepCSV":{ # era: (loose, medium, tight)
"2016":(0.2217, 0.6321, 0.8953),
"2017":(0.1522, 0.4941, 0.8001),
"2018":(0.1241, 0.4184, 0.7527)
},
"DeepFlavour":{
"2016":(0.0614, 0.3093, 0.7221),
"2017":(0.0521, 0.3033, 0.7489),
"2018":(0.0494, 0.2770, 0.7264)
}
}
# bjets ={ "DeepFlavour": {"L": jets pass loose , "M": jets pass medium , "T":jets pass tight }
# "DeepCSV": {"L": --- , "M": --- , "T": ---- }
# }
# FIXME
bjets_boosted = {}
bjets_resolved = {}
#WorkingPoints = ["L", "M", "T"]
WorkingPoints = ["M"]
for tagger in btagging.keys():
bJets_AK4_deepflavour ={}
bJets_AK4_deepcsv ={}
bJets_AK8_deepcsv ={}
# FIXME idx is not propagated properly when i pass only one or two wp !!
for wp in sorted(WorkingPoints):
suffix = ("loose" if wp=='L' else ("medium" if wp=='M' else "tight"))
idx = ( 0 if wp=="L" else ( 1 if wp=="M" else 2))
if tagger=="DeepFlavour":
print ("Btagging: Era= {0}, Tagger={1}, Pass_{2}_working_point={3}".format(era, tagger, suffix, btagging[tagger][era][idx] ))
print ("***********************************************", idx, wp)
print ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "{0}_{1}".format('DeepJet', suffix))
bJets_AK4_deepflavour[wp] = op.select(cleaned_AK4JetsByDeepFlav, lambda j : j.btagDeepFlavB >= btagging[tagger][era][idx] )
Jet_DeepFlavourBDisc = { "BTagDiscri": lambda j : j.btagDeepFlavB }
deepBFlavScaleFactor = get_scalefactor("jet", ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "{0}_{1}".format('DeepJet', suffix)),
additionalVariables=Jet_DeepFlavourBDisc,
getFlavour=(lambda j : j.hadronFlavour),
systName="btagging{0}".format(era))
bjets_resolved[tagger]=bJets_AK4_deepflavour
else:
print ("Btagging: Era= {0}, Tagger={1}, Pass_{2}_working_point={3}".format(era, tagger, suffix, btagging[tagger][era][idx] ))
print ("***********************************************", idx, wp)
print ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "{0}_{1}".format('DeepCSV', suffix))
bJets_AK4_deepcsv[wp] = op.select(cleaned_AK4JetsByDeepB, lambda j : j.btagDeepB >= btagging[tagger][era][idx] )
bJets_AK8_deepcsv[wp] = op.select(cleaned_AK8JetsByDeepB, lambda j : op.AND(j.subJet1.btagDeepB >= btagging[tagger][era][idx] , j.subJet2.btagDeepB >= btagging[tagger][era][idx]))
Jet_DeepCSVBDis = { "BTagDiscri": lambda j : j.btagDeepB }
subJet_DeepCSVBDis = { "BTagDiscri": lambda j : op.AND(j.subJet1.btagDeepB, j.subJet2.btagDeepB) }
# FIXME for boosted and resolved i will use # tagger need to pass jsons files to scale factors above !
deepB_AK4ScaleFactor = get_scalefactor("jet", ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "{0}_{1}".format('DeepCSV', suffix)),
additionalVariables=Jet_DeepCSVBDis,
getFlavour=(lambda j : j.hadronFlavour),
systName="btagging{0}".format(era))
# FIXME
#deepB_AK8ScaleFactor = get_scalefactor("jet", ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "subjet_{0}_{1}".format('DeepCSV', suffix)),
#additionalVariables=Jet_DeepCSVBDis,
#getFlavour=(lambda j : j.subJet1.hadronFlavour),
#systName="btagging{0}".format(era))
bjets_resolved[tagger]=bJets_AK4_deepcsv
bjets_boosted[tagger]=bJets_AK8_deepcsv
bestDeepFlavourPair={}
bestDeepCSVPair={}
bestJetPairs= {}
bjets = {}
# For the Resolved only
class GetBestJetPair(object):
JetsPair={}
def __init__(self, JetsPair, tagger, wp):
def ReturnHighestDiscriminatorJet(tagger, wp):
if tagger=="DeepCSV":
return op.sort(safeget(bjets_resolved, tagger, wp), lambda j: - j.btagDeepB)
elif tagger=="DeepFlavour":
return op.sort(safeget(bjets_resolved, tagger, wp), lambda j: - j.btagDeepFlavB)
else:
raise RuntimeError("Something went wrong in returning {0} discriminator !".format(tagger))
firstBest=ReturnHighestDiscriminatorJet(tagger, wp)[0]
JetsPair[0]=firstBest
secondBest=ReturnHighestDiscriminatorJet(tagger, wp)[1]
JetsPair[1]=secondBest
# bestJetPairs= { "DeepFlavour": bestDeepFlavourPair,
# "DeepCSV": bestDeepCSVPair
# }
####### Zmass reconstruction : Opposite Sign , Same Flavour leptons
########################################################
# supress quaronika resonances and jets misidentified as leptons
LowMass_cut = lambda dilep: op.invariant_mass(dilep[0].p4, dilep[1].p4)>12.
## Dilepton selection: opposite sign leptons in range 70.<mll<120. GeV
osdilep_Z = lambda l1,l2 : op.AND(l1.charge != l2.charge, op.in_range(70., op.invariant_mass(l1.p4, l2.p4), 120.))
osLLRng = {
"MuMu" : op.combine(muons, N=2, pred= osdilep_Z),
"ElEl" : op.combine(electrons, N=2, pred=osdilep_Z),
#"ElMu" : op.combine((electrons, muons), pred=lambda ele,mu : op.AND(osdilep_Z(ele,mu), ele.pt > mu.pt )),
#"MuEl" : op.combine((muons, electrons), pred=lambda mu,ele : op.AND(osdilep_Z(mu,ele), mu.pt > ele.pt))
}
hasOSLL_cmbRng = lambda cmbRng : op.AND(op.rng_len(cmbRng) > 0, cmbRng[0][0].pt > 25.) # TODO The leading pT for the µµ channel should be above 20 Gev !
## helper selection (OR) to make sure jet calculations are only done once
hasOSLL = noSel.refine("hasOSLL", cut=op.OR(*( hasOSLL_cmbRng(rng) for rng in osLLRng.values())))
forceDefine(t._Jet.calcProd, hasOSLL)
forceDefine(getattr(t, "_{0}".format("MET" if era != "2017" else "METFixEE2017")).calcProd, hasOSLL)
llSFs = {
"MuMu" : (lambda ll : [ muMediumIDSF(ll[0]), muMediumIDSF(ll[1]), muMediumISOSF(ll[0]), muMediumISOSF(ll[1]), doubleMuTrigSF(ll) ]),
"ElMu" : (lambda ll : [ elMediumIDSF(ll[0]), muMediumIDSF(ll[1]), muMediumISOSF(ll[1]), elemuTrigSF(ll) ]),
"MuEl" : (lambda ll : [ muMediumIDSF(ll[0]), muMediumISOSF(ll[0]), elMediumIDSF(ll[1]), mueleTrigSF(ll) ]),
"ElEl" : (lambda ll : [ elMediumIDSF(ll[0]), elMediumIDSF(ll[1]), doubleEleTrigSF(ll) ])
}
categories = dict((channel, (catLLRng[0], hasOSLL.refine("hasOS{0}".format(channel), cut=hasOSLL_cmbRng(catLLRng), weight=(llSFs[channel](catLLRng[0]) if isMC else None)) )) for channel, catLLRng in osLLRng.items())
## btagging efficiencies plots
#plots.extend(MakeBtagEfficienciesPlots(self, jets, bjets, categories))
for channel, (dilepton, catSel) in categories.items():
#---- Zmass (2Lepton OS && SF ) --------
#plots.extend(MakeControlPlotsForZpic(self, catSel, dilepton, channel))
#---- add Jets selection
TwoLeptonsTwoJets_Resolved = catSel.refine("TwoJet_{0}Sel_resolved".format(channel), cut=[ op.rng_len(AK4jets) > 1 ])
TwoLeptonsTwoJets_Boosted = catSel.refine("OneJet_{0}Sel_boosted".format(channel), cut=[ op.rng_len(AK8jets) > 0 ])
#plots.extend(makeJetPlots(self, TwoLeptonsTwoJets_Resolved, AK4jets, channel))
#plots.extend(makeBoostedJetPLots(self, TwoLeptonsTwoJets_Boosted, AK8jets, channel))
# ----- plots : mll, mlljj, mjj, nVX, pT, eta : basic selection plots ------
#plots.extend(MakeControlPlotsForBasicSel(self, TwoLeptonsTwoJets_Resolved, AK4jets, dilepton, channel))
#plots.extend(MakeControlPlotsForBasicSel(self, TwoLeptonsTwoJets_boosted, AK8jets, dilepton, channel))
for wp in WorkingPoints:
# Get the best AK4 JETS
GetBestJetPair(bestDeepCSVPair,"DeepCSV", wp)
GetBestJetPair(bestDeepFlavourPair,"DeepFlavour", wp)
bestJetPairs["DeepCSV"]=bestDeepCSVPair
bestJetPairs["DeepFlavour"]=bestDeepFlavourPair
print ("bestJetPairs AK4--->", bestJetPairs, wp)
print ("bestJetPairs_deepcsv AK4--->", bestJetPairs["DeepCSV"][0], bestJetPairs["DeepCSV"][1], wp)
print ("bestJetPairs_deepflavour AK4 --->", bestJetPairs["DeepFlavour"][0],bestJetPairs["DeepFlavour"][1], wp)
# resolved
bJets_resolved_PassdeepflavourWP=safeget(bjets_resolved, "DeepFlavour", wp)
bJets_resolved_PassdeepcsvWP=safeget(bjets_resolved, "DeepCSV", wp)
# boosted
bJets_boosted_PassdeepcsvWP=safeget(bjets_boosted, "DeepCSV", wp)
TwoLeptonsTwoBjets_NoMETCut_Res = {
"DeepFlavour{0}".format(wp) : TwoLeptonsTwoJets_Resolved.refine("TwoLeptonsTwoBjets_NoMETcut_DeepFlavour{0}_{1}_Resolved".format(wp, channel),
cut=[ op.rng_len(bJets_resolved_PassdeepflavourWP) > 1 ],
weight=([ deepBFlavScaleFactor(bJets_resolved_PassdeepflavourWP[0]), deepBFlavScaleFactor(bJets_resolved_PassdeepflavourWP[1]) ]if isMC else None)),
"DeepCSV{0}".format(wp) : TwoLeptonsTwoJets_Resolved.refine("TwoLeptonsTwoBjets_NoMETcut_DeepCSV{0}_{1}_Resolved".format(wp, channel),
cut=[ op.rng_len(bJets_resolved_PassdeepcsvWP) > 1 ],
weight=([ deepB_AK4ScaleFactor(bJets_resolved_PassdeepcsvWP[0]), deepB_AK4ScaleFactor(bJets_resolved_PassdeepcsvWP[1]) ]if isMC else None))
}
TwoLeptonsTwoBjets_NoMETCut_Boo = {
"DeepCSV{0}".format(wp) : TwoLeptonsTwoJets_Boosted.refine("TwoLeptonsTwoBjets_NoMETcut_DeepCSV{0}_{1}_Boosted".format(wp, channel),
cut=[ op.rng_len(bJets_boosted_PassdeepcsvWP) > 1 ]),
# FIXME ! can't pass boosted jets SFs with current version ---> move to v7
#weight=([ deepB_AK8ScaleFactor(bJets_boosted_PassdeepcsvWP[0]), deepB_AK8ScaleFactor(bJets_boosted_PassdeepcsvWP[1]) ]if isMC else None))
}
## needed to optimize the MET cut
# FIXME Rerun again &&& pass signal and bkg
# The MET cut is passed to TwoLeptonsTwoBjets selection for the # tagger and for the # wp
plots.extend(MakeMETPlots(self, TwoLeptonsTwoBjets_NoMETCut_Res, corrMET, MET, channel, "resolved"))
plots.extend(MakeMETPlots(self, TwoLeptonsTwoBjets_NoMETCut_Boo, corrMET, MET, channel, "boosted"))
plots.extend(MakeExtraMETPlots(self, TwoLeptonsTwoBjets_NoMETCut_Res, dilepton, MET, channel, "resolved"))
plots.extend(MakeExtraMETPlots(self, TwoLeptonsTwoBjets_NoMETCut_Boo, dilepton, MET, channel, "boosted"))
TwoLeptonsTwoBjets_Res = dict((key, selNoMET.refine("TwoLeptonsTwoBjets_{0}_{1}_Resolved".format(key, channel), cut=[ corrMET.pt < 80. ])) for key, selNoMET in TwoLeptonsTwoBjets_NoMETCut_Res.items())
TwoLeptonsTwoBjets_Boo = dict((key, selNoMET.refine("TwoLeptonsTwoBjets_{0}_{1}_Boosted".format(key, channel), cut=[ corrMET.pt < 80. ])) for key, selNoMET in TwoLeptonsTwoBjets_NoMETCut_Boo.items())
#plots.extend(MakeDiscriminatorPlots(self, TwoLeptonsTwoBjets_Res, bjets_resolved, wp, channel, "resolved"))
#plots.extend(MakeDiscriminatorPlots(self, TwoLeptonsTwoBjets_Boo, bjets_boosted, wp, channel, "boosted"))
#plots.extend(makeResolvedBJetPlots(self, TwoLeptonsTwoBjets_Res, bjets_resolved, dilepton, wp, channel))
#plots.extend(makeBoostedBJetPlots(self, TwoLeptonsTwoBjets_NoMETCut_Boo, bjets_boosted, dilepton, wp, channel))
# --- to get the Ellipses plots
plots.extend(MakeEllipsesPLots(self, TwoLeptonsTwoBjets_Res, bjets_resolved, dilepton, wp, channel, "resolved"))
plots.extend(MakeEllipsesPLots(self, TwoLeptonsTwoBjets_Boo, bjets_boosted, dilepton, wp, channel, "boosted"))
return plots
def __init__(self):
self.binningVariables = {
"Eta": lambda obj: obj.eta,
"ClusEta": lambda obj: obj.eta + obj.deltaEtaSC,
"AbsEta": lambda obj: op.abs(obj.eta),
"AbsClusEta": lambda obj: op.abs(obj.clusterEta + obj.deltaEtaSC),
"Pt": lambda obj: obj.pt,
}
# Scale factors dictionary construction #
# (better to do that here so that it is not buil everytime we ask for scalefactors #
instance = MakeScaleFactorsDict(paths={
'ttH_SF':
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data',
'ScaleFactors_ttH'),
'DY_SF':
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data',
'ScaleFactors_DY'),
'POG_SF':
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data',
'ScaleFactors_POG'),
'Btag_SF':
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data',
'ScaleFactors_Btag'),
'FR':
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data',
'FakeRates')
},
check_path=True)
#----- 2016 -----#
# Single Trigger SF # (Double triggers are single numbers and are in Base)
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='singleTrigger_electron_2016',
base_str="TTH_trigger_SingleElectron_2016.json")
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='singleTrigger_muon_2016',
base_str="TTH_trigger_SingleMuon_2016.json")
### ttH mva ###
# Electrons Loose #
# https://gitlab.cern.ch/ttH_leptons/doc/blob/master/Legacy/data_to_mc_corrections.md#electron-id-efficiency-scale-factors-for-loose-lepton-id
instance.AddScaleFactorWithWorkingPoint(
path_key='ttH_SF',
entry_key='electron_loosereco_2016',
base_key="electron_loosereco_{pt}",
base_str="Electron_EGamma_SF2D_RecoEff2016{pt}.json",
format_dict={'pt': ['PtLt20', 'PtGt20']},
lowercase_keys=True)
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_looseid_2016',
base_str="Electron_EGamma_SF2D_LooseMVA2016.json")
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_looseeff_2016',
base_str="Electron_EGamma_SF2D_LooseEff2016.json")
# Muons Loose #
# https://gitlab.cern.ch/ttH_leptons/doc/-/blob/master/Legacy/data_to_mc_corrections.md#muon-id-efficiency-scale-factors-for-loose-lepton-id
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='muon_loose_2016',
base_str="Muon_EGamma_SF2D_Loose2016.json")
# Electrons tight #
# https://gitlab.cern.ch/ttH_leptons/doc/-/blob/master/Legacy/data_to_mc_corrections.md#lepton-id-efficiency-scale-factors-for-tight-lepton-id
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_tightMVA_2016',
base_str="TTHSF_EGamma_SF2D_ElectronTight2016.json")
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_tightMVArelaxed_2016',
base_str="TTHSF_ElectronRelaxedttHID_2016.json")
# Muons tight #
# https://gitlab.cern.ch/ttH_leptons/doc/-/blob/master/Legacy/data_to_mc_corrections.md#lepton-id-efficiency-scale-factors-for-tight-lepton-id
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='muon_tightMVA_2016',
base_str="TTHSF_EGamma_SF2D_MuonTight2016.json")
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='muon_tightMVArelaxed_2016',
base_str="TTHSF_MuonRelaxedttHID_2016.json")
### POG ID ###
# https://twiki.cern.ch/twiki/bin/view/CMS/MuonReferenceEffs2016LegacyRereco
# -> SF root in "Systematic uncertainties" : ID + ISO /!\ needs to be lumi reweighted
instance.AddScaleFactor(
path_key='POG_SF',
entry_key='muon_POGSF_ID_2016',
base_str=
'Muon_NUM_TightID_DEN_genTracks_pt_eta_statPlusSyst_2016_RunBCDEFGH_ID.json'
)
instance.AddScaleFactor(
path_key='POG_SF',
entry_key='muon_POGSF_ISO_2016',
base_str=
'Muon_NUM_TightRelIso_DEN_TightIDandIPCut_pt_eta_statPlusSyst_2016_RunBCDEFGH_ISO.json'
)
instance.AddScaleFactor(path_key='POG_SF',
entry_key='electron_POGSF_2016',
base_str='Electron_EGamma_SF2D_2016.json')
# DY weight #
instance.AddScaleFactorWithWorkingPoint(
path_key='DY_SF',
entry_key='DY_resolved_2016',
base_key='{channel}_{variable}_{type}_{btag}',
base_str=
'weight_{variable}_{channel}_{type}_1D_weight_{btag}_2016.json',
format_dict={
'channel': ['ElEl', 'MuMu', 'SF'],
'type': ['data', 'mc'],
'btag': ['1b', '2b'],
'variable': ['HT']
})
instance.AddScaleFactorWithWorkingPoint(
path_key='DY_SF',
entry_key='DY_boosted_2016',
base_key='{channel}_{variable}_{type}_{btag}',
base_str=
'weight_{variable}_{channel}_{type}_1D_weight_{btag}_2016.json',
format_dict={
'channel': ['ElEl', 'MuMu', 'SF'],
'type': ['data', 'mc'],
'btag': ['1b'],
'variable': ['fatjetsoftDropmass']
})
# Fake rates #
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='electron_fakerates_2016',
base_key='{wp}_{channel}_{syst}_syst',
base_str=
'TTHFakeRates_{wp}MVA_{channel}_Electron_2016_{syst}Syst.json',
format_dict={
'wp': ['Loose'],
'channel': ['DL', 'SL'],
'syst': ['pt', 'barrel', 'norm']
})
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='muon_fakerates_2016',
base_key='{wp}_{channel}_{syst}_syst',
base_str='TTHFakeRates_{wp}MVA_{channel}_Muon_2016_{syst}Syst.json',
format_dict={
'wp': ['Loose'],
'channel': ['DL', 'SL'],
'syst': ['pt', 'barrel', 'norm']
})
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='fakerates_nonclosure_2016',
base_key='{wp}_{lepton}_SL_2016',
base_str='TTHFakeRates_{wp}MVA_SL_{lepton}_2016_QCD.json',
format_dict={
'wp': ['Loose'],
'lepton': ['Electron', 'Muon']
})
# PU ID SF #
instance.AddScaleFactorWithWorkingPoint(
path_key='POG_SF',
entry_key='jet_puid_eff',
base_key='{eom}_{era}_{wp}',
base_str='PUID_EFF_h2_{eom}_mc{era}_{wp}.json',
format_dict={
'eom': ["eff", "mistag"],
'wp': ['L', 'M', 'T'],
'era': ['2016', '2017', '2018']
})
instance.AddScaleFactorWithWorkingPoint(
path_key='POG_SF',
entry_key='jet_puid_sf',
base_key='{eom}_{era}_{wp}',
base_str='PUID_SF_h2_{eom}_sf{era}_{wp}.json',
format_dict={
'eom': ["eff", "mistag"],
'wp': ['L', 'M', 'T'],
'era': ['2016', '2017', '2018']
})
# Ak8 efficiency btagging #
instance.AddScaleFactorWithWorkingPoint(
path_key='Btag_SF',
entry_key='ak8btag_eff',
base_key='eff_{flav}_{era}',
base_str='BtagEff_ak8_{flav}_{era}.json',
format_dict={
'flav': ['bjets', 'cjets', 'lightjets'],
'era': ['2016', '2017', '2018']
})
#----- 2017 -----#
# Check links of 2016 #
# Single Trigger SF # (Double triggers are single numbers and are in Base)
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='singleTrigger_electron_2017',
base_str="TTH_trigger_SingleElectron_2017.json")
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='singleTrigger_muon_2017',
base_str="TTH_trigger_SingleMuon_2017.json")
# Electrons Loose #
instance.AddScaleFactorWithWorkingPoint(
path_key='ttH_SF',
entry_key='electron_loosereco_2017',
base_key="electron_loosereco_{pt}",
base_str="Electron_EGamma_SF2D_RecoEff2017{pt}.json",
format_dict={'pt': ['PtLt20', 'PtGt20']},
lowercase_keys=True)
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_looseid_2017',
base_str="Electron_EGamma_SF2D_LooseMVA2017.json")
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_looseeff_2017',
base_str="Electron_EGamma_SF2D_LooseEff2017.json")
# Muons Loose #
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='muon_loose_2017',
base_str="Muon_EGamma_SF2D_Loose2017.json")
# Electrons tight #
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_tightMVA_2017',
base_str="TTHSF_EGamma_SF2D_ElectronTight2017.json")
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_tightMVArelaxed_2017',
base_str="TTHSF_ElectronRelaxedttHID_2017.json")
# Muons tight #
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='muon_tightMVA_2017',
base_str="TTHSF_EGamma_SF2D_MuonTight2017.json")
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='muon_tightMVArelaxed_2017',
base_str="TTHSF_MuonRelaxedttHID_2017.json")
# DY weight #
instance.AddScaleFactorWithWorkingPoint(
path_key='DY_SF',
entry_key='DY_resolved_2017',
base_key='{channel}_{variable}_{type}_{btag}',
base_str=
'weight_{variable}_{channel}_{type}_1D_weight_{btag}_2017.json',
format_dict={
'channel': ['ElEl', 'MuMu', 'SF'],
'type': ['data', 'mc'],
'btag': ['1b', '2b'],
'variable': ['HT']
})
instance.AddScaleFactorWithWorkingPoint(
path_key='DY_SF',
entry_key='DY_boosted_2017',
base_key='{channel}_{variable}_{type}_{btag}',
base_str=
'weight_{variable}_{channel}_{type}_1D_weight_{btag}_2017.json',
format_dict={
'channel': ['ElEl', 'MuMu', 'SF'],
'type': ['data', 'mc'],
'btag': ['1b'],
'variable': ['fatjetsoftDropmass']
})
### POG ID ###
# https://twiki.cern.ch/twiki/bin/view/CMS/MuonReferenceEffs2017
# -> SF root in "Systematic uncertainties"
instance.AddScaleFactor(
path_key='POG_SF',
entry_key='muon_POGSF_ID_2017',
base_str=
'Muon_NUM_TightID_DEN_genTracks_abseta_pt_statPlusSyst_2017_RunBCDEF_ID.json'
)
instance.AddScaleFactor(
path_key='POG_SF',
entry_key='muon_POGSF_ISO_2017',
base_str=
'Muon_NUM_TightRelIso_DEN_TightIDandIPCut_abseta_pt_statPlusSyst_2017_RunBCDEF_ISO.json'
)
instance.AddScaleFactor(path_key='POG_SF',
entry_key='electron_POGSF_2017',
base_str='Electron_EGamma_SF2D_2017.json')
# Fake rates #
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='electron_fakerates_2017',
base_key='{wp}_{channel}_{syst}_syst',
base_str=
'TTHFakeRates_{wp}MVA_{channel}_Electron_2017_{syst}Syst.json',
format_dict={
'wp': ['Loose'],
'channel': ['DL', 'SL'],
'syst': ['pt', 'barrel', 'norm']
})
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='muon_fakerates_2017',
base_key='{wp}_{channel}_{syst}_syst',
base_str='TTHFakeRates_{wp}MVA_{channel}_Muon_2017_{syst}Syst.json',
format_dict={
'wp': ['Loose'],
'channel': ['DL', 'SL'],
'syst': ['pt', 'barrel', 'norm']
})
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='fakerates_nonclosure_2017',
base_key='{wp}_{lepton}_SL_2017',
base_str='TTHFakeRates_{wp}MVA_SL_{lepton}_2017_QCD.json',
format_dict={
'wp': ['Loose'],
'lepton': ['Electron', 'Muon']
})
#----- 2018 -----#
# Check links of 2016 #
# Single Trigger SF # (Double triggers are single numbers and are in Base)
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='singleTrigger_electron_2018',
base_str="TTH_trigger_SingleElectron_2018.json")
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='singleTrigger_muon_2018',
base_str="TTH_trigger_SingleMuon_2018.json")
# Electrons Loose #
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_loosereco_2018',
base_str="Electron_EGamma_SF2D_RecoEff2018.json")
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_looseid_2018',
base_str="Electron_EGamma_SF2D_LooseMVA2018.json")
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_looseeff_2018',
base_str="Electron_EGamma_SF2D_LooseEff2018.json")
# Muons Loose #
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='muon_loose_2018',
base_str="Muon_EGamma_SF2D_Loose2018.json")
# Electrons tight #
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_tightMVA_2018',
base_str="TTHSF_EGamma_SF2D_ElectronTight2018.json")
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_tightMVArelaxed_2018',
base_str="TTHSF_ElectronRelaxedttHID_2018.json")
# Muons tight #
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='muon_tightMVA_2018',
base_str="TTHSF_EGamma_SF2D_MuonTight2018.json")
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='muon_tightMVArelaxed_2018',
base_str="TTHSF_MuonRelaxedttHID_2018.json")
# DY weight #
instance.AddScaleFactorWithWorkingPoint(
path_key='DY_SF',
entry_key='DY_resolved_2018',
base_key='{channel}_{variable}_{type}_{btag}',
base_str=
'weight_{variable}_{channel}_{type}_1D_weight_{btag}_2018.json',
format_dict={
'channel': ['ElEl', 'MuMu', 'SF'],
'type': ['data', 'mc'],
'btag': ['1b', '2b'],
'variable': ['HT']
})
instance.AddScaleFactorWithWorkingPoint(
path_key='DY_SF',
entry_key='DY_boosted_2018',
base_key='{channel}_{variable}_{type}_{btag}',
base_str=
'weight_{variable}_{channel}_{type}_1D_weight_{btag}_2018.json',
format_dict={
'channel': ['ElEl', 'MuMu', 'SF'],
'type': ['data', 'mc'],
'btag': ['1b'],
'variable': ['fatjetsoftDropmass']
})
### POG ID ###
# https://twiki.cern.ch/twiki/bin/view/CMS/MuonReferenceEffs2017
# -> SF root in "Systematic uncertainties"
instance.AddScaleFactor(
path_key='POG_SF',
entry_key='muon_POGSF_ID_2018',
base_str=
'Muon_NUM_TightID_DEN_TrackerMuons_abseta_pt_statPlusSyst_2018_RunABCD_ID.json'
)
instance.AddScaleFactor(
path_key='POG_SF',
entry_key='muon_POGSF_ISO_2018',
base_str=
'Muon_NUM_TightRelIso_DEN_TightIDandIPCut_abseta_pt_statPlusSyst_2018_RunABCD_ISO.json'
)
instance.AddScaleFactor(path_key='POG_SF',
entry_key='electron_POGSF_2018',
base_str='Electron_EGamma_SF2D_2018.json')
# Fake rates #
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='electron_fakerates_2018',
base_key='{wp}_{channel}_{syst}_syst',
base_str=
'TTHFakeRates_{wp}MVA_{channel}_Electron_2018_{syst}Syst.json',
format_dict={
'wp': ['Loose'],
'channel': ['DL', 'SL'],
'syst': ['pt', 'barrel', 'norm']
})
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='muon_fakerates_2018',
base_key='{wp}_{channel}_{syst}_syst',
base_str='TTHFakeRates_{wp}MVA_{channel}_Muon_2018_{syst}Syst.json',
format_dict={
'wp': ['Loose'],
'channel': ['DL', 'SL'],
'syst': ['pt', 'barrel', 'norm']
})
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='fakerates_nonclosure_2018',
base_key='{wp}_{lepton}_SL_2018',
base_str='TTHFakeRates_{wp}MVA_SL_{lepton}_2018_QCD.json',
format_dict={
'wp': ['Loose'],
'lepton': ['Electron', 'Muon']
})
# Get full dict #
self.all_scalefactors = instance.GetScaleFactorsDict()
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, SummedPlot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
if self.args.examples == "all":
examples = list(i+1 for i in range(self.nExamples)) # 1-4 are fine, so is 7
else:
examples = list(set(int(tk) for tk in self.args.examples.split(",")))
logger.info("Running the following examples: {0}".format(",".join(str(i) for i in examples)))
plots = []
if 1 in examples:
## Example 1: Plot the missing ET of all events.
plots.append(Plot.make1D("Ex1_MET",
tree.MET.pt, noSel,
EqBin(100, 0., 2000.), title="MET (GeV)"))
if 2 in examples:
## Example 2: Plot pT of all jets in all events.
plots.append(Plot.make1D("Ex2_jetPt",
op.map(tree.Jet, lambda j : j.pt), noSel,
EqBin(100, 15., 60.), title="Jet p_{T} (GeV/c)"))
if 3 in examples:
## Example 3: Plot pT of jets with |η| < 1.
centralJets1 = op.select(tree.Jet, lambda j : op.abs(j.eta) < 1.)
plots.append(Plot.make1D("Ex3_central1_jetPt",
op.map(centralJets1, lambda j : j.pt), noSel,
EqBin(100, 15., 60.), title="Jet p_{T} (GeV/c)"))
if 4 in examples:
## Example 4: Plot the missing ET of events that have at least two jets with pT > 40 GeV.
jets40 = op.select(tree.Jet, lambda j : j.pt > 40)
hasTwoJets40 = noSel.refine("twoJets40", cut=(op.rng_len(jets40) >= 2))
plots.append(Plot.make1D("Ex4_twoJets40_MET",
tree.MET.pt, hasTwoJets40,
EqBin(100, 0., 2000.), title="MET (GeV)"))
if 5 in examples:
## Example 5: Plot the missing ET of events that have an opposite-sign muon pair with an invariant mass between 60 and 120 GeV.
dimu_Z = op.combine(tree.Muon, N=2, pred=(lambda mu1, mu2 : op.AND(
mu1.charge != mu2.charge,
op.in_range(60., op.invariant_mass(mu1.p4, mu2.p4), 120.)
)))
hasDiMuZ = noSel.refine("hasDiMuZ", cut=(op.rng_len(dimu_Z) > 0))
plots.append(Plot.make1D("Ex5_dimuZ_MET",
tree.MET.pt, hasDiMuZ,
EqBin(100, 0., 2000.), title="MET (GeV)"))
if 6 in examples:
## Example 6: Plot pT of the trijet system with the mass closest to 172.5 GeV in each event and plot the maximum b-tagging discriminant value among the jets in the triplet.
trijets = op.combine(tree.Jet, N=3)
hadTop = op.rng_min_element_by(trijets,
fun=lambda comb: op.abs((comb[0].p4+comb[1].p4+comb[2].p4).M()-172.5))
hadTop_p4 = (hadTop[0].p4 + hadTop[1].p4 + hadTop[2].p4)
hasTriJet = noSel.refine("hasTriJet", cut=(op.rng_len(trijets) > 0))
plots.append(Plot.make1D("Ex6_trijet_topPt",
hadTop_p4.Pt(), hasTriJet,
EqBin(100, 15., 40.), title="Trijet p_{T} (GeV/c)"))
plots.append(Plot.make1D("Ex6_trijet_maxbtag",
op.max(op.max(hadTop[0].btag, hadTop[1].btag), hadTop[2].btag), hasTriJet,
EqBin(100, 0., 1.), title="Trijet maximum b-tag"))
if verbose:
plots.append(Plot.make1D("Ex6_njets",
op.rng_len(tree.Jet), noSel,
EqBin(20, 0., 20.), title="Number of jets"))
plots.append(Plot.make1D("Ex6_ntrijets",
op.rng_len(trijets), noSel,
EqBin(100, 0., 1000.), title="Number of 3-jet combinations"))
plots.append(Plot.make1D("Ex6_trijet_mass",
hadTop_p4.M(), hasTriJet,
EqBin(100, 0., 250.), title="Trijet mass (GeV/c^{2})"))
if 7 in examples:
## Example 7: Plot the sum of pT of jets with pT > 30 GeV that are not within 0.4 in ΔR of any lepton with pT > 10 GeV.
el10 = op.select(tree.Electron, lambda el : el.pt > 10.)
mu10 = op.select(tree.Muon , lambda mu : mu.pt > 10.)
cleanedJets30 = op.select(tree.Jet, lambda j : op.AND(
j.pt > 30.,
op.NOT(op.rng_any(el10, lambda el : op.deltaR(j.p4, el.p4) < 0.4 )),
op.NOT(op.rng_any(mu10, lambda mu : op.deltaR(j.p4, mu.p4) < 0.4 ))
))
plots.append(Plot.make1D("Ex7_sumCleanedJetPt",
op.rng_sum(cleanedJets30, lambda j : j.pt), noSel,
EqBin(100, 15., 200.), title="Sum p_{T} (GeV/c)"))
if 8 in examples:
## Example 8: For events with at least three leptons and a same-flavor opposite-sign lepton pair, find the same-flavor opposite-sign lepton pair with the mass closest to 91.2 GeV and plot the transverse mass of the missing energy and the leading other lepton.
# The plot is made for each of the different flavour categories (l+/- l-/+ l') and then summed,
# because concatenation of containers is not (yet) supported.
lepColl = { "El" : tree.Electron, "Mu" : tree.Muon }
mt3lPlots = []
for dlNm,dlCol in lepColl.items():
dilep = op.combine(dlCol, N=2, pred=(lambda l1,l2 : op.AND(l1.charge != l2.charge)))
hasDiLep = noSel.refine("hasDilep{0}{0}".format(dlNm), cut=(op.rng_len(dilep) > 0))
dilepZ = op.rng_min_element_by(dilep, fun=lambda ll : op.abs(op.invariant_mass(ll[0].p4, ll[1].p4)-91.2))
for tlNm,tlCol in lepColl.items():
if tlCol == dlCol:
hasTriLep = hasDiLep.refine("hasTrilep{0}{0}{1}".format(dlNm,tlNm),
cut=(op.rng_len(tlCol) > 2))
residLep = op.select(tlCol, lambda l : op.AND(l.idx != dilepZ[0].idx, l.idx != dilepZ[1].idx))
l3 = op.rng_max_element_by(residLep, lambda l : l.pt)
else:
hasTriLep = hasDiLep.refine("hasTriLep{0}{0}{1}".format(dlNm,tlNm),
cut=(op.rng_len(tlCol) > 0))
l3 = op.rng_max_element_by(tlCol, lambda l : l.pt)
mtPlot = Plot.make1D("Ex8_3lMT_{0}{0}{1}".format(dlNm,tlNm),
op.sqrt(2*l3.pt*tree.MET.pt*(1-op.cos(l3.phi-tree.MET.phi))), hasTriLep,
EqBin(100, 15., 250.), title="M_{T} (GeV/c^2)")
mt3lPlots.append(mtPlot)
plots.append(mtPlot)
plots.append(SummedPlot("Ex8_3lMT", mt3lPlots))
return plots
def returnResonantMVAInputs(self, l1, l2, channel, jets, bjets, fatjets, met,
electrons, muons):
if channel == "ElEl":
l1conept = lambda l1: self.electron_conept[l1.idx]
l2conept = lambda l2: self.electron_conept[l2.idx]
elif channel == "MuMu":
l1conept = lambda l1: self.muon_conept[l1.idx]
l2conept = lambda l2: self.muon_conept[l2.idx]
elif channel == "ElMu":
l1conept = lambda l1: self.electron_conept[l1.idx]
l2conept = lambda l2: self.muon_conept[l2.idx]
else:
raise RuntimeError("Wrong channel")
dijets = op.combine(jets, N=2)
import bamboo.treeoperations as _to
def rng_min(rng, fun=(lambda x: x), typeName="float"):
return op._to.Reduce.fromRngFun(
rng, op.c_float(float("+inf"), typeName),
(lambda fn:
(lambda res, elm: op.extMethod("std::min", returnType="Float_t")
(res, fn(elm))))(fun))
if self.args.era is None:
era = op.c_int(int(self.era))
else:
era = op.c_int(int(self.args.era))
print(f'Using {self.args.era} as DNN input')
return {
('eventnr', 'Event number', (100, 0., 1e6)):
self.tree.event,
('era', 'Era', (3, 2016., 2019.)):
era,
('l1_E', 'Lead lepton E [GeV]', (50, 0., 500.)):
op.switch(l1conept(l1) >= l2conept(l2), l1.p4.E(), l2.p4.E()),
('l1_Px', 'Lead lepton P_x [GeV]', (40, -200., 200.)):
op.switch(l1conept(l1) >= l2conept(l2), l1.p4.Px(), l2.p4.Px()),
('l1_Py', 'Lead lepton P_y [GeV]', (40, -200., 200.)):
op.switch(l1conept(l1) >= l2conept(l2), l1.p4.Py(), l2.p4.Py()),
('l1_Pz', 'Lead lepton P_z [GeV]', (40, -200., 200.)):
op.switch(l1conept(l1) >= l2conept(l2), l1.p4.Pz(), l2.p4.Pz()),
('l1_charge', 'Lead lepton charge', (2, 0., 2.)):
op.switch(l1conept(l1) >= l2conept(l2), l1.charge, l2.charge),
('l1_pdgId', 'Lead lepton pdg ID', (45, -22., 22.)):
op.switch(l1conept(l1) >= l2conept(l2), l1.pdgId, l2.pdgId),
('l2_E', 'Sublead lepton E [GeV]', (50, 0., 500.)):
op.switch(l1conept(l1) >= l2conept(l2), l2.p4.E(), l1.p4.E()),
('l2_Px', 'Sublead lepton P_x [GeV]', (40, -200., 200.)):
op.switch(l1conept(l1) >= l2conept(l2), l2.p4.Px(), l1.p4.Px()),
('l2_Py', 'Sublead lepton P_y [GeV]', (40, -200., 200.)):
op.switch(l1conept(l1) >= l2conept(l2), l2.p4.Py(), l1.p4.Py()),
('l2_Pz', 'Sublead lepton P_z [GeV]', (40, -200., 200.)):
op.switch(l1conept(l1) >= l2conept(l2), l2.p4.Pz(), l1.p4.Pz()),
('l2_charge', 'Sublead lepton charge', (2, 0., 2.)):
op.switch(l1conept(l1) >= l2conept(l2), l2.charge, l1.charge),
('l2_pdgId', 'Sublead lepton pdg ID', (45, -22., 22.)):
op.switch(l1conept(l1) >= l2conept(l2), l2.pdgId, l1.pdgId),
('j1_E', 'Lead jet E [GeV]', (50, 0., 500.)):
op.switch(op.rng_len(jets) > 0, jets[0].p4.E(), op.c_float(0.)),
('j1_Px', 'Lead jet P_x [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(jets) > 0, jets[0].p4.Px(), op.c_float(0.)),
('j1_Py', 'Lead jet P_y [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(jets) > 0, jets[0].p4.Py(), op.c_float(0.)),
('j1_Pz', 'Lead jet P_z [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(jets) > 0, jets[0].p4.Pz(), op.c_float(0.)),
('j2_E', 'Sublead jet E [GeV]', (50, 0., 500.)):
op.switch(op.rng_len(jets) > 1, jets[1].p4.E(), op.c_float(0.)),
('j2_Px', 'Sublead jet P_x [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(jets) > 1, jets[1].p4.Px(), op.c_float(0.)),
('j2_Py', 'Sublead jet P_y [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(jets) > 1, jets[1].p4.Py(), op.c_float(0.)),
('j2_Pz', 'Sublead jet P_z [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(jets) > 1, jets[1].p4.Pz(), op.c_float(0.)),
('j3_E', 'Subsublead jet E [GeV]', (50, 0., 500.)):
op.switch(op.rng_len(jets) > 2, jets[2].p4.E(), op.c_float(0.)),
('j3_Px', 'Subsublead jet P_x [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(jets) > 2, jets[2].p4.Px(), op.c_float(0.)),
('j3_Py', 'Subsublead jet P_y [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(jets) > 2, jets[2].p4.Py(), op.c_float(0.)),
('j3_Pz', 'Subsublead jet P_z [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(jets) > 2, jets[2].p4.Pz(), op.c_float(0.)),
('j4_E', 'Subsubsublead jet E [GeV]', (50, 0., 500.)):
op.switch(op.rng_len(jets) > 3, jets[3].p4.E(), op.c_float(0.)),
('j4_Px', 'Subsubsublead jet P_x [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(jets) > 3, jets[3].p4.Px(), op.c_float(0.)),
('j4_Py', 'Subsubsublead jet P_y [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(jets) > 3, jets[3].p4.Py(), op.c_float(0.)),
('j4_Pz', 'Subsubsublead jet P_z [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(jets) > 3, jets[3].p4.Pz(), op.c_float(0.)),
('fatjet_E', 'Fatjet E [GeV]', (50, 0., 500.)):
op.switch(op.rng_len(fatjets) > 0, fatjets[0].p4.E(), op.c_float(0.)),
('fatjet_Px', 'Fatjet P_x [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(fatjets) > 0, fatjets[0].p4.Px(), op.c_float(0.)),
('fatjet_Py', 'Fatjet P_y [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(fatjets) > 0, fatjets[0].p4.Py(), op.c_float(0.)),
('fatjet_Pz', 'Fatjet P_z [GeV]', (40, -200., 200.)):
op.switch(op.rng_len(fatjets) > 0, fatjets[0].p4.Pz(), op.c_float(0.)),
('fatjet_tau1', 'Fatjet #tau_1', (50, 0., 1.)):
op.switch(op.rng_len(fatjets) > 0, fatjets[0].tau1, op.c_float(0.)),
('fatjet_tau2', 'Fatjet #tau_2', (50, 0., 1.)):
op.switch(op.rng_len(fatjets) > 0, fatjets[0].tau2, op.c_float(0.)),
('fatjet_tau3', 'Fatjet #tau_3', (50, 0., 1.)):
op.switch(op.rng_len(fatjets) > 0, fatjets[0].tau3, op.c_float(0.)),
('fatjet_tau4', 'Fatjet #tau_4', (50, 0., 1.)):
op.switch(op.rng_len(fatjets) > 0, fatjets[0].tau4, op.c_float(0.)),
('fatjet_softdrop', 'Fatjet softdrop mass [GeV]', (50, 0., 1000.)):
op.switch(
op.rng_len(fatjets) > 0, fatjets[0].msoftdrop, op.c_float(0.)),
('met_E', 'MET Energy', (50, 0., 500.)):
met.p4.E(),
('met_Px', 'MET P_x', (40, -200., 200.)):
met.p4.Px(),
('met_Py', 'MET P_y', (40, -200., 200.)):
met.p4.Py(),
('met_Pz', 'MET P_z', (40, -200., 200.)):
met.p4.Pz(),
('m_bb_bregcorr', 'Di-bjet invariant mass (regcorr) [GeV]', (100, 0., 1000.)):
op.multiSwitch(
(op.rng_len(bjets) == 0, op.c_float(0.)),
(op.rng_len(bjets) == 1, self.HLL.getCorrBp4(bjets[0]).M()),
op.invariant_mass(self.HLL.getCorrBp4(bjets[0]),
self.HLL.getCorrBp4(bjets[1]))),
('ht', 'HT(jets) [GeV]', (100, 0., 1000.)):
op.rng_sum(jets, lambda j: j.pt),
('min_dr_jets_lep1', 'Min(#Delta R(lead lepton,jets))', (25, 0., 5.)):
op.switch(
op.rng_len(jets) > 0,
op.switch(
l1conept(l1) >= l2conept(l2),
self.HLL.MinDR_part1_partCont(l1.p4, jets),
self.HLL.MinDR_part1_partCont(l2.p4, jets)), op.c_float(0.)),
('min_dr_jets_lep2', 'Min(#Delta R(sublead lepton,jets))', (25, 0., 5.)):
op.switch(
op.rng_len(jets) > 0,
op.switch(
l1conept(l1) >= l2conept(l2),
self.HLL.MinDR_part1_partCont(l2.p4, jets),
self.HLL.MinDR_part1_partCont(l1.p4, jets)), op.c_float(0.)),
('m_ll', 'Dilepton invariant mass [GeV]', (100, 0., 1000.)):
op.invariant_mass(l1.p4, l2.p4),
('dr_ll', 'Dilepton #Delta R', (25, 0., 5.)):
op.deltaR(l1.p4, l2.p4),
('min_dr_jet', 'Min(#Delta R(jets))', (25, 0., 5.)):
op.switch(
op.rng_len(dijets) > 0,
op.rng_min(dijets,
lambda dijet: op.deltaR(dijet[0].p4, dijet[1].p4)),
op.c_float(0.)),
('min_dphi_jet', 'Min(#Delta #Phi(jets))', (16, 0., 3.2)):
op.switch(
op.rng_len(dijets) > 0,
rng_min(
dijets,
lambda dijet: op.abs(op.deltaPhi(dijet[0].p4, dijet[1].p4)),
typeName='double'), op.c_float(0.)),
('m_hh_simplemet_bregcorr', 'M_{HH} (simple MET) (regcorr) [GeV]', (100, 0., 1000.)):
op.invariant_mass(
op.rng_sum(bjets,
lambda bjet: self.HLL.getCorrBp4(bjet),
start=self.HLL.empty_p4), l1.p4, l2.p4, met.p4),
('met_ld', 'MET_{LD}', (100, 0., 1000.)):
self.HLL.MET_LD_DL(met, jets, electrons, muons),
('dr_bb', 'Di-bjet #Delta R', (25, 0., 5.)):
op.switch(
op.rng_len(bjets) >= 2, op.deltaR(bjets[0].p4, bjets[1].p4),
op.c_float(0.)),
('min_dr_leps_b1', 'Min(#Delta R(lead bjet,dilepton))', (25, 0., 5.)):
op.switch(
op.rng_len(bjets) >= 1,
self.HLL.MinDR_part1_dipart(bjets[0].p4, [l1.p4, l2.p4]),
op.c_float(0.)),
('min_dr_leps_b2', 'Min(#Delta R(sublead bjet,dilepton))', (25, 0., 5.)):
op.switch(
op.rng_len(bjets) >= 2,
self.HLL.MinDR_part1_dipart(bjets[1].p4, [l1.p4, l2.p4]),
op.c_float(0.)),
('lep1_conept', 'Lead lepton cone-P_T [GeV]', (40, 0., 200.)):
op.switch(l1conept(l1) >= l2conept(l2), l1conept(l1), l2conept(l2)),
('lep2_conept', 'Sublead lepton cone-P_T [GeV]', (40, 0., 200.)):
op.switch(l1conept(l1) >= l2conept(l2), l2conept(l2), l1conept(l1)),
('mww_simplemet', 'M_{WW} (simple MET) [GeV]', (100, 0., 1000.)):
op.invariant_mass(l1.p4, l2.p4, met.p4),
('boosted_tag', 'Boosted tag', (2, 0., 2.)):
op.c_int(
op.OR(
op.rng_len(self.ak8BJets) > 0, # Boosted 1B
op.AND(
op.rng_len(self.ak8BJets) == 0, # Boosted 0B
op.rng_len(self.ak8Jets) > 0,
op.rng_len(self.ak4BJets) == 0))),
('dphi_met_dilep', 'Dilepton-MET #Delta #Phi', (32, -3.2, 3.2)):
op.abs(op.deltaPhi(met.p4, (l1.p4 + l2.p4))),
('dphi_met_dibjet', 'Dibjet-MET #Delta #Phi', (32, -3.2, 3.2)):
op.multiSwitch(
(op.rng_len(bjets) == 0, op.c_float(0.)),
(op.rng_len(bjets) == 1, op.abs(op.deltaPhi(met.p4, bjets[0].p4))),
op.abs(op.deltaPhi(met.p4, (bjets[0].p4 + bjets[1].p4)))),
('dr_dilep_dijet', 'Dilepton-dijet #Delta R', (25, 0., 5.)):
op.multiSwitch(
(op.rng_len(jets) == 0, op.c_float(0.)),
(op.rng_len(jets) == 1, op.deltaR((l1.p4 + l2.p4), jets[0].p4)),
op.deltaR((l1.p4 + l2.p4), (jets[0].p4 + jets[1].p4))),
('dr_dilep_dibjet', 'Dilepton-dibjet #Delta R', (25, 0., 5.)):
op.multiSwitch(
(op.rng_len(bjets) == 0, op.c_float(0.)),
(op.rng_len(bjets) == 1, op.deltaR((l1.p4 + l2.p4), bjets[0].p4)),
op.deltaR((l1.p4 + l2.p4), (bjets[0].p4 + bjets[1].p4))),
('m_T', 'Transverse mass', (100, 0., 1000.)):
op.sqrt(2 * (l1.p4 + l2.p4).Pt() * met.p4.E() *
(1 - op.cos((l1.p4 + l2.p4).Phi() - met.p4.Phi()))),
('cosThetaS_Hbb', 'Helicity angle between Hbb and bjet', (20, 0., 1.)):
op.switch(
op.rng_len(bjets) == 2,
op.extMethod("HHbbWWJPA::cosThetaS",
returnType="float")(bjets[0].p4, bjets[1].p4),
op.c_float(0.)),
('LBN_inputs', 'LBN inputs', None): [
op.switch(l1conept(l1) >= l2conept(l2), l1.p4.E(), l2.p4.E()),
op.switch(l1conept(l1) >= l2conept(l2), l1.p4.Px(), l2.p4.Px()),
op.switch(l1conept(l1) >= l2conept(l2), l1.p4.Py(), l2.p4.Py()),
op.switch(l1conept(l1) >= l2conept(l2), l1.p4.Pz(), l2.p4.Pz()),
op.switch(l1conept(l1) >= l2conept(l2), l2.p4.E(), l1.p4.E()),
op.switch(l1conept(l1) >= l2conept(l2), l2.p4.Px(), l1.p4.Px()),
op.switch(l1conept(l1) >= l2conept(l2), l2.p4.Py(), l1.p4.Py()),
op.switch(l1conept(l1) >= l2conept(l2), l2.p4.Pz(), l1.p4.Pz()),
op.switch(op.rng_len(jets) > 0, jets[0].p4.E(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 0, jets[0].p4.Px(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 0, jets[0].p4.Py(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 0, jets[0].p4.Pz(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 1, jets[1].p4.E(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 1, jets[1].p4.Px(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 1, jets[1].p4.Py(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 1, jets[1].p4.Pz(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 2, jets[2].p4.E(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 2, jets[2].p4.Px(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 2, jets[2].p4.Py(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 2, jets[2].p4.Pz(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 3, jets[3].p4.E(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 3, jets[3].p4.Px(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 3, jets[3].p4.Py(), op.c_float(0.)),
op.switch(op.rng_len(jets) > 3, jets[3].p4.Pz(), op.c_float(0.)),
op.switch(
op.rng_len(fatjets) > 0, fatjets[0].p4.E(), op.c_float(0.)),
op.switch(
op.rng_len(fatjets) > 0, fatjets[0].p4.Px(), op.c_float(0.)),
op.switch(
op.rng_len(fatjets) > 0, fatjets[0].p4.Py(), op.c_float(0.)),
op.switch(
op.rng_len(fatjets) > 0, fatjets[0].p4.Pz(), op.c_float(0.))
]
}
def __init__(self):
self.binningVariables = {
"Eta": lambda obj: obj.eta,
"ClusEta": lambda obj: obj.eta + obj.deltaEtaSC,
"AbsEta": lambda obj: op.abs(obj.eta),
"AbsClusEta": lambda obj: op.abs(obj.clusterEta + obj.deltaEtaSC),
"Pt": lambda obj: obj.pt,
}
# Scale factors dictionary construction #
# (better to do that here so that it is not buil everytime we ask for scalefactors #
instance = MakeScaleFactorsDict(paths={
'ttH_SF':
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data',
'ScaleFactors_ttH'),
'DY_SF':
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data',
'ScaleFactors_DY'),
'POG_SF':
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data',
'ScaleFactors_POG'),
'Btag_SF':
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data',
'ScaleFactors_Btag'),
'FR':
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data',
'FakeRates')
},
check_path=True)
#----- 2016 -----#
# Single Trigger SF # (Double triggers are single numbers and are in Base)
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='singleTrigger_electron_2016',
base_str="TTH_trigger_SingleElectron_2016.json")
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='singleTrigger_muon_2016',
base_str="TTH_trigger_SingleMuon_2016.json")
# Electrons Loose #
# https://gitlab.cern.ch/ttH_leptons/doc/blob/master/Legacy/data_to_mc_corrections.md#electron-id-efficiency-scale-factors-for-loose-lepton-id
instance.AddScaleFactorWithWorkingPoint(
path_key='ttH_SF',
entry_key='electron_loosereco_2016',
base_key="electron_loosereco_{pt}",
base_str="Electron_EGamma_SF2D_RecoEff2016{pt}.json",
format_dict={'pt': ['PtLt20', 'PtGt20']},
lowercase_keys=True)
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_looseid_2016',
base_str="Electron_EGamma_SF2D_LooseMVA2016.json")
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_looseeff_2016',
base_str="Electron_EGamma_SF2D_LooseEff2016.json")
# Muons Loose #
# https://gitlab.cern.ch/ttH_leptons/doc/-/blob/master/Legacy/data_to_mc_corrections.md#muon-id-efficiency-scale-factors-for-loose-lepton-id
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='muon_loose_2016',
base_str="Muon_EGamma_SF2D_Loose2016.json")
# Electrons tight #
# https://gitlab.cern.ch/ttH_leptons/doc/-/blob/master/Legacy/data_to_mc_corrections.md#lepton-id-efficiency-scale-factors-for-tight-lepton-id
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_tightMVA_2016',
base_str="TTHSF_EGamma_SF2D_ElectronTight2016.json")
# Muons tight #
# https://gitlab.cern.ch/ttH_leptons/doc/-/blob/master/Legacy/data_to_mc_corrections.md#lepton-id-efficiency-scale-factors-for-tight-lepton-id
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='muon_tightMVA_2016',
base_str="TTHSF_EGamma_SF2D_MuonTight2016.json")
# Btagging #
# https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation2016Legacy#Supported_Algorithms_and_Operati
instance.AddScaleFactorWithWorkingPoint(
path_key='POG_SF',
entry_key='btag_2016',
base_key='{algo}_{wp}',
base_str="BTagging_{wp}_{flav}_{calib}_{algo}_2016.json",
format_dict={
'algo': ["DeepCSV", "DeepJet"],
'wp': ["loose", "medium", "tight"],
('flav', 'calib'): [("lightjets", "incl"), ("cjets", "comb"),
("bjets", "comb")]
})
# https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation2016Legacy#Subjet_b_tagging
instance.AddScaleFactorWithWorkingPoint(
path_key='POG_SF',
entry_key='subjet_btag_2016',
base_key='{algo}_{wp}',
base_str="BTagging_{wp}_{flav}_{calib}_subjet_{algo}_2016.json",
format_dict={
'algo': ["DeepCSV"],
'wp': ["loose", "medium"],
('flav', 'calib'): [("lightjets", "incl"), ("cjets", "lt"),
("bjets", "lt")]
})
# Btagging split #
instance.AddScaleFactorWithWorkingPoint(
path_key='POG_SF',
entry_key='btag_lightjets_2016',
base_key='{algo}_{wp}',
base_str="BTagging_{wp}_lightjets_incl_{algo}_2016.json",
format_dict={
'algo': ["DeepCSV", "DeepJet"],
'wp': ["loose", "medium", "tight"]
})
instance.AddScaleFactorWithWorkingPoint(
path_key='POG_SF',
entry_key='btag_cjets_2016',
base_key='{algo}_{wp}',
base_str="BTagging_{wp}_cjets_comb_{algo}_2016.json",
format_dict={
'algo': ["DeepCSV", "DeepJet"],
'wp': ["loose", "medium", "tight"]
})
instance.AddScaleFactorWithWorkingPoint(
path_key='POG_SF',
entry_key='btag_bjets_2016',
base_key='{algo}_{wp}',
base_str="BTagging_{wp}_bjets_comb_{algo}_2016.json",
format_dict={
'algo': ["DeepCSV", "DeepJet"],
'wp': ["loose", "medium", "tight"]
})
# DY weight for 1 and 2 btag #
instance.AddScaleFactorWithWorkingPoint(
path_key='DY_SF',
entry_key='DY_2016',
base_key='{channel}_{type}_{btag}',
#base_str = 'weight_firstLeptonPtVSLeadjetPt_{channel}_{type}_2D_weight_{btag}_2016.json',
#base_str = 'weight_firstleptonPtVsEta_{channel}_{type}_2D_weight_{btag}_2016.json',
base_str=
'weight_leadjetPt_{channel}_{type}_1D_weight_{btag}_2016.json',
format_dict={
'channel': ['ElEl', 'MuMu'],
'type': ['data', 'mc'],
'btag': ['1b', '2b']
})
# Fake rates #
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='electron_fakerates_2016',
base_key='{syst}_syst',
base_str='TTHFakeRates_Electron_2016_{syst}Syst.json',
format_dict={'syst': ['pt', 'barrel', 'norm']})
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='muon_fakerates_2016',
base_key='{syst}_syst',
base_str='TTHFakeRates_Muon_2016_{syst}Syst.json',
format_dict={'syst': ['pt', 'barrel', 'norm']})
#----- 2017 -----#
# Check links of 2016 #
# Single Trigger SF # (Double triggers are single numbers and are in Base)
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='singleTrigger_electron_2017',
base_str="TTH_trigger_SingleElectron_2017.json")
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='singleTrigger_muon_2017',
base_str="TTH_trigger_SingleMuon_2017.json")
# Electrons Loose #
instance.AddScaleFactorWithWorkingPoint(
path_key='ttH_SF',
entry_key='electron_loosereco_2017',
base_key="electron_loosereco_{pt}",
base_str="Electron_EGamma_SF2D_RecoEff2017{pt}.json",
format_dict={'pt': ['PtLt20', 'PtGt20']},
lowercase_keys=True)
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_looseid_2017',
base_str="Electron_EGamma_SF2D_LooseMVA2017.json")
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_looseeff_2017',
base_str="Electron_EGamma_SF2D_LooseEff2017.json")
# Muons Loose #
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='muon_loose_2017',
base_str="Muon_EGamma_SF2D_Loose2017.json")
# Electrons tight #
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_tightMVA_2017',
base_str="TTHSF_EGamma_SF2D_ElectronTight2017.json")
# Muons tight #
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='muon_tightMVA_2017',
base_str="TTHSF_EGamma_SF2D_MuonTight2017.json")
# Btagging #
# https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation94X#Supported_Algorithms_and_Operati
instance.AddScaleFactorWithWorkingPoint(
path_key='POG_SF',
entry_key='btag_2017',
base_key='{algo}_{wp}',
base_str="BTagging_{wp}_{flav}_{calib}_{algo}_2017.json",
format_dict={
'algo': ["DeepCSV", "DeepJet"],
'wp': ["loose", "medium", "tight"],
('flav', 'calib'): [("lightjets", "incl"), ("cjets", "comb"),
("bjets", "comb")]
})
# https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation94X#Boosted_event_topologies
instance.AddScaleFactorWithWorkingPoint(
path_key='POG_SF',
entry_key='subjet_btag_2017',
base_key='{algo}_{wp}',
base_str="BTagging_{wp}_{flav}_{calib}_subjet_{algo}_2017.json",
format_dict={
'algo': ["DeepCSV"],
'wp': ["loose", "medium"],
('flav', 'calib'): [("lightjets", "incl"), ("cjets", "lt"),
("bjets", "lt")]
})
# Fake rates #
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='electron_fakerates_2017',
base_key='{syst}_syst',
base_str='TTHFakeRates_Electron_2017_{syst}Syst.json',
format_dict={'syst': ['pt', 'barrel', 'norm']})
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='muon_fakerates_2017',
base_key='{syst}_syst',
base_str='TTHFakeRates_Muon_2017_{syst}Syst.json',
format_dict={'syst': ['pt', 'barrel', 'norm']})
#----- 2018 -----#
# Check links of 2016 #
# Single Trigger SF # (Double triggers are single numbers and are in Base)
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='singleTrigger_electron_2018',
base_str="TTH_trigger_SingleElectron_2018.json")
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='singleTrigger_muon_2018',
base_str="TTH_trigger_SingleMuon_2018.json")
# Electrons Loose #
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_loosereco_2018',
base_str="Electron_EGamma_SF2D_RecoEff2018.json")
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_looseid_2018',
base_str="Electron_EGamma_SF2D_LooseMVA2018.json")
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_looseeff_2018',
base_str="Electron_EGamma_SF2D_LooseEff2018.json")
# Muons Loose #
instance.AddScaleFactor(path_key='ttH_SF',
entry_key='muon_loose_2018',
base_str="Muon_EGamma_SF2D_Loose2018.json")
# Electrons tight #
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='electron_tightMVA_2018',
base_str="TTHSF_EGamma_SF2D_ElectronTight2018.json")
# Muons tight #
instance.AddScaleFactor(
path_key='ttH_SF',
entry_key='muon_tightMVA_2018',
base_str="TTHSF_EGamma_SF2D_MuonTight2018.json")
# Btagging #
# https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation102X
instance.AddScaleFactorWithWorkingPoint(
path_key='POG_SF',
entry_key='btag_2018',
base_key='{algo}_{wp}',
base_str="BTagging_{wp}_{flav}_{calib}_{algo}_2018.json",
format_dict={
'algo': ["DeepCSV", "DeepJet"],
'wp': ["loose", "medium", "tight"],
('flav', 'calib'): [("lightjets", "incl"), ("cjets", "comb"),
("bjets", "comb")]
})
# https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation102X#Boosted_event_topologies
instance.AddScaleFactorWithWorkingPoint(
path_key='POG_SF',
entry_key='subjet_btag_2018',
base_key='{algo}_{wp}',
base_str="BTagging_{wp}_{flav}_{calib}_subjet_{algo}_2018.json",
format_dict={
'algo': ["DeepCSV"],
'wp': ["loose", "medium"],
('flav', 'calib'): [("lightjets", "incl"), ("cjets", "lt"),
("bjets", "lt")]
})
# Fake rates #
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='electron_fakerates_2018',
base_key='{syst}_syst',
base_str='TTHFakeRates_Electron_2018_{syst}Syst.json',
format_dict={'syst': ['pt', 'barrel', 'norm']})
instance.AddScaleFactorWithWorkingPoint(
path_key='FR',
entry_key='muon_fakerates_2018',
base_key='{syst}_syst',
base_str='TTHFakeRates_Muon_2018_{syst}Syst.json',
format_dict={'syst': ['pt', 'barrel', 'norm']})
# Get full dict #
self.all_scalefactors = instance.GetScaleFactorsDict()
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, CutFlowReport
from bamboo.plots import EquidistantBinning as EqB
from bamboo import treefunctions as op
plots = []
#definitions
electrons = op.select(t.elec, lambda el : op.AND(
el.pt > 20., op.abs(el.eta) < 2.5
))
muons = op.select(t.muon, lambda mu : op.AND(
mu.pt > 20., op.abs(mu.eta) < 2.5
))
cleanedElectrons = op.select(electrons, lambda el : op.NOT(
op.rng_any(muons, lambda mu : op.deltaR(el.p4, mu.p4) < 0.3 )
))
# we are taking the second isopass to be on which is equal to the medium working point
isolatedElectrons = op.select(cleanedElectrons, lambda el : el.isopass & (1<<2) )
identifiedElectrons = op.select(isolatedElectrons, lambda el : el.idpass & (1<<2) )
cleanedMuons = op.select(muons, lambda mu : op.NOT(
op.rng_any(electrons, lambda el : op.deltaR(mu.p4, el.p4) < 0.3 )
))
isolatedMuons = op.select(cleanedMuons, lambda mu : mu.isopass & (1<<2) )
identifiedMuons = op.select(isolatedMuons, lambda mu : mu.idpass & (1<<2) )
InvMassMuMU = op.invariant_mass(identifiedMuons[0].p4, identifiedMuons[1].p4 )
cleanedJets = op.select(t.jetpuppi, lambda j : op.AND(
op.NOT(op.rng_any(identifiedElectrons, lambda el : op.deltaR(el.p4, j.p4) < 0.3) ),
op.NOT(op.rng_any(identifiedMuons, lambda mu : op.deltaR(mu.p4, j.p4) < 0.3) )
))
cleanedGoodJets = op.select(cleanedJets, lambda j : op.AND(
j.pt > 30, op.abs(j.eta) < 2.5
))
btaggedJets = op.select(cleanedGoodJets, lambda j : j.btag & (1<<2))
met = op.select(t.metpuppi)
#selections
#selection1 : Oppositely charged MuMu selection
sel1 = noSel.refine("nmumu", cut = [op.AND(
(op.rng_len(identifiedMuons) > 1), (op.product(identifiedMuons[0].charge, identifiedMuons[1].charge) < 0 ))])
#selection2 : Invariant mass selection
sel2 = sel1.refine("InvM", cut = [op.NOT(op.in_range(76, InvMassMuMU, 106))])
#selection3 : two jets selection
sel3 = sel2.refine("njet", cut = [op.rng_len(cleanedGoodJets) > 1])
#selection4 : at least 1 among two leading jets is b-tagged
sel4 = sel3.refine("btag", cut = [op.OR(
cleanedGoodJets[0].btag & (1<<2), cleanedGoodJets[1].btag & (1<<2))])
#selection5 : MET > 40 GeV
sel5 = sel4.refine("MET", cut = [met[0].pt > 40])
#plots
#noSel
plots.append(Plot.make1D("nJetsNoSel", op.rng_len(cleanedGoodJets), noSel, EqB(10, 0., 10.), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsNoSel", op.rng_len(btaggedJets), noSel, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("nMuNoSel", op.rng_len(identifiedMuons), noSel, EqB(15, 0., 15.), title="nMuons"))
plots.append(Plot.make1D("METptNoSel", met[0].pt, noSel, EqB(50, 0., 250), title="MET_PT"))
#sel1
plots.append(Plot.make1D("nJetsSel1", op.rng_len(cleanedGoodJets), sel1, EqB(10, 0., 10.), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel1", op.rng_len(btaggedJets), sel1, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("nMuSel1", op.rng_len(identifiedMuons), sel1, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("InvMassTwoMuonsSel1", InvMassMuMU, sel1, EqB(30, 0, 300), title="m(ll)"))
plots.append(Plot.make1D("LeadingMuonPTSel1", muons[0].pt, sel1, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel1", muons[1].pt, sel1, EqB(30, 0., 250.), title="SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel1", muons[0].eta, sel1, EqB(30, -3, 3), title=" Leading Muon eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel1", muons[1].eta, sel1, EqB(30, -3, 3), title="SubLeading Muon eta"))
plots.append(Plot.make1D("METptSel1", met[0].pt, sel1, EqB(50, 0., 250), title="MET_PT"))
#sel2
plots.append(Plot.make1D("nJetsSel2", op.rng_len(cleanedGoodJets), sel2, EqB(10, 0., 10.), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel2", op.rng_len(btaggedJets), sel2, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("nMuSel2", op.rng_len(identifiedMuons), sel2, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("InvMassTwoMuonsSel2", InvMassMuMU, sel2, EqB(20, 20., 300.), title="m(ll)"))
plots.append(Plot.make1D("LeadingMuonPTSel2", muons[0].pt, sel2, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel2", muons[1].pt, sel2, EqB(30, 0., 200.), title=" SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel2", muons[0].eta, sel2, EqB(30, -3, 3), title=" Leading Muon Eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel2", muons[1].eta, sel2, EqB(30, -3, 3), title=" SubLeading Muon Eta"))
plots.append(Plot.make1D("METptSel2", met[0].pt, sel2, EqB(50, 0., 250), title="MET_PT"))
#sel3
plots.append(Plot.make1D("nJetsSel3", op.rng_len(cleanedGoodJets), sel3, EqB(10, 0., 10.), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel3", op.rng_len(btaggedJets), sel3, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("LeadingJetPTSel3", cleanedGoodJets[0].pt, sel3, EqB(50, 0., 350.), title="Leading jet PT"))
plots.append(Plot.make1D("SubLeadingJetPTSel3", cleanedGoodJets[1].pt, sel3, EqB(50, 0., 350.), title="SubLeading jet PT"))
plots.append(Plot.make1D("LeadingJetEtaSel3", cleanedGoodJets[0].eta, sel3, EqB(30, -3, 3), title="Leading jet Eta"))
plots.append(Plot.make1D("SubLeadingJetEtaSel3", cleanedGoodJets[1].eta, sel3, EqB(30, -3, 3), title="SubLeading jet Eta"))
plots.append(Plot.make1D("nMuSel3", op.rng_len(identifiedMuons), sel3, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("LeadingMuonPTSel3", muons[0].pt, sel3, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel3", muons[1].pt, sel3, EqB(30, 0., 200.), title=" SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel3", muons[0].eta, sel3, EqB(30, -3, 3), title=" Leading Muon Eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel3", muons[1].eta, sel3, EqB(30, -3, 3), title=" SubLeading Muon Eta"))
plots.append(Plot.make1D("InvMassTwoMuonsSel3", InvMassMuMU, sel3, EqB(30, 0, 300), title="m(ll)"))
plots.append(Plot.make1D("METptSel3", met[0].pt, sel3, EqB(50, 0., 250), title="MET_PT"))
#sel4
plots.append(Plot.make1D("nJetsSel4", op.rng_len(cleanedGoodJets), sel4, EqB(10, 0, 10), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel4", op.rng_len(btaggedJets), sel4, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("LeadingJetPTSel4", cleanedGoodJets[0].pt, sel4, EqB(50, 0., 250.), title="Leading jet PT"))
plots.append(Plot.make1D("SubLeadingJetPTSel4", cleanedGoodJets[1].pt, sel4, EqB(50, 0., 250.), title="SubLeading jet PT"))
plots.append(Plot.make1D("LeadingJetEtaSel4", cleanedGoodJets[0].eta, sel4, EqB(30, -3, 3.), title="Leading jet Eta"))
plots.append(Plot.make1D("SubLeadingJetEtaSel4", cleanedGoodJets[1].eta, sel4, EqB(30, -3, 3.), title="SubLeading jet Eta"))
plots.append(Plot.make1D("nMuSel4", op.rng_len(identifiedMuons), sel4, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("LeadingMuonPTSel4", muons[0].pt, sel4, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel4", muons[1].pt, sel4, EqB(30, 0., 200.), title=" SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel4", muons[0].eta, sel4, EqB(30, -3, 3), title=" Leading Muon Eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel4", muons[1].eta, sel4, EqB(30, -3, 3), title=" SubLeading Muon Eta"))
plots.append(Plot.make1D("InvMassTwoMuonsSel4", InvMassMuMU, sel4, EqB(30, 0, 300), title="m(ll)"))
plots.append(Plot.make1D("METptSel4", met[0].pt, sel4, EqB(50, 0., 250), title="MET_PT"))
#sel5
plots.append(Plot.make1D("nJetsSel5", op.rng_len(cleanedGoodJets), sel5, EqB(10, 0, 10), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel5", op.rng_len(btaggedJets), sel5, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("LeadingJetPTSel5", cleanedGoodJets[0].pt, sel5, EqB(50, 0., 250.), title="Leading jet PT"))
plots.append(Plot.make1D("SubLeadingJetPTSel5", cleanedGoodJets[1].pt, sel5, EqB(50, 0., 250.), title="SubLeading jet PT"))
plots.append(Plot.make1D("LeadingJetEtaSel5", cleanedGoodJets[0].eta, sel5, EqB(30, -3, 3.), title="Leading jet Eta"))
plots.append(Plot.make1D("SubLeadingJetEtaSel5", cleanedGoodJets[1].eta, sel5, EqB(30, -3, 3.), title="SubLeading jet Eta"))
plots.append(Plot.make1D("nMuSel5", op.rng_len(identifiedMuons), sel5, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("LeadingMuonPTSel5", muons[0].pt, sel5, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel5", muons[1].pt, sel5, EqB(30, 0., 200.), title=" SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel5", muons[0].eta, sel5, EqB(30, -3, 3), title=" Leading Muon Eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel5", muons[1].eta, sel5, EqB(30, -3, 3), title=" SubLeading Muon Eta"))
plots.append(Plot.make1D("InvMassTwoMuonsSel5", InvMassMuMU, sel5, EqB(30, 0, 300), title="m(ll)"))
plots.append(Plot.make1D("METptSel5", met[0].pt, sel5, EqB(50, 0., 250), title="MET_PT > 40"))
# Efficiency Report on terminal and the .tex output
cfr = CutFlowReport("yields")
cfr.add(noSel, "Sel0: No selection")
cfr.add(sel1, "Sel1: nMuMu >= 2")
cfr.add(sel2, "Sel2: InvM")
cfr.add(sel3, "Sel3: nJet >= 2")
cfr.add(sel4, "Sel4: btag")
cfr.add(sel5, "Sel5: MET")
plots.append(cfr)
return plots
