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