def test_highlevel(): array = ak.Array([[3.3, 1.1, 5.5, 1.1, 4.4], [4.4, 2.2, 1.1, 6.6], [2.2, 3.3, -1.1]]) assert ak.argmin(array) == 11 assert ak.argmax(array) == 8 assert ak.to_list(ak.argmin(array, axis=0)) == [2, 0, 2, 0, 0] assert ak.to_list(ak.argmax(array, axis=0)) == [1, 2, 0, 1, 0] assert ak.to_list(ak.argmin(array, axis=1)) == [1, 2, 2] assert ak.to_list(ak.argmax(array, axis=1)) == [2, 3, 1]
def test_mine(): array = ak.Array( ak.layout.ListOffsetArray64( ak.layout.Index64(np.array([1, 2, 4, 7], np.int64)), ak.layout.NumpyArray( np.array([0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9])), )) assert (ak.argmax(array, axis=-1).tolist() == ak.argmax(ak.Array([[1.1], [2.2, 3.3], [4.4, 5.5, 6.6]]), axis=-1).tolist())
def test_argmin_argmax(): array = ak.Array( [ [ [ np.datetime64("2022"), np.datetime64("2023"), np.datetime64("2025") ], [], [np.datetime64("2027"), np.datetime64("2011")], [np.datetime64("2013")], ], [], [[np.datetime64("2017"), np.datetime64("2019")], [np.datetime64("2023")]], ], check_valid=True, ) assert ak.argmin(array) == 4 assert ak.argmax(array) == 3 assert ak.to_list(ak.argmin(array, axis=0)) == [[1, 1, 0], [1], [0, 0], [0]] assert ak.to_list(ak.argmax(array, axis=0)) == [[0, 0, 0], [1], [0, 0], [0]] assert ak.to_list(ak.argmin(array, axis=1)) == [[3, 2, 0], [], [0, 0]] assert ak.to_list(ak.argmax(array, axis=1)) == [[2, 0, 0], [], [1, 0]] array = ak.from_iter( [ [[ np.datetime64("2021-01-20"), np.datetime64("2021-01-10"), np.datetime64("2021-01-30"), ]], [[]], [None, None, None], [[ np.datetime64("2021-01-14"), np.datetime64("2021-01-15"), np.datetime64("2021-01-16"), ]], ], highlevel=False, ) assert ak.to_list(array.argmin(axis=2)) == [[1], [None], [None, None, None], [0]]
def test_issue434(): a = ak.Array([[0.0, 1.1, 2.2], [3.3, 4.4], [5.5]]) b = ak.Array([[9.9, 8.8, 7.7], [6.6, 5.5], [4.4]]) assert ak.to_list(b[ak.argmin(a, axis=1, keepdims=True)]) == [[9.9], [6.6], [4.4]] assert ak.to_list(b[ak.argmax(a, axis=1, keepdims=True)]) == [[7.7], [5.5], [4.4]]
def process(self, events): events["Electron", "pdgId"] = -11 * events.Electron.charge events["Muon", "pdgId"] = -13 * events.Muon.charge events["leptons"] = ak.concatenate( [events.Electron, events.Muon], axis=1, ) events = events[ak.num(events.leptons) >= 3] pair = ak.argcombinations(events.leptons, 2, fields=["l1", "l2"]) pair = pair[( events.leptons[pair.l1].pdgId == -events.leptons[pair.l2].pdgId)] with np.errstate(invalid="ignore"): pair = pair[ak.singletons( ak.argmin( abs((events.leptons[pair.l1] + events.leptons[pair.l2]).mass - 91.2), axis=1, ))] events = events[ak.num(pair) > 0] pair = pair[ak.num(pair) > 0][:, 0] l3 = ak.local_index(events.leptons) l3 = l3[(l3 != pair.l1) & (l3 != pair.l2)] l3 = l3[ak.argmax(events.leptons[l3].pt, axis=1, keepdims=True)] l3 = events.leptons[l3][:, 0] mt = np.sqrt(2 * l3.pt * events.MET.pt * (1 - np.cos(events.MET.delta_phi(l3)))) return (hist.Hist.new.Reg( 100, 0, 200, name="mt", label=r"$\ell$-MET transverse mass [GeV]").Double().fill(mt))
def test_998(): array = ak.Array( ak.layout.ListOffsetArray64( ak.layout.Index64(np.array([1, 2, 4, 7], np.int64)), ak.layout.NumpyArray(np.r_[1.8125, 0.8125, -0.9375, 1.1875, -0.6875, 1.3125, 21.3125, ]), )) assert ak.to_list(ak.argmax(array, axis=-1)) == [0, 1, 2]
def test_ByteMaskedArray(): content = ak.Array([1.1, 2.2, 3.3, 999, 999, 4.4, 5.5]).layout mask = ak.layout.Index8( np.array([False, False, False, True, True, False, False])) bytemaskedarray = ak.layout.ByteMaskedArray(mask, content, valid_when=False) array = ak.Array(bytemaskedarray) assert array.tolist() == [1.1, 2.2, 3.3, None, None, 4.4, 5.5] assert ak.max(array, axis=0) == 5.5 assert ak.argmax(array, axis=0) == 6 offsets = ak.layout.Index64(np.array([0, 2, 4, 7], dtype=np.int64)) listoffsetarray = ak.layout.ListOffsetArray64(offsets, bytemaskedarray) array = ak.Array(listoffsetarray) assert array.tolist() == [[1.1, 2.2], [3.3, None], [None, 4.4, 5.5]] assert ak.max(array, axis=1).tolist() == [2.2, 3.3, 5.5] assert ak.argmax(array, axis=1).tolist() == [1, 0, 2]
def test_1000(): array = ak.Array( ak.layout.ListOffsetArray64( ak.layout.Index64(np.array([1, 3, 5], np.int64)), ak.layout.ListOffsetArray64( ak.layout.Index64(np.array([0, 3, 5, 8, 10, 12], np.int64)), ak.layout.NumpyArray(np.r_[1.8125, 0.81252, -0.937, 6.0, -0.6875, 1.3125, 21.3125, 4.0, 9.8, 2.2, 33.0, 44.6, ]), ), )) assert ak.to_list(ak.argmax(array, axis=1)) == [[0, 1, 1], [1, 1]]
def mjj_filter(events, fatjets, jets, mjj_range, debug): fatjets.Tbb = fatjets.particleNetMD_Xbb / \ (1 - fatjets.particleNetMD_Xqq - fatjets.particleNetMD_Xcc) fatjet = fatjets[ak.argmax(fatjets.Tbb, axis=1, keepdims=True)] print("best fatjet eta {} and jet eta {}".format(fatjet.eta, fatjet.phi)) match_mask = object_selections.mask_nearest(ak.flatten(fatjet.eta), ak.flatten(fatjet.phi), jets.eta, jets.phi, threshold=0.4) matched_jet = jets[match_mask] other_jets = jets[~match_mask] mjj_cut = object_selections.select_mass(events, matched_jet, other_jets, mjj_range, debug) selected_events = events[mjj_cut] return selected_events
def test_minmax(): assert ak.min(ak.from_iter([[1 + 5j, 2 + 4j], [], [3 + 3j]])) == 1 + 5j assert ak.max(ak.from_iter([[1 + 5j, 2 + 4j], [], [3 + 3j]])) == 3 + 3j assert ak.min(ak.from_iter([[1 + 5j, 2 + 4j], [], [3 + 3j]]), axis=1).tolist() == [ 1 + 5j, None, 3 + 3j, ] assert ak.max(ak.from_iter([[1 + 5j, 2 + 4j], [], [3 + 3j]]), axis=1).tolist() == [ 2 + 4j, None, 3 + 3j, ] assert ak.argmin(ak.from_iter([[1 + 5j, 2 + 4j], [], [3 + 3j]]), axis=1).tolist() == [0, None, 0] assert ak.argmax(ak.from_iter([[1 + 5j, 2 + 4j], [], [3 + 3j]]), axis=1).tolist() == [1, None, 0]
def process(self, events): output = self.accumulator.identity() output['total']['all'] += len(events) # use a very loose preselection to filter the events presel = ak.num(events.Jet) > 2 ev = events[presel] dataset = ev.metadata['dataset'] # load the config - probably not needed anymore cfg = loadConfig() ## Muons muon = Collections(ev, "Muon", "vetoTTH").get() tightmuon = Collections(ev, "Muon", "tightTTH").get() dimuon = choose(muon, 2) SSmuon = ak.any((dimuon['0'].charge * dimuon['1'].charge) > 0, axis=1) leading_muon_idx = ak.singletons(ak.argmax(muon.pt, axis=1)) leading_muon = muon[leading_muon_idx] ## Electrons electron = Collections(ev, "Electron", "vetoTTH").get() tightelectron = Collections(ev, "Electron", "tightTTH").get() dielectron = choose(electron, 2) SSelectron = ak.any( (dielectron['0'].charge * dielectron['1'].charge) > 0, axis=1) leading_electron_idx = ak.singletons(ak.argmax(electron.pt, axis=1)) leading_electron = electron[leading_electron_idx] ## Merge electrons and muons - this should work better now in ak1 dilepton = cross(muon, electron) SSlepton = ak.any((dilepton['0'].charge * dilepton['1'].charge) > 0, axis=1) lepton = ak.concatenate([muon, electron], axis=1) leading_lepton_idx = ak.singletons(ak.argmax(lepton.pt, axis=1)) leading_lepton = lepton[leading_lepton_idx] trailing_lepton_idx = ak.singletons(ak.argmin(lepton.pt, axis=1)) trailing_lepton = lepton[trailing_lepton_idx] dilepton_mass = (leading_lepton + trailing_lepton).mass dilepton_pt = (leading_lepton + trailing_lepton).pt dilepton_dR = delta_r(leading_lepton, trailing_lepton) mt_lep_met = mt(lepton.pt, lepton.phi, ev.MET.pt, ev.MET.phi) min_mt_lep_met = ak.min(mt_lep_met, axis=1) ## Jets jet = getJets(ev, minPt=25, maxEta=4.7, pt_var='pt_nom') jet = jet[ak.argsort( jet.pt_nom, ascending=False )] # need to sort wrt smeared and recorrected jet pt jet = jet[~match(jet, muon, deltaRCut=0.4)] # remove jets that overlap with muons jet = jet[~match( jet, electron, deltaRCut=0.4)] # remove jets that overlap with electrons central = jet[(abs(jet.eta) < 2.4)] btag = getBTagsDeepFlavB( jet, year=self.year) # should study working point for DeepJet light = getBTagsDeepFlavB(jet, year=self.year, invert=True) fwd = getFwdJet(light) fwd_noPU = getFwdJet(light, puId=False) tau = getTaus(ev) track = getIsoTracks(ev) ## forward jets j_fwd = fwd[ak.singletons(ak.argmax( fwd.p, axis=1))] # highest momentum spectator high_score_btag = central[ak.argsort(central.btagDeepFlavB)][:, :2] bl = cross(lepton, high_score_btag) bl_dR = delta_r(bl['0'], bl['1']) min_bl_dR = ak.min(bl_dR, axis=1) jf = cross(j_fwd, jet) mjf = (jf['0'] + jf['1']).mass j_fwd2 = jf[ak.singletons( ak.argmax(mjf, axis=1) )]['1'] # this is the jet that forms the largest invariant mass with j_fwd delta_eta = ak.fill_none( ak.pad_none(abs(j_fwd2.eta - j_fwd.eta), 1, clip=True), 0) ## MET -> can switch to puppi MET met_pt = ev.MET.pt met_phi = ev.MET.phi ## other variables ht = ak.sum(jet.pt, axis=1) st = met_pt + ht + ak.sum(muon.pt, axis=1) + ak.sum(electron.pt, axis=1) ## event selectors filters = getFilters(ev, year=self.year, dataset=dataset) dilep = ((ak.num(tightelectron) + ak.num(tightmuon)) == 2) lep0pt = ((ak.num(electron[(electron.pt > 25)]) + ak.num(muon[(muon.pt > 25)])) > 0) lep1pt = ((ak.num(electron[(electron.pt > 20)]) + ak.num(muon[(muon.pt > 20)])) > 1) lepveto = ((ak.num(electron) + ak.num(muon)) == 2) selection = PackedSelection() selection.add('lepveto', lepveto) selection.add('dilep', dilep) selection.add('filter', (filters)) selection.add('p_T(lep0)>25', lep0pt) selection.add('p_T(lep1)>20', lep1pt) selection.add('SS', (SSlepton | SSelectron | SSmuon)) selection.add('N_jet>3', (ak.num(jet) >= 4)) selection.add('N_central>2', (ak.num(central) >= 3)) selection.add('N_btag>0', (ak.num(btag) >= 1)) selection.add('N_fwd>0', (ak.num(fwd) >= 1)) #ss_reqs = ['lepveto', 'dilep', 'filter', 'p_T(lep0)>25', 'p_T(lep1)>20', 'SS'] ss_reqs = [ 'lepveto', 'dilep', 'filter', 'p_T(lep0)>25', 'p_T(lep1)>20', 'SS' ] #bl_reqs = ss_reqs + ['N_jet>3', 'N_central>2', 'N_btag>0', 'N_fwd>0'] bl_reqs = ss_reqs + ['N_jet>3', 'N_central>2', 'N_btag>0'] ss_reqs_d = {sel: True for sel in ss_reqs} ss_selection = selection.require(**ss_reqs_d) bl_reqs_d = {sel: True for sel in bl_reqs} BL = selection.require(**bl_reqs_d) weight = Weights(len(ev)) if not dataset == 'MuonEG': # lumi weight weight.add("weight", ev.weight) # PU weight - not in the babies... weight.add("PU", ev.puWeight, weightUp=ev.puWeightUp, weightDown=ev.puWeightDown, shift=False) # b-tag SFs weight.add("btag", self.btagSF.Method1a(btag, light)) # lepton SFs weight.add("lepton", self.leptonSF.get(electron, muon)) #cutflow = Cutflow(output, ev, weight=weight) #cutflow_reqs_d = {} #for req in bl_reqs: # cutflow_reqs_d.update({req: True}) # cutflow.addRow( req, selection.require(**cutflow_reqs_d) ) labels = { 'topW_v3': 0, 'TTW': 1, 'TTZ': 2, 'TTH': 3, 'ttbar': 4, 'ttbar1l_MG': 4 } if dataset in labels: label_mult = labels[dataset] else: label_mult = 5 label = np.ones(len(ev[BL])) * label_mult output["n_lep"] += processor.column_accumulator( ak.to_numpy((ak.num(electron) + ak.num(muon))[BL])) output["n_lep_tight"] += processor.column_accumulator( ak.to_numpy((ak.num(tightelectron) + ak.num(tightmuon))[BL])) output["lead_lep_pt"] += processor.column_accumulator( ak.to_numpy(ak.flatten(leading_lepton[BL].pt, axis=1))) output["lead_lep_eta"] += processor.column_accumulator( ak.to_numpy(ak.flatten(leading_lepton[BL].eta, axis=1))) output["lead_lep_phi"] += processor.column_accumulator( ak.to_numpy(ak.flatten(leading_lepton[BL].phi, axis=1))) output["lead_lep_charge"] += processor.column_accumulator( ak.to_numpy(ak.flatten(leading_lepton[BL].charge, axis=1))) output["sublead_lep_pt"] += processor.column_accumulator( ak.to_numpy(ak.flatten(trailing_lepton[BL].pt, axis=1))) output["sublead_lep_eta"] += processor.column_accumulator( ak.to_numpy(ak.flatten(trailing_lepton[BL].eta, axis=1))) output["sublead_lep_phi"] += processor.column_accumulator( ak.to_numpy(ak.flatten(trailing_lepton[BL].phi, axis=1))) output["sublead_lep_charge"] += processor.column_accumulator( ak.to_numpy(ak.flatten(trailing_lepton[BL].charge, axis=1))) output["lead_jet_pt"] += processor.column_accumulator( ak.to_numpy(ak.flatten(jet[:, 0:1][BL].pt, axis=1))) output["lead_jet_eta"] += processor.column_accumulator( ak.to_numpy(ak.flatten(jet[:, 0:1][BL].eta, axis=1))) output["lead_jet_phi"] += processor.column_accumulator( ak.to_numpy(ak.flatten(jet[:, 0:1][BL].phi, axis=1))) output["sublead_jet_pt"] += processor.column_accumulator( ak.to_numpy(ak.flatten(jet[:, 1:2][BL].pt, axis=1))) output["sublead_jet_eta"] += processor.column_accumulator( ak.to_numpy(ak.flatten(jet[:, 1:2][BL].eta, axis=1))) output["sublead_jet_phi"] += processor.column_accumulator( ak.to_numpy(ak.flatten(jet[:, 1:2][BL].phi, axis=1))) output["lead_btag_pt"] += processor.column_accumulator( ak.to_numpy(ak.flatten(high_score_btag[:, 0:1][BL].pt, axis=1))) output["lead_btag_eta"] += processor.column_accumulator( ak.to_numpy(ak.flatten(high_score_btag[:, 0:1][BL].eta, axis=1))) output["lead_btag_phi"] += processor.column_accumulator( ak.to_numpy(ak.flatten(high_score_btag[:, 0:1][BL].phi, axis=1))) output["sublead_btag_pt"] += processor.column_accumulator( ak.to_numpy(ak.flatten(high_score_btag[:, 1:2][BL].pt, axis=1))) output["sublead_btag_eta"] += processor.column_accumulator( ak.to_numpy(ak.flatten(high_score_btag[:, 1:2][BL].eta, axis=1))) output["sublead_btag_phi"] += processor.column_accumulator( ak.to_numpy(ak.flatten(high_score_btag[:, 1:2][BL].phi, axis=1))) output["fwd_jet_p"] += processor.column_accumulator( ak.to_numpy( ak.flatten(ak.fill_none(ak.pad_none(j_fwd[BL].p, 1, clip=True), 0), axis=1))) output["fwd_jet_pt"] += processor.column_accumulator( ak.to_numpy( ak.flatten(ak.fill_none( ak.pad_none(j_fwd[BL].pt, 1, clip=True), 0), axis=1))) output["fwd_jet_eta"] += processor.column_accumulator( ak.to_numpy( ak.flatten(ak.fill_none( ak.pad_none(j_fwd[BL].eta, 1, clip=True), 0), axis=1))) output["fwd_jet_phi"] += processor.column_accumulator( ak.to_numpy( ak.flatten(ak.fill_none( ak.pad_none(j_fwd[BL].phi, 1, clip=True), 0), axis=1))) output["mjj_max"] += processor.column_accumulator( ak.to_numpy(ak.fill_none(ak.max(mjf[BL], axis=1), 0))) output["delta_eta_jj"] += processor.column_accumulator( ak.to_numpy(ak.flatten(delta_eta[BL], axis=1))) output["met"] += processor.column_accumulator(ak.to_numpy(met_pt[BL])) output["ht"] += processor.column_accumulator(ak.to_numpy(ht[BL])) output["st"] += processor.column_accumulator(ak.to_numpy(st[BL])) output["n_jet"] += processor.column_accumulator( ak.to_numpy(ak.num(jet[BL]))) output["n_btag"] += processor.column_accumulator( ak.to_numpy(ak.num(btag[BL]))) output["n_fwd"] += processor.column_accumulator( ak.to_numpy(ak.num(fwd[BL]))) output["n_central"] += processor.column_accumulator( ak.to_numpy(ak.num(central[BL]))) output["n_tau"] += processor.column_accumulator( ak.to_numpy(ak.num(tau[BL]))) output["n_track"] += processor.column_accumulator( ak.to_numpy(ak.num(track[BL]))) output["dilepton_pt"] += processor.column_accumulator( ak.to_numpy(ak.flatten(dilepton_pt[BL], axis=1))) output["dilepton_mass"] += processor.column_accumulator( ak.to_numpy(ak.flatten(dilepton_mass[BL], axis=1))) output["min_bl_dR"] += processor.column_accumulator( ak.to_numpy(min_bl_dR[BL])) output["min_mt_lep_met"] += processor.column_accumulator( ak.to_numpy(min_mt_lep_met[BL])) output["label"] += processor.column_accumulator(label) output["weight"] += processor.column_accumulator(weight.weight()[BL]) output["presel"]["all"] += len(ev[ss_selection]) output["sel"]["all"] += len(ev[BL]) return output
def process(self, events): output = self.accumulator.identity() # we can use a very loose preselection to filter the events. nothing is done with this presel, though presel = ak.num(events.Jet) > 0 ev = events[presel] dataset = ev.metadata['dataset'] # load the config - probably not needed anymore cfg = loadConfig() output['totalEvents']['all'] += len(events) output['skimmedEvents']['all'] += len(ev) ## Electrons electron = Collections(ev, "Electron", "tightFCNC", 0, self.year).get() electron = electron[(electron.pt > 15) & (np.abs(electron.eta) < 2.4)] electron = electron[(electron.genPartIdx >= 0)] electron = electron[(np.abs(electron.matched_gen.pdgId) == 11 )] #from here on all leptons are gen-matched electron = electron[((electron.genPartFlav == 1) | (electron.genPartFlav == 15))] #and now they are all prompt is_flipped = (((electron.matched_gen.pdgId * (-1) == electron.pdgId) | (find_first_parent(electron.matched_gen) * (-1) == electron.pdgId)) & (np.abs(electron.pdgId) == 11)) flipped_electron = electron[is_flipped] n_flips = ak.num(flipped_electron) leading_electron_idx = ak.singletons(ak.argmax(electron.pt, axis=1)) leading_electron = electron[leading_electron_idx] leading_flipped_electron_idx = ak.singletons( ak.argmax(flipped_electron.pt, axis=1)) leading_flipped_electron = electron[leading_flipped_electron_idx] def getMVAscore(electron): if self.year == 2016: MVA = electron.mvaSpring16GP return MVA elif self.year == 2017: MVA = electron.mvaFall17V2noIso return MVA elif self.year == 2018: MVA = np.minimum( np.maximum(electron.mvaFall17V2noIso, -1.0 + 1.e-6), 1.0 - 1.e-6) return -0.5 * np.log(2 / (MVA + 1) - 1) # setting up the various weights #weight = Weights( len(ev) ) #if not dataset=='MuonEG': # generator weight # weight.add("weight", ev.genWeight) #selections filters = getFilters(ev, year=self.year, dataset=dataset) electr = ((ak.num(electron) >= 1)) flip = (n_flips >= 1) selection = PackedSelection() selection.add('filter', (filters)) selection.add('electr', electr) selection.add('flip', flip) bl_reqs = ['filter', 'electr'] bl_reqs_d = {sel: True for sel in bl_reqs} baseline = selection.require(**bl_reqs_d) f_reqs = bl_reqs + ['flip'] f_reqs_d = {sel: True for sel in f_reqs} flip_sel = selection.require(**f_reqs_d) #adjust weights to prevent length mismatch #ak_weight_gen = ak.ones_like(electron[baseline].pt) * weight.weight()[baseline] #ak_weight_flip = ak.ones_like(flipped_electron[flip_sel].pt) * weight.weight()[flip_sel] #output['N_ele'].fill(dataset=dataset, multiplicity=ak.num(electron)[baseline], weight=weight.weight()[baseline]) #output['electron_flips'].fill(dataset=dataset, multiplicity=n_flips[baseline], weight=weight.weight()[baseline]) output["electron"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(electron[baseline].pt)), eta=abs(ak.to_numpy(ak.flatten(electron[baseline].eta))), ) output["electron2"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(electron[baseline].pt)), eta=ak.to_numpy(ak.flatten(electron[baseline].eta)), ) output["flipped_electron"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(flipped_electron[flip_sel].pt)), eta=abs(ak.to_numpy(ak.flatten(flipped_electron[flip_sel].eta))), ) output["flipped_electron2"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(flipped_electron[flip_sel].pt)), eta=ak.to_numpy(ak.flatten(flipped_electron[flip_sel].eta)), ) output["mva_id"].fill( dataset=dataset, mva_id=ak.to_numpy(ak.flatten(getMVAscore(electron)[baseline])), eta=np.abs(ak.to_numpy(ak.flatten(electron.etaSC[baseline]))), ) output["mva_id2"].fill( dataset=dataset, mva_id=ak.to_numpy(ak.flatten(getMVAscore(electron)[baseline])), pt=ak.to_numpy(ak.flatten(electron.pt[baseline])), ) output["isolation"].fill( dataset=dataset, isolation1=ak.to_numpy(ak.flatten(electron.jetRelIso[baseline])), isolation2=ak.to_numpy(ak.flatten(electron.jetPtRelv2[baseline])), ) return output
def make_leading_pair(target, base): return target[ak.argmax(base.pt, axis=1, keepdims=True)]
def process(self, events): output = self.accumulator.identity() # use a very loose preselection to filter the events presel = ak.num(events.Jet) > 2 ev = events[presel] dataset = ev.metadata['dataset'] # load the config - probably not needed anymore cfg = loadConfig() output['totalEvents']['all'] += len(events) output['skimmedEvents']['all'] += len(ev) ## Generated leptons '''gen_lep = ev.GenL leading_gen_lep = gen_lep[ak.singletons(ak.argmax(gen_lep.pt, axis=1))] trailing_gen_lep = gen_lep[ak.singletons(ak.argmin(gen_lep.pt, axis=1))]''' ## Muons muon = Collections(ev, "Muon", "tightTTH").get() vetomuon = Collections(ev, "Muon", "vetoTTH").get() leading_muon_idx = ak.singletons(ak.argmax(muon.pt, axis=1)) leading_muon = muon[leading_muon_idx] ## Electrons electron = Collections(ev, "Electron", "tightTTH").get() vetoelectron = Collections(ev, "Electron", "vetoTTH").get() leading_electron_idx = ak.singletons(ak.argmax(electron.pt, axis=1)) leading_electron = electron[leading_electron_idx] ## Merge electrons and muons - this should work better now in ak1 dilepton = cross(muon, electron) dimuon = choose(muon, 2) OS_dimuon = dimuon[(dimuon['0'].charge * dimuon['1'].charge < 0)] dielectron = choose(electron, 2) OS_dielectron = dielectron[( dielectron['0'].charge * dielectron['1'].charge < 0)] OS_dimuon_bestZmumu = OS_dimuon[ak.singletons( ak.argmin(abs(OS_dimuon.mass - 91.2), axis=1))] OS_dielectron_bestZee = OS_dielectron[ak.singletons( ak.argmin(abs(OS_dielectron.mass - 91.2), axis=1))] OS_dilepton_mass = ak.fill_none( ak.pad_none(ak.concatenate( [OS_dimuon_bestZmumu.mass, OS_dielectron_bestZee.mass], axis=1), 1, clip=True), -1) lepton = ak.concatenate([muon, electron], axis=1) leading_lepton_idx = ak.singletons(ak.argmax(lepton.pt, axis=1)) leading_lepton = lepton[leading_lepton_idx] trailing_lepton_idx = ak.singletons(ak.argmin(lepton.pt, axis=1)) trailing_lepton = lepton[trailing_lepton_idx] ## Jets jet = getJets(ev, minPt=25, maxEta=4.7, pt_var='pt_nom') jet = jet[ak.argsort( jet.pt_nom, ascending=False )] # need to sort wrt smeared and recorrected jet pt jet = jet[~match(jet, muon, deltaRCut=0.4)] # remove jets that overlap with muons jet = jet[~match( jet, electron, deltaRCut=0.4)] # remove jets that overlap with electrons central = jet[(abs(jet.eta) < 2.4)] btag = getBTagsDeepFlavB( jet, year=self.year) # should study working point for DeepJet light = getBTagsDeepFlavB(jet, year=self.year, invert=True) fwd = getFwdJet(light) fwd_noPU = getFwdJet(light, puId=False) ## forward jets j_fwd = fwd[ak.singletons(ak.argmax( fwd.p, axis=1))] # highest momentum spectator jf = cross(j_fwd, jet) mjf = (jf['0'] + jf['1']).mass # j_fwd2 = jf[ak.singletons(ak.argmax(mjf, axis=1))]['1'] # this is the jet that forms the largest invariant mass with j_fwd # delta_eta = abs(j_fwd2.eta - j_fwd.eta) ## MET -> can switch to puppi MET met_pt = ev.MET.pt met_phi = ev.MET.phi ## other variables ht = ak.sum(jet.pt, axis=1) st = met_pt + ht + ak.sum(muon.pt, axis=1) + ak.sum(electron.pt, axis=1) # define the weight weight = Weights(len(ev)) if not re.search(re.compile('MuonEG|DoubleMuon|DoubleEG|EGamma'), dataset): # lumi weight weight.add("weight", ev.weight * cfg['lumi'][self.year]) # PU weight - not in the babies... weight.add("PU", ev.puWeight, weightUp=ev.puWeightUp, weightDown=ev.puWeightDown, shift=False) # b-tag SFs weight.add("btag", self.btagSF.Method1a(btag, light)) # lepton SFs # weight.add("lepton", self.leptonSF.get(electron, muon)) cutflow = Cutflow(output, ev, weight=weight) sel = Selection( dataset=dataset, events=ev, year=self.year, ele=electron, ele_veto=vetoelectron, mu=muon, mu_veto=vetomuon, jet_all=jet, jet_central=central, jet_btag=btag, jet_fwd=fwd, met=ev.MET, ) BL = sel.trilep_baseline(cutflow=cutflow) # first, make a few super inclusive plots output['ST'].fill(dataset=dataset, ht=st[BL], weight=weight.weight()[BL]) output['PV_npvs'].fill(dataset=dataset, multiplicity=ev.PV[BL].npvs, weight=weight.weight()[BL]) output['PV_npvsGood'].fill(dataset=dataset, multiplicity=ev.PV[BL].npvsGood, weight=weight.weight()[BL]) output['N_jet'].fill(dataset=dataset, multiplicity=ak.num(jet)[BL], weight=weight.weight()[BL]) output['N_b'].fill(dataset=dataset, multiplicity=ak.num(btag)[BL], weight=weight.weight()[BL]) output['N_central'].fill(dataset=dataset, multiplicity=ak.num(central)[BL], weight=weight.weight()[BL]) output['N_ele'].fill(dataset=dataset, multiplicity=ak.num(vetoelectron)[BL], weight=weight.weight()[BL]) output['N_mu'].fill(dataset=dataset, multiplicity=ak.num(vetomuon)[BL], weight=weight.weight()[BL]) output['N_fwd'].fill(dataset=dataset, multiplicity=ak.num(fwd)[BL], weight=weight.weight()[BL]) '''output['nLepFromTop'].fill(dataset=dataset, multiplicity=ev[BL].nLepFromTop, weight=weight.weight()[BL]) output['nLepFromTau'].fill(dataset=dataset, multiplicity=ev.nLepFromTau[BL], weight=weight.weight()[BL]) output['nLepFromZ'].fill(dataset=dataset, multiplicity=ev.nLepFromZ[BL], weight=weight.weight()[BL]) output['nLepFromW'].fill(dataset=dataset, multiplicity=ev.nLepFromW[BL], weight=weight.weight()[BL]) output['nGenTau'].fill(dataset=dataset, multiplicity=ev.nGenTau[BL], weight=weight.weight()[BL]) output['nGenL'].fill(dataset=dataset, multiplicity=ak.num(ev.GenL[BL], axis=1), weight=weight.weight()[BL])''' # make a plot of the dilepton mass, but without applying the cut on the dilepton mass itself (N-1 plot) output['dilep_mass'].fill( dataset=dataset, mass=ak.flatten( OS_dilepton_mass[sel.trilep_baseline(omit=['offZ'])]), weight=weight.weight()[sel.trilep_baseline(omit=['offZ'])]) output['MET'].fill(dataset=dataset, pt=ev.MET[BL].pt, phi=ev.MET[BL].phi, weight=weight.weight()[BL]) '''output['lead_gen_lep'].fill( dataset = dataset, pt = ak.to_numpy(ak.flatten(leading_gen_lep[BL].pt)), eta = ak.to_numpy(ak.flatten(leading_gen_lep[BL].eta)), phi = ak.to_numpy(ak.flatten(leading_gen_lep[BL].phi)), weight = weight.weight()[BL] ) output['trail_gen_lep'].fill( dataset = dataset, pt = ak.to_numpy(ak.flatten(trailing_gen_lep[BL].pt)), eta = ak.to_numpy(ak.flatten(trailing_gen_lep[BL].eta)), phi = ak.to_numpy(ak.flatten(trailing_gen_lep[BL].phi)), weight = weight.weight()[BL] )''' output['lead_lep'].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(leading_lepton[BL].pt)), eta=ak.to_numpy(ak.flatten(leading_lepton[BL].eta)), phi=ak.to_numpy(ak.flatten(leading_lepton[BL].phi)), weight=weight.weight()[BL]) output['trail_lep'].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(trailing_lepton[BL].pt)), eta=ak.to_numpy(ak.flatten(trailing_lepton[BL].eta)), phi=ak.to_numpy(ak.flatten(trailing_lepton[BL].phi)), weight=weight.weight()[BL]) output['j1'].fill(dataset=dataset, pt=ak.flatten(jet.pt_nom[:, 0:1][BL]), eta=ak.flatten(jet.eta[:, 0:1][BL]), phi=ak.flatten(jet.phi[:, 0:1][BL]), weight=weight.weight()[BL]) output['j2'].fill(dataset=dataset, pt=ak.flatten(jet[:, 1:2][BL].pt_nom), eta=ak.flatten(jet[:, 1:2][BL].eta), phi=ak.flatten(jet[:, 1:2][BL].phi), weight=weight.weight()[BL]) #output['j3'].fill( # dataset = dataset, # pt = ak.flatten(jet[:, 2:3][BL].pt_nom), # eta = ak.flatten(jet[:, 2:3][BL].eta), # phi = ak.flatten(jet[:, 2:3][BL].phi), # weight = weight.weight()[BL] #) output['fwd_jet'].fill(dataset=dataset, pt=ak.flatten(j_fwd[BL].pt), eta=ak.flatten(j_fwd[BL].eta), phi=ak.flatten(j_fwd[BL].phi), weight=weight.weight()[BL]) output['high_p_fwd_p'].fill(dataset=dataset, p=ak.flatten(j_fwd[BL].p), weight=weight.weight()[BL]) vetolepton = ak.concatenate([vetomuon, vetoelectron], axis=1) trilep = choose3(vetolepton, 3) trilep_m = trilep.mass output['m3l'].fill(dataset=dataset, mass=ak.flatten(trilep_m[BL]), weight=weight.weight()[BL]) return output
def process(self, events): # Dataset parameters dataset = events.metadata['dataset'] year = self._samples[dataset]['year'] xsec = self._samples[dataset]['xsec'] sow = self._samples[dataset]['nSumOfWeights'] isData = self._samples[dataset]['isData'] datasets = [ 'SingleMuon', 'SingleElectron', 'EGamma', 'MuonEG', 'DoubleMuon', 'DoubleElectron' ] for d in datasets: if d in dataset: dataset = dataset.split('_')[0] # Initialize objects met = events.MET e = events.Electron mu = events.Muon j = events.Jet # Muon selection mu['isPres'] = isPresMuon(mu.dxy, mu.dz, mu.sip3d, mu.looseId) mu['isTight'] = isTightMuon(mu.pt, mu.eta, mu.dxy, mu.dz, mu.pfRelIso03_all, mu.sip3d, mu.mvaTTH, mu.mediumPromptId, mu.tightCharge, mu.looseId, minpt=10) mu['isGood'] = mu['isPres'] & mu['isTight'] leading_mu = mu[ak.argmax(mu.pt, axis=-1, keepdims=True)] leading_mu = leading_mu[leading_mu.isGood] mu = mu[mu.isGood] mu_pres = mu[mu.isPres] # Electron selection e['isPres'] = isPresElec(e.pt, e.eta, e.dxy, e.dz, e.miniPFRelIso_all, e.sip3d, e.lostHits, minpt=15) e['isTight'] = isTightElec(e.pt, e.eta, e.dxy, e.dz, e.miniPFRelIso_all, e.sip3d, e.mvaTTH, e.mvaFall17V2Iso, e.lostHits, e.convVeto, e.tightCharge, e.sieie, e.hoe, e.eInvMinusPInv, minpt=15) e['isClean'] = isClean(e, mu, drmin=0.05) e['isGood'] = e['isPres'] & e['isTight'] & e['isClean'] leading_e = e[ak.argmax(e.pt, axis=-1, keepdims=True)] leading_e = leading_e[leading_e.isGood] e = e[e.isGood] e_pres = e[e.isPres & e.isClean] nElec = ak.num(e) nMuon = ak.num(mu) twoLeps = (nElec + nMuon) == 2 threeLeps = (nElec + nMuon) == 3 twoElec = (nElec == 2) twoMuon = (nMuon == 2) e0 = e[ak.argmax(e.pt, axis=-1, keepdims=True)] m0 = mu[ak.argmax(mu.pt, axis=-1, keepdims=True)] # Jet selection jetptname = 'pt_nom' if hasattr(j, 'pt_nom') else 'pt' j['isGood'] = isTightJet(getattr(j, jetptname), j.eta, j.jetId, j.neHEF, j.neEmEF, j.chHEF, j.chEmEF, j.nConstituents) j['isClean'] = isClean(j, e, drmin=0.4) & isClean(j, mu, drmin=0.4) goodJets = j[(j.isClean) & (j.isGood)] njets = ak.num(goodJets) ht = ak.sum(goodJets.pt, axis=-1) j0 = goodJets[ak.argmax(goodJets.pt, axis=-1, keepdims=True)] ### We need weights for: normalization, lepSF, triggerSF, pileup, btagSF... weights = coffea.analysis_tools.Weights(len(events)) weights.add('norm', np.ones_like(met.pt)) eftweights = events['EFTfitCoefficients'] if hasattr( events, "EFTfitCoefficients") else [] hout = self.accumulator.identity() normweights = weights.weight().flatten() #hout['SumOfEFTweights'].fill(eftweights, sample=dataset, SumOfEFTweights=varnames['counts'], weight=normweights) flavSelection = { 'b': (np.abs(goodJets.hadronFlavour) == 5), 'c': (np.abs(goodJets.hadronFlavour) == 4), 'l': (np.abs(goodJets.hadronFlavour) <= 3) } WP = {'all': -999., 'loose': 0.0490, 'medium': 0.2783, 'tight': 0.7100} btagSelection = {} for wp, wpvals in WP.items(): btagSelection[wp] = (goodJets.btagDeepFlavB > wpvals) for jetype in ['b', 'c', 'l']: for wp in WP.keys(): mask = (flavSelection[jetype]) & (btagSelection[wp]) selectjets = goodJets[mask] pts = ak.flatten(selectjets.pt) etas = ak.flatten(selectjets.eta) absetas = ak.flatten(np.abs(selectjets.eta)) flavarray = np.zeros_like(pts) if jetype == 'l' else ( np.ones_like(pts) * (4 if jetype == 'c' else 5)) weights = np.ones_like(pts) hout['jetpt'].fill(WP=wp, Flav=jetype, pt=pts, weight=weights) hout['jeteta'].fill(WP=wp, Flav=jetype, eta=etas, weight=weights) hout['jetpteta'].fill(WP=wp, Flav=jetype, pt=pts, abseta=absetas, weight=weights) hout['jetptetaflav'].fill(WP=wp, pt=pts, abseta=absetas, flav=flavarray, weight=weights) return hout
def process(self, events): # Dataset parameters dataset = events.metadata['dataset'] histAxisName = self._samples[dataset]['histAxisName'] year = self._samples[dataset]['year'] xsec = self._samples[dataset]['xsec'] sow = self._samples[dataset]['nSumOfWeights'] isData = self._samples[dataset]['isData'] datasets = [ 'SingleMuon', 'SingleElectron', 'EGamma', 'MuonEG', 'DoubleMuon', 'DoubleElectron' ] for d in datasets: if d in dataset: dataset = dataset.split('_')[0] # Initialize objects met = events.MET e = events.Electron mu = events.Muon tau = events.Tau j = events.Jet # Muon selection mu['isPres'] = isPresMuon(mu.dxy, mu.dz, mu.sip3d, mu.looseId) mu['isTight'] = isTightMuon(mu.pt, mu.eta, mu.dxy, mu.dz, mu.pfRelIso03_all, mu.sip3d, mu.mvaTTH, mu.mediumPromptId, mu.tightCharge, mu.looseId, minpt=10) mu['isGood'] = mu['isPres'] & mu['isTight'] leading_mu = mu[ak.argmax(mu.pt, axis=-1, keepdims=True)] leading_mu = leading_mu[leading_mu.isGood] mu = mu[mu.isGood] mu_pres = mu[mu.isPres] # Electron selection e['isPres'] = isPresElec(e.pt, e.eta, e.dxy, e.dz, e.miniPFRelIso_all, e.sip3d, e.lostHits, minpt=15) e['isTight'] = isTightElec(e.pt, e.eta, e.dxy, e.dz, e.miniPFRelIso_all, e.sip3d, e.mvaTTH, e.mvaFall17V2Iso, e.lostHits, e.convVeto, e.tightCharge, e.sieie, e.hoe, e.eInvMinusPInv, minpt=15) e['isClean'] = isClean(e, mu, drmin=0.05) e['isGood'] = e['isPres'] & e['isTight'] & e['isClean'] leading_e = e[ak.argmax(e.pt, axis=-1, keepdims=True)] leading_e = leading_e[leading_e.isGood] e = e[e.isGood] e_pres = e[e.isPres & e.isClean] # Tau selection tau['isPres'] = isPresTau(tau.pt, tau.eta, tau.dxy, tau.dz, tau.leadTkPtOverTauPt, tau.idAntiMu, tau.idAntiEle, tau.rawIso, tau.idDecayModeNewDMs, minpt=20) tau['isClean'] = isClean(tau, e_pres, drmin=0.4) & isClean( tau, mu_pres, drmin=0.4) tau['isGood'] = tau['isPres'] # & tau['isClean'], for the moment tau = tau[tau.isGood] nElec = ak.num(e) nMuon = ak.num(mu) nTau = ak.num(tau) twoLeps = (nElec + nMuon) == 2 threeLeps = (nElec + nMuon) == 3 twoElec = (nElec == 2) twoMuon = (nMuon == 2) e0 = e[ak.argmax(e.pt, axis=-1, keepdims=True)] m0 = mu[ak.argmax(mu.pt, axis=-1, keepdims=True)] # Attach the lepton SFs to the electron and muons collections AttachElectronSF(e, year=year) AttachMuonSF(mu, year=year) # Create a lepton (muon+electron) collection and calculate a per event lepton SF leps = ak.concatenate([e, mu], axis=-1) events['lepSF_nom'] = ak.prod(leps.sf_nom, axis=-1) events['lepSF_hi'] = ak.prod(leps.sf_hi, axis=-1) events['lepSF_lo'] = ak.prod(leps.sf_lo, axis=-1) # Jet selection jetptname = 'pt_nom' if hasattr(j, 'pt_nom') else 'pt' ### Jet energy corrections if not isData: j["pt_raw"] = (1 - j.rawFactor) * j.pt j["mass_raw"] = (1 - j.rawFactor) * j.mass j["pt_gen"] = ak.values_astype(ak.fill_none(j.matched_gen.pt, 0), np.float32) j["rho"] = ak.broadcast_arrays(events.fixedGridRhoFastjetAll, j.pt)[0] events_cache = events.caches[0] corrected_jets = jet_factory.build(j, lazy_cache=events_cache) #print('jet pt: ',j.pt) #print('cor pt: ',corrected_jets.pt) #print('jes up: ',corrected_jets.JES_jes.up.pt) #print('jes down: ',corrected_jets.JES_jes.down.pt) #print(ak.fields(corrected_jets)) ''' # SYSTEMATICS jets = corrected_jets if(self.jetSyst == 'JERUp'): jets = corrected_jets.JER.up elif(self.jetSyst == 'JERDown'): jets = corrected_jets.JER.down elif(self.jetSyst == 'JESUp'): jets = corrected_jets.JES_jes.up elif(self.jetSyst == 'JESDown'): jets = corrected_jets.JES_jes.down ''' j['isGood'] = isTightJet(getattr(j, jetptname), j.eta, j.jetId, j.neHEF, j.neEmEF, j.chHEF, j.chEmEF, j.nConstituents) #j['isgood'] = isGoodJet(j.pt, j.eta, j.jetId) #j['isclean'] = isClean(j, e, mu) j['isClean'] = isClean(j, e, drmin=0.4) & isClean( j, mu, drmin=0.4) # & isClean(j, tau, drmin=0.4) goodJets = j[(j.isClean) & (j.isGood)] njets = ak.num(goodJets) ht = ak.sum(goodJets.pt, axis=-1) j0 = goodJets[ak.argmax(goodJets.pt, axis=-1, keepdims=True)] #nbtags = ak.num(goodJets[goodJets.btagDeepFlavB > 0.2770]) # Loose DeepJet WP if year == 2017: btagwpl = 0.0532 #WP loose else: btagwpl = 0.0490 #WP loose isBtagJetsLoose = (goodJets.btagDeepB > btagwpl) isNotBtagJetsLoose = np.invert(isBtagJetsLoose) nbtagsl = ak.num(goodJets[isBtagJetsLoose]) # Medium DeepJet WP if year == 2017: btagwpm = 0.3040 #WP medium else: btagwpm = 0.2783 #WP medium isBtagJetsMedium = (goodJets.btagDeepB > btagwpm) isNotBtagJetsMedium = np.invert(isBtagJetsMedium) nbtagsm = ak.num(goodJets[isBtagJetsMedium]) # Btag SF following 1a) in https://twiki.cern.ch/twiki/bin/viewauth/CMS/BTagSFMethods btagSF = np.ones_like(ht) btagSFUp = np.ones_like(ht) btagSFDo = np.ones_like(ht) if not isData: pt = goodJets.pt abseta = np.abs(goodJets.eta) flav = goodJets.hadronFlavour bJetSF = GetBTagSF(abseta, pt, flav) bJetSFUp = GetBTagSF(abseta, pt, flav, sys=1) bJetSFDo = GetBTagSF(abseta, pt, flav, sys=-1) bJetEff = GetBtagEff(abseta, pt, flav, year) bJetEff_data = bJetEff * bJetSF bJetEff_dataUp = bJetEff * bJetSFUp bJetEff_dataDo = bJetEff * bJetSFDo pMC = ak.prod(bJetEff[isBtagJetsMedium], axis=-1) * ak.prod( (1 - bJetEff[isNotBtagJetsMedium]), axis=-1) pData = ak.prod(bJetEff_data[isBtagJetsMedium], axis=-1) * ak.prod( (1 - bJetEff_data[isNotBtagJetsMedium]), axis=-1) pDataUp = ak.prod( bJetEff_dataUp[isBtagJetsMedium], axis=-1) * ak.prod( (1 - bJetEff_dataUp[isNotBtagJetsMedium]), axis=-1) pDataDo = ak.prod( bJetEff_dataDo[isBtagJetsMedium], axis=-1) * ak.prod( (1 - bJetEff_dataDo[isNotBtagJetsMedium]), axis=-1) pMC = ak.where(pMC == 0, 1, pMC) # removeing zeroes from denominator... btagSF = pData / pMC btagSFUp = pDataUp / pMC btagSFDo = pDataUp / pMC ################################################################## ### 2 same-sign leptons ################################################################## # emu singe = e[(nElec == 1) & (nMuon == 1) & (e.pt > -1)] singm = mu[(nElec == 1) & (nMuon == 1) & (mu.pt > -1)] em = ak.cartesian({"e": singe, "m": singm}) emSSmask = (em.e.charge * em.m.charge > 0) emSS = em[emSSmask] nemSS = len(ak.flatten(emSS)) # ee and mumu # pt>-1 to preserve jagged dimensions ee = e[(nElec == 2) & (nMuon == 0) & (e.pt > -1)] mm = mu[(nElec == 0) & (nMuon == 2) & (mu.pt > -1)] sumcharge = ak.sum(e.charge, axis=-1) + ak.sum(mu.charge, axis=-1) eepairs = ak.combinations(ee, 2, fields=["e0", "e1"]) eeSSmask = (eepairs.e0.charge * eepairs.e1.charge > 0) eeonZmask = (np.abs((eepairs.e0 + eepairs.e1).mass - 91.2) < 10) eeoffZmask = (eeonZmask == 0) mmpairs = ak.combinations(mm, 2, fields=["m0", "m1"]) mmSSmask = (mmpairs.m0.charge * mmpairs.m1.charge > 0) mmonZmask = (np.abs((mmpairs.m0 + mmpairs.m1).mass - 91.2) < 10) mmoffZmask = (mmonZmask == 0) eeSSonZ = eepairs[eeSSmask & eeonZmask] eeSSoffZ = eepairs[eeSSmask & eeoffZmask] mmSSonZ = mmpairs[mmSSmask & mmonZmask] mmSSoffZ = mmpairs[mmSSmask & mmoffZmask] neeSS = len(ak.flatten(eeSSonZ)) + len(ak.flatten(eeSSoffZ)) nmmSS = len(ak.flatten(mmSSonZ)) + len(ak.flatten(mmSSoffZ)) print('Same-sign events [ee, emu, mumu] = [%i, %i, %i]' % (neeSS, nemSS, nmmSS)) # Cuts eeSSmask = (ak.num(eeSSmask[eeSSmask]) > 0) mmSSmask = (ak.num(mmSSmask[mmSSmask]) > 0) eeonZmask = (ak.num(eeonZmask[eeonZmask]) > 0) eeoffZmask = (ak.num(eeoffZmask[eeoffZmask]) > 0) mmonZmask = (ak.num(mmonZmask[mmonZmask]) > 0) mmoffZmask = (ak.num(mmoffZmask[mmoffZmask]) > 0) emSSmask = (ak.num(emSSmask[emSSmask]) > 0) ################################################################## ### 3 leptons ################################################################## # eem muon_eem = mu[(nElec == 2) & (nMuon == 1) & (mu.pt > -1)] elec_eem = e[(nElec == 2) & (nMuon == 1) & (e.pt > -1)] ee_eem = ak.combinations(elec_eem, 2, fields=["e0", "e1"]) ee_eemZmask = (ee_eem.e0.charge * ee_eem.e1.charge < 1) & (np.abs( (ee_eem.e0 + ee_eem.e1).mass - 91.2) < 10) ee_eemOffZmask = (ee_eem.e0.charge * ee_eem.e1.charge < 1) & (np.abs( (ee_eem.e0 + ee_eem.e1).mass - 91.2) > 10) ee_eemZmask = (ak.num(ee_eemZmask[ee_eemZmask]) > 0) ee_eemOffZmask = (ak.num(ee_eemOffZmask[ee_eemOffZmask]) > 0) eepair_eem = (ee_eem.e0 + ee_eem.e1) trilep_eem = eepair_eem + muon_eem #ak.cartesian({"e0":ee_eem.e0,"e1":ee_eem.e1, "m":muon_eem}) # mme muon_mme = mu[(nElec == 1) & (nMuon == 2) & (mu.pt > -1)] elec_mme = e[(nElec == 1) & (nMuon == 2) & (e.pt > -1)] mm_mme = ak.combinations(muon_mme, 2, fields=["m0", "m1"]) mm_mmeZmask = (mm_mme.m0.charge * mm_mme.m1.charge < 1) & (np.abs( (mm_mme.m0 + mm_mme.m1).mass - 91.2) < 10) mm_mmeOffZmask = (mm_mme.m0.charge * mm_mme.m1.charge < 1) & (np.abs( (mm_mme.m0 + mm_mme.m1).mass - 91.2) > 10) mm_mmeZmask = (ak.num(mm_mmeZmask[mm_mmeZmask]) > 0) mm_mmeOffZmask = (ak.num(mm_mmeOffZmask[mm_mmeOffZmask]) > 0) mmpair_mme = (mm_mme.m0 + mm_mme.m1) trilep_mme = mmpair_mme + elec_mme mZ_mme = mmpair_mme.mass mZ_eem = eepair_eem.mass m3l_eem = trilep_eem.mass m3l_mme = trilep_mme.mass # eee and mmm eee = e[(nElec == 3) & (nMuon == 0) & (e.pt > -1)] mmm = mu[(nElec == 0) & (nMuon == 3) & (mu.pt > -1)] eee_leps = ak.combinations(eee, 3, fields=["e0", "e1", "e2"]) mmm_leps = ak.combinations(mmm, 3, fields=["m0", "m1", "m2"]) ee_pairs = ak.combinations(eee, 2, fields=["e0", "e1"]) mm_pairs = ak.combinations(mmm, 2, fields=["m0", "m1"]) ee_pairs_index = ak.argcombinations(eee, 2, fields=["e0", "e1"]) mm_pairs_index = ak.argcombinations(mmm, 2, fields=["m0", "m1"]) mmSFOS_pairs = mm_pairs[ (np.abs(mm_pairs.m0.pdgId) == np.abs(mm_pairs.m1.pdgId)) & (mm_pairs.m0.charge != mm_pairs.m1.charge)] offZmask_mm = ak.all( np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2) > 10., axis=1, keepdims=True) & (ak.num(mmSFOS_pairs) > 0) onZmask_mm = ak.any( np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2) < 10., axis=1, keepdims=True) eeSFOS_pairs = ee_pairs[ (np.abs(ee_pairs.e0.pdgId) == np.abs(ee_pairs.e1.pdgId)) & (ee_pairs.e0.charge != ee_pairs.e1.charge)] offZmask_ee = ak.all( np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2) > 10, axis=1, keepdims=True) & (ak.num(eeSFOS_pairs) > 0) onZmask_ee = ak.any( np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2) < 10, axis=1, keepdims=True) # Create masks **for event selection** eeeOnZmask = (ak.num(onZmask_ee[onZmask_ee]) > 0) eeeOffZmask = (ak.num(offZmask_ee[offZmask_ee]) > 0) mmmOnZmask = (ak.num(onZmask_mm[onZmask_mm]) > 0) mmmOffZmask = (ak.num(offZmask_mm[offZmask_mm]) > 0) # Now we need to create masks for the leptons in order to select leptons from the Z boson candidate (in onZ categories) ZeeMask = ak.argmin(np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2), axis=1, keepdims=True) ZmmMask = ak.argmin(np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2), axis=1, keepdims=True) Zee = eeSFOS_pairs[ZeeMask] Zmm = mmSFOS_pairs[ZmmMask] eZ0 = Zee.e0[ak.num(eeSFOS_pairs) > 0] eZ1 = Zee.e1[ak.num(eeSFOS_pairs) > 0] eZ = eZ0 + eZ1 mZ0 = Zmm.m0[ak.num(mmSFOS_pairs) > 0] mZ1 = Zmm.m1[ak.num(mmSFOS_pairs) > 0] mZ = mZ0 + mZ1 mZ_eee = eZ.mass mZ_mmm = mZ.mass # And for the W boson ZmmIndices = mm_pairs_index[ZmmMask] ZeeIndices = ee_pairs_index[ZeeMask] eW = eee[~ZeeIndices.e0 | ~ZeeIndices.e1] mW = mmm[~ZmmIndices.m0 | ~ZmmIndices.m1] triElec = eee_leps.e0 + eee_leps.e1 + eee_leps.e2 triMuon = mmm_leps.m0 + mmm_leps.m1 + mmm_leps.m2 m3l_eee = triElec.mass m3l_mmm = triMuon.mass ################################################################## ### >=4 leptons ################################################################## # 4lep cat is4lmask = ((nElec + nMuon) >= 4) muon_4l = mu[(is4lmask) & (mu.pt > -1)] elec_4l = e[(is4lmask) & (e.pt > -1)] # selecting 4 leading leptons leptons = ak.concatenate([e, mu], axis=-1) leptons_sorted = leptons[ak.argsort(leptons.pt, axis=-1, ascending=False)] lep4l = leptons_sorted[:, 0:4] e4l = lep4l[abs(lep4l.pdgId) == 11] mu4l = lep4l[abs(lep4l.pdgId) == 13] nElec4l = ak.num(e4l) nMuon4l = ak.num(mu4l) # Triggers trig_eeSS = passTrigger(events, 'ee', isData, dataset) trig_mmSS = passTrigger(events, 'mm', isData, dataset) trig_emSS = passTrigger(events, 'em', isData, dataset) trig_eee = passTrigger(events, 'eee', isData, dataset) trig_mmm = passTrigger(events, 'mmm', isData, dataset) trig_eem = passTrigger(events, 'eem', isData, dataset) trig_mme = passTrigger(events, 'mme', isData, dataset) trig_4l = triggerFor4l(events, nMuon, nElec, isData, dataset) # MET filters # Weights genw = np.ones_like(events['event']) if ( isData or len(self._wc_names_lst) > 0) else events['genWeight'] ### We need weights for: normalization, lepSF, triggerSF, pileup, btagSF... weights = {} for r in [ 'all', 'ee', 'mm', 'em', 'eee', 'mmm', 'eem', 'mme', 'eeee', 'eeem', 'eemm', 'mmme', 'mmmm' ]: # weights[r] = coffea.analysis_tools.Weights(len(events)) weights[r] = coffea.analysis_tools.Weights(len(events), storeIndividual=True) if len(self._wc_names_lst) > 0: sow = np.ones_like( sow ) # Not valid in nanoAOD for EFT samples, MUST use SumOfEFTweights at analysis level weights[r].add('norm', genw if isData else (xsec / sow) * genw) weights[r].add('btagSF', btagSF, btagSFUp, btagSFDo) weights[r].add('lepSF', events.lepSF_nom, events.lepSF_hi, events.lepSF_lo) # Extract the EFT quadratic coefficients and optionally use them to calculate the coefficients on the w**2 quartic function # eft_coeffs is never Jagged so convert immediately to numpy for ease of use. eft_coeffs = ak.to_numpy(events['EFTfitCoefficients']) if hasattr( events, "EFTfitCoefficients") else None if eft_coeffs is not None: # Check to see if the ordering of WCs for this sample matches what want if self._samples[dataset]['WCnames'] != self._wc_names_lst: eft_coeffs = efth.remap_coeffs( self._samples[dataset]['WCnames'], self._wc_names_lst, eft_coeffs) eft_w2_coeffs = efth.calc_w2_coeffs(eft_coeffs, self._dtype) if ( self._do_errors and eft_coeffs is not None) else None # Selections and cuts selections = PackedSelection() #(dtype='uint64') channels2LSS = ['eeSSonZ', 'eeSSoffZ', 'mmSSonZ', 'mmSSoffZ', 'emSS'] selections.add('eeSSonZ', (eeonZmask) & (eeSSmask) & (trig_eeSS)) selections.add('eeSSoffZ', (eeoffZmask) & (eeSSmask) & (trig_eeSS)) selections.add('mmSSonZ', (mmonZmask) & (mmSSmask) & (trig_mmSS)) selections.add('mmSSoffZ', (mmoffZmask) & (mmSSmask) & (trig_mmSS)) selections.add('emSS', (emSSmask) & (trig_emSS)) channels3L = ['eemSSonZ', 'eemSSoffZ', 'mmeSSonZ', 'mmeSSoffZ'] selections.add('eemSSonZ', (ee_eemZmask) & (trig_eem)) selections.add('eemSSoffZ', (ee_eemOffZmask) & (trig_eem)) selections.add('mmeSSonZ', (mm_mmeZmask) & (trig_mme)) selections.add('mmeSSoffZ', (mm_mmeOffZmask) & (trig_mme)) channels3L += ['eeeSSonZ', 'eeeSSoffZ', 'mmmSSonZ', 'mmmSSoffZ'] selections.add('eeeSSonZ', (eeeOnZmask) & (trig_eee)) selections.add('eeeSSoffZ', (eeeOffZmask) & (trig_eee)) selections.add('mmmSSonZ', (mmmOnZmask) & (trig_mmm)) selections.add('mmmSSoffZ', (mmmOffZmask) & (trig_mmm)) channels4L = ['eeee', 'eeem', 'eemm', 'mmme', 'mmmm'] selections.add('eeee', ((nElec4l == 4) & (nMuon4l == 0)) & (trig_4l)) selections.add('eeem', ((nElec4l == 3) & (nMuon4l == 1)) & (trig_4l)) selections.add('eemm', ((nElec4l == 2) & (nMuon4l == 2)) & (trig_4l)) selections.add('mmme', ((nElec4l == 1) & (nMuon4l == 3)) & (trig_4l)) selections.add('mmmm', ((nElec4l == 0) & (nMuon4l == 4)) & (trig_4l)) selections.add('ch+', (sumcharge > 0)) selections.add('ch-', (sumcharge < 0)) selections.add('ch0', (sumcharge == 0)) levels = ['base', '1+bm2+bl', '1bm', '2+bm'] selections.add('base', (nElec + nMuon >= 2)) selections.add('1+bm2+bl', (nElec + nMuon >= 2) & ((nbtagsm >= 1) & (nbtagsl >= 2))) selections.add('1bm', (nElec + nMuon >= 2) & (nbtagsm == 1)) selections.add('2+bm', (nElec + nMuon >= 2) & (nbtagsm >= 2)) # Variables invMass_eeSSonZ = (eeSSonZ.e0 + eeSSonZ.e1).mass invMass_eeSSoffZ = (eeSSoffZ.e0 + eeSSoffZ.e1).mass invMass_mmSSonZ = (mmSSonZ.m0 + mmSSonZ.m1).mass invMass_mmSSoffZ = (mmSSoffZ.m0 + mmSSoffZ.m1).mass invMass_emSS = (emSS.e + emSS.m).mass varnames = {} varnames['met'] = met.pt varnames['ht'] = ht varnames['njets'] = njets varnames['invmass'] = { 'eeSSonZ': invMass_eeSSonZ, 'eeSSoffZ': invMass_eeSSoffZ, 'mmSSonZ': invMass_mmSSonZ, 'mmSSoffZ': invMass_mmSSoffZ, 'emSS': invMass_emSS, 'eemSSonZ': mZ_eem, 'eemSSoffZ': mZ_eem, 'mmeSSonZ': mZ_mme, 'mmeSSoffZ': mZ_mme, 'eeeSSonZ': mZ_eee, 'eeeSSoffZ': mZ_eee, 'mmmSSonZ': mZ_mmm, 'mmmSSoffZ': mZ_mmm, } varnames['m3l'] = { 'eemSSonZ': m3l_eem, 'eemSSoffZ': m3l_eem, 'mmeSSonZ': m3l_mme, 'mmeSSoffZ': m3l_mme, 'eeeSSonZ': m3l_eee, 'eeeSSoffZ': m3l_eee, 'mmmSSonZ': m3l_mmm, 'mmmSSoffZ': m3l_mmm, } varnames['e0pt'] = e0.pt varnames['e0eta'] = e0.eta varnames['m0pt'] = m0.pt varnames['m0eta'] = m0.eta varnames['j0pt'] = j0.pt varnames['j0eta'] = j0.eta varnames['counts'] = np.ones_like(events['event']) # systematics systList = [] if isData == False: systList = ['nominal'] if self._do_systematics: systList = systList + [ 'lepSFUp', 'lepSFDown', 'btagSFUp', 'btagSFDown' ] else: systList = ['noweight'] # fill Histos hout = self.accumulator.identity() normweights = weights['all'].weight().flatten( ) # Why does it not complain about .flatten() here? sowweights = np.ones_like(normweights) if len( self._wc_names_lst) > 0 else normweights hout['SumOfEFTweights'].fill(sample=histAxisName, SumOfEFTweights=varnames['counts'], weight=sowweights, eft_coeff=eft_coeffs, eft_err_coeff=eft_w2_coeffs) for syst in systList: for var, v in varnames.items(): for ch in channels2LSS + channels3L + channels4L: for sumcharge in ['ch+', 'ch-', 'ch0']: for lev in levels: #find the event weight to be used when filling the histograms weightSyst = syst #in the case of 'nominal', or the jet energy systematics, no weight systematic variation is used (weightSyst=None) if syst in [ 'nominal', 'JERUp', 'JERDown', 'JESUp', 'JESDown' ]: weightSyst = None # no weight systematic for these variations if syst == 'noweight': weight = np.ones(len(events)) # for data else: # call weights.weight() with the name of the systematic to be varied if ch in channels3L: ch_w = ch[:3] elif ch in channels2LSS: ch_w = ch[:2] else: ch_w = ch weight = weights['all'].weight( weightSyst ) if isData else weights[ch_w].weight( weightSyst) cuts = [ch] + [lev] + [sumcharge] cut = selections.all(*cuts) weights_flat = weight[cut].flatten( ) # Why does it not complain about .flatten() here? weights_ones = np.ones_like(weights_flat, dtype=np.int) eft_coeffs_cut = eft_coeffs[ cut] if eft_coeffs is not None else None eft_w2_coeffs_cut = eft_w2_coeffs[ cut] if eft_w2_coeffs is not None else None # filling histos if var == 'invmass': if ((ch in [ 'eeeSSoffZ', 'mmmSSoffZ', 'eeeSSonZ', 'mmmSSonZ' ]) or (ch in channels4L)): continue else: values = ak.flatten(v[ch][cut]) hout['invmass'].fill( eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, invmass=values, weight=weights_flat, systematic=syst) elif var == 'm3l': if ((ch in channels2LSS) or (ch in [ 'eeeSSoffZ', 'mmmSSoffZ', 'eeeSSonZ', 'mmmSSonZ' ]) or (ch in channels4L)): continue values = ak.flatten(v[ch][cut]) hout['m3l'].fill( eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, m3l=values, weight=weights_flat, systematic=syst) else: values = v[cut] # These all look identical, do we need if/else here? if var == 'ht': hout[var].fill( eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, ht=values, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, weight=weights_flat, systematic=syst) elif var == 'met': hout[var].fill( eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, met=values, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, weight=weights_flat, systematic=syst) elif var == 'njets': hout[var].fill( eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, njets=values, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, weight=weights_flat, systematic=syst) elif var == 'nbtags': hout[var].fill( eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, nbtags=values, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, weight=weights_flat, systematic=syst) elif var == 'counts': hout[var].fill(counts=values, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, weight=weights_ones, systematic=syst) elif var == 'j0eta': if lev == 'base': continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill( eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, j0eta=values, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, weight=weights_flat, systematic=syst) elif var == 'e0pt': if ch in [ 'mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ', 'mmmSSonZ', 'mmmm' ]: continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill( eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, e0pt=values, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, weight=weights_flat, systematic=syst ) # Crashing here, not sure why. Related to values? elif var == 'm0pt': if ch in [ 'eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ', 'eeee' ]: continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill( eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, m0pt=values, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, weight=weights_flat, systematic=syst) elif var == 'e0eta': if ch in [ 'mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ', 'mmmSSonZ', 'mmmm' ]: continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill( eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, e0eta=values, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, weight=weights_flat, systematic=syst) elif var == 'm0eta': if ch in [ 'eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ', 'eeee' ]: continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill( eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, m0eta=values, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, weight=weights_flat, systematic=syst) elif var == 'j0pt': if lev == 'base': continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill( eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, j0pt=values, sample=histAxisName, channel=ch, cut=lev, sumcharge=sumcharge, weight=weights_flat, systematic=syst) return hout
def test(): x = ak.Array([1, 2, 3, None, 4]) assert ak.argmax(x) == 4
def process(self, events): # Dataset parameters dataset = events.metadata['dataset'] year = self._samples[dataset]['year'] xsec = self._samples[dataset]['xsec'] sow = self._samples[dataset]['nSumOfWeights' ] isData = self._samples[dataset]['isData'] datasets = ['SingleMuon', 'SingleElectron', 'EGamma', 'MuonEG', 'DoubleMuon', 'DoubleElectron'] for d in datasets: if d in dataset: dataset = dataset.split('_')[0] # Inittialize objects met = events.GenMET e = events.GenPart[abs(events.GenPart.pdgId)==11] mu = events.GenPart[abs(events.GenPart.pdgId)==13] tau = events.GenPart[abs(events.GenPart.pdgId)==15] j = events.GenJet leading_mu = mu[ak.argmax(mu.pt,axis=-1,keepdims=True)] leading_e = e[ak.argmax(e.pt,axis=-1,keepdims=True)] nElec = ak.num(e) nMuon = ak.num(mu) nTau = ak.num(tau) twoLeps = (nElec+nMuon) == 2 threeLeps = (nElec+nMuon) == 3 twoElec = (nElec == 2) twoMuon = (nMuon == 2) e0 = e[ak.argmax(e.pt,axis=-1,keepdims=True)] m0 = mu[ak.argmax(mu.pt,axis=-1,keepdims=True)] elecs = e[ak.argsort(e.pt, ascending=False)] muons = mu[ak.argsort(mu.pt, ascending=False)] e1 = elecs e2 = elecs m1 = muons m2 = muons # Jet selection jetptname = 'pt_nom' if hasattr(j, 'pt_nom') else 'pt' njets = ak.num(j) ht = ak.sum(j.pt,axis=-1) jets = j[ak.argsort(j.pt, ascending=False)] j0 = j[ak.argmax(j.pt,axis=-1,keepdims=True)] j1 = jets j2 = jets j3 = jets nbtags = ak.num(j[abs(j.hadronFlavour)==5]) ################################################################## ### 2 same-sign leptons ################################################################## # emu singe = e [(nElec==1)&(nMuon==1)&(e .pt>-1)] singm = mu[(nElec==1)&(nMuon==1)&(mu.pt>-1)] em = ak.cartesian({"e":singe,"m":singm}) emSSmask = (em.e.pdgId*em.m.pdgId>0) emSS = em[emSSmask] nemSS = len(ak.flatten(emSS)) year = 2018 lepSF_emSS = GetLeptonSF(mu.pt, mu.eta, 'm', e.pt, e.eta, 'e', year=year) # ee and mumu # pt>-1 to preserve jagged dimensions ee = e [(nElec==2)&(nMuon==0)&(e.pt>-1)] mm = mu[(nElec==0)&(nMuon==2)&(mu.pt>-1)] eepairs = ak.combinations(ee, 2, fields=["e0","e1"]) eeSSmask = (eepairs.e0.pdgId*eepairs.e1.pdgId>0) eeonZmask = (np.abs((eepairs.e0+eepairs.e1).mass-91.2)<10) eeoffZmask = (eeonZmask==0) mmpairs = ak.combinations(mm, 2, fields=["m0","m1"]) mmSSmask = (mmpairs.m0.pdgId*mmpairs.m1.pdgId>0) mmonZmask = (np.abs((mmpairs.m0+mmpairs.m1).mass-91.2)<10) mmoffZmask = (mmonZmask==0) eeSSonZ = eepairs[eeSSmask & eeonZmask] eeSSoffZ = eepairs[eeSSmask & eeoffZmask] mmSSonZ = mmpairs[mmSSmask & mmonZmask] mmSSoffZ = mmpairs[mmSSmask & mmoffZmask] neeSS = len(ak.flatten(eeSSonZ)) + len(ak.flatten(eeSSoffZ)) nmmSS = len(ak.flatten(mmSSonZ)) + len(ak.flatten(mmSSoffZ)) lepSF_eeSS = GetLeptonSF(eepairs.e0.pt, eepairs.e0.eta, 'e', eepairs.e1.pt, eepairs.e1.eta, 'e', year=year) lepSF_mumuSS = GetLeptonSF(mmpairs.m0.pt, mmpairs.m0.eta, 'm', mmpairs.m1.pt, mmpairs.m1.eta, 'm', year=year) print('Same-sign events [ee, emu, mumu] = [%i, %i, %i]'%(neeSS, nemSS, nmmSS)) # Cuts eeSSmask = (ak.num(eeSSmask[eeSSmask])>0) mmSSmask = (ak.num(mmSSmask[mmSSmask])>0) eeonZmask = (ak.num(eeonZmask[eeonZmask])>0) eeoffZmask = (ak.num(eeoffZmask[eeoffZmask])>0) mmonZmask = (ak.num(mmonZmask[mmonZmask])>0) mmoffZmask = (ak.num(mmoffZmask[mmoffZmask])>0) emSSmask = (ak.num(emSSmask[emSSmask])>0) ################################################################## ### 3 leptons ################################################################## # eem muon_eem = mu[(nElec==2)&(nMuon==1)&(mu.pt>-1)] elec_eem = e[(nElec==2)&(nMuon==1)&( e.pt>-1)] ee_eem = ak.combinations(elec_eem, 2, fields=["e0", "e1"]) ee_eemZmask = (ee_eem.e0.pdgId*ee_eem.e1.pdgId<1)&(np.abs((ee_eem.e0+ee_eem.e1).mass-91.2)<10) ee_eemOffZmask = (ee_eem.e0.pdgId*ee_eem.e1.pdgId<1)&(np.abs((ee_eem.e0+ee_eem.e1).mass-91.2)>10) ee_eemZmask = (ak.num(ee_eemZmask[ee_eemZmask])>0) ee_eemOffZmask = (ak.num(ee_eemOffZmask[ee_eemOffZmask])>0) eepair_eem = (ee_eem.e0+ee_eem.e1) trilep_eem = eepair_eem+muon_eem #ak.cartesian({"e0":ee_eem.e0,"e1":ee_eem.e1, "m":muon_eem}) lepSF_eem = GetLeptonSF(ee_eem.e0.pt, ee_eem.e0.eta, 'e', ee_eem.e1.pt, ee_eem.e1.eta, 'e', mu.pt, mu.eta, 'm', year) # mme muon_mme = mu[(nElec==1)&(nMuon==2)&(mu.pt>-1)] elec_mme = e[(nElec==1)&(nMuon==2)&( e.pt>-1)] mm_mme = ak.combinations(muon_mme, 2, fields=["m0", "m1"]) mm_mmeZmask = (mm_mme.m0.pdgId*mm_mme.m1.pdgId<1)&(np.abs((mm_mme.m0+mm_mme.m1).mass-91.2)<10) mm_mmeOffZmask = (mm_mme.m0.pdgId*mm_mme.m1.pdgId<1)&(np.abs((mm_mme.m0+mm_mme.m1).mass-91.2)>10) mm_mmeZmask = (ak.num(mm_mmeZmask[mm_mmeZmask])>0) mm_mmeOffZmask = (ak.num(mm_mmeOffZmask[mm_mmeOffZmask])>0) mmpair_mme = (mm_mme.m0+mm_mme.m1) trilep_mme = mmpair_mme+elec_mme mZ_mme = mmpair_mme.mass mZ_eem = eepair_eem.mass m3l_eem = trilep_eem.mass m3l_mme = trilep_mme.mass lepSF_mme = GetLeptonSF(mm_mme.m0.pt, mm_mme.m0.eta, 'm', mm_mme.m1.pt, mm_mme.m1.eta, 'm', e.pt, e.eta, 'e', year) # eee and mmm eee = e[(nElec==3)&(nMuon==0)&( e.pt>-1)] mmm = mu[(nElec==0)&(nMuon==3)&(mu.pt>-1)] eee_leps = ak.combinations(eee, 3, fields=["e0", "e1", "e2"]) mmm_leps = ak.combinations(mmm, 3, fields=["m0", "m1", "m2"]) ee_pairs = ak.combinations(eee, 2, fields=["e0", "e1"]) mm_pairs = ak.combinations(mmm, 2, fields=["m0", "m1"]) ee_pairs_index = ak.argcombinations(eee, 2, fields=["e0", "e1"]) mm_pairs_index = ak.argcombinations(mmm, 2, fields=["m0", "m1"]) lepSF_eee = GetLeptonSF(eee_leps.e0.pt, eee_leps.e0.eta, 'e', eee_leps.e1.pt, eee_leps.e1.eta, 'e', eee_leps.e2.pt, eee_leps.e2.eta, 'e', year) lepSF_mmm = GetLeptonSF(mmm_leps.m0.pt, mmm_leps.m0.eta, 'm', mmm_leps.m1.pt, mmm_leps.m1.eta, 'm', mmm_leps.m2.pt, mmm_leps.m2.eta, 'm', year) mmSFOS_pairs = mm_pairs[(np.abs(mm_pairs.m0.pdgId) == np.abs(mm_pairs.m1.pdgId)) & (mm_pairs.m0.pdgId != mm_pairs.m1.pdgId)] offZmask_mm = ak.all(np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2)>10., axis=1, keepdims=True) & (ak.num(mmSFOS_pairs)>0) onZmask_mm = ak.any(np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2)<10., axis=1, keepdims=True) eeSFOS_pairs = ee_pairs[(np.abs(ee_pairs.e0.pdgId) == np.abs(ee_pairs.e1.pdgId)) & (ee_pairs.e0.pdgId != ee_pairs.e1.pdgId)] offZmask_ee = ak.all(np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2)>10, axis=1, keepdims=True) & (ak.num(eeSFOS_pairs)>0) onZmask_ee = ak.any(np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2)<10, axis=1, keepdims=True) # Create masks **for event selection** eeeOnZmask = (ak.num(onZmask_ee[onZmask_ee])>0) eeeOffZmask = (ak.num(offZmask_ee[offZmask_ee])>0) mmmOnZmask = (ak.num(onZmask_mm[onZmask_mm])>0) mmmOffZmask = (ak.num(offZmask_mm[offZmask_mm])>0) # Now we need to create masks for the leptons in order to select leptons from the Z boson candidate (in onZ categories) ZeeMask = ak.argmin(np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2),axis=1,keepdims=True) ZmmMask = ak.argmin(np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2),axis=1,keepdims=True) Zee = eeSFOS_pairs[ZeeMask] Zmm = mmSFOS_pairs[ZmmMask] eZ0= Zee.e0[ak.num(eeSFOS_pairs)>0] eZ1= Zee.e1[ak.num(eeSFOS_pairs)>0] eZ = eZ0+eZ1 mZ0= Zmm.m0[ak.num(mmSFOS_pairs)>0] mZ1= Zmm.m1[ak.num(mmSFOS_pairs)>0] mZ = mZ0+mZ1 mZ_eee = eZ.mass mZ_mmm = mZ.mass # And for the W boson ZmmIndices = mm_pairs_index[ZmmMask] ZeeIndices = ee_pairs_index[ZeeMask] eW = eee[~ZeeIndices.e0 | ~ZeeIndices.e1] mW = mmm[~ZmmIndices.m0 | ~ZmmIndices.m1] triElec = eee_leps.e0+eee_leps.e1+eee_leps.e2 triMuon = mmm_leps.m0+mmm_leps.m1+mmm_leps.m2 m3l_eee = triElec.mass m3l_mmm = triMuon.mass # Triggers trig_eeSS = passTrigger(events,'ee',isData,dataset) trig_mmSS = passTrigger(events,'mm',isData,dataset) trig_emSS = passTrigger(events,'em',isData,dataset) trig_eee = passTrigger(events,'eee',isData,dataset) trig_mmm = passTrigger(events,'mmm',isData,dataset) trig_eem = passTrigger(events,'eem',isData,dataset) trig_mme = passTrigger(events,'mme',isData,dataset) # MET filters # Weights genw = np.ones_like(events['MET_pt']) if isData else events['genWeight'] ### We need weights for: normalization, lepSF, triggerSF, pileup, btagSF... weights = {} for r in ['all', 'ee', 'mm', 'em', 'eee', 'mmm', 'eem', 'mme']: weights[r] = coffea.analysis_tools.Weights(len(events)) weights[r].add('norm',genw if isData else (xsec/sow)*genw) weights['ee'].add('lepSF_eeSS', lepSF_eeSS) weights['em'].add('lepSF_emSS', lepSF_emSS) weights['mm'].add('lepSF_mmSS', lepSF_mumuSS) weights['eee'].add('lepSF_eee', lepSF_eee) weights['mmm'].add('lepSF_mmm', lepSF_mmm) weights['mme'].add('lepSF_mme', lepSF_mme) weights['eem'].add('lepSF_eem', lepSF_eem) # Extract the EFT quadratic coefficients and optionally use them to calculate the coefficients on the w**2 quartic function # eft_coeffs is never Jagged so convert immediately to numpy for ease of use. eft_coeffs = ak.to_numpy(events['EFTfitCoefficients']) if hasattr(events, "EFTfitCoefficients") else None eft_w2_coeffs = efth.calc_w2_coeffs(eft_coeffs,self._dtype) if (self._do_errors and eft_coeffs is not None) else None # Selections and cuts selections = PackedSelection() channels2LSS = ['eeSSonZ', 'eeSSoffZ', 'mmSSonZ', 'mmSSoffZ', 'emSS'] selections.add('eeSSonZ', (eeonZmask)&(eeSSmask)&(trig_eeSS)) selections.add('eeSSoffZ', (eeoffZmask)&(eeSSmask)&(trig_eeSS)) selections.add('mmSSonZ', (mmonZmask)&(mmSSmask)&(trig_mmSS)) selections.add('mmSSoffZ', (mmoffZmask)&(mmSSmask)&(trig_mmSS)) selections.add('emSS', (emSSmask)&(trig_emSS)) channels3L = ['eemSSonZ', 'eemSSoffZ', 'mmeSSonZ', 'mmeSSoffZ'] selections.add('eemSSonZ', (ee_eemZmask)&(trig_eem)) selections.add('eemSSoffZ', (ee_eemOffZmask)&(trig_eem)) selections.add('mmeSSonZ', (mm_mmeZmask)&(trig_mme)) selections.add('mmeSSoffZ', (mm_mmeOffZmask)&(trig_mme)) channels3L += ['eeeSSonZ', 'eeeSSoffZ', 'mmmSSonZ', 'mmmSSoffZ'] selections.add('eeeSSonZ', (eeeOnZmask)&(trig_eee)) selections.add('eeeSSoffZ', (eeeOffZmask)&(trig_eee)) selections.add('mmmSSonZ', (mmmOnZmask)&(trig_mmm)) selections.add('mmmSSoffZ', (mmmOffZmask)&(trig_mmm)) levels = ['base', '2jets', '4jets', '4j1b', '4j2b'] selections.add('base', (nElec+nMuon>=2)) selections.add('2jets',(njets>=2)) selections.add('4jets',(njets>=4)) selections.add('4j1b',(njets>=4)&(nbtags>=1)) selections.add('4j2b',(njets>=4)&(nbtags>=2)) # Variables invMass_eeSSonZ = ( eeSSonZ.e0+ eeSSonZ.e1).mass invMass_eeSSoffZ = (eeSSoffZ.e0+eeSSoffZ.e1).mass invMass_mmSSonZ = ( mmSSonZ.m0+ mmSSonZ.m1).mass invMass_mmSSoffZ = (mmSSoffZ.m0+mmSSoffZ.m1).mass invMass_emSS = (emSS.e+emSS.m).mass varnames = {} varnames['met'] = met.pt varnames['ht'] = ht varnames['njets'] = njets varnames['nbtags'] = nbtags varnames['invmass'] = { 'eeSSonZ' : invMass_eeSSonZ, 'eeSSoffZ' : invMass_eeSSoffZ, 'mmSSonZ' : invMass_mmSSonZ, 'mmSSoffZ' : invMass_mmSSoffZ, 'emSS' : invMass_emSS, 'eemSSonZ' : mZ_eem, 'eemSSoffZ' : mZ_eem, 'mmeSSonZ' : mZ_mme, 'mmeSSoffZ' : mZ_mme, 'eeeSSonZ' : mZ_eee, 'eeeSSoffZ' : mZ_eee, 'mmmSSonZ' : mZ_mmm, 'mmmSSoffZ' : mZ_mmm, } varnames['m3l'] = { 'eemSSonZ' : m3l_eem, 'eemSSoffZ' : m3l_eem, 'mmeSSonZ' : m3l_mme, 'mmeSSoffZ' : m3l_mme, 'eeeSSonZ' : m3l_eee, 'eeeSSoffZ' : m3l_eee, 'mmmSSonZ' : m3l_mmm, 'mmmSSoffZ' : m3l_mmm, } varnames['e0pt' ] = e0.pt varnames['e0eta'] = e0.eta varnames['m0pt' ] = m0.pt varnames['m0eta'] = m0.eta varnames['e1pt' ] = e1 varnames['e1eta'] = e1 varnames['e2pt' ] = e2 varnames['e2eta'] = e2 varnames['m1pt' ] = m1 varnames['m1eta'] = m1 varnames['m2pt' ] = m2 varnames['m2eta'] = m2 varnames['j0pt' ] = j0.pt varnames['j0eta'] = j0.eta varnames['j1pt'] = j1 varnames['j1eta'] = j1 varnames['j2pt'] = j2 varnames['j2eta'] = j2 varnames['j3pt'] = j3 varnames['j3eta'] = j3 varnames['counts'] = np.ones_like(events.GenMET.pt) # fill Histos hout = self.accumulator.identity() normweights = weights['all'].weight().flatten() # Why does it not complain about .flatten() here? hout['SumOfEFTweights'].fill(sample=dataset, SumOfEFTweights=varnames['counts'], weight=normweights, eft_coeff=eft_coeffs, eft_err_coeff=eft_w2_coeffs) for var, v in varnames.items(): for ch in channels2LSS+channels3L: for lev in levels: weight = weights[ ch[:3] if (ch.startswith('eee') or ch.startswith('mmm') or ch.startswith('eem') or ch.startswith('mme')) else ch[:2]].weight() cuts = [ch] + [lev] cut = selections.all(*cuts) weights_flat = weight[cut].flatten() # Why does it not complain about .flatten() here? weights_ones = np.ones_like(weights_flat, dtype=np.int) eft_coeffs_cut = eft_coeffs[cut] if eft_coeffs is not None else None eft_w2_coeffs_cut = eft_w2_coeffs[cut] if eft_w2_coeffs is not None else None if var == 'invmass': if ch in ['eeeSSoffZ', 'mmmSSoffZ']: continue elif ch in ['eeeSSonZ' , 'mmmSSonZ' ]: continue #values = v[ch] else : values = ak.flatten(v[ch][cut]) hout['invmass'].fill(eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, sample=dataset, channel=ch, cut=lev, invmass=values, weight=weights_flat) elif var == 'm3l': if ch in ['eeSSonZ','eeSSoffZ', 'mmSSonZ', 'mmSSoffZ','emSS', 'eeeSSoffZ', 'mmmSSoffZ', 'eeeSSonZ' , 'mmmSSonZ']: continue values = ak.flatten(v[ch][cut]) hout['m3l'].fill(sample=dataset, channel=ch, cut=lev, m3l=values, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) else: values = v[cut] if var == 'ht' : hout[var].fill(ht=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'met' : hout[var].fill(met=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'njets' : hout[var].fill(njets=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'nbtags': hout[var].fill(nbtags=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'counts': hout[var].fill(counts=values, sample=dataset, channel=ch, cut=lev, weight=weights_ones) elif var == 'j0eta' : if lev == 'base': continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill(j0eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'e0pt' : if ch in ['mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ', 'mmmSSonZ']: continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill(e0pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'm0pt' : if ch in ['eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ']: continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill(m0pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'e0eta' : if ch in ['mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ', 'mmmSSonZ']: continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill(e0eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'm0eta': if ch in ['eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ']: continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill(m0eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'j0pt' : if lev == 'base': continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill(j0pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'j1pt': if lev == "base": continue values = values.pt[:,1] #values = ak.flatten(values) hout[var].fill(j1pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var =='j1eta': if lev == 'base': continue values = values.eta[:,1] hout[var].fill(j1eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'j2pt': if lev in ['base', "2jets"]: continue values = values.pt[:,2] hout[var].fill(j2pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'j2eta': if lev in ['base', "2jets"]: continue values = values.eta[:,2] hout[var].fill(j2eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'j3pt': if lev in ['base', "2jets"]: continue values = values.pt[:,3] hout[var].fill(j3pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'j3eta': if lev in ['base', "2jets"]: continue values = values.eta[:,3] hout[var].fill(j3eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'e1pt': if ch in ['mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ', 'mmmSSonZ', 'mmeSSonZ', 'mmeSSoffZ', 'emSS']: continue values = values.pt[:,1] hout[var].fill(e1pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'e1eta': if ch in ['mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ', 'mmmSSonZ', 'mmeSSonZ', 'mmeSSoffZ', 'emSS']: continue values = values.eta[:,1] hout[var].fill(e1eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'e2pt': if ch in ['eeeSSonZ', 'eeeSSoffZ']: values = values.pt[:,2] hout[var].fill(e2pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'e2eta': if ch in ['eeeSSonZ', 'eeeSSoffZ']: values = values.eta[:,2] hout[var].fill(e2eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'm1pt': if ch in ['eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ', 'eemSSonZ', 'eemSSoffZ', 'emSS']: continue values = values.pt[:,1] hout[var].fill(m1pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'm1eta': if ch in ['eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ', 'eemSSonZ', 'eemSSoffZ', 'emSS']: continue values = values.eta[:,1] hout[var].fill(m1eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'm2pt': if ch in ['mmmSSonZ', 'mmmSSoffZ']: values = values.pt[:,2] hout[var].fill(m2pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == 'm2eta': if ch in ['mmmSSonZ', 'mmmSSoffZ']: values = values.eta[:,2] hout[var].fill(m2eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) return hout
def process(self, events): # Dataset parameters dataset = events.metadata['dataset'] year = self._samples[dataset]['year'] xsec = self._samples[dataset]['xsec'] sow = self._samples[dataset]['nSumOfWeights'] isData = self._samples[dataset]['isData'] datasets = [ 'SingleMuon', 'SingleElectron', 'EGamma', 'MuonEG', 'DoubleMuon', 'DoubleElectron' ] for d in datasets: if d in dataset: dataset = dataset.split('_')[0] # Initialize objects met = events.MET e = events.Electron mu = events.Muon tau = events.Tau j = events.Jet # Muon selection #mu['isGood'] = isMuonMVA(mu.pt, mu.eta, mu.dxy, mu.dz, mu.miniPFRelIso_all, mu.sip3d, mu.mvaTTH, mu.mediumPromptId, mu.tightCharge, minpt=10) mu['isPres'] = isPresMuon(mu.dxy, mu.dz, mu.sip3d, mu.looseId) mu['isTight'] = isTightMuon(mu.pt, mu.eta, mu.dxy, mu.dz, mu.pfRelIso03_all, mu.sip3d, mu.mvaTTH, mu.mediumPromptId, mu.tightCharge, mu.looseId, minpt=10) mu['isGood'] = mu['isPres'] & mu['isTight'] leading_mu = mu[ak.argmax(mu.pt, axis=-1, keepdims=True)] leading_mu = leading_mu[leading_mu.isGood] mu = mu[mu.isGood] mu_pres = mu[mu.isPres] # Electron selection #e['isGood'] = isElecMVA(e.pt, e.eta, e.dxy, e.dz, e.miniPFRelIso_all, e.sip3d, e.mvaTTH, e.mvaFall17V2Iso, e.lostHits, e.convVeto, e.tightCharge, minpt=10) e['isPres'] = isPresElec(e.pt, e.eta, e.dxy, e.dz, e.miniPFRelIso_all, e.sip3d, e.lostHits, minpt=15) e['isTight'] = isTightElec(e.pt, e.eta, e.dxy, e.dz, e.miniPFRelIso_all, e.sip3d, e.mvaTTH, e.mvaFall17V2Iso, e.lostHits, e.convVeto, e.tightCharge, e.sieie, e.hoe, e.eInvMinusPInv, minpt=15) e['isClean'] = isClean(e, mu, drmin=0.05) e['isGood'] = e['isPres'] & e['isTight'] & e['isClean'] leading_e = e[ak.argmax(e.pt, axis=-1, keepdims=True)] leading_e = leading_e[leading_e.isGood] e = e[e.isGood] e_pres = e[e.isPres & e.isClean] # Tau selection tau['isPres'] = isPresTau(tau.pt, tau.eta, tau.dxy, tau.dz, tau.leadTkPtOverTauPt, tau.idAntiMu, tau.idAntiEle, tau.rawIso, tau.idDecayModeNewDMs, minpt=20) tau['isClean'] = isClean(tau, e_pres, drmin=0.4) & isClean( tau, mu_pres, drmin=0.4) tau['isGood'] = tau['isPres'] # & tau['isClean'], for the moment tau = tau[tau.isGood] nElec = ak.num(e) nMuon = ak.num(mu) nTau = ak.num(tau) twoLeps = (nElec + nMuon) == 2 threeLeps = (nElec + nMuon) == 3 twoElec = (nElec == 2) twoMuon = (nMuon == 2) e0 = e[ak.argmax(e.pt, axis=-1, keepdims=True)] m0 = mu[ak.argmax(mu.pt, axis=-1, keepdims=True)] # Jet selection jetptname = 'pt_nom' if hasattr(j, 'pt_nom') else 'pt' j['isGood'] = isTightJet(getattr(j, jetptname), j.eta, j.jetId, j.neHEF, j.neEmEF, j.chHEF, j.chEmEF, j.nConstituents) #j['isgood'] = isGoodJet(j.pt, j.eta, j.jetId) #j['isclean'] = isClean(j, e, mu) j['isClean'] = isClean(j, e, drmin=0.4) & isClean( j, mu, drmin=0.4) # & isClean(j, tau, drmin=0.4) goodJets = j[(j.isClean) & (j.isGood)] njets = ak.num(goodJets) ht = ak.sum(goodJets.pt, axis=-1) j0 = goodJets[ak.argmax(goodJets.pt, axis=-1, keepdims=True)] #nbtags = ak.num(goodJets[goodJets.btagDeepFlavB > 0.2770]) nbtags = ak.num(goodJets[goodJets.btagDeepB > 0.4941]) ################################################################## ### 2 same-sign leptons ################################################################## # emu singe = e[(nElec == 1) & (nMuon == 1) & (e.pt > -1)] singm = mu[(nElec == 1) & (nMuon == 1) & (mu.pt > -1)] em = ak.cartesian({"e": singe, "m": singm}) emSSmask = (em.e.charge * em.m.charge > 0) emSS = em[emSSmask] nemSS = len(ak.flatten(emSS)) # ee and mumu # pt>-1 to preserve jagged dimensions ee = e[(nElec == 2) & (nMuon == 0) & (e.pt > -1)] mm = mu[(nElec == 0) & (nMuon == 2) & (mu.pt > -1)] eepairs = ak.combinations(ee, 2, fields=["e0", "e1"]) eeSSmask = (eepairs.e0.charge * eepairs.e1.charge > 0) eeonZmask = (np.abs((eepairs.e0 + eepairs.e1).mass - 91.2) < 10) eeoffZmask = (eeonZmask == 0) mmpairs = ak.combinations(mm, 2, fields=["m0", "m1"]) mmSSmask = (mmpairs.m0.charge * mmpairs.m1.charge > 0) mmonZmask = (np.abs((mmpairs.m0 + mmpairs.m1).mass - 91.2) < 10) mmoffZmask = (mmonZmask == 0) eeSSonZ = eepairs[eeSSmask & eeonZmask] eeSSoffZ = eepairs[eeSSmask & eeoffZmask] mmSSonZ = mmpairs[mmSSmask & mmonZmask] mmSSoffZ = mmpairs[mmSSmask & mmoffZmask] neeSS = len(ak.flatten(eeSSonZ)) + len(ak.flatten(eeSSoffZ)) nmmSS = len(ak.flatten(mmSSonZ)) + len(ak.flatten(mmSSoffZ)) print('Same-sign events [ee, emu, mumu] = [%i, %i, %i]' % (neeSS, nemSS, nmmSS)) # Cuts eeSSmask = (ak.num(eeSSmask[eeSSmask]) > 0) mmSSmask = (ak.num(mmSSmask[mmSSmask]) > 0) eeonZmask = (ak.num(eeonZmask[eeonZmask]) > 0) eeoffZmask = (ak.num(eeoffZmask[eeoffZmask]) > 0) mmonZmask = (ak.num(mmonZmask[mmonZmask]) > 0) mmoffZmask = (ak.num(mmoffZmask[mmoffZmask]) > 0) emSSmask = (ak.num(emSSmask[emSSmask]) > 0) ################################################################## ### 3 leptons ################################################################## # eem muon_eem = mu[(nElec == 2) & (nMuon == 1) & (mu.pt > -1)] elec_eem = e[(nElec == 2) & (nMuon == 1) & (e.pt > -1)] ee_eem = ak.combinations(elec_eem, 2, fields=["e0", "e1"]) ee_eemZmask = (ee_eem.e0.charge * ee_eem.e1.charge < 1) & (np.abs( (ee_eem.e0 + ee_eem.e1).mass - 91.2) < 10) ee_eemOffZmask = (ee_eem.e0.charge * ee_eem.e1.charge < 1) & (np.abs( (ee_eem.e0 + ee_eem.e1).mass - 91.2) > 10) ee_eemZmask = (ak.num(ee_eemZmask[ee_eemZmask]) > 0) ee_eemOffZmask = (ak.num(ee_eemOffZmask[ee_eemOffZmask]) > 0) eepair_eem = (ee_eem.e0 + ee_eem.e1) trilep_eem = eepair_eem + muon_eem #ak.cartesian({"e0":ee_eem.e0,"e1":ee_eem.e1, "m":muon_eem}) # mme muon_mme = mu[(nElec == 1) & (nMuon == 2) & (mu.pt > -1)] elec_mme = e[(nElec == 1) & (nMuon == 2) & (e.pt > -1)] mm_mme = ak.combinations(muon_mme, 2, fields=["m0", "m1"]) mm_mmeZmask = (mm_mme.m0.charge * mm_mme.m1.charge < 1) & (np.abs( (mm_mme.m0 + mm_mme.m1).mass - 91.2) < 10) mm_mmeOffZmask = (mm_mme.m0.charge * mm_mme.m1.charge < 1) & (np.abs( (mm_mme.m0 + mm_mme.m1).mass - 91.2) > 10) mm_mmeZmask = (ak.num(mm_mmeZmask[mm_mmeZmask]) > 0) mm_mmeOffZmask = (ak.num(mm_mmeOffZmask[mm_mmeOffZmask]) > 0) mmpair_mme = (mm_mme.m0 + mm_mme.m1) trilep_mme = mmpair_mme + elec_mme mZ_mme = mmpair_mme.mass mZ_eem = eepair_eem.mass m3l_eem = trilep_eem.mass m3l_mme = trilep_mme.mass # eee and mmm eee = e[(nElec == 3) & (nMuon == 0) & (e.pt > -1)] mmm = mu[(nElec == 0) & (nMuon == 3) & (mu.pt > -1)] eee_leps = ak.combinations(eee, 3, fields=["e0", "e1", "e2"]) mmm_leps = ak.combinations(mmm, 3, fields=["m0", "m1", "m2"]) ee_pairs = ak.combinations(eee, 2, fields=["e0", "e1"]) mm_pairs = ak.combinations(mmm, 2, fields=["m0", "m1"]) ee_pairs_index = ak.argcombinations(eee, 2, fields=["e0", "e1"]) mm_pairs_index = ak.argcombinations(mmm, 2, fields=["m0", "m1"]) mmSFOS_pairs = mm_pairs[ (np.abs(mm_pairs.m0.pdgId) == np.abs(mm_pairs.m1.pdgId)) & (mm_pairs.m0.charge != mm_pairs.m1.charge)] offZmask_mm = ak.all( np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2) > 10., axis=1, keepdims=True) & (ak.num(mmSFOS_pairs) > 0) onZmask_mm = ak.any( np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2) < 10., axis=1, keepdims=True) eeSFOS_pairs = ee_pairs[ (np.abs(ee_pairs.e0.pdgId) == np.abs(ee_pairs.e1.pdgId)) & (ee_pairs.e0.charge != ee_pairs.e1.charge)] offZmask_ee = ak.all( np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2) > 10, axis=1, keepdims=True) & (ak.num(eeSFOS_pairs) > 0) onZmask_ee = ak.any( np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2) < 10, axis=1, keepdims=True) # Create masks **for event selection** eeeOnZmask = (ak.num(onZmask_ee[onZmask_ee]) > 0) eeeOffZmask = (ak.num(offZmask_ee[offZmask_ee]) > 0) mmmOnZmask = (ak.num(onZmask_mm[onZmask_mm]) > 0) mmmOffZmask = (ak.num(offZmask_mm[offZmask_mm]) > 0) # Now we need to create masks for the leptons in order to select leptons from the Z boson candidate (in onZ categories) ZeeMask = ak.argmin(np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2), axis=1, keepdims=True) ZmmMask = ak.argmin(np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2), axis=1, keepdims=True) Zee = eeSFOS_pairs[ZeeMask] Zmm = mmSFOS_pairs[ZmmMask] eZ0 = Zee.e0[ak.num(eeSFOS_pairs) > 0] eZ1 = Zee.e1[ak.num(eeSFOS_pairs) > 0] eZ = eZ0 + eZ1 mZ0 = Zmm.m0[ak.num(mmSFOS_pairs) > 0] mZ1 = Zmm.m1[ak.num(mmSFOS_pairs) > 0] mZ = mZ0 + mZ1 mZ_eee = eZ.mass mZ_mmm = mZ.mass # And for the W boson ZmmIndices = mm_pairs_index[ZmmMask] ZeeIndices = ee_pairs_index[ZeeMask] eW = eee[~ZeeIndices.e0 | ~ZeeIndices.e1] mW = mmm[~ZmmIndices.m0 | ~ZmmIndices.m1] triElec = eee_leps.e0 + eee_leps.e1 + eee_leps.e2 triMuon = mmm_leps.m0 + mmm_leps.m1 + mmm_leps.m2 m3l_eee = triElec.mass m3l_mmm = triMuon.mass # Triggers trig_eeSS = passTrigger(events, 'ee', isData, dataset) trig_mmSS = passTrigger(events, 'mm', isData, dataset) trig_emSS = passTrigger(events, 'em', isData, dataset) trig_eee = passTrigger(events, 'eee', isData, dataset) trig_mmm = passTrigger(events, 'mmm', isData, dataset) trig_eem = passTrigger(events, 'eem', isData, dataset) trig_mme = passTrigger(events, 'mme', isData, dataset) # MET filters # Weights genw = np.ones_like( events['MET_pt']) if isData else events['genWeight'] weights = coffea.analysis_tools.Weights(len(events)) weights.add('norm', genw if isData else (xsec / sow) * genw) eftweights = events['EFTfitCoefficients'] if hasattr( events, "EFTfitCoefficients") else [] # Selections and cuts selections = PackedSelection() channels2LSS = ['eeSSonZ', 'eeSSoffZ', 'mmSSonZ', 'mmSSoffZ', 'emSS'] selections.add('eeSSonZ', (eeonZmask) & (eeSSmask) & (trig_eeSS)) selections.add('eeSSoffZ', (eeoffZmask) & (eeSSmask) & (trig_eeSS)) selections.add('mmSSonZ', (mmonZmask) & (mmSSmask) & (trig_mmSS)) selections.add('mmSSoffZ', (mmoffZmask) & (mmSSmask) & (trig_mmSS)) selections.add('emSS', (emSSmask) & (trig_emSS)) channels3L = ['eemSSonZ', 'eemSSoffZ', 'mmeSSonZ', 'mmeSSoffZ'] selections.add('eemSSonZ', (ee_eemZmask) & (trig_eem)) selections.add('eemSSoffZ', (ee_eemOffZmask) & (trig_eem)) selections.add('mmeSSonZ', (mm_mmeZmask) & (trig_mme)) selections.add('mmeSSoffZ', (mm_mmeOffZmask) & (trig_mme)) channels3L += ['eeeSSonZ', 'eeeSSoffZ', 'mmmSSonZ', 'mmmSSoffZ'] selections.add('eeeSSonZ', (eeeOnZmask) & (trig_eee)) selections.add('eeeSSoffZ', (eeeOffZmask) & (trig_eee)) selections.add('mmmSSonZ', (mmmOnZmask) & (trig_mmm)) selections.add('mmmSSoffZ', (mmmOffZmask) & (trig_mmm)) levels = ['base', '2jets', '4jets', '4j1b', '4j2b'] selections.add('base', (nElec + nMuon >= 2)) selections.add('2jets', (njets >= 2)) selections.add('4jets', (njets >= 4)) selections.add('4j1b', (njets >= 4) & (nbtags >= 1)) selections.add('4j2b', (njets >= 4) & (nbtags >= 2)) # Variables invMass_eeSSonZ = (eeSSonZ.e0 + eeSSonZ.e1).mass invMass_eeSSoffZ = (eeSSoffZ.e0 + eeSSoffZ.e1).mass invMass_mmSSonZ = (mmSSonZ.m0 + mmSSonZ.m1).mass invMass_mmSSoffZ = (mmSSoffZ.m0 + mmSSoffZ.m1).mass invMass_emSS = (emSS.e + emSS.m).mass varnames = {} varnames['met'] = met.pt varnames['ht'] = ht varnames['njets'] = njets varnames['nbtags'] = nbtags varnames['invmass'] = { 'eeSSonZ': invMass_eeSSonZ, 'eeSSoffZ': invMass_eeSSoffZ, 'mmSSonZ': invMass_mmSSonZ, 'mmSSoffZ': invMass_mmSSoffZ, 'emSS': invMass_emSS, 'eemSSonZ': mZ_eem, 'eemSSoffZ': mZ_eem, 'mmeSSonZ': mZ_mme, 'mmeSSoffZ': mZ_mme, 'eeeSSonZ': mZ_eee, 'eeeSSoffZ': mZ_eee, 'mmmSSonZ': mZ_mmm, 'mmmSSoffZ': mZ_mmm, } varnames['m3l'] = { 'eemSSonZ': m3l_eem, 'eemSSoffZ': m3l_eem, 'mmeSSonZ': m3l_mme, 'mmeSSoffZ': m3l_mme, 'eeeSSonZ': m3l_eee, 'eeeSSoffZ': m3l_eee, 'mmmSSonZ': m3l_mmm, 'mmmSSoffZ': m3l_mmm, } varnames['e0pt'] = e0.pt varnames['e0eta'] = e0.eta varnames['m0pt'] = m0.pt varnames['m0eta'] = m0.eta varnames['j0pt'] = j0.pt varnames['j0eta'] = j0.eta varnames['counts'] = np.ones_like(events.MET.pt) # fill Histos hout = self.accumulator.identity() allweights = weights.weight().flatten( ) # Why does it not complain about .flatten() here? hout['SumOfEFTweights'].fill(eftweights, sample=dataset, SumOfEFTweights=varnames['counts'], weight=allweights) for var, v in varnames.items(): for ch in channels2LSS + channels3L: for lev in levels: weight = weights.weight() cuts = [ch] + [lev] cut = selections.all(*cuts) weights_flat = weight[cut].flatten( ) # Why does it not complain about .flatten() here? weights_ones = np.ones_like(weights_flat, dtype=np.int) eftweightsvalues = eftweights[cut] if len( eftweights) > 0 else [] if var == 'invmass': if ch in ['eeeSSoffZ', 'mmmSSoffZ']: continue elif ch in ['eeeSSonZ', 'mmmSSonZ']: continue #values = v[ch] else: values = ak.flatten(v[ch][cut]) hout['invmass'].fill(sample=dataset, channel=ch, cut=lev, invmass=values, weight=weights_flat) elif var == 'm3l': if ch in [ 'eeSSonZ', 'eeSSoffZ', 'mmSSonZ', 'mmSSoffZ', 'emSS', 'eeeSSoffZ', 'mmmSSoffZ', 'eeeSSonZ', 'mmmSSonZ' ]: continue values = ak.flatten(v[ch][cut]) hout['m3l'].fill(eftweightsvalues, sample=dataset, channel=ch, cut=lev, m3l=values, weight=weights_flat) else: values = v[cut] if var == 'ht': hout[var].fill(eftweightsvalues, ht=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat) elif var == 'met': hout[var].fill(eftweightsvalues, met=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat) elif var == 'njets': hout[var].fill(eftweightsvalues, njets=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat) elif var == 'nbtags': hout[var].fill(eftweightsvalues, nbtags=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat) elif var == 'counts': hout[var].fill(counts=values, sample=dataset, channel=ch, cut=lev, weight=weights_ones) elif var == 'j0eta': if lev == 'base': continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill(eftweightsvalues, j0eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat) elif var == 'e0pt': if ch in [ 'mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ', 'mmmSSonZ' ]: continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill( eftweightsvalues, e0pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat ) # Crashing here, not sure why. Related to values? elif var == 'm0pt': if ch in [ 'eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ' ]: continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill(eftweightsvalues, m0pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat) elif var == 'e0eta': if ch in [ 'mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ', 'mmmSSonZ' ]: continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill(eftweightsvalues, e0eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat) elif var == 'm0eta': if ch in [ 'eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ' ]: continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill(eftweightsvalues, m0eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat) elif var == 'j0pt': if lev == 'base': continue values = ak.flatten(values) #values=np.asarray(values) hout[var].fill(eftweightsvalues, j0pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat) return hout
def process(self, events): # Dataset parameters dataset = events.metadata['dataset'] year = self._samples[dataset]['year'] xsec = self._samples[dataset]['xsec'] sow = self._samples[dataset]['nSumOfWeights'] isData = self._samples[dataset]['isData'] datasets = [ 'SingleMuon', 'SingleElectron', 'EGamma', 'MuonEG', 'DoubleMuon', 'DoubleElectron' ] for d in datasets: if d in dataset: dataset = dataset.split('_')[0] # Extract the EFT quadratic coefficients and optionally use them to calculate the coefficients on the w**2 quartic function # eft_coeffs is never Jagged so convert immediately to numpy for ease of use. eft_coeffs = ak.to_numpy(events['EFTfitCoefficients']) if hasattr( events, "EFTfitCoefficients") else None if eft_coeffs is not None: # Check to see if the ordering of WCs for this sample matches what want if self._samples[dataset]['WCnames'] != self._wc_names_lst: eft_coeffs = efth.remap_coeffs( self._samples[dataset]['WCnames'], self._wc_names_lst, eft_coeffs) eft_w2_coeffs = efth.calc_w2_coeffs(eft_coeffs, self._dtype) if ( self._do_errors and eft_coeffs is not None) else None # Initialize objects (GEN objects) e = events.GenPart[abs(events.GenPart.pdgId) == 11] m = events.GenPart[abs(events.GenPart.pdgId) == 13] tau = events.GenPart[abs(events.GenPart.pdgId) == 15] j = events.GenJet run = events.run luminosityBlock = events.luminosityBlock event = events.event print("\n\nInfo about events:") print("\trun:", run) print("\tluminosityBlock:", luminosityBlock) print("\tevent:", event) print("\nLeptons before selection:") print("\te pt", e.pt) print("\te eta", e.eta) print("\tm pt", m.pt) print("\tm eta", m.eta) ######## Lep selection ######## e_selec = ((e.pt > 15) & (abs(e.eta) < 2.5)) m_selec = ((m.pt > 15) & (abs(m.eta) < 2.5)) e = e[e_selec] m = m[m_selec] # Put the e and mu togheter l = ak.concatenate([e, m], axis=1) n_e = ak.num(e) n_m = ak.num(m) n_l = ak.num(l) at_least_two_leps = (n_l >= 2) e0 = e[ak.argmax(e.pt, axis=-1, keepdims=True)] m0 = m[ak.argmax(m.pt, axis=-1, keepdims=True)] l0 = l[ak.argmax(l.pt, axis=-1, keepdims=True)] print("\nLeptons after selection:") print("\te pt", e.pt) print("\tm pt", m.pt) print("\tl pt:", l.pt) print("\tn e", n_e) print("\tn m", n_m) print("\tn l", n_l) print("\nMask for at least two lep:", at_least_two_leps) print("\nLeading lepton info:") print("\te0", e0.pt) print("\tm0", m0.pt) print("\tl0", l0.pt) ######## Jet selection ######## print("\nJet info:") print("\tjpt before selection", j.pt) j_selec = ((j.pt > 30) & (abs(j.eta) < 2.5)) print("\tjselect", j_selec) j = j[j_selec] print("\tjpt", j.pt) j['isClean'] = isClean(j, e, drmin=0.4) & isClean(j, m, drmin=0.4) j_isclean = isClean(j, e, drmin=0.4) & isClean(j, m, drmin=0.4) print("\tj is clean", j_isclean) j = j[j_isclean] print("\tclean jets pt", j.pt) n_j = ak.num(j) print("\tn_j", n_j) j0 = j[ak.argmax(j.pt, axis=-1, keepdims=True)] print("\tj0pt", j0.pt) at_least_two_jets = (n_j >= 2) print("\tat_least_two_jets", at_least_two_jets) ######## Selections and cuts ######## event_selec = (at_least_two_leps & at_least_two_jets) print("\nEvent selection:", event_selec, "\n") selections = PackedSelection() selections.add('2l2j', event_selec) varnames = {} varnames['counts'] = np.ones_like(events.MET.pt) varnames['njets'] = n_j varnames['j0pt'] = j0.pt varnames['j0eta'] = j0.eta varnames['l0pt'] = l0.pt ######## Fill histos ######## print("\nFilling hists now...\n") hout = self.accumulator.identity() for var, v in varnames.items(): cut = selections.all("2l2j") values = v[cut] eft_coeffs_cut = eft_coeffs[cut] if eft_coeffs is not None else None eft_w2_coeffs_cut = eft_w2_coeffs[ cut] if eft_w2_coeffs is not None else None if var == "counts": hout[var].fill(counts=values, sample=dataset, channel="2l", cut="2l") elif var == "njets": hout[var].fill(njets=values, sample=dataset, channel="2l", cut="2l", eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == "j0pt": hout[var].fill(j0pt=values, sample=dataset, channel="2l", cut="2l", eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == "j0eta": hout[var].fill(j0eta=values, sample=dataset, channel="2l", cut="2l", eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) elif var == "l0pt": hout[var].fill(l0pt=values, sample=dataset, channel="2l", cut="2l", eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut) return hout
def process(self, events): output = self.accumulator.identity() # we can use a very loose preselection to filter the events. nothing is done with this presel, though presel = ak.num(events.Jet) > 0 ev = events[presel] dataset = ev.metadata['dataset'] # load the config - probably not needed anymore cfg = loadConfig() #output['totalEvents']['all'] += len(events) #output['skimmedEvents']['all'] += len(ev) if self.year == 2018: triggers = ev.HLT.Ele23_Ele12_CaloIdL_TrackIdL_IsoVL elif self.year == 2017: triggers = ev.HLT.Ele23_Ele12_CaloIdL_TrackIdL_IsoVL elif self.year == 2016: triggers = ev.HLT.Ele23_Ele12_CaloIdL_TrackIdL_IsoVL_DZ if self.year == 2018: lumimask = LumiMask( 'processors/Cert_314472-325175_13TeV_Legacy2018_Collisions18_JSON.txt' ) ## Electrons electron = Collections(ev, "Electron", "tight").get() electron = electron[(electron.pt > 25) & (np.abs(electron.eta) < 2.4)] loose_electron = Collections(ev, "Electron", "veto").get() loose_electron = loose_electron[(loose_electron.pt > 25) & (np.abs(loose_electron.eta) < 2.4)] SSelectron = (ak.sum(electron.charge, axis=1) != 0) & (ak.num(electron) == 2) OSelectron = (ak.sum(electron.charge, axis=1) == 0) & (ak.num(electron) == 2) dielectron = choose(electron, 2) dielectron_mass = (dielectron['0'] + dielectron['1']).mass dielectron_pt = (dielectron['0'] + dielectron['1']).pt leading_electron_idx = ak.singletons(ak.argmax(electron.pt, axis=1)) leading_electron = electron[(leading_electron_idx)] leading_electron = leading_electron[(leading_electron.pt > 30)] trailing_electron_idx = ak.singletons(ak.argmin(electron.pt, axis=1)) trailing_electron = electron[trailing_electron_idx] ##Muons loose_muon = Collections(ev, "Muon", "veto").get() loose_muon = loose_muon[(loose_muon.pt > 20) & (np.abs(loose_muon.eta) < 2.4)] #jets jet = getJets(ev, minPt=40, maxEta=2.4, pt_var='pt', UL=False) jet = jet[ak.argsort( jet.pt, ascending=False )] # need to sort wrt smeared and recorrected jet pt jet = jet[~match(jet, loose_muon, deltaRCut=0.4)] # remove jets that overlap with muons jet = jet[~match( jet, electron, deltaRCut=0.4)] # remove jets that overlap with electrons ## MET -> can switch to puppi MET met_pt = ev.MET.pt met_phi = ev.MET.phi #selections filters = getFilters(ev, year=self.year, dataset=dataset) mask = lumimask(ev.run, ev.luminosityBlock) ss = (SSelectron) os = (OSelectron) mass = (ak.min(np.abs(dielectron_mass - 91.2), axis=1) < 15) lead_electron = (ak.min(leading_electron.pt, axis=1) > 30) jet1 = (ak.num(jet) >= 1) jet2 = (ak.num(jet) >= 2) num_loose = ((ak.num(loose_electron) == 2) & (ak.num(loose_muon) == 0)) selection = PackedSelection() selection.add('filter', (filters)) selection.add('mask', (mask)) selection.add('ss', ss) selection.add('os', os) selection.add('mass', mass) selection.add('leading', lead_electron) selection.add('triggers', triggers) selection.add('one jet', jet1) selection.add('two jets', jet2) selection.add('num_loose', num_loose) bl_reqs = ['filter'] + ['mass'] + ['mask'] + ['triggers'] + [ 'leading' ] + ['num_loose'] #bl_reqs = ['filter'] + ['mass'] + ['triggers'] + ['leading'] + ['num_loose'] bl_reqs_d = {sel: True for sel in bl_reqs} baseline = selection.require(**bl_reqs_d) s_reqs = bl_reqs + ['ss'] s_reqs_d = {sel: True for sel in s_reqs} ss_sel = selection.require(**s_reqs_d) o_reqs = bl_reqs + ['os'] o_reqs_d = {sel: True for sel in o_reqs} os_sel = selection.require(**o_reqs_d) j1s_reqs = s_reqs + ['one jet'] j1s_reqs_d = {sel: True for sel in j1s_reqs} j1ss_sel = selection.require(**j1s_reqs_d) j1o_reqs = o_reqs + ['one jet'] j1o_reqs_d = {sel: True for sel in j1o_reqs} j1os_sel = selection.require(**j1o_reqs_d) j2s_reqs = s_reqs + ['two jets'] j2s_reqs_d = {sel: True for sel in j2s_reqs} j2ss_sel = selection.require(**j2s_reqs_d) j2o_reqs = o_reqs + ['two jets'] j2o_reqs_d = {sel: True for sel in j2o_reqs} j2os_sel = selection.require(**j2o_reqs_d) output["N_jet"].fill( dataset=dataset, multiplicity=ak.num(jet)[os_sel], ) return output
def set_fatjets(events, fatjets, genparts, options, debug): n_fatjets = awkward.num(fatjets) events["n_fatjets"] = n_fatjets n_save = 3 if options["signal"] and genparts is not None: #print ("\n\nSIGNAL\n\n") mask_genB = abs(genparts.pdgId) == 5 mIdx = genparts.genPartIdxMother mask_fromH = genparts.pdgId[mIdx] == 25 genB_fromH = genparts[mask_genB & mask_fromH] HbbIdx = genB_fromH.genPartIdxMother[:,0] Hbb_Pt = object_selections.getGenPartPtFromIdx(genparts,HbbIdx) Hbb_Eta = object_selections.getGenPartEtaFromIdx(genparts,HbbIdx) Hbb_Phi = object_selections.getGenPartPhiFromIdx(genparts,HbbIdx) dR_bb = object_selections.compute_deltaR(genB_fromH.phi[:,0], genB_fromH.phi[:,1], genB_fromH.eta[:,0], genB_fromH.eta[:,1]) events["Hbb_pt"] = Hbb_Pt events["Hbb_eta"] = Hbb_Eta events["Hbb_phi"] = Hbb_Phi events["genBBfromH_delta_R"] = dR_bb # matching fatjet to Hbb: # closest fatjet # match_mask = object_selections.mask_nearest(Hbb_Eta,Hbb_Phi,fatjets.eta,fatjets.phi,threshold=0.4) # matched_fatjet = fatjets[match_mask] # highest PNet fatjet fatjets.Tbb = fatjets.particleNetMD_Xbb / \ (1 - fatjets.particleNetMD_Xqq - fatjets.particleNetMD_Xcc) matched_fatjet = fatjets[awkward.argmax(fatjets.Tbb,axis=1,keepdims=True)] try: dR_HbbFatjet = object_selections.compute_deltaR(Hbb_Phi,matched_fatjet.phi,Hbb_Eta,matched_fatjet.eta) except: dR_HbbFatjet = -9 matched_fatjet.pt = -9 matched_fatjet.eta = -9 matched_fatjet.phi = -9 matched_fatjet.mass = -9 matched_fatjet.msoftdrop = -9 matched_fatjet.btagDDBvL_noMD = -9 matched_fatjet.deepTagMD_HbbvsQCD = -9 matched_fatjet.particleNetMD_Xqq = -9 matched_fatjet.particleNetMD_Xcc = -9 matched_fatjet.particleNetMD_Xbb = -9 matched_fatjet.particleNetMD_QCD = -9 events["HbbFatjet_delta_R"] = dR_HbbFatjet events["matched_fatjet_pt"] = matched_fatjet.pt events["matched_fatjet_eta"] = matched_fatjet.eta events["matched_fatjet_phi"] = matched_fatjet.phi events["matched_fatjet_mass"] = matched_fatjet.mass events["matched_fatjet_msoftdrop"] = matched_fatjet.msoftdrop events["matched_fatjet_btag"] = matched_fatjet.btagDDBvL_noMD events["matched_fatjet_deepbtag_md"] = matched_fatjet.deepTagMD_HbbvsQCD if hasattr(matched_fatjet, "particleNetMD_Xqq"): events["matched_fatjet_particleNet_Xqq"] = matched_fatjet.particleNetMD_Xqq events["matched_fatjet_particleNet_Xcc"] = matched_fatjet.particleNetMD_Xcc events["matched_fatjet_particleNet_Xbb"] = matched_fatjet.particleNetMD_Xbb events["matched_fatjet_particleNet_XQCD"] = matched_fatjet.particleNetMD_QCD events["matched_fatjet_particleNet_Tbb"] = matched_fatjet.particleNetMD_Xbb/( 1 - matched_fatjet.particleNetMD_Xcc - matched_fatjet.particleNetMD_Xqq) fatjet_pt_padded = utils.pad_awkward_array(fatjets.pt, n_save, -9) fatjet_eta_padded = utils.pad_awkward_array(fatjets.eta, n_save, -9) fatjet_phi_padded = utils.pad_awkward_array(fatjets.phi, n_save, -9) fatjet_mass_padded = utils.pad_awkward_array(fatjets.mass, n_save, -9) fatjet_msoftdrop_padded = utils.pad_awkward_array(fatjets.msoftdrop, n_save, -9) fatjet_btag_padded = utils.pad_awkward_array(fatjets.btagDDBvL_noMD, n_save, -9) fatjet_deepbtag_md_padded = utils.pad_awkward_array(fatjets.deepTagMD_HbbvsQCD, n_save, -9) if hasattr(fatjets,"particleNetMD_Xqq"): fatjet_PNet_Xqq_padded = utils.pad_awkward_array(fatjets.particleNetMD_Xqq, n_save, -9) fatjet_PNet_Xcc_padded = utils.pad_awkward_array(fatjets.particleNetMD_Xcc, n_save, -9) fatjet_PNet_Xbb_padded = utils.pad_awkward_array(fatjets.particleNetMD_Xbb, n_save, -9) fatjet_PNet_XQCD_padded = utils.pad_awkward_array(fatjets.particleNetMD_QCD, n_save, -9) for i in range(n_save): events["fatjet%s_pt" % str(i+1)] = fatjet_pt_padded[:,i] events["fatjet%s_eta" % str(i+1)] = fatjet_eta_padded[:,i] events["fatjet%s_phi" % str(i+1)] = fatjet_phi_padded[:,i] events["fatjet%s_msoftdrop" % str(i+1)] = fatjet_msoftdrop_padded[:,i] events["fatjet%s_mass" % str(i+1)] = fatjet_mass_padded[:,i] events["fatjet%s_btag" % str(i+1)] = fatjet_btag_padded[:,i] events["fatjet%s_deepbtag_md" % str(i+1)] = fatjet_deepbtag_md_padded[:,i] if hasattr(fatjets, "particleNetMD_Xqq"): events["fatjet%s_particleNet_Tbb" % str(i+1)] = fatjet_PNet_Xbb_padded[:,i] / ( #taken from Hbb meeting 02/24/2021 1 - fatjet_PNet_Xcc_padded[:,i] - fatjet_PNet_Xqq_padded[:,i] ) events["fatjet%s_particleNet_Xqq" % str(i+1)] = fatjet_PNet_Xqq_padded[:,i] events["fatjet%s_particleNet_Xcc" % str(i+1)] = fatjet_PNet_Xcc_padded[:,i] events["fatjet%s_particleNet_Xbb" % str(i+1)] = fatjet_PNet_Xbb_padded[:,i] events["fatjet%s_particleNet_XQCD" % str(i+1)] = fatjet_PNet_XQCD_padded[:,i] # for i in range(100,160): # print("\nmatched fatjet_pt : {}, Tbb : {}".format(matched_fatjet[i].pt, matched_fatjet.particleNetMD_Xbb[i]/( # 1 - matched_fatjet.particleNetMD_Xcc[i] # - matched_fatjet.particleNetMD_Xqq[i]))) # for j in range(n_save): # print("fatjet{}_pt: {}, Tbb : {}".format( # str(j+1), fatjet_pt_padded[i, j], fatjet_PNet_Xbb_padded[i, j] / ( # 1 - fatjet_PNet_Xcc_padded[i, j] # - fatjet_PNet_Xqq_padded[i, j]))) return events
def process_shift(self, events, shift_name): dataset = events.metadata['dataset'] isRealData = not hasattr(events, "genWeight") selection = PackedSelection() weights = Weights(len(events), storeIndividual=True) output = self.make_output() if shift_name is None and not isRealData: output['sumw'] = ak.sum(events.genWeight) if isRealData or self._newTrigger: trigger = np.zeros(len(events), dtype='bool') for t in self._triggers[self._year]: if t in events.HLT.fields: trigger = trigger | events.HLT[t] selection.add('trigger', trigger) del trigger else: selection.add('trigger', np.ones(len(events), dtype='bool')) if isRealData: selection.add( 'lumimask', lumiMasks[self._year](events.run, events.luminosityBlock)) else: selection.add('lumimask', np.ones(len(events), dtype='bool')) if isRealData and self._skipRunB and self._year == '2017': selection.add('dropB', events.run > 299329) else: selection.add('dropB', np.ones(len(events), dtype='bool')) if isRealData: trigger = np.zeros(len(events), dtype='bool') for t in self._muontriggers[self._year]: if t in events.HLT.fields: trigger |= np.array(events.HLT[t]) selection.add('muontrigger', trigger) del trigger else: selection.add('muontrigger', np.ones(len(events), dtype='bool')) metfilter = np.ones(len(events), dtype='bool') for flag in self._met_filters[ self._year]['data' if isRealData else 'mc']: metfilter &= np.array(events.Flag[flag]) selection.add('metfilter', metfilter) del metfilter fatjets = events.FatJet fatjets['msdcorr'] = corrected_msoftdrop(fatjets) fatjets['qcdrho'] = 2 * np.log(fatjets.msdcorr / fatjets.pt) fatjets['n2ddt'] = fatjets.n2b1 - n2ddt_shift(fatjets, year=self._year) fatjets['msdcorr_full'] = fatjets['msdcorr'] * self._msdSF[self._year] candidatejet = fatjets[ # https://github.com/DAZSLE/BaconAnalyzer/blob/master/Analyzer/src/VJetLoader.cc#L269 (fatjets.pt > 200) & (abs(fatjets.eta) < 2.5) & fatjets.isTight # this is loose in sampleContainer ] candidatejet = candidatejet[:, : 2] # Only consider first two to match generators if self._jet_arbitration == 'pt': candidatejet = ak.firsts(candidatejet) elif self._jet_arbitration == 'mass': candidatejet = ak.firsts(candidatejet[ak.argmax( candidatejet.msdcorr, axis=1, keepdims=True)]) elif self._jet_arbitration == 'n2': candidatejet = ak.firsts(candidatejet[ak.argmin(candidatejet.n2ddt, axis=1, keepdims=True)]) elif self._jet_arbitration == 'ddb': candidatejet = ak.firsts(candidatejet[ak.argmax( candidatejet.btagDDBvLV2, axis=1, keepdims=True)]) elif self._jet_arbitration == 'ddc': candidatejet = ak.firsts(candidatejet[ak.argmax( candidatejet.btagDDCvLV2, axis=1, keepdims=True)]) else: raise RuntimeError("Unknown candidate jet arbitration") if self._tagger == 'v1': bvl = candidatejet.btagDDBvL cvl = candidatejet.btagDDCvL cvb = candidatejet.btagDDCvB elif self._tagger == 'v2': bvl = candidatejet.btagDDBvLV2 cvl = candidatejet.btagDDCvLV2 cvb = candidatejet.btagDDCvBV2 elif self._tagger == 'v3': bvl = candidatejet.particleNetMD_Xbb cvl = candidatejet.particleNetMD_Xcc / ( 1 - candidatejet.particleNetMD_Xbb) cvb = candidatejet.particleNetMD_Xcc / ( candidatejet.particleNetMD_Xcc + candidatejet.particleNetMD_Xbb) elif self._tagger == 'v4': bvl = candidatejet.particleNetMD_Xbb cvl = candidatejet.btagDDCvLV2 cvb = candidatejet.particleNetMD_Xcc / ( candidatejet.particleNetMD_Xcc + candidatejet.particleNetMD_Xbb) else: raise ValueError("Not an option") selection.add('minjetkin', (candidatejet.pt >= 450) & (candidatejet.pt < 1200) & (candidatejet.msdcorr >= 40.) & (candidatejet.msdcorr < 201.) & (abs(candidatejet.eta) < 2.5)) selection.add('_strict_mass', (candidatejet.msdcorr > 85) & (candidatejet.msdcorr < 130)) selection.add('_high_score', cvl > 0.8) selection.add('minjetkinmu', (candidatejet.pt >= 400) & (candidatejet.pt < 1200) & (candidatejet.msdcorr >= 40.) & (candidatejet.msdcorr < 201.) & (abs(candidatejet.eta) < 2.5)) selection.add('minjetkinw', (candidatejet.pt >= 200) & (candidatejet.pt < 1200) & (candidatejet.msdcorr >= 40.) & (candidatejet.msdcorr < 201.) & (abs(candidatejet.eta) < 2.5)) selection.add('jetid', candidatejet.isTight) selection.add('n2ddt', (candidatejet.n2ddt < 0.)) if not self._tagger == 'v2': selection.add('ddbpass', (bvl >= 0.89)) selection.add('ddcpass', (cvl >= 0.83)) selection.add('ddcvbpass', (cvb >= 0.2)) else: selection.add('ddbpass', (bvl >= 0.7)) selection.add('ddcpass', (cvl >= 0.45)) selection.add('ddcvbpass', (cvb >= 0.03)) jets = events.Jet jets = jets[(jets.pt > 30.) & (abs(jets.eta) < 2.5) & jets.isTight] # only consider first 4 jets to be consistent with old framework jets = jets[:, :4] dphi = abs(jets.delta_phi(candidatejet)) selection.add( 'antiak4btagMediumOppHem', ak.max(jets[dphi > np.pi / 2][self._ak4tagBranch], axis=1, mask_identity=False) < BTagEfficiency.btagWPs[self._ak4tagger][self._year]['medium']) ak4_away = jets[dphi > 0.8] selection.add( 'ak4btagMedium08', ak.max(ak4_away[self._ak4tagBranch], axis=1, mask_identity=False) > BTagEfficiency.btagWPs[self._ak4tagger][self._year]['medium']) met = events.MET selection.add('met', met.pt < 140.) goodmuon = ((events.Muon.pt > 10) & (abs(events.Muon.eta) < 2.4) & (events.Muon.pfRelIso04_all < 0.25) & events.Muon.looseId) nmuons = ak.sum(goodmuon, axis=1) leadingmuon = ak.firsts(events.Muon[goodmuon]) if self._looseTau: goodelectron = ((events.Electron.pt > 10) & (abs(events.Electron.eta) < 2.5) & (events.Electron.cutBased >= events.Electron.VETO)) nelectrons = ak.sum(goodelectron, axis=1) ntaus = ak.sum( ((events.Tau.pt > 20) & (abs(events.Tau.eta) < 2.3) & events.Tau.idDecayMode & ((events.Tau.idMVAoldDM2017v2 & 2) != 0) & ak.all(events.Tau.metric_table(events.Muon[goodmuon]) > 0.4, axis=2) & ak.all(events.Tau.metric_table( events.Electron[goodelectron]) > 0.4, axis=2)), axis=1, ) else: goodelectron = ( (events.Electron.pt > 10) & (abs(events.Electron.eta) < 2.5) & (events.Electron.cutBased >= events.Electron.LOOSE)) nelectrons = ak.sum(goodelectron, axis=1) ntaus = ak.sum( (events.Tau.pt > 20) & events.Tau.idDecayMode # bacon iso looser than Nano selection & ak.all(events.Tau.metric_table(events.Muon[goodmuon]) > 0.4, axis=2) & ak.all(events.Tau.metric_table(events.Electron[goodelectron]) > 0.4, axis=2), axis=1, ) selection.add('noleptons', (nmuons == 0) & (nelectrons == 0) & (ntaus == 0)) selection.add('onemuon', (nmuons == 1) & (nelectrons == 0) & (ntaus == 0)) selection.add('muonkin', (leadingmuon.pt > 55.) & (abs(leadingmuon.eta) < 2.1)) selection.add('muonDphiAK8', abs(leadingmuon.delta_phi(candidatejet)) > 2 * np.pi / 3) # W-Tag (Tag and Probe) # tag side selection.add( 'ak4btagMediumOppHem', ak.max(jets[dphi > np.pi / 2][self._ak4tagBranch], axis=1, mask_identity=False) > BTagEfficiency.btagWPs[self._ak4tagger][self._year]['medium']) selection.add('met40p', met.pt > 40.) selection.add('tightMuon', (leadingmuon.tightId) & (leadingmuon.pt > 53.)) # selection.add('ptrecoW', (leadingmuon + met).pt > 250.) selection.add('ptrecoW200', (leadingmuon + met).pt > 200.) selection.add( 'ak4btagNearMu', leadingmuon.delta_r(leadingmuon.nearest(ak4_away, axis=None)) < 2.0) _bjets = jets[self._ak4tagBranch] > BTagEfficiency.btagWPs[ self._ak4tagger][self._year]['medium'] # _nearAK8 = jets.delta_r(candidatejet) < 0.8 # _nearMu = jets.delta_r(ak.firsts(events.Muon)) < 0.3 # selection.add('ak4btagOld', ak.sum(_bjets & ~_nearAK8 & ~_nearMu, axis=1) >= 1) _nearAK8 = jets.delta_r(candidatejet) < 0.8 _nearMu = jets.delta_r(leadingmuon) < 0.3 selection.add('ak4btagOld', ak.sum(_bjets & ~_nearAK8 & ~_nearMu, axis=1) >= 1) # _nearAK8 = jets.delta_r(candidatejet) < 0.8 # _nearMu = jets.delta_r(candidatejet.nearest(events.Muon[goodmuon], axis=None)) < 0.3 # selection.add('ak4btagNew', ak.sum(_bjets & ~_nearAK8 & ~_nearMu, axis=1) >= 1) # probe side selection.add('minWjetpteta', (candidatejet.pt >= 200) & (abs(candidatejet.eta) < 2.4)) # selection.add('noNearMuon', candidatejet.delta_r(candidatejet.nearest(events.Muon[goodmuon], axis=None)) > 1.0) selection.add('noNearMuon', candidatejet.delta_r(leadingmuon) > 1.0) ##### if isRealData: genflavor = ak.zeros_like(candidatejet.pt) else: if 'HToCC' in dataset or 'HToBB' in dataset: if self._ewkHcorr: add_HiggsEW_kFactors(weights, events.GenPart, dataset) weights.add('genweight', events.genWeight) if "PSWeight" in events.fields: add_ps_weight(weights, events.PSWeight) else: add_ps_weight(weights, None) if "LHEPdfWeight" in events.fields: add_pdf_weight(weights, events.LHEPdfWeight) else: add_pdf_weight(weights, None) if "LHEScaleWeight" in events.fields: add_scalevar_7pt(weights, events.LHEScaleWeight) add_scalevar_3pt(weights, events.LHEScaleWeight) else: add_scalevar_7pt(weights, []) add_scalevar_3pt(weights, []) add_pileup_weight(weights, events.Pileup.nPU, self._year, dataset) bosons = getBosons(events.GenPart) matchedBoson = candidatejet.nearest(bosons, axis=None, threshold=0.8) if self._tightMatch: match_mask = ( (candidatejet.pt - matchedBoson.pt) / matchedBoson.pt < 0.5) & ((candidatejet.msdcorr - matchedBoson.mass) / matchedBoson.mass < 0.3) selmatchedBoson = ak.mask(matchedBoson, match_mask) genflavor = bosonFlavor(selmatchedBoson) else: genflavor = bosonFlavor(matchedBoson) genBosonPt = ak.fill_none(ak.firsts(bosons.pt), 0) if self._newVjetsKfactor: add_VJets_kFactors(weights, events.GenPart, dataset) else: add_VJets_NLOkFactor(weights, genBosonPt, self._year, dataset) if shift_name is None: output['btagWeight'].fill(val=self._btagSF.addBtagWeight( weights, ak4_away, self._ak4tagBranch)) if self._nnlops_rew and dataset in [ 'GluGluHToCC_M125_13TeV_powheg_pythia8' ]: weights.add('minlo_rew', powheg_to_nnlops(ak.to_numpy(genBosonPt))) if self._newTrigger: add_jetTriggerSF( weights, ak.firsts(fatjets), self._year if not self._skipRunB else f'{self._year}CDEF', selection) else: add_jetTriggerWeight(weights, candidatejet.msdcorr, candidatejet.pt, self._year) add_mutriggerSF(weights, leadingmuon, self._year, selection) add_mucorrectionsSF(weights, leadingmuon, self._year, selection) if self._year in ("2016", "2017"): weights.add("L1Prefiring", events.L1PreFiringWeight.Nom, events.L1PreFiringWeight.Up, events.L1PreFiringWeight.Dn) logger.debug("Weight statistics: %r" % weights.weightStatistics) msd_matched = candidatejet.msdcorr * self._msdSF[self._year] * ( genflavor > 0) + candidatejet.msdcorr * (genflavor == 0) regions = { 'signal': [ 'noleptons', 'minjetkin', 'met', 'metfilter', 'jetid', 'antiak4btagMediumOppHem', 'n2ddt', 'trigger', 'lumimask' ], 'signal_noddt': [ 'noleptons', 'minjetkin', 'met', 'jetid', 'antiak4btagMediumOppHem', 'trigger', 'lumimask', 'metfilter' ], # 'muoncontrol': ['minjetkinmu', 'jetid', 'n2ddt', 'ak4btagMedium08', 'onemuon', 'muonkin', 'muonDphiAK8', 'muontrigger', 'lumimask', 'metfilter'], 'muoncontrol': [ 'onemuon', 'muonkin', 'muonDphiAK8', 'metfilter', 'minjetkinmu', 'jetid', 'ak4btagMedium08', 'n2ddt', 'muontrigger', 'lumimask' ], 'muoncontrol_noddt': [ 'onemuon', 'muonkin', 'muonDphiAK8', 'jetid', 'metfilter', 'minjetkinmu', 'jetid', 'ak4btagMedium08', 'muontrigger', 'lumimask' ], 'wtag': [ 'onemuon', 'tightMuon', 'minjetkinw', 'jetid', 'met40p', 'metfilter', 'ptrecoW200', 'ak4btagOld', 'muontrigger', 'lumimask' ], 'wtag0': [ 'onemuon', 'tightMuon', 'met40p', 'metfilter', 'ptrecoW200', 'ak4btagOld', 'muontrigger', 'lumimask' ], 'wtag2': [ 'onemuon', 'tightMuon', 'minjetkinw', 'jetid', 'ak4btagMediumOppHem', 'met40p', 'metfilter', 'ptrecoW200', 'ak4btagOld', 'muontrigger', 'lumimask' ], 'noselection': [], } def normalize(val, cut): if cut is None: ar = ak.to_numpy(ak.fill_none(val, np.nan)) return ar else: ar = ak.to_numpy(ak.fill_none(val[cut], np.nan)) return ar import time tic = time.time() if shift_name is None: for region, cuts in regions.items(): allcuts = set([]) cut = selection.all(*allcuts) output['cutflow_msd'].fill(region=region, genflavor=normalize( genflavor, None), cut=0, weight=weights.weight(), msd=normalize(msd_matched, None)) output['cutflow_eta'].fill(region=region, genflavor=normalize(genflavor, cut), cut=0, weight=weights.weight()[cut], eta=normalize( candidatejet.eta, cut)) output['cutflow_pt'].fill(region=region, genflavor=normalize(genflavor, cut), cut=0, weight=weights.weight()[cut], pt=normalize(candidatejet.pt, cut)) for i, cut in enumerate(cuts + ['ddcvbpass', 'ddcpass']): allcuts.add(cut) cut = selection.all(*allcuts) output['cutflow_msd'].fill(region=region, genflavor=normalize( genflavor, cut), cut=i + 1, weight=weights.weight()[cut], msd=normalize(msd_matched, cut)) output['cutflow_eta'].fill( region=region, genflavor=normalize(genflavor, cut), cut=i + 1, weight=weights.weight()[cut], eta=normalize(candidatejet.eta, cut)) output['cutflow_pt'].fill( region=region, genflavor=normalize(genflavor, cut), cut=i + 1, weight=weights.weight()[cut], pt=normalize(candidatejet.pt, cut)) if self._evtVizInfo and 'ddcpass' in allcuts and isRealData and region == 'signal': if 'event' not in events.fields: continue _cut = selection.all(*allcuts, '_strict_mass', '_high_score') # _cut = selection.all('_strict_mass'') output['to_check'][ 'mass'] += processor.column_accumulator( normalize(msd_matched, _cut)) nfatjet = ak.sum( ((fatjets.pt > 200) & (abs(fatjets.eta) < 2.5) & fatjets.isTight), axis=1) output['to_check'][ 'njet'] += processor.column_accumulator( normalize(nfatjet, _cut)) output['to_check'][ 'fname'] += processor.column_accumulator( np.array([events.metadata['filename']] * len(normalize(msd_matched, _cut)))) output['to_check'][ 'event'] += processor.column_accumulator( normalize(events.event, _cut)) output['to_check'][ 'luminosityBlock'] += processor.column_accumulator( normalize(events.luminosityBlock, _cut)) output['to_check'][ 'run'] += processor.column_accumulator( normalize(events.run, _cut)) if shift_name is None: systematics = [None] + list(weights.variations) else: systematics = [shift_name] def fill(region, systematic, wmod=None): selections = regions[region] cut = selection.all(*selections) sname = 'nominal' if systematic is None else systematic if wmod is None: if systematic in weights.variations: weight = weights.weight(modifier=systematic)[cut] else: weight = weights.weight()[cut] else: weight = weights.weight()[cut] * wmod[cut] output['templates'].fill( region=region, systematic=sname, runid=runmap(events.run)[cut], genflavor=normalize(genflavor, cut), pt=normalize(candidatejet.pt, cut), msd=normalize(msd_matched, cut), ddb=normalize(bvl, cut), ddc=normalize(cvl, cut), ddcvb=normalize(cvb, cut), weight=weight, ) if region in [ 'wtag', 'wtag0', 'wtag2', 'wtag3', 'wtag4', 'wtag5', 'wtag6', 'wtag7', 'noselection' ]: # and sname in ['nominal', 'pileup_weightDown', 'pileup_weightUp', 'jet_triggerDown', 'jet_triggerUp']: output['wtag'].fill( region=region, systematic=sname, genflavor=normalize(genflavor, cut), pt=normalize(candidatejet.pt, cut), msd=normalize(msd_matched, cut), n2ddt=normalize(candidatejet.n2ddt, cut), ddc=normalize(cvl, cut), ddcvb=normalize(cvb, cut), weight=weight, ) # if region in ['signal', 'noselection']: # output['etaphi'].fill( # region=region, # systematic=sname, # runid=runmap(events.run)[cut], # genflavor=normalize(genflavor, cut), # pt=normalize(candidatejet.pt, cut), # eta=normalize(candidatejet.eta, cut), # phi=normalize(candidatejet.phi, cut), # ddc=normalize(cvl, cut), # ddcvb=normalize(cvb, cut), # ), if not isRealData: if wmod is not None: _custom_weight = events.genWeight[cut] * wmod[cut] else: _custom_weight = np.ones_like(weight) output['genresponse_noweight'].fill( region=region, systematic=sname, pt=normalize(candidatejet.pt, cut), genpt=normalize(genBosonPt, cut), weight=_custom_weight, ) output['genresponse'].fill( region=region, systematic=sname, pt=normalize(candidatejet.pt, cut), genpt=normalize(genBosonPt, cut), weight=weight, ) if systematic is None: output['signal_opt'].fill( region=region, genflavor=normalize(genflavor, cut), ddc=normalize(cvl, cut), ddcvb=normalize(cvb, cut), msd=normalize(msd_matched, cut), weight=weight, ) output['signal_optb'].fill( region=region, genflavor=normalize(genflavor, cut), ddb=normalize(bvl, cut), msd=normalize(msd_matched, cut), weight=weight, ) for region in regions: cut = selection.all(*(set(regions[region]) - {'n2ddt'})) if shift_name is None: output['nminus1_n2ddt'].fill( region=region, n2ddt=normalize(candidatejet.n2ddt, cut), weight=weights.weight()[cut], ) for systematic in systematics: if isRealData and systematic is not None: continue fill(region, systematic) if shift_name is None and 'GluGluH' in dataset and 'LHEWeight' in events.fields: for i in range(9): fill(region, 'LHEScale_%d' % i, events.LHEScaleWeight[:, i]) for c in events.LHEWeight.fields[1:]: fill(region, 'LHEWeight_%s' % c, events.LHEWeight[c]) toc = time.time() output["filltime"] = toc - tic if shift_name is None: output["weightStats"] = weights.weightStatistics return {dataset: output}
def process(self, events): output = self.accumulator.identity() # we can use a very loose preselection to filter the events. nothing is done with this presel, though presel = ak.num(events.Jet) > 0 if self.year == 2016: lumimask = LumiMask( '../data/lumi/Cert_271036-284044_13TeV_Legacy2016_Collisions16_JSON.txt' ) if self.year == 2017: lumimask = LumiMask( '../data/lumi/Cert_294927-306462_13TeV_UL2017_Collisions17_GoldenJSON.txt' ) if self.year == 2018: lumimask = LumiMask( '../data/lumi/Cert_314472-325175_13TeV_Legacy2018_Collisions18_JSON.txt' ) ev = events[presel] dataset = ev.metadata['dataset'] # load the config - probably not needed anymore cfg = loadConfig() output['totalEvents']['all'] += len(events) output['skimmedEvents']['all'] += len(ev) if self.year == 2018: triggers = ev.HLT.Ele23_Ele12_CaloIdL_TrackIdL_IsoVL elif self.year == 2017: triggers = ev.HLT.Ele23_Ele12_CaloIdL_TrackIdL_IsoVL elif self.year == 2016: triggers = ev.HLT.Ele23_Ele12_CaloIdL_TrackIdL_IsoVL_DZ ## Electrons electron = Collections(ev, "Electron", "tightFCNC", 0, self.year).get() electron = electron[(electron.pt > 25) & (np.abs(electron.eta) < 2.4)] loose_electron = Collections(ev, "Electron", "looseFCNC", 0, self.year).get() loose_electron = loose_electron[(loose_electron.pt > 25) & (np.abs(loose_electron.eta) < 2.4)] SSelectron = (ak.sum(electron.charge, axis=1) != 0) & (ak.num(electron) == 2) OSelectron = (ak.sum(electron.charge, axis=1) == 0) & (ak.num(electron) == 2) dielectron = choose(electron, 2) dielectron_mass = (dielectron['0'] + dielectron['1']).mass dielectron_pt = (dielectron['0'] + dielectron['1']).pt leading_electron_idx = ak.singletons(ak.argmax(electron.pt, axis=1)) leading_electron = electron[(leading_electron_idx)] leading_electron = leading_electron[(leading_electron.pt > 30)] trailing_electron_idx = ak.singletons(ak.argmin(electron.pt, axis=1)) trailing_electron = electron[trailing_electron_idx] ##Muons loose_muon = Collections(ev, "Muon", "looseFCNC", 0, self.year).get() loose_muon = loose_muon[(loose_muon.pt > 20) & (np.abs(loose_muon.eta) < 2.4)] #jets jet = getJets(ev, minPt=40, maxEta=2.4, pt_var='pt') jet = jet[~match(jet, loose_muon, deltaRCut=0.4)] # remove jets that overlap with muons jet = jet[~match( jet, electron, deltaRCut=0.4)] # remove jets that overlap with electrons ## MET -> can switch to puppi MET met_pt = ev.MET.pt met_phi = ev.MET.phi #weights weight = Weights(len(ev)) weight2 = Weights(len(ev)) weight2.add("charge flip", self.charge_flip_ratio.flip_weight(electron)) #selections filters = getFilters(ev, year=self.year, dataset=dataset, UL=False) mask = lumimask(ev.run, ev.luminosityBlock) ss = (SSelectron) os = (OSelectron) mass = (ak.min(np.abs(dielectron_mass - 91.2), axis=1) < 15) lead_electron = (ak.min(leading_electron.pt, axis=1) > 30) jet1 = (ak.num(jet) >= 1) jet2 = (ak.num(jet) >= 2) num_loose = ((ak.num(loose_electron) == 2) & (ak.num(loose_muon) == 0)) selection = PackedSelection() selection.add('filter', (filters)) selection.add('mask', (mask)) selection.add('ss', ss) selection.add('os', os) selection.add('mass', mass) selection.add('leading', lead_electron) selection.add('triggers', triggers) selection.add('one jet', jet1) selection.add('two jets', jet2) selection.add('num_loose', num_loose) bl_reqs = ['filter'] + ['triggers'] + ['mask'] bl_reqs_d = {sel: True for sel in bl_reqs} baseline = selection.require(**bl_reqs_d) s_reqs = bl_reqs + ['ss'] + ['mass'] + ['num_loose'] + ['leading'] s_reqs_d = {sel: True for sel in s_reqs} ss_sel = selection.require(**s_reqs_d) o_reqs = bl_reqs + ['os'] + ['mass'] + ['num_loose'] + ['leading'] o_reqs_d = {sel: True for sel in o_reqs} os_sel = selection.require(**o_reqs_d) j1s_reqs = s_reqs + ['one jet'] j1s_reqs_d = {sel: True for sel in j1s_reqs} j1ss_sel = selection.require(**j1s_reqs_d) j1o_reqs = o_reqs + ['one jet'] j1o_reqs_d = {sel: True for sel in j1o_reqs} j1os_sel = selection.require(**j1o_reqs_d) j2s_reqs = s_reqs + ['two jets'] j2s_reqs_d = {sel: True for sel in j2s_reqs} j2ss_sel = selection.require(**j2s_reqs_d) j2o_reqs = o_reqs + ['two jets'] j2o_reqs_d = {sel: True for sel in j2o_reqs} j2os_sel = selection.require(**j2o_reqs_d) #outputs output["electron_data1"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(leading_electron[os_sel].pt)), eta=ak.to_numpy(ak.flatten(leading_electron[os_sel].eta)), phi=ak.to_numpy(ak.flatten(leading_electron[os_sel].phi)), weight=weight2.weight()[os_sel]) output["electron_data2"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(trailing_electron[os_sel].pt)), eta=ak.to_numpy(ak.flatten(trailing_electron[os_sel].eta)), phi=ak.to_numpy(ak.flatten(trailing_electron[os_sel].phi)), weight=weight2.weight()[os_sel]) output["electron_data3"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(leading_electron[j1os_sel].pt)), eta=ak.to_numpy(ak.flatten(leading_electron[j1os_sel].eta)), phi=ak.to_numpy(ak.flatten(leading_electron[j1os_sel].phi)), weight=weight2.weight()[j1os_sel]) output["electron_data4"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(trailing_electron[j1os_sel].pt)), eta=ak.to_numpy(ak.flatten(trailing_electron[j1os_sel].eta)), phi=ak.to_numpy(ak.flatten(trailing_electron[j1os_sel].phi)), weight=weight2.weight()[j1os_sel]) output["electron_data5"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(leading_electron[j2os_sel].pt)), eta=ak.to_numpy(ak.flatten(leading_electron[j2os_sel].eta)), phi=ak.to_numpy(ak.flatten(leading_electron[j2os_sel].phi)), weight=weight2.weight()[j2os_sel]) output["electron_data6"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(trailing_electron[j2os_sel].pt)), eta=ak.to_numpy(ak.flatten(trailing_electron[j2os_sel].eta)), phi=ak.to_numpy(ak.flatten(trailing_electron[j2os_sel].phi)), weight=weight2.weight()[j2os_sel]) output["electron_data7"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(leading_electron[ss_sel].pt)), eta=ak.to_numpy(ak.flatten(leading_electron[ss_sel].eta)), phi=ak.to_numpy(ak.flatten(leading_electron[ss_sel].phi)), weight=weight.weight()[ss_sel]) output["electron_data8"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(trailing_electron[ss_sel].pt)), eta=ak.to_numpy(ak.flatten(trailing_electron[ss_sel].eta)), phi=ak.to_numpy(ak.flatten(trailing_electron[ss_sel].phi)), weight=weight.weight()[ss_sel]) output["electron_data9"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(leading_electron[j1ss_sel].pt)), eta=ak.to_numpy(ak.flatten(leading_electron[j1ss_sel].eta)), phi=ak.to_numpy(ak.flatten(leading_electron[j1ss_sel].phi)), weight=weight.weight()[j1ss_sel]) output["electron_data10"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(trailing_electron[j1ss_sel].pt)), eta=ak.to_numpy(ak.flatten(trailing_electron[j1ss_sel].eta)), phi=ak.to_numpy(ak.flatten(trailing_electron[j1ss_sel].phi)), weight=weight.weight()[j1ss_sel]) output["electron_data11"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(leading_electron[j2ss_sel].pt)), eta=ak.to_numpy(ak.flatten(leading_electron[j2ss_sel].eta)), phi=ak.to_numpy(ak.flatten(leading_electron[j2ss_sel].phi)), weight=weight.weight()[j2ss_sel]) output["electron_data12"].fill( dataset=dataset, pt=ak.to_numpy(ak.flatten(trailing_electron[j2ss_sel].pt)), eta=ak.to_numpy(ak.flatten(trailing_electron[j2ss_sel].eta)), phi=ak.to_numpy(ak.flatten(trailing_electron[j2ss_sel].phi)), weight=weight.weight()[j2ss_sel]) output["dilep_mass1"].fill( dataset=dataset, mass=ak.to_numpy(ak.flatten(dielectron_mass[os_sel])), pt=ak.to_numpy(ak.flatten(dielectron_pt[os_sel])), weight=weight2.weight()[os_sel]) output["dilep_mass2"].fill( dataset=dataset, mass=ak.to_numpy(ak.flatten(dielectron_mass[j1os_sel])), pt=ak.to_numpy(ak.flatten(dielectron_pt[j1os_sel])), weight=weight2.weight()[j1os_sel]) output["dilep_mass3"].fill( dataset=dataset, mass=ak.to_numpy(ak.flatten(dielectron_mass[j2os_sel])), pt=ak.to_numpy(ak.flatten(dielectron_pt[j2os_sel])), weight=weight2.weight()[j2os_sel]) output["dilep_mass4"].fill( dataset=dataset, mass=ak.to_numpy(ak.flatten(dielectron_mass[ss_sel])), pt=ak.to_numpy(ak.flatten(dielectron_pt[ss_sel])), weight=weight.weight()[ss_sel]) output["dilep_mass5"].fill( dataset=dataset, mass=ak.to_numpy(ak.flatten(dielectron_mass[j1ss_sel])), pt=ak.to_numpy(ak.flatten(dielectron_pt[j1ss_sel])), weight=weight.weight()[j1ss_sel]) output["dilep_mass6"].fill( dataset=dataset, mass=ak.to_numpy(ak.flatten(dielectron_mass[j2ss_sel])), pt=ak.to_numpy(ak.flatten(dielectron_pt[j2ss_sel])), weight=weight.weight()[j2ss_sel]) output["MET"].fill(dataset=dataset, pt=met_pt[os_sel], weight=weight2.weight()[os_sel]) output["MET2"].fill(dataset=dataset, pt=met_pt[j1os_sel], weight=weight2.weight()[j1os_sel]) output["MET3"].fill(dataset=dataset, pt=met_pt[j2os_sel], weight=weight2.weight()[j2os_sel]) output["MET4"].fill(dataset=dataset, pt=met_pt[ss_sel], weight=weight.weight()[ss_sel]) output["MET5"].fill(dataset=dataset, pt=met_pt[j1ss_sel], weight=weight.weight()[j1ss_sel]) output["MET6"].fill(dataset=dataset, pt=met_pt[j2ss_sel], weight=weight.weight()[j2ss_sel]) output["N_jet"].fill(dataset=dataset, multiplicity=ak.num(jet)[os_sel], weight=weight2.weight()[os_sel]) output["N_jet2"].fill(dataset=dataset, multiplicity=ak.num(jet)[j1os_sel], weight=weight2.weight()[j1os_sel]) output["N_jet3"].fill(dataset=dataset, multiplicity=ak.num(jet)[j2os_sel], weight=weight2.weight()[j2os_sel]) output["N_jet4"].fill(dataset=dataset, multiplicity=ak.num(jet)[ss_sel], weight=weight.weight()[ss_sel]) output["N_jet5"].fill(dataset=dataset, multiplicity=ak.num(jet)[j1ss_sel], weight=weight.weight()[j1ss_sel]) output["N_jet6"].fill(dataset=dataset, multiplicity=ak.num(jet)[j2ss_sel], weight=weight.weight()[j2ss_sel]) output["PV_npvsGood"].fill(dataset=dataset, multiplicity=ev.PV[os_sel].npvsGood, weight=weight2.weight()[os_sel]) output["PV_npvsGood2"].fill(dataset=dataset, multiplicity=ev.PV[j1os_sel].npvsGood, weight=weight2.weight()[j1os_sel]) output["PV_npvsGood3"].fill(dataset=dataset, multiplicity=ev.PV[j2os_sel].npvsGood, weight=weight2.weight()[j2os_sel]) output["PV_npvsGood4"].fill(dataset=dataset, multiplicity=ev.PV[ss_sel].npvsGood, weight=weight.weight()[ss_sel]) output["PV_npvsGood5"].fill(dataset=dataset, multiplicity=ev.PV[j1ss_sel].npvsGood, weight=weight.weight()[j1ss_sel]) output["PV_npvsGood6"].fill(dataset=dataset, multiplicity=ev.PV[j2ss_sel].npvsGood, weight=weight.weight()[j2ss_sel]) return output