def lorentzVecsLeps(nom, is3l):
'''
Higgs decays to two jets and one lepton, or two leptons. This returns lorentzVectors for each decay product candidate
For H -> 2j, 1l case (not isF): returns jet0, jet1, met, lep0, lep1, (lep2 if is3l)
For H -> 2l case (isF): Return met, lep0, lep1, lep2
'''
met = LorentzVector()
met.SetPtEtaPhiE(nom.met_met, 0, nom.met_phi, nom.met_met)
lep0 = LorentzVector()
lep0.SetPtEtaPhiE(nom.lep_Pt_0, nom.lep_Eta_0, nom.lep_Phi_0, nom.lep_E_0)
lep1 = LorentzVector()
lep1.SetPtEtaPhiE(nom.lep_Pt_1, nom.lep_Eta_1, nom.lep_Phi_1, nom.lep_E_1)
if is3l:
lep2 = LorentzVector()
lep2.SetPtEtaPhiE(nom.lep_Pt_2, nom.lep_Eta_2, nom.lep_Phi_2, nom.lep_E_2)
if is3l:
return (lep0, lep1, lep2, met)
else:
return (lep0, lep1, met)
def lorentzVecsTop(nom, topIdx0, topIdx1):
'''
Takes the indices of two jets identified to be bjets from top decay, return their LorentzVectors
'''
top0 = LorentzVector()
top0.SetPtEtaPhiE(nom.jet_pt[topIdx0], nom.jet_eta[topIdx0], nom.jet_phi[topIdx0], nom.jet_e[topIdx0])
top1 = LorentzVector()
top1.SetPtEtaPhiE(nom.jet_pt[topIdx1], nom.jet_eta[topIdx1], nom.jet_phi[topIdx1], nom.jet_e[topIdx1])
return (top0, top1)
def calcTopMass(nom, topMasses):
current = 0
for e in nom:
current += 1
if current % 10000 == 0:
print(current)
#if current==200000:
# break
if e.nJets_OR != 1:
continue
if e.nJets_OR_DL1_70 != 1:
continue
if abs(e.Mll01 - 91.2e3) > 10e3 and abs(e.Mll02 - 91.2e3) > 10e3:
continue
if e.trilep_type == 0: continue
lep = LorentzVector()
if abs(e.Mll02 - 91.2e3) < abs(e.Mll01 - 91.2e3):
lep.SetPtEtaPhiE(e.lep_Pt_1, e.lep_Eta_1, e.lep_Phi_1, e.lep_E_1)
else:
lep.SetPtEtaPhiE(e.lep_Pt_2, e.lep_Eta_2, e.lep_Phi_2, e.lep_E_2)
met = neutrinoPz(lep, e.met_met, e.met_phi)
w = lep + met
#w_eta = np.arccosh( abs( np.sqrt(wt.E()**2 - 80e3**2)/ wt.Pt() ) )
#w = LorentzVector()
#w.SetPtEtaPhiE(wt.Pt(), w_eta, wt.Phi(), wt.E())
#print('M', w.M())
jet = LorentzVector()
jet.SetPtEtaPhiE(e.jets_Pt_0, e.jets_Eta_0, e.jets_Phi_0, e.jets_E_0)
top = LorentzVector()
top = w + jet
topMasses.append(top.M())
if top.M() < 0:
print(top.M(), w.M())
def lorentzVecs(nom, jetIdx0, jetIdx1, is3l):
'''
Initialize met, lepton, and jet lorentz vectors
Return jet0, jet1, met, lep0, lep1, (lep2 if is3l)
'''
met = LorentzVector()
met.SetPtEtaPhiE(nom.met_met, 0, nom.met_phi, nom.met_met)
lep0 = LorentzVector()
lep0.SetPtEtaPhiE(nom.lep_Pt_0, nom.lep_Eta_0, nom.lep_Phi_0, nom.lep_E_0)
lep1 = LorentzVector()
lep1.SetPtEtaPhiE(nom.lep_Pt_1, nom.lep_Eta_1, nom.lep_Phi_1, nom.lep_E_1)
if is3l:
lep2 = LorentzVector()
lep2.SetPtEtaPhiE(nom.lep_Pt_2, nom.lep_Eta_2, nom.lep_Phi_2, nom.lep_E_2)
jet0 = LorentzVector()
jet0.SetPtEtaPhiE(nom.jet_pt[jetIdx0], nom.jet_eta[jetIdx0], nom.jet_phi[jetIdx0], nom.jet_e[jetIdx0])
jet1 = LorentzVector()
jet1.SetPtEtaPhiE(nom.jet_pt[jetIdx1], nom.jet_eta[jetIdx1], nom.jet_phi[jetIdx1], nom.jet_e[jetIdx1])
if is3l:
return (jet0, jet1, met, lep0, lep1, lep2)
else:
return (jet0, jet1, met, lep0, lep1)
def fourvect(self):
if ((self.nSCTHits + self.nPixHits) < 4):
# electron with low number of tracker hits
eta = self.cl_eta
phi = self.cl_phi
et = self.cl_E / math.cosh(self.cl_eta)
else:
eta = self.tracketa
phi = self.trackphi
et = self.cl_E / math.cosh(self.tracketa)
vect = LorentzVector()
vect.SetPtEtaPhiE(et, eta, phi, self.cl_E)
return vect
def create_dict(nom):
current = 0
events = []
bestScores = []
nEntries = nom.GetEntries()
print(nEntries)
for idx in range(nEntries):
if idx % 10000 == 0:
print(str(idx) + '/' + str(nEntries))
nom.GetEntry(idx)
higgCand = LorentzVector()
lep4Vecs = []
jet4Vecs = []
btags = []
met = LorentzVector()
met.SetPtEtaPhiE(nom.MET_RefFinal_et, 0, nom.MET_RefFinal_phi,
nom.MET_RefFinal_et)
#for i in range(2):
lep_Pt_0 = nom.lep_Pt_0
lep_Eta_0 = nom.lep_Eta_0
lep_Phi_0 = nom.lep_Phi_0
lep_E_0 = nom.lep_E_0
lepVec_0 = LorentzVector()
lepVec_0.SetPtEtaPhiE(lep_Pt_0, lep_Eta_0, lep_Phi_0, lep_E_0)
lep4Vecs.append(lepVec_0)
lep_Pt_1 = nom.lep_Pt_1
lep_Eta_1 = nom.lep_Eta_1
lep_Phi_1 = nom.lep_Phi_1
lep_E_1 = nom.lep_E_1
lepVec_1 = LorentzVector()
lepVec_1.SetPtEtaPhiE(lep_Pt_1, lep_Eta_1, lep_Phi_1, lep_E_1)
lep4Vecs.append(lepVec_1)
for j in range(len(nom.m_pflow_jet_pt)): #nom.selected_jets'][i]:
jetVec = LorentzVector()
jetVec.SetPtEtaPhiM(nom.m_pflow_jet_pt[j], nom.m_pflow_jet_eta[j],
nom.m_pflow_jet_phi[j], nom.m_pflow_jet_m[j])
jet4Vecs.append(jetVec)
btags.append(nom.m_pflow_jet_flavor_weight_MV2c10[j])
combos = []
combosTop = []
for l in range(len(lep4Vecs)):
for i in range(len(jet4Vecs) - 1):
for j in range(i + 1, len(jet4Vecs)):
comb = [l, i, j]
t = topDict(jet4Vecs[i], jet4Vecs[j], lep4Vecs[0],
lep4Vecs[1], met, btags[i], btags[j],
nom.m_pflow_jet_jvt[i], nom.m_pflow_jet_jvt[j],
nom.m_pflow_jet_numTrk[i],
nom.m_pflow_jet_numTrk[j])
combosTop.append([t, comb])
#loop over combinations, score them in the BDT, figure out the best result
topDF = pd.DataFrame.from_dict([x[0] for x in combosTop])
topMat = xgb.DMatrix(topDF, feature_names=list(topDF))
topPred = topModel.predict(topMat)
topBest = np.argmax(topPred)
bestTopComb = combosTop[topBest][1]
topMatches = bestTopComb[1:]
for l in range(len(lep4Vecs)):
for i in range(len(jet4Vecs) - 1):
for j in range(i + 1, len(jet4Vecs)):
comb = [l, i, j]
if l == 0:
k = higgsDict(jet4Vecs[i], jet4Vecs[j], lep4Vecs[l],
met, btags[i], btags[j], lep4Vecs[1],
nom.m_pflow_jet_jvt[i],
nom.m_pflow_jet_jvt[j],
nom.m_pflow_jet_numTrk[i],
nom.m_pflow_jet_numTrk[j])
else:
k = higgsDict(jet4Vecs[i], jet4Vecs[j], lep4Vecs[l],
met, btags[i], btags[j], lep4Vecs[0],
nom.m_pflow_jet_jvt[i],
nom.m_pflow_jet_jvt[j],
nom.m_pflow_jet_numTrk[i],
nom.m_pflow_jet_numTrk[j])
combos.append([k, comb])
###Evaluate higgsTop BDT
df = pd.DataFrame.from_dict([x[0] for x in combos])
xgbMat = xgb.DMatrix(df, feature_names=list(df))
pred = xgbModel.predict(xgbMat)
best = np.argmax(pred)
bestScores.append(pred[best])
bestComb = combos[best][1]
lepMatch = bestComb[0]
jetMatches = bestComb[1:]
k = {}
#k['higgs_pt'] = nom.higgs_pt
k['comboScore'] = pred[best]
k['topScore'] = topPred[topBest]
if lepMatch == 0:
k['lep_Pt_H'] = nom.lep_Pt_0
k['lep_Eta_H'] = nom.lep_Eta_0
phi_0 = nom.lep_Phi_0
k['lep_E_H'] = nom.lep_E_0
k['lep_Pt_O'] = nom.lep_Pt_1
k['lep_Eta_O'] = nom.lep_Eta_1
k['lep_Phi_O'] = calc_phi(phi_0, nom.lep_Phi_1)
k['lep_E_O'] = nom.lep_E_1
elif lepMatch == 1:
k['lep_Pt_H'] = nom.lep_Pt_1
k['lep_Eta_H'] = nom.lep_Eta_1
phi_0 = nom.lep_Phi_1
k['lep_E_H'] = nom.lep_E_1
k['lep_Pt_O'] = nom.lep_Pt_0
k['lep_Eta_O'] = nom.lep_Eta_0
k['lep_Phi_O'] = calc_phi(phi_0, nom.lep_Phi_0)
k['lep_E_O'] = nom.lep_E_0
n = 0
for i in jetMatches: #nom.nJets_OR_T):
k['jet_Pt_h' + str(n)] = nom.m_pflow_jet_pt[i]
k['jet_Eta_h' + str(n)] = nom.m_pflow_jet_eta[i]
k['jet_E_h' + str(n)] = jet4Vecs[i].E() #nom.m_pflow_jet_E[i]
k['jet_Phi_h' + str(n)] = calc_phi(phi_0, nom.m_pflow_jet_phi[i])
k['jet_MV2c10_h' +
str(n)] = nom.m_pflow_jet_flavor_weight_MV2c10[i]
n += 1
btags = np.array(btags)
btags[jetMatches[0]] = 0
btags[jetMatches[1]] = 0
bestBtags = np.argpartition(btags, -2)[-2:]
n = 0
for i in topMatches: #bestBtags:#nom.nJets_OR_T):
k['top_Pt_' + str(n)] = nom.m_pflow_jet_pt[i]
k['top_Eta_' + str(n)] = nom.m_pflow_jet_eta[i]
k['top_E_' + str(n)] = jet4Vecs[i].E() #nom.m_pflow_jet_E[i]
k['top_Phi_' + str(n)] = calc_phi(phi_0, nom.m_pflow_jet_phi[i])
k['top_MV2c10_' + str(n)] = nom.m_pflow_jet_flavor_weight_MV2c10[i]
n += 1
k['MET'] = nom.MET_RefFinal_et
k['MET_phi'] = calc_phi(phi_0, nom.MET_RefFinal_phi)
events.append(k)
return events
def run_top_mass(inputPath):
topMassesWZ = []
topMassestZ = []
topMassReco = []
f = TFile(inputPath, "READ")
dsid = inputPath.split('/')[-1]
dsid = dsid.replace('.root', '')
#print(inputPath)
nom = f.Get('nominal')
try:
nom.GetEntries()
except:
print('failed for ' + inputPath)
return 0
try:
nom.Mll01
except:
print('failed for ' + inputPath)
return 0
if nom.GetEntries() == 0:
return 0
if hasattr(nom, "topMassReco"):
print('already there', inputPath)
return 0
nEntries = nom.GetEntries()
for idx in range(nEntries):
if idx % 10000 == 0:
print(str(idx) + '/' + str(nEntries))
nom.GetEntry(idx)
lep = LorentzVector()
if abs(nom.Mll02 - 91.2e3) < abs(nom.Mll01 - 91.2e3):
lep.SetPtEtaPhiE(nom.lep_Pt_1, nom.lep_Eta_1, nom.lep_Phi_1,
nom.lep_E_1)
else:
lep.SetPtEtaPhiE(nom.lep_Pt_2, nom.lep_Eta_2, nom.lep_Phi_2,
nom.lep_E_2)
met = neutrinoPz(lep, nom.met_met, nom.met_phi)
w = lep + met
jet = LorentzVector()
#jet.SetPtEtaPhiE( nom.jet_Pt_0, nom.jet_Eta_0, nom.jet_Phi_0, nom.jet_E_0 )
if len(nom.jet_pt):
jet.SetPtEtaPhiE(nom.jet_pt[0], nom.jet_eta[0], nom.jet_phi[0],
nom.jet_e[0])
else:
jet.SetPtEtaPhiE(0, 0, 0, 0)
top = LorentzVector()
top = w + jet
topMassReco.append(top.M())
f.Close()
with root_open(inputPath, mode='a') as myfile:
topMassReco = np.asarray(topMassReco)
topMassReco.dtype = [('topMassReco', 'float64')]
topMassReco.dtype.names = ['topMassReco']
root_numpy.array2tree(topMassReco, tree=myfile.nominal)
myfile.write()
myfile.Close()
print(current)
#if current==200000:
# break
#if e.nJets_OR_T!=1:
# continue
#if e.nJets_OR_T_MV2c10_70!=1:
# continue
#if abs(e.Mll01 - 91.2e3) > 10e3 and abs(e.Mll02 - 91.2e3) > 10e3:
# continue
#if e.trilep_type==0: continue
lep = LorentzVector()
if abs(e.Mll02 - 91.2e3) < abs(e.Mll01 - 91.2e3):
lep.SetPtEtaPhiE( e.lep_Pt_1, e.lep_Eta_1, e.lep_Phi_1, e.lep_E_1 )
else:
lep.SetPtEtaPhiE( e.lep_Pt_2, e.lep_Eta_2, e.lep_Phi_2, e.lep_E_2 )
met = neutrinoPz(lep, e.met_met, e.met_phi)
w = lep+met
jet = LorentzVector()
jet.SetPtEtaPhiE( e.jets_Pt_0, e.jets_Eta_0, e.jets_Phi_0, e.jets_E_0 )
top = LorentzVector()
top = w+jet
topMassReco.append(top.M())
def create_dict(nom):
current = 0
events1l = []
events2l = []
decayDicts = []
bestScores = []
nEntries = nom.GetEntries()
for idx in range(nEntries):
if idx % 10000 == 0:
print(str(idx) + '/' + str(nEntries))
#if current%100000==0:
#break
nom.GetEntry(idx)
higgCand = LorentzVector()
lep4Vecs = []
jet4Vecs = []
btags = []
met = LorentzVector()
met.SetPtEtaPhiE(nom.MET_RefFinal_et, 0, nom.MET_RefFinal_phi,
nom.MET_RefFinal_et)
lepVec_0 = LorentzVector()
lepVec_0.SetPtEtaPhiE(nom.lep_Pt_0, nom.lep_Eta_0, nom.lep_Phi_0,
nom.lep_E_0)
lep4Vecs.append(lepVec_0)
lepVec_1 = LorentzVector()
lepVec_1.SetPtEtaPhiE(nom.lep_Pt_1, nom.lep_Eta_1, nom.lep_Phi_1,
nom.lep_E_1)
lep4Vecs.append(lepVec_1)
lepVec_2 = LorentzVector()
lepVec_2.SetPtEtaPhiE(nom.lep_Pt_2, nom.lep_Eta_2, nom.lep_Phi_2,
nom.lep_E_2)
lep4Vecs.append(lepVec_2)
for j in range(len(nom.m_pflow_jet_pt)): #nom.selected_jets'][i]:
jetVec = LorentzVector()
jetVec.SetPtEtaPhiM(nom.m_pflow_jet_pt[j], nom.m_pflow_jet_eta[j],
nom.m_pflow_jet_phi[j], nom.m_pflow_jet_m[j])
jet4Vecs.append(jetVec)
btags.append(nom.m_pflow_jet_flavor_weight_MV2c10[j])
combosTop = []
for l in range(len(lep4Vecs)):
for i in range(len(jet4Vecs) - 1):
for j in range(i + 1, len(jet4Vecs)):
comb = [l, i, j]
t = topDict(jet4Vecs[i], jet4Vecs[j], lep4Vecs[0],
lep4Vecs[1], lep4Vecs[2], met, btags[i],
btags[j], nom.m_pflow_jet_jvt[i],
nom.m_pflow_jet_jvt[j],
nom.m_pflow_jet_numTrk[i],
nom.m_pflow_jet_numTrk[j])
combosTop.append([t, comb])
#loop over combinations, score them in the BDT, figure out the best result
topDF = pd.DataFrame.from_dict([x[0] for x in combosTop])
topMat = xgb.DMatrix(topDF, feature_names=list(topDF))
topPred = topModel.predict(topMat)
topBest = np.argmax(topPred)
bestTopComb = combosTop[topBest][1]
topMatches = bestTopComb[1:]
combos1l = []
for l in range(1, len(lep4Vecs)):
for i in range(len(jet4Vecs) - 1):
for j in range(i + 1, len(jet4Vecs)):
comb = [l, i, j]
if l == 1:
k = higgs1lDict(
jet4Vecs[i], jet4Vecs[j], lep4Vecs[l], met,
nom.m_pflow_jet_flavor_weight_MV2c10[i],
nom.m_pflow_jet_flavor_weight_MV2c10[j],
lep4Vecs[0], lep4Vecs[2], nom.m_pflow_jet_jvt[i],
nom.m_pflow_jet_jvt[j], nom.m_pflow_jet_numTrk[i],
nom.m_pflow_jet_numTrk[j])
else:
k = higgs1lDict(
jet4Vecs[i], jet4Vecs[j], lep4Vecs[l], met,
nom.m_pflow_jet_flavor_weight_MV2c10[i],
nom.m_pflow_jet_flavor_weight_MV2c10[j],
lep4Vecs[0], lep4Vecs[1], nom.m_pflow_jet_jvt[i],
nom.m_pflow_jet_jvt[j], nom.m_pflow_jet_numTrk[i],
nom.m_pflow_jet_numTrk[j])
combos1l.append([k, comb])
combos2l = []
possCombs = [[0, 1, 2], [0, 2, 1]]
for comb in possCombs:
k = higgs2lDict(lep4Vecs[comb[0]], lep4Vecs[comb[1]],
lep4Vecs[comb[2]], met)
combos2l.append([k, [comb[0], comb[1]]])
#Run 2l XGB, find best match
df2l = pd.DataFrame.from_dict([x[0] for x in combos2l])
xgbMat2l = xgb.DMatrix(df2l, feature_names=list(df2l))
pred2l = higgs2lModel.predict(xgbMat2l)
best2l = np.argmax(pred2l)
bestComb2l = combos2l[best2l][1]
lepMatch2l = bestComb2l[1]
#Run 1l XGB, find best match
df1l = pd.DataFrame.from_dict([x[0] for x in combos1l])
xgbMat1l = xgb.DMatrix(df1l, feature_names=list(df1l))
pred1l = higgs1lModel.predict(xgbMat1l)
best1l = np.argmax(pred1l)
bestComb1l = combos1l[best1l][1]
lepMatch1l = bestComb1l[0]
jetMatches1l = bestComb1l[1:]
### Add decay dict
k = decayDict(lep4Vecs[0], lep4Vecs[1], lep4Vecs[2], met,
jet4Vecs[topMatches[0]], jet4Vecs[topMatches[1]])
k['nJets'] = nom.nJets_OR_T
k['nJets_MV2c10_70'] = nom.nJets_OR_T_MV2c10_70
k['higgs2l_score'] = pred2l[best2l]
k['higgs1l_score'] = pred1l[best1l]
decayDicts.append(k)
### Add 2l pt prediction dict
q = {}
q['comboScore'] = pred2l[best2l]
if lepMatch2l == 1:
q['lep_Pt_0'] = nom.lep_Pt_0
q['lep_Eta_0'] = nom.lep_Eta_0
phi_0 = nom.lep_Phi_0
q['lep_E_0'] = nom.lep_E_0
q['lep_Pt_1'] = nom.lep_Pt_1
q['lep_Eta_1'] = nom.lep_Eta_1
q['lep_Phi_1'] = calc_phi(phi_0, nom.lep_Phi_1)
q['lep_E_1'] = nom.lep_E_1
q['lep_Pt_2'] = nom.lep_Pt_2
q['lep_Eta_2'] = nom.lep_Eta_2
q['lep_Phi_2'] = calc_phi(phi_0, nom.lep_Phi_2)
q['lep_E_2'] = nom.lep_E_2
elif lepMatch2l == 2:
q['lep_Pt_0'] = nom.lep_Pt_0
q['lep_Eta_0'] = nom.lep_Eta_0
phi_0 = nom.lep_Phi_0
q['lep_E_0'] = nom.lep_E_0
q['lep_Pt_1'] = nom.lep_Pt_2
q['lep_Eta_1'] = nom.lep_Eta_2
q['lep_Phi_1'] = calc_phi(phi_0, nom.lep_Phi_2)
q['lep_E_1'] = nom.lep_E_2
q['lep_Pt_2'] = nom.lep_Pt_1
q['lep_Eta_2'] = nom.lep_Eta_1
q['lep_Phi_2'] = calc_phi(phi_0, nom.lep_Phi_1)
q['lep_E_2'] = nom.lep_E_1
n = 0
for i in topMatches:
q['top_Pt_' + str(n)] = nom.m_pflow_jet_pt[i]
q['top_Eta_' + str(n)] = nom.m_pflow_jet_eta[i]
q['top_E_' + str(n)] = jet4Vecs[i].E() #nom.m_pflow_jet_E[i]
q['top_Phi_' + str(n)] = calc_phi(phi_0, nom.m_pflow_jet_phi[i])
q['top_MV2c10_' + str(n)] = nom.m_pflow_jet_flavor_weight_MV2c10[i]
n += 1
q['MET'] = nom.MET_RefFinal_et
q['MET_phi'] = calc_phi(phi_0, nom.MET_RefFinal_phi)
events2l.append(q)
### Add 1l Pt prediction dict
y = {}
#y['higgs_pt'] = nom.higgs_pt
y['comboScore'] = pred1l[best1l]
if lepMatch1l == 1:
y['lep_Pt_H'] = nom.lep_Pt_1
y['lep_Eta_H'] = nom.lep_Eta_1
phi_0 = nom.lep_Phi_1
y['lep_E_H'] = nom.lep_E_1
y['lep_Pt_0'] = nom.lep_Pt_0
y['lep_Eta_0'] = nom.lep_Eta_0
y['lep_Phi_0'] = calc_phi(phi_0, nom.lep_Phi_0)
y['lep_E_0'] = nom.lep_E_0
y['lep_Pt_1'] = nom.lep_Pt_2
y['lep_Eta_1'] = nom.lep_Eta_2
y['lep_Phi_1'] = calc_phi(phi_0, nom.lep_Phi_2)
y['lep_E_1'] = nom.lep_E_2
elif lepMatch1l == 2:
y['lep_Pt_H'] = nom.lep_Pt_2
y['lep_Eta_H'] = nom.lep_Eta_2
phi_0 = nom.lep_Phi_2
y['lep_E_H'] = nom.lep_E_2
y['lep_Pt_0'] = nom.lep_Pt_0
y['lep_Eta_0'] = nom.lep_Eta_0
y['lep_Phi_0'] = calc_phi(phi_0, nom.lep_Phi_0)
y['lep_E_0'] = nom.lep_E_0
y['lep_Pt_1'] = nom.lep_Pt_1
y['lep_Eta_1'] = nom.lep_Eta_1
y['lep_Phi_1'] = calc_phi(phi_0, nom.lep_Phi_1)
y['lep_E_1'] = nom.lep_E_1
n = 0
for i in jetMatches1l: #nom.nJets_OR_T):
y['jet_Pt_h' + str(n)] = nom.m_pflow_jet_pt[i]
y['jet_Eta_h' + str(n)] = nom.m_pflow_jet_eta[i]
y['jet_E_h' + str(n)] = jet4Vecs[i].E() #nom.m_pflow_jet_E[i]
y['jet_Phi_h' + str(n)] = calc_phi(phi_0, nom.m_pflow_jet_phi[i])
y['jet_MV2c10_h' +
str(n)] = nom.m_pflow_jet_flavor_weight_MV2c10[i]
n += 1
n = 0
for i in topMatches: #bestBtags:#nom.nJets_OR_T):
y['top_Pt_' + str(n)] = nom.m_pflow_jet_pt[i]
y['top_Eta_' + str(n)] = nom.m_pflow_jet_eta[i]
y['top_E_' + str(n)] = jet4Vecs[i].E() #nom.m_pflow_jet_E[i]
y['top_Phi_' + str(n)] = calc_phi(phi_0, nom.m_pflow_jet_phi[i])
y['top_MV2c10_' + str(n)] = nom.m_pflow_jet_flavor_weight_MV2c10[i]
n += 1
y['MET'] = nom.MET_RefFinal_et
y['MET_phi'] = calc_phi(phi_0, nom.MET_RefFinal_phi)
events1l.append(y)
return decayDicts, events1l, events2l
