def fourvect_vis(self):
vect = LorentzVector()
try:
vect.SetPtEtaPhiM(et2pt(self.vis_Et, self.vis_eta, self.vis_m),
self.eta, self.phi, self.m)
except ValueError:
log.warning("DOMAIN ERROR ON TRUTH 4-VECT: "
"Et: {0} eta: {1} m: {2}".format(
self.vis_Et, self.vis_eta, self.vis_m))
vect.SetPtEtaPhiM(0, self.eta, self.phi, self.m)
return vect
def mass(tau1, tau2, METpx, METpy):
"""
Calculate and return the collinear mass and momentum fractions
of tau1 and tau2
TODO: set visible mass of taus. 1.2 GeV for 3p and 0.8 GeV for 1p
"""
recTau1 = LorentzVector()
recTau2 = LorentzVector()
# tau 4-vector; synchronize for MMC calculation
if tau1.nTracks() < 3:
recTau1.SetPtEtaPhiM(tau1.pt(), tau1.eta(), tau1.phi(), 800.) # MeV
else:
recTau1.SetPtEtaPhiM(tau1.pt(), tau1.eta(), tau1.phi(), 1200.) # MeV
if tau2.nTracks() < 3:
recTau2.SetPtEtaPhiM(tau2.pt(), tau2.eta(), tau2.phi(), 800.) # MeV
else:
recTau2.SetPtEtaPhiM(tau2.pt(), tau2.eta(), tau2.phi(), 1200.) # MeV
K = ROOT.TMatrixD(2, 2)
K[0][0] = recTau1.Px()
K[0][1] = recTau2.Px()
K[1][0] = recTau1.Py()
K[1][1] = recTau2.Py()
if K.Determinant() == 0:
return -1., -1111., -1111.
M = ROOT.TMatrixD(2, 1)
M[0][0] = METpx
M[1][0] = METpy
Kinv = K.Invert()
X = Kinv * M
X1 = X(0, 0)
X2 = X(1, 0)
x1 = 1. / (1. + X1)
x2 = 1. / (1. + X2)
p1 = recTau1 * (1. / x1)
p2 = recTau2 * (1. / x2)
m_col = (p1 + p2).M()
m_vis = (recTau1 + recTau2).M()
return m_vis, m_col, x1, x2
def fourvect(self):
vect = LorentzVector()
vect.SetPtEtaPhiM(
self.pt,
self.eta,
self.phi,
self.m)
# self._particle.Mass() * GeV)
return vect
def fourvect_clbased(self):
vect = LorentzVector()
tau_numTrack = self.numTrack
tau_nPi0s = self.pi0_n
if tau_nPi0s == 0:
if self.track_n > 0:
sumTrk = LorentzVector()
for trk_ind in xrange(0, self.track_n):
curTrk = LorentzVector()
curTrk.SetPtEtaPhiM(self.track_atTJVA_pt[trk_ind],
self.track_atTJVA_eta[trk_ind],
self.track_atTJVA_phi[trk_ind], 139.8)
sumTrk += curTrk
vect.SetPtEtaPhiM(sumTrk.Pt(), sumTrk.Eta(), sumTrk.Phi(),
sumTrk.M())
else:
vect.SetPtEtaPhiM(self.pt, self.eta, self.phi, self.m)
elif tau_nPi0s == 1 or tau_nPi0s == 2:
if self.pi0_vistau_pt == 0:
vect.SetPtEtaPhiM(self.pt, self.eta, self.phi, self.m)
else:
vect.SetPtEtaPhiM(self.pi0_vistau_pt, self.pi0_vistau_eta,
self.pi0_vistau_phi, self.pi0_vistau_m)
else:
vect.SetPtEtaPhiM(self.pi0_vistau_pt, self.pi0_vistau_eta,
self.pi0_vistau_phi, self.pi0_vistau_m)
return vect
def transformVars(df):
'''
modifies the variables to create the ones that mv2 uses, inserts default values when needed, saves new variables
in the dataframe
Args:
-----
df: pandas dataframe containing all the interesting variables as extracted from the .root file
Returns:
--------
modified mv2-compliant dataframe
'''
from rootpy.vector import LorentzVector, Vector3
import pandautils as pup
# -- modify features and set default values
df['abs(jet_eta)'] = abs(df['jet_eta'])
# -- create new IPxD features
for (pu, pb, pc) in zip(df['jet_ip2d_pu'], df['jet_ip2d_pb'],
df['jet_ip2d_pc']):
pu[np.logical_or(pu >= 10, pu < -1)] = -1
pb[np.logical_or(pu >= 10, pu < -1)] = -1
pc[np.logical_or(pu >= 10, pu < -1)] = -1
for (pu, pb, pc) in zip(df['jet_ip3d_pu'], df['jet_ip3d_pb'],
df['jet_ip3d_pc']):
pu[pu >= 10] = -1
pb[pu >= 10] = -1
pc[pu >= 10] = -1
df['jet_ip2'] = (df['jet_ip2d_pb'] / df['jet_ip2d_pu']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip2_c'] = (df['jet_ip2d_pb'] / df['jet_ip2d_pc']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip2_cu'] = (df['jet_ip2d_pc'] / df['jet_ip2d_pu']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip3'] = (df['jet_ip3d_pb'] / df['jet_ip3d_pu']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip3_c'] = (df['jet_ip3d_pb'] / df['jet_ip3d_pc']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip3_cu'] = (df['jet_ip3d_pc'] / df['jet_ip3d_pu']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
# -- create new IPMP features
for (pu, pb, pc) in zip(df['jet_ipmp_pu'], df['jet_ipmp_pb'],
df['jet_ipmp_pc']):
pu[pu >= 10] = -1
pb[pu >= 10] = -1
pc[pu >= 10] = -1
df['jet_ip'] = (df['jet_ipmp_pb'] / df['jet_ipmp_pu']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip_c'] = (df['jet_ipmp_pb'] / df['jet_ipmp_pc']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
df['jet_ip_cu'] = (df['jet_ipmp_pc'] / df['jet_ipmp_pu']).apply(
lambda x: np.log(x)).apply(lambda x: _replaceInfNaN(x, -20))
# -- SV1 features
dx = df['jet_sv1_vtx_x'] - df['PVx']
dy = df['jet_sv1_vtx_y'] - df['PVy']
dz = df['jet_sv1_vtx_z'] - df['PVz']
v_jet = LorentzVector()
pv2sv = Vector3()
sv1_L3d = []
sv1_Lxy = []
dR = []
for index, dxi in enumerate(dx): # loop thru events
sv1_L3d_ev = []
sv1L_ev = []
dR_ev = []
for jet in xrange(len(dxi)): # loop thru jets
v_jet.SetPtEtaPhiM(df['jet_pt'][index][jet],
df['jet_eta'][index][jet],
df['jet_phi'][index][jet],
df['jet_m'][index][jet])
if (dxi[jet].size != 0):
sv1_L3d_ev.append(
np.sqrt(
pow(dx[index][jet], 2) + pow(dy[index][jet], 2) +
pow(dz[index][jet], 2))[0])
sv1L_ev.append(math.hypot(dx[index][jet], dy[index][jet]))
pv2sv.SetXYZ(dx[index][jet], dy[index][jet], dz[index][jet])
jetAxis = Vector3(v_jet.Px(), v_jet.Py(), v_jet.Pz())
dR_ev.append(pv2sv.DeltaR(jetAxis))
else:
dR_ev.append(-1)
sv1L_ev.append(-100)
sv1_L3d_ev.append(-100)
sv1_Lxy.append(sv1L_ev)
dR.append(dR_ev)
sv1_L3d.append(sv1_L3d_ev)
df['jet_sv1_dR'] = dR
df['jet_sv1_Lxy'] = sv1_Lxy
df['jet_sv1_L3d'] = sv1_L3d
# -- add more default values for sv1 variables
sv1_vtx_ok = pup.match_shape(
np.asarray([len(el) for event in df['jet_sv1_vtx_x'] for el in event]),
df['jet_pt'])
for (ok4event, sv1_ntkv4event, sv1_n2t4event, sv1_mass4event,
sv1_efrc4event,
sv1_sig34event) in zip(sv1_vtx_ok, df['jet_sv1_ntrkv'],
df['jet_sv1_n2t'], df['jet_sv1_m'],
df['jet_sv1_efc'], df['jet_sv1_sig3d']):
sv1_ntkv4event[np.asarray(ok4event) == 0] = -1
sv1_n2t4event[np.asarray(ok4event) == 0] = -1
sv1_mass4event[np.asarray(ok4event) == 0] = -1000
sv1_efrc4event[np.asarray(ok4event) == 0] = -1
sv1_sig34event[np.asarray(ok4event) == 0] = -100
# -- JF features
jf_dR = []
for eventN, (etas, phis, masses) in enumerate(
zip(df['jet_jf_deta'], df['jet_jf_dphi'],
df['jet_jf_m'])): # loop thru events
jf_dR_ev = []
for m in xrange(len(masses)): # loop thru jets
if (masses[m] > 0):
jf_dR_ev.append(np.sqrt(etas[m] * etas[m] + phis[m] * phis[m]))
else:
jf_dR_ev.append(-10)
jf_dR.append(jf_dR_ev)
df['jet_jf_dR'] = jf_dR
# -- add more default values for jf variables
for (jf_mass, jf_n2tv, jf_ntrkv, jf_nvtx, jf_nvtx1t, jf_efrc,
jf_sig3) in zip(df['jet_jf_m'], df['jet_jf_n2t'],
df['jet_jf_ntrkAtVx'], df['jet_jf_nvtx'],
df['jet_jf_nvtx1t'], df['jet_jf_efc'],
df['jet_jf_sig3d']):
jf_n2tv[jf_mass <= 0] = -1
jf_ntrkv[jf_mass <= 0] = -1
jf_nvtx[jf_mass <= 0] = -1
jf_nvtx1t[jf_mass <= 0] = -1
jf_mass[jf_mass <= 0] = -1e3
jf_efrc[jf_mass <= 0] = -1
jf_sig3[jf_mass <= 0] = -100
return df
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 fourvect(self):
vect = LorentzVector()
vect.SetPtEtaPhiM(self.pt * GeV, self.eta, self.phi, self.m)
return vect
def getTruthVis4Vector(self):
"""Get the LorentzVector for the visible truth tau """
vector = LorentzVector()
vector.SetPtEtaPhiM(self.vis_Et, self.vis_eta, self.vis_phi,
self.vis_m)
return vector
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