def work(self):
D4PD_model = RecoTauBlock + RecoJetBlock + EventVariables + TrueTauBlock + PartonBlock
# initialize the TreeChain of all input files (each containing one tree named self.metadata.treename)
tree = TreeChain(self.metadata.treename,
files=self.files,
events=self.events,
cache=True,
cache_size=10000000,
learn_entries=30)
# create output tree
self.output.cd()
D4PD = Tree(name=self.metadata.name, model=D4PD_model)
copied_variables = ['actualIntPerXing',
'averageIntPerXing']
copied_variables += mc_triggers
D4PD.set_buffer(tree.buffer, variables=copied_variables, create_branches=True, visible=False)
tree.always_read(copied_variables)
# set the event filters
# passthrough for MC for trigger acceptance studies
self.event_filters = EventFilterList([
GRLFilter(self.grl, passthrough = self.metadata.datatype != datasets.DATA),
PriVertex(),
LArError(),
JetCleaningLoose(passthrough = self.metadata.datatype != datasets.DATA),
#JetCleaningMedium(passthrough = self.metadata.datatype != datasets.DATA),
LArHole(),
#JetCrackVeto(),
ElectronVeto(),
MuonVeto(),
TauElectronVeto(),
TauMuonVeto(),
TauAuthorTrack(),
])
self.event_filters.insert(1, MCTriggers())
tree.filters += self.event_filters
cutflow = Cutflow()
# define tree collections
tree.define_collection(name="taus", prefix="tau_", size="tau_n", mix=TauFourMomentum)
# jet_eta etc is AntiKt4LCTopo in tau-perf D3PDs
tree.define_collection(name="jets", prefix="jet_", size="jet_n", mix=FourMomentum)
tree.define_collection(name="truetaus", prefix="trueTau_", size="trueTau_n", mix=MCTauFourMomentum)
tree.define_collection(name="mc", prefix="mc_", size="mc_n", mix=MCParticle)
tree.define_collection(name="muons", prefix="mu_staco_", size="mu_staco_n")
tree.define_collection(name="electrons", prefix="el_", size="el_n")
tree.define_collection(name="vertices", prefix="vxp_", size="vxp_n")
# define tree objects
D4PD.define_object(name='tau1', prefix='tau1_')
D4PD.define_object(name='tau2', prefix='tau2_')
D4PD.define_object(name='jet1', prefix='jet1_')
D4PD.define_object(name='jet2', prefix='jet2_')
"""
tree.define_association(origin='taus', target='truetaus', prefix='trueTauAssoc_', link='index')
tree.define_association(origin='truetaus', target='taus', prefix='tauAssoc_', link='index')
"""
# entering the main event loop...
for event in tree:
D4PD.reset()
cutflow.reset()
# tau selection
event.taus.select(lambda tau: tau.pt > 15*GeV)
# Jet selection
event.jets.select(lambda jet: jet.fourvect.P() > 25*GeV and abs(jet.emscale_eta) < 4.5)
"""
Get VBF jets
"""
# get partons (already sorted by eta in hepmc)
parton1, parton2 = hepmc.get_VBF_partons(event)
PartonBlock.set(D4PD, parton1, parton2)
D4PD.dR_quarks = parton1.fourvect.DeltaR(parton2.fourvect)
"""
Get true taus
"""
event.truetaus.select(lambda tau: tau.vis_Et > 10 * GeV and abs(tau.vis_eta) < 2.5)
if len(event.truetaus) > 2:
print "ERROR: too many true taus: %i" % len(event.truetaus)
D4PD.error = 1
D4PD.Fill()
continue
elif len(event.truetaus) < 2:
print "ERROR: too few true taus: %i" % len(event.truetaus)
D4PD.error = 2
D4PD.Fill()
continue
"""
fourvects = []
colors = []
radii = []
for thing in event.jets:
fourvects.append(thing.fourvect)
colors.append('blue')
radii.append(.4)
for parton in (parton1, parton2):
fourvects.append(parton.fourvect)
colors.append('green')
radii.append(.1)
for thing in event.taus:
fourvects.append(thing.fourvect)
colors.append('red')
radii.append(.2)
for tau in event.truetaus:
fourvects.append(tau.fourvect)
colors.append('purple')
radii.append(.1)
eventview.draw(event, fourvects, colors=colors, radii=radii)
"""
TrueTauBlock.set(D4PD, 1, event.truetaus[0])
TrueTauBlock.set(D4PD, 2, event.truetaus[1])
D4PD.dR_truetaus = event.truetaus[0].fourvect.DeltaR(event.truetaus[1].fourvect)
D4PD.dR_quark_tau = min([
parton1.fourvect.DeltaR(event.truetaus[0].fourvect),
parton2.fourvect.DeltaR(event.truetaus[0].fourvect),
parton1.fourvect.DeltaR(event.truetaus[1].fourvect),
parton2.fourvect.DeltaR(event.truetaus[1].fourvect),
])
taus = []
if event.taus:
for truetau in event.truetaus:
closest_tau = min(event.taus, key=lambda tau: utils.dR(tau.seedCalo_eta, tau.seedCalo_phi, truetau.vis_eta, truetau.vis_phi))
if utils.dR(closest_tau.seedCalo_eta, closest_tau.seedCalo_phi, truetau.vis_eta, truetau.vis_phi) < 0.2:
if closest_tau in taus:
# collision
D4PD.error = 3
break
taus.append(closest_tau)
if len(taus) < 2:
# collision
D4PD.Fill()
continue
"""
# Overlap removal between taus and jets
event.jets.select(lambda jet: not any([tau for tau in taus if
utils.dR(jet.emscale_eta, jet.emscale_phi, tau.seedCalo_eta, tau.seedCalo_phi) < .2]))
"""
jets = []
if event.jets:
for quark in (parton1, parton2):
closest_jet = min(event.jets, key=lambda jet: utils.dR(jet.eta, jet.phi, quark.eta, quark.phi))
if utils.dR(closest_jet.eta, closest_jet.phi, quark.eta, quark.phi) < 0.4:
if closest_jet in jets:
# collision
D4PD.error = 4
break
jets.append(closest_jet)
if len(jets) < 2:
# collision
D4PD.Fill()
continue
"""
Jet variables
"""
RecoJetBlock.set(D4PD, jets[0], jets[1])
"""
Reco tau variables
This must come after the RecoJetBlock is filled since
that sets the jet_beta for boosting the taus
"""
RecoTauBlock.set(event, D4PD, taus[0], taus[1])
D4PD.true_Mvis_tau1_tau2 = (D4PD.trueTau1_fourvect_vis + D4PD.trueTau2_fourvect_vis).M()
"""
MET
"""
METx = event.MET_RefFinal_etx
METy = event.MET_RefFinal_ety
MET_vect = Vector2(METx, METy)
MET_3vect = Vector3(METx, METy, 0.)
D4PD.MET = event.MET_RefFinal_et
D4PD.MET_phi = event.MET_RefFinal_phi
# HT TODO: Fix
sumET = event.MET_RefFinal_sumet
D4PD.HT = sumET
D4PD.numVertices = len([vtx for vtx in event.vertices if (vtx.type == 1 and vtx.nTracks >= 4) or
(vtx.type == 3 and vtx.nTracks >= 2)])
# fill output ntuple
# use reset=True to reset all variables to their defaults after the fill
# to avoid any values from this event carrying over into the next
D4PD.cutflow = cutflow.int()
D4PD.Fill()
self.output.cd()
D4PD.FlushBaskets()
D4PD.Write()
