def neutrinoPz(lepton_fourVector, neutrino_pt, neutrino_phi):
"""
Calculate the z-component of the nu momentum by using the W-boson mass as the constraint
General idea:
If the discriminant is less than zero, then force it to be zero.
You solve with the discriminant set to zero to get a scaled value
for the term "mu" and "pt". The neutrino Pt will be the
(lepton_pz*scaled_pt)/lepton_pt
"""
m_w = 80.4e3 # mass of the W boson
delta_phi = lepton_fourVector.Phi() - neutrino_phi
# Simplifying term you get when solve for neutrino Pz using transverse mass of W boson
mu = (m_w)**2/2 + np.cos(delta_phi)*lepton_fourVector.Pt()*neutrino_pt
pz_l = lepton_fourVector.Pz() # Lepton Pz
pt_l = lepton_fourVector.Pt() # lepton Pt
e_l = lepton_fourVector.E() # Lepton energy
p_l = sqrt(pt_l**2 + pz_l**2) # Lepton momentum
el_px = lepton_fourVector.Px()
el_py = lepton_fourVector.Py()
nu_px = neutrino_pt*np.cos(neutrino_phi)
nu_py = neutrino_pt*np.sin(neutrino_phi)
if e_l == 0:
nu = LorentzVector()
return nu
discriminant = ((mu**2*pz_l**2)/(e_l**2 - pz_l**2)**2) - ((e_l**2*neutrino_pt**2 - mu**2)/(e_l**2 - pz_l**2))
if discriminant>0:
pZ_nu_A = mu*lepton_fourVector.Pz()/(pt_l**2) + sqrt(discriminant)
pZ_nu_B = mu*lepton_fourVector.Pz()/(pt_l**2) - sqrt(discriminant)
elif discriminant<0:
scaled_mu = sqrt(pt_l**2*e_l**2*neutrino_pt**2/(pz_l**2+pt_l**2))
scaled_pt = m_w**2/(2*pt_l*(1-np.cos(delta_phi)))
pZ_nu_A = pZ_nu_B = (pz_l*scaled_pt)/pt_l
elif discriminant==0:
pZ_nu_A = pZ_nu_B = mu*lepton_fourVector.Pz()/(pt_l**2)
if abs(pZ_nu_A) < abs(pZ_nu_B):
nu_pz = pZ_nu_A
else:
nu_pz = pZ_nu_B
nu = LorentzVector()
nu.SetPxPyPzE(nu_px, nu_py, nu_pz, neutrino_pt)
return nu
def work(self):
# get argument values
local = self.args.local
syst_terms = self.args.syst_terms
datatype = self.metadata.datatype
year = self.metadata.year
verbose = self.args.student_verbose
very_verbose = self.args.student_very_verbose
redo_selection = self.args.redo_selection
nominal_values = self.args.nominal_values
# get the dataset name
dsname = os.getenv('INPUT_DATASET_NAME', None)
if dsname is None:
# attempt to guess dsname from dirname
if self.files:
dsname = os.path.basename(os.path.dirname(self.files[0]))
# is this a signal sample?
# if so we will also keep some truth information in the output below
is_signal = datatype == datasets.MC and (
'_VBFH' in dsname or '_ggH' in dsname or '_ZH' in dsname
or '_WH' in dsname or '_ttH' in dsname)
log.info("DATASET: {0}".format(dsname))
log.info("IS SIGNAL: {0}".format(is_signal))
# is this an inclusive signal sample for overlap studies?
is_inclusive_signal = is_signal and '_inclusive' in dsname
# is this a BCH-fixed sample? (temporary)
is_bch_sample = 'r5470_r4540_p1344' in dsname
if is_bch_sample:
log.warning("this is a BCH-fixed r5470 sample")
# onfilechange will contain a list of functions to be called as the
# chain rolls over to each new file
onfilechange = []
count_funcs = {}
if datatype != datasets.DATA:
# count the weighted number of events
if local:
def mc_weight_count(event):
return event.hh_mc_weight
else:
def mc_weight_count(event):
return event.TruthEvent[0].weights()[0]
count_funcs = {
'mc_weight': mc_weight_count,
}
if local:
# local means running on the skims, the output of this script
# running on the grid
if datatype == datasets.DATA:
# merge the GRL fragments
merged_grl = goodruns.GRL()
def update_grl(student, grl, name, file, tree):
grl |= str(
file.Get('Lumi/%s' %
student.metadata.treename).GetString())
onfilechange.append((update_grl, (
self,
merged_grl,
)))
if datatype == datasets.DATA:
merged_cutflow = Hist(1, 0, 1, name='cutflow', type='D')
else:
merged_cutflow = Hist(2, 0, 2, name='cutflow', type='D')
def update_cutflow(student, cutflow, name, file, tree):
# record a cut-flow
year = student.metadata.year
datatype = student.metadata.datatype
cutflow[1].value += file.cutflow_event[1].value
if datatype != datasets.DATA:
cutflow[2].value += file.cutflow_event_mc_weight[1].value
onfilechange.append((update_cutflow, (
self,
merged_cutflow,
)))
else:
# NEED TO BE CONVERTED TO XAOD
# if datatype not in (datasets.EMBED, datasets.MCEMBED):
# # merge TrigConfTrees
# metadirname = '%sMeta' % self.metadata.treename
# trigconfchain = ROOT.TChain('%s/TrigConfTree' % metadirname)
# map(trigconfchain.Add, self.files)
# metadir = self.output.mkdir(metadirname)
# metadir.cd()
# trigconfchain.Merge(self.output, -1, 'fast keep')
# self.output.cd()
if datatype == datasets.DATA:
# merge GRL XML strings
merged_grl = goodruns.GRL()
# for fname in self.files:
# with root_open(fname) as f:
# for key in f.Lumi.keys():
# merged_grl |= goodruns.GRL(
# str(key.ReadObj().GetString()),
# from_string=True)
# lumi_dir = self.output.mkdir('Lumi')
# lumi_dir.cd()
# xml_string= ROOT.TObjString(merged_grl.str())
# xml_string.Write(self.metadata.treename)
# self.output.cd()
self.output.cd()
# create the output tree
model = get_model(datatype,
dsname,
prefix=None if local else 'hh_',
is_inclusive_signal=is_inclusive_signal)
log.info("Output Model:\n\n{0}\n\n".format(model))
outtree = Tree(name=self.metadata.treename, model=model)
if local:
tree = outtree
else:
tree = outtree.define_object(name='tree', prefix='hh_')
#tree.define_object(name='tau', prefix='tau_')
tree.define_object(name='tau1', prefix='tau1_')
tree.define_object(name='tau2', prefix='tau2_')
tree.define_object(name='truetau1', prefix='truetau1_')
tree.define_object(name='truetau2', prefix='truetau2_')
tree.define_object(name='jet1', prefix='jet1_')
tree.define_object(name='jet2', prefix='jet2_')
tree.define_object(name='jet3', prefix='jet3_')
mmc_objects = [
tree.define_object(name='mmc0', prefix='mmc0_'),
tree.define_object(name='mmc1', prefix='mmc1_'),
tree.define_object(name='mmc2', prefix='mmc2_'),
]
for mmc_obj in mmc_objects:
mmc_obj.define_object(name='resonance', prefix='resonance_')
# NEED TO BE CONVERTED TO XAOD
# trigger_emulation = TauTriggerEmulation(
# year=year,
# passthrough=local or datatype != datasets.MC or year > 2011,
# count_funcs=count_funcs)
# if not trigger_emulation.passthrough:
# onfilechange.append(
# (update_trigger_trees, (self, trigger_emulation,)))
# trigger_config = None
# if datatype not in (datasets.EMBED, datasets.MCEMBED):
# # trigger config tool to read trigger info in the ntuples
# trigger_config = get_trigger_config()
# # update the trigger config maps on every file change
# onfilechange.append((update_trigger_config, (trigger_config,)))
# define the list of event filters
if local and syst_terms is None and not redo_selection:
event_filters = None
else:
tau_ntrack_recounted_use_ntup = False
if year > 2011:
# peek at first tree to determine if the extended number of
# tracks is already stored
with root_open(self.files[0]) as test_file:
test_tree = test_file.Get(self.metadata.treename)
tau_ntrack_recounted_use_ntup = ('tau_out_track_n_extended'
in test_tree)
log.info(self.grl)
event_filters = EventFilterList([
GRLFilter(self.grl,
passthrough=(local
or (datatype not in (datasets.DATA,
datasets.EMBED))),
count_funcs=count_funcs),
CoreFlags(passthrough=local, count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# EmbeddingPileupPatch(
# passthrough=(
# local or year > 2011 or datatype != datasets.EMBED),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD (not a priority)
# PileupTemplates(
# year=year,
# passthrough=(
# local or is_bch_sample or datatype not in (
# datasets.MC, datasets.MCEMBED)),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# RandomSeed(
# datatype=datatype,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# BCHSampleRunNumber(
# passthrough=not is_bch_sample,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# RandomRunNumber(
# tree=tree,
# datatype=datatype,
# pileup_tool=pileup_tool,
# passthrough=local,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# trigger_emulation,
# NEED TO BE CONVERTED TO XAOD
# Triggers(
# year=year,
# tree=tree,
# datatype=datatype,
# passthrough=datatype in (datasets.EMBED, datasets.MCEMBED),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
PileupReweight_xAOD(
tree=tree,
passthrough=(local
or (datatype
not in (datasets.MC, datasets.MCEMBED))),
count_funcs=count_funcs),
PriVertex(passthrough=local, count_funcs=count_funcs),
LArError(passthrough=local, count_funcs=count_funcs),
TileError(passthrough=local, count_funcs=count_funcs),
TileTrips(passthrough=(local or datatype
in (datasets.MC, datasets.MCEMBED)),
count_funcs=count_funcs),
JetCalibration(datatype=datatype,
passthrough=local,
count_funcs=count_funcs),
JetResolution(
passthrough=(local
or (datatype
not in (datasets.MC, datasets.MCEMBED))),
count_funcs=count_funcs),
TauCalibration(datatype,
passthrough=local,
count_funcs=count_funcs),
# # truth matching must come before systematics due to
# # TES_TRUE/FAKE
# NEED TO BE CONVERTED TO XAOD
TrueTauSelection(passthrough=datatype == datasets.DATA,
count_funcs=count_funcs),
TruthMatching(passthrough=datatype == datasets.DATA,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
NvtxJets(tree=tree, count_funcs=count_funcs),
# # PUT THE SYSTEMATICS "FILTER" BEFORE
# # ANY FILTERS THAT REFER TO OBJECTS
# # BUT AFTER CALIBRATIONS
# # Systematics must also come before anything that refers to
# # thing.fourvect since fourvect is cached!
# NEED TO BE CONVERTED TO XAOD
# Systematics(
# terms=syst_terms,
# year=year,
# datatype=datatype,
# tree=tree,
# verbose=verbose,
# passthrough=not syst_terms,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# JetIsPileup(
# passthrough=(
# local or year < 2012 or
# datatype not in (datasets.MC, datasets.MCEMBED)),
# count_funcs=count_funcs),
JetCleaning(datatype=datatype,
year=year,
count_funcs=count_funcs),
ElectronVeto(el_sel='Medium', count_funcs=count_funcs),
MuonVeto(count_funcs=count_funcs),
TauPT(2, thresh=20 * GeV, count_funcs=count_funcs),
TauHasTrack(2, count_funcs=count_funcs),
TauEta(2, count_funcs=count_funcs),
TauElectronVeto(2, count_funcs=count_funcs),
TauMuonVeto(2, count_funcs=count_funcs),
TauCrack(2, count_funcs=count_funcs),
# # before selecting the leading and subleading taus
# # be sure to only consider good candidates
TauIDMedium(2, count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# but not used by default
# #TauTriggerMatchIndex(
# # config=trigger_config,
# # year=year,
# # datatype=datatype,
# # passthrough=datatype == datasets.EMBED,
# # count_funcs=count_funcs),
# Select two leading taus at this point
# 25 and 35 for data
# 20 and 30 for MC to leave room for TES uncertainty
TauLeadSublead(lead=(35 * GeV if datatype == datasets.DATA
or local else 30 * GeV),
sublead=(25 * GeV if datatype == datasets.DATA
or local else 20 * GeV),
count_funcs=count_funcs),
# taus are sorted (in decreasing order) by pT from here on
TauIDSelection(count_funcs=count_funcs),
TaudR(3.2, count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# but not used by default
# #TauTriggerMatchThreshold(
# # datatype=datatype,
# # tree=tree,
# # count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# TauTriggerEfficiency(
# year=year,
# datatype=datatype,
# tree=tree,
# tes_systematic=self.args.syst_terms and (
# Systematics.TES_TERMS & self.args.syst_terms),
# passthrough=datatype == datasets.DATA,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
PileupScale(tree=tree,
year=year,
datatype=datatype,
passthrough=local,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
TauIDScaleFactors(year=year,
passthrough=datatype == datasets.DATA,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# TauFakeRateScaleFactors(
# year=year,
# datatype=datatype,
# tree=tree,
# tes_up=(self.args.syst_terms is not None and
# (Systematics.TES_FAKE_TOTAL_UP in self.args.syst_terms or
# Systematics.TES_FAKE_FINAL_UP in self.args.syst_terms)),
# tes_down=(self.args.syst_terms is not None and
# (Systematics.TES_FAKE_TOTAL_DOWN in self.args.syst_terms or
# Systematics.TES_FAKE_FINAL_DOWN in self.args.syst_terms)),
# passthrough=datatype in (datasets.DATA, datasets.EMBED),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
HiggsPT(year=year,
tree=tree,
passthrough=not is_signal or local,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# TauTrackRecounting(
# year=year,
# use_ntup_value=tau_ntrack_recounted_use_ntup,
# passthrough=local,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# MCWeight(
# datatype=datatype,
# tree=tree,
# passthrough=local or datatype == datasets.DATA,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# EmbeddingIsolation(
# tree=tree,
# passthrough=(
# local or year < 2012 or
# datatype not in (datasets.EMBED, datasets.MCEMBED)),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# EmbeddingCorrections(
# tree=tree,
# year=year,
# passthrough=(
# local or
# datatype not in (datasets.EMBED, datasets.MCEMBED)),
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# EmbeddingTauSpinner(
# year=year,
# tree=tree,
# passthrough=(
# local or datatype not in (
# datasets.EMBED, datasets.MCEMBED)),
# count_funcs=count_funcs),
# # put MET recalculation after tau selection but before tau-jet
# # overlap removal and jet selection because of the RefAntiTau
# # MET correction
# NEED TO BE CONVERTED TO XAOD
# METRecalculation(
# terms=syst_terms,
# year=year,
# tree=tree,
# refantitau=not nominal_values,
# verbose=verbose,
# very_verbose=very_verbose,
# count_funcs=count_funcs),
TauJetOverlapRemoval(count_funcs=count_funcs),
JetPreselection(count_funcs=count_funcs),
NonIsolatedJet(tree=tree, count_funcs=count_funcs),
JetSelection(year=year, count_funcs=count_funcs),
RecoJetTrueTauMatching(passthrough=datatype == datasets.DATA
or local,
count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
# BCHCleaning(
# tree=tree,
# passthrough=year == 2011 or local,
# datatype=datatype,
# count_funcs=count_funcs),
# NEED TO BE CONVERTED TO XAOD
ClassifyInclusiveHiggsSample(
tree=tree,
passthrough=not is_inclusive_signal,
count_funcs=count_funcs),
])
# set the event filters
self.filters['event'] = event_filters
hh_buffer = TreeBuffer()
if local:
chain = TreeChain(
self.metadata.treename,
files=self.files,
# ignore_branches=ignore_branches,
events=self.events,
onfilechange=onfilechange,
filters=event_filters,
cache=True,
cache_size=50000000,
learn_entries=100)
buffer = TreeBuffer()
for name, value in chain._buffer.items():
if name.startswith('hh_'):
hh_buffer[name[3:]] = value
elif name in copied:
buffer[name] = value
outtree.set_buffer(hh_buffer, create_branches=False, visible=True)
outtree.set_buffer(buffer, create_branches=True, visible=False)
else:
root_chain = ROOT.TChain(self.metadata.treename)
for f in self.files:
log.info(f)
root_chain.Add(f)
# if len(self.files) != 1:
# raise RuntimeError('lenght of files has to be 1 for now (no xAOD chaining available)')
# self.files = self.files[0]
# root_chain = ROOT.TFile(self.files)
chain = xAODTree(root_chain,
filters=event_filters,
events=self.events)
define_objects(chain, datatype=datatype)
outtree.set_buffer(hh_buffer, create_branches=True, visible=False)
# create the MMC
mmc = mass.MMC(year=year)
# report which packages have been loaded
# externaltools.report()
self.output.cd()
# The main event loop
# the event filters above are automatically run for each event and only
# the surviving events are looped on
for event in chain:
if local and syst_terms is None and not redo_selection:
outtree.Fill()
continue
# sort taus and jets in decreasing order by pT
event.taus.sort(key=lambda tau: tau.pt(), reverse=True)
event.jets.sort(key=lambda jet: jet.pt(), reverse=True)
# tau1 is the leading tau
# tau2 is the subleading tau
tau1, tau2 = event.taus
tau1.fourvect = asrootpy(tau1.p4())
tau2.fourvect = asrootpy(tau2.p4())
beta_taus = (tau1.fourvect + tau2.fourvect).BoostVector()
tau1.fourvect_boosted = LorentzVector()
tau1.fourvect_boosted.copy_from(tau1.fourvect)
tau1.fourvect_boosted.Boost(beta_taus * -1)
tau2.fourvect_boosted = LorentzVector()
tau2.fourvect_boosted.copy_from(tau2.fourvect)
tau2.fourvect_boosted.Boost(beta_taus * -1)
jets = list(event.jets)
for jet in jets:
jet.fourvect = asrootpy(jet.p4())
jet1, jet2, jet3 = None, None, None
beta = None
if len(jets) >= 2:
jet1, jet2 = jets[:2]
# determine boost of system
# determine jet CoM frame
beta = (jet1.fourvect + jet2.fourvect).BoostVector()
tree.jet_beta.copy_from(beta)
jet1.fourvect_boosted = LorentzVector()
jet1.fourvect_boosted.copy_from(jet1.fourvect)
jet1.fourvect_boosted.Boost(beta * -1)
jet2.fourvect_boosted = LorentzVector()
jet2.fourvect_boosted.copy_from(jet2.fourvect)
jet2.fourvect_boosted.Boost(beta * -1)
tau1.min_dr_jet = min(tau1.fourvect.DeltaR(jet1.fourvect),
tau1.fourvect.DeltaR(jet2.fourvect))
tau2.min_dr_jet = min(tau2.fourvect.DeltaR(jet1.fourvect),
tau2.fourvect.DeltaR(jet2.fourvect))
# tau centrality (degree to which they are between the two jets)
tau1.centrality = eventshapes.eta_centrality(
tau1.fourvect.Eta(), jet1.fourvect.Eta(),
jet2.fourvect.Eta())
tau2.centrality = eventshapes.eta_centrality(
tau2.fourvect.Eta(), jet1.fourvect.Eta(),
jet2.fourvect.Eta())
# boosted tau centrality
tau1.centrality_boosted = eventshapes.eta_centrality(
tau1.fourvect_boosted.Eta(), jet1.fourvect_boosted.Eta(),
jet2.fourvect_boosted.Eta())
tau2.centrality_boosted = eventshapes.eta_centrality(
tau2.fourvect_boosted.Eta(), jet1.fourvect_boosted.Eta(),
jet2.fourvect_boosted.Eta())
# 3rd leading jet
if len(jets) >= 3:
jet3 = jets[2]
jet3.fourvect_boosted = LorentzVector()
jet3.fourvect_boosted.copy_from(jet3.fourvect)
jet3.fourvect_boosted.Boost(beta * -1)
elif len(jets) == 1:
jet1 = jets[0]
tau1.min_dr_jet = tau1.fourvect.DeltaR(jet1.fourvect)
tau2.min_dr_jet = tau2.fourvect.DeltaR(jet1.fourvect)
RecoJetBlock.set(tree, jet1, jet2, jet3, local=local)
# mass of ditau + leading jet system
if jet1 is not None:
tree.mass_tau1_tau2_jet1 = (tau1.fourvect + tau2.fourvect +
jet1.fourvect).M()
#####################################
# number of tracks from PV minus taus
#####################################
ntrack_pv = 0
ntrack_nontau_pv = 0
for vxp in event.vertices:
# primary vertex
if vxp.vertexType() == 1:
ntrack_pv = vxp.nTrackParticles()
ntrack_nontau_pv = ntrack_pv - tau1.nTracks(
) - tau2.nTracks()
break
tree.ntrack_pv = ntrack_pv
tree.ntrack_nontau_pv = ntrack_nontau_pv
#########################
# MET variables
#########################
MET = event.MET[0]
METx = MET.mpx()
METy = MET.mpy()
METet = MET.met()
MET_vect = Vector2(METx, METy)
MET_4vect = LorentzVector()
MET_4vect.SetPxPyPzE(METx, METy, 0., METet)
MET_4vect_boosted = LorentzVector()
MET_4vect_boosted.copy_from(MET_4vect)
if beta is not None:
MET_4vect_boosted.Boost(beta * -1)
tree.MET_et = METet
tree.MET_etx = METx
tree.MET_ety = METy
tree.MET_phi = MET.phi()
dPhi_tau1_tau2 = abs(tau1.fourvect.DeltaPhi(tau2.fourvect))
dPhi_tau1_MET = abs(tau1.fourvect.DeltaPhi(MET_4vect))
dPhi_tau2_MET = abs(tau2.fourvect.DeltaPhi(MET_4vect))
tree.dPhi_tau1_tau2 = dPhi_tau1_tau2
tree.dPhi_tau1_MET = dPhi_tau1_MET
tree.dPhi_tau2_MET = dPhi_tau2_MET
tree.dPhi_min_tau_MET = min(dPhi_tau1_MET, dPhi_tau2_MET)
tree.MET_bisecting = is_MET_bisecting(dPhi_tau1_tau2,
dPhi_tau1_MET, dPhi_tau2_MET)
sumET = MET.sumet()
tree.MET_sumet = sumET
if sumET != 0:
tree.MET_sig = ((2. * METet / GeV) /
(utils.sign(sumET) * sqrt(abs(sumET / GeV))))
else:
tree.MET_sig = -1.
tree.MET_centrality = eventshapes.phi_centrality(
tau1.fourvect, tau2.fourvect, MET_vect)
tree.MET_centrality_boosted = eventshapes.phi_centrality(
tau1.fourvect_boosted, tau2.fourvect_boosted,
MET_4vect_boosted)
tree.number_of_good_vertices = len(event.vertices)
##########################
# Jet and sum pt variables
##########################
tree.numJets = len(event.jets)
# sum pT with only the two leading jets
tree.sum_pt = sum([tau1.pt(), tau2.pt()] +
[jet.pt() for jet in jets[:2]])
# sum pT with all selected jets
tree.sum_pt_full = sum([tau1.pt(), tau2.pt()] +
[jet.pt() for jet in jets])
# vector sum pT with two leading jets and MET
tree.vector_sum_pt = sum([tau1.fourvect, tau2.fourvect] +
[jet.fourvect
for jet in jets[:2]] + [MET_4vect]).Pt()
# vector sum pT with all selected jets and MET
tree.vector_sum_pt_full = sum([tau1.fourvect, tau2.fourvect] +
[jet.fourvect for jet in jets] +
[MET_4vect]).Pt()
# resonance pT
tree.resonance_pt = sum([tau1.fourvect, tau2.fourvect,
MET_4vect]).Pt()
# #############################
# # tau <-> vertex association
# #############################
tree.tau_same_vertex = (tau1.vertex() == tau2.vertex())
tau1.vertex_prob = ROOT.TMath.Prob(tau1.vertex().chiSquared(),
int(tau1.vertex().numberDoF()))
tau2.vertex_prob = ROOT.TMath.Prob(tau2.vertex().chiSquared(),
int(tau2.vertex().numberDoF()))
# ##########################
# # MMC Mass
# ##########################
mmc_result = mmc.mass(tau1,
tau2,
METx,
METy,
sumET,
njets=len(event.jets))
for mmc_method, mmc_object in enumerate(mmc_objects):
mmc_mass, mmc_resonance, mmc_met = mmc_result[mmc_method]
if verbose:
log.info("MMC (method %d): %f" % (mmc_method, mmc_mass))
mmc_object.mass = mmc_mass
mmc_object.MET_et = mmc_met.Mod()
mmc_object.MET_etx = mmc_met.X()
mmc_object.MET_ety = mmc_met.Y()
mmc_object.MET_phi = math.pi - mmc_met.Phi()
if mmc_mass > 0:
FourMomentum.set(mmc_object.resonance, mmc_resonance)
# ############################
# # collinear and visible mass
# ############################
vis_mass, collin_mass, tau1_x, tau2_x = mass.collinearmass(
tau1, tau2, METx, METy)
tree.mass_vis_tau1_tau2 = vis_mass
tree.mass_collinear_tau1_tau2 = collin_mass
tau1.collinear_momentum_fraction = tau1_x
tau2.collinear_momentum_fraction = tau2_x
# # Fill the tau block
# # This must come after the RecoJetBlock is filled since
# # that sets the jet_beta for boosting the taus
RecoTauBlock.set(event, tree, datatype, tau1, tau2, local=local)
# NEED TO BE CONVERTED TO XAOD
if datatype != datasets.DATA:
TrueTauBlock.set(tree, tau1, tau2)
# fill the output tree
outtree.Fill(reset=True)
# externaltools.report()
# flush any baskets remaining in memory to disk
self.output.cd()
outtree.FlushBaskets()
outtree.Write()
if local:
if datatype == datasets.DATA:
xml_string = ROOT.TObjString(merged_grl.str())
xml_string.Write('lumi')
merged_cutflow.Write()
