def sum(self, axis=-1):
return awkward1.zip(
{
"x": awkward1.sum(self.x, axis=axis),
"y": awkward1.sum(self.y, axis=axis),
},
with_name="TwoVector",
)
def sum(self, axis=-1):
return awkward1.zip(
{
"x": awkward1.sum(self.x, axis=axis),
"y": awkward1.sum(self.y, axis=axis),
"z": awkward1.sum(self.z, axis=axis),
"t": awkward1.sum(self.t, axis=axis),
},
with_name="LorentzVector",
)
def sum(self, axis=-1):
"""Sum an array of vectors elementwise using `x` and `y` components"""
out = awkward1.zip(
{
"x": awkward1.sum(self.x, axis=axis),
"y": awkward1.sum(self.y, axis=axis),
},
with_name="TwoVector",
highlevel=False,
)
return awkward1._util.wrap(out, cache=self.cache, behavior=self.behavior)
def sum(self, axis=-1):
return awkward1.zip(
{
"x": awkward1.sum(self.x, axis=axis),
"y": awkward1.sum(self.y, axis=axis),
"z": awkward1.sum(self.z, axis=axis),
"t": awkward1.sum(self.t, axis=axis),
"charge": awkward1.sum(self.charge, axis=axis),
},
with_name="Candidate",
)
def sum(self, axis=-1):
"""Sum an array of vectors elementwise using `x`, `y`, `z`, `t`, and `charge` components"""
return awkward1.zip(
{
"x": awkward1.sum(self.x, axis=axis),
"y": awkward1.sum(self.y, axis=axis),
"z": awkward1.sum(self.z, axis=axis),
"t": awkward1.sum(self.t, axis=axis),
"charge": awkward1.sum(self.charge, axis=axis),
},
with_name="Candidate",
)
def sum(self, axis=-1):
"""Sum an array of vectors elementwise using `x`, `y`, and `z` components"""
out = awkward1.zip(
{
"x": awkward1.sum(self.x, axis=axis),
"y": awkward1.sum(self.y, axis=axis),
"z": awkward1.sum(self.z, axis=axis),
},
with_name="ThreeVector",
highlevel=False,
)
return awkward1.Array(out, behavior=self.behavior)
def flip_weight(self, electron):
f_1 = self.evaluator['el'](electron.pt[:,0:1], abs(electron.eta[:,0:1]))
f_2 = self.evaluator['el'](electron.pt[:,1:2], abs(electron.eta[:,1:2]))
# For custom measurements
#f_1 = yahist_2D_lookup(self.ratio, electron.pt[:,0:1], abs(electron.eta[:,0:1]))
#f_2 = yahist_2D_lookup(self.ratio, electron.pt[:,1:2], abs(electron.eta[:,1:2]))
# I'm using ak.prod and ak.sum to replace empty arrays by 1 and 0, respectively
weight = ak.sum(f_1/(1-f_1), axis=1)*ak.prod(1-f_2/(1-f_2), axis=1) + ak.sum(f_2/(1-f_2), axis=1)*ak.prod(1-f_1/(1-f_1), axis=1)
return weight
def lct_check(csc_accept, csc_chamber, lct_chamber, alct_chamber,
clct_chamber):
csc_pass = np.array(ak.num(csc_accept, axis=1) > 0)
if np.count_nonzero(csc_pass) == 0: return [0, 0, 0, 0]
csc_cham_pass = csc_chamber[csc_pass]
lct_cham_pass = lct_chamber[csc_pass]
alct_cham_pass = alct_chamber[csc_pass]
clct_cham_pass = clct_chamber[csc_pass]
#print(csc_cham_pass)
#print(lct_cham_pass)
#print(alct_cham_pass)
#print(clct_cham_pass)
lct_corr = 0
alct_corr = 0
clct_corr = 0
for i in range(len(csc_cham_pass)):
for j in range(len(csc_cham_pass[i])):
if csc_cham_pass[i][j] in lct_cham_pass[i]: lct_corr += 1
if csc_cham_pass[i][j] in alct_cham_pass[i]: alct_corr += 1
if csc_cham_pass[i][j] in clct_cham_pass[i]: clct_corr += 1
total_cham = ak.sum(ak.num(csc_cham_pass))
lct_eff = lct_corr / total_cham * 100
alct_eff = alct_corr / total_cham * 100
clct_eff = clct_corr / total_cham * 100
return [total_cham, lct_eff, alct_eff, clct_eff]
def process(self, events):
output = self.accumulator.identity()
dataset = events.metadata['dataset']
muons = ak.zip(
{
"pt": events.Muon_pt,
"eta": events.Muon_eta,
"phi": events.Muon_phi,
"mass": events.Muon_mass,
"charge": events.Muon_charge,
},
with_name="PtEtaPhiMCandidate")
cut = (ak.num(muons) == 2) & (ak.sum(muons.charge) == 0)
# add first and second muon in every event together
dimuon = muons[cut][:, 0] + muons[cut][:, 1]
output["sumw"][dataset] += len(events)
output["mass"].fill(
dataset=dataset,
mass=dimuon.mass,
)
return output
def test_awkward_accessor():
x = fletcher.FletcherContinuousArray([[1.0, 2.0], [], [3.0, 4.0, 5.0]])
y = np.zeros(len(x), dtype=float)
df = pd.DataFrame(dict(x=x, y=y))
df.to_root(".test.root", compression_jagged=None)
df = pd.read_root(".test.root")
assert df["x"].ak(0).sum().tolist() == [3.0, 0.0, 12.0]
assert awkward1.sum(df["x"], axis=-1).tolist() == [3.0, 0.0, 12.0]
def debug_eff(llp_accept, csc_accept, emtf_accept, gmt_accept, csc_endcap,
csc_station, csc_ring, csc_chamber, emtf_endcap, emtf_station):
# Create a mask for events in acceptance
llp_pass = np.array(ak.sum(llp_accept, axis=-1) > 0)
# Apppply mask to the csc, emtf, and gmt
csc_pass = csc_accept[llp_pass]
emtf_pass = emtf_accept[llp_pass]
gmt_pass = gmt_accept[llp_pass]
mismatch = ak.num(emtf_pass) > ak.flatten(gmt_pass) * 5
print("Mismatch:")
print(mismatch)
print('')
csc_pass_endcap = csc_endcap[llp_pass]
csc_pass_station = csc_station[llp_pass]
csc_pass_ring = csc_ring[llp_pass]
csc_pass_chamber = csc_chamber[llp_pass]
print("CSC Masked Array:")
print(csc_pass[mismatch])
print("CSC endcap:")
print(csc_pass_endcap[mismatch])
print("CSC station:")
print(csc_pass_station[mismatch])
print("CSC ring:")
print(csc_pass_ring[mismatch])
print("CSC chamber:")
print(csc_pass_chamber[mismatch])
print("")
emtf_pass_endcap = emtf_endcap[llp_pass]
emtf_pass_station = emtf_station[llp_pass]
print("EMTF Masked Array:")
print(emtf_pass[mismatch])
print("EMTF endcap:")
print(emtf_pass_endcap[mismatch])
print("EMTF sector:")
print(emtf_pass_station[mismatch])
print("")
print("GMT Masked Array:")
print(gmt_pass[mismatch])
print("")
# Calculate efficiency for each
n_acc = np.count_nonzero(llp_pass)
csc_eff = np.count_nonzero(ak.num(csc_pass, axis=1)) / n_acc * 100
emtf_eff = np.count_nonzero(ak.num(emtf_pass, axis=1)) / n_acc * 100
gmt_eff = np.count_nonzero(gmt_pass) / n_acc * 100
return [n_acc, csc_eff, emtf_eff, gmt_eff]
def get_eff(llp_accept, csc_accept, csc_accept_loose, csc_accept_tight,
emtf_accept, emtf_accept_loose, gmt_accept):
# Create a mask for events in acceptance
llp_pass = np.array(ak.sum(llp_accept, axis=-1) > 0)
n_acc = np.count_nonzero(llp_pass)
if n_acc == 0: return [0, 0, 0, 0]
# Apppply mask to the csc, emtf, and gmt
csc_pass = csc_accept[llp_pass]
emtf_pass = emtf_accept[llp_pass]
gmt_pass = gmt_accept[llp_pass]
csc_pass_loose = csc_accept_loose[llp_pass]
csc_pass_tight = csc_accept_tight[llp_pass]
emtf_pass_loose = emtf_accept_loose[llp_pass]
# Calculate efficiency for each
csc_eff = np.count_nonzero(ak.sum(csc_pass, axis=1)) / n_acc * 100
emtf_eff = np.count_nonzero(ak.sum(emtf_pass, axis=1)) / n_acc * 100
gmt_eff = np.count_nonzero((gmt_pass)) / n_acc * 100
csc_eff_loose = np.count_nonzero(ak.sum(csc_pass_loose,
axis=1)) / n_acc * 100
csc_eff_tight = np.count_nonzero(ak.sum(csc_pass_tight,
axis=1)) / n_acc * 100
emtf_eff_loose = np.count_nonzero(ak.sum(emtf_pass_loose,
axis=1)) / n_acc * 100
csc_eff_2loose = np.count_nonzero(
np.array(ak.sum(csc_pass_loose, axis=1) > 1)) / n_acc * 100
emtf_one = np.array(ak.sum(emtf_pass, axis=1) > 0)
emtf_two = np.array(ak.sum(emtf_pass_loose, axis=1) > 0)
emtf_or = emtf_one | emtf_two
emtf_eff_or = np.count_nonzero(emtf_or) / n_acc * 100
return [
n_acc, csc_eff_loose, csc_eff, csc_eff_tight, csc_eff_2loose, emtf_eff,
emtf_eff_loose, emtf_eff_or, gmt_eff
]
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)
## Muons
muon = Collections(ev, "Muon", "tightSSTTH").get()
vetomuon = Collections(ev, "Muon", "vetoTTH").get()
dimuon = choose(muon, 2)
SSmuon = ak.any((dimuon['0'].charge * dimuon['1'].charge) > 0, axis=1)
OSmuon = 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", "tightSSTTH").get()
vetoelectron = Collections(ev, "Electron", "vetoTTH").get()
dielectron = choose(electron, 2)
SSelectron = ak.any(
(dielectron['0'].charge * dielectron['1'].charge) > 0, axis=1)
OSelectron = 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
lepton = ak.concatenate([muon, electron], axis=1)
dilepton = cross(muon, electron)
SSlepton = ak.any((dilepton['0'].charge * dilepton['1'].charge) > 0,
axis=1)
OSlepton = ak.any((dilepton['0'].charge * dilepton['1'].charge) < 0,
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]
second_lepton = lepton[~(trailing_lepton_idx & leading_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
high_p_fwd = fwd[ak.singletons(ak.argmax(
fwd.p, axis=1))] # highest momentum spectator
high_pt_fwd = fwd[ak.singletons(ak.argmax(
fwd.pt_nom, axis=1))] # highest transverse momentum spectator
high_eta_fwd = fwd[ak.singletons(ak.argmax(abs(
fwd.eta), axis=1))] # most forward spectator
## Get the two leading b-jets in terms of btag score
high_score_btag = central[ak.argsort(central.btagDeepFlavB)][:, :2]
jf = cross(high_p_fwd, jet)
mjf = (jf['0'] + jf['1']).mass
deltaEta = abs(high_p_fwd.eta -
jf[ak.singletons(ak.argmax(mjf, axis=1))]['1'].eta)
deltaEtaMax = ak.max(deltaEta, axis=1)
mjf_max = ak.max(mjf, axis=1)
jj = choose(jet, 2)
mjj_max = ak.max((jj['0'] + jj['1']).mass, axis=1)
## 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)
lt = met_pt + ak.sum(muon.pt, axis=1) + ak.sum(electron.pt, axis=1)
ht_central = ak.sum(central.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,
b_direction='central',
c_direction='central'))
# lepton SFs
weight.add("lepton", self.leptonSF.get(electron, muon))
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.dilep_baseline(SS=False)
BL_minusNb = sel.dilep_baseline(SS=False, omit=['N_btag>0'])
output['N_b'].fill(dataset=dataset,
multiplicity=ak.num(btag)[BL_minusNb],
weight=weight.weight()[BL_minusNb])
if re.search(re.compile('MuonEG|DoubleMuon|DoubleEG|EGamma'), dataset):
#rle = ak.to_numpy(ak.zip([ev.run, ev.luminosityBlock, ev.event]))
run_ = ak.to_numpy(ev.run)
lumi_ = ak.to_numpy(ev.luminosityBlock)
event_ = ak.to_numpy(ev.event)
output['%s_run' % dataset] += processor.column_accumulator(
run_[BL])
output['%s_lumi' % dataset] += processor.column_accumulator(
lumi_[BL])
output['%s_event' % dataset] += processor.column_accumulator(
event_[BL])
# Now, take care of systematic unceratinties
if not re.search(re.compile('MuonEG|DoubleMuon|DoubleEG|EGamma'),
dataset):
alljets = getJets(ev, minPt=0, maxEta=4.7)
alljets = alljets[(alljets.jetId > 1)]
for var in self.variations:
# get the collections that change with the variations
btag = getBTagsDeepFlavB(
jet,
year=self.year) # should study working point for DeepJet
weight = Weights(len(ev))
weight.add("weight", ev.weight * cfg['lumi'][self.year])
weight.add("PU",
ev.puWeight,
weightUp=ev.puWeightUp,
weightDown=ev.puWeightDown,
shift=False)
if var == 'centralUp':
weight.add(
"btag",
self.btagSF.Method1a(btag,
light,
b_direction='central',
c_direction='up'))
elif var == 'centralDown':
weight.add(
"btag",
self.btagSF.Method1a(btag,
light,
b_direction='central',
c_direction='down'))
elif var == 'upCentral':
weight.add(
"btag",
self.btagSF.Method1a(btag,
light,
b_direction='up',
c_direction='central'))
elif var == 'downCentral':
weight.add(
"btag",
self.btagSF.Method1a(btag,
light,
b_direction='down',
c_direction='central'))
weight.add("lepton", self.leptonSF.get(electron, muon))
met = ev.MET
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=met,
)
BL = sel.dilep_baseline(SS=False)
BL_minusNb = sel.dilep_baseline(SS=False, omit=['N_btag>0'])
output['N_b_' + var].fill(
dataset=dataset,
multiplicity=ak.num(btag)[BL_minusNb],
weight=weight.weight()[BL_minusNb])
return output
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)
## Muons
muon = Collections(ev, "Muon", "tightSSTTH").get()
vetomuon = Collections(ev, "Muon", "vetoTTH").get()
dimuon = choose(muon, 2)
SSmuon = ak.any((dimuon['0'].charge * dimuon['1'].charge)>0, axis=1)
OSmuon = 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", "tightSSTTH").get()
vetoelectron = Collections(ev, "Electron", "vetoTTH").get()
dielectron = choose(electron, 2)
SSelectron = ak.any((dielectron['0'].charge * dielectron['1'].charge)>0, axis=1)
OSelectron = 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
lepton = ak.concatenate([muon, electron], axis=1)
dilepton = cross(muon, electron)
SSlepton = ak.any((dilepton['0'].charge * dilepton['1'].charge)>0, axis=1)
OSlepton = ak.any((dilepton['0'].charge * dilepton['1'].charge)<0, 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
high_p_fwd = fwd[ak.singletons(ak.argmax(fwd.p, axis=1))] # highest momentum spectator
high_pt_fwd = fwd[ak.singletons(ak.argmax(fwd.pt_nom, axis=1))] # highest transverse momentum spectator
high_eta_fwd = fwd[ak.singletons(ak.argmax(abs(fwd.eta), axis=1))] # most forward spectator
## Get the two leading b-jets in terms of btag score
high_score_btag = central[ak.argsort(central.btagDeepFlavB)][:,:2]
jf = cross(high_p_fwd, jet)
mjf = (jf['0']+jf['1']).mass
deltaEta = abs(high_p_fwd.eta - jf[ak.singletons(ak.argmax(mjf, axis=1))]['1'].eta)
deltaEtaMax = ak.max(deltaEta, axis=1)
mjf_max = ak.max(mjf, axis=1)
jj = choose(jet, 2)
mjj_max = ak.max((jj['0']+jj['1']).mass, axis=1)
## 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)
ht_central = ak.sum(central.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.dilep_baseline(cutflow=cutflow, SS=False)
# first, make a few super inclusive plots
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])
BL_minusNb = sel.dilep_baseline(SS=False, omit=['N_btag>0'])
output['N_b'].fill(dataset=dataset, multiplicity=ak.num(btag)[BL_minusNb], weight=weight.weight()[BL_minusNb])
output['N_central'].fill(dataset=dataset, multiplicity=ak.num(central)[BL], weight=weight.weight()[BL])
output['N_ele'].fill(dataset=dataset, multiplicity=ak.num(electron)[BL], weight=weight.weight()[BL])
output['N_mu'].fill(dataset=dataset, multiplicity=ak.num(electron)[BL], weight=weight.weight()[BL])
BL_minusFwd = sel.dilep_baseline(SS=False, omit=['N_fwd>0'])
output['N_fwd'].fill(dataset=dataset, multiplicity=ak.num(fwd)[BL_minusFwd], weight=weight.weight()[BL_minusFwd])
BL_minusMET = sel.dilep_baseline(SS=False, omit=['MET>50'])
output['MET'].fill(
dataset = dataset,
pt = ev.MET[BL_minusMET].pt,
phi = ev.MET[BL_minusMET].phi,
weight = weight.weight()[BL_minusMET]
)
#output['electron'].fill(
# dataset = dataset,
# pt = ak.to_numpy(ak.flatten(electron[BL].pt)),
# eta = ak.to_numpy(ak.flatten(electron[BL].eta)),
# phi = ak.to_numpy(ak.flatten(electron[BL].phi)),
# weight = weight.weight()[BL]
#)
#
#output['muon'].fill(
# dataset = dataset,
# pt = ak.to_numpy(ak.flatten(muon[BL].pt)),
# eta = ak.to_numpy(ak.flatten(muon[BL].eta)),
# phi = ak.to_numpy(ak.flatten(muon[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['fwd_jet'].fill(
dataset = dataset,
pt = ak.flatten(high_p_fwd[BL].pt_nom),
eta = ak.flatten(high_p_fwd[BL].eta),
phi = ak.flatten(high_p_fwd[BL].phi),
weight = weight.weight()[BL]
)
output['b1'].fill(
dataset = dataset,
pt = ak.flatten(high_score_btag[:, 0:1][BL].pt_nom),
eta = ak.flatten(high_score_btag[:, 0:1][BL].eta),
phi = ak.flatten(high_score_btag[:, 0:1][BL].phi),
weight = weight.weight()[BL]
)
output['b2'].fill(
dataset = dataset,
pt = ak.flatten(high_score_btag[:, 1:2][BL].pt_nom),
eta = ak.flatten(high_score_btag[:, 1:2][BL].eta),
phi = ak.flatten(high_score_btag[:, 1:2][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]
)
if re.search(re.compile('MuonEG|DoubleMuon|DoubleEG|EGamma'), dataset):
#rle = ak.to_numpy(ak.zip([ev.run, ev.luminosityBlock, ev.event]))
run_ = ak.to_numpy(ev.run)
lumi_ = ak.to_numpy(ev.luminosityBlock)
event_ = ak.to_numpy(ev.event)
output['%s_run'%dataset] += processor.column_accumulator(run_[BL])
output['%s_lumi'%dataset] += processor.column_accumulator(lumi_[BL])
output['%s_event'%dataset] += processor.column_accumulator(event_[BL])
# Now, take care of systematic unceratinties
if not re.search(re.compile('MuonEG|DoubleMuon|DoubleEG|EGamma'), dataset):
alljets = getJets(ev, minPt=0, maxEta=4.7)
alljets = alljets[(alljets.jetId>1)]
for var in self.variations:
# get the collections that change with the variations
jet = getPtEtaPhi(alljets, pt_var=var)
jet = jet[(jet.pt>25)]
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
high_p_fwd = fwd[ak.singletons(ak.argmax(fwd.p, axis=1))] # highest momentum spectator
high_pt_fwd = fwd[ak.singletons(ak.argmax(fwd.pt, axis=1))] # highest transverse momentum spectator
high_eta_fwd = fwd[ak.singletons(ak.argmax(abs(fwd.eta), axis=1))] # most forward spectator
## Get the two leading b-jets in terms of btag score
high_score_btag = central[ak.argsort(central.btagDeepFlavB)][:,:2]
met = ev.MET
met['pt'] = getattr(met, var)
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 = met,
)
BL = sel.dilep_baseline(SS=False)
# get the modified selection -> more difficult
#selection.add('N_jet>2_'+var, (ak.num(jet.pt)>=3)) # stupid bug here...
#selection.add('N_btag=2_'+var, (ak.num(btag)==2) )
#selection.add('N_central>1_'+var, (ak.num(central)>=2) )
#selection.add('N_fwd>0_'+var, (ak.num(fwd)>=1) )
#selection.add('MET>30_'+var, (getattr(ev.MET, var)>30) )
### Don't change the selection for now...
#bl_reqs = os_reqs + ['N_jet>2_'+var, 'MET>30_'+var, 'N_btag=2_'+var, 'N_central>1_'+var, 'N_fwd>0_'+var]
#bl_reqs_d = { sel: True for sel in bl_reqs }
#BL = selection.require(**bl_reqs_d)
# the OS selection remains unchanged
output['N_jet_'+var].fill(dataset=dataset, multiplicity=ak.num(jet)[BL], weight=weight.weight()[BL])
BL_minusFwd = sel.dilep_baseline(SS=False, omit=['N_fwd>0'])
output['N_fwd_'+var].fill(dataset=dataset, multiplicity=ak.num(fwd)[BL_minusFwd], weight=weight.weight()[BL_minusFwd])
BL_minusNb = sel.dilep_baseline(SS=False, omit=['N_btag>0'])
output['N_b_'+var].fill(dataset=dataset, multiplicity=ak.num(btag)[BL_minusNb], weight=weight.weight()[BL_minusNb])
output['N_central_'+var].fill(dataset=dataset, multiplicity=ak.num(central)[BL], weight=weight.weight()[BL])
# We don't need to redo all plots with variations. E.g., just add uncertainties to the jet plots.
output['j1_'+var].fill(
dataset = dataset,
pt = ak.flatten(jet.pt[:, 0:1][BL]),
eta = ak.flatten(jet.eta[:, 0:1][BL]),
phi = ak.flatten(jet.phi[:, 0:1][BL]),
weight = weight.weight()[BL]
)
output['b1_'+var].fill(
dataset = dataset,
pt = ak.flatten(high_score_btag[:, 0:1].pt[:, 0:1][BL]),
eta = ak.flatten(high_score_btag[:, 0:1].eta[:, 0:1][BL]),
phi = ak.flatten(high_score_btag[:, 0:1].phi[:, 0:1][BL]),
weight = weight.weight()[BL]
)
output['fwd_jet_'+var].fill(
dataset = dataset,
pt = ak.flatten(high_p_fwd[BL].pt),
#p = ak.flatten(high_p_fwd[BL].p),
eta = ak.flatten(high_p_fwd[BL].eta),
phi = ak.flatten(high_p_fwd[BL].phi),
weight = weight.weight()[BL]
)
BL_minusMET = sel.dilep_baseline(SS=False, omit=['MET>50'])
output['MET_'+var].fill(
dataset = dataset,
pt = getattr(ev.MET, var)[BL_minusMET],
phi = ev.MET[BL_minusMET].phi,
weight = weight.weight()[BL_minusMET]
)
return output
def dilep_baseline(self, omit=[], cutflow=None, tight=False, SS=True):
'''
give it a cutflow object if you want it to be filed.
cuts in the omit list will not be applied
'''
self.selection = PackedSelection()
is_dilep = ((ak.num(self.ele) + ak.num(self.mu))==2)
pos_charge = ((ak.sum(self.ele.pdgId, axis=1) + ak.sum(self.mu.pdgId, axis=1))<0)
neg_charge = ((ak.sum(self.ele.pdgId, axis=1) + ak.sum(self.mu.pdgId, axis=1))>0)
lep0pt = ((ak.num(self.ele[(self.ele.pt>25)]) + ak.num(self.mu[(self.mu.pt>25)]))>0)
lep1pt = ((ak.num(self.ele[(self.ele.pt>20)]) + ak.num(self.mu[(self.mu.pt>20)]))>1)
lepveto = ((ak.num(self.ele_veto) + ak.num(self.mu_veto))==2)
dimu = choose(self.mu, 2)
diele = choose(self.ele, 2)
dilep = cross(self.mu, self.ele)
if SS:
is_SS = ( ak.any((dimu['0'].charge * dimu['1'].charge)>0, axis=1) | \
ak.any((diele['0'].charge * diele['1'].charge)>0, axis=1) | \
ak.any((dilep['0'].charge * dilep['1'].charge)>0, axis=1) )
else:
is_OS = ( ak.any((dimu['0'].charge * dimu['1'].charge)<0, axis=1) | \
ak.any((diele['0'].charge * diele['1'].charge)<0, axis=1) | \
ak.any((dilep['0'].charge * dilep['1'].charge)<0, axis=1) )
lepton = ak.concatenate([self.ele, self.mu], axis=1)
lepton_pdgId_pt_ordered = ak.fill_none(
ak.pad_none(
lepton[ak.argsort(lepton.pt, ascending=False)].pdgId, 2, clip=True),
0)
triggers = getTriggers(self.events,
ak.flatten(lepton_pdgId_pt_ordered[:,0:1]),
ak.flatten(lepton_pdgId_pt_ordered[:,1:2]), year=self.year, dataset=self.dataset)
ht = ak.sum(self.jet_all.pt, axis=1)
st = self.met.pt + ht + ak.sum(self.mu.pt, axis=1) + ak.sum(self.ele.pt, axis=1)
self.selection.add('lepveto', lepveto)
self.selection.add('dilep', is_dilep)
#self.selection.add('filter', self.filters)
self.selection.add('trigger', triggers)
self.selection.add('p_T(lep0)>25', lep0pt)
self.selection.add('p_T(lep1)>20', lep1pt)
if SS:
self.selection.add('SS', is_SS )
else:
self.selection.add('OS', is_OS )
self.selection.add('N_jet>3', (ak.num(self.jet_all)>3) )
self.selection.add('N_jet>4', (ak.num(self.jet_all)>4) )
self.selection.add('N_central>2', (ak.num(self.jet_central)>2) )
self.selection.add('N_central>3', (ak.num(self.jet_central)>3) )
self.selection.add('N_btag>0', (ak.num(self.jet_btag)>0) )
self.selection.add('N_fwd>0', (ak.num(self.jet_fwd)>0) )
self.selection.add('MET>30', (self.met.pt>30) )
self.selection.add('MET>50', (self.met.pt>50) )
self.selection.add('ST>600', (st>600) )
ss_reqs = [
# 'filter',
'lepveto',
'dilep',
'p_T(lep0)>25',
'p_T(lep1)>20',
'trigger',
'SS' if SS else 'OS',
'N_jet>3',
'N_central>2',
'N_btag>0',
'MET>30',
'N_fwd>0',
]
if tight:
ss_reqs += [
'N_jet>4',
'N_central>3',
'ST>600',
'MET>50',
#'delta_eta',
]
ss_reqs_d = { sel: True for sel in ss_reqs if not sel in omit }
ss_selection = self.selection.require(**ss_reqs_d)
if cutflow:
#
cutflow_reqs_d = {}
for req in ss_reqs:
cutflow_reqs_d.update({req: True})
cutflow.addRow( req, self.selection.require(**cutflow_reqs_d) )
return ss_selection
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) >= 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)
## Electrons
electron = Collections(ev, "Electron", "tight").get()
electron = electron[(electron.pt > 20) & (abs(electron.eta) < 2.4)]
electron = electron[((electron.genPartIdx >= 0) &
(np.abs(electron.matched_gen.pdgId) == 11)
)] #from here on all leptons are gen-matched
##Muons
muon = Collections(ev, "Muon", "tight").get()
muon = muon[(muon.pt > 20) & (abs(muon.eta) < 2.4)]
muon = muon[((muon.genPartIdx >= 0) &
(np.abs(muon.matched_gen.pdgId) == 13))]
##Leptons
lepton = ak.concatenate([muon, electron], axis=1)
SSlepton = (ak.sum(lepton.charge, axis=1) != 0) & (ak.num(lepton) == 2)
OSlepton = (ak.sum(lepton.charge, axis=1) == 0) & (ak.num(lepton) == 2)
leading_lepton_idx = ak.singletons(ak.argmax(lepton.pt, axis=1))
leading_lepton = lepton[leading_lepton_idx]
#jets
jet = getJets(ev, minPt=40, maxEta=2.4, pt_var='pt')
jet = jet[ak.argsort(
jet.pt, 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)]
## MET -> can switch to puppi MET
met_pt = ev.MET.pt
met_phi = ev.MET.phi
# setting up the various weights
weight = Weights(len(ev))
weight2 = Weights(len(ev))
if not dataset == 'MuonEG':
# generator weight
weight.add("weight", ev.genWeight)
weight2.add("weight", ev.genWeight)
weight2.add("charge flip",
self.charge_flip_ratio.flip_weight(electron))
#selections
filters = getFilters(ev, year=self.year, dataset=dataset)
ss = (SSlepton)
os = (OSlepton)
jet_all = (ak.num(jet) >= 2)
selection = PackedSelection()
selection.add('filter', (filters))
selection.add('ss', ss)
selection.add('os', os)
selection.add('jet', jet_all)
bl_reqs = ['filter', 'jet']
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)
#outputs
output['N_jet'].fill(dataset=dataset,
multiplicity=ak.num(jet)[baseline],
weight=weight.weight()[baseline])
output['N_ele'].fill(dataset=dataset,
multiplicity=ak.num(lepton)[ss_sel],
weight=weight.weight()[ss_sel])
output['N_ele2'].fill(dataset=dataset,
multiplicity=ak.num(lepton)[os_sel],
weight=weight2.weight()[os_sel])
output["electron"].fill(
dataset=dataset,
pt=ak.to_numpy(ak.flatten(leading_lepton[ss_sel].pt)),
eta=abs(ak.to_numpy(ak.flatten(leading_lepton[ss_sel].eta))),
phi=ak.to_numpy(ak.flatten(leading_lepton[ss_sel].phi)),
weight=weight.weight()[ss_sel])
output["electron2"].fill(
dataset=dataset,
pt=ak.to_numpy(ak.flatten(leading_lepton[os_sel].pt)),
eta=abs(ak.to_numpy(ak.flatten(leading_lepton[os_sel].eta))),
phi=ak.to_numpy(ak.flatten(leading_lepton[os_sel].phi)),
weight=weight2.weight()[os_sel])
return output
genpart_pt = full_hh4b_samples['GenPart_pt']
genpart_status = full_hh4b_samples['GenPart_status']
full_hh4b_samples['GenPart_status']
full_hh4b_samples['GenPart_statusFlags']
higgses = (full_hh4b_samples['GenPart_pdgId'] == 25)
full_hh4b_samples['GenPart_status'][higgses]
np.unique(
ak.to_numpy(
ak.pad_none(full_hh4b_samples['GenPart_status'][higgses], 40, axis=1)))
ak.pad_none(hh4b_samples['GenPart_status'][higgses], 40, axis=1)
higgs_pt = genpart_pt[(full_hh4b_samples['GenPart_pdgId'] == 25)]
gt_300 = ak.sort(higgs_pt, axis=1)[:, -1] > 300
num_gt_300 = ak.sum(ak.sort(higgs_pt, axis=1)[:, -1] > 300)
num_gt_300 / len(higgs_pt)
fhiggs_pt = genpart_pt[(full_hh4b_samples['GenPart_pdgId'] == 25
)][full_hh4b_samples['GenPart_status'][higgses] == 22]
jet_pt = ak.pad_none(full_hh4b_samples['FatJet_pt'], 2, axis=1)[:, :2][gt_300]
jet_msd = ak.pad_none(full_hh4b_samples['FatJet_msoftdrop'], 2,
axis=1)[:, :2][gt_300]
ak.sum((jet_pt[:, 0] > 250) * (jet_pt[:, 1] > 250) * (jet_msd[:, 0] > 20) *
(jet_msd[:, 1] > 20)) / len(jet_pt)
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)>=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)
## 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
leading_electron_idx = ak.singletons(ak.argmax(electron.pt, axis=1))
leading_electron = electron[leading_electron_idx]
trailing_electron_idx = ak.singletons(ak.argmin(electron.pt, axis=1))
trailing_electron = electron[trailing_electron_idx]
leading_parent = find_first_parent(leading_electron.matched_gen)
trailing_parent = find_first_parent(trailing_electron.matched_gen)
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]
flipped_electron = flipped_electron[(ak.fill_none(flipped_electron.pt, 0)>0)]
flipped_electron = flipped_electron[~(ak.is_none(flipped_electron))]
n_flips = ak.num(flipped_electron)
##Muons
muon = Collections(ev, "Muon", "tightFCNC").get()
muon = muon[(muon.pt > 15) & (np.abs(muon.eta) < 2.4)]
muon = muon[(muon.genPartIdx >= 0)]
muon = muon[(np.abs(muon.matched_gen.pdgId)==13)] #from here, all muons are gen-matched
muon = muon[( (muon.genPartFlav==1) | (muon.genPartFlav==15) )] #and now they are all prompt
##Leptons
lepton = ak.concatenate([muon, electron], axis=1)
SSlepton = (ak.sum(lepton.charge, axis=1) != 0) & (ak.num(lepton)==2)
OSlepton = (ak.sum(lepton.charge, axis=1) == 0) & (ak.num(lepton)==2)
emulepton = (ak.num(electron) == 1) & (ak.num(muon) == 1)
no_mumu = (ak.num(muon) <= 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=40, maxEta=2.4, pt_var='pt')
jet = jet[ak.argsort(jet.pt, 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)]
## MET -> can switch to puppi MET
met_pt = ev.MET.pt
met_phi = ev.MET.phi
# setting up the various weights
weight = Weights( len(ev) )
weight2 = Weights( len(ev))
if not dataset=='MuonEG':
# generator weight
weight.add("weight", ev.genWeight)
weight2.add("weight", ev.genWeight)
weight2.add("charge flip", self.charge_flip_ratio.flip_weight(electron))
#selections
filters = getFilters(ev, year=self.year, dataset=dataset)
ss = (SSlepton)
os = (OSlepton)
jet_all = (ak.num(jet) >= 2)
diele = (ak.num(electron) == 2)
emu = (emulepton)
flips = (n_flips == 1)
no_flips = (n_flips == 0)
nmm = no_mumu
selection = PackedSelection()
selection.add('filter', (filters) )
selection.add('ss', ss )
selection.add('os', os )
selection.add('jet', jet_all )
selection.add('ee', diele)
selection.add('emu', emu)
selection.add('flip', flips)
selection.add('nflip', no_flips)
selection.add('no_mumu', nmm)
bl_reqs = ['filter'] + ['jet']
bl_reqs_d = { sel: True for sel in bl_reqs }
baseline = selection.require(**bl_reqs_d)
f_reqs = bl_reqs + ['flip'] + ['ss'] + ['ee']
f_reqs_d = {sel: True for sel in f_reqs}
flip_sel = selection.require(**f_reqs_d)
f2_reqs = bl_reqs + ['flip'] + ['ss'] + ['emu']
f2_reqs_d = {sel: True for sel in f2_reqs}
flip_sel2 = selection.require(**f2_reqs_d)
f3_reqs = bl_reqs + ['flip'] + ['ss'] + ['no_mumu']
f3_reqs_d = {sel: True for sel in f3_reqs}
flip_sel3 = selection.require(**f3_reqs_d)
nf_reqs = bl_reqs + ['nflip'] + ['os'] + ['ee']
nf_reqs_d = {sel: True for sel in nf_reqs}
n_flip_sel = selection.require(**nf_reqs_d)
nf2_reqs = bl_reqs + ['nflip'] + ['os'] + ['emu']
nf2_reqs_d = {sel: True for sel in nf2_reqs}
n_flip_sel2 = selection.require(**nf2_reqs_d)
nf3_reqs = bl_reqs + ['nflip'] + ['os'] + ['no_mumu']
nf3_reqs_d = {sel: True for sel in nf3_reqs}
n_flip_sel3 = selection.require(**nf3_reqs_d)
s_reqs = bl_reqs + ['ss'] + ['no_mumu']
s_reqs_d = { sel: True for sel in s_reqs }
ss_sel = selection.require(**s_reqs_d)
o_reqs = bl_reqs + ['os'] + ['no_mumu']
o_reqs_d = {sel: True for sel in o_reqs }
os_sel = selection.require(**o_reqs_d)
ees_reqs = bl_reqs + ['ss'] + ['ee']
ees_reqs_d = { sel: True for sel in ees_reqs }
eess_sel = selection.require(**ees_reqs_d)
eeo_reqs = bl_reqs + ['os'] + ['ee']
eeo_reqs_d = {sel: True for sel in eeo_reqs }
eeos_sel = selection.require(**eeo_reqs_d)
ems_reqs = bl_reqs + ['ss'] + ['emu']
ems_reqs_d = { sel: True for sel in ems_reqs }
emss_sel = selection.require(**ems_reqs_d)
emo_reqs = bl_reqs + ['os'] + ['emu']
emo_reqs_d = {sel: True for sel in emo_reqs }
emos_sel = selection.require(**emo_reqs_d)
#outputs
output['N_jet'].fill(dataset=dataset, multiplicity=ak.num(jet)[baseline], weight=weight.weight()[baseline])
output['N_ele'].fill(dataset=dataset, multiplicity=ak.num(lepton)[ss_sel], weight=weight.weight()[ss_sel])
output['N_ele2'].fill(dataset=dataset, multiplicity=ak.num(lepton)[os_sel], weight=weight2.weight()[os_sel])
output['electron_flips'].fill(dataset=dataset, multiplicity = n_flips[flip_sel], weight=weight.weight()[flip_sel])
output['electron_flips2'].fill(dataset=dataset, multiplicity = n_flips[n_flip_sel], weight=weight2.weight()[n_flip_sel])
output['electron_flips3'].fill(dataset=dataset, multiplicity = n_flips[flip_sel2], weight=weight.weight()[flip_sel2])
output['electron_flips4'].fill(dataset=dataset, multiplicity = n_flips[n_flip_sel2], weight=weight2.weight()[n_flip_sel2])
output["electron"].fill(
dataset = dataset,
pt = ak.to_numpy(ak.flatten(leading_electron[flip_sel3].pt)),
eta = np.abs(ak.to_numpy(ak.flatten(leading_electron[flip_sel3].eta))),
weight = weight.weight()[flip_sel3]
)
output["electron2"].fill(
dataset = dataset,
pt = ak.to_numpy(ak.flatten(leading_electron[n_flip_sel3].pt)),
eta = np.abs(ak.to_numpy(ak.flatten(leading_electron[n_flip_sel3].eta))),
weight = weight2.weight()[n_flip_sel3]
)
output["flipped_electron"].fill(
dataset = dataset,
pt = ak.to_numpy(ak.flatten(leading_electron[flip_sel].pt)),
eta = np.abs(ak.to_numpy(ak.flatten(leading_electron[flip_sel].eta))),
weight = weight.weight()[flip_sel]
)
output["flipped_electron2"].fill(
dataset = dataset,
pt = ak.to_numpy(ak.flatten(leading_electron[n_flip_sel].pt)),
eta = np.abs(ak.to_numpy(ak.flatten(leading_electron[n_flip_sel].eta))),
weight = weight2.weight()[n_flip_sel]
)
output["flipped_electron3"].fill(
dataset = dataset,
pt = ak.to_numpy(ak.flatten(leading_electron[flip_sel2].pt)),
eta = np.abs(ak.to_numpy(ak.flatten(leading_electron[flip_sel2].eta))),
weight = weight.weight()[flip_sel2]
)
output["flipped_electron4"].fill(
dataset = dataset,
pt = ak.to_numpy(ak.flatten(leading_electron[n_flip_sel2].pt)),
eta = np.abs(ak.to_numpy(ak.flatten(leading_electron[n_flip_sel2].eta))),
weight = weight2.weight()[n_flip_sel2]
)
#output["lepton_parent"].fill(
# dataset = dataset,
# pdgID = np.abs(ak.to_numpy(ak.flatten(leading_parent[ss_sel]))),
# weight = weight.weight()[ss_sel]
#)
#
#output["lepton_parent2"].fill(
# dataset = dataset,
# pdgID = np.abs(ak.to_numpy(ak.flatten(trailing_parent[ss_sel]))),
# weight = weight.weight()[ss_sel]
#)
return output
lct_trigger_rate = []
alct_daq_rate = []
clct_daq_rate = []
lct_daq_rate = []
gem_trigger_rate = []
gem_daq_rate = []
## trigger rates for each station
for ids in range(0, 9):
station = id_to_station[ids][0]
ring = id_to_station[ids][1]
cuts = ((csc_alct.bx == 3) & (csc_alct.station == station) &
(csc_alct.ring == ring))
ak.to_list(cuts)
passEvents = ak.sum(cuts)
objectRate = passEvents * normalization
print(
"Trigger Rate for ALCT for CSC station = %d, ring = %d (BX3): %f kHz" %
(station, ring, objectRate))
for ids in range(0, 9):
station = id_to_station[ids][0]
ring = id_to_station[ids][1]
cuts = ((csc_clct.bx == 7) & (csc_clct.station == station) &
(csc_clct.ring == ring))
ak.to_list(cuts)
passEvents = ak.sum(cuts)
objectRate = passEvents * normalization
print(
"Trigger Rate for CLCT for CSC station = %d, ring = %d (BX7): %f kHz" %
def process(self, df):
output = self.accumulator.identity()
# important variables
obj = Objects(df)
mask = bl.getBaselineMask(df)
electrons = obj.goodElectrons()[mask]
muons = obj.goodMuons()[mask]
jets = obj.goodJets()[mask]
madHT_cut = df['madHT'][mask]
met = df['MET'][mask]
metPhi = df['METPhi'][mask]
tracks = obj.goodTracks()[mask]
output['cutflow']['all events'] += jets.size
# Hack: defining weights: same length as the number of events in the chunk
# Check if this is what other people do too
luminosity = 21071.0 + 38654.0
evtw = df['Weight'][mask] * luminosity
ew = utl.awkwardReshape(electrons, evtw)
mw = utl.awkwardReshape(muons, evtw)
jw = utl.awkwardReshape(jets, evtw)
tw = utl.awkwardReshape(tracks, evtw)
# Getting subset of variables based on number of AK8 jets
# calculating event variables
ht = ak.sum(jets.pt, axis=1)
st = ht + met
#dPhiMinJ = utl.deltaPhi(fjets.phi,metPhi).min()
dPhiMinj = utl.deltaPhi(jets.phi, metPhi).min()
#
# Trigger Study...
#
cut_met = met > 250
cut_ht_offline = ht > 1200
cut_ht_scouting = ht > 500
jets_met = jets[cut_met]
jets_ht_off = jets[cut_ht_offline]
jets_ht_scout = jets[cut_ht_scouting]
output['trigger']['all events'] += jets.size
output['trigger']['met'] += jets_met.size
output['trigger']['ht_offline'] += jets_ht_off.size
output['trigger']['ht_scouting'] += jets_ht_scout.size
## at least 1 AK4 Jet
cut_1j = jets.counts >= 1
jets_1j = jets[cut_1j]
metPhi_1j = metPhi[cut_1j]
evtw_1j = evtw[cut_1j]
dPhij1 = utl.deltaPhi(jets_1j.phi[:, 0], metPhi_1j)
output['cutflow']['one jet'] += jets_1j.size
## at least 2 AK4 Jets
cut_2j = jets.counts >= 2
jets_2j = jets[cut_2j]
metPhi_2j = metPhi[cut_2j]
evtw_2j = evtw[cut_2j]
j1_eta = np.array(jets_2j.eta[:, 0])
j2_eta = np.array(jets_2j.eta[:, 1])
j1_phi = np.array(jets_2j.phi[:, 0])
j2_phi = np.array(jets_2j.phi[:, 1])
dEtaj12 = abs(j1_eta - j2_eta)
deltaR12j = utl.delta_R(j1_phi, j2_phi, j1_eta, j2_eta)
dPhij1_2j = utl.deltaPhi(j1_phi, metPhi_2j)
dPhij2 = utl.deltaPhi(j2_phi, metPhi_2j)
output['cutflow']['two jets'] += jets_2j.size
### at least 1 AK8 Jet
#cut_1fj = fjets.counts >= 1
#fjets_1fj = fjets[cut_1fj]
#metPhi_1fj = metPhi[cut_1fj]
#evtw_1fj = evtw[cut_1fj]
#dPhiJ1 = utl.deltaPhi(fjets_1fj.phi[:,0],metPhi_1fj)
### at least 2 AK8 Jets
#cut_2fj = fjets.counts >= 2
#fjets_2fj = fjets[cut_2fj]
#metPhi_2fj = metPhi[cut_2fj]
#evtw_2fj = evtw[cut_2fj]
#J1_eta = np.array(fjets_2fj.eta[:,0])
#J2_eta = np.array(fjets_2fj.eta[:,1])
#J1_phi = np.array(fjets_2fj.phi[:,0])
#J2_phi = np.array(fjets_2fj.phi[:,1])
#dEtaJ12 = abs(J1_eta - J2_eta)
#deltaR12J = utl.delta_R(J1_phi,J2_phi,J1_eta,J2_eta)
#dPhiJ1_2fj = utl.deltaPhi(J1_phi,metPhi_2fj)
#dPhiJ2 = utl.deltaPhi(J2_phi,metPhi_2fj)
## twofjets = (fjets.counts >= 2)
## difjets = fjets[twofjets]
## ptcut = (difjets.pt[:,0] > 200) & (difjets.pt[:,1] > 200)
## difjets_pt200 = difjets[ptcut]
#twofjets = (fjets.counts >= 2)
#output['cutflow']['two fjets'] += twofjets.sum()
## difjets = fjets[twofjets]
## difjets_pt200 = difjets[ptcut]
## output['jtpt'].fill(dataset=dataset, pt=fjets.pt.flatten())
## output['jteta'].fill(dataset=dataset, eta=fjets.eta.flatten())
output['h_ntracks'].fill(ntracks=tracks.counts.flatten(), weight=evtw)
output['h_njets'].fill(njets=jets.counts.flatten(), weight=evtw)
output['h_ht'].fill(ht=ht, weight=evtw)
output['h_st'].fill(st=st, weight=evtw)
output['h_met'].fill(MET=met, weight=evtw)
#output['h_jPt'].fill(pt=jets.pt.flatten(),weight=ak.flatten(jw))
#output['h_jEta'].fill(eta=jets.eta.flatten(),weight=ak.flatten(jw))
#output['h_jPhi'].fill(phi=jets.phi.flatten(),weight=ak.flatten(jw))
#output['h_jAxismajor'].fill(axismajor=jets.axismajor.flatten(),weight=ak.flatten(jw))
#output['h_jAxisminor'].fill(axisminor=jets.axisminor.flatten(),weight=ak.flatten(jw))
#output['h_jPtD'].fill(ptD=jets.ptD.flatten(),weight=ak.flatten(jw))
#output['h_jPtAK8'].fill(pt=fjets.pt.flatten(),weight=ak.flatten(fjw))
#output['h_jEtaAK8'].fill(eta=fjets.eta.flatten(),weight=ak.flatten(fjw))
#output['h_jPhiAK8'].fill(phi=fjets.phi.flatten(),weight=ak.flatten(fjw))
#output['h_jAxismajorAK8'].fill(axismajor=fjets.axismajor.flatten(),weight=ak.flatten(fjw))
#output['h_jAxisminorAK8'].fill(axisminor=fjets.axisminor.flatten(),weight=ak.flatten(fjw))
#output['h_jGirthAK8'].fill(girth=fjets.girth.flatten(),weight=ak.flatten(fjw))
#output['h_jPtDAK8'].fill(ptD=fjets.ptD.flatten(),weight=ak.flatten(fjw))
#output['h_jTau1AK8'].fill(tau1=fjets.tau1.flatten(),weight=ak.flatten(fjw))
#output['h_jTau2AK8'].fill(tau2=fjets.tau2.flatten(),weight=ak.flatten(fjw))
#output['h_jTau3AK8'].fill(tau3=fjets.tau3.flatten(),weight=ak.flatten(fjw))
#output['h_jTau21AK8'].fill(tau21=fjets[fjets.tau1 > 0].tau2.flatten()/fjets[fjets.tau1 > 0].tau1.flatten(),weight=ak.flatten(fjw)[(fjets.tau1 > 0).flatten()])
#output['h_jTau32AK8'].fill(tau32=fjets[fjets.tau2 > 0].tau3.flatten()/fjets[fjets.tau2 > 0].tau2.flatten(),weight=ak.flatten(fjw)[(fjets.tau2 > 0).flatten()])
#output['h_jSoftDropMassAK8'].fill(softDropMass=fjets.softDropMass.flatten(),weight=ak.flatten(fjw))
#output['h_dEtaJ12'].fill(dEtaJ12=dEtaJ12,weight=evtw_2fj)
#output['h_dRJ12'].fill(dRJ12=deltaR12J,weight=evtw_2fj)
#output['h_dPhiJ1MET'].fill(dPhiJMET=dPhiJ1,weight=evtw_1fj)
#output['h_dPhiJ2MET'].fill(dPhiJMET=dPhiJ2,weight=evtw_2fj)
#output['h_dPhiMinJMET'].fill(dPhiJMET=dPhiMinJ,weight=evtw)
#output['h_dPhiJ1METrdPhiJ2MET'].fill(dPhiJ1METrdPhiJ2MET=dPhiJ1_2fj[dPhiJ2>0]/dPhiJ2[dPhiJ2>0],weight=evtw_2fj[dPhiJ2>0])
#output['h_mT'].fill(mT=mtAK8,weight=evtw)
#output['h_METrHT_pt30'].fill(METrHT_pt30=met[ht>0]/ht[ht>0],weight=evtw[ht>0])
#output['h_METrST_pt30'].fill(METrST_pt30=met[st>0]/st[st>0],weight=evtw[st>0])
## Cut: at least 2 AK8 jets
#output['h_njets_ge2AK8j'].fill(njets=jets[cut_2fj].counts.flatten(),weight=evtw[cut_2fj])
#output['h_njetsAK8_ge2AK8j'].fill(njets=fjets[cut_2fj].counts.flatten(),weight=evtw[cut_2fj])
#output['h_ht_ge2AK8j'].fill(ht=ht[cut_2fj],weight=evtw[cut_2fj])
#output['h_st_ge2AK8j'].fill(st=st[cut_2fj],weight=evtw[cut_2fj])
#output['h_met_ge2AK8j'].fill(MET=met[cut_2fj],weight=evtw[cut_2fj])
#output['h_dPhiJ1MET_ge2AK8j'].fill(dPhiJMET=dPhiJ1[fjets_1fj.counts >= 2],weight=evtw_1fj[fjets_1fj.counts >= 2])
#output['h_dPhiMinJMET_ge2AK8j'].fill(dPhiJMET=dPhiMinJ[cut_2fj],weight=evtw[cut_2fj])
#output['h_METrHT_pt30_ge2AK8j'].fill(METrHT_pt30=met[(cut_2fj) & (ht>0)]/ht[(cut_2fj) & (ht>0)],weight=evtw[(cut_2fj) & (ht>0)])
#output['h_METrST_pt30_ge2AK8j'].fill(METrST_pt30=met[(cut_2fj) & (st>0)]/st[(cut_2fj) & (st>0)],weight=evtw[(cut_2fj) & (st>0)])
#output['h_mT_ge2AK8j'].fill(mT=mtAK8[cut_2fj],weight=evtw[cut_2fj])
## more AK4 jets variables
#output['h_dEtaj12'].fill(dEtaJ12=dEtaj12,weight=evtw_2j)
#output['h_dRj12'].fill(dRJ12=deltaR12j,weight=evtw_2j)
#output['h_dPhij1MET'].fill(dPhiJMET=dPhij1,weight=evtw_1j)
#output['h_dPhij2MET'].fill(dPhiJMET=dPhij2,weight=evtw_2j)
#output['h_dPhiMinjMET'].fill(dPhiJMET=dPhiMinj,weight=evtw)
#output['h_dPhij1METrdPhij2MET'].fill(dPhiJ1METrdPhiJ2MET=dPhij1_2j[dPhij2>0]/dPhij2[dPhij2>0],weight=evtw_2j[dPhij2>0])
## Cut: at least 2 AK4 jets
#output['h_njets_ge2AK4j'].fill(njets=jets[cut_2j].counts.flatten(),weight=evtw[cut_2j])
#output['h_njetsAK8_ge2AK4j'].fill(njets=fjets[cut_2j].counts.flatten(),weight=evtw[cut_2j])
#output['h_ht_ge2AK4j'].fill(ht=ht[cut_2j],weight=evtw[cut_2j])
#output['h_st_ge2AK4j'].fill(st=st[cut_2j],weight=evtw[cut_2j])
#output['h_met_ge2AK4j'].fill(MET=met[cut_2j],weight=evtw[cut_2j])
#output['h_dPhij1MET_ge2AK4j'].fill(dPhiJMET=dPhij1[jets_1j.counts >= 2],weight=evtw_1j[jets_1j.counts >= 2])
#output['h_dPhiMinjMET_ge2AK4j'].fill(dPhiJMET=dPhiMinj[cut_2j],weight=evtw[cut_2j])
#output['h_METrHT_pt30_ge2AK4j'].fill(METrHT_pt30=met[(cut_2j) & (ht>0)]/ht[(cut_2j) & (ht>0)],weight=evtw[(cut_2j) & (ht>0)])
#output['h_METrST_pt30_ge2AK4j'].fill(METrST_pt30=met[(cut_2j) & (st>0)]/st[(cut_2j) & (st>0)],weight=evtw[(cut_2j) & (st>0)])
#output['h_mT_ge2AK4j'].fill(mT=mtAK8[cut_2j],weight=evtw[cut_2j])
#output['h_madHT'].fill(ht=madHT_cut,weight=evtw)
return output
def process(self, events):
output = self._accumulator.identity()
dataset_name = events.metadata['dataset']
output["total_events"][dataset_name] += events.__len__()
# HLT selection
HLT_mask = []
if year == "2016":
if "SingleMuon" in dataset_name: #this does not work, as the name of file which is under processing is unknown
if "2016B2" in dataset_name:
HLT_mask = events.HLT.IsoMu24 | events.HLT.IsoTkMu24 | events.HLT.Mu50
else:
HLT_mask = events.HLT.IsoMu24 | events.HLT.IsoTkMu24 | events.HLT.Mu50 | events.HLT.TkMu50
else: #https://twiki.cern.ch/twiki/bin/view/CMS/HLTPathsRunIIList
if "2016B2" in dataset_name:
HLT_mask = events.HLT.PFHT800 | events.HLT.PFHT900 | events.HLT.PFJet500 | events.HLT.CaloJet500_NoJetID
elif "2016H" in dataset_name:
HLT_mask = events.HLT.PFHT900 | events.HLT.AK8PFJet450 | events.HLT.AK8PFJet500 | events.HLT.PFJet500 | events.HLT.CaloJet500_NoJetID
else:
HLT_mask = events.HLT.PFHT800 | events.HLT.PFHT900 | events.HLT.AK8PFJet450 | events.HLT.AK8PFJet500 | events.HLT.PFJet500 | events.HLT.CaloJet500_NoJetID
if year == "2017":
if "SingleMuon" in dataset_name:
if "2017B" in dataset_name:
HLT_mask = events.HLT.IsoMu27 | events.HLT.Mu50
else:
HLT_mask = events.HLT.IsoMu27 | events.HLT.Mu50 | events.HLT.OldMu100 | events.HLT.TkMu100
else:
HLT_mask = events.HLT.PFHT1050 | events.HLT.AK8PFJet500 | events.HLT.AK8PFJet550 | events.HLT.CaloJet500_NoJetID | events.HLT.CaloJet550_NoJetID | events.HLT.PFJet500
if year == "2018":
if "SingleMuon" in dataset_name:
HLT_mask = events.HLT.IsoMu24 | events.HLT.Mu50 | events.HLT.OldMu100 | events.HLT.TkMu100
else:
HLT_mask = events.HLT.PFHT1050 | events.HLT.AK8PFJet500 | events.HLT.AK8PFJet550 | events.HLT.CaloJet500_NoJetID | events.HLT.CaloJet550_NoJetID | events.HLT.PFJet500
# Require 3 jets
jet_mask = (events.Jet.pt > 30.) & (abs(events.Jet.eta) <
2.5) & (events.Jet.isTight)
event_mask = (awk.sum(jet_mask, axis=1) >= 3)
event_mask = event_mask & HLT_mask
events_3j = events[event_mask]
# Reduce jet mask to only events with 3 good jets
jet_mask = jet_mask[event_mask]
# Array of the jets to consider for trijet resonance
selected_jets = events_3j.Jet[jet_mask][:, :3]
# Pairs of jets
#pairs = awk.argcombinations(selected_jets, 2)
#jet_i, jet_j = awk.unzip(pairs)
pairs = [(0, 1), (1, 2), (2, 0)]
jet_i, jet_j = zip(*pairs) # Returns [0, 1, 2] , [1, 2, 0]
m_ij = (selected_jets[:, jet_i] + selected_jets[:, jet_j]).mass
dR_ij = selected_jets[:, jet_i].delta_r(selected_jets[:, jet_j])
dEta_ij = abs(selected_jets[:, jet_i].eta -
selected_jets[:, jet_j].eta)
jet_k = [2, 0, 1]
dR_i_jk = selected_jets[:, jet_i].delta_r(selected_jets[:, jet_j] +
selected_jets[:, jet_k])
dEta_i_jk = abs(selected_jets[:, jet_i].eta -
(selected_jets[:, jet_j] +
selected_jets[:, jet_k]).eta)
dPhi_i_jk = abs(selected_jets[:, jet_i].phi -
(selected_jets[:, jet_j] +
selected_jets[:, jet_k]).phi)
m3j = selected_jets.sum().mass
pt_i_overM = selected_jets.pt / m3j
m_01_overM = m_ij[:, 0] / m3j
m_12_overM = m_ij[:, 1] / m3j
m_20_overM = m_ij[:, 2] / m3j
dPtoverM_0_12 = abs(selected_jets[:, 0].pt -
(selected_jets[:, 1] +
selected_jets[:, 2]).pt) / m3j
dPtoverM_1_20 = abs(selected_jets[:, 1].pt -
(selected_jets[:, 2] +
selected_jets[:, 0]).pt) / m3j
dPtoverM_2_01 = abs(selected_jets[:, 2].pt -
(selected_jets[:, 0] +
selected_jets[:, 1]).pt) / m3j
# Event selection masks
selection_masks = {}
# Pre-selection
selection = PackedSelection()
selection.add("Dummy", m3j > 000)
sel_mask = selection.require(
**{name: True
for name in selection.names})
selection_masks["Pre-selection"] = sel_mask
# HLT_trigger (this is already done at the beginning)
# if year == "2016":
# JetHLT_mask = []
# if "2016B2" in dataset_name:
# JetHLT_mask = events.HLT.PFHT800 | events.HLT.PFHT900 | events.HLT.PFJet500 | events.HLT.CaloJet500_NoJetID
# elif "2016H" in dataset_name:
# JetHLT_mask = events.HLT.PFHT900 | events.HLT.AK8PFJet450 | events.HLT.AK8PFJet500 | events.HLT.PFJet500 | events.HLT.CaloJet500_NoJetID
# else:
# JetHLT_mask = events.HLT.PFHT800 | events.HLT.PFHT900 | events.HLT.AK8PFJet450 | events.HLT.AK8PFJet500 | events.HLT.PFJet500 | events.HLT.CaloJet500_NoJetID
# selection_masks["JetHLT"] = JetHLT_mask[event_mask]
# if year == "2017":
# JetHLT_mask = events.HLT.PFHT1050 | events.HLT.AK8PFJet500 | events.HLT.AK8PFJet550 | events.HLT.CaloJet500_NoJetID | events.HLT.CaloJet550_NoJetID | events.HLT.PFJet500
# selection_masks["JetHLT"] = JetHLT_mask[event_mask]
# if year == "2018":
# JetHLT_mask = events.HLT.PFHT1050 | events.HLT.AK8PFJet500 | events.HLT.AK8PFJet550 | events.HLT.CaloJet500_NoJetID | events.HLT.CaloJet550_NoJetID | events.HLT.PFJet500
# selection_masks["JetHLT"] = JetHLT_mask[event_mask]
# Fill histograms
for selection, selection_mask in selection_masks.items():
output["mjjj"].fill(dataset=dataset_name,
selection=selection,
mjjj=m3j[selection_mask])
output["m_ij"].fill(dataset=dataset_name,
selection=selection,
m_01=m_ij[:, 0][selection_mask],
m_12=m_ij[:, 1][selection_mask],
m_20=m_ij[:, 2][selection_mask])
output["dR_ij"].fill(dataset=dataset_name,
selection=selection,
dR_01=dR_ij[:, 0][selection_mask],
dR_12=dR_ij[:, 1][selection_mask],
dR_20=dR_ij[:, 2][selection_mask])
output["dEta_ij"].fill(dataset=dataset_name,
selection=selection,
dEta_01=dEta_ij[:, 0][selection_mask],
dEta_12=dEta_ij[:, 1][selection_mask],
dEta_20=dEta_ij[:, 2][selection_mask])
output["moverM_ij"].fill(dataset=dataset_name,
selection=selection,
moverM_01=m_01_overM[selection_mask],
moverM_12=m_12_overM[selection_mask],
moverM_20=m_20_overM[selection_mask])
output["pt_i"].fill(dataset=dataset_name,
selection=selection,
pt_0=selected_jets[:, 0][selection_mask].pt,
pt_1=selected_jets[:, 1][selection_mask].pt,
pt_2=selected_jets[:, 2][selection_mask].pt)
output["eta_i"].fill(dataset=dataset_name,
selection=selection,
eta_0=selected_jets[:, 0][selection_mask].eta,
eta_1=selected_jets[:, 1][selection_mask].eta,
eta_2=selected_jets[:, 2][selection_mask].eta)
output["ptoverM_i"].fill(dataset=dataset_name,
selection=selection,
ptoverM_0=pt_i_overM[:,
0][selection_mask],
ptoverM_1=pt_i_overM[:,
1][selection_mask],
ptoverM_2=pt_i_overM[:,
2][selection_mask])
output["dR_i_jk"].fill(dataset=dataset_name,
selection=selection,
dR_0_12=dR_i_jk[:, 0][selection_mask],
dR_1_20=dR_i_jk[:, 1][selection_mask],
dR_2_01=dR_i_jk[:, 2][selection_mask])
output["dEta_i_jk"].fill(dataset=dataset_name,
selection=selection,
dEta_0_12=dEta_i_jk[:, 0][selection_mask],
dEta_1_20=dEta_i_jk[:, 1][selection_mask],
dEta_2_01=dEta_i_jk[:, 2][selection_mask])
output["dPhi_i_jk"].fill(dataset=dataset_name,
selection=selection,
dPhi_0_12=dPhi_i_jk[:, 0][selection_mask],
dPhi_1_20=dPhi_i_jk[:, 1][selection_mask],
dPhi_2_01=dPhi_i_jk[:, 2][selection_mask])
output["dPtoverM_i_jk"].fill(
dataset=dataset_name,
selection=selection,
dPtoverM_0_12=dPtoverM_0_12[selection_mask],
dPtoverM_1_20=dPtoverM_1_20[selection_mask],
dPtoverM_2_01=dPtoverM_2_01[selection_mask])
pt_i_overM_2fill = pt_i_overM[selection_mask]
dR_ij_2fill = dR_ij[selection_mask]
dEta_ij_2fill = dEta_ij[selection_mask]
dR_i_jk_2fill = dR_i_jk[selection_mask]
dEta_i_jk_2fill = dEta_i_jk[selection_mask]
dPhi_i_jk_2fill = dPhi_i_jk[selection_mask]
dPtoverM_0_12_2fill = dPtoverM_0_12[selection_mask]
dPtoverM_1_20_2fill = dPtoverM_1_20[selection_mask]
dPtoverM_2_01_2fill = dPtoverM_2_01[selection_mask]
selected_jets_2fill = selected_jets[selection_mask]
max_pt_overM_2fill = awk.max(pt_i_overM_2fill, axis=1)
min_pt_overM_2fill = awk.min(pt_i_overM_2fill, axis=1)
max_dR_2fill = awk.max(dR_ij_2fill, axis=1)
max_dEta_2fill = awk.max(dEta_ij_2fill, axis=1)
min_dR_2fill = awk.min(dR_ij_2fill, axis=1)
min_dEta_2fill = awk.min(dEta_ij_2fill, axis=1)
min_pt_2fill = awk.min(selected_jets_2fill.pt, axis=1)
max_eta_2fill = awk.max(abs(selected_jets_2fill.eta), axis=1)
max_dR_i_jk_2fill = awk.max(dR_i_jk_2fill, axis=1)
min_dR_i_jk_2fill = awk.min(dR_i_jk_2fill, axis=1)
max_dEta_i_jk_2fill = awk.max(dEta_i_jk_2fill, axis=1)
min_dEta_i_jk_2fill = awk.min(dEta_i_jk_2fill, axis=1)
max_dPhi_i_jk_2fill = awk.max(dPhi_i_jk_2fill, axis=1)
min_dPhi_i_jk_2fill = awk.min(dPhi_i_jk_2fill, axis=1)
max_dPtoverM_i_jk_2fill = []
min_dPtoverM_i_jk_2fill = []
for pair in zip(dPtoverM_0_12_2fill, dPtoverM_1_20_2fill,
dPtoverM_2_01_2fill):
max_dPtoverM_i_jk_2fill.append(max(pair))
min_dPtoverM_i_jk_2fill.append(min(pair))
max_dPtoverM_i_jk_2fill = np.array(max_dPtoverM_i_jk_2fill)
min_dPtoverM_i_jk_2fill = np.array(min_dPtoverM_i_jk_2fill)
max_pt_overM_2fill = awk.fill_none(max_pt_overM_2fill, -99)
min_pt_overM_2fill = awk.fill_none(min_pt_overM_2fill, -99)
max_dR_2fill = awk.fill_none(max_dR_2fill, -99)
max_dEta_2fill = awk.fill_none(max_dEta_2fill, -99)
min_dR_2fill = awk.fill_none(min_dR_2fill, -99)
min_dEta_2fill = awk.fill_none(min_dEta_2fill, -99)
min_pt_2fill = awk.fill_none(min_pt_2fill, -99)
max_eta_2fill = awk.fill_none(max_eta_2fill, -99)
max_dR_i_jk_2fill = awk.fill_none(max_dR_i_jk_2fill, -99)
min_dR_i_jk_2fill = awk.fill_none(min_dR_i_jk_2fill, -99)
max_dEta_i_jk_2fill = awk.fill_none(max_dEta_i_jk_2fill, -99)
min_dEta_i_jk_2fill = awk.fill_none(min_dEta_i_jk_2fill, -99)
max_dPhi_i_jk_2fill = awk.fill_none(max_dPhi_i_jk_2fill, -99)
min_dPhi_i_jk_2fill = awk.fill_none(min_dPhi_i_jk_2fill, -99)
output["max_dR"].fill(dataset=dataset_name,
selection=selection,
max_dR=max_dR_2fill)
output["max_dEta"].fill(dataset=dataset_name,
selection=selection,
max_dEta=max_dEta_2fill)
output["min_dR"].fill(dataset=dataset_name,
selection=selection,
min_dR=min_dR_2fill)
output["min_dEta"].fill(dataset=dataset_name,
selection=selection,
min_dEta=min_dEta_2fill)
output["min_pt"].fill(dataset=dataset_name,
selection=selection,
min_pt=min_pt_2fill)
output["max_eta"].fill(dataset=dataset_name,
selection=selection,
max_eta=max_eta_2fill)
output["max_ptoverM"].fill(dataset=dataset_name,
selection=selection,
max_ptoverM=max_pt_overM_2fill)
output["min_ptoverM"].fill(dataset=dataset_name,
selection=selection,
min_ptoverM=min_pt_overM_2fill)
output["max_dR_j_jj"].fill(dataset=dataset_name,
selection=selection,
max_dR_j_jj=max_dR_i_jk_2fill)
output["max_dEta_j_jj"].fill(dataset=dataset_name,
selection=selection,
max_dEta_j_jj=max_dEta_i_jk_2fill)
output["max_dPhi_j_jj"].fill(dataset=dataset_name,
selection=selection,
max_dPhi_j_jj=max_dPhi_i_jk_2fill)
output["max_dPtoverM_j_jj"].fill(
dataset=dataset_name,
selection=selection,
max_dPtoverM_j_jj=max_dPtoverM_i_jk_2fill)
output["min_dR_j_jj"].fill(dataset=dataset_name,
selection=selection,
min_dR_j_jj=min_dR_i_jk_2fill)
output["min_dEta_j_jj"].fill(dataset=dataset_name,
selection=selection,
min_dEta_j_jj=min_dEta_i_jk_2fill)
output["min_dPhi_j_jj"].fill(dataset=dataset_name,
selection=selection,
min_dPhi_j_jj=min_dPhi_i_jk_2fill)
output["min_dPtoverM_j_jj"].fill(
dataset=dataset_name,
selection=selection,
min_dPtoverM_j_jj=min_dPtoverM_i_jk_2fill)
return output
def process(self, events):
output = self.accumulator.identity()
dataset = events.metadata['dataset']
output['sumw'][dataset] += ak.sum(events.genWeight)
##############
# Trigger level
triggers = [
"HLT_Mu12_TrkIsoVVL_Ele23_CaloIdL_TrackIdL_IsoVL_DZ",
"HLT_Mu23_TrkIsoVVL_Ele12_CaloIdL_TrackIdL_IsoVL_DZ",
]
trig_arrs = [events.HLT[_trig.strip("HLT_")] for _trig in triggers]
req_trig = np.zeros(len(events), dtype='bool')
for t in trig_arrs:
req_trig = req_trig | t
############
# Event level
## Muon cuts
# muon twiki: https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideMuonIdRun2
events.Muon = events.Muon[(events.Muon.pt > 30) & (abs(events.Muon.eta < 2.4))] # & (events.Muon.tightId > .5)
events.Muon = ak.pad_none(events.Muon, 1, axis=1)
req_muon =(ak.count(events.Muon.pt, axis=1) == 1)
## Electron cuts
# electron twiki: https://twiki.cern.ch/twiki/bin/viewauth/CMS/CutBasedElectronIdentificationRun2
events.Electron = events.Electron[(events.Electron.pt > 30) & (abs(events.Electron.eta) < 2.4)]
events.Electron = ak.pad_none(events.Electron, 1, axis=1)
req_ele = (ak.count(events.Electron.pt, axis=1) == 1)
## Jet cuts
events.Jet = events.Jet[(events.Jet.pt > 25) & (abs(events.Jet.eta) <= 2.5)]
req_jets = (ak.count(events.Jet.pt, axis=1) >= 2)
req_opposite_charge = events.Electron[:, 0].charge * events.Muon[:, 0].charge == -1
event_level = req_trig & req_muon & req_ele & req_opposite_charge & req_jets
# Selected
selev = events[event_level]
#########
# Per electron
el_eta = (abs(selev.Electron.eta) <= 2.4)
el_pt = selev.Electron.pt > 30
el_level = el_eta & el_pt
# Per muon
mu_eta = (abs(selev.Muon.eta) <= 2.4)
mu_pt = selev.Muon.pt > 30
mu_level = mu_eta & mu_pt
# Per jet
jet_eta = (abs(selev.Jet.eta) <= 2.4)
jet_pt = selev.Jet.pt > 25
jet_pu = ( ((selev.Jet.puId > 6) & (selev.Jet.pt < 50)) | (selev.Jet.pt > 50) )
jet_id = selev.Jet.jetId >= 2
#jet_id = selev.Jet.isTight() == 1 & selev.Jet.isTightLeptonVeto() == 0
jet_level = jet_pu & jet_eta & jet_pt & jet_id
# b-tag twiki : https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation102X
bjet_disc_t = selev.Jet.btagDeepB > 0.7264 # L=0.0494, M=0.2770, T=0.7264
bjet_disc_m = selev.Jet.btagDeepB > 0.2770 # L=0.0494, M=0.2770, T=0.7264
bjet_disc_l = selev.Jet.btagDeepB > 0.0494 # L=0.0494, M=0.2770, T=0.7264
bjet_level_t = jet_level & bjet_disc_t
bjet_level_m = jet_level & bjet_disc_m
bjet_level_l = jet_level & bjet_disc_l
sel = selev.Electron[el_level]
smu = selev.Muon[mu_level]
sjets = selev.Jet[jet_level]
sbjets_t = selev.Jet[bjet_level_t]
sbjets_m = selev.Jet[bjet_level_m]
sbjets_l = selev.Jet[bjet_level_l]
# output['pt'].fill(dataset=dataset, pt=selev.Jet.pt.flatten())
# Fill histograms dynamically
for histname, h in output.items():
if (histname not in self.jet_hists) and (histname not in self.deepcsv_hists): continue
# Get valid fields perhistogram to fill
fields = {k: ak.flatten(sjets[k], axis=None) for k in h.fields if k in dir(sjets)}
h.fill(dataset=dataset, **fields)
def flatten(ar): # flatten awkward into a 1d array to hist
return ak.flatten(ar, axis=None)
def num(ar):
return ak.num(ak.fill_none(ar[~ak.is_none(ar)], 0), axis=0)
output['njet'].fill(dataset=dataset, njet=flatten(ak.num(sjets)))
output['nbjet_t'].fill(dataset=dataset, nbjet_t=flatten(ak.num(sbjets_t)))
output['nbjet_m'].fill(dataset=dataset, nbjet_m=flatten(ak.num(sbjets_m)))
output['nbjet_l'].fill(dataset=dataset, nbjet_l=flatten(ak.num(sbjets_l)))
output['nel'].fill(dataset=dataset, nel=flatten(ak.num(sel)))
output['nmu'].fill(dataset=dataset, nmu=flatten(ak.num(smu)))
output['lelpt'].fill(dataset=dataset, lelpt=flatten(selev.Electron[:, 0].pt))
output['lmupt'].fill(dataset=dataset, lmupt=flatten(selev.Muon[:, 0].pt))
output['ljpt'].fill(dataset=dataset, ljpt=flatten(selev.Jet[:, 0].pt))
output['sljpt'].fill(dataset=dataset, sljpt=flatten(selev.Jet[:, 1].pt))
return output
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", "tightSSTTH").get()
vetomuon = Collections(ev, "Muon", "vetoTTH").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", "tightSSTTH").get()
vetoelectron = Collections(ev, "Electron", "vetoTTH").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]
n_nonprompt = getNonPromptFromFlavour(electron) + getNonPromptFromFlavour(muon)
n_chargeflip = getChargeFlips(electron, ev.GenPart) + getChargeFlips(muon, ev.GenPart)
## 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)
## event selectors
filters = getFilters(ev, year=self.year, dataset=dataset)
dilep = ((ak.num(electron) + ak.num(muon))==2)
pos_charge = ((ak.sum(electron.pdgId, axis=1) + ak.sum(muon.pdgId, axis=1))<0)
neg_charge = ((ak.sum(electron.pdgId, axis=1) + ak.sum(muon.pdgId, axis=1))>0)
lep0pt = ((ak.num(electron[(electron.pt>25)]) + ak.num(muon[(muon.pt>25)]))>0)
lep0pt_40 = ((ak.num(electron[(electron.pt>40)]) + ak.num(muon[(muon.pt>40)]))>0)
lep0pt_100 = ((ak.num(electron[(electron.pt>100)]) + ak.num(muon[(muon.pt>100)]))>0)
lep1pt = ((ak.num(electron[(electron.pt>20)]) + ak.num(muon[(muon.pt>20)]))>1)
lep1pt_30 = ((ak.num(electron[(electron.pt>30)]) + ak.num(muon[(muon.pt>30)]))>1)
lepveto = ((ak.num(vetoelectron) + ak.num(vetomuon))==2)
# define the weight
weight = Weights( len(ev) )
#mult = 1
#if dataset=='inclusive': mult = 0.0478/47.448
#if dataset=='plus': mult = 0.0036/7.205
if not dataset=='MuonEG':
# lumi weight
weight.add("weight", ev.weight*cfg['lumi'][self.year])
#weight.add("weight", ev.genWeight*cfg['lumi'][self.year]*mult)
# 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))
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(lep0)>40', lep0pt_40 )
selection.add('p_T(lep1)>20', lep1pt )
selection.add('p_T(lep1)>30', lep1pt_30 )
selection.add('SS', ( SSlepton | SSelectron | SSmuon) )
selection.add('pos', ( pos_charge ) )
selection.add('neg', ( neg_charge ) )
selection.add('N_jet>3', (ak.num(jet)>=4) )
selection.add('N_jet>4', (ak.num(jet)>=5) )
selection.add('N_central>2', (ak.num(central)>=3) )
selection.add('N_central>3', (ak.num(central)>=4) )
selection.add('N_btag>0', (ak.num(btag)>=1) )
selection.add('MET>50', (ev.MET.pt>50) )
selection.add('ST', (st>600) )
selection.add('N_fwd>0', (ak.num(fwd)>=1 ))
selection.add('delta_eta', (ak.any(delta_eta>2, axis=1) ) )
selection.add('fwd_p>500', (ak.any(j_fwd.p>500, axis=1) ) )
ss_reqs = ['lepveto', 'dilep', 'SS', 'filter', 'p_T(lep0)>25', 'p_T(lep1)>20', 'N_jet>3', 'N_central>2', 'N_btag>0']
bl_reqs = ss_reqs + ['N_fwd>0', 'N_jet>4', 'N_central>3', 'ST', 'MET>50', 'delta_eta']
sr_reqs = bl_reqs + ['fwd_p>500', 'p_T(lep0)>40', 'p_T(lep1)>30']
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)
sr_reqs_d = { sel: True for sel in sr_reqs }
SR = selection.require(**sr_reqs_d)
cutflow = Cutflow(output, ev, weight=weight)
cutflow_reqs_d = {}
for req in sr_reqs:
cutflow_reqs_d.update({req: True})
cutflow.addRow( req, selection.require(**cutflow_reqs_d) )
# first, make a few super inclusive plots
output['PV_npvs'].fill(dataset=dataset, multiplicity=ev.PV[ss_selection].npvs, weight=weight.weight()[ss_selection])
output['PV_npvsGood'].fill(dataset=dataset, multiplicity=ev.PV[ss_selection].npvsGood, weight=weight.weight()[ss_selection])
output['N_jet'].fill(dataset=dataset, multiplicity=ak.num(jet)[ss_selection], weight=weight.weight()[ss_selection])
output['N_b'].fill(dataset=dataset, multiplicity=ak.num(btag)[ss_selection], weight=weight.weight()[ss_selection])
output['N_central'].fill(dataset=dataset, multiplicity=ak.num(central)[ss_selection], weight=weight.weight()[ss_selection])
output['N_ele'].fill(dataset=dataset, multiplicity=ak.num(electron)[ss_selection], weight=weight.weight()[ss_selection])
output['N_mu'].fill(dataset=dataset, multiplicity=ak.num(electron)[ss_selection], weight=weight.weight()[ss_selection])
output['N_fwd'].fill(dataset=dataset, multiplicity=ak.num(fwd)[ss_selection], weight=weight.weight()[ss_selection])
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])
output['chargeFlip_vs_nonprompt'].fill(dataset=dataset, n1=n_chargeflip[ss_selection], n2=n_nonprompt[ss_selection], n_ele=ak.num(electron)[ss_selection], weight=weight.weight()[ss_selection])
output['MET'].fill(
dataset = dataset,
pt = ev.MET[ss_selection].pt,
phi = ev.MET[ss_selection].phi,
weight = weight.weight()[ss_selection]
)
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]
)
return output
def process(self, events):
output = self._accumulator.identity()
dataset_name = events.metadata['dataset']
output["total_events"][dataset_name] += events.__len__()
# HLT selection
HLT_mask = []
if year == "2016":
if "SingleMuon" in dataset_name:
if "2016B2" in dataset_name:
HLT_mask = events.HLT.IsoMu24 | events.HLT.IsoTkMu24 | events.HLT.Mu50
else:
HLT_mask = events.HLT.IsoMu24 | events.HLT.IsoTkMu24 | events.HLT.Mu50 | events.HLT.TkMu50
else: #https://twiki.cern.ch/twiki/bin/view/CMS/HLTPathsRunIIList
if "2016B2" in dataset_name:
HLT_mask = events.HLT.PFHT800 | events.HLT.PFHT900 | events.HLT.PFJet500 | events.HLT.CaloJet500_NoJetID
elif "2016H" in dataset_name:
HLT_mask = events.HLT.PFHT900 | events.HLT.AK8PFJet450 | events.HLT.AK8PFJet500 | events.HLT.PFJet500 | events.HLT.CaloJet500_NoJetID
else:
HLT_mask = events.HLT.PFHT800 | events.HLT.PFHT900 | events.HLT.AK8PFJet450 | events.HLT.AK8PFJet500 | events.HLT.PFJet500 | events.HLT.CaloJet500_NoJetID
if year == "2017":
if "SingleMuon" in dataset_name:
if "2017B" in dataset_name:
HLT_mask = events.HLT.IsoMu27 | events.HLT.Mu50
else:
HLT_mask = events.HLT.IsoMu27 | events.HLT.Mu50 | events.HLT.OldMu100 | events.HLT.TkMu100
else:
HLT_mask = events.HLT.PFHT1050 | events.HLT.AK8PFJet500 | events.HLT.AK8PFJet550 | events.HLT.CaloJet500_NoJetID | events.HLT.CaloJet550_NoJetID | events.HLT.PFJet500
if year == "2018":
if "SingleMuon" in dataset_name:
HLT_mask = events.HLT.IsoMu24 | events.HLT.Mu50 | events.HLT.OldMu100 | events.HLT.TkMu100
else:
HLT_mask = events.HLT.PFHT1050 | events.HLT.AK8PFJet500 | events.HLT.AK8PFJet550 | events.HLT.CaloJet500_NoJetID | events.HLT.CaloJet550_NoJetID | events.HLT.PFJet500
# Require 3 jets
jet_mask = (events.Jet.pt > 30.) & (abs(events.Jet.eta) <
2.5) & (events.Jet.isTight)
event_mask = (awk.sum(jet_mask, axis=1) >= 3)
event_mask = event_mask & HLT_mask
events_3j = events[event_mask]
# Reduce jet mask to only events with 3 good jets
jet_mask = jet_mask[event_mask]
# Array of the jets to consider for trijet resonance
selected_jets = events_3j.Jet[jet_mask][:, :3]
# Pairs of jets
pairs = [(0, 1), (1, 2), (2, 0)]
jet_i, jet_j = zip(*pairs) # Returns [0, 1, 2] , [1, 2, 0]
m_ij = (selected_jets[:, jet_i] + selected_jets[:, jet_j]).mass
dR_ij = selected_jets[:, jet_i].delta_r(selected_jets[:, jet_j])
dEta_ij = abs(selected_jets[:, jet_i].eta -
selected_jets[:, jet_j].eta)
max_dR = awk.max(dR_ij, axis=1)
max_dEta = awk.max(dEta_ij, axis=1)
min_dR = awk.min(dR_ij, axis=1)
min_dEta = awk.min(dEta_ij, axis=1)
min_pT = awk.min(selected_jets.pt, axis=1)
max_eta = abs(awk.max(selected_jets.eta, axis=1))
jet_k = [2, 0, 1]
dR_i_jk = selected_jets[:, jet_i].delta_r(selected_jets[:, jet_j] +
selected_jets[:, jet_k])
dEta_i_jk = abs(selected_jets[:, jet_i].eta -
(selected_jets[:, jet_j] +
selected_jets[:, jet_k]).eta)
dPhi_i_jk = abs(selected_jets[:, jet_i].phi -
(selected_jets[:, jet_j] +
selected_jets[:, jet_k]).phi)
dPt_i_jk = abs(selected_jets[:, jet_i].pt -
(selected_jets[:, jet_j] + selected_jets[:, jet_k]).pt)
max_dPhi_jjj = awk.max(dPhi_i_jk, axis=1)
m3j = selected_jets.sum().mass
pt_i_overM = selected_jets.pt / m3j
max_pt_overM = awk.max(pt_i_overM, axis=1)
min_pt_overM = awk.min(pt_i_overM, axis=1)
m_01_overM = m_ij[:, 0] / m3j
m_12_overM = m_ij[:, 1] / m3j
m_20_overM = m_ij[:, 2] / m3j
for pt_cut in range(30, 1150, 5):
cut_name = f"min_pT_cut{pt_cut}".format(pt_cut)
selection = PackedSelection()
selection.add("MinJetPt_cut", min_pT > pt_cut)
sel_mask = selection.require(
**{name: True
for name in selection.names})
output[f"N_min_pT_cut{pt_cut}".format(
pt_cut)][dataset_name] += events_3j[sel_mask].__len__()
for eta_cut in np.arange(0, 2.5, 0.05):
cut_name = f"max_eta_cut{eta_cut}".format(eta_cut)
selection = PackedSelection()
selection.add("MaxJetEta_cut", max_eta < eta_cut)
sel_mask = selection.require(
**{name: True
for name in selection.names})
output[f"N_max_eta_cut{eta_cut}".format(
eta_cut)][dataset_name] += events_3j[sel_mask].__len__()
for dEta_max_cut in np.arange(0, 5, 0.1):
cut_name = f"dEta_max_cut{dEta_max_cut}".format(dEta_max_cut)
selection = PackedSelection()
selection.add("MaxJJdEta_cut", max_dEta < dEta_max_cut)
sel_mask = selection.require(
**{name: True
for name in selection.names})
output[f"N_dEta_jj_max_cut{dEta_max_cut}".format(
dEta_max_cut)][dataset_name] += events_3j[sel_mask].__len__()
for dR_min_cut in np.arange(0, 5, 0.1):
cut_name = f"dR_min_cut{dR_min_cut}".format(dR_min_cut)
selection = PackedSelection()
selection.add("MinJJdR_cut", min_dR > dR_min_cut)
sel_mask = selection.require(
**{name: True
for name in selection.names})
output[f"N_dR_jj_min_cut{dR_min_cut}".format(
dR_min_cut)][dataset_name] += events_3j[sel_mask].__len__()
#min cut for the variable dPhi_jjj_max
# for dPhi_jjj_max_min_cut in range(0,6,0.1):
# cut_name = f"dPhi_jjj_max_min_cut{dPhi_jjj_max_min_cut}".format(dPhi_jjj_max_min_cut)
# selections[cut_name] = PackedSelection()
# selections[cut_name].add("j_jj_dPhi_max_cut", min_dR > dPhi_jjj_max_min_cut)
# selection_items[cut_name] = []
# selection_items[cut_name].append("j_jj_dPhi_max_cut")
# sel_mask = HLT_mask & selections[cut_name].require(**{name: True for name in selection_items[cut_name]})
# output[f"N_dPhi_jjj_max_min_cut{dPhi_jjj_max_min_cut}".format(dPhi_jjj_max_min_cut)][dataset_name] += events_3j[sel_mask].__len__()
# for dPhi_jjj_min_max_cut in range(0,6,0.1):
# cut_name = f"dPhi_jjj_max_min_cut{dPhi_jjj_max_min_cut}".format(dPhi_jjj_max_min_cut)
# selections[cut_name] = PackedSelection()
# selections[cut_name].add("j_jj_dPhi_max_cut", min_dR > dPhi_jjj_max_min_cut)
# selection_items[cut_name] = []
# selection_items[cut_name].append("j_jj_dPhi_max_cut")
# sel_mask = HLT_mask & selections[cut_name].require(**{name: True for name in selection_items[cut_name]})
# output[f"N_dPhi_jjj_max_min_cut{dPhi_jjj_max_min_cut}".format(dPhi_jjj_max_min_cut)][dataset_name] += events_3j[sel_mask].__len__()
return output
Lep5 = ((Elec5_PT_arr.count()) + (Muon5_PT_arr.count()) >= 2)
Lep6 = ((Elec6_PT_arr.count()) + (Muon6_PT_arr.count()) >= 2)
Lep7 = ((Elec7_PT_arr.count()) + (Muon7_PT_arr.count()) >= 2)
Lep8 = ((Elec8_PT_arr.count()) + (Muon8_PT_arr.count()) >= 2)
Jet1 = ((Jet1_PT_arr.count()) >= 2)
Jet2 = ((Jet2_PT_arr.count()) >= 2)
Jet3 = ((Jet3_PT_arr.count()) >= 2)
Jet4 = ((Jet4_PT_arr.count()) >= 2)
Jet5 = ((Jet5_PT_arr.count()) >= 2)
Jet6 = ((Jet6_PT_arr.count()) >= 2)
Jet7 = ((Jet7_PT_arr.count()) >= 2)
Jet8 = ((Jet8_PT_arr.count()) >= 2)
## scalar sum of lepton pt
lep1_pt_sum = ak.sum(Elec1_PT_arr, axis=-1) + ak.sum(Muon1_PT_arr,
axis=-1) > 120
lep2_pt_sum = ak.sum(Elec2_PT_arr, axis=-1) + ak.sum(Muon2_PT_arr,
axis=-1) > 120
lep3_pt_sum = ak.sum(Elec3_PT_arr, axis=-1) + ak.sum(Muon3_PT_arr,
axis=-1) > 120
lep4_pt_sum = ak.sum(Elec4_PT_arr, axis=-1) + ak.sum(Muon4_PT_arr,
axis=-1) > 120
lep5_pt_sum = ak.sum(Elec5_PT_arr, axis=-1) + ak.sum(Muon5_PT_arr,
axis=-1) > 120
lep6_pt_sum = ak.sum(Elec6_PT_arr, axis=-1) + ak.sum(Muon6_PT_arr,
axis=-1) > 120
lep7_pt_sum = ak.sum(Elec7_PT_arr, axis=-1) + ak.sum(Muon7_PT_arr,
axis=-1) > 120
lep8_pt_sum = ak.sum(Elec8_PT_arr, axis=-1) + ak.sum(Muon8_PT_arr,
axis=-1) > 120
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)
## Muons
muon = Collections(ev, "Muon", "tightTTH").get()
vetomuon = Collections(ev, "Muon", "vetoTTH").get()
dimuon = choose(muon, 2)
SSmuon = ak.any((dimuon['0'].charge * dimuon['1'].charge) > 0, axis=1)
OSmuon = 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", "tightTTH").get()
vetoelectron = Collections(ev, "Electron", "vetoTTH").get()
dielectron = choose(electron, 2)
SSelectron = ak.any(
(dielectron['0'].charge * dielectron['1'].charge) > 0, axis=1)
OSelectron = 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
lepton = ak.concatenate([muon, electron], axis=1)
dilepton = cross(muon, electron)
SSlepton = ak.any((dilepton['0'].charge * dilepton['1'].charge) > 0,
axis=1)
OSlepton = ak.any((dilepton['0'].charge * dilepton['1'].charge) < 0,
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
high_p_fwd = fwd[ak.singletons(ak.argmax(
fwd.p, axis=1))] # highest momentum spectator
high_pt_fwd = fwd[ak.singletons(ak.argmax(
fwd.pt_nom, axis=1))] # highest transverse momentum spectator
high_eta_fwd = fwd[ak.singletons(ak.argmax(abs(
fwd.eta), axis=1))] # most forward spectator
## Get the two leading b-jets in terms of btag score
high_score_btag = central[ak.argsort(central.btagDeepFlavB)][:, :2]
jf = cross(high_p_fwd, jet)
mjf = (jf['0'] + jf['1']).mass
deltaEta = abs(high_p_fwd.eta -
jf[ak.singletons(ak.argmax(mjf, axis=1))]['1'].eta)
deltaEtaMax = ak.max(deltaEta, axis=1)
mjf_max = ak.max(mjf, axis=1)
jj = choose(jet, 2)
mjj_max = ak.max((jj['0'] + jj['1']).mass, axis=1)
## 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)
ht_central = ak.sum(central.pt, axis=1)
## event selectors
filters = getFilters(ev, year=self.year, dataset=dataset)
triggers = getTriggers(ev, year=self.year, dataset=dataset)
dilep = ((ak.num(electron) == 1) & (ak.num(muon) == 1))
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(vetoelectron) + ak.num(vetomuon)) == 2)
# define the weight
weight = Weights(len(ev))
if not dataset == 'MuonEG':
# 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))
selection = PackedSelection()
selection.add('lepveto', lepveto)
selection.add('dilep', dilep)
selection.add('trigger', (triggers))
selection.add('filter', (filters))
selection.add('p_T(lep0)>25', lep0pt)
selection.add('p_T(lep1)>20', lep1pt)
selection.add('OS', OSlepton)
selection.add('N_btag=2', (ak.num(btag) == 2))
selection.add('N_jet>2', (ak.num(jet) >= 3))
selection.add('N_central>1', (ak.num(central) >= 2))
selection.add('N_fwd>0', (ak.num(fwd) >= 1))
selection.add('MET>30', (ev.MET.pt > 30))
os_reqs = [
'lepveto', 'dilep', 'trigger', 'filter', 'p_T(lep0)>25',
'p_T(lep1)>20', 'OS'
]
bl_reqs = os_reqs + [
'N_btag=2', 'N_jet>2', 'N_central>1', 'N_fwd>0', 'MET>30'
]
os_reqs_d = {sel: True for sel in os_reqs}
os_selection = selection.require(**os_reqs_d)
bl_reqs_d = {sel: True for sel in bl_reqs}
BL = selection.require(**bl_reqs_d)
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))
# first, make a few super inclusive plots
output['PV_npvs'].fill(dataset=dataset,
multiplicity=ev.PV[os_selection].npvs,
weight=weight.weight()[os_selection])
output['PV_npvsGood'].fill(dataset=dataset,
multiplicity=ev.PV[os_selection].npvsGood,
weight=weight.weight()[os_selection])
output['N_jet'].fill(dataset=dataset,
multiplicity=ak.num(jet)[os_selection],
weight=weight.weight()[os_selection])
output['N_b'].fill(dataset=dataset,
multiplicity=ak.num(btag)[os_selection],
weight=weight.weight()[os_selection])
output['N_central'].fill(dataset=dataset,
multiplicity=ak.num(central)[os_selection],
weight=weight.weight()[os_selection])
output['N_ele'].fill(dataset=dataset,
multiplicity=ak.num(electron)[os_selection],
weight=weight.weight()[os_selection])
output['N_mu'].fill(dataset=dataset,
multiplicity=ak.num(electron)[os_selection],
weight=weight.weight()[os_selection])
output['N_fwd'].fill(dataset=dataset,
multiplicity=ak.num(fwd)[os_selection],
weight=weight.weight()[os_selection])
output['MET'].fill(dataset=dataset,
pt=ev.MET[os_selection].pt,
phi=ev.MET[os_selection].phi,
weight=weight.weight()[os_selection])
output['electron'].fill(dataset=dataset,
pt=ak.to_numpy(ak.flatten(electron[BL].pt)),
eta=ak.to_numpy(ak.flatten(electron[BL].eta)),
phi=ak.to_numpy(ak.flatten(electron[BL].phi)),
weight=weight.weight()[BL])
output['muon'].fill(dataset=dataset,
pt=ak.to_numpy(ak.flatten(muon[BL].pt)),
eta=ak.to_numpy(ak.flatten(muon[BL].eta)),
phi=ak.to_numpy(ak.flatten(muon[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['fwd_jet'].fill(dataset=dataset,
pt=ak.flatten(high_p_fwd[BL].pt_nom),
eta=ak.flatten(high_p_fwd[BL].eta),
phi=ak.flatten(high_p_fwd[BL].phi),
weight=weight.weight()[BL])
output['b1'].fill(dataset=dataset,
pt=ak.flatten(high_score_btag[:, 0:1][BL].pt_nom),
eta=ak.flatten(high_score_btag[:, 0:1][BL].eta),
phi=ak.flatten(high_score_btag[:, 0:1][BL].phi),
weight=weight.weight()[BL])
output['b2'].fill(dataset=dataset,
pt=ak.flatten(high_score_btag[:, 1:2][BL].pt_nom),
eta=ak.flatten(high_score_btag[:, 1:2][BL].eta),
phi=ak.flatten(high_score_btag[:, 1:2][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])
# Now, take care of systematic unceratinties
if not dataset == 'MuonEG':
alljets = getJets(ev, minPt=0, maxEta=4.7)
alljets = alljets[(alljets.jetId > 1)]
for var in self.variations:
# get the collections that change with the variations
jet = getPtEtaPhi(alljets, pt_var=var)
jet = jet[(jet.pt > 25)]
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
high_p_fwd = fwd[ak.singletons(ak.argmax(
fwd.p, axis=1))] # highest momentum spectator
high_pt_fwd = fwd[ak.singletons(ak.argmax(
fwd.pt, axis=1))] # highest transverse momentum spectator
high_eta_fwd = fwd[ak.singletons(
ak.argmax(abs(fwd.eta), axis=1))] # most forward spectator
## Get the two leading b-jets in terms of btag score
high_score_btag = central[ak.argsort(
central.btagDeepFlavB)][:, :2]
# get the modified selection -> more difficult
selection.add('N_jet>2_' + var,
(ak.num(jet.pt) >= 3)) # stupid bug here...
selection.add('N_btag=2_' + var, (ak.num(btag) == 2))
selection.add('N_central>1_' + var, (ak.num(central) >= 2))
selection.add('N_fwd>0_' + var, (ak.num(fwd) >= 1))
selection.add('MET>30_' + var, (getattr(ev.MET, var) > 30))
## Don't change the selection for now...
bl_reqs = os_reqs + [
'N_jet>2_' + var, 'MET>30_' + var, 'N_btag=2_' + var,
'N_central>1_' + var, 'N_fwd>0_' + var
]
bl_reqs_d = {sel: True for sel in bl_reqs}
BL = selection.require(**bl_reqs_d)
# the OS selection remains unchanged
output['N_jet_' + var].fill(
dataset=dataset,
multiplicity=ak.num(jet)[os_selection],
weight=weight.weight()[os_selection])
output['N_fwd_' + var].fill(
dataset=dataset,
multiplicity=ak.num(fwd)[os_selection],
weight=weight.weight()[os_selection])
output['N_b_' + var].fill(
dataset=dataset,
multiplicity=ak.num(btag)[os_selection],
weight=weight.weight()[os_selection])
output['N_central_' + var].fill(
dataset=dataset,
multiplicity=ak.num(central)[os_selection],
weight=weight.weight()[os_selection])
# We don't need to redo all plots with variations. E.g., just add uncertainties to the jet plots.
output['j1_' + var].fill(dataset=dataset,
pt=ak.flatten(jet.pt[:, 0:1][BL]),
eta=ak.flatten(jet.eta[:, 0:1][BL]),
phi=ak.flatten(jet.phi[:, 0:1][BL]),
weight=weight.weight()[BL])
output['b1_' + var].fill(
dataset=dataset,
pt=ak.flatten(high_score_btag[:, 0:1].pt[:, 0:1][BL]),
eta=ak.flatten(high_score_btag[:, 0:1].eta[:, 0:1][BL]),
phi=ak.flatten(high_score_btag[:, 0:1].phi[:, 0:1][BL]),
weight=weight.weight()[BL])
output['fwd_jet_' + var].fill(
dataset=dataset,
pt=ak.flatten(high_p_fwd[BL].pt),
eta=ak.flatten(high_p_fwd[BL].eta),
phi=ak.flatten(high_p_fwd[BL].phi),
weight=weight.weight()[BL])
output['MET_' + var].fill(dataset=dataset,
pt=getattr(ev.MET,
var)[os_selection],
phi=ev.MET[os_selection].phi,
weight=weight.weight()[os_selection])
return output
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)
if not re.search(re.compile('MuonEG|DoubleMuon|DoubleEG|EGamma'), dataset):
## 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", "tightSSTTH").get()
vetomuon = Collections(ev, "Muon", "vetoTTH").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", "tightSSTTH").get()
vetoelectron = Collections(ev, "Electron", "vetoTTH").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)
lepton_pdgId_pt_ordered = ak.fill_none(ak.pad_none(lepton[ak.argsort(lepton.pt, ascending=False)].pdgId, 2, clip=True), 0)
if not re.search(re.compile('MuonEG|DoubleMuon|DoubleEG|EGamma'), dataset):
n_nonprompt = getNonPromptFromFlavour(electron) + getNonPromptFromFlavour(muon)
n_chargeflip = getChargeFlips(electron, ev.GenPart) + getChargeFlips(muon, ev.GenPart)
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)
## Tau and other stuff
tau = getTaus(ev)
track = getIsoTracks(ev)
## 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)
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)
## 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])
#weight.add("weight", ev.genWeight*cfg['lumi'][self.year]*mult)
# 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.dilep_baseline(cutflow=cutflow, SS=True)
weight_BL = weight.weight()[BL]
if True:
# define the inputs to the NN
# this is super stupid. there must be a better way.
NN_inputs = np.stack([
ak.to_numpy(ak.num(jet[BL])),
ak.to_numpy(ak.num(tau[BL])),
ak.to_numpy(ak.num(track[BL])),
ak.to_numpy(st[BL]),
ak.to_numpy(ev.MET[BL].pt),
ak.to_numpy(ak.max(mjf[BL], axis=1)),
ak.to_numpy(pad_and_flatten(delta_eta[BL])),
ak.to_numpy(pad_and_flatten(leading_lepton[BL].pt)),
ak.to_numpy(pad_and_flatten(leading_lepton[BL].eta)),
ak.to_numpy(pad_and_flatten(trailing_lepton[BL].pt)),
ak.to_numpy(pad_and_flatten(trailing_lepton[BL].eta)),
ak.to_numpy(pad_and_flatten(dilepton_mass[BL])),
ak.to_numpy(pad_and_flatten(dilepton_pt[BL])),
ak.to_numpy(pad_and_flatten(j_fwd[BL].pt)),
ak.to_numpy(pad_and_flatten(j_fwd[BL].p)),
ak.to_numpy(pad_and_flatten(j_fwd[BL].eta)),
ak.to_numpy(pad_and_flatten(jet[:, 0:1][BL].pt)),
ak.to_numpy(pad_and_flatten(jet[:, 1:2][BL].pt)),
ak.to_numpy(pad_and_flatten(jet[:, 0:1][BL].eta)),
ak.to_numpy(pad_and_flatten(jet[:, 1:2][BL].eta)),
ak.to_numpy(pad_and_flatten(high_score_btag[:, 0:1][BL].pt)),
ak.to_numpy(pad_and_flatten(high_score_btag[:, 1:2][BL].pt)),
ak.to_numpy(pad_and_flatten(high_score_btag[:, 0:1][BL].eta)),
ak.to_numpy(pad_and_flatten(high_score_btag[:, 1:2][BL].eta)),
ak.to_numpy(min_bl_dR[BL]),
ak.to_numpy(min_mt_lep_met[BL]),
])
NN_inputs = np.moveaxis(NN_inputs, 0, 1)
model, scaler = load_onnx_model('v8')
try:
NN_inputs_scaled = scaler.transform(NN_inputs)
NN_pred = predict_onnx(model, NN_inputs_scaled)
best_score = np.argmax(NN_pred, axis=1)
except ValueError:
#print ("Empty NN_inputs")
NN_pred = np.array([])
best_score = np.array([])
NN_inputs_scaled = NN_inputs
#k.clear_session()
output['node'].fill(dataset=dataset, multiplicity=best_score, weight=weight_BL)
output['node0_score_incl'].fill(dataset=dataset, score=NN_pred[:,0] if np.shape(NN_pred)[0]>0 else np.array([]), weight=weight_BL)
output['node0_score'].fill(dataset=dataset, score=NN_pred[best_score==0][:,0] if np.shape(NN_pred)[0]>0 else np.array([]), weight=weight_BL[best_score==0])
output['node1_score'].fill(dataset=dataset, score=NN_pred[best_score==1][:,1] if np.shape(NN_pred)[0]>0 else np.array([]), weight=weight_BL[best_score==1])
output['node2_score'].fill(dataset=dataset, score=NN_pred[best_score==2][:,2] if np.shape(NN_pred)[0]>0 else np.array([]), weight=weight_BL[best_score==2])
output['node3_score'].fill(dataset=dataset, score=NN_pred[best_score==3][:,3] if np.shape(NN_pred)[0]>0 else np.array([]), weight=weight_BL[best_score==3])
output['node4_score'].fill(dataset=dataset, score=NN_pred[best_score==4][:,4] if np.shape(NN_pred)[0]>0 else np.array([]), weight=weight_BL[best_score==4])
SR_sel_pp = ((best_score==0) & ak.flatten((leading_lepton[BL].pdgId<0)))
SR_sel_mm = ((best_score==0) & ak.flatten((leading_lepton[BL].pdgId>0)))
leading_lepton_BL = leading_lepton[BL]
output['lead_lep_SR_pp'].fill(
dataset = dataset,
pt = ak.to_numpy(ak.flatten(leading_lepton_BL[SR_sel_pp].pt)),
weight = weight_BL[SR_sel_pp]
)
output['lead_lep_SR_mm'].fill(
dataset = dataset,
pt = ak.to_numpy(ak.flatten(leading_lepton_BL[SR_sel_mm].pt)),
weight = weight_BL[SR_sel_mm]
)
del model
del scaler
del NN_inputs, NN_inputs_scaled, NN_pred
# first, make a few super inclusive plots
output['PV_npvs'].fill(dataset=dataset, multiplicity=ev.PV[BL].npvs, weight=weight_BL)
output['PV_npvsGood'].fill(dataset=dataset, multiplicity=ev.PV[BL].npvsGood, weight=weight_BL)
output['N_jet'].fill(dataset=dataset, multiplicity=ak.num(jet)[BL], weight=weight_BL)
output['N_b'].fill(dataset=dataset, multiplicity=ak.num(btag)[BL], weight=weight_BL)
output['N_central'].fill(dataset=dataset, multiplicity=ak.num(central)[BL], weight=weight_BL)
output['N_ele'].fill(dataset=dataset, multiplicity=ak.num(electron)[BL], weight=weight_BL)
output['N_mu'].fill(dataset=dataset, multiplicity=ak.num(electron)[BL], weight=weight_BL)
output['N_fwd'].fill(dataset=dataset, multiplicity=ak.num(fwd)[BL], weight=weight_BL)
output['ST'].fill(dataset=dataset, pt=st[BL], weight=weight_BL)
output['HT'].fill(dataset=dataset, pt=ht[BL], weight=weight_BL)
if not re.search(re.compile('MuonEG|DoubleMuon|DoubleEG|EGamma'), dataset):
output['nLepFromTop'].fill(dataset=dataset, multiplicity=ev[BL].nLepFromTop, weight=weight_BL)
output['nLepFromTau'].fill(dataset=dataset, multiplicity=ev.nLepFromTau[BL], weight=weight_BL)
output['nLepFromZ'].fill(dataset=dataset, multiplicity=ev.nLepFromZ[BL], weight=weight_BL)
output['nLepFromW'].fill(dataset=dataset, multiplicity=ev.nLepFromW[BL], weight=weight_BL)
output['nGenTau'].fill(dataset=dataset, multiplicity=ev.nGenTau[BL], weight=weight_BL)
output['nGenL'].fill(dataset=dataset, multiplicity=ak.num(ev.GenL[BL], axis=1), weight=weight_BL)
output['chargeFlip_vs_nonprompt'].fill(dataset=dataset, n1=n_chargeflip[BL], n2=n_nonprompt[BL], n_ele=ak.num(electron)[BL], weight=weight_BL)
output['MET'].fill(
dataset = dataset,
pt = ev.MET[BL].pt,
phi = ev.MET[BL].phi,
weight = weight_BL
)
if not re.search(re.compile('MuonEG|DoubleMuon|DoubleEG|EGamma'), dataset):
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_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_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_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_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_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_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_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_BL
)
output['high_p_fwd_p'].fill(dataset=dataset, p = ak.flatten(j_fwd[BL].p), weight = weight_BL)
return output
def process(self, events):
# get meta infos
dataset = events.metadata["dataset"]
isRealData = not hasattr(events, "genWeight")
n_events = len(events)
selection = processor.PackedSelection()
weights = processor.Weights(n_events)
output = self.accumulator.identity()
# weights
if not isRealData:
output['sumw'][dataset] += awkward1.sum(events.genWeight)
# trigger
triggers = {}
for channel in ["e","mu"]:
trigger = np.zeros(len(events), dtype='bool')
for t in self._trigger[channel]:
try:
trigger = trigger | events.HLT[t]
except:
warnings.warn("Missing trigger %s" % t, RuntimeWarning)
triggers[channel] = trigger
# met filter
met_filters = ["goodVertices",
"globalSuperTightHalo2016Filter",
"HBHENoiseFilter",
"HBHENoiseIsoFilter",
"EcalDeadCellTriggerPrimitiveFilter",
"BadPFMuonFilter",
]
met_filters_mask = np.ones(len(events), dtype='bool')
for t in met_filters:
met_filters_mask = met_filters_mask & events.Flag[t]
selection.add("met_filter", awkward1.to_numpy(met_filters_mask))
# load objects
muons = events.Muon
electrons = events.Electron
jets = events.Jet
fatjets = events.FatJet
subjets = events.SubJet
fatjetsLS = events.FatJetLS
met = events.MET
# muons
goodmuon = (
(muons.mediumId)
& (muons.miniPFRelIso_all <= 0.2)
& (muons.pt >= 27)
& (abs(muons.eta) <= 2.4)
& (abs(muons.dz) < 0.1)
& (abs(muons.dxy) < 0.05)
& (muons.sip3d < 4)
)
good_muons = muons[goodmuon]
ngood_muons = awkward1.sum(goodmuon, axis=1)
# electrons
goodelectron = (
(electrons.mvaFall17V2noIso_WP90)
& (electrons.pt >= 30)
& (abs(electrons.eta) <= 1.479)
& (abs(electrons.dz) < 0.1)
& (abs(electrons.dxy) < 0.05)
& (electrons.sip3d < 4)
)
good_electrons = electrons[goodelectron]
ngood_electrons = awkward1.sum(goodelectron, axis=1)
# good leptons
good_leptons = awkward1.concatenate([good_muons, good_electrons], axis=1)
good_leptons = good_leptons[awkward1.argsort(good_leptons.pt)]
# lepton candidate
candidatelep = awkward1.firsts(good_leptons)
# lepton channel selection
selection.add("ch_e", awkward1.to_numpy((triggers["e"]) & (ngood_electrons==1) & (ngood_muons==0))) # not sure if need to require 0 muons or 0 electrons in the next line
selection.add("ch_mu", awkward1.to_numpy((triggers["mu"]) & (ngood_electrons==0) & (ngood_muons==1)))
# jets
ht = awkward1.sum(jets[jets.pt > 30].pt,axis=1)
selection.add("ht_400", awkward1.to_numpy(ht>=400))
goodjet = (
(jets.isTight)
& (jets.pt > 30)
& (abs(jets.eta) <= 2.5)
)
good_jets = jets[goodjet]
# fat jets
jID = "isTight"
# TODO: add mass correction
# a way to get the first two subjets
# cart = awkward1.cartesian([fatjets, subjets], nested=True)
# idxes = awkward1.pad_none(awkward1.argsort(cart['0'].delta_r(cart['1'])), 2, axis=2)
# sj1 = subjets[idxes[:,:,0]]
# sj2 = subjets[idxes[:,:,1]]
good_fatjet = (
(getattr(fatjets, jID))
& (abs(fatjets.eta) <= 2.4)
& (fatjets.pt > 50)
& (fatjets.msoftdrop > 30)
& (fatjets.msoftdrop < 210)
#& (fatjets.pt.copy(content=fatjets.subjets.content.counts) == 2) # TODO: require 2 subjets?
# this can probably be done w FatJet_subJetIdx1 or FatJet_subJetIdx2
& (awkward1.all(fatjets.subjets.pt >= 20))
& (awkward1.all(abs(fatjets.subjets.eta) <= 2.4))
)
good_fatjets = fatjets[good_fatjet]
# hbb candidate
mask_hbb = (
(good_fatjets.pt > 200)
& (good_fatjets.delta_r(candidatelep) > 2.0)
)
candidateHbb = awkward1.firsts(good_fatjets[mask_hbb])
# b-tag #& (good_fatjets.particleNetMD_Xbb > 0.9)
selection.add('hbb_btag',awkward1.to_numpy(candidateHbb.deepTagMD_ZHbbvsQCD >= 0.8)) # score would be larger for tight category (0.97)
# No AK4 b-tagged jets away from bb jet
jets_HbbV = jets[good_jets.delta_r(candidateHbb) >= 1.2]
selection.add('hbb_vetobtagaway', awkward1.to_numpy(awkward1.max(jets_HbbV.btagDeepB, axis=1, mask_identity=False) > BTagEfficiency.btagWPs[self._year]['medium']))
# fat jets Lepton Subtracted
# wjj candidate
mask_wjj = (
(fatjetsLS.pt > 50)
& (fatjetsLS.delta_r(candidatelep) > 1.2)
# need to add 2 subjets w pt > 20 & eta<2.4
# need to add ID?
)
candidateWjj = awkward1.firsts(fatjetsLS[mask_wjj][awkward1.argmin(fatjetsLS[mask_wjj].delta_r(candidatelep),axis=1,keepdims=True)])
# add t2/t1 <= 0.75 (0.45 HP)
selection.add('hww_mass', awkward1.to_numpy(candidateWjj.mass >= 10))
print('met ',met)
# wjjlnu info
#HSolverLiInfo hwwInfoLi;
# qqSDmass = candidateWjj.msoftdrop
# hwwLi = hSolverLi->minimize(candidatelep.p4(), met.p4(), wjjcand.p4(), qqSDmass, hwwInfoLi)
#neutrino = hwwInfoLi.neutrino;
#wlnu = hwwInfoLi.wlnu;
#wqq = hwwInfoLi.wqqjet;
#hWW = hwwInfoLi.hWW;
#wwDM = PhysicsUtilities::deltaR( wlnu,wqq) * hWW.pt()/2.0;
# add dlvqq <= 11 (2.5 HP)
# in the meantime let's add the mass
'''
mm = (candidatejet - candidatelep).mass2
jmass = (mm>0)*np.sqrt(np.maximum(0, mm)) + (mm<0)*candidatejet.mass
joffshell = jmass < 62.5
massassumption = 80.*joffshell + (125 - 80.)*~joffshell
x = massassumption**2/(2*candidatelep.pt*met.pt) + np.cos(candidatelep.phi - met.phi)
met_eta = (
(x < 1)*np.arcsinh(x*np.sinh(candidatelep.eta))
+ (x > 1)*(
candidatelep.eta - np.sign(candidatelep.eta)*np.arccosh(candidatelep.eta)
)
)
met_p4 = TLorentzVectorArray.from_ptetaphim(np.array([0.]),np.array([0.]),np.array([0.]),np.array([0.]))
if met.size > 0:
met_p4 = TLorentzVectorArray.from_ptetaphim(met.pt, met_eta.fillna(0.), met.phi, np.zeros(met.size))
# hh system
candidateHH = candidateWjj + met_p4 + candidateHbb
selection.add('hh_mass', candidateHH.mass >= 700)
selection.add('hh_centrality', candidateHH.pt/candidateHH.mass >= 0.3)
'''
channels = {"e": ["met_filter","ch_e","ht_400","hbb_btag","hbb_vetobtagaway","hww_mass"], #,"hh_mass","hh_centrality"],
"mu": ["met_filter","ch_mu","ht_400","hbb_btag","hbb_vetobtagaway","hww_mass"] #,"hh_mass","hh_centrality"],
}
# need to add gen info
if not isRealData:
weights.add('genweight', events.genWeight)
add_pileup_weight(weights, events.Pileup.nPU, self._year, dataset)
for channel, cuts in channels.items():
allcuts = set()
output['cutflow'].fill(dataset=dataset, channel=channel, cut=0, weight=weights.weight())
for i, cut in enumerate(cuts):
allcuts.add(cut)
cut = selection.all(*allcuts)
output['cutflow'].fill(dataset=dataset, channel=channel, cut=i + 1, weight=weights.weight()[cut])
return output
def process(self, events):
output = self._accumulator.identity()
dataset_name = events.metadata['dataset']
output["total_events"][dataset_name] += events.__len__()
# Initialize dict accumulators, if have not been initialized
for jet in [0, 1, 2]:
if dataset_name not in output[f"eta_{jet}_final"].keys():
output[f"eta_{jet}_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"ptoverM_{jet}_final"].keys():
output[f"ptoverM_{jet}_final"][dataset_name] = processor.column_accumulator(np.array([]))
for pair in [(0, 1), (1, 2), (2, 0)]:
if dataset_name not in output[f"dEta_{pair[0]}{pair[1]}_final"].keys():
output[f"dEta_{pair[0]}{pair[1]}_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dR_{pair[0]}{pair[1]}_final"].keys():
output[f"dR_{pair[0]}{pair[1]}_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"moverM_{pair[0]}{pair[1]}_final"].keys():
output[f"moverM_{pair[0]}{pair[1]}_final"][dataset_name] = processor.column_accumulator(np.array([]))
for pair in [(0, 1, 2), (1, 2, 0), (2, 0, 1)]:
if dataset_name not in output[f"dR_{pair[0]}_{pair[1]}{pair[2]}_final"].keys():
output[f"dR_{pair[0]}_{pair[1]}{pair[2]}_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dEta_{pair[0]}_{pair[1]}{pair[2]}_final"].keys():
output[f"dEta_{pair[0]}_{pair[1]}{pair[2]}_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"Phi_{pair[0]}_{pair[1]}{pair[2]}_final"].keys():
output[f"Phi_{pair[0]}_{pair[1]}{pair[2]}_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dPtoverM_{pair[0]}_{pair[1]}{pair[2]}_final"].keys():
output[f"dPtoverM_{pair[0]}_{pair[1]}{pair[2]}_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"ptoverM_max_final"].keys():
output[f"ptoverM_max_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"ptoverM_min_final"].keys():
output[f"ptoverM_min_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"eta_max_final"].keys():
output[f"eta_max_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dR_max_final"].keys():
output[f"dR_max_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dR_min_final"].keys():
output[f"dR_min_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dEta_max_final"].keys():
output[f"dEta_max_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dEta_min_final"].keys():
output[f"dEta_min_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dR_j_jj_max_final"].keys():
output[f"dR_j_jj_max_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dR_j_jj_min_final"].keys():
output[f"dR_j_jj_min_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dEta_j_jj_max_final"].keys():
output[f"dEta_j_jj_max_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dEta_j_jj_min_final"].keys():
output[f"dEta_j_jj_min_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dPhi_j_jj_max_final"].keys():
output[f"dPhi_j_jj_max_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dPhi_j_jj_min_final"].keys():
output[f"dPhi_j_jj_min_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dPtoverM_j_jj_max_final"].keys():
output[f"dPtoverM_j_jj_max_final"][dataset_name] = processor.column_accumulator(np.array([]))
if dataset_name not in output[f"dPtoverM_j_jj_min_final"].keys():
output[f"dPtoverM_j_jj_min_final"][dataset_name] = processor.column_accumulator(np.array([]))
# HLT selection
HLT_mask = []
if year == "2016":
if "SingleMuon" in dataset_name: #this does not work, as the name of file which is under processing is unknown
if "2016B2" in dataset_name:
HLT_mask = events.HLT.IsoMu24 | events.HLT.IsoTkMu24 | events.HLT.Mu50
else:
HLT_mask = events.HLT.IsoMu24 | events.HLT.IsoTkMu24 | events.HLT.Mu50 | events.HLT.TkMu50
else: #https://twiki.cern.ch/twiki/bin/view/CMS/HLTPathsRunIIList
if "2016B2" in dataset_name:
HLT_mask = events.HLT.PFHT800 | events.HLT.PFHT900 | events.HLT.PFJet500 | events.HLT.CaloJet500_NoJetID
elif "2016H" in dataset_name:
HLT_mask = events.HLT.PFHT900 | events.HLT.AK8PFJet450 | events.HLT.AK8PFJet500 | events.HLT.PFJet500 | events.HLT.CaloJet500_NoJetID
else:
HLT_mask = events.HLT.PFHT800 | events.HLT.PFHT900 | events.HLT.AK8PFJet450 | events.HLT.AK8PFJet500 | events.HLT.PFJet500 | events.HLT.CaloJet500_NoJetID
if year == "2017":
if "SingleMuon" in dataset_name:
if "2017B" in dataset_name:
HLT_mask = events.HLT.IsoMu27 | events.HLT.Mu50
else:
HLT_mask = events.HLT.IsoMu27 | events.HLT.Mu50 | events.HLT.OldMu100 | events.HLT.TkMu100
else:
HLT_mask = events.HLT.PFHT1050 | events.HLT.AK8PFJet500 | events.HLT.AK8PFJet550 | events.HLT.CaloJet500_NoJetID | events.HLT.CaloJet550_NoJetID | events.HLT.PFJet500
if year == "2018":
if "SingleMuon" in dataset_name:
HLT_mask = events.HLT.IsoMu24 | events.HLT.Mu50 | events.HLT.OldMu100 | events.HLT.TkMu100
else:
HLT_mask = events.HLT.PFHT1050 | events.HLT.AK8PFJet500 | events.HLT.AK8PFJet550 | events.HLT.CaloJet500_NoJetID | events.HLT.CaloJet550_NoJetID | events.HLT.PFJet500
# Require 3 jets
jet_mask = (events.Jet.pt > 30.) & (abs(events.Jet.eta) < 2.5) & (events.Jet.isTight)
event_mask = (awk.sum(jet_mask, axis=1) >= 3)
event_mask = event_mask & HLT_mask
events_3j = events[event_mask]
# Reduce jet mask to only events with 3 good jets
jet_mask = jet_mask[event_mask]
# Array of the jets to consider for trijet resonance
selected_jets = events_3j.Jet[jet_mask][:, :3]
# Pairs of jets
#pairs = awk.argcombinations(selected_jets, 2)
#jet_i, jet_j = awk.unzip(pairs)
pairs = [(0, 1), (1, 2), (2, 0)]
jet_i, jet_j = zip(*pairs) # Returns [0, 1, 2] , [1, 2, 0]
m_ij = (selected_jets[:, jet_i] + selected_jets[:, jet_j]).mass
dR_ij = selected_jets[:, jet_i].delta_r(selected_jets[:, jet_j])
dEta_ij = abs(selected_jets[:, jet_i].eta - selected_jets[:, jet_j].eta)
jet_k = [2, 0, 1]
dR_i_jk = selected_jets[:, jet_i].delta_r(selected_jets[:, jet_j] + selected_jets[:, jet_k])
dEta_i_jk = abs(selected_jets[:, jet_i].eta - (selected_jets[:, jet_j] + selected_jets[:, jet_k]).eta)
dPhi_i_jk = abs(selected_jets[:, jet_i].phi - (selected_jets[:, jet_j] + selected_jets[:, jet_k]).phi)
m3j = selected_jets.sum().mass
pt_i_overM = selected_jets.pt / m3j
m_01_overM = m_ij[:,0] / m3j
m_12_overM = m_ij[:,1] / m3j
m_20_overM = m_ij[:,2] / m3j
dPtoverM_0_12 = abs(selected_jets[:, 0].pt - (selected_jets[:, 1] + selected_jets[:, 2]).pt) / m3j
dPtoverM_1_20 = abs(selected_jets[:, 1].pt - (selected_jets[:, 2] + selected_jets[:, 0]).pt) / m3j
dPtoverM_2_01 = abs(selected_jets[:, 2].pt - (selected_jets[:, 0] + selected_jets[:, 1]).pt) / m3j
# Event selection masks
# selection_masks = {}
# Pre-selection
selection = PackedSelection()
selection.add("Dummy", m3j > 000)
sel_mask = selection.require(**{name: True for name in selection.names})
# selection_masks["Pre-selection"] = sel_mask
output["selected_events"][dataset_name] += events_3j[sel_mask].__len__()
for jet in [0, 1, 2]:
output[f"eta_{jet}_final"][dataset_name] += processor.column_accumulator(np.array(selected_jets[:, jet][sel_mask].eta))
output[f"ptoverM_{jet}_final"][dataset_name] += processor.column_accumulator(np.array(pt_i_overM[:, jet][sel_mask]))
for pair in [(0, 1), (1, 2), (2, 0)]:
output[f"dEta_{pair[0]}{pair[1]}_final"][dataset_name] += processor.column_accumulator(np.array(dEta_ij[:, pair[0]][sel_mask]))
output[f"dR_{pair[0]}{pair[1]}_final"][dataset_name] += processor.column_accumulator(np.array(dR_ij[:, pair[0]][sel_mask]))
output[f"moverM_01_final"][dataset_name] += processor.column_accumulator(np.array(m_01_overM[sel_mask]))
output[f"moverM_12_final"][dataset_name] += processor.column_accumulator(np.array(m_12_overM[sel_mask]))
output[f"moverM_20_final"][dataset_name] += processor.column_accumulator(np.array(m_20_overM[sel_mask]))
for pair in [(0, 1, 2), (1, 2, 0), (2, 0, 1)]:
output[f"dR_{pair[0]}_{pair[1]}{pair[2]}_final"][dataset_name] += processor.column_accumulator(np.array(dR_i_jk[:, pair[0]][sel_mask]))
output[f"dEta_{pair[0]}_{pair[1]}{pair[2]}_final"][dataset_name] += processor.column_accumulator(np.array(dEta_i_jk[:, pair[0]][sel_mask]))
output[f"Phi_{pair[0]}_{pair[1]}{pair[2]}_final"][dataset_name] += processor.column_accumulator(np.array(dPhi_i_jk[:, pair[0]][sel_mask]))
output[f"dPtoverM_0_12_final"][dataset_name] += processor.column_accumulator(np.array(dPtoverM_0_12[sel_mask]))
output[f"dPtoverM_1_20_final"][dataset_name] += processor.column_accumulator(np.array(dPtoverM_1_20[sel_mask]))
output[f"dPtoverM_2_01_final"][dataset_name] += processor.column_accumulator(np.array(dPtoverM_2_01[sel_mask]))
max_pt_overM_2fill = awk.max(pt_i_overM[sel_mask], axis=1)
min_pt_overM_2fill = awk.min(pt_i_overM[sel_mask], axis=1)
max_dR_2fill = awk.max(dR_ij[sel_mask], axis=1)
max_dEta_2fill = awk.max(dEta_ij[sel_mask], axis=1)
min_dR_2fill = awk.min(dR_ij[sel_mask], axis=1)
min_dEta_2fill = awk.min(dEta_ij[sel_mask], axis=1)
min_pt_2fill = awk.min(selected_jets[sel_mask].pt, axis=1)
max_eta_2fill = awk.max(abs(selected_jets[sel_mask].eta), axis=1)
max_dR_i_jk_2fill = awk.max(dR_i_jk[sel_mask], axis=1)
min_dR_i_jk_2fill = awk.min(dR_i_jk[sel_mask], axis=1)
max_dEta_i_jk_2fill = awk.max(dEta_i_jk[sel_mask], axis=1)
min_dEta_i_jk_2fill = awk.min(dEta_i_jk[sel_mask], axis=1)
max_dPhi_i_jk_2fill = awk.max(dPhi_i_jk[sel_mask], axis=1)
min_dPhi_i_jk_2fill = awk.min(dPhi_i_jk[sel_mask], axis=1)
max_dPtoverM_i_jk_2fill = []
min_dPtoverM_i_jk_2fill = []
dPtoverM_0_12_2fill = dPtoverM_0_12[sel_mask]
dPtoverM_1_20_2fill = dPtoverM_1_20[sel_mask]
dPtoverM_2_01_2fill = dPtoverM_2_01[sel_mask]
for pair in zip(dPtoverM_0_12_2fill, dPtoverM_1_20_2fill, dPtoverM_2_01_2fill):
max_dPtoverM_i_jk_2fill.append(max(pair))
min_dPtoverM_i_jk_2fill.append(min(pair))
max_pt_overM_2fill = awk.fill_none(max_pt_overM_2fill, -99)
min_pt_overM_2fill = awk.fill_none(min_pt_overM_2fill, -99)
max_dR_2fill = awk.fill_none(max_dR_2fill, -99)
max_dEta_2fill = awk.fill_none(max_dEta_2fill, -99)
min_dR_2fill = awk.fill_none(min_dR_2fill, -99)
min_dEta_2fill = awk.fill_none(min_dEta_2fill, -99)
min_pt_2fill = awk.fill_none(min_pt_2fill, -99)
max_eta_2fill = awk.fill_none(max_eta_2fill, -99)
max_dR_i_jk_2fill = awk.fill_none(max_dR_i_jk_2fill, -99)
min_dR_i_jk_2fill = awk.fill_none(min_dR_i_jk_2fill, -99)
max_dEta_i_jk_2fill = awk.fill_none(max_dEta_i_jk_2fill, -99)
min_dEta_i_jk_2fill = awk.fill_none(min_dEta_i_jk_2fill, -99)
max_dPhi_i_jk_2fill = awk.fill_none(max_dPhi_i_jk_2fill, -99)
min_dPhi_i_jk_2fill = awk.fill_none(min_dPhi_i_jk_2fill, -99)
output[f"ptoverM_max_final"][dataset_name] += processor.column_accumulator(np.array(max_pt_overM_2fill))
output[f"ptoverM_min_final"][dataset_name] += processor.column_accumulator(np.array(min_pt_overM_2fill))
output[f"eta_max_final"][dataset_name] += processor.column_accumulator(np.array(max_eta_2fill))
output[f"dR_max_final"][dataset_name] += processor.column_accumulator(np.array(max_dR_2fill))
output[f"dR_min_final"][dataset_name] += processor.column_accumulator(np.array(min_dR_2fill))
output[f"dEta_max_final"][dataset_name] += processor.column_accumulator(np.array(max_dEta_2fill))
output[f"dEta_min_final"][dataset_name] += processor.column_accumulator(np.array(min_dEta_2fill))
output[f"dR_j_jj_max_final"][dataset_name] += processor.column_accumulator(np.array(max_dR_i_jk_2fill))
output[f"dR_j_jj_min_final"][dataset_name] += processor.column_accumulator(np.array(min_dR_i_jk_2fill))
output[f"dEta_j_jj_max_final"][dataset_name] += processor.column_accumulator(np.array(max_dEta_i_jk_2fill))
output[f"dEta_j_jj_min_final"][dataset_name] += processor.column_accumulator(np.array(min_dEta_i_jk_2fill))
output[f"dPhi_j_jj_max_final"][dataset_name] += processor.column_accumulator(np.array(max_dPhi_i_jk_2fill))
output[f"dPhi_j_jj_min_final"][dataset_name] += processor.column_accumulator(np.array(min_dPhi_i_jk_2fill))
output[f"dPtoverM_j_jj_max_final"][dataset_name] += processor.column_accumulator(np.array(max_dPtoverM_i_jk_2fill))
output[f"dPtoverM_j_jj_min_final"][dataset_name] += processor.column_accumulator(np.array(min_dPtoverM_i_jk_2fill))
return output
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)
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 dataset=='MuonEG':
# lumi weight
weight.add("weight", ev.weight*cfg['lumi'][self.year])
#weight.add("weight", ev.genWeight*cfg['lumi'][self.year]*mult)
# 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['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(electron)[BL], weight=weight.weight()[BL])
output['N_mu'].fill(dataset=dataset, multiplicity=ak.num(electron)[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])
return output
