def process(self, events):
events["Electron", "pdgId"] = -11 * events.Electron.charge
events["Muon", "pdgId"] = -13 * events.Muon.charge
events["leptons"] = ak.concatenate(
[events.Electron, events.Muon],
axis=1,
)
events = events[ak.num(events.leptons) >= 3]
pair = ak.argcombinations(events.leptons, 2, fields=["l1", "l2"])
pair = pair[(
events.leptons[pair.l1].pdgId == -events.leptons[pair.l2].pdgId)]
with np.errstate(invalid="ignore"):
pair = pair[ak.singletons(
ak.argmin(
abs((events.leptons[pair.l1] +
events.leptons[pair.l2]).mass - 91.2),
axis=1,
))]
events = events[ak.num(pair) > 0]
pair = pair[ak.num(pair) > 0][:, 0]
l3 = ak.local_index(events.leptons)
l3 = l3[(l3 != pair.l1) & (l3 != pair.l2)]
l3 = l3[ak.argmax(events.leptons[l3].pt, axis=1, keepdims=True)]
l3 = events.leptons[l3][:, 0]
mt = np.sqrt(2 * l3.pt * events.MET.pt *
(1 - np.cos(events.MET.delta_phi(l3))))
return (hist.Hist.new.Reg(
100, 0, 200, name="mt",
label=r"$\ell$-MET transverse mass [GeV]").Double().fill(mt))
def test_argcombinations():
array = ak.Array([[0.0, 1.1, 2.2, 3.3], [], [4.4, 5.5, 6.6], [7.7],
[8.8, 9.9, 10.0, 11.1, 12.2]])
assert ak.to_list(ak.argcombinations(array, 2, replacement=False)) == [
[(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)],
[],
[(0, 1), (0, 2), (1, 2)],
[],
[
(0, 1),
(0, 2),
(0, 3),
(0, 4),
(1, 2),
(1, 3),
(1, 4),
(2, 3),
(2, 4),
(3, 4),
],
]
def process(self, events):
# Dataset parameters
dataset = events.metadata['dataset']
year = self._samples[dataset]['year']
xsec = self._samples[dataset]['xsec']
sow = self._samples[dataset]['nSumOfWeights']
isData = self._samples[dataset]['isData']
datasets = [
'SingleMuon', 'SingleElectron', 'EGamma', 'MuonEG', 'DoubleMuon',
'DoubleElectron'
]
for d in datasets:
if d in dataset: dataset = dataset.split('_')[0]
# Initialize objects
met = events.MET
e = events.Electron
mu = events.Muon
tau = events.Tau
j = events.Jet
# Muon selection
#mu['isGood'] = isMuonMVA(mu.pt, mu.eta, mu.dxy, mu.dz, mu.miniPFRelIso_all, mu.sip3d, mu.mvaTTH, mu.mediumPromptId, mu.tightCharge, minpt=10)
mu['isPres'] = isPresMuon(mu.dxy, mu.dz, mu.sip3d, mu.looseId)
mu['isTight'] = isTightMuon(mu.pt,
mu.eta,
mu.dxy,
mu.dz,
mu.pfRelIso03_all,
mu.sip3d,
mu.mvaTTH,
mu.mediumPromptId,
mu.tightCharge,
mu.looseId,
minpt=10)
mu['isGood'] = mu['isPres'] & mu['isTight']
leading_mu = mu[ak.argmax(mu.pt, axis=-1, keepdims=True)]
leading_mu = leading_mu[leading_mu.isGood]
mu = mu[mu.isGood]
mu_pres = mu[mu.isPres]
# Electron selection
#e['isGood'] = isElecMVA(e.pt, e.eta, e.dxy, e.dz, e.miniPFRelIso_all, e.sip3d, e.mvaTTH, e.mvaFall17V2Iso, e.lostHits, e.convVeto, e.tightCharge, minpt=10)
e['isPres'] = isPresElec(e.pt,
e.eta,
e.dxy,
e.dz,
e.miniPFRelIso_all,
e.sip3d,
e.lostHits,
minpt=15)
e['isTight'] = isTightElec(e.pt,
e.eta,
e.dxy,
e.dz,
e.miniPFRelIso_all,
e.sip3d,
e.mvaTTH,
e.mvaFall17V2Iso,
e.lostHits,
e.convVeto,
e.tightCharge,
e.sieie,
e.hoe,
e.eInvMinusPInv,
minpt=15)
e['isClean'] = isClean(e, mu, drmin=0.05)
e['isGood'] = e['isPres'] & e['isTight'] & e['isClean']
leading_e = e[ak.argmax(e.pt, axis=-1, keepdims=True)]
leading_e = leading_e[leading_e.isGood]
e = e[e.isGood]
e_pres = e[e.isPres & e.isClean]
# Tau selection
tau['isPres'] = isPresTau(tau.pt,
tau.eta,
tau.dxy,
tau.dz,
tau.leadTkPtOverTauPt,
tau.idAntiMu,
tau.idAntiEle,
tau.rawIso,
tau.idDecayModeNewDMs,
minpt=20)
tau['isClean'] = isClean(tau, e_pres, drmin=0.4) & isClean(
tau, mu_pres, drmin=0.4)
tau['isGood'] = tau['isPres'] # & tau['isClean'], for the moment
tau = tau[tau.isGood]
nElec = ak.num(e)
nMuon = ak.num(mu)
nTau = ak.num(tau)
twoLeps = (nElec + nMuon) == 2
threeLeps = (nElec + nMuon) == 3
twoElec = (nElec == 2)
twoMuon = (nMuon == 2)
e0 = e[ak.argmax(e.pt, axis=-1, keepdims=True)]
m0 = mu[ak.argmax(mu.pt, axis=-1, keepdims=True)]
# Jet selection
jetptname = 'pt_nom' if hasattr(j, 'pt_nom') else 'pt'
j['isGood'] = isTightJet(getattr(j,
jetptname), j.eta, j.jetId, j.neHEF,
j.neEmEF, j.chHEF, j.chEmEF, j.nConstituents)
#j['isgood'] = isGoodJet(j.pt, j.eta, j.jetId)
#j['isclean'] = isClean(j, e, mu)
j['isClean'] = isClean(j, e, drmin=0.4) & isClean(
j, mu, drmin=0.4) # & isClean(j, tau, drmin=0.4)
goodJets = j[(j.isClean) & (j.isGood)]
njets = ak.num(goodJets)
ht = ak.sum(goodJets.pt, axis=-1)
j0 = goodJets[ak.argmax(goodJets.pt, axis=-1, keepdims=True)]
#nbtags = ak.num(goodJets[goodJets.btagDeepFlavB > 0.2770])
nbtags = ak.num(goodJets[goodJets.btagDeepB > 0.4941])
##################################################################
### 2 same-sign leptons
##################################################################
# emu
singe = e[(nElec == 1) & (nMuon == 1) & (e.pt > -1)]
singm = mu[(nElec == 1) & (nMuon == 1) & (mu.pt > -1)]
em = ak.cartesian({"e": singe, "m": singm})
emSSmask = (em.e.charge * em.m.charge > 0)
emSS = em[emSSmask]
nemSS = len(ak.flatten(emSS))
# ee and mumu
# pt>-1 to preserve jagged dimensions
ee = e[(nElec == 2) & (nMuon == 0) & (e.pt > -1)]
mm = mu[(nElec == 0) & (nMuon == 2) & (mu.pt > -1)]
eepairs = ak.combinations(ee, 2, fields=["e0", "e1"])
eeSSmask = (eepairs.e0.charge * eepairs.e1.charge > 0)
eeonZmask = (np.abs((eepairs.e0 + eepairs.e1).mass - 91.2) < 10)
eeoffZmask = (eeonZmask == 0)
mmpairs = ak.combinations(mm, 2, fields=["m0", "m1"])
mmSSmask = (mmpairs.m0.charge * mmpairs.m1.charge > 0)
mmonZmask = (np.abs((mmpairs.m0 + mmpairs.m1).mass - 91.2) < 10)
mmoffZmask = (mmonZmask == 0)
eeSSonZ = eepairs[eeSSmask & eeonZmask]
eeSSoffZ = eepairs[eeSSmask & eeoffZmask]
mmSSonZ = mmpairs[mmSSmask & mmonZmask]
mmSSoffZ = mmpairs[mmSSmask & mmoffZmask]
neeSS = len(ak.flatten(eeSSonZ)) + len(ak.flatten(eeSSoffZ))
nmmSS = len(ak.flatten(mmSSonZ)) + len(ak.flatten(mmSSoffZ))
print('Same-sign events [ee, emu, mumu] = [%i, %i, %i]' %
(neeSS, nemSS, nmmSS))
# Cuts
eeSSmask = (ak.num(eeSSmask[eeSSmask]) > 0)
mmSSmask = (ak.num(mmSSmask[mmSSmask]) > 0)
eeonZmask = (ak.num(eeonZmask[eeonZmask]) > 0)
eeoffZmask = (ak.num(eeoffZmask[eeoffZmask]) > 0)
mmonZmask = (ak.num(mmonZmask[mmonZmask]) > 0)
mmoffZmask = (ak.num(mmoffZmask[mmoffZmask]) > 0)
emSSmask = (ak.num(emSSmask[emSSmask]) > 0)
##################################################################
### 3 leptons
##################################################################
# eem
muon_eem = mu[(nElec == 2) & (nMuon == 1) & (mu.pt > -1)]
elec_eem = e[(nElec == 2) & (nMuon == 1) & (e.pt > -1)]
ee_eem = ak.combinations(elec_eem, 2, fields=["e0", "e1"])
ee_eemZmask = (ee_eem.e0.charge * ee_eem.e1.charge < 1) & (np.abs(
(ee_eem.e0 + ee_eem.e1).mass - 91.2) < 10)
ee_eemOffZmask = (ee_eem.e0.charge * ee_eem.e1.charge < 1) & (np.abs(
(ee_eem.e0 + ee_eem.e1).mass - 91.2) > 10)
ee_eemZmask = (ak.num(ee_eemZmask[ee_eemZmask]) > 0)
ee_eemOffZmask = (ak.num(ee_eemOffZmask[ee_eemOffZmask]) > 0)
eepair_eem = (ee_eem.e0 + ee_eem.e1)
trilep_eem = eepair_eem + muon_eem #ak.cartesian({"e0":ee_eem.e0,"e1":ee_eem.e1, "m":muon_eem})
# mme
muon_mme = mu[(nElec == 1) & (nMuon == 2) & (mu.pt > -1)]
elec_mme = e[(nElec == 1) & (nMuon == 2) & (e.pt > -1)]
mm_mme = ak.combinations(muon_mme, 2, fields=["m0", "m1"])
mm_mmeZmask = (mm_mme.m0.charge * mm_mme.m1.charge < 1) & (np.abs(
(mm_mme.m0 + mm_mme.m1).mass - 91.2) < 10)
mm_mmeOffZmask = (mm_mme.m0.charge * mm_mme.m1.charge < 1) & (np.abs(
(mm_mme.m0 + mm_mme.m1).mass - 91.2) > 10)
mm_mmeZmask = (ak.num(mm_mmeZmask[mm_mmeZmask]) > 0)
mm_mmeOffZmask = (ak.num(mm_mmeOffZmask[mm_mmeOffZmask]) > 0)
mmpair_mme = (mm_mme.m0 + mm_mme.m1)
trilep_mme = mmpair_mme + elec_mme
mZ_mme = mmpair_mme.mass
mZ_eem = eepair_eem.mass
m3l_eem = trilep_eem.mass
m3l_mme = trilep_mme.mass
# eee and mmm
eee = e[(nElec == 3) & (nMuon == 0) & (e.pt > -1)]
mmm = mu[(nElec == 0) & (nMuon == 3) & (mu.pt > -1)]
eee_leps = ak.combinations(eee, 3, fields=["e0", "e1", "e2"])
mmm_leps = ak.combinations(mmm, 3, fields=["m0", "m1", "m2"])
ee_pairs = ak.combinations(eee, 2, fields=["e0", "e1"])
mm_pairs = ak.combinations(mmm, 2, fields=["m0", "m1"])
ee_pairs_index = ak.argcombinations(eee, 2, fields=["e0", "e1"])
mm_pairs_index = ak.argcombinations(mmm, 2, fields=["m0", "m1"])
mmSFOS_pairs = mm_pairs[
(np.abs(mm_pairs.m0.pdgId) == np.abs(mm_pairs.m1.pdgId))
& (mm_pairs.m0.charge != mm_pairs.m1.charge)]
offZmask_mm = ak.all(
np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2) > 10.,
axis=1,
keepdims=True) & (ak.num(mmSFOS_pairs) > 0)
onZmask_mm = ak.any(
np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2) < 10.,
axis=1,
keepdims=True)
eeSFOS_pairs = ee_pairs[
(np.abs(ee_pairs.e0.pdgId) == np.abs(ee_pairs.e1.pdgId))
& (ee_pairs.e0.charge != ee_pairs.e1.charge)]
offZmask_ee = ak.all(
np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2) > 10,
axis=1,
keepdims=True) & (ak.num(eeSFOS_pairs) > 0)
onZmask_ee = ak.any(
np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2) < 10,
axis=1,
keepdims=True)
# Create masks **for event selection**
eeeOnZmask = (ak.num(onZmask_ee[onZmask_ee]) > 0)
eeeOffZmask = (ak.num(offZmask_ee[offZmask_ee]) > 0)
mmmOnZmask = (ak.num(onZmask_mm[onZmask_mm]) > 0)
mmmOffZmask = (ak.num(offZmask_mm[offZmask_mm]) > 0)
# Now we need to create masks for the leptons in order to select leptons from the Z boson candidate (in onZ categories)
ZeeMask = ak.argmin(np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass -
91.2),
axis=1,
keepdims=True)
ZmmMask = ak.argmin(np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass -
91.2),
axis=1,
keepdims=True)
Zee = eeSFOS_pairs[ZeeMask]
Zmm = mmSFOS_pairs[ZmmMask]
eZ0 = Zee.e0[ak.num(eeSFOS_pairs) > 0]
eZ1 = Zee.e1[ak.num(eeSFOS_pairs) > 0]
eZ = eZ0 + eZ1
mZ0 = Zmm.m0[ak.num(mmSFOS_pairs) > 0]
mZ1 = Zmm.m1[ak.num(mmSFOS_pairs) > 0]
mZ = mZ0 + mZ1
mZ_eee = eZ.mass
mZ_mmm = mZ.mass
# And for the W boson
ZmmIndices = mm_pairs_index[ZmmMask]
ZeeIndices = ee_pairs_index[ZeeMask]
eW = eee[~ZeeIndices.e0 | ~ZeeIndices.e1]
mW = mmm[~ZmmIndices.m0 | ~ZmmIndices.m1]
triElec = eee_leps.e0 + eee_leps.e1 + eee_leps.e2
triMuon = mmm_leps.m0 + mmm_leps.m1 + mmm_leps.m2
m3l_eee = triElec.mass
m3l_mmm = triMuon.mass
# Triggers
trig_eeSS = passTrigger(events, 'ee', isData, dataset)
trig_mmSS = passTrigger(events, 'mm', isData, dataset)
trig_emSS = passTrigger(events, 'em', isData, dataset)
trig_eee = passTrigger(events, 'eee', isData, dataset)
trig_mmm = passTrigger(events, 'mmm', isData, dataset)
trig_eem = passTrigger(events, 'eem', isData, dataset)
trig_mme = passTrigger(events, 'mme', isData, dataset)
# MET filters
# Weights
genw = np.ones_like(
events['MET_pt']) if isData else events['genWeight']
weights = coffea.analysis_tools.Weights(len(events))
weights.add('norm', genw if isData else (xsec / sow) * genw)
eftweights = events['EFTfitCoefficients'] if hasattr(
events, "EFTfitCoefficients") else []
# Selections and cuts
selections = PackedSelection()
channels2LSS = ['eeSSonZ', 'eeSSoffZ', 'mmSSonZ', 'mmSSoffZ', 'emSS']
selections.add('eeSSonZ', (eeonZmask) & (eeSSmask) & (trig_eeSS))
selections.add('eeSSoffZ', (eeoffZmask) & (eeSSmask) & (trig_eeSS))
selections.add('mmSSonZ', (mmonZmask) & (mmSSmask) & (trig_mmSS))
selections.add('mmSSoffZ', (mmoffZmask) & (mmSSmask) & (trig_mmSS))
selections.add('emSS', (emSSmask) & (trig_emSS))
channels3L = ['eemSSonZ', 'eemSSoffZ', 'mmeSSonZ', 'mmeSSoffZ']
selections.add('eemSSonZ', (ee_eemZmask) & (trig_eem))
selections.add('eemSSoffZ', (ee_eemOffZmask) & (trig_eem))
selections.add('mmeSSonZ', (mm_mmeZmask) & (trig_mme))
selections.add('mmeSSoffZ', (mm_mmeOffZmask) & (trig_mme))
channels3L += ['eeeSSonZ', 'eeeSSoffZ', 'mmmSSonZ', 'mmmSSoffZ']
selections.add('eeeSSonZ', (eeeOnZmask) & (trig_eee))
selections.add('eeeSSoffZ', (eeeOffZmask) & (trig_eee))
selections.add('mmmSSonZ', (mmmOnZmask) & (trig_mmm))
selections.add('mmmSSoffZ', (mmmOffZmask) & (trig_mmm))
levels = ['base', '2jets', '4jets', '4j1b', '4j2b']
selections.add('base', (nElec + nMuon >= 2))
selections.add('2jets', (njets >= 2))
selections.add('4jets', (njets >= 4))
selections.add('4j1b', (njets >= 4) & (nbtags >= 1))
selections.add('4j2b', (njets >= 4) & (nbtags >= 2))
# Variables
invMass_eeSSonZ = (eeSSonZ.e0 + eeSSonZ.e1).mass
invMass_eeSSoffZ = (eeSSoffZ.e0 + eeSSoffZ.e1).mass
invMass_mmSSonZ = (mmSSonZ.m0 + mmSSonZ.m1).mass
invMass_mmSSoffZ = (mmSSoffZ.m0 + mmSSoffZ.m1).mass
invMass_emSS = (emSS.e + emSS.m).mass
varnames = {}
varnames['met'] = met.pt
varnames['ht'] = ht
varnames['njets'] = njets
varnames['nbtags'] = nbtags
varnames['invmass'] = {
'eeSSonZ': invMass_eeSSonZ,
'eeSSoffZ': invMass_eeSSoffZ,
'mmSSonZ': invMass_mmSSonZ,
'mmSSoffZ': invMass_mmSSoffZ,
'emSS': invMass_emSS,
'eemSSonZ': mZ_eem,
'eemSSoffZ': mZ_eem,
'mmeSSonZ': mZ_mme,
'mmeSSoffZ': mZ_mme,
'eeeSSonZ': mZ_eee,
'eeeSSoffZ': mZ_eee,
'mmmSSonZ': mZ_mmm,
'mmmSSoffZ': mZ_mmm,
}
varnames['m3l'] = {
'eemSSonZ': m3l_eem,
'eemSSoffZ': m3l_eem,
'mmeSSonZ': m3l_mme,
'mmeSSoffZ': m3l_mme,
'eeeSSonZ': m3l_eee,
'eeeSSoffZ': m3l_eee,
'mmmSSonZ': m3l_mmm,
'mmmSSoffZ': m3l_mmm,
}
varnames['e0pt'] = e0.pt
varnames['e0eta'] = e0.eta
varnames['m0pt'] = m0.pt
varnames['m0eta'] = m0.eta
varnames['j0pt'] = j0.pt
varnames['j0eta'] = j0.eta
varnames['counts'] = np.ones_like(events.MET.pt)
# fill Histos
hout = self.accumulator.identity()
allweights = weights.weight().flatten(
) # Why does it not complain about .flatten() here?
hout['SumOfEFTweights'].fill(eftweights,
sample=dataset,
SumOfEFTweights=varnames['counts'],
weight=allweights)
for var, v in varnames.items():
for ch in channels2LSS + channels3L:
for lev in levels:
weight = weights.weight()
cuts = [ch] + [lev]
cut = selections.all(*cuts)
weights_flat = weight[cut].flatten(
) # Why does it not complain about .flatten() here?
weights_ones = np.ones_like(weights_flat, dtype=np.int)
eftweightsvalues = eftweights[cut] if len(
eftweights) > 0 else []
if var == 'invmass':
if ch in ['eeeSSoffZ', 'mmmSSoffZ']: continue
elif ch in ['eeeSSonZ', 'mmmSSonZ']:
continue #values = v[ch]
else:
values = ak.flatten(v[ch][cut])
hout['invmass'].fill(sample=dataset,
channel=ch,
cut=lev,
invmass=values,
weight=weights_flat)
elif var == 'm3l':
if ch in [
'eeSSonZ', 'eeSSoffZ', 'mmSSonZ', 'mmSSoffZ',
'emSS', 'eeeSSoffZ', 'mmmSSoffZ', 'eeeSSonZ',
'mmmSSonZ'
]:
continue
values = ak.flatten(v[ch][cut])
hout['m3l'].fill(eftweightsvalues,
sample=dataset,
channel=ch,
cut=lev,
m3l=values,
weight=weights_flat)
else:
values = v[cut]
if var == 'ht':
hout[var].fill(eftweightsvalues,
ht=values,
sample=dataset,
channel=ch,
cut=lev,
weight=weights_flat)
elif var == 'met':
hout[var].fill(eftweightsvalues,
met=values,
sample=dataset,
channel=ch,
cut=lev,
weight=weights_flat)
elif var == 'njets':
hout[var].fill(eftweightsvalues,
njets=values,
sample=dataset,
channel=ch,
cut=lev,
weight=weights_flat)
elif var == 'nbtags':
hout[var].fill(eftweightsvalues,
nbtags=values,
sample=dataset,
channel=ch,
cut=lev,
weight=weights_flat)
elif var == 'counts':
hout[var].fill(counts=values,
sample=dataset,
channel=ch,
cut=lev,
weight=weights_ones)
elif var == 'j0eta':
if lev == 'base': continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(eftweightsvalues,
j0eta=values,
sample=dataset,
channel=ch,
cut=lev,
weight=weights_flat)
elif var == 'e0pt':
if ch in [
'mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ',
'mmmSSonZ'
]:
continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(
eftweightsvalues,
e0pt=values,
sample=dataset,
channel=ch,
cut=lev,
weight=weights_flat
) # Crashing here, not sure why. Related to values?
elif var == 'm0pt':
if ch in [
'eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ',
'eeeSSonZ'
]:
continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(eftweightsvalues,
m0pt=values,
sample=dataset,
channel=ch,
cut=lev,
weight=weights_flat)
elif var == 'e0eta':
if ch in [
'mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ',
'mmmSSonZ'
]:
continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(eftweightsvalues,
e0eta=values,
sample=dataset,
channel=ch,
cut=lev,
weight=weights_flat)
elif var == 'm0eta':
if ch in [
'eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ',
'eeeSSonZ'
]:
continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(eftweightsvalues,
m0eta=values,
sample=dataset,
channel=ch,
cut=lev,
weight=weights_flat)
elif var == 'j0pt':
if lev == 'base': continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(eftweightsvalues,
j0pt=values,
sample=dataset,
channel=ch,
cut=lev,
weight=weights_flat)
return hout
def process(self, events):
# Dataset parameters
dataset = events.metadata['dataset']
histAxisName = self._samples[dataset]['histAxisName']
year = self._samples[dataset]['year']
xsec = self._samples[dataset]['xsec']
sow = self._samples[dataset]['nSumOfWeights']
isData = self._samples[dataset]['isData']
datasets = [
'SingleMuon', 'SingleElectron', 'EGamma', 'MuonEG', 'DoubleMuon',
'DoubleElectron'
]
for d in datasets:
if d in dataset: dataset = dataset.split('_')[0]
# Initialize objects
met = events.MET
e = events.Electron
mu = events.Muon
tau = events.Tau
j = events.Jet
# Muon selection
mu['isPres'] = isPresMuon(mu.dxy, mu.dz, mu.sip3d, mu.looseId)
mu['isTight'] = isTightMuon(mu.pt,
mu.eta,
mu.dxy,
mu.dz,
mu.pfRelIso03_all,
mu.sip3d,
mu.mvaTTH,
mu.mediumPromptId,
mu.tightCharge,
mu.looseId,
minpt=10)
mu['isGood'] = mu['isPres'] & mu['isTight']
leading_mu = mu[ak.argmax(mu.pt, axis=-1, keepdims=True)]
leading_mu = leading_mu[leading_mu.isGood]
mu = mu[mu.isGood]
mu_pres = mu[mu.isPres]
# Electron selection
e['isPres'] = isPresElec(e.pt,
e.eta,
e.dxy,
e.dz,
e.miniPFRelIso_all,
e.sip3d,
e.lostHits,
minpt=15)
e['isTight'] = isTightElec(e.pt,
e.eta,
e.dxy,
e.dz,
e.miniPFRelIso_all,
e.sip3d,
e.mvaTTH,
e.mvaFall17V2Iso,
e.lostHits,
e.convVeto,
e.tightCharge,
e.sieie,
e.hoe,
e.eInvMinusPInv,
minpt=15)
e['isClean'] = isClean(e, mu, drmin=0.05)
e['isGood'] = e['isPres'] & e['isTight'] & e['isClean']
leading_e = e[ak.argmax(e.pt, axis=-1, keepdims=True)]
leading_e = leading_e[leading_e.isGood]
e = e[e.isGood]
e_pres = e[e.isPres & e.isClean]
# Tau selection
tau['isPres'] = isPresTau(tau.pt,
tau.eta,
tau.dxy,
tau.dz,
tau.leadTkPtOverTauPt,
tau.idAntiMu,
tau.idAntiEle,
tau.rawIso,
tau.idDecayModeNewDMs,
minpt=20)
tau['isClean'] = isClean(tau, e_pres, drmin=0.4) & isClean(
tau, mu_pres, drmin=0.4)
tau['isGood'] = tau['isPres'] # & tau['isClean'], for the moment
tau = tau[tau.isGood]
nElec = ak.num(e)
nMuon = ak.num(mu)
nTau = ak.num(tau)
twoLeps = (nElec + nMuon) == 2
threeLeps = (nElec + nMuon) == 3
twoElec = (nElec == 2)
twoMuon = (nMuon == 2)
e0 = e[ak.argmax(e.pt, axis=-1, keepdims=True)]
m0 = mu[ak.argmax(mu.pt, axis=-1, keepdims=True)]
# Attach the lepton SFs to the electron and muons collections
AttachElectronSF(e, year=year)
AttachMuonSF(mu, year=year)
# Create a lepton (muon+electron) collection and calculate a per event lepton SF
leps = ak.concatenate([e, mu], axis=-1)
events['lepSF_nom'] = ak.prod(leps.sf_nom, axis=-1)
events['lepSF_hi'] = ak.prod(leps.sf_hi, axis=-1)
events['lepSF_lo'] = ak.prod(leps.sf_lo, axis=-1)
# Jet selection
jetptname = 'pt_nom' if hasattr(j, 'pt_nom') else 'pt'
### Jet energy corrections
if not isData:
j["pt_raw"] = (1 - j.rawFactor) * j.pt
j["mass_raw"] = (1 - j.rawFactor) * j.mass
j["pt_gen"] = ak.values_astype(ak.fill_none(j.matched_gen.pt, 0),
np.float32)
j["rho"] = ak.broadcast_arrays(events.fixedGridRhoFastjetAll,
j.pt)[0]
events_cache = events.caches[0]
corrected_jets = jet_factory.build(j, lazy_cache=events_cache)
#print('jet pt: ',j.pt)
#print('cor pt: ',corrected_jets.pt)
#print('jes up: ',corrected_jets.JES_jes.up.pt)
#print('jes down: ',corrected_jets.JES_jes.down.pt)
#print(ak.fields(corrected_jets))
'''
# SYSTEMATICS
jets = corrected_jets
if(self.jetSyst == 'JERUp'):
jets = corrected_jets.JER.up
elif(self.jetSyst == 'JERDown'):
jets = corrected_jets.JER.down
elif(self.jetSyst == 'JESUp'):
jets = corrected_jets.JES_jes.up
elif(self.jetSyst == 'JESDown'):
jets = corrected_jets.JES_jes.down
'''
j['isGood'] = isTightJet(getattr(j,
jetptname), j.eta, j.jetId, j.neHEF,
j.neEmEF, j.chHEF, j.chEmEF, j.nConstituents)
#j['isgood'] = isGoodJet(j.pt, j.eta, j.jetId)
#j['isclean'] = isClean(j, e, mu)
j['isClean'] = isClean(j, e, drmin=0.4) & isClean(
j, mu, drmin=0.4) # & isClean(j, tau, drmin=0.4)
goodJets = j[(j.isClean) & (j.isGood)]
njets = ak.num(goodJets)
ht = ak.sum(goodJets.pt, axis=-1)
j0 = goodJets[ak.argmax(goodJets.pt, axis=-1, keepdims=True)]
#nbtags = ak.num(goodJets[goodJets.btagDeepFlavB > 0.2770])
# Loose DeepJet WP
if year == 2017: btagwpl = 0.0532 #WP loose
else: btagwpl = 0.0490 #WP loose
isBtagJetsLoose = (goodJets.btagDeepB > btagwpl)
isNotBtagJetsLoose = np.invert(isBtagJetsLoose)
nbtagsl = ak.num(goodJets[isBtagJetsLoose])
# Medium DeepJet WP
if year == 2017: btagwpm = 0.3040 #WP medium
else: btagwpm = 0.2783 #WP medium
isBtagJetsMedium = (goodJets.btagDeepB > btagwpm)
isNotBtagJetsMedium = np.invert(isBtagJetsMedium)
nbtagsm = ak.num(goodJets[isBtagJetsMedium])
# Btag SF following 1a) in https://twiki.cern.ch/twiki/bin/viewauth/CMS/BTagSFMethods
btagSF = np.ones_like(ht)
btagSFUp = np.ones_like(ht)
btagSFDo = np.ones_like(ht)
if not isData:
pt = goodJets.pt
abseta = np.abs(goodJets.eta)
flav = goodJets.hadronFlavour
bJetSF = GetBTagSF(abseta, pt, flav)
bJetSFUp = GetBTagSF(abseta, pt, flav, sys=1)
bJetSFDo = GetBTagSF(abseta, pt, flav, sys=-1)
bJetEff = GetBtagEff(abseta, pt, flav, year)
bJetEff_data = bJetEff * bJetSF
bJetEff_dataUp = bJetEff * bJetSFUp
bJetEff_dataDo = bJetEff * bJetSFDo
pMC = ak.prod(bJetEff[isBtagJetsMedium], axis=-1) * ak.prod(
(1 - bJetEff[isNotBtagJetsMedium]), axis=-1)
pData = ak.prod(bJetEff_data[isBtagJetsMedium], axis=-1) * ak.prod(
(1 - bJetEff_data[isNotBtagJetsMedium]), axis=-1)
pDataUp = ak.prod(
bJetEff_dataUp[isBtagJetsMedium], axis=-1) * ak.prod(
(1 - bJetEff_dataUp[isNotBtagJetsMedium]), axis=-1)
pDataDo = ak.prod(
bJetEff_dataDo[isBtagJetsMedium], axis=-1) * ak.prod(
(1 - bJetEff_dataDo[isNotBtagJetsMedium]), axis=-1)
pMC = ak.where(pMC == 0, 1,
pMC) # removeing zeroes from denominator...
btagSF = pData / pMC
btagSFUp = pDataUp / pMC
btagSFDo = pDataUp / pMC
##################################################################
### 2 same-sign leptons
##################################################################
# emu
singe = e[(nElec == 1) & (nMuon == 1) & (e.pt > -1)]
singm = mu[(nElec == 1) & (nMuon == 1) & (mu.pt > -1)]
em = ak.cartesian({"e": singe, "m": singm})
emSSmask = (em.e.charge * em.m.charge > 0)
emSS = em[emSSmask]
nemSS = len(ak.flatten(emSS))
# ee and mumu
# pt>-1 to preserve jagged dimensions
ee = e[(nElec == 2) & (nMuon == 0) & (e.pt > -1)]
mm = mu[(nElec == 0) & (nMuon == 2) & (mu.pt > -1)]
sumcharge = ak.sum(e.charge, axis=-1) + ak.sum(mu.charge, axis=-1)
eepairs = ak.combinations(ee, 2, fields=["e0", "e1"])
eeSSmask = (eepairs.e0.charge * eepairs.e1.charge > 0)
eeonZmask = (np.abs((eepairs.e0 + eepairs.e1).mass - 91.2) < 10)
eeoffZmask = (eeonZmask == 0)
mmpairs = ak.combinations(mm, 2, fields=["m0", "m1"])
mmSSmask = (mmpairs.m0.charge * mmpairs.m1.charge > 0)
mmonZmask = (np.abs((mmpairs.m0 + mmpairs.m1).mass - 91.2) < 10)
mmoffZmask = (mmonZmask == 0)
eeSSonZ = eepairs[eeSSmask & eeonZmask]
eeSSoffZ = eepairs[eeSSmask & eeoffZmask]
mmSSonZ = mmpairs[mmSSmask & mmonZmask]
mmSSoffZ = mmpairs[mmSSmask & mmoffZmask]
neeSS = len(ak.flatten(eeSSonZ)) + len(ak.flatten(eeSSoffZ))
nmmSS = len(ak.flatten(mmSSonZ)) + len(ak.flatten(mmSSoffZ))
print('Same-sign events [ee, emu, mumu] = [%i, %i, %i]' %
(neeSS, nemSS, nmmSS))
# Cuts
eeSSmask = (ak.num(eeSSmask[eeSSmask]) > 0)
mmSSmask = (ak.num(mmSSmask[mmSSmask]) > 0)
eeonZmask = (ak.num(eeonZmask[eeonZmask]) > 0)
eeoffZmask = (ak.num(eeoffZmask[eeoffZmask]) > 0)
mmonZmask = (ak.num(mmonZmask[mmonZmask]) > 0)
mmoffZmask = (ak.num(mmoffZmask[mmoffZmask]) > 0)
emSSmask = (ak.num(emSSmask[emSSmask]) > 0)
##################################################################
### 3 leptons
##################################################################
# eem
muon_eem = mu[(nElec == 2) & (nMuon == 1) & (mu.pt > -1)]
elec_eem = e[(nElec == 2) & (nMuon == 1) & (e.pt > -1)]
ee_eem = ak.combinations(elec_eem, 2, fields=["e0", "e1"])
ee_eemZmask = (ee_eem.e0.charge * ee_eem.e1.charge < 1) & (np.abs(
(ee_eem.e0 + ee_eem.e1).mass - 91.2) < 10)
ee_eemOffZmask = (ee_eem.e0.charge * ee_eem.e1.charge < 1) & (np.abs(
(ee_eem.e0 + ee_eem.e1).mass - 91.2) > 10)
ee_eemZmask = (ak.num(ee_eemZmask[ee_eemZmask]) > 0)
ee_eemOffZmask = (ak.num(ee_eemOffZmask[ee_eemOffZmask]) > 0)
eepair_eem = (ee_eem.e0 + ee_eem.e1)
trilep_eem = eepair_eem + muon_eem #ak.cartesian({"e0":ee_eem.e0,"e1":ee_eem.e1, "m":muon_eem})
# mme
muon_mme = mu[(nElec == 1) & (nMuon == 2) & (mu.pt > -1)]
elec_mme = e[(nElec == 1) & (nMuon == 2) & (e.pt > -1)]
mm_mme = ak.combinations(muon_mme, 2, fields=["m0", "m1"])
mm_mmeZmask = (mm_mme.m0.charge * mm_mme.m1.charge < 1) & (np.abs(
(mm_mme.m0 + mm_mme.m1).mass - 91.2) < 10)
mm_mmeOffZmask = (mm_mme.m0.charge * mm_mme.m1.charge < 1) & (np.abs(
(mm_mme.m0 + mm_mme.m1).mass - 91.2) > 10)
mm_mmeZmask = (ak.num(mm_mmeZmask[mm_mmeZmask]) > 0)
mm_mmeOffZmask = (ak.num(mm_mmeOffZmask[mm_mmeOffZmask]) > 0)
mmpair_mme = (mm_mme.m0 + mm_mme.m1)
trilep_mme = mmpair_mme + elec_mme
mZ_mme = mmpair_mme.mass
mZ_eem = eepair_eem.mass
m3l_eem = trilep_eem.mass
m3l_mme = trilep_mme.mass
# eee and mmm
eee = e[(nElec == 3) & (nMuon == 0) & (e.pt > -1)]
mmm = mu[(nElec == 0) & (nMuon == 3) & (mu.pt > -1)]
eee_leps = ak.combinations(eee, 3, fields=["e0", "e1", "e2"])
mmm_leps = ak.combinations(mmm, 3, fields=["m0", "m1", "m2"])
ee_pairs = ak.combinations(eee, 2, fields=["e0", "e1"])
mm_pairs = ak.combinations(mmm, 2, fields=["m0", "m1"])
ee_pairs_index = ak.argcombinations(eee, 2, fields=["e0", "e1"])
mm_pairs_index = ak.argcombinations(mmm, 2, fields=["m0", "m1"])
mmSFOS_pairs = mm_pairs[
(np.abs(mm_pairs.m0.pdgId) == np.abs(mm_pairs.m1.pdgId))
& (mm_pairs.m0.charge != mm_pairs.m1.charge)]
offZmask_mm = ak.all(
np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2) > 10.,
axis=1,
keepdims=True) & (ak.num(mmSFOS_pairs) > 0)
onZmask_mm = ak.any(
np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2) < 10.,
axis=1,
keepdims=True)
eeSFOS_pairs = ee_pairs[
(np.abs(ee_pairs.e0.pdgId) == np.abs(ee_pairs.e1.pdgId))
& (ee_pairs.e0.charge != ee_pairs.e1.charge)]
offZmask_ee = ak.all(
np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2) > 10,
axis=1,
keepdims=True) & (ak.num(eeSFOS_pairs) > 0)
onZmask_ee = ak.any(
np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2) < 10,
axis=1,
keepdims=True)
# Create masks **for event selection**
eeeOnZmask = (ak.num(onZmask_ee[onZmask_ee]) > 0)
eeeOffZmask = (ak.num(offZmask_ee[offZmask_ee]) > 0)
mmmOnZmask = (ak.num(onZmask_mm[onZmask_mm]) > 0)
mmmOffZmask = (ak.num(offZmask_mm[offZmask_mm]) > 0)
# Now we need to create masks for the leptons in order to select leptons from the Z boson candidate (in onZ categories)
ZeeMask = ak.argmin(np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass -
91.2),
axis=1,
keepdims=True)
ZmmMask = ak.argmin(np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass -
91.2),
axis=1,
keepdims=True)
Zee = eeSFOS_pairs[ZeeMask]
Zmm = mmSFOS_pairs[ZmmMask]
eZ0 = Zee.e0[ak.num(eeSFOS_pairs) > 0]
eZ1 = Zee.e1[ak.num(eeSFOS_pairs) > 0]
eZ = eZ0 + eZ1
mZ0 = Zmm.m0[ak.num(mmSFOS_pairs) > 0]
mZ1 = Zmm.m1[ak.num(mmSFOS_pairs) > 0]
mZ = mZ0 + mZ1
mZ_eee = eZ.mass
mZ_mmm = mZ.mass
# And for the W boson
ZmmIndices = mm_pairs_index[ZmmMask]
ZeeIndices = ee_pairs_index[ZeeMask]
eW = eee[~ZeeIndices.e0 | ~ZeeIndices.e1]
mW = mmm[~ZmmIndices.m0 | ~ZmmIndices.m1]
triElec = eee_leps.e0 + eee_leps.e1 + eee_leps.e2
triMuon = mmm_leps.m0 + mmm_leps.m1 + mmm_leps.m2
m3l_eee = triElec.mass
m3l_mmm = triMuon.mass
##################################################################
### >=4 leptons
##################################################################
# 4lep cat
is4lmask = ((nElec + nMuon) >= 4)
muon_4l = mu[(is4lmask) & (mu.pt > -1)]
elec_4l = e[(is4lmask) & (e.pt > -1)]
# selecting 4 leading leptons
leptons = ak.concatenate([e, mu], axis=-1)
leptons_sorted = leptons[ak.argsort(leptons.pt,
axis=-1,
ascending=False)]
lep4l = leptons_sorted[:, 0:4]
e4l = lep4l[abs(lep4l.pdgId) == 11]
mu4l = lep4l[abs(lep4l.pdgId) == 13]
nElec4l = ak.num(e4l)
nMuon4l = ak.num(mu4l)
# Triggers
trig_eeSS = passTrigger(events, 'ee', isData, dataset)
trig_mmSS = passTrigger(events, 'mm', isData, dataset)
trig_emSS = passTrigger(events, 'em', isData, dataset)
trig_eee = passTrigger(events, 'eee', isData, dataset)
trig_mmm = passTrigger(events, 'mmm', isData, dataset)
trig_eem = passTrigger(events, 'eem', isData, dataset)
trig_mme = passTrigger(events, 'mme', isData, dataset)
trig_4l = triggerFor4l(events, nMuon, nElec, isData, dataset)
# MET filters
# Weights
genw = np.ones_like(events['event']) if (
isData or len(self._wc_names_lst) > 0) else events['genWeight']
### We need weights for: normalization, lepSF, triggerSF, pileup, btagSF...
weights = {}
for r in [
'all', 'ee', 'mm', 'em', 'eee', 'mmm', 'eem', 'mme', 'eeee',
'eeem', 'eemm', 'mmme', 'mmmm'
]:
# weights[r] = coffea.analysis_tools.Weights(len(events))
weights[r] = coffea.analysis_tools.Weights(len(events),
storeIndividual=True)
if len(self._wc_names_lst) > 0:
sow = np.ones_like(
sow
) # Not valid in nanoAOD for EFT samples, MUST use SumOfEFTweights at analysis level
weights[r].add('norm', genw if isData else (xsec / sow) * genw)
weights[r].add('btagSF', btagSF, btagSFUp, btagSFDo)
weights[r].add('lepSF', events.lepSF_nom, events.lepSF_hi,
events.lepSF_lo)
# Extract the EFT quadratic coefficients and optionally use them to calculate the coefficients on the w**2 quartic function
# eft_coeffs is never Jagged so convert immediately to numpy for ease of use.
eft_coeffs = ak.to_numpy(events['EFTfitCoefficients']) if hasattr(
events, "EFTfitCoefficients") else None
if eft_coeffs is not None:
# Check to see if the ordering of WCs for this sample matches what want
if self._samples[dataset]['WCnames'] != self._wc_names_lst:
eft_coeffs = efth.remap_coeffs(
self._samples[dataset]['WCnames'], self._wc_names_lst,
eft_coeffs)
eft_w2_coeffs = efth.calc_w2_coeffs(eft_coeffs, self._dtype) if (
self._do_errors and eft_coeffs is not None) else None
# Selections and cuts
selections = PackedSelection() #(dtype='uint64')
channels2LSS = ['eeSSonZ', 'eeSSoffZ', 'mmSSonZ', 'mmSSoffZ', 'emSS']
selections.add('eeSSonZ', (eeonZmask) & (eeSSmask) & (trig_eeSS))
selections.add('eeSSoffZ', (eeoffZmask) & (eeSSmask) & (trig_eeSS))
selections.add('mmSSonZ', (mmonZmask) & (mmSSmask) & (trig_mmSS))
selections.add('mmSSoffZ', (mmoffZmask) & (mmSSmask) & (trig_mmSS))
selections.add('emSS', (emSSmask) & (trig_emSS))
channels3L = ['eemSSonZ', 'eemSSoffZ', 'mmeSSonZ', 'mmeSSoffZ']
selections.add('eemSSonZ', (ee_eemZmask) & (trig_eem))
selections.add('eemSSoffZ', (ee_eemOffZmask) & (trig_eem))
selections.add('mmeSSonZ', (mm_mmeZmask) & (trig_mme))
selections.add('mmeSSoffZ', (mm_mmeOffZmask) & (trig_mme))
channels3L += ['eeeSSonZ', 'eeeSSoffZ', 'mmmSSonZ', 'mmmSSoffZ']
selections.add('eeeSSonZ', (eeeOnZmask) & (trig_eee))
selections.add('eeeSSoffZ', (eeeOffZmask) & (trig_eee))
selections.add('mmmSSonZ', (mmmOnZmask) & (trig_mmm))
selections.add('mmmSSoffZ', (mmmOffZmask) & (trig_mmm))
channels4L = ['eeee', 'eeem', 'eemm', 'mmme', 'mmmm']
selections.add('eeee', ((nElec4l == 4) & (nMuon4l == 0)) & (trig_4l))
selections.add('eeem', ((nElec4l == 3) & (nMuon4l == 1)) & (trig_4l))
selections.add('eemm', ((nElec4l == 2) & (nMuon4l == 2)) & (trig_4l))
selections.add('mmme', ((nElec4l == 1) & (nMuon4l == 3)) & (trig_4l))
selections.add('mmmm', ((nElec4l == 0) & (nMuon4l == 4)) & (trig_4l))
selections.add('ch+', (sumcharge > 0))
selections.add('ch-', (sumcharge < 0))
selections.add('ch0', (sumcharge == 0))
levels = ['base', '1+bm2+bl', '1bm', '2+bm']
selections.add('base', (nElec + nMuon >= 2))
selections.add('1+bm2+bl', (nElec + nMuon >= 2) & ((nbtagsm >= 1) &
(nbtagsl >= 2)))
selections.add('1bm', (nElec + nMuon >= 2) & (nbtagsm == 1))
selections.add('2+bm', (nElec + nMuon >= 2) & (nbtagsm >= 2))
# Variables
invMass_eeSSonZ = (eeSSonZ.e0 + eeSSonZ.e1).mass
invMass_eeSSoffZ = (eeSSoffZ.e0 + eeSSoffZ.e1).mass
invMass_mmSSonZ = (mmSSonZ.m0 + mmSSonZ.m1).mass
invMass_mmSSoffZ = (mmSSoffZ.m0 + mmSSoffZ.m1).mass
invMass_emSS = (emSS.e + emSS.m).mass
varnames = {}
varnames['met'] = met.pt
varnames['ht'] = ht
varnames['njets'] = njets
varnames['invmass'] = {
'eeSSonZ': invMass_eeSSonZ,
'eeSSoffZ': invMass_eeSSoffZ,
'mmSSonZ': invMass_mmSSonZ,
'mmSSoffZ': invMass_mmSSoffZ,
'emSS': invMass_emSS,
'eemSSonZ': mZ_eem,
'eemSSoffZ': mZ_eem,
'mmeSSonZ': mZ_mme,
'mmeSSoffZ': mZ_mme,
'eeeSSonZ': mZ_eee,
'eeeSSoffZ': mZ_eee,
'mmmSSonZ': mZ_mmm,
'mmmSSoffZ': mZ_mmm,
}
varnames['m3l'] = {
'eemSSonZ': m3l_eem,
'eemSSoffZ': m3l_eem,
'mmeSSonZ': m3l_mme,
'mmeSSoffZ': m3l_mme,
'eeeSSonZ': m3l_eee,
'eeeSSoffZ': m3l_eee,
'mmmSSonZ': m3l_mmm,
'mmmSSoffZ': m3l_mmm,
}
varnames['e0pt'] = e0.pt
varnames['e0eta'] = e0.eta
varnames['m0pt'] = m0.pt
varnames['m0eta'] = m0.eta
varnames['j0pt'] = j0.pt
varnames['j0eta'] = j0.eta
varnames['counts'] = np.ones_like(events['event'])
# systematics
systList = []
if isData == False:
systList = ['nominal']
if self._do_systematics:
systList = systList + [
'lepSFUp', 'lepSFDown', 'btagSFUp', 'btagSFDown'
]
else:
systList = ['noweight']
# fill Histos
hout = self.accumulator.identity()
normweights = weights['all'].weight().flatten(
) # Why does it not complain about .flatten() here?
sowweights = np.ones_like(normweights) if len(
self._wc_names_lst) > 0 else normweights
hout['SumOfEFTweights'].fill(sample=histAxisName,
SumOfEFTweights=varnames['counts'],
weight=sowweights,
eft_coeff=eft_coeffs,
eft_err_coeff=eft_w2_coeffs)
for syst in systList:
for var, v in varnames.items():
for ch in channels2LSS + channels3L + channels4L:
for sumcharge in ['ch+', 'ch-', 'ch0']:
for lev in levels:
#find the event weight to be used when filling the histograms
weightSyst = syst
#in the case of 'nominal', or the jet energy systematics, no weight systematic variation is used (weightSyst=None)
if syst in [
'nominal', 'JERUp', 'JERDown', 'JESUp',
'JESDown'
]:
weightSyst = None # no weight systematic for these variations
if syst == 'noweight':
weight = np.ones(len(events)) # for data
else:
# call weights.weight() with the name of the systematic to be varied
if ch in channels3L: ch_w = ch[:3]
elif ch in channels2LSS: ch_w = ch[:2]
else: ch_w = ch
weight = weights['all'].weight(
weightSyst
) if isData else weights[ch_w].weight(
weightSyst)
cuts = [ch] + [lev] + [sumcharge]
cut = selections.all(*cuts)
weights_flat = weight[cut].flatten(
) # Why does it not complain about .flatten() here?
weights_ones = np.ones_like(weights_flat,
dtype=np.int)
eft_coeffs_cut = eft_coeffs[
cut] if eft_coeffs is not None else None
eft_w2_coeffs_cut = eft_w2_coeffs[
cut] if eft_w2_coeffs is not None else None
# filling histos
if var == 'invmass':
if ((ch in [
'eeeSSoffZ', 'mmmSSoffZ', 'eeeSSonZ',
'mmmSSonZ'
]) or (ch in channels4L)):
continue
else:
values = ak.flatten(v[ch][cut])
hout['invmass'].fill(
eft_coeff=eft_coeffs_cut,
eft_err_coeff=eft_w2_coeffs_cut,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
invmass=values,
weight=weights_flat,
systematic=syst)
elif var == 'm3l':
if ((ch in channels2LSS) or (ch in [
'eeeSSoffZ', 'mmmSSoffZ', 'eeeSSonZ',
'mmmSSonZ'
]) or (ch in channels4L)):
continue
values = ak.flatten(v[ch][cut])
hout['m3l'].fill(
eft_coeff=eft_coeffs_cut,
eft_err_coeff=eft_w2_coeffs_cut,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
m3l=values,
weight=weights_flat,
systematic=syst)
else:
values = v[cut]
# These all look identical, do we need if/else here?
if var == 'ht':
hout[var].fill(
eft_coeff=eft_coeffs_cut,
eft_err_coeff=eft_w2_coeffs_cut,
ht=values,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
weight=weights_flat,
systematic=syst)
elif var == 'met':
hout[var].fill(
eft_coeff=eft_coeffs_cut,
eft_err_coeff=eft_w2_coeffs_cut,
met=values,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
weight=weights_flat,
systematic=syst)
elif var == 'njets':
hout[var].fill(
eft_coeff=eft_coeffs_cut,
eft_err_coeff=eft_w2_coeffs_cut,
njets=values,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
weight=weights_flat,
systematic=syst)
elif var == 'nbtags':
hout[var].fill(
eft_coeff=eft_coeffs_cut,
eft_err_coeff=eft_w2_coeffs_cut,
nbtags=values,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
weight=weights_flat,
systematic=syst)
elif var == 'counts':
hout[var].fill(counts=values,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
weight=weights_ones,
systematic=syst)
elif var == 'j0eta':
if lev == 'base': continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(
eft_coeff=eft_coeffs_cut,
eft_err_coeff=eft_w2_coeffs_cut,
j0eta=values,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
weight=weights_flat,
systematic=syst)
elif var == 'e0pt':
if ch in [
'mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ',
'mmmSSonZ', 'mmmm'
]:
continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(
eft_coeff=eft_coeffs_cut,
eft_err_coeff=eft_w2_coeffs_cut,
e0pt=values,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
weight=weights_flat,
systematic=syst
) # Crashing here, not sure why. Related to values?
elif var == 'm0pt':
if ch in [
'eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ',
'eeeSSonZ', 'eeee'
]:
continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(
eft_coeff=eft_coeffs_cut,
eft_err_coeff=eft_w2_coeffs_cut,
m0pt=values,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
weight=weights_flat,
systematic=syst)
elif var == 'e0eta':
if ch in [
'mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ',
'mmmSSonZ', 'mmmm'
]:
continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(
eft_coeff=eft_coeffs_cut,
eft_err_coeff=eft_w2_coeffs_cut,
e0eta=values,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
weight=weights_flat,
systematic=syst)
elif var == 'm0eta':
if ch in [
'eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ',
'eeeSSonZ', 'eeee'
]:
continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(
eft_coeff=eft_coeffs_cut,
eft_err_coeff=eft_w2_coeffs_cut,
m0eta=values,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
weight=weights_flat,
systematic=syst)
elif var == 'j0pt':
if lev == 'base': continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(
eft_coeff=eft_coeffs_cut,
eft_err_coeff=eft_w2_coeffs_cut,
j0pt=values,
sample=histAxisName,
channel=ch,
cut=lev,
sumcharge=sumcharge,
weight=weights_flat,
systematic=syst)
return hout
def process(self, events):
# Dataset parameters
dataset = events.metadata['dataset']
year = self._samples[dataset]['year']
xsec = self._samples[dataset]['xsec']
sow = self._samples[dataset]['nSumOfWeights' ]
isData = self._samples[dataset]['isData']
datasets = ['SingleMuon', 'SingleElectron', 'EGamma', 'MuonEG', 'DoubleMuon', 'DoubleElectron']
for d in datasets:
if d in dataset: dataset = dataset.split('_')[0]
# Inittialize objects
met = events.GenMET
e = events.GenPart[abs(events.GenPart.pdgId)==11]
mu = events.GenPart[abs(events.GenPart.pdgId)==13]
tau = events.GenPart[abs(events.GenPart.pdgId)==15]
j = events.GenJet
leading_mu = mu[ak.argmax(mu.pt,axis=-1,keepdims=True)]
leading_e = e[ak.argmax(e.pt,axis=-1,keepdims=True)]
nElec = ak.num(e)
nMuon = ak.num(mu)
nTau = ak.num(tau)
twoLeps = (nElec+nMuon) == 2
threeLeps = (nElec+nMuon) == 3
twoElec = (nElec == 2)
twoMuon = (nMuon == 2)
e0 = e[ak.argmax(e.pt,axis=-1,keepdims=True)]
m0 = mu[ak.argmax(mu.pt,axis=-1,keepdims=True)]
elecs = e[ak.argsort(e.pt, ascending=False)]
muons = mu[ak.argsort(mu.pt, ascending=False)]
e1 = elecs
e2 = elecs
m1 = muons
m2 = muons
# Jet selection
jetptname = 'pt_nom' if hasattr(j, 'pt_nom') else 'pt'
njets = ak.num(j)
ht = ak.sum(j.pt,axis=-1)
jets = j[ak.argsort(j.pt, ascending=False)]
j0 = j[ak.argmax(j.pt,axis=-1,keepdims=True)]
j1 = jets
j2 = jets
j3 = jets
nbtags = ak.num(j[abs(j.hadronFlavour)==5])
##################################################################
### 2 same-sign leptons
##################################################################
# emu
singe = e [(nElec==1)&(nMuon==1)&(e .pt>-1)]
singm = mu[(nElec==1)&(nMuon==1)&(mu.pt>-1)]
em = ak.cartesian({"e":singe,"m":singm})
emSSmask = (em.e.pdgId*em.m.pdgId>0)
emSS = em[emSSmask]
nemSS = len(ak.flatten(emSS))
year = 2018
lepSF_emSS = GetLeptonSF(mu.pt, mu.eta, 'm', e.pt, e.eta, 'e', year=year)
# ee and mumu
# pt>-1 to preserve jagged dimensions
ee = e [(nElec==2)&(nMuon==0)&(e.pt>-1)]
mm = mu[(nElec==0)&(nMuon==2)&(mu.pt>-1)]
eepairs = ak.combinations(ee, 2, fields=["e0","e1"])
eeSSmask = (eepairs.e0.pdgId*eepairs.e1.pdgId>0)
eeonZmask = (np.abs((eepairs.e0+eepairs.e1).mass-91.2)<10)
eeoffZmask = (eeonZmask==0)
mmpairs = ak.combinations(mm, 2, fields=["m0","m1"])
mmSSmask = (mmpairs.m0.pdgId*mmpairs.m1.pdgId>0)
mmonZmask = (np.abs((mmpairs.m0+mmpairs.m1).mass-91.2)<10)
mmoffZmask = (mmonZmask==0)
eeSSonZ = eepairs[eeSSmask & eeonZmask]
eeSSoffZ = eepairs[eeSSmask & eeoffZmask]
mmSSonZ = mmpairs[mmSSmask & mmonZmask]
mmSSoffZ = mmpairs[mmSSmask & mmoffZmask]
neeSS = len(ak.flatten(eeSSonZ)) + len(ak.flatten(eeSSoffZ))
nmmSS = len(ak.flatten(mmSSonZ)) + len(ak.flatten(mmSSoffZ))
lepSF_eeSS = GetLeptonSF(eepairs.e0.pt, eepairs.e0.eta, 'e', eepairs.e1.pt, eepairs.e1.eta, 'e', year=year)
lepSF_mumuSS = GetLeptonSF(mmpairs.m0.pt, mmpairs.m0.eta, 'm', mmpairs.m1.pt, mmpairs.m1.eta, 'm', year=year)
print('Same-sign events [ee, emu, mumu] = [%i, %i, %i]'%(neeSS, nemSS, nmmSS))
# Cuts
eeSSmask = (ak.num(eeSSmask[eeSSmask])>0)
mmSSmask = (ak.num(mmSSmask[mmSSmask])>0)
eeonZmask = (ak.num(eeonZmask[eeonZmask])>0)
eeoffZmask = (ak.num(eeoffZmask[eeoffZmask])>0)
mmonZmask = (ak.num(mmonZmask[mmonZmask])>0)
mmoffZmask = (ak.num(mmoffZmask[mmoffZmask])>0)
emSSmask = (ak.num(emSSmask[emSSmask])>0)
##################################################################
### 3 leptons
##################################################################
# eem
muon_eem = mu[(nElec==2)&(nMuon==1)&(mu.pt>-1)]
elec_eem = e[(nElec==2)&(nMuon==1)&( e.pt>-1)]
ee_eem = ak.combinations(elec_eem, 2, fields=["e0", "e1"])
ee_eemZmask = (ee_eem.e0.pdgId*ee_eem.e1.pdgId<1)&(np.abs((ee_eem.e0+ee_eem.e1).mass-91.2)<10)
ee_eemOffZmask = (ee_eem.e0.pdgId*ee_eem.e1.pdgId<1)&(np.abs((ee_eem.e0+ee_eem.e1).mass-91.2)>10)
ee_eemZmask = (ak.num(ee_eemZmask[ee_eemZmask])>0)
ee_eemOffZmask = (ak.num(ee_eemOffZmask[ee_eemOffZmask])>0)
eepair_eem = (ee_eem.e0+ee_eem.e1)
trilep_eem = eepair_eem+muon_eem #ak.cartesian({"e0":ee_eem.e0,"e1":ee_eem.e1, "m":muon_eem})
lepSF_eem = GetLeptonSF(ee_eem.e0.pt, ee_eem.e0.eta, 'e', ee_eem.e1.pt, ee_eem.e1.eta, 'e', mu.pt, mu.eta, 'm', year)
# mme
muon_mme = mu[(nElec==1)&(nMuon==2)&(mu.pt>-1)]
elec_mme = e[(nElec==1)&(nMuon==2)&( e.pt>-1)]
mm_mme = ak.combinations(muon_mme, 2, fields=["m0", "m1"])
mm_mmeZmask = (mm_mme.m0.pdgId*mm_mme.m1.pdgId<1)&(np.abs((mm_mme.m0+mm_mme.m1).mass-91.2)<10)
mm_mmeOffZmask = (mm_mme.m0.pdgId*mm_mme.m1.pdgId<1)&(np.abs((mm_mme.m0+mm_mme.m1).mass-91.2)>10)
mm_mmeZmask = (ak.num(mm_mmeZmask[mm_mmeZmask])>0)
mm_mmeOffZmask = (ak.num(mm_mmeOffZmask[mm_mmeOffZmask])>0)
mmpair_mme = (mm_mme.m0+mm_mme.m1)
trilep_mme = mmpair_mme+elec_mme
mZ_mme = mmpair_mme.mass
mZ_eem = eepair_eem.mass
m3l_eem = trilep_eem.mass
m3l_mme = trilep_mme.mass
lepSF_mme = GetLeptonSF(mm_mme.m0.pt, mm_mme.m0.eta, 'm', mm_mme.m1.pt, mm_mme.m1.eta, 'm', e.pt, e.eta, 'e', year)
# eee and mmm
eee = e[(nElec==3)&(nMuon==0)&( e.pt>-1)]
mmm = mu[(nElec==0)&(nMuon==3)&(mu.pt>-1)]
eee_leps = ak.combinations(eee, 3, fields=["e0", "e1", "e2"])
mmm_leps = ak.combinations(mmm, 3, fields=["m0", "m1", "m2"])
ee_pairs = ak.combinations(eee, 2, fields=["e0", "e1"])
mm_pairs = ak.combinations(mmm, 2, fields=["m0", "m1"])
ee_pairs_index = ak.argcombinations(eee, 2, fields=["e0", "e1"])
mm_pairs_index = ak.argcombinations(mmm, 2, fields=["m0", "m1"])
lepSF_eee = GetLeptonSF(eee_leps.e0.pt, eee_leps.e0.eta, 'e', eee_leps.e1.pt, eee_leps.e1.eta, 'e', eee_leps.e2.pt, eee_leps.e2.eta, 'e', year)
lepSF_mmm = GetLeptonSF(mmm_leps.m0.pt, mmm_leps.m0.eta, 'm', mmm_leps.m1.pt, mmm_leps.m1.eta, 'm', mmm_leps.m2.pt, mmm_leps.m2.eta, 'm', year)
mmSFOS_pairs = mm_pairs[(np.abs(mm_pairs.m0.pdgId) == np.abs(mm_pairs.m1.pdgId)) & (mm_pairs.m0.pdgId != mm_pairs.m1.pdgId)]
offZmask_mm = ak.all(np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2)>10., axis=1, keepdims=True) & (ak.num(mmSFOS_pairs)>0)
onZmask_mm = ak.any(np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2)<10., axis=1, keepdims=True)
eeSFOS_pairs = ee_pairs[(np.abs(ee_pairs.e0.pdgId) == np.abs(ee_pairs.e1.pdgId)) & (ee_pairs.e0.pdgId != ee_pairs.e1.pdgId)]
offZmask_ee = ak.all(np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2)>10, axis=1, keepdims=True) & (ak.num(eeSFOS_pairs)>0)
onZmask_ee = ak.any(np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2)<10, axis=1, keepdims=True)
# Create masks **for event selection**
eeeOnZmask = (ak.num(onZmask_ee[onZmask_ee])>0)
eeeOffZmask = (ak.num(offZmask_ee[offZmask_ee])>0)
mmmOnZmask = (ak.num(onZmask_mm[onZmask_mm])>0)
mmmOffZmask = (ak.num(offZmask_mm[offZmask_mm])>0)
# Now we need to create masks for the leptons in order to select leptons from the Z boson candidate (in onZ categories)
ZeeMask = ak.argmin(np.abs((eeSFOS_pairs.e0 + eeSFOS_pairs.e1).mass - 91.2),axis=1,keepdims=True)
ZmmMask = ak.argmin(np.abs((mmSFOS_pairs.m0 + mmSFOS_pairs.m1).mass - 91.2),axis=1,keepdims=True)
Zee = eeSFOS_pairs[ZeeMask]
Zmm = mmSFOS_pairs[ZmmMask]
eZ0= Zee.e0[ak.num(eeSFOS_pairs)>0]
eZ1= Zee.e1[ak.num(eeSFOS_pairs)>0]
eZ = eZ0+eZ1
mZ0= Zmm.m0[ak.num(mmSFOS_pairs)>0]
mZ1= Zmm.m1[ak.num(mmSFOS_pairs)>0]
mZ = mZ0+mZ1
mZ_eee = eZ.mass
mZ_mmm = mZ.mass
# And for the W boson
ZmmIndices = mm_pairs_index[ZmmMask]
ZeeIndices = ee_pairs_index[ZeeMask]
eW = eee[~ZeeIndices.e0 | ~ZeeIndices.e1]
mW = mmm[~ZmmIndices.m0 | ~ZmmIndices.m1]
triElec = eee_leps.e0+eee_leps.e1+eee_leps.e2
triMuon = mmm_leps.m0+mmm_leps.m1+mmm_leps.m2
m3l_eee = triElec.mass
m3l_mmm = triMuon.mass
# Triggers
trig_eeSS = passTrigger(events,'ee',isData,dataset)
trig_mmSS = passTrigger(events,'mm',isData,dataset)
trig_emSS = passTrigger(events,'em',isData,dataset)
trig_eee = passTrigger(events,'eee',isData,dataset)
trig_mmm = passTrigger(events,'mmm',isData,dataset)
trig_eem = passTrigger(events,'eem',isData,dataset)
trig_mme = passTrigger(events,'mme',isData,dataset)
# MET filters
# Weights
genw = np.ones_like(events['MET_pt']) if isData else events['genWeight']
### We need weights for: normalization, lepSF, triggerSF, pileup, btagSF...
weights = {}
for r in ['all', 'ee', 'mm', 'em', 'eee', 'mmm', 'eem', 'mme']:
weights[r] = coffea.analysis_tools.Weights(len(events))
weights[r].add('norm',genw if isData else (xsec/sow)*genw)
weights['ee'].add('lepSF_eeSS', lepSF_eeSS)
weights['em'].add('lepSF_emSS', lepSF_emSS)
weights['mm'].add('lepSF_mmSS', lepSF_mumuSS)
weights['eee'].add('lepSF_eee', lepSF_eee)
weights['mmm'].add('lepSF_mmm', lepSF_mmm)
weights['mme'].add('lepSF_mme', lepSF_mme)
weights['eem'].add('lepSF_eem', lepSF_eem)
# Extract the EFT quadratic coefficients and optionally use them to calculate the coefficients on the w**2 quartic function
# eft_coeffs is never Jagged so convert immediately to numpy for ease of use.
eft_coeffs = ak.to_numpy(events['EFTfitCoefficients']) if hasattr(events, "EFTfitCoefficients") else None
eft_w2_coeffs = efth.calc_w2_coeffs(eft_coeffs,self._dtype) if (self._do_errors and eft_coeffs is not None) else None
# Selections and cuts
selections = PackedSelection()
channels2LSS = ['eeSSonZ', 'eeSSoffZ', 'mmSSonZ', 'mmSSoffZ', 'emSS']
selections.add('eeSSonZ', (eeonZmask)&(eeSSmask)&(trig_eeSS))
selections.add('eeSSoffZ', (eeoffZmask)&(eeSSmask)&(trig_eeSS))
selections.add('mmSSonZ', (mmonZmask)&(mmSSmask)&(trig_mmSS))
selections.add('mmSSoffZ', (mmoffZmask)&(mmSSmask)&(trig_mmSS))
selections.add('emSS', (emSSmask)&(trig_emSS))
channels3L = ['eemSSonZ', 'eemSSoffZ', 'mmeSSonZ', 'mmeSSoffZ']
selections.add('eemSSonZ', (ee_eemZmask)&(trig_eem))
selections.add('eemSSoffZ', (ee_eemOffZmask)&(trig_eem))
selections.add('mmeSSonZ', (mm_mmeZmask)&(trig_mme))
selections.add('mmeSSoffZ', (mm_mmeOffZmask)&(trig_mme))
channels3L += ['eeeSSonZ', 'eeeSSoffZ', 'mmmSSonZ', 'mmmSSoffZ']
selections.add('eeeSSonZ', (eeeOnZmask)&(trig_eee))
selections.add('eeeSSoffZ', (eeeOffZmask)&(trig_eee))
selections.add('mmmSSonZ', (mmmOnZmask)&(trig_mmm))
selections.add('mmmSSoffZ', (mmmOffZmask)&(trig_mmm))
levels = ['base', '2jets', '4jets', '4j1b', '4j2b']
selections.add('base', (nElec+nMuon>=2))
selections.add('2jets',(njets>=2))
selections.add('4jets',(njets>=4))
selections.add('4j1b',(njets>=4)&(nbtags>=1))
selections.add('4j2b',(njets>=4)&(nbtags>=2))
# Variables
invMass_eeSSonZ = ( eeSSonZ.e0+ eeSSonZ.e1).mass
invMass_eeSSoffZ = (eeSSoffZ.e0+eeSSoffZ.e1).mass
invMass_mmSSonZ = ( mmSSonZ.m0+ mmSSonZ.m1).mass
invMass_mmSSoffZ = (mmSSoffZ.m0+mmSSoffZ.m1).mass
invMass_emSS = (emSS.e+emSS.m).mass
varnames = {}
varnames['met'] = met.pt
varnames['ht'] = ht
varnames['njets'] = njets
varnames['nbtags'] = nbtags
varnames['invmass'] = {
'eeSSonZ' : invMass_eeSSonZ,
'eeSSoffZ' : invMass_eeSSoffZ,
'mmSSonZ' : invMass_mmSSonZ,
'mmSSoffZ' : invMass_mmSSoffZ,
'emSS' : invMass_emSS,
'eemSSonZ' : mZ_eem,
'eemSSoffZ' : mZ_eem,
'mmeSSonZ' : mZ_mme,
'mmeSSoffZ' : mZ_mme,
'eeeSSonZ' : mZ_eee,
'eeeSSoffZ' : mZ_eee,
'mmmSSonZ' : mZ_mmm,
'mmmSSoffZ' : mZ_mmm,
}
varnames['m3l'] = {
'eemSSonZ' : m3l_eem,
'eemSSoffZ' : m3l_eem,
'mmeSSonZ' : m3l_mme,
'mmeSSoffZ' : m3l_mme,
'eeeSSonZ' : m3l_eee,
'eeeSSoffZ' : m3l_eee,
'mmmSSonZ' : m3l_mmm,
'mmmSSoffZ' : m3l_mmm,
}
varnames['e0pt' ] = e0.pt
varnames['e0eta'] = e0.eta
varnames['m0pt' ] = m0.pt
varnames['m0eta'] = m0.eta
varnames['e1pt' ] = e1
varnames['e1eta'] = e1
varnames['e2pt' ] = e2
varnames['e2eta'] = e2
varnames['m1pt' ] = m1
varnames['m1eta'] = m1
varnames['m2pt' ] = m2
varnames['m2eta'] = m2
varnames['j0pt' ] = j0.pt
varnames['j0eta'] = j0.eta
varnames['j1pt'] = j1
varnames['j1eta'] = j1
varnames['j2pt'] = j2
varnames['j2eta'] = j2
varnames['j3pt'] = j3
varnames['j3eta'] = j3
varnames['counts'] = np.ones_like(events.GenMET.pt)
# fill Histos
hout = self.accumulator.identity()
normweights = weights['all'].weight().flatten() # Why does it not complain about .flatten() here?
hout['SumOfEFTweights'].fill(sample=dataset, SumOfEFTweights=varnames['counts'], weight=normweights, eft_coeff=eft_coeffs, eft_err_coeff=eft_w2_coeffs)
for var, v in varnames.items():
for ch in channels2LSS+channels3L:
for lev in levels:
weight = weights[ ch[:3] if (ch.startswith('eee') or ch.startswith('mmm') or ch.startswith('eem') or ch.startswith('mme')) else ch[:2]].weight()
cuts = [ch] + [lev]
cut = selections.all(*cuts)
weights_flat = weight[cut].flatten() # Why does it not complain about .flatten() here?
weights_ones = np.ones_like(weights_flat, dtype=np.int)
eft_coeffs_cut = eft_coeffs[cut] if eft_coeffs is not None else None
eft_w2_coeffs_cut = eft_w2_coeffs[cut] if eft_w2_coeffs is not None else None
if var == 'invmass':
if ch in ['eeeSSoffZ', 'mmmSSoffZ']: continue
elif ch in ['eeeSSonZ' , 'mmmSSonZ' ]: continue #values = v[ch]
else : values = ak.flatten(v[ch][cut])
hout['invmass'].fill(eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut, sample=dataset, channel=ch, cut=lev, invmass=values, weight=weights_flat)
elif var == 'm3l':
if ch in ['eeSSonZ','eeSSoffZ', 'mmSSonZ', 'mmSSoffZ','emSS', 'eeeSSoffZ', 'mmmSSoffZ', 'eeeSSonZ' , 'mmmSSonZ']: continue
values = ak.flatten(v[ch][cut])
hout['m3l'].fill(sample=dataset, channel=ch, cut=lev, m3l=values, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
else:
values = v[cut]
if var == 'ht' : hout[var].fill(ht=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'met' : hout[var].fill(met=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'njets' : hout[var].fill(njets=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'nbtags': hout[var].fill(nbtags=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'counts': hout[var].fill(counts=values, sample=dataset, channel=ch, cut=lev, weight=weights_ones)
elif var == 'j0eta' :
if lev == 'base': continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(j0eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'e0pt' :
if ch in ['mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ', 'mmmSSonZ']: continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(e0pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'm0pt' :
if ch in ['eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ']: continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(m0pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'e0eta' :
if ch in ['mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ', 'mmmSSonZ']: continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(e0eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'm0eta':
if ch in ['eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ']: continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(m0eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'j0pt' :
if lev == 'base': continue
values = ak.flatten(values)
#values=np.asarray(values)
hout[var].fill(j0pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'j1pt':
if lev == "base": continue
values = values.pt[:,1]
#values = ak.flatten(values)
hout[var].fill(j1pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var =='j1eta':
if lev == 'base': continue
values = values.eta[:,1]
hout[var].fill(j1eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'j2pt':
if lev in ['base', "2jets"]: continue
values = values.pt[:,2]
hout[var].fill(j2pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'j2eta':
if lev in ['base', "2jets"]: continue
values = values.eta[:,2]
hout[var].fill(j2eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'j3pt':
if lev in ['base', "2jets"]: continue
values = values.pt[:,3]
hout[var].fill(j3pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'j3eta':
if lev in ['base', "2jets"]: continue
values = values.eta[:,3]
hout[var].fill(j3eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'e1pt':
if ch in ['mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ', 'mmmSSonZ', 'mmeSSonZ', 'mmeSSoffZ', 'emSS']: continue
values = values.pt[:,1]
hout[var].fill(e1pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'e1eta':
if ch in ['mmSSonZ', 'mmSSoffZ', 'mmmSSoffZ', 'mmmSSonZ', 'mmeSSonZ', 'mmeSSoffZ', 'emSS']: continue
values = values.eta[:,1]
hout[var].fill(e1eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'e2pt':
if ch in ['eeeSSonZ', 'eeeSSoffZ']:
values = values.pt[:,2]
hout[var].fill(e2pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'e2eta':
if ch in ['eeeSSonZ', 'eeeSSoffZ']:
values = values.eta[:,2]
hout[var].fill(e2eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'm1pt':
if ch in ['eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ', 'eemSSonZ', 'eemSSoffZ', 'emSS']: continue
values = values.pt[:,1]
hout[var].fill(m1pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'm1eta':
if ch in ['eeSSonZ', 'eeSSoffZ', 'eeeSSoffZ', 'eeeSSonZ', 'eemSSonZ', 'eemSSoffZ', 'emSS']: continue
values = values.eta[:,1]
hout[var].fill(m1eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'm2pt':
if ch in ['mmmSSonZ', 'mmmSSoffZ']:
values = values.pt[:,2]
hout[var].fill(m2pt=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
elif var == 'm2eta':
if ch in ['mmmSSonZ', 'mmmSSoffZ']:
values = values.eta[:,2]
hout[var].fill(m2eta=values, sample=dataset, channel=ch, cut=lev, weight=weights_flat, eft_coeff=eft_coeffs_cut, eft_err_coeff=eft_w2_coeffs_cut)
return hout