def pad_and_flatten(val):
import awkward as ak
try:
return ak.flatten(ak.fill_none(ak.pad_none(val, 1, clip=True), 0))
#return val.pad(1, clip=True).fillna(0.).flatten()#.reshape(-1, 1)
except ValueError:
return ak.flatten(val)
def combine(eff, sf):
# tagged SF = SF*eff / eff = SF
tagged_sf = awkward1.prod(sf[passbtag], axis=-1)
# untagged SF = (1 - SF*eff) / (1 - eff)
untagged_sf = awkward1.prod(
((1 - sf * eff) / (1 - eff))[~passbtag], axis=-1)
return awkward1.fill_none(tagged_sf * untagged_sf,
1.) # TODO: move None guard to coffea
def globalindex():
gidx = awkward1.Array(
awkward1.layout.ListOffsetArray32(
awkward1.layout.Index32(source_offsets), index.generator(),
)
)
gidx = gidx.mask[gidx >= 0] + target_offsets[:-1]
return awkward1.fill_none(awkward1.flatten(gidx), -1)
def test_highlevel():
array = awkward1.Array([[1.1, 2.2, None, 3.3], [], [4.4, None, 5.5]])
assert awkward1.to_list(awkward1.fill_none(array, 999)) == [[1.1, 2.2, 999, 3.3], [], [4.4, 999, 5.5]]
assert awkward1.to_list(awkward1.fill_none(array, [1, 2, 3])) == [[1.1, 2.2, [1, 2, 3], 3.3], [], [4.4, [1, 2, 3], 5.5]]
assert awkward1.to_list(awkward1.fill_none(array, [])) == [[1.1, 2.2, [], 3.3], [], [4.4, [], 5.5]]
assert awkward1.to_list(awkward1.fill_none(array, {"x": 999})) == [[1.1, 2.2, {"x": 999}, 3.3], [], [4.4, {"x": 999}, 5.5]]
array = awkward1.Array([[1.1, 2.2, 3.3], None, [], None, [4.4, 5.5]])
assert awkward1.to_list(awkward1.fill_none(array, 999)) == [[1.1, 2.2, 3.3], 999, [], 999, [4.4, 5.5]]
assert awkward1.to_list(awkward1.fill_none(array, [1, 2, 3])) == [[1.1, 2.2, 3.3], [1, 2, 3], [], [1, 2, 3], [4.4, 5.5]]
assert awkward1.to_list(awkward1.fill_none(array, {"x": 999})) == [[1.1, 2.2, 3.3], {"x": 999}, [], {"x": 999}, [4.4, 5.5]]
assert awkward1.to_list(awkward1.fill_none(array, [])) == [[1.1, 2.2, 3.3], [], [], [], [4.4, 5.5]]
def local2global(stack):
"""Turn jagged local index into global index
Signature: index,target_offsets,!local2global
Outputs a content array with same shape as index content
"""
target_offsets = stack.pop()
index = stack.pop()
index = index.mask[index >= 0] + target_offsets[:-1]
out = numpy.array(awkward1.flatten(awkward1.fill_none(index, -1)))
if out.dtype != numpy.int64:
raise RuntimeError
stack.append(out)
def matchJets(self, obj, jet, deltaRCut=0.4):
combs = ak.cartesian([obj, jet], nested=True)
jet_index = ak.local_index(delta_r(
combs['0'], combs['1']))[delta_r(combs['0'], combs['1']) < 0.4]
jet_index_pad = ak.flatten(ak.fill_none(
ak.pad_none(jet_index, target=1, clip=True, axis=2), 0),
axis=2)
mask = ak.num(jet_index,
axis=2) > 0 # a mask for obj with a matched jet
mask_match = mask * 1 + ~mask * 0
mask_nomatch = mask * 0 + ~mask * 1
return jet_index_pad, mask_match, mask_nomatch
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) > 0
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 = ev.Muon
## Electrons
electron = Collections(ev, "Electron", "tight").get()
#electron = electron[(ak.nan_to_num(electron.eta, 99))]
electron = electron[(electron.miniPFRelIso_all < 0.12)
& (electron.pt > 20) & (abs(electron.eta) < 2.4)]
gen_matched_electron = electron[((electron.genPartIdx >= 0) & (abs(
electron.matched_gen.pdgId) == 11))]
is_flipped = ((gen_matched_electron.matched_gen.pdgId *
(-1) == gen_matched_electron.pdgId) &
(abs(gen_matched_electron.pdgId) == 11))
#(abs(ev.GenPart[gen_matched_electron.genPartIdx].pdgId) ==abs(gen_matched_electron.pdgId))&(ev.GenPart[gen_matched_electron.genPartIdx].pdgId/abs(ev.GenPart[gen_matched_electron.genPartIdx].pdgId) != gen_matched_electron.pdgId/abs(gen_matched_electron.pdgId))
flipped_electron = gen_matched_electron[is_flipped]
flipped_electron = flipped_electron[(ak.fill_none(
flipped_electron.pt, 0) > 0)]
n_flips = ak.num(flipped_electron)
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]
leading_flipped_electron_idx = ak.singletons(
ak.argmax(flipped_electron.pt, axis=1))
leading_flipped_electron = electron[leading_flipped_electron_idx]
## 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))
if not dataset == 'MuonEG':
# generator weight
weight.add("weight", ev.genWeight)
#selections
filters = getFilters(ev, year=self.year, dataset=dataset)
electr = ((ak.num(electron) >= 1))
gen_electr = ((ak.num(gen_matched_electron) >= 1))
ss = (SSelectron)
flip = (n_flips >= 1)
selection = PackedSelection()
selection.add('filter', (filters))
selection.add('electr', electr)
selection.add('ss', ss)
selection.add('flip', flip)
selection.add('gen_electr', gen_electr)
bl_reqs = ['filter', 'electr', 'gen_electr']
bl_reqs_d = {sel: True for sel in bl_reqs}
baseline = selection.require(**bl_reqs_d)
f_reqs = bl_reqs + ['flip']
f_reqs_d = {sel: True for sel in f_reqs}
flip_sel = selection.require(**f_reqs_d)
#adjust weights to prevent length mismatch
ak_weight_gen = ak.ones_like(
gen_matched_electron[baseline].pt) * weight.weight()[baseline]
ak_weight_flip = ak.ones_like(
flipped_electron[flip_sel].pt) * weight.weight()[flip_sel]
output['N_ele'].fill(dataset=dataset,
multiplicity=ak.num(electron)[baseline],
weight=weight.weight()[baseline])
output['electron_flips'].fill(dataset=dataset,
multiplicity=n_flips[baseline],
weight=weight.weight()[baseline])
output["electron"].fill(
dataset=dataset,
pt=ak.to_numpy(ak.flatten(gen_matched_electron[baseline].pt)),
eta=abs(ak.to_numpy(ak.flatten(
gen_matched_electron[baseline].eta))),
#phi = ak.to_numpy(ak.flatten(leading_electron[baseline].phi)),
#weight = ak.to_numpy(ak.flatten(ak_weight_gen))
)
output["electron2"].fill(
dataset=dataset,
pt=ak.to_numpy(ak.flatten(gen_matched_electron[baseline].pt)),
eta=ak.to_numpy(ak.flatten(gen_matched_electron[baseline].eta)),
#phi = ak.to_numpy(ak.flatten(leading_electron[baseline].phi)),
#weight = ak.to_numpy(ak.flatten(ak_weight_gen))
)
output["flipped_electron"].fill(
dataset=dataset,
pt=ak.to_numpy(ak.flatten(flipped_electron[flip_sel].pt)),
eta=abs(ak.to_numpy(ak.flatten(flipped_electron[flip_sel].eta))),
#phi = ak.to_numpy(ak.flatten(flipped_electron[flip_sel].phi)),
#weight = ak.to_numpy(ak.flatten(ak_weight_flip))
)
output["flipped_electron2"].fill(
dataset=dataset,
pt=ak.to_numpy(ak.flatten(flipped_electron[flip_sel].pt)),
eta=ak.to_numpy(ak.flatten(flipped_electron[flip_sel].eta)),
#phi = ak.to_numpy(ak.flatten(flipped_electron[flip_sel].phi)),
#weight = ak.to_numpy(ak.flatten(ak_weight_flip))
)
return output
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
def process(self, events):
dataset = events.metadata['dataset']
isRealData = not hasattr(events, "genWeight")
selection = PackedSelection()
weights = Weights(len(events))
output = self.accumulator.identity()
if not isRealData:
output['sumw'][dataset] += ak.sum(events.genWeight)
if isRealData:
trigger = np.zeros(len(events), dtype='bool')
for t in self._triggers[self._year]:
trigger = trigger | events.HLT[t]
else:
trigger = np.ones(len(events), dtype='bool')
selection.add('trigger', trigger)
if isRealData:
trigger = np.zeros(len(events), dtype='bool')
for t in self._muontriggers[self._year]:
trigger = trigger | events.HLT[t]
else:
trigger = np.ones(len(events), dtype='bool')
selection.add('muontrigger', trigger)
fatjets = events.FatJet
fatjets['msdcorr'] = corrected_msoftdrop(fatjets)
fatjets['qcdrho'] = 2 * np.log(fatjets.msdcorr / fatjets.pt)
fatjets['n2ddt'] = fatjets.n2b1 - n2ddt_shift(fatjets, year=self._year)
fatjets['msdcorr_full'] = fatjets['msdcorr'] * self._msdSF[self._year]
candidatejet = fatjets[
# https://github.com/DAZSLE/BaconAnalyzer/blob/master/Analyzer/src/VJetLoader.cc#L269
(fatjets.pt > 200)
& (abs(fatjets.eta) < 2.5)
& fatjets.isTight # this is loose in sampleContainer
]
if self._jet_arbitration == 'pt':
candidatejet = ak.firsts(candidatejet)
elif self._jet_arbitration == 'mass':
candidatejet = candidatejet[ak.argmax(candidatejet.msdcorr)]
elif self._jet_arbitration == 'n2':
candidatejet = candidatejet[ak.argmin(candidatejet.n2ddt)]
elif self._jet_arbitration == 'ddb':
candidatejet = candidatejet[ak.argmax(candidatejet.btagDDBvL)]
else:
raise RuntimeError("Unknown candidate jet arbitration")
selection.add('minjetkin', (candidatejet.pt >= 450)
& (candidatejet.msdcorr >= 40.)
& (abs(candidatejet.eta) < 2.5))
selection.add('jetacceptance', (candidatejet.msdcorr >= 47.)
& (candidatejet.pt < 1200)
& (candidatejet.msdcorr < 201.))
selection.add('jetid', candidatejet.isTight)
selection.add('n2ddt', (candidatejet.n2ddt < 0.))
selection.add('ddbpass', (candidatejet.btagDDBvL >= 0.89))
jets = events.Jet[(events.Jet.pt > 30.)
& (abs(events.Jet.eta) < 2.5)
& events.Jet.isTight]
# only consider first 4 jets to be consistent with old framework
jets = jets[:, :4]
dphi = abs(jets.delta_phi(candidatejet))
selection.add(
'antiak4btagMediumOppHem',
ak.max(
jets[dphi > np.pi / 2].btagDeepB, axis=1, mask_identity=False)
< BTagEfficiency.btagWPs[self._year]['medium'])
ak4_away = jets[dphi > 0.8]
selection.add(
'ak4btagMedium08',
ak.max(ak4_away.btagDeepB, axis=1, mask_identity=False) >
BTagEfficiency.btagWPs[self._year]['medium'])
selection.add('met', events.MET.pt < 140.)
goodmuon = ((events.Muon.pt > 10)
& (abs(events.Muon.eta) < 2.4)
& (events.Muon.pfRelIso04_all < 0.25)
& events.Muon.looseId)
nmuons = ak.sum(goodmuon, axis=1)
leadingmuon = ak.firsts(events.Muon[goodmuon])
nelectrons = ak.sum(
(events.Electron.pt > 10)
& (abs(events.Electron.eta) < 2.5)
& (events.Electron.cutBased >= events.Electron.LOOSE),
axis=1,
)
ntaus = ak.sum(
(events.Tau.pt > 20)
& events.Tau.idDecayMode, # bacon iso looser than Nano selection
axis=1,
)
selection.add('noleptons',
(nmuons == 0) & (nelectrons == 0) & (ntaus == 0))
selection.add('onemuon',
(nmuons == 1) & (nelectrons == 0) & (ntaus == 0))
selection.add('muonkin',
(leadingmuon.pt > 55.) & (abs(leadingmuon.eta) < 2.1))
selection.add('muonDphiAK8',
abs(leadingmuon.delta_phi(candidatejet)) > 2 * np.pi / 3)
if isRealData:
genflavor = 0
else:
weights.add('genweight', events.genWeight)
add_pileup_weight(weights, events.Pileup.nPU, self._year, dataset)
bosons = getBosons(events.GenPart)
matchedBoson = candidatejet.nearest(bosons,
axis=None,
threshold=0.8)
genflavor = bosonFlavor(matchedBoson)
genBosonPt = ak.fill_none(ak.firsts(bosons.pt), 0)
add_VJets_NLOkFactor(weights, genBosonPt, self._year, dataset)
add_jetTriggerWeight(weights, candidatejet.msdcorr,
candidatejet.pt, self._year)
output['btagWeight'].fill(dataset=dataset,
val=self._btagSF.addBtagWeight(
weights, ak4_away))
logger.debug("Weight statistics: %r" % weights.weightStatistics)
msd_matched = candidatejet.msdcorr * self._msdSF[self._year] * (
genflavor > 0) + candidatejet.msdcorr * (genflavor == 0)
regions = {
'signal': [
'trigger', 'minjetkin', 'jetacceptance', 'jetid', 'n2ddt',
'antiak4btagMediumOppHem', 'met', 'noleptons'
],
'muoncontrol': [
'muontrigger', 'minjetkin', 'jetacceptance', 'jetid', 'n2ddt',
'ak4btagMedium08', 'onemuon', 'muonkin', 'muonDphiAK8'
],
'noselection': [],
}
for region, cuts in regions.items():
allcuts = set()
output['cutflow'].fill(dataset=dataset,
region=region,
genflavor=genflavor,
cut=0,
weight=weights.weight())
for i, cut in enumerate(cuts + ['ddbpass']):
allcuts.add(cut)
cut = selection.all(*allcuts)
output['cutflow'].fill(dataset=dataset,
region=region,
genflavor=genflavor[cut],
cut=i + 1,
weight=weights.weight()[cut])
systematics = [
None,
'jet_triggerUp',
'jet_triggerDown',
'btagWeightUp',
'btagWeightDown',
'btagEffStatUp',
'btagEffStatDown',
]
def normalize(val, cut):
return ak.to_numpy(ak.fill_none(val[cut], np.nan))
def fill(region, systematic, wmod=None):
selections = regions[region]
cut = selection.all(*selections)
sname = 'nominal' if systematic is None else systematic
if wmod is None:
weight = weights.weight(modifier=systematic)[cut]
else:
weight = weights.weight()[cut] * wmod[cut]
output['templates'].fill(
dataset=dataset,
region=region,
systematic=sname,
genflavor=genflavor[cut],
pt=normalize(candidatejet.pt, cut),
msd=normalize(msd_matched, cut),
ddb=normalize(candidatejet.btagDDBvL, cut),
weight=weight,
)
if wmod is not None:
output['genresponse_noweight'].fill(
dataset=dataset,
region=region,
systematic=sname,
pt=normalize(candidatejet.pt, cut),
genpt=normalize(genBosonPt, cut),
weight=events.genWeight[cut] * wmod[cut],
)
output['genresponse'].fill(
dataset=dataset,
region=region,
systematic=sname,
pt=normalize(candidatejet.pt, cut),
genpt=normalize(genBosonPt, cut),
weight=weight,
)
for region in regions:
cut = selection.all(*(set(regions[region]) - {'n2ddt'}))
output['nminus1_n2ddt'].fill(
dataset=dataset,
region=region,
n2ddt=normalize(candidatejet.n2ddt, cut),
weight=weights.weight()[cut],
)
for systematic in systematics:
fill(region, systematic)
if 'GluGluHToBB' in dataset:
for i in range(9):
fill(region, 'LHEScale_%d' % i, events.LHEScaleWeight[:,
i])
for c in events.LHEWeight.columns[1:]:
fill(region, 'LHEWeight_%s' % c, events.LHEWeight[c])
output["weightStats"] = weights.weightStatistics
return output
def __init__(self, ev, obj, wp, year=2018, verbose=0):
self.obj = obj
self.wp = wp
if self.wp == None:
self.selection_dict = {}
else:
self.selection_dict = obj_def[self.obj][self.wp]
self.v = verbose
self.year = year
id_level = None
if wp.lower().count('veto'):
id_level = 0
elif wp.lower().count('fake'):
id_level = 1
elif wp.lower().count('tight'):
id_level = 2
if self.obj == "Muon":
# collections are already there, so we just need to calculate missing ones
ev['Muon', 'absMiniIso'] = ev.Muon.miniPFRelIso_all * ev.Muon.pt
ev['Muon', 'ptErrRel'] = ev.Muon.ptErr / ev.Muon.pt
# this is what we are using:
# - jetRelIso if the matched jet is within deltaR<0.4, pfRelIso03_all otherwise
# - btagDeepFlavB discriminator of the matched jet if jet is within deltaR<0.4, 0 otherwise
# - pt_cone = 0.9*pt of matched jet if jet is within deltaR<0.4, pt/(pt+iso) otherwise
mask_close = (ak.fill_none(ev.Muon.delta_r(ev.Muon.matched_jet),
99) < 0.4) * 1
mask_far = ~(ak.fill_none(ev.Muon.delta_r(ev.Muon.matched_jet), 99)
< 0.4) * 1
deepJet = ak.fill_none(ev.Muon.matched_jet.btagDeepFlavB,
0) * mask_close + 0 * mask_far
jetRelIsoV2 = ev.Muon.jetRelIso * mask_close + ev.Muon.pfRelIso03_all * mask_far # default to 0 if no match
conePt = 0.9 * ak.fill_none(
ev.Muon.matched_jet.pt,
0) * mask_close + (ev.Muon.pt *
(1 + ev.Muon.miniPFRelIso_all)) * mask_far
#conePt = 0.8 * ak.fill_none(ev.Muon.matched_jet.pt,0) * mask_close + (ev.Muon.pt/(1 + ev.Muon.miniPFRelIso_all))*mask_far
ev['Muon', 'deepJet'] = ak.copy(deepJet)
ev['Muon', 'jetRelIsoV2'] = jetRelIsoV2
ev['Muon', 'conePt'] = conePt
ev['Muon', 'id'] = ak.ones_like(conePt) * id_level
self.cand = ev.Muon
elif self.obj == "Electron":
# calculate new variables. asignment is awkward, but what can you do.
ev['Electron',
'absMiniIso'] = ev.Electron.miniPFRelIso_all * ev.Electron.pt
ev['Electron', 'etaSC'] = ev.Electron.eta + ev.Electron.deltaEtaSC
# the following line is only needed if we do our own matching.
# right now, we keep using the NanoAOD match, but check the deltaR distance
# jet_index, mask_match, mask_nomatch = self.matchJets(ev.Electron, ev.Jet)
# this is what we are using:
# - jetRelIso if the matched jet is within deltaR<0.4, pfRelIso03_all otherwise
# - btagDeepFlavB discriminator of the matched jet if jet is within deltaR<0.4, 0 otherwise
# - pt_cone = 0.9*pt of matched jet if jet is within deltaR<0.4, pt/(pt+iso) otherwise
mask_close = (ak.fill_none(
ev.Electron.delta_r(ev.Electron.matched_jet), 99) < 0.4) * 1
mask_far = ~(ak.fill_none(
ev.Electron.delta_r(ev.Electron.matched_jet), 99) < 0.4) * 1
deepJet = ak.fill_none(ev.Electron.matched_jet.btagDeepFlavB,
0) * mask_close
jetRelIsoV2 = ev.Electron.jetRelIso * mask_close + ev.Electron.pfRelIso03_all * mask_far # default to 0 if no match
conePt = 0.9 * ak.fill_none(
ev.Electron.matched_jet.pt, 0) * mask_close + (
ev.Electron.pt *
(1 + ev.Electron.miniPFRelIso_all)) * mask_far
#conePt = 0.8 * ak.fill_none(ev.Electron.matched_jet.pt,0) * mask_close + (ev.Electron.pt/(1 + ev.Electron.miniPFRelIso_all))*mask_far
ev['Electron', 'deepJet'] = ak.copy(deepJet)
ev['Electron', 'jetRelIsoV2'] = jetRelIsoV2
ev['Electron', 'conePt'] = conePt
ev['Electron', 'id'] = ak.ones_like(conePt) * id_level
self.cand = ev.Electron
self.getSelection()
if self.obj == "Electron" and self.wp == "tight":
self.selection = self.selection & self.getElectronMVAID(
) & self.getIsolation(0.07, 0.78, 8.0) & self.isTriggerSafeNoIso()
if self.v > 0: print(" - custom ID and multi-isolation")
if self.obj == "Muon" and self.wp == "tight":
self.selection = self.selection & self.getIsolation(
0.11, 0.74, 6.8)
if self.v > 0: print(" - custom multi-isolation")
#self.selection = self.selection & ak.fill_none(ev.Muon.matched_jet.btagDeepFlavB<0.2770, True)
#self.selection = self.selection & (ev.Muon.matched_jet.btagDeepFlavB<0.2770)
#if self.v>0: print (" - deepJet")
if self.obj == "Electron" and (self.wp == "tightTTH"
or self.wp == 'fakeableTTH'
or self.wp == "tightSSTTH"
or self.wp == 'fakeableSSTTH'):
self.selection = self.selection & self.getSigmaIEtaIEta()
if self.v > 0: print(" - SigmaIEtaIEta")
#self.selection = self.selection & ak.fill_none(ev.Electron.matched_jet.btagDeepFlavB<0.2770, True)
#self.selection = self.selection & (ev.Electron.matched_jet.btagDeepFlavB<0.2770)
#self.selection = self.selection & (ev.Jet[ev.Electron.jetIdx].btagDeepFlavB<0.2770)
#if self.v>0: print (" - deepJet")
if self.obj == 'Muon' and (self.wp == 'fakeableTTH'
or self.wp == 'fakeableSSTTH'):
#self.selection = self.selection & (self.cand.deepJet < self.getThreshold(self.cand.conePt, min_pt=20, max_pt=45, low=0.2770, high=0.0494))
self.selection = self.selection & (ak.fill_none(
ev.Muon.matched_jet.btagDeepFlavB, 0) < self.getThreshold(
self.cand.conePt, min_pt=20, max_pt=45))
if self.v > 0: print(" - interpolated deepJet")
def bosonFlavor(bosons):
childid = abs(bosons.children.pdgId)
genflavor = ak.any(childid == 5, axis=-1) * 3 + ak.any(
childid == 4, axis=-1) * 2 + ak.any(childid < 4, axis=-1) * 1
return ak.fill_none(genflavor, 0)
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 runOneFile(filename):
#print ("filename: ", filename)
#inputfile=filename
outputfile = "output/" + inputfile.split("/")[-1]
#outputfile = "tmp.root"
mycache = uproot4.LRUArrayCache("1 MB")
file_ = uproot4.open(inputfile, num_workers=10)
#print ("root file opened: ", filename)
nevents = ak.to_list(file_["h_total_mcweight"].values())[2]
#nevents = 1000000
print("histogram opened: ", nevents)
#tree_ = uproot4.open(inputfile, num_workers=10)["outTree"].arrays(array_cache=mycache)
tree_ = file_["outTree"].arrays(array_cache=mycache)
print("tree length", len(tree_))
#tree_ = uproot4.open(inputfile)[trees[0]].arrays()
#tree_ = uproot4.open(inputfile)["outTree"].arrays(array_cache=mycache)
#tree_ = uproot4.open("Merged_WJetsInclusiveSkim.root")["outTree"].arrays(array_cache=mycache)
#tree_ = uproot4.open("/eos/cms/store/group/phys_exotica/bbMET/2016_SkimmedFiles/skim_setup_2016_v16_07-00/crab_DYJetsToLL_M-50_HT-400to600_TuneCUETP8M1_13TeV-madgraphMLM-pythia8_200918_215129_0000_0.root")["outTree"].arrays(array_cache=mycache)
#tree_ = uproot4.open("/eos/cms/store/group/phys_exotica/bbMET/2016_SkimmedFiles/skim_setup_2016_v16_07-00/crab_ttHTobb_M125_13TeV_powheg_pythia8_200918_215950_0000_0.root")["outTree"].arrays(array_cache=mycache)
#print ((tree_))
cms_events = ak.zip(
{
"run":
tree_["st_runId"],
"lumi":
tree_["st_lumiSection"],
"event":
tree_["st_eventId"],
"jetpx":
tree_["st_THINjetPx"],
"jetpy":
tree_["st_THINjetPy"],
"jetpz":
tree_["st_THINjetPz"],
"jete":
tree_["st_THINjetEnergy"],
"jetpt":
getpt(tree_["st_THINjetPx"], tree_["st_THINjetPy"]),
"jeteta":
geteta(tree_["st_THINjetPx"], tree_["st_THINjetPy"],
tree_["st_THINjetPz"]),
"jetphi":
getphi(tree_["st_THINjetPx"], tree_["st_THINjetPy"]),
"jetcsv":
tree_["st_THINjetDeepCSV"],
"jetflav":
tree_["st_THINjetHadronFlavor"],
"metpt":
tree_["st_pfMetCorrPt"],
"metphi":
tree_["st_pfMetCorrPhi"],
"mettrig":
tree_["st_mettrigdecision"],
"elepx":
tree_["st_elePx"],
"elepy":
tree_["st_elePy"],
"elepz":
tree_["st_elePz"],
"elee":
tree_["st_eleEnergy"],
"eleidL":
tree_["st_eleIsPassLoose"],
"eleidT":
tree_["st_eleIsPassTight"],
"eleq":
tree_["st_eleCharge"],
"elept":
getpt(tree_["st_elePx"], tree_["st_elePy"]),
"eleeta":
geteta(tree_["st_elePx"], tree_["st_elePy"], tree_["st_elePz"]),
"elephi":
getphi(tree_["st_elePx"], tree_["st_elePy"]),
"mupx":
tree_["st_muPx"],
"mupy":
tree_["st_muPy"],
"mupz":
tree_["st_muPz"],
"mue":
tree_["st_muEnergy"],
"muidT":
tree_["st_isTightMuon"],
"muq":
tree_["st_muCharge"],
"mupt":
getpt(tree_["st_muPx"], tree_["st_muPy"]),
"mueta":
geteta(tree_["st_muPx"], tree_["st_muPy"], tree_["st_muPz"]),
"muphi":
getphi(tree_["st_muPx"], tree_["st_muPy"]),
"ntau":
tree_["st_nTau_discBased_TightEleTightMuVeto"],
"npho":
tree_["st_nPho"],
"phopx":
tree_["st_phoPx"],
"phopy":
tree_["st_phoPy"],
"phopz":
tree_["st_phoPz"],
"phoe":
tree_["st_phoEnergy"],
"phopt":
getpt(tree_["st_phoPx"], tree_["st_phoPy"]),
"phoeta":
geteta(tree_["st_phoPx"], tree_["st_phoPy"], tree_["st_phoPz"]),
"nTrueInt":
tree_["st_pu_nTrueInt"],
"nPUVert":
tree_["st_pu_nPUVert"],
"genpt":
tree_["st_genParPt"]
},
depth_limit=1)
out_events = ak.zip(
{
"run": tree_["st_runId"],
"lumi": tree_["st_lumiSection"],
"event": tree_["st_eventId"]
},
depth_limit=1)
print("event loading done")
print("# of events: ", len(cms_events))
## add more columns/properties to the event
cms_events["mu_sel_tight"] = (cms_events.mupt > 30) & (
cms_events.muidT == True) & (numpy.abs(cms_events.mueta) < 2.4)
cms_events["mu_sel_tight0"] = ak.Array(getN(cms_events.mu_sel_tight, 0))
cms_events["nMuTight"] = ak.sum(cms_events.mu_sel_tight, axis=-1)
cms_events["nMuLoose"] = ak.sum((cms_events.mupt > 10), axis=-1)
cms_events["mu_q0"] = ak.Array(getN(cms_events.muq, 0))
cms_events["mu_q1"] = ak.Array(getN(cms_events.muq, 1))
cms_events["ele_sel_tight"] = (cms_events.eleidT == True) & (
cms_events.elept > 30) & (numpy.abs(cms_events.eleeta) < 2.5)
cms_events["ele_sel_tight0"] = ak.Array(getN(cms_events.ele_sel_tight, 0))
cms_events["nEleTight"] = ak.sum(cms_events.ele_sel_tight, axis=-1)
cms_events["nEleLoose"] = ak.sum((cms_events.elept > 10), axis=-1)
cms_events["ele_q0"] = ak.Array(getN(cms_events.eleq, 0))
cms_events["ele_q1"] = ak.Array(getN(cms_events.eleq, 1))
cms_events["recoil_Wmunu"] = getrecoil(cms_events.nMuTight,
cms_events.mupt, cms_events.muphi,
cms_events.mupx, cms_events.mupy,
cms_events.metpt, cms_events.metphi)
cms_events["recoil_Wmunu0"] = ak.firsts(cms_events.recoil_Wmunu)
cms_events["recoil_Wenu"] = getrecoil(cms_events.nEleTight,
cms_events.elept, cms_events.elephi,
cms_events.elepx, cms_events.elepy,
cms_events.metpt, cms_events.metphi)
cms_events["recoil_Wenu0"] = ak.firsts(cms_events.recoil_Wenu)
elepx0 = ak.Array(getN(cms_events.elepx, 0))
elepx1 = ak.Array(getN(cms_events.elepx, 1))
elepy0 = ak.Array(getN(cms_events.elepy, 0))
elepy1 = ak.Array(getN(cms_events.elepy, 1))
elepz0 = ak.Array(getN(cms_events.elepz, 0))
elepz1 = ak.Array(getN(cms_events.elepz, 1))
elee0 = ak.Array(getN(cms_events.elee, 0))
elee1 = ak.Array(getN(cms_events.elee, 1))
cms_events["Zee_mass"] = numpy.sqrt((elee0 + elee1)**2 -
(elepx0 + elepx1)**2 -
(elepy0 + elepy1)**2 -
(elepz0 + elepz1)**2)
cms_events["Zee_pt"] = numpy.sqrt((elepx0 + elepx1)**2 +
(elepy0 + elepy1)**2)
cms_events["Zee_recoil"] = getrecoil1((elepx0 + elepx1), (elepy0 + elepy1),
cms_events.metpt, cms_events.metphi)
mupx0 = ak.Array(getN(cms_events.mupx, 0))
mupx1 = ak.Array(getN(cms_events.mupx, 1))
mupy0 = ak.Array(getN(cms_events.mupy, 0))
mupy1 = ak.Array(getN(cms_events.mupy, 1))
mupz0 = ak.Array(getN(cms_events.mupz, 0))
mupz1 = ak.Array(getN(cms_events.mupz, 1))
mue0 = ak.Array(getN(cms_events.mue, 0))
mue1 = ak.Array(getN(cms_events.mue, 1))
cms_events["Zmumu_mass"] = numpy.sqrt((mue0 + mue1)**2 -
(mupx0 + mupx1)**2 -
(mupy0 + mupy1)**2 -
(mupz0 + mupz1)**2)
cms_events["Zmumu_pt"] = numpy.sqrt((mupx0 + mupx1)**2 +
(mupy0 + mupy1)**2)
cms_events["Zmumu_recoil"] = getrecoil1(
(mupx0 + mupx1), (mupy0 + mupy1), cms_events.metpt, cms_events.metphi)
#cms_events["recoil_Zmumu"] = getrecoil
cms_events["recoil_WmunuPhi"] = getRecoilPhi(
cms_events.nMuTight, cms_events.mupt, cms_events.muphi,
cms_events.mupx, cms_events.mupy, cms_events.metpt, cms_events.metphi)
cms_events["recoil_WmunuPhi0"] = ak.firsts(cms_events.recoil_WmunuPhi)
cms_events["recoil_WenuPhi"] = getRecoilPhi(
cms_events.nEleTight, cms_events.elept, cms_events.elephi,
cms_events.elepx, cms_events.elepy, cms_events.metpt,
cms_events.metphi)
cms_events["recoil_WenuPhi0"] = ak.firsts(cms_events.recoil_WenuPhi)
cms_events["mt_Wmunu"] = getMT(cms_events.nMuTight, cms_events.mupt,
cms_events.muphi, cms_events.mupx,
cms_events.mupy, cms_events.metpt,
cms_events.metphi)
cms_events["mt_Wmunu0"] = ak.firsts(cms_events.mt_Wmunu)
cms_events["mt_Wenu"] = getMT(cms_events.nEleTight, cms_events.elept,
cms_events.elephi, cms_events.elepx,
cms_events.elepy, cms_events.metpt,
cms_events.metphi)
cms_events["mt_Wenu0"] = ak.firsts(cms_events.mt_Wenu)
cms_events["jet_sel_loose"] = (cms_events.jetpt > 30.0) & (numpy.abs(
cms_events.jeteta) < 2.5)
cms_events["jet_sel_tight"] = (cms_events.jetpt > 50.0) & (numpy.abs(
cms_events.jeteta) < 2.5)
#cms_events["jet_sel_b"] = (cms_events.jetcsv > 0.6321) & (numpy.abs(cms_events.jeteta)<2.4)
cms_events["jet_sel_b"] = (
cms_events.jetcsv[cms_events.jet_sel_loose == True] > 0.6321
) & (numpy.abs(cms_events.jeteta[cms_events.jet_sel_loose == True]) < 2.4)
cms_events["jetptTight"] = cms_events.jetpt[cms_events.jet_sel_tight ==
True]
cms_events["jetetaTight"] = cms_events.jeteta[cms_events.jet_sel_tight ==
True]
cms_events["jetphiTight"] = cms_events.jetphi[cms_events.jet_sel_tight ==
True]
cms_events["jetptLoose"] = cms_events.jetpt[cms_events.jet_sel_loose ==
True]
cms_events["jetetaLoose"] = cms_events.jeteta[cms_events.jet_sel_loose ==
True]
cms_events["jetphiLoose"] = cms_events.jetphi[cms_events.jet_sel_loose ==
True]
cms_events["jet_sel_tight0"] = ak.Array(
getN(cms_events.jet_sel_tight[cms_events.jet_sel_loose == True], 0))
cms_events["jet_sel_b_0"] = ak.Array(getN(cms_events.jet_sel_b, 0))
cms_events["jet_sel_b_1"] = ak.Array(getN(cms_events.jet_sel_b, 1))
cms_events["nJetLoose"] = ak.sum(cms_events.jet_sel_loose, axis=-1)
cms_events["nJetTight"] = ak.sum(cms_events.jet_sel_tight, axis=-1)
cms_events["nJetb"] = ak.sum(cms_events.jet_sel_b, axis=-1)
cms_events["dphi_jet_met"] = DeltaPhi(
cms_events.jetphi[cms_events.jet_sel_loose == True], cms_events.metphi)
cms_events["min_dphi_jet_met"] = ak.min(cms_events.dphi_jet_met, axis=-1)
#--------------------------------------------------------------------------------------------------
## W --> lepton + nu
#--------------------------------------------------------------------------------------------------
from regions import get_mask_wmunu1b, get_mask_wmunu2b, get_mask_wenu1b, get_mask_wenu2b, get_mask_topmunu1b, get_mask_topmunu2b, get_mask_topenu1b, get_mask_topenu2b, get_mask_Zmumu1b, get_mask_Zmumu2b, get_mask_Zee1b, get_mask_Zee2b, get_mask_SR1b, get_mask_SR2b
cms_events["mask_wmunu1b"] = get_mask_wmunu1b(cms_events)
cms_events["mask_wmunu2b"] = get_mask_wmunu2b(cms_events)
cms_events["mask_wenu1b"] = get_mask_wenu1b(cms_events)
cms_events["mask_wenu2b"] = get_mask_wenu2b(cms_events)
cms_events["mask_topmunu1b"] = get_mask_topmunu1b(cms_events)
cms_events["mask_topmunu2b"] = get_mask_topmunu2b(cms_events)
cms_events["mask_topenu1b"] = get_mask_topenu1b(cms_events)
cms_events["mask_topenu2b"] = get_mask_topenu2b(cms_events)
cms_events["mask_Zmumu1b"] = get_mask_Zmumu1b(cms_events)
cms_events["mask_Zmumu2b"] = get_mask_Zmumu2b(cms_events)
cms_events["mask_Zee1b"] = get_mask_Zee1b(cms_events)
cms_events["mask_Zee2b"] = get_mask_Zee2b(cms_events)
cms_events["mask_SR1b"] = get_mask_SR1b(cms_events)
cms_events["mask_SR2b"] = get_mask_SR2b(cms_events)
'''
wm = cms_events.event[mask_SR2b]
wm[~ak.is_none(wm)]
'''
###############
out_events["metpt"] = cms_events["metpt"]
out_events["metphi"] = cms_events["metphi"]
out_events["nTrueInt"] = cms_events["nTrueInt"]
out_events["nJetLoose"] = cms_events["nJetLoose"]
out_events["mu_sel_tight0"] = cms_events["mu_sel_tight0"]
out_events["nMuTight"] = cms_events["nMuTight"]
out_events["nMuLoose"] = cms_events["nMuLoose"]
out_events["mu_q0"] = cms_events["mu_q0"]
out_events["mu_q1"] = cms_events["mu_q1"]
out_events["mupt0"] = ak.Array(getN(cms_events.mupt, 0))
out_events["mupt1"] = ak.Array(getN(cms_events.mupt, 1))
out_events["mueta0"] = ak.Array(getN(cms_events.mueta, 0))
out_events["mueta1"] = ak.Array(getN(cms_events.mueta, 1))
out_events["muphi0"] = ak.Array(getN(cms_events.muphi, 0))
out_events["muphi1"] = ak.Array(getN(cms_events.muphi, 1))
out_events["ele_sel_tight0"] = cms_events["ele_sel_tight0"]
out_events["nEleTight"] = cms_events["nEleTight"]
out_events["nEleLoose"] = cms_events["nEleLoose"]
out_events["ele_q0"] = cms_events["ele_q0"]
out_events["ele_q1"] = cms_events["ele_q1"]
out_events["elept0"] = ak.Array(getN(cms_events.elept, 0))
out_events["elept1"] = ak.Array(getN(cms_events.elept, 1))
out_events["eleeta0"] = ak.Array(getN(cms_events.eleeta, 0))
out_events["eleeta1"] = ak.Array(getN(cms_events.eleeta, 1))
out_events["elephi0"] = ak.Array(getN(cms_events.elephi, 0))
out_events["elephi1"] = ak.Array(getN(cms_events.elephi, 1))
out_events["recoil_Wmunu0"] = cms_events["recoil_Wmunu0"]
out_events["recoil_Wenu0"] = cms_events["recoil_Wenu0"]
out_events["recoil_WmunuPhi0"] = cms_events["recoil_WmunuPhi0"]
out_events["recoil_WenuPhi0"] = cms_events["recoil_WenuPhi0"]
out_events["mt_Wmunu0"] = cms_events["mt_Wmunu0"]
out_events["mt_Wenu0"] = cms_events["mt_Wenu0"]
out_events["Zee_mass"] = cms_events["Zee_mass"]
out_events["Zee_pt"] = cms_events["Zee_pt"]
out_events["Zee_recoil"] = cms_events["Zee_recoil"]
out_events["Zmumu_mass"] = cms_events["Zmumu_mass"]
out_events["Zmumu_pt"] = cms_events["Zmumu_pt"]
out_events["Zmumu_recoil"] = cms_events["Zmumu_recoil"]
out_events["nJetLoose"] = cms_events["nJetLoose"]
out_events["nJetTight"] = cms_events["nJetTight"]
out_events["nJetb"] = cms_events["nJetb"]
out_events["min_dphi_jet_met"] = cms_events["min_dphi_jet_met"]
cms_events["jet_sel_tight0"] = cms_events["jet_sel_tight0"]
cms_events["jet_sel_b_0"] = cms_events["jet_sel_b_0"]
cms_events["jet_sel_b_1"] = cms_events["jet_sel_b_1"]
out_events["jetpt0"] = ak.Array(getN(cms_events.jetptTight, 0))
out_events["jetpt1"] = ak.Array(getN(cms_events.jetptLoose, 1))
out_events["jetpt2"] = ak.Array(getN(cms_events.jetptLoose, 2))
out_events["jetpt3"] = ak.Array(getN(cms_events.jetptLoose, 3))
out_events["jetpt4"] = ak.Array(getN(cms_events.jetptLoose, 4))
out_events["jetpt5"] = ak.Array(getN(cms_events.jetptLoose, 5))
out_events["jetpt6"] = ak.Array(getN(cms_events.jetptLoose, 6))
out_events["jeteta0"] = ak.Array(getN(cms_events.jetetaTight, 0))
out_events["jeteta1"] = ak.Array(getN(cms_events.jetetaLoose, 1))
out_events["jeteta2"] = ak.Array(getN(cms_events.jetetaLoose, 2))
out_events["jeteta3"] = ak.Array(getN(cms_events.jetetaLoose, 3))
out_events["jeteta4"] = ak.Array(getN(cms_events.jetetaLoose, 4))
out_events["jeteta5"] = ak.Array(getN(cms_events.jetetaLoose, 5))
out_events["jeteta6"] = ak.Array(getN(cms_events.jetetaLoose, 6))
out_events["jetphi0"] = ak.Array(getN(cms_events.jetphiTight, 0))
out_events["jetphi1"] = ak.Array(getN(cms_events.jetphiLoose, 1))
out_events["jetphi2"] = ak.Array(getN(cms_events.jetphiLoose, 2))
out_events["jetflav0"] = ak.Array(
getN(cms_events.jetflav[cms_events.jet_sel_tight == True], 0))
out_events["jetflav1"] = ak.Array(
getN(cms_events.jetflav[cms_events.jet_sel_loose == True], 1))
out_events["jetflav2"] = ak.Array(
getN(cms_events.jetflav[cms_events.jet_sel_loose == True], 2))
out_events["jetflav3"] = ak.Array(
getN(cms_events.jetflav[cms_events.jet_sel_loose == True], 3))
out_events["jetflav4"] = ak.Array(
getN(cms_events.jetflav[cms_events.jet_sel_loose == True], 4))
out_events["jetflav5"] = ak.Array(
getN(cms_events.jetflav[cms_events.jet_sel_loose == True], 5))
out_events["jetflav6"] = ak.Array(
getN(cms_events.jetflav[cms_events.jet_sel_loose == True], 6))
out_events["csv0"] = ak.Array(
getN(cms_events.jetcsv[cms_events.jet_sel_tight == True], 0))
out_events["csv1"] = ak.Array(
getN(cms_events.jetcsv[cms_events.jet_sel_loose == True], 1))
out_events["csv2"] = ak.Array(
getN(cms_events.jetcsv[cms_events.jet_sel_loose == True], 2))
out_events["csv3"] = ak.Array(
getN(cms_events.jetcsv[cms_events.jet_sel_loose == True], 3))
out_events["SR_2b"] = cms_events["mask_SR2b"]
out_events["SR_1b"] = cms_events["mask_SR1b"]
out_events["ZeeCR_2b"] = cms_events["mask_Zee2b"]
out_events["ZeeCR_1b"] = cms_events["mask_Zee1b"]
out_events["ZmumuCR_2b"] = cms_events["mask_Zmumu2b"]
out_events["ZmumuCR_1b"] = cms_events["mask_Zmumu1b"]
out_events["TopenuCR_2b"] = cms_events["mask_topenu2b"]
out_events["TopenuCR_1b"] = cms_events["mask_topenu1b"]
out_events["TopmunuCR_2b"] = cms_events["mask_topmunu2b"]
out_events["TopmunuCR_1b"] = cms_events["mask_topmunu1b"]
out_events["WenuCR_1b"] = cms_events["mask_wenu1b"]
out_events["WenuCR_2b"] = cms_events["mask_wenu2b"]
out_events["WmunuCR_1b"] = cms_events["mask_wmunu1b"]
out_events["WmunuCR_2b"] = cms_events["mask_wmunu2b"]
## btagging SFs
from read_sfs import btag_sf
from read_sfs import evaluator
out_events["btagsf0"] = btag_sf.eval("central", out_events.jetflav0,
abs(out_events.jeteta0),
out_events.jetpt0)
out_events["btagsf1"] = btag_sf.eval("central", out_events.jetflav1,
abs(out_events.jeteta1),
out_events.jetpt1)
out_events["btagsf2"] = btag_sf.eval("central", out_events.jetflav2,
abs(out_events.jeteta2),
out_events.jetpt2)
out_events["btagsf3"] = btag_sf.eval("central", out_events.jetflav3,
abs(out_events.jeteta3),
out_events.jetpt3)
out_events["btagsf4"] = btag_sf.eval("central", out_events.jetflav4,
abs(out_events.jeteta4),
out_events.jetpt4)
out_events["btagsf5"] = btag_sf.eval("central", out_events.jetflav5,
abs(out_events.jeteta5),
out_events.jetpt5)
out_events["btagsf6"] = btag_sf.eval("central", out_events.jetflav6,
abs(out_events.jeteta6),
out_events.jetpt6)
## btag efficiency
out_events["btag_eff_lwp_0"] = evaluator["btag_eff_lwp"](
out_events.jeteta0, out_events.jetpt0)
out_events["btag_eff_lwp_1"] = evaluator["btag_eff_lwp"](
out_events.jeteta1, out_events.jetpt1)
out_events["ctag_eff_lwp_0"] = evaluator["ctag_eff_lwp"](
out_events.jeteta0, out_events.jetpt0)
out_events["ctag_eff_lwp_1"] = evaluator["ctag_eff_lwp"](
out_events.jeteta1, out_events.jetpt1)
out_events["ltag_eff_lwp_0"] = evaluator["ltag_eff_lwp"](
out_events.jeteta0, out_events.jetpt0)
out_events["ltag_eff_lwp_1"] = evaluator["ltag_eff_lwp"](
out_events.jeteta1, out_events.jetpt1)
out_events["btag_eff_mwp_0"] = evaluator["btag_eff_mwp"](
out_events.jeteta0, out_events.jetpt0)
out_events["btag_eff_mwp_1"] = evaluator["btag_eff_mwp"](
out_events.jeteta1, out_events.jetpt1)
out_events["ctag_eff_mwp_0"] = evaluator["ctag_eff_mwp"](
out_events.jeteta0, out_events.jetpt0)
out_events["ctag_eff_mwp_1"] = evaluator["ctag_eff_mwp"](
out_events.jeteta1, out_events.jetpt1)
out_events["ltag_eff_mwp_0"] = evaluator["ltag_eff_mwp"](
out_events.jeteta0, out_events.jetpt0)
out_events["ltag_eff_mwp_1"] = evaluator["ltag_eff_mwp"](
out_events.jeteta1, out_events.jetpt1)
## ele sfs
out_events["eleTightSF0"] = evaluator["EGamma_SF2D_T"](out_events.eleeta0,
out_events.elept0)
out_events["eleLooseSF1"] = evaluator["EGamma_SF2D_L"](out_events.eleeta1,
out_events.elept1)
out_events["eleTrigSF0"] = evaluator["EGamma_SF2D_Trig"](
out_events.eleeta0, out_events.elept0)
out_events["eleRecoSF0"] = evaluator["EGamma_SF2D_Reco"](
out_events.eleeta0, out_events.elept0)
eleRecoSF1_hi = evaluator["EGamma_SF2D_Reco"](out_events.eleeta1,
out_events.elept1)
eleRecoSF1_lo = evaluator["EGamma_SF2D_Reco_lowpt"](out_events.eleeta1,
out_events.elept1)
eleRecoSF1_hi_ = ak.fill_none(
ak.mask(eleRecoSF1_hi, out_events.elept1 > 20.), 0)
eleRecoSF1_lo_ = ak.fill_none(
ak.mask(eleRecoSF1_lo, out_events.elept1 > 20.), 0)
out_events["eleRecoSF1"] = eleRecoSF1_hi_ + eleRecoSF1_lo_
## muon sfs
bcdef_lumi = 19.554725529
gh_lumi = 16.224846377
total_lumi = bcdef_lumi + gh_lumi
##--------low pt Loose
muonLooseIDSF_lowpt1 = (
(bcdef_lumi * evaluator["muon_lowpt_BCDEF_LooseID"]
(out_events.mupt1, abs(out_events.mueta1))) +
(gh_lumi * evaluator["muon_lowpt_GH_LooseID"]
(out_events.mupt1, abs(out_events.mueta1)))) / total_lumi
##----------- medium pt Loose
muonLooseIDSF1 = ((bcdef_lumi * evaluator["muon_highpt_BCDEF_LooseID"]
(out_events.mueta1, out_events.mupt1)) +
(gh_lumi * evaluator["muon_highpt_GH_LooseID"]
(out_events.mueta1, out_events.mupt1))) / total_lumi
muonLooseISOSF1 = ((bcdef_lumi * evaluator["muon_highpt_BCDEF_LooseISO"]
(out_events.mueta1, out_events.mupt1)) +
(gh_lumi * evaluator["muon_highpt_GH_LooseISO"]
(out_events.mueta1, out_events.mupt1))) / total_lumi
muon_loose_ID_low_SF_1 = ak.fill_none(
ak.mask(muonLooseIDSF_lowpt1, out_events.mupt1 < 20.), 0)
muon_loose_ID_high_SF_1 = ak.fill_none(
ak.mask(muonLooseIDSF1, out_events.mupt1 > 20.), 0)
muon_loose_ID_SF_1 = muon_loose_ID_low_SF_1 + muon_loose_ID_high_SF_1
out_events["muLooseSF1"] = muon_loose_ID_SF_1 * muonLooseISOSF1
##------------medium pt tight
muonTightIDSF0 = ((bcdef_lumi * evaluator["muon_highpt_BCDEF_TightID"]
(out_events.mueta0, out_events.mupt0)) +
(gh_lumi * evaluator["muon_highpt_GH_TightID"]
(out_events.mueta0, out_events.mupt0))) / total_lumi
muonTightISOSF0 = ((bcdef_lumi * evaluator["muon_highpt_BCDEF_TightISO"]
(out_events.mueta0, out_events.mupt0)) +
(gh_lumi * evaluator["muon_highpt_GH_TightISO"]
(out_events.mueta0, out_events.mupt0))) / total_lumi
out_events["muTightSF0"] = muonTightIDSF0 * muonTightISOSF0
out_events["puweight"] = evaluator["pu_weight"](cms_events.nTrueInt)
## trigger sfs
out_events["mettrigWeight"] = evaluator["met_trig"](cms_events.metpt)
out_events["recoilWmunutrigWeight"] = evaluator["met_trig"](
cms_events.recoil_Wmunu0)
out_events["recoilWenutrigWeight"] = evaluator["met_trig"](
cms_events.recoil_Wenu0)
out_events["recoilZmumutrigWeight"] = evaluator["met_trig"](
cms_events.Zmumu_recoil)
out_events["recoilZeetrigWeight"] = evaluator["met_trig"](
cms_events.Zee_recoil)
## Fill weights for each CR so that we don't need to worry later
out_events["weight_SR_2b"] = out_events.puweight * out_events.mettrigWeight
out_events["weight_SR_1b"] = out_events.puweight * out_events.mettrigWeight
out_events["weight_ZeeCR_2b"] = out_events.puweight * out_events.eleTrigSF0
out_events["weight_ZeeCR_1b"] = out_events.puweight * out_events.eleTrigSF0
out_events[
"weight_ZmumuCR_2b"] = out_events.puweight * out_events.recoilZmumutrigWeight
out_events[
"weight_ZmumuCR_1b"] = out_events.puweight * out_events.recoilZmumutrigWeight
out_events[
"weight_TopenuCR_2b"] = out_events.puweight * out_events.eleTrigSF0
out_events[
"weight_TopenuCR_1b"] = out_events.puweight * out_events.eleTrigSF0
out_events[
"weight_TopmunuCR_2b"] = out_events.puweight * out_events.recoilWmunutrigWeight
out_events[
"weight_TopmunuCR_1b"] = out_events.puweight * out_events.recoilWmunutrigWeight
out_events[
"weight_WenuCR_1b"] = out_events.puweight * out_events.eleTrigSF0
out_events[
"weight_WenuCR_2b"] = out_events.puweight * out_events.eleTrigSF0
out_events[
"weight_WmunuCR_1b"] = out_events.puweight * out_events.recoilWmunutrigWeight
out_events[
"weight_WmunuCR_2b"] = out_events.puweight * out_events.recoilWmunutrigWeight
## Fill Histograms
from variables import vardict, regions, variables_common
from binning import binning
f = TFile(outputfile, "RECREATE")
for ireg in regions:
thisregion = out_events[out_events[ireg] == True]
thisregion_ = thisregion[~(ak.is_none(thisregion))]
weight_ = "weight_" + ireg
for ivar in variables_common[ireg]:
hist_name_ = "h_reg_" + ireg + "_" + vardict[ivar]
h = VarToHist(thisregion_[ivar], thisregion_[weight_], hist_name_,
binning[ireg][ivar])
f.cd()
h.Write()
h_total = TH1F("h_total_mcweight", "h_total_mcweight", 2, 0, 2)
h_total.SetBinContent(1, nevents)
f.cd()
h_total.Write()
write_parquet = False
if write_parquet:
ak.to_parquet(out_events, "analysis_wjets_allevents.parquet")
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))]
# Get the leptons. This has changed a couple of times now, but we are using fakeable objects as baseline leptons.
# The added p4 instance has the corrected pt (conePt for fakeable) and should be used for any following selection or calculation
# Any additional correction (if we choose to do so) should be added here, e.g. Rochester corrections, ...
## Muons
mu_v = Collections(ev, "Muon", "vetoTTH", year=year).get() # these include all muons, tight and fakeable
mu_t = Collections(ev, "Muon", "tightSSTTH", year=year).get()
mu_f = Collections(ev, "Muon", "fakeableSSTTH", year=year).get()
muon = ak.concatenate([mu_t, mu_f], axis=1)
muon['p4'] = get_four_vec_fromPtEtaPhiM(muon, get_pt(muon), muon.eta, muon.phi, muon.mass, copy=False) #FIXME new
## Electrons
el_v = Collections(ev, "Electron", "vetoTTH", year=year).get()
el_t = Collections(ev, "Electron", "tightSSTTH", year=year).get()
el_f = Collections(ev, "Electron", "fakeableSSTTH", year=year).get()
electron = ak.concatenate([el_t, el_f], axis=1)
electron['p4'] = get_four_vec_fromPtEtaPhiM(electron, get_pt(electron), electron.eta, electron.phi, electron.mass, copy=False) #FIXME new
if not re.search(re.compile('MuonEG|DoubleMuon|DoubleEG|EGamma'), dataset):
el_t_p = prompt(el_t)
el_t_np = nonprompt(el_t)
el_f_p = prompt(el_f)
el_f_np = nonprompt(el_f)
mu_t_p = prompt(mu_t)
mu_t_np = nonprompt(mu_t)
mu_f_p = prompt(mu_f)
mu_f_np = nonprompt(mu_f)
is_flipped = ( (el_t_p.matched_gen.pdgId*(-1) == el_t_p.pdgId) & (abs(el_t_p.pdgId) == 11) )
el_t_p_cc = el_t_p[~is_flipped] # this is tight, prompt, and charge consistent
el_t_p_cf = el_t_p[is_flipped] # this is tight, prompt, and charge flipped
## Merge electrons and muons. These are fakeable leptons now
lepton = ak.concatenate([muon, electron], axis=1)
leading_lepton_idx = ak.singletons(ak.argmax(lepton.p4.pt, axis=1))
leading_lepton = lepton[leading_lepton_idx]
trailing_lepton_idx = ak.singletons(ak.argmin(lepton.p4.pt, axis=1))
trailing_lepton = lepton[trailing_lepton_idx]
dilepton_mass = (leading_lepton.p4 + trailing_lepton.p4).mass
dilepton_pt = (leading_lepton.p4 + trailing_lepton.p4).pt
#dilepton_dR = delta_r(leading_lepton, trailing_lepton)
dilepton_dR = leading_lepton.p4.delta_r(trailing_lepton.p4)
lepton_pdgId_pt_ordered = ak.fill_none(ak.pad_none(lepton[ak.argsort(lepton.p4.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)
gp = ev.GenPart
gp_e = gp[((abs(gp.pdgId)==11)&(gp.status==1)&((gp.statusFlags&(1<<0))==1)&(gp.statusFlags&(1<<8)==256))]
gp_m = gp[((abs(gp.pdgId)==13)&(gp.status==1)&((gp.statusFlags&(1<<0))==1)&(gp.statusFlags&(1<<8)==256))]
n_gen_lep = ak.num(gp_e) + ak.num(gp_m)
else:
n_gen_lep = np.zeros(len(ev))
LL = (n_gen_lep > 2) # this is the classifier for LL events (should mainly be ttZ/tZ/WZ...)
mt_lep_met = mt(lepton.p4.pt, lepton.p4.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)
tau = tau[~match(tau, muon, deltaRCut=0.4)]
tau = tau[~match(tau, electron, deltaRCut=0.4)]
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
# try to get either the most forward light jet, or if there's more than one with eta>1.7, the highest pt one
most_fwd = light[ak.argsort(abs(light.eta))][:,0:1]
#most_fwd = light[ak.singletons(ak.argmax(abs(light.eta)))]
best_fwd = ak.concatenate([j_fwd, most_fwd], axis=1)[:,0:1]
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(get_pt(muon), axis=1) + ak.sum(get_pt(electron), axis=1)
st = met_pt + ht + ak.sum(lepton.p4.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
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)
# slightly restructured
# calculate everything from loose, require two tights on top
# since n_tight == n_loose == 2, the tight and loose leptons are the same in the end
# in this selection we'll get events with exactly two fakeable+tight and two loose leptons.
sel = Selection(
dataset = dataset,
events = ev,
year = self.year,
ele = electron,
ele_veto = el_v,
mu = muon,
mu_veto = mu_v,
jet_all = jet,
jet_central = central,
jet_btag = btag,
jet_fwd = fwd,
jet_light = light,
met = ev.MET,
)
baseline = sel.dilep_baseline(cutflow=cutflow, SS=True, omit=['N_fwd>0'])
baseline_OS = sel.dilep_baseline(cutflow=cutflow, SS=False, omit=['N_fwd>0']) # this is for charge flip estimation
if not re.search(re.compile('MuonEG|DoubleMuon|DoubleEG|EGamma'), dataset):
BL = (baseline & ((ak.num(el_t_p_cc)+ak.num(mu_t_p))==2)) # this is the MC baseline for events with two tight prompt leptons
BL_incl = (baseline & ((ak.num(el_t)+ak.num(mu_t))==2)) # this is the MC baseline for events with two tight leptons
np_est_sel_mc = (baseline & \
((((ak.num(el_t_p_cc)+ak.num(mu_t_p))==1) & ((ak.num(el_f_np)+ak.num(mu_f_np))==1)) | (((ak.num(el_t_p_cc)+ak.num(mu_t_p))==0) & ((ak.num(el_f_np)+ak.num(mu_f_np))==2)) )) # no overlap between tight and nonprompt, and veto on additional leptons. this should be enough
np_obs_sel_mc = (baseline & ((ak.num(el_t)+ak.num(mu_t))==2) & ((ak.num(el_t_np)+ak.num(mu_t_np))>=1) ) # two tight leptons, at least one nonprompt
np_est_sel_data = (baseline & ~baseline) # this has to be false
cf_est_sel_mc = (baseline_OS & ((ak.num(el_t_p)+ak.num(mu_t_p))==2))
cf_obs_sel_mc = (baseline & ((ak.num(el_t)+ak.num(mu_t))==2) & ((ak.num(el_t_p_cf))>=1) ) # two tight leptons, at least one electron charge flip
cf_est_sel_data = (baseline & ~baseline) # this has to be false
weight_np_mc = self.nonpromptWeight.get(el_f_np, mu_f_np, meas='TT')
weight_cf_mc = self.chargeflipWeight.flip_weight(el_t_p)
else:
BL = (baseline & ((ak.num(el_t)+ak.num(mu_t))==2))
BL_incl = BL
np_est_sel_mc = (baseline & ~baseline)
np_obs_sel_mc = (baseline & ~baseline)
np_est_sel_data = (baseline & (ak.num(el_t)+ak.num(mu_t)==1) & (ak.num(el_f)+ak.num(mu_f)==1) )
cf_est_sel_mc = (baseline & ~baseline)
cf_obs_sel_mc = (baseline & ~baseline)
cf_est_sel_data = (baseline_OS & ((ak.num(el_t)+ak.num(mu_t))==2) )
weight_np_mc = np.zeros(len(ev))
weight_cf_mc = np.zeros(len(ev))
#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)
if False:
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])
weight_BL = weight.weight()[BL] # this is just a shortened weight list for the two prompt selection
weight_np_data = self.nonpromptWeight.get(el_f, mu_f, meas='data')
weight_cf_data = self.chargeflipWeight.flip_weight(el_t)
out_sel = (BL | np_est_sel_mc | cf_est_sel_mc)
dummy = (np.ones(len(ev))==1)
def fill_multiple_np(hist, arrays, add_sel=dummy):
#reg_sel = [BL, np_est_sel_mc, np_obs_sel_mc, np_est_sel_data, cf_est_sel_mc, cf_obs_sel_mc, cf_est_sel_data],
#print ('len', len(reg_sel[0]))
#print ('sel', reg_sel[0])
reg_sel = [BL&add_sel, BL_incl&add_sel, np_est_sel_mc&add_sel, np_obs_sel_mc&add_sel, np_est_sel_data&add_sel, cf_est_sel_mc&add_sel, cf_obs_sel_mc&add_sel, cf_est_sel_data&add_sel],
fill_multiple(
hist,
datasets=[
dataset, # only prompt contribution from process
dataset+"_incl", # everything from process (inclusive MC truth)
"np_est_mc", # MC based NP estimate
"np_obs_mc", # MC based NP observation
"np_est_data",
"cf_est_mc",
"cf_obs_mc",
"cf_est_data",
],
arrays=arrays,
selections=reg_sel[0], # no idea where the additional dimension is coming from...
weights=[
weight.weight()[reg_sel[0][0]],
weight.weight()[reg_sel[0][1]],
weight.weight()[reg_sel[0][2]]*weight_np_mc[reg_sel[0][2]],
weight.weight()[reg_sel[0][3]],
weight.weight()[reg_sel[0][4]]*weight_np_data[reg_sel[0][4]],
weight.weight()[reg_sel[0][5]]*weight_cf_mc[reg_sel[0][5]],
weight.weight()[reg_sel[0][6]],
weight.weight()[reg_sel[0][7]]*weight_cf_data[reg_sel[0][7]],
],
)
if self.evaluate or self.dump:
# define the inputs to the NN
# this is super stupid. there must be a better way.
# used a np.stack which is ok performance wise. pandas data frame seems to be slow and memory inefficient
#FIXME no n_b, n_fwd back in v13/v14 of the DNN
NN_inputs_d = {
'n_jet': ak.to_numpy(ak.num(jet)),
'n_fwd': ak.to_numpy(ak.num(fwd)),
'n_b': ak.to_numpy(ak.num(btag)),
'n_tau': ak.to_numpy(ak.num(tau)),
#'n_track': ak.to_numpy(ak.num(track)),
'st': ak.to_numpy(st),
'met': ak.to_numpy(ev.MET.pt),
'mjj_max': ak.to_numpy(ak.fill_none(ak.max(mjf, axis=1),0)),
'delta_eta_jj': ak.to_numpy(pad_and_flatten(delta_eta)),
'lead_lep_pt': ak.to_numpy(pad_and_flatten(leading_lepton.p4.pt)),
'lead_lep_eta': ak.to_numpy(pad_and_flatten(leading_lepton.p4.eta)),
'sublead_lep_pt': ak.to_numpy(pad_and_flatten(trailing_lepton.p4.pt)),
'sublead_lep_eta': ak.to_numpy(pad_and_flatten(trailing_lepton.p4.eta)),
'dilepton_mass': ak.to_numpy(pad_and_flatten(dilepton_mass)),
'dilepton_pt': ak.to_numpy(pad_and_flatten(dilepton_pt)),
'fwd_jet_pt': ak.to_numpy(pad_and_flatten(best_fwd.pt)),
'fwd_jet_p': ak.to_numpy(pad_and_flatten(best_fwd.p)),
'fwd_jet_eta': ak.to_numpy(pad_and_flatten(best_fwd.eta)),
'lead_jet_pt': ak.to_numpy(pad_and_flatten(jet[:, 0:1].pt)),
'sublead_jet_pt': ak.to_numpy(pad_and_flatten(jet[:, 1:2].pt)),
'lead_jet_eta': ak.to_numpy(pad_and_flatten(jet[:, 0:1].eta)),
'sublead_jet_eta': ak.to_numpy(pad_and_flatten(jet[:, 1:2].eta)),
'lead_btag_pt': ak.to_numpy(pad_and_flatten(high_score_btag[:, 0:1].pt)),
'sublead_btag_pt': ak.to_numpy(pad_and_flatten(high_score_btag[:, 1:2].pt)),
'lead_btag_eta': ak.to_numpy(pad_and_flatten(high_score_btag[:, 0:1].eta)),
'sublead_btag_eta': ak.to_numpy(pad_and_flatten(high_score_btag[:, 1:2].eta)),
'min_bl_dR': ak.to_numpy(ak.fill_none(min_bl_dR, 0)),
'min_mt_lep_met': ak.to_numpy(ak.fill_none(min_mt_lep_met, 0)),
}
if self.dump:
for k in NN_inputs_d.keys():
output[k] += processor.column_accumulator(NN_inputs_d[k][out_sel])
if self.evaluate:
NN_inputs = np.stack( [NN_inputs_d[k] for k in NN_inputs_d.keys()] )
NN_inputs = np.nan_to_num(NN_inputs, 0, posinf=1e5, neginf=-1e5) # events with posinf/neginf/nan will not pass the BL selection anyway
NN_inputs = np.moveaxis(NN_inputs, 0, 1) # this is needed for a np.stack (old version)
model, scaler = load_onnx_model(self.training)
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 ("Problem with prediction. Showing the shapes here:")
print (np.shape(NN_inputs))
print (np.shape(weight_BL))
NN_pred = np.array([])
best_score = np.array([])
NN_inputs_scaled = NN_inputs
raise
##k.clear_session()
#FIXME below needs to be fixed again with changed NN evaluation. Should work now
fill_multiple_np(output['node'], {'multiplicity':best_score})
fill_multiple_np(output['node0_score_incl'], {'score':NN_pred[:,0]})
fill_multiple_np(output['node1_score_incl'], {'score':NN_pred[:,1]})
fill_multiple_np(output['node2_score_incl'], {'score':NN_pred[:,2]})
fill_multiple_np(output['node3_score_incl'], {'score':NN_pred[:,3]})
fill_multiple_np(output['node4_score_incl'], {'score':NN_pred[:,4]})
fill_multiple_np(output['node0_score'], {'score':NN_pred[:,0]}, add_sel=(best_score==0))
fill_multiple_np(output['node1_score'], {'score':NN_pred[:,1]}, add_sel=(best_score==1))
fill_multiple_np(output['node2_score'], {'score':NN_pred[:,2]}, add_sel=(best_score==2))
fill_multiple_np(output['node3_score'], {'score':NN_pred[:,3]}, add_sel=(best_score==3))
fill_multiple_np(output['node4_score'], {'score':NN_pred[:,4]}, add_sel=(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
labels = {'topW_v3': 0, 'TTW':1, 'TTZ': 2, 'TTH': 3, 'ttbar': 4, 'rare':5, 'diboson':6} # these should be all?
if dataset in labels:
label_mult = labels[dataset]
else:
label_mult = 7 # data or anything else
if self.dump:
output['label'] += processor.column_accumulator(np.ones(len(ev[out_sel])) * label_mult)
output['SS'] += processor.column_accumulator(ak.to_numpy(BL[out_sel]))
output['OS'] += processor.column_accumulator(ak.to_numpy(cf_est_sel_mc[out_sel]))
output['AR'] += processor.column_accumulator(ak.to_numpy(np_est_sel_mc[out_sel]))
output['LL'] += processor.column_accumulator(ak.to_numpy(LL[out_sel]))
output['weight'] += processor.column_accumulator(ak.to_numpy(weight.weight()[out_sel]))
output['weight_np'] += processor.column_accumulator(ak.to_numpy(weight_np_mc[out_sel]))
output['weight_cf'] += processor.column_accumulator(ak.to_numpy(weight_cf_mc[out_sel]))
# 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)
fill_multiple_np(output['N_jet'], {'multiplicity': ak.num(jet)})
fill_multiple_np(output['N_b'], {'multiplicity': ak.num(btag)})
fill_multiple_np(output['N_central'], {'multiplicity': ak.num(central)})
fill_multiple_np(output['N_ele'], {'multiplicity':ak.num(electron)})
fill_multiple_np(output['N_mu'], {'multiplicity':ak.num(muon)})
fill_multiple_np(output['N_fwd'], {'multiplicity':ak.num(fwd)})
fill_multiple_np(output['ST'], {'ht': st})
fill_multiple_np(output['HT'], {'ht': ht})
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)
fill_multiple_np(output['MET'], {'pt':ev.MET.pt, 'phi':ev.MET.phi})
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
)
fill_multiple_np(
output['lead_lep'],
{
'pt': pad_and_flatten(leading_lepton.p4.pt),
'eta': pad_and_flatten(leading_lepton.eta),
'phi': pad_and_flatten(leading_lepton.phi),
},
)
fill_multiple_np(
output['trail_lep'],
{
'pt': pad_and_flatten(trailing_lepton.p4.pt),
'eta': pad_and_flatten(trailing_lepton.eta),
'phi': pad_and_flatten(trailing_lepton.phi),
},
)
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
)
fill_multiple_np(
output['fwd_jet'],
{
'pt': pad_and_flatten(best_fwd.pt),
'eta': pad_and_flatten(best_fwd.eta),
'phi': pad_and_flatten(best_fwd.phi),
},
)
#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(best_fwd[BL].p), weight = weight_BL)
return output
def normalize(val, cut):
return ak.to_numpy(ak.fill_none(val[cut], np.nan))
def __init__(self, ev, obj, wp, verbose=0, year=2018):
self.obj = obj
self.wp = wp
if self.wp == None:
self.selection_dict = {}
else:
self.selection_dict = obj_def[self.obj][self.wp]
self.v = verbose
#self.year = df['year'][0] ## to be implemented in next verison of babies
self.year = year
if self.obj == "Muon":
# collections are already there, so we just need to calculate missing ones
ev['Muon', 'absMiniIso'] = ev.Muon.miniPFRelIso_all * ev.Muon.pt
ev['Muon', 'ptErrRel'] = ev.Muon.ptErr / ev.Muon.pt
# this is what we are using:
# - jetRelIso if the matched jet is within deltaR<0.4, pfRelIso03_all otherwise
# - btagDeepFlavB discriminator of the matched jet if jet is within deltaR<0.4, 0 otherwise
# (FOR TTH) - pt_cone = 0.9*pt of matched jet if jet is within deltaR<0.4, pt/(pt+iso) otherwise
# (FOR SS) - pt_cone = pt*(1 + max(0,I_m-I_1)) if pt_rel > I_3; max(pt, pt(matched_jet)*I_2) otherwise
#TTH conePt
mask_close = (ak.fill_none(ev.Muon.delta_r(ev.Muon.matched_jet),
99) < 0.4) * 1
mask_far = ~(ak.fill_none(ev.Muon.delta_r(ev.Muon.matched_jet), 99)
< 0.4) * 1
#conePt = 0.9 * ak.fill_none(ev.Muon.matched_jet.pt,0) * mask_close + ev.Muon.pt*(1 + ev.Muon.miniPFRelIso_all)*mask_far
#SS conePt
if (self.year == 2017) or (self.year == 2018):
I_1 = 0.11
I_2 = 0.74
I_3 = 6.8
elif (self.year == 2016):
I_1 = 0.16
I_2 = 0.76
I_3 = 7.2
PF_unflatten = ak.from_regular(
ev.Muon.miniPFRelIso_all[:, :, np.newaxis])
max_miniIso = ak.max(
ak.concatenate(
[PF_unflatten - I_1,
ak.zeros_like(PF_unflatten)], axis=2),
axis=2) #equivalent to max(0, ev.Muon.miniPFRelIso_all - I_1)
muon_pt_unflatten = ak.from_regular(ev.Muon.pt[:, :, np.newaxis])
jet_pt_unflatten = ak.from_regular(
ev.Muon.matched_jet.pt[:, :, np.newaxis])
max_pt = ak.max(
ak.concatenate([muon_pt_unflatten, jet_pt_unflatten * I_2],
axis=2),
axis=2) #max(ev.Muon.pt, ev.Muon.matched_jet.pt * I_2)
conePt = (ev.Muon.pt *
(1 + max_miniIso)) * (ev.Muon.jetPtRelv2 > I_3) + (
max_pt * ~(ev.Muon.jetPtRelv2 > I_3))
deepJet = ak.fill_none(ev.Muon.matched_jet.btagDeepFlavB,
0) * mask_close
jetRelIsoV2 = ev.Muon.jetRelIso * mask_close + ev.Muon.pfRelIso03_all * mask_far # default to 0 if no match
ev['Muon', 'deepJet'] = ak.copy(deepJet)
ev['Muon', 'jetRelIsoV2'] = jetRelIsoV2
ev['Muon', 'conePt'] = conePt
ev['Muon', 'jetRelIso'] = ev.Muon.jetRelIso
ev['Muon', 'jetPtRelv2'] = ev.Muon.jetPtRelv2
ev['Muon', 'boolFCNCIso'] = self.getFCNCIsolation(
ev.Muon.jetRelIso, ev.Muon.jetPtRelv2, I_2,
I_3) & (ev.Muon.miniPFRelIso_all < I_1)
ev['Muon', 'boolFCNCfake'] = (ev.Muon.genPartFlav !=
1) & (ev.Muon.genPartFlav != 15)
self.cand = ev.Muon
elif self.obj == "Electron":
# calculate new variables. asignment is awkward, but what can you do.
ev['Electron',
'absMiniIso'] = ev.Electron.miniPFRelIso_all * ev.Electron.pt
ev['Electron', 'etaSC'] = ev.Electron.eta + ev.Electron.deltaEtaSC
# the following line is only needed if we do our own matching.
# right now, we keep using the NanoAOD match, but check the deltaR distance
# jet_index, mask_match, mask_nomatch = self.matchJets(ev.Electron, ev.Jet)
# this is what we are using:
# - jetRelIso if the matched jet is within deltaR<0.4, pfRelIso03_all otherwise
# - btagDeepFlavB discriminator of the matched jet if jet is within deltaR<0.4, 0 otherwise
# - pt_cone = 0.9*pt of matched jet if jet is within deltaR<0.4, pt/(pt+iso), 0 otherwise
mask_close = (ak.fill_none(
ev.Electron.delta_r(ev.Electron.matched_jet), 99) < 0.4) * 1
mask_far = ~(ak.fill_none(
ev.Electron.delta_r(ev.Electron.matched_jet), 99) < 0.4) * 1
deepJet = ak.fill_none(ev.Electron.matched_jet.btagDeepFlavB,
0) * mask_close
jetRelIsoV2 = ev.Electron.jetRelIso * mask_close + ev.Electron.pfRelIso03_all * mask_far # default to 0 if no match
#TTH conePt
#conePt = 0.9 * ak.fill_none(ev.Electron.matched_jet.pt,0) * mask_close + ev.Electron.pt*(1 + ev.Electron.miniPFRelIso_all)*mask_far
#SS conePt
if (self.year == 2017) or (self.year == 2018):
I_1 = 0.07
I_2 = 0.78
I_3 = 8.0
elif (self.year == 2016):
I_1 = 0.12
I_2 = 0.8
I_3 = 7.2
PF_unflatten = ak.from_regular(
ev.Electron.miniPFRelIso_all[:, :, np.newaxis])
max_miniIso = ak.max(
ak.concatenate(
[PF_unflatten - I_1,
ak.zeros_like(PF_unflatten)], axis=2),
axis=2) #equivalent to max(0, ev.Muon.miniPFRelIso_all - I_1)
electron_pt_unflatten = ak.from_regular(ev.Electron.pt[:, :,
np.newaxis])
jet_pt_unflatten = ak.from_regular(
ev.Electron.matched_jet.pt[:, :, np.newaxis])
max_pt = ak.max(
ak.concatenate([electron_pt_unflatten, jet_pt_unflatten * I_2],
axis=2),
axis=2) #max(ev.Muon.pt, ev.Muon.matched_jet.pt * I_2)
conePt = (ev.Electron.pt *
(1 + max_miniIso)) * (ev.Electron.jetPtRelv2 > I_3) + (
max_pt * ~(ev.Electron.jetPtRelv2 > I_3))
ev['Electron', 'deepJet'] = ak.copy(deepJet)
ev['Electron', 'jetRelIsoV2'] = jetRelIsoV2
ev['Electron', 'conePt'] = conePt
ev['Electron', 'jetRelIso'] = ev.Electron.jetRelIso
ev['Electron', 'jetPtRelv2'] = ev.Electron.jetPtRelv2
ev['Electron', 'boolFCNCIso'] = self.getFCNCIsolation(
ev.Electron.jetRelIso, ev.Electron.jetPtRelv2, I_2,
I_3) & (ev.Electron.miniPFRelIso_all < I_1)
ev['Electron',
'boolFCNCfake'] = (ev.Electron.genPartFlav !=
1) & (ev.Electron.genPartFlav != 15)
self.cand = ev.Electron
self.getSelection()
if self.obj == "Electron" and self.wp == "tight":
self.selection = self.selection & self.getElectronMVAID(
) & self.getIsolation(0.07, 0.78, 8.0) & self.isTriggerSafeNoIso()
if self.v > 0: print(" - custom ID and multi-isolation")
if self.obj == "Muon" and self.wp == "tight":
self.selection = self.selection & self.getIsolation(
0.11, 0.74, 6.8)
if self.v > 0: print(" - custom multi-isolation")
#self.selection = self.selection & ak.fill_none(ev.Muon.matched_jet.btagDeepFlavB<0.2770, True)
#self.selection = self.selection & (ev.Muon.matched_jet.btagDeepFlavB<0.2770)
#if self.v>0: print (" - deepJet")
if self.obj == "Electron" and (self.wp == "tightTTH"
or self.wp == 'fakeableTTH'
or self.wp == "tightSSTTH"
or self.wp == 'fakeableSSTTH'):
self.selection = self.selection & self.getSigmaIEtaIEta()
if self.v > 0: print(" - SigmaIEtaIEta")
#self.selection = self.selection & ak.fill_none(ev.Electron.matched_jet.btagDeepFlavB<0.2770, True)
#self.selection = self.selection & (ev.Electron.matched_jet.btagDeepFlavB<0.2770)
#self.selection = self.selection & (ev.Jet[ev.Electron.jetIdx].btagDeepFlavB<0.2770)
#if self.v>0: print (" - deepJet")
if self.obj == 'Muon' and (self.wp == 'fakeableTTH'
or self.wp == 'fakeableSSTTH'):
self.selection = self.selection & (
self.cand.deepJet < self.getThreshold(self.cand.conePt,
min_pt=20,
max_pt=45,
low=0.2770,
high=0.0494))
if self.v > 0: print(" - interpolated deepJet")
def num(ar):
return ak.num(ak.fill_none(ar[~ak.is_none(ar)], 0), axis=0)
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
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 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()
# 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
out_events["eleTightSF0"] = evaluator["EGamma_SF2D_T"](out_events.eleeta0,
out_events.elept0)
out_events["eleLooseSF1"] = evaluator["EGamma_SF2D_L"](out_events.eleeta1,
out_events.elept1)
out_events["eleTrigSF0"] = evaluator["EGamma_SF2D_Trig"](out_events.eleeta0,
out_events.elept0)
out_events["eleRecoSF0"] = evaluator["EGamma_SF2D_Reco"](out_events.eleeta0,
out_events.elept0)
eleRecoSF1_hi = evaluator["EGamma_SF2D_Reco"](out_events.eleeta1,
out_events.elept1)
eleRecoSF1_lo = evaluator["EGamma_SF2D_Reco_lowpt"](out_events.eleeta1,
out_events.elept1)
eleRecoSF1_hi_ = ak.fill_none(ak.mask(eleRecoSF1_hi, out_events.elept1 > 20.),
0)
eleRecoSF1_lo_ = ak.fill_none(ak.mask(eleRecoSF1_lo, out_events.elept1 > 20.),
0)
out_events["eleRecoSF1"] = eleRecoSF1_hi_ + eleRecoSF1_lo_
bcdef_lumi = 19.554725529
gh_lumi = 16.224846377
total_lumi = bcdef_lumi + gh_lumi
##--------low pt Loose
muonLooseIDSF_lowpt1 = (
(bcdef_lumi * evaluator["muon_lowpt_BCDEF_LooseID"]
(out_events.mupt1, abs(out_events.mueta1))) +
(gh_lumi * evaluator["muon_lowpt_GH_LooseID"]
(out_events.mupt1, abs(out_events.mueta1)))) / total_lumi
