def process(self, df):
output = self.accumulator.identity()
dataset = df['dataset']
## construct weights ##
wgts = processor.Weights(df.size)
if self.data_type != 'data':
wgts.add('genw', df['weight'])
npv = df['trueInteractionNum']
wgts.add('pileup', *(f(npv) for f in self.pucorrs))
triggermask = np.logical_or.reduce([df[t] for t in Triggers])
wgts.add('trigger', triggermask)
cosmicpairmask = df['cosmicveto_result']
wgts.add('cosmicveto', cosmicpairmask)
pvmask = df['metfilters_PrimaryVertexFilter']
wgts.add('primaryvtx', pvmask)
weight = wgts.weight()
########################
ak4jets = JaggedCandidateArray.candidatesfromcounts(
df['akjet_ak4PFJetsCHS_p4'],
px=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fX'].content,
py=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fY'].content,
pz=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fZ'].content,
energy=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fT'].content,
jetid=df['akjet_ak4PFJetsCHS_jetid'].content,
)
ak4jets = ak4jets[ak4jets.jetid & (ak4jets.pt > 30) &
(np.abs(ak4jets.eta) < 2.4)]
leptonjets = JaggedCandidateArray.candidatesfromcounts(
df['pfjet_p4'],
px=df['pfjet_p4.fCoordinates.fX'].content,
py=df['pfjet_p4.fCoordinates.fY'].content,
pz=df['pfjet_p4.fCoordinates.fZ'].content,
energy=df['pfjet_p4.fCoordinates.fT'].content,
sumtkpt=df['pfjet_tkPtSum05'].content,
pfiso=df['pfjet_pfIsolationNoPU05'].content,
mintkdist=df['pfjet_pfcands_minTwoTkDist'].content,
)
ljdautype = awkward.fromiter(df['pfjet_pfcand_type'])
npfmu = (ljdautype == 3).sum()
ndsa = (ljdautype == 8).sum()
isegammajet = (npfmu == 0) & (ndsa == 0)
ispfmujet = (npfmu >= 2) & (ndsa == 0)
isdsajet = ndsa > 0
label = isegammajet.astype(int) * 1 + ispfmujet.astype(
int) * 2 + isdsajet.astype(int) * 3
leptonjets.add_attributes(label=label, ndsa=ndsa)
nmu = ((ljdautype == 3) | (ljdautype == 8)).sum()
leptonjets.add_attributes(ismutype=(nmu >= 2), iseltype=(nmu == 0))
ljdaucharge = awkward.fromiter(df['pfjet_pfcand_charge']).sum()
leptonjets.add_attributes(qsum=ljdaucharge)
leptonjets.add_attributes(
isneutral=(leptonjets.iseltype
| (leptonjets.ismutype & (leptonjets.qsum == 0))))
leptonjets.add_attributes(
mucharged=(leptonjets.iseltype
| (leptonjets.ismutype & (leptonjets.qsum != 0))))
ljdsamuSubset = fromNestNestIndexArray(
df['dsamuon_isSubsetFilteredCosmic1Leg'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
leptonjets.add_attributes(nocosmic=(ljdsamuSubset.sum() == 0))
leptonjets = leptonjets[(leptonjets.nocosmic) & (leptonjets.pt > 30) &
(leptonjets.mintkdist < 50)]
## __ twoleptonjets__
twoleptonjets = leptonjets.counts >= 2
dileptonjets = leptonjets[twoleptonjets]
ak4jets = ak4jets[twoleptonjets]
wgt = weight[twoleptonjets]
if dileptonjets.size == 0: return output
lj0 = dileptonjets[dileptonjets.pt.argmax()]
lj1 = dileptonjets[dileptonjets.pt.argsort()[:, 1:2]]
ak4jets = ak4jets[ak4jets.pt > (lj0.pt.flatten())]
ak4jetCounts = (ak4jets.counts > 0).astype(int)
minpfiso = ((lj0.pfiso > lj1.pfiso).astype(int) * lj1.pfiso +
(lj0.pfiso < lj1.pfiso).astype(int) * lj0.pfiso).flatten()
ljneutrality = (
(lj0.isneutral & lj1.isneutral).astype(int) * 1 +
(lj0.mucharged & lj1.mucharged).astype(int) * 2).flatten()
## channel def ##
#### 2mu2e
singleMuljEvents = dileptonjets.ismutype.sum() == 1
muljInLeading2Events = (lj0.ismutype | lj1.ismutype).flatten()
channel_2mu2e = singleMuljEvents & muljInLeading2Events
output['chan-2mu2e'].fill(dataset=dataset,
iso=minpfiso[channel_2mu2e],
val=ak4jetCounts[channel_2mu2e],
weight=wgt[channel_2mu2e])
#### 4mu
doubleMuljEvents = dileptonjets.ismutype.sum() == 2
muljIsLeading2Events = (lj0.ismutype & lj1.ismutype).flatten()
channel_4mu = doubleMuljEvents & muljIsLeading2Events
output['chan-4mu'].fill(dataset=dataset,
iso=minpfiso[channel_4mu],
val=ljneutrality[channel_4mu],
weight=wgt[channel_4mu])
###########
return output
def process(self, df):
output = self.accumulator.identity()
if df.size==0: return output
dataset = df['dataset']
## construct weights ##
wgts = processor.Weights(df.size)
if self.data_type!='data':
wgts.add('genw', df['weight'])
npv = df['trueInteractionNum']
wgts.add('pileup', *(f(npv) for f in self.pucorrs))
triggermask = np.logical_or.reduce([df[t] for t in Triggers])
wgts.add('trigger', triggermask)
cosmicpairmask = df['cosmicveto_result']
wgts.add('cosmicveto', cosmicpairmask)
pvmask = df['metfilters_PrimaryVertexFilter']
wgts.add('primaryvtx', pvmask)
# ...bla bla, other weights goes here
weight = wgts.weight()
########################
ak4jets = JaggedCandidateArray.candidatesfromcounts(
df['akjet_ak4PFJetsCHS_p4'],
px=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fX'].content,
py=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fY'].content,
pz=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fZ'].content,
energy=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fT'].content,
hadfrac=df['akjet_ak4PFJetsCHS_hadronEnergyFraction'].content,
jetid=df['akjet_ak4PFJetsCHS_jetid'].content,
deepcsv=df['hftagscore_DeepCSV_b'].content,
)
deepcsv_tight = np.bitwise_and(ak4jets.deepcsv, 1<<2)==(1<<2)
ak4jets.add_attributes(deepcsvTight=deepcsv_tight)
ak4jets=ak4jets[ak4jets.jetid&(ak4jets.pt>20)&(np.abs(ak4jets.eta)<2.5)]
leptonjets = JaggedCandidateArray.candidatesfromcounts(
df['pfjet_p4'],
px=df['pfjet_p4.fCoordinates.fX'].content,
py=df['pfjet_p4.fCoordinates.fY'].content,
pz=df['pfjet_p4.fCoordinates.fZ'].content,
energy=df['pfjet_p4.fCoordinates.fT'].content,
ncands=df['pfjet_pfcands_n'].content,
)
ljdautype = awkward.fromiter(df['pfjet_pfcand_type'])
npfmu = (ljdautype==3).sum()
ndsa = (ljdautype==8).sum()
isegammajet = (npfmu==0)&(ndsa==0)
ispfmujet = (npfmu>=2)&(ndsa==0)
isdsajet = ndsa>0
label = isegammajet.astype(int)*1+ispfmujet.astype(int)*2+isdsajet.astype(int)*3
leptonjets.add_attributes(label=label)
nmu = ((ljdautype==3)|(ljdautype==8)).sum()
leptonjets.add_attributes(ismutype=(nmu>=2), iseltype=(nmu==0))
ljpfmuId = fromNestNestIndexArray(df['muon_selectors'], awkward.fromiter(df['pfjet_pfcand_pfmuonIdx']))
leptonjets.add_attributes(nloose=(ljpfmuId&(1<<0)==(1<<0)).sum(),
nmedium=(ljpfmuId&(1<<1)==(1<<1)).sum(),
ntight=(ljpfmuId&(1<<3)==(1<<3)).sum(),
nisoloose=(ljpfmuId&(1<<7)==(1<<7)).sum(),
nisomedium=(ljpfmuId&(1<<8)==(1<<8)).sum(),
nisotight=(ljpfmuId&(1<<9)==(1<<9)).sum(),
)
## __ twoleptonjets__
twoleptonjets = leptonjets.counts>=2
dileptonjets = leptonjets[twoleptonjets]
ak4jets = ak4jets[twoleptonjets]
wgt = weight[twoleptonjets]
if dileptonjets.size==0: return output
lj0 = dileptonjets[dileptonjets.pt.argmax()]
lj1 = dileptonjets[dileptonjets.pt.argsort()[:, 1:2]]
## channel def ##
singleMuljEvents = dileptonjets.ismutype.sum()==1
muljInLeading2Events = (lj0.ismutype | lj1.ismutype).flatten()
channel_2mu2e = (singleMuljEvents&muljInLeading2Events).astype(int)*1
doubleMuljEvents = dileptonjets.ismutype.sum()==2
muljIsLeading2Events = (lj0.ismutype & lj1.ismutype).flatten()
channel_4mu = (doubleMuljEvents&muljIsLeading2Events).astype(int)*2
channel_ = channel_2mu2e + channel_4mu
###########
cuts = [
np.ones_like(wgt).astype(bool), # all
(np.abs(lj0.p4.delta_phi(lj1.p4))>np.pi/2).flatten(), # dphi > pi/2
ak4jets.counts<4, # N(jets) < 4
ak4jets[(ak4jets.pt>30)&(np.abs(ak4jets.eta)<2.4)&ak4jets.deepcsvTight].counts==0, # N(tightB)==0
(~channel_2mu2e.astype(bool)) | (channel_2mu2e.astype(bool)&(((lj0.iseltype)&(lj0.pt>40)) | ((lj1.iseltype)&(lj1.pt>40))).flatten() ), # EGMpt0>40
( (lj0.ismutype&(lj0.pt>40)) | ((~lj0.ismutype)&(lj1.ismutype&(lj1.pt>40))) ).flatten(), # Mupt0>40
( (~(channel_==2)) | (channel_==2)&((lj1.pt>30).flatten()) ), # Mupt1>30
]
if self.region == 'CR':
cuts[1] = ~cuts[1]
totcut = np.logical_and.reduce(cuts)
dileptonjets = dileptonjets[totcut]
wgt = wgt[totcut]
channel_ = channel_[totcut]
ljpfmu = dileptonjets[dileptonjets.label==2]
ljpfmuones = ljpfmu.pt.ones_like()
if ljpfmu.size==0: return output
output['nloose'].fill(dataset=dataset, cnt=ljpfmu.nloose.flatten(), weight=(wgt*ljpfmuones).flatten(), channel=(channel_*ljpfmuones).flatten())
output['nmedium'].fill(dataset=dataset, cnt=ljpfmu.nmedium.flatten(), weight=(wgt*ljpfmuones).flatten(), channel=(channel_*ljpfmuones).flatten())
output['ntight'].fill(dataset=dataset, cnt=ljpfmu.ntight.flatten(), weight=(wgt*ljpfmuones).flatten(), channel=(channel_*ljpfmuones).flatten())
output['nisoloose'].fill(dataset=dataset, cnt=ljpfmu.nisoloose.flatten(), weight=(wgt*ljpfmuones).flatten(), channel=(channel_*ljpfmuones).flatten())
output['nisomedium'].fill(dataset=dataset, cnt=ljpfmu.nisomedium.flatten(), weight=(wgt*ljpfmuones).flatten(), channel=(channel_*ljpfmuones).flatten())
output['nisotight'].fill(dataset=dataset, cnt=ljpfmu.nisotight.flatten(), weight=(wgt*ljpfmuones).flatten(), channel=(channel_*ljpfmuones).flatten())
return output
def process(self, df):
output = self.accumulator.identity()
if df.size == 0: return output
dataset = df['dataset']
## construct weights ##
wgts = processor.Weights(df.size)
if self.data_type != 'data':
wgts.add('genw', df['weight'])
npv = df['trueInteractionNum']
wgts.add('pileup', *(f(npv) for f in self.pucorrs))
triggermask = np.logical_or.reduce([df[t] for t in Triggers])
wgts.add('trigger', triggermask)
cosmicpairmask = df['cosmicveto_result']
wgts.add('cosmicveto', cosmicpairmask)
pvmask = df['metfilters_PrimaryVertexFilter']
wgts.add('primaryvtx', pvmask)
# ...bla bla, other weights goes here
weight = wgts.weight()
########################
ak4jets = JaggedCandidateArray.candidatesfromcounts(
df['akjet_ak4PFJetsCHS_p4'],
px=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fX'].content,
py=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fY'].content,
pz=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fZ'].content,
energy=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fT'].content,
jetid=df['akjet_ak4PFJetsCHS_jetid'].content,
muefrac=df['akjet_ak4PFJetsCHS_muonEnergyFraction'].content,
chaemefrac=df['akjet_ak4PFJetsCHS_chaEmEnergyFraction'].content,
emefrac=df['akjet_ak4PFJetsCHS_emEnergyFraction'].content,
hadfrac=df['akjet_ak4PFJetsCHS_hadronEnergyFraction'].content,
chahadfrac=df['akjet_ak4PFJetsCHS_chaHadEnergyFraction'].content,
deepcsv=df['hftagscore_DeepCSV_b'].content,
)
deepcsv_tight = np.bitwise_and(ak4jets.deepcsv, 1 << 2) == (1 << 2)
ak4jets.add_attributes(deepcsvTight=deepcsv_tight, )
ak4jets = ak4jets[ak4jets.jetid & (ak4jets.pt > 30) &
(np.abs(ak4jets.eta) < 2.4)]
leptonjets = JaggedCandidateArray.candidatesfromcounts(
df['pfjet_p4'],
px=df['pfjet_p4.fCoordinates.fX'].content,
py=df['pfjet_p4.fCoordinates.fY'].content,
pz=df['pfjet_p4.fCoordinates.fZ'].content,
energy=df['pfjet_p4.fCoordinates.fT'].content,
vx=df['pfjet_klmvtx.fCoordinates.fX'].content,
vy=df['pfjet_klmvtx.fCoordinates.fY'].content,
vz=df['pfjet_klmvtx.fCoordinates.fZ'].content,
mintkdist=df['pfjet_pfcands_minTwoTkDist'].content,
)
leptonjets.add_attributes(vxy=np.hypot(leptonjets.vx, leptonjets.vy))
ljdautype = awkward.fromiter(df['pfjet_pfcand_type'])
npfmu = (ljdautype == 3).sum()
ndsa = (ljdautype == 8).sum()
isegammajet = (npfmu == 0) & (ndsa == 0)
ispfmujet = (npfmu >= 2) & (ndsa == 0)
isdsajet = ndsa > 0
label = isegammajet.astype(int) * 1 + ispfmujet.astype(
int) * 2 + isdsajet.astype(int) * 3
leptonjets.add_attributes(label=label)
nmu = ((ljdautype == 3) | (ljdautype == 8)).sum()
leptonjets.add_attributes(ismutype=(nmu >= 2), iseltype=(nmu == 0))
ljdaucharge = awkward.fromiter(df['pfjet_pfcand_charge']).sum()
leptonjets.add_attributes(qsum=ljdaucharge)
leptonjets.add_attributes(
isneutral=(leptonjets.iseltype
| (leptonjets.ismutype & (leptonjets.qsum == 0))))
leptonjets.add_attributes(
displaced=((leptonjets.vxy >= 5) |
(np.isnan(leptonjets.vxy) & leptonjets.ismutype)
)) # non-vertex treated as displaced too
ljdsamuSubset = fromNestNestIndexArray(
df['dsamuon_isSubsetFilteredCosmic1Leg'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
leptonjets.add_attributes(nocosmic=(ljdsamuSubset.sum() == 0))
leptonjets = leptonjets[(leptonjets.isneutral) & (leptonjets.nocosmic)
& (leptonjets.pt > 30) &
(leptonjets.mintkdist < 50)]
# mask_ = ak4jets.match(leptonjets, deltaRCut=0.4)
# ak4jets = ak4jets[~mask_]
## __ twoleptonjets__
twoleptonjets = (leptonjets.counts >= 2) & (leptonjets.ismutype.sum()
>= 1)
dileptonjets = leptonjets[twoleptonjets]
ak4jets = ak4jets[twoleptonjets]
wgt = weight[twoleptonjets]
if dileptonjets.size == 0: return output
lj0 = dileptonjets[dileptonjets.pt.argmax()]
lj1 = dileptonjets[dileptonjets.pt.argsort()[:, 1:2]]
## channel def ##
singleMuljEvents = dileptonjets.ismutype.sum() == 1
muljInLeading2Events = (lj0.ismutype | lj1.ismutype).flatten()
channel_2mu2e = (singleMuljEvents
& muljInLeading2Events).astype(int) * 1
doubleMuljEvents = dileptonjets.ismutype.sum() == 2
muljIsLeading2Events = (lj0.ismutype & lj1.ismutype).flatten()
channel_4mu = (doubleMuljEvents & muljIsLeading2Events).astype(int) * 2
channel_ = channel_2mu2e + channel_4mu
###########
ak4jets = ak4jets[ak4jets.pt > (lj0.pt.flatten())]
output['njets'].fill(dataset=dataset,
cnt=ak4jets.counts,
weight=wgt,
channel=channel_)
if ak4jets.flatten().size != 0:
ak4jets = ak4jets[(ak4jets.pt > 30) & (np.abs(ak4jets.eta) < 2.4) &
(ak4jets.deepcsvTight)]
output['ntightb'].fill(dataset=dataset,
cnt=ak4jets.counts,
weight=wgt,
channel=channel_)
return output
def process(self, df):
output = self.accumulator.identity()
dataset = df['dataset']
leptonjets = JaggedCandidateArray.candidatesfromcounts(
df['pfjet_p4'],
px=df['pfjet_p4.fCoordinates.fX'].content,
py=df['pfjet_p4.fCoordinates.fY'].content,
pz=df['pfjet_p4.fCoordinates.fZ'].content,
energy=df['pfjet_p4.fCoordinates.fT'].content,
sumtkpt=df['pfjet_tkPtSum05'].content,
pfiso=df['pfjet_pfIsolationNoPU05'].content,
)
ljdautype = awkward.fromiter(df['pfjet_pfcand_type'])
npfmu = (ljdautype == 3).sum()
ndsa = (ljdautype == 8).sum()
isegammajet = (npfmu == 0) & (ndsa == 0)
ispfmujet = (npfmu >= 2) & (ndsa == 0)
isdsajet = ndsa > 0
label = isegammajet.astype(int) * 1 + ispfmujet.astype(
int) * 2 + isdsajet.astype(int) * 3
leptonjets.add_attributes(label=label)
nmu = ((ljdautype == 3) | (ljdautype == 8)).sum()
leptonjets.add_attributes(ismutype=(nmu >= 2), iseltype=(nmu == 0))
ljdaucharge = awkward.fromiter(df['pfjet_pfcand_charge']).sum()
leptonjets.add_attributes(qsum=ljdaucharge)
leptonjets.add_attributes(
isneutral=(leptonjets.iseltype
| (leptonjets.ismutype & (leptonjets.qsum == 0))))
ljdsamuSubset = fromNestNestIndexArray(
df['dsamuon_isSubsetFilteredCosmic1Leg'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
leptonjets.add_attributes(nocosmic=(ljdsamuSubset.sum() == 0))
leptonjets = leptonjets[(leptonjets.nocosmic) & (leptonjets.isneutral)]
## __ twoleptonjets__
twoleptonjets = leptonjets.counts >= 2
dileptonjets = leptonjets[twoleptonjets]
# wgt = weight[twoleptonjets]
if dileptonjets.size == 0: return output
lj0 = dileptonjets[dileptonjets.pt.argmax()]
lj1 = dileptonjets[dileptonjets.pt.argsort()[:, 1:2]]
## channel def ##
singleMuljEvents = dileptonjets.ismutype.sum() == 1
muljInLeading2Events = (lj0.ismutype | lj1.ismutype).flatten()
channel_2mu2e = (singleMuljEvents
& muljInLeading2Events).astype(int) * 1
doubleMuljEvents = dileptonjets.ismutype.sum() == 2
muljIsLeading2Events = (lj0.ismutype & lj1.ismutype).flatten()
channel_4mu = (doubleMuljEvents & muljIsLeading2Events).astype(int) * 2
channel_ = channel_2mu2e + channel_4mu
###########
ljones = dileptonjets.pt.ones_like()
output['sumpt'].fill(
dataset=dataset,
sumpt=dileptonjets.sumtkpt.flatten(),
)
output['pfiso'].fill(
dataset=dataset,
iso=dileptonjets.pfiso.flatten(),
)
return output
def process(self, df):
output = self.accumulator.identity()
if df.size == 0: return output
dataset = df['dataset']
## construct weights ##
wgts = processor.Weights(df.size)
if self.data_type != 'data':
wgts.add('genw', df['weight'])
npv = df['trueInteractionNum']
wgts.add('pileup', *(f(npv) for f in self.pucorrs))
triggermask = np.logical_or.reduce([df[t] for t in Triggers])
wgts.add('trigger', triggermask)
cosmicpairmask = df['cosmicveto_result']
wgts.add('cosmicveto', cosmicpairmask)
pvmask = df['metfilters_PrimaryVertexFilter']
wgts.add('primaryvtx', pvmask)
# ...bla bla, other weights goes here
weight = wgts.weight()
########################
ak4jets = JaggedCandidateArray.candidatesfromcounts(
df['akjet_ak4PFJetsCHS_p4'],
px=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fX'].content,
py=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fY'].content,
pz=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fZ'].content,
energy=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fT'].content,
jetid=df['akjet_ak4PFJetsCHS_jetid'].content,
deepcsv=df['hftagscore_DeepCSV_b'].content,
)
deepcsv_tight = np.bitwise_and(ak4jets.deepcsv, 1 << 2) == (1 << 2)
ak4jets.add_attributes(deepcsvTight=deepcsv_tight)
ak4jets = ak4jets[ak4jets.jetid & (ak4jets.pt > 20) &
(np.abs(ak4jets.eta) < 2.5)]
leptonjets = JaggedCandidateArray.candidatesfromcounts(
df['pfjet_p4'],
px=df['pfjet_p4.fCoordinates.fX'].content,
py=df['pfjet_p4.fCoordinates.fY'].content,
pz=df['pfjet_p4.fCoordinates.fZ'].content,
energy=df['pfjet_p4.fCoordinates.fT'].content,
lxy=df['pfjet_klmvtx_lxy'].content,
)
ljdautype = awkward.fromiter(df['pfjet_pfcand_type'])
npfmu = (ljdautype == 3).sum()
ndsa = (ljdautype == 8).sum()
isegammajet = (npfmu == 0) & (ndsa == 0)
ispfmujet = (npfmu >= 2) & (ndsa == 0)
isdsajet = ndsa > 0
label = isegammajet.astype(int) * 1 + ispfmujet.astype(
int) * 2 + isdsajet.astype(int) * 3
leptonjets.add_attributes(label=label, ndsa=ndsa)
nmu = ((ljdautype == 3) | (ljdautype == 8)).sum()
leptonjets.add_attributes(ismutype=(nmu >= 2), iseltype=(nmu == 0))
ljdaucharge = awkward.fromiter(df['pfjet_pfcand_charge']).sum()
leptonjets.add_attributes(qsum=ljdaucharge)
leptonjets.add_attributes(
isneutral=(leptonjets.iseltype
| (leptonjets.ismutype & (leptonjets.qsum == 0))))
leptonjets.add_attributes(
displaced=((np.abs(leptonjets.lxy) >= 5) |
(np.isnan(leptonjets.lxy) & leptonjets.ismutype)
)) # non-vertex treated as displaced too
ljdsamuSubset = fromNestNestIndexArray(
df['dsamuon_isSubsetFilteredCosmic1Leg'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
leptonjets.add_attributes(nocosmic=(ljdsamuSubset.sum() == 0))
leptonjets = leptonjets[(leptonjets.nocosmic) & (leptonjets.pt > 30)]
## __twoleptonjets__ AND >=1 displaced
twoleptonjets = (leptonjets.counts >= 2) & (
leptonjets.ismutype.sum() >= 1) & (leptonjets.displaced.sum() >= 1)
dileptonjets = leptonjets[twoleptonjets]
ak4jets = ak4jets[twoleptonjets]
wgt = weight[twoleptonjets]
if dileptonjets.size == 0: return output
lj0 = dileptonjets[dileptonjets.pt.argmax()]
lj1 = dileptonjets[dileptonjets.pt.argsort()[:, 1:2]]
## channel def ##
singleMuljEvents = dileptonjets.ismutype.sum() == 1
muljInLeading2Events = (lj0.ismutype | lj1.ismutype).flatten()
channel_2mu2e = (singleMuljEvents
& muljInLeading2Events).astype(int) * 1
doubleMuljEvents = dileptonjets.ismutype.sum() == 2
muljIsLeading2Events = (lj0.ismutype & lj1.ismutype).flatten()
channel_4mu = (doubleMuljEvents & muljIsLeading2Events).astype(int) * 2
channel_ = channel_2mu2e + channel_4mu
###########
cuts = [
((lj0.isneutral) & (lj1.isneutral)).flatten(), # both 'neutral'
(np.abs(lj0.p4.delta_phi(lj1.p4)) >
np.pi / 2).flatten(), # dphi > pi/2
(~channel_2mu2e.astype(bool)) |
(channel_2mu2e.astype(bool) &
(((lj0.iseltype) & (lj0.pt > 60)) |
((lj1.iseltype) & (lj1.pt > 60))).flatten()), # EGMpt0>60
ak4jets.counts < 3, # N(jets) < 4
ak4jets[(ak4jets.pt > 30) & (np.abs(ak4jets.eta) < 2.4)
& ak4jets.deepcsvTight].counts == 0, # N(tightB)==0
]
if self.region == 'CR':
cuts[1] = ~cuts[1]
if self.enforceNeutral == False:
cuts[0] = ~cuts[0]
for i, c in enumerate(itertools.accumulate(cuts, np.logical_and)):
output['count'].fill(dataset=dataset,
cnt=np.ones_like(wgt[c]) * i,
weight=wgt[c],
channel=channel_[c])
return output
def process(self, df):
output = self.accumulator.identity()
dataset = df['dataset']
## construct weights ##
wgts = processor.Weights(df.size)
if self.data_type != 'data':
wgts.add('genw', df['weight'])
npv = df['trueInteractionNum']
wgts.add('pileup', *(f(npv) for f in self.pucorrs))
triggermask = np.logical_or.reduce([df[t] for t in Triggers])
wgts.add('trigger', triggermask)
cosmicpairmask = df['cosmicveto_result']
wgts.add('cosmicveto', cosmicpairmask)
pvmask = df['metfilters_PrimaryVertexFilter']
wgts.add('primaryvtx', pvmask)
# ...bla bla, other weights goes here
weight = wgts.weight()
########################
leptonjets = JaggedCandidateArray.candidatesfromcounts(
df['pfjet_p4'],
px=df['pfjet_p4.fCoordinates.fX'].content,
py=df['pfjet_p4.fCoordinates.fY'].content,
pz=df['pfjet_p4.fCoordinates.fZ'].content,
energy=df['pfjet_p4.fCoordinates.fT'].content,
sumtkpt=df['pfjet_tkPtSum05'].content,
pfiso=df['pfjet_pfIsolationNoPU05'].content,
isodbeta=df['pfjet_pfiso'].content,
mintkdist=df['pfjet_pfcands_minTwoTkDist'].content,
)
ljdautype = awkward.fromiter(df['pfjet_pfcand_type'])
npfmu = (ljdautype == 3).sum()
ndsa = (ljdautype == 8).sum()
isegammajet = (npfmu == 0) & (ndsa == 0)
ispfmujet = (npfmu >= 2) & (ndsa == 0)
isdsajet = ndsa > 0
label = isegammajet.astype(int) * 1 + ispfmujet.astype(
int) * 2 + isdsajet.astype(int) * 3
leptonjets.add_attributes(label=label)
nmu = ((ljdautype == 3) | (ljdautype == 8)).sum()
leptonjets.add_attributes(ismutype=(nmu >= 2), iseltype=(nmu == 0))
ljdaucharge = awkward.fromiter(df['pfjet_pfcand_charge']).sum()
leptonjets.add_attributes(qsum=ljdaucharge)
leptonjets.add_attributes(
isneutral=(leptonjets.iseltype
| (leptonjets.ismutype & (leptonjets.qsum == 0))))
leptonjets.add_attributes(
mucharged=(leptonjets.iseltype
| (leptonjets.ismutype & (leptonjets.qsum != 0))))
ljdsamuSubset = fromNestNestIndexArray(
df['dsamuon_isSubsetFilteredCosmic1Leg'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
leptonjets.add_attributes(nocosmic=(ljdsamuSubset.sum() == 0))
leptonjets = leptonjets[(leptonjets.nocosmic) & (leptonjets.pt > 30) &
(leptonjets.mintkdist < 50)]
## __ twoleptonjets__
twoleptonjets = leptonjets.counts >= 2
dileptonjets = leptonjets[twoleptonjets]
wgt = weight[twoleptonjets]
if dileptonjets.size == 0: return output
lj0 = dileptonjets[dileptonjets.pt.argmax()]
lj1 = dileptonjets[dileptonjets.pt.argsort()[:, 1:2]]
## channel def ##
singleMuljEvents = dileptonjets.ismutype.sum() == 1
muljInLeading2Events = (lj0.ismutype | lj1.ismutype).flatten()
channel_2mu2e = (singleMuljEvents
& muljInLeading2Events).astype(int) * 1
doubleMuljEvents = dileptonjets.ismutype.sum() == 2
muljIsLeading2Events = (lj0.ismutype & lj1.ismutype).flatten()
channel_4mu = (doubleMuljEvents & muljIsLeading2Events).astype(int) * 2
channel_ = channel_2mu2e + channel_4mu
###########
# isControl = (np.abs(lj0.p4.delta_phi(lj1.p4))<np.pi/2).flatten()
# if self.data_type!='data':
# dileptonjets = dileptonjets[isControl]
# channel_ = channel_[isControl]
# wgt = wgt[isControl]
mask_ = (lj0.isneutral & lj1.isneutral).flatten()
if self.bothNeutral is False:
mask_ = (lj0.mucharged & lj1.mucharged).flatten()
# mask_ = ((channel_==2)&((~lj0.isneutral&(~lj1.isneutral)).flatten())) | ((channel_==1)&mask_)
channel_ = channel_[mask_]
wgt = wgt[mask_]
dileptonjets = dileptonjets[mask_]
minpfiso = (lj0.pfiso > lj1.pfiso).astype(int) * lj1.pfiso + (
lj0.pfiso < lj1.pfiso).astype(int) * lj0.pfiso
output['minpfiso'].fill(dataset=dataset,
iso=minpfiso[mask_].flatten(),
channel=channel_,
weight=wgt)
maxpfiso = (lj0.pfiso > lj1.pfiso).astype(int) * lj0.pfiso + (
lj0.pfiso < lj1.pfiso).astype(int) * lj1.pfiso
output['maxpfiso'].fill(dataset=dataset,
iso=maxpfiso[mask_].flatten(),
channel=channel_,
weight=wgt)
output['lj0pfiso'].fill(dataset=dataset,
iso=lj0.pfiso[mask_].flatten(),
channel=channel_,
weight=wgt)
ljones = dileptonjets.pt.ones_like()
output['sumpt'].fill(dataset=dataset,
sumpt=dileptonjets.sumtkpt.flatten(),
channel=(channel_ * ljones).flatten(),
weight=(wgt * ljones).flatten())
output['pfiso'].fill(dataset=dataset,
iso=dileptonjets.pfiso.flatten(),
channel=(channel_ * ljones).flatten(),
weight=(wgt * ljones).flatten())
output['isodbeta'].fill(dataset=dataset,
iso=dileptonjets.isodbeta.flatten(),
channel=(channel_ * ljones).flatten(),
weight=(wgt * ljones).flatten())
return output
def process(self, df):
output = self.accumulator.identity()
if df.size == 0: return output
dataset = df['dataset']
run = df['run']
lumi = df['lumi']
event = df['event']
## construct weights ##
wgts = processor.Weights(df.size)
if self.data_type != 'data':
wgts.add('genw', df['weight'])
npv = df['trueInteractionNum']
wgts.add('pileup', *(f(npv) for f in self.pucorrs))
triggermask = np.logical_or.reduce([df[t] for t in Triggers])
wgts.add('trigger', triggermask)
cosmicpairmask = df['cosmicveto_result']
wgts.add('cosmicveto', cosmicpairmask)
pvmask = df['metfilters_PrimaryVertexFilter']
wgts.add('primaryvtx', pvmask)
# ...bla bla, other weights goes here
weight = wgts.weight()
########################
ak4jets = JaggedCandidateArray.candidatesfromcounts(
df['akjet_ak4PFJetsCHS_p4'],
px=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fX'].content,
py=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fY'].content,
pz=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fZ'].content,
energy=df['akjet_ak4PFJetsCHS_p4.fCoordinates.fT'].content,
jetid=df['akjet_ak4PFJetsCHS_jetid'].content,
deepcsv=df['hftagscore_DeepCSV_b'].content,
)
deepcsv_tight = np.bitwise_and(ak4jets.deepcsv, 1 << 2) == (1 << 2)
ak4jets.add_attributes(deepcsvTight=deepcsv_tight)
ak4jets = ak4jets[ak4jets.jetid & (ak4jets.pt > 20) &
(np.abs(ak4jets.eta) < 2.5)]
leptonjets = JaggedCandidateArray.candidatesfromcounts(
df['pfjet_p4'],
px=df['pfjet_p4.fCoordinates.fX'].content,
py=df['pfjet_p4.fCoordinates.fY'].content,
pz=df['pfjet_p4.fCoordinates.fZ'].content,
energy=df['pfjet_p4.fCoordinates.fT'].content,
vx=df['pfjet_klmvtx.fCoordinates.fX'].content,
vy=df['pfjet_klmvtx.fCoordinates.fY'].content,
vz=df['pfjet_klmvtx.fCoordinates.fZ'].content,
mintkdist=df['pfjet_pfcands_minTwoTkDist'].content,
)
## attribute: `label, ismutype, iseltype`
ljdautype = awkward.fromiter(df['pfjet_pfcand_type'])
npfmu = (ljdautype == 3).sum()
ndsa = (ljdautype == 8).sum()
isegammajet = (npfmu == 0) & (ndsa == 0)
ispfmujet = (npfmu >= 2) & (ndsa == 0)
isdsajet = ndsa > 0
label = isegammajet.astype(int) * 1 + ispfmujet.astype(
int) * 2 + isdsajet.astype(int) * 3
leptonjets.add_attributes(label=label, ndsa=ndsa)
nmu = ((ljdautype == 3) | (ljdautype == 8)).sum()
leptonjets.add_attributes(ismutype=(nmu >= 2), iseltype=(nmu == 0))
## attribute: `isneutral`
ljdaucharge = awkward.fromiter(df['pfjet_pfcand_charge']).sum()
leptonjets.add_attributes(qsum=ljdaucharge)
leptonjets.add_attributes(
isneutral=(leptonjets.iseltype
| (leptonjets.ismutype & (leptonjets.qsum == 0))))
## attribute: `displaced`
leptonjets.add_attributes(vxy=np.hypot(leptonjets.vx, leptonjets.vy))
leptonjets.add_attributes(
displaced=((leptonjets.vxy >= 5) |
(np.isnan(leptonjets.vxy) & leptonjets.ismutype)
)) # non-vertex treated as displaced too
## attribute: `nocosmic`
ljdsamuFoundOppo = fromNestNestIndexArray(
df['dsamuon_hasOppositeMuon'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
dtcscTime = fromNestNestIndexArray(
df['dsamuon_timeDiffDTCSC'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
rpcTime = fromNestNestIndexArray(
df['dsamuon_timeDiffRPC'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
if len(dtcscTime.flatten().flatten()):
dtcscTime = dtcscTime[ljdsamuFoundOppo]
rpcTime = rpcTime[ljdsamuFoundOppo]
ljdsamuSubset = fromNestNestIndexArray(
df['dsamuon_isSubsetFilteredCosmic1Leg'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
leptonjets.add_attributes(nocosmic=(((dtcscTime < -20).sum() == 0)
& ((rpcTime < -7.5).sum() == 0)
& (ljdsamuSubset.sum() == 0)))
leptonjets = leptonjets[(leptonjets.nocosmic) & (leptonjets.pt > 30) &
(leptonjets.mintkdist < 30)]
## __twoleptonjets__
twoleptonjets = (leptonjets.counts >= 2) & (
leptonjets.ismutype.sum() >= 1) #&(leptonjets.displaced.sum()>=1)
dileptonjets = leptonjets[twoleptonjets]
ak4jets = ak4jets[twoleptonjets]
wgt = weight[twoleptonjets]
run = run[twoleptonjets]
lumi = lumi[twoleptonjets]
event = event[twoleptonjets]
if dileptonjets.size == 0: return output
lj0 = dileptonjets[dileptonjets.pt.argmax()]
lj1 = dileptonjets[dileptonjets.pt.argsort()[:, 1:2]]
## channel def ##
singleMuljEvents = dileptonjets.ismutype.sum() == 1
muljInLeading2Events = (lj0.ismutype | lj1.ismutype).flatten()
channel_2mu2e = (singleMuljEvents
& muljInLeading2Events).astype(int) * 1
doubleMuljEvents = dileptonjets.ismutype.sum() == 2
muljIsLeading2Events = (lj0.ismutype & lj1.ismutype).flatten()
channel_4mu = (doubleMuljEvents & muljIsLeading2Events).astype(int) * 2
channel_ = channel_2mu2e + channel_4mu
###########
# cuts = [
# wgt.astype(bool),
# ((lj0.isneutral)&(lj1.isneutral)).flatten(), # both 'neutral'
# (np.abs(lj0.p4.delta_phi(lj1.p4)) > np.pi / 2).flatten(), # dphi > pi/2
# (~channel_2mu2e.astype(bool)) | (channel_2mu2e.astype(bool)&(((lj0.iseltype)&(lj0.pt>60)) | ((lj1.iseltype)&(lj1.pt>60))).flatten() ), # EGMpt0>60
# ak4jets.counts<4, # N(jets) < 4
# ak4jets[(ak4jets.pt > 30) & (np.abs(ak4jets.eta) < 2.4) & ak4jets.deepcsvTight].counts == 0, # N(tightB)==0
# ]
# if self.data_type == 'data':
# cuts[1] = ~cuts[1]
cuts = [
wgt.astype(bool),
((~lj0.isneutral) & (~lj1.isneutral)).flatten(),
(np.abs(lj0.p4.delta_phi(lj1.p4)) > 2.98).flatten(),
]
totcut = np.logical_and.reduce(cuts)
output['run_1'] += processor.column_accumulator(run[totcut
& (channel_ == 1)])
output['lumi_1'] += processor.column_accumulator(
lumi[totcut & (channel_ == 1)])
output['event_1'] += processor.column_accumulator(
event[totcut & (channel_ == 1)])
output['run_2'] += processor.column_accumulator(run[totcut
& (channel_ == 2)])
output['lumi_2'] += processor.column_accumulator(
lumi[totcut & (channel_ == 2)])
output['event_2'] += processor.column_accumulator(
event[totcut & (channel_ == 2)])
if self.data_type == 'data':
era = np.ones_like(run) * list('ABCD').index(dataset)
output['era_1'] += processor.column_accumulator(
era[totcut & (channel_ == 1)])
output['era_2'] += processor.column_accumulator(
era[totcut & (channel_ == 2)])
return output
def process(self, df):
output = self.accumulator.identity()
dataset = df['dataset']
## construct weights ##
wgts = processor.Weights(df.size)
triggermask = np.logical_or.reduce([df[t] for t in Triggers])
wgts.add('trigger', triggermask)
cosmicpairmask = df['cosmicveto_result']
wgts.add('cosmicveto', cosmicpairmask)
pvmask = df['metfilters_PrimaryVertexFilter']
wgts.add('primaryvtx', pvmask)
weight = wgts.weight()
########################
leptonjets = JaggedCandidateArray.candidatesfromcounts(
df['pfjet_p4'],
px=df['pfjet_p4.fCoordinates.fX'].content,
py=df['pfjet_p4.fCoordinates.fY'].content,
pz=df['pfjet_p4.fCoordinates.fZ'].content,
energy=df['pfjet_p4.fCoordinates.fT'].content,
pfiso=df['pfjet_pfIsolationNoPU05'].content,
mintkdist=df['pfjet_pfcands_minTwoTkDist'].content,
)
ljdautype = awkward.fromiter(df['pfjet_pfcand_type'])
npfmu = (ljdautype == 3).sum()
ndsa = (ljdautype == 8).sum()
isegammajet = (npfmu == 0) & (ndsa == 0)
ispfmujet = (npfmu >= 2) & (ndsa == 0)
isdsajet = ndsa > 0
label = isegammajet.astype(int) * 1 + ispfmujet.astype(
int) * 2 + isdsajet.astype(int) * 3
leptonjets.add_attributes(label=label, ndsa=ndsa)
nmu = ((ljdautype == 3) | (ljdautype == 8)).sum()
leptonjets.add_attributes(ismutype=(nmu >= 2), iseltype=(nmu == 0))
ljdaucharge = awkward.fromiter(df['pfjet_pfcand_charge']).sum()
leptonjets.add_attributes(qsum=ljdaucharge)
leptonjets.add_attributes(
isneutral=(leptonjets.iseltype
| (leptonjets.ismutype & (leptonjets.qsum == 0))))
## attribute: `nocosmic`
ljdsamuFoundOppo = fromNestNestIndexArray(
df['dsamuon_hasOppositeMuon'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
dtcscTime = fromNestNestIndexArray(
df['dsamuon_timeDiffDTCSC'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
rpcTime = fromNestNestIndexArray(
df['dsamuon_timeDiffRPC'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
if len(dtcscTime.flatten().flatten()):
dtcscTime = dtcscTime[ljdsamuFoundOppo]
rpcTime = rpcTime[ljdsamuFoundOppo]
ljdsamuSubset = fromNestNestIndexArray(
df['dsamuon_isSubsetFilteredCosmic1Leg'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
leptonjets.add_attributes(nocosmic=(((dtcscTime < -20).sum() == 0)
& ((rpcTime < -7.5).sum() == 0)
& (ljdsamuSubset.sum() == 0)))
leptonjets = leptonjets[(leptonjets.nocosmic) & (leptonjets.pt > 30) &
(leptonjets.mintkdist < 30)]
## __ twoleptonjets__
twoleptonjets = leptonjets.counts >= 2
dileptonjets = leptonjets[twoleptonjets]
wgt = weight[twoleptonjets]
if dileptonjets.size == 0: return output
lj0 = dileptonjets[dileptonjets.pt.argmax()]
lj1 = dileptonjets[dileptonjets.pt.argsort()[:, 1:2]]
## channel def ##
singleMuljEvents = dileptonjets.ismutype.sum() == 1
muljInLeading2Events = (lj0.ismutype | lj1.ismutype).flatten()
channel_2mu2e = (singleMuljEvents
& muljInLeading2Events).astype(int) * 1
doubleMuljEvents = dileptonjets.ismutype.sum() == 2
muljIsLeading2Events = (lj0.ismutype & lj1.ismutype).flatten()
channel_4mu = doubleMuljEvents & muljIsLeading2Events
###########
dphi = np.abs(lj0.p4.delta_phi(lj1.p4))
if self.category == '00':
channel_4mu = channel_4mu & (
(lj0.isneutral & lj1.isneutral).flatten())
elif self.category == '01':
channel_4mu = channel_4mu & ((lj0.isneutral &
(~lj1.isneutral)).flatten())
elif self.category == '10':
channel_4mu = channel_4mu & ((
(~lj0.isneutral) & lj1.isneutral).flatten())
elif self.category == '11':
channel_4mu = channel_4mu & (((~lj0.isneutral) &
(~lj1.isneutral)).flatten())
dphi = dphi[channel_4mu]
wgt = wgt[channel_4mu]
output['dphi'] += processor.column_accumulator(
dphi[wgt.astype(bool)].flatten())
return output
def process(self, df):
output = self.accumulator.identity()
dataset = df['dataset']
## construct weights ##
wgts = processor.Weights(df.size)
if self.data_type!='data':
wgts.add('genw', df['weight'])
npv = df['trueInteractionNum']
wgts.add('pileup', *(f(npv) for f in self.pucorrs))
triggermask = np.logical_or.reduce([df[t] for t in Triggers])
wgts.add('trigger', triggermask)
cosmicpairmask = df['cosmicveto_result']
wgts.add('cosmicveto', cosmicpairmask)
pvmask = df['metfilters_PrimaryVertexFilter']
wgts.add('primaryvtx', pvmask)
# ...bla bla, other weights goes here
weight = wgts.weight()
########################
leptonjets = JaggedCandidateArray.candidatesfromcounts(
df['pfjet_p4'],
px=df['pfjet_p4.fCoordinates.fX'].content,
py=df['pfjet_p4.fCoordinates.fY'].content,
pz=df['pfjet_p4.fCoordinates.fZ'].content,
energy=df['pfjet_p4.fCoordinates.fT'].content,
mintkdist=df['pfjet_pfcands_minTwoTkDist'].content,
)
ljdautype = awkward.fromiter(df['pfjet_pfcand_type'])
npfmu = (ljdautype==3).sum()
ndsa = (ljdautype==8).sum()
isegammajet = (npfmu==0)&(ndsa==0)
ispfmujet = (npfmu>=2)&(ndsa==0)
isdsajet = ndsa>0
label = isegammajet.astype(int)*1+ispfmujet.astype(int)*2+isdsajet.astype(int)*3
leptonjets.add_attributes(label=label)
nmu = ((ljdautype==3)|(ljdautype==8)).sum()
leptonjets.add_attributes(ismutype=(nmu>=2), iseltype=(nmu==0))
ljdaucharge = awkward.fromiter(df['pfjet_pfcand_charge']).sum()
leptonjets.add_attributes(qsum=ljdaucharge)
leptonjets.add_attributes(isneutral=(leptonjets.iseltype | (leptonjets.ismutype&(leptonjets.qsum==0))))
leptonjets.add_attributes(mucharged=(leptonjets.iseltype | (leptonjets.ismutype&(leptonjets.qsum!=0))))
ljdsamuSubset = fromNestNestIndexArray(df['dsamuon_isSubsetFilteredCosmic1Leg'], awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
leptonjets.add_attributes(nocosmic=(ljdsamuSubset.sum()==0))
leptonjets = leptonjets[(leptonjets.nocosmic)&(leptonjets.pt>30)&(leptonjets.mintkdist<50)]
## __ twoleptonjets__
twoleptonjets = leptonjets.counts>=2
dileptonjets = leptonjets[twoleptonjets]
wgt = weight[twoleptonjets]
if dileptonjets.size==0: return output
lj0 = dileptonjets[dileptonjets.pt.argmax()]
lj1 = dileptonjets[dileptonjets.pt.argsort()[:, 1:2]]
## channel def ##
singleMuljEvents = dileptonjets.ismutype.sum()==1
muljInLeading2Events = (lj0.ismutype | lj1.ismutype).flatten()
channel_2mu2e = (singleMuljEvents&muljInLeading2Events).astype(int)*1
doubleMuljEvents = dileptonjets.ismutype.sum()==2
muljIsLeading2Events = (lj0.ismutype & lj1.ismutype).flatten()
channel_4mu = (doubleMuljEvents&muljIsLeading2Events).astype(int)*2
channel_ = channel_2mu2e + channel_4mu
###########
ljBothNeutral = ((lj0.isneutral)&(lj1.isneutral)).flatten()
ljBothCharged = ((~lj0.isneutral).sum()+(~lj1.isneutral).sum())==((lj0.ismutype).sum()+(lj1.ismutype).sum())
lj4mu0cha = (channel_==2)&((lj0.mucharged&lj1.isneutral).flatten())
lj4mu1cha = (channel_==2)&((lj0.isneutral&(lj1.mucharged)).flatten())
lj4mu0or1cha = (channel_==2)&((lj0.isneutral&(lj1.mucharged)).flatten()|(lj0.mucharged&lj1.isneutral).flatten())
output['dphi-neu'].fill(dataset=dataset, dphi=np.abs(lj0.p4.delta_phi(lj1.p4)[ljBothNeutral].flatten()), channel=channel_[ljBothNeutral], weight=wgt[ljBothNeutral])
output['dphi-cha'].fill(dataset=dataset, dphi=np.abs(lj0.p4.delta_phi(lj1.p4)[ljBothCharged].flatten()), channel=channel_[ljBothCharged], weight=wgt[ljBothCharged])
output['dphi-0mucha'].fill(dataset=dataset, dphi=np.abs(lj0.p4.delta_phi(lj1.p4)[lj4mu0cha].flatten()), channel=channel_[lj4mu0cha], weight=wgt[lj4mu0cha])
output['dphi-1mucha'].fill(dataset=dataset, dphi=np.abs(lj0.p4.delta_phi(lj1.p4)[lj4mu1cha].flatten()), channel=channel_[lj4mu1cha], weight=wgt[lj4mu1cha])
output['dphi-01mucha'].fill(dataset=dataset, dphi=np.abs(lj0.p4.delta_phi(lj1.p4)[lj4mu0or1cha].flatten()), channel=channel_[lj4mu0or1cha], weight=wgt[lj4mu0or1cha])
return output
def process(self, df):
output = self.accumulator.identity()
dataset = df['dataset']
parallelpairs = df['cosmicveto_parallelpairs']
leptonjets = JaggedCandidateArray.candidatesfromcounts(
df['pfjet_p4'],
px=df['pfjet_p4.fCoordinates.fX'].content,
py=df['pfjet_p4.fCoordinates.fY'].content,
pz=df['pfjet_p4.fCoordinates.fZ'].content,
energy=df['pfjet_p4.fCoordinates.fT'].content,
mintkdist=df['pfjet_pfcands_minTwoTkDist'].content,
)
ljdautype = awkward.fromiter(df['pfjet_pfcand_type'])
npfmu = (ljdautype == 3).sum()
ndsa = (ljdautype == 8).sum()
isegammajet = (npfmu == 0) & (ndsa == 0)
ispfmujet = (npfmu >= 2) & (ndsa == 0)
isdsajet = ndsa > 0
label = isegammajet.astype(int) * 1 + ispfmujet.astype(
int) * 2 + isdsajet.astype(int) * 3
leptonjets.add_attributes(label=label)
nmu = ((ljdautype == 3) | (ljdautype == 8)).sum()
leptonjets.add_attributes(ismutype=(nmu >= 2), iseltype=(nmu == 0))
ljdaucharge = awkward.fromiter(df['pfjet_pfcand_charge']).sum()
leptonjets.add_attributes(qsum=ljdaucharge)
leptonjets.add_attributes(
isneutral=(leptonjets.iseltype
| (leptonjets.ismutype & (leptonjets.qsum == 0))))
ljdsamuFoundOppo = fromNestNestIndexArray(
df['dsamuon_hasOppositeMuon'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
dtcscTime = fromNestNestIndexArray(
df['dsamuon_timeDiffDTCSC'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))[ljdsamuFoundOppo]
rpcTime = fromNestNestIndexArray(
df['dsamuon_timeDiffRPC'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))[ljdsamuFoundOppo]
ljdsamuSubset = fromNestNestIndexArray(
df['dsamuon_isSubsetFilteredCosmic1Leg'],
awkward.fromiter(df['pfjet_pfcand_dsamuonIdx']))
leptonjets_ = leptonjets[leptonjets.isneutral]
ljdsamuFoundOppo = ljdsamuFoundOppo[leptonjets.isneutral]
dtcscTime = dtcscTime[leptonjets.isneutral]
rpcTime = rpcTime[leptonjets.isneutral]
ljdsamuSubset = ljdsamuSubset[leptonjets.isneutral]
## __ twoleptonjets__
twoleptonjets = leptonjets_.counts >= 2
dileptonjets = leptonjets_[twoleptonjets]
parallelpairs = parallelpairs[twoleptonjets]
ljdsamuFoundOppo = ljdsamuFoundOppo[twoleptonjets]
dtcscTime = dtcscTime[twoleptonjets]
rpcTime = rpcTime[twoleptonjets]
ljdsamuSubset = ljdsamuSubset[twoleptonjets]
if dileptonjets.size == 0: return output
lj0 = dileptonjets[dileptonjets.pt.argmax()]
lj1 = dileptonjets[dileptonjets.pt.argsort()[:, 1:2]]
## channel def ##
singleMuljEvents = dileptonjets.ismutype.sum() == 1
muljInLeading2Events = (lj0.ismutype | lj1.ismutype).flatten()
channel_2mu2e = (singleMuljEvents
& muljInLeading2Events).astype(int) * 1
doubleMuljEvents = dileptonjets.ismutype.sum() == 2
muljIsLeading2Events = (lj0.ismutype & lj1.ismutype).flatten()
channel_4mu = (doubleMuljEvents & muljIsLeading2Events).astype(int) * 2
channel_ = channel_2mu2e + channel_4mu
###########
output['npairs'].fill(dataset=dataset, num=parallelpairs[channel_ > 0])
output['ljdsaOppo'].fill(
dataset=dataset,
frac=ljdsamuFoundOppo[channel_ > 0].flatten().flatten())
output['dtcscTime'].fill(dataset=dataset,
t=dtcscTime[channel_ > 0].flatten().flatten())
output['rpcTime'].fill(dataset=dataset,
t=rpcTime[channel_ > 0].flatten().flatten())
output['ljdsaSubset'].fill(
dataset=dataset,
frac=ljdsamuSubset[channel_ > 0].flatten().flatten())
output['tkDist'].fill(
dataset=dataset,
dist=dileptonjets.mintkdist[channel_ > 0].flatten())
return output