def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
plots = []
el10 = op.select(tree.Electron, lambda el: el.pt > 10.)
mu10 = op.select(tree.Muon, lambda mu: mu.pt > 10.)
cleanedJets30 = op.select(
tree.Jet, lambda j: op.AND(
j.pt > 30.,
op.NOT(
op.rng_any(el10, lambda el: op.deltaR(j.p4, el.p4) < 0.4)),
op.NOT(
op.rng_any(mu10, lambda mu: op.deltaR(j.p4, mu.p4) < 0.4)))
)
plots.append(
Plot.make1D("sumCleanedJetPt",
op.rng_sum(cleanedJets30, lambda j: j.pt),
noSel,
EqBin(100, 15., 200.),
title="Sum p_{T} (GeV/c)"))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, CutFlowReport, SummedPlot
from bamboo.plots import EquidistantBinning as EqB
from bamboo import treefunctions as op
isMC = self.isMC(sample)
if isMC:
noSel = noSel.refine("mcWeight", weight=[t.genWeight])
noSel = noSel.refine(
"trig",
cut=op.OR(t.HLT.HIL3DoubleMu0,
t.HLT.HIEle20_Ele12_CaloIdL_TrackIdL_IsoVL_DZ))
plots = []
muons = op.select(t.Muon, lambda mu: mu.pt > 20.)
twoMuSel = noSel.refine("twoMuons", cut=[op.rng_len(muons) > 1])
plots.append(
Plot.make1D("dimu_M",
op.invariant_mass(muons[0].p4, muons[1].p4),
twoMuSel,
EqB(100, 20., 120.),
title="Dimuon invariant mass",
plotopts={
"show-overflow": False,
"legend-position": [0.2, 0.6, 0.5, 0.9]
}))
return plots
def reco_4l(leptons, lName, baseSel):
## select events with four leptons, and find the best Z candidate
## shared between 4el and 4mu
has4l = baseSel.refine(f"has4{lName}", cut=[
op.rng_len(leptons) == 4,
op.rng_sum(leptons, lambda l : l.charge) == 0,
])
allZcand = op.combine(leptons, N=2, pred=lambda l1,l2 : l1.charge != l2.charge)
bestZ = op.rng_min_element_by(allZcand, lambda ll : op.abs(op.invariant_mass(ll[0].p4, ll[1].p4)-mZ))
otherLeptons = op.select(leptons, partial(lambda l,oz=None : op.AND(l.idx != oz[0].idx, l.idx != oz[1].idx), oz=bestZ))
return has4l, bestZ, otherLeptons
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, SummedPlot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
plots = []
# The plot is made for each of the different flavour categories (l+/- l-/+ l') and then summed,
# because concatenation of containers is not (yet) supported.
lepColl = {"El": tree.Electron, "Mu": tree.Muon}
mt3lPlots = []
for dlNm, dlCol in lepColl.items():
dilep = op.combine(
dlCol,
N=2,
pred=(lambda l1, l2: op.AND(l1.charge != l2.charge)))
hasDiLep = noSel.refine("hasDilep{0}{0}".format(dlNm),
cut=(op.rng_len(dilep) > 0))
dilepZ = op.rng_min_element_by(
dilep,
fun=lambda ll: op.abs(
op.invariant_mass(ll[0].p4, ll[1].p4) - 91.2))
for tlNm, tlCol in lepColl.items():
if tlCol == dlCol:
hasTriLep = hasDiLep.refine("hasTrilep{0}{0}{1}".format(
dlNm, tlNm),
cut=(op.rng_len(tlCol) > 2))
residLep = op.select(
tlCol, lambda l: op.AND(l.idx != dilepZ[0].idx, l.idx
!= dilepZ[1].idx))
l3 = op.rng_max_element_by(residLep, lambda l: l.pt)
else:
hasTriLep = hasDiLep.refine("hasTriLep{0}{0}{1}".format(
dlNm, tlNm),
cut=(op.rng_len(tlCol) > 0))
l3 = op.rng_max_element_by(tlCol, lambda l: l.pt)
mtPlot = Plot.make1D(
"3lMT_{0}{0}{1}".format(dlNm, tlNm),
op.sqrt(2 * l3.pt * tree.MET.pt *
(1 - op.cos(l3.phi - tree.MET.phi))),
hasTriLep,
EqBin(100, 15., 250.),
title="M_{T} (GeV/c^2)")
mt3lPlots.append(mtPlot)
plots.append(mtPlot)
plots.append(SummedPlot("3lMT", mt3lPlots))
return plots
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
plots = []
centralJets1 = op.select(tree.Jet, lambda j: op.abs(j.eta) < 1.)
plots.append(
Plot.make1D("central1_jetPt",
op.map(centralJets1, lambda j: j.pt),
noSel,
EqBin(100, 15., 60.),
title="Jet p_{T} (GeV/c)"))
return plots
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
plots = []
jets40 = op.select(tree.Jet, lambda j: j.pt > 40)
hasTwoJets40 = noSel.refine("twoJets40", cut=(op.rng_len(jets40) >= 2))
plots.append(
Plot.make1D("twoJets40_MET",
tree.MET.pt,
hasTwoJets40,
EqBin(100, 0., 2000.),
title="MET (GeV)"))
return plots
def plotRecoForGen(sel, gen, reco, lambda_match, name):
n_reco = [
op.select(
reco, lambda r: op.switch(
op.rng_len(gen) > i, lambda_match(gen[i], r), op.c_bool(False))
) for i in range(10)
]
plots = SummedPlot("n_reco_{}_per_gen".format(name), [
Plot.make1D("n_reco_{}_per_gen_{}".format(name, i),
op.rng_len(nre),
sel,
EquidistantBinning(10, 0, 10),
xTitle="N reco {} for gen {}".format(name, i))
for i, nre in enumerate(n_reco)
],
xTitle="N reco {} for each gen".format(name))
return plots
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, SummedPlot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
plots = []
metSel = noSel.refine("MET", cut=(tree.met.et > 30000))
trigSel = metSel.refine("trig", cut=op.OR(tree.trigE, tree.trigM))
goodLeptons = op.select(
tree.lep, lambda l: op.AND(l.isTightID, l.pt > 35000., l.ptcone30 /
l.pt < 0.1, l.etcone20 / l.pt < 0.1))
oneLepSel = trigSel.refine("1goodlep",
cut=(op.rng_len(goodLeptons) == 1))
lep = goodLeptons[0]
signalSel = oneLepSel.refine(
"signalRegion",
cut=op.AND(
op.abs(lep.z0 * op.sin(lep.p4.theta())) < 0.5,
op.multiSwitch(
(lep.type == 11,
op.AND(
op.abs(lep.eta) < 2.46,
op.NOT(op.in_range(1.37, op.abs(lep.eta), 1.52)),
op.abs(lep.trackd0pvunbiased /
lep.tracksigd0pvunbiased) < 5)),
(lep.type == 13,
op.AND(
op.abs(lep.eta) < 2.5,
op.abs(lep.trackd0pvunbiased /
lep.tracksigd0pvunbiased) < 3)),
op.c_bool(False))))
metp4 = makePtEtaPhiEP4(tree.met.et, op.c_float(0.), tree.met.phi,
tree.met.et)
plots.append(
Plot.make1D("mt_w", (lep.p4 + metp4).Mt() / 1000.,
signalSel,
EqBin(40, 60., 180.),
title="m_{T}^{W#rightarrow l#nu} (GeV)",
plotopts={"log-y-axis-range": [25., 2.5e8]}))
return plots
def comp_cosThetaSbetBeamAndHiggs(self, genColl):
genh = op.select(
genColl,
lambda g: op.AND(g.pdgId == 25, g.statusFlags & (0x1 << 13)))
HH_p4 = genh[0].p4 + genh[1].p4
cm = HH_p4.BoostToCM()
boosted_h1 = op.extMethod("ROOT::Math::VectorUtil::boost",
returnType=genh[0].p4._typeName)(genh[0].p4,
cm)
boosted_h2 = op.extMethod("ROOT::Math::VectorUtil::boost",
returnType=genh[1].p4._typeName)(genh[1].p4,
cm)
mHH = op.switch(
op.rng_len(genh) == 2, op.invariant_mass(genh[0].p4, genh[1].p4),
op.c_float(-9999))
cosTheta1 = op.switch(
op.rng_len(genh) == 2, op.abs(boosted_h1.Pz() / boosted_h1.P()),
op.c_float(-9999))
cosTheta2 = op.switch(
op.rng_len(genh) == 2, op.abs(boosted_h1.Pz() / boosted_h2.P()),
op.c_float(-9999))
return [mHH, cosTheta1, cosTheta2]
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
plots = []
muons = op.sort(op.select(tree.Muon, lambda mu : op.AND(
mu.pt > 5,
op.abs(mu.eta) < 2.4,
op.abs(mu.pfRelIso04_all) < 0.40,
op.abs(mu.dxy) < 0.5,
op.abs(mu.dz ) < 1.,
op.sqrt(mu.dxy**2 + mu.dz**2)/op.sqrt(mu.dxyErr**2+mu.dzErr**2) < 4, ## SIP3D
)), lambda mu : -mu.pt)
electrons = op.sort(op.select(tree.Electron, lambda el : op.AND(
el.pt > 7.,
op.abs(el.eta) < 2.5,
op.abs(el.pfRelIso03_all) < 0.40,
op.abs(el.dxy) < 0.5,
op.abs(el.dz ) < 1.,
op.sqrt(el.dxy**2 + el.dz**2)/op.sqrt(el.dxyErr**2+el.dzErr**2) < 4, ## SIP3D
)), lambda el : -el.pt)
plots += self.controlPlots_2l(noSel, muons, electrons)
mZ = 91.1876
def reco_4l(leptons, lName, baseSel):
## select events with four leptons, and find the best Z candidate
## shared between 4el and 4mu
has4l = baseSel.refine(f"has4{lName}", cut=[
op.rng_len(leptons) == 4,
op.rng_sum(leptons, lambda l : l.charge) == 0,
])
allZcand = op.combine(leptons, N=2, pred=lambda l1,l2 : l1.charge != l2.charge)
bestZ = op.rng_min_element_by(allZcand, lambda ll : op.abs(op.invariant_mass(ll[0].p4, ll[1].p4)-mZ))
otherLeptons = op.select(leptons, partial(lambda l,oz=None : op.AND(l.idx != oz[0].idx, l.idx != oz[1].idx), oz=bestZ))
return has4l, bestZ, otherLeptons
## Mixed category: take leading two for each (as in the other implementations
def cuts2lMixed(leptons):
return [ op.rng_len(leptons) == 2,
leptons[0].charge != leptons[1].charge,
leptons[0].pt > 20.,
leptons[1].pt > 10.
]
has4lMixed = noSel.refine(f"has4lMixed", cut=cuts2lMixed(muons)+cuts2lMixed(electrons))
mixed_mElEl = op.invariant_mass(electrons[0].p4, electrons[1].p4)
mixed_mMuMu = op.invariant_mass(muons[0].p4, muons[1].p4)
## two categories: elel closest to Z or mumu closest to Z
has2El2Mu = has4lMixed.refine(f"has2El2Mu", cut=(op.abs(mixed_mElEl-mZ) < op.abs(mixed_mMuMu-mZ)))
has2Mu2El = has4lMixed.refine(f"has2Mu2El", cut=(op.abs(mixed_mElEl-mZ) > op.abs(mixed_mMuMu-mZ)))
mH_cats = []
for catNm, (has4l, bestZ, otherZ) in {
"4Mu" : reco_4l(muons , "Mu", noSel),
"4El" : reco_4l(electrons, "El", noSel),
"2El2Mu" : (has2El2Mu, electrons, muons),
"2Mu2El" : (has2Mu2El, muons, electrons)
}.items():
bestZp4 = bestZ[0].p4 + bestZ[1].p4
otherZp4 = otherZ[0].p4 + otherZ[1].p4
hasZZ = has4l.refine(f"{has4l.name}ZZ", cut=[
op.deltaR(bestZ[0].p4 , bestZ[1].p4 ) > 0.02,
op.deltaR(otherZ[0].p4, otherZ[1].p4) > 0.02,
op.in_range(40., bestZp4.M(), 120.),
op.in_range(12., otherZp4.M(), 120.)
])
plots += self.controlPlots_4l(hasZZ, bestZ, otherZ)
m4l = (bestZ[0].p4+bestZ[1].p4+otherZ[0].p4+otherZ[1].p4).M()
hasZZ_m4l70 = hasZZ.refine(f"{hasZZ.name}m4l70", cut=(m4l > 70.))
p_mH = Plot.make1D(f"H_mass_{catNm}", m4l, hasZZ_m4l70, EqBin(36, 70., 180.),
plotopts={"show-overflow": False, "log-y": False, "y-axis-range": [0., 18.]})
mH_cats.append(p_mH)
plots.append(p_mH)
plots.append(SummedPlot("H_mass", mH_cats))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, CutFlowReport, EquidistantBinning, VariableBinning
from bamboo import treefunctions as op
plots = []
# definitions
electrons = op.select(
t.elec, lambda el: op.AND(el.pt > 20.,
op.abs(el.eta) < 2.5))
muons = op.select(t.muon, lambda mu: op.AND(mu.pt > 20.,
op.abs(mu.eta) < 2.5))
cleanedElectrons = op.select(
electrons, lambda el: op.NOT(
op.rng_any(muons, lambda mu: op.deltaR(el.p4, mu.p4) < 0.3)))
# we are taking the second isopass to be on which is equal to the medium working point
isolatedElectrons = op.select(cleanedElectrons,
lambda el: el.isopass & (1 << 2))
identifiedElectrons = op.select(isolatedElectrons,
lambda el: el.idpass & (1 << 2))
cleanedMuons = op.select(
muons, lambda mu: op.NOT(
op.rng_any(electrons, lambda el: op.deltaR(mu.p4, el.p4) < 0.3)
))
isolatedMuons = op.select(cleanedMuons,
lambda mu: mu.isopass & (1 << 2))
identifiedMuons = op.select(isolatedMuons,
lambda mu: mu.idpass & (1 << 2))
cleanedJets = op.select(
t.jetpuppi, lambda j: op.AND(
op.NOT(
op.rng_any(identifiedElectrons, lambda el: op.deltaR(
el.p4, j.p4) < 0.3)),
op.NOT(
op.rng_any(identifiedMuons, lambda mu: op.deltaR(
mu.p4, j.p4) < 0.3))))
cleanedGoodJets30_0to1_5 = op.select(
cleanedJets, lambda j: op.AND(j.pt > 30,
op.abs(j.eta) < 1.5))
cleanedGoodJets30_1_5to3 = op.select(
cleanedJets, lambda j: op.AND(
j.pt > 30,
op.NOT(op.AND(op.abs(j.eta) < 1.5,
op.abs(j.eta) > 3))))
cleanedGoodJets30_3toInf = op.select(
cleanedJets, lambda j: op.AND(j.pt > 30,
op.abs(j.eta) > 3))
cleanedGoodJets50_0to1_5 = op.select(
cleanedJets, lambda j: op.AND(j.pt > 50,
op.abs(j.eta) < 1.5))
cleanedGoodJets50_1_5to3 = op.select(
cleanedJets, lambda j: op.AND(
j.pt > 50,
op.NOT(op.AND(op.abs(j.eta) < 1.5,
op.abs(j.eta) > 3))))
cleanedGoodJets50_3toInf = op.select(
cleanedJets, lambda j: op.AND(j.pt > 50,
op.abs(j.eta) > 3))
cleanedGoodJets100_0to1_5 = op.select(
cleanedJets, lambda j: op.AND(j.pt > 100,
op.abs(j.eta) < 1.5))
cleanedGoodJets100_1_5to3 = op.select(
cleanedJets, lambda j: op.AND(
j.pt > 100,
op.NOT(op.AND(op.abs(j.eta) < 1.5,
op.abs(j.eta) > 3))))
cleanedGoodJets100_3toInf = op.select(
cleanedJets, lambda j: op.AND(j.pt > 100,
op.abs(j.eta) > 3))
cleanedGoodJets30 = op.select(cleanedJets, lambda j: j.pt > 30)
cleanedGoodJets50 = op.select(cleanedJets, lambda j: j.pt > 50)
cleanedGoodJets100 = op.select(cleanedJets, lambda j: j.pt > 100)
met = op.select(t.metpuppi)
sel1 = noSel.refine("nJet30", cut=[op.rng_len(cleanedGoodJets30) > 0])
sel2 = noSel.refine("nJet50", cut=[op.rng_len(cleanedGoodJets50) > 0])
sel3 = noSel.refine("nJet100",
cut=[op.rng_len(cleanedGoodJets100) > 0])
sel1_1 = noSel.refine("nJet30_1",
cut=[op.rng_len(cleanedGoodJets30) > 0])
sel1_1_1 = noSel.refine("nJet30_1_1",
cut=[op.rng_len(cleanedGoodJets30_0to1_5) > 0])
sel1_1_2 = noSel.refine("nJet30_1_2",
cut=[op.rng_len(cleanedGoodJets30_1_5to3) > 0])
sel1_1_3 = noSel.refine("nJet30_1_3",
cut=[op.rng_len(cleanedGoodJets30_3toInf) > 0])
sel2_1 = noSel.refine("nJet50_1",
cut=[op.rng_len(cleanedGoodJets50) > 0])
sel1_2_1 = noSel.refine("nJet50_1_1",
cut=[op.rng_len(cleanedGoodJets50_0to1_5) > 0])
sel1_2_2 = noSel.refine("nJet50_1_2",
cut=[op.rng_len(cleanedGoodJets50_1_5to3) > 0])
sel1_2_3 = noSel.refine("nJet50_1_3",
cut=[op.rng_len(cleanedGoodJets50_3toInf) > 0])
sel3_1 = noSel.refine("nJet100_1",
cut=[op.rng_len(cleanedGoodJets100) > 0])
sel1_3_1 = noSel.refine(
"nJet100_1_1", cut=[op.rng_len(cleanedGoodJets100_0to1_5) > 0])
sel1_3_2 = noSel.refine(
"nJet100_1_2", cut=[op.rng_len(cleanedGoodJets100_1_5to3) > 0])
sel1_3_3 = noSel.refine(
"nJet100_1_3", cut=[op.rng_len(cleanedGoodJets100_3toInf) > 0])
sel1_2 = noSel.refine("nJet30_2",
cut=[op.rng_len(cleanedGoodJets30) > 1])
sel2_1_1 = noSel.refine("nJet30_2_1",
cut=[op.rng_len(cleanedGoodJets30_0to1_5) > 1])
sel2_1_2 = noSel.refine("nJet30_2_2",
cut=[op.rng_len(cleanedGoodJets30_1_5to3) > 1])
sel2_1_3 = noSel.refine("nJet30_2_3",
cut=[op.rng_len(cleanedGoodJets30_3toInf) > 1])
sel2_2 = noSel.refine("nJet50_2",
cut=[op.rng_len(cleanedGoodJets50) > 1])
sel2_2_1 = noSel.refine("nJet50_2_1",
cut=[op.rng_len(cleanedGoodJets50_0to1_5) > 1])
sel2_2_2 = noSel.refine("nJet50_2_2",
cut=[op.rng_len(cleanedGoodJets50_1_5to3) > 1])
sel2_2_3 = noSel.refine("nJet50_2_3",
cut=[op.rng_len(cleanedGoodJets50_3toInf) > 1])
sel3_2 = noSel.refine("nJet100_2",
cut=[op.rng_len(cleanedGoodJets100) > 1])
sel2_3_1 = noSel.refine(
"nJet100_2_1", cut=[op.rng_len(cleanedGoodJets100_0to1_5) > 1])
sel2_3_2 = noSel.refine(
"nJet100_2_2", cut=[op.rng_len(cleanedGoodJets100_1_5to3) > 1])
sel2_3_3 = noSel.refine(
"nJet100_2_3", cut=[op.rng_len(cleanedGoodJets100_3toInf) > 1])
sel1_3 = noSel.refine("nJet30_3",
cut=[op.rng_len(cleanedGoodJets30) > 2])
sel3_1_1 = noSel.refine("nJet30_3_1",
cut=[op.rng_len(cleanedGoodJets30_0to1_5) > 2])
sel3_1_2 = noSel.refine("nJet30_3_2",
cut=[op.rng_len(cleanedGoodJets30_1_5to3) > 2])
sel3_1_3 = noSel.refine("nJet30_3_3",
cut=[op.rng_len(cleanedGoodJets30_3toInf) > 2])
sel2_3 = noSel.refine("nJet50_3",
cut=[op.rng_len(cleanedGoodJets50) > 2])
sel3_2_1 = noSel.refine("nJet50_3_1",
cut=[op.rng_len(cleanedGoodJets50_0to1_5) > 2])
sel3_2_2 = noSel.refine("nJet50_3_2",
cut=[op.rng_len(cleanedGoodJets50_1_5to3) > 2])
sel3_2_3 = noSel.refine("nJet50_3_3",
cut=[op.rng_len(cleanedGoodJets50_3toInf) > 2])
sel3_3 = noSel.refine("nJet100_3",
cut=[op.rng_len(cleanedGoodJets100) > 2])
sel3_3_1 = noSel.refine(
"nJet100_3_1", cut=[op.rng_len(cleanedGoodJets100_0to1_5) > 2])
sel3_3_2 = noSel.refine(
"nJet100_3_2", cut=[op.rng_len(cleanedGoodJets100_1_5to3) > 2])
sel3_3_3 = noSel.refine(
"nJet100_3_3", cut=[op.rng_len(cleanedGoodJets100_3toInf) > 2])
sel1_4 = noSel.refine("nJet30_4",
cut=[op.rng_len(cleanedGoodJets30) > 3])
sel4_1_1 = noSel.refine("nJet30_4_1",
cut=[op.rng_len(cleanedGoodJets30_0to1_5) > 3])
sel4_1_2 = noSel.refine("nJet30_4_2",
cut=[op.rng_len(cleanedGoodJets30_1_5to3) > 3])
sel4_1_3 = noSel.refine("nJet30_4_3",
cut=[op.rng_len(cleanedGoodJets30_3toInf) > 3])
sel2_4 = noSel.refine("nJet50_4",
cut=[op.rng_len(cleanedGoodJets50) > 3])
sel4_2_1 = noSel.refine("nJet50_4_1",
cut=[op.rng_len(cleanedGoodJets50_0to1_5) > 3])
sel4_2_2 = noSel.refine("nJet50_4_2",
cut=[op.rng_len(cleanedGoodJets50_1_5to3) > 3])
sel4_2_3 = noSel.refine("nJet50_4_3",
cut=[op.rng_len(cleanedGoodJets50_3toInf) > 3])
sel3_4 = noSel.refine("nJet100_4",
cut=[op.rng_len(cleanedGoodJets100) > 3])
sel4_3_1 = noSel.refine(
"nJet100_4_1", cut=[op.rng_len(cleanedGoodJets100_0to1_5) > 3])
sel4_3_2 = noSel.refine(
"nJet100_4_2", cut=[op.rng_len(cleanedGoodJets100_1_5to3) > 3])
sel4_3_3 = noSel.refine(
"nJet100_4_3", cut=[op.rng_len(cleanedGoodJets100_3toInf) > 3])
# plots
# # ### 30 GeV
plots.append(
Plot.make1D("nJets_jetPT_30GeV",
op.rng_len(cleanedGoodJets30),
noSel,
EquidistantBinning(15, 0., 15.),
title="nJets (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet1_pT_jetPT_30GeV",
cleanedGoodJets30[0].pt,
sel1_1,
EquidistantBinning(50, 0., 4000.),
title="Jet1_pT (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet1_eta_jetPT_30GeV",
cleanedGoodJets30[0].eta,
sel1_1,
EquidistantBinning(30, -3, 3),
title="Jet1_eta (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet1_pT_0to1_5_jetPT_30GeV",
cleanedGoodJets30_0to1_5[0].pt,
sel1_1_1,
EquidistantBinning(50, 0, 4000),
title="Jet1_pT 0 < eta < 1.5, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet1_pT_1_5to3_jetPT_30GeV",
cleanedGoodJets30_1_5to3[0].pt,
sel1_1_2,
EquidistantBinning(50, 0, 4000),
title="Jet1_pT 1.5 < eta < 3, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet1_pT_3toInf_jetPT_30GeV",
cleanedGoodJets30_3toInf[0].pt,
sel1_1_3,
EquidistantBinning(50, 0, 1000),
title="Jet1_pT eta > 3, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet2_pT_jetPT_30GeV",
cleanedGoodJets30[1].pt,
sel1_2,
EquidistantBinning(50, 0., 4000.),
title="Jet2_pT (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet2_eta_jetPT_30GeV",
cleanedGoodJets30[1].eta,
sel1_2,
EquidistantBinning(30, -3, 3),
title="Jet2_eta (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet2_pT_0to1_5_jetPT_30GeV",
cleanedGoodJets30_0to1_5[1].pt,
sel2_1_1,
EquidistantBinning(50, 0, 4000),
title="Jet2_pT 0 < eta < 1.5, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet2_pT_1_5to3_jetPT_30GeV",
cleanedGoodJets30_1_5to3[1].pt,
sel2_1_2,
EquidistantBinning(50, 0, 4000),
title="Jet2_pT 1.5 < eta < 3, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet2_pT_3toInf_jetPT_30GeV",
cleanedGoodJets30_3toInf[1].pt,
sel2_1_3,
EquidistantBinning(50, 0, 500),
title="Jet2_pT eta > 3, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet3_pT_jetPT_30GeV",
cleanedGoodJets30[2].pt,
sel1_3,
EquidistantBinning(50, 0., 2000.),
title="Jet3_pT (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet3_eta_jetPT_30GeV",
cleanedGoodJets30[2].eta,
sel1_3,
EquidistantBinning(30, -3, 3),
title="Jet3_eta (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet3_pT_0to1_5_jetPT_30GeV",
cleanedGoodJets30_0to1_5[2].pt,
sel3_1_1,
EquidistantBinning(50, 0, 2000),
title="Jet3_pT 0 < eta < 1.5, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet3_pT_1_5to3_jetPT_30GeV",
cleanedGoodJets30_1_5to3[2].pt,
sel3_1_2,
EquidistantBinning(50, 0, 2000),
title="Jet3_pT 1.5 < eta < 3, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet3_pT_3toInf_jetPT_30GeV",
cleanedGoodJets30_3toInf[2].pt,
sel3_1_3,
EquidistantBinning(50, 0, 200),
title="Jet3_pT eta > 3, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet4_pT_jetPT_30GeV",
cleanedGoodJets30[3].pt,
sel1_4,
EquidistantBinning(50, 0., 2000.),
title="Jet4_pT (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet4_eta_jetPT_30GeV",
cleanedGoodJets30[3].eta,
sel1_4,
EquidistantBinning(30, -3, 3),
title="Jet4_eta (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet4_pT_0to1_5_jetPT_30GeV",
cleanedGoodJets30_0to1_5[3].pt,
sel4_1_1,
EquidistantBinning(50, 0, 2000),
title="Jet4_pT 0 < eta < 1.5, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet4_pT_1_5to3_jetPT_30GeV",
cleanedGoodJets30_1_5to3[3].pt,
sel4_1_2,
EquidistantBinning(50, 0, 2000),
title="Jet4_pT 1.5 < eta < 3, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet4_pT_3toInf_jetPT_30GeV",
cleanedGoodJets30_3toInf[3].pt,
sel4_1_3,
EquidistantBinning(50, 0, 200),
title="Jet4_pT eta > 3, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("MET_jetPT_30GeV",
met[0].pt,
sel1,
EquidistantBinning(50, 0, 1000),
title="MET (jet p_{T} > 30GeV)"))
# # ### 50 GeV
plots.append(
Plot.make1D("nJets_jetPT_50GeV",
op.rng_len(cleanedGoodJets50),
noSel,
EquidistantBinning(15, 0., 15.),
title="nJets (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet1_pT_jetPT_50GeV",
cleanedGoodJets50[0].pt,
sel2_1,
EquidistantBinning(50, 0., 4000.),
title="Jet1_pT (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet1_eta_jetPT_50GeV",
cleanedGoodJets50[0].eta,
sel2_1,
EquidistantBinning(30, -3, 3),
title="Jet1_eta (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet1_pT_0to1_5_jetPT_50GeV",
cleanedGoodJets50_0to1_5[0].pt,
sel1_2_1,
EquidistantBinning(50, 0, 4000),
title="Jet1_pT 0 < eta < 1.5, (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet1_pT_1_5to3_jetPT_50GeV",
cleanedGoodJets50_1_5to3[0].pt,
sel1_2_2,
EquidistantBinning(50, 0, 4000),
title="Jet1_pT 1.5 < eta < 3, (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet1_pT_3toInf_jetPT_50GeV",
cleanedGoodJets50_3toInf[0].pt,
sel1_2_3,
EquidistantBinning(50, 0, 1000),
title="Jet1_pT eta > 3, (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet2_pT_jetPT_50GeV",
cleanedGoodJets50[1].pt,
sel2_2,
EquidistantBinning(50, 0., 4000.),
title="Jet2_pT (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet2_eta_jetPT_50GeV",
cleanedGoodJets50[1].eta,
sel2_2,
EquidistantBinning(30, -3, 3),
title="Jet2_eta (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet2_pT_0to1_5_jetPT_50GeV",
cleanedGoodJets50_0to1_5[1].pt,
sel2_2_1,
EquidistantBinning(50, 0, 4000),
title="Jet2_pT 0 < eta < 1.5, (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet2_pT_1_5to3_jetPT_50GeV",
cleanedGoodJets50_1_5to3[1].pt,
sel2_2_2,
EquidistantBinning(50, 0, 4000),
title="Jet2_pT 1.5 < eta < 3, (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet2_pT_3toInf_jetPT_50GeV",
cleanedGoodJets50_3toInf[1].pt,
sel2_2_3,
EquidistantBinning(50, 0, 500),
title="Jet2_pT eta > 3, (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet3_pT_jetPT_50GeV",
cleanedGoodJets50[2].pt,
sel2_3,
EquidistantBinning(50, 0., 2000.),
title="Jet3_pT (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet3_eta_jetPT_50GeV",
cleanedGoodJets50[2].eta,
sel2_3,
EquidistantBinning(30, -3, 3),
title="Jet3_eta (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet3_pT_0to1_5_jetPT_50GeV",
cleanedGoodJets50_0to1_5[2].pt,
sel3_2_1,
EquidistantBinning(50, 0, 2000),
title="Jet3_pT 0 < eta < 1.5, (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet3_pT_1_5to3_jetPT_50GeV",
cleanedGoodJets50_1_5to3[2].pt,
sel3_2_2,
EquidistantBinning(50, 0, 2000),
title="Jet3_pT 1.5 < eta < 3, (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet3_pT_3toInf_jetPT_50GeV",
cleanedGoodJets50_3toInf[2].pt,
sel3_2_3,
EquidistantBinning(50, 0, 200),
title="Jet3_pT eta > 3, (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet4_pT_jetPT_50GeV",
cleanedGoodJets50[3].pt,
sel2_4,
EquidistantBinning(50, 0., 2000.),
title="Jet4_pT (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet4_eta_jetPT_50GeV",
cleanedGoodJets50[3].eta,
sel2_4,
EquidistantBinning(30, -3, 3),
title="Jet4_eta (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet4_pT_0to1_5_jetPT_50GeV",
cleanedGoodJets50_0to1_5[3].pt,
sel4_2_1,
EquidistantBinning(50, 0, 2000),
title="Jet4_pT 0 < eta < 1.5, (jet p_{T} > 30GeV)"))
plots.append(
Plot.make1D("Jet4_pT_1_5to3_jetPT_50GeV",
cleanedGoodJets50_1_5to3[3].pt,
sel4_2_2,
EquidistantBinning(50, 0, 2000),
title="Jet4_pT 1.5 < eta < 3, (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("Jet4_pT_3toInf_jetPT_50GeV",
cleanedGoodJets50_3toInf[3].pt,
sel4_2_3,
EquidistantBinning(50, 0, 200),
title="Jet4_pT eta > 3, (jet p_{T} > 50GeV)"))
plots.append(
Plot.make1D("MET_jetPT_50GeV",
met[0].pt,
sel2,
EquidistantBinning(50, 0, 1000),
title="MET (jet p_{T} > 50GeV)"))
# ### 100 GeV
plots.append(
Plot.make1D("nJets_jetPT_100GeV",
op.rng_len(cleanedGoodJets100),
noSel,
EquidistantBinning(15, 0., 15.),
title="nJets (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet1_pT_jetPT_100GeV",
cleanedGoodJets100[0].pt,
sel3_1,
EquidistantBinning(50, 0., 4000.),
title="Jet1_pT (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet1_eta_jetPT_100GeV",
cleanedGoodJets100[0].eta,
sel3_1,
EquidistantBinning(30, -3, 3),
title="Jet1_eta (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet1_pT_0to1_5_jetPT_100GeV",
cleanedGoodJets100_0to1_5[0].pt,
sel1_3_1,
EquidistantBinning(50, 0, 4000),
title="Jet1_pT 0 < eta < 1.5, (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet1_pT_1_5to3_jetPT_100GeV",
cleanedGoodJets100_1_5to3[0].pt,
sel1_3_2,
EquidistantBinning(50, 0, 4000),
title="Jet1_pT 1.5 < eta < 3, (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet1_pT_3toInf_jetPT_100GeV",
cleanedGoodJets100_3toInf[0].pt,
sel1_3_3,
EquidistantBinning(50, 0, 1000),
title="Jet1_pT eta > 3, (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet2_pT_jetPT_100GeV",
cleanedGoodJets100[1].pt,
sel3_2,
EquidistantBinning(50, 0., 4000.),
title="Jet2_pT (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet2_eta_jetPT_100GeV",
cleanedGoodJets100[1].eta,
sel3_2,
EquidistantBinning(30, -3, 3),
title="Jet2_eta (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet2_pT_0to1_5_jetPT_100GeV",
cleanedGoodJets100_0to1_5[1].pt,
sel2_3_1,
EquidistantBinning(50, 0, 4000),
title="Jet2_pT 0 < eta < 1.5, (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet2_pT_1_5to3_jetPT_100GeV",
cleanedGoodJets100_1_5to3[1].pt,
sel2_3_2,
EquidistantBinning(50, 0, 4000),
title="Jet2_pT 1.5 < eta < 3, (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet2_pT_3toInf_jetPT_100GeV",
cleanedGoodJets100_3toInf[1].pt,
sel2_3_3,
EquidistantBinning(50, 0, 500),
title="Jet2_pT eta > 3, (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet3_pT_jetPT_100GeV",
cleanedGoodJets100[2].pt,
sel3_3,
EquidistantBinning(50, 0., 2000.),
title="Jet3_pT (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet3_eta_jetPT_100GeV",
cleanedGoodJets100[2].eta,
sel3_3,
EquidistantBinning(30, -3, 3),
title="Jet3_eta (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet3_pT_0to1_5_jetPT_100GeV",
cleanedGoodJets100_0to1_5[2].pt,
sel3_3_1,
EquidistantBinning(50, 0, 2000),
title="Jet3_pT 0 < eta < 1.5, (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet3_pT_1_5to3_jetPT_100GeV",
cleanedGoodJets100_1_5to3[2].pt,
sel3_3_2,
EquidistantBinning(50, 0, 2000),
title="Jet3_pT 1.5 < eta < 3, (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet3_pT_3toInf_jetPT_100GeV",
cleanedGoodJets100_3toInf[2].pt,
sel3_3_3,
EquidistantBinning(50, 0, 200),
title="Jet3_pT eta > 3, (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet4_pT_jetPT_100GeV",
cleanedGoodJets100[3].pt,
sel3_4,
EquidistantBinning(50, 0., 2000.),
title="Jet4_pT (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet4_eta_jetPT_100GeV",
cleanedGoodJets100[3].eta,
sel3_4,
EquidistantBinning(30, -3, 3),
title="Jet4_eta (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet4_pT_0to1_5_jetPT_100GeV",
cleanedGoodJets100_0to1_5[3].pt,
sel4_3_1,
EquidistantBinning(50, 0, 2000),
title="Jet4_pT 0 < eta < 1.5, (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet4_pT_1_5to3_jetPT_100GeV",
cleanedGoodJets100_1_5to3[3].pt,
sel4_3_2,
EquidistantBinning(50, 0, 2000),
title="Jet4_pT 1.5 < eta < 3, (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("Jet4_pT_3toInf_jetPT_100GeV",
cleanedGoodJets100_3toInf[3].pt,
sel4_3_3,
EquidistantBinning(50, 0, 200),
title="Jet4_pT eta > 3, (jet p_{T} > 100GeV)"))
plots.append(
Plot.make1D("MET_jetPT_100GeV",
met[0].pt,
sel3,
EquidistantBinning(50, 0, 1000),
title="MET (jet p_{T} > 100GeV)"))
# Efficiency Report on terminal and the .tex output
cfr = CutFlowReport("yields")
cfr.add(noSel, "None")
cfr.add(sel1, "30GeV")
cfr.add(sel2, "50GeV")
cfr.add(sel3, "100GeV")
plots.append(cfr)
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
if 'type' not in sampleCfg.keys() or sampleCfg["type"] != "signal":
raise RuntimeError("Sample needs to be HH signal LO GGF sample")
era = sampleCfg.get("era") if sampleCfg else None
# Select gen level Higgs #
genh = op.select(
t.GenPart,
lambda g: op.AND(g.pdgId == 25, g.statusFlags & (0x1 << 13)))
HH_p4 = genh[0].p4 + genh[1].p4
cm = HH_p4.BoostToCM()
boosted_h = op.extMethod("ROOT::Math::VectorUtil::boost",
returnType=genh[0].p4._typeName)(genh[0].p4,
cm)
mHH = op.invariant_mass(genh[0].p4, genh[1].p4)
cosHH = op.abs(boosted_h.Pz() / boosted_h.P())
# Apply reweighting #
benchmarks = [
'BenchmarkSM',
'Benchmark1',
'Benchmark2',
'Benchmark3',
'Benchmark4',
'Benchmark5',
'Benchmark6',
'Benchmark7',
'Benchmark8',
'Benchmark8a',
'Benchmark9',
'Benchmark10',
'Benchmark11',
'Benchmark12',
'BenchmarkcHHH0',
'BenchmarkcHHH1',
'BenchmarkcHHH2p45',
'BenchmarkcHHH5',
'Benchmarkcluster1',
'Benchmarkcluster2',
'Benchmarkcluster3',
'Benchmarkcluster4',
'Benchmarkcluster5',
'Benchmarkcluster6',
'Benchmarkcluster7',
]
selections = {'': noSel}
reweights = {}
if self.args.reweighting:
for benchmark in benchmarks:
json_file = os.path.join(
os.path.abspath(os.path.dirname(__file__)), 'data',
'ScaleFactors_GGF_LO',
'{}_to_{}_{}.json'.format(sample, benchmark, era))
if os.path.exists(json_file):
print("Found file {}".format(json_file))
reweightLO = get_scalefactor("lepton",
json_file,
paramDefs={
'Eta': lambda x: mHH,
'Pt': lambda x: cosHH
})
selections[benchmark] = SelectionWithDataDriven.create(
parent=noSel,
name='noSel' + benchmark,
ddSuffix=benchmark,
cut=op.c_bool(True),
ddCut=op.c_bool(True),
weight=op.c_float(1.),
ddWeight=reweightLO(op.c_float(1.)),
enable=True)
reweights[benchmark] = reweightLO(op.c_float(1.))
else:
print("Could not find file {}".format(json_file))
# Plots #
plots = []
for name, reweight in reweights.items():
plots.append(
Plot.make1D("weight_{}".format(name),
reweight,
noSel,
EquidistantBinning(100, 0, 5.),
xTitle='weight'))
for selName, sel in selections.items():
plots.append(
Plot.make2D(
f"mHHvsCosThetaStar{selName}", [mHH, cosHH],
sel, [
VariableBinning([
250., 270., 290., 310., 330., 350., 370., 390.,
410., 430., 450., 470., 490., 510., 530., 550.,
570., 590., 610., 630., 650., 670., 700., 750.,
800., 850., 900., 950., 1000., 1100., 1200., 1300.,
1400., 1500., 1750., 2000., 5000.
]),
VariableBinning([0.0, 0.4, 0.6, 0.8, 1.0])
],
xTitle='m_{HH}',
yTitle='cos(#theta^{*})'))
plots.append(
Plot.make1D(f"mHH{selName}",
mHH,
sel,
VariableBinning([
250., 270., 290., 310., 330., 350., 370., 390.,
410., 430., 450., 470., 490., 510., 530., 550.,
570., 590., 610., 630., 650., 670., 700., 750.,
800., 850., 900., 950., 1000., 1100., 1200.,
1300., 1400., 1500., 1750., 2000., 5000.
]),
xTitle='m_{HH}'))
plots.append(
Plot.make1D(f"cosThetaStar{selName}",
cosHH,
sel,
VariableBinning([0.0, 0.4, 0.6, 0.8, 1.0]),
xTitle='cos(#theta^{*})'))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
# Some imports #
from bamboo.analysisutils import forceDefine
era = sampleCfg['era']
# Get pile-up configs #
puWeightsFile = None
if era == "2016":
sfTag = "94X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data",
"puweights2016.json")
elif era == "2017":
sfTag = "94X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data",
"puweights2017.json")
elif era == "2018":
sfTag = "102X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data",
"puweights2018.json")
if self.isMC(sample) and puWeightsFile is not None:
from bamboo.analysisutils import makePileupWeight
noSel = noSel.refine("puWeight",
weight=makePileupWeight(puWeightsFile,
t.Pileup_nTrueInt,
systName="pileup"))
isMC = self.isMC(sample)
plots = []
forceDefine(t._Muon.calcProd, noSel)
#############################################################################
################################ Muons #####################################
#############################################################################
# Wp // 2016- 2017 -2018 : Muon_mediumId // https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideMuonIdRun2#Muon_Isolation
muonsByPt = op.sort(t.Muon, lambda mu: -mu.p4.Pt())
muons = op.select(
muonsByPt, lambda mu: op.AND(mu.p4.Pt() > 10.,
op.abs(mu.p4.Eta()) < 2.4, mu.tightId,
mu.pfRelIso04_all < 0.15))
# Scalefactors #
#if era=="2016":
# doubleMuTrigSF = get_scalefactor("dilepton", ("doubleMuLeg_HHMoriond17_2016"), systName="mumutrig")
# muMediumIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "id_medium"), combine="weight", systName="muid")
# muMediumISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "iso_tight_id_medium"), combine="weight", systName="muiso")
# TrkIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "highpt"), combine="weight")
# TrkISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "isotrk_loose_idtrk_tightidandipcut"), combine="weight")
#else:
# muMediumIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "id_medium"), systName="muid")
# muMediumISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "iso_tight_id_medium"), systName="muiso")
#############################################################################
############################# Electrons ###################################
#############################################################################
#Wp // 2016: Electron_cutBased_Sum16==3 -> medium // 2017 -2018 : Electron_cutBased ==3 --> medium ( Fall17_V2)
# asking for electrons to be in the Barrel region with dz<1mm & dxy< 0.5mm // Endcap region dz<2mm & dxy< 0.5mm
electronsByPt = op.sort(t.Electron, lambda ele: -ele.p4.Pt())
electrons = op.select(
electronsByPt,
lambda ele: op.AND(ele.p4.Pt() > 15.,
op.abs(ele.p4.Eta()) < 2.5, ele.cutBased >= 3)
) # //cut-based ID Fall17 V2 the recomended one from POG for the FullRunII
# Scalefactors #
#elMediumIDSF = get_scalefactor("lepton", ("electron_{0}_{1}".format(era,sfTag), "id_medium"), systName="elid")
#doubleEleTrigSF = get_scalefactor("dilepton", ("doubleEleLeg_HHMoriond17_2016"), systName="eleltrig")
#elemuTrigSF = get_scalefactor("dilepton", ("elemuLeg_HHMoriond17_2016"), systName="elmutrig")
#mueleTrigSF = get_scalefactor("dilepton", ("mueleLeg_HHMoriond17_2016"), systName="mueltrig")
OsElEl = op.combine(
electrons,
N=2,
pred=lambda el1, el2: op.AND(el1.charge != el2.charge,
el1.p4.Pt() > 25,
el2.p4.Pt() > 15))
OsMuMu = op.combine(
muons,
N=2,
pred=lambda mu1, mu2: op.AND(mu1.charge != mu2.charge,
mu1.p4.Pt() > 25,
mu2.p4.Pt() > 15))
OsElMu = op.combine((electrons, muons),
pred=lambda el, mu: op.AND(el.charge != mu.charge,
el.p4.Pt() > 25,
mu.p4.Pt() > 15))
OsMuEl = op.combine((electrons, muons),
pred=lambda el, mu: op.AND(el.charge != mu.charge,
el.p4.Pt() > 15,
mu.p4.Pt() > 25))
hasOsElEl = noSel.refine("hasOsElEl", cut=[op.rng_len(OsElEl) >= 1])
hasOsMuMu = noSel.refine("hasOsMuMu", cut=[op.rng_len(OsMuMu) >= 1])
hasOsElMu = noSel.refine("hasOsElMu", cut=[op.rng_len(OsElMu) >= 1])
hasOsMuEl = noSel.refine("hasOsMuEl", cut=[op.rng_len(OsMuEl) >= 1])
plots.append(
Plot.make1D("ElEl_channel",
op.rng_len(OsElEl),
noSel,
EquidistantBinning(10, 0, 10.),
title='Number of dilepton events in ElEl channel',
xTitle='N_{dilepton} (ElEl channel)'))
plots.append(
Plot.make1D("MuMu_channel",
op.rng_len(OsMuMu),
noSel,
EquidistantBinning(10, 0, 10.),
title='Number of dilepton events in MuMu channel',
xTitle='N_{dilepton} (MuMu channel)'))
plots.append(
Plot.make1D("ElMu_channel",
op.rng_len(OsElMu),
noSel,
EquidistantBinning(10, 0, 10.),
title='Number of dilepton events in ElMu channel',
xTitle='N_{dilepton} (ElMu channel)'))
plots.append(
Plot.make1D("MuEl_channel",
op.rng_len(OsMuEl),
noSel,
EquidistantBinning(10, 0, 10.),
title='Number of dilepton events in MuEl channel',
xTitle='N_{dilepton} (MuEl channel)'))
plots += makeDileptonPlots(self,
sel=hasOsElEl,
dilepton=OsElEl[0],
suffix='hasOsdilep',
channel='ElEl')
plots += makeDileptonPlots(self,
sel=hasOsMuMu,
dilepton=OsMuMu[0],
suffix='hasOsdilep',
channel='MuMu')
plots += makeDileptonPlots(self,
sel=hasOsElMu,
dilepton=OsElMu[0],
suffix='hasOsdilep',
channel='ElMu')
plots += makeDileptonPlots(self,
sel=hasOsMuEl,
dilepton=OsMuEl[0],
suffix='hasOsdilep',
channel='MuEl')
# Dilepton Z peak exclusion (charge already done in previous selection) #
lambda_mllLowerband = lambda dilep: op.in_range(
12., op.invariant_mass(dilep[0].p4, dilep[1].p4), 80.)
lambda_mllUpperband = lambda dilep: op.invariant_mass(
dilep[0].p4, dilep[1].p4) > 100.
lambda_mllCut = lambda dilep: op.OR(lambda_mllLowerband(dilep),
lambda_mllUpperband(dilep))
hasOsElElOutZ = hasOsElEl.refine("hasOsElElOutZ",
cut=[lambda_mllCut(OsElEl[0])])
hasOsMuMuOutZ = hasOsMuMu.refine("hasOsMuMuOutZ",
cut=[lambda_mllCut(OsMuMu[0])])
hasOsElMuOutZ = hasOsElMu.refine("hasOsElMuOutZ",
cut=[lambda_mllCut(OsElMu[0])])
hasOsMuElOutZ = hasOsMuEl.refine("hasOsMuElOutZ",
cut=[lambda_mllCut(OsMuEl[0])])
plots += makeDileptonPlots(self,
sel=hasOsElElOutZ,
dilepton=OsElEl[0],
suffix='hasOsdilep_OutZ',
channel='ElEl')
plots += makeDileptonPlots(self,
sel=hasOsMuMuOutZ,
dilepton=OsMuMu[0],
suffix='hasOsdilep_OutZ',
channel='MuMu')
plots += makeDileptonPlots(self,
sel=hasOsElMuOutZ,
dilepton=OsElMu[0],
suffix='hasOsdilep_OutZ',
channel='ElMu')
plots += makeDileptonPlots(self,
sel=hasOsMuElOutZ,
dilepton=OsMuEl[0],
suffix='hasOsdilep_OutZ',
channel='MuEl')
#############################################################################
################################ Jets #####################################
#############################################################################
# select jets // 2016 - 2017 - 2018 ( j.jetId &2) -> tight jet ID
jetsByPt = op.sort(t.Jet, lambda jet: -jet.p4.Pt())
jetsSel = op.select(jetsByPt,
lambda j: op.AND(j.p4.Pt() > 20.,
op.abs(j.p4.Eta()) < 2.4,
(j.jetId & 2))) # Jets = AK4 jets
fatjetsByPt = op.sort(t.FatJet, lambda fatjet: -fatjet.p4.Pt())
fatjetsSel = op.select(
fatjetsByPt, lambda j: op.AND(j.p4.Pt() > 20.,
op.abs(j.p4.Eta()) < 2.4,
(j.jetId & 2))) # FatJets = AK8 jets
# exclude from the jetsSel any jet that happens to include within its reconstruction cone a muon or an electron.
jets = op.select(
jetsSel, lambda j: op.AND(
op.NOT(
op.rng_any(electrons, lambda ele: op.deltaR(j.p4, ele.p4) <
0.3)),
op.NOT(
op.rng_any(muons, lambda mu: op.deltaR(j.p4, mu.p4) < 0.3))
))
fatjets = op.select(
fatjetsSel, lambda j: op.AND(
op.NOT(
op.rng_any(electrons, lambda ele: op.deltaR(j.p4, ele.p4) <
0.3)),
op.NOT(
op.rng_any(muons, lambda mu: op.deltaR(j.p4, mu.p4) < 0.3))
))
# Boosted and resolved jets categories #
if era == "2016": # Must check that subJet exists before looking at the btag
lambda_boosted = lambda fatjet: op.OR(
op.AND(fatjet.subJet1._idx.result != -1, fatjet.subJet1.
btagDeepB > 0.6321),
op.AND(fatjet.subJet2._idx.result != -1, fatjet.subJet2.
btagDeepB > 0.6321))
lambda_resolved = lambda jet: jet.btagDeepB > 0.6321
elif era == "2017":
lambda_boosted = lambda fatjet: op.OR(
op.AND(fatjet.subJet1._idx.result != -1, fatjet.subJet1.
btagDeepB > 0.4941),
op.AND(fatjet.subJet2._idx.result != -1, fatjet.subJet2.
btagDeepB > 0.4941))
lambda_resolved = lambda jet: jet.btagDeepB > 0.4941
elif era == "2018":
lambda_boosted = lambda fatjet: op.OR(
op.AND(fatjet.subJet1._idx.result != -1, fatjet.subJet1.
btagDeepB > 0.4184),
op.AND(fatjet.subJet2._idx.result != -1, fatjet.subJet2.
btagDeepB > 0.4184))
lambda_resolved = lambda jet: jet.btagDeepB > 0.4184
# Select the bjets we want #
bjetsResolved = op.select(jets, lambda_resolved)
bjetsBoosted = op.select(fatjets, lambda_boosted)
# Define the boosted and Resolved (+exclusive) selections #
hasBoostedJets = noSel.refine("hasBoostedJets",
cut=[op.rng_len(bjetsBoosted) >= 1])
hasNotBoostedJets = noSel.refine("hasNotBoostedJets",
cut=[op.rng_len(bjetsBoosted) == 0])
hasResolvedJets = noSel.refine(
"hasResolvedJets",
cut=[op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1])
hasNotResolvedJets = noSel.refine(
"hasNotResolvedJets",
cut=[op.OR(op.rng_len(jets) <= 1,
op.rng_len(bjetsResolved) == 0)])
hasBoostedAndResolvedJets = noSel.refine(
"hasBoostedAndResolvedJets",
cut=[
op.rng_len(bjetsBoosted) >= 1,
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1
])
hasNotBoostedAndResolvedJets = noSel.refine(
"hasNotBoostedAndResolvedJets",
cut=[
op.OR(
op.rng_len(bjetsBoosted) == 0,
op.rng_len(jets) <= 1,
op.rng_len(bjetsResolved) == 0)
])
hasExlusiveResolvedJets = noSel.refine(
"hasExlusiveResolved",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
hasNotExlusiveResolvedJets = noSel.refine(
"hasNotExlusiveResolved",
cut=[
op.OR(
op.OR(
op.rng_len(jets) <= 1,
op.rng_len(bjetsResolved) == 0),
op.AND(
op.rng_len(bjetsBoosted) >= 1,
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1))
])
hasExlusiveBoostedJets = noSel.refine(
"hasExlusiveBoostedJets",
cut=[
op.rng_len(bjetsBoosted) >= 1,
op.OR(op.rng_len(jets) <= 1,
op.rng_len(bjetsResolved) == 0)
])
hasNotExlusiveBoostedJets = noSel.refine(
"hasNotExlusiveBoostedJets",
cut=[
op.OR(
op.rng_len(bjetsBoosted) == 0,
op.AND(
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1))
])
# Counting events from different selections for debugging #
# Passing Boosted selection #
PassedBoosted = Plot.make1D("PassedBoosted",
op.c_int(1),
hasBoostedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Boosted',
xTitle='Passed Boosted')
FailedBoosted = Plot.make1D("FailedBoosted",
op.c_int(0),
hasNotBoostedJets,
EquidistantBinning(2, 0., 2.),
title='Failed Boosted',
xTitle='Failed Boosted')
plots.append(
SummedPlot("BoostedCase", [FailedBoosted, PassedBoosted],
xTitle="Boosted selection"))
# Passing Resolved selection #
PassedResolved = Plot.make1D("PassedResolved",
op.c_int(1),
hasResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Resolved',
xTitle='Passed Resolved')
FailedResolved = Plot.make1D("FailedResolved",
op.c_int(0),
hasNotResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Failed Resolved',
xTitle='Failed Resolved')
plots.append(
SummedPlot("ResolvedCase", [FailedResolved, PassedResolved],
xTitle="Resolved selection"))
# Passing Exclusive Resolved (Resolved AND NOT Boosted) #
PassedExclusiveResolved = Plot.make1D(
"PassedExclusiveResolved",
op.c_int(1),
hasExlusiveResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Exclusive Resolved',
xTitle='Passed Exclusive Resolved')
FailedExclusiveResolved = Plot.make1D(
"FailedExclusiveResolved",
op.c_int(0),
hasNotExlusiveResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Failed Exclusive Resolved',
xTitle='Failed Exclusive Resolved')
plots.append(
SummedPlot("ExclusiveResolvedCase",
[FailedExclusiveResolved, PassedExclusiveResolved],
xTitle="Exclusive Resolved selection"))
# Passing Exclusive Boosted (Boosted AND NOT Resolved) #
PassedExclusiveBoosted = Plot.make1D("PassedExclusiveBoosted",
op.c_int(1),
hasExlusiveBoostedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Exclusive Boosted',
xTitle='Passed Exclusive Boosted')
FailedExclusiveBoosted = Plot.make1D("FailedExclusiveBoosted",
op.c_int(0),
hasNotExlusiveBoostedJets,
EquidistantBinning(2, 0., 2.),
title='Failed Exclusive Boosted',
xTitle='Failed Exclusive Boosted')
plots.append(
SummedPlot("ExclusiveBoostedCase",
[FailedExclusiveBoosted, PassedExclusiveBoosted],
xTitle="Exclusive Boosted selection"))
# Passing Boosted AND Resolved #
PassedBoth = Plot.make1D("PassedBoth",
op.c_int(1),
hasBoostedAndResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Passed Both Boosted and Resolved',
xTitle='Passed Boosted and Resolved')
FailedBoth = Plot.make1D(
"FailedBoth", # Means failed the (Boosted AND Resolved) = either one or the other
op.c_int(0),
hasNotBoostedAndResolvedJets,
EquidistantBinning(2, 0., 2.),
title='Failed combination Boosted and Resolved',
xTitle='Failed combination')
plots.append(
SummedPlot("BoostedAndResolvedCase", [FailedBoth, PassedBoth],
xTitle="Boosted and Resolved selection"))
# Count number of boosted and resolved jets #
plots.append(
Plot.make1D("NBoostedJets",
op.rng_len(bjetsBoosted),
hasBoostedJets,
EquidistantBinning(5, 0., 5.),
title='Number of boosted jets in boosted case',
xTitle='N boosted bjets'))
plots.append(
Plot.make1D(
"NResolvedJets",
op.rng_len(bjetsResolved),
hasExlusiveResolvedJets,
EquidistantBinning(5, 0., 5.),
title='Number of resolved jets in exclusive resolved case',
xTitle='N resolved bjets'))
# Plot number of subjets in the boosted fatjets #
lambda_noSubjet = lambda fatjet: op.AND(
fatjet.subJet1._idx.result == -1,
op.AND(fatjet.subJet2._idx.result == -1))
lambda_oneSubjet = lambda fatjet: op.AND(
fatjet.subJet1._idx.result != -1,
op.AND(fatjet.subJet2._idx.result == -1))
lambda_twoSubjet = lambda fatjet: op.AND(
fatjet.subJet1._idx.result != -1,
op.AND(fatjet.subJet2._idx.result != -1))
hasNoSubjet = hasBoostedJets.refine(
"hasNoSubjet", cut=[lambda_noSubjet(bjetsBoosted[0])])
hasOneSubjet = hasBoostedJets.refine(
"hasOneSubjet", cut=[lambda_oneSubjet(bjetsBoosted[0])])
hasTwoSubjet = hasBoostedJets.refine(
"hasTwoSubjet", cut=[lambda_twoSubjet(bjetsBoosted[0])])
plot_hasNoSubjet = Plot.make1D(
"plot_hasNoSubjet", # Fill bin 0
op.c_int(0),
hasNoSubjet,
EquidistantBinning(3, 0., 3.),
title='Boosted jet without subjet')
plot_hasOneSubjet = Plot.make1D(
"plot_hasOneSubjet", # Fill bin 1
op.c_int(1),
hasOneSubjet,
EquidistantBinning(3, 0., 3.),
title='Boosted jet with one subjet')
plot_hasTwoSubjet = Plot.make1D(
"plot_hasTwoSubjet", # Fill bin 2
op.c_int(2),
hasTwoSubjet,
EquidistantBinning(3, 0., 3.),
title='Boosted jet with two subjets')
plots.append(
SummedPlot(
"NumberOfSubjets",
[plot_hasNoSubjet, plot_hasOneSubjet, plot_hasTwoSubjet],
xTitle="Number of subjets in boosted jet"))
# Plot jets quantities without the dilepton selections #
plots += makeFatJetPlots(self,
sel=hasBoostedJets,
fatjet=bjetsBoosted[0],
suffix="BoostedJets",
channel="NoChannel")
plots += makeJetsPlots(self,
sel=hasResolvedJets,
jets=bjetsResolved,
suffix="ResolvedJets",
channel="NoChannel")
#############################################################################
##################### Jets + Dilepton combination ###########################
#############################################################################
# Combine dilepton and Resolved (Exclusive = NOT Boosted) selections #
hasOsElElOutZResolvedJets = hasOsElElOutZ.refine(
"hasOsElElOutZResolvedJets",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
hasOsMuMuOutZResolvedJets = hasOsMuMuOutZ.refine(
"hasOsMuMuOutZResolvedJets",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
hasOsElMuOutZResolvedJets = hasOsElMuOutZ.refine(
"hasOsElMuOutZResolvedJets",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
hasOsMuElOutZResolvedJets = hasOsMuElOutZ.refine(
"hasOsMuElOutZResolvedJets",
cut=[
op.rng_len(jets) >= 2,
op.rng_len(bjetsResolved) >= 1,
op.rng_len(bjetsBoosted) == 0
])
# Combine dilepton and Boosted selections #
hasOsElElOutZBoostedJets = hasOsElElOutZ.refine(
"hasOsElElOutZBoostedJets", cut=[op.rng_len(bjetsBoosted) >= 1])
hasOsMuMuOutZBoostedJets = hasOsMuMuOutZ.refine(
"hasOsMuMuOutZBoostedJets", cut=[op.rng_len(bjetsBoosted) >= 1])
hasOsElMuOutZBoostedJets = hasOsElMuOutZ.refine(
"hasOsElMuOutZBoostedJets", cut=[op.rng_len(bjetsBoosted) >= 1])
hasOsMuElOutZBoostedJets = hasOsMuElOutZ.refine(
"hasOsMuElOutZBoostedJets", cut=[op.rng_len(bjetsBoosted) >= 1])
# Plot dilepton with OS, Z peak and Resolved jets selections #
plots += makeDileptonPlots(self,
sel=hasOsElElOutZResolvedJets,
dilepton=OsElEl[0],
suffix='hasOsdilep_OutZ_ResolvedJets',
channel='ElEl')
plots += makeDileptonPlots(self,
sel=hasOsMuMuOutZResolvedJets,
dilepton=OsMuMu[0],
suffix='hasOsdilep_OutZ_ResolvedJets',
channel='MuMu')
plots += makeDileptonPlots(self,
sel=hasOsElMuOutZResolvedJets,
dilepton=OsElMu[0],
suffix='hasOsdilep_OutZ_ResolvedJets',
channel='ElMu')
plots += makeDileptonPlots(self,
sel=hasOsMuElOutZResolvedJets,
dilepton=OsMuEl[0],
suffix='hasOsdilep_OutZ_ResolvedJets',
channel='MuEl')
# Plot dilepton with OS dilepton, Z peak and Boosted jets selections #
plots += makeDileptonPlots(self,
sel=hasOsElElOutZBoostedJets,
dilepton=OsElEl[0],
suffix='hasOsdilep_OutZ_BoostedJets',
channel='ElEl')
plots += makeDileptonPlots(self,
sel=hasOsMuMuOutZBoostedJets,
dilepton=OsMuMu[0],
suffix='hasOsdilep_OutZ_BoostedJets',
channel='MuMu')
plots += makeDileptonPlots(self,
sel=hasOsElMuOutZBoostedJets,
dilepton=OsElMu[0],
suffix='hasOsdilep_OutZ_BoostedJets',
channel='ElMu')
plots += makeDileptonPlots(self,
sel=hasOsMuElOutZBoostedJets,
dilepton=OsMuEl[0],
suffix='hasOsdilep_OutZ_BoostedJets',
channel='MuEl')
# Plotting the fatjet for OS dilepton, Z peak and Boosted jets selections #
plots += makeFatJetPlots(self,
sel=hasOsElElOutZBoostedJets,
fatjet=bjetsBoosted[0],
suffix="hasOsdilep_OutZ_BoostedJets",
channel="ElEl")
plots += makeFatJetPlots(self,
sel=hasOsMuMuOutZBoostedJets,
fatjet=bjetsBoosted[0],
suffix="hasOsdilep_OutZ_BoostedJets",
channel="MuMu")
plots += makeFatJetPlots(self,
sel=hasOsElMuOutZBoostedJets,
fatjet=bjetsBoosted[0],
suffix="hasOsdilep_OutZ_BoostedJets",
channel="ElMu")
plots += makeFatJetPlots(self,
sel=hasOsMuElOutZBoostedJets,
fatjet=bjetsBoosted[0],
suffix="hasOsdilep_OutZ_BoostedJets",
channel="MuEl")
# Plotting the jets for OS dilepton, Z peak and Resolved jets selections #
plots += makeJetsPlots(self,
sel=hasOsElElOutZResolvedJets,
jets=bjetsResolved,
suffix="hasOsdilep_OutZ_ResolvedJets",
channel="ElEl")
plots += makeJetsPlots(self,
sel=hasOsMuMuOutZResolvedJets,
jets=bjetsResolved,
suffix="hasOsdilep_OutZ_ResolvedJets",
channel="MuMu")
plots += makeJetsPlots(self,
sel=hasOsElMuOutZResolvedJets,
jets=bjetsResolved,
suffix="hasOsdilep_OutZ_ResolvedJets",
channel="ElMu")
plots += makeJetsPlots(self,
sel=hasOsMuElOutZResolvedJets,
jets=bjetsResolved,
suffix="hasOsdilep_OutZ_ResolvedJets",
channel="MuEl")
## helper selection (OR) to make sure jet calculations are only done once
#hasOSLL = noSel.refine("hasOSLL", cut=op.OR(*( hasOSLL_cmbRng(rng) for rng in osLLRng.values())))
#forceDefine(t._Jet.calcProd, hasOSLL)
#for varNm in t._Jet.available:
# forceDefine(t._Jet[varNm], hasOSLL)
return plots
def createHistos(self):
''' Create all the histos for the analysis '''
### Analysis histos
for key_chan in channel:
ichan = channnel[key_chan]
# Event
self.NewHisto('HT', ichan, 80, 0, 400)
self.NewHisto('MET', ichan, 30, 0, 150)
self.NewHisto('NJets',ichan, 8 ,-0.5, 7.5)
self.NewHisto('Btags',ichan, 4 ,-0.5, 3.5)
self.NewHisto('Vtx', ichan, 10, -0.5, 9.5)
self.NewHisto('NBtagNJets', ichan, 7, -0.5, 6.5)
self.NewHisto('NBtagNJets_3bins', ichan, 3, -0.5, 2.5)
# Leptons
self.NewHisto('Lep0Pt', ichan, 20, 20, 120)
self.NewHisto('Lep1Pt', ichan, 16, 10, 90)
self.NewHisto('Lep0Eta', ichan, 50, -2.5, 2.5)
self.NewHisto('Lep1Eta', ichan, 50, -2.5, 2.5)
self.NewHisto('Lep0Phi', ichan, 20, -1, 1)
self.NewHisto('Lep1Phi', ichan, 20, -1, 1)
self.NewHisto('DilepPt', ichan, 40, 0, 200)
self.NewHisto('InvMass', ichan, 60, 0, 300)
self.NewHisto('DYMass', ichan, 200, 70, 110)
self.NewHisto('DYMassBB', ichan, 200, 70, 110)
self.NewHisto('DYMassBE', ichan, 200, 70, 110)
self.NewHisto('DYMassEB', ichan, 200, 70, 110)
self.NewHisto('DYMassEE', ichan, 200, 70, 110)
self.NewHisto('DeltaPhi', ichan, 20, 0, 1)
if ichan == chan[ch.ElMu]:
self.NewHisto('ElecEta', 'ElMu', 50, -2.5, 2.5)
self.NewHisto('MuonEta', 'ElMu', 50, -2.5, 2.5)
self.NewHisto('ElecPt', 'ElMu', 20, 10, 110)
self.NewHisto('MuonPt', 'ElMu', 20, 10, 110)
self.NewHisto('ElecPhi', 'ElMu', 20, -1, 1)
self.NewHisto('MuonPhi', 'ElMu', 20, -1, 1)
def FillHistograms(self, leptons, jets, pmet, ich, ilev, isys):
''' Fill all the histograms. Take the inputs from lepton list, jet list, pmet '''
if self.SS: return # Do not fill histograms for same-sign events
if not len(leptons) >= 2: return # Just in case
# Re-calculate the observables
lep0 = leptons[0]; lep1 = leptons[1]
l0pt = lep0.Pt(); l1pt = lep1.Pt()
l0eta = lep0.Eta(); l1eta = lep1.Eta()
l0phi = lep0.Phi(); l1phi = lep1.Phi()
dphi = DeltaPhi(lep0, lep1)
mll = InvMass(lep0, lep1)
dipt = DiPt(lep0, lep1)
mupt = 0; elpt = 0
mueta = 0; eleta = 0
muphi = 0; elphi = 0
if ich == channel.ElMu:
if lep0.IsMuon():
mu = lep0
el = lep1
else:
mu = lep1
el = lep0
elpt = el.Pt(); mupt = mu.Pt()
eleta = el.Eta(); mueta = mu.Eta()
elphi = el.Phi(); muphi = mu.Phi()
met = pmet.Pt()
ht = 0;
### Fill the histograms
#if ich == ch.ElMu and ilev == lev.dilepton: print 'Syst = ', isys, ', weight = ', self.weight
self.GetHisto('HT', ich).Fill(ht)
self.GetHisto('MET', ich).Fill(met)
self.GetHisto('NJets',ich).Fill(njet)
self.GetHisto('Btags',ich).Fill(nbtag)
self.GetHisto('Vtx', ich).Fill(self.nvtx)
self.GetHisto("InvMass", ich, ).Fill(mll)
# Leptons
self.GetHisto('Lep0Pt', ich).Fill(l0pt)
self.GetHisto('Lep1Pt', ich).Fill(l1pt)
self.GetHisto('Lep0Eta', ich).Fill(l0eta)
self.GetHisto('Lep1Eta', ich).Fill(l1eta)
self.GetHisto('Lep0Phi', ich).Fill(l0phi/3.141592)
self.GetHisto('Lep1Phi', ich).Fill(l1phi/3.141592)
self.GetHisto('DilepPt', ich).Fill(dipt, self.weight)
self.GetHisto('DeltaPhi', ich).Fill(dphi/3.141592)
self.GetHisto('InvMass', ich).Fill(mll, self.weight)
if mll > 70 and mll < 110:
self.GetHisto('DYMass', ich).Fill(mll)
l0eta = abs(l0eta); l1eta = abs(l1eta)
if ich == chan.ElEl:
if l0eta <= 1.479 and l1eta <= 1.479: self.GetHisto('DYMassBB', ich).Fill(mll)
elif l0eta <= 1.479 and l1eta > 1.479: self.GetHisto('DYMassBE', ich).Fill(mll)
elif l0eta > 1.479 and l1eta <= 1.479: self.GetHisto('DYMassEB', ich).Fill(mll)
elif l0eta > 1.479 and l1eta > 1.479: self.GetHisto('DYMassEE', ich).Fill(mll)
if ich == chan.ElMu:
self.GetHisto('ElecEta', ich).Fill(eleta)
self.GetHisto('ElecPt', ich).Fill(elpt)
self.GetHisto('ElecPhi', ich).Fill(elphi)
self.GetHisto('MuonEta', ich).Fill(mueta)
self.GetHisto('MuonPt', ich).Fill(mupt)
self.GetHisto('MuonPhi', ich).Fill(muphi)
muons = op.select(t.Muon, lambda mu : mu.pt > 20.)
twoMuSel = noSel.refine("twoMuons", cut=[ op.rng_len(muons) > 1 ])
plots.append(Plot.make1D("dimu_M",
op.invariant_mass(muons[0].p4, muons[1].p4), twoMuSel, EqB(100, 20., 120.),
title="Dimuon invariant mass", plotopts={"show-overflow":False,
"legend-position": [0.2, 0.6, 0.5, 0.9]}))
electrons = op.select(t.Electron, lambda el : el.pt > 22.)
twoElSel = noSel.refine("twoElectrons", cut=[ op.rng_len(electrons) > 1 ])
plots.append(Plot.make1D("diel_M",
op.invariant_mass(electrons[0].p4, electrons[1].p4), twoElSel, EqB(100, 20., 120.),
title="Dielectron invariant mass", plotopts={"show-overflow":False,
"legend-position": [0.2, 0.6, 0.5, 0.9]}))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
noSel = super(BtagEffAndMistagNano,
self).prepareObjects(t,
noSel,
sample,
sampleCfg,
channel='DL',
forSkimmer=True)
era = sampleCfg['era']
plots = []
forceDefine(self.tree._Jet.calcProd, noSel) # Jets for configureJets
forceDefine(self.tree._FatJet.calcProd,
noSel) # FatJets for configureJets
forceDefine(
getattr(
self.tree, "_{0}".format(
"MET" if self.era != "2017" else "METFixEE2017")).calcProd,
noSel) # MET for configureMET
# protection against data #
if not self.is_MC:
return []
#############################################################################
# AK4 #
#############################################################################
ak4_truth_lightjets = op.select(self.ak4Jets,
lambda j: j.hadronFlavour == 0)
ak4_truth_cjets = op.select(self.ak4Jets,
lambda j: j.hadronFlavour == 4)
ak4_truth_bjets = op.select(self.ak4Jets,
lambda j: j.hadronFlavour == 5)
N_ak4_truth_lightjets = Plot.make3D(
'N_ak4_truth_lightjets', [
op.map(ak4_truth_lightjets, lambda j: op.abs(j.eta)),
op.map(ak4_truth_lightjets, lambda j: j.pt),
op.map(ak4_truth_lightjets, lambda j: j.btagDeepFlavB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='lightjet #eta',
yTitle='lightjet P_{T}',
zTitle='lightjet Btagging score')
N_ak4_truth_cjets = Plot.make3D('N_ak4_truth_cjets', [
op.map(ak4_truth_cjets, lambda j: op.abs(j.eta)),
op.map(ak4_truth_cjets, lambda j: j.pt),
op.map(ak4_truth_cjets, lambda j: j.btagDeepFlavB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='cjet #eta',
yTitle='cjet P_{T}',
zTitle='cjet Btagging score')
N_ak4_truth_bjets = Plot.make3D('N_ak4_truth_bjets', [
op.map(ak4_truth_bjets, lambda j: op.abs(j.eta)),
op.map(ak4_truth_bjets, lambda j: j.pt),
op.map(ak4_truth_bjets, lambda j: j.btagDeepFlavB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='bjet #eta',
yTitle='bjet P_{T}',
zTitle='bjet Btagging score')
plots.extend(
[N_ak4_truth_lightjets, N_ak4_truth_cjets, N_ak4_truth_bjets])
ak4_btagged_lightjets = op.select(ak4_truth_lightjets,
self.lambda_ak4Btag)
ak4_btagged_cjets = op.select(ak4_truth_cjets, self.lambda_ak4Btag)
ak4_btagged_bjets = op.select(ak4_truth_bjets, self.lambda_ak4Btag)
N_ak4_btagged_lightjets = Plot.make3D(
'N_ak4_btagged_lightjets', [
op.map(ak4_btagged_lightjets, lambda j: op.abs(j.eta)),
op.map(ak4_btagged_lightjets, lambda j: j.pt),
op.map(ak4_btagged_lightjets, lambda j: j.btagDeepFlavB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='lightjet #eta',
yTitle='lightjet P_{T}',
zTitle='lightjet Btagging score')
N_ak4_btagged_cjets = Plot.make3D(
'N_ak4_btagged_cjets', [
op.map(ak4_btagged_cjets, lambda j: op.abs(j.eta)),
op.map(ak4_btagged_cjets, lambda j: j.pt),
op.map(ak4_btagged_cjets, lambda j: j.btagDeepFlavB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='cjet #eta',
yTitle='cjet P_{T}',
zTitle='cjet Btagging score')
N_ak4_btagged_bjets = Plot.make3D(
'N_ak4_btagged_bjets', [
op.map(ak4_btagged_bjets, lambda j: op.abs(j.eta)),
op.map(ak4_btagged_bjets, lambda j: j.pt),
op.map(ak4_btagged_bjets, lambda j: j.btagDeepFlavB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='bjet #eta',
yTitle='bjet P_{T}',
zTitle='bjet Btagging score')
plots.extend([
N_ak4_btagged_lightjets, N_ak4_btagged_cjets, N_ak4_btagged_bjets
])
#############################################################################
# AK8 #
#############################################################################
# Truth MC object hadron flavour #
# subjet.nBHadrons>0 : bjet
# subjet.nCHadrons>0 : cjet
# else : lightjet
ak8subJet1_truth_lightjets = op.select(
self.ak8Jets, lambda j: op.AND(j.subJet1.nBHadrons == 0, j.subJet1.
nCHadrons == 0))
ak8subJet2_truth_lightjets = op.select(
self.ak8Jets, lambda j: op.AND(j.subJet2.nBHadrons == 0, j.subJet2.
nCHadrons == 0))
ak8subJet1_truth_cjets = op.select(self.ak8Jets,
lambda j: j.subJet1.nCHadrons > 0)
ak8subJet2_truth_cjets = op.select(self.ak8Jets,
lambda j: j.subJet2.nCHadrons > 0)
ak8subJet1_truth_bjets = op.select(self.ak8Jets,
lambda j: j.subJet1.nBHadrons > 0)
ak8subJet2_truth_bjets = op.select(self.ak8Jets,
lambda j: j.subJet2.nBHadrons > 0)
N_ak8_truth_subJet1_lightjets = Plot.make3D(
'N_ak8_truth_subJet1_lightjets', [
op.map(ak8subJet1_truth_lightjets,
lambda j: op.abs(j.subJet1.eta)),
op.map(ak8subJet1_truth_lightjets, lambda j: j.subJet1.pt),
op.map(ak8subJet1_truth_lightjets,
lambda j: j.subJet1.btagDeepB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='lightjet subJet1 #eta',
yTitle='lightjet subJet1 P_{T}',
zTitle='lightjet subJet1 Btagging score')
N_ak8_truth_subJet2_lightjets = Plot.make3D(
'N_ak8_truth_subJet2_lightjets', [
op.map(ak8subJet2_truth_lightjets,
lambda j: op.abs(j.subJet2.eta)),
op.map(ak8subJet2_truth_lightjets, lambda j: j.subJet2.pt),
op.map(ak8subJet2_truth_lightjets,
lambda j: j.subJet2.btagDeepB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='lightjet subJet2 #eta',
yTitle='lightjet subJet2 P_{T}',
zTitle='lightjet subJet2 Btagging score')
N_ak8_truth_subJet1_cjets = Plot.make3D(
'N_ak8_truth_subJet1_cjets', [
op.map(ak8subJet1_truth_cjets,
lambda j: op.abs(j.subJet1.eta)),
op.map(ak8subJet1_truth_cjets, lambda j: j.subJet1.pt),
op.map(ak8subJet1_truth_cjets, lambda j: j.subJet1.btagDeepB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='cjet subJet1 #eta',
yTitle='cjet subJet1 P_{T}',
zTitle='cjet subJet1 Btagging score')
N_ak8_truth_subJet2_cjets = Plot.make3D(
'N_ak8_truth_subJet2_cjets', [
op.map(ak8subJet2_truth_cjets,
lambda j: op.abs(j.subJet2.eta)),
op.map(ak8subJet2_truth_cjets, lambda j: j.subJet2.pt),
op.map(ak8subJet2_truth_cjets, lambda j: j.subJet2.btagDeepB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='cjet subJet2 #eta',
yTitle='cjet subJet2 P_{T}',
zTitle='cjet subJet2 Btagging score')
N_ak8_truth_subJet1_bjets = Plot.make3D(
'N_ak8_truth_subJet1_bjets', [
op.map(ak8subJet1_truth_bjets,
lambda j: op.abs(j.subJet1.eta)),
op.map(ak8subJet1_truth_bjets, lambda j: j.subJet1.pt),
op.map(ak8subJet1_truth_bjets, lambda j: j.subJet1.btagDeepB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='bjet subJet1 #eta',
yTitle='bjet subJet1 P_{T}',
zTitle='bjet subJet1 Btagging score')
N_ak8_truth_subJet2_bjets = Plot.make3D(
'N_ak8_truth_subJet2_bjets', [
op.map(ak8subJet2_truth_bjets,
lambda j: op.abs(j.subJet2.eta)),
op.map(ak8subJet2_truth_bjets, lambda j: j.subJet2.pt),
op.map(ak8subJet2_truth_bjets, lambda j: j.subJet2.btagDeepB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='bjet subJet2 #eta',
yTitle='bjet subJet2 P_{T}',
zTitle='bjet subJet2 Btagging score')
plots.extend([
N_ak8_truth_subJet1_lightjets, N_ak8_truth_subJet2_lightjets,
N_ak8_truth_subJet1_cjets, N_ak8_truth_subJet2_cjets,
N_ak8_truth_subJet1_bjets, N_ak8_truth_subJet2_bjets
])
plots.append(
SummedPlot(
'N_ak8_truth_lightjets',
[N_ak8_truth_subJet1_lightjets, N_ak8_truth_subJet2_lightjets],
xTitle='lightjet #eta',
yTitle='lightjet P_{T}',
zTitle='lightjet Btagging score'))
plots.append(
SummedPlot('N_ak8_truth_cjets',
[N_ak8_truth_subJet1_cjets, N_ak8_truth_subJet2_cjets],
xTitle='cjet #eta',
yTitle='cjet P_{T}',
zTitle='cjet Btagging score'))
plots.append(
SummedPlot('N_ak8_truth_bjets',
[N_ak8_truth_subJet1_bjets, N_ak8_truth_subJet2_bjets],
xTitle='bjet #eta',
yTitle='bjet P_{T}',
zTitle='bjet Btagging score'))
# Btagged objects per flavour #
ak8subJet1_btagged_lightjets = op.select(
ak8subJet1_truth_lightjets,
lambda j: self.lambda_subjetBtag(j.subJet1))
ak8subJet2_btagged_lightjets = op.select(
ak8subJet2_truth_lightjets,
lambda j: self.lambda_subjetBtag(j.subJet2))
ak8subJet1_btagged_cjets = op.select(
ak8subJet1_truth_cjets,
lambda j: self.lambda_subjetBtag(j.subJet1))
ak8subJet2_btagged_cjets = op.select(
ak8subJet2_truth_cjets,
lambda j: self.lambda_subjetBtag(j.subJet2))
ak8subJet1_btagged_bjets = op.select(
ak8subJet1_truth_bjets,
lambda j: self.lambda_subjetBtag(j.subJet1))
ak8subJet2_btagged_bjets = op.select(
ak8subJet2_truth_bjets,
lambda j: self.lambda_subjetBtag(j.subJet2))
N_ak8_btagged_subJet1_lightjets = Plot.make3D(
'N_ak8_btagged_subJet1_lightjets', [
op.map(ak8subJet1_btagged_lightjets,
lambda j: op.abs(j.subJet1.eta)),
op.map(ak8subJet1_btagged_lightjets, lambda j: j.subJet1.pt),
op.map(ak8subJet1_btagged_lightjets,
lambda j: j.subJet1.btagDeepB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='lightjet subJet1 #eta',
yTitle='lightjet subJet1 P_{T}',
zTitle='lightjet subJet1 Btagging score')
N_ak8_btagged_subJet2_lightjets = Plot.make3D(
'N_ak8_btagged_subJet2_lightjets', [
op.map(ak8subJet2_btagged_lightjets,
lambda j: op.abs(j.subJet2.eta)),
op.map(ak8subJet2_btagged_lightjets, lambda j: j.subJet2.pt),
op.map(ak8subJet2_btagged_lightjets,
lambda j: j.subJet2.btagDeepB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='lightjet subJet2 #eta',
yTitle='lightjet subJet2 P_{T}',
zTitle='lightjet subJet2 Btagging score')
N_ak8_btagged_subJet1_cjets = Plot.make3D(
'N_ak8_btagged_subJet1_cjets', [
op.map(ak8subJet1_btagged_cjets,
lambda j: op.abs(j.subJet1.eta)),
op.map(ak8subJet1_btagged_cjets, lambda j: j.subJet1.pt),
op.map(ak8subJet1_btagged_cjets, lambda j: j.subJet1.btagDeepB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='cjet subJet1 #eta',
yTitle='cjet subJet1 P_{T}',
zTitle='cjet subJet1 Btagging score')
N_ak8_btagged_subJet2_cjets = Plot.make3D(
'N_ak8_btagged_subJet2_cjets', [
op.map(ak8subJet2_btagged_cjets,
lambda j: op.abs(j.subJet2.eta)),
op.map(ak8subJet2_btagged_cjets, lambda j: j.subJet2.pt),
op.map(ak8subJet2_btagged_cjets, lambda j: j.subJet2.btagDeepB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='cjet subJet2 #eta',
yTitle='cjet subJet2 P_{T}',
zTitle='cjet subJet2 Btagging score')
N_ak8_btagged_subJet1_bjets = Plot.make3D(
'N_ak8_btagged_subJet1_bjets', [
op.map(ak8subJet1_btagged_bjets,
lambda j: op.abs(j.subJet1.eta)),
op.map(ak8subJet1_btagged_bjets, lambda j: j.subJet1.pt),
op.map(ak8subJet1_btagged_bjets, lambda j: j.subJet1.btagDeepB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='bjet subJet1 #eta',
yTitle='bjet subJet1 P_{T}',
zTitle='bjet subJet1 Btagging score')
N_ak8_btagged_subJet2_bjets = Plot.make3D(
'N_ak8_btagged_subJet2_bjets', [
op.map(ak8subJet2_btagged_bjets,
lambda j: op.abs(j.subJet2.eta)),
op.map(ak8subJet2_btagged_bjets, lambda j: j.subJet2.pt),
op.map(ak8subJet2_btagged_bjets, lambda j: j.subJet2.btagDeepB)
],
noSel, [
EquidistantBinning(100, 0., 2.5),
EquidistantBinning(100, 0., 1000),
EquidistantBinning(100, 0., 1.)
],
xTitle='bjet subJet2 #eta',
yTitle='bjet subJet2 P_{T}',
zTitle='bjet subJet2 Btagging score')
plots.extend([
N_ak8_btagged_subJet1_lightjets, N_ak8_btagged_subJet2_lightjets,
N_ak8_btagged_subJet1_cjets, N_ak8_btagged_subJet2_cjets,
N_ak8_btagged_subJet1_bjets, N_ak8_btagged_subJet2_bjets
])
plots.append(
SummedPlot('N_ak8_btagged_lightjets', [
N_ak8_btagged_subJet1_lightjets,
N_ak8_btagged_subJet2_lightjets
],
xTitle='lightjet #eta',
yTitle='lightjet P_{T}',
zTitle='lightjet Btagging score'))
plots.append(
SummedPlot(
'N_ak8_btagged_cjets',
[N_ak8_btagged_subJet1_cjets, N_ak8_btagged_subJet2_cjets],
xTitle='cjet #eta',
yTitle='cjet P_{T}',
zTitle='cjet Btagging score'))
plots.append(
SummedPlot(
'N_ak8_btagged_bjets',
[N_ak8_btagged_subJet1_bjets, N_ak8_btagged_subJet2_bjets],
xTitle='bjet #eta',
yTitle='bjet P_{T}',
zTitle='bjet Btagging score'))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.plots import CutFlowReport, SummedPlot
from bamboo.plots import EquidistantBinning as EqB
from bamboo import treefunctions as op
isMC = self.isMC(sample)
trigCut, trigWeight = None, None
if isMC:
trigCut = op.OR(t.HLT.HIEle20_Ele12_CaloIdL_TrackIdL_IsoVL_DZ, t.HLT.HIL3DoubleMu0, t.HLT.HIL3Mu20, t.HLT.HIEle20_WPLoose_Gsf)
trigWeight = op.switch(op.OR(t.HLT.HIEle20_Ele12_CaloIdL_TrackIdL_IsoVL_DZ, t.HLT.HIL3DoubleMu0), op.c_float(1.),
op.switch(t.HLT.HIL3Mu20, op.c_float(306.913/308.545), op.c_float(264.410/308.545))) ## FIXME these are wrong - you will get the final values from team A
else:
## trigger order: dielectron, dimuon or single muon, single electron
pd = sample.split("_")[0]
if pd == "SingleMuon":
trigCut = op.AND(op.NOT(t.HLT.HIEle20_Ele12_CaloIdL_TrackIdL_IsoVL_DZ),
op.OR(t.HLT.HIL3DoubleMu0, t.HLT.HIL3Mu20))
elif pd == "HighEGJet":
trigCut = op.OR(t.HLT.HIEle20_Ele12_CaloIdL_TrackIdL_IsoVL_DZ,
op.AND(op.NOT(op.OR(t.HLT.HIL3DoubleMu0, t.HLT.HIL3Mu20)),
t.HLT.HIEle20_WPLoose_Gsf))
noSel = noSel.refine("trig", cut=trigCut, weight=trigWeight)
plots = []
def isGoodElectron(el, ptCut=10.):
return op.AND(
el.pt > ptCut,
op.abs(el.eta) < 2.5,
el.lostHits == 0, ## do you want this?
op.abs(el.sip3d) < 8.,
op.abs(el.dxy) < .05,
op.abs(el.dz ) < .1,
el.miniPFRelIso_all < 0.085,
el.mvaTTH > 0.125,
op.NOT(op.AND(el.jet.isValid, op.OR(el.jet.btagDeepB > .1522, el.jet.btagDeepB <= -999.)))
)
def isGoodMuon(mu, ptCut=10.):
return op.AND(
mu.pt > ptCut,
op.abs(mu.eta) < 2.4,
mu.mediumPromptId,
op.abs(mu.sip3d) < 8.,
op.abs(mu.dxy) < .05,
op.abs(mu.dz ) < .1,
mu.miniPFRelIso_all < 0.325,
mu.mvaTTH > 0.55,
op.NOT(op.AND(mu.jet.isValid, op.OR(mu.jet.btagDeepB > .1522, mu.jet.btagDeepB <= -999.)))
)
goodLeptons = {
"el" : op.select(t.Electron, partial(isGoodElectron, ptCut=15.)),
"mu" : op.select(t.Muon, partial(isGoodMuon, ptCut=15.))
}
plots += [
Plot.make1D("trig_nLeptons15", op.rng_len(goodLeptons["el"])+op.rng_len(goodLeptons["mu"]), noSel, EqB(15, 0., 15.)),
Plot.make1D("trig_nEl15", op.rng_len(goodLeptons["el"]), noSel, EqB(15, 0., 15.)),
Plot.make1D("trig_nMu15", op.rng_len(goodLeptons["mu"]), noSel, EqB(15, 0., 15.))
]
from bamboo.scalefactors import get_scalefactor
sf_loose = {
"mu": get_scalefactor("lepton", "Muon_RecoToLoose", sfLib=scalefactors_lepMVA, paramDefs=binningVariables_nano_noScaleSyst, systName="muLoose"),
"el": get_scalefactor("lepton", "Electron_RecoToLoose", sfLib=scalefactors_lepMVA, paramDefs=binningVariables_nano_noScaleSyst, systName="elLoose")
}
sf_tight = {
"mu": get_scalefactor("lepton", "Muon_LooseToTight", sfLib=scalefactors_lepMVA, paramDefs=binningVariables_nano_noScaleSyst, systName="muTight"),
"el": get_scalefactor("lepton", "Electron_LooseToTight", sfLib=scalefactors_lepMVA, paramDefs=binningVariables_nano_noScaleSyst, systName="elTight")
}
nGoodLeptons = op.rng_len(goodLeptons["el"])+op.rng_len(goodLeptons["mu"])
hasTwoGoodLeptons = noSel.refine("has2Lep", cut=(nGoodLeptons > 1)) # avoid overlap with 1l
jets = op.sort(op.select(t.Jet, lambda j : op.AND(
j.pt > 25.,
op.abs(j.eta) < 2.4,
j.jetId & 0x2,
op.AND(
op.NOT(op.rng_any(goodLeptons["el"], lambda l : op.deltaR(l.p4, j.p4) < 0.4)),
op.NOT(op.rng_any(goodLeptons["mu"], lambda l : op.deltaR(l.p4, j.p4) < 0.4)))
)), lambda j : -j.pt)
## WP: see https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation94X
loosebjets = op.select(jets, lambda j : j.btagDeepB > 0.1522)
mediumbjets = op.select(jets, lambda j : j.btagDeepB > 0.4941)
for fl1,fl2 in product(*repeat(goodLeptons.keys(), 2)):
dilepSel = lambda l1,l2 : op.AND(
l1.charge != l2.charge,
(l1.p4+l2.p4).M() > 12.
)
if fl1 == fl2:
lGood = op.sort(goodLeptons[fl1], lambda l : -l.pt)
dilep = op.combine(lGood, N=2, pred=dilepSel)
else:
l1Good = op.sort(goodLeptons[fl1], lambda l : -l.pt)
l2Good = op.sort(goodLeptons[fl2], lambda l : -l.pt)
dilep = op.combine((l1Good, l2Good), pred=dilepSel)
ll = dilep[0]
hasDilep = hasTwoGoodLeptons.refine(f"hasDilep{fl1}{fl2}", cut=(op.rng_len(dilep) > 0, ll[0].pt > 25.),
weight=([ sf_loose[fl1](ll[0]), sf_loose[fl2](ll[1]), sf_tight[fl1](ll[0]), sf_tight[fl2](ll[1]) ] if isMC else None))
plots += [
Plot.make1D(f"dilepton_{fl1}{fl2}_Mll", (ll[0].p4+ll[1].p4).M(), hasDilep, EqB(50, 70, 120.), title="Dilepton mass"),
]
# for il,ifl in enumerate((fl1, fl2)):
## plots += [
# Plot.make1D(f"dilepton_{fl1}{fl2}_L{il:d}PT", ll[il].pt, hasDilep, EqB(50, 0., 100.), title=f"Lepton {il:d} PT"),
# Plot.make1D(f"dilepton_{fl1}{fl2}_L{il:d}ETA", ll[il].eta, hasDilep, EqB(50, -2.5, 2.5), title=f"Lepton {il:d} ETA"),
# ]
# plots += [
# Plot.make1D(f"dilepton_{fl1}{fl2}_nJets", op.rng_len(jets), hasDilep, EqB(15, 0, 15.), title="Jet multiplicity"),
# Plot.make1D(f"dilepton_{fl1}{fl2}_nLooseBJets", op.rng_len(loosebjets), hasDilep, EqB(15, 0, 15.), title="Loose b-jet multiplicity"),
# Plot.make1D(f"dilepton_{fl1}{fl2}_nMediumBJets", op.rng_len(mediumbjets), hasDilep, EqB(15, 0, 15.), title="Medium b-jet multiplicity")
# ]
return plots
def definePlots(self, tree, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, SummedPlot
from bamboo.plots import EquidistantBinning as EqBin
from bamboo import treefunctions as op
if self.args.examples == "all":
examples = list(i+1 for i in range(self.nExamples)) # 1-4 are fine, so is 7
else:
examples = list(set(int(tk) for tk in self.args.examples.split(",")))
logger.info("Running the following examples: {0}".format(",".join(str(i) for i in examples)))
plots = []
if 1 in examples:
## Example 1: Plot the missing ET of all events.
plots.append(Plot.make1D("Ex1_MET",
tree.MET.pt, noSel,
EqBin(100, 0., 2000.), title="MET (GeV)"))
if 2 in examples:
## Example 2: Plot pT of all jets in all events.
plots.append(Plot.make1D("Ex2_jetPt",
op.map(tree.Jet, lambda j : j.pt), noSel,
EqBin(100, 15., 60.), title="Jet p_{T} (GeV/c)"))
if 3 in examples:
## Example 3: Plot pT of jets with |η| < 1.
centralJets1 = op.select(tree.Jet, lambda j : op.abs(j.eta) < 1.)
plots.append(Plot.make1D("Ex3_central1_jetPt",
op.map(centralJets1, lambda j : j.pt), noSel,
EqBin(100, 15., 60.), title="Jet p_{T} (GeV/c)"))
if 4 in examples:
## Example 4: Plot the missing ET of events that have at least two jets with pT > 40 GeV.
jets40 = op.select(tree.Jet, lambda j : j.pt > 40)
hasTwoJets40 = noSel.refine("twoJets40", cut=(op.rng_len(jets40) >= 2))
plots.append(Plot.make1D("Ex4_twoJets40_MET",
tree.MET.pt, hasTwoJets40,
EqBin(100, 0., 2000.), title="MET (GeV)"))
if 5 in examples:
## Example 5: Plot the missing ET of events that have an opposite-sign muon pair with an invariant mass between 60 and 120 GeV.
dimu_Z = op.combine(tree.Muon, N=2, pred=(lambda mu1, mu2 : op.AND(
mu1.charge != mu2.charge,
op.in_range(60., op.invariant_mass(mu1.p4, mu2.p4), 120.)
)))
hasDiMuZ = noSel.refine("hasDiMuZ", cut=(op.rng_len(dimu_Z) > 0))
plots.append(Plot.make1D("Ex5_dimuZ_MET",
tree.MET.pt, hasDiMuZ,
EqBin(100, 0., 2000.), title="MET (GeV)"))
if 6 in examples:
## Example 6: Plot pT of the trijet system with the mass closest to 172.5 GeV in each event and plot the maximum b-tagging discriminant value among the jets in the triplet.
trijets = op.combine(tree.Jet, N=3)
hadTop = op.rng_min_element_by(trijets,
fun=lambda comb: op.abs((comb[0].p4+comb[1].p4+comb[2].p4).M()-172.5))
hadTop_p4 = (hadTop[0].p4 + hadTop[1].p4 + hadTop[2].p4)
hasTriJet = noSel.refine("hasTriJet", cut=(op.rng_len(trijets) > 0))
plots.append(Plot.make1D("Ex6_trijet_topPt",
hadTop_p4.Pt(), hasTriJet,
EqBin(100, 15., 40.), title="Trijet p_{T} (GeV/c)"))
plots.append(Plot.make1D("Ex6_trijet_maxbtag",
op.max(op.max(hadTop[0].btag, hadTop[1].btag), hadTop[2].btag), hasTriJet,
EqBin(100, 0., 1.), title="Trijet maximum b-tag"))
if verbose:
plots.append(Plot.make1D("Ex6_njets",
op.rng_len(tree.Jet), noSel,
EqBin(20, 0., 20.), title="Number of jets"))
plots.append(Plot.make1D("Ex6_ntrijets",
op.rng_len(trijets), noSel,
EqBin(100, 0., 1000.), title="Number of 3-jet combinations"))
plots.append(Plot.make1D("Ex6_trijet_mass",
hadTop_p4.M(), hasTriJet,
EqBin(100, 0., 250.), title="Trijet mass (GeV/c^{2})"))
if 7 in examples:
## Example 7: Plot the sum of pT of jets with pT > 30 GeV that are not within 0.4 in ΔR of any lepton with pT > 10 GeV.
el10 = op.select(tree.Electron, lambda el : el.pt > 10.)
mu10 = op.select(tree.Muon , lambda mu : mu.pt > 10.)
cleanedJets30 = op.select(tree.Jet, lambda j : op.AND(
j.pt > 30.,
op.NOT(op.rng_any(el10, lambda el : op.deltaR(j.p4, el.p4) < 0.4 )),
op.NOT(op.rng_any(mu10, lambda mu : op.deltaR(j.p4, mu.p4) < 0.4 ))
))
plots.append(Plot.make1D("Ex7_sumCleanedJetPt",
op.rng_sum(cleanedJets30, lambda j : j.pt), noSel,
EqBin(100, 15., 200.), title="Sum p_{T} (GeV/c)"))
if 8 in examples:
## Example 8: For events with at least three leptons and a same-flavor opposite-sign lepton pair, find the same-flavor opposite-sign lepton pair with the mass closest to 91.2 GeV and plot the transverse mass of the missing energy and the leading other lepton.
# The plot is made for each of the different flavour categories (l+/- l-/+ l') and then summed,
# because concatenation of containers is not (yet) supported.
lepColl = { "El" : tree.Electron, "Mu" : tree.Muon }
mt3lPlots = []
for dlNm,dlCol in lepColl.items():
dilep = op.combine(dlCol, N=2, pred=(lambda l1,l2 : op.AND(l1.charge != l2.charge)))
hasDiLep = noSel.refine("hasDilep{0}{0}".format(dlNm), cut=(op.rng_len(dilep) > 0))
dilepZ = op.rng_min_element_by(dilep, fun=lambda ll : op.abs(op.invariant_mass(ll[0].p4, ll[1].p4)-91.2))
for tlNm,tlCol in lepColl.items():
if tlCol == dlCol:
hasTriLep = hasDiLep.refine("hasTrilep{0}{0}{1}".format(dlNm,tlNm),
cut=(op.rng_len(tlCol) > 2))
residLep = op.select(tlCol, lambda l : op.AND(l.idx != dilepZ[0].idx, l.idx != dilepZ[1].idx))
l3 = op.rng_max_element_by(residLep, lambda l : l.pt)
else:
hasTriLep = hasDiLep.refine("hasTriLep{0}{0}{1}".format(dlNm,tlNm),
cut=(op.rng_len(tlCol) > 0))
l3 = op.rng_max_element_by(tlCol, lambda l : l.pt)
mtPlot = Plot.make1D("Ex8_3lMT_{0}{0}{1}".format(dlNm,tlNm),
op.sqrt(2*l3.pt*tree.MET.pt*(1-op.cos(l3.phi-tree.MET.phi))), hasTriLep,
EqBin(100, 15., 250.), title="M_{T} (GeV/c^2)")
mt3lPlots.append(mtPlot)
plots.append(mtPlot)
plots.append(SummedPlot("Ex8_3lMT", mt3lPlots))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, CutFlowReport, SummedPlot
from bamboo.plots import EquidistantBinning as EqB
from bamboo import treefunctions as op
isMC = self.isMC(sample)
trigCut, trigWeight = None, None
if isMC:
noSel = noSel.refine("mcWeight", weight=[ t.genWeight, t.puWeight, t.PrefireWeight ])
trigCut = op.OR(t.HLT.HIEle20_Ele12_CaloIdL_TrackIdL_IsoVL_DZ, t.HLT.HIL3DoubleMu0, t.HLT.HIL3Mu20, t.HLT.HIEle20_WPLoose_Gsf)
## TODO add a correction for prescaled triggers
else:
## suggested trigger order: dielectron, dimuon or single muon, single electron (to minimise loss due to prescales). Electron triggered-events should be taken from the HighEGJet primary datasets, muon-triggered events from the SingleMuon primary datset
pd = sample.split("_")[0]
if pd == "SingleMuon":
## TODO fill trigger cut
elif pd == "HighEGJet":
## TODO fill trigger cut
noSel = noSel.refine("trig", cut=trigCut, weight=trigWeight)
plots = []
goodLeptons = {
"el" : op.select(t.Electron, partial(isGoodElectron, ptCut=15.)),
"mu" : op.select(t.Muon, partial(isGoodMuon, ptCut=15.))
}
plots += [
Plot.make1D("trig_nLeptons15", op.rng_len(goodLeptons["el"])+op.rng_len(goodLeptons["mu"]), noSel, EqB(15, 0., 15.)),
Plot.make1D("trig_nEl15", op.rng_len(goodLeptons["el"]), noSel, EqB(15, 0., 15.)),
Plot.make1D("trig_nMu15", op.rng_len(goodLeptons["mu"]), noSel, EqB(15, 0., 15.))
]
from bamboo.scalefactors import get_scalefactor
sf_loose = {
"mu": get_scalefactor("lepton", "Muon_RecoToLoose", sfLib=scalefactors_lepMVA, paramDefs=binningVariables_nano_noScaleSyst, systName="muLoose"),
"el": get_scalefactor("lepton", "Electron_RecoToLoose", sfLib=scalefactors_lepMVA, paramDefs=binningVariables_nano_noScaleSyst, systName="elLoose")
}
sf_tight = {
"mu": get_scalefactor("lepton", "Muon_LooseToTight", sfLib=scalefactors_lepMVA, paramDefs=binningVariables_nano_noScaleSyst, systName="muTight"),
"el": get_scalefactor("lepton", "Electron_LooseToTight", sfLib=scalefactors_lepMVA, paramDefs=binningVariables_nano_noScaleSyst, systName="elTight")
}
nGoodLeptons = op.rng_len(goodLeptons["el"])+op.rng_len(goodLeptons["mu"])
hasTwoGoodLeptons = noSel.refine("has2Lep", cut=(nGoodLeptons > 1)) # avoid overlap with 1l
jets = op.sort(op.select(t.Jet, lambda j : op.AND(
j.pt > 25., ## you decide...
op.abs(j.eta) < 2.4,
j.jetId & 0x2, ## tight JetID
op.AND( ## lepton-jet cross-cleaning
op.NOT(op.rng_any(goodLeptons["el"], lambda l : op.deltaR(l.p4, j.p4) < 0.4)),
op.NOT(op.rng_any(goodLeptons["mu"], lambda l : op.deltaR(l.p4, j.p4) < 0.4)))
)), lambda j : -j.pt)
for fl1,fl2 in product(*repeat(goodLeptons.keys(), 2)):
dilepSel = lambda l1,l2 : op.AND(
l1.charge != l2.charge,
(l1.p4+l2.p4).M() > 12.
)
if fl1 == fl2:
lGood = op.sort(goodLeptons[fl1], lambda l : -l.pt)
dilep = op.combine(lGood, N=2, pred=dilepSel)
else:
l1Good = op.sort(goodLeptons[fl1], lambda l : -l.pt)
l2Good = op.sort(goodLeptons[fl2], lambda l : -l.pt)
dilep = op.combine((l1Good, l2Good), pred=dilepSel)
ll = dilep[0]
hasDilep = hasTwoGoodLeptons.refine(f"hasDilep{fl1}{fl2}", cut=(op.rng_len(dilep) > 0, ll[0].pt > 25.),
weight=([ sf_loose[fl1](ll[0]), sf_loose[fl2](ll[1]), sf_tight[fl1](ll[0]), sf_tight[fl2](ll[1]) ] if isMC else None))
plots += [
Plot.make1D(f"dilepton_{fl1}{fl2}_Mll", (ll[0].p4+ll[1].p4).M(), hasDilep, EqB(50, 70, 120.), title="Dilepton mass"),
]
for il,ifl in enumerate((fl1, fl2)):
plots += [
Plot.make1D(f"dilepton_{fl1}{fl2}_L{il:d}PT", ll[il].pt, hasDilep, EqB(50, 0., 100.), title=f"Lepton {il:d} PT"),
Plot.make1D(f"dilepton_{fl1}{fl2}_L{il:d}ETA", ll[il].eta, hasDilep, EqB(50, -2.5, 2.5), title=f"Lepton {il:d} ETA"),
]
plots += [
Plot.make1D(f"dilepton_{fl1}{fl2}_nJets", op.rng_len(jets), hasDilep, EqB(15, 0, 15.), title="Jet multiplicity"),
]
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.analysisutils import forceDefine
from bamboo.plots import Plot
from bamboo.plots import EquidistantBinning as EqB
from bamboo import treefunctions as op
era = sampleCfg.get("era") if sampleCfg else None
noSel = noSel.refine("passMETFlags", cut=METFilter(t.Flag, era) )
puWeightsFile = None
if era == "2016":
sfTag="94X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data/PileupFullRunII", "puweights2016.json")
elif era == "2017":
sfTag="94X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data/PileupFullRunII", "puweights2017.json")
elif era == "2018":
sfTag="102X"
puWeightsFile = os.path.join(os.path.dirname(__file__), "data/PileupFullRunII", "puweights2018.json")
if self.isMC(sample) and puWeightsFile is not None:
from bamboo.analysisutils import makePileupWeight
noSel = noSel.refine("puWeight", weight=makePileupWeight(puWeightsFile, t.Pileup_nTrueInt, systName="pileup"))
isMC = self.isMC(sample)
plots = []
forceDefine(t._Muon.calcProd, noSel)
# Wp // 2016- 2017 -2018 : Muon_mediumId // https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideMuonIdRun2#Muon_Isolation
#To suppress nonprompt lep-tons, the impact parameter in three dimensions of the lepton track, with respect to the primaryvertex, is required to be less than 4 times its uncertainty (|SIP3D|<4)
sorted_muons = op.sort(t.Muon, lambda mu : -mu.pt)
muons = op.select(sorted_muons, lambda mu : op.AND(mu.pt > 10., op.abs(mu.eta) < 2.4, mu.mediumId, mu.pfRelIso04_all<0.15, op.abs(mu.sip3d < 4.)))
# i pass 2016 seprate from 2017 &2018 because SFs need to be combined for BCDEF and GH eras !
if era=="2016":
doubleMuTrigSF = get_scalefactor("dilepton", ("doubleMuLeg_HHMoriond17_2016"), systName="mumutrig")
muMediumIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "id_medium"), combine="weight", systName="muid")
muMediumISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "iso_tight_id_medium"), combine="weight", systName="muiso")
else:
doubleMuTrigSF = get_scalefactor("dilepton", ("doubleMuLeg_HHMoriond17_2016"), systName="mumutrig")
muMediumIDSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "id_medium"), systName="muid")
muMediumISOSF = get_scalefactor("lepton", ("muon_{0}_{1}".format(era, sfTag), "iso_tight_id_medium"), systName="muiso")
#Wp // 2016: Electron_cutBased_Sum16==3 -> medium // 2017 -2018 : Electron_cutBased ==3 --> medium ( Fall17_V2)
# asking for electrons to be in the Barrel region with dz<1mm & dxy< 0.5mm // Endcap region dz<2mm & dxy< 0.5mm
# cut-based ID Fall17 V2 the recomended one from POG for the FullRunII
sorted_electrons = op.sort(t.Electron, lambda ele : -ele.pt)
electrons = op.select(sorted_electrons, lambda ele : op.AND(ele.pt > 15., op.abs(ele.eta) < 2.5 , ele.cutBased>=3, op.abs(ele.sip3d)< 4., op.OR(op.AND(op.abs(ele.dxy) < 0.05, op.abs(ele.dz) < 0.1), op.AND(op.abs(ele.dxy) < 0.05, op.abs(ele.dz) < 0.2) )))
elMediumIDSF = get_scalefactor("lepton", ("electron_{0}_{1}".format(era,sfTag), "id_medium"), systName="elid")
doubleEleTrigSF = get_scalefactor("dilepton", ("doubleEleLeg_HHMoriond17_2016"), systName="eleltrig")
elemuTrigSF = get_scalefactor("dilepton", ("elemuLeg_HHMoriond17_2016"), systName="elmutrig")
mueleTrigSF = get_scalefactor("dilepton", ("mueleLeg_HHMoriond17_2016"), systName="mueltrig")
MET = t.MET if era != "2017" else t.METFixEE2017
corrMET=METcorrection(MET,t.PV,sample,era,self.isMC(sample))
####### select jets
##################################
#// 2016 - 2017 - 2018 ( j.jetId &2) -> tight jet ID
# For 2017 data, there is the option of "Tight" or "TightLepVeto", depending on how much you want to veto jets that overlap with/are faked by leptons
sorted_AK4jets=op.sort(t.Jet, lambda j : -j.pt)
AK4jetsSel = op.select(sorted_AK4jets, lambda j : op.AND(j.pt > 20., op.abs(j.eta)< 2.4, (j.jetId &2)))# j.jetId == 6))# oldcut: (j.jetId &2)))
# exclude from the jetsSel any jet that happens to include within its reconstruction cone a muon or an electron.
AK4jets= op.select(AK4jetsSel, lambda j : op.AND(op.NOT(op.rng_any(electrons, lambda ele : op.deltaR(j.p4, ele.p4) < 0.3 )), op.NOT(op.rng_any(muons, lambda mu : op.deltaR(j.p4, mu.p4) < 0.3 ))))
# order jets by *decreasing* deepFlavour
cleaned_AK4JetsByDeepFlav = op.sort(AK4jets, lambda j: -j.btagDeepFlavB)
cleaned_AK4JetsByDeepB = op.sort(AK4jets, lambda j: -j.btagDeepB)
# Boosted Region
sorted_AK8jets=op.sort(t.FatJet, lambda j : -j.pt)
AK8jetsSel = op.select(sorted_AK8jets, lambda j : op.AND(j.pt > 200., op.abs(j.eta)< 2.4, (j.jetId &2), j.subJet1._idx.result != -1, j.subJet2._idx.result != -1))
AK8jets= op.select(AK8jetsSel, lambda j : op.AND(op.NOT(op.rng_any(electrons, lambda ele : op.deltaR(j.p4, ele.p4) < 0.3 )), op.NOT(op.rng_any(muons, lambda mu : op.deltaR(j.p4, mu.p4) < 0.3 ))))
cleaned_AK8JetsByDeepB = op.sort(AK8jets, lambda j: -j.btagDeepB)
# Now, let's ask for the jets to be a b-jets
# DeepCSV or deepJet Medium b-tag working point
btagging = {
"DeepCSV":{ # era: (loose, medium, tight)
"2016":(0.2217, 0.6321, 0.8953),
"2017":(0.1522, 0.4941, 0.8001),
"2018":(0.1241, 0.4184, 0.7527)
},
"DeepFlavour":{
"2016":(0.0614, 0.3093, 0.7221),
"2017":(0.0521, 0.3033, 0.7489),
"2018":(0.0494, 0.2770, 0.7264)
}
}
# bjets ={ "DeepFlavour": {"L": jets pass loose , "M": jets pass medium , "T":jets pass tight }
# "DeepCSV": {"L": --- , "M": --- , "T": ---- }
# }
# FIXME
bjets_boosted = {}
bjets_resolved = {}
#WorkingPoints = ["L", "M", "T"]
WorkingPoints = ["M"]
for tagger in btagging.keys():
bJets_AK4_deepflavour ={}
bJets_AK4_deepcsv ={}
bJets_AK8_deepcsv ={}
# FIXME idx is not propagated properly when i pass only one or two wp !!
for wp in sorted(WorkingPoints):
suffix = ("loose" if wp=='L' else ("medium" if wp=='M' else "tight"))
idx = ( 0 if wp=="L" else ( 1 if wp=="M" else 2))
if tagger=="DeepFlavour":
print ("Btagging: Era= {0}, Tagger={1}, Pass_{2}_working_point={3}".format(era, tagger, suffix, btagging[tagger][era][idx] ))
print ("***********************************************", idx, wp)
print ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "{0}_{1}".format('DeepJet', suffix))
bJets_AK4_deepflavour[wp] = op.select(cleaned_AK4JetsByDeepFlav, lambda j : j.btagDeepFlavB >= btagging[tagger][era][idx] )
Jet_DeepFlavourBDisc = { "BTagDiscri": lambda j : j.btagDeepFlavB }
deepBFlavScaleFactor = get_scalefactor("jet", ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "{0}_{1}".format('DeepJet', suffix)),
additionalVariables=Jet_DeepFlavourBDisc,
getFlavour=(lambda j : j.hadronFlavour),
systName="btagging{0}".format(era))
bjets_resolved[tagger]=bJets_AK4_deepflavour
else:
print ("Btagging: Era= {0}, Tagger={1}, Pass_{2}_working_point={3}".format(era, tagger, suffix, btagging[tagger][era][idx] ))
print ("***********************************************", idx, wp)
print ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "{0}_{1}".format('DeepCSV', suffix))
bJets_AK4_deepcsv[wp] = op.select(cleaned_AK4JetsByDeepB, lambda j : j.btagDeepB >= btagging[tagger][era][idx] )
bJets_AK8_deepcsv[wp] = op.select(cleaned_AK8JetsByDeepB, lambda j : op.AND(j.subJet1.btagDeepB >= btagging[tagger][era][idx] , j.subJet2.btagDeepB >= btagging[tagger][era][idx]))
Jet_DeepCSVBDis = { "BTagDiscri": lambda j : j.btagDeepB }
subJet_DeepCSVBDis = { "BTagDiscri": lambda j : op.AND(j.subJet1.btagDeepB, j.subJet2.btagDeepB) }
# FIXME for boosted and resolved i will use # tagger need to pass jsons files to scale factors above !
deepB_AK4ScaleFactor = get_scalefactor("jet", ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "{0}_{1}".format('DeepCSV', suffix)),
additionalVariables=Jet_DeepCSVBDis,
getFlavour=(lambda j : j.hadronFlavour),
systName="btagging{0}".format(era))
# FIXME
#deepB_AK8ScaleFactor = get_scalefactor("jet", ("btag_{0}_94X".format(era).replace("94X", "102X" if era=="2018" else "94X"), "subjet_{0}_{1}".format('DeepCSV', suffix)),
#additionalVariables=Jet_DeepCSVBDis,
#getFlavour=(lambda j : j.subJet1.hadronFlavour),
#systName="btagging{0}".format(era))
bjets_resolved[tagger]=bJets_AK4_deepcsv
bjets_boosted[tagger]=bJets_AK8_deepcsv
bestDeepFlavourPair={}
bestDeepCSVPair={}
bestJetPairs= {}
bjets = {}
# For the Resolved only
class GetBestJetPair(object):
JetsPair={}
def __init__(self, JetsPair, tagger, wp):
def ReturnHighestDiscriminatorJet(tagger, wp):
if tagger=="DeepCSV":
return op.sort(safeget(bjets_resolved, tagger, wp), lambda j: - j.btagDeepB)
elif tagger=="DeepFlavour":
return op.sort(safeget(bjets_resolved, tagger, wp), lambda j: - j.btagDeepFlavB)
else:
raise RuntimeError("Something went wrong in returning {0} discriminator !".format(tagger))
firstBest=ReturnHighestDiscriminatorJet(tagger, wp)[0]
JetsPair[0]=firstBest
secondBest=ReturnHighestDiscriminatorJet(tagger, wp)[1]
JetsPair[1]=secondBest
# bestJetPairs= { "DeepFlavour": bestDeepFlavourPair,
# "DeepCSV": bestDeepCSVPair
# }
####### Zmass reconstruction : Opposite Sign , Same Flavour leptons
########################################################
# supress quaronika resonances and jets misidentified as leptons
LowMass_cut = lambda dilep: op.invariant_mass(dilep[0].p4, dilep[1].p4)>12.
## Dilepton selection: opposite sign leptons in range 70.<mll<120. GeV
osdilep_Z = lambda l1,l2 : op.AND(l1.charge != l2.charge, op.in_range(70., op.invariant_mass(l1.p4, l2.p4), 120.))
osLLRng = {
"MuMu" : op.combine(muons, N=2, pred= osdilep_Z),
"ElEl" : op.combine(electrons, N=2, pred=osdilep_Z),
#"ElMu" : op.combine((electrons, muons), pred=lambda ele,mu : op.AND(osdilep_Z(ele,mu), ele.pt > mu.pt )),
#"MuEl" : op.combine((muons, electrons), pred=lambda mu,ele : op.AND(osdilep_Z(mu,ele), mu.pt > ele.pt))
}
hasOSLL_cmbRng = lambda cmbRng : op.AND(op.rng_len(cmbRng) > 0, cmbRng[0][0].pt > 25.) # TODO The leading pT for the µµ channel should be above 20 Gev !
## helper selection (OR) to make sure jet calculations are only done once
hasOSLL = noSel.refine("hasOSLL", cut=op.OR(*( hasOSLL_cmbRng(rng) for rng in osLLRng.values())))
forceDefine(t._Jet.calcProd, hasOSLL)
forceDefine(getattr(t, "_{0}".format("MET" if era != "2017" else "METFixEE2017")).calcProd, hasOSLL)
llSFs = {
"MuMu" : (lambda ll : [ muMediumIDSF(ll[0]), muMediumIDSF(ll[1]), muMediumISOSF(ll[0]), muMediumISOSF(ll[1]), doubleMuTrigSF(ll) ]),
"ElMu" : (lambda ll : [ elMediumIDSF(ll[0]), muMediumIDSF(ll[1]), muMediumISOSF(ll[1]), elemuTrigSF(ll) ]),
"MuEl" : (lambda ll : [ muMediumIDSF(ll[0]), muMediumISOSF(ll[0]), elMediumIDSF(ll[1]), mueleTrigSF(ll) ]),
"ElEl" : (lambda ll : [ elMediumIDSF(ll[0]), elMediumIDSF(ll[1]), doubleEleTrigSF(ll) ])
}
categories = dict((channel, (catLLRng[0], hasOSLL.refine("hasOS{0}".format(channel), cut=hasOSLL_cmbRng(catLLRng), weight=(llSFs[channel](catLLRng[0]) if isMC else None)) )) for channel, catLLRng in osLLRng.items())
## btagging efficiencies plots
#plots.extend(MakeBtagEfficienciesPlots(self, jets, bjets, categories))
for channel, (dilepton, catSel) in categories.items():
#---- Zmass (2Lepton OS && SF ) --------
#plots.extend(MakeControlPlotsForZpic(self, catSel, dilepton, channel))
#---- add Jets selection
TwoLeptonsTwoJets_Resolved = catSel.refine("TwoJet_{0}Sel_resolved".format(channel), cut=[ op.rng_len(AK4jets) > 1 ])
TwoLeptonsTwoJets_Boosted = catSel.refine("OneJet_{0}Sel_boosted".format(channel), cut=[ op.rng_len(AK8jets) > 0 ])
#plots.extend(makeJetPlots(self, TwoLeptonsTwoJets_Resolved, AK4jets, channel))
#plots.extend(makeBoostedJetPLots(self, TwoLeptonsTwoJets_Boosted, AK8jets, channel))
# ----- plots : mll, mlljj, mjj, nVX, pT, eta : basic selection plots ------
#plots.extend(MakeControlPlotsForBasicSel(self, TwoLeptonsTwoJets_Resolved, AK4jets, dilepton, channel))
#plots.extend(MakeControlPlotsForBasicSel(self, TwoLeptonsTwoJets_boosted, AK8jets, dilepton, channel))
for wp in WorkingPoints:
# Get the best AK4 JETS
GetBestJetPair(bestDeepCSVPair,"DeepCSV", wp)
GetBestJetPair(bestDeepFlavourPair,"DeepFlavour", wp)
bestJetPairs["DeepCSV"]=bestDeepCSVPair
bestJetPairs["DeepFlavour"]=bestDeepFlavourPair
print ("bestJetPairs AK4--->", bestJetPairs, wp)
print ("bestJetPairs_deepcsv AK4--->", bestJetPairs["DeepCSV"][0], bestJetPairs["DeepCSV"][1], wp)
print ("bestJetPairs_deepflavour AK4 --->", bestJetPairs["DeepFlavour"][0],bestJetPairs["DeepFlavour"][1], wp)
# resolved
bJets_resolved_PassdeepflavourWP=safeget(bjets_resolved, "DeepFlavour", wp)
bJets_resolved_PassdeepcsvWP=safeget(bjets_resolved, "DeepCSV", wp)
# boosted
bJets_boosted_PassdeepcsvWP=safeget(bjets_boosted, "DeepCSV", wp)
TwoLeptonsTwoBjets_NoMETCut_Res = {
"DeepFlavour{0}".format(wp) : TwoLeptonsTwoJets_Resolved.refine("TwoLeptonsTwoBjets_NoMETcut_DeepFlavour{0}_{1}_Resolved".format(wp, channel),
cut=[ op.rng_len(bJets_resolved_PassdeepflavourWP) > 1 ],
weight=([ deepBFlavScaleFactor(bJets_resolved_PassdeepflavourWP[0]), deepBFlavScaleFactor(bJets_resolved_PassdeepflavourWP[1]) ]if isMC else None)),
"DeepCSV{0}".format(wp) : TwoLeptonsTwoJets_Resolved.refine("TwoLeptonsTwoBjets_NoMETcut_DeepCSV{0}_{1}_Resolved".format(wp, channel),
cut=[ op.rng_len(bJets_resolved_PassdeepcsvWP) > 1 ],
weight=([ deepB_AK4ScaleFactor(bJets_resolved_PassdeepcsvWP[0]), deepB_AK4ScaleFactor(bJets_resolved_PassdeepcsvWP[1]) ]if isMC else None))
}
TwoLeptonsTwoBjets_NoMETCut_Boo = {
"DeepCSV{0}".format(wp) : TwoLeptonsTwoJets_Boosted.refine("TwoLeptonsTwoBjets_NoMETcut_DeepCSV{0}_{1}_Boosted".format(wp, channel),
cut=[ op.rng_len(bJets_boosted_PassdeepcsvWP) > 1 ]),
# FIXME ! can't pass boosted jets SFs with current version ---> move to v7
#weight=([ deepB_AK8ScaleFactor(bJets_boosted_PassdeepcsvWP[0]), deepB_AK8ScaleFactor(bJets_boosted_PassdeepcsvWP[1]) ]if isMC else None))
}
## needed to optimize the MET cut
# FIXME Rerun again &&& pass signal and bkg
# The MET cut is passed to TwoLeptonsTwoBjets selection for the # tagger and for the # wp
plots.extend(MakeMETPlots(self, TwoLeptonsTwoBjets_NoMETCut_Res, corrMET, MET, channel, "resolved"))
plots.extend(MakeMETPlots(self, TwoLeptonsTwoBjets_NoMETCut_Boo, corrMET, MET, channel, "boosted"))
plots.extend(MakeExtraMETPlots(self, TwoLeptonsTwoBjets_NoMETCut_Res, dilepton, MET, channel, "resolved"))
plots.extend(MakeExtraMETPlots(self, TwoLeptonsTwoBjets_NoMETCut_Boo, dilepton, MET, channel, "boosted"))
TwoLeptonsTwoBjets_Res = dict((key, selNoMET.refine("TwoLeptonsTwoBjets_{0}_{1}_Resolved".format(key, channel), cut=[ corrMET.pt < 80. ])) for key, selNoMET in TwoLeptonsTwoBjets_NoMETCut_Res.items())
TwoLeptonsTwoBjets_Boo = dict((key, selNoMET.refine("TwoLeptonsTwoBjets_{0}_{1}_Boosted".format(key, channel), cut=[ corrMET.pt < 80. ])) for key, selNoMET in TwoLeptonsTwoBjets_NoMETCut_Boo.items())
#plots.extend(MakeDiscriminatorPlots(self, TwoLeptonsTwoBjets_Res, bjets_resolved, wp, channel, "resolved"))
#plots.extend(MakeDiscriminatorPlots(self, TwoLeptonsTwoBjets_Boo, bjets_boosted, wp, channel, "boosted"))
#plots.extend(makeResolvedBJetPlots(self, TwoLeptonsTwoBjets_Res, bjets_resolved, dilepton, wp, channel))
#plots.extend(makeBoostedBJetPlots(self, TwoLeptonsTwoBjets_NoMETCut_Boo, bjets_boosted, dilepton, wp, channel))
# --- to get the Ellipses plots
plots.extend(MakeEllipsesPLots(self, TwoLeptonsTwoBjets_Res, bjets_resolved, dilepton, wp, channel, "resolved"))
plots.extend(MakeEllipsesPLots(self, TwoLeptonsTwoBjets_Boo, bjets_boosted, dilepton, wp, channel, "boosted"))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, CutFlowReport, SummedPlot
from bamboo.plots import EquidistantBinning as EqB
from bamboo import treefunctions as op
isMC = self.isMC(sample)
if sampleCfg.get("alt-syst"):
noSel = noSel.refine("withoutsyst", autoSyst=False)
plots = []
trigCut, trigWeight = None, None
if isMC:
muR = op.systematic(op.c_float(1.),
name="muR",
up=t.PSWeight[2],
down=t.PSWeight[0])
muF = op.systematic(op.c_float(1.),
name="muF",
up=t.PSWeight[3],
down=t.PSWeight[1])
noSel = noSel.refine(
"mcWeight",
weight=[t.genWeight, t.puWeight, t.PrefireWeight, muR, muF])
trigCut = op.OR(t.HLT.HIEle20_Ele12_CaloIdL_TrackIdL_IsoVL_DZ,
t.HLT.HIL3DoubleMu0, t.HLT.HIL3Mu20,
t.HLT.HIEle20_WPLoose_Gsf)
trigWeight = op.switch(
op.OR(t.HLT.HIEle20_Ele12_CaloIdL_TrackIdL_IsoVL_DZ,
t.HLT.HIL3DoubleMu0), op.c_float(1.),
op.switch(t.HLT.HIL3Mu20, op.c_float(306.913 / 308.545),
op.c_float(264.410 / 308.545))
) ## FIXME these are wrong - you will get the final values from team A
else:
## trigger order: dielectron, dimuon or single muon, single electron
pd = sample.split("_")[0]
if pd == "SingleMuon":
trigCut = op.AND(
op.NOT(t.HLT.HIEle20_Ele12_CaloIdL_TrackIdL_IsoVL_DZ),
op.OR(t.HLT.HIL3DoubleMu0, t.HLT.HIL3Mu20))
elif pd == "HighEGJet":
trigCut = op.OR(
t.HLT.HIEle20_Ele12_CaloIdL_TrackIdL_IsoVL_DZ,
op.AND(op.NOT(op.OR(t.HLT.HIL3DoubleMu0, t.HLT.HIL3Mu20)),
t.HLT.HIEle20_WPLoose_Gsf))
noSel = noSel.refine("trig", cut=trigCut, weight=trigWeight)
#plots += [Plot.make1D("nTotalEvents", op.rng_len([1]), noSel , EqB(1, 0, 1.), title="nTotalEvents")]
plots.append(
Plot.make1D("nTotalJets",
op.rng_len(t.Jet),
noSel,
EqB(15, 0, 15.),
title="Initial Jet multiplicity"))
#noSel = noSel.refine("trig", cut=op.OR(t.HLT.HIL3DoubleMu0, t.HLT.HIEle20_Ele12_CaloIdL_TrackIdL_IsoVL_DZ))
# plots = []
goodLeptons = {
"el":
op.select(
t.Electron, lambda el: op.AND(el.pt > 15.,
op.abs(el.p4.Eta()) < 2.5)
), # op.select(t.Electron, partial(isGoodElectron, ptCut=15.)),
"mu":
op.select(t.Muon, lambda mu: mu.pt > 20.
) # op.select(t.Muon, partial(isGoodMuon, ptCut=15.))
}
plots += [
Plot.make1D(
"trig_nLeptons15",
op.rng_len(goodLeptons["el"]) + op.rng_len(goodLeptons["mu"]),
noSel, EqB(15, 0., 15.)),
Plot.make1D("trig_nEl15", op.rng_len(goodLeptons["el"]), noSel,
EqB(15, 0., 15.)),
Plot.make1D("trig_nMu15", op.rng_len(goodLeptons["mu"]), noSel,
EqB(15, 0., 15.))
]
from bamboo.scalefactors import get_scalefactor
sf_loose = {
"mu":
get_scalefactor("lepton",
"Muon_RecoToLoose",
sfLib=scalefactors_lepMVA,
paramDefs=binningVariables_nano_noScaleSyst,
systName="muLoose"),
"el":
get_scalefactor("lepton",
"Electron_RecoToLoose",
sfLib=scalefactors_lepMVA,
paramDefs=binningVariables_nano_noScaleSyst,
systName="elLoose")
}
sf_tight = {
"mu":
get_scalefactor("lepton",
"Muon_LooseToTight",
sfLib=scalefactors_lepMVA,
paramDefs=binningVariables_nano_noScaleSyst,
systName="muTight"),
"el":
get_scalefactor("lepton",
"Electron_LooseToTight",
sfLib=scalefactors_lepMVA,
paramDefs=binningVariables_nano_noScaleSyst,
systName="elTight")
}
nGoodLeptons = op.rng_len(goodLeptons["el"]) + op.rng_len(
goodLeptons["mu"])
hasTwoGoodLeptons = noSel.refine(
"has2Lep", cut=(nGoodLeptons > 1)) # avoid overlap with 1l
jets = op.sort(
op.select(
t.Jet, lambda j: op.AND(
j.pt > 25.,
op.abs(j.eta) < 2.4, j.jetId & 0x2,
op.AND(
op.NOT(
op.rng_any(goodLeptons["el"], lambda l: op.deltaR(
l.p4, j.p4) < 0.4)),
op.NOT(
op.rng_any(goodLeptons["mu"], lambda l: op.deltaR(
l.p4, j.p4) < 0.4))))), lambda j: -j.pt)
## WP: see https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation94X
loosebjets = op.select(jets, lambda j: j.btagDeepB > 0.1522)
mediumbjets = op.select(jets, lambda j: j.btagDeepB > 0.4941)
for fl1, fl2 in product(*repeat(goodLeptons.keys(), 2)):
dilepSel = lambda l1, l2: op.AND(l1.charge != l2.charge,
(l1.p4 + l2.p4).M() > 12.)
if fl1 == fl2:
lGood = op.sort(goodLeptons[fl1], lambda l: -l.pt)
dilep = op.combine(lGood, N=2, pred=dilepSel)
else:
l1Good = op.sort(goodLeptons[fl1], lambda l: -l.pt)
l2Good = op.sort(goodLeptons[fl2], lambda l: -l.pt)
dilep = op.combine((l1Good, l2Good), pred=dilepSel)
ll = dilep[0]
hasDilep = hasTwoGoodLeptons.refine(
f"hasDilep{fl1}{fl2}",
cut=(op.rng_len(dilep) > 0, ll[0].pt > 25.),
weight=([
sf_loose[fl1](ll[0]), sf_loose[fl2](ll[1]), sf_tight[fl1](
ll[0]), sf_tight[fl2](ll[1])
] if isMC else None))
plots += [
Plot.make1D(f"dilepton_{fl1}{fl2}_Mll",
(ll[0].p4 + ll[1].p4).M(),
hasDilep,
EqB(50, 70, 120.),
title="Dilepton mass"),
]
for il, ifl in enumerate((fl1, fl2)):
plots += [
Plot.make1D(f"dilepton_{fl1}{fl2}_L{il:d}PT",
ll[il].pt,
hasDilep,
EqB(50, 0., 100.),
title=f"Lepton {il:d} PT"),
Plot.make1D(f"dilepton_{fl1}{fl2}_L{il:d}ETA",
ll[il].eta,
hasDilep,
EqB(50, -2.5, 2.5),
title=f"Lepton {il:d} ETA"),
]
plots += [
Plot.make1D(f"dilepton_{fl1}{fl2}_nJets",
op.rng_len(jets),
hasDilep,
EqB(15, 0, 15.),
title="Jet multiplicity"),
Plot.make1D(f"dilepton_{fl1}{fl2}_nLooseBJets",
op.rng_len(loosebjets),
hasDilep,
EqB(15, 0, 15.),
title="Loose b-jet multiplicity"),
Plot.make1D(f"dilepton_{fl1}{fl2}_nMediumBJets",
op.rng_len(mediumbjets),
hasDilep,
EqB(15, 0, 15.),
title="Medium b-jet multiplicity"),
#Plot.make1D(f"dilepton_{fl1}{fl2}_nSelectedEvents", 1, hasDilep, EqB(1, 0, 1.), title="nSelectedEvents")
]
#muons = op.select(t.Muon, lambda mu : mu.pt > 20.)
#twoMuSel = noSel.refine("twoMuons", cut=[ op.rng_len(muons) > 1 ])
#electrons = op.select(t.Electron, lambda el : op.AND(el.pt > 15. , op.abs(el.p4.Eta()) < 2.5))
#twoElSel = noSel.refine("twoElectrons", cut=[ op.rng_len(electrons) > 1 ])
#oselmu = op.combine((electrons, muons))
#leptons = oselmu[0]
#twoLepSel = noSel.refine("twoLeptons", cut=[ op.rng_len(electrons) == 1 , op.rng_len(muons) == 1 ])
#jets = op.select(t.Jet, lambda j : j.pt > 30.)
#bjets = op.select(jets, lambda j : j.btagDeepFlavB > 0.2217)
#plots.append(Plot.make1D("dimu_M",
# op.invariant_mass(muons[0].p4, muons[1].p4), twoMuSel, EqB(100, 20., 120.),
# title="Dimuon invariant mass", plotopts={"show-overflow":False,
# "legend-position": [0.2, 0.6, 0.5, 0.9]}))
#plots.append(Plot.make1D("diel_M",
# op.invariant_mass(electrons[0].p4, electrons[1].p4), twoElSel, EqB(100, 20., 120.),
# title="Dielectron invariant mass", plotopts={"show-overflow":False,
# "legend-position": [0.2, 0.6, 0.5, 0.9]}))
#plots.append(Plot.make1D("dilep_M",
# op.invariant_mass(leptons[0].p4, leptons[1].p4) , twoLepSel, EqB(100, 20., 120.),
# title="Dimuon invariant mass", plotopts={"show-overflow":False,
# "legend-position": [0.2, 0.6, 0.5, 0.9]}))
#plots.append(SummedPlot("Mjj", plots, title="m(jj)"))
#plots.append(Plot.make1D("nJets_dimu",op.rng_len(jets), twoMuSel, EqB(10, -0.5, 9.5),
# title="Jet multiplicity", plotopts={"show-overflow":False,
# "legend-position": [0.2, 0.6, 0.5, 0.9]}))
#plots.append(Plot.make1D("nBJets_dimu",op.rng_len(bjets), twoMuSel, EqB(10, -0.5, 9.5),
# title="Jet multiplicity", plotopts={"show-overflow":False,
# "legend-position": [0.2, 0.6, 0.5, 0.9]}))
#plots.append(Plot.make1D("nJets_diel",op.rng_len(jets), twoElSel, EqB(10, -0.5, 9.5),
# title="Jet multiplicity", plotopts={"show-overflow":False,
# "legend-position": [0.2, 0.6, 0.5, 0.9]}))
#plots.append(Plot.make1D("nBJets_diel",op.rng_len(bjets), twoElSel, EqB(10, -0.5, 9.5),
# title="Jet multiplicity", plotopts={"show-overflow":False,
# "legend-position": [0.2, 0.6, 0.5, 0.9]}))
#plots.append(Plot.make1D("nJets_elmu",op.rng_len(jets), twoLepSel, EqB(10, -0.5, 9.5),
# title="Jet multiplicity", plotopts={"show-overflow":False,
# "legend-position": [0.2, 0.6, 0.5, 0.9]}))
#plots.append(Plot.make1D("nBJets_elmu",op.rng_len(bjets), twoLepSel, EqB(10, -0.5, 9.5),
# title="Jet multiplicity", plotopts={"show-overflow":False,
# "legend-position": [0.2, 0.6, 0.5, 0.9]}))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
noSel = super(TransferFunction,
self).prepareObjects(t, noSel, sample, sampleCfg, 'DL')
noSel = self.beforeJetselection(noSel)
era = sampleCfg['era']
plots = []
if not self.is_MC:
return plots
lambda_is_matched = lambda lep: op.OR(
lep.genPartFlav == 1, # Prompt muon or electron
lep.genPartFlav == 15) # From tau decay
#lep.genPartFlav==22) # From photon conversion (only available for electrons)
plots.append(
Plot.make1D("totalWeight",
noSel.weight,
noSel,
EquidistantBinning(100, -int(10**5), int(10**5)),
xTitle="total weight"))
plots.append(
Plot.make1D("genWeight",
t.genWeight,
noSel,
EquidistantBinning(100, -int(10**5), int(10**5)),
xTitle="gen weight"))
##########################################################
# Leptons #
##########################################################
#----- Gen particles -----#
gen_e = op.select(
t.GenPart, lambda g: op.AND(
op.abs(g.pdgId) == 11, g.statusFlags & (0x1 << 13), g.pt >= 10,
op.abs(g.eta) < 2.5))
gen_m = op.select(
t.GenPart, lambda g: op.AND(
op.abs(g.pdgId) == 13, g.statusFlags & (0x1 << 13), g.pt >= 10,
op.abs(g.eta) < 2.4))
#----- Reco particles -----#
lambda_reco_e = lambda el: op.AND(
op.abs(el.pdgId) == 11, el.pt >= 10,
op.abs(el.eta) < 2.5, lambda_is_matched(el))
lambda_reco_m = lambda mu: op.AND(
op.abs(mu.pdgId) == 13, mu.pt >= 10,
op.abs(mu.eta) < 2.4, lambda_is_matched(mu))
reco_e = op.select(self.electronsFakeSel, lambda_reco_e)
reco_m = op.select(self.muonsFakeSel, lambda_reco_m)
#---- Matching -----#
lambda_lepton_match = lambda gen, reco: op.AND(
op.deltaR(gen.p4, reco.p4) < 0.1, (op.abs(gen.pt - reco.pt) /
(gen.pt + reco.pt)) < 0.2)
# reco_e_matched = op.select(reco_e, lambda re : op.rng_any(gen_e, lambda ge: lambda_lepton_match(ge,re)))
# reco_m_matched = op.select(reco_m, lambda rm : op.rng_any(gen_m, lambda gm: lambda_lepton_match(gm,rm)))
#match_e = op.combine((gen_e,reco_e), pred = lambda ge,re : re.idx == op.rng_min_element_by(reco_e_matched,lambda re_matched : op.deltaR(ge.p4,re_matched.p4)).idx)
#match_m = op.combine((gen_m,reco_m), pred = lambda gm,rm : rm.idx == op.rng_min_element_by(reco_m_matched,lambda rm_matched : op.deltaR(gm.p4,rm_matched.p4)).idx)
match_e = op.combine((gen_e, reco_e), pred=lambda_lepton_match)
match_m = op.combine((gen_m, reco_m), pred=lambda_lepton_match)
plots.extend(plotMatching("e", match_e, noSel, "e^{#pm}"))
plots.extend(plotMatching("m", match_m, noSel, "#mu^{#pm}"))
# plots.append(plotRecoForGen(noSel,gen_e,tight_e,lambda_lepton_match,"e_tight"))
# plots.append(plotRecoForGen(noSel,gen_e,tight_m,lambda_lepton_match,"m_tight"))
##########################################################
# Ak4 B jets #
##########################################################
#----- RecoJets -----#
recoJet_b = op.select(
t.Jet, lambda j: op.AND(
op.abs(j.partonFlavour) == 5, j.pt >= 20,
op.abs(j.eta) < 2.4, j.genJet.isValid))
#----- Parton -----#
gen_b = op.select(
t.GenPart, lambda g: op.AND(
op.abs(g.pdgId) == 5, g.statusFlags & (0x1 << 13), g.pt >= 20,
op.abs(g.eta) < 2.4))
#----- Matching -----#
lambda_jet_match = lambda gen, reco: op.AND(
op.deltaR(gen.p4, reco.p4) < 0.2)
reco_b_matched = op.select(
recoJet_b, lambda jetb: op.rng_any(
gen_b, lambda gb: lambda_jet_match(gb, jetb)))
match_b = op.combine(
(gen_b, recoJet_b),
pred=lambda gb, jetb: jetb.idx == op.rng_min_element_by(
reco_b_matched, lambda rb_matched: op.deltaR(
gb.p4, rb_matched.p4)).idx)
plots.extend(plotMatching("ak4b", match_b, noSel, "b"))
##########################################################
# Ak4 C jets #
##########################################################
#----- RecoJets -----#
recoJet_c = op.select(
t.Jet, lambda j: op.AND(
op.abs(j.partonFlavour) == 4, j.pt >= 20,
op.abs(j.eta) < 2.4, j.genJet.isValid))
#----- Parton -----#
gen_c = op.select(
t.GenPart, lambda g: op.AND(
op.abs(g.pdgId) == 4, g.statusFlags & (0x1 << 13), g.pt >= 20,
op.abs(g.eta) < 2.4))
#----- Matching -----#
reco_c_matched = op.select(
recoJet_c, lambda jetc: op.rng_any(
gen_c, lambda gc: lambda_jet_match(gc, jetc)))
match_c = op.combine(
(gen_c, recoJet_c),
pred=lambda gc, jetc: jetc.idx == op.rng_min_element_by(
reco_c_matched, lambda rc_matched: op.deltaR(
gc.p4, rc_matched.p4)).idx)
plots.extend(plotMatching("ak4c", match_c, noSel, "c"))
##########################################################
# Ak4 lightjets #
##########################################################
#----- RecoJets -----#
recoJet_l = op.select(
t.Jet, lambda j: op.AND(
op.OR(
op.abs(j.partonFlavour) == 1,
op.abs(j.partonFlavour) == 2,
op.abs(j.partonFlavour) == 3), j.pt >= 20,
op.abs(j.eta) < 2.4, j.genJet.isValid))
#----- Parton -----#
gen_l = op.select(
t.GenPart, lambda g: op.AND(
op.OR(
op.abs(g.pdgId) == 1,
op.abs(g.pdgId) == 2,
op.abs(g.pdgId) == 3), g.statusFlags &
(0x1 << 13), g.pt >= 20,
op.abs(g.eta) < 2.4))
#----- Matching -----#
reco_l_matched = op.select(
recoJet_l, lambda jetl: op.rng_any(
gen_l, lambda gl: lambda_jet_match(gl, jetl)))
match_l = op.combine(
(gen_l, recoJet_l),
pred=lambda gl, jetl: jetl.idx == op.rng_min_element_by(
reco_l_matched, lambda rl_matched: op.deltaR(
gl.p4, rl_matched.p4)).idx)
plots.extend(plotMatching("ak4l", match_l, noSel, "l"))
##########################################################
# Ak8 jets #
##########################################################
#----- RecoJets -----#
recoFatJet = op.select(
t.FatJet, lambda j: op.AND(
j.pt >= 100,
op.abs(j.eta) < 2.4,
op.AND(j.subJet1.isValid, j.subJet1.pt >= 20.,
op.abs(j.subJet1.eta) <= 2.4, j.subJet2.isValid, j.
subJet2.pt >= 20.,
op.abs(j.subJet2.eta) <= 2.4),
op.AND(j.msoftdrop >= 30, j.msoftdrop <= 210), j.tau2 / j.tau1
<= 0.75))
#plots.extend(fatjetPlots(noSel,recoFatJet,"fatjet"))
reco_subJet1_b_matched = op.select(
recoFatJet, lambda fat: op.rng_any(
gen_b, lambda gb: lambda_jet_match(gb, fat.subJet1)))
reco_subJet2_b_matched = op.select(
recoFatJet, lambda fat: op.rng_any(
gen_b, lambda gb: lambda_jet_match(gb, fat.subJet2)))
reco_subJet1_c_matched = op.select(
recoFatJet, lambda fat: op.rng_any(
gen_c, lambda gc: lambda_jet_match(gc, fat.subJet1)))
reco_subJet2_c_matched = op.select(
recoFatJet, lambda fat: op.rng_any(
gen_c, lambda gc: lambda_jet_match(gc, fat.subJet2)))
reco_subJet1_l_matched = op.select(
recoFatJet, lambda fat: op.rng_any(
gen_l, lambda gl: lambda_jet_match(gl, fat.subJet1)))
reco_subJet2_l_matched = op.select(
recoFatJet, lambda fat: op.rng_any(
gen_l, lambda gl: lambda_jet_match(gl, fat.subJet2)))
def makeFatjetMatch(gen1, gen2, recoFatJet):
return op.combine(
(gen1, gen2, recoFatJet),
pred=lambda g1, g2, fat: op.AND(
fat.subJet1.idx == op.rng_min_element_by(
reco_subJet1_b_matched, lambda sub1_matched: op.deltaR(
g1.p4, sub1_matched.p4)).idx, fat.subJet2.idx == op
.rng_min_element_by(
reco_subJet2_b_matched, lambda sub2_matched: op.deltaR(
g2.p4, sub2_matched.p4)).idx))
match_fat_bb = makeFatjetMatch(gen_b, gen_b, recoFatJet)
match_fat_bc = makeFatjetMatch(gen_b, gen_c, recoFatJet)
match_fat_bl = makeFatjetMatch(gen_b, gen_l, recoFatJet)
match_fat_cb = makeFatjetMatch(gen_c, gen_b, recoFatJet)
match_fat_cc = makeFatjetMatch(gen_c, gen_c, recoFatJet)
match_fat_cl = makeFatjetMatch(gen_c, gen_l, recoFatJet)
match_fat_lb = makeFatjetMatch(gen_l, gen_b, recoFatJet)
match_fat_lc = makeFatjetMatch(gen_l, gen_c, recoFatJet)
match_fat_ll = makeFatjetMatch(gen_l, gen_l, recoFatJet)
# match_fat_bb = op.combine((gen_b,gen_b,recoFatJet), pred = lambda g1,g2,fat : op.AND(
# fat.subJet1.idx == op.rng_min_element_by(reco_subJet1_b_matched,
# lambda sub1_matched : op.deltaR(gb1.p4,sub1_matched.p4)).idx,
# fat.subJet2.idx == op.rng_min_element_by(reco_subJet2_b_matched,
# lambda sub2_matched : op.deltaR(gb2.p4,sub2_matched.p4)).idx))
# match_fat_bc = op.combine((gen_b,gen_b,recoFatJet), pred = lambda gb1,gb2,fat : op.AND(
# fat.subJet1.idx == op.rng_min_element_by(reco_subJet1_b_matched,
# lambda sub1_matched : op.deltaR(gb1.p4,sub1_matched.p4)).idx,
# fat.subJet2.idx == op.rng_min_element_by(reco_subJet2_b_matched,
# lambda sub2_matched : op.deltaR(gb2.p4,sub2_matched.p4)).idx))
plots.extend(plotFatjetMatching(match_fat_bb, noSel, 'b', 'b'))
plots.extend(plotFatjetMatching(match_fat_bc, noSel, 'b', 'c'))
plots.extend(plotFatjetMatching(match_fat_bl, noSel, 'b', 'l'))
plots.extend(plotFatjetMatching(match_fat_cb, noSel, 'c', 'b'))
plots.extend(plotFatjetMatching(match_fat_cc, noSel, 'c', 'c'))
plots.extend(plotFatjetMatching(match_fat_cl, noSel, 'c', 'l'))
plots.extend(plotFatjetMatching(match_fat_bb, noSel, 'l', 'b'))
plots.extend(plotFatjetMatching(match_fat_bc, noSel, 'l', 'c'))
plots.extend(plotFatjetMatching(match_fat_bl, noSel, 'l', 'l'))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
era = sampleCfg['era']
plots = []
##########################################################
# Leptons #
##########################################################
#----- Gen particles -----#
gen_e_minus = op.select(
t.GenPart, lambda g: op.AND(g.pdgId == 11, g.statusFlags &
(0x1 << 13), g.pt > 10,
op.abs(g.eta) < 2.5))
gen_e_plus = op.select(
t.GenPart, lambda g: op.AND(g.pdgId == -11, g.statusFlags &
(0x1 << 13), g.pt > 10,
op.abs(g.eta) < 2.5))
gen_mu_minus = op.select(
t.GenPart, lambda g: op.AND(g.pdgId == 13, g.statusFlags &
(0x1 << 13), g.pt > 10,
op.abs(g.eta) < 2.4))
gen_mu_plus = op.select(
t.GenPart, lambda g: op.AND(g.pdgId == -13, g.statusFlags &
(0x1 << 13), g.pt > 10,
op.abs(g.eta) < 2.4))
plots.extend(
plotNumber("N_e_minus_gen", gen_e_minus, noSel, 10,
"N(e^{-}) gen level"))
plots.extend(
plotNumber("N_e_plus_gen", gen_e_plus, noSel, 10,
"N(e^{+}) gen level"))
plots.extend(
plotNumber("N_mu_minus_gen", gen_mu_minus, noSel, 10,
"N(\mu^{-}) gen level"))
plots.extend(
plotNumber("N_mu_plus_gen", gen_mu_plus, noSel, 10,
"N(\mu^{+}) gen level"))
#----- Reco particles -----#
reco_e_minus = op.select(
t.Electron, lambda ele: op.AND(
ele.charge == -1, ele.pdgId == 11, ele.pt > 10,
op.abs(ele.eta) < 2.5,
op.OR(ele.genPartFlav == 1, ele.genPartFlav == 15, ele.
genPartFlav == 22)))
reco_e_plus = op.select(
t.Electron, lambda ele: op.AND(
ele.charge == +1, ele.pdgId == -11, ele.pt > 10,
op.abs(ele.eta) < 2.5,
op.OR(ele.genPartFlav == 1, ele.genPartFlav == 15, ele.
genPartFlav == 22)))
reco_mu_minus = op.select(
t.Muon, lambda mu: op.AND(
mu.charge == -1, mu.pdgId == 13, mu.pt > 10,
op.abs(mu.eta) < 2.4,
op.OR(mu.genPartFlav == 1, mu.genPartFlav == 15)))
reco_mu_plus = op.select(
t.Muon, lambda mu: op.AND(
mu.charge == +1, mu.pdgId == -13, mu.pt > 10,
op.abs(mu.eta) < 2.4,
op.OR(mu.genPartFlav == 1, mu.genPartFlav == 15)))
plots.extend(
plotNumber("N_e_minus_reco", reco_e_minus, noSel, 10,
"N(e^{-}) reco level"))
plots.extend(
plotNumber("N_e_plus_reco", reco_e_plus, noSel, 10,
"N(e^{+}) reco level"))
plots.extend(
plotNumber("N_mu_minus_reco", reco_mu_minus, noSel, 10,
"N(\mu^{-}) reco level"))
plots.extend(
plotNumber("N_mu_plus_reco", reco_mu_plus, noSel, 10,
"N(\mu^{+}) reco level"))
#---- Matching -----#
lambda_lepton_match = lambda l_gen, l_reco: op.AND(
op.deltaR(l_gen.p4, l_reco.p4) < 0.1,
op.abs(l_gen.pt - l_reco.pt) / l_gen.pt < 0.2)
match_e_minus = op.combine((gen_e_minus, reco_e_minus),
pred=lambda_lepton_match)
match_e_plus = op.combine((gen_e_plus, reco_e_plus),
pred=lambda_lepton_match)
match_mu_minus = op.combine((gen_mu_minus, reco_mu_minus),
pred=lambda_lepton_match)
match_mu_plus = op.combine((gen_mu_plus, reco_mu_plus),
pred=lambda_lepton_match)
SelEMinusMatch = noSel.refine("SelEMinusMatch",
cut=[op.rng_len(match_e_minus) >= 1])
SelEPlusMatch = noSel.refine("SelEPlusMatch",
cut=[op.rng_len(match_e_plus) >= 1])
SelMuMinusMatch = noSel.refine("SelMuMinusMatch",
cut=[op.rng_len(match_mu_minus) >= 1])
SelMuPlusMatch = noSel.refine("SelMuPlusMatch",
cut=[op.rng_len(match_mu_plus) >= 1])
plots.extend(
plotNumber("N_e_minus_match", match_e_minus, noSel, 10,
"N(e^{-}) match level"))
plots.extend(
plotNumber("N_e_plus_match", match_e_plus, noSel, 10,
"N(e^{+}) match level"))
plots.extend(
plotNumber("N_mu_minus_match", match_mu_minus, noSel, 10,
"N(\mu^{-}) match level"))
plots.extend(
plotNumber("N_mu_plus_match", match_mu_plus, noSel, 10,
"N(\mu^{+}) match level"))
plots.extend(
plotMatching("e", [match_e_minus, match_e_plus],
[SelEMinusMatch, SelEPlusMatch], "e^{#pm}"))
plots.extend(
plotMatching("mu", [match_mu_minus, match_mu_plus],
[SelMuMinusMatch, SelMuPlusMatch], "#mu^{#pm}"))
##########################################################
# B jets #
##########################################################
#----- RecoJets -----#
#recoJet_b = op.select(t.Jet, lambda j : op.AND(j.partonFlavour == 5, j.pt>10, op.abs(j.eta)<2.4 ,(op.abs(j.pt-j.genJet.pt)/j.genJet.pt)<1 ))
#recoJet_bbar = op.select(t.Jet, lambda j : op.AND(j.partonFlavour == -5, j.pt>10, op.abs(j.eta)<2.4 ,(op.abs(j.pt-j.genJet.pt)/j.genJet.pt)<1 ))
recoJet_b = op.select(
t.Jet, lambda j: op.AND(j.partonFlavour == 5, j.pt > 10,
op.abs(j.eta) < 2.4, j.genJet.isValid))
recoJet_bbar = op.select(
t.Jet, lambda j: op.AND(j.partonFlavour == -5, j.pt > 10,
op.abs(j.eta) < 2.4, j.genJet.isValid))
plots.extend(
plotNumber("N_jetb_reco", recoJet_b, noSel, 5, "N(b) reco jet"))
plots.extend(
plotNumber("N_jetbbar_reco", recoJet_bbar, noSel, 5,
"N(#bar{b}) reco jet"))
SelBReco = noSel.refine("SelBReco", cut=[op.rng_len(recoJet_b) >= 1])
SelBBarReco = noSel.refine("SelBBarReco",
cut=[op.rng_len(recoJet_bbar) >= 1])
# plots.extend(plotJetReco("bjet",recoJet_b,SelBReco,"b jet"))
# plots.extend(plotJetReco("bbarjet",recoJet_bbar,SelBBarReco,"#bar{b} jet"))
#
#
# #----- Parton -----#
# gen_b = op.select(t.GenPart,lambda g : op.AND( g.pdgId==5 , g.statusFlags & ( 0x1 << 13) , g.pt>10, op.abs(g.eta)<2.4))
# gen_bbar = op.select(t.GenPart,lambda g : op.AND( g.pdgId==-5 , g.statusFlags & ( 0x1 << 13) , g.pt>10, op.abs(g.eta)<2.4))
# plots.extend(plotNumber("N_b_gen",gen_b,noSel,5,"N(b) gen quark"))
# plots.extend(plotNumber("N_bbar_gen",gen_bbar,noSel,5,"N(#bar{b}) gen quark"))
#
# #----- Matching -----#
# lambda_bjet_match = lambda b_gen,b_jet : op.deltaR(b_gen.p4,b_jet.genJet.p4)<0.2
#
# match_b = op.combine((gen_b,recoJet_b),pred=lambda_bjet_match)
# match_bbar = op.combine((gen_bbar,recoJet_bbar),pred=lambda_bjet_match)
#
# plots.extend(plotNumber("N_b_match",match_b,noSel,5,"N(b) match level"))
# plots.extend(plotNumber("N_bbar_match",match_bbar,noSel,5,"N(#bar{b}) match level"))
#
# SelBMatch = noSel.refine("SelBMatch",cut=[op.rng_len(match_b)>=1])
# SelBBarMatch = noSel.refine("SelBBarMatch",cut=[op.rng_len(match_bbar)>=1])
#
# plots.extend(plotMatching("b",[match_b,match_bbar],[SelBMatch,SelBBarMatch],"b"))
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
from bamboo.plots import Plot, CutFlowReport
from bamboo.plots import EquidistantBinning as EqB
from bamboo import treefunctions as op
plots = []
#definitions
electrons = op.select(t.elec, lambda el : op.AND(
el.pt > 20., op.abs(el.eta) < 2.5
))
muons = op.select(t.muon, lambda mu : op.AND(
mu.pt > 20., op.abs(mu.eta) < 2.5
))
cleanedElectrons = op.select(electrons, lambda el : op.NOT(
op.rng_any(muons, lambda mu : op.deltaR(el.p4, mu.p4) < 0.3 )
))
# we are taking the second isopass to be on which is equal to the medium working point
isolatedElectrons = op.select(cleanedElectrons, lambda el : el.isopass & (1<<2) )
identifiedElectrons = op.select(isolatedElectrons, lambda el : el.idpass & (1<<2) )
cleanedMuons = op.select(muons, lambda mu : op.NOT(
op.rng_any(electrons, lambda el : op.deltaR(mu.p4, el.p4) < 0.3 )
))
isolatedMuons = op.select(cleanedMuons, lambda mu : mu.isopass & (1<<2) )
identifiedMuons = op.select(isolatedMuons, lambda mu : mu.idpass & (1<<2) )
InvMassMuMU = op.invariant_mass(identifiedMuons[0].p4, identifiedMuons[1].p4 )
cleanedJets = op.select(t.jetpuppi, lambda j : op.AND(
op.NOT(op.rng_any(identifiedElectrons, lambda el : op.deltaR(el.p4, j.p4) < 0.3) ),
op.NOT(op.rng_any(identifiedMuons, lambda mu : op.deltaR(mu.p4, j.p4) < 0.3) )
))
cleanedGoodJets = op.select(cleanedJets, lambda j : op.AND(
j.pt > 30, op.abs(j.eta) < 2.5
))
btaggedJets = op.select(cleanedGoodJets, lambda j : j.btag & (1<<2))
met = op.select(t.metpuppi)
#selections
#selection1 : Oppositely charged MuMu selection
sel1 = noSel.refine("nmumu", cut = [op.AND(
(op.rng_len(identifiedMuons) > 1), (op.product(identifiedMuons[0].charge, identifiedMuons[1].charge) < 0 ))])
#selection2 : Invariant mass selection
sel2 = sel1.refine("InvM", cut = [op.NOT(op.in_range(76, InvMassMuMU, 106))])
#selection3 : two jets selection
sel3 = sel2.refine("njet", cut = [op.rng_len(cleanedGoodJets) > 1])
#selection4 : at least 1 among two leading jets is b-tagged
sel4 = sel3.refine("btag", cut = [op.OR(
cleanedGoodJets[0].btag & (1<<2), cleanedGoodJets[1].btag & (1<<2))])
#selection5 : MET > 40 GeV
sel5 = sel4.refine("MET", cut = [met[0].pt > 40])
#plots
#noSel
plots.append(Plot.make1D("nJetsNoSel", op.rng_len(cleanedGoodJets), noSel, EqB(10, 0., 10.), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsNoSel", op.rng_len(btaggedJets), noSel, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("nMuNoSel", op.rng_len(identifiedMuons), noSel, EqB(15, 0., 15.), title="nMuons"))
plots.append(Plot.make1D("METptNoSel", met[0].pt, noSel, EqB(50, 0., 250), title="MET_PT"))
#sel1
plots.append(Plot.make1D("nJetsSel1", op.rng_len(cleanedGoodJets), sel1, EqB(10, 0., 10.), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel1", op.rng_len(btaggedJets), sel1, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("nMuSel1", op.rng_len(identifiedMuons), sel1, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("InvMassTwoMuonsSel1", InvMassMuMU, sel1, EqB(30, 0, 300), title="m(ll)"))
plots.append(Plot.make1D("LeadingMuonPTSel1", muons[0].pt, sel1, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel1", muons[1].pt, sel1, EqB(30, 0., 250.), title="SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel1", muons[0].eta, sel1, EqB(30, -3, 3), title=" Leading Muon eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel1", muons[1].eta, sel1, EqB(30, -3, 3), title="SubLeading Muon eta"))
plots.append(Plot.make1D("METptSel1", met[0].pt, sel1, EqB(50, 0., 250), title="MET_PT"))
#sel2
plots.append(Plot.make1D("nJetsSel2", op.rng_len(cleanedGoodJets), sel2, EqB(10, 0., 10.), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel2", op.rng_len(btaggedJets), sel2, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("nMuSel2", op.rng_len(identifiedMuons), sel2, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("InvMassTwoMuonsSel2", InvMassMuMU, sel2, EqB(20, 20., 300.), title="m(ll)"))
plots.append(Plot.make1D("LeadingMuonPTSel2", muons[0].pt, sel2, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel2", muons[1].pt, sel2, EqB(30, 0., 200.), title=" SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel2", muons[0].eta, sel2, EqB(30, -3, 3), title=" Leading Muon Eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel2", muons[1].eta, sel2, EqB(30, -3, 3), title=" SubLeading Muon Eta"))
plots.append(Plot.make1D("METptSel2", met[0].pt, sel2, EqB(50, 0., 250), title="MET_PT"))
#sel3
plots.append(Plot.make1D("nJetsSel3", op.rng_len(cleanedGoodJets), sel3, EqB(10, 0., 10.), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel3", op.rng_len(btaggedJets), sel3, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("LeadingJetPTSel3", cleanedGoodJets[0].pt, sel3, EqB(50, 0., 350.), title="Leading jet PT"))
plots.append(Plot.make1D("SubLeadingJetPTSel3", cleanedGoodJets[1].pt, sel3, EqB(50, 0., 350.), title="SubLeading jet PT"))
plots.append(Plot.make1D("LeadingJetEtaSel3", cleanedGoodJets[0].eta, sel3, EqB(30, -3, 3), title="Leading jet Eta"))
plots.append(Plot.make1D("SubLeadingJetEtaSel3", cleanedGoodJets[1].eta, sel3, EqB(30, -3, 3), title="SubLeading jet Eta"))
plots.append(Plot.make1D("nMuSel3", op.rng_len(identifiedMuons), sel3, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("LeadingMuonPTSel3", muons[0].pt, sel3, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel3", muons[1].pt, sel3, EqB(30, 0., 200.), title=" SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel3", muons[0].eta, sel3, EqB(30, -3, 3), title=" Leading Muon Eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel3", muons[1].eta, sel3, EqB(30, -3, 3), title=" SubLeading Muon Eta"))
plots.append(Plot.make1D("InvMassTwoMuonsSel3", InvMassMuMU, sel3, EqB(30, 0, 300), title="m(ll)"))
plots.append(Plot.make1D("METptSel3", met[0].pt, sel3, EqB(50, 0., 250), title="MET_PT"))
#sel4
plots.append(Plot.make1D("nJetsSel4", op.rng_len(cleanedGoodJets), sel4, EqB(10, 0, 10), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel4", op.rng_len(btaggedJets), sel4, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("LeadingJetPTSel4", cleanedGoodJets[0].pt, sel4, EqB(50, 0., 250.), title="Leading jet PT"))
plots.append(Plot.make1D("SubLeadingJetPTSel4", cleanedGoodJets[1].pt, sel4, EqB(50, 0., 250.), title="SubLeading jet PT"))
plots.append(Plot.make1D("LeadingJetEtaSel4", cleanedGoodJets[0].eta, sel4, EqB(30, -3, 3.), title="Leading jet Eta"))
plots.append(Plot.make1D("SubLeadingJetEtaSel4", cleanedGoodJets[1].eta, sel4, EqB(30, -3, 3.), title="SubLeading jet Eta"))
plots.append(Plot.make1D("nMuSel4", op.rng_len(identifiedMuons), sel4, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("LeadingMuonPTSel4", muons[0].pt, sel4, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel4", muons[1].pt, sel4, EqB(30, 0., 200.), title=" SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel4", muons[0].eta, sel4, EqB(30, -3, 3), title=" Leading Muon Eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel4", muons[1].eta, sel4, EqB(30, -3, 3), title=" SubLeading Muon Eta"))
plots.append(Plot.make1D("InvMassTwoMuonsSel4", InvMassMuMU, sel4, EqB(30, 0, 300), title="m(ll)"))
plots.append(Plot.make1D("METptSel4", met[0].pt, sel4, EqB(50, 0., 250), title="MET_PT"))
#sel5
plots.append(Plot.make1D("nJetsSel5", op.rng_len(cleanedGoodJets), sel5, EqB(10, 0, 10), title="nJets"))
plots.append(Plot.make1D("nbtaggedJetsSel5", op.rng_len(btaggedJets), sel5, EqB(10, 0., 10.), title="nbtaggedJets"))
plots.append(Plot.make1D("LeadingJetPTSel5", cleanedGoodJets[0].pt, sel5, EqB(50, 0., 250.), title="Leading jet PT"))
plots.append(Plot.make1D("SubLeadingJetPTSel5", cleanedGoodJets[1].pt, sel5, EqB(50, 0., 250.), title="SubLeading jet PT"))
plots.append(Plot.make1D("LeadingJetEtaSel5", cleanedGoodJets[0].eta, sel5, EqB(30, -3, 3.), title="Leading jet Eta"))
plots.append(Plot.make1D("SubLeadingJetEtaSel5", cleanedGoodJets[1].eta, sel5, EqB(30, -3, 3.), title="SubLeading jet Eta"))
plots.append(Plot.make1D("nMuSel5", op.rng_len(identifiedMuons), sel5, EqB(10, 0., 10.), title="nMuons"))
plots.append(Plot.make1D("LeadingMuonPTSel5", muons[0].pt, sel5, EqB(30, 0., 250.), title=" Leading Muon PT"))
plots.append(Plot.make1D("SubLeadingMuonPTSel5", muons[1].pt, sel5, EqB(30, 0., 200.), title=" SubLeading Muon PT"))
plots.append(Plot.make1D("LeadingMuonEtaSel5", muons[0].eta, sel5, EqB(30, -3, 3), title=" Leading Muon Eta"))
plots.append(Plot.make1D("SubLeadingMuonEtaSel5", muons[1].eta, sel5, EqB(30, -3, 3), title=" SubLeading Muon Eta"))
plots.append(Plot.make1D("InvMassTwoMuonsSel5", InvMassMuMU, sel5, EqB(30, 0, 300), title="m(ll)"))
plots.append(Plot.make1D("METptSel5", met[0].pt, sel5, EqB(50, 0., 250), title="MET_PT > 40"))
# Efficiency Report on terminal and the .tex output
cfr = CutFlowReport("yields")
cfr.add(noSel, "Sel0: No selection")
cfr.add(sel1, "Sel1: nMuMu >= 2")
cfr.add(sel2, "Sel2: InvM")
cfr.add(sel3, "Sel3: nJet >= 2")
cfr.add(sel4, "Sel4: btag")
cfr.add(sel5, "Sel5: MET")
plots.append(cfr)
return plots
def definePlots(self, t, noSel, sample=None, sampleCfg=None):
noSel = super(BtagEffAndMistagNano,
self).prepareObjects(t, noSel, sample, sampleCfg)
era = sampleCfg['era']
plots = []
# protection against data #
if not self.is_MC:
return []
# Get ScaleFactor binning #
instance = ScaleFactorsbbWW()
all_SF = instance.all_scalefactors
tuple_medium_DeepJet_json = all_SF['btag_' + era]['DeepJet_medium']
variables = []
binning = []
index = ['x', 'y', 'z']
for json_file in tuple_medium_DeepJet_json:
with open(json_file, 'r') as handle:
content = json.load(handle)
var = content['variables']
bins = content['binning']
variables.append(var)
binning.append(bins)
print('Variables in file %s' % json_file)
for var, bin_name in zip(var, index):
print(('... Variable : %s' % var).ljust(30, ' ') +
'Binning (index %s) :' % bin_name, bins[bin_name])
# Find best binning (finest) #
opt_binning = {}
for var, bins in zip(variables, binning):
for v, bn in zip(var, index):
if v == 'AbsEta':
continue
b = bins[bn]
if not v in opt_binning.keys():
opt_binning[v] = b
else:
opt_binning[v].extend(
[a for a in b if a not in opt_binning[v]])
# Sort #
for var, bins in opt_binning.items():
opt_binning[var] = sorted(opt_binning[var])
print("Optimal Binning chosen")
for var, bins in opt_binning.items():
print(('... Variable : %s' % var).ljust(30, ' ') + 'Binning :',
bins)
# Truth MC object hadron flavour #
ak4_truth_lightjets = op.select(self.ak4Jets,
lambda j: op.abs(j.hadronFlavour) == 0)
ak4_truth_cjets = op.select(self.ak4Jets,
lambda j: op.abs(j.hadronFlavour) == 4)
ak4_truth_bjets = op.select(self.ak4Jets,
lambda j: op.abs(j.hadronFlavour) == 5)
plots.append(
Plot.make3D('N_total_lightjets', [
op.map(ak4_truth_lightjets, lambda j: j.eta),
op.map(ak4_truth_lightjets, lambda j: j.pt),
op.map(ak4_truth_lightjets, lambda j: j.btagDeepFlavB)
],
noSel, [
VariableBinning(opt_binning['Eta']),
VariableBinning(opt_binning['Pt']),
VariableBinning(opt_binning['BTagDiscri'])
],
xTitle='lightjet #eta',
yTitle='lightjet P_{T}',
zTitle='lightjet Btagging score'))
plots.append(
Plot.make3D('N_total_cjets', [
op.map(ak4_truth_cjets, lambda j: j.eta),
op.map(ak4_truth_cjets, lambda j: j.pt),
op.map(ak4_truth_cjets, lambda j: j.btagDeepFlavB)
],
noSel, [
VariableBinning(opt_binning['Eta']),
VariableBinning(opt_binning['Pt']),
VariableBinning(opt_binning['BTagDiscri'])
],
xTitle='cjet #eta',
yTitle='cjet P_{T}',
zTitle='cjet Btagging score'))
plots.append(
Plot.make3D('N_total_bjets', [
op.map(ak4_truth_bjets, lambda j: j.eta),
op.map(ak4_truth_bjets, lambda j: j.pt),
op.map(ak4_truth_bjets, lambda j: j.btagDeepFlavB)
],
noSel, [
VariableBinning(opt_binning['Eta']),
VariableBinning(opt_binning['Pt']),
VariableBinning(opt_binning['BTagDiscri'])
],
xTitle='bjet #eta',
yTitle='bjet P_{T}',
zTitle='bjet Btagging score'))
# Btagged objects per flavour #
ak4_btagged_lightjets = op.select(ak4_truth_lightjets,
self.lambda_ak4Btag)
ak4_btagged_cjets = op.select(ak4_truth_cjets, self.lambda_ak4Btag)
ak4_btagged_bjets = op.select(ak4_truth_bjets, self.lambda_ak4Btag)
plots.append(
Plot.make3D('N_btagged_lightjets', [
op.map(ak4_btagged_lightjets, lambda j: j.eta),
op.map(ak4_btagged_lightjets, lambda j: j.pt),
op.map(ak4_btagged_lightjets, lambda j: j.btagDeepFlavB)
],
noSel, [
VariableBinning(opt_binning['Eta']),
VariableBinning(opt_binning['Pt']),
VariableBinning(opt_binning['BTagDiscri'])
],
xTitle='lightjet #eta',
yTitle='lightjet P_{T}',
zTitle='lightjet Btagging score'))
plots.append(
Plot.make3D('N_btagged_cjets', [
op.map(ak4_btagged_cjets, lambda j: j.eta),
op.map(ak4_btagged_cjets, lambda j: j.pt),
op.map(ak4_btagged_cjets, lambda j: j.btagDeepFlavB)
],
noSel, [
VariableBinning(opt_binning['Eta']),
VariableBinning(opt_binning['Pt']),
VariableBinning(opt_binning['BTagDiscri'])
],
xTitle='cjet #eta',
yTitle='cjet P_{T}',
zTitle='cjet Btagging score'))
plots.append(
Plot.make3D('N_btagged_bjets', [
op.map(ak4_btagged_bjets, lambda j: j.eta),
op.map(ak4_btagged_bjets, lambda j: j.pt),
op.map(ak4_btagged_bjets, lambda j: j.btagDeepFlavB)
],
noSel, [
VariableBinning(opt_binning['Eta']),
VariableBinning(opt_binning['Pt']),
VariableBinning(opt_binning['BTagDiscri'])
],
xTitle='bjet #eta',
yTitle='bjet P_{T}',
zTitle='bjet Btagging score'))
return plots
