def FillEfficiency(Chain, sample, args):
global roc
global ptHist
global etaHist
for lepton in xrange(Chain._nL):
if not objSel.isGoodBaseTau(Chain, lepton): continue
if Chain._tauGenStatus[lepton] != 5: continue
for i in xrange(len(tau_id_algos)):
roc[i].fill_eff_denominator(Chain._weight)
ptHist[i].fill_denominator(Chain._lPt[lepton], Chain._weight)
etaHist[i].fill_denominator(Chain._lEta[lepton], Chain._weight)
if not Chain._tauEleVetoLoose[lepton]: continue
if not Chain._tauMuonVetoLoose[lepton]: continue
DMfinding = objSel.getDMfinding(Chain, lepton)
discriminators = objSel.getTauIDs(Chain, lepton)
for i, discr in enumerate(discriminators):
if not DMfinding[i]: continue
for j, WP in enumerate(discr):
if WP:
roc[i].fill_eff_numerator(j, Chain._weight)
ptHist[i].fill_numerator(Chain._lPt[lepton], j, Chain._weight)
etaHist[i].fill_numerator(Chain._lEta[lepton], j, Chain._weight)
def CalcFakeRate(Chain, sample, args):
global roc
global ptHist
global etaHist
for lepton in xrange(Chain._nL):
if not objSel.isFakeTau(Chain, lepton): continue
for i in xrange(len(tau_id_algos)):
roc[i].fill_misid_denominator(Chain._weight)
ptHist[i].fill_denominator(Chain._lPt[lepton], Chain._weight)
etaHist[i].fill_denominator(Chain._lEta[lepton], Chain._weight)
if objSel.lepHasOverlap(Chain, lepton): continue
if not Chain._tauEleVetoVLoose[lepton]: continue
if not Chain._tauMuonVetoLoose[lepton]: continue
DMfinding = objSel.getDMfinding(Chain, lepton)
discriminators = objSel.getTauIDs(Chain, lepton)
for i, discr in enumerate(discriminators):
if not DMfinding[i]: continue
for j, WP in enumerate(discr):
if WP:
roc[i].fill_misid_numerator(j, Chain._weight)
ptHist[i].fill_numerator(Chain._lPt[lepton], j,
Chain._weight)
etaHist[i].fill_numerator(Chain._lEta[lepton], j,
Chain._weight)
def CalcBlujFakeRate(Chain, sample, args):
global roc
global ptHist
global etaHist
for lepton in xrange(Chain._nLight, Chain._nL):
if not objSel.isGoodBaseTauIso(Chain, lepton): continue
if Chain._tauGenStatus[lepton] != 6: continue
for i in xrange(len(tau_id_algos)):
roc[i].fill_misid_denominator(Chain._weight)
ptHist[i].fill_denominator(Chain._lPt[lepton], Chain._weight)
etaHist[i].fill_denominator(Chain._lEta[lepton], Chain._weight)
DMfinding = objSel.getDMfinding(Chain, lepton)
discriminators = objSel.getTauIDs(Chain, lepton)
for i, discr in enumerate(discriminators):
if not DMfinding[i]: continue
for j, WP in enumerate(discr):
if WP:
roc[i].fill_misid_numerator(j, Chain._weight)
ptHist[i].fill_numerator(Chain._lPt[lepton], j,
Chain._weight)
etaHist[i].fill_numerator(Chain._lEta[lepton], j,
Chain._weight)
def CalcANFakeRate(Chain, sample, args):
global roc
global ptHist
global etaHist
for jet in xrange(Chain._nJets):
if Chain._jetPt[jet] < 20 or abs(Chain._jetEta[jet]) > 2.3: continue
jetVec = objSel.getFourVec(Chain._jetPt[jet], Chain._jetEta[jet],
Chain._jetPhi[jet], Chain._jetE[jet])
#Find matching reco tau
matchindex = objSel.tauMatchIndexIso(Chain, jetVec)
if matchindex != 1 and Chain._tauGenStatus[matchindex] == 5: continue
for i in xrange(len(tau_id_algos)):
roc[i].fill_misid_denominator(CHain._weight)
ptHist[i].fill_denominator(Chain._jetPt[jet], Chain._weight)
etaHist[i].fill_denominator(Chain._jetEta[jet], Chain._weight)
DMfinding = objSel.getDMfinding(Chain, matchindex)
discriminators = objSel.getTauIDs(Chain, matchindex)
for i, discr in enumerate(discriminators):
if not DMfinding[i]: continue
for j, WP in enumerate(discr):
if WP:
passedTau[i][j] += 1.
ptHist[i].fill_numerator(Chain._lPt[matchindex], j,
Chain._weight)
etaHist[i].fill_numerator(Chain._lEta[matchindex], j,
Chain._weight)
def FillANEfficiency(Chain, sample, args):
global roc
global ptHist
global etaHist
for genlepton in xrange(Chain._gen_nL):
if not Chain._gen_lFlavor[genlepton] == 2: continue
if not Chain._gen_lIsPrompt[genlepton]: continue
if Chain._gen_lPt[genlepton] < 20 or abs(
Chain._gen_lEta[genlepton]) > 2.3:
continue
genTauVec = objSel.GetFourVec(Chain._gen_lPt[genlepton],
Chain._gen_lEta[genlepton],
Chain._gen_lPhi[genlepton],
Chain._gen_lE[genlepton])
#Find matching reco tau
matchindex = objSel.tauMatchIndexIso(Chain, genTauVec, needFake=False)
if matchindex == -1: continue
for i in xrange(len(tau_id_algos)):
roc[i].fill_eff_denominator(Chain._weight)
ptHist[i].fill_denominator(Chain._gen_lPt[genlepton],
Chain._weight)
etaHist[i].fill_denominator(Chain._gen_lEta[genlepton],
Chain._weight)
DMfinding = objSel.getDMfinding(Chain, matchindex)
discriminators = objSel.getTauIDs(Chain, matchindex)
for i, discr in enumerate(discriminators):
if not DMfinding[i]: continue
for j, WP in enumerate(discr):
if WP:
roc[i].fill_eff_numerator(j, Chain._weight)
ptHist[i].fill_numerator(Chain._lPt[matchindex], j,
Chain._weight)
etaHist[i].fill_numerator(Chain._lEta[matchindex], j,
Chain._weight)