def KstarMuMuMC(process):
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.genDecayConstructor import genDecayConstructor
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.genRecoMatcher import genRecoMatcher
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.compositeRecoMatcher import compositeRecoMatcher
GenDecay = genDecayConstructor(
momPdgId=511,
daughtersPdgId=[13, -13, 321, -211],
outputMomColl="genB",
interDecay=["313->321,-211"],
outputDaughterColls=["genMu1", "genMu2", "genK", "genPi"])
process.append(GenDecay)
RecoMu1 = genRecoMatcher(recoInput="Muon",
genInput="genMu1",
output="recoMu1",
branches=["pt", "eta", "phi"])
process.append(RecoMu1)
RecoMu2 = genRecoMatcher(recoInput="Muon",
genInput="genMu2",
output="recoMu2",
branches=["pt", "eta", "phi"])
process.append(RecoMu2)
RecoK = genRecoMatcher(recoInput="ProbeTracks",
genInput="genK",
output="recoK",
branches=["pt", "eta", "phi"])
process.append(RecoK)
RecoPi = genRecoMatcher(recoInput="ProbeTracks",
genInput="genPi",
output="recoPi",
branches=["pt", "eta", "phi"])
process.append(RecoPi)
RecoB = compositeRecoMatcher(
compositeColl="BToKsMuMu",
lepCompositeIdxs=["l1_idx", "l2_idx"],
hadronCompositeIdxs=["trk1_idx", "trk2_idx"],
lepMatchedRecoIdxs=["recoMu1_Idx", "recoMu2_Idx"],
hadronMatchedRecoIdxs=["recoK_Idx", "recoPi_Idx"],
outputColl="recoB",
branches=["pt", "eta", "phi"])
process.append(RecoB)
return process
def KEEMC(process, Jpsi=[], use_PF=False, use_1lowPt_1PF=False):
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.genDecayConstructorPython import genDecayConstructorPython
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.genRecoMatcher import genRecoMatcher
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.compositeRecoMatcher import compositeRecoMatcher
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.branchCreatorMC import branchCreatorMC
cuts_on_lep = lambda l: True
cuts_on_B = "True"
cuts_on_B_vars = []
if use_PF and not use_1lowPt_1PF:
cuts_on_lep = lambda l: l.isPF == 1 and l.pfmvaId > -5000
cuts_on_B_vars = ["recoE1_pfmvaId", "recoE2_pfmvaId"]
cuts_on_B = cuts_on_B + " and ( {0}>-300.5 or {1}>-300.5 )"
elif use_1lowPt_1PF and not use_PF:
cuts_on_lep = lambda l: (
(l.isPF == 1 and l.pfmvaId > -1.25) or
(l.isPF == 0 and l.isPFoverlap == 0 and l.mvaId > -1.5))
cuts_on_B_vars = ["recoE1_isPF", "recoE2_isPF"]
cuts_on_B = cuts_on_B + " and ( ({0}==1 and {1}==0) or ( {0}==0 and {1}==1) )"
GenDecay = genDecayConstructorPython(
momPdgId=521,
daughtersPdgId=[11, -11, 321],
outputMomColl="genB",
intermediateDecay=Jpsi,
trgMuonPtEtaThresholds=[], #was 7,1.6
outputDaughterColls=["genE1", "genE2", "genK"])
process.append(GenDecay)
RecoE1 = genRecoMatcher(recoInput="Electron",
genInput="genE1",
output="recoE1",
branches=[
"pt", "eta", "phi", "vx", "vy", "vz", "isPF",
"pfmvaId", "isPFoverlap", "mvaId"
],
cuts=cuts_on_lep,
skipNotMatched=False)
process.append(RecoE1)
RecoE2 = genRecoMatcher(recoInput="Electron",
genInput="genE2",
output="recoE2",
branches=[
"pt", "eta", "phi", "vx", "vy", "vz", "isPF",
"pfmvaId", "isPFoverlap", "mvaId"
],
cuts=cuts_on_lep,
skipNotMatched=False)
process.append(RecoE2)
RecoK = genRecoMatcher(recoInput="ProbeTracks",
genInput="genK",
output="recoK",
branches=["pt", "eta", "phi", "vx", "vy", "vz"],
skipNotMatched=False)
process.append(RecoK)
RecoB = compositeRecoMatcher(
compositeColl="BToKEE",
lepCompositeIdxs=["l1Idx", "l2Idx"],
hadronCompositeIdxs=["kIdx"],
lepMatchedRecoIdxs=["recoE1_Idx", "recoE2_Idx"],
hadronMatchedRecoIdxs=["recoK_Idx"],
outputColl="recoB",
cuts_vars=cuts_on_B_vars,
cuts=cuts_on_B,
branches=[
"fit_pt",
"fit_eta",
"fit_phi",
"fit_mass",
"l_xy",
"l_xy_unc",
"fit_cos2D",
"svprob",
"fit_massErr",
"b_iso04",
"mll_fullfit",
"l1Idx",
"l2Idx",
"kIdx",
"vtx_x",
"vtx_y",
"vtx_z",
"fit_l1_pt",
"fit_l1_eta",
"fit_l1_phi",
"l1_iso04",
"fit_l2_pt",
"fit_l2_eta",
"fit_l2_phi",
"l2_iso04",
"fit_k_pt",
"fit_k_eta",
"fit_k_phi",
"k_iso04",
],
sortTwoLepByIdx=True,
lepLabelsToSort=[
"l1", "l2"
] # branches need to have lep labels between "_" eg fit_l1_pt or l1_iso - lep indexes also sorted
)
process.append(RecoB)
# in case of inf in L_xy/unc produces -99
CreateVars = branchCreatorMC(
inputBranches=[["recoB_l_xy", "recoB_l_xy_unc"],
["recoE1_vz", "recoE2_vz"],
["recoK_vz", "recoE1_vz", "recoE2_vz"],
[
"recoE1_eta", "recoE1_phi", "recoE2_eta",
"recoE2_phi"
],
[
"recoK_eta", "recoK_phi", "recoE1_eta",
"recoE1_phi", "recoE2_eta", "recoE2_phi"
]],
operation=[
"{0}/{1}", "abs({0}-{1})", "min(abs({0}-{1}),abs({0}-{2}))",
"deltaR({0},{1},{2},{3})",
"min(deltaR({0},{1},{2},{3}),deltaR({0},{1},{4},{5}))"
],
createdBranches=[
"recoB_l_xy_sig", "recoB_l1l2Dz", "recoB_lKDz", "recoB_l1l2Dr",
"recoB_lKDr"
],
checkForBCandBranch=
"recoB_l_xy" #if provided branch puts -99 when branch value is -99. eg Useful for evt where recoK is found but
)
process.append(CreateVars)
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.functionWrapper import functionWrapper
TagVars = functionWrapper(
functionName="TagVarsMC",
collections=[
"ProbeTracks", "TriggerMuon", "recoB_fit_pt", "recoB_fit_eta",
"recoB_fit_phi", "recoB_fit_mass"
],
createdBranches=[
"recoB_TagMuEtRatio", "recoB_TagMuDphi", "recoB_TagMu4Prod"
],
)
process.append(TagVars)
ClosestTrkVars = functionWrapper(
functionName="ClosestTrkVarsMC",
collections=[
"ProbeTracks", "BToKEE", "recoB_Idx", "Electron", "recoB_l1Idx",
"recoB_l2Idx"
],
createdBranches=[
"recoB_l1_trk_mass", "recoB_l2_trk_mass", "recoB_trk_minxy1",
"recoB_trk_minxy2", "recoB_trk_minxy3", "recoB_trk_mean"
],
)
process.append(ClosestTrkVars)
return process
def KEEMC(process, Jpsi=[]):
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.genDecayConstructorPython import genDecayConstructorPython
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.genRecoMatcher import genRecoMatcher
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.compositeRecoMatcher import compositeRecoMatcher
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.branchCreatorMC import branchCreatorMC
GenDecay = genDecayConstructorPython(
momPdgId=521,
daughtersPdgId=[11, -11, 321],
outputMomColl="genB",
intermediateDecay=Jpsi,
trgMuonPtEtaThresholds=[], #was 7,1.6
selectTrgMuon=False,
excludeTrgMuon=False,
outputDaughterColls=["genE1", "genE2", "genK"])
process.append(GenDecay)
RecoE1 = genRecoMatcher(
recoInput="Electron",
genInput="genE1",
output="recoE1",
branches=["pt", "eta", "phi", "fBrem", "vz", "hoe"],
skipNotMatched=False)
process.append(RecoE1)
RecoE2 = genRecoMatcher(
recoInput="Electron",
genInput="genE2",
output="recoE2",
branches=["pt", "eta", "phi", "fBrem", "vz", "hoe"],
skipNotMatched=False)
process.append(RecoE2)
RecoK = genRecoMatcher(recoInput="ProbeTracks",
genInput="genK",
output="recoK",
branches=["pt", "eta", "phi", "vz"],
skipNotMatched=False)
process.append(RecoK)
RecoB = compositeRecoMatcher(
compositeColl="BToKEE",
lepCompositeIdxs=["l1Idx", "l2Idx"],
hadronCompositeIdxs=["kIdx"],
lepMatchedRecoIdxs=["recoE1_Idx", "recoE2_Idx"],
hadronMatchedRecoIdxs=["recoK_Idx"],
outputColl="recoB",
branches=[
"fit_pt", "fit_eta", "fit_phi", "fit_mass", "mll_fullfit", "l_xy",
"l_xy_unc", "fit_cos2D", "svprob", "l1Idx", "l2Idx", "kIdx"
])
process.append(RecoB)
CreateVars = branchCreatorMC(
inputBranches=[["recoB_l_xy", "recoB_l_xy_unc"],
["recoE1_vz", "recoE2_vz"],
["recoK_vz", "recoE1_vz", "recoE2_vz"],
[
"recoE1_eta", "recoE1_phi", "recoE2_eta",
"recoE2_phi"
],
[
"recoK_eta", "recoK_phi", "recoE1_eta",
"recoE1_phi", "recoE2_eta", "recoE2_phi"
]],
operation=[
"{0}/{1}", "abs({0}-{1})", "min(abs({0}-{1}),abs({0}-{2}))",
"deltaR({0},{1},{2},{3})",
"min(deltaR({0},{1},{2},{3}),deltaR({0},{1},{4},{5}))"
],
createdBranches=[
"recoB_l_xy_sig", "recoB_e1e2Dz", "recoB_eKDz", "recoB_e1e2Dr",
"recoB_eKDr"
])
process.append(CreateVars)
return process
def KMuMuMC(process, Jpsi=[]):
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.genDecayConstructorPython import genDecayConstructorPython
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.genRecoMatcher import genRecoMatcher
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.compositeRecoMatcher import compositeRecoMatcher
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.branchCreatorMC import branchCreatorMC
from PhysicsTools.NanoAODTools.postprocessing.modules.bpark.genTriggerMuon import genTriggerMuon
#cuts_on_lep = lambda l: True
cuts_on_B = "True"
cuts_on_B_vars = []
GenDecay = genDecayConstructorPython(
momPdgId=521,
daughtersPdgId=[13, -13, 321],
outputMomColl="genB",
intermediateDecay=Jpsi,
trgMuonPtEtaThresholds=
[], #best for training - probe/tag side kinematics no trigger eff reduction
selectTrgMuon=False,
excludeTrgMuon=False,
outputDaughterColls=["genMu1", "genMu2", "genK"])
process.append(GenDecay)
RecoMu1 = genRecoMatcher(recoInput="Muon",
genInput="genMu1",
output="recoMu1",
branches=[
"pt", "eta", "phi", "softId", "vz",
"pfRelIso03_all", "dxy", "dxyErr"
],
addChargeMatching=False,
skipNotMatched=False,
DRcut=0.1)
process.append(RecoMu1)
RecoMu2 = genRecoMatcher(recoInput="Muon",
genInput="genMu2",
output="recoMu2",
branches=[
"pt", "eta", "phi", "softId", "vz",
"pfRelIso03_all", "dxy", "dxyErr"
],
addChargeMatching=False,
skipNotMatched=False,
DRcut=0.1)
process.append(RecoMu2)
#deal with trg muon
TriggerObj = genTriggerMuon(trgBranch="Muon_isTriggering",
skipNoTrgEvt=True,
skipProbe=False,
skipTag=True,
selectionPathList=["HLT_Mu9_IP6"],
outputColl="trgMu",
recoIdx=["recoMu1_Idx", "recoMu2_Idx"],
trgMuMinPt=8.5,
branches=["pt", "eta", "phi", "dxy", "dxyErr"])
process.append(TriggerObj)
RecoK = genRecoMatcher(recoInput="ProbeTracks",
genInput="genK",
output="recoK",
branches=["vz"],
skipNotMatched=False,
DRcut=0.1)
process.append(RecoK)
RecoB = compositeRecoMatcher(
compositeColl="BToKMuMu",
lepCompositeIdxs=["l1Idx", "l2Idx"],
hadronCompositeIdxs=["kIdx"],
lepMatchedRecoIdxs=["recoMu1_Idx", "recoMu2_Idx"],
hadronMatchedRecoIdxs=["recoK_Idx"],
outputColl="recoB",
cuts_vars=cuts_on_B_vars,
cuts=cuts_on_B,
branches=[
"fit_pt", "fit_eta", "fit_phi", "fit_mass", "mll_fullfit", "l_xy",
"l_xy_unc", "fit_cos2D", "svprob", "fit_massErr", "b_iso04",
"vtx_x", "vtx_y", "vtx_z", "l1Idx", "l2Idx", "kIdx", "fit_l1_pt",
"fit_l1_eta", "fit_l1_phi", "l1_iso04", "n_l1_used", "fit_l2_pt",
"fit_l2_eta", "fit_l2_phi", "l2_iso04", "n_l2_used", "fit_k_pt",
"fit_k_eta", "fit_k_phi", "k_iso04", "n_k_used"
],
sortTwoLepByIdx=True,
lepLabelsToSort=[
"l1", "l2"
] # branches need to have lep labels between "_" eg fit_l1_pt or l1_iso - lep indexes also sorted
)
process.append(RecoB)
# in case of inf in L_xy/unc produces -99
CreateVars = branchCreatorMC(
inputBranches=[["recoB_l_xy", "recoB_l_xy_unc"],
["recoMu1_vz", "recoMu2_vz"],
["recoK_vz", "recoMu1_vz", "recoMu2_vz"],
[
"recoB_fit_l1_eta", "recoB_fit_l1_phi",
"recoB_fit_l2_eta", "recoB_fit_l2_phi"
],
[
"recoB_fit_k_eta", "recoB_fit_k_phi",
"recoB_fit_l1_eta", "recoB_fit_l1_phi",
"recoB_fit_l2_eta", "recoB_fit_l2_phi"
]],
operation=[
"{0}/{1}", "abs({0}-{1})", "min(abs({0}-{1}),abs({0}-{2}))",
"deltaR({0},{1},{2},{3})",
"min(deltaR({0},{1},{2},{3}),deltaR({0},{1},{4},{5}))"
],
createdBranches=[
"recoB_l_xy_sig", "recoB_l1l2Dz", "recoB_lKDz", "recoB_l1l2Dr",
"recoB_lKDr"
],
checkForBCandBranch=
"recoB_l_xy" #if provided branch puts -99 when branch value is -99. eg Useful for evt where recoK is found but B not
)
process.append(CreateVars)
return process