def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.initReaders(inputTree) # initReaders must be called in beginFile
#wrappedOutputTree.SetAutoSave()
self.out = mappedOutputTree(wrappedOutputTree,
mapname=self._branch_map)
print "[jhchoi]self.out=", type(self.out)
print "before,autosave=", self.out._tree.GetAutoSave()
self.out._tree.SetAutoSave()
print "after,autosave,=", self.out._tree.GetAutoSave()
# New Branches
self.out.branch('Lepton_RecoSF', 'F', lenVar='nLepton')
self.out.branch('Lepton_RecoSF_Up', 'F', lenVar='nLepton')
self.out.branch('Lepton_RecoSF_Down', 'F', lenVar='nLepton')
self.wp_sf_pf = [
'_IdIsoSF', '_IdIsoSF_Up', '_IdIsoSF_Down', '_IdIsoSF_Syst',
'_TotSF', '_TotSF_Up', '_TotSF_Down'
]
for wp in self.ElectronWP[self.cmssw]['TightObjWP']:
for postfix in self.wp_sf_pf:
self.out.branch('Lepton_tightElectron_' + wp + postfix,
'F',
lenVar='nLepton')
for wp in self.MuonWP[self.cmssw]['TightObjWP']:
for postfix in self.wp_sf_pf:
self.out.branch('Lepton_tightMuon_' + wp + postfix,
'F',
lenVar='nLepton')
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.jet_idx_name = ''
self.out = mappedOutputTree(wrappedOutputTree, mapname=self._branch_map)
for var in self.angle_var:
for obj in self.angle_obj:
self.out.branch('MHlnjj_'+var+'_'+obj, 'F')
for var in self.var:
for obj in self.obj:
self.out.branch('MHlnjj_'+var+'_'+obj, 'F')
self.out.branch('MHlnjj_pt_l', 'F')
self.out.branch('MHlnjj_pt_j1', 'F')
self.out.branch('MHlnjj_pt_j2', 'F')
self.out.branch('MHlnjj_eta_l', 'F')
self.out.branch('MHlnjj_eta_j1', 'F')
self.out.branch('MHlnjj_eta_j2', 'F')
self.out.branch('MHlnjj_idx_j3', 'I')
self.out.branch('MHlnjj_PTljj_D_PTmet', 'F')
self.out.branch('MHlnjj_PTljj_D_Mlmetjj', 'F')
self.out.branch('MHlnjj_MINPTlj_D_PTmet', 'F')
self.out.branch('MHlnjj_MINPTlj_D_Mlmetjj', 'F')
self.out.branch('MHlnjj_MAXPTlj_D_PTmet', 'F')
self.out.branch('MHlnjj_MAXPTlj_D_Mlmetjj', 'F')
self.out.branch('MHlnjj_MTljj_D_PTmet', 'F')
self.out.branch('MHlnjj_MTljj_D_Mlmetjj', 'F')
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.out = mappedOutputTree(wrappedOutputTree,
mapname=self._branch_map)
self.itree = inputTree
for key in self.formulas.keys():
self.formulas[key] = eval('lambda event:' + self.formulas[key])
self.out.branch(key, 'F')
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.out = mappedOutputTree(wrappedOutputTree,
mapname=self._branch_map)
self.newbranches = [
'hm',
'me_vbf_hsm',
'me_vbf_hm',
'me_vbf_hp',
'me_vbf_hl',
'me_vbf_mixhm',
'me_vbf_mixhp',
'me_wh_hsm',
'me_wh_hm',
'me_wh_hp',
'me_wh_hl',
'me_wh_mixhm',
'me_wh_mixhp',
'me_zh_hsm',
'me_zh_hm',
'me_zh_hp',
'me_zh_hl',
'me_zh_mixhm',
'me_zh_mixhp',
'me_qcd_hsm',
]
for nameBranches in self.newbranches:
self.out.branch(nameBranches, "F")
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.out = mappedOutputTree(wrappedOutputTree,
mapname=self._output_branch_map)
# New Branches
self.out.branch("BoostedWtagSF_nominal", "F")
self.out.branch("BoostedWtagSF_up", "F")
self.out.branch("BoostedWtagSF_down", "F")
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.classifiers = []
self.preprocessing = []
for c, p in zip(["best_model_1.hdf5","best_model_0.hdf5"], ["fold0_keras_preprocessing_neutrino.pickle","fold1_keras_preprocessing_neutrino.pickle"]):
self.classifiers.append(load_model(self.pathtotraining+c, custom_objects={'customLoss': self.customLoss}))
self.preprocessing.append(pickle.load(open(self.pathtotraining+p, "rb")))
self.out = mappedOutputTree(wrappedOutputTree, suffix= "_"+self._suffix)
self.out.branch("DNN_mth", "F")
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.initReaders(inputTree) # initReaders must be called in beginFile
self.out = mappedOutputTree(wrappedOutputTree, mapname=self._branch_map)
self.metCollections = {"Puppi":"PuppiMET"} # Name for branch and name of MET collection # "PF":"MET",
# New branches
for MET in self.metCollections:
for typ in Wlep_br:
for var in Wlep_br[typ]:
if 'Wlep_' in var: self.out.branch("HM_"+var+"_"+MET, typ)
#self.out.branch("IsWlepEvt", "I")
self.out.branch("HM_Wlep_mt" , "F")
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.classifiers = []
self.preprocessing = []
for c, p in zip(["best_model_1.hdf5", "best_model_0.hdf5"], [
"fold0_keras_preprocessing_production.pickle",
"fold1_keras_preprocessing_production.pickle"
]):
self.classifiers.append(load_model(self.pathtotraining + c))
self.preprocessing.append(
pickle.load(open(self.pathtotraining + p, "rb")))
self.out = mappedOutputTree(wrappedOutputTree,
suffix="_" + self._suffix)
self.out.branch("DNN_isVBF", "F")
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.classifiers = []
self.preprocessing = []
for c, p in zip(["best_model_1.hdf5", "best_model_0.hdf5"], [
"fold0_keras_preprocessing_neutrinosemi.pickle",
"fold1_keras_preprocessing_neutrinosemi.pickle"
]):
self.classifiers.append(load_model(self.pathtotraining + c))
self.preprocessing.append(
pickle.load(open(self.pathtotraining + p, "rb")))
suffix = "" if self._branch_map == "" else "_" + self._branch_map
self.out = mappedOutputTree(wrappedOutputTree, suffix=suffix)
self.out.branch("DNN_mth", "F")
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
# Using branchmap (Only JEs and Fatjet systematics change the pairing)
self.out = mappedOutputTree(wrappedOutputTree,
mapname=self._branch_map)
# New Branches
for key in pairing_strategies_resolved.keys():
self.out.branch("VBS_jets_" + key, "I", n=2)
self.out.branch("V_jets_" + key, "I", n=2)
for key in pairing_strategies_fatjet.keys():
self.out.branch("VBS_jets_" + key, "I", n=2)
self.out.branch("V_jets_" + key, "I", n=2)
# 0-boosted, 1-resolved (4jets)
self.out.branch("VBS_category", "I")
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.classifiers = []
self.preprocessing = []
for c, p in zip(["best_model_1.hdf5","best_model_0.hdf5"], ["fold0_keras_preprocessing_category.pickle","fold1_keras_preprocessing_category.pickle"]):
self.classifiers.append(load_model(self.pathtotraining+c))
self.preprocessing.append(pickle.load(open(self.pathtotraining+p, "rb")))
self.out = mappedOutputTree(wrappedOutputTree, suffix= "_"+self._suffix)
self.classes = ["WW", "ggH115_190", "ggH200_450", "ggH500_5000", "VBF115_190", "VBF200_450", "VBF500_5000", "other"]
for cla in self.classes:
self.out.branch("DNN_"+cla, "F")
self.out.branch("DNN_categ", "I")
self.out.branch("DNN_categ_maxscore", "I")
self.out.branch("DNN_categ_difftosecond", "I")
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.out = mappedOutputTree(wrappedOutputTree,
mapname=self._branch_map)
#self.out.branch('Whad_px','F')
#self.out.branch('Whad_py','F')
#self.out.branch('Whad_pz','F')
#self.out.branch('Whad_E','F')
self.out.branch(self.prefix + 'Whad_pt', 'F')
self.out.branch(self.prefix + 'Whad_eta', 'F')
self.out.branch(self.prefix + 'Whad_phi', 'F')
self.out.branch(self.prefix + 'Whad_mass', 'F')
self.out.branch(self.prefix + 'idx_j1', 'I')
self.out.branch(self.prefix + 'idx_j2', 'I')
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
#using suffix instead of mapname to save all branches with same suffix.
suffix = "" if self._branch_map == "" else "_" + self._branch_map
self.out = mappedOutputTree(wrappedOutputTree, suffix=suffix)
# New Branches
for typ, branches in vbs_vars.VBSjjlnu_branches.items():
for var in branches:
self.out.branch(var, typ)
for vec_branch in vbs_vars.VBSjjlnu_vector_branches:
if type(vec_branch["len"]) == str:
self.out.branch(vec_branch["name"],
vec_branch["type"],
lenVar=vec_branch["len"])
elif type(vec_branch["len"]) == int:
self.out.branch(vec_branch["name"],
vec_branch["type"],
n=vec_branch["len"])
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.initReaders() # initReaders must be called in beginFile
self.out = mappedOutputTree(wrappedOutputTree, mapname=self._branch_map)
if self.keepRunP:
# Check if input tree indeed contains run_period
#isThere = False
#for br in wrappedOutputTree.GetListOfBranches():
# if br.GetName() == 'run_period': isThere = True
#if not isThere: print("TrigMaker WARNING: Input tree does not contain the 'run_period' branch, while 'keepRunP' is True.")
#else:
try: self.NewVar['I'].remove('run_period')
except: pass
for typ in self.NewVar:
for name in self.NewVar[typ]:
if 'TriggerEmulator' in name: self.out.branch(name, typ, 6)
else: self.out.branch(name, typ)
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.out = mappedOutputTree(wrappedOutputTree,
mapname=self._branch_map)
#New Branches ##For Event
self.out.branch("HM_Flavlnjj", "I")
self.out.branch("HM_IsBoosted", "O")
self.out.branch("HM_IsResolved", "O")
self.out.branch("HM_IsBTopTagged", "O")
##For Boosted Selection ##For FatJet
self.list_WJetVar = [
'pt', 'eta', 'phi', 'mass', 'tau21', 'WptOvHfatM', 'HlnFat_mass',
'CFatJetIdx'
]
self.suffix = '' if (
self._branch_map == ''
or 'fatjet' not in self._branch_map) else '_' + self._branch_map
for myvar in self.list_WJetVar:
self.out.branch("HM_CleanFatJetPassMBoosted_" + myvar,
'F',
lenVar='HM_nCleanFatJetPassMBoosted' + self.suffix)
# For Boosted-like selection, not requiring tau21 cut
self.out.branch("HM_idxWfat_noTau21Cut", "I")
self.out.branch("HM_HlnFatMass_noTau21Cut", "F")
self.out.branch("HM_WptOvHak4M", "F")
self.out.branch("HM_Hlnjj_mass", "F")
#self.out.branch("Wlep_mt" , "F")
self.out.branch("HM_Hlnjj_mt", "F")
self.out.branch("HM_vbfFat_jj_dEta", "F")
self.out.branch("HM_vbfFat_jj_mass", "F")
self.out.branch("HM_vbfjj_jj_dEta", "F")
self.out.branch("HM_vbfjj_jj_mass", "F")
self.out.branch("HM_largest_nonW_mjj", "F")
self.out.branch("HM_IsVbfFat", "O")
self.out.branch("HM_IsVbfjj", "O")
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.out = mappedOutputTree(wrappedOutputTree,
mapname=self._branch_map)
self.newbranches = [
'pfmetPhi_zh4l',
'z0Mass_zh4l',
'z0Pt_zh4l',
'z1Mass_zh4l',
'z1Pt_zh4l',
'zaMass_zh4l',
'zbMass_zh4l',
'flagZ1SF_zh4l',
'z0DeltaPhi_zh4l',
'z1DeltaPhi_zh4l',
'zaDeltaPhi_zh4l',
'zbDeltaPhi_zh4l',
'minDeltaPhi_zh4l',
'z0DeltaR_zh4l',
'z1DeltaR_zh4l',
'zaDeltaR_zh4l',
'zbDeltaR_zh4l',
'lep1Mt_zh4l',
'lep2Mt_zh4l',
'lep3Mt_zh4l',
'lep4Mt_zh4l',
'minMt_zh4l',
'z1Mt_zh4l',
'mllll_zh4l',
'chllll_zh4l',
'z1dPhi_lep1MET_zh4l',
'z1dPhi_lep2MET_zh4l',
'z1mindPhi_lepMET_zh4l',
]
for nameBranches in self.newbranches:
self.out.branch(nameBranches, "F")
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.out = mappedOutputTree(wrappedOutputTree,
mapname=self._output_branch_map)
if self._output_branch_map == "":
# New Branches
for typ in CleanFatJet_br:
for var in CleanFatJet_br[typ]:
self.out.branch(var, typ, lenVar='nCleanFatJet')
#vector of CleanJet idx not overlapping with Fatjets
self.out.branch('CleanJetNotFat_jetIdx',
"I",
lenVar="nCleanJetNotFat")
# Distance from the first FatJet
self.out.branch('CleanJetNotFat_deltaR',
"F",
lenVar="nCleanJetNotFat")
else:
##################
#N.B little hack for lenVar with variation
# New Branches
for typ in CleanFatJet_br:
for var in CleanFatJet_br[typ]:
self.out.branch(var,
typ,
lenVar='nCleanFatJet_' +
self._output_branch_map)
#vector of CleanJet idx not overlapping with Fatjets
self.out.branch('CleanJetNotFat_jetIdx',
"I",
lenVar="nCleanJetNotFat_" +
self._output_branch_map)
# Distance from the first FatJet
self.out.branch('CleanJetNotFat_deltaR',
"F",
lenVar="nCleanJetNotFat_" +
self._output_branch_map)
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.initReaders(inputTree) # initReaders must be called in beginFile
self.out = mappedOutputTree(wrappedOutputTree,
mapname=self._branch_map)
# New Branches
self.out.branch('Lepton_RecoSF', 'F', lenVar='nLepton')
self.out.branch('Lepton_RecoSF_Up', 'F', lenVar='nLepton')
self.out.branch('Lepton_RecoSF_Down', 'F', lenVar='nLepton')
self.wp_sf_pf = [
'_IdIsoSF', '_IdIsoSF_Up', '_IdIsoSF_Down', '_IdIsoSF_Syst',
'_TotSF', '_TotSF_Up', '_TotSF_Down'
]
for wp in self.ElectronWP[self.cmssw]['TightObjWP']:
for postfix in self.wp_sf_pf:
self.out.branch('Lepton_tightElectron_' + wp + postfix,
'F',
lenVar='nLepton')
for wp in self.MuonWP[self.cmssw]['TightObjWP']:
for postfix in self.wp_sf_pf:
self.out.branch('Lepton_tightMuon_' + wp + postfix,
'F',
lenVar='nLepton')
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.out = mappedOutputTree(wrappedOutputTree,
mapname=self._branch_map)
self.itree = inputTree
for iMva in self.mvaDic:
self.out.branch(iMva, 'F')
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.out = mappedOutputTree(wrappedOutputTree, mapname=self._branch_map)
self.newbranches = [
'mll',
'dphill',
'yll',
'ptll',
'pt1',
'pt2',
'mth',
'mcoll',
'mcollWW',
'mTi',
'mTe',
'choiMass',
'mR',
'mT2',
'channel',
'drll',
'dphilljet',
'dphilljetjet',
'dphilljetjet_cut',
'dphillmet',
'dphilmet',
'dphilmet1',
'dphilmet2',
'mtw1',
'mtw2',
'mjj',
'detajj',
'njet',
'mllWgSt',
'drllWgSt',
'mllThird',
'mllOneThree',
'mllTwoThree',
'drllOneThree',
'drllTwoThree',
'dphijet1met',
'dphijet2met',
'dphijjmet',
'dphijjmet_cut',
'dphilep1jet1',
'dphilep1jet2',
'dphilep2jet1',
'dphilep2jet2',
'mindetajl',
'detall',
'dphijj',
'maxdphilepjj',
'dphilep1jj',
'dphilep2jj',
'ht',
'vht_pt',
'vht_phi',
'projpfmet',
'dphiltkmet',
'projtkmet',
'mpmet',
'pTWW',
'pTHjj',
'recoil',
'jetpt1_cut',
'jetpt2_cut',
'dphilljet_cut',
'dphijet1met_cut',
'dphijet2met_cut',
'PfMetDivSumMet',
'upara',
'uperp',
'm2ljj20',
'm2ljj30',
# for VBF training
'ptTOT_cut',
'mTOT_cut',
'OLV1_cut',
'OLV2_cut',
'Ceta_cut',
#whss
'mlljj20_whss',
'mlljj30_whss',
'WlepPt_whss',
'WlepMt_whss'
]
for nameBranches in self.newbranches :
self.out.branch(nameBranches , "F");
