Exercise-1-Introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step1 Getting familiar with the muon objects and nanoaod\n",
    "In this exercise we will get familiar with the muon objects in an [nanoAOD](https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookNanoAOD) file. We will mostly use the following file, containing simulated Drell–Yan dimuon events generated at NLO.<br>\n",
    "* DYJetsToMuMu_M-50_TuneCP5_13TeV-madgraphMLM-pythia8.root\n",
    "\n",
    "Further information and a list of those variables stored in nanoAOD can be found [here](https://cms-nanoaod-integration.web.cern.ch/integration/master-106X/mc102X_doc.html).<br>\n",
    "In nanoAOD, data are stored as arrays. \n",
    "[Uproot](https://github.com/scikit-hep/uproot#exploring-a-file) will be used to explore the nanoAOD file.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not importing? Don't forget to select the correct kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import awkward as ak\n",
    "from math import pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, lets take a look of the variables stored in NanoAOD. Uproot.open( ) return a ROOTDirectory similar to a python dictionary. Function key( ) prints all the dicinory keywords. The b' ahead of the names is due to ROOT object names having no encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#events=uproot.open('/eos/purdue/store/mc/Run3Summer22NanoAODv13/DYJetsToLL_M-50_TuneCP5_13p6TeV-madgraphMLM-pythia8/NANOAODSIM/133X_mcRun3_2022_realistic_ForNanov13_v1-v1/50000/1c16ff7d-a4ad-44cd-80fd-42840958ce79.root')['Events']\n",
    "events=uproot.open('~/work/projects/hats2024/data/DYtoMuMu.root')['Events']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['run',\n",
       " 'luminosityBlock',\n",
       " 'event',\n",
       " 'bunchCrossing',\n",
       " 'HTXS_njets25',\n",
       " 'HTXS_njets30',\n",
       " 'HTXS_stage1_1_cat_pTjet25GeV',\n",
       " 'HTXS_stage1_1_cat_pTjet30GeV',\n",
       " 'HTXS_stage1_1_fine_cat_pTjet25GeV',\n",
       " 'HTXS_stage1_1_fine_cat_pTjet30GeV',\n",
       " 'HTXS_stage1_2_cat_pTjet25GeV',\n",
       " 'HTXS_stage1_2_cat_pTjet30GeV',\n",
       " 'HTXS_stage1_2_fine_cat_pTjet25GeV',\n",
       " 'HTXS_stage1_2_fine_cat_pTjet30GeV',\n",
       " 'HTXS_stage_0',\n",
       " 'HTXS_stage_1_pTjet25',\n",
       " 'HTXS_stage_1_pTjet30',\n",
       " 'HTXS_Higgs_pt',\n",
       " 'HTXS_Higgs_y',\n",
       " 'BeamSpot_type',\n",
       " 'BeamSpot_sigmaZ',\n",
       " 'BeamSpot_sigmaZError',\n",
       " 'BeamSpot_z',\n",
       " 'BeamSpot_zError',\n",
       " 'nboostedTau',\n",
       " 'boostedTau_idAntiEle2018',\n",
       " 'boostedTau_idAntiMu',\n",
       " 'boostedTau_idMVAnewDM2017v2',\n",
       " 'boostedTau_idMVAoldDM2017v2',\n",
       " 'boostedTau_jetIdx',\n",
       " 'boostedTau_rawAntiEleCat2018',\n",
       " 'boostedTau_charge',\n",
       " 'boostedTau_decayMode',\n",
       " 'boostedTau_chargedIso',\n",
       " 'boostedTau_eta',\n",
       " 'boostedTau_leadTkDeltaEta',\n",
       " 'boostedTau_leadTkDeltaPhi',\n",
       " 'boostedTau_leadTkPtOverTauPt',\n",
       " 'boostedTau_mass',\n",
       " 'boostedTau_neutralIso',\n",
       " 'boostedTau_phi',\n",
       " 'boostedTau_photonsOutsideSignalCone',\n",
       " 'boostedTau_pt',\n",
       " 'boostedTau_puCorr',\n",
       " 'boostedTau_rawAntiEle2018',\n",
       " 'boostedTau_rawIso',\n",
       " 'boostedTau_rawIsodR03',\n",
       " 'boostedTau_rawMVAnewDM2017v2',\n",
       " 'boostedTau_rawMVAoldDM2017v2',\n",
       " 'CaloMET_phi',\n",
       " 'CaloMET_pt',\n",
       " 'CaloMET_sumEt',\n",
       " 'ChsMET_phi',\n",
       " 'ChsMET_pt',\n",
       " 'ChsMET_sumEt',\n",
       " 'nCorrT1METJet',\n",
       " 'CorrT1METJet_area',\n",
       " 'CorrT1METJet_eta',\n",
       " 'CorrT1METJet_muonSubtrFactor',\n",
       " 'CorrT1METJet_phi',\n",
       " 'CorrT1METJet_rawPt',\n",
       " 'DeepMETResolutionTune_phi',\n",
       " 'DeepMETResolutionTune_pt',\n",
       " 'DeepMETResponseTune_phi',\n",
       " 'DeepMETResponseTune_pt',\n",
       " 'nElectron',\n",
       " 'Electron_seediEtaOriX',\n",
       " 'Electron_convVeto',\n",
       " 'Electron_cutBased',\n",
       " 'Electron_cutBased_HEEP',\n",
       " 'Electron_isPFcand',\n",
       " 'Electron_jetNDauCharged',\n",
       " 'Electron_lostHits',\n",
       " 'Electron_mvaIso_WP80',\n",
       " 'Electron_mvaIso_WP90',\n",
       " 'Electron_mvaNoIso_WP80',\n",
       " 'Electron_mvaNoIso_WP90',\n",
       " 'Electron_seedGain',\n",
       " 'Electron_tightCharge',\n",
       " 'Electron_jetIdx',\n",
       " 'Electron_photonIdx',\n",
       " 'Electron_svIdx',\n",
       " 'Electron_fsrPhotonIdx',\n",
       " 'Electron_charge',\n",
       " 'Electron_pdgId',\n",
       " 'Electron_seediPhiOriY',\n",
       " 'Electron_vidNestedWPBitmap',\n",
       " 'Electron_vidNestedWPBitmapHEEP',\n",
       " 'Electron_deltaEtaSC',\n",
       " 'Electron_dr03EcalRecHitSumEt',\n",
       " 'Electron_dr03HcalDepth1TowerSumEt',\n",
       " 'Electron_dr03TkSumPt',\n",
       " 'Electron_dr03TkSumPtHEEP',\n",
       " 'Electron_dxy',\n",
       " 'Electron_dxyErr',\n",
       " 'Electron_dz',\n",
       " 'Electron_dzErr',\n",
       " 'Electron_eInvMinusPInv',\n",
       " 'Electron_energyErr',\n",
       " 'Electron_eta',\n",
       " 'Electron_hoe',\n",
       " 'Electron_ip3d',\n",
       " 'Electron_jetPtRelv2',\n",
       " 'Electron_jetRelIso',\n",
       " 'Electron_mass',\n",
       " 'Electron_miniPFRelIso_all',\n",
       " 'Electron_miniPFRelIso_chg',\n",
       " 'Electron_mvaHZZIso',\n",
       " 'Electron_mvaIso',\n",
       " 'Electron_mvaNoIso',\n",
       " 'Electron_pfRelIso03_all',\n",
       " 'Electron_pfRelIso03_chg',\n",
       " 'Electron_phi',\n",
       " 'Electron_pt',\n",
       " 'Electron_r9',\n",
       " 'Electron_scEtOverPt',\n",
       " 'Electron_sieie',\n",
       " 'Electron_sip3d',\n",
       " 'Electron_mvaTTH',\n",
       " 'nFatJet',\n",
       " 'FatJet_jetId',\n",
       " 'FatJet_nConstituents',\n",
       " 'FatJet_subJetIdx1',\n",
       " 'FatJet_subJetIdx2',\n",
       " 'FatJet_electronIdx3SJ',\n",
       " 'FatJet_muonIdx3SJ',\n",
       " 'FatJet_area',\n",
       " 'FatJet_btagDDBvLV2',\n",
       " 'FatJet_btagDDCvBV2',\n",
       " 'FatJet_btagDDCvLV2',\n",
       " 'FatJet_btagDeepB',\n",
       " 'FatJet_btagHbb',\n",
       " 'FatJet_eta',\n",
       " 'FatJet_mass',\n",
       " 'FatJet_msoftdrop',\n",
       " 'FatJet_n2b1',\n",
       " 'FatJet_n3b1',\n",
       " 'FatJet_particleNetWithMass_H4qvsQCD',\n",
       " 'FatJet_particleNetWithMass_HbbvsQCD',\n",
       " 'FatJet_particleNetWithMass_HccvsQCD',\n",
       " 'FatJet_particleNetWithMass_QCD',\n",
       " 'FatJet_particleNetWithMass_TvsQCD',\n",
       " 'FatJet_particleNetWithMass_WvsQCD',\n",
       " 'FatJet_particleNetWithMass_ZvsQCD',\n",
       " 'FatJet_particleNet_QCD',\n",
       " 'FatJet_particleNet_QCD0HF',\n",
       " 'FatJet_particleNet_QCD1HF',\n",
       " 'FatJet_particleNet_QCD2HF',\n",
       " 'FatJet_particleNet_XbbVsQCD',\n",
       " 'FatJet_particleNet_XccVsQCD',\n",
       " 'FatJet_particleNet_XggVsQCD',\n",
       " 'FatJet_particleNet_XqqVsQCD',\n",
       " 'FatJet_particleNet_XteVsQCD',\n",
       " 'FatJet_particleNet_XtmVsQCD',\n",
       " 'FatJet_particleNet_XttVsQCD',\n",
       " 'FatJet_particleNet_massCorr',\n",
       " 'FatJet_phi',\n",
       " 'FatJet_pt',\n",
       " 'FatJet_rawFactor',\n",
       " 'FatJet_tau1',\n",
       " 'FatJet_tau2',\n",
       " 'FatJet_tau3',\n",
       " 'FatJet_tau4',\n",
       " 'FatJet_lsf3',\n",
       " 'nFsrPhoton',\n",
       " 'FsrPhoton_electronIdx',\n",
       " 'FsrPhoton_muonIdx',\n",
       " 'FsrPhoton_dROverEt2',\n",
       " 'FsrPhoton_eta',\n",
       " 'FsrPhoton_phi',\n",
       " 'FsrPhoton_pt',\n",
       " 'FsrPhoton_relIso03',\n",
       " 'nGenJetAK8',\n",
       " 'GenJetAK8_eta',\n",
       " 'GenJetAK8_mass',\n",
       " 'GenJetAK8_phi',\n",
       " 'GenJetAK8_pt',\n",
       " 'nGenJet',\n",
       " 'GenJet_eta',\n",
       " 'GenJet_mass',\n",
       " 'GenJet_phi',\n",
       " 'GenJet_pt',\n",
       " 'nGenPart',\n",
       " 'GenPart_genPartIdxMother',\n",
       " 'GenPart_statusFlags',\n",
       " 'GenPart_pdgId',\n",
       " 'GenPart_status',\n",
       " 'GenPart_eta',\n",
       " 'GenPart_mass',\n",
       " 'GenPart_phi',\n",
       " 'GenPart_pt',\n",
       " 'nGenProton',\n",
       " 'GenProton_isPU',\n",
       " 'GenProton_px',\n",
       " 'GenProton_py',\n",
       " 'GenProton_pz',\n",
       " 'GenProton_vz',\n",
       " 'nSubGenJetAK8',\n",
       " 'SubGenJetAK8_eta',\n",
       " 'SubGenJetAK8_mass',\n",
       " 'SubGenJetAK8_phi',\n",
       " 'SubGenJetAK8_pt',\n",
       " 'Generator_id1',\n",
       " 'Generator_id2',\n",
       " 'Generator_binvar',\n",
       " 'Generator_scalePDF',\n",
       " 'Generator_weight',\n",
       " 'Generator_x1',\n",
       " 'Generator_x2',\n",
       " 'Generator_xpdf1',\n",
       " 'Generator_xpdf2',\n",
       " 'GenVtx_x',\n",
       " 'GenVtx_y',\n",
       " 'GenVtx_z',\n",
       " 'nGenVisTau',\n",
       " 'GenVisTau_status',\n",
       " 'GenVisTau_charge',\n",
       " 'GenVisTau_genPartIdxMother',\n",
       " 'GenVisTau_eta',\n",
       " 'GenVisTau_mass',\n",
       " 'GenVisTau_phi',\n",
       " 'GenVisTau_pt',\n",
       " 'genWeight',\n",
       " 'LHEWeight_originalXWGTUP',\n",
       " 'nLHEPdfWeight',\n",
       " 'LHEPdfWeight',\n",
       " 'nLHEReweightingWeight',\n",
       " 'LHEReweightingWeight',\n",
       " 'nLHEScaleWeight',\n",
       " 'LHEScaleWeight',\n",
       " 'nPSWeight',\n",
       " 'PSWeight',\n",
       " 'nIsoTrack',\n",
       " 'IsoTrack_isHighPurityTrack',\n",
       " 'IsoTrack_isPFcand',\n",
       " 'IsoTrack_isFromLostTrack',\n",
       " 'IsoTrack_charge',\n",
       " 'IsoTrack_fromPV',\n",
       " 'IsoTrack_pdgId',\n",
       " 'IsoTrack_dxy',\n",
       " 'IsoTrack_dz',\n",
       " 'IsoTrack_eta',\n",
       " 'IsoTrack_pfRelIso03_all',\n",
       " 'IsoTrack_pfRelIso03_chg',\n",
       " 'IsoTrack_phi',\n",
       " 'IsoTrack_pt',\n",
       " 'IsoTrack_miniPFRelIso_all',\n",
       " 'IsoTrack_miniPFRelIso_chg',\n",
       " 'nJet',\n",
       " 'Jet_jetId',\n",
       " 'Jet_nConstituents',\n",
       " 'Jet_nElectrons',\n",
       " 'Jet_nMuons',\n",
       " 'Jet_nSVs',\n",
       " 'Jet_electronIdx1',\n",
       " 'Jet_electronIdx2',\n",
       " 'Jet_muonIdx1',\n",
       " 'Jet_muonIdx2',\n",
       " 'Jet_svIdx1',\n",
       " 'Jet_svIdx2',\n",
       " 'Jet_hfadjacentEtaStripsSize',\n",
       " 'Jet_hfcentralEtaStripSize',\n",
       " 'Jet_PNetRegPtRawCorr',\n",
       " 'Jet_PNetRegPtRawCorrNeutrino',\n",
       " 'Jet_PNetRegPtRawRes',\n",
       " 'Jet_area',\n",
       " 'Jet_btagDeepFlavB',\n",
       " 'Jet_btagDeepFlavCvB',\n",
       " 'Jet_btagDeepFlavCvL',\n",
       " 'Jet_btagDeepFlavQG',\n",
       " 'Jet_btagPNetB',\n",
       " 'Jet_btagPNetCvB',\n",
       " 'Jet_btagPNetCvL',\n",
       " 'Jet_btagPNetQvG',\n",
       " 'Jet_btagPNetTauVJet',\n",
       " 'Jet_btagRobustParTAK4B',\n",
       " 'Jet_btagRobustParTAK4CvB',\n",
       " 'Jet_btagRobustParTAK4CvL',\n",
       " 'Jet_btagRobustParTAK4QG',\n",
       " 'Jet_chEmEF',\n",
       " 'Jet_chHEF',\n",
       " 'Jet_eta',\n",
       " 'Jet_hfsigmaEtaEta',\n",
       " 'Jet_hfsigmaPhiPhi',\n",
       " 'Jet_mass',\n",
       " 'Jet_muEF',\n",
       " 'Jet_muonSubtrFactor',\n",
       " 'Jet_neEmEF',\n",
       " 'Jet_neHEF',\n",
       " 'Jet_phi',\n",
       " 'Jet_pt',\n",
       " 'Jet_rawFactor',\n",
       " 'LHE_Njets',\n",
       " 'LHE_Nb',\n",
       " 'LHE_Nc',\n",
       " 'LHE_Nuds',\n",
       " 'LHE_Nglu',\n",
       " 'LHE_NpNLO',\n",
       " 'LHE_NpLO',\n",
       " 'LHE_HT',\n",
       " 'LHE_HTIncoming',\n",
       " 'LHE_Vpt',\n",
       " 'LHE_AlphaS',\n",
       " 'nLHEPart',\n",
       " 'LHEPart_pdgId',\n",
       " 'LHEPart_status',\n",
       " 'LHEPart_spin',\n",
       " 'LHEPart_pt',\n",
       " 'LHEPart_eta',\n",
       " 'LHEPart_phi',\n",
       " 'LHEPart_mass',\n",
       " 'LHEPart_incomingpz',\n",
       " 'nLowPtElectron',\n",
       " 'LowPtElectron_convVeto',\n",
       " 'LowPtElectron_convWP',\n",
       " 'LowPtElectron_lostHits',\n",
       " 'LowPtElectron_electronIdx',\n",
       " 'LowPtElectron_photonIdx',\n",
       " 'LowPtElectron_charge',\n",
       " 'LowPtElectron_pdgId',\n",
       " 'LowPtElectron_ID',\n",
       " 'LowPtElectron_convVtxRadius',\n",
       " 'LowPtElectron_deltaEtaSC',\n",
       " 'LowPtElectron_dxy',\n",
       " 'LowPtElectron_dxyErr',\n",
       " 'LowPtElectron_dz',\n",
       " 'LowPtElectron_dzErr',\n",
       " 'LowPtElectron_eInvMinusPInv',\n",
       " 'LowPtElectron_energyErr',\n",
       " 'LowPtElectron_eta',\n",
       " 'LowPtElectron_hoe',\n",
       " 'LowPtElectron_mass',\n",
       " 'LowPtElectron_miniPFRelIso_all',\n",
       " 'LowPtElectron_miniPFRelIso_chg',\n",
       " 'LowPtElectron_phi',\n",
       " 'LowPtElectron_pt',\n",
       " 'LowPtElectron_ptbiased',\n",
       " 'LowPtElectron_r9',\n",
       " 'LowPtElectron_scEtOverPt',\n",
       " 'LowPtElectron_sieie',\n",
       " 'LowPtElectron_unbiased',\n",
       " 'GenMET_phi',\n",
       " 'GenMET_pt',\n",
       " 'MET_MetUnclustEnUpDeltaX',\n",
       " 'MET_MetUnclustEnUpDeltaY',\n",
       " 'MET_covXX',\n",
       " 'MET_covXY',\n",
       " 'MET_covYY',\n",
       " 'MET_phi',\n",
       " 'MET_pt',\n",
       " 'MET_significance',\n",
       " 'MET_sumEt',\n",
       " 'MET_sumPtUnclustered',\n",
       " 'nMuon',\n",
       " 'Muon_highPtId',\n",
       " 'Muon_highPurity',\n",
       " 'Muon_inTimeMuon',\n",
       " 'Muon_isGlobal',\n",
       " 'Muon_isPFcand',\n",
       " 'Muon_isStandalone',\n",
       " 'Muon_isTracker',\n",
       " 'Muon_jetNDauCharged',\n",
       " 'Muon_looseId',\n",
       " 'Muon_mediumId',\n",
       " 'Muon_mediumPromptId',\n",
       " 'Muon_miniIsoId',\n",
       " 'Muon_multiIsoId',\n",
       " 'Muon_mvaMuID_WP',\n",
       " 'Muon_nStations',\n",
       " 'Muon_nTrackerLayers',\n",
       " 'Muon_pfIsoId',\n",
       " 'Muon_puppiIsoId',\n",
       " 'Muon_softId',\n",
       " 'Muon_softMvaId',\n",
       " 'Muon_tightCharge',\n",
       " 'Muon_tightId',\n",
       " 'Muon_tkIsoId',\n",
       " 'Muon_triggerIdLoose',\n",
       " 'Muon_jetIdx',\n",
       " 'Muon_svIdx',\n",
       " 'Muon_fsrPhotonIdx',\n",
       " 'Muon_charge',\n",
       " 'Muon_pdgId',\n",
       " 'Muon_dxy',\n",
       " 'Muon_dxyErr',\n",
       " 'Muon_dxybs',\n",
       " 'Muon_dz',\n",
       " 'Muon_dzErr',\n",
       " 'Muon_eta',\n",
       " 'Muon_ip3d',\n",
       " 'Muon_jetPtRelv2',\n",
       " 'Muon_jetRelIso',\n",
       " 'Muon_mass',\n",
       " 'Muon_miniPFRelIso_all',\n",
       " 'Muon_miniPFRelIso_chg',\n",
       " 'Muon_mvaMuID',\n",
       " 'Muon_pfRelIso03_all',\n",
       " 'Muon_pfRelIso03_chg',\n",
       " 'Muon_pfRelIso04_all',\n",
       " 'Muon_phi',\n",
       " 'Muon_pt',\n",
       " 'Muon_ptErr',\n",
       " 'Muon_segmentComp',\n",
       " 'Muon_sip3d',\n",
       " 'Muon_softMva',\n",
       " 'Muon_tkRelIso',\n",
       " 'Muon_tunepRelPt',\n",
       " 'Muon_bsConstrainedChi2',\n",
       " 'Muon_bsConstrainedPt',\n",
       " 'Muon_bsConstrainedPtErr',\n",
       " 'Muon_mvaLowPt',\n",
       " 'Muon_mvaTTH',\n",
       " 'nPhoton',\n",
       " 'Photon_seediEtaOriX',\n",
       " 'Photon_cutBased',\n",
       " 'Photon_electronVeto',\n",
       " 'Photon_hasConversionTracks',\n",
       " 'Photon_isScEtaEB',\n",
       " 'Photon_isScEtaEE',\n",
       " 'Photon_mvaID_WP80',\n",
       " 'Photon_mvaID_WP90',\n",
       " 'Photon_pixelSeed',\n",
       " 'Photon_seedGain',\n",
       " 'Photon_electronIdx',\n",
       " 'Photon_jetIdx',\n",
       " 'Photon_seediPhiOriY',\n",
       " 'Photon_vidNestedWPBitmap',\n",
       " 'Photon_ecalPFClusterIso',\n",
       " 'Photon_energyErr',\n",
       " 'Photon_energyRaw',\n",
       " 'Photon_esEffSigmaRR',\n",
       " 'Photon_esEnergyOverRawE',\n",
       " 'Photon_eta',\n",
       " 'Photon_etaWidth',\n",
       " 'Photon_haloTaggerMVAVal',\n",
       " 'Photon_hcalPFClusterIso',\n",
       " 'Photon_hoe',\n",
       " 'Photon_hoe_PUcorr',\n",
       " 'Photon_mvaID',\n",
       " 'Photon_pfChargedIso',\n",
       " 'Photon_pfChargedIsoPFPV',\n",
       " 'Photon_pfChargedIsoWorstVtx',\n",
       " 'Photon_pfPhoIso03',\n",
       " 'Photon_pfRelIso03_all_quadratic',\n",
       " 'Photon_pfRelIso03_chg_quadratic',\n",
       " 'Photon_phi',\n",
       " 'Photon_phiWidth',\n",
       " 'Photon_pt',\n",
       " 'Photon_r9',\n",
       " 'Photon_s4',\n",
       " 'Photon_sieie',\n",
       " 'Photon_sieip',\n",
       " 'Photon_sipip',\n",
       " 'Photon_trkSumPtHollowConeDR03',\n",
       " 'Photon_trkSumPtSolidConeDR04',\n",
       " 'Photon_x_calo',\n",
       " 'Photon_y_calo',\n",
       " 'Photon_z_calo',\n",
       " 'Pileup_nPU',\n",
       " 'Pileup_sumEOOT',\n",
       " 'Pileup_sumLOOT',\n",
       " 'Pileup_nTrueInt',\n",
       " 'Pileup_pudensity',\n",
       " 'Pileup_gpudensity',\n",
       " 'PuppiMET_phi',\n",
       " 'PuppiMET_phiJERDown',\n",
       " 'PuppiMET_phiJERUp',\n",
       " 'PuppiMET_phiJESDown',\n",
       " 'PuppiMET_phiJESUp',\n",
       " 'PuppiMET_phiUnclusteredDown',\n",
       " 'PuppiMET_phiUnclusteredUp',\n",
       " 'PuppiMET_pt',\n",
       " 'PuppiMET_ptJERDown',\n",
       " 'PuppiMET_ptJERUp',\n",
       " 'PuppiMET_ptJESDown',\n",
       " 'PuppiMET_ptJESUp',\n",
       " 'PuppiMET_ptUnclusteredDown',\n",
       " 'PuppiMET_ptUnclusteredUp',\n",
       " 'PuppiMET_sumEt',\n",
       " 'RawMET_phi',\n",
       " 'RawMET_pt',\n",
       " 'RawMET_sumEt',\n",
       " 'RawPuppiMET_phi',\n",
       " 'RawPuppiMET_pt',\n",
       " 'RawPuppiMET_sumEt',\n",
       " 'Rho_fixedGridRhoAll',\n",
       " 'Rho_fixedGridRhoFastjetAll',\n",
       " 'Rho_fixedGridRhoFastjetCentral',\n",
       " 'Rho_fixedGridRhoFastjetCentralCalo',\n",
       " 'Rho_fixedGridRhoFastjetCentralChargedPileUp',\n",
       " 'Rho_fixedGridRhoFastjetCentralNeutral',\n",
       " 'nGenDressedLepton',\n",
       " 'GenDressedLepton_hasTauAnc',\n",
       " 'GenDressedLepton_pdgId',\n",
       " 'GenDressedLepton_eta',\n",
       " 'GenDressedLepton_mass',\n",
       " 'GenDressedLepton_phi',\n",
       " 'GenDressedLepton_pt',\n",
       " 'nGenIsolatedPhoton',\n",
       " 'GenIsolatedPhoton_eta',\n",
       " 'GenIsolatedPhoton_mass',\n",
       " 'GenIsolatedPhoton_phi',\n",
       " 'GenIsolatedPhoton_pt',\n",
       " 'nSoftActivityJet',\n",
       " 'SoftActivityJet_eta',\n",
       " 'SoftActivityJet_phi',\n",
       " 'SoftActivityJet_pt',\n",
       " 'SoftActivityJetNjets10',\n",
       " 'SoftActivityJetNjets2',\n",
       " 'SoftActivityJetNjets5',\n",
       " 'SoftActivityJetHT',\n",
       " 'SoftActivityJetHT10',\n",
       " 'SoftActivityJetHT2',\n",
       " 'SoftActivityJetHT5',\n",
       " 'nSubJet',\n",
       " 'SubJet_btagDeepB',\n",
       " 'SubJet_eta',\n",
       " 'SubJet_mass',\n",
       " 'SubJet_n2b1',\n",
       " 'SubJet_n3b1',\n",
       " 'SubJet_phi',\n",
       " 'SubJet_pt',\n",
       " 'SubJet_rawFactor',\n",
       " 'SubJet_tau1',\n",
       " 'SubJet_tau2',\n",
       " 'SubJet_tau3',\n",
       " 'SubJet_tau4',\n",
       " 'nTau',\n",
       " 'Tau_decayMode',\n",
       " 'Tau_idAntiEleDeadECal',\n",
       " 'Tau_idAntiMu',\n",
       " 'Tau_idDecayModeNewDMs',\n",
       " 'Tau_idDecayModeOldDMs',\n",
       " 'Tau_idDeepTau2017v2p1VSe',\n",
       " 'Tau_idDeepTau2017v2p1VSjet',\n",
       " 'Tau_idDeepTau2017v2p1VSmu',\n",
       " 'Tau_idDeepTau2018v2p5VSe',\n",
       " 'Tau_idDeepTau2018v2p5VSjet',\n",
       " 'Tau_idDeepTau2018v2p5VSmu',\n",
       " 'Tau_nSVs',\n",
       " 'Tau_charge',\n",
       " 'Tau_decayModePNet',\n",
       " 'Tau_eleIdx',\n",
       " 'Tau_jetIdx',\n",
       " 'Tau_muIdx',\n",
       " 'Tau_svIdx1',\n",
       " 'Tau_svIdx2',\n",
       " 'Tau_chargedIso',\n",
       " 'Tau_dxy',\n",
       " 'Tau_dz',\n",
       " 'Tau_eta',\n",
       " 'Tau_leadTkDeltaEta',\n",
       " 'Tau_leadTkDeltaPhi',\n",
       " 'Tau_leadTkPtOverTauPt',\n",
       " 'Tau_mass',\n",
       " 'Tau_neutralIso',\n",
       " 'Tau_phi',\n",
       " 'Tau_photonsOutsideSignalCone',\n",
       " 'Tau_probDM0PNet',\n",
       " 'Tau_probDM10PNet',\n",
       " 'Tau_probDM11PNet',\n",
       " 'Tau_probDM1PNet',\n",
       " 'Tau_probDM2PNet',\n",
       " 'Tau_pt',\n",
       " 'Tau_ptCorrPNet',\n",
       " 'Tau_puCorr',\n",
       " 'Tau_qConfPNet',\n",
       " 'Tau_rawDeepTau2017v2p1VSe',\n",
       " 'Tau_rawDeepTau2017v2p1VSjet',\n",
       " 'Tau_rawDeepTau2017v2p1VSmu',\n",
       " 'Tau_rawDeepTau2018v2p5VSe',\n",
       " 'Tau_rawDeepTau2018v2p5VSjet',\n",
       " 'Tau_rawDeepTau2018v2p5VSmu',\n",
       " 'Tau_rawIso',\n",
       " 'Tau_rawIsodR03',\n",
       " 'Tau_rawPNetVSe',\n",
       " 'Tau_rawPNetVSjet',\n",
       " 'Tau_rawPNetVSmu',\n",
       " 'TkMET_phi',\n",
       " 'TkMET_pt',\n",
       " 'TkMET_sumEt',\n",
       " 'nTrigObj',\n",
       " 'TrigObj_l1charge',\n",
       " 'TrigObj_id',\n",
       " 'TrigObj_l1iso',\n",
       " 'TrigObj_filterBits',\n",
       " 'TrigObj_pt',\n",
       " 'TrigObj_eta',\n",
       " 'TrigObj_phi',\n",
       " 'TrigObj_l1pt',\n",
       " 'TrigObj_l1pt_2',\n",
       " 'TrigObj_l2pt',\n",
       " 'genTtbarId',\n",
       " 'nOtherPV',\n",
       " 'OtherPV_z',\n",
       " 'OtherPV_score',\n",
       " 'PV_npvs',\n",
       " 'PV_npvsGood',\n",
       " 'PV_ndof',\n",
       " 'PV_x',\n",
       " 'PV_y',\n",
       " 'PV_z',\n",
       " 'PV_chi2',\n",
       " 'PV_score',\n",
       " 'nSV',\n",
       " 'SV_charge',\n",
       " 'SV_dlen',\n",
       " 'SV_dlenSig',\n",
       " 'SV_dxy',\n",
       " 'SV_dxySig',\n",
       " 'SV_pAngle',\n",
       " 'boostedTau_genPartFlav',\n",
       " 'boostedTau_genPartIdx',\n",
       " 'Electron_genPartFlav',\n",
       " 'Electron_genPartIdx',\n",
       " 'FatJet_hadronFlavour',\n",
       " 'FatJet_nBHadrons',\n",
       " 'FatJet_nCHadrons',\n",
       " 'FatJet_genJetAK8Idx',\n",
       " 'GenJetAK8_hadronFlavour',\n",
       " 'GenJetAK8_partonFlavour',\n",
       " 'GenJet_hadronFlavour',\n",
       " 'GenJet_partonFlavour',\n",
       " 'GenVtx_t0',\n",
       " 'Jet_hadronFlavour',\n",
       " 'Jet_genJetIdx',\n",
       " 'Jet_partonFlavour',\n",
       " 'LowPtElectron_genPartFlav',\n",
       " 'LowPtElectron_genPartIdx',\n",
       " 'Muon_genPartFlav',\n",
       " 'Muon_genPartIdx',\n",
       " 'Photon_genPartFlav',\n",
       " 'Photon_genPartIdx',\n",
       " 'MET_fiducialGenPhi',\n",
       " 'MET_fiducialGenPt',\n",
       " 'SubJet_hadronFlavour',\n",
       " 'SubJet_nBHadrons',\n",
       " 'SubJet_nCHadrons',\n",
       " 'SV_ntracks',\n",
       " 'SV_chi2',\n",
       " 'SV_eta',\n",
       " 'SV_mass',\n",
       " 'SV_ndof',\n",
       " 'SV_phi',\n",
       " 'SV_pt',\n",
       " 'SV_x',\n",
       " 'SV_y',\n",
       " 'SV_z',\n",
       " 'Tau_genPartFlav',\n",
       " 'Tau_genPartIdx',\n",
       " 'L1_AlwaysTrue',\n",
       " 'L1_BPTX_AND_Ref1_VME',\n",
       " 'L1_BPTX_AND_Ref3_VME',\n",
       " 'L1_BPTX_AND_Ref4_VME',\n",
       " 'L1_BPTX_BeamGas_B1_VME',\n",
       " 'L1_BPTX_BeamGas_B2_VME',\n",
       " 'L1_BPTX_BeamGas_Ref1_VME',\n",
       " 'L1_BPTX_BeamGas_Ref2_VME',\n",
       " 'L1_BPTX_NotOR_VME',\n",
       " 'L1_BPTX_OR_Ref3_VME',\n",
       " 'L1_BPTX_OR_Ref4_VME',\n",
       " 'L1_BPTX_RefAND_VME',\n",
       " 'L1_BptxMinus',\n",
       " 'L1_BptxOR',\n",
       " 'L1_BptxPlus',\n",
       " 'L1_BptxXOR',\n",
       " 'L1_CDC_SingleMu_3_er1p2_TOP120_DPHI2p618_3p142',\n",
       " 'L1_DoubleEG10_er1p2_dR_Max0p6',\n",
       " 'L1_DoubleEG10p5_er1p2_dR_Max0p6',\n",
       " 'L1_DoubleEG11_er1p2_dR_Max0p6',\n",
       " 'L1_DoubleEG4_er1p2_dR_Max0p9',\n",
       " 'L1_DoubleEG4p5_er1p2_dR_Max0p9',\n",
       " 'L1_DoubleEG5_er1p2_dR_Max0p9',\n",
       " 'L1_DoubleEG5p5_er1p2_dR_Max0p8',\n",
       " 'L1_DoubleEG6_er1p2_dR_Max0p8',\n",
       " 'L1_DoubleEG6p5_er1p2_dR_Max0p8',\n",
       " 'L1_DoubleEG7_er1p2_dR_Max0p8',\n",
       " 'L1_DoubleEG7p5_er1p2_dR_Max0p7',\n",
       " 'L1_DoubleEG8_er1p2_dR_Max0p7',\n",
       " 'L1_DoubleEG8er2p5_HTT260er',\n",
       " 'L1_DoubleEG8er2p5_HTT280er',\n",
       " 'L1_DoubleEG8er2p5_HTT300er',\n",
       " 'L1_DoubleEG8er2p5_HTT320er',\n",
       " 'L1_DoubleEG8er2p5_HTT340er',\n",
       " 'L1_DoubleEG8p5_er1p2_dR_Max0p7',\n",
       " 'L1_DoubleEG9_er1p2_dR_Max0p7',\n",
       " 'L1_DoubleEG9p5_er1p2_dR_Max0p6',\n",
       " 'L1_DoubleEG_15_10_er2p5',\n",
       " 'L1_DoubleEG_20_10_er2p5',\n",
       " 'L1_DoubleEG_22_10_er2p5',\n",
       " 'L1_DoubleEG_25_12_er2p5',\n",
       " 'L1_DoubleEG_25_14_er2p5',\n",
       " 'L1_DoubleEG_27_14_er2p5',\n",
       " 'L1_DoubleEG_LooseIso16_LooseIso12_er1p5',\n",
       " 'L1_DoubleEG_LooseIso18_LooseIso12_er1p5',\n",
       " 'L1_DoubleEG_LooseIso20_10_er2p5',\n",
       " 'L1_DoubleEG_LooseIso20_LooseIso12_er1p5',\n",
       " 'L1_DoubleEG_LooseIso22_10_er2p5',\n",
       " 'L1_DoubleEG_LooseIso22_12_er2p5',\n",
       " 'L1_DoubleEG_LooseIso22_LooseIso12_er1p5',\n",
       " 'L1_DoubleEG_LooseIso25_12_er2p5',\n",
       " 'L1_DoubleEG_LooseIso25_LooseIso12_er1p5',\n",
       " 'L1_DoubleIsoTau26er2p1_Jet55_RmOvlp_dR0p5',\n",
       " 'L1_DoubleIsoTau26er2p1_Jet70_RmOvlp_dR0p5',\n",
       " 'L1_DoubleIsoTau28er2p1',\n",
       " 'L1_DoubleIsoTau28er2p1_Mass_Max80',\n",
       " 'L1_DoubleIsoTau28er2p1_Mass_Max90',\n",
       " 'L1_DoubleIsoTau30er2p1',\n",
       " 'L1_DoubleIsoTau30er2p1_Mass_Max80',\n",
       " 'L1_DoubleIsoTau30er2p1_Mass_Max90',\n",
       " 'L1_DoubleIsoTau32er2p1',\n",
       " 'L1_DoubleIsoTau34er2p1',\n",
       " 'L1_DoubleIsoTau35er2p1',\n",
       " 'L1_DoubleIsoTau36er2p1',\n",
       " 'L1_DoubleJet100er2p3_dEta_Max1p6',\n",
       " 'L1_DoubleJet100er2p5',\n",
       " 'L1_DoubleJet112er2p3_dEta_Max1p6',\n",
       " 'L1_DoubleJet120er2p5',\n",
       " 'L1_DoubleJet150er2p5',\n",
       " 'L1_DoubleJet30er2p5_Mass_Min150_dEta_Max1p5',\n",
       " 'L1_DoubleJet30er2p5_Mass_Min200_dEta_Max1p5',\n",
       " 'L1_DoubleJet30er2p5_Mass_Min250_dEta_Max1p5',\n",
       " 'L1_DoubleJet30er2p5_Mass_Min300_dEta_Max1p5',\n",
       " 'L1_DoubleJet30er2p5_Mass_Min330_dEta_Max1p5',\n",
       " 'L1_DoubleJet30er2p5_Mass_Min360_dEta_Max1p5',\n",
       " 'L1_DoubleJet35_Mass_Min450_IsoTau45_RmOvlp',\n",
       " 'L1_DoubleJet35_Mass_Min450_IsoTau45er2p1_RmOvlp_dR0p5',\n",
       " 'L1_DoubleJet40er2p5',\n",
       " 'L1_DoubleJet_100_30_DoubleJet30_Mass_Min620',\n",
       " 'L1_DoubleJet_110_35_DoubleJet35_Mass_Min620',\n",
       " 'L1_DoubleJet_115_40_DoubleJet40_Mass_Min620',\n",
       " 'L1_DoubleJet_115_40_DoubleJet40_Mass_Min620_Jet60TT28',\n",
       " 'L1_DoubleJet_120_45_DoubleJet45_Mass_Min620',\n",
       " 'L1_DoubleJet_120_45_DoubleJet45_Mass_Min620_Jet60TT28',\n",
       " 'L1_DoubleJet_80_30_Mass_Min420_DoubleMu0_SQ',\n",
       " 'L1_DoubleJet_80_30_Mass_Min420_IsoTau40_RmOvlp',\n",
       " 'L1_DoubleJet_80_30_Mass_Min420_Mu8',\n",
       " 'L1_DoubleJet_90_30_DoubleJet30_Mass_Min620',\n",
       " 'L1_DoubleLLPJet40',\n",
       " 'L1_DoubleLooseIsoEG22er2p1',\n",
       " 'L1_DoubleLooseIsoEG24er2p1',\n",
       " 'L1_DoubleMu0',\n",
       " 'L1_DoubleMu0_Mass_Min1',\n",
       " 'L1_DoubleMu0_OQ',\n",
       " 'L1_DoubleMu0_SQ',\n",
       " 'L1_DoubleMu0_SQ_OS',\n",
       " 'L1_DoubleMu0_Upt15_Upt7',\n",
       " 'L1_DoubleMu0_Upt5_Upt5',\n",
       " 'L1_DoubleMu0_Upt6_IP_Min1_Upt4',\n",
       " 'L1_DoubleMu0_dR_Max1p6_Jet90er2p5_dR_Max0p8',\n",
       " 'L1_DoubleMu0er1p4_OQ_OS_dEta_Max1p6',\n",
       " 'L1_DoubleMu0er1p4_SQ_OS_dR_Max1p4',\n",
       " 'L1_DoubleMu0er1p5_SQ',\n",
       " 'L1_DoubleMu0er1p5_SQ_OS',\n",
       " 'L1_DoubleMu0er1p5_SQ_OS_dR_Max1p4',\n",
       " 'L1_DoubleMu0er1p5_SQ_dR_Max1p4',\n",
       " 'L1_DoubleMu0er2p0_SQ_OS_dEta_Max1p5',\n",
       " 'L1_DoubleMu0er2p0_SQ_OS_dEta_Max1p6',\n",
       " 'L1_DoubleMu0er2p0_SQ_OS_dR_Max1p4',\n",
       " 'L1_DoubleMu0er2p0_SQ_dEta_Max1p5',\n",
       " 'L1_DoubleMu0er2p0_SQ_dEta_Max1p6',\n",
       " 'L1_DoubleMu0er2p0_SQ_dR_Max1p4',\n",
       " 'L1_DoubleMu18er2p1_SQ',\n",
       " 'L1_DoubleMu3_OS_er2p3_Mass_Max14_DoubleEG7p5_er2p1_Mass_Max20',\n",
       " 'L1_DoubleMu3_SQ_ETMHF30_HTT60er',\n",
       " 'L1_DoubleMu3_SQ_ETMHF30_Jet60er2p5_OR_DoubleJet40er2p5',\n",
       " 'L1_DoubleMu3_SQ_ETMHF40_HTT60er',\n",
       " 'L1_DoubleMu3_SQ_ETMHF40_Jet60er2p5_OR_DoubleJet40er2p5',\n",
       " 'L1_DoubleMu3_SQ_ETMHF50_HTT60er',\n",
       " 'L1_DoubleMu3_SQ_ETMHF50_Jet60er2p5',\n",
       " 'L1_DoubleMu3_SQ_ETMHF50_Jet60er2p5_OR_DoubleJet40er2p5',\n",
       " 'L1_DoubleMu3_SQ_ETMHF60_Jet60er2p5',\n",
       " 'L1_DoubleMu3_SQ_HTT220er',\n",
       " 'L1_DoubleMu3_SQ_HTT240er',\n",
       " 'L1_DoubleMu3_SQ_HTT260er',\n",
       " 'L1_DoubleMu3_dR_Max1p6_Jet90er2p5_dR_Max0p8',\n",
       " 'L1_DoubleMu3er2p0_SQ_OS_dR_Max1p4',\n",
       " 'L1_DoubleMu4_SQ_EG9er2p5',\n",
       " 'L1_DoubleMu4_SQ_OS',\n",
       " 'L1_DoubleMu4_SQ_OS_dR_Max1p2',\n",
       " 'L1_DoubleMu4p5_SQ_OS',\n",
       " 'L1_DoubleMu4p5_SQ_OS_dR_Max1p2',\n",
       " 'L1_DoubleMu4p5er2p0_SQ_OS',\n",
       " 'L1_DoubleMu4p5er2p0_SQ_OS_Mass_7to18',\n",
       " 'L1_DoubleMu4p5er2p0_SQ_OS_Mass_Min7',\n",
       " 'L1_DoubleMu5_OS_er2p3_Mass_8to14_DoubleEG3er2p1_Mass_Max20',\n",
       " 'L1_DoubleMu5_SQ_EG9er2p5',\n",
       " 'L1_DoubleMu8_SQ',\n",
       " 'L1_DoubleMu9_SQ',\n",
       " 'L1_DoubleMu_12_5',\n",
       " 'L1_DoubleMu_15_5_SQ',\n",
       " 'L1_DoubleMu_15_7',\n",
       " 'L1_DoubleMu_15_7_Mass_Min1',\n",
       " 'L1_DoubleMu_15_7_SQ',\n",
       " 'L1_DoubleTau70er2p1',\n",
       " 'L1_ETM120',\n",
       " 'L1_ETM150',\n",
       " 'L1_ETMHF100',\n",
       " 'L1_ETMHF100_HTT60er',\n",
       " 'L1_ETMHF110',\n",
       " 'L1_ETMHF110_HTT60er',\n",
       " 'L1_ETMHF110_HTT60er_NotSecondBunchInTrain',\n",
       " 'L1_ETMHF120',\n",
       " 'L1_ETMHF120_HTT60er',\n",
       " 'L1_ETMHF120_NotSecondBunchInTrain',\n",
       " 'L1_ETMHF130',\n",
       " 'L1_ETMHF130_HTT60er',\n",
       " 'L1_ETMHF140',\n",
       " 'L1_ETMHF150',\n",
       " 'L1_ETMHF70',\n",
       " 'L1_ETMHF70_HTT60er',\n",
       " 'L1_ETMHF80',\n",
       " 'L1_ETMHF80_HTT60er',\n",
       " 'L1_ETMHF80_SingleJet55er2p5_dPhi_Min2p1',\n",
       " 'L1_ETMHF80_SingleJet55er2p5_dPhi_Min2p6',\n",
       " 'L1_ETMHF90',\n",
       " 'L1_ETMHF90_HTT60er',\n",
       " 'L1_ETMHF90_SingleJet60er2p5_dPhi_Min2p1',\n",
       " 'L1_ETMHF90_SingleJet60er2p5_dPhi_Min2p6',\n",
       " 'L1_ETMHF90_SingleJet80er2p5_dPhi_Min2p1',\n",
       " 'L1_ETMHF90_SingleJet80er2p5_dPhi_Min2p6',\n",
       " 'L1_ETT1200',\n",
       " 'L1_ETT1600',\n",
       " 'L1_ETT2000',\n",
       " 'L1_FirstBunchAfterTrain',\n",
       " 'L1_FirstBunchBeforeTrain',\n",
       " 'L1_FirstBunchInTrain',\n",
       " 'L1_FirstCollisionInOrbit',\n",
       " 'L1_FirstCollisionInTrain',\n",
       " 'L1_HCAL_LaserMon_Trig',\n",
       " 'L1_HCAL_LaserMon_Veto',\n",
       " 'L1_HTT120_SingleLLPJet40',\n",
       " 'L1_HTT120er',\n",
       " 'L1_HTT160_SingleLLPJet50',\n",
       " 'L1_HTT160er',\n",
       " 'L1_HTT200_SingleLLPJet60',\n",
       " 'L1_HTT200er',\n",
       " 'L1_HTT240_SingleLLPJet70',\n",
       " 'L1_HTT255er',\n",
       " 'L1_HTT280er',\n",
       " 'L1_HTT280er_QuadJet_70_55_40_35_er2p5',\n",
       " 'L1_HTT320er',\n",
       " 'L1_HTT320er_QuadJet_70_55_40_40_er2p5',\n",
       " 'L1_HTT320er_QuadJet_80_60_er2p1_45_40_er2p3',\n",
       " 'L1_HTT320er_QuadJet_80_60_er2p1_50_45_er2p3',\n",
       " 'L1_HTT360er',\n",
       " 'L1_HTT400er',\n",
       " 'L1_HTT450er',\n",
       " 'L1_IsoEG32er2p5_Mt40',\n",
       " 'L1_IsoTau52er2p1_QuadJet36er2p5',\n",
       " 'L1_IsolatedBunch',\n",
       " 'L1_LastBunchInTrain',\n",
       " 'L1_LastCollisionInTrain',\n",
       " 'L1_LooseIsoEG22er2p1_IsoTau26er2p1_dR_Min0p3',\n",
       " 'L1_LooseIsoEG22er2p1_Tau70er2p1_dR_Min0p3',\n",
       " 'L1_LooseIsoEG24er2p1_HTT100er',\n",
       " 'L1_LooseIsoEG24er2p1_IsoTau27er2p1_dR_Min0p3',\n",
       " 'L1_LooseIsoEG26er2p1_HTT100er',\n",
       " 'L1_LooseIsoEG26er2p1_Jet34er2p5_dR_Min0p3',\n",
       " 'L1_LooseIsoEG28er2p1_HTT100er',\n",
       " 'L1_LooseIsoEG28er2p1_Jet34er2p5_dR_Min0p3',\n",
       " 'L1_LooseIsoEG30er2p1_HTT100er',\n",
       " 'L1_LooseIsoEG30er2p1_Jet34er2p5_dR_Min0p3',\n",
       " 'L1_MinimumBiasHF0',\n",
       " 'L1_MinimumBiasHF0_AND_BptxAND',\n",
       " 'L1_Mu10er2p3_Jet32er2p3_dR_Max0p4_DoubleJet32er2p3_dEta_Max1p6',\n",
       " 'L1_Mu12er2p3_Jet40er2p1_dR_Max0p4_DoubleJet40er2p1_dEta_Max1p6',\n",
       " 'L1_Mu12er2p3_Jet40er2p3_dR_Max0p4_DoubleJet40er2p3_dEta_Max1p6',\n",
       " 'L1_Mu18er2p1_Tau24er2p1',\n",
       " 'L1_Mu18er2p1_Tau26er2p1',\n",
       " 'L1_Mu18er2p1_Tau26er2p1_Jet55',\n",
       " 'L1_Mu18er2p1_Tau26er2p1_Jet70',\n",
       " 'L1_Mu20_EG10er2p5',\n",
       " 'L1_Mu22er2p1_IsoTau28er2p1',\n",
       " 'L1_Mu22er2p1_IsoTau30er2p1',\n",
       " 'L1_Mu22er2p1_IsoTau32er2p1',\n",
       " 'L1_Mu22er2p1_IsoTau34er2p1',\n",
       " 'L1_Mu22er2p1_IsoTau36er2p1',\n",
       " 'L1_Mu22er2p1_IsoTau40er2p1',\n",
       " 'L1_Mu22er2p1_Tau70er2p1',\n",
       " 'L1_Mu3_Jet120er2p5_dR_Max0p4',\n",
       " 'L1_Mu3_Jet120er2p5_dR_Max0p8',\n",
       " 'L1_Mu3_Jet16er2p5_dR_Max0p4',\n",
       " 'L1_Mu3_Jet30er2p5',\n",
       " 'L1_Mu3_Jet35er2p5_dR_Max0p4',\n",
       " 'L1_Mu3_Jet60er2p5_dR_Max0p4',\n",
       " 'L1_Mu3_Jet80er2p5_dR_Max0p4',\n",
       " 'L1_Mu3er1p5_Jet100er2p5_ETMHF30',\n",
       " 'L1_Mu3er1p5_Jet100er2p5_ETMHF40',\n",
       " 'L1_Mu3er1p5_Jet100er2p5_ETMHF50',\n",
       " 'L1_Mu5_EG23er2p5',\n",
       " 'L1_Mu5_LooseIsoEG20er2p5',\n",
       " 'L1_Mu6_DoubleEG10er2p5',\n",
       " 'L1_Mu6_DoubleEG12er2p5',\n",
       " 'L1_Mu6_DoubleEG15er2p5',\n",
       " 'L1_Mu6_DoubleEG17er2p5',\n",
       " 'L1_Mu6_HTT240er',\n",
       " 'L1_Mu6_HTT250er',\n",
       " 'L1_Mu7_EG20er2p5',\n",
       " 'L1_Mu7_EG23er2p5',\n",
       " 'L1_Mu7_LooseIsoEG20er2p5',\n",
       " 'L1_Mu7_LooseIsoEG23er2p5',\n",
       " 'L1_NotBptxOR',\n",
       " 'L1_QuadJet60er2p5',\n",
       " 'L1_QuadJet_95_75_65_20_DoubleJet_75_65_er2p5_Jet20_FWD3p0',\n",
       " 'L1_QuadMu0',\n",
       " 'L1_QuadMu0_OQ',\n",
       " 'L1_QuadMu0_SQ',\n",
       " 'L1_SecondBunchInTrain',\n",
       " 'L1_SecondLastBunchInTrain',\n",
       " 'L1_SingleEG10er2p5',\n",
       " 'L1_SingleEG15er2p5',\n",
       " 'L1_SingleEG26er2p5',\n",
       " 'L1_SingleEG28_FWD2p5',\n",
       " 'L1_SingleEG28er1p5',\n",
       " 'L1_SingleEG28er2p1',\n",
       " 'L1_SingleEG28er2p5',\n",
       " 'L1_SingleEG34er2p5',\n",
       " 'L1_SingleEG36er2p5',\n",
       " 'L1_SingleEG38er2p5',\n",
       " 'L1_SingleEG40er2p5',\n",
       " 'L1_SingleEG42er2p5',\n",
       " 'L1_SingleEG45er2p5',\n",
       " 'L1_SingleEG50',\n",
       " 'L1_SingleEG60',\n",
       " 'L1_SingleEG8er2p5',\n",
       " 'L1_SingleIsoEG24er1p5',\n",
       " 'L1_SingleIsoEG24er2p1',\n",
       " 'L1_SingleIsoEG26er1p5',\n",
       " 'L1_SingleIsoEG26er2p1',\n",
       " 'L1_SingleIsoEG26er2p5',\n",
       " 'L1_SingleIsoEG28_FWD2p5',\n",
       " 'L1_SingleIsoEG28er1p5',\n",
       " 'L1_SingleIsoEG28er2p1',\n",
       " 'L1_SingleIsoEG28er2p5',\n",
       " 'L1_SingleIsoEG30er2p1',\n",
       " 'L1_SingleIsoEG30er2p5',\n",
       " 'L1_SingleIsoEG32er2p1',\n",
       " 'L1_SingleIsoEG32er2p5',\n",
       " 'L1_SingleIsoEG34er2p5',\n",
       " 'L1_SingleIsoTau32er2p1',\n",
       " 'L1_SingleJet10erHE',\n",
       " 'L1_SingleJet120',\n",
       " 'L1_SingleJet120_FWD3p0',\n",
       " 'L1_SingleJet120er2p5',\n",
       " 'L1_SingleJet12erHE',\n",
       " 'L1_SingleJet140er2p5',\n",
       " 'L1_SingleJet140er2p5_ETMHF70',\n",
       " 'L1_SingleJet140er2p5_ETMHF80',\n",
       " 'L1_SingleJet140er2p5_ETMHF90',\n",
       " 'L1_SingleJet160er2p5',\n",
       " 'L1_SingleJet180',\n",
       " 'L1_SingleJet180er2p5',\n",
       " 'L1_SingleJet200',\n",
       " 'L1_SingleJet20er2p5_NotBptxOR',\n",
       " 'L1_SingleJet20er2p5_NotBptxOR_3BX',\n",
       " 'L1_SingleJet35',\n",
       " 'L1_SingleJet35_FWD3p0',\n",
       " 'L1_SingleJet35er2p5',\n",
       " 'L1_SingleJet43er2p5_NotBptxOR_3BX',\n",
       " 'L1_SingleJet46er2p5_NotBptxOR_3BX',\n",
       " 'L1_SingleJet60',\n",
       " 'L1_SingleJet60_FWD3p0',\n",
       " 'L1_SingleJet60er2p5',\n",
       " 'L1_SingleJet8erHE',\n",
       " 'L1_SingleJet90',\n",
       " 'L1_SingleJet90_FWD3p0',\n",
       " 'L1_SingleJet90er2p5',\n",
       " 'L1_SingleLooseIsoEG26er1p5',\n",
       " 'L1_SingleLooseIsoEG26er2p5',\n",
       " 'L1_SingleLooseIsoEG28_FWD2p5',\n",
       " 'L1_SingleLooseIsoEG28er1p5',\n",
       " 'L1_SingleLooseIsoEG28er2p1',\n",
       " 'L1_SingleLooseIsoEG28er2p5',\n",
       " 'L1_SingleLooseIsoEG30er1p5',\n",
       " 'L1_SingleLooseIsoEG30er2p5',\n",
       " 'L1_SingleMu0_BMTF',\n",
       " 'L1_SingleMu0_DQ',\n",
       " 'L1_SingleMu0_EMTF',\n",
       " 'L1_SingleMu0_OMTF',\n",
       " 'L1_SingleMu10er1p5',\n",
       " 'L1_SingleMu12_DQ_BMTF',\n",
       " 'L1_SingleMu12_DQ_EMTF',\n",
       " 'L1_SingleMu12_DQ_OMTF',\n",
       " 'L1_SingleMu12er1p5',\n",
       " 'L1_SingleMu14er1p5',\n",
       " 'L1_SingleMu15_DQ',\n",
       " 'L1_SingleMu16er1p5',\n",
       " 'L1_SingleMu18',\n",
       " 'L1_SingleMu18er1p5',\n",
       " 'L1_SingleMu20',\n",
       " 'L1_SingleMu22',\n",
       " 'L1_SingleMu22_BMTF',\n",
       " 'L1_SingleMu22_DQ',\n",
       " 'L1_SingleMu22_EMTF',\n",
       " 'L1_SingleMu22_OMTF',\n",
       " 'L1_SingleMu22_OQ',\n",
       " 'L1_SingleMu25',\n",
       " 'L1_SingleMu3',\n",
       " 'L1_SingleMu5',\n",
       " ...]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.keys()\n",
    "#events.values()\n",
    "#events.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1**: Which collections contain muons or muon candidates? (At any level: generated, reconstructed, trigger, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer 1:**\n",
    "Muon, Trigobj, GenPart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step2 Print and plot some muon objects \n",
    "We can print different objects such as transeverse momentum($p_T$ pt), pseudorapidity(eta $\\eta$) and azimuthal angle(phi $\\phi$) and apply cut on $p_T$, $\\eta$ and $\\phi$ using uproot.<br>\n",
    "First lets print pt eta phi of reconstructed muon "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "muon_pt,muon_eta,muon_phi,nmuon,ngenpart=events.arrays([\"Muon_pt\", \"Muon_eta\", \"Muon_phi\", \"nMuon\", \"nGenPart\"],library=\"ak\",how=tuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "muon_pt= [[], [], [14.2], [41.4, 37.2], ..., [33.7, ...], [26.8, 24.4], [], [41.6, 32.2]] \n",
      " muon_eta= [[], [], [-0.359], [1.06, ...], ..., [-0.719, 0.676], [], [0.722, -0.701]] \n",
      " muon_phi= [[], [], [-3], [2.61, -0.37], ..., [...], [0.317, -2.96], [], [-0.0455, -3.06]]\n"
     ]
    }
   ],
   "source": [
    "print('muon_pt=', muon_pt, '\\n',\n",
    "      'muon_eta=', muon_eta, '\\n',\n",
    "      'muon_phi=', muon_phi)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot them using matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.hist(ak.flatten(muon_pt), bins=150, range=[0,150], histtype='step',linewidth=2, color='blue', label='$p_T$ distribution of all Muons')\n",
    "plt.title('$p_T$ distribution of PAT Muon objects')\n",
    "plt.xlabel(r'Muon $p_T$ (GeV)')\n",
    "plt.ylabel('Events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:2: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:3: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:2: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:3: SyntaxWarning: invalid escape sequence '\\e'\n",
      "/tmp/ipykernel_7829/442157781.py:2: SyntaxWarning: invalid escape sequence '\\e'\n",
      "  plt.hist(ak.flatten(muon_eta), bins=60, range=[-3, 3], histtype='step',linewidth=2, color='red', label='muon_$\\eta$')\n",
      "/tmp/ipykernel_7829/442157781.py:3: SyntaxWarning: invalid escape sequence '\\e'\n",
      "  plt.title('$\\eta$ distribution of PAT Muon objects')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.hist(ak.flatten(muon_eta), bins=60, range=[-3, 3], histtype='step',linewidth=2, color='red', label='muon_$\\eta$')\n",
    "plt.title('$\\eta$ distribution of PAT Muon objects')\n",
    "plt.xlabel(r'Muon $\\eta$')\n",
    "plt.ylabel('Events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:2: SyntaxWarning: invalid escape sequence '\\p'\n",
      "<>:3: SyntaxWarning: invalid escape sequence '\\p'\n",
      "<>:2: SyntaxWarning: invalid escape sequence '\\p'\n",
      "<>:3: SyntaxWarning: invalid escape sequence '\\p'\n",
      "/tmp/ipykernel_7829/3680516053.py:2: SyntaxWarning: invalid escape sequence '\\p'\n",
      "  plt.hist(ak.flatten(muon_phi), bins=70, range=[-3.5, 3.5], histtype='step',linewidth=2, color='green', label='muon_$\\phi$')\n",
      "/tmp/ipykernel_7829/3680516053.py:3: SyntaxWarning: invalid escape sequence '\\p'\n",
      "  plt.title('$\\phi$ distribution of PAT Muon objects')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g3bp1uvfee7Vnzx5FRUW5uxwA1+HVV19VQkKCcnJyXO5CBaDm4rQoANXKzp07Va9ePbVu3drdpQCoIIfDoe3bt2vZsmWKiooiWAA3EC7oBlCt7NixQx07dlStWnw8AZ7q66+/1r333isfHx8tX77c3eUAqEKcFgUAAADACL4aBAAAAGAE4QIAAACAEYQLAAAAAEZwtyiDCgsLdfz4cQUEBFTaD0QBAAAAVcmyLOXk5Cg8PLzUG64QLgw6fvy4IiMj3V0GAAAAYNzRo0cVERFRYhvChUEBAQGSftnwgYGBbq4GAAAAuH7Z2dmKjIx0HuuWhHBh0OVToQIDAwkXAAAAqFHKcto/F3QDAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAhvdxcAAABQVh3/3lEZ5zLK1DasXph2PbSrkisCcCXCBQAA8BgZ5zJ0LOeYu8sAcA2ECwAA4HFq2WqpUb1GxY5LP5euQquwiisCIBEuAACAB2pUr5HSpqYVOy5iXgS9G4CbcEE3AAAAACMIFwAAAACM4LQoAABKwR2KAKBsCBeoNPwxRlVgP7sxmXzdyzIvzt8HgLIhXKDScLtAXC8O+nAt5fl8OZZzTBHzIkocXx43BdxU7HDuUITqgi9d4E6EC1Q6T7pdoKkP5Kr8YK/Jf0TKG1A56Kv+TO+vJX2+XLnvlHU/utY+VJaaLt+hKP1ceolhpqw87f1qQln2j/Rz6VW2rMs87bWoyi/3avJ2RMUQLlDpPOl2gaY+kKvyg/1G6CEq6QDyMlMHfTfiH7+qDNWm99WSPl/Ks16Sude+0Cr0mPdkdftChc9Os6riyz1T25Ev5WoOwgVQDFMfyFXZa+NJPUTlVdIBZHlc70FfeQ9WTaiKP2zuCtWV3dNU1QcEYfXCjMynLOvvjoP5kk4vK8/rXtJ8LvdKlPULBROu97OzMj4XTL3vy/LlnqkvXa53O1ZlSKnpodLdCBdAMUz1tlRlr42pPyJVqaq+ESrLQcjlP34lbaPq9gepKk8xKsv2Ke+BYWk9TZ7G1L5clvdrVR7Ml/f0smuFxvLMx9QXCmVxvZ/TldEDUtp1QlL162kz9ffuej+HuIbK/QgXuGGYPpe3LAdZ1Y0nna5hUln+AF/+41fWbVTaufnXy/Q3fWU5wCzLwUFZto+pA0NPfI+ZdL37oqmDeXedOlWdmArVpanIdUIVVZ4vXaqSqc8hycw1VCg/wgVuGKa/XTJ1oH69vQllOciqjn+oq9s3QuXZRlXR41KWb68r41vnaynv9jHhRg3DpvZFUwfzpvZ1Tz5vvapCdVm/BKuq0war2wF2dfucRvEIF7jhXO+5vKYPsqriAKq6fcBWtz9YUvXbRpdd70FNdTvAvN46rqetp+Bgvvqo6n2xPAf8VdmzZ2pZ1zsf9mnPQLhAtWfqYrmynPpRFqY+3EwfFHniQVZpvTY3wqkv12LqoMYT/xh7Ys2omarzvliVPXumlnWj9kbeaAgXqPZq6u0Cq/MfrarCH5prY/8AUJyq7E0xtawbvTfyRkO4gMcwcbGcxAdXdVDe14DXDAB+UZVfPHCKHiqCcAGPUZW3JkTl4g8NAAA1E+EC1cKNfstJAACAmoBwgWqBc+8BAKbxxRVQ9QgXcCsu8gIAVBa+uAKqHuECbsW59wAA0/jiCnAfwgUAAKhR+OIKcJ9a7i4AAAAAQM1AuAAAAABgBOECAAAAgBGECwAAAABGEC4AAAAAGEG4AAAAAGAE4QIAAACAEYQLAAAAAEYQLgAAAAAYQbgAAAAAYAThAgAAAIARhAsAAAAARhAuAAAAABhBuAAAAABgBOECAAAAgBGECwAAAABGEC4AAAAAGEG4AAAAAGAE4QIAAACAEYQLAAAAAEYQLgAAAAAYQbgAAAAAYAThAgAAAIARhAsAAAAARrg1XMyZM0e33367AgICFBISosGDB+vgwYMubSzL0qxZsxQeHi4/Pz9169ZN+/btc2mTl5enyZMnKzg4WP7+/ho0aJDS0tJc2mRlZSkuLk52u112u11xcXE6e/asS5sjR45o4MCB8vf3V3BwsKZMmaL8/PxKWXcAAACgpnFruNiyZYseffRRJScna+PGjbp06ZL69Omj3NxcZ5sXX3xR8+bN04IFC5SSkqKwsDD17t1bOTk5zjYJCQlau3atEhMTtXXrVp07d04DBgxQQUGBs82oUaOUmpqqpKQkJSUlKTU1VXFxcc7xBQUF6t+/v3Jzc7V161YlJiZq9erVmjZtWtVsDAAAAMDD2SzLstxdxGUnT55USEiItmzZorvvvluWZSk8PFwJCQn64x//KOmXXorQ0FC98MILevjhh+VwONSwYUO9/fbbGj58uCTp+PHjioyM1Mcff6zY2FgdOHBArVu3VnJysjp16iRJSk5OVnR0tL799lu1bNlSn3zyiQYMGKCjR48qPDxckpSYmKj4+HhlZmYqMDCwSL15eXnKy8tzPs/OzlZkZKQcDkex7W80EfMidCznmG4KuElpU9NKnwAAAKAa4BjGVXZ2tux2e5mOcavVNRcOh0OSFBQUJEk6fPiwMjIy1KdPH2cbX19fde3aVdu2bZMk7d69WxcvXnRpEx4erqioKGeb7du3y263O4OFJHXu3Fl2u92lTVRUlDNYSFJsbKzy8vK0e/fuYuudM2eO8zQru92uyMhIE5sBAAAA8EjVJlxYlqWpU6eqS5cuioqKkiRlZGRIkkJDQ13ahoaGOsdlZGTIx8dH9evXL7FNSEhIkWWGhIS4tLl6OfXr15ePj4+zzdVmzpwph8PhfBw9erS8qw0AAADUGN7uLuCySZMm6ZtvvtHWrVuLjLPZbC7PLcsqMuxqV7cprn1F2lzJ19dXvr6+JdYBAAAA3CiqRc/F5MmT9cEHH2jTpk2KiIhwDg8LC5OkIj0HmZmZzl6GsLAw5efnKysrq8Q2J06cKLLckydPurS5ejlZWVm6ePFikR4NAAAAAEW5NVxYlqVJkyZpzZo1+vzzz9W0aVOX8U2bNlVYWJg2btzoHJafn68tW7YoJiZGktShQwfVrl3bpU16err27t3rbBMdHS2Hw6GdO3c62+zYsUMOh8Olzd69e5Wenu5ss2HDBvn6+qpDhw7mVx4AAACoYdx6WtSjjz6qd999V//6178UEBDg7Dmw2+3y8/OTzWZTQkKCZs+erRYtWqhFixaaPXu26tatq1GjRjnbjhs3TtOmTVODBg0UFBSk6dOnq02bNurVq5ckqVWrVurbt6/Gjx+vRYsWSZIeeughDRgwQC1btpQk9enTR61bt1ZcXJzmzp2rM2fOaPr06Ro/fjx3fgIAAADKwK3hYuHChZKkbt26uQxfunSp4uPjJUkzZszQ+fPnNXHiRGVlZalTp07asGGDAgICnO3nz58vb29vDRs2TOfPn1fPnj21bNkyeXl5OdusWLFCU6ZMcd5VatCgQVqwYIFzvJeXl9avX6+JEyfqzjvvlJ+fn0aNGqWXXnqpktYeAAAAqFmq1e9ceLry3AP4RsA9ogEAgCfiGMaVx/7OBQAAAADPRbgAAAAAYAThAgAAAIARhAsAAAAARhAuAAAAABhBuAAAAABgBOECAAAAgBGECwAAAABGEC4AAAAAGEG4AAAAAGAE4QIAAACAEYQLAAAAAEYQLgAAAAAYQbgAAAAAYAThAgAAAIARhAsAAAAARhAuAAAAABhBuAAAAABgBOECAAAAgBGECwAAAABGEC4AAAAAGEG4AAAAAGAE4QIAAACAEYQLAAAAAEYQLgAAAAAYQbgAAAAAYAThAgAAAIARhAsAAAAARhAuAAAAABhBuAAAAABgBOECAAAAgBGECwAAAABGEC4AAAAAGEG4AAAAAGAE4QIAAACAEYQLAAAAAEYQLgAAAAAYQbgAAAAAYAThAgAAAIARhAsAAAAARhAuAAAAABhBuAAAAABgBOECAAAAgBGECwAAAABGEC4AAAAAGEG4AAAAAGAE4QIAAACAEYQLAAAAAEYQLgAAAAAYQbgAAAAAYAThAgAAAIARhAsAAAAARhAuAAAAABhBuAAAAABgBOECAAAAgBGECwAAAABGEC4AAAAAGEG4AAAAAGAE4QIAAACAEYQLAAAAAEYQLgAAAAAYQbgAAAAAYAThAgAAAIARhAsAAAAARhAuAAAAABhBuAAAAABgBOECAAAAgBGECwAAAABGEC4AAAAAGEG4AAAAAGAE4QIAAACAEYQLAAAAAEYQLgAAAAAYQbgAAAAAYAThAgAAAIARhAsAAAAARhAuAAAAABhBuAAAAABgBOECAAAAgBGECwAAAABGEC4AAAAAGEG4AAAAAGAE4QIAAACAEYQLAAAAAEYQLgAAAAAYQbgAAAAAYIRbw8UXX3yhgQMHKjw8XDabTevWrXMZHx8fL5vN5vLo3LmzS5u8vDxNnjxZwcHB8vf316BBg5SWlubSJisrS3FxcbLb7bLb7YqLi9PZs2dd2hw5ckQDBw6Uv7+/goODNWXKFOXn51fGagMAAAA1klvDRW5urtq1a6cFCxZcs03fvn2Vnp7ufHz88ccu4xMSErR27VolJiZq69atOnfunAYMGKCCggJnm1GjRik1NVVJSUlKSkpSamqq4uLinOMLCgrUv39/5ebmauvWrUpMTNTq1as1bdo08ysNAAAA1FDe7lx4v3791K9fvxLb+Pr6KiwsrNhxDodDixcv1ttvv61evXpJkt555x1FRkbqs88+U2xsrA4cOKCkpCQlJyerU6dOkqQ333xT0dHROnjwoFq2bKkNGzZo//79Onr0qMLDwyVJL7/8suLj4/Xcc88pMDDQ4FoDAAAANVO1v+Zi8+bNCgkJ0S233KLx48crMzPTOW737t26ePGi+vTp4xwWHh6uqKgobdu2TZK0fft22e12Z7CQpM6dO8tut7u0iYqKcgYLSYqNjVVeXp527959zdry8vKUnZ3t8gAAAABuVNU6XPTr108rVqzQ559/rpdfflkpKSnq0aOH8vLyJEkZGRny8fFR/fr1XaYLDQ1VRkaGs01ISEiReYeEhLi0CQ0NdRlfv359+fj4ONsUZ86cOc7rOOx2uyIjI69rfQEAAABP5tbTokozfPhw5/+joqLUsWNH3XzzzVq/fr2GDBlyzeksy5LNZnM+v/L/19PmajNnztTUqVOdz7OzswkYAAAAuGFV656LqzVq1Eg333yzvvvuO0lSWFiY8vPzlZWV5dIuMzPT2RMRFhamEydOFJnXyZMnXdpc3UORlZWlixcvFunRuJKvr68CAwNdHgAAAMCNyqPCxenTp3X06FE1atRIktShQwfVrl1bGzdudLZJT0/X3r17FRMTI0mKjo6Ww+HQzp07nW127Nghh8Ph0mbv3r1KT093ttmwYYN8fX3VoUOHqlg1AAAAwOO59bSoc+fO6dChQ87nhw8fVmpqqoKCghQUFKRZs2bpvvvuU6NGjfTjjz/qT3/6k4KDg3XvvfdKkux2u8aNG6dp06apQYMGCgoK0vTp09WmTRvn3aNatWqlvn37avz48Vq0aJEk6aGHHtKAAQPUsmVLSVKfPn3UunVrxcXFae7cuTpz5oymT5+u8ePH0xsBAAAAlJFbw8WuXbvUvXt35/PL1y+MHj1aCxcu1J49e/TWW2/p7NmzatSokbp3765Vq1YpICDAOc38+fPl7e2tYcOG6fz58+rZs6eWLVsmLy8vZ5sVK1ZoypQpzrtKDRo0yOW3Nby8vLR+/XpNnDhRd955p/z8/DRq1Ci99NJLlb0JAAAAgBrDZlmW5e4iaors7GzZ7XY5HA56PCRFzIvQsZxjuingJqVNTSt9AgAAgGqAYxhX5TnG9ahrLgAAAABUX4QLAAAAAEYQLgAAAAAYQbgAAAAAYAThAgAAAIARhAsAAAAARhAuAAAAABhBuAAAAABgBOECAAAAgBGECwAAAABGEC4AAAAAGEG4AAAAAGAE4QIAAACAEYQLAAAAAEYQLgAAAAAYQbgAAAAAYAThAgAAAIARhAsAAAAARhAuAAAAABhBuAAAAABgBOECAAAAgBGECwAAAABGEC4AAAAAGEG4AAAAAGAE4QIAAACAEYQLAAAAAEYQLgAAAAAYQbgAAAAAYAThAgAAAIARhAsAAAAARhAuAAAAABhBuAAAAABgBOECAAAAgBFGwkVBQYFSU1OVlZVlYnYAAAAAPFCFwkVCQoIWL14s6Zdg0bVrV912222KjIzU5s2bTdYHAAAAwENUKFz885//VLt27SRJH374oQ4fPqxvv/1WCQkJeuKJJ4wWCAAAAMAzVChcnDp1SmFhYZKkjz/+WEOHDtUtt9yicePGac+ePUYLBAAAAOAZKhQuQkNDtX//fhUUFCgpKUm9evWSJP3888/y8vIyWiAAAAAAz+BdkYnGjBmjYcOGqVGjRrLZbOrdu7ckaceOHbr11luNFggAAADAM1QoXMyaNUtRUVE6evSohg4dKl9fX0mSl5eXHn/8caMFAgAAAPAMFQoXb731loYPH+4MFZeNHDlSiYmJRgoDAAAA4FkqdM3FmDFj5HA4igzPycnRmDFjrrsoAAAAAJ6nQuHCsizZbLYiw9PS0mS326+7KAAAAACep1ynRf32t7+VzWaTzWZTz5495e39/09eUFCgw4cPq2/fvsaLBAAAAFD9lStcDB48WJKUmpqq2NhY1atXzznOx8dHTZo00X333We0QAAAAACeoVzh4umnn5YkNWnSRMOHD1edOnUqpSgAAAAAnqdCd4saPXq0JCk/P1+ZmZkqLCx0Gd+4cePrrwwAAACAR6lQuPjuu+80duxYbdu2zWX45Qu9CwoKjBQHAAAAwHNUKFzEx8fL29tbH330kfNXugEAAADc2CoULlJTU7V7927deuutpusBAAAA4KEq9DsXrVu31qlTp0zXAgAAAMCDVShcvPDCC5oxY4Y2b96s06dPKzs72+UBAAAA4MZTodOievXqJUnq2bOny3Au6AYAAABuXBUKF5s2bTJdBwAAAAAPV6Fw0bVrV9N1AAAAAPBwFbrmQpK+/PJLPfDAA4qJidGxY8ckSW+//ba2bt1qrDgAAAAAnqNC4WL16tWKjY2Vn5+fvvrqK+Xl5UmScnJyNHv2bKMFAgAAAPAMFQoXzz77rN544w29+eabql27tnN4TEyMvvrqK2PFAQAAAPAcFQoXBw8e1N13311keGBgoM6ePXu9NQEAAADwQBUKF40aNdKhQ4eKDN+6dat+/etfX3dRAAAAADxPhcLFww8/rP/+7//Wjh07ZLPZdPz4ca1YsULTp0/XxIkTTdcIAAAAwANU6Fa0M2bMkMPhUPfu3XXhwgXdfffd8vX11fTp0zVp0iTTNQIAAADwABUKF5L03HPP6YknntD+/ftVWFio1q1bq169eiZrAwAAAOBBKnRa1PLly5Wbm6u6deuqY8eOuuOOOwgWAAAAwA2uQuFi+vTpCgkJ0YgRI/TRRx/p0qVLpusCAAAA4GEqFC7S09O1atUqeXl5acSIEWrUqJEmTpyobdu2ma4PAAAAgIeoULjw9vbWgAEDtGLFCmVmZuqVV17RTz/9pO7du6tZs2amawQAAADgASp8QfdldevWVWxsrLKysvTTTz/pwIEDJuoCAAAA4GEq1HMhST///LNWrFihe+65R+Hh4Zo/f74GDx6svXv3mqwPAAAAgIeoUM/FyJEj9eGHH6pu3boaOnSoNm/erJiYGNO1AQAAAPAgFQoXNptNq1atUmxsrLy9r/vMKgAAAAA1QLlOi7rnnnvkcDj07rvvqn///nrhhRd09uxZ5/jTp0+rdevWpmsEAAAA4AHKFS4+/fRT5eXlOZ+/8MILOnPmjPP5pUuXdPDgQXPVAQAAAPAY5QoXlmWV+BwAAADAjavCd4sCAAAAgCuVK1zYbDbZbLYiwwAAAACgXLd6sixL8fHx8vX1lSRduHBBEyZMkL+/vyS5XI8BAAAA4MZSrnAxevRol+cPPPBAkTYPPvjg9VUEAAAAwCOVK1wsXbq0suoAAAAA4OG4oBsAAACAEYQLAAAAAEYQLgAAAAAYQbgAAAAAYAThAgAAAIARhAsAAAAARhAuAAAAABjh1nDxxRdfaODAgQoPD5fNZtO6detcxluWpVmzZik8PFx+fn7q1q2b9u3b59ImLy9PkydPVnBwsPz9/TVo0CClpaW5tMnKylJcXJzsdrvsdrvi4uJ09uxZlzZHjhzRwIED5e/vr+DgYE2ZMkX5+fmVsdoAAABAjeTWcJGbm6t27dppwYIFxY5/8cUXNW/ePC1YsEApKSkKCwtT7969lZOT42yTkJCgtWvXKjExUVu3btW5c+c0YMAAFRQUONuMGjVKqampSkpKUlJSklJTUxUXF+ccX1BQoP79+ys3N1dbt25VYmKiVq9erWnTplXeygMAAAA1TLl+odu0fv36qV+/fsWOsyxLr7zyip544gkNGTJEkrR8+XKFhobq3Xff1cMPPyyHw6HFixfr7bffVq9evSRJ77zzjiIjI/XZZ58pNjZWBw4cUFJSkpKTk9WpUydJ0ptvvqno6GgdPHhQLVu21IYNG7R//34dPXpU4eHhkqSXX35Z8fHxeu655xQYGFgFWwMAAADwbNX2movDhw8rIyNDffr0cQ7z9fVV165dtW3bNknS7t27dfHiRZc24eHhioqKcrbZvn277Ha7M1hIUufOnWW3213aREVFOYOFJMXGxiovL0+7d+++Zo15eXnKzs52eQAAAAA3qmobLjIyMiRJoaGhLsNDQ0Od4zIyMuTj46P69euX2CYkJKTI/ENCQlzaXL2c+vXry8fHx9mmOHPmzHFex2G32xUZGVnOtQQAAABqjmobLi6z2Wwuzy3LKjLsale3Ka59RdpcbebMmXI4HM7H0aNHS6wLAAAAqMmqbbgICwuTpCI9B5mZmc5ehrCwMOXn5ysrK6vENidOnCgy/5MnT7q0uXo5WVlZunjxYpEejSv5+voqMDDQ5QEAAADcqKptuGjatKnCwsK0ceNG57D8/Hxt2bJFMTExkqQOHTqodu3aLm3S09O1d+9eZ5vo6Gg5HA7t3LnT2WbHjh1yOBwubfbu3av09HRnmw0bNsjX11cdOnSo1PUEAAAAagq33i3q3LlzOnTokPP54cOHlZqaqqCgIDVu3FgJCQmaPXu2WrRooRYtWmj27NmqW7euRo0aJUmy2+0aN26cpk2bpgYNGigoKEjTp09XmzZtnHePatWqlfr27avx48dr0aJFkqSHHnpIAwYMUMuWLSVJffr0UevWrRUXF6e5c+fqzJkzmj59usaPH09vBAAAAFBGbg0Xu3btUvfu3Z3Pp06dKkkaPXq0li1bphkzZuj8+fOaOHGisrKy1KlTJ23YsEEBAQHOaebPny9vb28NGzZM58+fV8+ePbVs2TJ5eXk526xYsUJTpkxx3lVq0KBBLr+t4eXlpfXr12vixIm688475efnp1GjRumll16q7E0AAAAA1Bg2y7IsdxdRU2RnZ8tut8vhcNDjISliXoSO5RzTTQE3KW1qWukTAAAAVAMcw7gqzzFutb3mAgAAAIBnIVwAAAAAMIJwAQAAAMAIt17QDQAAAFRX6efSFTEvosQ2YfXCtOuhXVVUUfVHuAAAAACKUWgV6ljOMXeX4VEIF6iQjn/vqIxzGSW2ST+XXuJ4AACA6iisXlipbdLPpavQKqyCajwL4QIVknEugyQPAABqpLKc5nT5drVwRbjAdallq6VG9RqV2KYs6R8AAACej3CB69KoXiN+XAYAAACSuBUtAAAAAEMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAI6p1uJg1a5ZsNpvLIywszDnesizNmjVL4eHh8vPzU7du3bRv3z6XeeTl5Wny5MkKDg6Wv7+/Bg0apLS0NJc2WVlZiouLk91ul91uV1xcnM6ePVsVqwgAAADUGNU6XEjSb37zG6Wnpzsfe/bscY578cUXNW/ePC1YsEApKSkKCwtT7969lZOT42yTkJCgtWvXKjExUVu3btW5c+c0YMAAFRQUONuMGjVKqampSkpKUlJSklJTUxUXF1el6wkAAAB4Om93F1Aab29vl96KyyzL0iuvvKInnnhCQ4YMkSQtX75coaGhevfdd/Xwww/L4XBo8eLFevvtt9WrVy9J0jvvvKPIyEh99tlnio2N1YEDB5SUlKTk5GR16tRJkvTmm28qOjpaBw8eVMuWLatuZQEAAAAPVu17Lr777juFh4eradOmGjFihH744QdJ0uHDh5WRkaE+ffo42/r6+qpr167atm2bJGn37t26ePGiS5vw8HBFRUU522zfvl12u90ZLCSpc+fOstvtzjbXkpeXp+zsbJcHAAAAcKOq1uGiU6dOeuutt/Tpp5/qzTffVEZGhmJiYnT69GllZGRIkkJDQ12mCQ0NdY7LyMiQj4+P6tevX2KbkJCQIssOCQlxtrmWOXPmOK/TsNvtioyMrPC6AgAAAJ6uWoeLfv366b777lObNm3Uq1cvrV+/XtIvpz9dZrPZXKaxLKvIsKtd3aa49mWZz8yZM+VwOJyPo0ePlrpOAAAAQE1VrcPF1fz9/dWmTRt99913zuswru5dyMzMdPZmhIWFKT8/X1lZWSW2OXHiRJFlnTx5skivyNV8fX0VGBjo8gAAAABuVB4VLvLy8nTgwAE1atRITZs2VVhYmDZu3Ogcn5+fry1btigmJkaS1KFDB9WuXdulTXp6uvbu3etsEx0dLYfDoZ07dzrb7NixQw6Hw9kGAAAAQOmq9d2ipk+froEDB6px48bKzMzUs88+q+zsbI0ePVo2m00JCQmaPXu2WrRooRYtWmj27NmqW7euRo0aJUmy2+0aN26cpk2bpgYNGigoKEjTp093nmYlSa1atVLfvn01fvx4LVq0SJL00EMPacCAAdwpCgAAACiHah0u0tLSNHLkSJ06dUoNGzZU586dlZycrJtvvlmSNGPGDJ0/f14TJ05UVlaWOnXqpA0bNiggIMA5j/nz58vb21vDhg3T+fPn1bNnTy1btkxeXl7ONitWrNCUKVOcd5UaNGiQFixYULUrCwAAAHg4m2VZlruLqCmys7Nlt9vlcDhq/PUXEfMidCznmG4KuElpU9NKnwAAAKAGuZGOhcpzjOtR11wAAAAAqL4IFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMIJwAQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFxc5fXXX1fTpk1Vp04ddejQQV9++aW7SwIAAAA8AuHiCqtWrVJCQoKeeOIJff3117rrrrvUr18/HTlyxN2lAQAAANUe4eIK8+bN07hx4/T73/9erVq10iuvvKLIyEgtXLjQ3aUBAAAA1Z63uwuoLvLz87V79249/vjjLsP79Omjbdu2FTtNXl6e8vLynM8dDockKTs7u/IKLcGCnQu0YOeCKllWxrkMyZIKaxe6bX0BAADcpfBCoXRBOp53XOGzwyt9eZPumKRJd0yq9OUU5/KxnmVZpbYlXPyfU6dOqaCgQKGhoS7DQ0NDlZGRUew0c+bM0Z///OciwyMjIyulxuooXemyP2F3dxkAAABuYclSutIrfTlP/N8/d8rJyZHdXvJxH+HiKjabzeW5ZVlFhl02c+ZMTZ061fm8sLBQZ86cUYMGDa45DVxlZ2crMjJSR48eVWBgoLvLqbHYzlWHbV012M5Vh21dddjWVYPtXH6WZSknJ0fh4aX30BAu/k9wcLC8vLyK9FJkZmYW6c24zNfXV76+vi7DfvWrX1VWiTVaYGAgb/AqwHauOmzrqsF2rjps66rDtq4abOfyKa3H4jIu6P4/Pj4+6tChgzZu3OgyfOPGjYqJiXFTVQAAAIDnoOfiClOnTlVcXJw6duyo6Oho/f3vf9eRI0c0YcIEd5cGAAAAVHuEiysMHz5cp0+f1jPPPKP09HRFRUXp448/1s033+zu0mosX19fPf3000VOL4NZbOeqw7auGmznqsO2rjps66rBdq5cNqss95QCAAAAgFJwzQUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBaqNQYMGqXHjxqpTp44aNWqkuLg4HT9+3N1l1Sg//vijxo0bp6ZNm8rPz0/NmjXT008/rfz8fHeXViM999xziomJUd26dfmBTcNef/11NW3aVHXq1FGHDh305ZdfurukGueLL77QwIEDFR4eLpvNpnXr1rm7pBppzpw5uv322xUQEKCQkBANHjxYBw8edHdZNdLChQvVtm1b54/nRUdH65NPPnF3WTUO4QLVRvfu3fXee+/p4MGDWr16tb7//nvdf//97i6rRvn2229VWFioRYsWad++fZo/f77eeOMN/elPf3J3aTVSfn6+hg4dqkceecTdpdQoq1atUkJCgp544gl9/fXXuuuuu9SvXz8dOXLE3aXVKLm5uWrXrp0WLFjg7lJqtC1btujRRx9VcnKyNm7cqEuXLqlPnz7Kzc11d2k1TkREhJ5//nnt2rVLu3btUo8ePfS73/1O+/btc3dpNQq3okW19cEHH2jw4MHKy8tT7dq13V1OjTV37lwtXLhQP/zwg7tLqbGWLVumhIQEnT171t2l1AidOnXSbbfdpoULFzqHtWrVSoMHD9acOXPcWFnNZbPZtHbtWg0ePNjdpdR4J0+eVEhIiLZs2aK7777b3eXUeEFBQZo7d67GjRvn7lJqDHouUC2dOXNGK1asUExMDMGikjkcDgUFBbm7DKBM8vPztXv3bvXp08dleJ8+fbRt2zY3VQWY43A4JInP5UpWUFCgxMRE5ebmKjo62t3l1CiEC1Qrf/zjH+Xv768GDRroyJEj+te//uXukmq077//Xn/72980YcIEd5cClMmpU6dUUFCg0NBQl+GhoaHKyMhwU1WAGZZlaerUqerSpYuioqLcXU6NtGfPHtWrV0++vr6aMGGC1q5dq9atW7u7rBqFcIFKNWvWLNlsthIfu3btcrZ/7LHH9PXXX2vDhg3y8vLSgw8+KM7cK115t7MkHT9+XH379tXQoUP1+9//3k2Ve56KbGuYZ7PZXJ5bllVkGOBpJk2apG+++UYrV650dyk1VsuWLZWamqrk5GQ98sgjGj16tPbv3+/usmoUb3cXgJpt0qRJGjFiRIltmjRp4vx/cHCwgoODdcstt6hVq1aKjIxUcnIyXZalKO92Pn78uLp3767o6Gj9/e9/r+TqapbybmuYFRwcLC8vryK9FJmZmUV6MwBPMnnyZH3wwQf64osvFBER4e5yaiwfHx81b95cktSxY0elpKTo1Vdf1aJFi9xcWc1BuECluhwWKuJyj0VeXp7Jkmqk8mznY8eOqXv37urQoYOWLl2qWrXowCyP69mncf18fHzUoUMHbdy4Uffee69z+MaNG/W73/3OjZUBFWNZliZPnqy1a9dq8+bNatq0qbtLuqFYlsVxhmGEC1QLO3fu1M6dO9WlSxfVr19fP/zwg5566ik1a9aMXguDjh8/rm7duqlx48Z66aWXdPLkSee4sLAwN1ZWMx05ckRnzpzRkSNHVFBQoNTUVElS8+bNVa9ePfcW58GmTp2quLg4dezY0dn7duTIEa4dMuzcuXM6dOiQ8/nhw4eVmpqqoKAgNW7c2I2V1SyPPvqo3n33Xf3rX/9SQECAs1fObrfLz8/PzdXVLH/605/Ur18/RUZGKicnR4mJidq8ebOSkpLcXVrNYgHVwDfffGN1797dCgoKsnx9fa0mTZpYEyZMsNLS0txdWo2ydOlSS1KxD5g3evToYrf1pk2b3F2ax3vttdesm2++2fLx8bFuu+02a8uWLe4uqcbZtGlTsfvv6NGj3V1ajXKtz+SlS5e6u7QaZ+zYsc7PjYYNG1o9e/a0NmzY4O6yahx+5wIAAACAEZxsDQAAAMAIwgUAAAAAIwgXAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAXOHWW2/VP/7xD3eXAQAeiXABAKgU8fHxstlsmjBhQpFxEydOlM1mU3x8fNUXVoLz58/r0KFDateunbtLAQCPRLgAAFSayMhIJSYm6vz5885hFy5c0MqVK9W4cWM3Vla8vXv3yrIsRUVFubsUAPBIhAsAQKW57bbb1LhxY61Zs8Y5bM2aNYqMjNRvf/tb57AmTZrolVdecZm2ffv2mjVrlvN5Xl6epkyZopCQENWpU0ddunRRSkqKyzTdunXTlClTNGPGDAUFBSksLMxlHteSmpqqHj16qEuXLiosLFTjxo01f/78Cq0zANzICBcAgEo1ZswYLV261Pl8yZIlGjt2bLnnM2PGDK1evVrLly/XV199pebNmys2NlZnzpxxabd8+XL5+/trx44devHFF/XMM89o48aN15zv999/r65du6pHjx4aNGiQhgwZomnTpmnq1KnatWtXuesEgBsZ4QIAUKni4uK0detW/fjjj/rpp5/0P//zP3rggQfKNY/c3FwtXLhQc+fOVb9+/dS6dWu9+eab8vPz0+LFi13atm3bVk8//bRatGihBx98UB07dtS///3va857woQJGjJkiP7f//t/OnLkiKKjozVjxgz96le/0pdfflmhdQaAG5W3uwsAANRswcHB6t+/v5YvXy7LstS/f38FBweXax7ff/+9Ll68qDvvvNM5rHbt2rrjjjt04MABl7Zt27Z1ed6oUSNlZmYWO9+MjAx9/vnn2rZtmwoKCrRnzx7Nnj1btWrVkre3t3x8fMpVJwDc6AgXAIBKN3bsWE2aNEmS9NprrxUZX6tWLVmW5TLs4sWLzv9fHmez2VzaWJZVZFjt2rVdnttsNhUWFhZbV3JysgoLC9W+fXt9++23On/+vNq3b6+jR4/q1KlTLmEGAFA6TosCAFS6vn37Kj8/X/n5+YqNjS0yvmHDhkpPT3c+z87O1uHDh53PmzdvLh8fH23dutU57OLFi9q1a5datWpV4bry8/Ml/XIHq9TUVEVERKhBgwZatGiRWrdurfbt21d43gBwI6LnAgBQ6by8vJynL3l5eRUZ36NHDy1btkwDBw5U/fr19eSTT7q08/f31yOPPKLHHntMQUFBaty4sV588UX9/PPPGjduXIXr6ty5s7y9vfXMM8/o3LlzatasmV5//XXNnz9fmzZtqvB8AeBGRbgAAFSJwMDAa46bOXOmfvjhBw0YMEB2u11/+ctfXHouJOn5559XYWGh4uLilJOTo44dO+rTTz9V/fr1K1xT48aNtWTJEv3xj39Uenq6vL299fPPP+vjjz/WHXfcUeH5AsCNymZdfZIrAAA3oKCgIC1ZskSDBw92dykA4LG45gIAcMNLS0tTVlaW2rRp4+5SAMCjES4AADe8PXv2yN/fX7/+9a/dXQoAeDROiwIAAABgBD0XAAAAAIwgXAAAAAAwgnABAAAAwAjCBQAAAAAjCBcAAAAAjCBcAAAAADCCcAEAAADACMIFAAAAACMIFwAAAACMIFwAAAAAMOL/A9gkjZg8yE+uAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.hist(ak.flatten(muon_phi), bins=70, range=[-3.5, 3.5], histtype='step',linewidth=2, color='green', label='muon_$\\phi$')\n",
    "plt.title('$\\phi$ distribution of PAT Muon objects')\n",
    "plt.xlabel(r'Muon $\\phi$')\n",
    "plt.ylabel('Events')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then lets apply criteria such as pt>20 GeV and |eta|<2.4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>[[],\n",
       " [],\n",
       " [False],\n",
       " [True, True],\n",
       " [True, False],\n",
       " [False],\n",
       " [True],\n",
       " [False, False],\n",
       " [True, True],\n",
       " [True, True],\n",
       " ...,\n",
       " [True, True],\n",
       " [True, False],\n",
       " [True, True],\n",
       " [True],\n",
       " [],\n",
       " [True, True],\n",
       " [True, True],\n",
       " [],\n",
       " [True, True]]\n",
       "-------------------------\n",
       "type: 930236 * var * bool</pre>"
      ],
      "text/plain": [
       "<Array [[], [], [False], ..., [], [True, True]] type='930236 * var * bool'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masking=(muon_pt>20)&(abs(muon_eta)<2.4)\n",
    "masking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above, by performing masking, a jagged array of bool values is created.\n",
    "Then using the sytax array[masking], we keep only the varible that is true in the masking array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "muon_pt= [[], [], [], [41.4, 37.2], ..., [33.7, ...], [26.8, 24.4], [], [41.6, 32.2]] \n",
      " muon_eta= [[], [], [], [1.06, -0.0674], ..., [...], [-0.719, 0.676], [], [0.722, -0.701]] \n",
      " muon_phi= [[], [], [], [2.61, -0.37], ..., [...], [0.317, -2.96], [], [-0.0455, -3.06]]\n"
     ]
    }
   ],
   "source": [
    "print('muon_pt=', muon_pt[masking], '\\n',\n",
    "      'muon_eta=', muon_eta[masking], '\\n',\n",
    "      'muon_phi=', muon_phi[masking])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then lets plot pt of reconstructed muon and reconstructed muon with pt>20 GeV and |eta|<2.4, note that we can use flatten() to reduce the jagged 2 dimension array to 1 dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "muon_pt_masked=muon_pt[masking]\n",
    "muon_pt_flat=ak.flatten(muon_pt)\n",
    "muon_pt_masked_flat=ak.flatten(muon_pt_masked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.hist(muon_pt_flat, bins=150, range=[0,150], histtype='step',linewidth=2, color='red', label='muon_$p_T$')\n",
    "plt.hist(muon_pt_masked_flat, bins=150, range=[0,150], histtype='step',linewidth=2, linestyle=':', color='blue', label='muon_$p_T$ with criteria')\n",
    "plt.legend()\n",
    "plt.xlabel(r'Muon $p_T$ (GeV)')\n",
    "plt.ylabel('Events')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2**: Using the syntax above, plot the main kinematic observables for the following objects and selections (only the first few events):\n",
    "* all final-state muons in the GenPart collection\n",
    "* all final-state muons in the GenPart collection with pT > 20 GeV and |η| < 2.4\n",
    "* all final-state muons in the GenPart collection with pT > 20 GeV and |η| < 2.4, and not coming from a hadron or τ decay\n",
    "\n",
    "**Question 3**: Compare the numbers of reconstructed and generated muons and their pT spectra at different selection stages, by overlaying the distributions in the same frame.<br>\n",
    "Since you are running on a Drell–Yan sample, you would expect two muons per event. Explain why there are sometimes more than two muons in an event.<br>\n",
    "Look at the full distributions, without η and pT cuts. Can you explain the differences?<br>\n",
    "\n",
    "Hint (GenPart_statusFlags: gen status flags stored bitwise. Bits are: 0 : isPrompt, 1 : isDecayedLeptonHadron, 2 : isTauDecayProduct, 3 : isPromptTauDecayProduct, 4 : isDirectTauDecayProduct, 5 : isDirectPromptTauDecayProduct, 6 : isDirectHadronDecayProduct, 7 : isHardProcess, 8 : fromHardProcess, 9 : isHardProcessTauDecayProduct, 10 : isDirectHardProcessTauDecayProduct, 11 : fromHardProcessBeforeFSR, 12 : isFirstCopy, 13 : isLastCopy, 14 : isLastCopyBeforeFSR,\n",
    "\n",
    "GenPart_status\tParticle status. 1=stable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:7: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:8: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:7: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:8: SyntaxWarning: invalid escape sequence '\\e'\n",
      "/tmp/ipykernel_7829/1454705003.py:7: SyntaxWarning: invalid escape sequence '\\e'\n",
      "  plt.hist(ak.flatten(genpart_pt[masking_two]), bins=150, range=[0,150], histtype='step',linewidth=2, color='blue', label='Final state Muons with $p_T$ > 20GeV and $|\\eta|$<2.4')\n",
      "/tmp/ipykernel_7829/1454705003.py:8: SyntaxWarning: invalid escape sequence '\\e'\n",
      "  plt.hist(ak.flatten(genpart_pt[masking_three]), bins=150, range=[0,150], histtype='step',linewidth=2, color='brown', linestyle=('dashed'), label='Final state Muons with $p_T$ > 20GeV and $|\\eta|$<2.4\\nand not coming from hadron or $\\\\tau$ decay')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genpart_status, genpart_statusflags, genpart_pdgid, genpart_pt, genpart_eta, genpart_phi=events.arrays([\"GenPart_status\", \"GenPart_statusFlags\", \"GenPart_pdgId\", \"GenPart_pt\", \"GenPart_eta\", \"GenPart_phi\"], library='ak', how=tuple)\n",
    "masking_one=(abs(genpart_pdgid)==13)& (genpart_status==1)\n",
    "masking_two=(abs(genpart_pdgid)==13)& (genpart_status==1)&(genpart_pt>20)&(abs(genpart_eta)<2.4)\n",
    "masking_three=(abs(genpart_pdgid)==13)& (genpart_status==1)&(genpart_pt>20)&(abs(genpart_eta)<2.4)&((genpart_statusflags%2)==1)\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.hist(ak.flatten(genpart_pt[masking_one]), bins=150, range=[0,150], histtype='step',linewidth=2, color='red', label='All final state Muons')\n",
    "plt.hist(ak.flatten(genpart_pt[masking_two]), bins=150, range=[0,150], histtype='step',linewidth=2, color='blue', label='Final state Muons with $p_T$ > 20GeV and $|\\eta|$<2.4')\n",
    "plt.hist(ak.flatten(genpart_pt[masking_three]), bins=150, range=[0,150], histtype='step',linewidth=2, color='brown', linestyle=('dashed'), label='Final state Muons with $p_T$ > 20GeV and $|\\eta|$<2.4\\nand not coming from hadron or $\\\\tau$ decay')\n",
    "plt.legend()\n",
    "plt.title('$p_T$ distribution of the Generated Muons')\n",
    "plt.xlabel(r'generated Muon $p_T$ (GeV)')\n",
    "plt.ylabel('Events')\n",
    "plt. yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets also compare the number of muons in each event between reconstructed one and generated one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.hist(nmuon, bins=10, range=[0,10], histtype='step',linewidth=2, color='red', label='number of PAT muons')\n",
    "plt.hist(ak.count(genpart_pt[masking_one], axis=1), bins=10, range=[0,10], histtype='step',linewidth=2, color='blue', label='number of GEN muons')\n",
    "plt.legend()\n",
    "plt.xlabel('number of muon')\n",
    "plt.ylabel('Events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Events')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.hist(ak.flatten(muon_pt), bins=150, range=[0,150], histtype='step',linewidth=2, color='blue', label='recon_muon_$p_T$')\n",
    "plt.hist(ak.flatten(genpart_pt[masking_one]), bins=150, range=[0,150], histtype='step',linewidth=2, color='red', label='gen_muon_$p_T$')\n",
    "plt.legend()\n",
    "plt.xlabel(r'Muon $p_T$ (GeV)')\n",
    "plt.ylabel('Events')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3 Gen matching\n",
    "When working with simulated data, it is sometime useful to pair each reconstructed muon to its corresponding generated particle, e.g. for efficiency or resolution studies. In this part of the exercise we will learn possible ways to do this.<br>\n",
    "\n",
    "Gen matching information for muon is already stored in Muon_genPartIdx, which is matched on status=1 muon using particle flow algorithm. \n",
    "Lets print the Moun_genPartIdx. Note that -1 means cannot matched to generated particles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>[[],\n",
       " [],\n",
       " [6],\n",
       " [9, 11],\n",
       " [11, 10],\n",
       " [14],\n",
       " [16],\n",
       " [10, 9],\n",
       " [16, 14],\n",
       " [9, 10],\n",
       " ...,\n",
       " [12, 14],\n",
       " [13, 11],\n",
       " [10, 9],\n",
       " [7],\n",
       " [],\n",
       " [12, 10],\n",
       " [9, 8],\n",
       " [],\n",
       " [7, 8]]\n",
       "--------------------------\n",
       "type: 930236 * var * int16</pre>"
      ],
      "text/plain": [
       "<Array [[], [], [6], ..., [9, 8], [], [7, 8]] type='930236 * var * int16'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "muon_genpartidx=events[\"Muon_genPartIdx\"].array()\n",
    "muon_genpartidx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also do a matching ourselves using deltaR method, in deltaR method, we need to make sure the delta phi is between pi and -pi. Below defines a deltaR function\n",
    "&Delta;R = (&Delta;&eta;<sup>2</sup> + &Delta;&phi;<sup>2</sup>)<sup>1/2</sup> <br>\n",
    "Note that &Delta;&phi; is between &pi; to -&pi;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deltaR(genetas, eta, genphis, phi):\n",
    "    return np.sqrt((genetas-eta)**2+((genphis-phi+pi)%(2*pi)-pi)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "muon_genpartidx_deltar=[]\n",
    "\n",
    "for genetas, genphis, etas, phis in zip(genpart_eta, genpart_phi, muon_eta, muon_phi):\n",
    "  idx=[]\n",
    "  for eta, phi in zip(etas, phis):\n",
    "    deltar=deltaR(genetas, eta, genphis, phi)\n",
    "    if ak.min(deltar)<0.3:\n",
    "        idx.append(ak.argmin(deltar))\n",
    "    else:\n",
    "        idx.append(-1)\n",
    "  muon_genpartidx_deltar.append(idx)\n",
    "\n",
    "#print(muon_genpartidx_deltar)\n",
    "#for geneta, genphi, in zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 5**: Are there reconstructed muons that do not match any generated muons? Why? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 6**: Are there cases in which the &Delta;R matching fails to find the correct generated particle? Why? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets take a look at those reconstracted muons that do not match any generated muons, and plot their pt and eta distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "masking_nogenmatch=(muon_genpartidx==-1)\n",
    "masking_genmatched=(muon_genpartidx!=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(masking_nogenmatch)\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.hist(ak.flatten(muon_pt[masking_nogenmatch]), bins=150, range=[0,150], histtype='step',linewidth=2, color='blue', label='unmatched PAT muon $p_T$')\n",
    "plt.hist(ak.flatten(muon_pt[masking_genmatched]), bins=150, range=[0,150], histtype='step',linewidth=2, color='red', label='matched PAT muon $p_T$')\n",
    "plt.legend()\n",
    "plt.xlabel(r'PAT Muon $p_T$ (GeV)')\n",
    "plt.ylabel('Events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.hist(ak.flatten(muon_eta[masking_nogenmatch]), bins=60, range=[-3,3], histtype='step',linewidth=2, color='blue', label='unmatched PAT muon $\\eta$')\n",
    "plt.hist(ak.flatten(muon_eta[masking_genmatched]), bins=60, range=[-3,3], histtype='step',linewidth=2, color='red', label='matched PAT muon $\\eta$')\n",
    "plt.legend()\n",
    "plt.xlabel(r'PAT Muon $\\eta$')\n",
    "plt.ylabel('Events')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: Trigger matching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A physics analysis often requires a matching between trigger and offline objects for a proper assessment of trigger efficiencies and related systematics. In order to reduce size per event. NanoAODThe no longer store matching information between muon objects and trigger objects. \n",
    "TrigObj_id in nanoAOD stored the ID of the HLT object. Using previous recipe, we can perform a simple $\\Delta R$ matching between HLT and reconstructed objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hlt_pt, hlt_eta, hlt_phi, hlt_id= events.arrays([\"TrigObj_pt\", \"TrigObj_eta\", \"TrigObj_phi\", \"TrigObj_id\"], library='ak', how=tuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hltmuon_mask=abs(hlt_id)==13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hltmuon_genpartidx_deltar=[]\n",
    "\n",
    "for genetas, genphis, etas, phis in zip(genpart_eta, genpart_phi, hlt_eta[hltmuon_mask], hlt_phi[hltmuon_mask]):\n",
    "  idx=[]\n",
    "  for eta, phi in zip(etas, phis):\n",
    "    deltar=deltaR(genetas, eta, genphis, phi)\n",
    "    if ak.min(deltar)<0.1:\n",
    "        idx.append(ak.argmin(deltar))\n",
    "    else:\n",
    "        idx.append(-1)\n",
    "  hltmuon_genpartidx_deltar.append(idx)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hltmuon_genpartidx_deltar=ak.from_iter(hltmuon_genpartidx_deltar)\n",
    "hltmasking_nogenmatch=(hltmuon_genpartidx_deltar==-1)\n",
    "hltmasking_genmatched=(hltmuon_genpartidx_deltar!=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.hist(ak.flatten(hlt_pt[hltmuon_mask]), bins=150, range=[0,150], histtype='step', linewidth=2, color='green', label='HLT muon $p_T$')\n",
    "plt.hist(ak.flatten(hlt_pt[hltmuon_mask][hltmasking_nogenmatch]), bins=150, range=[0,150], histtype='step',linewidth=2, color='blue', label='unmatched HLT muon $p_T$')\n",
    "plt.hist(ak.flatten(hlt_pt[hltmuon_mask][hltmasking_genmatched]), bins=150, range=[0,150], histtype='step',linewidth=2, color='red', linestyle=(':'), label='matched HLT muon $p_T$')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(r'Muon $p_T$ (GeV)')\n",
    "plt.ylabel('Events')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.hist(ak.flatten(hlt_eta[hltmuon_mask][hltmasking_nogenmatch]), bins=60, range=[-3,3], histtype='step',linewidth=2, color='blue', label='unmatched HLT muon $\\eta$')\n",
    "plt.hist(ak.flatten(hlt_eta[hltmuon_mask][hltmasking_genmatched]), bins=60, range=[-3,3], histtype='step',linewidth=2, color='red', label='matched HLT muon $\\eta$')\n",
    "plt.hist(ak.flatten(hlt_eta[hltmuon_mask]), bins=60, range=[-3,3], histtype='step',linewidth=2, color='green', label='HLT muon $\\eta$')\n",
    "plt.legend()\n",
    "plt.xlabel(r'Muon $\\eta$')\n",
    "plt.ylabel('Events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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