nano_darkhiggs_uproot.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "## File required for this example at /uscms/home/fnavarro/nobackup/nano_darkhiggs_files/ZZTo2L2Nu.root \n",
    "import numpy as np \n",
    "from awkward import JaggedArray, Table  \n",
    "import uproot, uproot_methods\n",
    "import fnal_column_analysis_tools \n",
    "from fnal_column_analysis_tools.analysis_objects import JaggedCandidateArray\n",
    "from histbook import *\n",
    "from vega import VegaLite as canvas                \n",
    "import vega\n",
    "from striped.job import SinglePointStripedSession as Session, IPythonDisplay\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "extractor = fnal_column_analysis_tools.lookup_tools.extractor()\n",
    "extractor.add_weight_sets(['* * lookup_tables/eleTrig.root','* * lookup_tables/muon_trig_Run2016BtoF.root',\n",
    "                           '* * lookup_tables/ScaleFactor_etracker_80x.json', \n",
    "                            '* * lookup_tables/EIDISO_WH_out.json','* * lookup_tables/Tight27AfterIDISO_out.json', '* * lookup_tables/failingVeto_out.json', \n",
    "                            '* * lookup_tables/ElectronID_SFs_WP90.root', '* * lookup_tables/kfactor_24bins.root'])\n",
    "extractor.finalize()\n",
    "\n",
    "evaluator = extractor.make_evaluator()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist1 = Hist(bin(\"Dielectron_mass\",50,0,200),weight='e_weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "182987 events/sec\n"
     ]
    },
    {
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       ".vega-embed .error p {\n",
       "    color: firebrick;\n",
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      "jupyter-vega": "#54b12dfc-5bfc-41e0-b772-0a42f9fe615b"
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       "var output_area = this;\n",
       "\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
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"
     },
     "metadata": {
      "jupyter-vega": "#54b12dfc-5bfc-41e0-b772-0a42f9fe615b"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "singleele_trigger_paths = [\"HLT_Ele27_WPLoose_Gsf\",\n",
    "          \"HLT_Ele105_CaloIdVT_GsfTrkIdT\",\n",
    "          \"HLT_Ele27_WPTight_Gsf\",\n",
    "          \"HLT_Ele27_eta2p1_WPTight_Gsf\",\n",
    "          \"HLT_Ele32_eta2p1_WPTight_Gsf\",\n",
    "          \"HLT_Ele35_WPLoose_Gsf\",\n",
    "          \"HLT_ECALHT800\"]\n",
    "\n",
    "singleele_trigger_columns = {path:path for path in singleele_trigger_paths}\n",
    "electron_columns = {'pt':'Electron_pt','eta':'Electron_eta','phi':'Electron_phi','mass':'Electron_mass','iso':'Electron_pfRelIso03_all','dxy':'Electron_dxy','dz':'Electron_dz',\n",
    "                    'cutBased_HLTPreSel':'Electron_cutBased_HLTPreSel','mvaSpring16GP_WP80':'Electron_mvaSpring16GP_WP80','id':'Electron_mvaSpring16GP_WP90','charge':'Electron_charge',\n",
    "                    'pdgId':'Electron_pdgId','deltaEtaSC':'Electron_deltaEtaSC'}\n",
    "\n",
    "muon_columns = {'pt':'Muon_pt','eta':'Muon_eta','phi':'Muon_phi','mass':'Muon_mass','iso':'Muon_pfRelIso04_all','dxy':'Muon_dxy','dz':'Muon_dz','charge':'Muon_charge','tightId':'Muon_tightId',\n",
    "                 'pdgId':'Muon_pdgId'}\n",
    "\n",
    "jet_columns = {'pt':'Jet_pt','eta':'Jet_eta','phi':'Jet_phi','mass':'Jet_mass','id':'Jet_jetId'}\n",
    "\n",
    "tau_columns = {'pt':'Tau_pt','eta':'Tau_eta','phi':'Tau_phi','mass':'Tau_mass','decayMode':'Tau_idDecayMode','decayModeNew':'Tau_idDecayModeNewDMs','id':'Tau_idMVAnew'} # (idmVAnewDM does not exist in my file) idMVAnew\n",
    "\n",
    "photon_columns = {'pt':'Photon_pt','eta':'Photon_eta','phi':'Photon_phi','mass':'Photon_mass'}\n",
    "\n",
    "gen_columns = {'pt':'GenPart_pt','eta':'GenPart_eta','phi':'GenPart_phi','mass':'GenPart_mass','id':'GenPart_pdgId','status':'GenPart_status', 'statusFlags':'GenPart_statusFlags','mIdx':'GenPart_genPartIdxMother'}\n",
    "\n",
    "all_columns = [electron_columns,muon_columns,jet_columns,photon_columns,singleele_trigger_columns,tau_columns,gen_columns]\n",
    "\n",
    "columns = []\n",
    "\n",
    "\n",
    "events = 0;\n",
    "for cols in all_columns: columns.extend(list(cols.values()))\n",
    "\n",
    "   \n",
    "for arrays in uproot.iterate('ZZTo2L2Nu.root','Events',columns,entrysteps=300000):\n",
    "    t1 = time.time()   \n",
    "        # initialize phyisics object\n",
    "    triggers  = {'SingleEle':np.prod([arrays[val] for val in singleele_trigger_columns],axis=0)}\n",
    "      \n",
    "    electrons = JaggedCandidateArray.candidatesfromcounts(arrays[electron_columns['pt']].counts,\n",
    "                **{key:arrays[val].content for key,val in electron_columns.items()})\n",
    "    \n",
    "    muons     = JaggedCandidateArray.candidatesfromcounts(arrays[muon_columns['pt']].counts, \n",
    "                **{key:arrays[val].content for key,val in muon_columns.items()})\n",
    "    \n",
    "    taus      = JaggedCandidateArray.candidatesfromcounts(arrays[tau_columns['pt']].counts, \n",
    "                **{key:arrays[val].content for key,val in tau_columns.items()})                     \n",
    "    photons   = JaggedCandidateArray.candidatesfromcounts(arrays[photon_columns['pt']].counts, \n",
    "                **{key:arrays[val].content for key,val in photon_columns.items()})\n",
    "    jets      = JaggedCandidateArray.candidatesfromcounts(arrays[jet_columns['pt']].counts, \n",
    "                **{key:arrays[val].content for key,val in jet_columns.items()})\n",
    "    genPart   = JaggedCandidateArray.candidatesfromcounts(arrays[gen_columns['pt']].counts, \n",
    "                **{key:arrays[val].content for key,val in gen_columns.items()})\n",
    "    \n",
    "    events+= electrons.size\n",
    "    \n",
    "    # Scale Farctors to be used\n",
    "\n",
    "    sfReco = np.ones(electrons.size)\n",
    "    sfReco_up = np.ones(electrons.size)\n",
    "    sfReco_down = np.ones(electrons.size)\n",
    "    sfTrig = np.ones(electrons.size)\n",
    "    sfTrig_up = np.ones(electrons.size)\n",
    "    sfTrig_down = np.ones(electrons.size)\n",
    "    sfIdIso = np.ones(electrons.size)\n",
    "    sfIdIso_up = np.ones(electrons.size)\n",
    "    sfIdIso_down = np.ones(electrons.size)\n",
    "    sfId = np.ones(electrons.size)\n",
    "    sfId_up = np.ones(electrons.size)\n",
    "    sfId_down = np.ones(electrons.size)\n",
    "    \n",
    "    weights = np.ones(genPart.size)\n",
    "    weights_up = np.ones(genPart.size)\n",
    "    weights_down = np.ones(genPart.size)\n",
    "    # end initialize\n",
    "        # physical objects selection\n",
    "    loose_electron_selection = ((electrons.pt>7)&(abs(electrons.eta)<2.4)&(abs(electrons.dxy)<0.05)&\n",
    "                                (abs(electrons.dz)<0.2)&(electrons.iso<0.4))#&(electrons.id)\n",
    "    loose_muon_selection =  ((muons.pt>5)&(abs(muons.eta)<2.4)&(abs(muons.dxy)<0.5)&(abs(muons.dz)<1.0)&\n",
    "                             (muons.iso<0.4))\n",
    "    loose_photon_selection = (photons.pt>15)&(abs(photons.eta)<2.5)\n",
    "    tau_selection = (taus.pt>18)&(abs(taus.eta)<2.3)&(taus.decayMode)&((taus.id&2)!=0)\n",
    "    jet_selection = (jets.pt>25)&(abs(jets.eta)<4.5)&((jets.id&2)!=0)\n",
    "    \n",
    "    loose_electrons = electrons[loose_electron_selection]\n",
    "    loose_muons = muons[loose_muon_selection]\n",
    "    loose_photons = photons[loose_photon_selection]\n",
    "    selected_taus = taus[tau_selection]\n",
    "    selected_jets = jets[jet_selection]\n",
    "        # end seletion\n",
    "    \n",
    "        # lepton cleaned jets\n",
    "    jet_combinations = selected_jets.p4.cross(loose_electrons.p4, nested=True)\n",
    "    mask = (jet_combinations.i0.delta_r(jet_combinations.i1) < 0.3 ).any()\n",
    "    e_clean_jets = selected_jets[~mask]\n",
    "    \n",
    "    jet_combinations = e_clean_jets.p4.cross(loose_muons.p4, nested=True)\n",
    "    mask = (jet_combinations.i0.delta_r(jet_combinations.i1) < 0.3).any()\n",
    "    clean_jets = e_clean_jets[~mask]\n",
    "    \n",
    "    # once merge is done\n",
    "    # mask = selected_jets.p4.match(clean_electrons.p4, 0.3)\n",
    "    # clean_jets = selected_jets[~mask]\n",
    "        # end cleaning\n",
    "    \n",
    "    tight_electron_selection = ((loose_electrons.pt > 30)&(loose_electrons.id)&\n",
    "                              (loose_electrons.mvaSpring16GP_WP80)&(loose_electrons.iso < 0.06))\n",
    "\n",
    "    tight_electrons = loose_electrons[tight_electron_selection]\n",
    "    tight_electrons_1L =tight_electrons\n",
    "    \n",
    "    genPart_1LSfe_selection = (abs(genPart.id==11))&(genPart.statusFlags&1 !=0)&(genPart.statusFlags&8192 != 0)\n",
    "    \n",
    "    gen_prompt_electrons = genPart[genPart_1LSfe_selection]\n",
    "    gen_electrons_combinations = tight_electrons.p4.cross(gen_prompt_electrons.p4,nested=True)\n",
    "    gen_electrons_mask = (gen_electrons_combinations.i0.delta_r(gen_electrons_combinations.i1)<0.4).any()\n",
    "\n",
    "    _1L_electrons = tight_electrons[gen_electrons_mask]\n",
    "   \n",
    "    # 1L channel weights\n",
    "    sfReco_value ='ScaleFactor_tracker_80x/eta_pt_ratio_value'\n",
    "    sfReco_error = 'ScaleFactor_tracker_80x/eta_pt_ratio_error'\n",
    "    sfIdIso_value = 'EIDISO_WH/eta_pt_ratio_value'\n",
    "    sfIdIso_error = 'EIDISO_WH/eta_pt_ratio_error'\n",
    "    sfTrig_value = 'Tight27AfterIDISO/eta_pt_ratio_value'\n",
    "    sfTrig_error = 'Tight27AfterIDISO/eta_pt_ratio_error'\n",
    "    \n",
    "    c =_1L_electrons.counts\n",
    "    etaSC = _1L_electrons.eta+_1L_electrons.deltaEtaSC\n",
    "    pt = _1L_electrons.pt\n",
    "    sfReco[c>0] = evaluator[sfReco_value](etaSC,pt)[c>0][:,0]\n",
    "    sfReco_up[c>0] = sfReco[c>0] + evaluator[sfReco_error](etaSC,pt)[c>0][:,0]\n",
    "    sfReco_down[c>0] = sfReco[c>0] - evaluator[sfReco_error](etaSC,pt)[c>0][:,0]\n",
    "    sfReco_up[c>0][_1L_electrons[c>0].pt[:,0] >80] = sfReco_up[c>0][_1L_electrons[c>0].pt[:,0] >80] + 0.01\n",
    "    sfReco_down[c>0][_1L_electrons[c>0].pt[:,0] >80] = sfReco_down[c>0][_1L_electrons[c>0].pt[:,0] >80] - 0.01\n",
    "\n",
    "    sfIdIso[c >0] = evaluator[sfIdIso_value](etaSC,pt)[c>0][:,0]\n",
    "    sfIdIso_up[c>0] = sfIdIso[c > 0] + evaluator[sfIdIso_error](etaSC,pt)[c>0][:,0]\n",
    "    sfIdIso_down[c>0] =sfIdIso[c > 0] - evaluator[sfIdIso_error](etaSC,pt)[c>0][:,0]\n",
    "\n",
    "    sfTrig[c>0] = evaluator[sfTrig_value](etaSC,pt)[c>0][:,0]\n",
    "    sfTrig_up[c>0] = sfTrig[c>0] + evaluator[sfTrig_error](etaSC, pt)[c>0][:,0]\n",
    "    sfTrig_down[c>0] =sfTrig[c>0] - evaluator[sfTrig_error](etaSC,pt)[c>0][:,0]\n",
    "        \n",
    "    weight = sfReco*sfTrig*sfIdIso*sfId\n",
    "    weight_up = sfReco_up*sfTrig_up*sfIdIso_up*sfId\n",
    "    weight_down = sfReco_down*sfTrig_down*sfIdIso_down*sfId\n",
    "    \n",
    "    \n",
    "    dielectron_selection = (tight_electrons.counts>0)&(clean_jets.counts==0)#&(triggers[\"SingleEle\"]==1)#&(loose_photons.counts==0)\n",
    "    \n",
    "    dielectrons = loose_electrons[dielectron_selection].distincts()\n",
    "    dielectron_mass = dielectrons.mass\n",
    "    e_weight = weight[dielectron_selection][dielectrons.counts>0]\n",
    "    dielectron_mass = dielectron_mass[dielectron_mass.counts>0][:,0]\n",
    "        # end dileptons\n",
    "    \n",
    "        # plots\n",
    "    hist1.fill(Dielectron_mass=dielectron_mass,e_weight=e_weight)\n",
    "t2 = time.time()    \n",
    "print str(int(events/(t2-t1)))+\" events/sec\"\n",
    "disp = beside(hist1.step(\"Dielectron_mass\",width=300))\n",
    "display = IPythonDisplay(disp)\n",
    "display.init()\n",
    "disp.to(canvas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t =triggers['SingleEle']\n",
    "t[triggers['SingleEle']==1].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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