nano_darkhiggs_striped.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "from striped.job import SinglePointStripedSession as Session, IPythonDisplay\n",
    "from vega import VegaLite\n",
    "\n",
    "import cloudpickle\n",
    "import zlib\n",
    "\n",
    "import uproot, uproot_methods\n",
    "import awkward\n",
    "import numpy as np\n",
    "\n",
    "import fnal_column_analysis_tools\n",
    "from fnal_column_analysis_tools.lookup_tools import extractor\n",
    "from fnal_column_analysis_tools.lookup_tools import evaluator \n",
    "from fnal_column_analysis_tools.analysis_objects import JaggedCandidateArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_all = True\n",
    "datasets = [\"ZZTo2L2Nu_13TeV_powheg_pythia8\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "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/EIDISO_WH_out.json',\n",
    "                           '* * lookup_tables/Tight27AfterIDISO_out.json',\n",
    "                           '* * lookup_tables/failingVeto_out.json', \n",
    "                           '* * lookup_tables/ElectronID_SFs_WP90.root',\n",
    "                           '* * lookup_tables/ScaleFactor_etracker_80x.json', \n",
    "                           '* * lookup_tables/kfactor_24bins.root'])\n",
    "extractor.finalize()\n",
    "\n",
    "evaluator = extractor.make_evaluator()\n",
    "\n",
    "pickle_evaluator = zlib.compress(cloudpickle.dumps(evaluator))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_datasets(cfgfile):\n",
    "    xsec_dict = {}\n",
    "    sample_dict = {}\n",
    "    with open(cfgfile) as f:\n",
    "        for l in f:\n",
    "            l = l.strip()\n",
    "            if l.startswith('#'):\n",
    "                continue\n",
    "            pieces = l.split()\n",
    "            if(l == ''):\n",
    "                continue\n",
    "            if len(pieces) < 3:\n",
    "                print('Invalid input format:\\n%s' % l)\n",
    "                continue\n",
    "            samp = pieces[0]\n",
    "            sample_group = pieces[1]\n",
    "            sample_dict[samp] = sample_group\n",
    "            isData = not ('MC' in pieces[2])\n",
    "            if not isData:\n",
    "                if len(pieces) < 4:\n",
    "                    printf('Cannot find cross section:\\n%s' % l)\n",
    "                    continue\n",
    "                else:\n",
    "                    try:\n",
    "                        xsec = float(pieces[3])\n",
    "                    except ValueError:\n",
    "                        import numexpr\n",
    "                        xsec = numexpr.evaluate(pieces[3]).item()\n",
    "                    except:\n",
    "                        printf('Trouble parsing xsec:\\n%s' % l)\n",
    "                        continue\n",
    "                xsec_dict[samp] = xsec\n",
    "        \n",
    "    return xsec_dict, sample_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'DYJetsToNuNu_PtZ-100To250_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 164.46869999999998, 'WJetsToLNu_Pt-100To250_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 677.82, 'DYJetsToLL_Pt-50To100_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 374.68, 'DYJetsToNuNu_PtZ-50To100_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 668.9413199999999, 'DYJetsToNuNu_PtZ-650ToInf_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 0.07830000000000001, 'WJetsToLNu_Wpt-50To100_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 3298.37, 'WJetsToLNu_Pt-600ToInf_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 0.4602, 'WJetsToLNu_Pt-400To600_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 3.0563, 'DYJetsToLL_Pt-250To400_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 3.3247, 'DYJetsToLL_Pt-100To250_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 86.52, 'WJetsToLNu_Pt-250To400_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 24.083, 'DYJetsToNuNu_PtZ-250To400_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 6.2115, 'DYJetsToLL_Pt-400To650_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 0.4491, 'DYJetsToNuNu_PtZ-400To650_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 0.8336999999999999, 'DYJetsToLL_Pt-650ToInf_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8': 0.0422}\n"
     ]
    }
   ],
   "source": [
    "xsec_dict, sample_dict = parse_datasets('datasets_2016_minimal.txt')\n",
    "print (xsec_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "#__worker_class__\n",
    "import numpy as np\n",
    "import zlib\n",
    "import cloudpickle\n",
    "\n",
    "import uproot, uproot_methods\n",
    "import fnal_column_analysis_tools\n",
    "from fnal_column_analysis_tools.lookup_tools import extractor\n",
    "from fnal_column_analysis_tools.lookup_tools import evaluator \n",
    "from fnal_column_analysis_tools.analysis_objects import JaggedCandidateArray\n",
    "\n",
    "from collections import OrderedDict\n",
    "\n",
    "import awkward\n",
    "#import numba\n",
    "#from collections import OrderedDict\n",
    "#import matplotlib.pyplot as plt\n",
    "##%matplotlib inline\n",
    "#from fnal_column_analysis_tools import hist\n",
    "\n",
    "class Worker(object):\n",
    "    \n",
    "    Columns = [\"Photon.pt\",\"Photon.eta\",\"Photon.phi\",\"Photon.mass\",\"Jet.pt\",\"Jet.eta\",\"Jet.phi\",\"Jet.mass\",\"Jet.jetId\",\n",
    "              \"Electron.pt\",\"Electron.eta\",\"Electron.phi\",\"Electron.mass\",\"Electron.pfRelIso03_all\",\"Electron.dxy\",\n",
    "               \"Electron.dz\",\"Electron.cutBased\",\"Electron.charge\",\"Electron.deltaEtaSC\",\n",
    "               \"Electron.mvaSpring16GP_WP80\",\"Electron.mvaSpring16GP_WP90\",\"Muon.pt\",\"Muon.eta\",\"Muon.phi\",\"Muon.mass\",\n",
    "               \"Muon.pfRelIso04_all\",\"Muon.dxy\",\"Muon.dz\",\"Muon.charge\",\"Muon.tightId\",\n",
    "               \"nElectron\",\"nMuon\",\"nJet\",\"nPhoton\",\"nGenPart\",\"nTau\",\"nAK15Puppi\",\n",
    "               \"MET_pt\",\"MET_phi\",\n",
    "               \"GenPart.pt\",\"GenPart.eta\",\"GenPart.phi\",\n",
    "               \"GenPart.mass\",\"GenPart.pdgId\",\"GenPart.status\",\"GenPart.statusFlags\",\"GenPart.genPartIdxMother\",\n",
    "               \"Tau.pt\",\"Tau.eta\",\"Tau.phi\",\"Tau.mass\",\"Tau.idDecayMode\",\"Tau.idMVAnew\",\n",
    "               \"AK15Puppi.pt\",\"AK15Puppi.eta\",\"AK15Puppi.phi\",\"AK15Puppi.mass\",\"AK15Puppi.jetId\"]\n",
    "    \n",
    "    \n",
    "    def run(self, events, job, db):\n",
    "        dtype = job[\"dtype\"]\n",
    "        hists = job[\"hists\"]\n",
    "        dataset = job[\"dataset\"]\n",
    "                \n",
    "        photon_columns   = {'pt':events.Photon.pt,'eta':events.Photon.eta,'phi':events.Photon.phi,\n",
    "                             'mass':events.Photon.mass}\n",
    "        \n",
    "        jet_columns      = {'pt':events.Jet.pt,'eta':events.Jet.eta,'phi':events.Jet.phi,\n",
    "                            'mass':events.Jet.mass,'id':events.Jet.jetId}\n",
    "\n",
    "        fat_jet_columns = {'pt':events.AK15Puppi.pt,'eta':events.AK15Puppi.eta,'phi':events.AK15Puppi.phi,\n",
    "                           'mass':events.AK15Puppi.mass,'jetId':events.AK15Puppi.jetId}\n",
    "        \n",
    "        electron_columns = {'pt':events.Electron.pt,'eta':events.Electron.eta,'phi':events.Electron.phi,\n",
    "                            'mass':events.Electron.mass,\n",
    "                            'iso':events.Electron.pfRelIso03_all,'dxy':events.Electron.dxy,\n",
    "                            'dz':events.Electron.dz, 'cid':events.Electron.cutBased,\n",
    "                            'charge':events.Electron.charge,\n",
    "                            'deltaEtaSC':events.Electron.deltaEtaSC, \n",
    "                            'mva_WP80':events.Electron.mvaSpring16GP_WP80,\n",
    "                          #  'cutBased_HLTPreSel':events.Electron.cutBased_HLTPreSel,\n",
    "                            'mva_WP90':events.Electron.mvaSpring16GP_WP90}\n",
    "        \n",
    "        muon_columns     = {'pt':events.Muon.pt,'eta':events.Muon.eta,'phi':events.Muon.phi,\n",
    "                             'mass':events.Muon.mass,'iso':events.Muon.pfRelIso04_all,\n",
    "                             'dxy':events.Muon.dxy,'dz':events.Muon.dz,'charge':events.Muon.charge,\n",
    "                             'cid':events.Muon.tightId}\n",
    "            \n",
    "                                 \n",
    "        tau_columns      = {'pt':events.Tau.pt,'eta':events.Tau.eta,'phi':events.Tau.phi,\n",
    "                            'mass':events.Tau.mass, 'decayMode':events.Tau.idDecayMode, \n",
    "                            'id':events.Tau.idMVAnew}\n",
    "        \n",
    "  #     if 'mc' in dtype:\n",
    "  #          electron_columns.update({'pdgId':events.Electron.pdgId}) \n",
    "            \n",
    "  #          muon_columns.update({'pdgId':events.Muon.pdgId})\n",
    "                         \n",
    "        gen_columns      = {'pt':events.GenPart.pt,'eta':events.GenPart.eta,'phi':events.GenPart.phi,\n",
    "                            'mass':events.GenPart.mass,\n",
    "                            'id':events.GenPart.pdgId,'status':events.GenPart.status,\n",
    "                            'statusFlags':events.GenPart.statusFlags,\n",
    "                            'mIdx':events.GenPart.genPartIdxMother}\n",
    "          \n",
    "        eval_pickle = job[\"evaluator\"]\n",
    "        eval_pickle = zlib.decompress(eval_pickle)\n",
    "        evaluator = cloudpickle.loads(eval_pickle)\n",
    "           \n",
    "       # triggers  = {'SingleEle':np.prod([arrays[val] for val in singleele_trigger_columns],axis=0)}\n",
    "    \n",
    "        #job.message(awkward.__version__)\n",
    "    \n",
    "        electrons = JaggedCandidateArray.candidatesfromcounts(events.nElectron,\n",
    "                **{key:val for key,val in electron_columns.items()})\n",
    "        muons     = JaggedCandidateArray.candidatesfromcounts(events.nMuon, \n",
    "                **{key:val for key,val in muon_columns.items()})             \n",
    "        photons   = JaggedCandidateArray.candidatesfromcounts(events.nPhoton, \n",
    "               **{key:val for key,val in photon_columns.items()})\n",
    "        jets      = JaggedCandidateArray.candidatesfromcounts(events.nJet, \n",
    "               **{key:val for key,val in jet_columns.items()})\n",
    "        fat_jets = JaggedCandidateArray.candidatesfromcounts(events.nAK15Puppi,\n",
    "               **{key:val for key,val in fat_jet_columns.items()})\n",
    "        genPart   = JaggedCandidateArray.candidatesfromcounts(events.nGenPart, \n",
    "               **{key:val for key,val in gen_columns.items()})\n",
    "        taus      = JaggedCandidateArray.candidatesfromcounts(events.nTau, \n",
    "               **{key:val for key,val in tau_columns.items()})                                                         \n",
    "\n",
    "        met_vec = uproot_methods.TLorentzVectorArray.from_ptetaphi(events.MET_pt,0,events.MET_phi,0)\n",
    "        #jagged_met = JaggedCandidateArray.candidatesfromcounts(np.ones(events.MET_pt),p4=met_vec)\n",
    "        \n",
    "        # end initialize\n",
    "           \n",
    "             # physical objects selection\n",
    "        loose_electron_selection = (electrons.pt>7)&(abs(electrons.eta)<2.4)&((electrons.cid&2)!=0)&(electrons.mva_WP80)  \n",
    "        #(electrons.cid&2)!=0 for loose selection \n",
    "        tight_electron_selection = (electrons.pt>40)&(abs(electrons.eta)<2.4)&((electrons.cid&4)!=0)&(electrons.mva_WP80) #(electrons.cid&4)!=0 for tight selection\n",
    "        loose_muon_selection =  (muons.pt>5)&(abs(muons.eta)<2.4)   # \"isLooseMuon\" is already added in muon object\n",
    "        tight_muon_selection =  (muons.pt>10)&(abs(muons.eta)<2.4)&(muons.cid)   # muons.cid for tight muon selection\n",
    "        loose_photon_selection = (photons.pt>15)&(abs(photons.eta)<2.5)\n",
    "        #&((photons.cid&1)!=0)  #(photons.cid&1)!=0 for loose selection \n",
    "        tight_photon_selection = (photons.pt>175)&(abs(photons.eta)<2.5)#&((photons.cid&3)!=0) #(photons.cid&3)!=0 for tight selection \n",
    "        tau_selection = (taus.pt>18)&(abs(taus.eta)<2.3)&(taus.decayMode)&((taus.id&2)!=0)\n",
    "        fat_jet_selection = (fat_jets.pt > 200)&(abs(fat_jets.eta)<2.4)&(fat_jets.jetId > 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",
    "        tight_electrons = electrons[tight_electron_selection]\n",
    "        loose_muons = muons[loose_muon_selection]\n",
    "        tight_muons = muons[tight_muon_selection]\n",
    "        loose_photons = photons[loose_photon_selection]\n",
    "        tight_photons = photons[tight_photon_selection]\n",
    "        selected_taus = taus[tau_selection]\n",
    "        selected_fat_jets = fat_jets[fat_jet_selection]\n",
    "        selected_jets = jets[jet_selection]\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.mva_WP80)&(loose_electrons.iso < 0.06))\n",
    "\n",
    "        tight_electrons = loose_electrons[tight_electron_selection]\n",
    "        tight_electrons_1L =tight_electrons\n",
    "        \n",
    "        clean_electrons = loose_electrons\n",
    "        clean_muons = loose_muons\n",
    "        clean_fat_jets = selected_fat_jets\n",
    "        \n",
    "                #Get recoils\n",
    "            #ARH - this could be improved by defining a single recoil object regardless of region. \n",
    "        pfUA = self.get_recoil(met_vec,loose_photons)\n",
    "        pfUWe = self.get_recoil(met_vec,clean_electrons)\n",
    "        pfUWm = self.get_recoil(met_vec,clean_muons)\n",
    "        \n",
    "        dPhiMet = abs(jets.p4.delta_phi(met_vec)).min()\n",
    "        dPhipfUWm = abs(jets.p4.delta_phi(pfUWm)).min()\n",
    "        dPhipfUWe = abs(jets.p4.delta_phi(pfUWe)).min()\n",
    "        dPhipfUA = abs(jets.p4.delta_phi(pfUA)).min()\n",
    "            #End of recoils            \n",
    "\n",
    "            #signal and control region selections\n",
    "        preselection = (selected_taus.counts==0)&(clean_fat_jets.counts>0)\n",
    "        \n",
    "        \n",
    "                #No triggers are applied yet!\n",
    "        \n",
    "        selections = OrderedDict()\n",
    "        selections['signal'] = preselection&(clean_muons.counts==0)&(clean_electrons.counts==0)&(loose_photons.counts==0)&(met_vec.pt>200)&(dPhiMet > 0.8)\n",
    "\n",
    "        #Need to calculate mt and apply cuts of mT<160'. Need one tight muon.\n",
    "        selections['mn'] = preselection&(clean_muons.counts==1)&(clean_electrons.counts==0)&(loose_photons.counts==0)&(pfUWm.pt>200)&(dPhipfUWm > 0.8)&(met_vec.pt>50)\n",
    "        #Need to calculate mt and apply cuts of mT<160'. Need one tight electron\n",
    "        selections['en'] = preselection&(clean_muons.counts==0)&(clean_electrons.counts==1)&(loose_photons.counts==0)&(pfUWe.pt>200)&(dPhipfUWe > 0.8)\n",
    "\n",
    "        #Need at least one tight muon, 60<diLepMass && diLepMass<120\n",
    "        selections['zmm'] = preselection&(clean_muons.counts==2)&(clean_electrons.counts==0)&(loose_photons.counts==0)&(pfUWm.pt>200)&(dPhipfUWm > 0.8)\n",
    "        #Need at least one tight electron, 60<diLepMass && diLepMass<120\n",
    "        selections['zee'] = preselection&(clean_muons.counts==0)&(clean_electrons.counts==2)&(loose_photons.counts==0)&(pfUWe.pt>200)&(dPhipfUWe > 0.8)\n",
    "        #Need loosePho1IsTight\n",
    "        selections['pho'] = preselection&(clean_muons.counts==0)&(clean_electrons.counts==0)&(loose_photons.counts==1)&(pfUA.pt>200)&(dPhipfUA > 0.8)\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",
    "        #weight, weight_up, weight_down = self.calculateEleWeights(evaluator, _1L_electrons)\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",
    "        \n",
    "        for region in selections:\n",
    "            hists['recoil'].fill(dataset=dataset,region=region,recoil=met_vec[selections[region]].pt.flatten()) \n",
    "   #        hists['dPhi']\n",
    "            hists['fjPt'].fill(dataset=dataset,   region=region, fjPt=clean_fat_jets[selections[region]].pt.flatten())\n",
    "            hists['fjEta'].fill(dataset=dataset,  region=region, fjEta=clean_fat_jets[selections[region]].eta.flatten())\n",
    "            hists['phoPt'].fill(dataset=dataset,  region=region, phoPt=loose_photons[selections[region]].pt.flatten())\n",
    "            hists['phoEta'].fill(dataset=dataset, region=region, phoEta=loose_photons[selections[region]].eta.flatten())\n",
    "            hists['elePt'].fill(dataset=dataset,  region=region, elePt=clean_electrons[selections[region]].pt.flatten())\n",
    "            hists['eleEta'].fill(dataset=dataset, region=region, eleEta=clean_electrons[selections[region]].eta.flatten())\n",
    "            hists['muPt'].fill(dataset=dataset,   region=region, muPt=clean_muons[selections[region]].pt.flatten())\n",
    "            hists['muEta'].fill(dataset=dataset,  region=region, muEta=clean_muons[selections[region]].eta.flatten())\n",
    "            #hists[''].fill(dataset=dataset,region=region,=.flatten())\n",
    "\n",
    "        job.send(hists=hists)\n",
    "\n",
    "        \n",
    "    def calculateEleWeights(self, evaluator, electrons):\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",
    "        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 =electrons.counts\n",
    "        etaSC = electrons.eta+electrons.deltaEtaSC\n",
    "        pt = 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][electrons[c>0].pt[:,0] >80] = sfReco_up[c>0][electrons[c>0].pt[:,0] >80] + 0.01\n",
    "        sfReco_down[c>0][electrons[c>0].pt[:,0] >80] = sfReco_down[c>0][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 = sfTrig*sfIdIso*sfId*sfReco\n",
    "        weight_up = sfTrig_up*sfIdIso_up*sfId*sfReco_up\n",
    "        weight_down = sfTrig_down*sfIdIso_down*sfId*sfReco_down\n",
    "\n",
    "        return weight, weight_up, weight_down\n",
    "    \n",
    "    def get_recoil(self,met,add_objects):\n",
    "        #ARH: This worked until an update in awkward. Have to wait for a new release then it should work again\n",
    "        #highest_pt = add_objects.p4[add_objects.pt.argmax()]\n",
    "        \n",
    "        #recoil = met.zeros_like()\n",
    "        #recoil = recoil.cross(highest_pt)\n",
    "        #recoil = (recoil.i0+recoil.i1+met).p4\n",
    "        \n",
    "        #recoil = met\n",
    "        #recoil = add_objects.pt.sum() + met.pt\n",
    "        recoil = add_objects.p4.sum() + met\n",
    "        return recoil\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fnal_column_analysis_tools.hist as hist\n",
    "\n",
    "dataset = hist.Cat(\"dataset\", \"DAS name\")\n",
    "region =  hist.Cat(\"region\", \"Control or Signal region\")\n",
    "\n",
    "hists = OrderedDict()\n",
    "hists['recoil'] = hist.Hist(\"Events\", dataset, region, hist.Bin(\"recoil\", \"Recoil (GeV)\", [0.,100,200,300,400,600,1000,2000]))\n",
    "hists['dPhi']   = hist.Hist(\"Events\", dataset, region, hist.Bin(\"dPhi\",\"Min dPhi(recoil,jets)\",16,0,3.2))\n",
    "hists['fjEta']  = hist.Hist(\"Events\", dataset, region, hist.Bin(\"fjEta\",\"Fat Jet Eta\",50,-2.5,2.5))\n",
    "hists['fjPt']   = hist.Hist(\"Events\", dataset, region, hist.Bin(\"fjPt\",\"Fat Jet Pt (GeV)\",20,200,800))\n",
    "hists['phoPt']  = hist.Hist(\"Events\", dataset, region, hist.Bin(\"phoPt\",\"Photon Pt (GeV)\",20,200,800))\n",
    "hists['phoEta'] = hist.Hist(\"Events\", dataset, region, hist.Bin(\"phoEta\",\"Photon Eta\",50,-2.5,2.5))\n",
    "hists['elePt']  = hist.Hist(\"Events\", dataset, region, hist.Bin(\"elePt\",\"Electron Pt (GeV)\",20,200,800))\n",
    "hists['eleEta'] = hist.Hist(\"Events\", dataset, region, hist.Bin(\"eleEta\",\"Electron Eta\",50,-2.5,2.5))\n",
    "hists['muPt']   = hist.Hist(\"Events\", dataset, region, hist.Bin(\"muPt\",\"Muon Pt (GeV)\",20,200,800))\n",
    "hists['muEta']  = hist.Hist(\"Events\", dataset, region, hist.Bin(\"muEta\",\"Muon Eta\",50,-2.5,2.5))\n",
    "#hists['']= hist.Hist(\"Events\", dataset, region, hist.Bin())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import threading\n",
    "\n",
    "class MyHistCollectorCallback(object):\n",
    "    def __init__(self, hists, redraw_fcn, update=2e5):\n",
    "        \"\"\"\n",
    "            hists: a dictionary of histograms that will be kept by reference and updated\n",
    "                        with the partial results of the striped session\n",
    "            redraw_fcn: a callable method that will redraw whatever histograms you want\n",
    "                        to display in real time as the histograms are updated.  It must\n",
    "                        return a list of figures\n",
    "            update: the redraw function will only be called after this many events have\n",
    "                        been collected from the striped workers\n",
    "        \"\"\"\n",
    "        self._hists = hists\n",
    "        self._redraw = redraw_fcn\n",
    "        self._update = update\n",
    "        self._seen = 0\n",
    "        self._lock = threading.Lock()\n",
    "\n",
    "    @property\n",
    "    def lock(self):\n",
    "        return self._lock\n",
    "\n",
    "    def on_streams_update(self, nevents, data):\n",
    "        \"\"\"\n",
    "            This method is called by striped\n",
    "        \"\"\"\n",
    "        if \"hists\" in data:\n",
    "            for key in data[\"hists\"]:\n",
    "                for key2 in key:\n",
    "                    self._hists[key2] += key[key2]\n",
    "        self._seen += nevents\n",
    "        if self._seen > self._update:\n",
    "            self._seen = 0\n",
    "            self.update_histograms()\n",
    "    \n",
    "    def update_histograms(self):\n",
    "        figs = self._redraw()\n",
    "        for fig in figs:\n",
    "            fig.canvas.draw()\n",
    "    \n",
    "    def on_exception(self, wid, info):\n",
    "        print(\"Worker exception:\")\n",
    "        print(info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "session = Session(\"/Users/ahall/striped.yaml\")\n",
    "\n",
    "\n",
    "from fnal_column_analysis_tools.hist import plot\n",
    "\n",
    "class PlotDrawer(object):    \n",
    "    def __call__(self):\n",
    "        figs = []\n",
    "        return figs\n",
    "class PlotDrawer(object):    \n",
    "    def __call__(self):\n",
    "        figs = []\n",
    "        figs.append(self.draw_recoil())\n",
    "        return figs\n",
    "    \n",
    "    def draw_recoil(self):\n",
    "        figin = getattr(self, 'massfig', None)\n",
    "        hist2d = hists['recoil'].project('dataset')\n",
    "        fig, ax, temp= plot.plot1d(hist2d,overlay='region',ax=figin,fill_opts={})\n",
    "        self.massfig = ax\n",
    "        return fig\n",
    "        \n",
    "draw = PlotDrawer()\n",
    "callback = MyHistCollectorCallback(hists, draw, update=1e6)\n",
    "# This cell must stay separated from the update loop\n",
    "# https://github.com/matplotlib/jupyter-matplotlib/issues/17\n",
    "figs = draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Job 2110066: worker #135 failed to load frame 810\n",
      "Job 2110066: worker #152 failed to load frame 912\n",
      "Job 2110066: worker #4 failed to load frame 24\n",
      "Job 2110066: worker #108 failed to load frame 648\n",
      "Job 2110066: worker #232 failed to load frame 1347\n",
      "Job 2110066: worker #174 failed to load frame 1044\n",
      "Job 2110066: worker #183 failed to load frame 1098\n",
      "Job 2110066: worker #24 failed to load frame 144\n",
      "Job 2110066: worker #2 failed to load frame 12\n",
      "Job 2110066: worker #262 failed to load frame 1497\n",
      "Job 2110066: worker #216 failed to load frame 1267\n",
      "Job 2110066: worker #273 failed to load frame 1552\n",
      "Job 2110066: worker #35 failed to load frame 210\n",
      "Job 2110066: worker #115 failed to load frame 690\n",
      "Job 2110066: worker #253 failed to load frame 1452\n",
      "Job 2110066: worker #50 failed to load frame 300\n",
      "Job 2110066: worker #244 failed to load frame 1407\n",
      "Job 2110066: worker #243 failed to load frame 1402\n",
      "Job 2110066: worker #13 failed to load frame 78\n",
      "Job 2110066: worker #254 failed to load frame 1457\n",
      "Job 2110066: worker #214 failed to load frame 1257\n",
      "Job 2110066: worker #23 failed to load frame 138\n",
      "Job 2110066: worker #153 failed to load frame 918\n",
      "Job 2110066: worker #117 failed to load frame 702\n",
      "Job 2110066: worker #155 failed to load frame 930\n",
      "Job 2110066: worker #216 failed to load frame 1268\n",
      "Job 2110066: worker #271 failed to load frame 1542\n",
      "Job 2110066: worker #32 failed to load frame 192\n",
      "Job 2110066: worker #171 failed to load frame 1026\n",
      "Job 2110066: worker #217 failed to load frame 1272\n",
      "Job 2110066: worker #246 failed to load frame 1417\n",
      "Job 2110066: worker #133 failed to load frame 798\n",
      "Job 2110066: worker #43 failed to load frame 259\n",
      "Job 2110066: worker #125 failed to load frame 750\n",
      "Job 2110066: worker #78 failed to load frame 468\n",
      "Job 2110066: worker #187 failed to load frame 1122\n",
      "Job 2110066: worker #43 failed to load frame 260\n",
      "Job 2110066: worker #40 failed to load frame 240\n",
      "Job 2110066: worker #21 failed to load frame 126\n",
      "Job 2110066: worker #57 failed to load frame 342\n",
      "Job 2110066: worker #262 failed to load frame 1498\n",
      "Job 2110066: worker #12 failed to load frame 73\n",
      "Job 2110066: worker #3 failed to load frame 18\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Job 2110066: worker #266 failed to load frame 1518\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Job 2110066: worker #211 failed to load frame 1246\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Job 2110066: worker #122 failed to load frame 733\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Job 2110066: worker #28 failed to load frame 171\n",
      "Job 2110066: worker #156 failed to load frame 938\n",
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      "Job 2110066: worker #41 failed to load frame 247\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/Users/ahall/.local/lib/python2.7/site-packages/striped-1.12.2-py2.7-macosx-10.9-x86_64.egg/striped/common/DataExchange.py\", line 247, in recv\n",
      "    try:    msg = DXMessage.fromBufferedSocket(self)\n",
      "  File \"/Users/ahall/.local/lib/python2.7/site-packages/striped-1.12.2-py2.7-macosx-10.9-x86_64.egg/striped/common/DataExchange.py\", line 110, in fromBufferedSocket\n",
      "    sync = sock.readn(3)\n",
      "  File \"/Users/ahall/.local/lib/python2.7/site-packages/striped-1.12.2-py2.7-macosx-10.9-x86_64.egg/striped/common/DataExchange.py\", line 213, in readn\n",
      "    word = self.Sock.recv(100000)\n",
      "error: [Errno 4] Interrupted system call\n",
      "\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "global name 'nevents' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-168-ac3bf179aaf2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0muser_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"dataset\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"dtype\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"evaluator\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mpickle_evaluator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"hists\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mhists\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     )\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m     \u001b[0mruntime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTFinish\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTStart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;31m#print (dataset_name)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahall/.local/lib/python2.7/site-packages/striped-1.12.2-py2.7-macosx-10.9-x86_64.egg/striped/job/SinglePointJob.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    326\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitDone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mSPIPythonJob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSinglePointJob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahall/.local/lib/python2.7/site-packages/striped-1.12.2-py2.7-macosx-10.9-x86_64.egg/striped/job/SinglePointJob.pyc\u001b[0m in \u001b[0;36mwaitDone\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    263\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m                     \u001b[0mdone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjobFailed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Job server closed the connection\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    266\u001b[0m                     \u001b[0;31m#print \"empty message\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahall/.local/lib/python2.7/site-packages/striped-1.12.2-py2.7-macosx-10.9-x86_64.egg/striped/common/MyThread.pyc\u001b[0m in \u001b[0;36msmethod\u001b[0;34m(self, *params, **args)\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0;31m#print \"@synchronized: in %s\" % (method,)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0mIn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m         \u001b[0;31m#print \"@synchronized: out %s\" % (method,)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mIn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahall/.local/lib/python2.7/site-packages/striped-1.12.2-py2.7-macosx-10.9-x86_64.egg/striped/job/StripedJob.pyc\u001b[0m in \u001b[0;36mjobFailed\u001b[0;34m(self, error)\u001b[0m\n\u001b[1;32m    171\u001b[0m         \u001b[0mhandled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCallbackList\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"on_job_finish\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEventsProcessed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhandled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Job failed after %d events:\\n%s\\n\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnevents\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: global name 'nevents' is not defined"
     ]
    }
   ],
   "source": [
    "if run_all:\n",
    "    datasets=sample_dict\n",
    "for dataset_name in datasets:\n",
    "    if (\"madgraph\"in dataset_name) or (\"pythia\") in dataset_name:\n",
    "        dtype = 'mc'\n",
    "    else:\n",
    "        dtype = 'data'\n",
    "    job = session.createJob(dataset_name, \n",
    "        callbacks=callback,\n",
    "        user_params = {\"dataset\":dataset_name,\"dtype\":dtype, \"evaluator\":pickle_evaluator, \"hists\":hists}\n",
    "    )\n",
    "    job.run()\n",
    "    runtime = job.TFinish - job.TStart\n",
    "    #print (dataset_name)\n",
    "    nevents = job.EventsProcessed\n",
    "    print (\"%-70s %7.3f M events, %7.3f M events/sec\" % (dataset_name, float(nevents)/1000000, nevents/runtime/1000000))\n",
    "figs = draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist2d = hists['recoil'].project('region')\n",
    "\n",
    "fig, ax, _ = plot.plot1d(hist2d,overlay='dataset',fill_opts={})  \n",
    "lumi = 100\n",
    "ax.text(1., 1., r\"%.2f fb$^{-1}$ (13 TeV)\" % (lumi), fontsize=16, horizontalalignment='right', verticalalignment='bottom', transform=ax.transAxes)\n",
    "ax.set_yscale('log')\n",
    "ax.autoscale(axis='y', tight=False)\n",
    "ax.set_ylim(1, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist2d = hists['phoPt'].project('region','pho')\n",
    "\n",
    "fig, ax, _ = plot.plot1d(hist2d,overlay='dataset',fill_opts={})  \n",
    "lumi = 100\n",
    "ax.text(1., 1., r\"%.2f fb$^{-1}$ (13 TeV)\" % (lumi), fontsize=16, horizontalalignment='right', verticalalignment='bottom', transform=ax.transAxes)\n",
    "ax.set_yscale('log')\n",
    "ax.autoscale(axis='y', tight=False)\n",
    "ax.set_ylim(1, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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