Tutorials/InteractiveAnalysis/Notebooks/TagAndProbe.ipynb

{
 "metadata": {
  "name": "TagAndProbe"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import ROOT\n",
      "import rootnotes\n",
      "import utils\n",
      "import math"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd /Users/demattia/CMSSH/soft/Releases/CMSSW_5_3_8/src"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Users/demattia/CMSSH/soft/Releases/CMSSW_5_3_8/src\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ROOT.gROOT.LoadMacro(\"Loader.C+\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define cuts and some useful variables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "triggerMatchDeltaR = 0.1\n",
      "muMass = 0.1057"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the input file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tree = ROOT.TChain(\"T\")\n",
      "tree.Add(\"/Users/demattia/CMSSH/soft/Releases/CMSSW_5_3_8/src/Analysis/TagAndProbe/test/TagAndProbe_ZMuMu.root\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Define RooFit variables and PDFs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from ROOT import RooRealVar, RooFormulaVar, RooVoigtian, RooChebychev, RooArgList, RooArgSet, RooAddPdf, RooDataSet, RooCategory, RooSimultaneous"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define pdf for the passing probes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mass = RooRealVar(\"mass\", \"mass\", 60, 120)\n",
      "# Construct signal pdf\n",
      "mean = RooRealVar(\"mean\", \"mean\", 90, 80, 100)\n",
      "width = RooRealVar(\"width\", \"width\", 2.4952, 1, 3)\n",
      "width.setConstant(ROOT.kTRUE)\n",
      "sigma = RooRealVar(\"sigma\", \"sigma\", 1.2, 0.2, 2)\n",
      "signalAll = RooVoigtian(\"signalAll\", \"signalAll\", mass, mean, width, sigma)\n",
      "# Construct background pdf\n",
      "a0_all = RooRealVar(\"a0_all\",\"a0_all\",-0.1,-1,1)\n",
      "a1_all = RooRealVar(\"a1_all\",\"a1_all\",0.004,-1,1)\n",
      "backgroundAll = RooChebychev(\"backgroundAll\",\"backgroundAll\",mass,RooArgList(a0_all,a1_all))\n",
      "# Construct composite pdf\n",
      "sigAll = RooRealVar(\"sigAll\", \"sigAll\", 2000, 0, 100000)\n",
      "bkgAll = RooRealVar(\"bkgAll\", \"bkgAll\", 100, 0, 1000)\n",
      "modelAll = RooAddPdf(\"modelAll\", \"modelAll\", RooArgList(signalAll, backgroundAll), RooArgList(sigAll, bkgAll))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define pdf for all probes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Construct signal pdf. \n",
      "# NOTE that sigma is shared with the signal sample model\n",
      "signalPass = RooVoigtian(\"signalPass\",\"signalPass\",mass,mean,width,sigma)\n",
      "# Construct the background pdf\n",
      "a0_pass = RooRealVar(\"a0_pass\",\"a0_pass\",-0.1,-1,1)\n",
      "a1_pass = RooRealVar(\"a1_pass\",\"a1_pass\",0.5,-0.1,1)\n",
      "backgroundPass = RooChebychev(\"backgroundPass\",\"backgroundPass\",mass,RooArgList(a0_pass,a1_pass))\n",
      "# Construct the composite model\n",
      "fraction = RooRealVar(\"fraction\",\"fration\",0.8,0.,1.)\n",
      "sigPass = RooFormulaVar(\"sigPass\", \"@0*@1\", RooArgList(sigAll, fraction))\n",
      "bkgPass = RooRealVar(\"bkgPass\", \"bkgPass\", 100, 0, 1000)\n",
      "modelPass = RooAddPdf(\"modelPass\", \"modelPass\", RooArgList(signalPass, backgroundPass), RooArgList(sigPass, bkgPass))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frac = RooRealVar(\"frac\", \"frac\", 0.8, 0., 1.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define combined pdf for simultaneous fit"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Define category to distinguish physics and control samples events\n",
      "sample = RooCategory(\"sample\",\"sample\")\n",
      "sample.defineType(\"all\")\n",
      "sample.defineType(\"pass\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "simPdf = RooSimultaneous(\"simPdf\",\"simultaneous pdf\",sample)\n",
      "\n",
      "# Associate model with the physics state and model_ctl with the control state\n",
      "simPdf.addPdf(modelAll,\"all\")\n",
      "simPdf.addPdf(modelPass,\"pass\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Prepare datasets"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define binning"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import bisect\n",
      "\n",
      "# ptBins = [23, 26, 30, 35, 40, 45, 50, 60, 70]\n",
      "ptBins = [23, 100]\n",
      "\n",
      "# This returns the position in the array\n",
      "def findBins(pt1, pt2):\n",
      "    return (bisect.bisect_right(ptBins, pt1)-1, bisect.bisect_right(ptBins, pt2)-1)\n",
      "\n",
      "def findPosition(bin1, bin2):\n",
      "    return bin1+len(ptBins)*bin2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datasetAllMap = {}\n",
      "datasetPassMap = {}\n",
      "for ptBin1 in range(0, len(ptBins)):\n",
      "    for ptBin2 in range(0, len(ptBins)):\n",
      "        datasetAllMap[(ptBin1, ptBin2)] = RooDataSet(\"datasetAll_\"+str(ptBin1)+\"_\"+str(ptBin2), \"datasetAll_\"+str(ptBin1)+\"_\"+str(ptBin2), RooArgSet(mass))\n",
      "        datasetPassMap[(ptBin1, ptBin2)] = RooDataSet(\"datasetPass_\"+str(ptBin1)+\"_\"+str(ptBin2), \"datasetPass_\"+str(ptBin1)+\"_\"+str(ptBin2), RooArgSet(mass))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Event loop"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Trigger efficiency for new trigger over old trigger"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "oldTrigger = \"HLT_L2DoubleMu23_NoVertex_v\"\n",
      "newTrigger = \"HLT_L2DoubleMu23_NoVertex_2Cha_Angle2p5_v\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hAllMap = {}\n",
      "hPassMap = {}\n",
      "for ptBin1 in range(0, len(ptBins)):\n",
      "    for ptBin2 in range(0, len(ptBins)):\n",
      "        hAllMap[(ptBin1,ptBin2)] = ROOT.TH1F(\"hAll\"+\"_\"+str(ptBin1)+\"_\"+str(ptBin2), \"All events passing old trigger \"+str(ptBin1)+\"_\"+str(ptBin2), 100, 60, 120)\n",
      "        hPassMap[(ptBin1,ptBin2)] = ROOT.TH1F(\"hPass\"+\"_\"+str(ptBin1)+\"_\"+str(ptBin2), \"All events passing old trigger and new trigger \"+str(ptBin1)+\"_\"+str(ptBin2), 100, 60, 120)\n",
      "        \n",
      "        \n",
      "hPt1 = ROOT.TH1F(\"hPt1\", \"muon1 p_T\", 100, 0, 100)\n",
      "hPt2 = ROOT.TH1F(\"hPt2\", \"muon2 p_T\", 100, 0, 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def passSelection(track):\n",
      "    if ( track.pt > 26 and abs(track.eta) < 2 and track.isolation/track.pt < 0.1 and track.trackerLayersWithMeasurement >= 6\n",
      "         and track.trackQuality and track.dxy < 30. and track.dz < 30. ):\n",
      "        return True\n",
      "\n",
      "def passDileptonSelection(track1, track2, cosine):\n",
      "    if track1.charge == track2.charge: return False\n",
      "    if utils.deltaR(track1.phi, track1.eta, track2.phi, track2.eta) < 0.2: return False\n",
      "    # cosineAndMass = computeCosineAndMass(track1, track2)\n",
      "    if cosine <= -0.79: return False\n",
      "    return True\n",
      "\n",
      "def computeCosineAndMass(mu1, mu2):\n",
      "    muon1 = ROOT.TLorentzVector()\n",
      "    muon2 = ROOT.TLorentzVector()\n",
      "    muon1.SetPtEtaPhiM(mu1.pt, mu1.eta, mu1.phi, muMass)\n",
      "    muon2.SetPtEtaPhiM(mu2.pt, mu2.eta, mu2.phi, muMass)\n",
      "    cosine = math.cos(muon1.Angle(muon2.Vect()))\n",
      "    mass = (muon1+muon2).M()\n",
      "    return (cosine, mass)\n",
      "\n",
      "def fillTriggerMatchedTrack(track, triggerObjects, matchedTracks):\n",
      "    for triggerMuon in triggerObjects:\n",
      "        if utils.deltaR(triggerMuon.phi, triggerMuon.eta, track.phi, track.eta) < triggerMatchDeltaR and passSelection(track):\n",
      "            matchedTracks.append(track)\n",
      "\n",
      "def fillSingleCandidate(track1, track2, histoMap, datasetMap):\n",
      "    cosineAndMass = computeCosineAndMass(track1, track2)\n",
      "    if not passDileptonSelection(track1, track2, cosineAndMass[0]): return False\n",
      "    mass.setVal(cosineAndMass[1])\n",
      "    histoMap[findBins(track1.pt, track2.pt)].Fill(mass.getVal())\n",
      "    datasetMap[findBins(track1.pt, track2.pt)].add(RooArgSet(mass))\n",
      "    return True\n",
      "\n",
      "def fillCandidates(matchedTracks, histoMap, datasetMap):\n",
      "    if len(matchedTracks) == 2:\n",
      "        fillSingleCandidate(matchedTracks[0], matchedTracks[1], histoMap, datasetMap)\n",
      "    elif len(matchedTracks) > 2:\n",
      "        for i in range(0, len(matchedTracks)):\n",
      "            for j in range(i+1, len(matchedTracks)):\n",
      "                fillSingleCandidate(matchedTracks[i], matchedTracks[j], histoMap, datasetMap)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "allCandidates = 0\n",
      "passCandidates = 0\n",
      "\n",
      "processedEvents = 0\n",
      "\n",
      "for event in tree:\n",
      "    # if processedEvents > 10000:\n",
      "    #     break\n",
      "\n",
      "    # Get the trigger outcome for this event\n",
      "    oldTriggerFired = False\n",
      "    newTriggerFired = False\n",
      "    for name in event.triggerNames:\n",
      "        if name.find(oldTrigger) != -1: oldTriggerFired = True\n",
      "        if name.find(newTrigger) != -1: newTriggerFired = True\n",
      "    \n",
      "    # If none of the two triggers fired we can skip the event\n",
      "    if not oldTriggerFired and not newTriggerFired:\n",
      "        continue\n",
      "\n",
      "    # print \"event number\", processedEvents\n",
      "    processedEvents += 1\n",
      "\n",
      "    # print \"run, event =\", str(event.run)+\",\", event.event\n",
      "    oldTriggerObjects = event.triggerFilterObjectsMap[\"hltL2DoubleMu23NoVertexL2PreFiltered\"]\n",
      "    newTriggerObjects = event.triggerFilterObjectsMap[\"hltL2DoubleMu23NoVertexL2Filtered2ChaAngle2p5\"]\n",
      "    \n",
      "    matchedTracksOldTrigger = []\n",
      "    matchedTracksNewTrigger = []\n",
      "    for track in event.tracks:\n",
      "        # Find a matching trigger object in DeltaR\n",
      "        fillTriggerMatchedTrack(track, oldTriggerObjects, matchedTracksOldTrigger)\n",
      "        fillTriggerMatchedTrack(track, newTriggerObjects, matchedTracksNewTrigger)\n",
      "    \n",
      "    if len(matchedTracksOldTrigger) > 2:\n",
      "        # print \"too many matches old trigger:\", len(matchedTracksOldTrigger)\n",
      "        pass\n",
      "    if len(matchedTracksNewTrigger) > 2:\n",
      "        # print \"too many matches new trigger:\", len(matchedTracksNewTrigger)\n",
      "        pass\n",
      "    \n",
      "    if oldTriggerFired:\n",
      "        if len(matchedTracksOldTrigger) == 2:\n",
      "            # if matchedTracksOldTrigger[1].pt < matchedTracksOldTrigger[0].pt:\n",
      "            #     print matchedTracksOldTrigger[0].pt\n",
      "            hPt1.Fill(matchedTracksOldTrigger[0].pt)\n",
      "            hPt2.Fill(matchedTracksOldTrigger[1].pt)\n",
      "        # print \"old trigger fired\"\n",
      "        fillCandidates(matchedTracksOldTrigger, hAllMap, datasetAllMap)\n",
      "\n",
      "    # Note: we require both triggers. This is only needed in data because the old trigger is prescaled. In MC the old trigger always fires if the new one fires.\n",
      "    if oldTriggerFired and newTriggerFired:\n",
      "        # print \"new trigger fired\"\n",
      "        fillCandidates(matchedTracksNewTrigger, hPassMap, datasetPassMap)\n",
      "\n",
      "print \"all candidates =\", hAllMap[0,0].GetEntries()\n",
      "print \"pass candidates =\", hPassMap[0,0].GetEntries()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "all candidates = 126134.0\n",
        "pass candidates = 119684.0\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# print (162697. - 126134.)/162697.\n",
      "# passingNew = 120991\n",
      "# print 164388 - dimuonsWithoutCosineCut\n",
      "# 126134\n",
      "# 119684\n",
      "\n",
      "\n",
      "canvasPt = rootnotes.canvas(\"PtDistributions\", (800, 800))\n",
      "# canvasPt.Divide(2)\n",
      "# canvasPt.cd(1)\n",
      "hPt1.Draw()\n",
      "hPt2.Draw(\"same\")\n",
      "hPt1.GetYaxis().SetTitle(\"arbitrary units\")\n",
      "hPt1.GetYaxis().SetTitleOffset(1.3)\n",
      "hPt1.GetXaxis().SetTitle(\"muon p_{T} [GeV/c]\")\n",
      "hPt2.SetLineColor(2)\n",
      "legend = ROOT.TLegend(0.9,0.8,0.6,0.9);\n",
      "legend.AddEntry(hPt1,\"First muon p_{T}\",\"l\");\n",
      "legend.SetFillColor(0)\n",
      "legend.SetLineColor(0)\n",
      "legend.AddEntry(hPt2,\"Second muon p_{T}\",\"l\");\n",
      "legend.Draw(\"same\");\n",
      "\n",
      "canvasPt.SaveAs(\"trigger_match_first_second_pt.pdf\")\n",
      "canvasPt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": 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oAgDI4Jd3VwB4keh8KIUOTADYIaEKvkhkdGfZ0xMD7IjuPwCADIQqAIAMdP/d\nkF67sdipMgBgpwpfNTlBqLpBbOJjXCsjqIAdSF95S45cQhV8E38kAGzGmCoAgAyEKgCADIQqAIAM\nhCoAgAyEKgCADIQqAIAMhCoAgAyEKgCADIQqAIAMhCoAgAwsU3ODBZUB4JVKXt0vTai6QWwCgFfa\n74LKuv8AADIQqgAAMhCqAAAyEKoAADIQqgAAMhCqAAAyEKoAADIQqgAAMhCqAAAyEKoAADIQqgAA\nMhCqAAAysKDyDemFGy23DAB5lbxkcppQdYPYBACvlL7ylhy5dP8BAGQgVAEAZLB5qOr7vuu6tm3r\num7btuu6XDtXVRV2Hvbv+37l/msKBwBYr950zFDbtpfLZb49+qRd151Op5U7Vwu9qk3TRKNV3/eH\nw2G+/Xw+t20bLX94CmOq+Bx1Xe39/fwBhwA8p+RL84Y1C4mqaZohuPR9HzLW5HnHoed4PKZ3jhYe\nAtnxeJy0Qs0LH++fOAMlv3Jwtw9IJB9wCMBzir40X7dxPp+H8o/H43h7yDST7dH63FtI0zTRg0oX\n3jTN0lFsd37gDT7g/fwBhwA8p+RL81Y1W8o31x8RZxxlQr45n89rykkkwnnYWkpm11E4WzqKkl85\nuNsHvJ8/4BCA55R8ad5qoPrQNxdSy9j5fD6fz+MeuvDzfHhT2DIfKRUtfDAemxUeOB+ZnigcAOAu\nm4SqRI6pqqr9IWwJo6PmO4cSQpnhh+gA83nSSgSmUII7AQGAJ20yo/o8x6ST0M1f3VvUWCKxAQDk\nsm1LVVVVw7xQhx/qun6sWWhlD929PXrCFgCQxbZr/0WnkjqdTn3fr+zOm3ts/FO68OhkWoNn1hi6\nFnvPJ9+g4OWxgG9W8uJ9T9qkpWqcUaJ3+V0ul72MDX/mLoB3152vd71O/wG825N32L27+imbhKrQ\npzaZ37xt23A65vOb7yVmAQDMbbv2X3T4VLhBb0hRK3v9gnv3Hz8XAMBGtg1V0QD0WCpybyBQVVVV\n19N/AGXYJFRt2piUvr9vvnEITImh6NWjFQZerK5mA8UAivGGUHVX7gkdiPNZ16Ohat7ylKjMvc1a\nAABLtg1V0bAS1pCZT2iemDV0bMhM47VoJjuPn3cey+a/EqoAgCdtNaZqGI1+uVwmUSbEl/F6MmHj\n5JbAruuiawgu5aTw8Mn2aAjr+z6xQCEAwF3q7aZ8GM/uNengm0y1UFVV3zSr10AAACAASURBVPch\nEg0pJ4Se+c5VVbVtG347ZLKQmc7n87zlKVRmUniVnKKzrjc8P7Ctuv68IUeRj+QnHiaQUPKledua\nhegzFg1J1c+56ubOS4VHE9VS4dWtSc9LfuXghk9MG0IVUPKlefOa9T9UVdW27c2F/4Yd+r5vf7i5\n//rCQ2VWFl7yKwc3fGLaEKqAki/N5dasBCW/cnDDJ6aN+ZJh16r6vMMEEkq+NG+7oPIHSK/7WOzr\nCh8p8okz+Sd8nP2uuCxU3SA2AcAr3Rzu/LKa3GvbZWoAAL6EUAUAkIFQBQCQgVAFAJCBUAUAkIFQ\nBQCQgVAFAJCBUAUAkIFQBQCQgVAFAJCBUAUAkIG1/26woDIAvFLJq/ulCVU3iE3swG6/gADm9rug\nslAFH2H2HVTXtT8IAF5JqIJPUPKfbgBfQqiCT6CfGuDt3P0HAJCBUAUAkIFQBQCQgVAFAJCBUAUA\nkIFQBQCQgVAFAJCBUAUAkIFQBQCQgRnVb0iv/mEaawDIa7/rbglVN4hNAPBK6StvyZFL9x8AQAZC\nFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABtb+u8GCygDw\nSiWv7pcmVN0gNgHAK1lQGQDgqwlVAAAZbBiq6qS+7+cP6fu+67q2beu6btu267r0U4Sdh/2jZUb3\nX1M4AMB69XZjhtK9nufzuW3b8Zau606n03zPpRpGy2+aZimuHQ6HNdWYPIUxVexAXVdf+0b95mOH\nr1TypXmrgeoh2TRNs3L/kKiOx+Ow5XK5VAunLyShpmmGn4eHXy6XrusmrVDjRDUUHvY/HA7FvjYA\nwJ5ctxGyy8r9o/U5n8/DxuPxGC18sj0EuLsKb5omUauV9Yd3+uY36jcfO3ylki/NRQxUD81aIegM\n2rYdctKkWzD8d94iNd8eNoYoFgoftgztYQAAz9gqVA1RZmXfX8hA8+FNYct8pNQkJI2NQ1g0ad0s\nHIo1v+3j3TUCoKq2C1VD809iDPh852gCC0ko5J7wQ7TwedJKBKZQgjsB2ZdJg/O7qwNAVb1gnqqu\n68Z/Uidi1soElg5Vc4nEBgCQy7ah6nQ6TYZDXS6XpUmq0lY+5N4ePWELAMhik1A1DjRN05zP5+v1\nej6fQ4I5HA4ru/PSha+XLjwxVj09hWnaA/UEgM/2zIW18Gvrti1Vx+Ox7/sh0Awznof7+/YyjOmZ\nWyvfXXcAKM4zF9bCr62bTP7Ztu3SYQ+zJFwul3njUIhfQMK1qqqy/1YD+E5vmKdqMuzp3iD1WPAy\naQIf5Xqd/gPg3d4ZqrZ4lHsDAYC3KGJG9UGW+/vmG4fAlJ42XbcjAPCkTULVMD5/aSj6vDEpkXtC\nIfNZ16Ohat7ylAhM9zZrAQAs2SRURRfsC+bb59OmB9HktFR+NCTNY9n8V0IV7NfubroGPtUmoWrN\nWn7jtZPDxsPhMClnaHmaLD6zlJPCwyfboyGs7/to4cC+7OuOa+CD1Rt9AbVtG7rzQmfceMukCarv\n+xCJhpQTQs9853H5TdMMmSxkpvP5PA9z4S/XSeFVVSXOQF1vdX7gcXXtdr9/iJ0Nn1z4YCV/wDes\nWdd10R7A4/EYHW41zlVBNFENxrktiCaqpcKrZKKqyn7l+F5C1dhST59TBB+q5Evz5jUb8lOY2PPm\nROrj/Qc39w/zXd0svP9hZeElv3J8L6HqJqcIPlfJl+Zya1aCkl85vpfEcJNTBJ+r5EvzJsvUfJL0\nbUTFvq4AsFP7vYFXqLpBbAKAV7o53PllNblXQTOqAwDsl1AFAJCBUAUAkIFQBQCQgVAFAJCBUAUA\nkIFQBQCQgVAFAJCBUAUAkIFQBQCQgVAFAJCBUAUAkIEFlW9IL9xouWUAyKvkJZPThKobxCYAeKX0\nlbfkyCVUQcEK/u4AYEKogrLN/mKr61rzKUCBhCooWskN3QCMCVVQNKP6APbClAoAABkIVQAAGQhV\nAAAZCFUAABkIVQAAGQhVAAAZCFUAABmYp+oGCyoDwCvtd9JjoeoGsQkAXmm/Cyrr/gMAyECoAgDI\nQKgCAMhAqAIAyECoAgDIQKgCAMhAqAIAyECoAgDIQKgCAMhAqAIAyECoAgDIwNp/N1hQGQBeqeTV\n/dKEqhvEJgB4pf0uqCxUQSnm3xQSPcCOCFVQkOnfZwX/QQbAhIHqAAAZvDRUdV1X13XXdUs79H3f\ndV3btnVdt22b2DMUOOw87N/3/cr91xQOALBe/bKB2H3fHw6Hqqqapommn67rTqfTfPtSDaND1ZYK\nD88+cT6f27ZdqnNdv+78QOT9VteVd+ADnDf4XCVfml9Xs5CBorlnHHqOx+Ow5XK5DFvmlWzbdvht\n0zRDKgqB7Hg8Tlqh5oWP90+cgZJfOT6PUJWN8wafq+hL8/UlmqYJz9g0zXyHaH3O5/Ow8Xg8jreH\nYDTZHp7lrsKj9QkPXHFwkEfk/eYd+BjnDT5XyZfmV4ypGrc5Le0w/BCCzqBt2yEnTboFw3/nLVLz\n7WFjiGKh8GFLum7wMteqqur6p38A7McrQtXQ9TYJTGMhA82HN4Ut8x7DSUgaG4ewaNK6WTi8x/U6\n/QfATmweqoahVGHkU1QYHTX/VUhCIfeEH6IFzpNWIjCFEtwJCAA8adtQNY9ECYnUNZYOVXOJxAYA\nkMuGoarv+6EbLtHxd1dpa3a7t0dP2AIAsthwmZphKNXxeEw3Kd3b8vTY+Kd04Ymx6s8s3Hg1IAYA\nflbyishP2ipUDSGmaZq9D1cSjAAgoycvrCVnsk26/7quG9p+7mpVcgseALBfm4Squ4ZSrez1e3j/\ngcQGAGwqf6gK8eVwONQ/G7ZfLpfhv3f1DLo3EAAo2Ssm/1wyaT3Kcn/ffOMQmNLTpj/W+gUAEOQf\nqN627VLH33A/YBi9HqJM0zSXyyWae0Jr1nzW9b7v52Fo3vIUll6eu7dZCwBgyUuXeg6zq8/bqMJS\nNpN8EyLRuJ7RjeNyjsfjuG9xeN7JxkQ54we6+4/XqWvr0uThTMLnKvnS/M7uvyAEqSESBeEuwsni\nM/Pmq8nDJ9ujqzKHZZ4TawgCAKxUREtVNWpkqn6knBB6ovuHRqawqmDITPPmrmo0rcWk8Co5YUbJ\ncZgPpH0ll9g8Nj7L8BlKvjSXEqqqn3NVsLRztTBYKpqolgqvbn3PlvzK8YGEqlxmZ9JnGT5GyR/n\n4mo29NwNg9AHN/cfUlfbtjfnaOh/WFl4ya8cH0ioykWogs9V8se53JqVoORXjg8kVOUSXcXCuYWP\nUPKlecMFlT9Deo2hYl9X+GrzD2bBi4UBEyWv7pcmVN0gNgHAK90c7vyymtyriCkVAAD2TqgCAMhA\nqAIAyECoAgDIQKgCAMhAqAIAyECoAgDIQKgCAMhAqAIAyECoAgDIwDI18AbRZRasiASwa0LVDRZU\nZiORN0/BC1oBvEzJq/ulCVU3iE1s4VqJUABx+11QWaiCN5HXAT6LgeoAABkIVQAAGQhVAAAZCFUA\nABkIVQAAGQhVAAAZCFUAABkIVQAAGQhVAAAZCFUAABlYpuYGCyoDwCuVvLpfmlB1g9gEAK+03wWV\ndf8BAGQgVAEAZCBUAQBkIFQBAGQgVAEAZCBUAQBkIFQBAGQgVAEAZGDyT9jcfKo6U8oCfB6hCl5h\nOkFwwTMCA/AYoQr4DtEgax0qIB+hCvh8dXQ1Me2FQFZC1Q3phRstt8wa18r1G2CtkpdMThOqbhCb\nyMMbCWCd9JW35MhlSgUAgAyEKgCADLYNVX3fd13Xtm1d123btm3b9/36/buuS5cfdh72Txc+3n9N\n4QAA69XbjRlq2/Zyucy3N00TTT9d151Op/n2pRpGe1WXCu/7/nA4zLefz+e2baPlD09hTBUZ1LUx\nVe8V/yx7XWCHSr40b1WzcaI6Ho9DM1LITPPoMw49x+Nx2BJKmFcylN80zZCKQuHH43HSCjUvfLx/\n4gyU/MqxJy7e7yZUwcco+tJ83cZQeNM0k+1N0wy/Op/P8/0n9Tmfz8PG4/E43h6C0WR7KDxamaXC\n55UcPzBxjLCWN9K7xb/9vC6wQyVfmjcZUxUaiuY9cdFfhZ9D0Bm0bTvkpEm3YPjvvEVqvj1sDFEs\nFD5sifZRAp8k+vX37koBn+bVd/9FBzCFDDT/bdgyz2eTkDQ2DmHRpHWzcACAu2wVqpqmSYSeiTA6\nav6rectW+CGaz+ZPmghMoQR3AgIAT9pkRvVERlnTKJWWDlVzicQGAJDLK7r/+r4PE1ANHXPD/YD3\nFrJmt3t79IQtACCLV6z9N5kgajLlwb0tT4+Nf0oXnhir/swaQ0bCAsBEyYv3PekVLVWT1qDT6bSj\nMUzP3Fr57roDQHGeubAWfm19UfffcCLO53OYImHe4OQWPABgv146pcJ4eb7QJ3jv4Kp79x9IbADA\npl49T1WVnF8qwb2BAEDJNglVbdvWdX1v41CW+/vmG4fAlJ42/bHWL5irY95dKQBeYZNQNSSY9aPR\nE7knFDKf4CoaquYtT4nAdG+zFtx0jf0D4BtsEqrSjUOThfyq5FqB0eQUXRCwWghJ81g2/5VQRU7X\na+QfAJ9u2wWVE9Omj0dWhY2TGa26rhuS2WQY1lJOCg+fbI+GsL7vo4UDADyg3mjKh7ZtQ0tV6Iwb\nb5k0QfV9HyLRkHJC6JnvPC6/aZohk4XMdD6f52EujGuZFF4lp+is663ODx+rrrVL7YYXC3ao5Evz\nhjUb56qxaEiqfs5VN3deKj+aqJYKr25Nel7yK0ehXKd3xIsFO1TypXnzmg09cUMwatv25uj1sH/7\nw8391xfe/7Cy8JJfOQrlOr0jXizYoZIvzeXWrAQlv3IUynV6R7xYsEMlX5pfsaDyrqUnGSr2dQWA\nndrv9H5C1Q1iEwC80s3hzi+ryb3esEwNAMDnEaoAADIQqgAAMhCqAAAyMFAd+F7zEa/uTQEeJlQB\n32sSoUq+qwgon+4/AIAMhCoAgAyEKgCADIypAr7Yz4OojFEHniFUAd9qfqOfgerAE4SqGyyoDACv\ntN/7cIWqG8QmAHglCyoDAHw1oQoAIAOhCgAgA6EKACADA9XhcZHleN9SDwAKIFTBU6Z3qRR8WwoA\nm9L9BwCQgVAFAJCBUAUAkIFQBQCQgVAFAJCBUAUAkIEpFW5IL9xouWUAyKvkJZPThKobxCYAeKX0\nlbfkyCVUweOuldk+Afg7oQqeoy0TgKqqDFQHAMhCqAIAyECoAgDIQKgCAMhAqAIAyECoAgDIQKgC\nAMhAqAIAyECoAgDIwIzqN1hQGQBeqeTV/dKEqhvEJgB4JQsqA3yC6Pe1P66ANYQqgH+QnoCHbT5Q\nveu6tm3ruq7rum3brusSO/d9H/a/ufO88L7vV+6/pnDg61yvkX8A69TbNWt3XXc6naK/Op/Pbduu\n3H+phtFW+qZpotGq7/vD4bCyJuOn0OxPSl276H4+rzKUpORL81Y1G4eY4/E4NCP1fX+5XIaNkzQz\n2X/YEnaeV7Jt2+G3TdMM5YRAdjweJ61Q88LH+yfOQMmvHEVwuf0GXmUoScmX5q1qFpqRJuWP8834\nV9H9w86TnBTatCbbQ9KaPGm68KXGrarsV44iuNx+A68ylKTkS/MmY6pCRgnNQkHbtk3TLO1/Pp+j\nO0+6BcN/5y1S8+1LlWnbdtgS2sMgpa4j/wDgh21DVXQweNg4320+vClsmTcmzRNbMA5hicokCocI\no5gBWFbEMjVhdNT8V/MEFn6IDjCfJ61EYAoluBMQAHjSJqGq67rr9brU5bnULpW4C28sHarmEokN\nACCXV7dUhXv67k05K3vo7u3RE7YAgCxeOqP6+Na/ld150UIeeOp04Ymx6s+sMVTs7QnAfebfAz7d\n8KiSF+970otaqvq+b9s2JKrJXX4luz7h3XUHMqir2T0KwBOeubAWfm19RUtVmD6qSk4KNQSvF9QH\nACC7bVuq+r6v63o8i/o8Ud0bpB4LXiZNAAA2tWGo6rpuvDjM9Xp9piHKvYEAQMm2ClV93w8zcDZN\nc71e10wEleX+vvnGITClp03X7QgAPGmrUHVzWb2xRO4JaWw+u1W05HnLUyIw3dusBQCwZKvJP4cf\nVjY+JfaPlhBdELBaCEnzWDb/lVAFADxpk6Wew+1+ieX5qp9TTpi1YlyfruuG5HQ8HidrJA8tYZPt\n0ULG9RlvXypkrOSlsHm1unYv/Xeaz6lzrcxTBW9T8qV5k5qtnNfrfD6P+/LGo9qrn+dej94zGH47\nFBIarsbFzqs0KbxKTtFZ8ivHqwlVBN4M8D4lX5pLCVXVz7kqSIzKGk9/tVRmuvDq1qTnJb9yvJrr\nKIE3A7xPyZfm4mo29MQNE4EObu4/pK62bW/eY9j/sLLwkl85Xs11lMCbAd6n5EtzuTUrQcmvHK/m\nOkrgzQDvU/Kl+aULKu9Ruiuz2NcVAHZqvysuC1U3iE0A8Eo3hzu/rCb3EqogpuAPLQBlEqpgwc9/\nKtV1rdESgIQNF1QGAPgeQhUAQAZCFQBABsZUQVzJN5gAUCChCuLMpgHAXXT/AQBkIFQBAGQgVAEA\nZCBUAQBkIFQBAGTg7r8b0vfVu0EMAPLa74w2QtUNYhMAvFL6ylty5BKqAO4X/Vr3Nxh8N6EK4E7R\n8FTwX8/AaxioDgCQgVAFAJCBUAUAkIFQBQCQgVAFAJCBUAUAkIFQBQCQgVAFAJCBUAUAkIEZ1W+w\noDKw1vzrwlcE3K/k1f3ShKobxCZgjXr+dbHbCwO8134XVNb9BwCQgVAFAJCBUAUAkIFQBQCQgYHq\nAHlMxs+6yQW+jVAFkEHkfqWC71ECtqD7DwAgA6EKACADoQoAIAOhCgAgA6EKACADd//dYEFl4GHR\nLxDfG5BW8up+aULVDb7+gIfNv0D2e7WAl7GgMgDAV9NSBbCZ2Z/Umr7hgwlVANuIdmEU3HMBPOl1\n3X91Xbdtm96n7/uu69q2HXbuui69f9h52L/v+5X7rykcAGC9+jUDsbuuO51OTdMkcs+wz3z7Ug2j\nQ9WWnqLv+8PhMN9+Pp8TUa+uX3R+KE5dx5sZ4EneWvCcki/NL6rZEIASoWoceo7H47DlcrkMW+aV\nbNt2+G3TNEMqCoHseDxOWqHmhY/3T5yBkl85tuXKx0a8teA5RV+ar1s6n88hxFRV1TTN0p7R+pzP\n52Hj8Xgcbw9lTrY3TRM9qHTh6Volj4/P5aVnI95a8JySL80bxr1599zNvrl5Z1xokRrXM5Q8r/zw\nq3FjVSh83oIVOhyXTkLRcZhNaU5gI95a8JySL81FzFMVss58eFPYMk9j4zawifHYrPDA+cj0ROEA\nAHfZMFSNG8RCx1xUGB01/9W4wWnyQ3SA+TxpJQJTKMGdgMDr1PX0H/ARCpqn6uaEC4N0qJpLJDaA\nF5sHqEK7MYD7FdH9t8bKHrp7e/SELeCV5iNb310jIJv3t1Td2/L02PindOFh7oa5ZxZu9HUJABMl\nr4j8pPeHqsIJRgCQ0ZMX1pIzWUHdf27BA77UfOh6wZcNYMn7Q9XKXr+H9x9IbECZ6qqqrtfpP2CH\n3h+qVnJvIABQsoJCVZb7++Ybh8CUGIpePdr6BZBFPfPuGgGPKCJUJXJPmJZzPut6NFTNW54Sgene\nZi2A7KIriL27UsAjighV82nTg2hyGjLTeC2ayc7jkDSPZfNfCVUAwJOKCFUh0wwrHwdd1w0tT5PF\nZ5ZyUnj4ZHs0hPV9Hy0coAjuB4S9edFSz23bXi6XpmmWBk71fR8i0ZByQuiJPmoocPjtkMlCZjqf\nz/OWpzBGYVJ4lZwwo+SlsNlWXbsDizeKfPl4T0JVVWVfmksJVdXPuSpIPCTkqrFooloqvLo1BVnJ\nrxzbcgHjrYQqWFLypbm4mg09d33ftz/c3H9IXW3bzkdNTfQ/rCy85FeObbmA8VZCFSwp+dJcbs1K\nUPIrx7ZcwHir+awK16rynoSq7Euztf9uSE8YU+zrCuxa5LvFQHW+xn6nahOqbhCbAOCVbg53fllN\n7iVUAexE9FriDz8ohlAFsAO1PkEonlAFsA/x0etAMYQqgB2IjzLRUgUlEaoA9myeq4yygjcRqgB2\nyygrKEkRCyoDAOydUAUAkIFQBQCQgTFVAJ/FHKHwJkIVwOcwRyi8kVB1gwWVAeCVSl7dL02oukFs\nAoBXsqAyAAUzRyhsT6gC+CjRv+Onf/oX/Lc+7JdQBfA5ov0mJXeXwCcRqgA+3yRX6fmDLQhVAB8u\nPsmC6awgN6EK4PtEw5NeQniOZWoAADLQUgXAD2ZegCcIVQBUVRXLTzoE4R66/wAAMhCqAAAy0P13\ngwWVgW+2an52yGq/09UKVTf47gC+mW9AXs+CygB8nKXprNwkCDFCFQB3mLcSXKuF+wQlLb6MUAXA\nHeZdM3Vdx1fCgS8jVAGwifnYF6NU+WxCFQDPiuSnWYQqeXwxZCFUAfCUePvTbDy7oVd8PJN/frgP\n+NPQIZRg74ew9/pXezyE63Xyr66q+b+q+hG/xv9Ktb9XYeYDDqFkWqoAeJFVg9zr2oyj7JRQBUBZ\n4unJ5FgUT6gC4J3W3CQYbdDatFbwAKEKgLdZ36lXz4e9Q2GEKgBKF59c1L2EFEaouiF9o4SBkwDv\nsTSPw3RD/Dvct3fJ9nuLolB1gw8ewJ6s6yU023vJ0q9FyZFLqALgU6wMRnUd2U9nIk8z+WcGK1Pz\nW3ZbySFs+qR5Syv2Vdh7/bM/qUN4zCvqFpuYdL5xePz433W2JRrFvAqb7laybwxVx+Oxbdu6rtu2\nbdv2//7v/95dIwCKE58C/lbwWp+9+DyzmT8+XTQI//nPfz6dTtGd15wfu33eblVdr2n2L/kQXr9b\nsRWzm90y7jbf+NSsWgUf6d53e4tya7aF8HlomqZpmqqq/uM//mPY8vvvv//zP//zfP9i3y5223Q3\noeqB3YqtmN3stt1u63us4rNCFHAIn7rbW3zRQPXj8Tj8MM5Pp9Np+Ej8y7/8S7EvEgBlil44otlr\nHr/+3kt48yli0c0Fq0xfNKZqaJRqmmbSIvX7778PPxhcBcAWrjHx0fGx8fLX2b+1A7liu9U/W7lb\n1HvPaoG+JVSFwDT0+o2FjBW6AgGgEPPsFR1B/3BEq6JFxZJc9N88kM3FA98n+pZQdblchh+iA9KH\npBX2AYBiRdu9Vj42mr0ebkVbmcaqeWnVU41txfq6UBU1b74CAG6aZruF7DV/2NrZwtaUVoxvCVUA\nAJv6orv/HrD+1gy7fdhu1eo7pe323me0m93s9oW7FetbQtXQ/bfUzRe2/9///d9P9wa6Z/WLee0B\nuMu3dP+lh6KH7fP5PwEA1viWUAUAsCmhCgAgg28JVelJE8xQBQA86etCVVgBcCw9jB0A4KZvCVVh\nBPq8USqsYPPnP//5pXUCAD7It4Sq6kdmulwuk4WT/+Vf/mX4IQSvruvatq3rum3btm37vn9tTR8x\n1Da9T9/340Pruu4VNbslVGlNrQo8hEmVbr5hCjyEia7r6rpOVKyoQ0iv9hp9LYqqf3BXrco5hPDh\nTZvXsJxDGPuAz/IeD2GL69fkyvK663h0CaFPFTr4mqb585//PG6a+v3334d9omfpeDy+teI3DH2a\nTdPc3KeoN8BSlaqqOp/P6/d/ecX/ITHzWXT/Ag9h4nw+7+sQlt5CS2+k0uo/WHojRXcu6hBWjpqY\nvJ2KOoR0lfbyQUhUaen6VcghbHH9uuulzKteevpP1bbtvAfw999/H5qpwlyuTdMMwTkswHw+n29G\n6XcZqt00zVIY7/v+cDgMPw/vzr7vw3l4y3tgUqXhL4lxrSYnvMBDGL+XwiGEN8z85SjwEObGH4H5\n26m0Qwj1Wbq0D3+tzvevyqj/ILyRwtdOolalHULXdek2gKFu449zaYcQrVI1+vLfxWe567pQ4fkh\nHI/HSdNOOYeQ/fo1/0AlzkN+Lwhupfn999///Oc/D41VoYHqOsrCk79uiz1X5/N5nN8TMTx6CKFN\n4i3tcEtnNdRq8qtiD2F+2sMFfs0b6b2HMDGOJtG3U2mHEN7/K/cvrf6JZw+HNtle4CEkDO+oyXup\nwENY+jra0Wf55iGs2f+Vh7DR9Wvpg7N0HrIrLii80dJLG168aJ/Uu1QzS2/KRP1f9j5bqlL0ozuv\nVYGHkLicR4+uwEOYGMfZez8IBb4KcwXWP/3U8xeizENYEmob3VjUISS+jnbxWU5/EAo8hGom1/Ur\nFLj0pFvnxYI+ge+VvsynX/W3WP+mTHxIlhrntpb+Cph/inZ3CPP3UoGHMBHqsPR2KvAQog0h6Z2L\nqv81+UV//iFsKfMQlkTrU+AhpP9snn8cCjyEcBWI/nZe4bcfQjWT6/qV+EClz1IuQtXfpa+RBf4V\nOJa+tKTfsom34HbuDVVlHkLTNEtPOq9SgYcwr8BQvaWqFngIdz1pgfWPtuUkFHgIS5a+lMo8hJtX\n4sI/y+kqzb9vizqEjNevdD6+d7TAY75oSoW09FjLYoeor1fUIXRdN/5IzH87/DCpc2mHMNziG/3V\n8MO8wkUdQhAqvOau42IPYXwDf6KSRdU/eivAIPGoog4hKowjXjqQ0g5huK6H4cxBqOdePstRe3kV\nElZWNRzpGw/tl3c9MUUpZy6u8HW88lbt8QM3qdA9zz6+gXG4H/CBQl4s3LE4GVP1cGnPF3Kv+bXw\ncrnUdf3AHbsvrv/46eY3Jj92p1IJn+Wh2vd+hIPXH0Lf98M9aHVdR28ZK/yN1DTN5XJZWm/tsXXY\nSngjrbSyqm3bDq9p3/fbpS4tVR+uhOS+3vjW2VDzXRzC4XA4HA6n0ykkqvHlsORDGE74zctGgYcw\n/iZtmmZo8D+fz+Fafjgc9vIuqut6fuU7nU6T+SCGH8o8hCDRTFXyIYSG88vlcjqdxpPplP9ZDpWZ\np/B55cs8hKh7q1pCEBSq/i7dOjKeYeVVNfouw58OIVFlaTV5pck7nTXHDgAAC3dJREFU53Q6FTK9\nctrwxm6aZhe1XXI8HsOfnsNsYeH9U/hxjYNU9C6/y+Wyu++cENPfXZE7hJaqwfjjfDgcCn8XVaP3\n+RDEQ3gKbTO8jFD1d8OnaKmZdEfRfknJX81DnAq59nq9Rs9zyYfQ9324HIbxGYm/1EvQdV164EtU\nOYfQtu1wzufXvLZtlz7R5dS/Gl28JzMfDoc2/Bz+0giKOoSJULeby+xsX5c7jP+cu16v4eMcPsvR\nZXZeW8cbxkH8cDjUdR2+VEPAnXyvlnYICTuqqlD14QpPgcMfiONZ1OcfnsIPYWK8yFT4pi7zEO4a\nSlXmISRMWpcLr380goRr4S4OYZAeTVXmIYSTPx+EFz7Lob2nzEOofgTxyZkf38873vN11XrOvVUt\n4dCEKqrqTe/FruvGiw8sNVCtVMLHKXis7+OVhzBOfpO1b4ftw1jv9MrKc+W8Co/V5F31jz7vvg5h\nMPyB9GR/2YsPYQhMYXz6RPir467Gkne9CqGNbfBMA085n+Wbiro3UKj6u5WjdPertEMIN50Nf0ut\n+RYu6hDatq3r+t4qFXUIK03qvMdDGCuq/o99uRd1CGOJyUQmij2E9XZ0CENV582HuzuEm9Kjn190\nvNlnvtqp3c2oPpaePG3NjLTb1i/mrlNa4CGk6z+vVYGHcF4QDu2B6bxfV/tbUxTu5SVIPO98wugC\nD2FszYe6wENIV/uxBR42q2zc+Xw+Ho/r5yIu6hDyXr8SXwt3XXQeJlT9w9IZT0/SWoL0m/LtyzzN\n3fu5LfAQ0s9b/RC2FHgISx74ILzlED7jJUh80e/lEAYrvycLPIT0885/W+AhJKoU/bIt6hDyXr+W\n6p9uN8molO/xEiyteTT/aitN+k15XTiEpdW8XyC8749J44eUdghr1sgr/BCWLB3XtbBDWPMSrPks\nv/clWHr23b2L1v+lVNohjO/VmPwq1GryHivtEJaqlPiMlHMIea9fS+Fp6SXOrtyg8BbjO5yH63p4\nJYptprqueFOOvzWG4xof6esq+kO1zvicl3YI159vtGl+GG+Z7F/gIUQlqlTaIXzGSzB+z+/0EK73\ntHAUeAjjr/r5G2l+UAUewrhKN+t/LekQsl+/3nsdF6qmJu/Fl70Sz7j5prz+/L4cf/ZeVMWfzWsS\nNTntRR3CIPpuSdSqwEOY29chjL8xx5b+1C6t/oPoG2kvL8HgrmoUeAhLb6QdvQr3VqmQQ9ji+vXG\n63h9XX2F+x79D+0P765RNsPtObs+tAIPIVSpqqq2bW/eyVjgIdyrtEMY16dacUt/afWvfl5K2bvo\nXT7gVbi3SgUewpIHDm39S5mLUAUAkIF5qgAAMhCqAAAyEKoAADIQqgAAMhCqAAAyEKoAADIQqgAA\nMhCqAAAyEKoAADIQqgAAMhCqAAAyEKoAADIQqgAAMhCqAAAyEKoAADIQqgAAMhCqAAAyEKoAADIQ\nqgAAMvjl3RUAgE/Wdd1kS9u2bdumH9L3fVVVl8slbGyapuu69AMfFip5s273Pqrv+77v7yp28til\nJy1Qfb1e310HAPhYdV1PthyPx6Vk0LbtOEhFJR7+sPHzrgwGfd8fDofh5/P5vJSZhsN/LGx0XXc6\nnSYbS84tuv8A4P36vq/r+maiqqrqdDplb68ap7R549DNh2zUfrY7QhUAvML1h3k707jVp6qqpmmO\nx+N15Hw+H4/HsMPlcpk3gD1jnIpWNoOF/Deu2MRQVGKHtK7rwhl4rIQX0/0HABsK6WfpgjtJVImu\ntOrnfrq8/YDjvrab2eAFfX/RonKVthGhCgA2dDMNjNuc0olq8MD4p5VCTW5WY2UC+7ZQpfsPAN5m\n3NS0JlFVP4952uhWuJvFhkS1Xd/fHglVADvW/xC2DHfd13Vd13XbtpNBx8PN7eG3iWvnvOR7d5hU\nJlqfdIHra5tw8xS99xb9kE6aplk/3LtpmsnDo8Yn8Ob5r0YBKD1efmWqG3ZLTLUweRVWDpAv2hWA\n3QpXwfP5fD6fo9/zTdMMO4cr8cT5fF4qdjJceiyUNn/4uISVzzg5lkRtE1VK1/N6vS6doqUqZRGe\nIv3beytw/mHpt/ee//X1Gb+4iRou7ZCu282KJZ7x7bRUAXyCyWDnscvl0nXd+Hb9SV5ZeuDDotML\nTZ4x3SyRqO3pdHqsSSNxitZUaQvPzErQ/jD/VfpIq+TBhrOdaIJa3/c3T8Y365b3rsYXE6oAPsFw\nnRu34ozbA8JVcGh+6Pv++nN7Q8YusL7vx4lq3OAxrlI638xru/KxCZNiB+OTkD1c3hSSzVKz3GMm\n9xLedf7DO2GpB/Cuvr/5Dkt1G78QO571aovmLwBeY3wpmveLTfpZ5g8P1/LQRTgp9oHuv/B0kzLn\nO0wKnzR7zB84PpylWiXqmXjgzTo/I/HUidfuYePjTddn6UnTO4SXKX2uohVYer8N0q/vAy/961n7\nD+BDRNeYCz9He2rWLIpylzXNGMfjcWjKOp1OS/tEh90834CxNJonVCnv2cgl3Y446QS8OdvC+Xwe\nmouWzn/TNEMh0S7C0AyZeDmGB86b30Ldos87LnAYxr5UfrneneoAeNzNZoPwbR8ddxxCRq6Wqrua\nMZYem7g2PXDxutlyk6hSFolnX/Ok6WHdGc9/9BnX/2psOOeTwte8vok33gMv/esZUwXwCdbMGPmC\naoS2jfTThaATbQvJO8BoZbHpKm0t15NmOf+JJWtWDgIbWqQmdQiPTQ9vH9LJe+e5eJjuPwCyCf07\nz2S4jfJfutjQE1paqGrbNppC+r6fd1aGLenus5u9nEs9gKHv7+YQ9UTf3wcTqgB4tbckmALH6ITs\nknZzBNLcygSzdP67rhvGXS2Vk3j2obaJ1FXgC5GL7j8AeM+VPjzpu1pxlo462gMYfnig7+9LCFUA\nPOKZHLBy6E9e6Vaxt/T6Te53u+ux6fO/crh9oj0p9DmGLr9n+v6+hFAFQDbvHe6dtjJUvTLnjZ/r\nrqlHb86H/vz5f6z/LrHe38q6rVlTslhCFQDZhKtp+qKYZTz7vdJNO2+pUvXzrXDrb3lbSmArz/+w\npPT6I+26bmXf39CalR4ElqjbMJbrcDjs9O4/oQqARYmBzNHt40FCax774gSzVO1CqrRyWcNEDdec\n/2EdocvlcrMDd9wD+Hzf33gBnETdhh92OiRLqAJgKj2AerK6X/SB1XJryprleDcSrfb4cN4yGOg6\nmtnyZiNNehL8tm1vrogcXpebB3vvXYeJvr/JM0bfG+NJIrRUAfAhxtfFuq7Hv+r7Pj36Zzzpdl3X\nk4VrxqW95cJZ1/X4ecPcAYN3DeWZrH49VHI8tGiYd6qu6yF2NE2zFEnHDUKT89/3/fj8rznYSfB6\nuO9vUrcq9t5Yn/bKtd1k7QBs7eZ6Mulv+6Vlaq6z5Y0nxhf1+Y1ma5qgEo9KrI3zwMVrvIjvvVXK\nYmWdby5HE4Tzs1TtNUWtPNhJUelH5TrMROE3y38vLVUARHRdt5RCjsfjzXHQiWvnEOBeP2gmXavz\n+fzecTxt215X5NHz+Xyzha9t23R2WX+wk91uzvl5s/7pug3vjTUVK1O969oDsKlwf/vlcmmapv1h\n/cOHa+1jD88iDEIKSWLoWRuqVP24FW67CoQet/UX3Ml5q6qqbdsHektfef4n/ZXZ6/bAaXw9oQqA\nTzYPVS+2izRQvl2cRt1/AAAZCFUA8JNff/21XuHd1aQ4QhUA/OQPf/jDu6vALglVAPCT//7v/x7f\nJ/8///M/VVX94Q9/mNw/f2+xoYlrpzNbvt4wsdmO2gWFKgCADH55dwUAYENvX0VuPnXT26u0F7s7\nUaZUAICU//3f//23f/u3P/zhD3/729/eXReKpvsPACADoQoAIAOhCgAgA6EKACADoQoAIAOhCgAg\nA6EKACADoQoAIAOhCgAgg/8P+3NRIq7ffWYAAAAASUVORK5CYII=\n",
       "prompt_number": 54,
       "text": [
        "<ROOT.TCanvas object (\"PtDistributions\") at 0x7fe2a9154900>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "canvasPt = ROOT.TCanvas(\"PtDistributions\", \"test\", 800, 800)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: PtDistributions\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "canvas = rootnotes.canvas(\"AllAndPassCanvas\", (800, 400))\n",
      "canvas.Divide(len(ptBins),len(ptBins))\n",
      "for ptBin1 in range(0, len(ptBins)):\n",
      "    for ptBin2 in range(0, len(ptBins)):\n",
      "        canvas.cd(findPosition(ptBin1, ptBin2)+1)\n",
      "        # print findPosition(ptBin1, ptBin2)\n",
      "        hAllMap[(ptBin1, ptBin2)].Draw()\n",
      "        hPassMap[(ptBin1, ptBin2)].Draw(\"same\")\n",
      "        hPassMap[(ptBin1, ptBin2)].SetLineColor(2)\n",
      "canvas"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": 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75omxeRvqH5FiK3JX1jKGdA+WN6TaHPooIuaCGrxQ2QE6pXfeufk4fIivJOtT\npaARlpI8PfHOdk2/BJERSZti22Kwb4OnQ8mv4lubG7mV9Pn+1zYHX99fPbC+R7NSxFxQQ2Kies15\nWZzWUaY67dSC+B3pbJKpyFzmgjDOzbJfTBfNts+e3CfijOFlWWPu2YSnPp083Uv4rDY7l+k877Wh\nb9Y/ndteVrMpmFs8X96kdjXhxZfdxo2qxLRVuh5tbyY8m7Y4obtMDVnfnG1uF1yeN7cffaY4YbFH\n/cxVeIYoXnI27rwfXSazC9Wcf3LnJ4Qwz/ELf10Ib7/mIjYrgPMcUni36278hukz6br4mfZYC8yX\nuXdhPDC0+JTe/vC9znkcR4zezPSQ5vcpl2dduPf3vpU/r/+6WRayH5GHEJfn8G3n0+2dA663T1XL\nBz9THGTx9eJR2iZXsUSDRtU///wTN/LzMm1v7uyx/bKMzL6dBcncT7YuSJ/7syDn7PN3tVVWBSwO\nsti+XC7rmiKE7E3mXzk8TPjo4OvAJIx+Ocnc5Mfar0f7pxA+/u//vhKGMD39ufco7XzqV9o+Yu7d\ndpZ12s6/0Xw7ozYaAc7NDSH8/fffYdFciPSpAL8Onuuc73zm146/+wmTs1974695pvnO1ckQ8j23\n/RvfbvUxc3OX53CBoZFTFeAtyaLg5L4sTE/mpo8tPn+XNjtiOsgigLjz+bfID3Xm2xU3mhvMqfrr\nr78ul8vb29vH+Xuyj4+Pt7e3y+VSkO+PHz/+/PPPgrTFCcMt4BDC2eQfHx8hhIIvGxO+v79/nErW\niGRufsN90OZNc+cH2wt+/Pjx8+fPPO3mh9c71wmP83pzg6m/m+YeZA7h4+Pj58+f4faVjx+i2KMm\n5obvYW4I4b///vv161dBtRwqauZYu85bCZ/GUezvo5J7pNJI/m7GvJP825q7k3AdTdoTzY2l71TQ\nHx8fP9/eLsfMXacNIfx8eysr9WXmMq8cAAAAoAG95lQBAAAAfCtoVAEAAAA0gEYVAAAAQANoVAEA\nAAA0gEYVAAAAQANoVAEAAAA0gEYVAAAAQANoVAEAAAA0gEYVAAAAQANoVAEAAAA0gEYVAAAAQANo\nVAEAAAA0gEYVAAAAQANoVAEAAAA04I+Gx5qmqeHRYME8z4a5Y25vDP3F3N5g7sBg7sAUmNuyUZVH\nME1T3E4bvbdflpHVt9tW/OXM8ywli/eMdJwNx8x9tD2AF80zGsbcR9uOvOw+iasAABWCSURBVGie\n0fDmBgv9TTJta26v4b8UYtrY2d5MeDZtcUJ3mb6S6/W63vk02h5f+WlGXTMd0txNfJWFrpl6NzcV\n3nmeYzwUwIZnVFLVhBeYGx7ofyqhi0zbmtu4UTXdOJWq5tQsTusi0zI9G7JuVJ2NR0HnUzG/2Nyy\nvJpTXIm8vgDGhGXqvThakevulHHqCIa1q/J1pFjPhrg0d5qKHXrldaTS3KlJmb9er+/v73kfGrTF\nXNt0hnm0OAavGbmCsAoxnEXZ04SCsOZVRxku/A3W8jozNxYHPwGXydvSEmcGu8JcW/MAatCvoKma\nz6LvacLc3LjhQquEvr8KwirEcAI/jaoaYdsP/7U9IAQZVaWGq4bBStXL5ZKP7SqMaIyElJ62o5Cj\nIqKqSBiDUaMqPVU+MNfW2S3RPcp3vQrCKsRwFhcxKwSpEEMBymU2oiCsQgwnoKcKYPPpPxgPd/e7\ncwj64YqoKhLGYIioKhLGYGg9/df2gBBMVa1/+s+GaQryQUopKRXMccQvJk5VBUdwjvXA/um/FAdN\n5k6Ya+u6n1l5KEFBWPOzq4RpCvP8+a82TFQvQLnMRhSEVYjhBAz/1YQCDRFRlX7mHoioyv1uD0RU\nFTnHBsNQ1XwAAXN7UKNqr9fUQENmjVci6N87ekTB2YCtfZhv61jahkHJ7YG5rbFplYeBxU2odJbh\nPx+Ya+u6n1n5omIlbFyw1zaGKm7Df5Nw2ArCmlcdZSiX2RzzsV19ib7wM/wXKZO3dvgvNpYvl0sK\nQqTHewzQEzpxvV69PoXgASklpRbNekqMc/l05ysfPXmWl5SeImEMBhPVx8dK29SfsTjD1I1e3BJJ\n3iHpSOqy5HroqYpYyXu5XC6XiztzP/uo4i8mRfhMXvRUnUCyHt6hTN7Gc6pgeDyVYW1EJtxEvIy2\nrFGO2NbcfAAh+DRXGUruwNSY27inKuBuBxSE9X1LJH+HxP3uadJiCvKrKmDuKeipOo4zc+Xr4QUS\nPVWeDPYDT/8NjIKzAXP7gLkDg7kDU2Mu61Q5AFUBoBiWMuoBqsIm9FQ5QKSnCgaGktsDkWlzdGb0\nwNzWCLb2oKbk8kJl2CPNdU3oP8GbwhMPVSo8qWCGQURV+lR6YKtqWg9Fan2HbeISFZvV8jQFvcgr\n9WzcU+Vs3pwTDM+5daPKkb/iPXwi3RgRR7Y6QspiGBIfJXfxwIE2SVKJniofBntDRFXud3tgperl\ncskX/3Rwv3vDUZAioYqEMRi2quavQ8Dc5tSoyuKfPjDXNp1h+hZP05Sex/7UTfhRXgVhzc+uc6TK\n7rakgvj6n1byxpV7nZnLkgpn8GFuvujJrb6b03ooIciW3zJ527ymZj2+W3lYiKAnvAZnp9k8azaR\nF9iq6mnaTcbdC2rup+O8kh25Pv9kFNh2MNAUeqrGx/ZmN2h0qByEniqPMZxgsdon5j6LwYetibwn\nMjf3Zau83rpPwr5xAr0sPsylpwog4fGdu/ltrmJJvSGlpOMJc8Jhi6gqdaZBWzC3B0I9VUG1yeka\nBWEV7rmPcn9jtLzfFUNBWIUYTrDusVB9U42CsD46M3LoqTqMwgn2HIc9VTXCsvinA0SWBsDcHog8\nde/dXM2mg4i5Cndl0Als7YHQ4p/m1ceQiKhKP3MPRFQVCaMYzQuLiKoio5DQivz5MF9PIbigUk8m\nqvvAXFsf/cwRhv8OkB5BCAJn1zk2h4FURwCDtbyeSm6E4b/D+Ci5Dof/ImXyNh7+AwAXxNvcvF2V\n/qRZwflCqtsAQweGsd0e1JRfJqo7QEHYxUkm6vJiWcgg3VOlI6mzzgw/PVUKwirEcA56qg5DT1VX\nJJZU0JTGO4aqbr77D5ebIKWkVDDDIKKqSBjQA2ZT9YA5VeNjrq15AIdw1VOVw7SbE9BTdTIGH7Ym\n6Kk6jA9z6ak6Ba+p6YqUnjqRjISIql46MxS0Oo6tqqKvqXnZ210WGW3+uv65MadHSh8l1EDF1rGo\nUbV2onqc6Jqmu7qolx2R9KTYQFcU5u0dQT2+e0SKrb6tNuT92SHrGAtZ39ijhBrOgiCsU+UAQ1XX\nr6kBsGLaH8ZV6j9QIH+0k5p5VLz0MfuiRlUmqjtAYQQh4a+CvkWrFrltPAtnXVTNe/HN81fvgozL\nIqqKhAE96Du2u1+aikZCP8dV70dapQZVK/Xkhcqwh7+n/9LFNf2qirmSebtKa9rNDsKG5kjpKRIG\ntCWW3znDIIizmcb6ebOKlinalXqyTpUDFIT195hJvjP+H0KwlvER5k//acqy5OnzX+vHPwXA3Due\nPorb6um/RUbrXzfnVKV/02c2E2a1ysjP7T4dcM/l2jnIo8d1FwrLPMNbIywvVHbArPFCZYW23Xgo\nOBswtw+YC73B1h5EVcvKL6+pAQAPVbNGA+UUNVVz8zBgSGgx96CmzDZuVMl1NQ+BeaUMw+OjahYP\nb4VIyfVhLhSBrT2ouR3i6T8HfPen/449FfI0qq/V/DRg+nB7FnNgIYQg8EgE9INqpAeWi3/C94F6\nuS25nubVIub2QGT4DwaGktsDy54qXlPTFXM90/qBgVuiPoioKhLGYIioara+w8GVhx68KOb5Jx99\neP2n9OuRLPazvr/WmrdoRM6xwbBcp2r9mhq6mhuCnvAaOM16YK6qxFJGR0jLF+U/4X4899H22Swe\n/Wn/M9mH5ywMk+bMoi8j0LRqSqWSrFPlAAVhuy+Ispt3zPjZp6Z582OLEiJ2floJG2vkeDu0qERE\ni/DxNWwEVrvRkVSu5K53nvLryMpVufjrTx5cXSn/ZE46oMDSd92fDPuu61RFyuRt3KgSrY79Y66t\nXNW88SkaVY5j2OehuQ8+vXEtPJ68HQrCWlYdozeqwjybr+x6C4rFP1sitPgnDE/3brNFMb6v0Zbn\netnkhpRq3louObzo6ivVXS/bnAohhNiiKmB1ktz92v8ri0xUV+jqHhJzZwO29kFoSQWFk2w8bFVN\nI/dmMzPus1vmvp6Hcf6YJkjNdBltTsb+d3mV4CKqipxj46Ggqsg5Nhg1qjL85wNzbV83kLHuOsq6\nhaMOX2oc7MPPme/f8GXXU3XL8HuPEO2wHsQ5lfBBZ+fnn1440DD4CNFu3jHjJzvdDv8xp2rU4b9I\nmbwM/wGA6ghRWTwy30KkC0HO1lGYBd7KKlpynVNjK6+pcYB5uYXhkSu2Lzjn021xt+5J5lSNjbmz\nAVv7IDSnqszgmlOzfODTT6aui40jnStRqGGL2Z5DMD1fvPHot56y1Rqn1RKOiz9FHpz2VWdUOmxq\nTqWjPYpqerzU5DN9ROa7FM+p+g61a01ahZq5+BxzpPPrMzWeU3W9Xj8X/xSoPkbGogArmDvtPtn3\nNKzNtPvHtOHl/r7Y3FzztJ0yXn/515xwizCe7izNxq7weqiWj4eYW3NK0/WZVlx1LMP4xuYerEjX\nUW5WBXJ1cigpuQ16qr7eZDJvrYq7+3P999+CVJVpazI1+fkM2AIFc+fdTy74999/F3sepTK31dzf\nGnML/J2zhHO285FNa9bm1rOZ185OL+Z++fsScysTHvc3/3xBFvPWnv28No8Tt2PA39ncgy6szV0f\nobiIdfopLrn/c7lc6g2OB3l7ezub8Pfv379//y5IGEL49etXWabFCUMI1+u1LOaahCGEYpXqwdx+\nCYO1v8Xmhgp/Mfc1+DK3Jm2lRx5rZsztmrbGXOaVAwAAADSg8UR1AAAAgO8JjSoAAACABtCoAgAA\nAGgAjSoAAACABtCoAgAAAGgAjSoAAACABtCoAgAAAGgAjSoAAACABtCoAgAAAGgAjSoAAACABtCo\nAgAAAGgAjSoAAACABtCoAgAAAGgAjSoAAACABvzR8FjTNDU8GiyY59kwd8ztjaG/mNsbzB0YzB2Y\nAnNbNqryCKZpittpo/f2yzKy+nbbir+ceZ6lZPGekY6z4Zi5j7YH8KJ5RsOY+2jbkRfNMxre3GCh\nv0mmbc39OlY9eWTQFittr9fr+/u7YQDfBFt5MbcrtoUXc7tCyR2YMnkbz6mabrQ97LfFXM/r9boO\nRt3faXr4o4S5ni7NDUHTzQUKeiZ/FYJ5iosgIwqhXi4XnWCOoB9holLPNu1cbol6Y65tOsMcWJwK\nQww1/jrPYZqCXvCGwubdkFYxlBB9lHRzgYKwCjEcYXMsRhkFYRViOEgMVT/OSI2wjYf/yoKAfRSE\n9VLTheCsURVhEOEEfhpVEcw9grtGVQRzD6JwFTtLmby9JqpDQ0QmRXosFfooOBswtw+YOzCYOzA1\n5jZuVMHAUG57kO7OrQOBYaHk9kCk5GJuD2rMbdyoctQb6QjzchvhlqgHIuZiaw9ErruU3B6Y2xrB\n3B4I9VRhbQ9Ehv8wtwdcdwfG3NYItvZApORibg9qzOU1NXAUR8/EOsJQ1fRUdgKLWyGlpFQwwyCi\nqkgYg1Gjavt1qtoeEIKdqvk6RiGEeZ65K2qOoap5o2rOMAlmMMz1zAsvtvbAVtXkL+b2oEbVxo0q\n3O2Braq+lhB0hJSeImEMhrmqsfBKnWkDoKCnu2p5DsFL46BSz9p55XH9wMvlcrlc8iBoXTVBR9IU\niQNnXa1TpSCsQgwn8LNOlYKwCjEcYfK2TpWCsAoxHCXVwx6oEZZ3//nAXFvzAE7gqlEVsZXXk7nB\nU6MqgrlHcNeoimDuUVw1qiJl8rJOFRyFB8R6INJpj7k9wNyBwdyBqTGXdaocIFJ6cbYHIg9mw8BQ\ncnsgUnIxtwdCSypgcA9EVNWfC+kRQ1V5QKw3IqpScnsgoqpIGIMhtKQCDIzIFWIwRJ669/IMUcjD\nOxvqNJ1OUoqUnpTcHoioKhLGYNSo2niiepDpVhkJBWH9PWYSfExUVxDW06h9buJZQ42myprPZY4b\n4ha7m6iuIKxCDEdxNVG9RlheU+OAmdfUjIvIzAyFhvt4mNsawdYeiJRczO2B0JwqGAyXI0ROQE/o\niq/1IeNFLEzT50bkhSO2x5HSUyqYMajUk9fUOEBKVYbwWyGlpFQwwyClqlQw28zz5482CkouXtyp\nENIwVCrJ4p8+MNfWPIATuJpTFWHazVFczalSENZNyd10Vn4ijnnJ9WFucGDlmjJ5a3uqYg/zup+5\n8rAQkdJTJ5KRQFXoDedYD0RUFQljMGpUpafKB+bamgdwAnqqXOV+Dlc9VbdsMfcA9FR5y/0c8lau\nKZOX19TAUXhArAcid5l5GIIWf42j2cZxGBFbI5TcHohYjLk9qDGXieoOEFGVuZA9MFR1Mdc1YHE7\npJSUCmYYRFQVCWMwhBb/xN1OmGurMOX2KK6G/xSENT+7nnLXU8Xw38nc44a4xe6G/xSEVYjhKMJW\nrqkRlnWq4CjcEvVARFWmu/ZARFWRc2wwbFVND4dhbg9qVGX4zwFWqj56tBOXm4Ce0I+88HKmtUVB\nT6rlflTqyfCfD8y19dfPHBj+OxGDuK0M/9XkHjfELWb4z2kMRxG2ck2NsI2f/tOvnT0icguCsz2Y\nNd4gxjNEPTC3NYKtPRApuZjbgxpzGw//YXAPRFSlh7kHhqrmL3ZkZkYPRFSl5PZARFWRMAajRlUm\nqsNRRK4Qg2GoqqO3Zc+3nyXxhbvrH2uk9KTk9kBEVZEwBsNyojqvqemKlJ46kYyEoaou16lah5f2\nKEUupSQltwciqoqEMRhMVB8fc23NAziBq4nqEfO5zOrmLjzNd0Zn843woIH1XSeqq5sb8TZRPYK5\nR5G3ck2ZvLymBo7CXOYeiNxlYm4PMHdgMHdgaszl6T8HiJRenO2ByDNEMDCU3B6IlFzM7QFP/w2O\niKq95t5Wziw+lTx9UmA6M3OZh0dEVakz7eizBfnO/DMmJfc+UwU9vS7+uW+0BpV6sqK6A2xVzZ8R\nCzLXiSfM896MZg2klHRTKbvCXFXpVTMWweS/5uU3bitFLqekZEgb6EcYQqjuxWCiug/Mte0YQOUE\nxnXyxYT0/AOqE2BN/L1er+/v72HVahcqxesWiZOJ6jqSmlcdS3KPcpXWQS78ioXXpOQ+zpSJ6kdJ\n9i0CFquKc5ioDn1hRmQPRPqHdG21uo7WketpbjEltwfmtkYwtwc15jbuqQq42wEFYemp6or5/W7c\nkCu8+50Twj1VtwzthZXrzKCnqh1y5u5AT1UZbgx2xTzPCndFCm278VBwFsaGktsDkZKLuT0QWlIB\nBoZy2wMezB4YzB0YzB0YyyUVeE1NV6T01IlkJERUFQljMERUFQljMERUFQljMGpU5ek/H5hry5yq\nrpjPzIgbcuWXOVUtYtCylTlV7ZAzd4dvM6eq8TpVMBKPuiG5MWqClJ4+1rnxhqGqeeGVOtMGQEpP\nqWC+2FnZdedPncI4+ZlKPXn6zwEKwtJT1RXz+938V5tIFp0W6/6MUz1V8e9fxztm+vqAj8Lb/7CI\npLdItOpkeqoacb1e//d//1fL3ERuVrh5nVsZVsW8UxhPD97aXF5T4wARVeVuhobAVtXUDTlnWAUT\n5vuFs+dn62inDyw2soRWX0dCzxuU3B6Yq5pW7sXc5tBTNTgKwnacHfK9e6pEpt2Yx3BnzaP72pMH\nnG7Lkby+pyoLIf7xe5ubM0pPlYKwCjFs47ynqkZY1qlygMg6VZjbA5EHs2FgKLk9ECm5mNuDGnNZ\npwqOotBhNh7mlXIEW3sgct2l5PbA3NYI5vagxtzGjSq5SZFDIFJ6cbYHXHcHxtzWCLb2QKTkYm4P\nLBf/3AzlLDXnZflsMj+ZihSbsrl7jnQ2ydT7PNPXe+RaLhOKz7HvUACL04qUXMztkbbG3F7rVKWA\n8sgebW8mPJu2OKG7TK1IjzI9jfbsV370madpW2n1NNN+5oo8IHbc3NC5LBw5ZnFGp9Jibk5xAWxS\nubU6c4rPqPVnhjc3VJSFI7ygABYEXG9ug0bVP//8EzeisfEnbW/u7LH9sozMvp0F++a2kSVSKcvq\ngNON/NdQmukrjH45Zeb2kmjh0a12yyu+y+WSdqYPP9pO3+vEmZZSrWTJddv8sJq5IYS///47NDK3\npekPmKZp4W/a/3UO1FUX5d9oP1MLXmBujdHhvuTmezabMr1q3ccHX3ymobkN5lT99ddfl8vl7e3t\n43zL7uPj4+3t7XK5FOT748ePP//8syBtccJwCziEcDb5x8dHCKHgy8aE7+/vH6eSNcKpufMq4ZHo\nX29uMPW3xtxQ4e8jc+dnv/748ePnz59eSm6wLrz//fffr1+/RMxdc9DfriU3uK2Zxc0NK+N+/Pjx\n8z7twdDdmcu8cgAAAIAG8O4/AAAAgAbQqAIAAABoAI0qAAAAgAbQqAIAAABoAI0qAAAAgAbQqAIA\nAABoAI0qAAAAgAbQqAIAAABoAI0qAAAAgAb8f9Syp8DgD5QSAAAAAElFTkSuQmCC\n",
       "prompt_number": 140,
       "text": [
        "<ROOT.TCanvas object (\"AllAndPassCanvas\") at 0x7fd976c9ed30>"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "canvas = ROOT.TCanvas(\"AllAndPassCanvas\", \"test\", 800, 400)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: AllAndPassCanvas\n"
       ]
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After filling the datasets, build the combined dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "combDataMap = {}\n",
      "# Construct combined dataset in (x,sample)\n",
      "for ptBin1 in range(0, len(ptBins)):\n",
      "    for ptBin2 in range(0, len(ptBins)):\n",
      "        combDataMap[(ptBin1, ptBin2)] = RooDataSet(\"combData\"+\"_\"+str(ptBin1)+\"_\"+str(ptBin2),\"combined data \"+str(ptBin1)+\"_\"+str(ptBin2),\n",
      "                                                   RooArgSet(mass),ROOT.RooFit.Index(sample),\n",
      "                                                   ROOT.RooFit.Import(\"all\",datasetAllMap[(ptBin1, ptBin2)]),\n",
      "                                                   ROOT.RooFit.Import(\"pass\",datasetPassMap[(ptBin1, ptBin2)]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 145
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Simultaneous fit"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#%%rootprint\n",
      "# Perform simultaneous fit of model to data and model_ctl to data_ctl\n",
      "\n",
      "# Use this for minos (better error estimate, but takes longer)\n",
      "# fr = simPdf.fitTo(combData, ROOT.RooFit.Save(ROOT.kTRUE), ROOT.RooFit.Extended(ROOT.kTRUE), ROOT.RooFit.Minos(ROOT.kTRUE))\n",
      "\n",
      "# fr = simPdf.fitTo(combData, ROOT.RooFit.Save(ROOT.kTRUE), ROOT.RooFit.Extended(ROOT.kTRUE))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frMap = {}\n",
      "for ptBin1 in range(0, len(ptBins)):\n",
      "    for ptBin2 in range(0, len(ptBins)):\n",
      "        frMap[(ptBin1, ptBin2)] = simPdf.fitTo(combData, ROOT.RooFit.Save(ROOT.kTRUE), ROOT.RooFit.Extended(ROOT.kTRUE))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%rootprint\n",
      "for ptBin1 in range(0, len(ptBins)):\n",
      "    for ptBin2 in range(0, len(ptBins)):\n",
      "        frMap[(ptBin1, ptBin2)].Print(\"v\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.76\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.794, estimated distance to minimum: 6825.6\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.9809e-01   -9.0124e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.60e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.88\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.794, estimated distance to minimum: 6825.6\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.9809e-01   -9.0124e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.60e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.88\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.794, estimated distance to minimum: 6825.6\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.9809e-01   -9.0124e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.60e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.88\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.794, estimated distance to minimum: 6825.6\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.9809e-01   -9.0124e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.60e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.88\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.794, estimated distance to minimum: 6825.6\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.9809e-01   -9.0124e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.60e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.88\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.794, estimated distance to minimum: 6825.6\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.9809e-01   -9.0124e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.60e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.88\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.794, estimated distance to minimum: 6825.6\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.9809e-01   -9.0124e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.60e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.88\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.794, estimated distance to minimum: 6825.6\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.9809e-01   -9.0124e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.60e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.88\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.794, estimated distance to minimum: 6825.6\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.9809e-01   -9.0124e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.60e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.88\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.794, estimated distance to minimum: 6825.6\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.9809e-01   -9.0124e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.60e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.88\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.794, estimated distance to minimum: 6825.6\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.9809e-01   -9.0124e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.60e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.88\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.794, estimated distance to minimum: 6825.6\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.9809e-01   -9.0124e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.60e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n",
        "  RooFitResult: minimized FCN value: 260.755, estimated distance to minimum: 6821.88\n",
        "                covariance matrix quality: Approximation only, not accurate\n",
        "                Status : MIGRAD=4 HESSE=0 \n",
        "\n",
        "    Constant Parameter    Value     \n",
        "  --------------------  ------------\n",
        "                 width    2.4952e+00\n",
        "\n",
        "    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.\n",
        "  --------------------  ------------  --------------------------  --------\n",
        "                a0_all   -9.0124e-01   -9.9809e-01 +/-  9.68e-02  <none>\n",
        "               a0_pass   -2.5626e-01   -2.5626e-01 +/-  2.86e-01  <none>\n",
        "                a1_all   -9.9993e-01   -9.9993e-01 +/-  2.03e-09  <none>\n",
        "               a1_pass   -9.9533e-02   -9.9533e-02 +/-  2.53e-01  <none>\n",
        "                bkgAll    2.7165e+00    2.7165e+00 +/-  2.59e+00  <none>\n",
        "               bkgPass    2.0213e+00    2.0213e+00 +/-  1.46e+00  <none>\n",
        "              fraction    9.9330e-01    9.9330e-01 +/-  2.27e-01  <none>\n",
        "                  mean    8.9848e+01    8.9848e+01 +/-  3.06e-01  <none>\n",
        "                sigAll    3.3236e+01    3.3236e+01 +/-  4.34e+00  <none>\n",
        "                 sigma    2.0002e-01    2.0002e-01 +/-  1.77e+00  <none>\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 154
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the fit results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make a frame for the physics sample\n",
      "frame1 = mass.frame(ROOT.RooFit.Bins(30),ROOT.RooFit.Title(\"All events\"))\n",
      "\n",
      "# Plot all data tagged as physics sample\n",
      "combData.plotOn(frame1,ROOT.RooFit.Cut(\"sample==sample::all\"))\n",
      "\n",
      "# Plot \"physics\" slice of simultaneous pdf. \n",
      "# NBL You _must_ project the sample index category with data using ProjWData \n",
      "# as a RooSimultaneous makes no prediction on the shape in the index category \n",
      "# and can thus not be integrated\n",
      "simPdf.plotOn(frame1, ROOT.RooFit.Slice(sample,\"all\"), ROOT.RooFit.ProjWData(RooArgSet(sample),combData))\n",
      "simPdf.plotOn(frame1, ROOT.RooFit.Slice(sample,\"all\"), ROOT.RooFit.Components(\"backgroundAll\"), ROOT.RooFit.ProjWData(RooArgSet(sample),combData), ROOT.RooFit.LineStyle(ROOT.kDashed))\n",
      "\n",
      "# The same plot for the control sample slice\n",
      "frame2 = mass.frame(ROOT.RooFit.Bins(30),ROOT.RooFit.Title(\"Passing events\"))\n",
      "combDataMap[(1,1)].plotOn(frame2,ROOT.RooFit.Cut(\"sample==sample::pass\"))\n",
      "simPdf.plotOn(frame2,ROOT.RooFit.Slice(sample,\"pass\"),ROOT.RooFit.ProjWData(RooArgSet(sample),combData))\n",
      "simPdf.plotOn(frame2,ROOT.RooFit.Slice(sample,\"pass\"),ROOT.RooFit.Components(\"backgroundPass\"),ROOT.RooFit.ProjWData(RooArgSet(sample),combData),ROOT.RooFit.LineStyle(ROOT.kDashed))\n",
      "\n",
      "canvas2 = rootnotes.canvas(\"RooFitCanvas\", (800, 400))\n",
      "canvas2.Divide(2)\n",
      "canvas2.cd(1)\n",
      "# gPad.SetLeftMargin(0.15)\n",
      "frame1.GetYaxis().SetTitleOffset(1.4)\n",
      "frame1.Draw()\n",
      "\n",
      "canvas2.cd(2)\n",
      "# gPad.SetLeftMargin(0.15)\n",
      "frame2.GetYaxis().SetTitleOffset(1.4)\n",
      "frame2.Draw()\n",
      "\n",
      "canvas2\n",
      "# canvas2.Print(\"save.pdf\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": 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N+1rp/w50nudFUTSXxHFs8yrukcu03W7fvn17eXl5f3+vlFLq+zH673+//vjj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\ngu0j5nEL+wAAAABJRU5ErkJggg==\n",
       "prompt_number": 156,
       "text": [
        "<ROOT.TCanvas object (\"RooFitCanvas\") at 0x7fd976d10870>"
       ]
      }
     ],
     "prompt_number": 156
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "canvas2 = ROOT.TCanvas(\"RooFitCanvas\", \"test\", 800, 400)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: RooFitCanvas\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pars = simPdf.getParameters(combData)\n",
      "# pars.writeToFile(\"file.txt\")\n",
      "# for line in open(\"file.txt\"):\n",
      "#     print line.strip()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Resulting efficiency:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print fraction.getVal(), \"+/-\", fraction.getError()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.955125189711 +/- 0.0042785569915\n"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    }
   ],
   "metadata": {}
  }
 ]
}