LxyResolution.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:2596a988b448e1b44e5c4d6df1151510ed060e0f1d071deb3c2568aebd824cc2"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import ROOT\n",
      "from ROOT import TCanvas\n",
      "import rootprint\n",
      "import rootnotes\n",
      "from array import array"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ROOT.gStyle.SetOptStat(0000000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inputFile = ROOT.TFile(\"/home/demattia/cmslpc/uscms_data/d3/demattia/DisplacedLeptons/RefittedStandAloneMuons/Analysis/CMSSW_5_3_8/src/AnalysisMacros/Latest_Version_Code_June_19/WeightedFiles/HTo2LongLivedTo4F_MH400_MFF50_CTau8To800_analysis_20140602_weighted_2saMu.root\")\n",
      "\n",
      "# inputFile = ROOT.TFile(\"/home/demattia/cmslpc/uscms_data/d3/demattia/DisplacedLeptons/RefittedStandAloneMuons/Analysis/CMSSW_5_3_8/src/AnalysisMacros/Latest_Version_Code_June_19/WeightedFiles_NoTrigger/HTo2LongLivedTo4F_MH400_MFF50_CTau8To800_analysis_20140602_weighted_2saMu.root\")\n",
      "\n",
      "# inputFile = ROOT.TFile(\"/home/demattia/cmslpc/uscms_data/d3/demattia/DisplacedLeptons/RefittedStandAloneMuons/Analysis/CMSSW_5_3_8/src/AnalysisMacros/Latest_Version_Code_June_19/WeightedFiles_Trigger/HTo2LongLivedTo4F_MH400_MFF50_CTau8To800_analysis_20140602_weighted_2saMu.root\")\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We run over the cosmic data analysis trees and check when given two good back to back RSA we can reconstruct a SV."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from RefittedStandAloneMuons import *\n",
      "from ProgressBar import ProgressBar"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ROOT.gROOT.LoadMacro(\"interfaceRefittedStandAloneMuons/Loader.C+\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inputData = ROOT.TFile(\"/home/demattia/cmslpc/eos/uscms/store/user/lpcdve/noreplica/DisplacedLeptons/Trees/DYJets50_analysis_20140601/histograms.root\", \"READ\")\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TStreamerInfo::BuildCheck:0: RuntimeWarning: \n",
        "   The StreamerInfo of class TreeDipseudoLeptonCandidate read from file /home/demattia/cmslpc/eos/uscms/store/user/lpcdve/noreplica/DisplacedLeptons/Trees/DYJets50_analysis_20140601/histograms.root\n",
        "   has the same version (=1) as the active class but a different checksum.\n",
        "   You should update the version to ClassDef(TreeDipseudoLeptonCandidate,2).\n",
        "   Do not try to write objects with the current class definition,\n",
        "   the files will not be readable.\n",
        "\n",
        "TStreamerInfo::CompareContent:0: RuntimeWarning: The following data member of\n",
        "the on-file layout version 1 of class 'TreeDipseudoLeptonCandidate' differs from \n",
        "the in-memory layout version 1:\n",
        "   vector<pair<Int_t,Float_t> > tkMatches; //\n",
        "vs\n",
        "   vector<pair<int,float> > tkMatches; //\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Resolution with Tracker Tracks"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "directory = inputData.Get(\"muTrackAnalysis\")\n",
      "tree = directory.Get(\"outputTree\")\n",
      "\n",
      "cosmicVsD0 = ROOT.TH1F(\"cosmicVsD0\", \"cosmicVsD0\", 100, 0, 0.01)\n",
      "cosmicVsD0Vertex = ROOT.TH1F(\"cosmicVsD0Vertex\", \"cosmicVsD0Vertex\", 100, 0, 0.01)\n",
      "cosmicVsLxyVertex = ROOT.TH1F(\"cosmicVsLxyVertex\", \"cosmicVsLxyVertex\", 100, 0, 0.01)\n",
      "cosmicVsVx = ROOT.TH1F(\"cosmicVsVx\", \"cosmicVsVx\", 100, 0, 0.01)\n",
      "cosmicVsVy = ROOT.TH1F(\"cosmicVsVy\", \"cosmicVsVy\", 100, 0, 0.01)\n",
      "\n",
      "p = ProgressBar(tree.GetEntries())\n",
      "\n",
      "num = 0\n",
      "for event in tree:\n",
      "    if num%(event.GetEntries()/100) == 0:\n",
      "        p.animate(num)\n",
      "    \n",
      "    num += 1\n",
      "    \n",
      "    for cand in event.candidates.candidates_:\n",
      "        # print cand.cosine\n",
      "        # if cand.cosine < -0.99 and cand.leptonPtL > 26. and cand.leptonPtH > 26. and \\\n",
      "        # abs(cand.leptonEtaL) < 2. and abs(cand.leptonEtaH) < 2.:\n",
      "        leptonL = get_lepton(event.candidates.leptons_, cand.leptonIndexL)\n",
      "        leptonH = get_lepton(event.candidates.leptons_, cand.leptonIndexH)\n",
      "        # if not leptonL.isStandAloneMuon or not leptonH.isStandAloneMuon:\n",
      "        if not leptonL.isCentralTrack or not leptonH.isCentralTrack:\n",
      "            continue\n",
      "            # if not(leptonL.dtStationsWithValidHits+leptonL.cscStationsWithValidHits >= 2 and \\\n",
      "            # leptonH.dtStationsWithValidHits+leptonH.cscStationsWithValidHits >= 2):\n",
      "            #     continue\n",
      "            # print \"found\"\n",
      "        if cand.validVertex == True:\n",
      "            cosmicVsD0Vertex.Fill(cand.decayLength_PV/cand.decayLengthSignificance_PV)\n",
      "            cosmicVsLxyVertex.Fill(cand.decayLength_PV)\n",
      "            # print \"found and valid vertex\"\n",
      "            pass\n",
      "        # else:\n",
      "            # print \"found, but no valid vertex\"\n",
      "    if num > 10000:\n",
      "        break\n",
      "    pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [                       0%                       ]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: cosmicVsD0 (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: cosmicVsD0Vertex (Potential memory leak).\n",
        "TFile::Append:0: RuntimeWarning: Replacing existing TH1: cosmicVsLxyVertex (Potential memory leak).\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cEffData = ROOT.TCanvas()\n",
      "#cosmicVsD0.Draw()\n",
      "cosmicVsD0Vertex.Draw()\n",
      "#cosmicVsD0Vertex.SetLineColor(2)\n",
      "# cosmicVsLxyVertex.Draw()\n",
      "# cosmicVsLxyVertex.Fit(\"gaus\", \"\", \"\", 0.001, 0.01)\n",
      "#cosmicVsLxyVertex.SetLineColor(3)\n",
      "cEffData"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAHYCAIAAAApvgy/AAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE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\nKAAAgKD/Dyqh853/94WXAAAAAElFTkSuQmCC\n",
       "prompt_number": 42,
       "text": [
        "<ROOT.TCanvas object (\"c1_n21\") at 0x4904d20>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Resolution with RefittedStandAloneMuons"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compute Lxy resolution by looking at the x and y coordinates separately."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "directory = inputData.Get(\"muTrackAnalysis\")\n",
      "tree = directory.Get(\"outputTree\")\n",
      "\n",
      "cosmicVsD0 = ROOT.TH1F(\"cosmicVsD0\", \"cosmicVsD0\", 100, 0, 20)\n",
      "cosmicVsD0Vertex = ROOT.TH1F(\"cosmicVsD0Vertex\", \"cosmicVsD0Vertex\", 100, 0, 20)\n",
      "cosmicVsLxyVertex = ROOT.TH1F(\"cosmicVsLxyVertex\", \"cosmicVsLxyVertex\", 100, 0, 20)\n",
      "cosmicVsVx = ROOT.TH1F(\"cosmicVsVx\", \"cosmicVsVx\", 100, -10, 10)\n",
      "cosmicVsVy = ROOT.TH1F(\"cosmicVsVy\", \"cosmicVsVy\", 100, -10, 10)\n",
      "\n",
      "p = ProgressBar(tree.GetEntries())\n",
      "\n",
      "num = 0\n",
      "for event in tree:\n",
      "    if num%(event.GetEntries()/100) == 0:\n",
      "        p.animate(num)\n",
      "    \n",
      "    num += 1\n",
      "    \n",
      "    for cand in event.candidates.candidates_:\n",
      "        # print cand.cosine\n",
      "        if not(cand.cosine > -0.75 and cand.leptonPtL > 26. and cand.leptonPtH > 26. and \\\n",
      "        abs(cand.leptonEtaL) < 2. and abs(cand.leptonEtaH) < 2.):\n",
      "            continue\n",
      "        leptonL = get_lepton(event.candidates.leptons_, cand.leptonIndexL)\n",
      "        leptonH = get_lepton(event.candidates.leptons_, cand.leptonIndexH)\n",
      "        if not leptonL.isStandAloneMuon or not leptonH.isStandAloneMuon:\n",
      "        # if not leptonL.isCentralTrack or not leptonH.isCentralTrack:\n",
      "            continue\n",
      "        # if not(leptonL.dtStationsWithValidHits+leptonL.cscStationsWithValidHits >= 2 and \\\n",
      "        # leptonH.dtStationsWithValidHits+leptonH.cscStationsWithValidHits >= 2):\n",
      "        #     continue\n",
      "            # print \"found\"\n",
      "        if cand.validVertex == True:\n",
      "            LxyError = cand.decayLength_PV/cand.decayLengthSignificance_PV\n",
      "            cosmicVsD0Vertex.Fill(leptonH.d0_PV)\n",
      "            cosmicVsLxyVertex.Fill(cand.decayLength_PV)\n",
      "            cosmicVsVx.Fill(event.candidates.pv_x - cand.vx)\n",
      "            cosmicVsVy.Fill(event.candidates.pv_y - cand.vy)\n",
      "            # print \"found and valid vertex\"\n",
      "            pass\n",
      "        # else:\n",
      "            # print \"found, but no valid vertex\"\n",
      "    if num > 30000:\n",
      "        break\n",
      "    pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [                       0%                       ]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [                       0%                       ]  1 of 1904679 complete"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cEffData = ROOT.TCanvas(\"\", \"\", 800, 400)\n",
      "cEffData.Divide(2,1)\n",
      "#cosmicVsD0.Draw()\n",
      "# cEffData.cd(1)\n",
      "# cosmicVsD0Vertex.Draw()\n",
      "cEffData.cd(1)\n",
      "cosmicVsLxyVertex.Draw()\n",
      "cosmicVsLxyVertex.SetTitle(\"\")\n",
      "cosmicVsLxyVertex.GetYaxis().SetTitle(\"Entries\")\n",
      "cosmicVsLxyVertex.GetYaxis().SetTitleOffset(1.5)\n",
      "cosmicVsLxyVertex.GetXaxis().SetTitle(\"Lxy [cm]\")\n",
      "# cosmicVsLxyVertex.Fit(\"gaus\", \"\", \"\", 2, 6)\n",
      "cEffData.cd(2)\n",
      "cosmicVsVx.Draw()\n",
      "cosmicVsVx.SetTitle(\"\")\n",
      "cosmicVsVx.GetYaxis().SetTitle(\"Entries\")\n",
      "cosmicVsVx.GetYaxis().SetTitleOffset(1.5)\n",
      "cosmicVsVx.GetXaxis().SetTitle(\"Vertex distance [cm]\")\n",
      "cosmicVsVx.Fit(\"gaus\")\n",
      "cosmicVsVy.Draw(\"same\")\n",
      "\n",
      "leg = ROOT.TLegend(0.6,0.7,0.85,0.89)\n",
      "leg.SetBorderSize(0)\n",
      "leg.AddEntry(cosmicVsVx,\"x_{PV} - x_{SV}\",\"l\")\n",
      "leg.AddEntry(cosmicVsVy,\"y_{PV} - y_{SV}\",\"l\")\n",
      "leg.Draw()\n",
      "\n",
      "cosmicVsVy.SetLineColor(2)\n",
      "#cosmicVsLxyVertex.SetLineColor(3)\n",
      "cEffData"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "TCanvas::Constructor:0: RuntimeWarning: Deleting canvas with same name: \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAxwAAAF0CAIAAAAmXahBAAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE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\nAAAASUVORK5CYII=\n",
       "prompt_number": 10,
       "text": [
        "<ROOT.TCanvas object at 0x45dcdc0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}