LimitSetting_T6gg_6bins.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LimitSetting\n",
    "\n",
    "### Adaptation of [LimitSetting](https://github.com/asantra/DiPhotonClassicCodeRepository/tree/master/LimitSetting) in a Jupyter notebook\n",
    "\n",
    "#### thomas.mccauley@cern.ch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is recommended to run this using a combination of [CERNBox](https://cernbox.cern.ch/) and                      [SWAN](https://swan.web.cern.ch/). \n",
    "\n",
    "In your CERNBox area git clone this repo and run SWAN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "require(['notebook'],\n",
       "  function() {\n",
       "    IPython.CodeCell.config_defaults.highlight_modes['magic_text/x-c++src'] = {'reg':[/^%%cpp/]};\n",
       "    console.log(\"JupyROOT - %%cpp magic configured\");\n",
       "  }\n",
       ");\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.08/06\n"
     ]
    }
   ],
   "source": [
    "import ROOT\n",
    "\n",
    "# enable JsVis for better graphics\n",
    "#ROOT.enableJSVis()\n",
    "ROOT.disableJSVis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's read in the root file and get the histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TFile**\t\t./hist_signal_binA.root\t\n",
      " TFile*\t\t./hist_signal_binA.root\t\n",
      "  KEY: TH1F\th_All_Signal_MET;1\tAll Signal MET\n",
      "  KEY: TH1F\th_DoublePhoton_Signal_MET;1\tDi-photon signal MET\n",
      "  KEY: TH1F\th_DoublePhoton_Signal_MET_NoTriggerCut;1\tDi-photon signal MET\n",
      "  KEY: TH1F\th_DoublePhoton_Signal_MET_AsymPtCut;1\tDi-photon signal MET\n",
      "  KEY: TH1F\th_DoublePhoton_Signal_MET_InvMassCut;1\tDi-photon signal MET\n",
      "  KEY: TH1F\th_DoublePhoton_Signal_MET_InvMassAndAsymPtCut;1\tDi-photon signal MET\n",
      "  KEY: TH1F\th_DoublePhoton_Signal_MET_HighInvMassAndAsymPtCut;1\tDi-photon signal MET\n",
      "  KEY: TH1F\th_DoublePhotonFromThree_Signal_MET;1\tDi-photon signal MET\n",
      "  KEY: TH1F\th_DoublePhoton_Signal_METExpected;1\tDouble Photon MET, MET>100\n",
      "  KEY: TH1F\th_DoublePhoton_Signal_METJESUpExpected;1\tDouble Photon MET, MET>100\n",
      "  KEY: TH1F\th_DoublePhoton_Signal_METJESDownExpected;1\tDouble Photon MET, MET>100\n",
      "  KEY: TH1F\th_DoublePhoton_Signal_METExpectedUnweighted;1\tDouble Photon MET, MET>100\n",
      "  KEY: TH1F\th_DoublePhoton_Signal_METJESUpExpectedUnweighted;1\tDouble Photon MET, MET>100\n",
      "  KEY: TH1F\th_DoublePhoton_Signal_METJESDownExpectedUnweighted;1\tDouble Photon MET, MET>100\n",
      "  KEY: TH2D\th_GridAllEvents;1\tAll T5gg Events per mass point\n",
      "  KEY: TH2D\th_MET100to115_GridAll;1\tEvents for MET >100 and MET < 115\n",
      "  KEY: TH2D\th_MET115to130_GridAll;1\tEvents for 115 < MET < 130 GeV\n",
      "  KEY: TH2D\th_MET130to150_GridAll;1\tEvents for 130 < MET < 150 GeV\n",
      "  KEY: TH2D\th_MET150to185_GridAll;1\tEvents for MET >150 and MET < 185 GeV\n",
      "  KEY: TH2D\th_MET185to250_GridAll;1\tEvents for 185 < MET < 250 GeV\n",
      "  KEY: TH2D\th_MET250_GridAll;1\tEvents for MET > 250 GeV\n",
      "  KEY: TH2D\th_MET100to115_Grid;1\tEvents for MET >100 and MET < 115\n",
      "  KEY: TH2D\th_MET115to130_Grid;1\tEvents for 115 < MET < 130 GeV\n",
      "  KEY: TH2D\th_MET130to150_Grid;1\tEvents for 130 < MET < 150 GeV\n",
      "  KEY: TH2D\th_MET150to185_Grid;1\tEvents for MET >150 and MET < 185 GeV\n",
      "  KEY: TH2D\th_MET185to250_Grid;1\tEvents for 185 < MET < 250 GeV\n",
      "  KEY: TH2D\th_MET250_Grid;1\tEvents for MET > 250 GeV\n",
      "  KEY: TH2D\th_MET100to115_GridUnweighted;1\tEvents for MET >100 and MET < 115\n",
      "  KEY: TH2D\th_MET115to130_GridUnweighted;1\tEvents for 115 < MET < 130 GeV\n",
      "  KEY: TH2D\th_MET130to150_GridUnweighted;1\tEvents for 130 < MET < 150 GeV\n",
      "  KEY: TH2D\th_MET150to185_GridUnweighted;1\tEvents for MET >150 and MET < 185 GeV\n",
      "  KEY: TH2D\th_MET185to250_GridUnweighted;1\tEvents for 185 < MET < 250 GeV\n",
      "  KEY: TH2D\th_MET250_GridUnweighted;1\tEvents for MET > 250 GeV\n",
      "  KEY: TH2D\th_MET100to115_Grid_JESUp;1\tEvents for MET >100 and MET < 115\n",
      "  KEY: TH2D\th_MET115to130_Grid_JESUp;1\tEvents for 115 < MET < 130 GeV\n",
      "  KEY: TH2D\th_MET130to150_Grid_JESUp;1\tEvents for 130 < MET < 150 GeV\n",
      "  KEY: TH2D\th_MET150to185_Grid_JESUp;1\tEvents for MET >150 and MET < 185 GeV\n",
      "  KEY: TH2D\th_MET185to250_Grid_JESUp;1\tEvents for 185 < MET < 250 GeV\n",
      "  KEY: TH2D\th_MET250_Grid_JESUp;1\tEvents for MET > 250 GeV\n",
      "  KEY: TH2D\th_MET100to115_Grid_JESDown;1\tEvents for MET >100 and MET < 115\n",
      "  KEY: TH2D\th_MET115to130_Grid_JESDown;1\tEvents for 115 < MET < 130 GeV\n",
      "  KEY: TH2D\th_MET130to150_Grid_JESDown;1\tEvents for 130 < MET < 150 GeV\n",
      "  KEY: TH2D\th_MET150to185_Grid_JESDown;1\tEvents for MET >150 and MET < 185 GeV\n",
      "  KEY: TH2D\th_MET185to250_Grid_JESDown;1\tEvents for 185 < MET < 250 GeV\n",
      "  KEY: TH2D\th_MET250_Grid_JESDown;1\tEvents for MET > 250 GeV\n",
      "  KEY: TH2D\th_MET100to115_GridUnweighted_JESUp;1\tEvents for MET >100 and MET < 115\n",
      "  KEY: TH2D\th_MET115to130_GridUnweighted_JESUp;1\tEvents for 115 < MET < 130 GeV\n",
      "  KEY: TH2D\th_MET130to150_GridUnweighted_JESUp;1\tEvents for 130 < MET < 150 GeV\n",
      "  KEY: TH2D\th_MET150to185_GridUnweighted_JESUp;1\tEvents for MET >150 and MET < 185 GeV\n",
      "  KEY: TH2D\th_MET185to250_GridUnweighted_JESUp;1\tEvents for 185 < MET < 250 GeV\n",
      "  KEY: TH2D\th_MET250_GridUnweighted_JESUp;1\tEvents for MET > 250 GeV\n",
      "  KEY: TH2D\th_MET100to115_GridUnweighted_JESDown;1\tEvents for MET >100 and MET < 115\n",
      "  KEY: TH2D\th_MET115to130_GridUnweighted_JESDown;1\tEvents for 115 < MET < 130 GeV\n",
      "  KEY: TH2D\th_MET130to150_GridUnweighted_JESDown;1\tEvents for 130 < MET < 150 GeV\n",
      "  KEY: TH2D\th_MET150to185_GridUnweighted_JESDown;1\tEvents for MET >150 and MET < 185 GeV\n",
      "  KEY: TH2D\th_MET185to250_GridUnweighted_JESDown;1\tEvents for 185 < MET < 250 GeV\n",
      "  KEY: TH2D\th_MET250_GridUnweighted_JESDown;1\tEvents for MET > 250 GeV\n",
      "  KEY: TH2D\th_MET100to115_GridStatError;1\tEvents for MET >100 and MET < 115\n",
      "  KEY: TH2D\th_MET115to130_GridStatError;1\tEvents for 115 < MET < 130 GeV\n",
      "  KEY: TH2D\th_MET130to150_GridStatError;1\tEvents for 130 < MET < 150 GeV\n",
      "  KEY: TH2D\th_MET150to185_GridStatError;1\tEvents for MET >150 and MET < 185 GeV\n",
      "  KEY: TH2D\th_MET185to250_GridStatError;1\tEvents for 185 < MET < 250 GeV\n",
      "  KEY: TH2D\th_MET250_GridStatError;1\tEvents for MET > 250 GeV\n",
      "  KEY: TH2D\th_MET100to115_GridJESUpError;1\tEvents for MET >100 and MET < 115\n",
      "  KEY: TH2D\th_MET115to130_GridJESUpError;1\tEvents for 115 < MET < 130 GeV\n",
      "  KEY: TH2D\th_MET130to150_GridJESUpError;1\tEvents for 130 < MET < 150 GeV\n",
      "  KEY: TH2D\th_MET150to185_GridJESUpError;1\tEvents for MET >150 and MET < 185 GeV\n",
      "  KEY: TH2D\th_MET185to250_GridJESUpError;1\tEvents for 185 < MET < 250 GeV\n",
      "  KEY: TH2D\th_MET250_GridJESUpError;1\tEvents for MET > 250 GeV\n",
      "  KEY: TH2D\th_MET100to115_GridJESDownError;1\tEvents for MET >100 and MET < 115\n",
      "  KEY: TH2D\th_MET115to130_GridJESDownError;1\tEvents for 115 < MET < 130 GeV\n",
      "  KEY: TH2D\th_MET130to150_GridJESDownError;1\tEvents for 130 < MET < 150 GeV\n",
      "  KEY: TH2D\th_MET150to185_GridJESDownError;1\tEvents for MET >150 and MET < 185 GeV\n",
      "  KEY: TH2D\th_MET185to250_GridJESDownError;1\tEvents for 185 < MET < 250 GeV\n",
      "  KEY: TH2D\th_MET250_GridJESDownError;1\tEvents for MET > 250 GeV\n"
     ]
    }
   ],
   "source": [
    "file_in = ROOT.TFile('./hist_signal_binA.root', 'READ')\n",
    "file_in.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dB5T+xdCGwp133hmlImWGE199wRbNyhWTXCUP9CjElUrCpSLI4IpLGBaICbnIrEdh8YXs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\noQAAAEaioQAAAEb6/wGr++oZZF6OjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c1 = ROOT.TCanvas()\n",
    "c1.Divide(2,2)\n",
    "\n",
    "h1 = file_in.Get('h_MET100to115_Grid')\n",
    "h1stat = file_in.Get('h_MET100to115_GridStatError')\n",
    "h1up = file_in.Get('h_MET100to115_GridJESUpError')\n",
    "h1down = file_in.Get('h_MET100to115_GridJESDownError')\n",
    "\n",
    "c1.cd(1)\n",
    "h1.Draw('colz')\n",
    "c1.cd(2)\n",
    "h1stat.Draw('colz')\n",
    "c1.cd(3)\n",
    "h1up.Draw('colz')\n",
    "c1.cd(4)\n",
    "h1down.Draw('colz')\n",
    "\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the rest of the histograms from the root file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h2 = file_in.Get('h_MET115to130_Grid')\n",
    "h3 = file_in.Get('h_MET130to150_Grid')\n",
    "h4 = file_in.Get('h_MET150to185_Grid')\n",
    "h5 = file_in.Get('h_MET185to250_Grid')\n",
    "h6 = file_in.Get('h_MET250_Grid')\n",
    "\n",
    "h2stat = file_in.Get('h_MET115to130_GridStatError')\n",
    "h3stat = file_in.Get('h_MET130to150_GridStatError')\n",
    "h4stat = file_in.Get('h_MET150to185_GridStatError')\n",
    "h5stat = file_in.Get('h_MET185to250_GridStatError')\n",
    "h6stat = file_in.Get('h_MET250_GridStatError')\n",
    "\n",
    "h2up = file_in.Get('h_MET115to130_GridJESUpError')\n",
    "h3up = file_in.Get('h_MET130to150_GridJESUpError')\n",
    "h4up = file_in.Get('h_MET150to185_GridJESUpError')\n",
    "h5up = file_in.Get('h_MET185to250_GridJESUpError')\n",
    "h6up = file_in.Get('h_MET250_GridJESUpError')\n",
    "\n",
    "h2down = file_in.Get('h_MET115to130_GridJESDownError')\n",
    "h3down = file_in.Get('h_MET130to150_GridJESDownError')\n",
    "h4down = file_in.Get('h_MET150to185_GridJESDownError')\n",
    "h5down = file_in.Get('h_MET185to250_GridJESDownError')\n",
    "h6down = file_in.Get('h_MET250_GridJESDownError')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's draw the rest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0WKvf/WrcImsOn6eoSLEiWe9ITvWQEQG5UcxRyC3HYl3lllKi/P+l3vHK2XGL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\nfMHBbc3EjQ/kUQCmcVPN666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bo5D/5UlYAOon0dCDrLjYsWPHs88++8d//MfyK7tf0kba\nx8Q1m02eBANEp0i4VOzUBAkLr7/++p49ex599FHZT0wAkiskCVsKkxnb7fb999/v/mg3JElkv98f\nDoeDwSB5XQAq4Tvf+Y5sExOAStM3FOzNgb3sb775ZrvTzSZt+Tohl5eXZbsRQn1KAIolYeHTn/60\n3UNMAKpOOfQg46DGmH6/v2HDBrvf9i4OBgPp//QllN62bZtsM3EJqBM3LLz++ut2JzEBqLpEDQUZ\ncdyxY4fd76aHlNFKz/OazaYdkkzjnAGUkRsWNm3aZHcSE4CqS/r0SJnhvHPnTvdX5syMK8xXAmaB\nLyy4+w0xAagmfY9C8rXa7tDjhdc+G6XIOaqKjl4QO43PhS9qcvgcVuUm0lFmGYr/UeiSXF2oKGPM\nB5uaN6X7Vqz/vCKRiyZ5zsmTmowxVZQ8LLgxYf2dP45UZK2motMbNNmTFFa//M+KUnlGEkX2JEW6\nKmPMGtXTA6+4+XuKUoO/ukpR6h+7vxm3yPrPr1FUpMss1zBfVJRKRdLlkXZblwMuYuMAQFUkDAsR\nGwcAonOXJYftmS7RHAUAEIQFoISkq0+WIEkG1Yj0PQoAsvaT0awMWwCIIjAmfPVKb8WCNn+J3R4M\nBrEGCotsKLz6yGWyzTAEgCN7LpZthiGAiP5gvxe4321AuG2CVqsl64+idAQW2VCgcQDAReMASJ1t\nDbiPi3OfwBDlCAw9AABQWzaFiWyb+A93TeFZDwAAoK5oKAAAgFBlmcz4gS3fyq6ic16LnVFEkaPJ\naJMg/ezb/05RSvGmdHQfhe5N6T5A3UehSBpzcr1mDcKHr/q6opQx16tKVZtiMuMbv6XJaLRWlfxH\nYf72/1dR6l3bNXXpLjpF8h/ddXrqPZrnI//F/f+7otSb79HkRjv7gdjv641vaD5zRZKrYhXZUMi0\ncQCgcpjMCJRQOkMPbponmTfhbnc6nVar1el0UqkOQMkRE4DaSKdHQZ4BIwsw3FRQ/X5/OBwOBgOC\nAhDLT06vL/oUlIgJQBYKiQmpNRTcp8PZHwOXYSwvL8vj5188GDwKy5AEUHW6mODOUXAxJAEUKOU5\nCm5KB9njxgWJCIYGATADYsUEGgRACaW8PFLuG+y/8mOz2ex0OsPhMN3qAJQcMQGounR6FNzJSu5O\n2V5aWkqlIgCVQEwAaqPI5ZHuHIWIz33ILXmAbkXyB5sPq2rT1JWbPD+KcxRljDk8vE5VLjbd8nHd\n6TUas55HIWJMGB3XPWAzdhaNE1doouXiff9RUSpP5951OG6R1aqKRsc1uQ10yUvWvBz7v68x5qx1\nZ8ctUrmMCDo8FApAWRATgBIihTMAAAhFQwGoG0l2ZNmUBnGfFwegHtxEZ1bcmEBDAagbt5Vgxu2G\niA+eB1Az9vL3PE8iQ9yYUJaHQjE2CUx66fS5kzt/eM2O6aV8IUDSHEm2xNIiJgDT6WKCMcYuUZZt\nEycmMJkRqJgPf/+hwP1hwWIyN2JpERMAhSgxwb15iBsTimwoAMiUJFG2NxOMPgAzyA46uDlS48YE\nGgpA3cg0pcmURwBmjTtvURcTyjJH4ZydL0Upcvjbmtw1iuQ/utQ65oL3Kwrp6tLlJjqqOsOS0yV3\nUuRB0uV+mZGULKlQxIRVG04qKjr+27GLrH77cUVFP/v2FkUpXUxQRq33vDNuCV0SNh1FPihjjDmi\nKfSP3d+MW+T8hb/R1FQ1RTYUIgYCADOCmACUUDrLI2Xdtrte093udDqtVotnzwMzgpgA1EY6DQVZ\nnSnrNeVfO2NiOBwOBoNU6gJQfsQEoDbSGXqYTN3gW68plpeX5fHzR/deFHg0uh+BqiMmALWR1RwF\nye3gW6YpEcFw8QMrOXpCN2m1jIgJQHKFxISUUzh7Y3YkUnoam81mp9MZDofpVgeg5IgJQNWl06Pg\nrtuWewU34fzS0lIqFQGoBGICUBtFLo90xyM/sOVbUYp8sKlaKByfbsX8hS/mdHp50mUp0CVsOOe1\n/FIOKBadr/rI2YqKztv/Q0Wpo4oy1efGhIjDEKdfWaOo6IKP/o+4RX710LsVFZnrNYVqSZdyYPXL\nbyhK6TI9KFKerNqgqKd6imwoRGwcAJgRzFEASojHTAMAgFA0FAAAQCgaCgAA1JabEdUV/aHzRc5R\nePHgv87zYb4CAMVkRgDTyTOm7UJluzN6K8EwmREos6MnNKtvqovGATBdYEz4xWebKxaUjGdm3HSo\nRo8CAABI6De+GZy4zG1AuCnVbSthMBhEbC7QUAAAoLbsiEN/zIwbDYETFwI1RqNRhic4peJGw/0x\n4mnokv8olD9fkI7ifeX5pg4Pr1OUyu1bkacXD14/Go3Wfutg9CK/+GyzqMs5Fb6YsP7OH0cptfrl\nf87mdPx0OXzOveuwotQbv6V5U+94RZMQTEGX5EqXj+7nV35YUerUgFctXwAACz5JREFU2lOKUquP\nr45bZNVfa/JBnXrPOxWljuy5uKiYUGSPQqWDGoDURWwcAMhTyssj3WUYYdsAZgcxAai6lHsUZBTE\nHRGRH+POtARQdcQEoOpSbij4bhfsTpls6QsHviFJwZAEUBuxYsKRPRcHHoQhCaBAKTcU7P2BOfN5\nsrJ803frQIMAqL1YMYEGAVBC6fcoSI+ibNtYIPvTrRGosVOvv73oU0iKmACkqJCYkH6Pgvuj3CsQ\nC4DcyCSAok/EGGICUH08FAqoG/4GA0hRkXkU3MmMZXvugy7LUJ5pmkqeEkp3errUSbq6FHQpd85f\n+BtNZeFZVU7svGx6UZkWUDnuZMaIz33Q5a5R0P13HB3X5CZa+11FIXNyfU55kHRX3P/8TPBM1en+\nl//6Q0Up3Rn+7NsfVZTKTWNPYVXzUCigYt6299nA/Ss2IMqPh0IBJcTQAwAACMVDoYC6IeMhgBQV\n2VB48eD1ss0wBICjey+SbYYhgFTIGiiZuiTTmCJOZmKOAoCyoHEApE6Sl9jnTdudsRKoM/QAlNea\nYzGu0BPZnQeAcgiMCcfvumTFgpIO1YybDgsLCxGXUtNQAACgwtZ+7YXA/W4Dwh1lsB0JvV6vAj0K\n9ZujkFuWgjyVPGFDntZ/XvOm1hx5I/UzqSt3jkLEmHB4eF1mp1MYXUKRV9dr8nwoLvA3/o/XFRW9\n+//+/xSlXv2A5k011p5WlHrX9tgpJXS5VRQVJWFHHOTBrXaP7WBgjgKAiiEmAKlzl0HJo1xjHYE8\nCgAAIFT6DQXf4+cntwHMFGICUGkpNxRkIES23X/dWZcAZgExAai6lOcouFHA3Wk3fHcP7mRGF+OU\nQG0QE4CqS38yow0H7vUvNw2+lRhc/MAsICYAlZZyQ8EuujDj0CC9ju12e/KuAsB0q4+vLvoUkiIm\nACkqJCak3FDwjTjKvQKxAJhNxASg6ki4VFW5ZTSqZeoknQtf1KRJ0WWsmk1uTMj0uQ+KjDcn169T\nVHTkG5r/+rosQ7l9P9ccPk9R0eHhFkUpZe4p1Qeo+E+8bs8PFBWtes87FaUKRMIlAGXBQ6GAEiLh\nEgAACEVDAQAAhGKOAoCycB8KxTAEUBLMUQBQFjQOgNTJImRZfzS5ZzqGHgAAqC1JWCJrkm0uE3fP\ndEX2KACY7s1jq4o+BQAlEhgTjuy5eMWCbkYT93krUSqloQAAQIWtv/PHgfvdBoTbJog17mCMaYxG\nI+25JdJoNNwfI56GLvmGIqNInlmGdAl5Sp4HiTclfvZtTe6XRqMxGo3Ovetw9CJH9lxc1OWcCl1M\n0Fn/+Zy+aY21pxWlVr/8z6mfSRhdIimFNUeOKUqdUuUmOr3hhKLUsTs/GreILuHSWT96h6LU0b0X\n6WJC/0zGmLm5uVarZZwM69MV2aNQ6aAGIHXEBCB17XZbWgN2I+6FlslkRnsq9rkvk9sAsjN5rdmb\nhmKvPmICUF3pNxRkFqU7XcL+63s8DIDUBc5wjj5rKaNTkg1iAlA5KQ89BF758qPv7sE3JCnofgSm\niDvD2V539rHO+d/BExOAqku5oWBvHQaDgbs6U8KEDRmyn4sfUNDNcJY7+EzPbRIxAai69HsUzPje\nRXoXbSyQPenWCMBlBx3cGc62I0F+zBkxAai6IpdHKqpmeaSo5UrCWr6pJMsjY63is6unFHWVhC4m\n6LA8UrA8UlRieWQhMaHI5ZHueGSmz30o+Z+fkp9e+f/k53aGuj/5utbtbFLEBN1//TeviB363vYX\nv1ZUdGqt5u+cju5P/pFvxP4AdV/pw8PrFKV0/s1/e0xRav3nY//9Xr1htaKiN1XNxwLxUCgAZUFM\nAEqIh0IBAIBQNBQAAEAoGgoAACBUkXMUXjx4vWwzNgmAmACUEJMZAZQFMQFInWQr8eVhi/6wFYYe\nAACorcDnv8RKv1ZkjwKA6XQ5agDUVWBMePWRy1Ys6Hv+S6yMqDMxR0GR6IMkOaKWqZN0cksMOrPc\nmHDhtc9GKaL7r3/4ztgxYf2PNBWtWndSUer0R85WlFrz15pm5bl3HY5bZPQBTeYxHW0OzZUfnDZJ\n0S4/vUHzX2p0fJWi1BRhF4vbgEiSwZ05CgDKImLjAEB0k89/iftwuJTnKNhnvcgTaGSuhLsNYHYQ\nE4BiyQPY7LY8dz761Zd+Q0G4D4uTR9wW8vw6AEUhJgBVl/LQg50f4bZfjDM04mu/uOORLoYkgNqI\nFRPC5mQxJAEUKOWGgjsKIuSmwd5AyH4aBEDtxYoJNAiAEkp56MHtTvTG7FiI/CrdGgGUGTEBqLr0\nexTcHyUExFqyCaA2iAlA1c1EHoXckiKUPA2AUZ1hnqcHn1gf/qvZnUde3DkK5+x8KVKZ1zQVKWLC\nhZp6zGtrL1GUWv3yP6tq03hnM9rn7Dj7a79UVPSu7arsYevXKQpd+OJzilKvxs8P8eZazUdhPnRK\nU8oYU1BMII8CgLKI2jgAkCOe9QAAAELRUAAAAKHK2FAIy69Q3YpMtId2pIUPMKHc3leeb6rSju69\nKJ+K8vxKH9mjeR6BTm7ftDw/wNy+FXnWdfwuzaSWrM3EZEYAleCGY+YrACVRZI+CbRx8YMu3orQS\nAhuqCXdmVFf0xnvgK2Odf/T3Ff1Uo59VrFMq4QdYoW/FjLCNg3N2vhSllRB4k1fO//rROw8CXxn9\nOp2yP+IrX/nMpyLuTB4Tor+v6Df0Cb8VsXoOAl8c2CUQfWegwrsZyjj0AECNhy0BcCWPCTQUgFqR\nRy6R0QiASSUmjAqS5scA1JHi2hmNRq1Wy76s2+3mcimnJrcPFqgoxbUzSiMmFDaZkbgA6ES5dqr4\n7GZiAqCTdUxocHECddJ3FH0uAIqXQkzQdUSkpdfruZ0htoek1+u1Wi27vWvXrmazuWvXrrQq6na7\ncvAsKnLrco+fdV3yvrrdbqYfYJ5vKuu65Ji9Xi/TutyK7I+ykcUHWGnEhLTqIiboKiImTCq4oWC/\nxLJtPx37r/1yN5vN0WiU/JOSiuxhZWfqFbl1tcYjQznUZbn1ZvQBugfP5wOUKzb1uuSjsyE1u7rc\niuzx7Zcwow+w0ogJadVlERNiISYEKnjVg0zCnJyQ6VvLsby8nEpFcnDphEm3Il9dnuflVpddAyM/\nZvQByjuS42f9puyU3SzqshX5Fg5lUZdbUbvdzvSrXnXEhBTrIiboKiIm+JRleaT9bz8YDOQ/iW80\nZdu2balUZL/T7vcso4qkujzrCvxyp1vX5Eqb7N6UPbL7vtKtSyJOYDBNsa7AitzfplVRnRATUqmL\nmBD3+MSESSVqKPT7/VarJc0r+wk2m81OpzMcDtOqyHNkWpExxh7fVpR1XZP1ZlRXnm9KAlBGdckB\nJcxlVJdbkbs/z29F5RATUq+XmBDx+MQEH1Y9AACAUGXpUQAAACVEQwEAAISioYBZYQf/ZAjQO3Oa\nsbdSFnR3/lR7YpbyimWNM5TrK+uezOSYJYCMEBMioqGAmSCzk9xpxt7EJG33ovXOXMY2qe88k81u\ne0Frq7wJ9rcSoaQWmaUVK+IAUCAmREdDAbPFveTkUnT/tYHDnfnsK+uGFfdlsh0YSqSUnEB/zDgx\nS0JMBm8dQABiwopoKGC2TL/e3Ks98Le+S1dChntnYPUnVj250UdqkTsJehGAQhATVlTY0yOBPLld\ngqIdMjZpxn2AgUV8x5RL3Y0U7TMT1bkH9J2VGyM8Z1Qy9jsEEAcxITryKADZko7EjF4PoFoqFxMY\negCypbi8aSUANVa5mECPAgAACEWPAgAACEVDAQAAhKKhAAAAQtFQAAAAoWgoAACAUDQUAABAKBoK\nAAAgFA0FAAAQioYCAAAI9f8DzOeX2omJQlYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c2 = ROOT.TCanvas()\n",
    "c2.Divide(2,2)\n",
    "\n",
    "c2.cd(1)\n",
    "h2.Draw('colz')\n",
    "c2.cd(2)\n",
    "h2stat.Draw('colz')\n",
    "c2.cd(3)\n",
    "h2up.Draw('colz')\n",
    "c2.cd(4)\n",
    "h2down.Draw('colz')\n",
    "\n",
    "c2.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YOADgcv+aDrLJWW3OtXSuJ0blaBwAJcMYBaBgRo34sw0IN1ErAEQX/xgF3/Lz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\nrkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c3 = ROOT.TCanvas()\n",
    "c3.Divide(2,2)\n",
    "\n",
    "c3.cd(1)\n",
    "h3.Draw('colz')\n",
    "c3.cd(2)\n",
    "h3stat.Draw('colz')\n",
    "c3.cd(3)\n",
    "h3up.Draw('colz')\n",
    "c3.cd(4)\n",
    "h3down.Draw('colz')\n",
    "\n",
    "c3.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VSilV5Acomufqvxx+Z9Ikq5YfFGQ0dMosKMQsHABIavASL5VFZ0aggmh6AOqG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\nREEBAABEoqAAAAAiUVAAAACR/n8mp6Yl4S1b5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c4 = ROOT.TCanvas()\n",
    "c4.Divide(2,2)\n",
    "\n",
    "c4.cd(1)\n",
    "h4.Draw('colz')\n",
    "c4.cd(2)\n",
    "h4stat.Draw('colz')\n",
    "c4.cd(3)\n",
    "h4up.Draw('colz')\n",
    "c4.cd(4)\n",
    "h4down.Draw('colz')\n",
    "\n",
    "c4.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JkqEp5yj4/Rsevu+rz2fuQWKMSGdfTvLDhw93Op3p6Wn34tj+2W44dy7lTG6cf/O+0Wj0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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c5 = ROOT.TCanvas()\n",
    "c5.Divide(2,2)\n",
    "\n",
    "c5.cd(1)\n",
    "h5.Draw('colz')\n",
    "c5.cd(2)\n",
    "h5stat.Draw('colz')\n",
    "c5.cd(3)\n",
    "h5up.Draw('colz')\n",
    "c5.cd(4)\n",
    "h5down.Draw('colz')\n",
    "\n",
    "c5.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LCifpn9y4QIMAqL1YMYEGAVBC6fcohGaFc9enWyNQY88+/eKiDyEpYgKQokJi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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c6 = ROOT.TCanvas()\n",
    "c6.Divide(2,2)\n",
    "\n",
    "c6.cd(1)\n",
    "h6.Draw('colz')\n",
    "c6.cd(2)\n",
    "h6stat.Draw('colz')\n",
    "c6.cd(3)\n",
    "h6up.Draw('colz')\n",
    "c6.cd(4)\n",
    "h6down.Draw('colz')\n",
    "\n",
    "c6.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now specify a range of masses and scale factors (from where do they come?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Masses of the squark (T6gg)\n",
    "\n",
    "masses = [1400,1450,1500,1550,1600,1650,1700,1750,1800,1850,1900,1950,2000,2050,2100]\n",
    "neutralino_masses = [100,200,300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000]\n",
    "\n",
    "scale_factor = 0.993 * 0.986 * 0.983 * 0.983\n",
    "\n",
    "h1.Scale(scale_factor)\n",
    "h2.Scale(scale_factor)\n",
    "h3.Scale(scale_factor)\n",
    "h4.Scale(scale_factor)\n",
    "h5.Scale(scale_factor)\n",
    "h6.Scale(scale_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import os\n",
    "import math\n",
    "\n",
    "cfpath = './counting_files'\n",
    "\n",
    "# Make the ./counting_files dir\n",
    "# If already there rm the files\n",
    "try:\n",
    "    os.mkdir(cfpath)\n",
    "except OSError:\n",
    "    cfiles = os.listdir(cfpath)\n",
    "    for cf in cfiles:\n",
    "        os.remove(cfpath+'/'+cf)\n",
    "    \n",
    "temp_file = open('./counting_T5Wg_6binsA_ff_XXX_YYY.txt', 'r')\n",
    "#temp_file = open('./counting_T5Wg_6binsA_ee_XXX_YYY.txt', 'r')\n",
    "\n",
    "lines = [line for line in temp_file.readlines()]\n",
    "\n",
    "for i in range(1,len(masses)+1):\n",
    "    for j in range(1,len(neutralino_masses)+1):\n",
    "        \n",
    "        if h6.GetBinContent(i,j) > 0.0:\n",
    "         \n",
    "            uncertUp1 = math.fabs(h1up.GetBinContent(i,j))\n",
    "            uncertUp2 = math.fabs(h2up.GetBinContent(i,j))\n",
    "            uncertUp3 = math.fabs(h3up.GetBinContent(i,j))\n",
    "            uncertUp4 = math.fabs(h4up.GetBinContent(i,j))\n",
    "            uncertUp5 = math.fabs(h5up.GetBinContent(i,j))\n",
    "            uncertUp6 = math.fabs(h6up.GetBinContent(i,j))\n",
    "            \n",
    "            uncertDown1 = math.fabs(h1down.GetBinContent(i,j))\n",
    "            uncertDown2 = math.fabs(h2down.GetBinContent(i,j))\n",
    "            uncertDown3 = math.fabs(h3down.GetBinContent(i,j))\n",
    "            uncertDown4 = math.fabs(h4down.GetBinContent(i,j))\n",
    "            uncertDown5 = math.fabs(h5down.GetBinContent(i,j))\n",
    "            uncertDown6 = math.fabs(h6down.GetBinContent(i,j))\n",
    "            \n",
    "            file_name = 'counting_t6gg_'+str(masses[i-1])+'_'+str(neutralino_masses[j-1])+'.txt'\n",
    "            file_name = cfpath+'/'+file_name\n",
    "            new_file = open(file_name, 'w')\n",
    "            \n",
    "            for line in lines:\n",
    "                if re.search('T6GG', line):\n",
    "                    l = line.replace('T6GG1', str(round(h1.GetBinContent(i,j),3)))\n",
    "                    l = l.replace('T6GG2', str(round(h2.GetBinContent(i,j),3))) \n",
    "                    l = l.replace('T6GG3', str(round(h3.GetBinContent(i,j),3)))\n",
    "                    l = l.replace('T6GG4', str(round(h4.GetBinContent(i,j),3)))\n",
    "                    l = l.replace('T6GG5', str(round(h5.GetBinContent(i,j),3)))\n",
    "                    l = l.replace('T6GG6', str(round(h6.GetBinContent(i,j),3)))\n",
    "                    new_file.write(l)\n",
    "                elif re.search('MCS', line):\n",
    "                    l = line.replace('MCS1', str(round(1.0 + h1stat.GetBinContent(i,j),4)))\n",
    "                    l = l.replace('MCS2', str(round(1.0 + h2stat.GetBinContent(i,j),4))) \n",
    "                    l = l.replace('MCS3', str(round(1.0 + h3stat.GetBinContent(i,j),4))) \n",
    "                    l = l.replace('MCS4', str(round(1.0 + h4stat.GetBinContent(i,j),4)))\n",
    "                    l = l.replace('MCS5', str(round(1.0 + h5stat.GetBinContent(i,j),4))) \n",
    "                    l = l.replace('MCS6', str(round(1.0 + h6stat.GetBinContent(i,j),4)))\n",
    "                    new_file.write(l)\n",
    "                elif re.search('JES', line):\n",
    "                    l = line.replace('JES1',  str(round(1.0 + max(uncertUp1, uncertDown1),4))) \n",
    "                    l = l.replace('JES2',  str(round(1.0 + max(uncertUp2, uncertDown2),4)))\n",
    "                    l = l.replace('JES3',  str(round(1.0 + max(uncertUp3, uncertDown3),4)))\n",
    "                    l = l.replace('JES4',  str(round(1.0 + max(uncertUp4, uncertDown4),4)))\n",
    "                    l = l.replace('JES5',  str(round(1.0 + max(uncertUp5, uncertDown5),4)))\n",
    "                    l = l.replace('JES6',  str(round(1.0 + max(uncertUp6, uncertDown6),4)))\n",
    "                    new_file.write(l)\n",
    "                else:\n",
    "                    new_file.write(line)\n",
    "                    \n",
    "            new_file.close()\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now the datacards should be in the counting_files dir and make the shell script to run the combine commands which follow the template:\n",
    "```\n",
    "combine -M Asymptotic counting_files/counting_t6gg_${i}_${j}.txt > limit_files/limit_t6gg_${i}_${j}.txt\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "limit_file = open('makelimitfiles.sh', 'w')\n",
    "\n",
    "for cf in os.listdir('./counting_files'):\n",
    "        \n",
    "        m1 = cf.split('_')[2]\n",
    "        m2 = cf.split('_')[3][:-4]\n",
    "        \n",
    "        print m1, m2\n",
    "        \n",
    "        command = 'combine -M Asymptotic counting_files/'\n",
    "        command += cf + ' > limit_files/'\n",
    "        command += 'limit_t6gg_'+m1+'_'+m2+'.txt'\n",
    "        \n",
    "        print command\n",
    "        \n",
    "        limit_file.write(command+'\\n')\n",
    "\n",
    "limit_file.close()\n",
    "\n",
    "#try:\n",
    "#    os.mkdir('./limit_files')\n",
    "#except OSError:\n",
    "#    lfiles = os.listdir('./limit_files')\n",
    "#    for lf in lfiles:\n",
    "#        os.remove('./limit_files/'+lf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In CMSSW_7_4_7 follow the instructions here:\n",
    "\n",
    "https://twiki.cern.ch/twiki/bin/viewauth/CMS/SWGuideHiggsAnalysisCombinedLimit#ROOT6_SLC6_release_CMSSW_7_4_X\n",
    "\n",
    "and copy over the counting files and makelimitfiles.sh. \n",
    "\n",
    "Run the script and then tar up the limit files dir to here and proceed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The next step is to parse the limit files to create four histograms. A limit file (e.g. limit_t6gg_2000_1000.txt) looks like this:\n",
    "\n",
    "```\n",
    "At r = 25.550304:\tq_mu = 4.42079\tq_A  = 2.74014\tCLsb = 0.01527\tCLb  = 0.30585\tCLs  = 0.04993\n",
    "\n",
    " -- Asymptotic -- \n",
    "Observed Limit: r < 25.5503\n",
    "Expected  2.5%: r < 14.6732\n",
    "Expected 16.0%: r < 20.8888\n",
    "Expected 50.0%: r < 31.9688\n",
    "Expected 84.0%: r < 50.7008\n",
    "Expected 97.5%: r < 77.9536\n",
    "\n",
    "Done in 0.00 min (cpu), 0.04 min (real)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to fetch the information:\n",
    "\n",
    "\"Down\" : 16.0%\n",
    "\n",
    "\"Mid\" : 50.0%\n",
    "\n",
    "\"Up\" : 84.0%\n",
    "\n",
    "\"Observed\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "limdir = './limit_files/'\n",
    "\n",
    "limit_files = os.listdir(limdir)\n",
    "\n",
    "# these number should come from the arrays we defined earlier rather than hard-coded\n",
    "mSquark = np.array([1375.0, 1425.0, 1475.0, 1525.0, 1575.0, 1625.0, 1675.0, 1725.0, 1775.0, 1825.0, 1875.0, 1925.0, 1975.0, 2025.0, 2075.0, 2125.0])\n",
    "mNeutralino = np.array([50.0,150.0,250.0,350.0,450.0,550.0,650.0,750.0,850.0,950.0,1050.0,1150.0,1250.0,1350.0,1450.0,1550.0,1650.0,1750.0,1850.0,1950.0,2050.0,2150.0])\n",
    "\n",
    "nbinsx = len(mSquark)-1\n",
    "nbinsy = len(mNeutralino)-1\n",
    "\n",
    "hExpLimUp = ROOT.TH2D('up', 'up', nbinsx, mSquark, nbinsy, mNeutralino)\n",
    "hExpLimMid = ROOT.TH2D('mid', 'mid', nbinsx, mSquark, nbinsy, mNeutralino)\n",
    "hExpLimDown = ROOT.TH2D('down', 'down', nbinsx, mSquark, nbinsy, mNeutralino)\n",
    "hObsLim = ROOT.TH2D('obs', 'obs', nbinsx, mSquark, nbinsy, mNeutralino)\n",
    "\n",
    "for i in masses:\n",
    "    for j in neutralino_masses:\n",
    "        if j < i and ((i-j) > 50):\n",
    "            \n",
    "            with open(limdir+'limit_t6gg_'+str(i)+'_'+str(j)+'.txt') as fin:\n",
    "                for line in fin:\n",
    "                    \n",
    "                    if re.search('Observed', line):\n",
    "                        l = line.split('<')\n",
    "                        v = float(l[1].strip())\n",
    "                        if v > 0:\n",
    "                            hObsLim.SetBinContent(hObsLim.GetXaxis().FindBin(i), hObsLim.GetYaxis().FindBin(j), v)\n",
    "                            #print \"Filled \" + str(i) + \" and \" + str(j) + \" with \" + str(v)\n",
    "                        else:\n",
    "                            print \"No observed limit for \" + str(i) + \" and \" + str(j) \n",
    "                    if re.search('16.0%', line):\n",
    "                        l = line.split('<')\n",
    "                        v = float(l[1].strip())\n",
    "                        if v > 0:\n",
    "                            hExpLimDown.SetBinContent(hExpLimDown.GetXaxis().FindBin(i), hExpLimDown.GetYaxis().FindBin(j), v)\n",
    "            \n",
    "                    if re.search('50.0%', line):\n",
    "                        l = line.split('<')\n",
    "                        v = float(l[1].strip())\n",
    "                        if v > 0:\n",
    "                            hExpLimMid.SetBinContent(hExpLimMid.GetXaxis().FindBin(i), hExpLimMid.GetYaxis().FindBin(j), v)\n",
    "            \n",
    "                    if re.search('84.0%', line):\n",
    "                        l = line.split('<')\n",
    "                        v = float(l[1].strip())\n",
    "                        if v > 0:\n",
    "                            hExpLimUp.SetBinContent(hExpLimUp.GetXaxis().FindBin(i), hExpLimUp.GetYaxis().FindBin(j), v)\n",
    "\n",
    "    \n",
    "hObsLim.GetZaxis().SetRangeUser(0.0, 0.5)\n",
    "hExpLimDown.GetZaxis().SetRangeUser(0.0, 0.50)\n",
    "hExpLimMid.GetZaxis().SetRangeUser(0.0, 0.50)\n",
    "hExpLimUp.GetZaxis().SetRangeUser(0.0, 0.50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a new canvas and see what the histograms look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Hg0Ecx4PBwEl1QEe8dOjW4v/5buxNiAlAFbzEBDcdhcWIoOa5n/RizTRNA1+XBcAhYgLQ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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c2 = ROOT.TCanvas()\n",
    "c2.Divide(2,2)\n",
    "\n",
    "c2.cd(1)\n",
    "hObsLim.Draw('colz')\n",
    "\n",
    "c2.cd(2)\n",
    "hExpLimDown.Draw('colz')\n",
    "\n",
    "c2.cd(3)\n",
    "hExpLimMid.Draw('colz')\n",
    "\n",
    "c2.cd(4)\n",
    "hExpLimUp.Draw('colz')\n",
    "\n",
    "c2.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def MakeLimitGraph(h):\n",
    "    \n",
    "    g = ROOT.TGraph()\n",
    "    n = 0\n",
    "    m = 19\n",
    "\n",
    "    for i in range(1, h.GetNbinsY()+1):\n",
    "        \n",
    "        x = 0.0\n",
    "        y = h.GetYaxis().GetBinCenter(i)\n",
    "        \n",
    "        for j in range(1, h.GetNbinsX() + 1):\n",
    "            \n",
    "            xLow = h.GetXaxis().GetBinCenter(j)\n",
    "            xHigh = h.GetXaxis().GetBinCenter(1+j)\n",
    "            zLow = h.GetBinContent(j, i)\n",
    "            zHigh = h.GetBinContent(1 + j, i)\n",
    "\n",
    "            if zLow < 1.0 and zHigh > 1.0: \n",
    "                x = xLow + (xHigh - xLow) * (1.0 - zLow) / (zHigh - zLow)\n",
    "\n",
    "                g.SetPoint(n, x, y)\n",
    "                n += 1\n",
    "                break\n",
    "        \n",
    "    return g\n",
    "    \n",
    "\n",
    "def ScaleLimit(h, s, scaleByXSec, sigma):\n",
    "    \n",
    "    h_new = h.Clone(s)\n",
    "    \n",
    "    for i in range(1, h_new.GetNbinsX()+1):\n",
    "        for j in range(1, h_new.GetNbinsY()+1):\n",
    "            \n",
    "            m = h_new.GetXaxis().GetBinCenter(i)\n",
    "            r = h_new.GetBinContent(i, j)\n",
    "            foundM = False\n",
    "            \n",
    "            with open('./SusyCrossSections13TeVGluGlu.txt') as f_xSec:\n",
    "                for line in f_xSec:\n",
    "                    \n",
    "                    l = line.split()\n",
    "                    mSusy = float(l[0])\n",
    "                    xSec = float(l[1])\n",
    "                    unc = float(l[2])\n",
    "                    \n",
    "                    if mSusy == m: # careful!\n",
    "                        if scaleByXSec:\n",
    "                            r *= xSec\n",
    "                        r /= 1.0 + sigma * unc / 100.0\n",
    "                        foundM = True\n",
    "                        break\n",
    "            if not foundM:\n",
    "                r = -1.0\n",
    "                \n",
    "            h_new.SetBinContent(i, j, r)\n",
    "    \n",
    "    return h_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_limit\n"
     ]
    }
   ],
   "source": [
    "h_xSecLimit = ScaleLimit(hObsLim, 'obs', True, 0.0)\n",
    "h_observedLimitLow = ScaleLimit(hObsLim, 'obs', False, -1.0)\n",
    "h_observedLimitHigh = ScaleLimit(hObsLim, 'obs' , False, 1.0)\n",
    "    \n",
    "g_expectedLimit = MakeLimitGraph(hExpLimMid)\n",
    "g_expectedLimitLow = MakeLimitGraph(hExpLimDown)\n",
    "g_expectedLimitHigh = MakeLimitGraph(hExpLimUp)\n",
    "\n",
    "g_observedLimit = MakeLimitGraph(hObsLim)\n",
    "g_observedLimitLow = MakeLimitGraph(h_observedLimitLow)\n",
    "g_observedLimitHigh = MakeLimitGraph(h_observedLimitHigh)\n",
    "\n",
    "ROOT.gStyle.SetPadRightMargin(0.16)\n",
    "c_limit = ROOT.TCanvas('c_limit', '', 800, 650)\n",
    "c_limit.SetLogz()\n",
    "    \n",
    "red = np.array([0.5, 0.5, 1.0, 1.0, 1.0])\n",
    "green = np.array([0.5, 1.0, 1.0, 0.6, 0.5])\n",
    "blue = np.array([1.0, 1.0, 0.5, 0.4, 0.5])\n",
    "length = np.array([0.0, 0.34, 0.61, 0.84, 1.0])\n",
    "                 \n",
    "table = ROOT.TColor.CreateGradientColorTable(5, length, red, green, blue, 255)\n",
    "limitPalette = [table+i for i in range(1,255+1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error in <TGraphPainter::PaintGraph>: illegal number of points (0)\n",
      "Error in <TGraphPainter::PaintGraph>: illegal number of points (0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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pmiRfJbkNTqVxFxlTj3Cyj+nJSAAAELdYUkGGh6QyQlVVUndKa80F8/dV17WU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\nAADs4D8AIAf6zFpTTa0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_xSecLimit.GetXaxis().SetTitle('M_{#tilde{g}} (GeV)')\n",
    "h_xSecLimit.GetXaxis().SetTitleOffset(1.0)\n",
    "\n",
    "h_xSecLimit.GetYaxis().SetTitle('M_{#tilde{#chi}_{1}^{0}} (GeV)')\n",
    "h_xSecLimit.GetYaxis().SetTitleOffset(1.25)\n",
    "\n",
    "h_xSecLimit.GetZaxis().SetLabelOffset(-0.003)\n",
    "h_xSecLimit.GetZaxis().SetLabelOffset(1)\n",
    "h_xSecLimit.GetZaxis().SetLabelSize(0.02)\n",
    "h_xSecLimit.GetZaxis().SetTitleOffset(0.9)\n",
    "h_xSecLimit.GetZaxis().SetTitle('95% CL limit on #sigma (pb)   ')\n",
    "h_xSecLimit.GetZaxis().SetRangeUser(0.0008, 0.0015)\n",
    "\n",
    "h_xSecLimit.SetContour(255)\n",
    "h_xSecLimit.SetTitle('')\n",
    "h_xSecLimit.SetStats(False)\n",
    "h_xSecLimit.Draw('colz')\n",
    "\n",
    "palette = h_xSecLimit.GetListOfFunctions().FindObject(\"palette\");\n",
    "palette.SetX1NDC(0.85)\n",
    "palette.SetX2NDC(0.9)\n",
    "\n",
    "g_expectedLimit.SetLineColor(ROOT.kRed+1)\n",
    "g_expectedLimit.SetLineWidth(2)\n",
    "g_expectedLimit.Draw('C same')\n",
    "\n",
    "g_expectedLimitLow.SetLineColor(ROOT.kRed+1)\n",
    "g_expectedLimitLow.SetLineStyle(2)\n",
    "g_expectedLimitLow.SetLineWidth(2)\n",
    "g_expectedLimitLow.Draw('C same')\n",
    "\n",
    "g_expectedLimitHigh.SetLineColor(ROOT.kRed+1)\n",
    "g_expectedLimitHigh.SetLineStyle(2)\n",
    "g_expectedLimitHigh.SetLineWidth(2)\n",
    "g_expectedLimitHigh.Draw('C same')\n",
    "\n",
    "g_observedLimit.SetLineWidth(2)\n",
    "#g_observedLimit.Draw('C same')\n",
    "\n",
    "g_observedLimitLow.SetLineStyle(2)\n",
    "g_observedLimitLow.SetLineWidth(2)\n",
    "#g_observedLimitLow.Draw('C same')\n",
    "\n",
    "g_observedLimitHigh.SetLineStyle(2)\n",
    "g_observedLimitHigh.SetLineWidth(2)\n",
    "#g_observedLimitHigh.Draw('C same')\n",
    "\n",
    "la_limit = ROOT.TLatex()\n",
    "la_limit.SetNDC()\n",
    "\n",
    "la_limit.SetTextAlign(13)\n",
    "la_limit.SetTextSize(0.044)\n",
    "la_limit.SetTextFont(62)\n",
    "la_limit.DrawLatex(0.14, 0.95, 'CMS')\n",
    "\n",
    "la_limit.SetTextAlign(13)\n",
    "la_limit.SetTextSize(0.044)\n",
    "la_limit.SetTextFont(42)\n",
    "la_limit.DrawLatex(0.23, 0.95, 'Preliminary')\n",
    "\n",
    "la_limit.SetTextAlign(13)\n",
    "la_limit.SetTextSize(0.044)\n",
    "la_limit.SetTextFont(42)\n",
    "la_limit.DrawLatex(0.6, 0.96, '35.8 fb^{-1} (13 TeV)')\n",
    "\n",
    "la_limit.SetTextSize(0.03)\n",
    "la_limit.SetTextFont(42)\n",
    "la_limit.DrawLatex(0.14, 0.89, 'pp #rightarrow #tilde{g}#tilde{g},#tilde{g} #rightarrow qq#tilde{#chi}_{1}^{0}, #tilde{#chi}_{1}^{0}#rightarrow #gamma#tilde{G}')\n",
    "la_limit.DrawLatex(0.14, 0.84, 'NLO + NLL exclusion')\n",
    "\n",
    "le_limit = ROOT.TLegend(0.13, 0.81, 0.39, 0.7)\n",
    "le_limit.SetFillStyle(0)\n",
    "le_limit.SetLineColor(ROOT.kWhite)\n",
    "le_limit.SetTextSize(0.02)\n",
    "le_limit.SetTextFont(42)\n",
    "#le_limit.AddEntry(g_observedLimit, 'Obs. limit', 'l')\n",
    "#le_limit.AddEntry(g_observedLimitLow, 'Obs. limit #pm 1 #sigma_{th}','l')\n",
    "le_limit.AddEntry(g_expectedLimit, 'Exp. limit', 'l')\n",
    "le_limit.AddEntry(g_expectedLimitLow, 'Exp. limit #pm 1 #sigma_{ex}','l')\n",
    "le_limit.Draw('same');\n",
    "\n",
    "c_limit.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}