{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from plotter import StackHist\n",
    "from utils import ResultSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading unchanged result file  ../data/TTZToLLNuNu_treeProducerSusyMultilepton_tree_result.root\n",
      "Loading unchanged result file  ../data/TTWToLNu_treeProducerSusyMultilepton_tree_result.root\n",
      "Loading unchanged result file  ../data/TTHnobb_mWCutfix_ext1_treeProducerSusyMultilepton_tree_result.root\n",
      "Loading unchanged result file  ../data/TTTT_ext_treeProducerSusyMultilepton_tree_result.root\n"
     ]
    }
   ],
   "source": [
    "rs_TTZ  = ResultSet(\"TTZ\",  \"../data/TTZToLLNuNu_treeProducerSusyMultilepton_tree.root\")\n",
    "rs_TTW  = ResultSet(\"TTW\",  \"../data/TTWToLNu_treeProducerSusyMultilepton_tree.root\")\n",
    "rs_TTH = ResultSet(\"TTH\", \"../data/TTHnobb_mWCutfix_ext1_treeProducerSusyMultilepton_tree.root\")\n",
    "rs_TTTT = ResultSet(\"TTTT\", \"../data/TTTT_ext_treeProducerSusyMultilepton_tree.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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k5LXf4/vk+Pi4bmxs1CQ1SV1bWxt+Dt2FfoO+g8/NafsZGN3n+LK8vHyq38rK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\nkAIAAACgU/8Ld3oToNbVpY0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66d55a3160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def stack(attr, ax, title, stack_signal=False):\n",
    "    hs = StackHist(title)\n",
    "    hs.add_mc_background(getattr(rs_TTZ,attr), 'TTZ', lumi=rs_TTZ.lumi)\n",
    "    hs.add_mc_background(getattr(rs_TTW,attr), 'TTW', lumi=rs_TTW.lumi)\n",
    "    hs.add_mc_background(getattr(rs_TTH,attr), 'TTH', lumi=rs_TTH.lumi)\n",
    "    \n",
    "    hs.set_mc_signal(getattr(rs_TTTT,attr), 'TTTT', lumi=rs_TTTT.lumi, stack=stack_signal, scale=20)\n",
    "    \n",
    "    hs.luminosity = 40\n",
    "    hs.energy = 13\n",
    "    hs.draw(ax)\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(1,2, figsize=(12,5))\n",
    "stack('b_jet_count', ax0, 'B-Jet Multiplicity', )\n",
    "stack('lepton_count', ax1, 'Lepton Multiplicity', )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}