{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from plotter import StackHist, StackHistWithSignificance\n",
    "from utils import ResultSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running analysis for sample:  TTZ\n",
      "Writing log data to ../ichep_data/TTZToLLNuNu_treeProducerSusyMultilepton_tree_result.log\n",
      "Running analysis for sample:  TTW\n",
      "Writing log data to ../ichep_data/TTWToLNu_treeProducerSusyMultilepton_tree_result.log\n",
      "Running analysis for sample:  TTH\n",
      "Writing log data to ../ichep_data/TTHnobb_mWCutfix_ext1_treeProducerSusyMultilepton_tree_result.log\n",
      "Running analysis for sample:  TTTT\n",
      "Writing log data to ../ichep_data/TTTT_ext_treeProducerSusyMultilepton_tree_result.log\n"
     ]
    }
   ],
   "source": [
    "rs_TTZ  = ResultSet(\"TTZ\",  \"../ichep_data/TTZToLLNuNu_treeProducerSusyMultilepton_tree.root\")\n",
    "rs_TTW  = ResultSet(\"TTW\",  \"../ichep_data/TTWToLNu_treeProducerSusyMultilepton_tree.root\")\n",
    "rs_TTH = ResultSet(\"TTH\", \"../ichep_data/TTHnobb_mWCutfix_ext1_treeProducerSusyMultilepton_tree.root\")\n",
    "rs_TTTT = ResultSet(\"TTTT\", \"../ichep_data/TTTT_ext_treeProducerSusyMultilepton_tree.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def stack(attr, ax, title, cls=StackHist, stack_signal=False, scale = None, **kwargs):\n",
    "    hs = cls(title)\n",
    "    hs.add_mc_background(getattr(rs_TTZ, attr), 'TTZ', lumi=rs_TTZ.lumi, plot_color='r')\n",
    "    hs.add_mc_background(getattr(rs_TTW, attr), 'TTW', lumi=rs_TTW.lumi, plot_color='g')\n",
    "    hs.add_mc_background(getattr(rs_TTH, attr), 'TTH', lumi=rs_TTH.lumi, plot_color='b')\n",
    "    if scale is None:\n",
    "        scale = 1 if stack_signal else 20\n",
    "    hs.set_mc_signal(getattr(rs_TTTT,attr), 'TTTT', lumi=rs_TTTT.lumi, stack=stack_signal, scale=scale, plot_color='k')\n",
    "    \n",
    "    hs.luminosity = 40\n",
    "    hs.energy = 13\n",
    "    hs.logy=False\n",
    "    hs.draw(ax, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main discriminating variables in the analysis are Jet and B-Jet Multiplicities\n",
    "\n",
    "Below are shown the distributions for the TTTT as well as relevant BG processes. The \"baseline\" selection for the trilepton channel is:\n",
    "  - Exactly three leptons\n",
    "  - Three or more B-Jets\n",
    "  - Z-Mass Window veto: The invariant mass of all same-flavor opposite-sign lepton pairs must be outside the Z-Mass window (70,105)GeV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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jgn4AAAAAAOixcwf9pZSliygEAAAAAAB4c7fe9oWllNkkd5MMk2xOtd9Pspdk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\nc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ctdt+BlMQ50GLUFgC6r2bhKO5c/oIpVT0g2orxTLtbA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\n5ot34V/kFgAAIO+YUFiDPvdVDZx8nfb+4T+069nvaes/X60+n56kYVd+T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ABAgFHoBwAA\nAAAAAAAgwCj0AwAAAAAAAAAQYBT6AQAAAAAAAAAIMAr9AAAAAAAAAAAE2P8HqxeRd37PTh4AAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8daa2e4390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(2,2, figsize=(26,20))\n",
    "stack('jet_count', ax0, 'Jet Multiplicity', cls=StackHistWithSignificance, high_cut_significance=True)\n",
    "stack('jet_count_trilepton', ax1, 'Jet Multiplicity', cls=StackHistWithSignificance, high_cut_significance=True)\n",
    "stack('jet_count_b_jet3', ax2, 'Jet Multiplicity', cls=StackHistWithSignificance, scale=1, high_cut_significance=True)\n",
    "stack('jet_count_z_mass_veto', ax3, 'Jet Multiplicity', cls=StackHistWithSignificance, scale=1, high_cut_significance=True)\n",
    "# stack('b_jet_count_base_selection', ax3, 'B-Jet Multiplicity', cls=StackHistWithSignificance, scale=1, high_cut_significance=True)\n",
    "# stack('b_jet_count', ax1, 'B-Jet Multiplicity', cls=StackHistWithSignificance, high_cut_significance=True)\n",
    "\n",
    "# stack('lepton_count', ax4, 'Lepton Multiplicity', )\n",
    "# stack('lepton_count', ax5, 'Lepton Multiplicity', stack_signal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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25iS5fT01EkiLv9cWrp5F3mtr5WokqyZduyGJYCSz6T4wEod6+lZy1NYq2d3d1dOnT6/E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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8daa412908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax0) = plt.subplots(1,1, figsize=(6,5))\n",
    "stack('leading_lepton_relIso', ax0, 'Leading Lepton Relative Isolation', stack_signal=False, cls=StackHistWithSignificance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49106\n",
      "41340\n"
     ]
    }
   ],
   "source": [
    "xs = list(rs_TTH.leading_lepton_relIso_all)\n",
    "print(len([x for x in xs if x == 0]))\n",
    "print(len([x for x in xs if x != 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pRiFw0B0AANgnKsUAAACoPCrFJcX6xAAAAJsjKcZhpXDqZ6ZSAACAtDF9AgAA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\nAJVHUgwAAIDK+/+P0VEPj9DxwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8daa3fd6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(1,2, figsize=(12,5))\n",
    "stack('reco_top_mass', ax0, 'Reconstructed Tri-jet Mass', stack_signal=True)\n",
    "stack('mc_top_mass', ax1, 'Reconstructed MC Tri-jet Mass', stack_signal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RxEOg+ugJRw8GGgEL71ydpLuti+Q7bVmPUJ6yK0nLy8ulOsXFeBwAZUQSDgCI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\nyyesAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8daa5325f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(1,2, figsize=(12,5))\n",
    "stack('leading_jet_pt', ax0, 'Leading Jet $P_t$', cls=StackHistWithSignificance, )\n",
    "stack('leading_lepton_pt', ax1, 'Leading Lepton $P_t$', cls=StackHistWithSignificance, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7095\n",
      "15515\n",
      "82006\n",
      "70289\n",
      "49972\n",
      "29240\n",
      "---\n",
      "138\n",
      "103\n",
      "62\n"
     ]
    }
   ],
   "source": [
    "print(rs_TTH.b_jet3_count)\n",
    "print(rs_TTH.trilepton_count)\n",
    "print(rs_TTH.z_mass_veto_count)\n",
    "print(rs_TTH.J4_count)\n",
    "print(rs_TTH.J5_count)\n",
    "print(rs_TTH.J6_count)\n",
    "print('---')\n",
    "print(rs_TTH.SR4j_count)\n",
    "print(rs_TTH.SR5j_count)\n",
    "print(rs_TTH.SR6j_count)\n",
    "\n",
    "# 'ROOT.vectos' in str(type(rs_TTH.b_jet3_count))"
   ]
  }
 ],
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