pyROOT5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<CENTER><img src=\"opendata-top-transblack.png\" style=\"width:60%\"></CENTER>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<CENTER><h1>Simple pyROOT notebook example</h1></CENTER>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "require(['codemirror/mode/clike/clike'], function(Clike) { console.log('ROOTaaS - C++ CodeMirror module loaded'); });"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "IPython.CodeCell.config_defaults.highlight_modes['magic_text/x-c++src'] = {'reg':[/^%%cpp/]};"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to ROOTaaS 6.06/08\n"
     ]
    }
   ],
   "source": [
    "import ROOT\n",
    "from math import *\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## f = ROOT.TFile.Open(\"http://opendata.atlas.cern/release/samples/MC/mc_105986.ZZ.root\")\n",
    "bgs= ROOT.TFile.Open(\"mc_105985.WW.root\")\n",
    "sig = ROOT.TFile.Open(\"mc_161005.ggH125_WW2lep.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c = ROOT.TCanvas(\"testCanvas\",\"a first way to plot a variable\",800,600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b = bgs.Get(\"mini\")\n",
    "s = sig.Get(\"mini\") # t for signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "h_bgs = ROOT.TH1F(\"variable2_bgs\",\"Example plot: Number of Jets\",10,0,10)\n",
    "h_sig = ROOT.TH1F(\"variable2_sig\",\"Example plot: Number of Jets\",10,0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "# Selection for signal\n",
    "weight = 0.00014\n",
    "\n",
    "for event in s:\n",
    "    # First, select only two lepton or more\n",
    "    if s.lep_n > 1:\n",
    "        # Make sure the e\n",
    "        if s.lep_type[0] != s.lep_type[1]:\n",
    "            # Let's define one TLorentz vector for each, e.i. two vectors!\n",
    "            leadingLep = ROOT.TLorentzVector(s.lep_pt[0], s.lep_eta[0], s.lep_phi[0], s.lep_E[0])\n",
    "            secondLep  = ROOT.TLorentzVector(s.lep_pt[1], s.lep_eta[1], s.lep_phi[1], s.lep_E[1])\n",
    "\n",
    "            # Next line does the addition of the two TLorentz vectors above and so, we can ask the mass very easy     \n",
    "            TL_ll = leadingLep + secondLep\n",
    "            mll = TL_ll.M()/1000.\n",
    "            if mll < 55 or mll > 10:\n",
    "                has_btags = any([w > 0.789 for w in s.jet_MV1])\n",
    "                if not has_btags:\n",
    "                #btags = sum([1 for count in range(0,3) if s.jet_MV1() > 0.789])\n",
    "                #if btags = 0:\n",
    "                # total tranvers momentum\n",
    "                    if s.lep_pt > 0 or s.lep_pt < 200:\n",
    "                        if s.met_et/1000. < 200:\n",
    "                            ll = s.lep_phi[0] - s.lep_phi[1]   \n",
    "                            if s.met_phi - ll < 180 or s.met_phi - ll > 10:\n",
    "                                if ll < 100:\n",
    "                                    h_sig.Fill(s.jet_n, weight)\n",
    "\n",
    "print \"Done\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "# selection for background\n",
    "for event in b:\n",
    "    # First is to use minimal two lepton\n",
    "    if b.lep_n > 1:\n",
    "        if b.lep_type[0] != b.lep_type[1]:\n",
    "            # Let's define one TLorentz vector for each, e.i. two vectors!\n",
    "            leadingLepB = ROOT.TLorentzVector(b.lep_pt[0], b.lep_eta[0], b.lep_phi[0], b.lep_E[0])\n",
    "            secondLepB  = ROOT.TLorentzVector(b.lep_pt[1], b.lep_eta[1], b.lep_phi[1], b.lep_E[1])\n",
    "\n",
    "            # Next line does the addition of the two TLorentz vectors above and so, we can ask the mass very easy     \n",
    "            TL_llB = leadingLepB + secondLepB\n",
    "            mllB = TL_ll.M()/1000.\n",
    "            if mllB < 55 or mllB > 10:\n",
    "                has_btagsB = any([w > 0.789 for w in b.jet_MV1])\n",
    "                if not has_btagsB:\n",
    "                    # total tranvers momentum\n",
    "                    if b.lep_pt > 0 or b.lep_pt < 200:\n",
    "                        if b.met_et/1000. < 200:\n",
    "                            llB = b.lep_phi[0] - b.lep_phi[1]   \n",
    "                            if b.met_phi - llB < 180 or b.met_phi - llB > 10:\n",
    "                                if llB < 100:\n",
    "                                    h_bgs.Fill(b.jet_n)\n",
    "print \"Done\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scale_sig =  4.94045042869\n",
      "scale_bgs =  34758.0\n",
      "original sb = 0.0197989898732\n",
      "new sb = 0.0264995901215\n"
     ]
    }
   ],
   "source": [
    "scale_sig = h_sig.Integral()\n",
    "h_sig.Scale(1/scale_sig)\n",
    "\n",
    "orig_scale_sig = 14.\n",
    "orig_scale_bgs = 500000.\n",
    "\n",
    "scale_bgs = h_bgs.Integral()\n",
    "h_sig.Scale(1/scale_bgs)\n",
    "\n",
    "print \"scale_sig = \", scale_sig\n",
    "print \"scale_bgs = \", scale_bgs\n",
    "\n",
    "# calculate significance\n",
    "orig_sb = orig_scale_sig / sqrt(orig_scale_bgs)\n",
    "sb = scale_sig / sqrt(scale_bgs)\n",
    "\n",
    "print \"original sb =\", orig_sb\n",
    "print \"new sb =\", sb "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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/9sSsVU1un/039KOtqqqqqrIsm1Y7tW+g18rpM32b31yfXe1WUF/DFHmdUAUA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\nTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_bgs.SetFillStyle(3008)\n",
    "h_bgs.SetFillColor(4)\n",
    "h_sig.SetFillStyle(3003)\n",
    "h_sig.SetFillColor(2)\n",
    "\n",
    "legend=ROOT.TLegend(0.75,0.7,0.9,0.9)\n",
    "legend.AddEntry(h_bgs,\"Background(WW)\",\"f\")\n",
    "legend.AddEntry(h_sig,\"Signal(Higgs)\",\"f\")\n",
    "\n",
    "h_sig.SetLineColor(2)\n",
    "h_sig.SetStats(0)\n",
    "h_bgs.SetStats(0)\n",
    "\n",
    "h_bgs.Draw()\n",
    "h_sig.Draw(\"same\")\n",
    "legend.Draw()\n",
    "c.Draw()\n"
   ]
  },
  {
   "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}