notebook_micro_HEP_analysis_cpp_TLorentz.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 ATLAS OpenData HEP analysis C++ notebook example</h1></CENTER>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#include <iostream>\n",
    "#include <string>\n",
    "#include <stdio.h>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TChain *dataset = new TChain(\"mini\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset->Add(\"mc_105986.ZZ.root\");\n",
    "\n",
    "//This input is readed directly from the Internet. If you are ofline, you can use the line above\n",
    "//dataset->Add(\"http://opendata.atlas.cern/release/samples/MC/mc_147770.Zee.root\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "const int vs = 5;\n",
    "\n",
    "Int_t   lepton_n = -1,\n",
    "        lepton_charge[vs], \n",
    "        lepton_type[vs];\n",
    "\n",
    "Float_t lepton_pt[vs],\n",
    "        lepton_eta[vs],\n",
    "        lepton_phi[vs],\n",
    "        lepton_E[vs];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset->SetBranchAddress(\"lep_n\",      &lepton_n);\n",
    "dataset->SetBranchAddress(\"lep_charge\", &lepton_charge);\n",
    "dataset->SetBranchAddress(\"lep_type\",   &lepton_type);\n",
    "dataset->SetBranchAddress(\"lep_pt\",     &lepton_pt);\n",
    "dataset->SetBranchAddress(\"lep_eta\",    &lepton_eta);\n",
    "dataset->SetBranchAddress(\"lep_phi\",    &lepton_phi);\n",
    "dataset->SetBranchAddress(\"lep_E\",   &lepton_E);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TH1F *h_lep_pt_leptons = new TH1F(\"h_lep_pt_leptons\",\"Lepton pt in GeV\",20,0,200);\n",
    "h_lep_pt_leptons->SetFillColor(kBlue);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "int nentries, nbytes, i;\n",
    "nentries = (Int_t)dataset->GetEntries();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total # events = 125000. Events to run = 125000 corresponding to 100% of total events!\r\n"
     ]
    }
   ],
   "source": [
    "// IMPORTANT: fraction events we want to run\n",
    "fraction_events = 1;\n",
    "events_to_run = nentries*fraction_events;\n",
    "\n",
    "std::cout << \"Total # events = \"  << nentries\n",
    "          << \". Events to run = \" << events_to_run\n",
    "          << \" corresponding to \" << fraction_events*100\n",
    "          << \"% of total events!\" << std::endl;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TLorentzVector l1;         \n",
    "TLorentzVector l2;  \n",
    "TLorentzVector l1plusl2;  \n",
    "\n",
    "for (i=0; i<events_to_run; i++)\n",
    "{\n",
    "    nbytes = dataset->GetEntry(i);\n",
    "    \n",
    "    if(lepton_n==2) // Number of leptons in the events has to be at least 2\n",
    "    {\n",
    "        if(lepton_type[0] == lepton_type[1]) //Leptons of the same family, i.e. 2 electrons or 2 muons (those are the two most energetic leptons)\n",
    "        {\n",
    "            if(lepton_charge[0] != lepton_charge[1]) // The two selected leptons must have opposite charge\n",
    "            {\n",
    "                //PT\n",
    "                float lepton_mass = lepton_pt[1]/1000.; // The default value in the root file is in MeV, so, we divide by 1000 to get it in GeV\n",
    "               // h_lep_pt_leptons->Fill(lepton_pt_inGeV);\n",
    "                \n",
    "                                \n",
    "              \n",
    "\n",
    "                l1.SetPtEtaPhiE(lepton_pt[0]/1000., lepton_eta[0], lepton_phi[0], lepton_E[0]/1000.);\n",
    "                l2.SetPtEtaPhiE(lepton_pt[1]/1000., lepton_eta[1], lepton_phi[1], lepton_E[1]/1000.);\n",
    "                l1plusl2=l1+l2;\n",
    "                //l1plusl2.M();\n",
    "                h_lep_pt_leptons->Fill(l1plusl2.M());\n",
    "\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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OCHIMPXBYuY/ioiiqH3FhaMzC/3FhaPxiExj7z2PLF58KHe9hlbhW/OElh/eESa2KzNBA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\nKAAAWf8fELRo8yWVBnUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TCanvas *cz = new TCanvas(\"cz\",\"cz\",10,10,700,700);\n",
    "h_lep_pt_leptons->Draw();\n",
    "cz->Draw();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ROOT Prompt",
   "language": "c++",
   "name": "root"
  },
  "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
}