01-Introduction-MC-v1-solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.22/00\n"
     ]
    }
   ],
   "source": [
    "#Import the packages\n",
    "import ROOT as r\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 0. The datasets\n",
    " \n",
    " 1. **Z to $\\mu\\mu$ simultion**\n",
    "\n",
    "We will begin by looking at simulation data corresponding to a Z boson decaying into two muons, which we will be comparing with real CMS data later on after getting familiar with the software.\n",
    "\n",
    "These simulated datasets correspond to the collision data collected by the CMS experiment in 2012.\n",
    "\n",
    "First, let's understand what we are looking at.\n",
    "\n",
    "The dataset name is: \n",
    "\n",
    "`/DYToMuMu_M-20_CT10_8TeV-powheg-pythia6/Summer12_DR53X-PU_S10_START53_V19-v1/AODSIM`\n",
    "\n",
    "The dataset name follows a specific convention to provide a self-explanatory description of the dataset.\n",
    "\n",
    "It follows a pattern like this:\n",
    "\n",
    "`/Primary dataset/Condition/Format/`\n",
    "\n",
    " - The `Primary dataset` tells you about the process for a simulation dataset, and the data taking period and trigger stream that the data was collected if it is data.\n",
    "\n",
    " - The `Condition` part tells you what reconstruction condition has been applied to the dataset.\n",
    "\n",
    " - Lastly, the `Format` part tells you the dataformat. We will work with the simplified version of `MINIAOD` format, called `NanoAOD`, which I have prepared for you in advance.**\n",
    "\n",
    "The dataset is downloaded from here. The files `dy_mc.root`/`dy_mc_small.root` is a tiny fraction of the whole dataset, as we do not need the whole 14TB of events.  \n",
    "http://opendata.cern.ch/record/7741\n",
    "\n",
    " 2. **Data - Double Muon data**\n",
    "\n",
    "We will be using proton-proton collision data at $\\sqrt{s}=8 TeV$ taken during 2012 by the CMS.\n",
    "\n",
    "Of course, we will not be using all of them, the dataset name \n",
    "\n",
    "`/DoubleMuParked/Run2012B-22Jan2013-v1/AOD`\n",
    "\n",
    "tells you with subset of data we will be using.\n",
    "\n",
    "Here `DoubleMuParked` means the data collected was triggered by a stream of High Level Trigger(HLT) trigger paths that targets muon, such as\n",
    "```\n",
    "HLT_DoubleMu5_IsoMu5\n",
    "```\n",
    "which means it requires two muons with at least 5 GeV and one of them is isolated from other particles.\n",
    "\n",
    "Like the simulation data, a skimmed version have been made for you: `Run2012B_DoubleMuParked_nanoaod.root`\n",
    "\n",
    "More details can be found in this record:\n",
    "http://opendata.cern.ch/record/6004\n",
    "\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "\n",
    "** In case you are curious, the conversion is done also with cms open software here:\n",
    "\n",
    "https://github.com/cms-opendata-analyses/AOD2NanoAODOutreachTool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1 Exploring ROOT files with pyROOT\n",
    "\n",
    "In this section, we will go over some of the basic operations to explore a root file, working with the small simulation file first.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TFile**\t\tRun2012B_DoubleMuParked_nanoaod.root\t\n",
      " TFile*\t\tRun2012B_DoubleMuParked_nanoaod.root\t\n",
      "  KEY: TDirectoryFile\taod2nanoaod;1\taod2nanoaod\n",
      "TDirectoryFile*\t\taod2nanoaod\taod2nanoaod\n",
      " KEY: TTree\tEvents;1\tEvents\n"
     ]
    }
   ],
   "source": [
    "#Opening a ROOT file\n",
    "f = r.TFile(\"dy_mc_small.root\")\n",
    "# list the content of the file\n",
    "f.ls()\n",
    "dir = f.GetDirectory(\"aod2nanoaod\")\n",
    "dir.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TTree\n",
    "TTree is the main object that holds the data. \n",
    "\n",
    "In general, all the objects in a TFile can be obtained with the `Get` Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = f.Get(\"aod2nanoaod/Events\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16922\n"
     ]
    }
   ],
   "source": [
    "## See how many events are there in this tree\n",
    "nEntries = t.GetEntries()\n",
    "print(nEntries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TTree.Show()\n",
    " There a several methods to see what's inside a tree\n",
    "\n",
    "`Show(n)` prints out the n-th event in the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======> EVENT:0\n",
      " run             = 1\n",
      " luminosityBlock = 155745\n",
      " event           = 15574401\n",
      " HLT_IsoMu24_eta2p1 = 0\n",
      " HLT_IsoMu24     = 0\n",
      " HLT_IsoMu17_eta2p1_LooseIsoPFTau20 = 0\n",
      " PV_npvs         = 10\n",
      " PV_x            = 0.242836\n",
      " PV_y            = 0.394151\n",
      " PV_z            = 0.156161\n",
      " nMuon           = 0\n",
      " nElectron       = 0\n",
      " nTau            = 1\n",
      " Tau_pt          = 15.8333\n",
      " Tau_eta         = 2.73775\n",
      " Tau_phi         = -0.505689\n",
      " Tau_mass        = 4.45695\n",
      " Tau_charge      = 0\n",
      " Tau_decayMode   = -1\n",
      " Tau_relIso_all  = nan\n",
      " Tau_jetIdx      = -1\n",
      " Tau_genPartIdx  = 0\n",
      " Tau_idDecayMode = 0\n",
      " Tau_idIsoRaw    = 0\n",
      " Tau_idIsoVLoose = 0\n",
      " Tau_idIsoLoose  = 0\n",
      " Tau_idIsoMedium = 0\n",
      " Tau_idIsoTight  = 0\n",
      " Tau_idAntiEleLoose = 0\n",
      " Tau_idAntiEleMedium = 0\n",
      " Tau_idAntiEleTight = 0\n",
      " Tau_idAntiMuLoose = 1\n",
      " Tau_idAntiMuMedium = 1\n",
      " Tau_idAntiMuTight = 1\n",
      " MET_pt          = 25.3113\n",
      " MET_phi         = -1.33268\n",
      " MET_sumet       = 544.79\n",
      " MET_significance = 4.06671\n",
      " MET_CovXX       = 157.977\n",
      " MET_CovXY       = 0.1051\n",
      " MET_CovYY       = 157.563\n",
      " nJet            = 0\n",
      " nGenPart        = 1\n",
      " GenPart_pt      = 0.00718976\n",
      " GenPart_eta     = 2.63121\n",
      " GenPart_phi     = -1.49576\n",
      " GenPart_mass    = 0.00051\n",
      " GenPart_pdgId   = 11\n",
      " GenPart_status  = 1\n"
     ]
    }
   ],
   "source": [
    "t.Show(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TTree.Print()\n",
    "\n",
    "`Print()` gives you more information about the branches, esp dataformat. \n",
    "\n",
    "Here, `run/I` tells you that the branch `run` is of type integer\n",
    "\n",
    "whereas, `PV_x/F` means PV_x is a float\n",
    "\n",
    "Finally, `Muon_pt[nMuon]/F ` is a array of floats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************************************************************************\n",
      "*Tree    :Events    : Events                                                 *\n",
      "*Entries :    12793 : Total =        12552048 bytes  File  Size =    5306422 *\n",
      "*        :          : Tree compression factor =   2.36                       *\n",
      "******************************************************************************\n",
      "*Br    0 :run       : run/I                                                  *\n",
      "*Entries :    12793 : Total  Size=      51797 bytes  File Size  =        464 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression= 110.59     *\n",
      "*............................................................................*\n",
      "*Br    1 :luminosityBlock : luminosityBlock/i                                *\n",
      "*Entries :    12793 : Total  Size=      51869 bytes  File Size  =        984 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=  52.17     *\n",
      "*............................................................................*\n",
      "*Br    2 :event     : event/l                                                *\n",
      "*Entries :    12793 : Total  Size=     103137 bytes  File Size  =      23047 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   4.45     *\n",
      "*............................................................................*\n",
      "*Br    3 :HLT_IsoMu24_eta2p1 : HLT_IsoMu24_eta2p1/O                          *\n",
      "*Entries :    12793 : Total  Size=      13415 bytes  File Size  =       2926 *\n",
      "*Baskets :        1 : Basket Size=      32000 bytes  Compression=   4.40     *\n",
      "*............................................................................*\n",
      "*Br    4 :HLT_IsoMu24 : HLT_IsoMu24/O                                        *\n",
      "*Entries :    12793 : Total  Size=      13380 bytes  File Size  =       2937 *\n",
      "*Baskets :        1 : Basket Size=      32000 bytes  Compression=   4.38     *\n",
      "*............................................................................*\n",
      "*Br    5 :HLT_IsoMu17_eta2p1_LooseIsoPFTau20 :                               *\n",
      "*         | HLT_IsoMu17_eta2p1_LooseIsoPFTau20/O                             *\n",
      "*Entries :    12793 : Total  Size=      13495 bytes  File Size  =       2822 *\n",
      "*Baskets :        1 : Basket Size=      32000 bytes  Compression=   4.57     *\n",
      "*............................................................................*\n",
      "*Br    6 :PV_npvs   : PV_npvs/I                                              *\n",
      "*Entries :    12793 : Total  Size=      51821 bytes  File Size  =      14055 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   3.65     *\n",
      "*............................................................................*\n",
      "*Br    7 :PV_x      : PV_x/F                                                 *\n",
      "*Entries :    12793 : Total  Size=      51803 bytes  File Size  =      40268 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   1.27     *\n",
      "*............................................................................*\n",
      "*Br    8 :PV_y      : PV_y/F                                                 *\n",
      "*Entries :    12793 : Total  Size=      51803 bytes  File Size  =      38811 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   1.32     *\n",
      "*............................................................................*\n",
      "*Br    9 :PV_z      : PV_z/F                                                 *\n",
      "*Entries :    12793 : Total  Size=      51803 bytes  File Size  =      47819 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   1.07     *\n",
      "*............................................................................*\n",
      "*Br   10 :nMuon     : nMuon/i                                                *\n",
      "*Entries :    12793 : Total  Size=      51809 bytes  File Size  =       7812 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   6.57     *\n",
      "*............................................................................*\n",
      "*Br   11 :Muon_pt   : Muon_pt[nMuon]/F                                       *\n",
      "*Entries :    12793 : Total  Size=     122816 bytes  File Size  =      85904 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   1.42     *\n",
      "*............................................................................*\n",
      "*Br   12 :Muon_eta  : Muon_eta[nMuon]/F                                      *\n",
      "*Entries :    12793 : Total  Size=     122826 bytes  File Size  =      87834 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   1.39     *\n",
      "*............................................................................*\n",
      "*Br   13 :Muon_phi  : Muon_phi[nMuon]/F                                      *\n",
      "*Entries :    12793 : Total  Size=     122826 bytes  File Size  =      87985 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   1.39     *\n",
      "*............................................................................*\n",
      "*Br   14 :Muon_mass : Muon_mass[nMuon]/F                                     *\n",
      "*Entries :    12793 : Total  Size=     122836 bytes  File Size  =      18606 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   6.57     *\n",
      "*............................................................................*\n",
      "*Br   15 :Muon_charge : Muon_charge[nMuon]/I                                 *\n",
      "*Entries :    12793 : Total  Size=     122849 bytes  File Size  =      28486 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   4.29     *\n",
      "*............................................................................*\n",
      "*Br   16 :Muon_pfRelIso03_all : Muon_pfRelIso03_all[nMuon]/F                 *\n",
      "*Entries :    12793 : Total  Size=     122936 bytes  File Size  =      71742 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   1.70     *\n",
      "*............................................................................*\n",
      "*Br   17 :Muon_pfRelIso04_all : Muon_pfRelIso04_all[nMuon]/F                 *\n",
      "*Entries :    12793 : Total  Size=     122936 bytes  File Size  =      78995 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   1.55     *\n",
      "*............................................................................*\n",
      "*Br   18 :Muon_tightId : Muon_tightId[nMuon]/O                               *\n",
      "*Entries :    12793 : Total  Size=      69768 bytes  File Size  =      23720 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   2.92     *\n",
      "*............................................................................*\n",
      "*Br   19 :Muon_softId : Muon_softId[nMuon]/O                                 *\n",
      "*Entries :    12793 : Total  Size=      69760 bytes  File Size  =      22925 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   3.02     *\n",
      "*............................................................................*\n",
      "*Br   20 :Muon_dxy  : Muon_dxy[nMuon]/F                                      *\n",
      "*Entries :    12793 : Total  Size=     122826 bytes  File Size  =      85515 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   1.43     *\n",
      "*............................................................................*\n",
      "*Br   21 :Muon_dxyErr : Muon_dxyErr[nMuon]/F                                 *\n",
      "*Entries :    12793 : Total  Size=     122856 bytes  File Size  =      82267 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   1.49     *\n",
      "*............................................................................*\n",
      "*Br   22 :Muon_dz   : Muon_dz[nMuon]/F                                       *\n",
      "*Entries :    12793 : Total  Size=     122816 bytes  File Size  =      85750 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   1.43     *\n",
      "*............................................................................*\n",
      "*Br   23 :Muon_dzErr : Muon_dzErr[nMuon]/F                                   *\n",
      "*Entries :    12793 : Total  Size=     122846 bytes  File Size  =      81952 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   1.49     *\n",
      "*............................................................................*\n",
      "*Br   24 :Muon_jetIdx : Muon_jetIdx[nMuon]/I                                 *\n",
      "*Entries :    12793 : Total  Size=     122849 bytes  File Size  =      37401 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   3.27     *\n",
      "*............................................................................*\n",
      "*Br   25 :Muon_genPartIdx : Muon_genPartIdx[nMuon]/I                         *\n",
      "*Entries :    12793 : Total  Size=     122889 bytes  File Size  =      28294 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   4.32     *\n",
      "*............................................................................*\n",
      "*Br   26 :nElectron : nElectron/i                                            *\n",
      "*Entries :    12793 : Total  Size=      51833 bytes  File Size  =       3504 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=  14.65     *\n",
      "*............................................................................*\n",
      "*Br   27 :Electron_pt : Electron_pt[nElectron]/F                             *\n",
      "*Entries :    12793 : Total  Size=      57832 bytes  File Size  =      10377 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   5.51     *\n",
      "*............................................................................*\n",
      "*Br   28 :Electron_eta : Electron_eta[nElectron]/F                           *\n",
      "*Entries :    12793 : Total  Size=      57840 bytes  File Size  =      10616 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   5.39     *\n",
      "*............................................................................*\n",
      "*Br   29 :Electron_phi : Electron_phi[nElectron]/F                           *\n",
      "*Entries :    12793 : Total  Size=      57840 bytes  File Size  =      10637 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   5.38     *\n",
      "*............................................................................*\n",
      "*Br   30 :Electron_mass : Electron_mass[nElectron]/F                         *\n",
      "*Entries :    12793 : Total  Size=      57848 bytes  File Size  =      10625 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   5.39     *\n",
      "*............................................................................*\n",
      "*Br   31 :Electron_charge : Electron_charge[nElectron]/I                     *\n",
      "*Entries :    12793 : Total  Size=      57857 bytes  File Size  =       5476 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=  10.45     *\n",
      "*............................................................................*\n",
      "*Br   32 :Electron_pfRelIso03_all : Electron_pfRelIso03_all[nElectron]/F     *\n",
      "*Entries :    12793 : Total  Size=      57928 bytes  File Size  =       7110 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   8.05     *\n",
      "*............................................................................*\n",
      "*Br   33 :Electron_dxy : Electron_dxy[nElectron]/F                           *\n",
      "*Entries :    12793 : Total  Size=      57840 bytes  File Size  =      10778 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   5.31     *\n",
      "*............................................................................*\n",
      "*Br   34 :Electron_dxyErr : Electron_dxyErr[nElectron]/F                     *\n",
      "*Entries :    12793 : Total  Size=      57864 bytes  File Size  =      10514 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   5.44     *\n",
      "*............................................................................*\n",
      "*Br   35 :Electron_dz : Electron_dz[nElectron]/F                             *\n",
      "*Entries :    12793 : Total  Size=      57832 bytes  File Size  =      10783 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   5.31     *\n",
      "*............................................................................*\n",
      "*Br   36 :Electron_dzErr : Electron_dzErr[nElectron]/F                       *\n",
      "*Entries :    12793 : Total  Size=      57856 bytes  File Size  =      10562 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   5.42     *\n",
      "*............................................................................*\n",
      "*Br   37 :Electron_cutBasedId : Electron_cutBasedId[nElectron]/O             *\n",
      "*Entries :    12793 : Total  Size=      53624 bytes  File Size  =       4988 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=  10.62     *\n",
      "*............................................................................*\n",
      "*Br   38 :Electron_pfId : Electron_pfId[nElectron]/O                         *\n",
      "*Entries :    12793 : Total  Size=      53576 bytes  File Size  =       5233 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=  10.12     *\n",
      "*............................................................................*\n",
      "*Br   39 :Electron_jetIdx : Electron_jetIdx[nElectron]/I                     *\n",
      "*Entries :    12793 : Total  Size=      57857 bytes  File Size  =       6435 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   8.89     *\n",
      "*............................................................................*\n",
      "*Br   40 :Electron_genPartIdx : Electron_genPartIdx[nElectron]/I             *\n",
      "*Entries :    12793 : Total  Size=      57889 bytes  File Size  =       5908 *\n",
      "*Baskets :        4 : Basket Size=      32000 bytes  Compression=   9.69     *\n",
      "*............................................................................*\n",
      "*Br   41 :nTau      : nTau/i                                                 *\n",
      "*Entries :    12793 : Total  Size=      51803 bytes  File Size  =      12827 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   4.00     *\n",
      "*............................................................................*\n",
      "*Br   42 :Tau_pt    : Tau_pt[nTau]/F                                         *\n",
      "*Entries :    12793 : Total  Size=     348399 bytes  File Size  =     285915 *\n",
      "*Baskets :       13 : Basket Size=      32000 bytes  Compression=   1.22     *\n",
      "*............................................................................*\n",
      "*Br   43 :Tau_eta   : Tau_eta[nTau]/F                                        *\n",
      "*Entries :    12793 : Total  Size=     348416 bytes  File Size  =     296703 *\n",
      "*Baskets :       13 : Basket Size=      32000 bytes  Compression=   1.17     *\n",
      "*............................................................................*\n",
      "*Br   44 :Tau_phi   : Tau_phi[nTau]/F                                        *\n",
      "*Entries :    12793 : Total  Size=     348416 bytes  File Size  =     301958 *\n",
      "*Baskets :       13 : Basket Size=      32000 bytes  Compression=   1.15     *\n",
      "*............................................................................*\n",
      "*Br   45 :Tau_mass  : Tau_mass[nTau]/F                                       *\n",
      "*Entries :    12793 : Total  Size=     348433 bytes  File Size  =     264618 *\n",
      "*Baskets :       13 : Basket Size=      32000 bytes  Compression=   1.31     *\n",
      "*............................................................................*\n",
      "*Br   46 :Tau_charge : Tau_charge[nTau]/I                                    *\n",
      "*Entries :    12793 : Total  Size=     348460 bytes  File Size  =      52628 *\n",
      "*Baskets :       13 : Basket Size=      32000 bytes  Compression=   6.61     *\n",
      "*............................................................................*\n",
      "*Br   47 :Tau_decayMode : Tau_decayMode[nTau]/I                              *\n",
      "*Entries :    12793 : Total  Size=     348511 bytes  File Size  =      54012 *\n",
      "*Baskets :       13 : Basket Size=      32000 bytes  Compression=   6.44     *\n",
      "*............................................................................*\n",
      "*Br   48 :Tau_relIso_all : Tau_relIso_all[nTau]/F                            *\n",
      "*Entries :    12793 : Total  Size=     348535 bytes  File Size  =      86700 *\n",
      "*Baskets :       13 : Basket Size=      32000 bytes  Compression=   4.01     *\n",
      "*............................................................................*\n",
      "*Br   49 :Tau_jetIdx : Tau_jetIdx[nTau]/I                                    *\n",
      "*Entries :    12793 : Total  Size=     348460 bytes  File Size  =     103050 *\n",
      "*Baskets :       13 : Basket Size=      32000 bytes  Compression=   3.37     *\n",
      "*............................................................................*\n",
      "*Br   50 :Tau_genPartIdx : Tau_genPartIdx[nTau]/I                            *\n",
      "*Entries :    12793 : Total  Size=     348528 bytes  File Size  =      62275 *\n",
      "*Baskets :       13 : Basket Size=      32000 bytes  Compression=   5.59     *\n",
      "*............................................................................*\n",
      "*Br   51 :Tau_idDecayMode : Tau_idDecayMode[nTau]/O                          *\n",
      "*Entries :    12793 : Total  Size=     126206 bytes  File Size  =      38633 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   3.25     *\n",
      "*............................................................................*\n",
      "*Br   52 :Tau_idIsoRaw : Tau_idIsoRaw[nTau]/F                                *\n",
      "*Entries :    12793 : Total  Size=     348501 bytes  File Size  =      59833 *\n",
      "*Baskets :       13 : Basket Size=      32000 bytes  Compression=   5.81     *\n",
      "*............................................................................*\n",
      "*Br   53 :Tau_idIsoVLoose : Tau_idIsoVLoose[nTau]/O                          *\n",
      "*Entries :    12793 : Total  Size=     126206 bytes  File Size  =      38400 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   3.27     *\n",
      "*............................................................................*\n",
      "*Br   54 :Tau_idIsoLoose : Tau_idIsoLoose[nTau]/O                            *\n",
      "*Entries :    12793 : Total  Size=     126196 bytes  File Size  =      38017 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   3.30     *\n",
      "*............................................................................*\n",
      "*Br   55 :Tau_idIsoMedium : Tau_idIsoMedium[nTau]/O                          *\n",
      "*Entries :    12793 : Total  Size=     126206 bytes  File Size  =      37310 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   3.37     *\n",
      "*............................................................................*\n",
      "*Br   56 :Tau_idIsoTight : Tau_idIsoTight[nTau]/O                            *\n",
      "*Entries :    12793 : Total  Size=     126196 bytes  File Size  =      37073 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   3.39     *\n",
      "*............................................................................*\n",
      "*Br   57 :Tau_idAntiEleLoose : Tau_idAntiEleLoose[nTau]/O                    *\n",
      "*Entries :    12793 : Total  Size=     126236 bytes  File Size  =      39196 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   3.20     *\n",
      "*............................................................................*\n",
      "*Br   58 :Tau_idAntiEleMedium : Tau_idAntiEleMedium[nTau]/O                  *\n",
      "*Entries :    12793 : Total  Size=     126246 bytes  File Size  =      38929 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   3.23     *\n",
      "*............................................................................*\n",
      "*Br   59 :Tau_idAntiEleTight : Tau_idAntiEleTight[nTau]/O                    *\n",
      "*Entries :    12793 : Total  Size=     126236 bytes  File Size  =      30317 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   4.14     *\n",
      "*............................................................................*\n",
      "*Br   60 :Tau_idAntiMuLoose : Tau_idAntiMuLoose[nTau]/O                      *\n",
      "*Entries :    12793 : Total  Size=     126226 bytes  File Size  =      38751 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   3.24     *\n",
      "*............................................................................*\n",
      "*Br   61 :Tau_idAntiMuMedium : Tau_idAntiMuMedium[nTau]/O                    *\n",
      "*Entries :    12793 : Total  Size=     126236 bytes  File Size  =      38748 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   3.24     *\n",
      "*............................................................................*\n",
      "*Br   62 :Tau_idAntiMuTight : Tau_idAntiMuTight[nTau]/O                      *\n",
      "*Entries :    12793 : Total  Size=     126226 bytes  File Size  =      38798 *\n",
      "*Baskets :        6 : Basket Size=      32000 bytes  Compression=   3.24     *\n",
      "*............................................................................*\n",
      "*Br   63 :MET_pt    : MET_pt/F                                               *\n",
      "*Entries :    12793 : Total  Size=      51815 bytes  File Size  =      46042 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   1.11     *\n",
      "*............................................................................*\n",
      "*Br   64 :MET_phi   : MET_phi/F                                              *\n",
      "*Entries :    12793 : Total  Size=      51821 bytes  File Size  =      47625 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   1.08     *\n",
      "*............................................................................*\n",
      "*Br   65 :MET_sumet : MET_sumet/F                                            *\n",
      "*Entries :    12793 : Total  Size=      51833 bytes  File Size  =      45272 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   1.13     *\n",
      "*............................................................................*\n",
      "*Br   66 :MET_significance : MET_significance/F                              *\n",
      "*Entries :    12793 : Total  Size=      51875 bytes  File Size  =      47109 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   1.09     *\n",
      "*............................................................................*\n",
      "*Br   67 :MET_CovXX : MET_CovXX/F                                            *\n",
      "*Entries :    12793 : Total  Size=      51833 bytes  File Size  =      45134 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   1.14     *\n",
      "*............................................................................*\n",
      "*Br   68 :MET_CovXY : MET_CovXY/F                                            *\n",
      "*Entries :    12793 : Total  Size=      51833 bytes  File Size  =      47971 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   1.07     *\n",
      "*............................................................................*\n",
      "*Br   69 :MET_CovYY : MET_CovYY/F                                            *\n",
      "*Entries :    12793 : Total  Size=      51833 bytes  File Size  =      45171 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   1.14     *\n",
      "*............................................................................*\n",
      "*Br   70 :nJet      : nJet/i                                                 *\n",
      "*Entries :    12793 : Total  Size=      51803 bytes  File Size  =      12234 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   4.19     *\n",
      "*............................................................................*\n",
      "*Br   71 :Jet_pt    : Jet_pt[nJet]/F                                         *\n",
      "*Entries :    12793 : Total  Size=     281849 bytes  File Size  =     221102 *\n",
      "*Baskets :       11 : Basket Size=      32000 bytes  Compression=   1.27     *\n",
      "*............................................................................*\n",
      "*Br   72 :Jet_eta   : Jet_eta[nJet]/F                                        *\n",
      "*Entries :    12793 : Total  Size=     281864 bytes  File Size  =     228530 *\n",
      "*Baskets :       11 : Basket Size=      32000 bytes  Compression=   1.23     *\n",
      "*............................................................................*\n",
      "*Br   73 :Jet_phi   : Jet_phi[nJet]/F                                        *\n",
      "*Entries :    12793 : Total  Size=     281864 bytes  File Size  =     236260 *\n",
      "*Baskets :       11 : Basket Size=      32000 bytes  Compression=   1.19     *\n",
      "*............................................................................*\n",
      "*Br   74 :Jet_mass  : Jet_mass[nJet]/F                                       *\n",
      "*Entries :    12793 : Total  Size=     281879 bytes  File Size  =     223885 *\n",
      "*Baskets :       11 : Basket Size=      32000 bytes  Compression=   1.26     *\n",
      "*............................................................................*\n",
      "*Br   75 :Jet_puId  : Jet_puId[nJet]/O                                       *\n",
      "*Entries :    12793 : Total  Size=     109465 bytes  File Size  =      20660 *\n",
      "*Baskets :        5 : Basket Size=      32000 bytes  Compression=   5.27     *\n",
      "*............................................................................*\n",
      "*Br   76 :Jet_btag  : Jet_btag[nJet]/F                                       *\n",
      "*Entries :    12793 : Total  Size=     281879 bytes  File Size  =      72942 *\n",
      "*Baskets :       11 : Basket Size=      32000 bytes  Compression=   3.86     *\n",
      "*............................................................................*\n",
      "*Br   77 :nGenPart  : nGenPart/i                                             *\n",
      "*Entries :    12793 : Total  Size=      51827 bytes  File Size  =      13249 *\n",
      "*Baskets :        2 : Basket Size=      32000 bytes  Compression=   3.87     *\n",
      "*............................................................................*\n",
      "*Br   78 :GenPart_pt : GenPart_pt[nGenPart]/F                                *\n",
      "*Entries :    12793 : Total  Size=     424937 bytes  File Size  =     185527 *\n",
      "*Baskets :       15 : Basket Size=      32000 bytes  Compression=   2.29     *\n",
      "*............................................................................*\n",
      "*Br   79 :GenPart_eta : GenPart_eta[nGenPart]/F                              *\n",
      "*Entries :    12793 : Total  Size=     424956 bytes  File Size  =     185664 *\n",
      "*Baskets :       15 : Basket Size=      32000 bytes  Compression=   2.28     *\n",
      "*............................................................................*\n",
      "*Br   80 :GenPart_phi : GenPart_phi[nGenPart]/F                              *\n",
      "*Entries :    12793 : Total  Size=     424956 bytes  File Size  =     185111 *\n",
      "*Baskets :       15 : Basket Size=      32000 bytes  Compression=   2.29     *\n",
      "*............................................................................*\n",
      "*Br   81 :GenPart_mass : GenPart_mass[nGenPart]/F                            *\n",
      "*Entries :    12793 : Total  Size=     424975 bytes  File Size  =      58863 *\n",
      "*Baskets :       15 : Basket Size=      32000 bytes  Compression=   7.21     *\n",
      "*............................................................................*\n",
      "*Br   82 :GenPart_pdgId : GenPart_pdgId[nGenPart]/I                          *\n",
      "*Entries :    12793 : Total  Size=     424987 bytes  File Size  =      90863 *\n",
      "*Baskets :       15 : Basket Size=      32000 bytes  Compression=   4.67     *\n",
      "*............................................................................*\n",
      "*Br   83 :GenPart_status : GenPart_status[nGenPart]/I                        *\n",
      "*Entries :    12793 : Total  Size=     425006 bytes  File Size  =      28832 *\n",
      "*Baskets :       15 : Basket Size=      32000 bytes  Compression=  14.72     *\n",
      "*............................................................................*\n"
     ]
    }
   ],
   "source": [
    "t.Print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: Why do we need a different data format for the Muon_pt branch?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TTree::Draw\n",
    "\n",
    "Another very handy way to see the data is the `Draw` method.\n",
    "\n",
    "This is an internal method to loop over all the events on a branch (very efficiently!),\n",
    "\n",
    "and fill an histogram from the branch content\n",
    "\n",
    "To visualize the histogram, we first need to create a Canvas.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = r.TCanvas('c1','c1',800,600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, we can look at the branch called `nMuon`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: Can you guess what does this histogram represents?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The branch names usually contains good description of what does it contain.\n",
    "\n",
    "Similarly, we can explore another branch called `PV_npvs` as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.Draw(\"PV_npvs\")\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: Can you guess what `PV_npvs` represents then?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looping a TTree with a histogram\n",
    "\n",
    "`TTree.Draw()` can only offer a simple plotting, for more custom analysis, we can go through the `TTree` event-by-event by building a for-loop.\n",
    "\n",
    "Let's use a simple for-loop to re-create the `nMuon` histogram.\n",
    "\n",
    " - Learn how to create a histogram\n",
    " - Learn how to write a loop structure for a TTree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining your own 1D histogram\n",
    "\n",
    "`ROOT` has a very powerful library of histogram class called `TH1` with extensive functionalities.\n",
    "\n",
    "You can construct a very simple 1D histogram like this:\n",
    "\n",
    "`h = r.TH1F(\"name\",\"title\", nBin, x_low, x_hi)` \n",
    "\n",
    " - `TH1F` is a sub-class of `TH1`, using a `float` to store the data of each bin of the histogram\n",
    " - `name` is the name of the histogram object, it is used to identify the histogram object (h, in this case) in a file\n",
    " - `title` is the title to be displayed in plotting\n",
    " - `nBin` is the number of bins \n",
    " - `x_low,x_hi` are the lower and upper range of the 1D histogram respectively.\n",
    " \n",
    " \n",
    "Further documentation about `TH1` can be found here:\n",
    "https://root.cern.ch/doc/master/classTH1.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's define our first histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_nMuon = r.TH1F('nMuon','nMuon',10,0,10)\n",
    "h_nMuon.Draw()\n",
    "c.Update()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filling a histogram\n",
    "\n",
    "Say, if you have an event with 4 muons in it. You will want to put 1 event in the 4-th bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_nMuon.Fill(4)\n",
    "c.Update()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## now let's clear the bin content, by setting the 5th bin to 0\n",
    "h_nMuon.SetBinContent(5,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looping a TTree\n",
    "\n",
    "By default, all the branches are loaded as the methods of the TTree object.\n",
    "\n",
    "For example, you can simply access the `nMuon` branch like this:\n",
    "\n",
    "`nMuon = t.nMuon`\n",
    "\n",
    "**It will return the value of the tree is currently located\n",
    "\n",
    "To access different events, we can do:\n",
    "\n",
    "`t.GetEntry(i)`\n",
    "\n",
    "This will set the memeory address to load the i-th entry in the Tree.\n",
    "\n",
    "Combining both, a TTree can be looped over with the simple loop structure like this:\n",
    "\n",
    "```\n",
    "for i in range(0,t.GetEntries()):\n",
    "    t.GetEntry(i)\n",
    "    nMuon = t.nMuon\n",
    "    h_nMuon.Fill(nMuon)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,t.GetEntries()):\n",
    "    t.GetEntry(i)\n",
    "    nMuon = t.nMuon\n",
    "    h_nMuon.Fill(nMuon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxwAAAI8CAIAAAD0vjrdAAAABmJLR0QAAAAAAAD5Q7t/AAAgAElEQVR4nO3dXbabOqIuUHRH+gWu6ss+vdhA9aLSlyqgZZwH3egQsIl/BDZmzoeMtRC2BfayvkhChHEcCwAAXvP/3l0BAIBvIFQBAGQgVAEAZCBUAQBkIFQBAGQgVAEAZCBUAQBkIFQBAGTw490VAL5f3/fxh6qqcu0J8GmEKmBbfd9fLpf4c9d1K2mpqqphGOLPbvYAHI7hP2A/qSPqqpSoAI5IqAL207btraL1vAXw+YQqYFe3wlPTNEVRlGW5Z2UAMjKnCsig7/uYlqqqujVrqq7rtm2bprmaq+LY33Ra1StiRFuvz2zne/YEWDMCPK7ruqIoyrIcr80oX+5ZTOae33q2oiimO0d1XacXuvW0U1f7uq7u03VdfPKpuq6fOh8Ao54q4CUhhKIoYjrp+z51OK3Mker7ftYnFPuKlhHnucoURVGWZaxDrE8IYVyEv6ZphmGIexa/5nu1bavLCnjSu1MdcEipo2j2NZKC0XLP8Xa3U9rhlZ6qaXfXcuO0Cyr1Zs36peLG5WsB3MNEdeAl03RVTCYzXe2piqWzWVO5rvuLq2HVdT3tZ6qqKtbw6oWHqbbR611lwJkJVcBLnhspmwapXGN/02ebSjWcpTdXGgJ5CVXArmJ4mkaf2HG1DEMPeaK7y8QpIC+hCtjVbAQwhqGtO43i81tfFNiUUAXsbRpx0hpRm76iG+AAOxCqgL3FCDXtsnpx7K+4L5YZ7wM2JVQBe0tx6pWxv1s5bLk9jfoJVcCmhCrgDWKQiosg3JN1lqswLEf04hT4tm1nc6fiq7jWD9iaUAW8wTRIrY/9pT1DCPG+gU3TXC6XZUhKz3O5XKqqapqmqqq0xrpZ6sDWhCrgDVIA+mMHUlVVaQmrtm0vl0vbtmVZXg1J469V0YdhaNs29WbNVigF2MKV+2EBfKCUw/54b76+76fzqEylAvYhVAEAZGD4DwAgA6EKACADoQoAIAOhCgAgA6EKACADoQoAIAOhCgAggx/vrgAAHFW6DxLf6qHlPIUqAHieNbS/2KOh2fAfAEAGQhUAQAZCFQBABkIVAJxOCCGEUFXV1dKqquIO+1bq8IIZdgDwnBCO2oymwHS1/uul5/Ho+6unCgDOq+/72Zamad5Qj68gVAHAN+v7fpmckmWEWtn5xddKO7zyEp9MqAKAbxNnRPV9H0K4XC6XyyWEMMtPZVmWZTkMw+yxwzCUZTnd0jTN8uGzKVmz11oWNU1TVdXll6+csCVUAcB3ulwudV13XVfXdVEUbdvOdojRZ9pvFJPTrQnst/R9f7lciqKILxez2iw2tW07DEPXdXGHJ17lAEYA4Ckf24zGJr6u67Ql5piu69IOZVlOf5julp4hboyZbPpsswcuE8X05bquW+5wiBDyaA31VAHAd5oO2N3qFpqNAC7H/v4odnQtRwxnFXj0aY9IqAKA84pha5qBnhuVmz0q/rqcsPXdhCoAOK8YpGJvU/x3oyUVvnAG1YJQBQCnlkYA7x/7W66JMNtydUzw6wlVAHBqsQ8pXqx3Z3/SNEJdHel7ZSTxuIQqADi16Xjf1bG/mI3atk3jg7PVGeLlgXFlrL7vm6aJGetsi7P/eHcFAIA3iyOAt0brqqqKO8TFqIqiqOt6mqtieGrbNu1QnPK+gUe9EyQAvN1xb6j8hNgLVVXVyqDePfscyKPv74k+DcXkttsAkMWpmtGzeTRUnW74z6cfgFz8X50pE9UBADI4XU8VAGSks4pEqAKA55lV8sUeTcyG/wAAMhCqAAAyEKoAADIQqgAAMjBRHQDyW94gLyrLcno34kdVVTUMw5fNjq+qanlO4uLssTStz351ofaqqtJNBtONnKd7xtsR3npIRkIVAOQXA8Hybnr33L8lhFDX9UnuRpzuvjw1veyubduURJd7Jn3fpzsPxjibomff97MHDsOwxek1/AcAW2mapv/di2153/df003VNE1VVcv+vHiK6roex3Ecx3gv5xiqxt/VdZ32j4mq67pxHLuuKyb59epjNzmk8UzOdrwAbGqlWYl9VLGNX+q6rizL8VcsKMsy7Zk6t2KqiEVxt7h/fOD0eWbPcPV5PtCtNHJ1y/Sop9vj0cUUNT3SdMbGX6fiuRo+tL/hPwB4g2EY4iBX7Im5XC4xGSRpwCuNahW/j2SlaVvTZ4jdM9Nnbts2zU/6KDG1LCefxTyU3Kp5PNL1nr++7+MstOJXr2H1ywsVv+2J4HZczgYAGa00HMvZVNG0ZyX1vsx+LSadLrPmadrpUvzef5P2jKEkdVx9eAM37VK6KtZ/1g8Xz9h04+xsxBMVT+Otd+GPHj1vp5tT9cfTBwC5lAvT0tTLst5xcjWfxf6baT9N6uBJXTjTuURPHsBbNU0Tu9zqup6doth7N90Yu+XiZX2pd2paOm3rr16Y+TrDfwCwldjAv/gkV58hBqbpyGDaXlVVGhAsXl7E4V3SBYBpTDOJh7McJYxZKsapuq7bto0PnGXKWBT3z1vn0/VUAcDXqOu6+10MCvEiwTirPU3eOpBY4Ti6t4w+sX9uOZsqHvUfe+bSKcpU2f8jVAHA8UzXw4zSeg2peyyOAMYenQN1VqXupVs9ScMwLIdEQwjT/eMAXxwNDCFME1haUzRjnSPDfwCwlavDf1mWnYxPm0a4+r6Pi2TG0ji7KC2OVWyTITYSx+9mFU5roN86nNgnF0943DNOZo9XFy5P1CYn5KFp7Ud3tuMFYFMrzcqtq//iQ9YvXpvuWfx+qdpsyaXp0y6vfUs+dqmqcXH132xdieXRza5tnLp1yLPnvLrk1VWPxoYwHvOKgOeEcK7j/Sgh/Hx3FV41jn+9uwrAZ9muWbm/eymN612dzX3nk3yNlfP2xAl59P09V8gQqt4ohJ+HDiVHrz+wBc3Kd3v0/TVRHQAgA6EKACADoQoAIAOhCgAgA6EKACADoQoAIAOhCgAgg9Pdpmb9ppKWGwEAnnO6UCU2AQBbOF2oAoAdxIGRuq6Xt0+ORV3XneoGMneKd0RePzPplE7P7dWHpJsrp53/+OSvONf6+u4n8EZHv83L0esPbGGlWUmzTWY7NE3Ttm0hVN0QQijLMt2nb6bv+8vlMt0ST+NyexSfall6ZxhwmxoA+FwxUTHT933TNOvznotfvU1d143jGONOTEtVVY2/q+s67R/3idu7ris2u8m0UAUAG1oO/13dJ4QwHauK+r6vqmpZ1DRN0zTT0rx13l/f9/fEzWEYyrJMx1uW5cqz1XUdO7GKoogZqyiKqqrKshyGIUu158YzOdvxfpSi+Pe7q/CSo9cf2MJKsxKb/Njqp42xaY//TrtbpvmgLMvpztOiuq6nD5m+RHrU0a0fS9d16byN4zg7vdMnSdtnZ/vqlpXK3FntSE8VAGwl9qmkGUJt2846V+IO4zj2fR/b+2EY4v6x5yYVFYuhw67r4oShDbtePkycZp666IZhiMN5U/Hspe2zt6DYcgRWqAKArcQxu2mLPhuqi+NZV/efdrpcnbidnuoLhv8elULk8sxcLpfpEGEc72vbNg6hbnquhCoA2FBs0YtfgWk5xWoYhjBRTIJC7I8JISwvbbs1negM0rT0eG6nuSr+vJyaFjvz4hux3amzThUAbKiqqjiitxz7i8qyvDqZPa10Fce8/nhl3NeLY53ThaaaprlcLnFj2lJc67pbdhZu0WWlpwoANjTtoLrakA/DUP1S/Lr+P12ztvWI1YFUVXW1X2p6fmbDqVEIYRpbt5t/JlQBwLbSRPJlj1S8Ei1Ovk5rVKaUMB0H3KeqH2jWS5dyVVqFYdZNdbXbr23btAhFMZnGnpfhPwDYVhwBvDr2FxNA27Zp1lRanTJGsZQn0q/jyW4NMu1YGsdxNsNsGo+WHVfTR7VtG0NYHFHdoqrnem9O+Fn8HEe/zcvR6w9sIWOzcjUQzHqqboWGs3muA++Js/fo+3uukCFUvdHRQ8nR6w9sQbPy3R59f083/Ld+9YS/DQDgOacLVWITALAFV/8BAGQgVAEAZCBUAQBkIFQBAGQgVAEAZCBUAQBkIFQBAGRwunWqAGAf6QbJUVmW6f4q9zyw67rlPVVmS1iXZVlV1dVbCH+meN/o4sZtj5e7VVW1vHXP1aJ4v+Tpnk3TTG9NfesJcxp3cfV20GVZTvdJd5qcbb+nNN7l+1ZpstvxslQU/353FV5y9PoDW1hpVlLDV9d1XdepFZs+tq7rq4+NjVrXdbees/zlnrbvc6TGOloe4NXdpidtVjQ9gcuYkUpnt7K+/3Q9Ghs+JVQtS3+r5Qulsz1zHxn3OnooOXr9gS2stziz0tgUppZ+pXVfD1VXuyRu5bPPMav8Sns93S2einh0s2eYnaVb5/Pqo+48XY/Ghp3mVMU+t9nnY3aX6XSE8YBTx2D84blSAPgQVVWlLpPY8A3DMB2fqqoqhPBoExYb07Ztp1viU1VVNW1qZ908yy2biseV6hNfejkemgbplo+aPUP8dXq6ro7rXX3U9HTl9FAEe1r8GN0qXdZkuuWV0uULPVF5sjh6T8/R6w9s4Y9NW1mWVwe5ZiN300ksqYG+s6dq/L2RnQ4Rxh9iv8OsIf7Pf/5TFMXff//96CE/7Wp7fbXHaHqM0+69lRY/nZlo+rRx+PXW8/+xzvfs9n/7P7T30+J72XVdPLbZB2V5eLPY9HTpjFD1RkcPJUevP7CFlWZlNu9lma6K38fCllNi7g9V04GwWSOYstQsRa13dmzhaiRaGbCbnrq4PWWJ5XNe7XK7Wo2HRksfPUU7Df8Nw1AUxeVyadu2bdvL5TLt8Fzun/L1SmmayX+r9PVqA8BzqqqK+SC2SsMwXC6X2bV70XIY6+lRudj2TR8eW9u+7//xj38URfGvf/0rbh+GYTZ9ewd3vmI6FenUxV/j0V0ul6ZpmqZZnsyUt+IZWF42GEKIB77RNKFd16mazsOfDiTfskUwCs/KXhMAvl6c1TTtI7nVoLxynX+ah5TmV6XGazp5KGWa//73v0VR/P3330+/4nNSPFrRNM0wDDEwxFNXTE5a7MSKHTTTiNY0zTiOs5lY05erqioub9F13XbdLjutUzVbbCOlxX1efSr1FgLARq4uNBXbvpWHPJ2rUntaVVXbtnVdz54q/vr333//85//rOs67h/7rt5lNiF9tn3akxQ7YuLPsf8vFYUQ7un9iqe9ruutr2PbqadqedbuGaRbX6Hrj6X3Vw8AMopt0J1NeBqhS1se6kpJV8EXk7av+mW6AGkaAXzL2N9sTPNWqFoRh/zSyZk+w9UxpXiM8fx0XbfHygAPzcB62nK23XS62bIm0y2vlM7sdrwsHX2i99HrD2xhpVmJzVycnx7NpkgXi0Wb4oVcKXysLP45W1B0Ot07vUp6qmlpGvL7z3/+k+MEPCZVJh1I3D6dgJ+K4kmbHcK0aPoMabfpqZ5mjPJ3G01U3yNkXL1aYXoultcgFIvZ/s+VzghVb3T0UHL0+gNbWG9Wlr1BV1cAn/1a/OpiWQlVt57z6utOi+I1gO9qDWf1n22fLaMwPRtpz5U12W8ttr48Y/efgUdP1E6nNR3h1bR+K6LeX1r8nltvrXz/ro8R4/FDydHrD2zhnmYl9VTdKrr16ytWXvTt7qzYym7PFT3h0dgQxr0mbs8GO2fzxZqmmV6hMJvct146u2Nld+0OlKkOux0vMyH8HMe/3l2L5x29/sAWNCvf7dH3d9dPQ1xZamWC+fq0tVdKI5/+Nzp6KDl6/YEtaFa+20eHqrfz6X+jo4eSo9cf2IJm5bs9+v7utE4VAHwlq0OTCFUA8CTdVOvO1pO3621qAAC+lVAFAJCBUAUAkIFQBQCQwekmqq9fpnGq+XQAQEanC1ViEwCwBcN/AAAZCFUAABkIVQAAGQhVAAAZCFUAABkIVQAAGQhVAAAZCFUAABkIVQAAGQhVAAAZCFUAABkIVQAAGQhVAAAZCFUAABn8eHcF9hZCWCkdx3G3mgAA3+R0oUpsAgC2YPgPACADoQoAIAOhCgAgA6EKACADoQoAIAOhCgAgA6EKACADoQoAIAOhCgAgA6EKACADoQoAIAOhCgAgA6EKACADoQoAIIMf767A3kIIK6XjOO5WEwDgm5wuVIlNAMAWDP8BAGQgVAEAZCBUAQBkIFQBAGQgVAEAZCBUAQBkIFQBAGQgVAEAZCBUAQBkIFQBAGQgVAEAZCBUAQBkIFQBAGQgVAEAZPDj3RXYWwhhpXQcx91qAgB8k9OFKrEJANiC4T8AgAyEKgCADIQqAIAMhCoAgAyEKgCADIQqAIAMhCoAgAyEKgCADN4TqkIITdPMNlZVFUIIIVRVtXzIemnTNCulAABbe8OK6vFGMX3fLzdGwzCEEKZLn79SCgCwg717qpYdVEVRxO6luq7HcRzHsa7r6Z7xh+dKAQD2sWunTt/3l8sl/lyWZeqsil1Ny86nuOWV0pmjd2KF8PPdVXjJOP717io8L4Sfh64/wP6O3uw+atfhv5iouq5L0Sopy3Llga+UfhntOgB8pv2G/+IYX9d1s+2zyVVRykkrpX3fr5c+XVUAgEftFKqaphmGoa7rh67O2yIYhWdlrwkA8E32GP7r+75t27IsP2H++KkGdwGA3ewRqlKWmnZTDcNQVdV6zFrv1nqlFAAgrz2m5VdVNQzD1aK6ruO6nYWr/+7gArQ3cvIBHnX0ZvdRe8yp6vt+nIhz1cuyHMcx9lRdvXwvbXylFABgHx9x778YreKAXd/3satptrznemkIIV4MOCsFANjHG25Ts1RVVV3Xbdumi+y6rkuTov5YGhe+SmtfTUsBAPbxWYOdcQ2FW5HoldLo6IO7pvW8kZMP8KijN7uPOtnRHvzd1a6/kZMP8KijN7uP+og5VQAARydUAQBkIFQBAGQgVAEAZCBUAQBkIFQBAGQgVAEAZPARK6rvKS3LftWpltMAADI6XagSmwCALRj+AwDIQKgCAMhAqAIAyECoAgDIQKgCAMhAqAIAyECoAgDIQKgCAMhAqAIAyECoAgDIQKgCAMhAqAIAyECoAgDI4Me7K7C3EMJK6TiOu9UEAPgmpwtVYhMAsAXDfwAAGQhVAAAZCFUAABkIVQAAGQhVAAAZCFUAABkIVQAAGQhVAAAZCFUAABkIVQAAGQhVAAAZCFUAABkIVQAAGQhVAAAZ/Hh3BfYWQlgpHcdxt5oAAN/kdKFKbAIAtmD4DwAgA6EKACADoQoAIAOhCgAgA6EKACADoQoAIAOhCgAgA6EKACADoQoAIAOhCgAgA6EKACADoQoAIAOhCgAgA6EKACCDH++uwN5CCCul4zjuVhMA4JucLlSJTQDAFgz/AQBkIFQBAGQgVAEAZCBUAQBkIFQBAGQgVAEAZCBUAQBkIFQBAGSwX6jq+76qqhBCCKGqquUOr5Q2TbNSCgCwtbDPCuN9318ul6IoyrIsimIYhuL3xc2Xd4/JVToVwk7Hu5EQfo7jX++uxUk5+QCPOnqz+6ideqpiohrHse/7vu/rui6KIvUqxR/quh7HcRzHWNo0TSyNPzxXCgCwj50iZAihLMu+76dbil9dStOf85Yuq3HoyKyz5I2cfIBHHb3ZfdRON1Tuum76a0xXcSgwmv689EopAMAOdhr+q6oqjvE1TdM0TRwNjIN00+6rJOWkldI4krhSmqHeAAD32amnKmnbNv5QluUfr9TbIhgtZ7Xf6VQdmADAo/Zep2ocx67ryrIchuEtyx+Mz9q/qgDAgezdU1UURVVVfd+HEOLCCut7blQKAJDXHj1VcWXO2VhemvkU088sYGX8FQBgB3uEqjRFfboxRp9YdPXyvbTxlVIAgH3sF6qGYWiaJl6yN8tSMW/FjXFksFgs77leGnvClqUAAPvY+zY1yWwt0KZp0oWBRVF0XTedFLVeOnvyWenU0Vchs/7kGzn5AI86erP7qF2PNqWoW6En7rBFaXT0d1e7/kZOPsCjjt7sPupkR3vwd1e7/kZOPsCjjt7sPmrvdaoAAL6SUAUAkIFQBQCQgVAFAJCBUAUAkIFQBQCQgVAFAJDBj3dXYG/xPja3nGo5DQAgo9OFKrEJANiC4T8AgAyEKgCADIQqAIAMhCoAgAyEKgCADIQqAIAMhCoAgAyEKgCADIQqAIAMhCoAgAyEKgCADIQqAIAMhCoAgAyEKgCADH68uwJ7CyGslI7juFtNAIBvcrpQJTYBAFsw/AcAkIFQBQCQgVAFAJCBUAUAkIFQBQCQgVAFAJCBUAUAkIFQBQCQgVAFAJCBUAUAkMHpblMTws93VwEA+EKnC1Xj+Ne7qwAAfCHDfwAAGZyupyqEsFI6juNuNQEAvsnpQpXYBABswfAfAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAY/3l2BvYUQVkrHcdytJgDANzldqBKbAIAtGP4DAMhAqAIAyECoAgDIQKgCAMhAqAIAyECoAgDIQKgCAMhAqAIAyGC/UNX3fVVVIYQQQlVVfd/PdpiWLh++Xto0zUopAMDWwj4rjPd9f7lciqIoy7IoimEYiqKo67ppmv9fj8XdY6YVe6V0KoSdjpfvE8LPcfzr3bUAOJKzNbs79VTFRNV1Xd/3fd/HU9y2bSyN3Ut1XY/jOI5jXddFUaS8FX94rhQAYB87RcjYmTR9raqqhmHoui6O6xXXOp/illdKl9U4VWQmIz1VAI86W7O70w2Vy7Jcn+0UhwW3KAUA2MFOw39930+H5Pq+j9Oqrs5YLyY5aaU0jiSulL5YZwCA+71hSYWmadIUq/U9twhG4VnZawIAfJOdhv+idA1gURRxNtWerx6danAXANjNfqGqaZp4ud90JYV166nrlVIAgLx2mpYfE1VZlldH9Fz9x+dz9R/Ao87W7O40pyr2Ud2aI3X18r208ZVSAIB97BGqUpaqFmJRHA2MA3Z938euptnynuulIYR4MeCsFABgH3vMqUqhKi6jsFRVVV3Xbdumi+ym09j/WNp13eVyefsUeADgzD5rsDPGr1uR6JXS6GyDu2RkThXAo87W7J7saE/27pKRUAXwqLM1u29Y/BMA4PsIVQAAGQhVAAAZCFUAABkIVQAAGQhVAAAZCFUAABnssaL6R0nLsl91quU0AICMTheqxCYAYAuG/wAAMhCqAAAyEKoAADIQqgAAMhCqAAAyEKoAADIQqgAAMhCqAAAyEKoAADIQqgAAMhCqAAAyEKoAADIQqgAAMhCqAAAy+PHuCuwthLBSOo7jbjUBAL7J6UKV2AQAbMHwHwBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABj/eXYG9hRBWSsdx3K0mAMA3OV2oEpsAgC0Y/gMAyECoAgDIQKgCAMhAqAIAyECoAgDIQKgCAMhAqAIAyECoAgDIQKgCAMhAqAIAyECoAgDIQKgCAMhAqAIAyECoAgDI4Me7K7C3EMJK6TiOu9UEAPgmpwtVYhMAsAXDfwAAGQhVAAAZCFUAABkIVQAAGQhVAAAZCFUAABkIVQAAGQhVAAAZvCFUhRD6vl9ur6oqhBBCqKrq0dKmaVZKAQC2tveK6k3TFEXR9/0s/UzvHjMMQwhhuvT5K6UAADvYr6eqaZqqqtq2XRbFgFXX9TiO4zjWdV38il/ph+dKAQD2sVOnTt/3l8sl/VrX9TT3xK6mZedT3PJK6YxOLJ4Wws9x/OvdtQA4krM1uzv1VFVVNe1JWirLcuXhr5QCAOzg/Vf/XZ20nnLSSmnf9+uluWoIAPBHe09Uf8gWwWg6q/0hp+rABAAe9dGhaguyEQCwhY8OVeuLTr1SCgCQ1/vnVMX0MwzDdGPGXwEAdvD+UFXcuHwvbXylFABgHx8RquKaVbHLqu/7OJd8trznemm89c2yFABgHx8xp6qqqrqu27ZNl+Z1XZcmRf2xtOu6y+WSFhedlgIA7OOzljqNayjcikSvlEZnW9qVjKyoDvCoszW7Jzvak727ZCRUATzqbM3uR8ypAgA4OqEKACADoQoAIAOhCgAgA6EKACADoQoAIAOhCgAgg49YUX1PaVn2q061nAYAkNHpQpXYBABswfAfAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABqdbUgGeFsLPd1fhJeP417urAPDNhCq4y9ETydETIcDnM/wHAJCBUAUAkIFQBQCQgVAFAJCBUAUAkIFQBQCQwemWVAghrJSO47hbTQCAb3K6UCU2AQBbMPwHAJCBUAUAkIFQBQCQgVAFAJCBUAUAkIFQBQCQgVAFAJCBUAUAkIFQBQCQgVAFAJCBUAUAkIFQBQCQgVAFAJCBUAUAkMGPd1dgbyGEldJxHHerCQDwTU4XqsQmAGALhv8AADIQqgAAMhCqAAAyEKoAADIQqgAAMhCqAAAyEKoAADIQqgAAMhCqAAAyEKoAADIQqgAAMhCqAAAyEKoAADL48e4K7C2EsFI6juNuNQEAvsnpQpXYBABswfAfAEAG4VQ9NyEc+3gPXf9DV75Q/3c7dP0PXflC/d/q0JUvjl//R+mpAgDIQKgCAMhAqAIAyECoAgDIQKgCAMjgdOtU3ePOqxXu2S3jU91v/4plrP/+Fdv/5N+5W/aTH8LPO/b6n6L4d459su/2Zz48WzzV/XzzPLSbD89X+qqTUlXVMAxFUZRl2ff9coeP/bM5Q8WcChX7voo5FSp2oFf85Ip9je/pqZref2YYhrO9kQDAe33JnKqqqoqiqOt6HMdxHOu6LoqiaZr31goAOI8v6c6J3VTTY1luKU7Tj/qZFXMqVOz7KuZUqNiBXvGTK/Y1vqGnKk6fKsvy3RUBAM7re+ZUzZRlGSetA9/hnqsX77vCMedudz4VcAbfE6ritKqZvu9n21JYRsYAAAWBSURBVKfz2Vdk3G3/V7xztzO84p27qdiju72lYkXxP5n2ybvbXU91kvdIxT78Fe/cLfdf7ol8T6i6uobCzKlGdgGAPX3DnKoVV7uvAACy+5Jp+Xde/QcAsJEv6amaXfrnekAAYGdfEqriOp9xsK/v+8vlUlj8EwDY0ZeEqqqq6rqOd6eJiarruumEqqqqQgghhKPPsgoh3DMl/3PECzDTyT9W5YtF/d9dnZeEEI71P42+78PCsd6Fpmni5+dYH/6rZz461kfouH+8B/3mudVCHfFYnjR+l67ruq6bbfyao46330l34/l8XdfFE16WZRqNPXr9312pJ6UDeXdFHpDO/9SBDiF9ZtIPy2+nz3T1zB/r7/fQf7zTyh+o2brVQn1NE3yPbz62KH4oZ7cFPMr3QlLX9RFDybIhOdZf1Ky28cNzoEY9iTU/XOVjtY8SRGZmXzWpmXxrpV5yrPrPvnzi+T/K5//qN88nf/OvtFDf0QTf7zB/IU9bfhEc66thXPyv8UAfx+Wpjn9gR2kml9/Ch/vwjL9/fo7SqETx0/LuWjxp+VGp6/pAf7wzx/rLHY/8zX81/31y5ddbqOO+Ec/5nsU/r/qOywCrqhrHsSiKpmnatn13dR5QluWhR9BnXxYH/TilWYbxh8Pp+z6e+aqqjvVxmn1UjjUbaarv+2EY6ro+1vmfOtAfb/q0Tzd+8o3X/thCHeK05/IlE9Ufcqo3+L36vp82JPGruTjOoqypFW+apmmaI15VGuu/MkXmk8VPy+Vyadu2bdvL5XKUT06aq5vm5x5uivfUET/58TMfT3tVVfEQDnGtQLqMfbox/i0cov5TVyv83U3wKULVrdsC7l2Pc0uh5IgNfGzUi6P1vTVNc/QOhmIymhD/s36gYxmGYRiGsizjPJK2bY+VS6L4VZmm5R1FVVWx8W7bNiaSo7Tl8RM+DMM0mr+vOlv51ib4FKHqW9+8o4iXZ8dQMlvq4ijGcey67liNet/3bduWZXnEhjyKM3hS/eMf8scOglw1jmPsr43jI8cavo+O2E1VFEUIIf6PIibyuObOUf544/88L5dL7OM81mf+5E4Rqq46yl/X0aUOqvjtdtzTntYZOsoXXGoF4yBm/DW2K0f5b8by0xI7G45S/1nXyLEqHx1oKtJUqnb6K2iaJv6n6BDnP85Sqn8Zf10ocNzvz6VvOpapL5+onvpRpxuP0ih+gThvsSzLQ3yRzcTKz7rW0vfyUb4Rlp/2A9X/KPVcOmi1l2IoOVw31dW53oczPe0HbbZO2AR/f0+V2wK+URzsOGKiKn59HcyakwNNtO/7fnqh7/Q67UO0kfF+U7NTfaDzX9xoS45S+eiIdS5W53of4ljiqF/69dDN1tVqH/RY7vH9ocptAd9lOsty5hAxK/0fq2maeFV/3PLFXwcf5db5P8qM6VjP+Gk/6IfnuG350f9402BfnJB3oEsXl2ZNcAyL39wEb70Q1ieYfQsfaP26mWOtRbvS+B3lLVheqHisxTOnjrWidDI7/0f58EezP4HDnfxjfeEsLee0vbtGD5hV/hDfmbc+MF/TBN8jjNfuy/OVvmOUnf1994XNny/1NBz0/Pvmea/jnv+jf/JnjvtGPOREoQoAYDvfP6cKAGAHQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAGQhUAQAZCFQBABkIVAEAG/wsEbNLQlfpwTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_nMuon.Draw()\n",
    "c.Update()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3. Plotting the di-muon mass spectrum\n",
    "\n",
    "Now we have all the ingredients to plot the di-muon mass spectrum.\n",
    "\n",
    "First, we will need a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "h_dimuon_mass = r.TH1F('DiMuon_mass','DiMuon_mass',200,0,200)\n",
    "h_dimuon_mass.Sumw2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may find the following formulas useful:\n",
    "\n",
    "Coordinate transformation between pesudeo-rapidity and cartesian coordinates:\n",
    "\n",
    "$\n",
    "p_x = p_T cos(\\phi)\\\\\n",
    "p_y = p_T sin(\\phi)\\\\\n",
    "p_z = p_T sinh(\\eta)\n",
    "$\n",
    "\n",
    "Vector additions and Energy-momentum relations:\n",
    "\n",
    "$\n",
    "E^2 = |\\vec{p}|^2 + m^2\\\\\n",
    "\\vec{p_1}+\\vec{p_2} =  (p1_x+p2_x) \\hat{x}+ (p1_y+p2_y) \\hat{y}+ (p1_z+p2_z) \\hat{z}\\\\\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## write a function that computes the invariant mass given the event\n",
    "## pt, phi, eta, mass are the arrays of the muons in each event\n",
    "##\n",
    "def invmass(pt,phi,eta,mass):\n",
    "    m = 0\n",
    "    px = pt[0]*np.cos(phi[0])+pt[1]*np.cos(phi[1])\n",
    "    py = pt[0]*np.sin(phi[0])+pt[1]*np.sin(phi[1])\n",
    "    theta0 = 2.*np.arctan(np.exp(-eta[0]))\n",
    "    theta1 = 2.*np.arctan(np.exp(-eta[1]))\n",
    "    pz = pt[0]/np.tan(theta0) + pt[1]/np.tan(theta1)\n",
    "    e0 = np.sqrt(pt[0]**2 + (pt[0]/np.tan(theta0))**2 + mass[0]**2)\n",
    "    e1 = np.sqrt(pt[1]**2 + (pt[1]/np.tan(theta1))**2 + mass[1]**2)\n",
    "    \n",
    "    m = np.sqrt((e0+e1)**2 - px**2 - py**2 - pz**2)\n",
    "    \n",
    "    ## OR in the massless muon limit, the formula can be more compact:\n",
    "    #m  = np.sqrt(2*pt[0]*pt[1]*(np.cosh(eta[0]-eta[1])-np.cos(phi[0]-phi[1])))\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## write a function that select \n",
    "def selectMuons(muons):\n",
    "    select = False\n",
    "    if muons['n']==2:        select = True\n",
    "    return select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0,nEntries):\n",
    "    t.GetEntry(i)\n",
    "    nMuon   = t.nMuon\n",
    "    pt      = np.array(t.Muon_pt)          # note: this is an array\n",
    "    phi     = np.array(t.Muon_phi)         # note: this is an array\n",
    "    eta     = np.array(t.Muon_eta)         # note: this is an array \n",
    "    mass    = np.array(t.Muon_mass)        # note: this is an array\n",
    "    isTight = np.array(t.Muon_tightId)     # note: this is an array\n",
    "    charge  = np.array(t.Muon_charge)      # note: this is an array\n",
    "    muons   = {\n",
    "        'n':nMuon,\n",
    "        'pt':pt,\n",
    "        'phi':phi,\n",
    "        'eta':eta,\n",
    "        'isTight':isTight,\n",
    "        'charge':charge,\n",
    "    }\n",
    "    if selectMuons(muons):\n",
    "        h_dimuon_mass.Fill(invmass(pt,phi,eta,mass))\n",
    "        \n",
    "h_dimuon_mass.Draw('hist')\n",
    "h_dimuon_mass.GetXaxis().SetTitle('m_{#mu#mu}[GeV]')\n",
    "c.Update()\n",
    "c.SetLogy(1)\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RESET the histogram if you want to try again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,201): h_dimuon_mass.SetBinContent(i,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3 Extracting the Z-boson mass\n",
    "\n",
    "Let's try to extract the Z-boson mass by fitting the peak with a Gaussian function.\n",
    "\n",
    "Mathematical functions in ROOT can be constructed with the class `TF1`*\n",
    "Gaussian function is one of the built-in function in ROOT with the following form:\n",
    "\n",
    "$f(x) = [0] exp\\left(-\\frac{1}{2}((x-[1])/[2]\\right)^2$\n",
    "\n",
    "Here `[0],[1],[2]` are the parameters of the function, corresponding to the `amplitude`, `mean` and `width` of the Gaussian function.\n",
    "\n",
    "Let's create a gaussian function, initialize it with some values, and draw it.\n",
    "\n",
    "* Full documentation of the `TF1` class\n",
    "https://root.cern.ch/doc/master/classTF1.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Here, 'f1' is the name of the object, \n",
    "## 'gaus' is a special string that corresponds to the gaussian formula above\n",
    "## 85 and 95 is the range of the function.\n",
    "## i.e. we are insterested only in that mass window.\n",
    "gaus = r.TF1('f1','gaus',75,105)\n",
    "gaus.SetParameters(1,90,10)\n",
    "gaus.Draw()\n",
    "c.SetLogy(0)\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`TH1` has basic fit function implemented.\n",
    "\n",
    "The default option is the least square fit, \n",
    "\n",
    "$\n",
    "\\chi^2 = \\sum \\left((y_i - f(x_i) )/e_i \\right)^2\n",
    "$\n",
    "\n",
    "The option `R` tells the fit to restrict to the range defined by the function\n",
    "\n",
    "The options `S` simply return the fit result as an object instead of just print-outs.\n",
    "\n",
    "The full options can be found here:\n",
    "\n",
    "https://root.cern.ch/doc/master/classTH1.html#a63eb028df86bc86c8e20c989eb23fb2a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " FCN=751.036 FROM MIGRAD    STATUS=CONVERGED      89 CALLS          90 TOTAL\n",
      "                     EDM=7.05961e-08    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  Constant     6.99743e+02   1.69184e+01   1.34272e-01   2.14013e-05\n",
      "   2  Mean         9.08432e+01   4.11189e-02   5.32781e-04  -4.32965e-03\n",
      "   3  Sigma        2.77650e+00   5.40462e-02   4.03131e-05   9.77698e-02\n"
     ]
    }
   ],
   "source": [
    "fitResult = h_dimuon_mass.Fit(gaus,'RS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_dimuon_mass.Draw()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 3a Quality of the fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking fit quality:\n",
      "chi2 = 751.036\n",
      "NDF = 27.000\n",
      "chi2/NDF = 27.816\n"
     ]
    }
   ],
   "source": [
    "chi2 = gaus.GetChisquare()\n",
    "NDF  = gaus.GetNDF()\n",
    "print('Checking fit quality:')\n",
    "print(\"chi2 = %.3f\\nNDF = %.3f\\nchi2/NDF = %.3f\"% (chi2,NDF, chi2/NDF))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions:\n",
    "- How does the width of the Gaussian (calculate the full width at half maximum from sigma) compares with the known intrinsic width of the Z boson?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.5381643516\n"
     ]
    }
   ],
   "source": [
    "FWHM = 2*(2*math.log(2))**0.5 * gaus.GetParameter('Sigma')\n",
    "print(FWHM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ANS:\n",
    " - The width you obtained is much bigger than the intrinsic width of the Z boson.\n",
    " - The reason can be attributed to:\n",
    "   - poor fit qualities (There are background events un-accounted for in the fit)\n",
    "   - Bin width (1GeV) is wider than detector resolution \n",
    "   - Fit window is too wide to accept background events\n",
    " - The current best fit value for Z boson width is `2.4952±0.0023` GeV*. This is a very precise measurement!\n",
    " - Width of Z boson has profound physics implication! For example, by comparing the width of the Z boson and measuring the branching ratios of all the decay modes, we can deduce if Z boson is having extra-undetected decay modes. This has put a very tight constain with the number of lepton generations that couples to the Z boson. \n",
    "\n",
    "*http://pdg.lbl.gov/2018/listings/rpp2018-list-z-boson.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4 Take-home exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4a Knowing more about the muons\n",
    "\n",
    "Now we have the basic tools to explore a ROOT file and analysis. Let's learn deeper about the muons!\n",
    "\n",
    "Here are some questions to think about:\n",
    "\n",
    "- plot muon pT for the leading, subleading, and third-leading muon (when it's there) and explain the features seen in the plot;\n",
    "- plot muon eta for the leading and subleading muons and explain the feature seen in the plot;\n",
    "- plot isTight flag for the three muons;\n",
    "- plot the muon multiplicity for isTight muons;\n",
    "\n",
    "These questions can be solved in a similar way as the `nMuon` example, with the addition of some control statements during the histogram filling.\n",
    "\n",
    "### Hint: all the `Muon_*` branch has already been sorted in decending order of `pt` \n",
    "i.e `Muon_pt[0]` corresponds to the pt of the leading muon, `Muon_eta[0]` would be the $\\eta$ of the same muon\n",
    "\n",
    "\n",
    "To help you get started, let's define some histograms for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Define the histograms\n",
    "h_mu0_pt      = r.TH1F('h_mu0_pt'     ,'Leading muon pt',200,0,200)\n",
    "h_mu0_eta     = r.TH1F('h_mu0_eta'    ,'Leading muon eta',100,-5,5)\n",
    "h_mu0_isTight = r.TH1F('h_mu0_isTight','Leading muon isTight',2,0,2)\n",
    "\n",
    "h_mu1_pt      = r.TH1F('h_mu1_pt'  ,'Sub-leading muon pt',200,0,200)\n",
    "h_mu1_eta     = r.TH1F('h_mu1_eta' ,'Sub-leading muon eta',100,-5,5)\n",
    "h_mu1_isTight = r.TH1F('h_mu1_isTight','Sub-leading muon isTight',2,0,2)\n",
    "\n",
    "h_mu2_pt      = r.TH1F('h_mu2_pt','Third-Leading muon pt',200,0,200)\n",
    "h_mu2_isTight = r.TH1F('h_mu2_isTight','Third-leading muon isTight',2,0,2)\n",
    "\n",
    "h_nTightMuon  = r.TH1F('h_nTightMuon','Number of tight muons',10,0,10)\n",
    "\n",
    "#collect the histograms into a dic\n",
    "histograms = {\n",
    "        'h_mu0_pt'     :h_mu0_pt,\n",
    "        'h_mu0_eta'    :h_mu0_eta,\n",
    "        'h_mu0_isTight':h_mu0_isTight,\n",
    "        'h_mu1_pt'     :h_mu1_pt,\n",
    "        'h_mu1_eta'    :h_mu1_eta,\n",
    "        'h_mu1_isTight':h_mu1_isTight,\n",
    "        'h_mu2_pt'     :h_mu2_pt,\n",
    "        'h_mu2_isTight':h_mu2_isTight,\n",
    "        'h_nTightMuon' :h_nTightMuon,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,nEntries):\n",
    "    t.GetEntry(i)\n",
    "    nMuon   = t.nMuon\n",
    "    pt      = np.array(t.Muon_pt)          # note: this is an array\n",
    "    phi     = np.array(t.Muon_phi)         # note: this is an array\n",
    "    eta     = np.array(t.Muon_eta)         # note: this is an array \n",
    "    mass    = np.array(t.Muon_mass)        # note: this is an array\n",
    "    isTight = np.array(t.Muon_tightId)     # note: this is an array\n",
    "    charge  = np.array(t.Muon_charge)      # note: this is an array\n",
    "    muons   = {\n",
    "        'n':nMuon,\n",
    "        'pt':pt,\n",
    "        'phi':phi,\n",
    "        'eta':eta,\n",
    "        'isTight':isTight,\n",
    "        'charge':charge,\n",
    "    }\n",
    "    if nMuon==2 and muons['pt'][0]>20:    \n",
    "        h_mu0_pt.Fill(pt[0])\n",
    "        h_mu0_eta.Fill(eta[0])\n",
    "        h_mu0_isTight.Fill(isTight[0])\n",
    "        h_mu1_pt.Fill(pt[1])\n",
    "        h_mu1_eta.Fill(eta[1])\n",
    "        h_mu1_isTight.Fill(isTight[1])\n",
    "    if nMuon>=3:\n",
    "        h_mu2_pt.Fill(pt[2])\n",
    "        h_mu2_isTight.Fill(isTight[2])\n",
    "    nTight = 0\n",
    "    for i in range(0,nMuon):\n",
    "        if isTight[0]: nTight+=1\n",
    "    h_nTightMuon.Fill(nTight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drawing the muon pT histograms\n",
    "\n",
    "You can execute the following cells to check the histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_mu0_pt.SetLineColor(r.kGreen)   ## r.kRed is a global constant stored in ROOT, corresponding to the color code\n",
    "h_mu1_pt.SetLineColor(r.kRed)     ## r.kRed is a global constant stored in ROOT, corresponding to the color code\n",
    "h_mu2_pt.SetLineColor(r.kBlue)\n",
    "h_mu0_pt.Draw()\n",
    "h_mu1_pt.Draw('same')             ## the \"same\" option overlays the histograms on the same canvas\n",
    "h_mu2_pt.Draw('same')\n",
    "h_mu0_pt.GetXaxis().SetTitle('pT[GeV]')\n",
    "c.Update()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: What do you observe? Can you understand the distribution?\n",
    "\n",
    " - You can clearly see the leading muon and sub-leading muon has very similar pT distribution. This makes sense becuase they come from the a 2-body decay of a Z-boson, in which energy of the Z-boson is shared among the 2 muons.\n",
    " - The 3-rd muon in the event has a much softer pT spectrum (i.e. lower pT), simply because they are not decay products of a very massive Z-boson. They are, for example, muons from the hadrons decay inside the jets from pile-up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drawing the muon eta histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_mu0_eta.Draw()\n",
    "h_mu0_eta.SetLineColor(r.kRed)     ## r.kRed is a global constant stored in ROOT, corresponding to the color code\n",
    "h_mu1_eta.SetLineColor(r.kBlue)\n",
    "h_mu1_eta.Draw('same')             ## the \"same\" option overlays the histograms on the same canvas\n",
    "h_mu0_eta.GetXaxis().SetTitle('#eta')\n",
    "c.Update()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drawing isTight histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_mu0_isTight.Draw()\n",
    "h_mu0_isTight.SetLineColor(r.kRed)     ## r.kRed is a global constant stored in ROOT, corresponding to the color code\n",
    "h_mu1_isTight.SetLineColor(r.kBlue)\n",
    "h_mu2_isTight.SetLineColor(r.kGreen)\n",
    "h_mu1_isTight.Draw('same')             ## the \"same\" option overlays the histograms on the same canvas\n",
    "h_mu2_isTight.Draw('same')             ## the \"same\" option overlays the histograms on the same canvas\n",
    "h_mu0_isTight.GetXaxis().SetTitle('isTight')\n",
    "c.Update()\n",
    "c.SetLogy(1)\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_nTightMuon.Draw()\n",
    "c.SetLogy(0)\n",
    "c.Update()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remember to reset the histograms if you want to re-fill them!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "for hname,h in histograms.items():\n",
    "    for b in range(0,h.GetNbinsX()):\n",
    "        h.SetBinContent(b,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4b Di-muon selections:\n",
    " - Can we increase the number of events by using events with exactly two isTight muons, not exactly two muons?\n",
    "   - Try modifying the `selectMuon` function to test your ideas!\n",
    " - histogram the product of electric charges for the events with two tight muons; How often do we have same-sign muons?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def selectGoodMuons(muons):\n",
    "    select = False\n",
    "    if muons['n']==2 and np.all(muons['isTight']) and muons['charge'][0]*muons['charge'][1]<0 and muons['pt'][0]>20:\n",
    "        select = True\n",
    "    return select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## same-signed muons\n",
    "h_q_prod = r.TH1F('h_q_prod','Muon charge product',3,-1,2)\n",
    "for i in range(0,nEntries):\n",
    "    t.GetEntry(i)\n",
    "    if t.nMuon==2 and t.Muon_tightId[0]==1 and t.Muon_tightId[1]==1:\n",
    "        h_q_prod.Fill(t.Muon_charge[0]*t.Muon_charge[1])\n",
    "h_q_prod.Draw()\n",
    "c.Update()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4c Z-boson decays\n",
    "\n",
    "Now that you have observe a Z boson, you do more in-depth analysis of those events coming from a Z boson decay.\n",
    "\n",
    "- For events within an invariant mass window near the Z peak, plot pT spectrum of the Z boson.\n",
    "- For these events, plot a correlation (2D histogram) of the opening azimuthal angle (phi) between the two muons and the pT of the Z boson.\n",
    "\n",
    "You can use the following histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "h_pT_z    = r.TH1F('pT_z','pT of Z boson',100,0,100)\n",
    "h_pTz_phi = r.TH2F('pTz_phi','pT of Z boson v.s. #Delta#phi(#mu#mu)',100,0,100,50,0,math.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to be careful when we calculate azimuthal angle difference, since computers do not know 2pi+phi = phi\n",
    "# so we need to have a function that returns deltaPhi to within 0 to pi\n",
    "def deltaPhi(phi1, phi2):\n",
    "    x = np.abs(phi1 - phi2)\n",
    "    if x >=  math.pi: \n",
    "        x = 2.*math.pi - x\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,nEntries):\n",
    "    t.GetEntry(i)\n",
    "    nMuon   = t.nMuon\n",
    "    pt      = np.array(t.Muon_pt)          # note: this is an array\n",
    "    phi     = np.array(t.Muon_phi)         # note: this is an array\n",
    "    eta     = np.array(t.Muon_eta)         # note: this is an array \n",
    "    mass    = np.array(t.Muon_mass)        # note: this is an array\n",
    "    isTight = np.array(t.Muon_tightId)     # note: this is an array\n",
    "    charge  = np.array(t.Muon_charge)      # note: this is an array\n",
    "    muons   = {\n",
    "        'n':nMuon,\n",
    "        'pt':pt,\n",
    "        'phi':phi,\n",
    "        'eta':eta,\n",
    "        'isTight':isTight,\n",
    "        'charge':charge,\n",
    "    }\n",
    "    if selectMuons(muons):\n",
    "        m_mumu = invmass(pt,phi,eta,mass)\n",
    "        if m_mumu >89 and m_mumu<93:\n",
    "            px = pt[0]*np.cos(phi[0])+pt[1]*np.cos(phi[1])\n",
    "            py = pt[0]*np.sin(phi[0])+pt[1]*np.sin(phi[1])\n",
    "            pt_z = (px**2+py**2)**0.5\n",
    "            delta_phi = deltaPhi(phi[0],phi[1])\n",
    "            h_pT_z.Fill(pt_z)\n",
    "            h_pTz_phi.Fill(pt_z,delta_phi) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_pT_z.Draw()\n",
    "c.Update()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_pTz_phi.Draw('COLZ')\n",
    "c.Update()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4d Improving fit quality\n",
    "\n",
    "- Modify the fit to have a sum of a Gaussian and an exponent to describe the background under the peak \n",
    "- Does this improve the fit chi2?\n",
    "- Can you think of other ways to improve the fit quality?\n",
    "\n",
    "You may want to read the `TF1` documentation to think of which functions to use!\n",
    "\n",
    "Here are some skeleton code for you to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[p0]*exp(-0.5*((x-[p1])/[p2])*((x-[p1])/[p2]))+exp([p3]+[p4]*x)\n"
     ]
    }
   ],
   "source": [
    "## Define the sum of gaussian and exponential:\n",
    "f2 = r.TF1('f2','gaus(0)+expo(3)',75,105)\n",
    "print(f2.GetFormula().GetExpFormula()) \n",
    "f2.SetParameters(1,90,10,6,-0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " FCN=222.768 FROM MIGRAD    STATUS=CONVERGED     307 CALLS         308 TOTAL\n",
      "                     EDM=9.57021e-10    STRATEGY= 1  ERROR MATRIX UNCERTAINTY   1.1 per cent\n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0           7.52620e+02   1.67563e+01  -4.48938e-03   4.05709e-08\n",
      "   2  p1           9.09204e+01   3.92270e-02   3.31470e-06   2.40169e-04\n",
      "   3  p2           2.34469e+00   4.34502e-02   2.18331e-05  -2.40812e-04\n",
      "   4  p3           5.48949e+00   2.98872e-01  -6.52326e-05   1.58055e-04\n",
      "   5  p4          -2.25256e-02   3.43054e-03   1.20555e-06   3.00455e-02\n"
     ]
    }
   ],
   "source": [
    "## ADD your fitting code here:\n",
    "fitResult = h_dimuon_mass.Fit(f2,'RS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_dimuon_mass.Draw()\n",
    "c.Update()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_data = h_dimuon_mass.Clone()\n",
    "f2_gaus = r.TF1('f2_gaus','gaus(0)',75,105)\n",
    "f2_expo = r.TF1('f2_expo','expo(0)',75,105)\n",
    "for i in range(0,3): f2_gaus.SetParameter(i,f2.GetParameter(i))\n",
    "for i in range(3,5): f2_expo.SetParameter(i-3,f2.GetParameter(i))\n",
    "f2.Draw()\n",
    "f2_gaus.SetLineColor(r.kBlue)\n",
    "f2_expo.SetLineColor(r.kGreen)\n",
    "f2_gaus.Draw('same')\n",
    "f2_expo.Draw('same')\n",
    "h_data.Draw('same E')\n",
    "f2.GetYaxis().SetRangeUser(0,1000)\n",
    "c.Update()\n",
    "c.SetLogy(0)\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking fit quality:\n",
      "chi2 = 222.768\n",
      "NDF = 25.000\n",
      "chi2/NDF = 8.911\n"
     ]
    }
   ],
   "source": [
    "## Now check your fit quality. Does it improve?\n",
    "chi2 = f2.GetChisquare()\n",
    "NDF  = f2.GetNDF()\n",
    "print('Checking fit quality:')\n",
    "print(\"chi2 = %.3f\\nNDF = %.3f\\nchi2/NDF = %.3f\"% (chi2,NDF, chi2/NDF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}