exercise-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EGamma short exercise\n",
    "This is the companion notebook to [short ex twiki link here](https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideCMSDataAnalysisSchoolCERN2023EGMExercise).\n",
    "\n",
    "Photons and electrons play a crucial role in the success of CMS. They were vital in the discovery of the Higgs boson and play an important role in searches for new physics and standard model measurements. The CMS detector features a high-resolution electromagnetic calorimeter (ECAL) and the world's largest active silicon tracker. These detectors provide clean object identification and excellent energy and momentum resolution of photons and electrons. Within CMS, the [EGamma POG](https://twiki.cern.ch/twiki/bin/view/CMS/EgammaPOG) has the responsibility to \"study, develop, characterize and validate the tools to identify and reconstruct electrons and photons.\" In this exercise, we will examine photon and electron identification and study how these objects are used in physics analyses. We will start with [NanoAOD](https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookNanoAOD) samples.\n",
    "\n",
    "Table of contents:\n",
    "   * [Using NanoAOD](#Using-NanoAOD)\n",
    "   * [Array programming primer](#Array-programming-primer)\n",
    "   * [Ground truth](#Ground-truth)\n",
    "   * [Discriminating variables](#Discriminating-variables)\n",
    "      * [Shower shape](#Shower-shape)\n",
    "      * [Isolation](#Isolation)\n",
    "      * [Electron-specific variables](#Electron-specific-variables)\n",
    "      * [Photon-specific variables](#Photon-specific-variables)\n",
    "   * [Cut optimization](#Cut-optimization)\n",
    "   * [Tag and probe](#Tag-and-probe)\n",
    "   * [Scale Factors](#Scale-Factors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using NanoAOD\n",
    "We will be using the [NanoAOD](https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookNanoAOD) data format in this exercise. It contains only information that are accessed in a typical CMS analysis, averaging 1-2 kB per event, in basic types like `int`, `float`, etc. This is in contrast to the [EDM format](https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookCMSSWFramework#EdM) used in upstream formats. This means that no CMSSW environment is necessary to read NanoAOD files.\n",
    "\n",
    "For analyses that require information not available in standard NanoAOD, one can create _private NanoAOD_, where  missing variables and/or CMSSW framework modules are added to the [configuration](https://github.com/cms-sw/cmssw/blob/master/PhysicsTools/NanoAOD/python/nano_cff.py) and then CRAB workflows are used to re-extract the information from the parent MiniAOD dataset.  The CMS [cross-POG forum](https://indico.cern.ch/category/5517/) exists to also allow users to petition for certain variables to be added to central NanoAOD.  For the purposes of this exercise, all standard EGamma variables can already be found in NanoAOD.\n",
    "\n",
    "Below, we examine the content of a NanoAOD file using [uproot](https://uproot.readthedocs.io/en/latest/?badge=latest#)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tag;1': <TObjString 'untagged' at 0x7f3b5e6b7dd0>,\n",
       " 'Events;1': <TTree 'Events' (1628 branches) at 0x7f3b496125e0>,\n",
       " 'LuminosityBlocks;1': <TTree 'LuminosityBlocks' (2 branches) at 0x7f3b5e410e80>,\n",
       " 'Runs;1': <TTree 'Runs' (8 branches) at 0x7f3b49612430>,\n",
       " 'MetaData;1': <TTree 'MetaData' (1 branches) at 0x7f3b5e410f70>,\n",
       " 'ParameterSets;1': <TTree 'ParameterSets' (1 branches) at 0x7f3b5e2a76d0>}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import uproot\n",
    "from glob import glob\n",
    "base_directory = '/eos/user/c/cmsdas/2023/short-ex-egamma/datasets/'\n",
    "dyfiles = sorted(glob(f'{base_directory}/DYJetsToLL_M-50_TuneCP5_13TeV-amcatnloFXFX-pythia8/*.root'))\n",
    "gjetfiles = sorted(glob(f'{base_directory}/GJets_HT-400To600_TuneCP5_13TeV-madgraphMLM-pythia8/*.root'))\n",
    "datafiles = sorted(glob(f'{base_directory}/EGamma-Run2018A/*.root'))\n",
    "file = uproot.open(dyfiles[0])\n",
    "dict(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The item of interest here is the `Events` [TTree](https://root.cern.ch/doc/master/classTTree.html) object. We will focus on electron/photon specific content. More information about the entire `Events` tree can be found in [this website](https://cms-nanoaod-integration.web.cern.ch/integration/master-106X/mc106X_doc.html) (and its' parent folder contains branch lists for other NanoAOD versions).\n",
    "\n",
    "To help organize all this content, we'll use the [coffea](https://coffeateam.github.io/coffea/) NanoEvents class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from coffea.nanoevents import NanoEventsFactory\n",
    "\n",
    "dyevents = NanoEventsFactory.from_root(dyfiles[0]).events()\n",
    "gevents = NanoEventsFactory.from_root(gjetfiles[0]).events()\n",
    "dataevents = NanoEventsFactory.from_root(datafiles[0]).events()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dEscaleDown',\n",
       " 'dEscaleUp',\n",
       " 'dEsigmaDown',\n",
       " 'dEsigmaUp',\n",
       " 'deltaEtaSC',\n",
       " 'dr03EcalRecHitSumEt',\n",
       " 'dr03HcalDepth1TowerSumEt',\n",
       " 'dr03TkSumPt',\n",
       " 'dr03TkSumPtHEEP',\n",
       " 'dxy',\n",
       " 'dxyErr',\n",
       " 'dz',\n",
       " 'dzErr',\n",
       " 'eCorr',\n",
       " 'eInvMinusPInv',\n",
       " 'energyErr',\n",
       " 'eta',\n",
       " 'hoe',\n",
       " 'ip3d',\n",
       " 'jetPtRelv2',\n",
       " 'jetRelIso',\n",
       " 'mass',\n",
       " 'miniPFRelIso_all',\n",
       " 'miniPFRelIso_chg',\n",
       " 'mvaFall17V2Iso',\n",
       " 'mvaFall17V2noIso',\n",
       " 'pfRelIso03_all',\n",
       " 'pfRelIso03_chg',\n",
       " 'phi',\n",
       " 'pt',\n",
       " 'r9',\n",
       " 'scEtOverPt',\n",
       " 'sieie',\n",
       " 'sip3d',\n",
       " 'mvaTTH',\n",
       " 'charge',\n",
       " 'cutBased',\n",
       " 'jetIdx',\n",
       " 'pdgId',\n",
       " 'photonIdx',\n",
       " 'tightCharge',\n",
       " 'vidNestedWPBitmap',\n",
       " 'vidNestedWPBitmapHEEP',\n",
       " 'convVeto',\n",
       " 'cutBased_HEEP',\n",
       " 'isPFcand',\n",
       " 'jetNDauCharged',\n",
       " 'lostHits',\n",
       " 'mvaFall17V2Iso_WP80',\n",
       " 'mvaFall17V2Iso_WP90',\n",
       " 'mvaFall17V2Iso_WPL',\n",
       " 'mvaFall17V2noIso_WP80',\n",
       " 'mvaFall17V2noIso_WP90',\n",
       " 'mvaFall17V2noIso_WPL',\n",
       " 'seedGain',\n",
       " 'genPartIdx',\n",
       " 'genPartFlav',\n",
       " 'cleanmask',\n",
       " 'genPartIdxG',\n",
       " 'jetIdxG',\n",
       " 'photonIdxG']"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dyevents.Electron.fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try replacing the above cell with `dyevents.Electron.fields` and `dyevents.Photon.fields` to see what photon-related columns exist in the tree.\n",
    "\n",
    "Below shows how to see a short description of a column. Try `dyevents.Electron.jetIdx.__doc__` and `dyevents.Electron.lostHits.__doc__`. **Q: What could these values be used for? Why are they associated with an electron?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cut-based ID Fall17 V2 (0:fail, 1:veto, 2:loose, 3:medium, 4:tight)'"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dyevents.Electron.cutBased.__doc__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array programming primer\n",
    "\n",
    "Before diving into the content, let's give a quick overview of [awkward](https://awkward-array.org/quickstart.html) array syntax, as the NanoEvents structure is an awkward array.  It is an extension of the [numpy](https://numpy.org/) array programming syntax.  If you are unfamiliar with numpy, perhaps glance through the [numpy quickstart](https://docs.scipy.org/doc/numpy/user/quickstart.html) before continuing, in particular the universal function and boolean indexing sections.  The main extensions to numpy that are used here are jagged arrays.  Some basic properties are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Array [[1, 2, 3], [4, -2, 4], [], [5]] type='4 * var * int64'>\n",
      "<Array [3, 3, 0, 1] type='4 * int64'>\n"
     ]
    }
   ],
   "source": [
    "import awkward as ak\n",
    "import numpy as np\n",
    "\n",
    "a = ak.Array([[1, 2, 3], [4, -2, 4], [], [5]])\n",
    "print(repr(a))\n",
    "print(repr(ak.num(a)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above is a singly-jagged array, where the first dimension has length 4, and the second dimension has variable length.  The length along that dimension is accessible via the [ak.num function](https://awkward-array.readthedocs.io/en/latest/_auto/ak.num.html) (the default axis is 1 for `ak.num`).  This is like the array `dyevents.Electrons`.  All comparison operators and numpy ufuncs are _broadcast_ over the content, meaning they are applied to each element in the jagged array. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[False, False, True], [True, False, True], [], [True]]\n",
      "[[1, 2, 3], [4, 2, 4], [], [5]]\n"
     ]
    }
   ],
   "source": [
    "print(a > 2)\n",
    "print(abs(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The jagged array `a > 2` can be used to filter (i.e. select) only those elements that pass the criteria. This is done using the `array[filter]` notation. We will use this syntax later to, for example, select only electrons above a certain pT threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[False, False, True], [True, False, True], [], [True]]\n",
      "[[3], [4, 4], [], [5]]\n"
     ]
    }
   ],
   "source": [
    "object_cut = (a > 2)\n",
    "print(object_cut)\n",
    "print(a[object_cut])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using jagged arrays in a typical physics analysis, in addition to `object_cuts` we can also have event-level selections. For example, we might want to only select _events_ where `all` electrons in the event have pT above some threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False, False, True, True]\n",
      "[[], [5]]\n"
     ]
    }
   ],
   "source": [
    "event_cut = ak.all(a > 2, axis = 1)\n",
    "print(event_cut)\n",
    "print(a[event_cut])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most utilities in the scientific python \"speak\" numpy arrays, however they do not know about jagged arrays.  So, for example, when interfacing with [pandas](https://pandas.pydata.org/) or [matplotlib](https://matplotlib.org/) we need to remove the jaggedness by using `ak.flatten`.  Of course, we should do this only after making any event-level selections, as after flattening, that structure is lost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[], [5]]\n",
      "[5]\n"
     ]
    }
   ],
   "source": [
    "print(a[event_cut])\n",
    "print(ak.flatten(a[event_cut]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another common awkward type is masked arrays.  The main feature is that certain elements can be [null](https://en.wikipedia.org/wiki/Nullable_type) (syn. N/A, not available, `None`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [1, 2, None, 4] type='4 * ?int64'>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = ak.Array([1, 2, None, 4])\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The null value propagates through any operation, until it is filled in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, None, 4]\n",
      "[1, 2, -1, 4]\n",
      "[True, True, None, True]\n",
      "[True, True, False, True]\n"
     ]
    }
   ],
   "source": [
    "print(b)\n",
    "print(ak.fill_none(b, -1))\n",
    "print(b > 0)\n",
    "print(ak.fill_none(b, -1) > 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the scientific python ecosystem, [matplotlib](https://matplotlib.org/) is the de facto standard plotting utility. Let's import it, along with [mplhep](https://mplhep.readthedocs.io/en/latest/), a helper library to make HEP plots easier (including CMS style!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import mplhep\n",
    "mplhep.style.use(mplhep.style.CMS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run this again to make sure the figure size updates (a known bug)\n",
    "mplhep.style.use(mplhep.style.CMS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kinematics\n",
    "Below is a plot of all reconstructed electrons' transverse momentum ($p_{T}$) in this NanoAOD file.\n",
    "Try plotting instead the $\\eta$ and $\\phi$ of the electrons.  Plot the electron `counts`.  Plot the $p_{T}$ of the electrons after applying a cut on $|\\eta| < 1.4$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([5.00000e+00, 1.32202e+05, 9.91960e+04, 7.50710e+04, 7.11350e+04,\n",
       "        7.18930e+04, 7.55690e+04, 7.80890e+04, 8.19400e+04, 8.52610e+04,\n",
       "        8.44390e+04, 5.88110e+04, 3.22630e+04, 2.26410e+04, 1.64680e+04,\n",
       "        1.24690e+04, 9.60200e+03, 7.43900e+03, 5.90700e+03, 4.68100e+03,\n",
       "        3.90100e+03, 3.12500e+03, 2.49800e+03, 2.08300e+03, 1.81400e+03,\n",
       "        1.50600e+03, 1.20700e+03, 1.07900e+03, 9.08000e+02, 8.12000e+02,\n",
       "        6.54000e+02, 5.94000e+02, 5.43000e+02, 4.59000e+02, 3.94000e+02,\n",
       "        3.74000e+02, 2.95000e+02, 2.81000e+02, 2.39000e+02, 2.17000e+02,\n",
       "        1.88000e+02, 1.76000e+02, 1.67000e+02, 1.40000e+02, 1.48000e+02,\n",
       "        1.06000e+02, 1.07000e+02, 9.70000e+01, 9.80000e+01]),\n",
       " array([  0.        ,   4.08163265,   8.16326531,  12.24489796,\n",
       "         16.32653061,  20.40816327,  24.48979592,  28.57142857,\n",
       "         32.65306122,  36.73469388,  40.81632653,  44.89795918,\n",
       "         48.97959184,  53.06122449,  57.14285714,  61.2244898 ,\n",
       "         65.30612245,  69.3877551 ,  73.46938776,  77.55102041,\n",
       "         81.63265306,  85.71428571,  89.79591837,  93.87755102,\n",
       "         97.95918367, 102.04081633, 106.12244898, 110.20408163,\n",
       "        114.28571429, 118.36734694, 122.44897959, 126.53061224,\n",
       "        130.6122449 , 134.69387755, 138.7755102 , 142.85714286,\n",
       "        146.93877551, 151.02040816, 155.10204082, 159.18367347,\n",
       "        163.26530612, 167.34693878, 171.42857143, 175.51020408,\n",
       "        179.59183673, 183.67346939, 187.75510204, 191.83673469,\n",
       "        195.91836735, 200.        ]),\n",
       " <BarContainer object of 49 artists>)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel(r'Electron $p_{T}$ [GeV]')\n",
    "ax.hist(ak.flatten(dyevents.Electron.pt), bins = np.linspace(0, 200, 50))\n",
    "#ax.set_xlabel(r'Electron $\\eta$')\n",
    "#ax.hist(ak.flatten(dyevents.Electron.eta), bins = np.linspace(-2.5, 2.5, 50))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we plot the invariant mass of all distinct pairs of electrons. The pairs made using the `combinations` [documentation](https://awkward-array.org/doc/main/reference/generated/ak.combinations.html) function. The `e0` and `e1` names are names we assign so we can separately access the first and second electron in the pair. **Q: What is the origin of the shoulder-like structure at low mass? Can this be removed by performing selections on the electron candidates, using the information above?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel(r'Dielectron mass [GeV]')\n",
    "\n",
    "ee = ak.combinations(dyevents.Electron, 2, fields = [\"e0\", \"e1\"])\n",
    "mass = (ee.e0 + ee.e1).mass\n",
    "ax.hist(ak.flatten(mass), bins = np.linspace(0, 200, 50));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ground truth\n",
    "If we want to identify variables useful in distinguishing electrons and photons candidates, we better have a good definition of \"true\".  Let's take a look at the generated particles that are matched to reconstructed electrons and photons to understand what kind of processes give rise to them.\n",
    "\n",
    "For electrons and photons, NanoAOD pre-computes the truth-matched particle by finding final-state electron and photon particles in the `GenPart` table that are directionally close (via the metric $\\Delta R = \\sqrt{(\\Delta \\eta)^2 + (\\Delta \\phi)^2}$) to the reconstructed particle.  When no match is found, we get a null/NA value.  Below, we put reconstructed electrons into three buckets: matched to a gen electron, matched to a gen photon, and unmatched, based on the [PDG id](http://pdg.lbl.gov/2018/reviews/rpp2018-rev-monte-carlo-numbering.pdf).  Since for this purpose we don't care so much which event each electron belongs to, we also flatten the array here for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "electrons = ak.flatten(dyevents.Electron)\n",
    "\n",
    "true_electrons = electrons[ak.fill_none(abs(electrons.matched_gen.pdgId) == 11, False)]\n",
    "photon_fakes = electrons[ak.fill_none(electrons.matched_gen.pdgId == 22, False)]\n",
    "unmatched_electrons = electrons[ak.is_none(electrons.matched_gen)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the true electrons, we would expect the parent to be a Z boson in these samples.  We can check this using the `distinctParent` attribute stored in our files.  There is also a `parent` attribute, which specifies the direct ancestor to the generated electron, however this could also be an electron (due to final state $\\gamma$ radiation and other generator-internal reasons).  The `distinctParent` attribute skips copies of the same particle that are the result of FSR.\n",
    "\n",
    "Below, we put the `distinctParent` value, along with some other (carefully chosen) gen particle flags, into a pandas dataframe by passing a dictionary. In this dictionary, the key signifies the parameter and the element is a flattened array of that value for every electron in the NanoAOD file. The `ak.zip` groups keyed elements together for each electron, making sure the dataframe is cast correctly where each electron is an entry. We then use `groupby` to create categories that share common values. They are then ranked by the number of electrons, or \"counts\" that are in that category. We only consider the categories with the top 10 number of entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parentId</th>\n",
       "      <th>fromHardProcess</th>\n",
       "      <th>isPrompt</th>\n",
       "      <th>isDirectPromptTauDecayProduct</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>655004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>83738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>19228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>6361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>5175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>521.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>411.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>511.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    parentId  fromHardProcess  isPrompt  isDirectPromptTauDecayProduct   count\n",
       "21      23.0             True      True                          False  655004\n",
       "17      15.0            False     False                           True   83738\n",
       "5        2.0             True      True                          False   19228\n",
       "19      21.0             True      True                          False    6361\n",
       "2        1.0             True      True                          False    5175\n",
       "11       4.0             True      True                          False    4321\n",
       "8        3.0             True      True                          False    2341\n",
       "32     521.0            False     False                          False    1328\n",
       "27     411.0            False     False                          False    1224\n",
       "31     511.0            False     False                          False    1211"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ak.to_pandas(\n",
    "    ak.zip(\n",
    "        {\n",
    "            \"parentId\": abs(true_electrons.matched_gen.distinctParent.pdgId),\n",
    "            \"fromHardProcess\": true_electrons.matched_gen.hasFlags([\"fromHardProcess\"]),\n",
    "            \"isPrompt\": true_electrons.matched_gen.hasFlags([\"isPrompt\"]),\n",
    "            \"isDirectPromptTauDecayProduct\": true_electrons.matched_gen.hasFlags(\n",
    "                [\"isDirectPromptTauDecayProduct\"]\n",
    "            ),\n",
    "        }\n",
    "    )\n",
    ")\n",
    "\n",
    "top10 = (\n",
    "    df.groupby(list(df.columns))\n",
    "    .size()\n",
    "    .rename(\"count\")\n",
    "    .reset_index()\n",
    "    .sort_values(\"count\", ascending = False)\n",
    ")[:10]\n",
    "\n",
    "top10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, in most cases, the parent is pdgId `23`, corresponding to the Z boson.  Sometimes the parent is a $\\tau$ lepton, and then the remainder are cases where the electron is from a hadron decay (`parentId > 100`) or where the parentage appears somewhat suspect (e.g. pdg IDs less than 6, corresponding to bare quarks... this is a quirk in how MadGraph records the parentage history for off-shell $Z^*/\\gamma^*$).  Conveniently, a set of flags is embedded in NanoAOD that are more straightforward than travsersing the NanoAOD parentage history.  In our case, we'll take `isPrompt` as the marker for true electrons coming from the hard scatter event.  Since direct $\\tau$ decays produce prompt isolated electrons, we include those with `isDirectPromptTauDecayProduct` as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "promptmatch = true_electrons.matched_gen.hasFlags(['isPrompt'])\n",
    "promptmatch = promptmatch | true_electrons.matched_gen.hasFlags(['isDirectPromptTauDecayProduct'])\n",
    "prompt_electrons = true_electrons[promptmatch]\n",
    "nonprompt_electrons = true_electrons[~promptmatch]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also a pre-computed variable `Electron.genPartFlav`, which according to the documentation, has the assignment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Flavour of genParticle (DressedLeptons for electrons) for MC matching to status==1 electrons or photons: 1 = prompt electron (including gamma*->mu mu), 15 = electron from prompt tau, 22 = prompt photon (likely conversion), 5 = electron from b, 4 = electron from c, 3 = electron from light or unknown, 0 = unmatched'"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dyevents.Electron.genPartFlav.__doc__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how this compares to our analysis: below we count the number of occurrences of each flavor using [np.unique](https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html) and then print the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GenPart flavor   1 has   693667 occurrences\n",
      "GenPart flavor  15 has    83738 occurrences\n",
      "GenPart flavor  22 has        8 occurrences\n"
     ]
    }
   ],
   "source": [
    "flavors, counts = np.unique(ak.to_numpy(prompt_electrons.genPartFlav), return_counts = True)\n",
    "for flavor, count in zip(flavors, counts):\n",
    "    print(\"GenPart flavor % 3d has % 8d occurrences\" % (flavor, count))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try checking our other categories against this pre-computed ground truth categorization by switching out the `prompt_electrons` variable with the others.\n",
    "For photons, `gevents.Photon.genPartFlav` provides similar categorization.  Below, we use this variable to sort photons into two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "photons = ak.flatten(gevents.Photon)\n",
    "prompt_photons = photons[photons.genPartFlav == 1]\n",
    "unmatched_photons = photons[photons.genPartFlav == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identification variables\n",
    "In the following few cells, we'll introduce typical identification variables that are useful in distinguishing electrons and photon candidates that are produced in hard scattering processes (the so-called true electrons/photons in HEPese).  We'll also look at electron- and photon-specific identification variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shower shape\n",
    "\n",
    "Shower shape variables are constructed from the energy-weighted spatial distribution of ECAL and HCAL deposits.\n",
    "\n",
    "   * $\\sigma_{i\\eta i\\eta}$ (`Electron.sieie`) is the energy-weighted standard deviation of integer eta (one per crystal) within the 5×5 crystal cluster centered at the crystal with maximum energy:\n",
    "\n",
    "$$ \\sigma_{i\\eta i\\eta} = \\frac{\\sum_{i}^{5x5} w_i (i\\eta_i - i\\eta_{\\text{seed}})}{\\sum_i^{5x5} w_i}, \\quad w_i = \\max(0, 4.7 + \\ln E_i - \\ln E_{5x5}),$$\n",
    "\n",
    "where the sum runs over the 5 × 5 crystal matrix around the most energetic crystal in the supercluster, and the eta distances are measured in units of the crystal size in the eta direction. This variable represents the second moment of the energy distribution along the eta coordinate.  The integer values are scaled by the nominal crystal $\\phi$ width in the barrel which is $\\pi / 180 \\approx 0.0175$. See: [definition](https://github.com/cms-sw/cmssw/blob/master/RecoEcal/EgammaCoreTools/interface/EcalClusterLazyTools.h#L252) and [implementation](https://github.com/cms-sw/cmssw/blob/master/RecoEcal/EgammaCoreTools/interface/EcalClusterTools.h#L1034) in CMSSW.\n",
    "\n",
    "   * $R_9$ (`Electron.r9`) is the ratio of the energy deposited in ECAL inside a 3 × 3 crystal matrix centered on the most energetic crystal in the supercluster and the supercluster raw energy.\n",
    "\n",
    "   * $H/E$ (`Electron.hoe`) is the ratio of the energy deposited in the single closest HCAL tower to the supercluster position inside a cone of radius 0.15 in the $\\eta, \\phi$ plane centered on the photon direction and the energy deposited in ECAL to that supercluster.\n",
    "\n",
    "   * $1/E - 1/P$ (`Electron.eInvMinusPInv`) is the signed difference between the reciprocal of the supercluster energy and the reciprocal of the GSF track momentum.\n",
    "\n",
    "Below, we plot the $\\sigma_{i\\eta i\\eta}$ variable for true prompt electrons and unmatched electrons, where the minimum electron $p_{T}$ is required to be at least 20 GeV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(1, 0, 'Electron $\\\\sigma_{i \\\\eta i \\\\eta}$')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "opts = {\n",
    "    'density': True,\n",
    "    'alpha': 0.5,\n",
    "}\n",
    "\n",
    "vars = {\n",
    "    'sieie': {\n",
    "        'bins': np.linspace(0, 0.05, 100),\n",
    "        'xlabel': r'Electron $\\sigma_{i \\eta i \\eta}$',\n",
    "        'log': False\n",
    "    },\n",
    "    'eInvMinusPInv': {\n",
    "        'bins': np.linspace(-0.2, 0.2, 100),\n",
    "        'xlabel': r'Electron $1/E - 1/P$ [/GeV]',\n",
    "        'log': True\n",
    "    },\n",
    "    'r9': {\n",
    "        'bins': np.linspace(0, 1, 100),\n",
    "        'xlabel': r'Electron $R_{9}$',\n",
    "        'log': True\n",
    "    },\n",
    "    'hoe': {\n",
    "        'bins': np.linspace(0, 1, 100),\n",
    "        'xlabel': r'Electron H/E',\n",
    "        'log': True\n",
    "    },\n",
    "    'pfRelIso03_chg': {\n",
    "        'bins': np.linspace(0, 1, 100),\n",
    "        'xlabel': r'Electron relative charged isolation',\n",
    "        'log': True\n",
    "    },\n",
    "}\n",
    "\n",
    "def cut_and_get(candidates, attribute):\n",
    "    return getattr(candidates[(candidates.pt > 20.)], attribute)\n",
    "\n",
    "var = 'sieie'\n",
    "ax.hist(cut_and_get(prompt_electrons, var), color = 'blue', label = 'Prompt electrons', bins = vars[var]['bins'], **opts)\n",
    "#ax.hist(cut_and_get(photon_fakes, var), color = 'green', label = 'Photons', bins = vars[var]['bins'], **opts)\n",
    "#ax.hist(cut_and_get(nonprompt_electrons, var), color = 'orange', label = 'Non-prompt electrons', bins = vars[var]['bins'], **opts)\n",
    "ax.hist(cut_and_get(unmatched_electrons, var), color = 'red', label = 'Unmatched', bins = vars[var]['bins'], **opts)\n",
    "\n",
    "ax.legend()\n",
    "ax.set_ylabel('Normalized' if opts['density'] else 'Events')\n",
    "ax.set_yscale('log' if vars[var]['log'] else 'linear')\n",
    "ax.set_xlabel(vars[var]['xlabel'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try uncommenting different categories and looking at the distributions.  Any guess as to why the true electrons have a double-peak?  It might help to try looking at `sieie` separately for barrel and endcap reconstructed electrons (hint: modify the filter function to also require `abs(electrons.eta) < 1.4447` for barrel electrons, and `abs(electrons.eta) > 1.566` for endcap).\n",
    "\n",
    "**Look at R9, H/E and 1/E - 1/P variables.** Which of the three is offers the strongest true vs fake electron discrimination? **Is the sign of 1/E - 1/P relevant for identification purposes?**\n",
    "\n",
    "Look at each variable as a function of $p_{T}$, for example by plotting a 2D histogram.  What trends do you notice?\n",
    "\n",
    "Plot `sieie` for prompt and unmatched photons in the barrel. Do you notice anything interesting?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Isolation\n",
    "\n",
    "Isolation variables compare the amount of energy deposited in a region around the reconstructed object to that of the object.  Typically, non-prompt electrons and photons (i.e. from hadron decays) have other energy deposited nearby.  Two particle flow (PF) isolation variables are saved in NanoAOD:\n",
    "\n",
    "   * PF charged hadron isolation `pfRelIso03_chg`: sum of $p_{T}$ of PF charged hadrons which are associated to the primary vertex, not in the \"footprint\" of the candidate, and within a cone of $\\Delta R < 0.3$,\n",
    "   * PF total isolation `pfRelIso03_all`: sum of $p_{T}$ of all PF candidates within the cone of $\\Delta R < 0.3$,\n",
    "   \n",
    "where in both cases, the values are relative to the object $p_{T}$.\n",
    "\n",
    "Due to pileup (multiple collisions in the same event), some of the PF candidates within this isolation cone may belong to a different collision and therefore this variable has a mild dependence on the number of pileup interactions. This pileup-dependence of total isolation can be corrected for, for example by computing,\n",
    "\n",
    "$$I = I_{\\text{raw}} - \\rho * \\text{EA}(|\\eta|),$$\n",
    "\n",
    "where $\\rho$ is the median pileup energy density per unit area, a measure of the pileup activity in the event (computed during the jet clustering stage); and EA is the effective area of the electron or photon object, a quantity computed and [made available](https://github.com/lsoffi/cmssw/blob/CMSSW_9_2_X_TnP/RecoEgamma/ElectronIdentification/data/Fall17/effAreaElectrons_cone03_pfNeuHadronsAndPhotons_92X.txt) by the Egamma POG, and is defined as explained in [slide 4 here](https://indico.cern.ch/event/662749/contributions/2763091/attachments/1545124/2424854/talk_electron_ID_fall17.pdf). **Q: Why is the EA parametrized as a function of $\\eta$, instead of say, $\\phi$? Can you offer an explanation?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Electron-specific variables\n",
    "\n",
    "   * Mini-isolation `miniPFRelIso_all, miniPFRelIso_chg`: uses a cone whose size varies as a function of the electron $p_{T}$ in order to reduce accidental overlap from hard scatter and pileup processes,\n",
    "   * Conversion veto `convVeto`: reject electrons that are from photons conversions in the innermost layers of the pixel or tracker detectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Photon-specific variables\n",
    "\n",
    "   * Conversion-safe electron veto `electronVeto`: this cut replaces the detector-based `pixelSeed` used to distinguish between photons and electrons. It checks the $\\Delta R$ distance between the photon direction and the nearest electron track and remove photons if there is a electron matching to the photon supercluster, with no missing hits and having no matching reconstructed conversion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cut optimization\n",
    "\n",
    "Now that we've seen a few discriminating variables, let's compare their power for differentiating true electrons from background using a [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic), which shows signal tagging efficiency as well as background rejection efficiency simultaneously on a 2D plot. Efficiency $\\epsilon$ is defined for a given selection as:\n",
    "\n",
    "$$\\epsilon = \\frac{n_{\\text{pass}}}{n_{\\text{pass}}+n_{\\text{fail}}} = \\frac{n_{\\text{pass}}}{n_{\\text{total}}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c52058316ec44f28a03e2632819684f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.2, description='hadronicOverEm:', layout=Layout(width='800px'), max=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0deaceb28ca7486fad6b4c1573d8aaae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IDTools.DrawSlider import make_slider\n",
    "make_slider(prompt_electrons, unmatched_electrons)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we plot the ROC curve for: selected discriminating variables; working points in the ROC of the POG-supported cut-based ID, which is a tuned selection on multiple variables; and the ROC for the POG-supported MVA classifier. Information on all of the POG-supported IDs can be found [here](https://twiki.cern.ch/twiki/bin/view/CMS/EgammaIDRecipesRun2).\n",
    "\n",
    "**Define a selection on multiple variables, measure its signal and background efficiency, and place a marker on the ROC corresponding to your ID.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeroc(sig, bkg, binning, direction = 1):\n",
    "    '''Given a signal and background discriminant array, along with a binning,\n",
    "    compute the ROC curve by integrating discriminant histograms along a direction:\n",
    "         1: cut < value\n",
    "        -1: cut >= value\n",
    "    Returns a tuple of (signal efficiency, background efficiency)'''\n",
    "    sighist, _ = np.histogram(ak.to_numpy(sig), bins = binning)\n",
    "    bkghist, _ = np.histogram(ak.to_numpy(bkg), bins = binning)\n",
    "    # numpy histogram will ignore values out of range of our binning (overflow)\n",
    "    # so add the overflow values that would pass given our cut direction\n",
    "    if direction == 1:\n",
    "        sighist[0] += sum(sig < binning[0])\n",
    "        bkghist[0] += sum(bkg < binning[0])\n",
    "    elif direction == -1:\n",
    "        sighist[-1] += sum(sig > binning[-1])\n",
    "        bkghist[-1] += sum(bkg > binning[-1])\n",
    "    else:\n",
    "        raise ValueError(\"Invalid direction\")\n",
    "    sigpass = (np.cumsum(sighist[::direction]) / len(sig))[::direction]\n",
    "    bkgpass = (np.cumsum(bkghist[::direction]) / len(bkg))[::direction]\n",
    "    return (sigpass, bkgpass)\n",
    "\n",
    "\n",
    "def cut(electrons):\n",
    "    return electrons[\n",
    "        (electrons.pt > 20)\n",
    "        & (abs(electrons.eta) < 1.4447)\n",
    "    ]\n",
    "\n",
    "\n",
    "roc_sieie = makeroc(\n",
    "    sig = cut(prompt_electrons).sieie,\n",
    "    bkg = cut(unmatched_electrons).sieie,\n",
    "    binning = np.linspace(0.005, 0.02, 100),\n",
    "    direction = 1,\n",
    ")\n",
    "\n",
    "roc_hoe = makeroc(\n",
    "    sig = cut(prompt_electrons).hoe,\n",
    "    bkg = cut(unmatched_electrons).hoe,\n",
    "    binning = np.linspace(0., 0.5, 100),\n",
    "    direction = 1,\n",
    ")\n",
    "\n",
    "roc_mva = makeroc(\n",
    "    sig = cut(prompt_electrons).mvaFall17V2Iso,\n",
    "    bkg = cut(unmatched_electrons).mvaFall17V2Iso,\n",
    "    binning = np.linspace(-1, 1., 100),\n",
    "    direction = -1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "sig_pass = cut(prompt_electrons).cutBased >= prompt_electrons.LOOSE\n",
    "bkg_pass = cut(unmatched_electrons).cutBased >= prompt_electrons.LOOSE\n",
    "wp_cutbased_loose = (\n",
    "    sum(sig_pass) / len(sig_pass),\n",
    "    sum(bkg_pass) / len(bkg_pass),\n",
    ")\n",
    "\n",
    "sig_pass = cut(prompt_electrons).cutBased >= prompt_electrons.TIGHT\n",
    "bkg_pass = cut(unmatched_electrons).cutBased >= prompt_electrons.TIGHT\n",
    "wp_cutbased_tight = (\n",
    "    sum(sig_pass) / len(sig_pass),\n",
    "    sum(bkg_pass) / len(bkg_pass),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3b44076490>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(*roc_sieie, label = r\"Cuts on $\\sigma_{i\\eta i\\eta}$\")\n",
    "ax.plot(*roc_hoe, label = r\"Cuts on $H/E$\")\n",
    "ax.plot(*wp_cutbased_loose, label = r\"POG loose ID\", marker = '*', markersize = 20, linestyle = 'none')\n",
    "ax.plot(*wp_cutbased_tight, label = r\"POG tight ID\", marker = '*', markersize = 20, linestyle = 'none')\n",
    "ax.plot(*roc_mva, label = r\"POG MVA ID\")\n",
    "\n",
    "diag = np.linspace(0, 1, 100)\n",
    "ax.plot(diag, diag, color = 'grey', linestyle = '--')\n",
    "ax.set_xlabel(\"Signal efficiency\")\n",
    "ax.set_ylabel(\"Background efficiency\")\n",
    "ax.set_xlim(1e-2, 1)\n",
    "ax.set_ylim(1e-2, 1)\n",
    "#ax.set_yscale('log')\n",
    "#ax.set_xscale('log')\n",
    "ax.legend(title = r'Barrel electrons')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are also often interested in the efficiency as a function of the electron's kinematics. To help explore the dependency, we can record the number of electrons passing and failing a given selection separately for many small regions in the electron $p_{T}$ and $\\eta$, which will allow us to compute the (binned) efficiency as a function of these variables, $\\epsilon(p_{T}, \\eta)$. We are using the [hist](https://hist.readthedocs.io/en/latest/) package to make a multidimensional histogram to record all these values in one object, which later can be projected as necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<html>\n",
       "<div style=\"display:flex; align-items:center;\">\n",
       "<div style=\"width:290px;\">\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"-10 -10 220 220\">\n",
       "<rect x=\"0\" y=\"0\" width=\"160\" height=\"160\" style=\"fill:white;opacity:.5;stroke-width:2;stroke:currentColor;\"/>\n",
       "<rect x=\"20\" y=\"20\" width=\"160\" height=\"160\" style=\"fill:white;opacity:.5;stroke-width:2;stroke:currentColor;\"/>\n",
       "<rect x=\"40\" y=\"40\" width=\"160\" height=\"160\" style=\"fill:white;opacity:.5;stroke-width:2;stroke:currentColor;\"/>\n",
       "<text x=\"120.0\" y=\"120.0\" style=\"font-size: 26pt; font-family: verdana; font-style: bold; fill: black;\" text-anchor=\"middle\" alignment-baseline=\"middle\">\n",
       "3D\n",
       "</text>\n",
       "</svg>\n",
       "</div>\n",
       "<div style=\"flex=grow:1;\">\n",
       "StrCategory(['pass', 'fail'], name='region', label='Region')<br/>\n",
       "Regular(18, 20, 200, name='pt', label='pt')<br/>\n",
       "Regular(25, -2.5, 2.5, name='eta', label='eta')<br/>\n",
       "<hr style=\"margin-top:.2em; margin-bottom:.2em;\"/>\n",
       "Double() Σ=607553.0 <em>(777413.0 with flow)</em>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</html>"
      ],
      "text/plain": [
       "Hist(\n",
       "  StrCategory(['pass', 'fail'], name='region', label='Region'),\n",
       "  Regular(18, 20, 200, name='pt', label='pt'),\n",
       "  Regular(25, -2.5, 2.5, name='eta', label='eta'),\n",
       "  storage=Double()) # Sum: 607553.0 (777413.0 with flow)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import hist\n",
    "\n",
    "efficiency = hist.Hist(\n",
    "    hist.axis.StrCategory([\"pass\", \"fail\"], name = \"region\", label = \"Region\"),\n",
    "    hist.axis.Regular(18, 20, 200, name = \"pt\", label = r\"pt\"),\n",
    "    hist.axis.Regular(25, -2.5, 2.5, name = \"eta\", label = r\"eta\")\n",
    ")\n",
    "\n",
    "passing = prompt_electrons.cutBased >= prompt_electrons.TIGHT\n",
    "efficiency.fill(region = \"pass\", pt = prompt_electrons[passing].pt, eta = prompt_electrons[passing].eta)\n",
    "efficiency.fill(region = \"fail\", pt = prompt_electrons[~passing].pt, eta = prompt_electrons[~passing].eta)\n",
    "\n",
    "efficiency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, we project the histogram to recover the binned $n_{\\text{pass}}(p_{T})$ (as `num`) and the binned $n_{\\text{total}}(p_{T})$ (as `denom`) and plot the ratio $\\epsilon(p_{T})$. Modify which axis the histogram is projected onto to see the efficiency as a function of $\\eta$.\n",
    "**Q: is the efficiency symmetric in $\\eta$?**\n",
    "**Q: what might explain the lower efficiency near $|\\eta|=1.4$?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 1, '$\\\\epsilon$')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from coffea.hist import clopper_pearson_interval\n",
    "\n",
    "num = efficiency[\"pass\", ...].project(\"pt\").view()\n",
    "denom = efficiency.project(\"pt\").view()\n",
    "edges = efficiency.axes[\"pt\"].edges\n",
    "err = abs(clopper_pearson_interval(num, denom) - num / denom)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "mplhep.histplot(num / denom, bins = edges, yerr = err, ax = ax)\n",
    "ax.set_xlabel(\"$p_{T}$ [GeV]\")\n",
    "ax.set_ylabel(\"$\\epsilon$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tag and probe\n",
    "\n",
    "So far, we've computed all our efficiencies using ground truth as provided by our simulation to categorize our signal and background reconstructed electrons.  If we trust our simulation to perfectly describe the detector response, then our job is done.  We cannot do that (**Q: why might we need to know true electron tagging efficiency in data?**), so we need to measure the efficiency in data, categorizing signal and background by inference.  How do we do this without selection bias?  Thanks to the Z boson, we have a solution: tag one electron in an event that passes some high quality requirement, then look for a \"probe\" electron such that the invariant mass of the di-electron pair ($m_{ee}$) matches that of the Z boson.  If we count the number of probes falling in some mass window that pass and fail our ID, we can infer the true electron tagging efficiency in data, despite not knowing the ground truth for each electron.\n",
    "Below is a basic example of making a tag and probe selection, then plotting the pass and fail mass distributions, for POG cut-based electron ID, using our simulated Z event sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "zcands = ak.combinations(dyevents.Electron, 2, fields = [\"tag\", \"probe\"])\n",
    "# filter pairs to have a good tag\n",
    "zcands = zcands[\n",
    "    (zcands.tag.pt > 20.)\n",
    "    & (abs(zcands.tag.eta) < 2.1)\n",
    "    & (zcands.tag.cutBased >= zcands.tag.TIGHT)\n",
    "    & (zcands.probe.pt > 10.)\n",
    "]\n",
    "# filter out events that have no z candidates\n",
    "zcands = zcands[ak.num(zcands) > 0]\n",
    "# some events may have multiple candidates, take the one with highest pt (i.e. the default ordering in NanoAOD)\n",
    "zcands = ak.firsts(zcands)\n",
    "# compute invariant mass\n",
    "mass = (zcands.tag + zcands.probe).mass\n",
    "\n",
    "goodprobe = zcands.probe.cutBased >= zcands.probe.LOOSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(1, 0, 'Dielectron mass [GeV]')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axp, axf) = plt.subplots(1, 2, sharey = True, figsize = (12, 6))\n",
    "mbins = np.linspace(60, 120, 61)\n",
    "\n",
    "axp.hist(mass[goodprobe], bins = mbins)\n",
    "axp.set_title('Passing probes')\n",
    "axp.set_ylabel('Events')\n",
    "axp.set_xlabel('Dielectron mass [GeV]')\n",
    "\n",
    "axf.hist(mass[~goodprobe], bins = mbins)\n",
    "axf.set_title('Failing probes')\n",
    "axf.set_xlabel('Dielectron mass [GeV]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now make the same plot with low and high probe $p_{T}$ requirements.  How does the mass shape and relative peak height change?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot the same distribution for electrons in real data**\n",
    "\n",
    "Try repeating the exercise above using `dataevents` instead of `dyevents`. You'll notice the failing distribution's peak is hidden under a much larger amount of background. This is combinatorial background from random fake electrons combining with the tag to create a continuous falling invariant mass spectrum. Try applying some selections on the identification variables above, and see how they impact signal and background distributions.\n",
    "\n",
    "To properly measure the peak height, a full analysis would have to subtract this background by fitting the pass and fail distributions.  If you got this far, try using [scipy.optimize.curve_fit](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html) to fit each distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale Factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tag and probe is the main method used derive the efficiencies that serve as inputs to the electron and photon *scale factors* (and most scale factors in general). The scale factors we will be using are defined as the ratio of the data efficieny to the Monte Carlo (MC) efficiency.\n",
    "\n",
    "\n",
    "Scale factors for electrons and photons are applied as event weights. Scale factors serve to match the MC object efficiency (ID, reconstruction, etc.) to the observed data efficiency. **Q: Why could this correction be needed?**. Scale factors are accessible via [ROOT Histograms](https://twiki.cern.ch/twiki/bin/view/CMS/EgammaUL2016To2018#General_note_about_ID_SFs) and [JSONs](https://twiki.cern.ch/twiki/bin/viewauth/CMS/EgammaSFJSON). The JSON format works particularly well with NanaAOD and Coffea-based analyeses, so we will include a brief example of how to access the electron ID scale factors using the `correctionlib` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "from correctionlib import CorrectionSet\n",
    "\n",
    "evaluator = CorrectionSet.from_file(\"/cvmfs/cms.cern.ch/rsync/cms-nanoAOD/jsonpog-integration/POG/EGM/2018_UL/electron.json.gz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have scale factors accessible, we need to apply them to our analysis! We already have a collection of Z candidates built (our tag and probe pairs), so we will use those. Normally you will not apply SFs to a tag and probe analyses, but this is just to serve as an example of how to apply scale factors to a Z candidate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3b44056bb0>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get the ID scale factor for each electron in the pair\n",
    "sfel1 = evaluator[\"UL-Electron-ID-SF\"].evaluate(\"2018\", \"sf\", \"Tight\", zcands.tag.eta.to_numpy(), zcands.tag.pt.to_numpy())\n",
    "sfel2 = evaluator[\"UL-Electron-ID-SF\"].evaluate(\"2018\", \"sf\", \"Loose\", zcands.probe.eta.to_numpy(), zcands.probe.pt.to_numpy())\n",
    "\n",
    "sftot = sfel1 * sfel2\n",
    "\n",
    "#Apply the weights to the 'Good probes' Z mass peak\n",
    "fig, ax = plt.subplots(figsize = (12, 6))\n",
    "mbins = np.linspace(60, 120, 61)\n",
    "\n",
    "ax.hist(mass[goodprobe], bins = mbins,label=\"no SF Event weight\",histtype='step')\n",
    "ax.hist(mass[goodprobe], bins = mbins, weights = sftot[goodprobe],label=\"with SF Event weight\",histtype='step')\n",
    "ax.set_ylabel('Events')\n",
    "ax.set_xlabel('Dielectron mass [GeV]')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very similar! We need a better way to decide if the scale factors are relevant. Plots that directly show the differences between the two, such as ratio plots, may be useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}