{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coffea-Casa Benchmark Example 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from coffea import hist\n",
    "import coffea.processor as processor\n",
    "import awkward as ak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This program plots the transverse mass of MET and a third lepton, where the third lepton is associated with a lepton pair\n",
    "# that has the same flavor, opposite charge, and closest mass to 91.2.\n",
    "\n",
    "import math\n",
    "\n",
    "class Processor(processor.ProcessorABC):\n",
    "    def __init__(self):\n",
    "        dataset_axis = hist.Cat(\"dataset\", \"MET and Third Lepton\")\n",
    "        muon_axis = hist.Bin(\"massT\", \"Transverse Mass\", 50, 15, 250)\n",
    "        \n",
    "        self._accumulator = processor.dict_accumulator({\n",
    "            'massT': hist.Hist(\"Counts\", dataset_axis, muon_axis),\n",
    "            'cutflow': processor.defaultdict_accumulator(int)\n",
    "        })\n",
    "    \n",
    "    @property\n",
    "    def accumulator(self):\n",
    "        return self._accumulator\n",
    "    \n",
    "    def process(self, events):\n",
    "        output = self.accumulator.identity()\n",
    "        \n",
    "        dataset = events.metadata[\"dataset\"]\n",
    "        \n",
    "        # Keep track of muons and electrons by tagging them 0/1.\n",
    "        muons = ak.with_field(events.Muon, 0, 'flavor')\n",
    "        electrons = ak.with_field(events.Electron, 1, 'flavor')\n",
    "        MET = events.MET\n",
    "        \n",
    "        output['cutflow']['all events'] += ak.size(events.MET, axis=0)\n",
    "        \n",
    "        # A few reasonable muon and electron selection cuts\n",
    "        muons = muons[(muons.pt > 10) & (np.abs(muons.eta) < 2.4)]\n",
    "        electrons = electrons[(electrons.pt > 10) & (np.abs(electrons.eta) < 2.5)]\n",
    "        \n",
    "        output['cutflow']['all muons'] += ak.sum(ak.count(muons, axis=1))\n",
    "        output['cutflow']['all electrons'] += ak.sum(ak.count(electrons, axis=1))\n",
    "\n",
    "        # Stack muons and electrons into a single array.\n",
    "        leptons = ak.with_name(ak.concatenate([muons, electrons], axis=1), 'PtEtaPhiMCandidate')\n",
    "        \n",
    "        # Filter out events with less than 3 leptons.\n",
    "        trileptons = leptons[ak.num(leptons, axis=1) >= 3]\n",
    "        output['cutflow']['trileptons'] += ak.sum(ak.num(trileptons, axis=1))\n",
    "        \n",
    "        # Generate the indices of every pair; indices because we'll be removing these elements later.\n",
    "        lepton_pairs = ak.argcombinations(trileptons, 2, fields=['i0', 'i1'])\n",
    "        \n",
    "        # Select pairs that are SFOS.\n",
    "        SFOS_pairs = lepton_pairs[(trileptons[lepton_pairs['i0']].flavor == trileptons[lepton_pairs['i1']].flavor) & (trileptons[lepton_pairs['i0']].charge != trileptons[lepton_pairs['i1']].charge)]\n",
    "        \n",
    "        # Find the pair with mass closest to Z.\n",
    "        closest_pairs = SFOS_pairs[ak.local_index(SFOS_pairs) == ak.argmin(np.abs((trileptons[SFOS_pairs['i0']] + trileptons[SFOS_pairs['i1']]).mass - 91.2), axis=1)]\n",
    "        \n",
    "        # Make trileptons and closest_pairs have same shape. First, fill nones with empty arrays. Then filter out events that don't meet the pair requirement.\n",
    "        closest_pairs = ak.fill_none(closest_pairs, [])\n",
    "        closest_pairs = closest_pairs[ak.num(closest_pairs) > 0]\n",
    "        trileptons = trileptons[ak.num(closest_pairs) > 0]\n",
    "        MET = MET[ak.num(closest_pairs) > 0]\n",
    "        \n",
    "        # Remove elements of the closest pairs from leptons, because we want the pt of the third lepton.\n",
    "        trileptons_no_pair = trileptons[(ak.local_index(trileptons) != ak.flatten(closest_pairs.i0)) & (ak.local_index(trileptons) != ak.flatten(closest_pairs.i1))]\n",
    "        \n",
    "        # Find the highest-pt lepton out of the ones that remain.\n",
    "        leading_lepton = trileptons_no_pair[ak.argmax(trileptons_no_pair.pt, axis=1)]\n",
    "        output['cutflow']['number of final leading leptons'] += ak.sum(ak.num(trileptons_no_pair, axis=1))\n",
    "        \n",
    "        # Cross MET with the leading lepton.\n",
    "        met_plus_lep = ak.cartesian({'i0': MET, 'i1': leading_lepton})\n",
    "        \n",
    "        # Do some math to get what we want.\n",
    "        dphi_met_lep = (met_plus_lep.i0.phi - met_plus_lep.i1.phi + math.pi) % (2*math.pi) - math.pi\n",
    "        mt_lep = np.sqrt(2.0*met_plus_lep.i0.pt*met_plus_lep.i1.pt*(1.0-np.cos(dphi_met_lep)))\n",
    "        \n",
    "        output['massT'].fill(dataset=dataset, massT=ak.flatten(mt_lep))\n",
    "        \n",
    "        return output\n",
    "\n",
    "    def postprocess(self, accumulator):\n",
    "        return accumulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  6min 59.8s\r"
     ]
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "import time\n",
    "\n",
    "client = Client(\"tls://localhost:8786\")\n",
    "\n",
    "fileset = {'SingleMu' : [\"root://eospublic.cern.ch//eos/root-eos/benchmark/Run2012B_SingleMu.root\"]}\n",
    "\n",
    "output = processor.run_uproot_job(fileset,\n",
    "                                treename = 'Events',\n",
    "                                processor_instance = Processor(),\n",
    "                                executor = processor.dask_executor,\n",
    "                                executor_args = {'schema': processor.NanoAODSchema, 'client': client}\n",
    "                                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Transverse Mass', ylabel='Counts'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist.plot1d(output['massT'], overlay='dataset', fill_opts={'edgecolor': (0,0,0,0.3), 'alpha': 0.8})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all events 53446198\n",
      "all muons 991421725\n",
      "all electrons 87391040\n",
      "trileptons 5043516\n",
      "number of final leading leptons 1227754\n"
     ]
    }
   ],
   "source": [
    "for key, value in output['cutflow'].items():\n",
    "    print(key, value)"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}