{
 "cells": [
  {
   "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 will graph the sum of Jet pT's which are greater than 30 GeV and farther than a Euclidean distance of 0.4 from any lepton with pT > 10 GeV.\n",
    "class Processor(processor.ProcessorABC):\n",
    "    def __init__(self):\n",
    "        dataset_axis = hist.Cat(\"dataset\", \"\")\n",
    "        muon_axis = hist.Bin(\"Jet_pt\", \"Jet_pt [GeV]\", 100, 15, 200)\n",
    "        \n",
    "        self._accumulator = processor.dict_accumulator({\n",
    "            'Jet_pt': 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",
    "        muons = events.Muon\n",
    "        electrons = events.Electron\n",
    "        jets = events.Jet\n",
    "        \n",
    "        output['cutflow']['all events'] += ak.size(jets, axis=0)\n",
    "        output['cutflow']['all jets'] += ak.sum(ak.num(jets, axis=1))\n",
    "        \n",
    "        # Get jets with higher GeV than 30.\n",
    "        min_jetpt = (jets.pt > 30)\n",
    "        output['cutflow']['jets with pt > 30'] += ak.sum(ak.sum(min_jetpt, axis=1))\n",
    "        \n",
    "        # Get all leptons with higher GeV than 10.\n",
    "        min_muonpt = (muons.pt > 10)\n",
    "        output['cutflow']['muons with pt > 10'] += ak.sum(ak.sum(min_muonpt, axis=1))\n",
    "        min_electronpt = (electrons.pt > 10)\n",
    "        output['cutflow']['electrons with pt > 10'] += ak.sum(ak.sum(min_electronpt, axis=1))\n",
    "        \n",
    "        # Mask jets and leptons with their minimum requirements/\n",
    "        goodjets = jets[min_jetpt]\n",
    "        goodmuons = muons[min_muonpt]\n",
    "        goodelectrons = electrons[min_electronpt]\n",
    "    \n",
    "        jet_muon_pairs = ak.cartesian({'jets': goodjets, 'muons': goodmuons}, nested=True)\n",
    "        jet_electron_pairs = ak.cartesian({'jets': goodjets, 'electrons': goodelectrons}, nested=True)\n",
    "    \n",
    "        # This long conditional checks that the jet is at least 0.4 euclidean distance from each lepton. It then checks if each unique jet contains a False, i.e., that a jet is 0.4 euclidean distance from EVERY specific lepton in the event.\n",
    "        good_jm_pairs = goodjets.nearest(goodmuons).delta_r(goodjets) > 0.4\n",
    "        good_je_pairs = goodjets.nearest(goodelectrons).delta_r(goodjets) > 0.4\n",
    "        good_jl_pairs = good_jm_pairs & good_je_pairs\n",
    "        \n",
    "        output['cutflow']['jet-muon pairs'] += ak.sum(ak.sum(good_jm_pairs, axis=1))\n",
    "        output['cutflow']['jet-electron pairs'] += ak.sum(ak.sum(good_je_pairs, axis=1))\n",
    "        output['cutflow']['jet-lepton pairs'] += ak.sum(ak.sum(good_jl_pairs, axis=1))\n",
    "        \n",
    "        # We then mask our jets with all three of the above good pairs to get only jets that are 0.4 distance from every type of lepton, and sum them.\n",
    "        sumjets = ak.sum(goodjets[good_jl_pairs].pt, axis=1)\n",
    "        output['cutflow']['final jets'] += ak.sum(ak.num(goodjets[good_jl_pairs], axis=1))\n",
    "        output['Jet_pt'].fill(dataset=dataset, Jet_pt=sumjets)\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 |  5min  3.3s\r"
     ]
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "import time\n",
    "\n",
    "client = Client(\"tls://localhost:8786\")\n",
    "\n",
    "# Our file is missing some cross-references, so we have to make NanoAOD push warnings instead of erroring out.\n",
    "# This ultimately isn't a problem, it's just a constraint of the public NanoAOD we're using.\n",
    "def fix():\n",
    "    from coffea.nanoevents import NanoAODSchema\n",
    "    NanoAODSchema.warn_missing_crossrefs = True\n",
    "\n",
    "client.register_worker_callbacks(fix)\n",
    "processor.NanoAODSchema.warn_missing_crossrefs = True\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='Jet_pt [GeV]', 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['Jet_pt'], 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 jets 170952895\n",
      "jets with pt > 30 37461601\n",
      "muons with pt > 10 58440651\n",
      "electrons with pt > 10 5483772\n",
      "jet-muon pairs 27423877\n",
      "jet-electron pairs 4918297\n",
      "jet-lepton pairs 3430495\n",
      "final jets 32545926\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
}