{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from coffea import hist\n",
    "from coffea.analysis_objects import JaggedCandidateArray\n",
    "import coffea.processor as processor"
   ]
  },
  {
   "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'] += jets.size\n",
    "        output['cutflow']['all jets'] += jets.counts.sum()\n",
    "        \n",
    "        # Get jets with higher GeV than 30.\n",
    "        min_jetpt = (jets.pt > 30)\n",
    "        output['cutflow']['jets with pt > 30'] += min_jetpt.sum().sum()\n",
    "        \n",
    "        # Get all leptons with higher GeV than 10.\n",
    "        min_muonpt = (muons.pt > 10)\n",
    "        output['cutflow']['muons with pt > 10'] += min_muonpt.sum().sum()\n",
    "        min_electronpt = (electrons.pt > 10)\n",
    "        output['cutflow']['electrons with pt > 10'] += min_electronpt.sum().sum()\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",
    "        # Cross is like distincts, but across multiple JCA's. So we cross jets with each lepton to generate all (jet, lepton) pairs. We have nested=True so that all jet values are stored in sublists together, and thus maintain uniqueness so we can get them back later.\n",
    "        jet_muon_pairs = goodjets.cross(goodmuons, nested=True)\n",
    "        jet_electron_pairs = goodjets.cross(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 = (jet_muon_pairs.i0.delta_r(jet_muon_pairs.i1) > 0.4).all()\n",
    "        good_je_pairs = (jet_electron_pairs.i0.delta_r(jet_electron_pairs.i1) > 0.4).all()\n",
    "        \n",
    "        output['cutflow']['jet-muon pairs'] += good_jm_pairs.sum().sum()\n",
    "        output['cutflow']['jet-electron pairs'] += good_je_pairs.sum().sum()\n",
    "        output['cutflow']['jet-lepton pairs'] += (good_jm_pairs & good_je_pairs).sum().sum()\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 = goodjets[good_jm_pairs & good_je_pairs].pt.sum()\n",
    "        output['cutflow']['final jets'] += goodjets[good_jm_pairs & good_je_pairs].counts.sum()\n",
    "        output['Jet_pt'].fill(dataset=dataset, Jet_pt=sumjets.flatten())\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": [
      "[#########                               ] | 23% Completed |  1min  1.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.comm.tcp - WARNING - Closing dangling stream in <TLS  local=tls://127.0.0.1:47974 remote=tls://localhost:8786>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[###########                             ] | 29% Completed |  1min  2.1s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.comm.tcp - WARNING - Closing dangling stream in <TLS  local=tls://127.0.0.1:47988 remote=tls://localhost:8786>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1min 58.5s\r"
     ]
    }
   ],
   "source": [
    "fileset = {'SingleMu' : [\"root://eospublic.cern.ch//eos/root-eos/benchmark/Run2012B_SingleMu.root\"]}\n",
    "\n",
    "from dask.distributed import Client\n",
    "from coffea_casa import CoffeaCasaCluster\n",
    "\n",
    "client = Client(\"tls://localhost:8786\")\n",
    "\n",
    "output = processor.run_uproot_job(fileset=fileset, \n",
    "                       treename=\"Events\", \n",
    "                       processor_instance=Processor(),\n",
    "                       executor=processor.dask_executor,\n",
    "                       executor_args={'client': client, 'nano': True},\n",
    "                       chunksize=250000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb293556c10>"
      ]
     },
     "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 28103661\n",
      "jet-electron pairs 33916050\n",
      "jet-lepton pairs 25397925\n",
      "final jets 25397925\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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}