{
 "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 plot the MET for events which have an opposite-sign muon pair that has mass in the range of 60-120 GeV.\n",
    "\n",
    "class Processor(processor.ProcessorABC):\n",
    "    def __init__(self):\n",
    "        dataset_axis = hist.Cat(\"dataset\", \"\")\n",
    "        muon_axis = hist.Bin(\"MET\", \"MET [GeV]\", 50, 1, 100)\n",
    "        \n",
    "        self._accumulator = processor.dict_accumulator({\n",
    "            'MET': 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",
    "        MET = events.MET.pt\n",
    "        \n",
    "        output['cutflow']['all events'] += muons.size\n",
    "        output['cutflow']['all muons'] += muons.mass.counts.sum()\n",
    "        \n",
    "        # Get all combinations of muon pairs in every event.\n",
    "        dimuons = muons.distincts()\n",
    "        \n",
    "        # Check that pairs have opposite charge.\n",
    "        opposites = (dimuons.i0.charge != dimuons.i1.charge)\n",
    "        \n",
    "        # Get only muons with mass between 60 and 120. Add the dimuon p4's, take mass.\n",
    "        limits = ((dimuons.i0 + dimuons.i1).mass >= 60) & ((dimuons.i0 + dimuons.i1).mass < 120)\n",
    "        \n",
    "        # Mask the dimuons with the opposites and the limits to get dimuons with opposite charge and mass between 60 and 120 GeV.\n",
    "        good_dimuons = dimuons[opposites & limits]\n",
    "        \n",
    "        # Mask the MET to get it only if an associated dimuon pair meeting the conditions exists.\n",
    "        good_MET = MET[good_dimuons.counts >= 1]\n",
    "        output['cutflow']['final events'] += good_MET.size\n",
    "        \n",
    "        output['MET'].fill(dataset=dataset, MET=good_MET)\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": [
      "[##                                      ] | 7% Completed | 34.0s0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.comm.tcp - WARNING - Closing dangling stream in <TLS  local=tls://127.0.0.1:45192 remote=tls://localhost:8786>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1min 39.3s\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 0x7fdc262c7580>"
      ]
     },
     "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['MET'], 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 74439173\n",
      "final events 2970884\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
}