{
 "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 plots a per-event array (in this case, Jet pT). In Coffea, this is not very dissimilar from the event-level process.\n",
    "\n",
    "class Processor(processor.ProcessorABC):\n",
    "    def __init__(self):\n",
    "        dataset_axis = hist.Cat(\"dataset\", \"\")\n",
    "        Jet_axis = hist.Bin(\"Jet_pt\", \"Jet_pt [GeV]\", 100, 15, 60)\n",
    "        \n",
    "        self._accumulator = processor.dict_accumulator({\n",
    "            'Jet_pt': hist.Hist(\"Counts\", dataset_axis, Jet_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",
    "        Jet_pt = events.Jet.pt\n",
    "        # As before, we can get the number of events by checking the size of the array. To get the number of jets, which varies per event, though, we need to count up the number in each event, and then sum those counts (count subarray sizes, sum them).\n",
    "        output['cutflow']['all events'] += Jet_pt.size\n",
    "        output['cutflow']['all jets'] += Jet_pt.counts.sum()\n",
    "        \n",
    "        # .flatten() removes jaggedness; plotting jagged data is meaningless, we just want to plot flat jets.\n",
    "        output['Jet_pt'].fill(dataset=dataset, Jet_pt=Jet_pt.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": [
      "[                                        ] | 2% Completed | 29.9s0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.comm.tcp - WARNING - Closing dangling stream in <TLS  local=tls://127.0.0.1:43642 remote=tls://localhost:8786>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1min 10.3s\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 0x7fefd9325610>"
      ]
     },
     "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"
     ]
    }
   ],
   "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
}