{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%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 the pT of the trijet system in each event with mass closest to 172.5, as well as the maximum b-tag among the three plotted jets.\n",
    "\n",
    "class Processor(processor.ProcessorABC):\n",
    "    def __init__(self):\n",
    "        dataset_axis = hist.Cat(\"dataset\", \"\")\n",
    "        Jet_axis = hist.Bin(\"Jet_pt\", \"Jet [GeV]\", 50, 15, 200)\n",
    "        b_tag_axis = hist.Bin(\"b_tag\", \"b-tagging discriminant\", 50, 0, 1)\n",
    "        \n",
    "        self._accumulator = processor.dict_accumulator({\n",
    "            'Jet_pt': hist.Hist(\"Counts\", dataset_axis, Jet_axis),\n",
    "            'b_tag': hist.Hist(\"Counts\", dataset_axis, b_tag_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",
    "        jets = events.Jet\n",
    "        \n",
    "        # Closest calculates the distance from 172.5 of a group of masses, finds the minimum distance, then returns a Boolean array of the original input array shape with True where the minimum-distance mass is located.\n",
    "        def closest(masses):\n",
    "            delta = abs(172.5 - masses)\n",
    "            closest_masses = delta.min()\n",
    "            is_closest = (delta == closest_masses)\n",
    "            return is_closest\n",
    "        \n",
    "        # We're going to be generating combinations of three jets - that's a lot, and cutting pt off at 30 reduces jets by half.\n",
    "        cut_jets = jets[jets.pt > 30]\n",
    "        \n",
    "        # Get all combinations of three jets.\n",
    "        trijets = cut_jets.choose(3)\n",
    "        # Get combined masses of those combinations, by adding all p4's and then taking .mass.\n",
    "        trijet_masses = (trijets.i0 + trijets.i1 + trijets.i2).mass\n",
    "        # Get the masses closest to specified value (see function above)\n",
    "        is_closest = closest(trijet_masses)\n",
    "        closest_trijets = trijets[is_closest]\n",
    "        # Get pt of the closest trijets.\n",
    "        closest_pt = (closest_trijets.i0 + closest_trijets.i1 + closest_trijets.i2).pt\n",
    "        # Get btag of the closest trijets. np.maximum(x,y) compares two arrays and gets element-wise maximums. We make two comparisons - once between the first and second jet, then between the first comparison and the third jet.\n",
    "        closest_btag = np.maximum(np.maximum(closest_trijets.i0.btag, closest_trijets.i1.btag), closest_trijets.i2.btag)\n",
    "        \n",
    "        output['Jet_pt'].fill(dataset=dataset, Jet_pt=closest_pt.flatten())\n",
    "        output['b_tag'].fill(dataset=dataset, b_tag=closest_btag.flatten())\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": [
      "[####                                    ] | 10% Completed | 31.7ss"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.comm.tcp - WARNING - Closing dangling stream in <TLS  local=tls://127.0.0.1:45900 remote=tls://localhost:8786>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1min 21.6s\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 0x7f201e2c86a0>"
      ]
     },
     "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": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f201e2a1730>"
      ]
     },
     "execution_count": 5,
     "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['b_tag'], overlay='dataset', fill_opts={'edgecolor': (0,0,0,0.3), 'alpha': 0.8})"
   ]
  }
 ],
 "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
}