docs/gallery/example4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coffea-Casa Benchmark Example 4"
   ]
  },
  {
   "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\n",
    "from coffea.nanoevents import schemas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This program plots an event-level variable (MET) based on conditionals with its associated Jet arrays (in this case, where at least 2 have pT > 40)\n",
    "\n",
    "class Processor(processor.ProcessorABC):\n",
    "    def __init__(self):\n",
    "        dataset_axis = hist.Cat(\"dataset\", \"\")\n",
    "        MET_axis = hist.Bin(\"MET_pt\", \"MET [GeV]\", 50, 0, 125)\n",
    "        \n",
    "        self._accumulator = processor.dict_accumulator({\n",
    "            'MET_pt': hist.Hist(\"Counts\", dataset_axis, MET_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",
    "        MET = events.MET.pt\n",
    "        \n",
    "        output['cutflow']['all events'] += ak.size(jets, axis=0)\n",
    "        \n",
    "        # We want jets with a pt of at least 40.\n",
    "        pt_min = (jets.pt > 40)\n",
    "\n",
    "        # We want MET where the above condition is met for at least two jets. The above is a list of Boolean sublists generated from the jet sublists (True if condition met, False if not). If we sum each sublist, we get the amount of jets matching the condition (since True = 1).\n",
    "        good_MET = MET[(ak.sum(pt_min, axis=1) >= 2)]\n",
    "        output['cutflow']['final events'] += ak.size(good_MET, axis=0)\n",
    "        \n",
    "        output['MET_pt'].fill(dataset=dataset, MET_pt=good_MET)\n",
    "        return output\n",
    "\n",
    "    def postprocess(self, accumulator):\n",
    "        return accumulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client\n",
    "\n",
    "client = Client(\"tls://localhost:8786\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1min 14.7s\r"
     ]
    }
   ],
   "source": [
    "fileset = {'SingleMu' : [\"root://eospublic.cern.ch//eos/root-eos/benchmark/Run2012B_SingleMu.root\"]}\n",
    "\n",
    "executor = processor.DaskExecutor(client=client)\n",
    "\n",
    "run = processor.Runner(executor=executor,\n",
    "                        schema=schemas.NanoAODSchema,\n",
    "                        savemetrics=True\n",
    "                      )\n",
    "\n",
    "output, metrics = run(fileset, \"Events\", processor_instance=Processor())\n",
    "\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MET [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['MET_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",
      "final events 6665702\n"
     ]
    }
   ],
   "source": [
    "for key, value in output['cutflow'].items():\n",
    "    print(key, value)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}