{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5efbd85a-a55f-41a9-a34e-452151c0a9a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import servicex as sx\n",
    "from func_adl_servicex import ServiceXSourceUpROOT\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 200 # make figures bigger and more readable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1caae859-3d03-44c1-ba5c-88da8b576dd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_name = \"cms:DYJetsToLL_M-50_TuneCP5_13TeV-amcatnloFXFX-pythia8/RunIIAutumn18NanoAODv7-Nano02Apr2020_102X_upgrade2018_realistic_v21_ext2-v1/NANOAODSIM\"\n",
    "sx_dataset = sx.ServiceXDataset(dataset_name, \"uproot\")\n",
    "ds = ServiceXSourceUpROOT(sx_dataset, \"Events\")\n",
    "\n",
    "\n",
    "filtered_jet_pT = ds.SelectMany(lambda event: {'pT': event.Jet_pt, 'eta': event.Jet_eta}.Zip()\n",
    "                                              .Where(lambda jet: abs(jet.eta) < 1)\n",
    "                                              .Select(lambda jet: jet.pT)).AsAwkwardArray().value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60ef3cd2-a47a-4dfc-8633-e6ec1fb045e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [50.5, 49.4, 24.3, ... 21.8, 61.8, 25.2] type='239719308 * float32'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_jet_pT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4cd0af13-76b9-4859-bf65-3a8eb19aa18e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([filtered_jet_pT], bins=100, range=(0, 100))\n",
    "plt.xlabel(r'$p_\\mathrm{T}$ [GeV]')\n",
    "plt.ylabel('Jets')\n",
    "plt.show()"
   ]
  }
 ],
 "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": 5
}