{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5efbd85a-a55f-41a9-a34e-452151c0a9a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import awkward as ak\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",
    "jet_pT = ds.Select(lambda event: event.Jet_pt).AsAwkwardArray().value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60ef3cd2-a47a-4dfc-8633-e6ec1fb045e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [156, 104, 50.5, ... 17.8, 17.2, 16.5] type='976406625 * float32'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.flatten(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([ak.flatten(jet_pT)], bins=100, range=(0, 100))\n",
    "plt.xlabel(r'$p_\\mathrm{T}$ [GeV]')\n",
    "plt.ylabel('Jets')\n",
    "plt.show()"
   ]
  }
 ],
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