{
 "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": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "cms:DYJetsToLL_M-50_...:   0%|          | 0/9000000000.0 [00:00]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "        cms:DYJetsToLL_M-50_... Downloaded:   0%|          | 0/9000000000.0 [00:00]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "missing_ET = ds.Select(lambda event: event.MET_pt).AsAwkwardArray().value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60ef3cd2-a47a-4dfc-8633-e6ec1fb045e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [46.6, 43.1, 22.5, ... 23.2, 98.6, 43.3] type='193119590 * float32'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_ET"
   ]
  },
  {
   "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([missing_ET], bins=100, range=(0, 100))\n",
    "plt.xlabel(r'$E_\\mathrm{T}^\\mathrm{miss}$ [GeV]')\n",
    "plt.ylabel('Events')\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
}