{
 "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",
    "import awkward as ak\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": [
       "[root://eospublic.ce...:   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": [
       "        [root://eospublic.ce... Downloaded:   0%|          | 0/9000000000.0 [00:00]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_name = [\"root://eospublic.cern.ch//eos/root-eos/benchmark/Run2012B_SingleMu.root\"]\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": "043b4fda-1806-4ca9-9881-dc8a9f09f1f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [22.2, 13.9, 15, ... 19.8, 19.9, 46.6] type='53446198 * float32'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_ET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70b0d95d-794d-4098-8f9a-5d52aa84d166",
   "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
}