{
 "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": [],
   "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",
    "\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 [27.8, 17.4, 33.7, ... 66.2, 22.1, 15.3] type='39563735 * 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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yQ0keW1V/3N3vXW+n3f0P620DAAAAZtFMroHR3Q/p7jcuEF7MXb80k1kYcx61NSMDAAAAFjKTAcYKvWeqfPvtGgQAAAAgwFjKPlPl72zbKAAAAICZXQNjJY6bKp+/EQ1W1dlJ7prkwCSXJflkkr9Kcmp3f30d7d5mmSqHrLVtAAAAGAMBxgKq6npJTp766o0b1PQDp8o3yyQkOS7JM6rqxO5+2xrbvXDdIwMAAIARE2As7KlJjh3KZ3T3h9fZ3ieSvDXJhzLZuvX6SX44yeOTPCjJTZK8uaoe2t1nrbMvAAAA2HUEGPNU1XFJnjecfjnJr6yzyRd39ykLfP/BJK+pqicneXmSvZK8oqqO6O6rVtnHoctcPyTJeatsEwAAAEZDgDGlqu6U5IxM/rlcneTR3X3Jetrs7suWuX5qVd09yZOS3CrJCUlet8o+LlrqelWtpjkAAAAYHbuQDKrqdkneleSgJNcleWx3n7tF3Z86VT5u0VoAAAAwowQYSarqVkn+JpMZEJ3kid19xhYO4ZNT5VtvYb8AAACwI8x8gFFVByc5O8kPDl/9ene/ZquHscX9AQAAwI4y0wFGVd04yV8nuePw1cnd/bJtGModp8oXb0P/AAAAMGozG2BU1f5Jzkxy1+Gr3+vuP9ym4Tx5qrxV624AAADAjjGTAUZV7ZPJbiP3Gr56SXf/zhraObGqevicssD1o6vqiGXaeHKSk4bTLw3jAgAAAKbM6jaqf5HkQUP53UleWVU/skT9a7r702vo525JXlFV5yQ5K8knknw1k3/uRyb52SQ/OdS9LsmTu/vKNfQDAAAAu9qsBhgnTJV/IsnHl6n/+SSHr7GvvZI8cPgs5qtJTurut6+xDwAAANjVZjXA2Cp/mcnrIfdMcpckt0hy00x2Hflako8l+askp3X35ds1SAAAABi7mQwwuntDti3t7tOSnLbE9S8n+bPhAwAAAKzRTC7iCQAAAOwsAgwAAABg9AQYAAAAwOgJMAAAAIDRE2AAAAAAoyfAAAAAAEZPgAEAAACMngADAAAAGD0BBgAAADB6AgwAAABg9AQYAAAAwOgJMAAAAIDRE2AAAAAAoyfAAAAAAEZPgAEAAACMngADAAAAGD0BBgAAADB6AgwAAABg9AQYAAAAwOgJMAAAAIDRE2AAAAAAoyfAAAAAAEZPgAEAAACMngADAAAAGD0BBgAAADB6AgwAAABg9AQYAAAAwOgJMAAAAIDRE2AAAAAAoyfAAAAAAEZPgAEAAACMngADAAAAGD0BBgAAADB6AgwAAABg9AQYAAAAwOgJMAAAAIDRE2AAAAAAoyfAAAAAAEZPgAEAAACMngADAAAAGD0BBgAAADB6AgwAAABg9AQYAAAAwOgJMAAAAIDRE2AAAAAAoyfAAAAAAEZPgAEAAACMngADAAAAGD0BBgAAADB6AgwAAABg9Pbe7gEAbKTDTz5zyesXPO/4LRoJAACwkczAAAAAAEZPgAEAAACMngADAAAAGD0BBgAAADB6AgwAAABg9AQYAAAAwOgJMAAAAIDRE2AAAAAAoyfAAAAAAEZPgAEAAACMngADAAAAGD0BBgAAADB6AgwAAABg9AQYAAAAwOgJMAAAAIDRE2AAAAAAoyfAAAAAAEZPgAEAAACM3swGGFV116p6ZlWdVVUXVtXVVXVFVX26qk6rqvtsQp+Pqaq/rqovVtW3quqCqnptVd1jo/sCAACA3WTv7R7Adqiqc5Pcd4FL+yS5w/D5+ap6bZIndfc16+xvvySnJ3nIvEu3HT6Pq6pTuvu56+kHAAAAdqtZnYFx6+F4cZKXJHlUkmOT3DPJbyb5wnD9CUlO24D+Xpk94cU5SR4+9HdSkn/J5N/Dc6rqSRvQFwAAAOw6MzkDI8n5SZ6Z5M3dfd28ax8YZl68L8kPJXlsVf1xd793LR1V1XFJHjecviPJI6b6PK+q3p7kI0kOS/L8qnpTd1+2lr4AAABgt5rJGRjd/ZDufuMC4cXc9UuTPG3qq0eto7vfGo7XJfnV+X0OfT1jOD0ok1kZAAAAwJSZDDBW6D1T5duvpYGqumGSBwynZ3f3RYtUfUuSy4fyCWvpCwAAAHYzAcbi9pkqf2eNbRybZN+hfO5ilYZFQj8wd09VXX+N/QEAAMCuNKtrYKzEcVPl89fYxlGraOP8JA/K5N/JHZJ8cqWdVNVtlqlyyErbAgAAgDESYCygqq6X5OSpr964xqYOnSov9vrInAvn3bfiAGPevQAAALDreIVkYU/N5PWPJDmjuz+8xnYOnCpfsUzdK6fKN1xjfwAAALArmYExz7Dt6fOG0y8n+ZV1NLffVPmaZepePVW+wSr7OXSZ64ckOW+VbQIAAMBoCDCmVNWdkpyRyT+Xq5M8ursvWUeT35oq77NorYl9p8pXraaTJXY3SZJU1WqaAwAAgNHxCsmgqm6X5F1JDkpyXZLHdveiO4es0Demysu9FnLAVHm5100AAABgpggwklTVrZL8TZJbJekkT+zuMzag6emZEcvtFDL9GohFOQEAAGDKzAcYVXVwkrOT/ODw1a9392s2qPnpnUSOXKbu3PVrk3xmg/oHAACAXWGmA4yqunGSv05yx+Grk7v7ZRvYxXnZs3jncUuMY58k95i7p7uXW/ATAAAAZsrMBhhVtX+SM5Pcdfjq97r7Dzeyj+7+RpK/HU4fWFWLvUZyQpIbDeWNeHUFAAAAdpWZDDCGGQ9nJLnX8NVLuvt31tDOiVXVw+eURaq9cDjuneRlVbXXvDYOTjIXnFyW5BWrHQcAAADsdrO6jepfJHnQUH53kldW1Y8sUf+a7v70Wjrq7ndX1RuSPCbJw5KcXVUvTnJxkqOT/HaSw4bqJ3f319fSDwAAAOxmsxpgnDBV/okkH1+m/ueTHL6O/p6YySsiD05y/+Ez7TtJntvdp66jDwAAANi1ZvIVkq3W3Vd19/FJHp/JjidfzmRxzwuTvD7Jvbv7lO0bIQAAAIzbTM7A6O7aoHZOS3LaKuq/PpPAAgAAAFgFMzAAAACA0RNgAAAAAKMnwAAAAABGT4ABAAAAjJ4AAwAAABg9AQYAAAAwegIMAAAAYPQEGAAAAMDoCTAAAACA0RNgAAAAAKMnwAAAAABGT4ABAAAAjJ4AAwAAABg9AQYAAAAwegIMAAAAYPQEGAAAAMDoCTAAAACA0dt7uwcAsJUOP/nMZetc8Lzjt2AkAADAapiBAQAAAIyeAAMAAAAYPQEGAAAAMHoCDAAAAGD0BBgAAADA6AkwAAAAgNETYAAAAACjJ8AAAAAARm/v7R5AklTVvknuneTgJJ/r7g9t85AAAACAEdn0AKOqbpvk14bT3+/uy+Zdv0eSNyW55dR3/zfJI7v7Xzd7fAAAAMD4bcUrJI9I8vQkP7FAeHFgkrdmEl7U1OduSc6sqlHMEAEAAAC211YEGD+ZpDMJKub7pSQ3H8p/lOSnk/zv4fyOSX5+swcHAAAAjN9WBBg/OBw/ssC1R2cSbpzR3U/p7nd0939KcnomMzEetQXjAwAAAEZuKwKMuRkWl0x/WVU3SnLX4fRV8+55w3D8sU0cFwAAALBDbEWAceBw3Gve9/cavrsuyXvmXbtwOP7A5g0LAAAA2Cm2IsD4t+F4q3nf3284fqy7r1zk3m9tyogAAACAHWUrAox/GI6PmPuiqvbKnvUvzlngnlsPx0sWuAYAAADMmK3YpvSMJMcleUJVXZLkvUmekOS2mQQYb1zgnrsPx3/dgvEBAAAAI7cVMzBOTfKpTHYVeXqSt2XP7iLv6O4PL3DPIzIJN96zBeMDAAAARm7TA4zuvjrJA5K8Jcm1mQQZ307y2kxmYnyPqrpvkjsOp2dv9vgAAACA8duKV0jS3V9K8qiq2jeTnUW+2t3XLFL9wiT3H8oLzc4AAAAAZsyWBBhzhtkYX1ymzueSfG5rRgQAAADsBJv+CklV/VlVvbKqbrmKe242d99mjg0AAADYGbZiEc8Th89Bq7jnRlP3AQAAADNuKwIMAAAAgHUZa4Cx33C8eltHAQAAAIzCWAOMew3HS7Z1FAAAAMAobPguJFX13xe59KtV9eVlbt83ye2TPCxJJ3nfRo4NAAAA2Jk2YxvVUzIJH6ZVkl9ZRRuV5FtJXrBBYwIAAAB2sM16haSmPj18agWfq5NckOR1Se7Z3R/bpPEBAAAAO8iGz8Do7u8JRarqO5kEGD/S3Z/c6P4AAACA3W8zXiGZ718zCTCu2YK+AAAAgF1o0wOM7j58s/sAAAAAdrexbqMKAAAA8F1b8QrJd1XVEUl+Lsk9kxyS5AZJfqq7PzNV50eSHJbkyu4+dyvHBwAAAIzTlgQYVXW9JH+Y5CmZzPqo4VIn2Wde9UOTvDPJtVV1u+7+wlaMEQAAABivrXqF5NQkv5lkryQXJ3nTYhW7+6wknx3qPmpLRgcAAACM2qYHGFV1vyQnDae/n+Tw7n70Mrednsksjftv3sgAAACAnWIrXiH55eH4l939Oyu850PD8U6bMB4AAABgh9mKV0jumclaF69cxT0XDcdDNn44AAAAwE6zFQHGzYfj51Zxz7XD8fobPBYAAABgB9qKAOOq4bj/Ku45bDh+fYPHAgAAAOxAWxFgzM28uMsq7nnIcPzkBo8FAAAA2IG2YhHPd2USXvxSVb28u7+zVOWquluSJ2SybsZfbcH4AL7H4SefueT1C553/BaNBAAAmLMVMzBemslrJEcn+dOqWnRdi6p6ZCahxT5JLk/yJ1swPgAAAGDkNn0GRnd/oar+c5I/TXJikgdV1TumqpxUVfsneWCSH0xSmcy++KXu/rfNHh8AAAAwflvxCkm6+5VV1Un+KMmtkzw5k5AiSZ4yHGs4Xp3kl7v79K0YGwAAADB+W/EKSZKku/8syZFJ/keSf8kksJj+fCHJHyc5qrtfvVXjAgAAAMZvS2ZgzOnui5I8PcnTq+pGSW6eZK8kX+3uS7dyLAAAAMDOsaUBxrTuvjyThToBAAAAlrRlr5AAAAAArNWGzsCoqv++gc11km8m+XqS85N8uLuv2ajGq+rmSY4dPscMn5sOl1/d3SduUD+nJHnWCqvfv7vfsxH9AgAAwG6y0a+QnJI9u4tstMur6k+TnNLd39yA9i7ZgDYAAACALbAZa2DU8lXW5MZJnpbkx6vq/t397Q1s+8Ikn0ryoA1scyFHL3P9c5vcPwAAAOxIGx1g3H+D29snyUFJ7pTksUmOSHLPJL+c5H+ts+3nJDkvyXndfUlVHZ5NDhC6+x82s30AAADYrTY0wOjuczeyvWlV9XtJ/jbJvZL8x6wzwOjula5LAQAAAGyzHbMLybCA5wuG06O2cywAAADA1toxAcbgguF44+0cBAAAALC1NmMRz810UZKnbvcg1qqqzk5y1yQHJrksySeT/FWSU7v76+to9zbLVDlkrW0DAADAGOyoAKO7v5bkJds9jnV44FT5ZkmOGz7PqKoTu/tta2z3wnWPDAAAAEZsRwUYO9gnkrw1yYeSXJzk+kl+OMnjM9m69SZJ3lxVD+3us7ZpjAAAADBaAozN9+LuPmWB7z+Y5DVV9eQkL0+yV5JXVNUR3X3VKvs4dJnrh2SyZSwAAADsSAKMTdbdly1z/dSqunuSJyW5VZITkrxulX1ctNT1qlpNcwAAADA6O20Xkt3q1Knycds2CgAAABgpAcY4fHKqfOttGwUAAACMlABjHLzjAQAAAEsQYIzDHafKF2/bKAAAAGCkBBjj8OSp8rnbNgoAAAAYKQHGOlTViVXVw+eUBa4fXVVHLNPGk5OcNJx+KckZGz9SAAAA2NlmdhvVqrp3kulw4eCp8hFVdeJ0/e4+bQ3d3C3JK6rqnCRnJflEkq9m8s/9yCQ/m+Qnh7rXJXlyd1+5hn4AAABgV5vZACPJk5L8/CLX7jV8pp22xn72SvLA4bOYryY5qbvfvsY+AAAAYFeb5QBjK/xlJq+H3DPJXZLcIslNM9l15GtJPpbkr5Kc1t2Xb9cgAQAAYOxmNsDo7hOTnLjONk7LEjMzuvvLSf5s+AAAAABrZBFPAAAAYPQEGAAAAMDoCTAAAACA0RNgAAAAAKM3s4t4AqzV4SefueT1C553/BaNBAAAZocZGAAAAMDoCTAAAACA0RNgAAAAAKMnwAAAAABGT4ABAAAAjJ4AAwAAABg9AQYAAAAwegIMAAAAYPQEGAAAAMDoCTAAAACA0RNgAAAAAKMnwAAAAABGT4ABAAAAjJ4AAwAAABg9AQYAAAAwegIMAAAAYPQEGAAAAMDoCTAAAACA0RNgAAAAAKMnwAAAAABGT4ABAAAAjJ4AAwAAABg9AQYAAAAwegIMAAAAYPQEGAAAAMDoCTAAAACA0RNgAAAAAKO393YPAGC3OfzkM5etc8Hzjt+CkQAAwO5hBgYAAAAwegIMAAAAYPQEGAAAAMDoCTAAAACA0RNgAAAAAKMnwAAAAABGT4ABAAAAjJ4AAwAAABg9AQYAAAAwegIMAAAAYPQEGAAAAMDoCTAAAACA0RNgAAAAAKMnwAAAAABGT4ABAAAAjJ4AAwAAABg9AQYAAAAwegIMAAAAYPQEGAAAAMDoCTAAAACA0dt7uwcAMIsOP/nMJa9f8Lzjt2gkAACwM5iBAQAAAIyeAAMAAAAYPQEGAAAAMHoCDAAAAGD0BBgAAADA6AkwAAAAgNETYAAAAACjJ8AAAAAARk+AAQAAAIyeAAMAAAAYPQEGAAAAMHoCDAAAAGD0BBgAAADA6AkwAAAAgNHbe7sHAMD3O/zkM5etc8Hzjt+CkQAAwDjM7AyMqrp5VT2kqp5TVWdV1aVV1cPntE3q8zFV9ddV9cWq+lZVXVBVr62qe2xGfwAAALBbzPIMjEu2qqOq2i/J6UkeMu/SbYfP46rqlO5+7laNCQAAAHaSmZ2BMc+FSd61ie2/MnvCi3OSPDzJsUlOSvIvmfx7eE5VPWkTxwAAAAA71izPwHhOkvOSnNfdl1TV4Uk+t9GdVNVxSR43nL4jySO6+7rh/LyqenuSjyQ5LMnzq+pN3X3ZRo8DAAAAdrKZnYHR3c/q7nd292a/SvJbw/G6JL86FV7MjePSJM8YTg/KZFYGAAAAMGVmA4ytUFU3TPKA4fTs7r5okapvSXL5UD5h0wcGAAAAO4wAY3Mdm2TfoXzuYpW6+5okH5i7p6quv9kDAwAAgJ1kltfA2ApHTZXPX6bu+UkelMm/kzsk+eRKO6mq2yxT5ZCVtgUAAABjJMDYXIdOlRd7fWTOhfPuW3GAMe9eAAAA2HW8QrK5DpwqX7FM3SunyjfchLEAAADAjmUGxubab6p8zTJ1r54q32CV/Ry6zPVDMtkyFgAAAHYkAcbm+tZUeZ9l6u47Vb5qNZ0ssbtJkqSqVtMcsEMcfvKZS16/4HnHb9FIAABg83mFZHN9Y6q83GshB0yVl3vdBAAAAGaKAGNzTc+MWG6nkOnXQCzKCQAAAFMEGJtreieRI5epO3f92iSf2ZzhAAAAwM4kwNhc52XP4p3HLVapqvZJco+5e7p7uQU/AQAAYKYIMDZRd38jyd8Opw+sqsVeIzkhyY2G8hmbPjAAAADYYQQY61BVJ1ZVD59TFqn2wuG4d5KXVdVe89o4OMkfDqeXJXnFZowVAAAAdrKZ3Ua1qu6d5Iiprw6eKh9RVSdO1+/u09bST3e/u6rekOQxSR6W5OyqenGSi5McneS3kxw2VD+5u7++ln4AAABgN5vZACPJk5L8/CLX7jV8pp22jr6emMkrIg9Ocv/hM+07SZ7b3aeuow8AAADYtWY5wNgy3X1VkuOr6nFJTkzyY0lukuSSJO9N8tLufv+2DRDYlQ4/+cxl61zwvOO3YCQAALB+MxtgdPeJmYQJ62njtKxiZkZ3vz7J69fTJwAAAMwii3gCAAAAoyfAAAAAAEZPgAEAAACMngADAAAAGD0BBgAAADB6M7sLCQDLb7Vqm1UAAMbCDAwAAABg9AQYAAAAwOgJMAAAAIDRE2AAAAAAoyfAAAAAAEZPgAEAAACMnm1UAVjUctusJrZaBQBga5iBAQAAAIyeAAMAAAAYPQEGAAAAMHoCDAAAAGD0BBgAAADA6AkwAAAAgNGzjSoA67LcVqu2WQUAYCOYgQEAAACMngADAAAAGD0BBgAAADB6AgwAAABg9AQYAAAAwOgJMAAAAIDRE2AAAAAAo7f3dg8AgN3t8JPPXLbOBc87fgtGAgDATmYGBgAAADB6AgwAAABg9AQYAAAAwOgJMAAAAIDRE2AAAAAAoyfAAAAAAEZPgAEAAACM3t7bPQAAOPzkM5e8fsHzjt+ikQAAMFZmYAAAAACjJ8AAAAAARk+AAQAAAIyeAAMAAAAYPQEGAAAAMHoCDAAAAGD0bKMKwOjZZhUAADMwAAAAgNETYAAAAACjJ8AAAAAARk+AAQAAAIyeAAMAAAAYPQEGAAAAMHq2UQVgx1tum9XEVqsAADudGRgAAADA6AkwAAAAgNETYAAAAACjJ8AAAAAARk+AAQAAAIyeAAMAAAAYPduoAjATlttq1TarAADjZgYGAAAAMHoCDAAAAGD0BBgAAADA6AkwAAAAgNETYAAAAACjZxcSAMjyu5QkdioBANhOZmAAAAAAoyfAAAAAAEZPgAEAAACMngADAAAAGD2LeALACi230KdFPgEANo8ZGAAAAMDoCTCSVNVhVfXCqvpUVV1ZVV+rqg9V1dOrav91tn1KVfUKP/fbmJ8IAAAAdpeZf4Wkqo5P8rokN576ev8kxwyfJ1XVg7v7s9sxPgAAAGDGA4yq+rEkb8wksLgiyR8kOSfJDZI8JskvJvnhJGdW1THdfcU6uzx6meufW2f7AAAAsCvNdICR5MWZhBfXJnlQd79/6tq7q+qfkzw/yZFJfjPJc9bTWXf/w3ruB2DcllvkM7HQJwDAWs3sGhhVdUyS+w2nr5wXXsx5UZJPDeWnVNX1t2JsAAAAwPea2QAjycOnyq9aqEJ3fyfJa4bTg7In8AAAAAC20CwHGPcZjlcm+cgS9c6dKt9784YDAAAALGaW18A4ajh+pruvXaLe+QvcsyZVdXaSuyY5MMllST6Z5K+SnNrdX19Hu7dZpsoha20bAAAAxmAmA4yq2i/JwcPpRUvV7e6vV9WVSQ5Icug6u37gVPlmSY4bPs+oqhO7+21rbPfCdY4LgC2y3EKfFvkEAFjYTAYYmcyAmLOSrVHnAowbrrG/TyR5a5IPJbk4yfUz2Z718UkelOQmSd5cVQ/t7rPW2AcAAADsWrMaYOw3Vb5mBfWvHo43WENfL+7uUxb4/oNJXlNVT07y8iR7JXlFVR3R3Vetso/lZoYckuS8VbYJAAAAozGrAca3psr7rKD+vsNxtcFCuvuyZa6fWlV3T/KkJLdKckKS162yjyVfg6mq1TQHAAAAozOru5B8Y6q8ktdCDhiOK3ndZC1OnSoft0l9AAAAwI41kzMwuvtbVXVpJgt5LrmDR1UdlD0BxmYtlvnJqfKtN6kPAHaA5Rb5TCz0CQDMplmdgZEknxqOR1TVUkHOkQvcs9G84wEAAABLmOUA4++H4wFJ7rZEvelXOt63SWO541T54k3qAwAAAHasWQ4w3jpV/oWFKlTV9ZL83HB6WZJzNmksT54qn7tJfQAAAMCONZNrYCRJd3+oqt6b5D5JTqqqV3f3++dVe1qSo4byS7r729MXq+rEJK8aTp89f7vUqjo6yVXd/ZnFxjFso3rScPqlJGes4ccBYIYst06GNTIAgN1oZgOMwW9k8lrIDZK8q6p+P5NZFjdI8pgkvzTU+3SSF62h/bsleUVVnZPkrCSfSPLVTP65H5nkZ5P85FD3uiRP7u4r1/ajAAAAwO410wFGd3+0qv5jkj9PcqMkv79AtU8nOb67v7HAtZXYK8kDh89ivprkpO5++xr7AAAAgF1tpgOMJOnud1TVj2YyG+P4TLZVvSbJZ5KcnuSl3f3NNTb/l5m8HnLPJHdJcoskN81k15GvJflYkr9Kclp3X76enwMAAAB2s+ru7R4Dm6yqbpPkwiS58MILc5vb3GabRwQLW+69fmDjWCcDANhMF110UQ499NC500O7+6L1tjnLu5AAAAAAO4QAAwAAABg9AQYAAAAwejO/iCcAzKLl1pyxRgYAMDZmYAAAAACjJ8AAAAAARs8rJADA9/GKCQAwNmZgAAAAAKMnwAAAAABGzyskAMCqLfeKSeI1EwBgY5mBAQAAAIyeAAMAAAAYPa+QAACbwk4mAMBGMgMDAAAAGD0BBgAAADB6XiEBALaFnUwAgNUwAwMAAAAYPQEGAAAAMHpeIQEARstOJgDAHDMwAAAAgNETYAAAAACj5xUSAGDHspMJAMwOMzAAAACA0TMDAwDY1SwECgC7gxkYAAAAwOiZgQEAzDTraADAzmAGBgAAADB6AgwAAABg9LxCAgCwDAuBAsD2MwMDAAAAGD0zMAAA1slCoACw+QQYAABbwGsoALA+XiEBAAAARs8MDACAETBDAwCWZgYGAAAAMHpmYAAA7AAWCgVg1pmBAQAAAIyeGRgAALuEdTQA2M0EGAAAM8JrKADsZF4hAQAAAEbPDAwAAL7LaygAjJUZGAAAAMDomYEBAMCKWUcDgO0iwAAAYEN5DQWAzSDAAABgS5nFAcBaCDAAABgdszgAmM8ingAAAMDomYEBAMCO4zUUgNkjwAAAYFfyGgrA7iLAAABgJgk4AHYWAQYAACzAayoA4yLAAACANTKLA2DrCDAAAGCTmMUBsHEEGAAAsI3M4gBYGQEGAACMmFkcABMCDAAA2OFWEnIsRwgCjJ0AAwAA8CoLMHoCDAAAYFleZQG2mwADAADYEOt9lUUAAixFgAEAAIyCWR7AUgQYAADAjmHBUphdAgwAAGCmCEFgZxJgAAAArJIQBLaeAAMAAGAbCEFgdQQYAAAAO5SdX5glAgwAAIAZZRYIO4kAAwAAgDXbiBBkJQQlCDAAAAAYva0ISoQk4ybAAAAAgAhJxk6AkaSqDkvyn5Mcn+SwJFcn+UySNyb53939zQ3q5zFJfiHJjyY5KMmXkrw3ycu6+wMb0QcAAADjZd2RtZv5AKOqjk/yuiQ3nvp6/yTHDJ8nVdWDu/uz6+hjvySnJ3nIvEu3HT6Pq6pTuvu5a+0DAAAAdrPrbfcAtlNV/VgmsyxunOSKJL+d5MeTPCDJnw7VfjjJmVV1w3V09crsCS/OSfLwJMcmOSnJv2Ty7+E5VfWkdfQBAAAAu9asz8B4cSazLa5N8qDufv/UtXdX1T8neX6SI5P8ZpLnrLaDqjouyeOG03ckeUR3Xzecn1dVb0/ykUxeXXl+Vb2puy9bw88CAAAAu9bMzsCoqmOS3G84feW88GLOi5J8aig/paquv4aufms4XpfkV6fCiyRJd1+a5BnD6UGZzMoAAAAApsxsgJHJaxxzXrVQhe7+TpLXDKcHZU/gsSLDaycPGE7P7u6LFqn6liSXD+UTVtMHAAAAzIJZDjDuMxyvzOQVjsWcO1W+9yr7ODbJvgu08z26+5okc7uQHLvGmR4AAACwa83yGhhHDcfPdPe1S9Q7f4F7VtvH/HYW6+dBmfw7uUOST660k6q6zTJVbj1X+OIXv7jSZmHLXXv5pds9BAAAGL2LLlpscv94zPvdc6+NaHMmA4xhW9ODh9Ml/81399er6sokByQ5dJVdTddf7gm7cN59Kw4w5t27pGOPPXYVzQIAADA2h/7xdo9g1W6W5PPrbWRWXyE5cKp8xQrqXzkcV7uV6mr6uXKqvJ4tWwEAAGDXmckZGEn2mypfs4L6Vw/HG2xiP1dPlVfbz3IzQ/bJZCvYLyf5SiY7oozRIUnOG8rHJPnSNo4FFuIZZew8o4ydZ5Sx84wydjvpGd0rk5kXSfKJjWhwVgOMb02V91lB/bmFOK/axH72nSqvqp8ldjeZ9tnVtLkdqmr69Esr/Llgy3hGGTvPKGPnGWXsPKOM3Q58Rtf92si0WX2F5BtT5ZW8rnHAcFzJ6yZr7eeAqfJq+wEAAIBdbSYDjO7+VpK57Q6W3MGjqg7KnnBhxYtlDqbTsOV2Cpl+DWS1/QAAAMCuNpMBxuBTw/GIqlrqVZojF7hnpaZ3Ejly0Vrfe/3aJJ9ZZT8AAACwq81ygPH3w/GAJHdbot5xU+X3rbKP87Jn8c7jFqtUVfskucfcPd29koVFAQAAYGbMcoDx1qnyLyxUoaqul+TnhtPLkpyzmg66+xtJ/nY4fWBVLfYayQlJbjSUz1hNHwAAADALZjbA6O4PJXnvcHpSVd1zgWpPS3LUUH5Jd397+mJVnVhVPXxOWaSrFw7HvZO8rKr2mtfGwUn+cDi9LMkrVvWDAAAAwAyY2QBj8BuZbFm6d5J3VdV/rap7VNX9q+rUJM8f6n06yYvW0kF3vzvJG4bThyU5u6oeVlV3r6pfSPKBJIcN10/u7q+v9YcBAACA3aq6e7vHsK2q6qFJ/jx7XuGY79NJju/u71tYs6pOTPKq4fTZ3X3KIn3cIMmbkjx4kT6+k+S5i90PAAAAs27WZ2Cku9+R5EeT/M9MwopvZvIqx4eTPCPJXRYKL1bZx1XdfXySxyc5O8mXM1nc88Ikr09yb+EFAAAALG7mZ2AAAAAA4zfzMzAAAACA8RNgAAAAAKMnwAAAAABGT4ABAAAAjJ4AAwAAABg9AQYAAAAwegIMAAAAYPQEGAAAAMDoCTAAAACA0RNgMApVdVhVvbCqPlVVV1bV16rqQ1X19Kraf7vHx+5TVXetqmdW1VlVdWFVXV1VV1TVp6vqtKq6zyrb+6mqektVXTS0ddFw/lOb9TMwu6rq+VXVU5/7reAezyibqqoOrqrfqqr3VdWXhufs4qr6YFW9oKruuYI2PKdsiqrap6pOqqq/qqovTv13/5+q6s+q6h4rbMczyopV1c2r6iFV9Zzhz5yXTv23+7Q1tLfu56+q9q+q/zL8rvW14X8Hnxp+FztstWPaatXd2z0GZlxVHZ/kdUluvEiVf0ry4O7+7NaNit2sqs5Nct8VVH1tkid19zVLtFVJXp7kl5Zo50+S/HL7P1w2QFX9WJIPJ9l76uv7d/d7FqnvGWXTVdXPJPnjJDddotrbuvvhi9zvOWXTVNWhSc5McvQyVf9nkqct9Ix5RlmLqlrqWXh1d5+4wnY25Pmrqttn8r+FH16kyr8leVx3/+VKxrUdzMBgWw1/EH9jJuHFFUl+O8mPJ3lAkj8dqv1wkjOr6obbMkh2o1sPx4uTvCTJo5Icm+SeSX4zyReG609Ictoybf1u9vzH5KNJHju09djhPMP1527AuJlxVXW9TP6/ce8kX17hbZ5RNlVV/VySN2QSXnw5ybOT/GSSuyU5Psl/TnJ2km8v0YznlE1RVXvne8OLjyc5MZP/5j8oyXOSXDlce2qSpy/SlGeU9bowybvWeO+6n7/hd6l3Zk948aeZ/M7145n8DnZFJr+TnV5VP7rGcW46MzDYVlV1TpL7Jbk2yX27+/3zrv+XJM8fTp/V3c/Z2hGyG1XVO5O8Jsmbu/u6Ba4fnOR9SX5o+Oq+3f3eBeodkeRTmfwy+eGh3lVT1/dPcm6Su2fyjB/Z3f+ywT8OM6SqnpLJ3xCen+SMJP91uLTgDAzPKJutqo7K5A/P+yZ5b5KHdve/LVJ3n4VmtHlO2UxV9cgkbxpO35/kPvP/219VdxuuXT/J15PcvLuvnbruGWVNqurZSc5Lcl53X1JVhyf53HB5RTMwNur5q6pTkjxrOP2t7n7BvOv3TPJ3Qz/ndPdPrPwn3TpmYLBtquqYTMKLJHnl/PBi8KJM/gebJE+pqutvxdjY3br7Id39xoXCi+H6pUmeNvXVoxZp6qnZM43/16f/YzK0880kvz6c7p3kKWseNDNvmAI99zcrv5Jk0VebpnhG2Wz/K5Pw4tIkJywWXiTJEq/jeU7ZTPeaKv/BQv/t7+6PZPI300lyUJIj51XxjLIm3f2s7n5nd1+yjmbW/fwNv0P9xnD6qUx+x5o/1vcneeVwev8h2BsdAQbb6eFT5VctVKG7v5PJ35Qnk/+g3G9zhwTf9Z6p8u3nXxzeRfzp4fT87v7AQo0M3//TcPrw4T5Yi/+d5IaZ/I3Ne5ar7Blls1XVkZlMP06Slw7h72rb8Jyy2faZKi+1ntr031jvO1fwjLKdNvD5u1+SmwzlVw+/Yy3ktKnyCasd71YQYLCd5nZ5uDLJR5aod+5U+d6bNxz4HtN/4Fno/+Rvlz1raZy7wPVpc9dvk+Tw9Q2LWVRVj07ykCRfS/JfVnibZ5TN9jNT5dPnClV1UFXdoaqWWtBzjueUzfbpqfIPLlFv7i8rOsk/T33vGWU7bdTzd58F6i3kw9mzJswof+8SYLCdjhqOn5l+z3AB5y9wD2y246bK5y9w/ahlrmeR655hVqWqbpLJYrNJ8ozu/soKb/WMstnmtp38tySfqqrHV9XHMgnaPp3k0qr6bFU9a4mFuD2nbLa/SHL5UH5GVe01v0JV3SWTBWeT5A3dffnUZc8o22mjnr8VtTP8TjY3G2mUz7AAg21RVfslOXg4vWiput399exJAg/dzHFB8t2dHk6e+uqNC1SbfhaXfIYzWXV6oftgJZ6f5JAk/1/2vJu6Ep5RNtsdh+MFmayF8edJ5q9cf7skpyR5f1XdaoE2PKdsqiH0PTHJVZmsh3FeVf1cVd2jqh5YVc/K5G+k90ny/zLZjWyaZ5TttFHP39z5ld192QrbuVlV7btkzW0gwGC7HDhVvmIF9ecCDFupshWemsnWVElyRnd/eIE6q3mGr5wqe4ZZsaq6d5InZbKq+JJ7uy/AM8pm+4HheGSSX0tyWZJfTnLzJPslOSbJWUOdH8lka775f/b0nLLpuvuMTHZoeGWSOyd5dSa7jpydScD2zUyCi3t395fm3e4ZZTtt1PM3185qfu9aqJ1tJ8Bgu+w3VV7JSvpXD8cbbMJY4Luq6rgkzxtOv5zJbg8LWc0zfPVU2TPMilTVPkn+JEkl+Z/d/YlVNuEZZbMdMBz3TXJdkv/Q3ad291e6++oh/H1I9oQYP57vXxTOc8qmG3ZgeFySh2by/6nz3SLJY7PwYvGeUbbTRj1/c+2s5veuhdrZdgIMtsu3psr7LFprj7npS1ctWQvWoarulOSMTLagujrJo5fY9mo1z/D09DvPMCv1zEzeP/3XJM9ew/2eUTbb9DN2+kKr4w8r3U8vPPvYJdrwnLLhquqAJH+T5LeT3DST1/KOyuR5unGSByX5+0xmDL2jqn5jXhOeUbbTRj1/c+2s5veuhdrZdgIMtss3psormZo097c8K5n2BKtWVbdL8q5Mtuu9Lslju3upVZpX8wwfMFX2DLOsYXvK/zqc/np3X7lU/UV4Rtls08/YWYtV6u5/TPKF4fSYJdrwnLIZnp3kvkP5pO5+Rnef393XdPfl3X12kvsnOSeT2Rn/o6qm13LxjLKdNur5m2tnNb93LdTOttt7uwfAbOrub1XVpZks5HmbpepW1UHZ8z+kC5eqC2sxLCz3N0lulcn2aU8c3pddyvRCSks+w/nehZQ8w6zEUzP5W5LPJtm/qh6zQJ0fmSr/RFUdMpTfMQQenlE224WZLDCbrGxxuVtnsj7GNM8pm6aqKskvDKef7u5XL1Svu6+tqv+WyUyM6w33PHW47BllO23U83dRkn+X5ICquskyC3nOtfOV7r56iXrbQoDBdvpUJnsSH1FVey+xleqR8+6BDVNVB2eyiNfc3vC/3t2vWcGtn5wqH7lore+/7hlmJeamb/5gJlsALue/TZVvl8kCXJ5RNts/Zs+Miu/bmnKeuevz/1vvOWUz3SJ7Fpv96DJ1PzJVnn7WPKNsp416/j6Z5JFT9b7vlb8kqaq9k9x+kTZGwSskbKe/H44HJLnbEvWOmyq/b/OGw6ypqhsn+evs2Qrw5O5+2Qpv/1ySi4fycUtVzJ6pq1/IZLtB2AqeUTbb302Vb79orYm5kPgL8773nLKZpgOz5f7i9vqL3OcZZTtt1PP391Plpdq5e/bMfB/l710CDLbTW6fKv7BQhWG7tZ8bTi/L5P1EWLeq2j/JmUnuOnz1e939hyu9f9jO8m3D6ZFVdY9F+rlH9iTib1vlNpjMqO4+sbtrqU++d2HP+09du2BowzPKZnt7km8P5fm7i3zXsLvTTYfT905f85yyyb6W5PKhfM/hb5cXM/1L3efmCp5RttMGPn/vSfJvQ/nnh9erFnLiVHm516m3hQCDbdPdH8qeP8icVFX3XKDa0zJZKTpJXtLd316gDqzKsD3lGUnuNXz1ku7+nTU09eLs+Vua/1VV37PV1HD+v4bTa4f6sJVeHM8om6S7v5rkFcPpTy60VktVHZjvfa5OXaCpF8dzyiYYdsE5czi9VSY7kXyfYb216b/EeOe8Ki+OZ5Tt8+Ks8/nr7muS/NFwelSSp8+vM/wudtJwem53n7fegW+GEg6ynarqLplMT7pBJqvc/n4msyxukOQxSX5pqPrpJHfv7m8s1A6sRlW9OXv+tvDdSZ6SyeKdi7mmuz+9SFt/kOTk4fSjmfwB6F8ymU79jCR3Ga79QXc/c30jhz2q6pQkzxpO79/d71mknmeUTVNVN0vy4SSHZfIH55cneUsmf+t9dCbP2NzfCv5xd//qIu14TtkUw65OH0my//DVO5K8OpNFkvdLco9M/hxw2HD9b7v7gQu04xll1arq3kmOmPrq4CQvGMrvy54QOEnS3act0s66n78hUP5wkh8avvqTJG/IZKvU+2eyffsNh/Mf7+7/t4IfccsJMNh2VfXQJH+e5EaLVPl0kuO7+zNbNyp2s6pa7f/xfb67D1+kresl+dMkT1zi/lcm+aXhb4JgQ6wiwPCMsqmq6qhMXic5Yolqf5bklxebSek5ZTNV1QMzWRD54GWqvjvJo7r76wu04Rll1arqtCQ/v9L6wyuiC7WzIc9fVR2R5C+T3GGRKpcneXx3z5+FNBoCDEahqm6b5DeSHJ/JFkHXJPlMktOTvLS7v7mNw2OX2cgAY6rNB2cyY+iYTP6AdGmS85Kc2t1nrWWcsJSVBhhT9T2jbJqqOiDJryR5VCZ/ML5hki9n8jeMp3b3itaw8pyyWarqpplMj/8PSe6U5CaZzBr6UibP2OuTvH25tSs8o6zGRgUYU+2t+/kb/v/615L8TCbB8z6ZbLv6l5m8Vv35lY53OwgwAAAAgNGziCcAAAAwegIMAAAAYPQEGAAAAMDoCTAAAACA0RNgAAAAAKMnwAAAAABGT4ABAAAAjJ4AAwAAABg9AQYAAAAwegIMAAAAYPQEGAAAAMDoCTAAAACA0RNgAAAAAKMnwAAAAABGT4ABAAAAjJ4AAwAAABg9AQYAAAAwegIMAAAAYPQEGADArlZVp1VVL/A5fLvHth2q6n6L/PM4ZbvHBgBLEWAAACyiqq5XVQ+vqj+uqo9V1SVVdU1VXV5Vn62qt1XVyVX1Q5vU/6lTAcP9V3nvA6bufelmjA8AttLe2z0AAIAtcnGSfz91/oWlKlfVg5O8KMmRC1y+fpIDk9wuycOS/EFVnZvkmd39/23McJMkr0nyS0P5CUnOWcW9PztVfu1U+bwkR0+df2JtQwOArSXAAABmxbe7+x9WUrGqnpHkD5LU8NX7krwjyUeTfDXJfklukeReSY5P8sNJjkvy35P81EYNuLvfV1X/kuT2SR5VVb/W3VetYPw3SPLI4fSfuvuDU21emeQfpupu1HABYFMJMAAAplTVzyV53nB6aZLHd/e7Fqn+lqp6epKHZhJ4bIbXJHl2JjM+fjrJG1Zwz8OH+sn3zr4AgB3LGhgAAIOqunWSlw+nVya57xLhRZKkJ96e5G5JXrkJw3ptkh7KT1jhPXP1Osmfb/iIAGAbCDAAgA1RVbeYWjTyyVV1o6r67ap6X1V9ZVj88l+HBTEP2e7xLuI3k9xgKP9Od39qpTd297e6+/Sl6lTVsVX1p1X16aq6oqqurKrzq+plVXWHRdr9XJK/H04fVFU3X6aPWyT5yeH03O7+/Ep/BgAYMwEGALBR7jxV7iT/nOR3k/x4koMzWfjy0CS/nOSDVXWrrR7gUmqyGMTPDadXZANnU1TV3lX1v5N8MMmTktwhyQFJ9s9k/YxfTfKPVfWLizTxmuG4d5LHLtPdY7PnNeHXLFURAHYSAQYAsFHuPFV+YZKbJzk9k3Ub7pbkPyb5+HD9sCT/cysHtwJ3yiRoSZL3dvc3NrDtVyb5laF8ViY7hByb5Jgkv5jkHzMJeP6kqh66wP1vTDK3eOdyr5HMXf9mkjetY8wAMCoW8QQANsqPTZVvkOTR816p+L9VdVYmMzNukeSEqrpJd1+2hWNcyo9Olf/vRjVaVY/Mnpkdv9jdr5hX5cNV9edJzkzyE0n+qKrO6u5r5yp09+VV9bYkj0lyt6o6aqHXW6rqjknuOpy+dYNDGADYVmZgAAAb5c5T5d9aaD2I4Rfqlw6neye5yxaMa6UOnip/ZamKVXWnqvqRRT4HzKv+X4fjGQuEF0km62ck+U/D6eFJ7rdAtenXQRabhTH9vddHANhVBBgAwLpV1X5Jfmg4/WSSP1qi+j9MlQ9etNbWO3CqfMUydT+W5BOLfI6ZqzTsanK34fSNSzU4zKi4dDi95wJV3pXki0P58cOaHd81nD9+OP1ikr9Z5mcAgB1FgAEAbISjk+w1lF/e3dctUffrU+VvTu1csqLP5v0ImX7dYv4sirW6+1T5L1bws80FOt+3S8vwz/T1w+lhSY6bV+V+mSySmiSvX+bfAQDsONbAAAA2wvT6F29fpu70L+eXJHn2AnWeleTfkrx4fcNala9OlW+2VMXu/p4/Q1XVKZmMeb4ltzxdwv6LfP/qJE8byk9I8p6pa14fAWBXE2AAABvhzsPx0u7+/DJ17zEcr07yqe7+8PwKVfWsJJd19ykbNsLlfWyqfNdFa63OXlPlx2fPLizL+fpCX3b3J6rqY5kERo+qqv/U3VdV1Q2SPHKo9rHuXmk/ALBjCDAAgI0wNwPjkqUqDes0PGQ4/bvuvnJTR7U6/5jJLIybJrlPVR2wAeObntXR3f0Pi9ZcuVcn+R9JbpTkYUn+TyZb1d5ouG72BQC7kjUwAIB1GUKJuS1IF3v1Yc5PJjliKL960wa1Bt3d2fPL/4FJTtyAZj86VX7QBrSXTNbBmNti9QnzjtPrZADAriLAAADW63bZ87f/h1bVDReqVFX7JHnBcPpPSf5iC8a2Wv8jyVVD+fer6oilKi+nuz+Tya4sSfKYqjpsPe0NbV6SyY4kSfLvq+pHsicceVd3f2m9fQDAGAkwAID1ml7Ac+/sWWTyu6pq3ySnZTJT47okv9Ld39mS0a1Cd1+U5NeG0xsleW9V3W8Ftx60xLXfHY77JXlLVS26QGhV7VtVvzpsS7uUudkreyd5Q/a8Fuz1EQB2LWtgAADrdefh+LUklyU5papumeStmSxGeeckv57kTkO93+nuc7Z0hKvQ3a+qqlsneU4mO6acU1V/l8nuKh/PZF2LymSHkR9L8ogkx041cdW89v6iqv59kp9Pcrckn6yqU5Ocm+QrmWzZevsk90lyQpIfyPJBxNsz+Wd9k+z553p5kret+gcGgB1CgAEArNfcDIyPJ/n9JO9I8uThM+3aJM/s7hdk5Lr7d4fdPl6U5A5J7jt8lvK+JM/o7g8ucO2kTBY4fVqSg5P89vBZyJWZzFJZanzfqqrTk/zi1Nend/dVi90DADudV0gAgPW683D8eHefnclMgjMy+YX96iSfS3JqkqN3Qngxp7vfkeSoTGZF/EmST2QyY+LaJN9I8vkkf5nklCR36u57d/f7Fmnruu5+RpI7ZhKKfDST2SnXDW39Y5LXZTJL45YrDCLmL4Lq9REAdjUzMACANauqGye57XD6iSTp7vMy+aV/x+vu6zIJY87YoPY+neTpG9TW+zJ5lQUAZoIAAwBYjztPlT+xXYNYoesPO3bM+afu/va2jWabVNUBmewcAwA7igADAFiPufUvOpPXIMbsVvnekOV2SS7YnqFsq2OSjHYRVQBYjDUwAID1uPNw/Gx3X7GdAwEAdrfq7u0eAwCwQ1XVR5LcNclbu/sR2z0eAGD3MgMDAFiTqto7yZ2G07GvfwEA7HBmYAAAAACjZwYGAAAAMHoCDAAAAGD0BBgAAADA6AkwAAAAgNETYAAAAACjJ8AAAAAARk+AAQAAAIyeAAMAAAAYPQEGAAAAMHoCDAAAAGD0BBgAAADA6AkwAAAAgNETYAAAAACjJ8AAAAAARk+AAQAAAIyeAAMAAAAYPQEGAAAAMHoCDAAAAGD0BBgAAADA6AkwAAAAgNH7/wHvZInyyUdyMAAAAABJRU5ErkJggg==\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
}