coffea-adl-benchmarks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "import awkward as ak\n",
    "import hist\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from coffea import processor\n",
    "\n",
    "# The opendata files are non-standard NanoAOD, so some optional data columns are missing\n",
    "processor.NanoAODSchema.warn_missing_crossrefs = False\n",
    "\n",
    "\n",
    "def run(query, chunksize=2**19, workers=24):\n",
    "    tic = time.monotonic()\n",
    "    output, metrics = processor.run_uproot_job(\n",
    "        fileset={\"SingleMu\": [\"/ssd/Run2012B_SingleMu.root\"]},\n",
    "        treename=\"Events\",\n",
    "        processor_instance=query(),\n",
    "        executor=processor.futures_executor,\n",
    "        executor_args={\n",
    "            \"workers\": workers,\n",
    "            \"schema\": processor.NanoAODSchema,\n",
    "            \"savemetrics\": True,\n",
    "            \"status\": False,\n",
    "        },\n",
    "        chunksize=chunksize,\n",
    "    )\n",
    "    toc = time.monotonic()\n",
    "    metrics[\"query\"] = query.__name__\n",
    "    metrics[\"chunksize\"] = chunksize\n",
    "    metrics[\"workers\"] = workers\n",
    "    metrics[\"walltime\"] = toc - tic\n",
    "    return output, metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 1\n",
    "\n",
    "Plot the <i>E</i><sub>T</sub><sup>miss</sup> of all events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bytesread': 314558409,\n",
       " 'entries': 53446198,\n",
       " 'columns': {'MET_pt'},\n",
       " 'processtime': 2.7774274349212646,\n",
       " 'chunks': 102,\n",
       " 'query': 'Q1Processor',\n",
       " 'chunksize': 524288,\n",
       " 'workers': 24,\n",
       " 'walltime': 1.6080826809998143}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1rc1Gq+spQ7Q5CJzSt3wycGgdbSRJYzRMoN8GbE9yWpLvAy4Crl/S5nrg9el5GfBN+88labJW7XKpqieSvAX4DL1hi1dU1YEklzbbdwM30huyeC+9YYsXj69kYATdNmMyrXXB9NZmXWtjXWuzoepKb2CKJGnWDdPlIkmaAQa6JHXEzAV6knOT3JPk3iRva7GOU5J8LsndSQ4k+YVm/W8m+Ysk+5uf81uo7atJ7mzef2+z7tlJPpvky83vvzXhmn6ob5/sT7KQ5K1t7K8kVyR5MMldfesG7p8kv9p83+5J8o8nXNe/T/KlJF9M8sdJntWs35bksb79tnvCdQ383FreX1f31fTVJPub9ZPcX4OyYfzfsaqamR96J2W/AjwP+D7gDmBHS7XMAWc2j58B/C9gB/CbwC+1vJ++Cmxesu53gLc1j98GvKvlz/H/0LtAYuL7C3glcCZw12r7p/lM7wCeCpzWfP+Om2Bd/wg4vnn8rr66tvW3a2F/Lfu5tb2/lmz/XeA3Wthfg7Jh7N+xWTtCPzINQVU9DixOQzBxVXW4mgnIqupbwN30ro6dVhcCH2kefwT46fZK4R8CX6mqVS6KHI+qugl4ZMnqQfvnQuDjVfX/qup+eiO5zppUXVX1J1X1RLN4C71rPCZqwP4apNX9taiZMPBngavG8d4rWSEbxv4dm7VAHzTFQKuSbANeDNzarHpL80/kKybdtdEo4E+S3N5MtwDwA9VcG9D8fk4LdS26iKP/R2t7f8Hg/TNN37k3Ap/uWz4tyf9M8t+TvKKFepb73KZlf70CeKCqvty3buL7a0k2jP07NmuBPtQUA5OU5OnAp4C3VtUCvZkmfxA4g95cNr/bQlkvr6oz6c2CeVmSV7ZQw7LSuzjtp4BPNqumYX+tZCq+c0neDjwBfLRZdRjYWlUvBv4N8LEkmwY9fwwGfW5Tsb+A13L0QcPE99cy2TCw6TLr1rXPZi3Qp2qKgSQn0PvAPlpV1wBU1QNV9Z2q+i7w+4zpn5srqapDze8HgT9uanggyVxT9xzw4KTrapwH7KuqB5oaW99fjUH7p/XvXJI3ABcAP1dNp2vzz/OHm8e30+t3ff6kalrhc5uG/XU88E+AqxfXTXp/LZcNTOA7NmuBPsw0BBPR9NF9CLi7qt7dt36ur9nPAHctfe6Y6/qbSZ6x+JjeSbW76O2nNzTN3gBcN8m6+hx15NT2/uozaP9cD1yU5KlJTqM35/+fT6qo9G4u8yvAT1XVt/vWb0nvXgUkeV5T130TrGvQ59bq/mr8BPClqjq4uGKS+2tQNjCJ79gkzvqO+Azy+fTOGn8FeHuLdfwovX8WfRHY3/ycD/wn4M5m/fXA3ITreh69M+Z3AAcW9xHwt4E/Bb7c/H52C/vs+4GHgWf2rZv4/qL3B+Uw8Nf0jo7etNL+Ad7efN/uAc6bcF330utfXfyO7W7a/tPm870D2Af85ITrGvi5tbm/mvUfBi5d0naS+2tQNoz9O+al/5LUEbPW5SJJGsBAl6SOMNAlqSMMdEnqCANdkjrCQJekjjDQ1WlJ/l3bNUiTYqBrZiS5JMnhHD2v+guTnNhMuPTLST7StPujJD8KHN/MT/37Sf5Dkp9YurzkPY7Mm9237geSfCzJfc2EZ19I8jOr1PpnS+e1Tm/+9/cneVpT++NJNo9yH2ljM9A1S14E/HpVndH3cye92eyuqarfAZ5Jb26RTzaP9wM/DDwO/F5V/ddllpf6SlWdAUcu474WuKmqnldVL6E35cRq09he1bTrdxFwVVU91rx+a/MQqZsMdM2SF9IL6KXOAD7TTIj0cPUmjDod2Ansr6rPAu8FLk9y0tLlVd7zx4HHq+rIHW6q6mtV9d7F5ST/IsmfN0fdH2zmDPkj4IIkT23abAOeC3x+Pf/h0jAMdM2SFwB/2NfdsjjX+9+hN7/PC+jdTAB6d6jZCnw5ybuAfwn8b+DBpctDvOe+QRuT/F3gNfSmLD4D+A69WREfpjfB0rlN04uAq8u5NjRGx7ddgDSMJKcAD1bVi5Zuq6o3NQ/3Nz9U1ev6mvzKkqcsXV5LHe+jN/nS41X19+jdfeklwG293hmexvf+SCx2u1zX/H7jet9XGoaBrlnxIuBLLbzvAXoz9QFQVZc1JzL3NqsCfKSqfnWZ514LvDvJmcDTqrktmTQudrloVryQZQI9ycVJdie5v/l9yYjf978BfyPJm/vWfX/f4z8FXp3kOU09z05yKkBVPQr8GXAFLdzbUhuPR+iaFS8EfizJec1yAa+oqj9Mch1wfFVdOuo3rapK8tPAf0zyy8BDwP+l6bapqvkkv07vHq5PoTc392XA4g2wrwKu4ckjXqSRcz50zbwkrwK2V9X7R/Ba24D/XFWnH3Nhq7/XV4GdVfWNcb+XNga7XNQFLwFuH9FrfQd4Zv+FRaO2eGERcALw3XG9jzYej9A185JcBbyxqh5ruxapTQa6JHWEXS6S1BEGuiR1hIEuSR1hoEtSRxjoktQRBrokdYSBLkkdYaBLUkcY6JLUEf8fvniWc2l5KVIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Q1Processor(processor.ProcessorABC):\n",
    "    def process(self, events):\n",
    "        return (\n",
    "            hist.Hist.new.Reg(100, 0, 200, name=\"met\", label=\"$E_{T}^{miss}$ [GeV]\")\n",
    "            .Double()\n",
    "            .fill(events.MET.pt)\n",
    "        )\n",
    "\n",
    "    def postprocess(self, accumulator):\n",
    "        return accumulator\n",
    "\n",
    "\n",
    "out, metrics = run(Q1Processor)\n",
    "out.plot1d()\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 2\n",
    "Plot the <i>p</i><sub>T</sub> of all jets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bytesread': 891917006,\n",
       " 'entries': 53446198,\n",
       " 'columns': {'Jet_pt', 'nJet'},\n",
       " 'processtime': 10.380706310272217,\n",
       " 'chunks': 102,\n",
       " 'query': 'Q2Processor',\n",
       " 'chunksize': 524288,\n",
       " 'workers': 24,\n",
       " 'walltime': 1.6780950600000324}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Q2Processor(processor.ProcessorABC):\n",
    "    def process(self, events):\n",
    "        return (\n",
    "            hist.Hist.new.Reg(100, 0, 200, name=\"ptj\", label=\"Jet $p_{T}$ [GeV]\")\n",
    "            .Double()\n",
    "            .fill(ak.flatten(events.Jet.pt))\n",
    "        )\n",
    "\n",
    "    def postprocess(self, accumulator):\n",
    "        return accumulator\n",
    "\n",
    "\n",
    "out, metrics = run(Q2Processor)\n",
    "out.plot1d()\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 3\n",
    "Plot the <i>p</i><sub>T</sub> of jets with |<i>η</i>| < 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bytesread': 1676450252,\n",
       " 'entries': 53446198,\n",
       " 'columns': {'Jet_eta', 'Jet_pt', 'nJet'},\n",
       " 'processtime': 20.93133306503296,\n",
       " 'chunks': 102,\n",
       " 'query': 'Q3Processor',\n",
       " 'chunksize': 524288,\n",
       " 'workers': 24,\n",
       " 'walltime': 2.5585319629999503}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Q3Processor(processor.ProcessorABC):\n",
    "    def process(self, events):\n",
    "        return (\n",
    "            hist.Hist.new.Reg(100, 0, 200, name=\"ptj\", label=\"Jet $p_{T}$ [GeV]\")\n",
    "            .Double()\n",
    "            .fill(ak.flatten(events.Jet[abs(events.Jet.eta) < 1].pt))\n",
    "        )\n",
    "\n",
    "    def postprocess(self, accumulator):\n",
    "        return accumulator\n",
    "\n",
    "\n",
    "out, metrics = run(Q3Processor)\n",
    "out.plot1d()\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 4\n",
    "Plot the <i>E</i><sub>T</sub><sup>miss</sup> of events that have at least two jets with <i>p</i><sub>T</sub> > 40 GeV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bytesread': 1119573557,\n",
       " 'entries': 53446198,\n",
       " 'columns': {'Jet_pt', 'MET_pt', 'nJet'},\n",
       " 'processtime': 13.629399061203003,\n",
       " 'chunks': 102,\n",
       " 'query': 'Q4Processor',\n",
       " 'chunksize': 524288,\n",
       " 'workers': 24,\n",
       " 'walltime': 2.007429679000097}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAENCAYAAAARyyJwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAehUlEQVR4nO3df5BV5Z3n8fcn4BBmkjag6LaAA4lkJ40zi6EXtTKZzcYMEGsymBkdOrUbqQlbEIvUJrXZmuhkahEtquLsJNY6iQZcGdFKFMYkI6RkDdG4qVQRsM2i2K0unUAi6V4hNmPjbjSBfPeP81w83d4+9/aP+6O7P6+qU/fc7z3Puc89XO63z/M85zmKCMzMzIbzlkZXwMzMmpsThZmZFXKiMDOzQk4UZmZWyInCzMwKOVGYmVmh6Y2uwHg7//zzY8GCBY2uhpnZhPLUU0/9IiLmlHtt0iWKBQsW0NnZ2ehqmJlNKJJ+OtxrbnoyM7NCThRmZlbIicLMzAo5UZiZWSEnCjMzK1QxUUh6q6QDkp6W1CVpU4rfLOnnkg6m5epcmZsk9Uh6QdKKXHyppEPptTskKcVnSNqR4vslLciVWSPpcFrWjOunNzOziqoZHvs68MGIeFXSOcAPJO1Jr90eEX+X31hSG9ABLAYuAr4r6d0RcQa4C1gH/BB4BFgJ7AHWAicj4hJJHcBtwGpJs4GNQDsQwFOSdkXEybF9bDMzq1bFRBHZDSteTU/PSUvRTSxWAQ9GxOvAEUk9wDJJR4GWiNgHIOk+4BqyRLEKuDmVfwj4cjrbWAHsjYj+VGYvWXJ5oPqP2Jw27e6iu3fg7PO2i1rY+JHFDayRmVl5VV1wJ2ka8BRwCfCViNgv6cPApyRdD3QCn01/6c8lO2MoOZZiv07rQ+OkxxcBIuK0pFeA8/LxMmUmnHxy2H+kH4DLF86mu2+gqJiZWUNV1ZkdEWciYgkwj+zs4FKyZqR3AUuAPuCLaXOV20VBfLRlzpK0TlKnpM4TJ04UfJLG6u4dOJsULl84m7983wJ2rL+SttYWuvsGWL1lH6u37GPT7q4G19TM7A0jmsIjIv5Z0hPAynzfhKS7gW+np8eA+bli84DeFJ9XJp4vc0zSdOBcoD/FPzCkzBNl6rUV2ArQ3t7e1Pd2bWttYcf6KwfHLmo5u+6zCzNrNtWMepoj6R1pfSbwIeB5Sa25zT4KPJvWdwEdaSTTQmARcCAi+oBTkq5I/Q/XAw/nypRGNF0LPJ76Rh4FlkuaJWkWsDzFJpWNH1nMjvVXnj27MDNrJtWcUbQC21M/xVuAnRHxbUn3S1pC1hR0FFgPEBFdknYC3cBpYEMa8QRwA3AvMJOsE7s0euoe4P7U8d1PNmqKiOiXdCvwZNrullLHtpmZ1Uc1o56eAS4rE/94QZnNwOYy8U7g0jLx14DrhtnXNmBbpXo2q3wHdnffgM8YzGzC8ZXZNZbvwG5rbRnUH2FmNhFMuvtRNKNyHdhFSiOgwNdXmFnjOVE0GY+AMrNm40TRZPJnD6WzCjOzRnIfhZmZFXKiMDOzQk4UZmZWyInCzMwKOVGYmVkhJwozMyvk4bFNzhffmVmjOVHUwHjN7+SL78ysGThR1EBpfqe21pYxze/ki+/MrBk4UdTISOd3MjNrVu7MNjOzQk4UZmZWyInCzMwKOVGYmVkhJwozMyvkRGFmZoU8PHYC8VXaZtYIFc8oJL1V0gFJT0vqkrQpxWdL2ivpcHqclStzk6QeSS9IWpGLL5V0KL12hySl+AxJO1J8v6QFuTJr0nsclrRmXD/9BNJ2UcvZK7y7+wbOXvltZlZr1ZxRvA58MCJelXQO8ANJe4A/Ax6LiC9IuhG4EficpDagA1gMXAR8V9K7I+IMcBewDvgh8AiwEtgDrAVORsQlkjqA24DVkmYDG4F2IICnJO2KiJPjdgQmCF+lbWaNUvGMIjKvpqfnpCWAVcD2FN8OXJPWVwEPRsTrEXEE6AGWSWoFWiJiX0QEcN+QMqV9PQRclc42VgB7I6I/JYe9ZMnFzMzqpKrObEnTJB0EjpP9cO8HLoyIPoD0eEHafC7wYq74sRSbm9aHxgeViYjTwCvAeQX7Glq/dZI6JXWeOHGimo9kZmZVqipRRMSZiFgCzCM7O7i0YHOV20VBfLRl8vXbGhHtEdE+Z86cgqqZmdlIjWh4bET8M/AEWfPPS6k5ifR4PG12DJifKzYP6E3xeWXig8pImg6cC/QX7MvMzOqkmlFPcyS9I63PBD4EPA/sAkqjkNYAD6f1XUBHGsm0EFgEHEjNU6ckXZH6H64fUqa0r2uBx1M/xqPAckmz0qiq5SnWdDbt7mL1ln2s3rLP944ws0mlmlFPrcB2SdPIEsvOiPi2pH3ATklrgZ8B1wFERJeknUA3cBrYkEY8AdwA3AvMJBvttCfF7wHul9RDdibRkfbVL+lW4Mm03S0R0T+WD1wr43UPCjOzZlMxUUTEM8BlZeIvA1cNU2YzsLlMvBN4U/9GRLxGSjRlXtsGbKtUz2bge1CY2WTkKTzMzKyQp/CYoDydh5nVixPFBJTv/3DHuZnVmhPFBOTpPMysntxHYWZmhZwozMyskBOFmZkVcqIwM7NCThRmZlbIicLMzAo5UZiZWSEnCjMzK+REYWZmhZwozMyskKfwmAQ8QaCZ1ZITxQTnCQLNrNacKCY4TxBoZrXmPgozMyvkRGFmZoWcKMzMrFDFRCFpvqTvSXpOUpekT6f4zZJ+LulgWq7OlblJUo+kFyStyMWXSjqUXrtDklJ8hqQdKb5f0oJcmTWSDqdlzbh++jHatLuL1Vv2sXrLPnckm9mkVc0ZxWngsxHxHuAKYIOktvTa7RGxJC2PAKTXOoDFwErgTknT0vZ3AeuARWlZmeJrgZMRcQlwO3Bb2tdsYCNwObAM2Chp1lg+8Hjq7h04myDaWlsGjUAyM5ssKo56iog+oC+tn5L0HDC3oMgq4MGIeB04IqkHWCbpKNASEfsAJN0HXAPsSWVuTuUfAr6czjZWAHsjoj+V2UuWXB4Y2cesnbbWFnasv7LR1TAzq5kR9VGkJqHLgP0p9ClJz0jalvtLfy7wYq7YsRSbm9aHxgeViYjTwCvAeQX7MjOzOqk6UUh6G/AN4DMRMUDWjPQuYAnZGccXS5uWKR4F8dGWyddtnaROSZ0nTpwo+hhmZjZCVSUKSeeQJYmvRcQ3ASLipYg4ExG/Ae4m60OA7K/++bni84DeFJ9XJj6ojKTpwLlAf8G+BomIrRHRHhHtc+bMqeYjmZlZlaoZ9STgHuC5iPhSLt6a2+yjwLNpfRfQkUYyLSTrtD6Q+jpOSboi7fN64OFcmdKIpmuBxyMigEeB5ZJmpaat5SlmZmZ1Us0UHu8DPg4cknQwxf4a+JikJWRNQUeB9QAR0SVpJ9BNNmJqQ0ScSeVuAO4FZpJ1Yu9J8XuA+1PHdz/ZqCkiol/SrcCTabtbSh3bVp4nCDSz8VbNqKcfUL6v4JGCMpuBzWXincClZeKvAdcNs69twLZK9TRPEGhmteFJAScRTxBoZrXgKTzMzKyQE4WZmRVyojAzs0JOFGZmVsiJwszMCjlRmJlZIScKMzMr5ERhZmaFnCjMzKyQE4WZmRXyFB6TmCcINLPx4EQxSXmCQDMbL04Uk5QnCDSz8eI+CjMzK+REYWZmhZwozMyskBOFmZkVcmf2CG3a3UV3bzaKqLtvgLbWlgolzMwmNp9RjFB378DZ4aZtrS2DhqGamU1GPqMYhbbWFnasv7LR1TAzq4uKZxSS5kv6nqTnJHVJ+nSKz5a0V9Lh9DgrV+YmST2SXpC0IhdfKulQeu0OSUrxGZJ2pPh+SQtyZdak9zgsac24fnozM6uomqan08BnI+I9wBXABkltwI3AYxGxCHgsPSe91gEsBlYCd0qalvZ1F7AOWJSWlSm+FjgZEZcAtwO3pX3NBjYClwPLgI35hGRmZrVXMVFERF9E/CitnwKeA+YCq4DtabPtwDVpfRXwYES8HhFHgB5gmaRWoCUi9kVEAPcNKVPa10PAVelsYwWwNyL6I+IksJc3kouNQGnep9Vb9rFpd1ejq2NmE8iI+ihSk9BlwH7gwojogyyZSLogbTYX+GGu2LEU+3VaHxovlXkx7eu0pFeA8/LxMmXy9VpHdqbCxRdfPJKPNCV43iczG4uqE4WktwHfAD4TEQOpe6HspmViURAfbZk3AhFbga0A7e3tb3p9qvO8T2Y2FlUNj5V0DlmS+FpEfDOFX0rNSaTH4yl+DJifKz4P6E3xeWXig8pImg6cC/QX7MvMzOqkmlFPAu4BnouIL+Ve2gWURiGtAR7OxTvSSKaFZJ3WB1Iz1SlJV6R9Xj+kTGlf1wKPp36MR4HlkmalTuzlKWZmZnVSTdPT+4CPA4ckHUyxvwa+AOyUtBb4GXAdQER0SdoJdJONmNoQEWdSuRuAe4GZwJ60QJaI7pfUQ3Ym0ZH21S/pVuDJtN0tEdE/uo9qZmajUTFRRMQPKN9XAHDVMGU2A5vLxDuBS8vEXyMlmjKvbQO2VaqnmZnVhqfwMDOzQk4UZmZWyInCzMwKOVGYmVkhzx47BZWm8yhpu6hl0EV5ZmZ5ThRTzND7Z3hKDzOrxIliihl65uApPcysEvdRmJlZIScKMzMr5ERhZmaFnCjMzKyQE4WZmRVyojAzs0JOFGZmVsiJwszMCvmCOxs0pYen8zCzoZwoprj8lB6ezsPMynGimOLyZw+ezsPMynGiqMKm3V1092Z/bXf3DdDW2lKhhJnZ5OHO7Cp09w6cbZZpa2150wysZmaTWcVEIWmbpOOSns3Fbpb0c0kH03J17rWbJPVIekHSilx8qaRD6bU7JCnFZ0jakeL7JS3IlVkj6XBa1ozbpx6FttYWdqy/kh3rr3Rnr5lNKdWcUdwLrCwTvz0ilqTlEQBJbUAHsDiVuVPStLT9XcA6YFFaSvtcC5yMiEuA24Hb0r5mAxuBy4FlwEZJs0b8Cc3MbEwqJoqI+D7QX+X+VgEPRsTrEXEE6AGWSWoFWiJiX0QEcB9wTa7M9rT+EHBVOttYAeyNiP6IOAnspXzCMjOzGhpLH8WnJD2TmqZKf+nPBV7MbXMsxeam9aHxQWUi4jTwCnBewb7MzKyORpso7gLeBSwB+oAvprjKbBsF8dGWGUTSOkmdkjpPnDhRUG0zMxupUSWKiHgpIs5ExG+Au8n6ECD7q39+btN5QG+KzysTH1RG0nTgXLKmruH2Va4+WyOiPSLa58yZM5qPZEnpKu3VW/axaXdXo6tjZk1gVIki9TmUfBQojYjaBXSkkUwLyTqtD0REH3BK0hWp/+F64OFcmdKIpmuBx1M/xqPAckmzUtPW8hSzGmm7qOXsNSLdfQNnrx0xs6mt4gV3kh4APgCcL+kY2UikD0haQtYUdBRYDxARXZJ2At3AaWBDRJxJu7qBbATVTGBPWgDuAe6X1EN2JtGR9tUv6VbgybTdLRFRbae6jYKv0jazciomioj4WJnwPQXbbwY2l4l3ApeWib8GXDfMvrYB2yrV0czMasdXZpuZWSEnCjMzK+REYWZmhZwozMyskBOFmZkV8v0obFi+RaqZgROFDcO3SDWzEicKK8sX35lZifsozMyskBOFmZkVcqIwM7NCThRmZlbIicLMzAo5UZiZWSEPj7Wq+OI7s6nLicIq8sV3ZlObE4VV5IvvzKY291GYmVkhJwozMyvkRGFmZoWcKMzMrFDFRCFpm6Tjkp7NxWZL2ivpcHqclXvtJkk9kl6QtCIXXyrpUHrtDklK8RmSdqT4fkkLcmXWpPc4LGnNuH1qG5PSUNnVW/axaXdXo6tjZjVWzRnFvcDKIbEbgcciYhHwWHqOpDagA1icytwpaVoqcxewDliUltI+1wInI+IS4HbgtrSv2cBG4HJgGbAxn5CsMdouaqGtNRsu2903QHevh8uaTXYVh8dGxPfzf+Unq4APpPXtwBPA51L8wYh4HTgiqQdYJuko0BIR+wAk3QdcA+xJZW5O+3oI+HI621gB7I2I/lRmL1lyeWDkH3PkNu3uOvsj2N03cPbHcarzUFmzqWe0fRQXRkQfQHq8IMXnAi/mtjuWYnPT+tD4oDIRcRp4BTivYF9vImmdpE5JnSdOnBjlRxqsu3fg7MVlba0tgy46MzObSsb7gjuViUVBfLRlBgcjtgJbAdrb28tuMxptrS3sWH/leO3OzGxCGu0ZxUuSWgHS4/EUPwbMz203D+hN8Xll4oPKSJoOnAv0F+zLzMzqaLSJYhdQGoW0Bng4F+9II5kWknVaH0jNU6ckXZH6H64fUqa0r2uBxyMigEeB5ZJmpU7s5SlmZmZ1VLHpSdIDZB3X50s6RjYS6QvATklrgZ8B1wFERJeknUA3cBrYEBFn0q5uIBtBNZOsE3tPit8D3J86vvvJRk0REf2SbgWeTNvdUurYtubhWWXNJr9qRj19bJiXrhpm+83A5jLxTuDSMvHXSImmzGvbgG2V6miN4VllzaYGzx5ro+ahsmZTg6fwMDOzQk4UZmZWyInCzMwKuY/Cxo1HQJlNTk4UNi48Asps8nKisHHhEVBmk5f7KMzMrJAThZmZFXLTk9WEO7bNJg8nCht37tg2m1ycKGzcuWPbbHJxH4WZmRVyojAzs0JuerKac8e22cTmRGE15Y5ts4nPicJqyh3bZhOf+yjMzKyQzyisrtxfYTbxOFFY3bi/wmxiGlOikHQUOAWcAU5HRLuk2cAOYAFwFPiLiDiZtr8JWJu2/48R8WiKLwXuBWYCjwCfjoiQNAO4D1gKvAysjoijY6mzNY77K8wmpvHoo/i3EbEkItrT8xuBxyJiEfBYeo6kNqADWAysBO6UNC2VuQtYByxKy8oUXwucjIhLgNuB28ahvmZmNgK16MxeBWxP69uBa3LxByPi9Yg4AvQAyyS1Ai0RsS8iguwM4poy+3oIuEqSalBnMzMbxlj7KAL4jqQAtkTEVuDCiOgDiIg+SRekbecCP8yVPZZiv07rQ+OlMi+mfZ2W9ApwHvCLMdbbmoA7ts0mhrEmivdFRG9KBnslPV+wbbkzgSiIF5UZvGNpHVnTFRdffHFxja0puGPbbOIYU6KIiN70eFzSt4BlwEuSWtPZRCtwPG1+DJifKz4P6E3xeWXi+TLHJE0HzgX6y9RjK7AVoL29/U2JxJqPO7bNJo5R91FI+h1Jby+tA8uBZ4FdwJq02Rrg4bS+C+iQNEPSQrJO6wOpmeqUpCtS/8P1Q8qU9nUt8Hjqx7BJptQMtXrLPjbt7mp0dcwsZyxnFBcC30p9y9OBr0fE/5D0JLBT0lrgZ8B1ABHRJWkn0A2cBjZExJm0rxt4Y3jsnrQA3APcL6mH7EyiYwz1rWjT7i66e7NmkO6+AdpaWyqUsPHgZiiz5jbqRBERPwH+VZn4y8BVw5TZDGwuE+8ELi0Tf42UaOqhu3fgbIJoa20Z9ANmteNmKLPm5iuzh2hrbWHH+isbXY0pzaOhzJqLE4U1FTdDmTUfJwprKkOboXx2YdZ4ThTWtHx2YdYcnCisabmT26w5OFHYhOFmKLPGcKKwCSHfDLX/SD/7j/SfvebFScOstpwobELIJ4KhF0aaWW05UdiE45FRZvXlRGETmkdGmdWeE4VNaD67MKs9JwqbNNzhbVYbThQ2abjD26w2nChsUipqksrzmYZZZU4UNukNN1380Oap0rZOHGaDOVHYpDfcD3++eQrcr2E2HCcKm7KGJoF84nDSMHuDE4VZMlxneLkmqhInEJsKnCjMyhguaeQ5gdhU4URhVkG1fRwlRQkkz8nEJgonCrNRGmkCyas2meQ5sVijTIhEIWkl8N+AacB/j4gvNLhKZsOq5se8mmSSN5rEUg0nH6tG0ycKSdOArwB/DBwDnpS0KyK6G1szs9Eb6Y/zSBNLNWqVfBrJia82mj5RAMuAnoj4CYCkB4FVgBOFTRm1+PGrRfJppMmY+EZqx/ora7LfiZAo5gIv5p4fAy7PbyBpHbAuPX1V0gtjeL/zd36SX4yhfK2cD67XCLheIzNp6vXTGlVkiKY8Xjs/OaZ6/e5wL0yERKEysRj0JGIrsHVc3kzqjIj28djXeHK9Rsb1GhnXa2SmWr3eMt47rIFjwPzc83lAb4PqYmY25UyERPEksEjSQkm/BXQAuxpcJzOzKaPpm54i4rSkTwGPkg2P3RYRXTV8y3FpwqoB12tkXK+Rcb1GZkrVSxFReSszM5uyJkLTk5mZNZAThZmZFXKiSCStlPSCpB5JNzawHvMlfU/Sc5K6JH06xW+W9HNJB9NydQPqdlTSofT+nSk2W9JeSYfT46w61+lf5o7JQUkDkj7TiOMlaZuk45KezcWGPT6Sbkrftxckrahzvf6rpOclPSPpW5LekeILJP0yd9y+Wqt6FdRt2H+7Bh+zHbk6HZV0MMXrcswKfhtq/x2LiCm/kHWS/xh4J/BbwNNAW4Pq0gq8N62/HfjfQBtwM/CfG3ycjgLnD4n9LXBjWr8RuK3B/47/h+zCobofL+CPgPcCz1Y6Punf9GlgBrAwff+m1bFey4Hpaf22XL0W5Ldr0DEr+2/X6GM25PUvAv+lnses4Leh5t8xn1Fkzk4TEhG/AkrThNRdRPRFxI/S+ingObKr05vVKmB7Wt8OXNO4qnAV8OOIqNPFuYNFxPeB/iHh4Y7PKuDBiHg9Io4APWTfw7rUKyK+ExGn09Mfkl2fVHfDHLPhNPSYlUgS8BfAA7V474I6DffbUPPvmBNFptw0IQ3/cZa0ALgM2J9Cn0pNBdvq3cSTBPAdSU+laVMALoyIPsi+yMAFDahXSQeD//M2+njB8Menmb5znwD25J4vlPS/JP1PSe9vUJ3K/ds1yzF7P/BSRBzOxep6zIb8NtT8O+ZEkak4TUi9SXob8A3gMxExANwFvAtYAvSRnfrW2/si4r3Ah4ENkv6oAXUoK12M+afAP6ZQMxyvIk3xnZP0eeA08LUU6gMujojLgP8EfF1SS52rNdy/XVMcM+BjDP6DpK7HrMxvw7CblomN6ng5UWSaapoQSeeQfRG+FhHfBIiIlyLiTET8BribGp1yF4mI3vR4HPhWqsNLklpTvVuB4/WuV/Jh4EcR8VKqY8OPVzLc8Wn4d07SGuBPgH8XqVE7NVO8nNafImvXfnc961Xwb9cMx2w68GfAjlKsnses3G8DdfiOOVFkmmaakNT+eQ/wXER8KRdvzW32UeDZoWVrXK/fkfT20jpZZ+izZMdpTdpsDfBwPeuVM+ivvEYfr5zhjs8uoEPSDEkLgUXAgXpVStnNwD4H/GlE/L9cfI6ye8Ag6Z2pXj+pV73S+w73b9fQY5Z8CHg+Io6VAvU6ZsP9NlCP71ite+onygJcTTaK4MfA5xtYjz8kOz18BjiYlquB+4FDKb4LaK1zvd5JNoLiaaCrdIyA84DHgMPpcXYDjtlvAy8D5+ZidT9eZImqD/g12V9za4uOD/D59H17AfhwnevVQ9Z+XfqOfTVt++fp3/dp4EfARxpwzIb9t2vkMUvxe4FPDtm2Lses4Leh5t8xT+FhZmaF3PRkZmaFnCjMzKyQE4WZmRVyojAzs0JOFGZmVsiJwszMCjlRmI2SpFsbXQezenCiMAMkrZfUp8H3tvh9Sf8iTfT2V5K2p+0ekvSHwPR0j4C7Jf2dpA8NfT7kPc7etyAXu1DS1yX9JE22uE/SRyvU9Ymh9xZQdg+OOyXNTHX/laTzx/MY2dTlRGGW+QPgbyJiSW45RDZD5zcj4m+Bc8nmHvrHtH4Q+D3gV8AdEfHdMs+H+nFELIGzUzL8E/D9iHhnRCwlmz6m0pTfD6Tt8jqAByLil2n/DZurzCYfJwqzzO+T/fAPtQR4NE3G9nJkE9VdCrQDByNiL/D3wJclzR36vMJ7fhD4VUScvSNaRPw0Iv6+9FzSv5d0IJ0lbElzCj0E/ImkGWmbBcBFwA9G88HNKnGiMMssBv4h1+xUut/GJWRzgC0mu1EMZHc0uxg4LOk24D8APwOOD31exXv+aLgXJb0HWE02vfsS4AzZTK8vk03utjJt2gHsCM/HYzUyvdEVMGs0SfOB4xHxB0Nfi4i1afVgWoiIj+c2+dyQIkOfj6QeXyGb+O1XEfGvye7YtxR4MmulYiZvJJ9S89PD6fETo31fs0qcKMyy/onnG/C+XWQzjwIQERtSB3RnCgnYHhE3lSn7T8CXJL0XmBnpFplmteCmJ7Osf+JNiULSX0r6qqQj6XH9OL/v48BbJd2Qi/12bv0x4FpJF6T6zJb0uwAR8SrwBLCNOt+72aYen1GYZYni30j6cHoewPsj4h8kPQxMj4hPjvebRkRIuga4XdJfASeA/0tqvoqIbkl/Q3af8reQ3RthA/DTtIsHgG/y5hFQZuPK96MwKyDpj4FFEXHnOOxrAfDtiLh0zBWr/F5HgfaI+EWt38smPzc9mRVbCjw1Tvs6A5ybv+BuvJUuuAPOAX5Tq/exqcVnFGYFJD0AfCIiftnoupg1ihOFmZkVctOTmZkVcqIwM7NCThRmZlbIicLMzAo5UZiZWSEnCjMzK+REYWZmhZwozMyskBOFmZkV+v9OP8qB3y8ORQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Q4Processor(processor.ProcessorABC):\n",
    "    def process(self, events):\n",
    "        has2jets = ak.sum(events.Jet.pt > 40, axis=1) >= 2\n",
    "        return (\n",
    "            hist.Hist.new.Reg(100, 0, 200, name=\"met\", label=\"$E_{T}^{miss}$ [GeV]\")\n",
    "            .Double()\n",
    "            .fill(events[has2jets].MET.pt)\n",
    "        )\n",
    "\n",
    "    def postprocess(self, accumulator):\n",
    "        return accumulator\n",
    "\n",
    "\n",
    "out, metrics = run(Q4Processor)\n",
    "out.plot1d()\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 5\n",
    "Plot the <i>E</i><sub>T</sub><sup>miss</sup> of events that have an\n",
    "opposite-charge muon pair with an invariant mass between 60 and 120 GeV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bytesread': 1687594871,\n",
       " 'entries': 53446198,\n",
       " 'columns': {'MET_pt',\n",
       "  'Muon_charge',\n",
       "  'Muon_eta',\n",
       "  'Muon_mass',\n",
       "  'Muon_phi',\n",
       "  'Muon_pt',\n",
       "  'nMuon'},\n",
       " 'processtime': 39.213595390319824,\n",
       " 'chunks': 102,\n",
       " 'query': 'Q5Processor',\n",
       " 'chunksize': 524288,\n",
       " 'workers': 24,\n",
       " 'walltime': 3.3579888279998613}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Q5Processor(processor.ProcessorABC):\n",
    "    def process(self, events):\n",
    "        mupair = ak.combinations(events.Muon, 2)\n",
    "        with np.errstate(invalid=\"ignore\"):\n",
    "            pairmass = (mupair.slot0 + mupair.slot1).mass\n",
    "        goodevent = ak.any(\n",
    "            (pairmass > 60)\n",
    "            & (pairmass < 120)\n",
    "            & (mupair.slot0.charge == -mupair.slot1.charge),\n",
    "            axis=1,\n",
    "        )\n",
    "        return (\n",
    "            hist.Hist.new.Reg(100, 0, 200, name=\"met\", label=\"$E_{T}^{miss}$ [GeV]\")\n",
    "            .Double()\n",
    "            .fill(events[goodevent].MET.pt)\n",
    "        )\n",
    "\n",
    "    def postprocess(self, accumulator):\n",
    "        return accumulator\n",
    "\n",
    "\n",
    "out, metrics = run(Q5Processor)\n",
    "out.plot1d()\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 6\n",
    "For events with at least three jets, plot the <i>p</i><sub>T</sub> of the trijet four-momentum that has the invariant mass closest to 172.5 GeV in each event and plot the maximum <i>b</i>-tagging discriminant value among the jets in this trijet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bytesread': 3660655077,\n",
       " 'entries': 53446198,\n",
       " 'columns': {'Jet_btag', 'Jet_eta', 'Jet_mass', 'Jet_phi', 'Jet_pt', 'nJet'},\n",
       " 'processtime': 247.16478276252747,\n",
       " 'chunks': 102,\n",
       " 'query': 'Q6Processor',\n",
       " 'chunksize': 524288,\n",
       " 'workers': 24,\n",
       " 'walltime': 13.536233111000001}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Q6Processor(processor.ProcessorABC):\n",
    "    def process(self, events):\n",
    "        # this line converts the coordinate system from (pt,eta,phi,mass) to (px,py,pz,E)\n",
    "        # which significantly speeds up later steps\n",
    "        jets = ak.zip(\n",
    "            {k: getattr(events.Jet, k) for k in [\"x\", \"y\", \"z\", \"t\", \"btag\"]},\n",
    "            with_name=\"LorentzVector\",\n",
    "            behavior=events.Jet.behavior,\n",
    "        )\n",
    "        trijet = ak.combinations(jets, 3, fields=[\"j1\", \"j2\", \"j3\"])\n",
    "        trijet[\"p4\"] = trijet.j1 + trijet.j2 + trijet.j3\n",
    "        trijet = ak.flatten(\n",
    "            trijet[ak.singletons(ak.argmin(abs(trijet.p4.mass - 172.5), axis=1))]\n",
    "        )\n",
    "        maxBtag = np.maximum(\n",
    "            trijet.j1.btag,\n",
    "            np.maximum(\n",
    "                trijet.j2.btag,\n",
    "                trijet.j3.btag,\n",
    "            ),\n",
    "        )\n",
    "        return {\n",
    "            \"trijetpt\": hist.Hist.new.Reg(\n",
    "                100, 0, 200, name=\"pt3j\", label=\"Trijet $p_{T}$ [GeV]\"\n",
    "            )\n",
    "            .Double()\n",
    "            .fill(trijet.p4.pt),\n",
    "            \"maxbtag\": hist.Hist.new.Reg(\n",
    "                100, 0, 1, name=\"btag\", label=\"Max jet b-tag score\"\n",
    "            )\n",
    "            .Double()\n",
    "            .fill(maxBtag),\n",
    "        }\n",
    "\n",
    "    def postprocess(self, accumulator):\n",
    "        return accumulator\n",
    "\n",
    "\n",
    "out, metrics = run(Q6Processor)\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharey=True)\n",
    "out[\"trijetpt\"].plot1d(ax=ax1)\n",
    "out[\"maxbtag\"].plot1d(ax=ax2)\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 7\n",
    "Plot the scalar sum in each event of the <i>p</i><sub>T</sub> of jets with <i>p</i><sub>T</sub> > 30 GeV that are not within 0.4 in Δ<i>R</i> of any light lepton with <i>p</i><sub>T</sub> > 10 GeV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bytesread': 3890192921,\n",
       " 'entries': 53446198,\n",
       " 'columns': {'Electron_eta',\n",
       "  'Electron_phi',\n",
       "  'Electron_pt',\n",
       "  'Jet_eta',\n",
       "  'Jet_phi',\n",
       "  'Jet_pt',\n",
       "  'Muon_eta',\n",
       "  'Muon_phi',\n",
       "  'Muon_pt',\n",
       "  'nElectron',\n",
       "  'nJet',\n",
       "  'nMuon'},\n",
       " 'processtime': 78.45437359809875,\n",
       " 'chunks': 102,\n",
       " 'query': 'Q7Processor',\n",
       " 'chunksize': 524288,\n",
       " 'workers': 24,\n",
       " 'walltime': 5.186383785000089}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEZCAYAAACHCd7XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVxElEQVR4nO3dfbBkdX3n8fdnYXxaJejOTRwBGbRYs2iKB6dA1o3LqskCwUwezDqUqy66wXFxI5XsbkzcwvBfEiumohhHXImyRQATDUELNlKJBEjJwzA7gzODxPFpnWVWrrAyzuLqgt/9o89gc+m+3Xemb/e9P96vqq4+53d+ffpbv+753DOnz0OqCknS6vcPZl2AJGkyDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEbMNNCTXJHkgSQ7x+j7h0m2d4+/T/KdKZQoSatGZnkcepJXAQeAK6vqZUt43b8HTq2qty5bcZK0ysx0C72qbgEe6m9L8uIk/y3J3UluTfKTA156PnD1VIqUpFXiyFkXMMDlwOaq+nKSM4A/Bl59cGGS44ETgL+ZUX2StCKtqEBP8mzgnwJ/luRg89MXdNsE/HlVPTbN2iRppVtRgU5vF9B3quqURfpsAi6aTjmStHqsqMMWq2o/8LUkvwKQnpMPLk/yEuC5wBdmVKIkrVizPmzxanrh/JIke5O8DXgj8LYkO4BdwMa+l5wPXFNeIlKSnmSmhy1KkiZnRe1ykSQdupn9KLp27dpav379rN5eklalu++++9tVNTdo2cwCff369WzdunVWby9Jq1KSbwxb5i4XSWqEgS5JjTDQJakRIwM9yTOS3JlkR5JdSS4d0OesJA/3Xd72kuUpV5I0zDg/in4feHVVHUiyBrgtyY1VdfuCfrdW1XmTL1GSNI6Rgd6dlXmgm13TPTwbSZJWmLH2oSc5Isl24AHgpqq6Y0C3M7vdMjcmeemQ9VyYZGuSrfPz84detSTpScYK9Kp6rLsC4rHA6UkW3l1oG3B8VZ0MfBC4bsh6Lq+qDVW1YW5u4HHxkqRDtKQTi6rqO0luBs4Gdva17++bviHJHydZW1XfnlilnUs/s4vd9z/+dpz0gqN47+sG/odAkp5SxjnKZS7J0d30M4HXAl9a0Of56e5IkeT0br0PTrxaYPf9+9m9rxfou/ftf0K4S9JT2Ti7XNYBn09yD3AXvX3on02yOcnmrs/rgZ3dJW8/AGxazkvcnrTuKK59+5mctO6o5XoLSVp1xjnK5R7g1AHtW/qmLwMum2xpkqSl8ExRSWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEaMDPQkz0hyZ5IdSXYluXRAnyT5QJI9Se5JctrylCtJGubIMfp8H3h1VR1Isga4LcmNVXV7X59zgBO7xxnAh7tnSdKUjNxCr54D3eya7lELum0Eruz63g4cnWTdZEuVJC1mrH3oSY5Ish14ALipqu5Y0OUY4Jt983u7toXruTDJ1iRb5+fnD7FkSdIgYwV6VT1WVacAxwKnJ3nZgi4Z9LIB67m8qjZU1Ya5ubklFytJGm5JR7lU1XeAm4GzFyzaCxzXN38scP/hFCZJWppxjnKZS3J0N/1M4LXAlxZ0ux54c3e0yyuAh6tq36SLlSQNN85RLuuATyQ5gt4fgE9W1WeTbAaoqi3ADcC5wB7gEeCCZapXkjTEyECvqnuAUwe0b+mbLuCiyZYmSVoKzxSVpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJasTIQE9yXJLPJ7k3ya4k7xrQ56wkDyfZ3j0uWZ5yJUnDHDlGn0eB36iqbUmeA9yd5Kaq2r2g361Vdd7kS5QkjWPkFnpV7auqbd30d4F7gWOWuzBJ0tIsaR96kvXAqcAdAxafmWRHkhuTvHTI6y9MsjXJ1vn5+aVXK0kaauxAT/Js4FPAxVW1f8HibcDxVXUy8EHgukHrqKrLq2pDVW2Ym5s7xJIlSYOMFehJ1tAL86uq6tMLl1fV/qo60E3fAKxJsnailUqSFjXOUS4BPgbcW1XvH9Ln+V0/kpzerffBSRYqSVrcOEe5vBJ4E/DFJNu7tt8GXghQVVuA1wPvSPIo8D1gU1XV5MuVJA0zMtCr6jYgI/pcBlw2qaIkSUvnmaKS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjRgZ6EmOS/L5JPcm2ZXkXQP6JMkHkuxJck+S05anXEnSMEeO0edR4DeqaluS5wB3J7mpqnb39TkHOLF7nAF8uHuWJE3JyC30qtpXVdu66e8C9wLHLOi2Ebiyem4Hjk6ybuLVSpKGWtI+9CTrgVOBOxYsOgb4Zt/8Xp4c+iS5MMnWJFvn5+eXWKokaTFjB3qSZwOfAi6uqv0LFw94ST2poeryqtpQVRvm5uaWVqkkaVFjBXqSNfTC/Kqq+vSALnuB4/rmjwXuP/zyJEnjGucolwAfA+6tqvcP6XY98ObuaJdXAA9X1b4J1ilJGmGco1xeCbwJ+GKS7V3bbwMvBKiqLcANwLnAHuAR4IKJVypJWtTIQK+q2xi8j7y/TwEXTaooSdLSeaaoJDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiNGBnqSK5I8kGTnkOVnJXk4yfbuccnky5QkjXLkGH0+DlwGXLlIn1ur6ryJVCRJOiQjt9Cr6hbgoSnUIkk6DJPah35mkh1Jbkzy0mGdklyYZGuSrfPz8xN6a0kSTCbQtwHHV9XJwAeB64Z1rKrLq2pDVW2Ym5ubwFtLkg467ECvqv1VdaCbvgFYk2TtYVcmSVqSww70JM9Pkm769G6dDx7ueiVJSzPyKJckVwNnAWuT7AXeC6wBqKotwOuBdyR5FPgesKmqatkqliQNNDLQq+r8Ecsvo3dYoyRphjxTVJIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRIwM9yRVJHkiyc8jyJPlAkj1J7kly2uTLlCSNMs4W+seBsxdZfg5wYve4EPjw4ZclSVqqkYFeVbcADy3SZSNwZfXcDhydZN2kCpQkjWcS+9CPAb7ZN7+3a3uSJBcm2Zpk6/z8/ATeWpJ00CQCPQPaalDHqrq8qjZU1Ya5ubkJvLUk6aBJBPpe4Li++WOB+yewXknSEkwi0K8H3twd7fIK4OGq2jeB9UqSluDIUR2SXA2cBaxNshd4L7AGoKq2ADcA5wJ7gEeAC5arWEnScCMDvarOH7G8gIsmVpEk6ZB4pqgkNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSI8YK9CRnJ7kvyZ4k7x6w/KwkDyfZ3j0umXypkqTFHDmqQ5IjgA8BPwPsBe5Kcn1V7V7Q9daqOm8ZapQkjWGcLfTTgT1V9dWq+gFwDbBxecuSJC3VOIF+DPDNvvm9XdtCZybZkeTGJC8dtKIkFybZmmTr/Pz8IZQrSRpmnEDPgLZaML8NOL6qTgY+CFw3aEVVdXlVbaiqDXNzc0sqVJK0uHECfS9wXN/8scD9/R2qan9VHeimbwDWJFk7sSolSSONE+h3AScmOSHJ04BNwPX9HZI8P0m66dO79T446WIlScONPMqlqh5N8k7gr4AjgCuqaleSzd3yLcDrgXckeRT4HrCpqhbulpEkLaORgQ6P70a5YUHblr7py4DLJluaJGkpPFNUkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDVirMvn6vBd+pld7L5//+PzJ73gKN77uoG3XpWkQ2KgT8nu+/eze99+Tlp3FLv37R/9AklaIne5TNFJ647i2ref+Xiov+EjX+ANH/kCl35m16xLk9QAt9Bn4KQXHPX49B1fe4g7vvbQE3bH9Pdzt4ykcRnoM9Af0gv3rR+0MOgNd0mjGOgzNiyk+4N+sa34foa+9NRmoK9Q42zF9xs39MHgl1o1VqAnORv4I+AI4L9U1e8uWJ5u+bnAI8C/qaptE671KWuc8B0n9GFpwb9S+QdJGmxkoCc5AvgQ8DPAXuCuJNdX1e6+bucAJ3aPM4APd8+aknEDbtzgX6la+IMk/ygvl3G20E8H9lTVVwGSXANsBPoDfSNwZVUVcHuSo5Osq6p9E694gYOH/610B49Bn7XV/o9otf9Bkn+UAa59+5nLst5xAv0Y4Jt983t58tb3oD7HAE8I9CQXAhd2sweS3Lekan9k7Sc38+2DMzsPcSXLYC38qK6FdgKf3Dy9YhZYtLYZsq6laaaubyxTIQusyPH65ObDquv4YQvGCfQMaKtD6ENVXQ5cPsZ7Ll5QsrWqNhzueiZtpdYFK7c261oa61qap1pd45wpuhc4rm/+WOD+Q+gjSVpG4wT6XcCJSU5I8jRgE3D9gj7XA29OzyuAh6ex/1yS9CMjd7lU1aNJ3gn8Fb3DFq+oql1JNnfLtwA30DtkcQ+9wxYvWL6SgQnstlkmK7UuWLm1WdfSWNfSPKXqSu/AFEnSaufVFiWpEQa6JDVi1QV6krOT3JdkT5J3z7CO45J8Psm9SXYleVfX/jtJ/meS7d3j3BnU9vUkX+zef2vX9rwkNyX5cvf83CnX9JK+MdmeZH+Si2cxXkmuSPJAkp19bUPHJ8lvdd+3+5L8yynX9b4kX0pyT5K/SHJ0174+yff6xm3LlOsa+rnNeLyu7avp60m2d+3THK9h2bD837GqWjUPej/KfgV4EfA0YAdw0oxqWQec1k0/B/h74CTgd4D/MONx+jqwdkHb7wPv7qbfDfzejD/H/0XvBImpjxfwKuA0YOeo8ek+0x3A04ETuu/fEVOs62eBI7vp3+ura31/vxmM18DPbdbjtWD5HwCXzGC8hmXDsn/HVtsW+uOXIaiqHwAHL0MwdVW1r7oLkFXVd4F76Z0du1JtBD7RTX8C+IXZlcJrgK9U1ZROFnyiqroFeGhB87Dx2QhcU1Xfr6qv0TuS6/Rp1VVVn6uqR7vZ2+md4zFVQ8ZrmJmO10HdBQP/FXD1crz3YhbJhmX/jq22QB92iYGZSrIeOBW4o2t6Z/df5CumvWujU8DnktzdXW4B4CeqOzege/7xGdR10Cae+A9t1uMFw8dnJX3n3grc2Dd/QpL/nuRvk/z0DOoZ9LmtlPH6aeBbVfXlvrapj9eCbFj279hqC/SxLjEwTUmeDXwKuLiq9tO70uSLgVPoXcvmD2ZQ1iur6jR6V8G8KMmrZlDDQOmdnPbzwJ91TSthvBazIr5zSd4DPApc1TXtA15YVacCvw78aZJpXv1t2Oe2IsYLOJ8nbjRMfbwGZMPQrgPaDmnMVlugr6hLDCRZQ+8Du6qqPg1QVd+qqseq6ofAR1mm/24upqru754fAP6iq+FbSdZ1da8DHph2XZ1zgG1V9a2uxpmPV2fY+Mz8O5fkLcB5wBur2+na/ff8wW76bnr7Xf/xtGpa5HNbCeN1JPBLwLUH26Y9XoOygSl8x1ZboI9zGYKp6PbRfQy4t6re39e+rq/bLzLli0Em+YdJnnNwmt6PajvpjdNbum5vAf5ymnX1ecKW06zHq8+w8bke2JTk6UlOoHfN/zunVVR6N5f5TeDnq+qRvva59O5VQJIXdXV9dYp1DfvcZjpendcCX6qqvQcbpjlew7KBaXzHpvGr74R/QT6X3q/GXwHeM8M6/hm9/xbdA2zvHucC/xX4Ytd+PbBuynW9iN4v5juAXQfHCPhHwF8DX+6enzeDMXsW8CDwY31tUx8ven9Q9gH/j97W0dsWGx/gPd337T7gnCnXtYfe/tWD37EtXd9f7j7fHcA24HVTrmvo5zbL8eraPw5sXtB3muM1LBuW/Tvmqf+S1IjVtstFkjSEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaMfKeotJq0l0/4xkjuj1YnoChBnlikZqS5KPAvx3Rba6qvj2NeqRpMtDVlCSnAX8LfIPe6daPDOjmFrqa5D50rThJDoxYfnSSfzdoWfVuLPBL9K6k9yHgf1fVtxc8DjnM+29l1tf2E0n+NMlXu2vQfyHJL45Yz80LbzWW3i35/qS7RdoPkqw91Dr11GSgazU6GhgY6ABVdRNwAfBz9K7bPWlfqapT4PEr610H3FJVL6qql9O7CuioOwtd3fXrtwm4olv3zC4LrdXLQNeKluRfJ7mz22r9SHcJ1N8FXty1vW/Q66rqKnqXnf3VJJcMWfc13U2F70jyjSQ/dwglvhr4QVU9ftPhqvpGVX1wkfoB/hw4L8nTu37rgRcAtx1CDRJgoGsFS/JPgDfQuwPTKcBjwBvp3WD3K1V1SlX9x2Gvr6r3AX8IXJrkrQO6nAx8tarO6Nb73kMo86X0Lse6lPqp3s0W7gTO7rpvAq51374Oh4Gulew1wMuBu7p91q+hd733pfh9ej+M/nqSx7/vSZ4JrAUu7Zp2A89NckGSLUm+1j2/fSlvluRDSXYkuWuM+vt3uyy8z6q0ZB6HrpUswCeq6ree0NjbPTH6xcmz6N184f/Qu+PPD/sWvwz4clX9327+NGBHVf1Jkr8EjqyqzWO8zS56N08AoKou6n7M3Dqs/j7XAe/vjsx5ZveDrnTI3ELXSvbXwOuT/DhAkuclOR74LvCcxV7YbY1fBfwUvTBfeLuxk4EXJnlGd6u+S+ntnoHeVvW44fo3wDOSvKOv7Vkj6gegqg4ANwNX4Na5JsBA14rS3eD3+wBVtRv4z8DnktwD3ETvVmcPAn+XZOewH0XphfNG4E1VdfuA5SfTC/yb6d2r9sNV9XfdspcDd49Tb7fP+xeAf97tprkT+ATwm8PqX7CKq7tarhnn/aTFeGKRVpQkJwMfrarTR3Yevo5fA/4I+E/dD6OD+twC/GpV3Tdg2dXAW6vqewOWrQc+W1UvO9T6xpHk68AGz2jVUrgPXStGks3ArwEXH8Y6NtLbOr8R+EySnxzQ7X8AL6Z3s94nqarzF3mLx4AfS7L94LHok9T9WPsFYA3wwxHdpSdwC11N6XZt/NSIbv+iqm6eQjnSVBnoktQIfxSVpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakR/x+i2Nt1INV2rwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Q7Processor(processor.ProcessorABC):\n",
    "    def process(self, events):\n",
    "        cleanjets = events.Jet[\n",
    "            ak.all(\n",
    "                events.Jet.metric_table(events.Muon[events.Muon.pt > 10]) >= 0.4, axis=2\n",
    "            )\n",
    "            & ak.all(\n",
    "                events.Jet.metric_table(events.Electron[events.Electron.pt > 10]) >= 0.4,\n",
    "                axis=2,\n",
    "            )\n",
    "            & (events.Jet.pt > 30)\n",
    "        ]\n",
    "        return (\n",
    "            hist.Hist.new.Reg(\n",
    "                100, 0, 200, name=\"sumjetpt\", label=\"Jet $\\sum p_{T}$ [GeV]\"\n",
    "            )\n",
    "            .Double()\n",
    "            .fill(ak.sum(cleanjets.pt, axis=1))\n",
    "        )\n",
    "\n",
    "    def postprocess(self, accumulator):\n",
    "        return accumulator\n",
    "\n",
    "\n",
    "out, metrics = run(Q7Processor)\n",
    "out.plot1d()\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 8\n",
    "For events with at least three light leptons and a same-flavor opposite-charge light lepton pair, find such a pair that has the invariant mass closest to 91.2 GeV in each event and plot the transverse mass of the system consisting of the missing tranverse momentum and the highest-<i>p</i><sub>T</sub> light lepton not in this pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bytesread': 2280546738,\n",
       " 'entries': 53446198,\n",
       " 'columns': {'Electron_charge',\n",
       "  'Electron_eta',\n",
       "  'Electron_mass',\n",
       "  'Electron_phi',\n",
       "  'Electron_pt',\n",
       "  'MET_phi',\n",
       "  'MET_pt',\n",
       "  'Muon_charge',\n",
       "  'Muon_eta',\n",
       "  'Muon_mass',\n",
       "  'Muon_phi',\n",
       "  'Muon_pt',\n",
       "  'nElectron',\n",
       "  'nMuon'},\n",
       " 'processtime': 61.07357311248779,\n",
       " 'chunks': 102,\n",
       " 'query': 'Q8Processor',\n",
       " 'chunksize': 524288,\n",
       " 'workers': 24,\n",
       " 'walltime': 4.237492516999964}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Q8Processor(processor.ProcessorABC):\n",
    "    def process(self, events):\n",
    "        events[\"Electron\", \"pdgId\"] = -11 * events.Electron.charge\n",
    "        events[\"Muon\", \"pdgId\"] = -13 * events.Muon.charge\n",
    "        events[\"leptons\"] = ak.concatenate(\n",
    "            [events.Electron, events.Muon],\n",
    "            axis=1,\n",
    "        )\n",
    "        events = events[ak.num(events.leptons) >= 3]\n",
    "\n",
    "        pair = ak.argcombinations(events.leptons, 2, fields=[\"l1\", \"l2\"])\n",
    "        pair = pair[(events.leptons[pair.l1].pdgId == -events.leptons[pair.l2].pdgId)]\n",
    "        with np.errstate(invalid=\"ignore\"):\n",
    "            pair = pair[\n",
    "                ak.singletons(\n",
    "                    ak.argmin(\n",
    "                        abs(\n",
    "                            (events.leptons[pair.l1] + events.leptons[pair.l2]).mass\n",
    "                            - 91.2\n",
    "                        ),\n",
    "                        axis=1,\n",
    "                    )\n",
    "                )\n",
    "            ]\n",
    "        events = events[ak.num(pair) > 0]\n",
    "        pair = pair[ak.num(pair) > 0][:, 0]\n",
    "\n",
    "        l3 = ak.local_index(events.leptons)\n",
    "        l3 = l3[(l3 != pair.l1) & (l3 != pair.l2)]\n",
    "        l3 = l3[ak.argmax(events.leptons[l3].pt, axis=1, keepdims=True)]\n",
    "        l3 = events.leptons[l3][:, 0]\n",
    "\n",
    "        mt = np.sqrt(2 * l3.pt * events.MET.pt * (1 - np.cos(events.MET.delta_phi(l3))))\n",
    "        return (\n",
    "            hist.Hist.new.Reg(\n",
    "                100, 0, 200, name=\"mt\", label=\"$\\ell$-MET transverse mass [GeV]\"\n",
    "            )\n",
    "            .Double()\n",
    "            .fill(mt)\n",
    "        )\n",
    "\n",
    "    def postprocess(self, accumulator):\n",
    "        return accumulator\n",
    "\n",
    "\n",
    "out, metrics = run(Q8Processor)\n",
    "out.plot1d()\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
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