coffea-adl-benchmarks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lgray/coffea-dev/coffea/src/coffea/nanoevents/factory.py:296: RuntimeWarning: You have set steps_per_file to 500, this should only be used for a\n",
      "                small number of inputs (e.g. for early-stage/exploratory analysis) since it does not\n",
      "                inform dask of each chunk lengths at creation time, which can cause unexpected\n",
      "                slowdowns at scale. If you would like to process larger datasets please specify steps\n",
      "                using the appropriate uproot \"files\" specification:\n",
      "                    https://github.com/scikit-hep/uproot5/blob/v5.1.2/src/uproot/_dask.py#L109-L132.\n",
      "                \n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "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.nanoevents import NanoEventsFactory, NanoAODSchema\n",
    "from coffea import processor\n",
    "\n",
    "import dask\n",
    "import dask_awkward as dak\n",
    "import hist.dask as hda\n",
    "\n",
    "from distributed import Client\n",
    "client=Client()\n",
    "\n",
    "# The opendata files are non-standard NanoAOD, so some optional data columns are missing\n",
    "NanoAODSchema.warn_missing_crossrefs = False\n",
    "\n",
    "# The warning emitted below indicates steps_per_file is for initial data exploration\n",
    "# and test. When running at scale there are better ways to specify processing chunks\n",
    "# of files.\n",
    "events = NanoEventsFactory.from_root(\n",
    "    \"file:/Users/lgray/coffea-dev/coffea/Run2012B_SingleMu.root:Events\",\n",
    "    steps_per_file=500,\n",
    "    metadata={\"dataset\": \"SingleMu\"}\n",
    ").events()"
   ]
  },
  {
   "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": [
       "{'from-uproot-d895a5d3a21bc4fd7e5f7c35639408ec': frozenset({'MET_pt'})}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q1_hist = (\n",
    "    hda.Hist.new.Reg(100, 0, 200, name=\"met\", label=\"$E_{T}^{miss}$ [GeV]\")\n",
    "    .Double()\n",
    "    .fill(events.MET.pt)\n",
    ")\n",
    "\n",
    "q1_hist.compute().plot1d(flow=\"none\")\n",
    "dak.necessary_columns(q1_hist)"
   ]
  },
  {
   "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": [
       "{'from-uproot-d895a5d3a21bc4fd7e5f7c35639408ec': frozenset({'Jet_pt', 'nJet'})}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q2_hist = (\n",
    "    hda.Hist.new.Reg(100, 0, 200, name=\"ptj\", label=\"Jet $p_{T}$ [GeV]\")\n",
    "    .Double()\n",
    "    .fill(dak.flatten(events.Jet.pt))\n",
    ")\n",
    "\n",
    "\n",
    "q2_hist.compute().plot1d(flow=\"none\")\n",
    "dak.necessary_columns(q2_hist)"
   ]
  },
  {
   "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": [
       "{'from-uproot-d895a5d3a21bc4fd7e5f7c35639408ec': frozenset({'Jet_eta',\n",
       "            'Jet_pt',\n",
       "            'nJet'})}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q3_hist = (\n",
    "    hda.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",
    "q3_hist.compute().plot1d(flow=\"none\")\n",
    "dak.necessary_columns(q3_hist)"
   ]
  },
  {
   "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": [
       "{'from-uproot-d895a5d3a21bc4fd7e5f7c35639408ec': frozenset({'Jet_pt',\n",
       "            'MET_pt',\n",
       "            'nJet'})}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "has2jets = ak.sum(events.Jet.pt > 40, axis=1) >= 2\n",
    "q4_hist = (\n",
    "    hda.Hist.new.Reg(100, 0, 200, name=\"met\", label=\"$E_{T}^{miss}$ [GeV]\")\n",
    "    .Double()\n",
    "    .fill(events[has2jets].MET.pt)\n",
    ")\n",
    "\n",
    "q4_hist.compute().plot1d(flow=\"none\")\n",
    "dak.necessary_columns(q4_hist)"
   ]
  },
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lgray/coffea-dev/awkward/src/awkward/_nplikes/array_module.py:245: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return impl(*broadcasted_args, **(kwargs or {}))\n",
      "2023-12-21 08:20:40,120 - distributed.utils_perf - WARNING - full garbage collections took 15% CPU time recently (threshold: 10%)\n",
      "/Users/lgray/coffea-dev/awkward/src/awkward/_nplikes/array_module.py:245: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return impl(*broadcasted_args, **(kwargs or {}))\n",
      "2023-12-21 08:20:41,035 - distributed.utils_perf - WARNING - full garbage collections took 15% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:41,425 - distributed.utils_perf - WARNING - full garbage collections took 15% CPU time recently (threshold: 10%)\n",
      "/Users/lgray/coffea-dev/awkward/src/awkward/_nplikes/array_module.py:245: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return impl(*broadcasted_args, **(kwargs or {}))\n",
      "2023-12-21 08:20:42,097 - distributed.utils_perf - WARNING - full garbage collections took 14% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:42,345 - distributed.utils_perf - WARNING - full garbage collections took 15% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:42,810 - distributed.utils_perf - WARNING - full garbage collections took 14% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:43,473 - distributed.utils_perf - WARNING - full garbage collections took 13% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:43,710 - distributed.utils_perf - WARNING - full garbage collections took 14% CPU time recently (threshold: 10%)\n",
      "/Users/lgray/coffea-dev/awkward/src/awkward/_nplikes/array_module.py:245: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return impl(*broadcasted_args, **(kwargs or {}))\n",
      "2023-12-21 08:20:43,997 - distributed.utils_perf - WARNING - full garbage collections took 13% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:44,172 - distributed.utils_perf - WARNING - full garbage collections took 14% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:44,761 - distributed.utils_perf - WARNING - full garbage collections took 13% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:45,122 - distributed.utils_perf - WARNING - full garbage collections took 13% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:45,365 - distributed.utils_perf - WARNING - full garbage collections took 13% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:45,583 - distributed.utils_perf - WARNING - full garbage collections took 13% CPU time recently (threshold: 10%)\n",
      "/Users/lgray/coffea-dev/awkward/src/awkward/_nplikes/array_module.py:245: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return impl(*broadcasted_args, **(kwargs or {}))\n",
      "2023-12-21 08:20:46,035 - distributed.utils_perf - WARNING - full garbage collections took 12% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:46,094 - distributed.utils_perf - WARNING - full garbage collections took 13% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:46,487 - distributed.utils_perf - WARNING - full garbage collections took 13% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:46,717 - distributed.utils_perf - WARNING - full garbage collections took 13% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:46,963 - distributed.utils_perf - WARNING - full garbage collections took 13% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:47,349 - distributed.utils_perf - WARNING - full garbage collections took 12% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:47,487 - distributed.utils_perf - WARNING - full garbage collections took 13% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:47,890 - distributed.utils_perf - WARNING - full garbage collections took 12% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:48,117 - distributed.utils_perf - WARNING - full garbage collections took 12% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:48,255 - distributed.utils_perf - WARNING - full garbage collections took 12% CPU time recently (threshold: 10%)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'from-uproot-d895a5d3a21bc4fd7e5f7c35639408ec': frozenset({'MET_pt',\n",
       "            'Muon_charge',\n",
       "            'Muon_eta',\n",
       "            'Muon_mass',\n",
       "            'Muon_phi',\n",
       "            'Muon_pt',\n",
       "            'nMuon'})}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mupair = ak.combinations(events.Muon, 2, fields=[\"mu1\", \"mu2\"])\n",
    "pairmass = (mupair.mu1 + mupair.mu2).mass\n",
    "goodevent = ak.any(\n",
    "    (pairmass > 60)\n",
    "    & (pairmass < 120)\n",
    "    & (mupair.mu1.charge == -mupair.mu2.charge),\n",
    "    axis=1,\n",
    ")\n",
    "q5_hist = (\n",
    "    hda.Hist.new.Reg(100, 0, 200, name=\"met\", label=\"$E_{T}^{miss}$ [GeV]\")\n",
    "    .Double()\n",
    "    .fill(events[goodevent].MET.pt)\n",
    ")\n",
    "\n",
    "\n",
    "q5_hist.compute().plot1d(flow=\"none\")\n",
    "dak.necessary_columns(q5_hist)"
   ]
  },
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-21 08:20:50,565 - distributed.utils_perf - WARNING - full garbage collections took 12% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:50,643 - distributed.utils_perf - WARNING - full garbage collections took 12% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:51,779 - distributed.utils_perf - WARNING - full garbage collections took 12% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:52,487 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:53,216 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:54,640 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:55,073 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:55,744 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:56,823 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:57,452 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:59,213 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:59,375 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)\n",
      "2023-12-21 08:20:59,800 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'from-uproot-d895a5d3a21bc4fd7e5f7c35639408ec': frozenset({'Jet_btag',\n",
       "            'Jet_eta',\n",
       "            'Jet_mass',\n",
       "            'Jet_phi',\n",
       "            'Jet_pt',\n",
       "            'nJet'})}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "q6_hists = {\n",
    "    \"trijetpt\": hda.Hist.new.Reg(\n",
    "        100, 0, 200, name=\"pt3j\", label=\"Trijet $p_{T}$ [GeV]\"\n",
    "    )\n",
    "    .Double()\n",
    "    .fill(trijet.p4.pt),\n",
    "    \"maxbtag\": hda.Hist.new.Reg(\n",
    "        100, 0, 1, name=\"btag\", label=\"Max jet b-tag score\"\n",
    "    )\n",
    "    .Double()\n",
    "    .fill(maxBtag),\n",
    "}\n",
    "\n",
    "out = dask.compute(q6_hists)[0]\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharey=True)\n",
    "out[\"trijetpt\"].plot1d(ax=ax1, flow=\"none\")\n",
    "out[\"maxbtag\"].plot1d(ax=ax2, flow=\"none\")\n",
    "dak.necessary_columns(q6_hists)"
   ]
  },
  {
   "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": [
       "{'from-uproot-d895a5d3a21bc4fd7e5f7c35639408ec': frozenset({'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'})}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S0tIUHh7uWWJiYmpbJgAAuMLVOoykpKRo7969WrZsWV3WI0lKTU1VYWGhZzl8+HCd7wMAAFwZfLpn5ILJkydr9erV2rhxo9q0aVNl36ioKBUUFHi1FRQUKCoqqtIxTqdTTqezNqUBAAA/49OZEWOMJk+erBUrVmj9+vVq3759tWPi4+O1bt06r7aMjAzFx8f7VikAAAhIPp0ZSUlJ0dKlS7Vq1So1adLEc99HeHi4GjZsKElKTk5W69atlZaWJkmaMmWKBg4cqJkzZ2ro0KFatmyZtm/frjfffLOOpwIAAPyRT2dG5s2bp8LCQg0aNEjR0dGe5d133/X0ycvLU35+vufxgAEDtHTpUr355puKjY3Ve++9p5UrV1Z50ysAALh6+HRmxBhTbZ/MzMxybSNHjtTIkSN92RUAALhK8N00AADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrfA4jGzdu1LBhw9SqVSs5HA6tXLmyyv6ZmZlyOBzlFpfLVduaAQBAAPE5jBQXFys2NlZz5871aVxOTo7y8/M9S0REhK+7BgAAAai+rwOSkpKUlJTk844iIiLUtGlTn8cBAIDAdtnuGendu7eio6N155136osvvqiyb0lJiYqKirwWAAAQmC55GImOjtb8+fP1/vvv6/3331dMTIwGDRqknTt3VjomLS1N4eHhniUmJuZSlwkAACzx+TKNr7p27aquXbt6Hg8YMEAHDhzQrFmz9N///d8VjklNTdXUqVM9j4uKiggkAAAEqEseRirSt29fbdq0qdL1TqdTTqfzMlYEAABssfI5I9nZ2YqOjraxawAAcIXx+czIqVOnlJub63l88OBBZWdnq1mzZmrbtq1SU1N19OhRvfPOO5Kk2bNnq3379rr++ut19uxZLVy4UOvXr9enn35ad7MAAAB+y+cwsn37dt12222exxfu7Rg7dqzS09OVn5+vvLw8z/rS0lI99thjOnr0qBo1aqRevXpp7dq1XtsAAABXL5/DyKBBg2SMqXR9enq61+Mnn3xSTz75pM+FAQCAqwPfTQMAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsMrnMLJx40YNGzZMrVq1ksPh0MqVK6sdk5mZqRtuuEFOp1OdOnVSenp6LUoFAACByOcwUlxcrNjYWM2dO7dG/Q8ePKihQ4fqtttuU3Z2th555BFNmDBBa9as8blYAAAQeOr7OiApKUlJSUk17j9//ny1b99eM2fOlCRdd9112rRpk2bNmqXExERfdw8AAALMJb9nJCsrSwkJCV5tiYmJysrKqnRMSUmJioqKvBYAABCYLnkYcblcioyM9GqLjIxUUVGRzpw5U+GYtLQ0hYeHe5aYmJhLXSYAALDkinw3TWpqqgoLCz3L4cOHbZcEAAAuEZ/vGfFVVFSUCgoKvNoKCgoUFhamhg0bVjjG6XTK6XRe6tIAAMAV4JKfGYmPj9e6deu82jIyMhQfH3+pdw0AAPyAz2Hk1KlTys7OVnZ2tqR/vHU3OztbeXl5kv5xiSU5OdnTf9KkSfr73/+uJ598Ul9//bXeeOMN/eUvf9Gjjz5aNzMAAAB+zecwsn37dsXFxSkuLk6SNHXqVMXFxWnatGmSpPz8fE8wkaT27dvrww8/VEZGhmJjYzVz5kwtXLiQt/UCAABJtbhnZNCgQTLGVLq+ok9XHTRokHbt2uXrrgAAwFXginw3DQAAuHoQRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWFWrMDJ37lxde+21CgkJUb9+/fTll19W2jc9PV0Oh8NrCQkJqXXBAAAgsPgcRt59911NnTpVzz33nHbu3KnY2FglJibq2LFjlY4JCwtTfn6+Zzl06NBFFQ0AAAKHz2Hktdde08SJE/XAAw+oe/fumj9/vho1aqRFixZVOsbhcCgqKsqzREZGXlTRAAAgcPgURkpLS7Vjxw4lJCT8tIGgICUkJCgrK6vScadOnVK7du0UExOj4cOHa9++fVXup6SkREVFRV4LAAAITD6Fke+//15lZWXlzmxERkbK5XJVOKZr165atGiRVq1apSVLlsjtdmvAgAE6cuRIpftJS0tTeHi4Z4mJifGlTAAA4Ecu+btp4uPjlZycrN69e2vgwIH64IMP1LJlSy1YsKDSMampqSosLPQshw8fvtRlAgAAS+r70rlFixaqV6+eCgoKvNoLCgoUFRVVo200aNBAcXFxys3NrbSP0+mU0+n0pTQAAOCnfDozEhwcrD59+mjdunWeNrfbrXXr1ik+Pr5G2ygrK9OePXsUHR3tW6UAACAg+XRmRJKmTp2qsWPH6sYbb1Tfvn01e/ZsFRcX64EHHpAkJScnq3Xr1kpLS5MkTZ8+Xf3791enTp104sQJzZgxQ4cOHdKECRPqdiYAAMAv+RxGRo0ape+++07Tpk2Ty+VS79699cknn3huas3Ly1NQ0E8nXI4fP66JEyfK5XLpmmuuUZ8+fbR582Z179697mYBAAD8lsMYY2wXUZ2ioiKFh4ersLBQYWFhdbLN06Xn1X3aGknSV9MT1Si4foVtAACgdmr6+s130wAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsKq+7QJwZTDG6My5sgrXNWxQTw6H4zJXBAC4WhBGIEk6c65M3aetqXDdV9MT1SiYfyoAgEuDyzQAAMAq/ruLcrY/kyBJuvGltZKk06XlL99w6QYAUFcIIyinUXA9r8cXQsnPcekGAFBXeDVBrfzybAlnSgAAtUUYQYUaNqinr6YnerWdLi3znCX55dmS7c8klDujQkABANQEYQQVcjgcPl2GqehSzi8DCuEEAFARwghq7JdnS35+pqQinD0BANQEYQQ19suzJdVdyvmlmpw9qQiBBQACG2EEtVbRpZyLPXtSEQILAAQ2wgjq1MWePalIXQWWihBiAMA+wgguqZqcPanIpQgsFaltiLmSEKgA+LtahZG5c+dqxowZcrlcio2N1R//+Ef17du30v7Lly/Xs88+q2+++UadO3fWf/zHf+iuu+6qddHwbzV5p86lCCwVudjxV4JACFQVIWQBVw+fw8i7776rqVOnav78+erXr59mz56txMRE5eTkKCIiolz/zZs3a/To0UpLS9Pdd9+tpUuXasSIEdq5c6d69OhRJ5NA4KmrwFKRuggxV5JAmsvPBWrIgu8IpoHPYYwxvgzo16+fbrrpJr3++uuSJLfbrZiYGD388MP6/e9/X67/qFGjVFxcrNWrV3va+vfvr969e2v+/Pk12mdRUZHCw8NVWFiosLAwX8qt1OnS855vqb3w0eY/b7vangh//gId6B/1bozRmXPlv2/HnwRaoAKqcrU9H9twqZ7za/r67dPeS0tLtWPHDqWmpnragoKClJCQoKysrArHZGVlaerUqV5tiYmJWrlyZaX7KSkpUUlJiedxYWGhpH9Mqq6cLj0vd8lpz3bP/38YudB2w7N/rbN9+ZsLvw9cuRoYoy2Px9suo86dKS3TwBmZtsvAFeZqfj6+XPa+4PtZ5pq48Lpd3XkPn15xvv/+e5WVlSkyMtKrPTIyUl9//XWFY1wuV4X9XS5XpftJS0vTCy+8UK49JibGl3JrLHr2Jdms3+L3AQBXl/DZl3b7J0+eVHh4eKXrr8j//qampnqdTXG73frxxx/VvHnzOr1uWFRUpJiYGB0+fLjOLv9caQJ9jszP/wX6HJmf/wv0OV7K+RljdPLkSbVq1arKfj6FkRYtWqhevXoqKCjwai8oKFBUVFSFY6KionzqL0lOp1NOp9OrrWnTpr6U6pOwsLCA/Af2c4E+R+bn/wJ9jszP/wX6HC/V/Ko6I3JBkC8bDA4OVp8+fbRu3TpPm9vt1rp16xQfX/H16/j4eK/+kpSRkVFpfwAAcHXx+TLN1KlTNXbsWN14443q27evZs+ereLiYj3wwAOSpOTkZLVu3VppaWmSpClTpmjgwIGaOXOmhg4dqmXLlmn79u16880363YmAADAL/kcRkaNGqXvvvtO06ZNk8vlUu/evfXJJ594blLNy8tTUNBPJ1wGDBigpUuX6plnntHTTz+tzp07a+XKlVfEZ4w4nU4999xz5S4JBZJAnyPz83+BPkfm5/8CfY5Xwvx8/pwRAACAuuTTPSMAAAB1jTACAACsIowAAACrCCMAAMCqqzqMzJ07V9dee61CQkLUr18/ffnll7ZLqpW0tDTddNNNatKkiSIiIjRixAjl5OR49Rk0aJAcDofXMmnSJEsV++b5558vV3u3bt0868+ePauUlBQ1b95coaGhuu+++8p90N6V7tprry03R4fDoZSUFEn+d/w2btyoYcOGqVWrVnI4HOW+i8oYo2nTpik6OloNGzZUQkKC9u/f79Xnxx9/1JgxYxQWFqamTZtq/PjxOnXq1GWcReWqmt+5c+f01FNPqWfPnmrcuLFatWql5ORkffvtt17bqOiYv/rqq5d5JpWr7hiOGzeuXP1Dhgzx6uOvx1BShX+PDodDM2bM8PS5ko9hTV4XavLcmZeXp6FDh6pRo0aKiIjQE088ofPnz9d5vVdtGHn33Xc1depUPffcc9q5c6diY2OVmJioY8eO2S7NZxs2bFBKSoq2bNmijIwMnTt3ToMHD1ZxcbFXv4kTJyo/P9+z/OEPf7BUse+uv/56r9o3bdrkWffoo4/qf/7nf7R8+XJt2LBB3377re69916L1fpu27ZtXvPLyMiQJI0cOdLTx5+OX3FxsWJjYzV37twK1//hD3/Qf/3Xf2n+/PnaunWrGjdurMTERJ09e9bTZ8yYMdq3b58yMjK0evVqbdy4Ub/97W8v1xSqVNX8Tp8+rZ07d+rZZ5/Vzp079cEHHygnJ0e//vWvy/WdPn261zF9+OGHL0f5NVLdMZSkIUOGeNX/5z//2Wu9vx5DSV7zys/P16JFi+RwOHTfffd59btSj2FNXheqe+4sKyvT0KFDVVpaqs2bN+vtt99Wenq6pk2bVvcFm6tU3759TUpKiudxWVmZadWqlUlLS7NYVd04duyYkWQ2bNjgaRs4cKCZMmWKvaIuwnPPPWdiY2MrXHfixAnToEEDs3z5ck/b3/72NyPJZGVlXaYK696UKVNMx44djdvtNsb49/GTZFasWOF57Ha7TVRUlJkxY4an7cSJE8bpdJo///nPxhhjvvrqKyPJbNu2zdPn448/Ng6Hwxw9evSy1V4Tv5xfRb788ksjyRw6dMjT1q5dOzNr1qxLW1wdqWiOY8eONcOHD690TKAdw+HDh5vbb7/dq82fjuEvXxdq8tz50UcfmaCgIONyuTx95s2bZ8LCwkxJSUmd1ndVnhkpLS3Vjh07lJCQ4GkLCgpSQkKCsrKyLFZWNwoLCyVJzZo182r/05/+pBYtWqhHjx5KTU3V6dOnbZRXK/v371erVq3UoUMHjRkzRnl5eZKkHTt26Ny5c17Hslu3bmrbtq3fHsvS0lItWbJEDz74oNcXQ/rz8fu5gwcPyuVyeR2z8PBw9evXz3PMsrKy1LRpU914442ePgkJCQoKCtLWrVsve80Xq7CwUA6Ho9x3bL366qtq3ry54uLiNGPGjEty+vtSyszMVEREhLp27aqHHnpIP/zwg2ddIB3DgoICffjhhxo/fny5df5yDH/5ulCT586srCz17NnT86GmkpSYmKiioiLt27evTuu7Ir+191L7/vvvVVZW5vULlqTIyEh9/fXXlqqqG263W4888ohuvvlmr0+5/Zd/+Re1a9dOrVq10u7du/XUU08pJydHH3zwgcVqa6Zfv35KT09X165dlZ+frxdeeEG33HKL9u7dK5fLpeDg4HJP8pGRkXK5XHYKvkgrV67UiRMnNG7cOE+bPx+/X7pwXCr6+7uwzuVyKSIiwmt9/fr11axZM787rmfPntVTTz2l0aNHe30J2b//+7/rhhtuULNmzbR582alpqYqPz9fr732msVqa27IkCG699571b59ex04cEBPP/20kpKSlJWVpXr16gXUMXz77bfVpEmTcpd//eUYVvS6UJPnTpfLVeHf6YV1demqDCOBLCUlRXv37vW6p0KS13Xanj17Kjo6WnfccYcOHDigjh07Xu4yfZKUlOT5uVevXurXr5/atWunv/zlL2rYsKHFyi6Nt956S0lJSV5fue3Px+9qdu7cOd1///0yxmjevHle66ZOner5uVevXgoODta//du/KS0tzS8+dvyf//mfPT/37NlTvXr1UseOHZWZmak77rjDYmV1b9GiRRozZoxCQkK82v3lGFb2unAluSov07Ro0UL16tUrd9dwQUGBoqKiLFV18SZPnqzVq1frs88+U5s2bars269fP0lSbm7u5SitTjVt2lRdunRRbm6uoqKiVFpaqhMnTnj18ddjeejQIa1du1YTJkyosp8/H78Lx6Wqv7+oqKhyN5OfP39eP/74o98c1wtB5NChQ8rIyKj2q9n79eun8+fP65tvvrk8BdaxDh06qEWLFp5/k4FwDCXp888/V05OTrV/k9KVeQwre12oyXNnVFRUhX+nF9bVpasyjAQHB6tPnz5at26dp83tdmvdunWKj4+3WFntGGM0efJkrVixQuvXr1f79u2rHZOdnS1Jio6OvsTV1b1Tp07pwIEDio6OVp8+fdSgQQOvY5mTk6O8vDy/PJaLFy9WRESEhg4dWmU/fz5+7du3V1RUlNcxKyoq0tatWz3HLD4+XidOnNCOHTs8fdavXy+32+0JYleyC0Fk//79Wrt2rZo3b17tmOzsbAUFBZW7tOEvjhw5oh9++MHzb9Lfj+EFb731lvr06aPY2Nhq+15Jx7C614WaPHfGx8drz549XqHyQrDu3r17nRd8VVq2bJlxOp0mPT3dfPXVV+a3v/2tadq0qdddw/7ioYceMuHh4SYzM9Pk5+d7ltOnTxtjjMnNzTXTp08327dvNwcPHjSrVq0yHTp0MLfeeqvlymvmscceM5mZmebgwYPmiy++MAkJCaZFixbm2LFjxhhjJk2aZNq2bWvWr19vtm/fbuLj4018fLzlqn1XVlZm2rZta5566imvdn88fidPnjS7du0yu3btMpLMa6+9Znbt2uV5N8mrr75qmjZtalatWmV2795thg8fbtq3b2/OnDnj2caQIUNMXFyc2bp1q9m0aZPp3LmzGT16tK0pealqfqWlpebXv/61adOmjcnOzvb6m7zwDoTNmzebWbNmmezsbHPgwAGzZMkS07JlS5OcnGx5Zj+pao4nT540jz/+uMnKyjIHDx40a9euNTfccIPp3LmzOXv2rGcb/noMLygsLDSNGjUy8+bNKzf+Sj+G1b0uGFP9c+f58+dNjx49zODBg012drb55JNPTMuWLU1qamqd13vVhhFjjPnjH/9o2rZta4KDg03fvn3Nli1bbJdUK5IqXBYvXmyMMSYvL8/ceuutplmzZsbpdJpOnTqZJ554whQWFtotvIZGjRploqOjTXBwsGndurUZNWqUyc3N9aw/c+aM+d3vfmeuueYa06hRI3PPPfeY/Px8ixXXzpo1a4wkk5OT49Xuj8fvs88+q/Df5NixY40x/3h777PPPmsiIyON0+k0d9xxR7l5//DDD2b06NEmNDTUhIWFmQceeMCcPHnSwmzKq2p+Bw8erPRv8rPPPjPGGLNjxw7Tr18/Ex4ebkJCQsx1111nXnnlFa8XctuqmuPp06fN4MGDTcuWLU2DBg1Mu3btzMSJE8v9Z85fj+EFCxYsMA0bNjQnTpwoN/5KP4bVvS4YU7Pnzm+++cYkJSWZhg0bmhYtWpjHHnvMnDt3rs7rdfx/0QAAAFZclfeMAACAKwdhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAFQrf/8z/+Uw+Go8TJp0iTbJQPwI/VtFwDgyte4cWPPz82bN1fTpk2r7H8lfIU6AP/BF+UBqNb58+f1T//0T1q1apViYmK0adMmtW3b1nZZAAIEl2kAVKt+/fp69913lZCQoMOHDyshIUEFBQWXtYZBgwZ5LgNlZ2df1n3/0rhx4zy1rFy50motQCAgjAABZNy4cRoxYoRPYwYNGqRHHnmk2n5Op1MrV65UfHy89u/fr8GDB+v48eO1K7SWJk6cqPz8fPXo0cOr3eVyacqUKerUqZNCQkIUGRmpm2++WfPmzdPp06drtO1hw4ZpyJAhFa77/PPP5XA4tHv3bknSnDlzlJ+ff3GTAeBBGAFQY40bN9ZHH32k2NhY7d69W3fddZdOnTp12fbfqFEjRUVFqX79n253+/vf/664uDh9+umneuWVV7Rr1y5lZWXpySef1OrVq7V27doabXv8+PHKyMjQkSNHyq1bvHixbrzxRvXq1UuSFB4erqioqLqZFADCCBDI3G630tLS1L59ezVs2FCxsbF67733POvHjRunDRs2aM6cOZ7LDt98802V22zatKk+/fRTde3aVVu2bNHw4cNVUlJSYV+XyyWHw6E5c+YoLi5OISEhuv7667Vp06Y6m+Pvfvc71a9fX9u3b9f999+v6667Th06dNDw4cP14YcfatiwYZ6+Vf0+7r77brVs2VLp6ele2z916pSWL1+u8ePH11nNALwRRoAAlpaWpnfeeUfz58/Xvn379Oijj+o3v/mNNmzYIOkflxvi4+M9lz/y8/MVExNT7XYjIiKUkZGhdu3aaf369Ro1apTOnz9frt+FezsWLVqk2bNnKzs7W23bttWYMWPkdrsven4//PCDPv30U6WkpHi94+fnHA6H5+eqfh/169dXcnKy0tPT9fP7+pcvX66ysjKNHj36ousFUDHCCBCgSkpK9Morr2jRokVKTExUhw4dNG7cOP3mN7/RggULJP3jckNwcLDn8kdUVJTq1atXo+3HxMRo7dq1aty4sVatWqXHHnusXJ///d//VYMGDbRq1SoNHDhQ3bp100svvaS8vDwdPXpU8+fPV+/evdWzZ08FBwerd+/e6t27t+bOnVujGnJzc2WMUdeuXb3aW7RoodDQUIWGhuqpp56q8e/jwQcf1IEDBzxhTfrHJZr77rtP4eHhNaoJgO/4nBEgQOXm5ur06dO68847vdpLS0sVFxdXJ/t4//33VVxcrKZNm2rChAnl1mdnZ+vee+/Vtdde62kLCwvz/Dxp0iRNmjRJu3fv1sSJE7V169Y6qevLL7+U2+3WmDFjPJeQavL76NatmwYMGKBFixZp0KBBys3N1eeff67p06fXSV0AKkYYAQLUhRtLP/zwQ7Vu3dprndPpvOjtp6en6/e//71CQkL017/+VT179izXJzs7W2PHjvVqy8rKUosWLbxq2rdvn66//nqfa+jUqZMcDodycnK82jt06CBJatiwoaetpr+P8ePH6+GHH9bcuXO1ePFidezYUQMHDvS5NgA1x2UaIEB1795dTqdTeXl56tSpk9fy8/tCgoODVVZW5tO2V69erYkTJ6pevXpatmyZbrnllnJ9zpw5o/3793tt2+12a/bs2Ro7dqyCgn56+tm7d2+twkjz5s1155136vXXX1dxcXGVfWv6+7j//vsVFBSkpUuX6p133tGDDz7odd8JgLrHmREgQDVp0kSPP/64Hn30Ubndbv3qV79SYWGhvvjiC4WFhXnOWFx77bXaunWrvvnmG4WGhqpZs2ZeQeGXNm/erPvvv1/nz5/XwoULNXz48Ar77dmzRw6HQ0uWLNHtt9+upk2batq0aTpx4oSeeeYZr7779u3TQw89VKt5vvHGG7r55pt144036vnnn1evXr0UFBSkbdu26euvv1afPn18+n2EhoZq1KhRSk1NVVFRkcaNG1erugD4wAAIGP/6r/9q7rvvPs9jt9ttZs+ebbp27WoaNGhgWrZsaRITE82GDRs8fXJyckz//v1Nw4YNjSRz8ODBSre/b98+c8011xhJ5pVXXqmylgULFpgePXqYd955x0RHR5tGjRqZe+65x+Tl5ZXr27FjR3P48OEqtzdw4EAzZcqUCtd9++23ZvLkyaZ9+/amQYMGJjQ01PTt29fMmDHDFBcX+/T7MMaYzZs3G0nmrrvuqrImSWbFihVV9gFQPb6bBgggQ4YMUadOnfT666/X+bYPHz6sAQMG6MiRI5oyZYpmz55dZf+UlBQdP35cS5curbLfmTNn1KZNG/3www9V9hs0aJB69+5d7X4vJ4fDoRUrVvj8qbcAvHHPCBAAjh8/rtWrVyszM1MJCQl1vv0ff/xRiYmJOnLkiEaPHq1Zs2ZVOyY7O9vziaVV+dvf/qZu3brVqI433nhDoaGh2rNnT436XyqTJk1SaGio1RqAQMKZESAA3HPPPdq2bZvGjh2rl156qc5vuBw5cqTXJ7dWp0uXLsrPz9eyZct011131UkNR48e1ZkzZyRJbdu2VXBwcJ1stzaOHTumoqIiSVJ0dHSlH7gGoGYIIwCqNWbMGJ8+A6Rjx45as2bNJawIQCAhjAAAAKu4ZwQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABW/R/z2DxPtePslAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "q7_hist = (\n",
    "    hda.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",
    "q7_hist.compute().plot1d(flow=\"none\")\n",
    "dak.necessary_columns(q7_hist)"
   ]
  },
  {
   "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": [
       "{'from-uproot-d895a5d3a21bc4fd7e5f7c35639408ec': frozenset({'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'})}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "pair = ak.argcombinations(events.leptons, 2, fields=[\"l1\", \"l2\"])\n",
    "pair = pair[(events.leptons[pair.l1].pdgId == -events.leptons[pair.l2].pdgId)]\n",
    "\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",
    "\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",
    "q8_hist = (\n",
    "    hda.Hist.new.Reg(\n",
    "        100, 0, 200, name=\"mt\", label=\"$\\ell$-MET transverse mass [GeV]\"\n",
    "    )\n",
    "    .Double()\n",
    "    .fill(mt)\n",
    ")\n",
    "\n",
    "q8_hist.compute().plot1d(flow=\"none\")\n",
    "dak.necessary_columns(q8_hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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