L1Ph2_Menu_Btag_seed_dev notebook

L1Ph2_Menu_Btag_seed_dev.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2cd747c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import awkward as ak                                                                                                                                                                                              \n",
    "                                                                                                                                                                                                            \n",
    "import matplotlib.pyplot as plt                                                                                                                                                                             \n",
    "import mplhep as hep       \n",
    "plt.style.use(hep.style.CMS)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56bac869",
   "metadata": {},
   "source": [
    "## HT bjets\n",
    "\n",
    "### use extended JETS\n",
    "\n",
    "btagSum = sum btag score of leading 4 jets with pt > 30 and eta < 2.4\n",
    "\n",
    "and then cuts:\n",
    "- scHT > 190\n",
    "- btagSum > 1.65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "481a0159",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/eos/user/a/alobanov/L1T/phase2/menu/ntuples/cache/V29_part/V29_part_MinBias_seededConeExtendedPuppiJet.parquet\r\n",
      "/eos/user/a/alobanov/L1T/phase2/menu/ntuples/cache/V29/V29_MinBias_seededConeExtendedPuppiJet.parquet\r\n"
     ]
    }
   ],
   "source": [
    "! ls /eos/user/a/alobanov/L1T/phase2/menu/ntuples/cache/V29*/V29*M*seed*Ex*Jet*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "619ae0d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#f\"/eos/cms/store/group/dpg_trigger/comm_trigger/L1Trigger/alobanov/phase2/menu/ntuples/cache/V29_hadd/V29_hadd_MinBias_{load_obj}.parquet\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "d2807a7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = \"/eos/cms/store/group/dpg_trigger/comm_trigger/L1Trigger/alobanov/phase2/menu/ntuples/cache/V29_hadd/V29_hadd_MinBias_%s.parquet\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "fcef57d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! ls /eos/cms/store/group/dpg_trigger/comm_trigger/L1Trigger/alobanov/phase2/menu/ntuples/cache/V29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f50b5c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# jets = ak.from_parquet(\"/eos/user/a/alobanov/L1T/phase2/menu/ntuples/cache/V29_fullstat/V29_fullstat_MinBias_seededConeExtendedPuppiJet.parquet\")\n",
    "# ht = ak.from_parquet(\"/eos/user/a/alobanov/L1T/phase2/menu/ntuples/cache/V29_fullstat/V29_fullstat_MinBias_seededConePuppiHT.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "24f7e9c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# jets = ak.from_parquet(\"/eos/user/a/alobanov/L1T/phase2/menu/ntuples/cache/V29/V29_MinBias_seededConeExtendedPuppiJet.parquet\")\n",
    "# ht = ak.from_parquet(\"/eos/user/a/alobanov/L1T/phase2/menu/ntuples/cache/V29/V29_MinBias_seededConePuppiHT.parquet\")\n",
    "\n",
    "jets = ak.from_parquet(pattern%(\"seededConeExtendedPuppiJet\"))\n",
    "ht = ak.from_parquet(pattern%(\"seededConePuppiHT\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "19524ee6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1869400"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(jets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "c64460b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['seededConeExtendedPuppiJetPt',\n",
       " 'seededConeExtendedPuppiJetEt',\n",
       " 'seededConeExtendedPuppiJetEta',\n",
       " 'seededConeExtendedPuppiJetPhi',\n",
       " 'seededConeExtendedPuppiJetBJetNN']"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jets.fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "989e161b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (jets[\"seededConeExtendedPuppiJetPt\"]) > 30\n",
    "mask = mask & (abs(jets[\"seededConeExtendedPuppiJetEta\"]) < 2.4)\n",
    "\n",
    "btag_sum = ak.sum(jets[mask][:,:4][\"seededConeExtendedPuppiJetBJetNN\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "0d6bc017",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAD2CAYAAAAZIgYrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPoklEQVR4nO3dX4xc91XA8e+JK1ZFlSwLHkpsb0CA4gckl90FAcFCaYvEUlmqFZmX9sEqyqiPCCEVqRGCClBRUEBUpXhWoBis8BCRWNlSUwTkwXaBZlcUDE+EOl0rKxD/bGhTGdU+PMysss7O7tyZuTN3fne/H8na9eje356bjc+ePffc30RmIkkqyyNNByBJGp3JW5IKZPKWpAKZvCWpQO+a9heICO+IStKYMjMGvW7lLUkFmlnyzsyH/jz99NN7Xhv0Z3l5udJxo6xZ93HTuJ5pfO26j/N7M9/XXff1NHndJfy/Vvf3Zhgrb0kqkMlbkgpk8pakAk192mRHp9N56O/Xrl1jfX2ds2fPzioESZpL6+vrrK+vj3TOzJJ3t9t96O/TSNxV16v7uFGPbeprT+O6q/J7M9vjRtHkf/MSrmcW13L27Nk956+trR14TlS5qzmJnTnvcb/OysoKGxsbtcbUpDZdT5uuBbyeedama4Fq1xPRG+9O57wlqT1M3pJUoJn1vNVz8827fPcv/ulDr73x6Q81FI2kUll5S1KBaqm8I+IM8CTwn5n52TrW3PHOEcPSvef0TzUdQm3a9r3xeuZXm64F6rmeStMm0bvt+VJmnouIBeAy8CiwlpnPR8SvAt8A/jEz199x7kTTJm3zzpYJ2DaRtNfE0yYRsQjcBE71XzoHXAfOABci4ghwFPg08L6JI5YkDTW0bZKZWxFxGtgpGZeBK5n5ICJuA4vAXwKfBP59v3VWVlYqB9XpdFr3a5IkdbvdPQ8sjqtSzzsz7+96U4WjwHb/823gWGZeAa4ctEabBuwlaRyjFKY7bZP9jHPD8i5wArgFnATuVDlpUMCDHgmVpMNmnL1NxhkV3ACWI+IR4DiwNcYakqQJVN7bJCKuZubqrmmT48DFzLw05DynTXZx2kRSFcOmTSq3TTJztf/xHnB+1EBsm0jSYOO0TeZ+V8G2sfKWVIW7CkpSCzX2Tjpg20SSwLZJEWybSKrCtokktZBtE0lqmG2TAtg2kVSFbRNJaiHbJpLUMNsmBbBtIqkK2yaS1EImb0kqkD1vSWqYPe8C2POWVIU9b0lqIZO3JBXI5C1JBTJ5S1KBnDaRpIY5bVIAp00kVeG0iSS1kMlbkgpk8pakApm8JalAJm9JKpDJW5IKVMucd0SsAsvA7cy8VMeakqT9Vaq8o+fl/ucLEfFiRNyIiAv9Q54A3gL+azphSpJ2G5q8I2IRuAmc6r90DrgOnAEuRMQR4HOZ+RzwQ9MKVJL0tqFtk8zciojTwM6jgcvAlcx8EBG3gUXgRyPiO4Dt/dZZWVmpHFSn0xn4OL0klazb7dLtdmtZq1LPOzPv7zzmDhzl7SS9DRzLzBeGrbGxsTFehJLUEqMUpjuPx+9nnBuWd4ETwC3gJHCnykluTDVb7qEilWOcjanGSd4bwHJE3ACOA1tjrKEaDUrUktqt8q6CEXE1M1cjYgG4TC9xXxw2Guiugg+bRkVcNXlbeUvlGLarYOXKOzNX+x/vAedHDcS2iSQN5n7eBbDyllRFbZW3yudNTKk9fBs0SWqYbZMCNNk2GcTKW5pPc9M2sfKWpMGsvAtg5S2pCt+AWJJayGmTgkzjSUonUKQy2fOWpIbZ8y7AJJXurPYwsfKWmmfPW5JayOQtSQWy5y1JDbPnXYCqPe8m9+i25y01z563JLWQc97aw9lvaf6ZvOeAb2MmaVS2TSSpQE6bSFLDnDYpQJtaJPbBpelx2kSSWsgblhrbKL9FWKVL9bLylqQCmbwlqUC2TTQTPvgj1cvKW5IKVFvyjoiPRcTP1rWeJGl/lZJ39Lzc/3whIl6MiBsRcaH/2vuBb00vTEnSbkN73hGxCHwBONJ/6RxwHfgM8FcR8UfAB+gl78eB359OqGob++DS+IYm78zciojTwM6/tGXgSmY+iIjbwGJmfjIiHgM+uN86KysrlYPqdDoDH6eXpJJ1u1263W4ta1WaNsnM+zuPuQNHge3+59vAMeBWZn6NA6rupaWlPa+5t4mkw2S/wnTQ3iabm5sHrlV5b5OIuJqZqxHxLPBKZl6LiBeAZzLzqwec594mu7Rpb5NpsG0i9Qzb22ScOe8NYDkibgDHga0qJ7mroCQNNtVdBXdV3gvAZXqJ+2JmXhpynpX3LlbeB7Pylnpqq7wzc7X/8R5wftRArLxVhRMoOozcz7sAVt6jM3nrMHI/b0lqId8GTZIaZtukALZNRmfbRIfRNEYFx2LlLUmDWXkXwMq7HlbjajtvWEpSC5m8JalA9rwlqWH2vAtgz7se9rzVdva8JamFfPd4FWm/32CsyHVY2POWpIbZ8y6APe/psvJWW8zNE5bSLLilrA4Lb1hKUoFM3pJUIJO3JBXIaRO1XtWbxPbG1RSnTQrgtMn8MnlrnviEpSS1kMlbkgpk8pakApm8JalAJm9JKpDJW5IKVMucd0R8FPhO4EFm/k4da0qS9lcpeUdv4PClzDwXEQvAZeBRYC0znwf+GjgDPDatQKVpc1MrlWRo2yQiFoGbwKn+S+eA6/SS9YWIOAK8t5/EF6YUpyRpl6GVd2ZuRcRpYKcsWQauZOaDiLgNLALfGxErwD/tt87KykrloDqdzsDH6SWpZN1ul263W8taldommXl/5zF34Ciw3f98GziWmX84bI2lpaU9r7m3iaTDZL/CdNDeJpubmweuVXlvk4i4mpmrEfEs8EpmXouIF4BnMvOrB5zn3ia7uLdJWex5qynTeCedDWA5Im4Ax4GtKie5q6AkDTbVXQV3Vd470ybHgYuZeWnIeVbeu1h5l89qXLNQW+Wdmav9j/eA86MGYuUtSYO5n3cBrLzLZ+WtWXA/b0lqId8GTZIaZtukALZN2st2iuo0jVHBsVh5S9JgVt4FsPJuLytv1ckblpLUQiZvSSqQPW9Japg97wLY8z5c7INrXPa8JamFTN6SVCB73pLUMHveBbDnfbjY89a45uYJS+kw8h3pNS32vCWpQCZvSSqQyVuSCuS0iSQ1zGmTAjhtIm9YqgqfsJSkFnJUUJoxxwdVBytvSSqQyVuSCmTylqQCmbwlqUC13LCMiLPAKYDMfLaONSVJ+6uUvKM3cPhSZp6LiAXgMvAosJaZzwPbwOeB56YVqNRmk0yg7PfsgBMs7Ta0bRIRi8BN+pU1cA64DpwBLkTEEeDfgF8Gfm86YUqSdhtaeWfmVkScBnZ+vC8DVzLzQUTcBhaBTwBvAk8Bvz5onZWVlcpBdTqdgY/TS1LJut0u3W63lrUqtU0y8/7OY+7AUXptEvofj2Xmx4etsbS0tOc19zaRdJjsV5gO2ttkc3PzwLXGuWF5FzgB3AJOAneqnFTXTxtJaptBheza2tqB54yTvDeA5Yi4ARwHtqqc5K6CkjTYVHcVjIirmbm6a9rkOHAxMy8NOc9dBXdxV0FV5bTJ4Vbbe1hm5mr/4z3g/KiBWHlL0mDu510AK29VZeV9uM3Nu8dbeUujcevYw8PKuwBW3prEoORt5d1OvpOOJLWQbRNJaphtkwLYNtEkbJscHrZNJKmFTN6SVCB73pLUMHveBbDnrUnY8z487HlLUgvNrG0iaXL+5qYd9rwlqWH2vAtg5aRZseddNnvektRCJm9JKpDJW5IKZPKWpAI5bSJJDXPapABOm2hWnDYpm9MmktRCJm9JKpDJW5IK5N4mkubKoPtC9u/3svKWpAKZvCWpQLUl74g4HhF/EhFH6lpTkjRY5eQdPS/3P1+IiBcj4kZEXOgf8g1gewoxSpLeoVLyjohF4CZwqv/SOeA6cAa4EBFHMvMO8B/TCFKS9LBK0yaZuRURp4Gd28DLwJXMfBARt4FF4NZBa6ysrFQOqtPpDHycXpJK1u126Xa7taxVeVQwM+/vPOoOHOXtFsk2cAy4lZm/st/5S0tLe15zbxNJh8l+hemgvU02NzcPXGvcOe+7wAl61fZJ4M6wE+r6aSNJbTOokF1bWzvwnHGT9wawHBE3gOPA1rAT3FVQkgab+q6CEXE1M1cjYgG4TC9xX8zMSwec466Cu7iroGal1KcSfcKyZ9iugiNV3pm52v94Dzg/yrlW3tJsmQTL4X7eBbDyVpNKSN7+0OmptfKehJW3JA1m5V0AK281qYQK1sq7x3fSkaQWsm0iSQ2zbVIA2yZqUgntB9smPbZNJKmFTN6SVCB73pLUMHveBbDnrSaV0Du2591TfM+7bbsR/u9X/qzpEGrTpmsBr2eetS0P1HE9Ju8Z+/rft+cfVJuuBbyeeda2PFDH9djzljT3br55d087pU2tlHF63jNL3m37ySlJdRnnzRjmvm0iSdrL5C1JBWoseY/a36lzzbqPG/XYKt56/W9rP7bu40bRZIx1X0/bvjdN/ntoUx6YxrUcxORdw3GjHlvFN1//cu3H1n3cKJqMse7radv3xuQ92+Pq0ti0ybVr11hfX3faRNKhV9S0SafTMXFLEk6bSNKhMbO9TSRJoyt2bxNJ0l5Tr7wlSfWz8pakApm8JalAJm9JKpDJW5IKZPKWpALNffKOiBMR8Zmm45hURCxExIsRcSMiLjQdTx2i5+Wm45hURHx7RPx5RHwlIorfeD4iTkbElyPiHyLiY03HU4eIOB8Rn2g6jklFxPdHxN9FxKsRcWWSteY6eUfEh4DXmOFj/FN0DrgOnAEuRMSRhuOZSEQsAjeBU03HUoOngFcz831ARsQTDcczqQ7wKeAHgY83HMvEIuJdwC81HUdNvg/4bGY+mZkfnmShuU7ewBeAH286iJosAxuZ+QC4DSw2HM9EMnMLOA18relYavDPwB/3P//XJgOpySvAq8C7gW82HEsdOsAXmw6iJt8DPBUR1yPio5MsNNfJO3tPEH2r6ThqchTY7n++DRxrMJZaZOZ9oPinvDLzbzLzjYj4CPAEUP+m2TOUma8B30WvSCj6XYgj4j3A+4HPNx1LTf6F3m8RZ4Gfi4ij4y40l8k7In6h3xP6YNOx1OgucKL/+UngTnOh6J0i4jeAnwQ+nJlFFwwRcSwzXwfeC/x0RLy76Zgm8PPAc7SgSADIzC9m5muZ+d/Al4DHxl1rLpN3Zv5mvyf0F03HUqMNYDkiHgGOA1sNx6O+iDgHkJkXMvPrTcdTg7WIeBz4PyD6f0r1OPBrwG8DnYh4stlwJhMRn4qIH+vf8/oB4I1x12rDjcBSXAEuAz8DXCy9umuZHwY+EBGv9v/+TGbeaDKgCf0W8DJwD/iDzHyr4XjGlpkfAYiInwB+JDNfHXLKvPscvXsS3wb8bmb+z7gLuTGVJBVoLtsmkqSDmbwlqUAmb0kqkMlbkgpk8pakApm8JalAJm9JKtD/AxdKJavGXMFyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(btag_sum, bins = np.arange(-1,5,.1), log = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d95177",
   "metadata": {},
   "source": [
    "### check HT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "bd5c0b7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ht = ht[\"seededConePuppiHT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "a9c8678c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [0, 0, 59.8, 41.5, ... 0, 31.2, 0, 46.8] type='1869400 * float64'>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ht"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "7243cc4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f928a324d90>"
      ]
     },
     "execution_count": 231,
     "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": [
    "mask = (jets[\"seededConeExtendedPuppiJetPt\"]) > 30\n",
    "mask = mask & (abs(jets[\"seededConeExtendedPuppiJetEta\"]) < 2.4)\n",
    "\n",
    "ht_jetsum = ak.sum(jets[mask][\"seededConeExtendedPuppiJetPt\"], axis = 1)\n",
    "\n",
    "_ = plt.hist(ht_jetsum, bins = np.linspace(0,500,50), log = True, label= \"HT from ext sc jets\")\n",
    "_ = plt.hist(ht, bins = np.linspace(0,500,50), log = True, label = \"HT producer\", histtype = \"step\")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "b2878aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import LogNorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "83798fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 1, 'HT online')"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z2yTpAne/TFLVzC7s0DgAAOgrnUrcn5F0s6RRScckneXuT0raI+mlaib021uxuyVd3KFxAADQVzpyjdvdbzOz50m6TdJ1kl7TenzazGqS1kna2wrfK+mF8+1r69at6eNu3759zqYkAAB0w/j4eNt7bnQkcZvZBne/x8yeKelvJQ21Hh+RNCjpgJ6eZW+WtH++fe3evbsTQwQAoONOZ0JpiUW5Uuc+Kt9pZi+QNC3JJB0xs7PU/Jh8j5ofk1/Uir1Y0tc7NA4AAPpKp8rBPijpU5KmJP25pC9Lmmh9/2Z3f6S14vwWSXe4+50dGgck1R7eGwcl3PBEez7u2faSd4Yx1claGFNbPRjGVKbjbl2WbECXiasPxe+FLVEOVkl09cqoJzqfVWfiY2VK7+qr4t+HJFUm499JprTMNqwPY3YlXrPbNnJ5DWXp1DXuL0l60ayHL50Vs0PSjk4cHwCAfsWd0wAAKAiJGwCAgpC4AQAoCIkbAICCmHtySe0yM2uu4e3V8a1EmUYkGX7ZRWFM5Vi8qtwTK6YzK88bA7nayXbxxPE885Y6UfNZmW7P6vRKLf47rCZ+Z5I0+MiBMKa+cW0Y41+9Kx5TohFJu6olgKU6Xsft7gv+cTPjBgCgICRuAAAKQuIGAKAgnbpzWtvMdY/XsbExjY2NdWE0AAAs3cTEhCYmJha1LYvTkMbitPZgcRqL04C5sDgNAIA+xIwbknKz6YFzNoUxjWfEM5zK0ekwpr5uND5WYsY9syaecVsj22UknuHOrI7HNHA00dRjJNGsJDPsxN9PZToxm040IqlMxc1DJMnqiTEdnYn389AjqeO1A7NyLAdm3AAA9CESNwAABSFxAwBQEBI3AAAFIXEDAFAQEjcAAAWhHAxp2zaeehe72Xzzs9pyrMaquIxr+szhMCZ1I5PkS6w+HO8sc6OSzJgyN4WpJsq4KlPtuQGLqomb1CTL6kYfOhjG1NbH5YDVRMlY5cDR+Fj33h8fixu5hDIlpbsa1y/DSMpFORgAAH2IxA0AQEFoMgIAwDJbSpORnk/c4+Mr+7oRAKD/zDUB3blzZ2pbPioHAKAgPT/jxvLIrAjNrKzNrAbP8DatYs6sBM80D5FyzTFqo/G+Mk1GUu+oExUXjczPn9hPdSrzs+fmAY3VcTVAZTLRsCTx+/B9+xMjitlovModWC7MuAEAKAiJGwCAgpC4AQAoCIkbAICCkLgBACgIiRsAgIJQDlawTNMPKdf8IFPqlVFJNH7IlIzNrI1jGkPx+85MWVkj+VdQqcX7skQV08zqeNzV6Xg/mUYklZm4ZGpgMlNWlzhWosGKJNXWxL/bynRcMjd4eDI+2Ib1YciuxN9HplwSWC7MuAEAKAiJGwCAgvT8R+U0GQEA9BuajAAAUBCajAAAsEKQuAEAKIh5ojNQN5iZS1Kvjq8XXLHlN3OBk1PtOeDa1fGhnr0ujEmVaA3GMfWh9uwnEyNJjWocY3EVk6qJEi1PDClzHjOdyDIx9ZH4Pf7g4UQtnHLjHn1gf7yfgXhMtu9QvJ9jx8KY+lP7wphdjevDGGAh1upU6L7wvwDMuAEAKAiJGwCAgpC4AQAoCIkbAICCkLgBAChIz9+ABfP73LevS8VlVp/76FAY0xiJYzKrjz3xdrE+HAdlmlpkGnFkm4xkxu2JledeyTTsiPeTGXfm9yFrTwORzLmWpKEDcQeVTHXC4KF4P5W9j4UxmSY8V2z+9TAGWC7MuAEAKAiJGwCAgvT8R+U0GQEA9BuajAAAUBCajAAAsEKQuAEAKEjPf1SOpcuUjWVKxqq1uBmF1UbCGE80B8mUXs2sioNqo4lj5aqYUqVeluiJU1EcNJM4R5V6okQrMeZK4lxn9jOQGI8kTZ85HMZUJ+OGJTaTiBkdDWMur1wZxgycvyWMAZYLM24AAApC4gYAoCAkbgAACkLiBgCgICRuAAAK0rHEbWarzOzzZnaHmY2b2SVmdquZfcHMNrdi3mVmt5jZhzs1DgAA+kkny8HeIOlmd3+vmf2ZpOskjUl6nqSrzex9ki5w98vM7KNmdqG739nB8fSdbS95ZyrO9h1qy/Gmzj0zjPHBTDeuZP1VoDaSKPVKlDGlJYadKYjK/PyZblyZLmONwXg/9Uam9CwMyXUikzRwNN5ZfTDe18Bg4pc7EpeeoT22bTz19tSzZTqxIdbJj8q/LekTra+/J+lH3f1JSXskvVTSRZJubz2/W9LFHRwLAAB9oWMzbnf/iiSZ2RslvVLSTa3Hp82sJmmdpL2t8L2SXjjXfrZu3Zo+5vbt2+dsSgIAQDeMj4+3vedGR++cZmZ/KOkZkl6nVuI2sxFJg5IO6OlZ9mZJ++fax+7duzs5RAAAOuZ0JpRmucuInVyc9npJcve3uPthSY+b2Vlqfky+R82PyS9qhV8s6eudGgsAAP2ikzPuH5L0GjO7ufX9DkkTkqYkvdndHzGzu8zsFkl3sDANAIBYJ69xv0PSO2Y9fOmsmB1qJnQsxt7HcnFnb4xjDh0JQyqJpg6NgfgllVkNPbM6sWJ6KAxJqScXHlvcYyW1rLxSi2Pq1fYsYbd6Yj9D8Y6qh+PdNDJjljSzOl4NPngk8VpLrDyvTk6FMbsa14cx2QoOYDlwAxYAAApC4gYAoCAkbgAACkLiBgCgICRuAAAKQuIGAKAgHb1zGnpDfc1IHHP2mjBm4PB0vJ+h+Fi10UxzjPaUQ7WrZEyS6oNxjGW6jCR+NEuUjFUS5WmZ8XiijCtTMpcql1OugUqmtKySuMuUb1gbxlxeuTKMqZ65IYxZ6epP7ev2EFYMZtwAABSk52fcc93jdWxsTGNjY10YDQAASzcxMaGJiYlFbdvzibvdXVUAAOi2uSagO3fuTG3LR+UAABSExA0AQEFI3AAAFKTnr3Fjfjc8kbv+/6qfuC6MsUZcolNbtSqM8cRbwUxMRqZEyeNGVGokYrIy1WApmfOYKBmrtqtkLFHC1kiUy0lSNa4qVKUeD6pyLD4BtfWjYczgD5wXxvi+/WEMsFyYcQMAUBASNwAABSFxAwBQEBI3AAAFIXEDAFAQVpUXLNMcQZKqr35ZGFMfjt/DzaxNLL/ONH6otKepRSPx6vVETGY/knLNQepxjLepEUkjEZPZT2WmPTHZJfW1xGuteixeDl9bF3eQGXziWBiTWTFuZ28MY1a6gXM2dXsIKwYzbgAACtLzM26ajAAA+g1NRgAAKAhNRgAAWCFI3AAAFITEDQBAQXr+GjfmN5BojiBJk2fEv+b6UFw3VBuNYzKlVbW474PqiYYVjUyTkXY2Pck048g02siUaCUacWT2s6yS4xlMlHpVp9pVVxfLNOv5l694T1uO1c8+99CHuj2EFYMZNwAABSFxAwBQEBI3AAAFIXEDAFAQEjcAAAUhcQMAUBDKwQrWWJOoq0qaWZUp9UrExA2bUiVjqf0kSq9S5WCJpmfNA8YhlqhQamSqmBLnyDLjSVRVZc61JttzLEmqDSfqxlbHJ2DoQKZmLrbtJe8MY3ztSFuOBbRDzydumowAAPrNUpqMmLfpJgbtZtacu/Tq+HrBay+6NhV39Llrw5jJ9fHUtF0z7lpi8lJPxGSO1YszbqvFMZn+15kZd3UqsZ/EmIcOxkGDR3J/q1aP4waOxj9cZsZdORqfSKvHHxXUEzPuXV+KZ+7AQsya/8a6+4L/2HKNGwCAgpC4AQAoCIkbAICC9PziNMyvMpW4ECppal17rl97poFIovFH21aeZ2JSTT+S6yga8TnKXHfOvFtOLe3I9OHINDRJvIwyMVmZ69fDT8TL2Kv7DocxtXvvD2N2Na4PY17zY+8NY1a6yytXhjGZc40YM24AAApC4gYAoCAkbgAACkLiBgCgICRuAAAKQuIGAKAglIMVbHLzulRcu8qmaomeJvVMTKJkLHMb0szP5QNxXVXmtqiSZInuIDadKKvL/GyJcrBqovQs83utZBqRJM515vaqUvI8zsSD8n37w5jqmRsyQwrVRrL3xQU6jxk3AAAF6fkZN93BAAD9ZindwXo+cY+Pj3d7CAAAtNVcE9CdO3emtuWjcgAACkLiBgCgICRuAAAK0vPXuDG/RjXR+knS9BmJEqXErjJlXJmYTIlSYzBRxpWo0PFq+8rBUmVTiTKuSqJkrJLpDpaIyXQry8g0UKvUcl3WKvVEOVg9cbIT6k/ta8t+Bo/U2rKffkbnr+XDjBsAgIKQuAEAKAiJGwCAgnQ0cVvTp1pfX2Jmt5rZF8xsc+uxd5nZLWb24U6OAwCAftGxxG1m50r6hqTzWw9dK2lM0jWSrjazTZIucPfLJFXN7MJOjQUAgH7RsVXl7v5gKxl/tvXQWe7+pJkdkvQHki6SdHvrud2SLpZ0Z6fG048mN+Z+fZm1vo2hOKY+kohJ7KcxlFjpnWkOklgxrkxviMySaUmNxPvczMpzS8RUpuOYgWNxTKbxR6aiYPBwvDx95MnEoCUN3vdoGFN7eG8Yk1nFfHnlytSYItWDyQ4qwDLo6Efl7l7X03njSOuxaUk1SeskHf/r3CupPW18AADoY8tZx71WksxsRNKgpANqzrIlabOk/XNttHXr1vQBtm/fPmdTEgAAumF8fLztPTeWM3E/bmZnSdoiaY+aH5P/Uuu5iyV9dK6Ndu/evTyjAwCgzU5nQmmWu6nWcibuHZImJE1JerO7P2Jmd5nZLZLucHeubwMAEOh44nb3K1r/3y3p0lnP7VAzoQMAgARuwAIAQEFoMlKw6TW56yH10URMptQr00BkJC4baiRKvdSmcjBr61vTRMeOWqb+LJYp4xo6GP/8wwfimGMb45O06pHJMGbgwcfDGKl9pV4Z7dpP486727IfoB2YcQMAUBASNwAABSFxAwBQEBI3AAAFIXEDAFAQEjcAAAWhHKxg02tzcbVEOVhjOC4bqq1KlGiNxK2vUiVaiVKvSiUuz6okysoa9VxZnVfigWf21RiMS8YyXdbqQ/Gxam0q85s+Mw6qTOX6BFWPxW3Ntm2MbxF5wxPtvf/zQgbO2bRsxwIiPZ+457rH69jYmMbGxrowGgAAlm5iYkITExOL2rbnE3e7u6oAANBtc01Ad+7cmdqWa9wAABSExA0AQEFI3AAAFKTnr3FjftPrc3H10cTK6sFE449V8Yrx6kgtjPFGbhV3O3jix8rvrD0xluhVYvGpTsUoc6oT41E9UVGQWC0vSTcmVoNfXrkyta/l8rmHPtTtIQD/jBk3AAAFIXEDAFAQEjcAAAUhcQMAUBASNwAABSFxAwBQEMrBClZbk6njkTzRaMOH4n1Vh+NSr4HBuEZpZip+2fmROGbwyThmIO5noepkHCNJ1ak4ZuhgojlKLY5Z9ehMGDNy/5NhTO3e+8OYDRe8IIyp//03w5jPN64PY7J2tXFfQL/p+cRNkxEAQL9ZSpMR87beoaJ9zMwlqVfH1wvO++MPpOLaNuMeTcy4h5hxRyrxaVzWGXe1TTNuZsnA0pg175jk7gveOolr3AAAFITEDQBAQUjcAAAUhMQNAEBBen5VOebn6xKrnCRVEwvGBofifa0djVdxrR6aDmMOTQ+HMQeHRsOYmUSXMa/GHas8+fa1kfhr8Wo8pqEDmQVs8WLBxrpVYczAOZvCmPrq+PfBwjNEMh3deB21BzNuAAAKQuIGAKAgJG4AAApC4gYAoCAkbgAACsKq8oKde84TqbiK4lXMqwfj1eAbR46EMd83fDiMeXxqTRizd/SMMOaByplhzPRwvGK6fjheeS5JlZl4xXj9cBzTSKw8r87E4x4+EP/5Dq0aDGOOPWMkjNm28dSeAbPVn9oXxkisLO5X/F6XT88nbpqMAAD6DU1GVqgfuek/p+KKnHEfTcy4n0zMuA/GM1dr44x7IDHjHjwUH2v1o/HvbPhAXJ8/tC/+vWZm3GtvjpuMMOMGloYmIwAA9CESNwAABSFxAwBQEBI3AAAF6flV5ZjfowfWpuKmHoubUfhg3NSiuipuRDIyGi+GmpocCmNqh+MypuHH4pfv6oNhiIYSMZJUSfR0GTocn8fRx2fi/TwaL/K74a7fjQeUkGkOcQMLyoCewYwbAICCkLgBACgIiRsAgIKQuAEAKAiJGwCAgnDLUwAAekD2lqc9Xw5GkxEAQL+hycgKNz4+PucbHJyM85THucrhPOVxrmI0GVlBxsfHuz2EInCe8jhXOZynPM5V+5C4AQAoCIkbAICCkLgBAChIz68qx/wa33t+84uZB5/+eg7f/8lfCfflo/UwZnBN3EBk3dpjYczRRJORySNxjD0xHMaMPvr0Go/7Hn5UL37HB0+NeTy3ANLi/iEaOhgHDR6Oz/XovU/GB5ucimMS/Nipv7Nv739A2zY+vZDohie4Pgn0ir6ccS92iX2p2y7F0W/8w6K3PfL1f1r0todu++aitjt6+z8u+phLsf/BxZ+np/bevehtHztyz+K3PXbforddim78HZT4t1fivzXdOFcl/qydPk8k7j7YdimOfWPxSeXI1xeXfCXp8O5FJu49i3+zsBQHHlz8edr3yFIS972L33by/kVvuxQk7s4ft8Rtu3HMErfN6GriNrNhM7vezL5kZm/p5lgAAChBt2fcr5f0RUmXSXqLmVW7PB4AAHpatxP3yyTtdveGpIckndvl8QAA0NO6estTMxuX9F53v9/M/lDS/3T321vPca9TAMCK0+u3PD0g6dmtrzdL2t+9oQAA0Pu6Xce9W9LLzOxLks6R9ODxJ6J3HAAArETdnnF/WtIr1Vyg9ufuXuvucAAA6G0929YTAACcqtszbgAAcBpI3AAAFKRnE7eZPdvM/rjb4+hl3Hnu9FjTp7o9jl5mZqvM7PNmdkerXBNzMLPNZvY1M7vLzN7a7fGUwMyuNLNruj2OXmVmW8xsj5ndbGafXii2JxO3mf0rSbep+6veex13nksys3MlfUPS+d0eS497g6Sb3f2lktzMXtnl8fSq7ZLeLekiSb/a5bH0PDMbkPQ73R5Hj3uepI+4+6vd/XULBfZk4pb0fyT9cLcHUQDuPJfk7g9KulDSA90eS4/7tqRPtL7+XjcH0uM+I+lmSaOS4l622C7pxm4PosedJ+kNZvZFM/uFhQJ7MnF7c6k7pWGxdZL2tr7eK2lDF8fS89y9LokyigW4+1fc/Ttm9kY1SzW/2u0x9SJ3v03Ss9R8w3xDl4fT08xsjaQfk/Q33R5Lj7tXzU8lxiT9RzNbN19gTyVuM/uN1uf7P97tsRSCO8+h7Vq3H75c0uu4t8LczGyDu98j6ZmSfsLMRrs9ph52taQ/Em+aF+TuN7r7be6+T9Ktkp4zX2xPJW53f3/r8/2/7fZYCnH8znMVzbrzHLAYZvZ6SXL3t7j74W6Pp4ftNLMXSJqWZK3/MLcXSPo9Sf9V0nYze3V3h9ObzOzdZnZpa63SBZK+M18si7/K9mlJfy3pZyT9GbMjtMEPSXqNmd3c+v6d7v6lbg6oR31Q0qckTal518ejXR5Pz3L3N0qSmb1K0svd/eZgk5Xqo2qunRiS9CfufnC+QO6cBgBAQXrqo3IAALAwEjcAAAUhcQMAUBASNwAABSFxAwBQEBI3AGDZmdmrzOygmX3bzB40s28er/E2s39tZvPegGSlI3EDALrlk+6+xd3PVfN+5u9rPf46Sc/t1qB6HYkbANBVZmaSzpD0HTPbpmaXuo+b2TPN7N+b2UNm9o9m9v5W/IiZfcLMvmVmHzCz3bP3Z2Z/dsJ2F7Yev8bM7mnN7l/ReuyXW7P+bx1v0Wpm/87M/puZfaPVbvPnWzF3m9kvzjH+q8zsO63j/ULrsftPeP7a1j6fY2Y3mNmEmT3SOvYuM7vfzH4ie764cxoAoFteb2YvlzSiZr+FH3b3r5jZJyX9hbt/z8x+XtJLJB2RdE8ref+cpMfd/flm9p8knTVrvy+R9GI17/f9U5KuaL05eIOkF6p5h8D3mdnPSvotNduzStIeMzvexewySS+XtEbSr0m6WFJV0t+Z2U2tjoPHvau133WS3q/mHS3n8+pW7IskfbL1/+dL+hU1O2OGmHEDALrlU+7+Qnc/T9L5kj4+R8wVarYwfruaCXpY0o+omfSk5q1nZ/tmK/a9kh529z9obfNZd59p3cZ3TNJWSV9w9/3uvl/S/1MzQUvSje5+RM0kf76kr0n6sqQz1WzBeaIvSvqImm8Yfi74mXe7+32S/r719b2SvqFm0k8hcQMAuq7Vbe2omf3z7NnMVku6Rc0Z8YSkPa2nKnq621h9jn1NSnqpmslxh5ldp+Zs+cTYUZ3aHKahp/Pi8T7rVUl/3XqD8UI1Z8sn3b/f3X9G0l9K+kk1k/hsZ5/w9fTxzWZ9nUbiBgB0nZltUnM2+1TroaqaM90D7v4+SUfV/Fh5QM3E+dOtuNdpVuJrXS/+H2p+ZP0bkl6jZqvMMTMbbF3z/t9qdlj8UTNbZ2br1fwY+6Tr5a3vrzCz9Wa2QdJdklafcKy1ZvZAa/9vk/RsMztD0pSZvaTV8nXbUs7NbFzjBgB0yxvM7IfVnPk2JP2Ku7uZfUXSn0p6paSHzOweNWfPfylph5rXgz9uZt9UM0HPbml8k5rXpb8r6bCkt7n7V83ss5L+SdKMpF9y9++a2e/p6WT9bnd/uHk5vKkVc52k29TMme9w9wMnPH/IzP5K0j1qdov7sLsfNLM/kHS9pAfU/LSgbegOBgAoipldIanm7rvM7BJJ/8Hd39ztcS0XEjcAoCitm7N8SNL3qfkx+Vvd/VvdHdXyIXEDAFAQFqcBAFAQEjcAAAUhcQMAUBASNwAABSFxAwBQEBI3AAAF+f95va3vLq+rqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = btag_sum\n",
    "y = ht\n",
    "\n",
    "plt.figure(figsize = (8,6))\n",
    "_ = plt.hist2d(x,y, bins = (np.linspace(-1,5,50),np.linspace(0,500,50)), norm = LogNorm())\n",
    "plt.xlabel(\"Btag score sum\")\n",
    "plt.ylabel(\"HT online\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52cd954f",
   "metadata": {},
   "source": [
    "### Use online HT for cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "0ead1dda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passing events:  3145\n",
      "Rate of passing events:  52.218641917192684  kHz\n"
     ]
    }
   ],
   "source": [
    "mask = (ht > 190) & (btag_sum > 1.65)\n",
    "npass = np.count_nonzero(mask)\n",
    "print(\"Number of passing events: \", npass)\n",
    "print(\"Rate of passing events: \", npass / len(mask) * 2760*11246 / 1e3, \" kHz\")\n",
    "# np.count_nonzero(mask) / len(mask) * 2760*11246 / 1e3 # this is the rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "e611d293",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passing events:  2024\n",
      "Rate of passing events:  33.605892286295074  kHz\n"
     ]
    }
   ],
   "source": [
    "jet_mask = (jets[\"seededConeExtendedPuppiJetPt\"]) > 30\n",
    "jet_mask = jet_mask & (abs(jets[\"seededConeExtendedPuppiJetEta\"]) < 2.4)\n",
    "\n",
    "\n",
    "mask = (ht > 190) & (btag_sum > 1.65) & (ak.num(jets[jet_mask[:,:4]]) > 3)\n",
    "npass = np.count_nonzero(mask)\n",
    "print(\"Number of passing events: \", npass)\n",
    "print(\"Rate of passing events: \", npass / len(mask) * 2760*11246 / 1e3, \" kHz\")\n",
    "# np.count_nonzero(mask) / len(mask) * 2760*11246 / 1e3 # this is the rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccc287fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2573d31",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9e487a43",
   "metadata": {},
   "source": [
    "Do scaling of HT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "ac2e471c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# offset = 45\n",
    "# slope = 1.086\n",
    "\n",
    "offset = 47.986\n",
    "slope = 1.084\n",
    "\n",
    "# new_pt = new_pt + eta_mask * (pt_orig * values[\"slope\"] + values[\"offset\"])\n",
    "offline_ht = ht * slope + offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "1b48b3bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158.68450184501845"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "off_ht_thr = 220\n",
    "onl_ht_thr = (off_ht_thr - offset) / slope\n",
    "onl_ht_thr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3873ff8a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "05110269",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 1, 'HT offline')"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = btag_sum\n",
    "y = offline_ht\n",
    "\n",
    "plt.figure(figsize = (8,6))\n",
    "_ = plt.hist2d(x,y, bins = (np.linspace(-1,5,50),np.linspace(0,500,50)), norm = LogNorm())\n",
    "plt.xlabel(\"Btag score sum\")\n",
    "plt.ylabel(\"HT offline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "fc9cce17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passing events:  6939\n",
      "Rate of passing events:  115.21308625227346  kHz\n"
     ]
    }
   ],
   "source": [
    "mask = (offline_ht > 190) & (btag_sum > 1.65)\n",
    "npass = np.count_nonzero(mask)\n",
    "print(\"Number of passing events: \", npass)\n",
    "print(\"Rate of passing events: \", npass / len(mask) * 2760*11246 / 1e3, \" kHz\")\n",
    "# np.count_nonzero(mask) / len(mask) * 2760*11246 / 1e3 # this is the rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "c80ce984",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passing events:  1077\n",
      "Rate of passing events:  17.882186755108588  kHz\n"
     ]
    }
   ],
   "source": [
    "mask = (offline_ht > 220) & (btag_sum > 2.2)\n",
    "npass = np.count_nonzero(mask)\n",
    "print(\"Number of passing events: \", npass)\n",
    "print(\"Rate of passing events: \", npass / len(mask) * 2760*11246 / 1e3, \" kHz\")\n",
    "# np.count_nonzero(mask) / len(mask) * 2760*11246 / 1e3 # this is the rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "af5f1382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [0, 0, 0.502, ... 0.525, 0, 0.543] type='1869400 * float64'>"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btag_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "376108af",
   "metadata": {},
   "source": [
    "# Menu style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "f43f80bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # define the trigger Legs\n",
    "# leg1_mask_str = \"(arr.pt >= 30) & (abs(arr.eta) < 2.4)\"\n",
    "# leg2_mask_str = \"(arr.pt >= 30) & (abs(arr.eta) < 2.4)\"\n",
    "# # obtain the corresponding leg arrays\n",
    "# leg1_mask = eval(leg1_mask_str)\n",
    "# leg2_mask = eval(leg2_mask_str)\n",
    "# leg1 = arr[leg1_mask]\n",
    "# leg2 = arr[leg2_mask]\n",
    "\n",
    "# # do the combinatorics -> pairs\n",
    "# combos = ak.cartesian({\"l1\":leg1, \"l2\":leg2})\n",
    "# # remove combinations of the same objects\n",
    "# nodup = combos.l1.idx != combos.l2.idx\n",
    "# combos = combos[nodup]\n",
    "\n",
    "# # apply cross-leg conditions\n",
    "# l1,l2 = ak.unzip(combos)\n",
    "# mask = (abs(l1.z0 - l2.z0) <=1)\n",
    "\n",
    "# # count the number of events/rows where at least one of the masks is True\n",
    "# np.sum(ak.any(mask, axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5266b0b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "b85afa79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mask = (jets[\"seededConeExtendedPuppiJetPt\"]) > 30\n",
    "mask = (jets[\"seededConeExtendedPuppiJetEt\"]) > 30\n",
    "\n",
    "mask = mask & (abs(jets[\"seededConeExtendedPuppiJetEta\"]) < 2.4)\n",
    "mask = mask & (abs(jets[\"seededConeExtendedPuppiJetBJetNN\"]) >= 0)\n",
    "\n",
    "\n",
    "#btag_sum = ak.sum(jets[mask][:,:4][\"seededConeExtendedPuppiJetBJetNN\"], axis = 1)\n",
    "good_jets = jets[mask]#[:,:4]\n",
    "\n",
    "btag_sum = ak.sum(good_jets[:,:4][\"seededConeExtendedPuppiJetBJetNN\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "6b12d17d",
   "metadata": {},
   "outputs": [],
   "source": [
    "jet4_combs = ak.combinations(good_jets, 4, fields = [\"leg1\",\"leg2\",\"leg3\",\"leg4\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "a37fd61a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[], [], [], [], ... [], [], [], []] type='1869400 * var * float64'>"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jet4_combs.leg1.seededConeExtendedPuppiJetEta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "9f3cae2a",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "_ = plt.hist(btag_sum, bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "6f92c1c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [41.5, 20.2, 19.5, 18] type='4 * float64'>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jets[\"seededConeExtendedPuppiJetPt\"][:,:4][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71b0467e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "a045952d",
   "metadata": {},
   "outputs": [],
   "source": [
    "btagSum = jet4_combs.leg1.seededConeExtendedPuppiJetBJetNN + jet4_combs.leg2.seededConeExtendedPuppiJetBJetNN + jet4_combs.leg3.seededConeExtendedPuppiJetBJetNN + jet4_combs.leg4.seededConeExtendedPuppiJetBJetNN\n",
    "btagSum_max = ak.max(btagSum, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "96b0a1a6",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# _ = plt.hist(btagSum_max, bins = 50)\n",
    "_ = plt.hist(btag_sum, bins = 50, histtype = \"step\", ls = \"--\")\n",
    "_ = plt.hist(btagSum_max, bins = _[1], log= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "91444cbb",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "mask_ht = ht > 220\n",
    "\n",
    "_ = plt.hist(btag_sum[mask_ht], bins = 50, histtype = \"step\", ls = \"--\")\n",
    "_ = plt.hist(btagSum_max[mask_ht], bins = _[1], log= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "f5854ff6",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x = btag_sum\n",
    "y = btagSum_max\n",
    "\n",
    "_ = plt.hist2d(ak.fill_none(x,0),ak.fill_none(y,0), bins = np.linspace(-1,5,50), norm = LogNorm())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "676dea1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passing events:  766\n",
      "Rate of passing events:  12.718435519417993  kHz\n"
     ]
    }
   ],
   "source": [
    "mask = (ht > 190) & (btag_sum > 2.2)\n",
    "npass = np.count_nonzero(mask)\n",
    "print(\"Number of passing events: \", npass)\n",
    "print(\"Rate of passing events: \", npass / len(mask) * 2760*11246 / 1e3, \" kHz\")\n",
    "# np.count_nonzero(mask) / len(mask) * 2760*11246 / 1e3 # this is the rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "536d92c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passing events:  1090\n",
      "Rate of passing events:  18.0980348775008  kHz\n"
     ]
    }
   ],
   "source": [
    "mask = (offline_ht > 220) & (btag_sum > 2.2)\n",
    "npass = np.count_nonzero(mask)\n",
    "print(\"Number of passing events: \", npass)\n",
    "print(\"Rate of passing events: \", npass / len(mask) * 2760*11246 / 1e3, \" kHz\")\n",
    "# np.count_nonzero(mask) / len(mask) * 2760*11246 / 1e3 # this is the rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "0d89fc7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passing events:  1090\n",
      "Rate of passing events:  18.0980348775008  kHz\n"
     ]
    }
   ],
   "source": [
    "mask = (offline_ht > 220) & (btag_sum > 2.2) #& (ak.num(good_jets) > 3)\n",
    "npass = np.count_nonzero(mask)\n",
    "print(\"Number of passing events: \", npass)\n",
    "print(\"Rate of passing events: \", npass / len(mask) * 2760*11246 / 1e3, \" kHz\")\n",
    "# np.count_nonzero(mask) / len(mask) * 2760*11246 / 1e3 # this is the rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "dfed8153",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passing events:  766\n",
      "Rate of passing events:  12.718435519417993  kHz\n"
     ]
    }
   ],
   "source": [
    "mask = (ht > 190) & (btag_sum > 2.2) #& (ak.num(good_jets) < 4)\n",
    "npass = np.count_nonzero(mask)\n",
    "print(\"Number of passing events: \", npass)\n",
    "print(\"Rate of passing events: \", npass / len(mask) * 2760*11246 / 1e3, \" kHz\")\n",
    "# np.count_nonzero(mask) / len(mask) * 2760*11246 / 1e3 # this is the rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "2d763d0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passing events:  951\n",
      "Rate of passing events:  15.79012033807639  kHz\n"
     ]
    }
   ],
   "source": [
    "mask = (ht > 190) & (btagSum_max > 2.2) #& (ak.num(good_jets) < 4)\n",
    "npass = np.count_nonzero(mask)\n",
    "print(\"Number of passing events: \", npass)\n",
    "print(\"Rate of passing events: \", npass / len(mask) * 2760*11246 / 1e3, \" kHz\")\n",
    "# np.count_nonzero(mask) / len(mask) * 2760*11246 / 1e3 # this is the rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "bd5c20f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passing events:  731\n",
      "Rate of passing events:  12.137305959131272  kHz\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "12.137305959131272"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (ht >= 220) & (btagSum_max > 2.2) \n",
    "npass = np.count_nonzero(mask)\n",
    "print(\"Number of passing events: \", npass)\n",
    "print(\"Rate of passing events: \", npass / len(mask) * 2760*11246 / 1e3, \" kHz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "5667ba4c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passing events:  1277\n",
      "Rate of passing events:  21.20292709960415  kHz\n"
     ]
    }
   ],
   "source": [
    "mask = (offline_ht >= 220) & (btagSum_max > 2.2) \n",
    "npass = np.count_nonzero(mask)\n",
    "print(\"Number of passing events: \", npass)\n",
    "print(\"Rate of passing events: \", npass / len(mask) * 2760*11246 / 1e3, \" kHz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af4044c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bc163f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "f0bfab35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 1, 'HT offline')"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = ak.fill_none(btagSum_max, 0)\n",
    "y = offline_ht\n",
    "\n",
    "plt.figure(figsize = (8,6))\n",
    "_ = plt.hist2d(x,y, bins = (np.linspace(-1,5,50),np.linspace(0,500,50)), norm = LogNorm())\n",
    "plt.xlabel(\"Btag score sum\")\n",
    "plt.ylabel(\"HT offline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38af2bc9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "513596bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ak.fill_none(btagSum_max, 0)\n",
    "y = offline_ht\n",
    "\n",
    "ht_bins = np.linspace(0,500,30)\n",
    "btag_bins = np.linspace(0,4,30)\n",
    "\n",
    "# cnts = {}\n",
    "cnts2 = []\n",
    "\n",
    "for ht_bin in ht_bins:\n",
    "#     cnts[ht_bin] = {}\n",
    "    for btag_bin in btag_bins:\n",
    "        \n",
    "        mask = (offline_ht >= ht_bin) & (btagSum_max > btag_bin) \n",
    "        npass = np.count_nonzero(mask)\n",
    "        \n",
    "#         cnts[ht_bin][btag_bin] = npass\n",
    "        cnts2.append(npass)\n",
    "\n",
    "# plt.figure(figsize = (8,6))\n",
    "# _ = plt.hist2d(x,y, bins = (np.linspace(-1,5,50),np.linspace(0,500,50)), norm = LogNorm())\n",
    "# plt.xlabel(\"Btag score sum\")\n",
    "# plt.ylabel(\"HT offline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "9b4f4b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f929a918430>"
      ]
     },
     "execution_count": 337,
     "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": [
    "cnts = np.array(cnts2).reshape(10,10)\n",
    "plt.imshow(cnts, norm = LogNorm())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "255790c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rate_fact = 1/len(jets) * 2760*11246 / 1e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "643a162c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "XX, YY = np.meshgrid(ht_bins, btag_bins)\n",
    "\n",
    "fig = plt.figure(figsize = (13,7))\n",
    "ax1=plt.subplot(111)\n",
    "plot1 = ax1.pcolormesh(XX,YY,cnts)\n",
    "cbar = plt.colorbar(plot1,ax=ax1, pad = .015, aspect=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "31f24c16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "XX, YY = np.meshgrid(ht_bins, btag_bins)\n",
    "\n",
    "fig = plt.figure(figsize = (13,7))\n",
    "ax1=plt.subplot(111)\n",
    "plot1 = ax1.pcolormesh(XX,YY,cnts*rate_fact)\n",
    "cbar = plt.colorbar(plot1,ax=ax1, pad = .015, aspect=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c460aa95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bb1463c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "567725e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(cnts.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69b64775",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "be47aafe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.array(cnts).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "a24158f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.0: {0.0: 5703,\n",
       "  0.4444444444444444: 5700,\n",
       "  0.8888888888888888: 5663,\n",
       "  1.3333333333333333: 5238,\n",
       "  1.7777777777777777: 3753,\n",
       "  2.2222222222222223: 1540,\n",
       "  2.6666666666666665: 243,\n",
       "  3.1111111111111107: 18,\n",
       "  3.5555555555555554: 18,\n",
       "  4.0: 0},\n",
       " 55.55555555555556: {0.0: 5703,\n",
       "  0.4444444444444444: 5700,\n",
       "  0.8888888888888888: 5663,\n",
       "  1.3333333333333333: 5238,\n",
       "  1.7777777777777777: 3753,\n",
       "  2.2222222222222223: 1540,\n",
       "  2.6666666666666665: 243,\n",
       "  3.1111111111111107: 18,\n",
       "  3.5555555555555554: 18,\n",
       "  4.0: 0},\n",
       " 111.11111111111111: {0.0: 5698,\n",
       "  0.4444444444444444: 5695,\n",
       "  0.8888888888888888: 5658,\n",
       "  1.3333333333333333: 5234,\n",
       "  1.7777777777777777: 3750,\n",
       "  2.2222222222222223: 1539,\n",
       "  2.6666666666666665: 242,\n",
       "  3.1111111111111107: 17,\n",
       "  3.5555555555555554: 17,\n",
       "  4.0: 0},\n",
       " 166.66666666666669: {0.0: 5558,\n",
       "  0.4444444444444444: 5555,\n",
       "  0.8888888888888888: 5518,\n",
       "  1.3333333333333333: 5101,\n",
       "  1.7777777777777777: 3652,\n",
       "  2.2222222222222223: 1510,\n",
       "  2.6666666666666665: 239,\n",
       "  3.1111111111111107: 17,\n",
       "  3.5555555555555554: 17,\n",
       "  4.0: 0},\n",
       " 222.22222222222223: {0.0: 4326,\n",
       "  0.4444444444444444: 4325,\n",
       "  0.8888888888888888: 4293,\n",
       "  1.3333333333333333: 3967,\n",
       "  1.7777777777777777: 2834,\n",
       "  2.2222222222222223: 1187,\n",
       "  2.6666666666666665: 195,\n",
       "  3.1111111111111107: 14,\n",
       "  3.5555555555555554: 14,\n",
       "  4.0: 0},\n",
       " 277.77777777777777: {0.0: 2439,\n",
       "  0.4444444444444444: 2439,\n",
       "  0.8888888888888888: 2423,\n",
       "  1.3333333333333333: 2255,\n",
       "  1.7777777777777777: 1649,\n",
       "  2.2222222222222223: 744,\n",
       "  2.6666666666666665: 133,\n",
       "  3.1111111111111107: 6,\n",
       "  3.5555555555555554: 6,\n",
       "  4.0: 0},\n",
       " 333.33333333333337: {0.0: 1377,\n",
       "  0.4444444444444444: 1377,\n",
       "  0.8888888888888888: 1367,\n",
       "  1.3333333333333333: 1256,\n",
       "  1.7777777777777777: 932,\n",
       "  2.2222222222222223: 418,\n",
       "  2.6666666666666665: 85,\n",
       "  3.1111111111111107: 3,\n",
       "  3.5555555555555554: 3,\n",
       "  4.0: 0},\n",
       " 388.8888888888889: {0.0: 764,\n",
       "  0.4444444444444444: 764,\n",
       "  0.8888888888888888: 755,\n",
       "  1.3333333333333333: 691,\n",
       "  1.7777777777777777: 492,\n",
       "  2.2222222222222223: 225,\n",
       "  2.6666666666666665: 42,\n",
       "  3.1111111111111107: 2,\n",
       "  3.5555555555555554: 2,\n",
       "  4.0: 0},\n",
       " 444.44444444444446: {0.0: 451,\n",
       "  0.4444444444444444: 451,\n",
       "  0.8888888888888888: 446,\n",
       "  1.3333333333333333: 416,\n",
       "  1.7777777777777777: 304,\n",
       "  2.2222222222222223: 130,\n",
       "  2.6666666666666665: 22,\n",
       "  3.1111111111111107: 2,\n",
       "  3.5555555555555554: 2,\n",
       "  4.0: 0},\n",
       " 500.0: {0.0: 241,\n",
       "  0.4444444444444444: 241,\n",
       "  0.8888888888888888: 239,\n",
       "  1.3333333333333333: 223,\n",
       "  1.7777777777777777: 173,\n",
       "  2.2222222222222223: 77,\n",
       "  2.6666666666666665: 12,\n",
       "  3.1111111111111107: 1,\n",
       "  3.5555555555555554: 1,\n",
       "  4.0: 0}}"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cnts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de02fa88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fc24bb6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "224c57bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "497997da",
   "metadata": {},
   "outputs": [],
   "source": [
    "btag_sum_ft =np.array(btag_sum,dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "1bd5374d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import binned_statistic_2d, binned_statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "008a801d",
   "metadata": {},
   "outputs": [],
   "source": [
    "btag_sum = ak.fill_none(btag_sum, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "47e103e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEACAYAAACXqUyYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPi0lEQVR4nO3dX4xcZ33G8e8TR3WJojhGSLR17EIFSiJVCnRXQGulaaC0pJWluKl70aBqqZRpbypV3IS0DRekQFGlqFL/KWMpKoIKlUTEwqCQXOCm2IiaaQmNuKiS1sQRC6qKsBtKlCrJ24udhc1k/pzxztkz7+b7kUaefV+f2Z9fjZ89OvM776aUgiRp+V3WdQGSpGYMbEmqhIEtSZUwsCWpEga2JFXCwJakSrQe2Nnw0JT5XpKvJPn7tmuRpJq1GthJDgFPANdNmL8K+G3g54FvJ7m+zXokqWatBnYp5TxwA/A0QJKbk3wtyZeT/BzwdmBQNu7e+RPgXJv1SFLNWr8kUkp5Edi8nfJDwC8CtwJ/CrwO+Jkk/wQcB9J2PZJUq53+0PFNwGeBfwB+HHgOeBa4mY1LJ+/d4XokqRqX7/D3exL4VWAPcAT4OvDuUsqLSb6HZ9iSNNFOB/ZHgLPAOvCRUsq5JM8m+Srw38Bv7nA9klSNuFufJNWhlTPsJP4UkKRLVEoZe3nYOx0lqRJt92FPfNxxxx1T51dWVi752Gnz2znWupanru3WbV3W1VVd047vNLAlSYtjYEtSJRoF9rQNnJIcS3LnYsuSJI2aGdjTNnBKcjnwwRbqkiSNmNnWV0o5n+QG4PNjpnvAI5OOXV1dnfi6Fy9efNl8r9ej1+vNKgeAI0eOXPL8do6dxbrms93X3k7dbX3fWfPWNd/8bqir3+/T7/dfNjaaf41N+yRz5NPLh0e+vhJ4EPgl4M6RubLx0pduZWVlW8e3xbrmY13zsa757La6tmTn2BzezoeO7wfu5Uc78UmSWrSdOx2vZWOXvauBq5KcLaWcWkhVkqRXmDuwk9wNPFhKuX349U3AOwxrSWpX48Aupdwy/POekfHHgMcWXBdPfOsib/jAuM854Zt/9uuL/naStPSW9saZK294T9cljNW0k2WnWdd8rGs+1jWftupqZXvVzd36tvPak86uwTNsSbtTsrFJX5mwW1+rv8Bg3E+ZI0eObKu3UpJ2g5MnT3Ly5Mm5jvEMW5KWxKwz7KW9hi1JejkDW5IqYWBLUiUMbEmqhIEtSZWwrU+SOmBbnyRVzLY+SdolDGxJqoSBLUmVMLAlqRIGtiRVwsCWpErYhy1JHbAPW5IqZh+2JO0SBrYkVcLAlqRKNArsbHhoZOyKJI8meTxJv53yJEmbZgZ2kkPAE8B1I1O3AadKKW8BSpLDiy9PkrRpZltfKeV8khuA0baNJ4EvDZ9/Z9yxq6urjQvp9Xpj2wAlqWb9fp9+fzEXIRq39SV5uJRyy5jx24H3Ae8ppbwwHLOtT5LmNKutb1s3ziT5GPB64NbNsJYkteOSAzvJUYBSytrCqpEkTTR3W1+Su5NcD7wNeFeSU8OHHzpKUosan2FvXr8updwzHLpr+JAk7QBvnJGkShjYklQJt1eVpA64vaokVcztVSVplzCwJakSBrYkVcLAlqRKGNiSVAkDW5IqYR+2JHXAPmxJqph92JK0SxjYklQJA1uSKmFgS1IlDGxJqoRtfZLUAdv6JKlitvVJ0i5hYEtSJQxsSapEo8DOhodGxvYmeSDJmSRrrVQnSfqhmYGd5BDwBHDdyNRR4DRwI7CWZM/iy5MkbZoZ2KWU88ANwNMjUyvAoJTyEvAMcGjx5UmSNjXqwy6lvLjZqrfFPmB9+Hwd2A+c2/oXVldXGxfS6/XG9m1LUs36/T79fn8hr7WdG2cuAtewEdIHgQujf2EwGGzj5SWpfvOcjG72YU+ynS6RAbCS5DLgAHB+G68lSZph7sBOcneS64ETwGE2Pni8v5TywoJrkyRt0fiSSCnlluGf92wZPrbwiiRJY3njjCRVwsCWpEq4vaokdcDtVSWpYm6vKkm7hIEtSZUwsCWpEga2JFXCwJakShjYklQJ+7AlqQP2YUtSxezDlqRdwsCWpEoY2JJUCQNbkiphYEtSJWzrk6QO2NYnSRWzrU+SdgkDW5IqYWBLUiVmBnaSvUkeSHImydqW8auTfDHJ40ne12qVkqRGZ9hHgdPAjcBakj3D8duBjwNvBV7ZDiJJWqgmgb0CDEopLwHPAIeG4z8AXgtc0VJtkqQtmvRh7wPWh8/Xgf3AOeAzwFPAHwP3jjtwdXW1cSG9Xm9s37Yk1azf79Pv9xfyWk0C+yJwDRshfRC4MBz/KPAbwFngZJL7Sinf3XrgYDBYSJGSVKt5TkY3+7AnaXJJZACsJLkMOACcH46/FvivUsrzwP8CVzWqSJJ0SZoE9gngMBsfPN4P3JXkeuDDwCeSfA34RinlXGtVSpJmXxIZnkEfmzD9tsWWI0maxBtnJKkSBrYkVcLtVSWpA26vKkkVc3tVSdolDGxJqoSBLUmVMLAlqRIGtiRVwsCWpErYhy1JHbAPW5IqZh+2JO0SBrYkVcLAlqRKGNiSVAkDW5IqYWBLUiXsw5akDtiHLUkVsw9bknYJA1uSKjEzsJPsTfJAkjNJ1raMX5bkb5P8a5I/aLVKSVKjM+yjwGngRmAtyZ7h+C8D/wOsAO9O4tm6JLWoSciuAINSykvAM8Ch4fhNwJfKxieL722pPknSUJO2vn3A+vD5OrAfOAe8Djia5I+Az5VSPjJ64OrqauNCer3e2DZASapZv9+n3+8v5LWaBPZF4Bo2QvogcGE4/hzwDeD3gM8keXMp5cmtBw4Gg4UUKUm1mudkdLOtb5Iml0QGwMrwGvUB4Pxw/HHgQinlBeDZhq8lSbpETUL2BHCYjQ8e7wfuSnI98Gngd5L8C/DtUsq/t1alJGn2JZFSyvPAsQnT3mMuSTvEyxiSVAkDW5IqYWBLUiXcXlWSOuD2qpJUMbdXlaRdwsCWpEoY2JJUCQNbkiphYEtSJWzrk6QO2NYnSRWzrU+SdgkDW5IqYWBLUiUMbEmqhIEtSZUwsCWpEvZhS1IH7MOWpIrZhy1Ju4SBLUmVMLAlqRIzAzvJ3iQPJDmTZG3M/LEkd7ZSnSTph5qcYR8FTgM3AmtJ9mxOJLkc+GBLtUmStmjS1rcCnCilvJTkGeAQcG441wMemXTg6upq40J6vd7YNkBJqlm/36ff7y/ktZoE9j5gffh8HdgPnEtyJfBO4K+At487cDAYLKJGSarWPCejm219kzS5JHIRuGb4/CBwYfj8/cC9wOIbuSVJr9DkDHsArCQ5AxwAzg/HrwVuBq4GrkpytpRyqpUqJUmNAvsE8Engt4D7gLuSPFhKuR0gyU3AOwxrSWrXzMAupTwPHJsy/xjw2CKLkiS9kjfOSFIlDGxJqoTbq0pSB9xeVZIq5vaqkrRLGNiSVAkDW5IqYWBLUiUMbEmqhG19ktQB2/okqWK29UnSLmFgS1IlDGxJqoSBLUmVMLAlqRIGtiRVwj5sSeqAfdiSVDH7sCVplzCwJakSBrYkVWJmYCfZm+SBJGeSrG0ZvyLJo0keT9JvtUpJUqMz7KPAaeBGYC3JnuH4bcCpUspbgJLkcDslSpKgWWCvAINSykvAM8Ch4fiTwKeGz7/TQm2SpC2a9GHvA9aHz9eB/cC5UspXAJLcDhwG7hk9cHV1tXEhvV5vbN+2JNWs3+/T7y/mqnGTwL4IXAOcAw4CFzYnknwMeD1waynlhdEDB4PBQoqUpFrNczK62Yc9SZNLIgNgJcllwAHg/PCFjwKUUtZKKd9vVI0k6ZI1CewTbFzyOA3cD9yV5HrgbcC7kpwaPvzQUZJaNPOSSCnleeDYmKm7hg9J0g7wxhlJqoSBLUmVcHtVSeqA26tKUsXcXlWSdgkDW5IqYWBLUiUMbEmqhIEtSZUwsCWpEvZhS1IH7MPeActal6T62YctSbvE0gb2s49/oZPv+4YPfH7qo6u6ZlnUb7RYNOuaj3XN59VW19IG9ve/vpzBuKx1vdreuNtlXfOxrvm0VVerHzq+2ky7vg1e45a0PUt7hi1Jejnb+iSpA5fS1tdqYC/j9aVZly26+t5eLpFeXcadvB4/fnzqMZ1dEpn3J8s8x06b/8FT/zz12Fnz2zl22vx2/k2zbOe1l7WuWfPWNd+8dc03v526tnP8qy6wn3vq7NRjZ81v59hp88sajMta16x565pv3rrmm+8qsKvsEvnU2fM8OuXywndnzC+jWf+mX9nBWiQtp5mBnWQv8Engp4DjpZS/mzaudswK9G9/6+LEa+SzfoD5w0CqQ5Mz7KPAaeAvgS8m+UQp5cUp46rMtB8Ghr20REopUx/AnwOHh88/Abxx2vjw6+LDhw8fPi7tMSmPm3zouA9YHz5fB/bPGJcktaDJJZGLwDXAOeAgcGHG+MStASVJl67JGfYAWElyGXAAOD9jXJLUgpm/wGBLN8gB4D7gEPAg8J9bx0spH2+3VEl6dWvlN85IkhbP3fokqRIGtiRVovPATrI3yQNJziRZmzW+BHW9OcnXkpxKcmKn69pSR5I8NDLW6ZpNqauzNUtyRZJHkzyepL9lvOv316S6On1/JTmY5GySf0vyu1vGu16vSXUty//HY0nu3PJ1K+vVeWDzozsmbwTWkuyZMd51XW8C/rqUcnMp5dYdrgmAJIeAJ4DrRqY6XbMpdXW5ZrcBp0opbwFKksPD8a7fX5Pq6vr91QM+BLwV+P0t412v16S6ul4vklwOfHBkuJX1WobAXgEGpZSXgGfY6EKZNt51XW8EbktyOsl7d7gmAEop54EbgKdHpjpdsyl1dblmTwKfGj7/zpbxrt9fk+rq+v31WeAU8BrguS3jXa/XpLq6Xi/Y+GHyyMhYK+u1DIG9rHdSTvr+/8HGT9MjwB8m2bfDdQEw3LdltMWn6zWbVFdna1ZK+Uop5ZtJbgcOA5ubkne6VlPq6vT9VUr5KvCTbITM1t843fV6Taqr0/VKciXwTuBzI1OtrNcyBPbmHZMw/k7K0fGdMvb7l1IeKaV8tZTyPeDLwE/vcF3TdL1mY3W9Zkk+BrwbuLWU8sJwuPO1GlfXEqzV/lLKU8BPAL+W5DXDqU7Xa1JdXa8X8H7gXl55ktLKei1DYC/rnZRjv3+SDyX5heE1qZ8FvrnDdU3T9ZqN1eWaJTkKUEpZK6V8f8tUp2s1qa4leH8dT3It8H9Ahg/o/r01tq4lWK9rgQ8DfwH0ktw8HG9lvTq/cSZLeifllLousHE97ceAvyml3LeTdY3U+HAp5ZYkd7MEazalrgt0tGZJPsrGWeyzw6F/BD5N9++vSXVdoMP31/DDz+PA88D9wNUswXtrSl0XWIL/j0luAt4xrKO19eo8sCVJzSzDJRFJUgMGtiRVwsCWpEoY2JJUCQNbkiphYEtSJQxsSarE/wMPlRsJTqHPZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(btag_sum, bins = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "3fecf750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.986"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2c8ac00",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dbebfe0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "c577f356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BinnedStatistic2dResult(statistic=array([[  61.17912095,  214.85078779,  356.70249319,  488.37390476,\n",
       "         623.52101818,  737.952     ,  913.018     ,           nan,\n",
       "                  nan,           nan],\n",
       "       [          nan,  243.7835    ,  349.338     ,  514.03825   ,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,           nan],\n",
       "       [ 143.58125   ,  242.4276478 ,  375.0387551 ,  491.26457143,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,           nan],\n",
       "       [ 164.70419444,  242.91300164,  363.69170098,  487.93191667,\n",
       "         637.86266667,           nan,           nan,           nan,\n",
       "        1204.2075    ,           nan],\n",
       "       [ 162.33043519,  243.27899755,  360.63945088,  488.28685849,\n",
       "         628.34755556,  729.18966667,  942.4215    , 1021.418     ,\n",
       "        1218.435     , 1354.11566667],\n",
       "       [ 162.24581967,  244.4874039 ,  353.80298228,  490.03627273,\n",
       "         624.674     ,  779.02081818,  899.197     , 1006.1878    ,\n",
       "                  nan, 1343.5015    ],\n",
       "       [ 166.0336    ,  245.13671711,  358.78228638,  491.95307843,\n",
       "         648.3594    ,  731.448     ,  854.753     ,           nan,\n",
       "                  nan,           nan],\n",
       "       [ 156.02466667,  239.79377778,  368.01308824,  482.73022222,\n",
       "                  nan,  793.778     ,           nan,           nan,\n",
       "                  nan,           nan],\n",
       "       [          nan,           nan,           nan,           nan,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,           nan],\n",
       "       [ 135.9255    ,  243.89436364,  343.28566667,  483.483     ,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,           nan]]), x_edge=array([0. , 0.4, 0.8, 1.2, 1.6, 2. , 2.4, 2.8, 3.2, 3.6, 4. ]), y_edge=array([  47.986 ,  179.0145,  310.043 ,  441.0715,  572.1   ,  703.1285,\n",
       "        834.157 ,  965.1855, 1096.214 , 1227.2425, 1358.271 ]), binnumber=array([13, 13, 13, ..., 13, 13, 13]))"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = ak.fill_none(btagSum_max, 0)\n",
    "y = offline_ht\n",
    "\n",
    "ret = binned_statistic_2d(x,y,y, statistic = \"mean\")\n",
    "ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "ee729253",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ret.statistic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "a6b86096",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ret = binned_statistic_2d(x,y,Jets2, statistic = lambda jt: ak.ravel(data.jt))\n",
    "# ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "503e6818",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "d60b1d20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mask_4jet = ak.num(good_jets) >= 4\n",
    "\n",
    "# btagSum = ak.sum(mask_4jet[:,:4][\"seededConeExtendedPuppiJetBJetNN\"], axis = 1)\n",
    "# mask_btag = btagSum \n",
    "\n",
    "\n",
    "# mask = mask4jet & (mask_btag > 2.2) & ()\n",
    "\n",
    "# # mask = (offline_ht > 220) & (btag_sum > 2.2)\n",
    "# npass = np.count_nonzero(mask)\n",
    "# print(\"Number of passing events: \", npass)\n",
    "# print(\"Rate of passing events: \", npass / len(mask) * 2760*11246 / 1e3, \" kHz\")\n",
    "# # np.count_nonzero(mask) / len(mask) * 2760*11246 / 1e3 # this is the rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c2d2aa1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "5a57e5c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Jets2 = ak.zip({br.replace(\"seededConeExtendedPuppiJet\",\"\").lower():jets[br] for br in jets.fields})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "885363a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pt', 'et', 'eta', 'phi', 'bjetnn']"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jets2.fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "f8646cd9",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "mask = (Jets2.pt > 30) & (abs(Jets2.eta) < 2.4)\n",
    "_ = plt.hist(ak.ravel(Jets2[mask].bjetnn), bins = 40, log = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "f6db5deb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [30.2, 31.2, 54.2, ... -1, 0.537, -1] type='250 * float64'>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (Jets2.pt > 30) & (abs(Jets2.eta) < 2.4)\n",
    "mask2 = Jets2[mask].bjetnn < 0\n",
    "ak.ravel(Jets2[mask2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fed75a46",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "28119fa0",
   "metadata": {},
   "source": [
    "# Save output score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2c35e7c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# out_fname = \"/eos/user/a/alobanov/L1T/phase2/menu/ntuples/cache/V29/V29_MinBias_seededConeExtendedPuppiBtag.parquet\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "42a0abe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# btag_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "07200f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ak.to_parquet(ak.zip({\"seededConeExtendedPuppiBtagSum\":btag_sum}), out_fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2533b67d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ak.from_parquet(out_fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b203b4fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# jets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "89c41789",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_dict = {k:jets[k] for k in jets.fields }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cc624816",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ht = ak.from_parquet(\"/eos/user/a/alobanov/L1T/phase2/menu/ntuples/cache/V29_fullstat/V29_fullstat_MinBias_seededConePuppiHT.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2664e7cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ht.fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1baa3e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_dict[\"seededConeExtendedPuppiBtagSum\":btag_sum]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8da147d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "08d3a3a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = ak.from_parquet(\"/eos/home-a/alobanov/L1T/phase2/menu/ntuples/nano/V29_nano_test_GenJet.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "42b9136f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GenJet_eta',\n",
       " 'GenJet_mass',\n",
       " 'GenJet_phi',\n",
       " 'GenJet_pt',\n",
       " 'GenJet_hadronFlavour',\n",
       " 'GenJet_partonFlavour']"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9592bc86",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}