{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import mplhep\n",
    "from mpl_toolkits import mplot3d\n",
    "import itertools\n",
    "import awkward as ak\n",
    "from scipy.optimize import curve_fit\n",
    "from utils.components import unique_name_ext_re\n",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "plt.rcParams[\"savefig.dpi\"] = 600\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = uproot.open(\n",
    "    \"/work/cetin/LHCb/reco_tuner/data/resolutions_and_effs_B_thesis.root:Track/MatchTrackChecker_8319528f/Match;1\",\n",
    ")\n",
    "\n",
    "P_recoed = file[\"01_long_P_reconstructed;1\"].to_numpy()\n",
    "P_recoable = file[\"01_long_P_reconstructible;1\"].to_numpy()\n",
    "\n",
    "Pt_recoed = file[\"01_long_Pt_reconstructed;1\"].to_numpy()\n",
    "Pt_recoable = file[\"01_long_Pt_reconstructible;1\"].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "control eff:  0.8626619913200968\n",
      "new eff:  0.8626619913200968\n",
      "control eff:  0.8629752409817771\n",
      "new eff:  0.8629752409817771\n"
     ]
    }
   ],
   "source": [
    "P_Velo_recoed = file[\"01_long_EndVelo_P_reconstructed;1\"].to_numpy()\n",
    "P_Velo_recoable = file[\"01_long_EndVelo_P_reconstructible;1\"].to_numpy()\n",
    "\n",
    "print(\"control eff: \", np.sum(P_recoed[0]) / np.sum(P_recoable[0]))\n",
    "print(\"new eff: \", np.sum(P_Velo_recoed[0]) / np.sum(P_Velo_recoable[0]))\n",
    "\n",
    "Pt_Velo_recoed = file[\"01_long_EndVelo_Pt_reconstructed;1\"].to_numpy()\n",
    "Pt_Velo_recoable = file[\"01_long_EndVelo_Pt_reconstructible;1\"].to_numpy()\n",
    "\n",
    "print(\"control eff: \", np.sum(Pt_recoed[0]) / np.sum(Pt_recoable[0]))\n",
    "print(\"new eff: \", np.sum(Pt_Velo_recoed[0]) / np.sum(Pt_Velo_recoable[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         20994.,  19297.,  17662.,  16196.,  14804.,  13679.,  12636.,\n",
       "         11687.,  10738.,  10125.,   9329.,   8681.,   8080.,   7424.,\n",
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       "          1875.,   1791.,   1684.,   1557.,   1559.,   1418.,   1389.,\n",
       "          1321.,   1245.,   1164.,   1122.,   1055.,   1008.,    961.,\n",
       "           920.,    899.,    833.,    839.,    746.,    744.,    725.,\n",
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       "           408.,    392.,    349.,    328.,    328.,    289.,    308.,\n",
       "           271.,    297.]),\n",
       " array([     0.,   1000.,   2000.,   3000.,   4000.,   5000.,   6000.,\n",
       "          7000.,   8000.,   9000.,  10000.,  11000.,  12000.,  13000.,\n",
       "         14000.,  15000.,  16000.,  17000.,  18000.,  19000.,  20000.,\n",
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       "         63000.,  64000.,  65000.,  66000.,  67000.,  68000.,  69000.,\n",
       "         70000.,  71000.,  72000.,  73000.,  74000.,  75000.,  76000.,\n",
       "         77000.,  78000.,  79000.,  80000.,  81000.,  82000.,  83000.,\n",
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       "         91000.,  92000.,  93000.,  94000.,  95000.,  96000.,  97000.,\n",
       "         98000.,  99000., 100000.]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P_recoed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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