{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import colormaps\n",
    "\n",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "plt.rcParams[\"savefig.dpi\"] = 600\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = uproot.open(\n",
    "    \"/work/cetin/LHCb/reco_tuner/data_matching/resolutions_and_effs_B_default_thesis.root:Track/MatchTrackChecker_8319528f/Match;1\",\n",
    ")\n",
    "\n",
    "P_recoed = file[\"07_long_electrons_P_reconstructed;1\"].to_numpy()\n",
    "P_recoable = file[\"07_long_electrons_P_reconstructible;1\"].to_numpy()\n",
    "\n",
    "Pt_recoed = file[\"07_long_electrons_Pt_reconstructed;1\"].to_numpy()\n",
    "Pt_recoable = file[\"07_long_electrons_Pt_reconstructible;1\"].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "control eff:  0.6145546589237905\n",
      "new eff:  0.6155293168395326\n",
      "control eff:  0.6173168233870217\n",
      "new eff:  0.6176270902698983\n",
      "212152\n",
      "130379.0\n",
      "213171.0\n",
      "131213.0\n"
     ]
    }
   ],
   "source": [
    "P_Velo_recoed = file[\"07_long_electrons_EndVelo_P_reconstructed;1\"].to_numpy()\n",
    "P_Velo_recoable = file[\n",
    "    \"07_long_electrons_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[\"07_long_electrons_EndVelo_Pt_reconstructed;1\"].to_numpy(\n",
    ")\n",
    "Pt_Velo_recoable = file[\n",
    "    \"07_long_electrons_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]))\n",
    "\n",
    "print(np.sum(P_recoable[0], dtype=int))\n",
    "print(np.sum(P_recoed[0]))\n",
    "print(np.sum(P_Velo_recoable[0]))\n",
    "print(np.sum(P_Velo_recoed[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         98000.,  99000., 100000.]),\n",
       " array([    0.,   100.,   200.,   300.,   400.,   500.,   600.,   700.,\n",
       "          800.,   900.,  1000.,  1100.,  1200.,  1300.,  1400.,  1500.,\n",
       "         1600.,  1700.,  1800.,  1900.,  2000.,  2100.,  2200.,  2300.,\n",
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       "         3200.,  3300.,  3400.,  3500.,  3600.,  3700.,  3800.,  3900.,\n",
       "         4000.,  4100.,  4200.,  4300.,  4400.,  4500.,  4600.,  4700.,\n",
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       "         7200.,  7300.,  7400.,  7500.,  7600.,  7700.,  7800.,  7900.,\n",
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       "         9600.,  9700.,  9800.,  9900., 10000.]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PdP_Velo_recoed = file[\"07_long_electrons_EndVelo_PdP_reconstructed;1\"].to_numpy()\n",
    "PdP_Velo_recoable = file[\"07_long_electrons_EndVelo_PdP_reconstructible;1\"].to_numpy()\n",
    "\n",
    "PdP_Velo_recoable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_733517/2776815717.py:1: RuntimeWarning: invalid value encountered in divide\n",
      "  effs = np.divide(PdP_Velo_recoed[0], PdP_Velo_recoable[0])\n"
     ]
    },
    {
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       "..       ...       ...       ...  ...  ..  ..  ...  ..  ...  ..  ..  ..  ...   \n",
       "95       NaN  1.000000  1.000000  ... NaN NaN  NaN NaN  NaN NaN NaN NaN  NaN   \n",
       "96       NaN       NaN       NaN  ... NaN NaN  NaN NaN  1.0 NaN NaN NaN  NaN   \n",
       "97  1.000000  1.000000  0.000000  ... NaN NaN  NaN NaN  NaN NaN NaN NaN  1.0   \n",
       "98       NaN  1.000000  1.000000  ... NaN NaN  NaN NaN  NaN NaN NaN NaN  NaN   \n",
       "99       NaN  1.000000  1.000000  ... NaN NaN  1.0 NaN  NaN NaN NaN NaN  NaN   \n",
       "\n",
       "    99  \n",
       "0  NaN  \n",
       "1  NaN  \n",
       "2  NaN  \n",
       "3  NaN  \n",
       "4  NaN  \n",
       "..  ..  \n",
       "95 NaN  \n",
       "96 NaN  \n",
       "97 NaN  \n",
       "98 NaN  \n",
       "99 NaN  \n",
       "\n",
       "[100 rows x 100 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "effs = np.divide(PdP_Velo_recoed[0], PdP_Velo_recoable[0])\n",
    "\n",
    "df = pd.DataFrame(effs)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig = plt.figure(figsize=(15, 7))\n",
    "ax = sns.heatmap(\n",
    "    effs,\n",
    "    robust=True,\n",
    "    square=False,\n",
    "    cmap=colormaps[\"rainbow\"],\n",
    "    xticklabels=False,\n",
    "    yticklabels=False,\n",
    "    vmax=1,\n",
    "    cbar_kws={\n",
    "        \"label\": \"Efficiency\",\n",
    "        \"pad\": 0.005,\n",
    "        \"shrink\": 1,\n",
    "        \"ticks\": [0.2, 0.4, 0.6, 0.8, 1.0],\n",
    "        \"aspect\": 15,\n",
    "    },\n",
    ")\n",
    "ax.set_ylabel(f\"$P$ [MeV]\")\n",
    "ax.set_xlabel(f\"$\\Delta P$ [MeV]\")\n",
    "ax.patch.set_edgecolor(\"black\")\n",
    "\n",
    "ax.set_yticks([0, 19, 39, 59, 79, 99], [0, 20000, 40000, 60000, 80000, 100000])\n",
    "ax.set_xticks([0, 19, 39, 59, 79, 99], [0, 2000, 4000, 6000, 8000, 10000])\n",
    "ax.invert_yaxis()\n",
    "# ax.set_ylim(0, 59)\n",
    "# ax.set_xlim(0, 59)\n",
    "\n",
    "ax.patch.set_linewidth(2)\n",
    "# ax.set_yticklabels([])\n",
    "# ax.set_title(\"EndVELO to EndUT $x/X_0$\", size=35)\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/Efficiency_PdP_Velo_hist2d.pdf\",\n",
    "#     format=\"PDF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([6.4927e+04, 8.7210e+03, 5.4020e+03, 4.0560e+03, 3.3500e+03,\n",
       "        2.8240e+03, 2.3990e+03, 2.0600e+03, 1.8280e+03, 1.5610e+03,\n",
       "        1.5130e+03, 1.3480e+03, 1.2090e+03, 1.1250e+03, 1.0270e+03,\n",
       "        9.3000e+02, 9.0200e+02, 8.2300e+02, 7.8100e+02, 6.7500e+02,\n",
       "        6.8700e+02, 6.5400e+02, 6.6000e+02, 6.2300e+02, 5.3100e+02,\n",
       "        5.1900e+02, 5.1200e+02, 4.4000e+02, 4.7200e+02, 4.6700e+02,\n",
       "        4.1100e+02, 4.1300e+02, 3.7700e+02, 3.4600e+02, 3.5400e+02,\n",
       "        3.3100e+02, 3.3400e+02, 2.8600e+02, 3.0600e+02, 2.8800e+02,\n",
       "        2.7000e+02, 2.8100e+02, 2.5100e+02, 2.2500e+02, 2.7100e+02,\n",
       "        2.3000e+02, 2.4100e+02, 2.1700e+02, 2.2900e+02, 2.0700e+02,\n",
       "        2.2500e+02, 1.8500e+02, 1.9000e+02, 1.8600e+02, 1.9100e+02,\n",
       "        1.9900e+02, 1.8400e+02, 1.8500e+02, 1.7200e+02, 1.5700e+02,\n",
       "        1.5300e+02, 1.7100e+02, 1.3700e+02, 1.5200e+02, 1.4500e+02,\n",
       "        1.4600e+02, 1.2900e+02, 1.3900e+02, 1.4300e+02, 1.3700e+02,\n",
       "        1.1600e+02, 1.2200e+02, 1.2600e+02, 1.1900e+02, 1.1800e+02,\n",
       "        1.3100e+02, 1.1400e+02, 1.0700e+02, 1.1700e+02, 1.1700e+02,\n",
       "        1.1500e+02, 1.3000e+02, 8.7000e+01, 1.0300e+02, 9.9000e+01,\n",
       "        1.0100e+02, 7.7000e+01, 8.6000e+01, 1.0500e+02, 7.6000e+01,\n",
       "        8.5000e+01, 8.6000e+01, 7.4000e+01, 8.6000e+01, 7.9000e+01,\n",
       "        6.8000e+01, 7.1000e+01, 7.9000e+01, 6.9000e+01, 6.3000e+01]),\n",
       " array([    0.,   100.,   200.,   300.,   400.,   500.,   600.,   700.,\n",
       "          800.,   900.,  1000.,  1100.,  1200.,  1300.,  1400.,  1500.,\n",
       "         1600.,  1700.,  1800.,  1900.,  2000.,  2100.,  2200.,  2300.,\n",
       "         2400.,  2500.,  2600.,  2700.,  2800.,  2900.,  3000.,  3100.,\n",
       "         3200.,  3300.,  3400.,  3500.,  3600.,  3700.,  3800.,  3900.,\n",
       "         4000.,  4100.,  4200.,  4300.,  4400.,  4500.,  4600.,  4700.,\n",
       "         4800.,  4900.,  5000.,  5100.,  5200.,  5300.,  5400.,  5500.,\n",
       "         5600.,  5700.,  5800.,  5900.,  6000.,  6100.,  6200.,  6300.,\n",
       "         6400.,  6500.,  6600.,  6700.,  6800.,  6900.,  7000.,  7100.,\n",
       "         7200.,  7300.,  7400.,  7500.,  7600.,  7700.,  7800.,  7900.,\n",
       "         8000.,  8100.,  8200.,  8300.,  8400.,  8500.,  8600.,  8700.,\n",
       "         8800.,  8900.,  9000.,  9100.,  9200.,  9300.,  9400.,  9500.,\n",
       "         9600.,  9700.,  9800.,  9900., 10000.]))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dP_Velo_recoed = file[\"07_long_electrons_EndVelo_dP_reconstructed;1\"].to_numpy()\n",
    "dP_Velo_recoable = file[\"07_long_electrons_EndVelo_dP_reconstructible;1\"].to_numpy()\n",
    "\n",
    "dP_Velo_recoed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "# plt.bar(\n",
    "#     dP_Velo_recoable[1][1:],\n",
    "#     dP_Velo_recoable[0] / np.max(dP_Velo_recoable[0]),\n",
    "#     alpha=0.5,\n",
    "#     color=\"#107E7D\",\n",
    "#     label=\"p distribution, e\",\n",
    "# )\n",
    "plt.errorbar(\n",
    "    dP_Velo_recoable[1][1:],\n",
    "    dP_Velo_recoed[0] / dP_Velo_recoable[0],\n",
    "    color=\"#107E7D\",\n",
    "    label=\"Efficiency\",\n",
    "    fmt=\"^\",\n",
    "    ms=10,\n",
    ")\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel(r\"$dp$ [MeV]\")\n",
    "plt.ylabel(\"Efficiency of Long Tracks\")\n",
    "plt.legend(loc=\"best\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\"/work/cetin/Projektpraktikum/thesis/Efficiency_dP_Velo.pdf\",\n",
    "#             format=\"PDF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tuner",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}