{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50501"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = uproot.open(\n",
    "    \"/work/cetin/Projektpraktikum/tracking_losses_ntuple_B_rad_length_endVelo2endUT.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
    ")\n",
    "\n",
    "# selektiere nur elektronen von B->K*ee\n",
    "allcolumns = file.arrays()\n",
    "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n",
    "\n",
    "ak.num(electrons, axis=0)\n",
    "# ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# electrons.type.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "found = electrons[~electrons.lost]\n",
    "lost = electrons[electrons.lost]\n",
    "\n",
    "eloss_found = (found[\"p\"] - found[\"p_end_scifi\"]) / found[\"p\"]\n",
    "eloss_lost = (lost[\"p\"] - lost[\"p_end_scifi\"]) / lost[\"p\"]\n",
    "\n",
    "eloss = (electrons[\"p\"] - electrons[\"p_end_scifi\"]) / electrons[\"p\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eloss_upstream_found = (found[\"p\"] - found[\"p_end_ut\"]) / found[\"p\"]\n",
    "# eloss_upstream_lost = (lost[\"p\"] - lost[\"p_end_ut\"]) / lost[\"p\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 50\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_lost),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#F05342\",\n",
    "    label=\"lost\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "# #2A9D8F another teal color\n",
    "plt.hist(\n",
    "    ak.to_numpy(eloss_found),\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"#107E7D\",\n",
    "    label=\"found\",\n",
    "    range=[0.001, 1],\n",
    ")\n",
    "\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"a.u.\")\n",
    "# plt.title(r'$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
    "plt.legend(title=\"LHCb Simulation\")\n",
    "\n",
    "plt.savefig(\"/work/cetin/Projektpraktikum/thesis/emitted_energy.pdf\", format=\"PDF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# magnet kick position\n",
    "input_tree = uproot.open(\n",
    "    {\n",
    "        \"/work/cetin/Projektpraktikum/param_data_B_default.root\": \"PrParameterisationData_2ece6184.PrDebugTrackingTool/Tuple;1\"\n",
    "    }\n",
    ")\n",
    "array = input_tree.arrays()\n",
    "array = array[array.isElectron & (array.fromB)]\n",
    "array[\"dSlope_fringe\"] = array[\"tx_ref\"] - array[\"tx\"]\n",
    "array[\"z_mag_x_fringe\"] = (\n",
    "    array[\"x\"]\n",
    "    - array[\"x_ref\"]\n",
    "    - array[\"tx\"] * array[\"z\"]\n",
    "    + array[\"tx_ref\"] * array[\"z_ref\"]\n",
    ") / array[\"dSlope_fringe\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(exptext: Custom Text(0.05, 0.95, 'LHCb'),\n",
       " expsuffix: Custom Text(0.05, 0.955, 'Simulation'))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.hist(\n",
    "    array[\"z_mag_x_fringe\"], bins=50, range=[5150, 5300], color=\"#2A9D8F\", density=True\n",
    ")\n",
    "plt.xlabel(r\"z$_{Mag}$ [mm]\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50501"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.num(array, axis=0)"
   ]
  },
  {
   "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
}