{
 "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": [
    {
     "data": {
      "text/plain": [
       "50501"
      ]
     },
     "execution_count": 2,
     "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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50501 * {\n",
      "    all_endvtx_types_length: int32,\n",
      "    all_endvtx_types: var * float32,\n",
      "    all_endvtx_x_length: int32,\n",
      "    all_endvtx_x: var * float32,\n",
      "    all_endvtx_y_length: int32,\n",
      "    all_endvtx_y: var * float32,\n",
      "    all_endvtx_z_length: int32,\n",
      "    all_endvtx_z: var * float32,\n",
      "    brem_photons_pe_length: int32,\n",
      "    brem_photons_pe: var * float32,\n",
      "    brem_photons_px_length: int32,\n",
      "    brem_photons_px: var * float32,\n",
      "    brem_photons_py_length: int32,\n",
      "    brem_photons_py: var * float32,\n",
      "    brem_photons_pz_length: int32,\n",
      "    brem_photons_pz: var * float32,\n",
      "    brem_vtx_x_length: int32,\n",
      "    brem_vtx_x: var * float32,\n",
      "    brem_vtx_y_length: int32,\n",
      "    brem_vtx_y: var * float32,\n",
      "    brem_vtx_z_length: int32,\n",
      "    brem_vtx_z: var * float32,\n",
      "    endvtx_type: int32,\n",
      "    endvtx_x: float64,\n",
      "    endvtx_y: float64,\n",
      "    endvtx_z: float64,\n",
      "    energy: float64,\n",
      "    eta: float64,\n",
      "    event_count: int32,\n",
      "    fromB: bool,\n",
      "    fromD: bool,\n",
      "    fromDecay: bool,\n",
      "    fromHadInt: bool,\n",
      "    fromPV: bool,\n",
      "    fromPairProd: bool,\n",
      "    fromSignal: bool,\n",
      "    fromStrange: bool,\n",
      "    ideal_state_770_qop: float64,\n",
      "    ideal_state_770_tx: float64,\n",
      "    ideal_state_770_ty: float64,\n",
      "    ideal_state_770_x: float64,\n",
      "    ideal_state_770_y: float64,\n",
      "    ideal_state_770_z: float64,\n",
      "    ideal_state_9410_qop: float64,\n",
      "    ideal_state_9410_tx: float64,\n",
      "    ideal_state_9410_ty: float64,\n",
      "    ideal_state_9410_x: float64,\n",
      "    ideal_state_9410_y: float64,\n",
      "    ideal_state_9410_z: float64,\n",
      "    isElectron: bool,\n",
      "    isKaon: bool,\n",
      "    isMuon: bool,\n",
      "    isPion: bool,\n",
      "    isProton: bool,\n",
      "    lost: bool,\n",
      "    lost_in_track_fit: bool,\n",
      "    match_fraction: float32,\n",
      "    mc_chi2: float32,\n",
      "    mc_dSlope: float32,\n",
      "    mc_dSlopeY: float32,\n",
      "    mc_distX: float32,\n",
      "    mc_distY: float32,\n",
      "    mc_teta2: float32,\n",
      "    mc_zMag: float32,\n",
      "    mcp_idx: int32,\n",
      "    mother_id: int32,\n",
      "    mother_key: int32,\n",
      "    originvtx_type: int32,\n",
      "    originvtx_x: float64,\n",
      "    originvtx_y: float64,\n",
      "    originvtx_z: float64,\n",
      "    p: float64,\n",
      "    p_end_scifi: float64,\n",
      "    p_end_ut: float64,\n",
      "    p_end_velo: float64,\n",
      "    phi: float64,\n",
      "    pid: int32,\n",
      "    pt: float64,\n",
      "    px: float64,\n",
      "    py: float64,\n",
      "    pz: float64,\n",
      "    quality: int32,\n",
      "    rad_length_frac: float64,\n",
      "    scifi_hit_pos_x_length: int32,\n",
      "    scifi_hit_pos_x: var * float32,\n",
      "    scifi_hit_pos_y_length: int32,\n",
      "    scifi_hit_pos_y: var * float32,\n",
      "    scifi_hit_pos_z_length: int32,\n",
      "    scifi_hit_pos_z: var * float32,\n",
      "    track_p: float64,\n",
      "    track_pt: float64,\n",
      "    tx: float64,\n",
      "    ty: float64,\n",
      "    ut_hit_pos_x_length: int32,\n",
      "    ut_hit_pos_x: var * float32,\n",
      "    ut_hit_pos_y_length: int32,\n",
      "    ut_hit_pos_y: var * float32,\n",
      "    ut_hit_pos_z_length: int32,\n",
      "    ut_hit_pos_z: var * float32,\n",
      "    velo_hit_pos_x_length: int32,\n",
      "    velo_hit_pos_x: var * float32,\n",
      "    velo_hit_pos_y_length: int32,\n",
      "    velo_hit_pos_y: var * float32,\n",
      "    velo_hit_pos_z_length: int32,\n",
      "    velo_hit_pos_z: var * float32,\n",
      "    velo_track_idx: int32,\n",
      "    velo_track_tx: float64,\n",
      "    velo_track_ty: float64,\n",
      "    velo_track_x: float64,\n",
      "    velo_track_y: float64,\n",
      "    velo_track_z: float64\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "electrons.type.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut_prop: bool = electrons.p_end_velo > 3e3\n",
    "found = electrons[~electrons.lost & cut_prop]\n",
    "lost = electrons[electrons.lost & cut_prop]\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\"]\n",
    "eloss_magnet_found = ak.to_numpy(\n",
    "    (found[\"p_end_velo\"] - found[\"p_end_scifi\"]) / found[\"p_end_velo\"]\n",
    ")\n",
    "eloss_magnet_lost = ak.to_numpy(\n",
    "    (lost[\"p_end_velo\"] - lost[\"p_end_scifi\"]) / lost[\"p_end_velo\"]\n",
    ")"
   ]
  },
  {
   "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": 18,
   "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_magnet_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_magnet_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\"$E_\\gamma/E_{VELO}$\")\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",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/emitted_energy_endVelo2endT.pdf\",\n",
    "#     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(array[\"z_mag_x_fringe\"],\n",
    "         bins=50,\n",
    "         range=[5150, 5300],\n",
    "         color=\"#2A9D8F\",\n",
    "         density=True)\n",
    "plt.xlabel(r\"z$_{Mag}$ [mm]\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "mplhep."
   ]
  },
  {
   "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
}