{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\t\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "import itertools\n",
    "import awkward as ak\n",
    "from scipy.optimize import curve_fit\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/lhcb/cetin/.config/matplotlib/matplotlibrc'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.matplotlib_fname()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10522"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "\n",
    "#selektiere nur elektronen von B->K*ee und nur solche mit einem momentum von ueber 5 GeV \n",
    "allcolumns = file.arrays()\n",
    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 9056\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 1466\n",
    "\n",
    "ak.num(found, axis=0) + ak.num(lost, axis=0)\n",
    "#ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 10\n",
      "10 10\n",
      "10 10\n",
      "15 15\n",
      "7 7\n",
      "7 7\n",
      "8 8\n",
      "3 3\n",
      "5 5\n",
      "15 15\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "\tprint(found[\"brem_vtx_x_length\"][i], found[\"brem_vtx_z_length\"][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eff all =  0.8606728758791105 +/- 0.003375885792719708\n"
     ]
    }
   ],
   "source": [
    "def t_eff(found, lost, axis = 0):\n",
    "    sel = ak.num(found, axis=axis)\n",
    "    des = ak.num(lost, axis=axis)\n",
    "    return sel/(sel + des)\n",
    "\n",
    "def eff_err(found, lost):\n",
    "    n_f = ak.num(found, axis=0)\n",
    "    n_all = ak.num(found, axis=0) + ak.num(lost,axis=0)\n",
    "    return 1/n_all * np.sqrt(np.abs(n_f*(1-n_f/n_all)))\n",
    "\n",
    "\n",
    "print(\"eff all = \", t_eff(found, lost), \"+/-\", eff_err(found, lost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>{energy: 4.62e+04,\n",
       " photon_length: 10,\n",
       " brem_photons_pe: [3.26e+03, 4.45e+03, 178, ..., 825, 8.99e+03, 3.48e+03],\n",
       " brem_vtx_z: [162, 187, 387, 487, ..., 9.49e+03, 1.21e+04, 1.21e+04, 1.21e+04],\n",
       " brem_vtx_x: [-5.37, -7, -20, -26.5, ..., -1.24e+03, -1.24e+03, -1.25e+03]}\n",
       "-------------------------------------------------------------------------------\n",
       "type: {\n",
       "    energy: float64,\n",
       "    photon_length: int64,\n",
       "    brem_photons_pe: var * float64,\n",
       "    brem_vtx_z: var * float64,\n",
       "    brem_vtx_x: var * float64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {energy: 4.62e+04, ...} type='{energy: float64, photon_length: int6...'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#try excluding all photons that originate from a vtx @ z>9500mm\n",
    "#ignore all brem vertices @ z>9500mm \n",
    "\n",
    "#found\n",
    "\n",
    "brem_e_f = found[\"brem_photons_pe\"]\n",
    "brem_z_f = found[\"brem_vtx_z\"]\n",
    "brem_x_f = found[\"brem_vtx_x\"]\n",
    "e_f = found[\"energy\"]\n",
    "length_f = found[\"brem_vtx_z_length\"]\n",
    "\n",
    "brem_f = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(found,axis=0)):\n",
    "    brem_f.begin_record()\n",
    "    #[:,\"energy\"] energy\n",
    "    brem_f.field(\"energy\").append(e_f[itr])\n",
    "    #[:,\"photon_length\"] number of vertices\n",
    "    brem_f.field(\"photon_length\").integer(length_f[itr])\n",
    "    #[:,\"brem_photons_pe\",:] photon energy \n",
    "    brem_f.field(\"brem_photons_pe\").append(brem_e_f[itr])\n",
    "    #[:,\"brem_vtx_z\",:] brem vtx z\n",
    "    brem_f.field(\"brem_vtx_z\").append(brem_z_f[itr])\n",
    "    brem_f.field(\"brem_vtx_x\").append(brem_x_f[itr])\n",
    "    brem_f.end_record()\n",
    "\n",
    "brem_f = ak.Array(brem_f)\n",
    "\n",
    "#lost\n",
    "\n",
    "brem_e_l = lost[\"brem_photons_pe\"]\n",
    "brem_z_l = lost[\"brem_vtx_z\"]\n",
    "brem_x_l = lost[\"brem_vtx_x\"]\n",
    "e_l = lost[\"energy\"]\n",
    "length_l = lost[\"brem_vtx_z_length\"]\n",
    "\n",
    "brem_l = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(lost,axis=0)):\n",
    "    brem_l.begin_record()\n",
    "    #[:,\"energy\"] energy\n",
    "    brem_l.field(\"energy\").append(e_l[itr])\n",
    "    #[:,\"photon_length\"] number of vertices\n",
    "    brem_l.field(\"photon_length\").integer(length_l[itr])\n",
    "    #[:,\"brem_photons_pe\",:] photon energy \n",
    "    brem_l.field(\"brem_photons_pe\").append(brem_e_l[itr])\n",
    "    #[:,\"brem_vtx_z\",:] brem vtx z\n",
    "    brem_l.field(\"brem_vtx_z\").append(brem_z_l[itr])\n",
    "    brem_l.field(\"brem_vtx_x\").append(brem_x_l[itr])\n",
    "    brem_l.end_record()\n",
    "\n",
    "brem_l = ak.Array(brem_l)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "brem_f[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_brem_found = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(brem_f, axis=0)):\n",
    "    acc_brem_found.begin_record()\n",
    "    acc_brem_found.field(\"energy\").real(brem_f[itr,\"energy\"])\n",
    "    \n",
    "    acc_brem_found.field(\"brem_photons_pe\")\n",
    "    acc_brem_found.begin_list()\n",
    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
    "        if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n",
    "            continue\n",
    "        else:\n",
    "            acc_brem_found.real(brem_f[itr,\"brem_photons_pe\", jentry])\n",
    "            \n",
    "            #acc_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
    "    acc_brem_found.end_list()\n",
    "    \n",
    "    acc_brem_found.field(\"brem_vtx_z\")\n",
    "    acc_brem_found.begin_list()\n",
    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
    "        if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n",
    "            continue\n",
    "        else:\n",
    "            acc_brem_found.real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
    "    acc_brem_found.end_list()\n",
    "    \n",
    "    acc_brem_found.field(\"brem_vtx_x\")\n",
    "    acc_brem_found.begin_list()\n",
    "    for jentry in range(brem_f[itr, \"photon_length\"]):\n",
    "        if brem_f[itr, \"brem_vtx_z\", jentry]>9500:\n",
    "            continue\n",
    "        else:\n",
    "            acc_brem_found.real(brem_f[itr, \"brem_vtx_x\",jentry])\n",
    "    acc_brem_found.end_list()\n",
    "    \n",
    "\n",
    "    \n",
    "    acc_brem_found.end_record()\n",
    "\n",
    "acc_brem_found = ak.Array(acc_brem_found)\n",
    "\n",
    "\n",
    "\n",
    "acc_brem_lost = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(brem_l, axis=0)):\n",
    "    acc_brem_lost.begin_record()\n",
    "    acc_brem_lost.field(\"energy\").real(brem_l[itr,\"energy\"])\n",
    "    \n",
    "    acc_brem_lost.field(\"brem_photons_pe\")\n",
    "    acc_brem_lost.begin_list()\n",
    "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
    "        if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n",
    "            continue\n",
    "        else:\n",
    "            acc_brem_lost.real(brem_l[itr,\"brem_photons_pe\", jentry])\n",
    "            \n",
    "            #acc_brem_found.field(\"brem_vtx_z\").real(brem_f[itr, \"brem_vtx_z\",jentry])\n",
    "    acc_brem_lost.end_list()\n",
    "    \n",
    "    acc_brem_lost.field(\"brem_vtx_z\")\n",
    "    acc_brem_lost.begin_list()\n",
    "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
    "        if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n",
    "            continue\n",
    "        else:\n",
    "            acc_brem_lost.real(brem_l[itr, \"brem_vtx_z\",jentry])\n",
    "    acc_brem_lost.end_list()\n",
    "    \n",
    "    acc_brem_lost.field(\"brem_vtx_x\")\n",
    "    acc_brem_lost.begin_list()\n",
    "    for jentry in range(brem_l[itr, \"photon_length\"]):\n",
    "        if brem_l[itr, \"brem_vtx_z\", jentry]>9500:\n",
    "            continue\n",
    "        else:\n",
    "            acc_brem_lost.real(brem_l[itr, \"brem_vtx_x\",jentry])\n",
    "    acc_brem_lost.end_list()\n",
    "    \n",
    "    acc_brem_lost.end_record()\n",
    "\n",
    "acc_brem_lost = ak.Array(acc_brem_lost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9056"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.num(acc_brem_found,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nph_e = found[\"brem_photons_pe\"]\\nevent_cut = ak.all(ph_e<cutoff_energy,axis=1)\\nph_e = ph_e[event_cut]\\n'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\"\"\"\n",
    "ph_e = found[\"brem_photons_pe\"]\n",
    "event_cut = ak.all(ph_e<cutoff_energy,axis=1)\n",
    "ph_e = ph_e[event_cut]\n",
    "\"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "cutoff energy = 350MeV, sample size: 693\n",
      "eff =  0.9481 +/- 0.0084\n"
     ]
    }
   ],
   "source": [
    "#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n",
    "#von energie der photonen abmachen\n",
    "#scan ab welcher energie der photonen die effizienz abfällt\n",
    "\n",
    "#abhängigkeit vom ort der emission untersuchen   <- noch nicht gemacht\n",
    "\n",
    "\n",
    "\n",
    "#idea: we make an event cut st all events that contain a photon of energy > cutoff_energy are not included\n",
    "\"\"\"\n",
    "ph_e = acc_brem_found[\"brem_photons_pe\"]\n",
    "event_cut = ak.all(ph_e<cutoff_energy,axis=1)\n",
    "ph_e = ph_e[event_cut]\n",
    "\"\"\"\n",
    "\n",
    "efficiencies_found = []\n",
    "deff_found = []\n",
    "\n",
    "\n",
    "for cutoff_energy in range(0,30050,1000):\n",
    "\tnobrem_f = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
    "\tnobrem_l = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
    "\n",
    "\tif ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0)==0:\n",
    "\t\tefficiencies_found.append(0)\n",
    "\t\tdeff_found.append(0)\n",
    "\t\tcontinue\n",
    "\t\n",
    "\teff = t_eff(nobrem_f, nobrem_l)\n",
    "\tdeff = eff_err(nobrem_f,nobrem_l)\n",
    "\tefficiencies_found.append(eff)\n",
    "\tdeff_found.append(deff)\n",
    "\t#print(\"cutoff = \",str(cutoff_energy) ,\"MeV, sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
    "\t#print(\"eff = \",np.round(t_eff(nobrem_f,nobrem_l),4), \"+/-\", np.round(eff_err(nobrem_f, nobrem_l),4))\n",
    "\n",
    "\"\"\"\n",
    "we see that a cutoff energy of xxxMeV is ideal because the efficiency drops significantly for higher values\n",
    "\"\"\"\n",
    "cutoff_energy = 350.0 #MeV\n",
    "\n",
    "\"\"\"\n",
    "better statistics: cutoff=xxxMeV - sample size: xxx events and efficiency=xxxx\n",
    "\"\"\"\n",
    "nobrem_found = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
    "nobrem_lost = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)<cutoff_energy]\n",
    "\n",
    "print(\"\\ncutoff energy = 350MeV, sample size:\",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n",
    "print(\"eff = \",np.round(t_eff(nobrem_found, nobrem_lost),4), \"+/-\", np.round(eff_err(nobrem_found, nobrem_lost),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_ = np.arange(0,30050,step=1000)\n",
    "\n",
    "plt.errorbar(x_,efficiencies_found, yerr=deff_found, ls=\"\", fmt=\".\", label=\"$\\epsilon(E_{\\gamma,tot})$ \") #, capsize=1)\t\n",
    "plt.xlabel(\"cutoff energy [MeV]\")\n",
    "plt.ylabel(r\"$\\epsilon$\")\n",
    "plt.title(r'$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
    "plt.ylim([0,1.1])\n",
    "plt.xlim([-1000,31000])\n",
    "\"\"\"\n",
    "plt.yticks(np.arange(0,1.04,step=0.02),minor=True)\n",
    "plt.xticks(np.arange(-200,10400,step=200),minor=True)\n",
    "\n",
    "plt.tick_params(left=True, bottom=True, top=True, right=True,\n",
    "\t\t\t\tlabelleft=True, labelbottom=True, labeltop=False, labelright=False,\n",
    "\t\t\t\twhich=\"major\",direction=\"in\", length=12, labelsize=\"large\")\n",
    "plt.tick_params(left=True, bottom=True, top=True, right=True,\n",
    "\t\t\t\tlabelleft=True, labelbottom=True, labeltop=False, labelright=False,\n",
    "\t\t\t\twhich=\"minor\",direction=\"in\", length=6)\n",
    "\"\"\"\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eff =  0.8545 +/- 0.0036\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>[{energy: 2.58e+04, brem_photons_pe: [9.97e+03, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 8.03e+04, brem_photons_pe: [4.91e+03, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 5.6e+03, brem_photons_pe: [320, ..., 392], brem_vtx_z: [...], ...},\n",
       " {energy: 6.36e+03, brem_photons_pe: [273, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 4.67e+04, brem_photons_pe: [8.96e+03, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 7.16e+04, brem_photons_pe: [544, ..., 142], brem_vtx_z: [...], ...},\n",
       " {energy: 5.15e+04, brem_photons_pe: [384, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 4.07e+04, brem_photons_pe: [2.7e+04, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 2.77e+04, brem_photons_pe: [2.24e+03, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 6.4e+04, brem_photons_pe: [686, ..., 796], brem_vtx_z: [...], ...},\n",
       " ...,\n",
       " {energy: 5.59e+03, brem_photons_pe: [901, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 2.13e+04, brem_photons_pe: [787, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 9.34e+03, brem_photons_pe: [762, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 5.08e+04, brem_photons_pe: [711, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 6.41e+04, brem_photons_pe: [4.17e+03, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 1.01e+04, brem_photons_pe: [220, ..., 156], brem_vtx_z: [...], ...},\n",
       " {energy: 1.96e+04, brem_photons_pe: [1.66e+03, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 2.98e+04, brem_photons_pe: [8.32e+03, ...], brem_vtx_z: [...], ...},\n",
       " {energy: 3.97e+04, brem_photons_pe: [9.36e+03, ...], brem_vtx_z: [...], ...}]\n",
       "------------------------------------------------------------------------------\n",
       "type: 1430 * {\n",
       "    energy: float64,\n",
       "    brem_photons_pe: var * float64,\n",
       "    brem_vtx_z: var * float64,\n",
       "    brem_vtx_x: var * float64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Array [{energy: 2.58e+04, ...}, ..., {...}] type='1430 * {energy: float64,...'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#wie viel energie relativ zur anfangsenergie verlieren die elektronen durch bremstrahlung und hat das einen einfluss darauf ob wir sie finden oder nicht?\n",
    "#if any photon of an electron has an energy higher the cutoff then it is included\n",
    "cutoff_energy=350\n",
    "\n",
    "brem_found = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n",
    "energy_found = ak.to_numpy(brem_found[\"energy\"])\n",
    "eph_found = ak.to_numpy(ak.sum(brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "residual_found = energy_found - eph_found\n",
    "energyloss_found = eph_found/energy_found\n",
    "\n",
    "brem_lost = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n",
    "energy_lost = ak.to_numpy(brem_lost[\"energy\"])\n",
    "eph_lost = ak.to_numpy(ak.sum(brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "residual_lost = energy_lost - eph_lost\n",
    "energyloss_lost = eph_lost/energy_lost\n",
    "\n",
    "print(\"eff = \", np.round(t_eff(brem_found,brem_lost),4), \"+/-\", np.round(eff_err(brem_found, brem_lost),4))\n",
    "brem_lost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean energyloss relative to initial energy (found):  0.40459562244424735\n",
      "mean energyloss relative to initial energy (lost):  0.7244570697471976\n"
     ]
    }
   ],
   "source": [
    "mean_energyloss_found = ak.mean(energyloss_found)\n",
    "mean_energyloss_lost = ak.mean(energyloss_lost)\n",
    "print(\"mean energyloss relative to initial energy (found): \", mean_energyloss_found)\n",
    "print(\"mean energyloss relative to initial energy (lost): \", mean_energyloss_lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#in abhängigkeit von der energie der elektronen\n",
    "plt.hist(energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
    "plt.hist(energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
    "#plt.xticks(np.arange(0,1.1,0.1), minor=True,)\n",
    "#plt.yticks(np.arange(0,5.5,0.5), minor=True)\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"counts (normed)\")\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\", title_fontsize=15)\n",
    "#plt.grid()\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "both_eloss = np.append(energyloss_found,energyloss_lost)\n",
    "plt.hist(both_eloss, bins=100, density=True, histtype='bar', color=\"cornflowerblue\", label=\"all\")\n",
    "plt.vlines(ak.mean(both_eloss),0,1.75,colors=\"red\", label=\"mean\")\n",
    "plt.xlabel(r\"Energyloss Ratio $E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"counts (normed)\")\n",
    "plt.title(r'$B^0\\rightarrow K^{\\ast 0} e^+e^-$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm')\n",
    "plt.legend(title=\"LHCb Simulation\", title_fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "brem_z_found = ak.to_numpy(ak.flatten(acc_brem_found[\"brem_vtx_z\"]))\n",
    "brem_x_found = ak.to_numpy(ak.flatten(acc_brem_found[\"brem_vtx_x\"]))\n",
    "\n",
    "brem_z_lost = ak.to_numpy(ak.flatten(acc_brem_lost[\"brem_vtx_z\"]))\n",
    "brem_x_lost = ak.to_numpy(ak.flatten(acc_brem_lost[\"brem_vtx_x\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7935"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_lost=np.array([])\n",
    "z_lost=np.array([])\n",
    "\n",
    "for i in range(4):\n",
    "    x_lost = np.append(x_lost, brem_x_lost)\n",
    "    z_lost = np.append(z_lost, brem_z_lost)\n",
    "\n",
    "len(brem_z_lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 150\n",
    "nmax = 30\n",
    "\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0 = ax0.hist2d(brem_z_found[:18000], brem_x_found[:18000], bins=nbins, density=False, cmap=plt.cm.jet, cmin=2, vmax=nmax)\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(r\"\\bf{}found\")\n",
    "\n",
    "#a1 = ax1.hist2d(z_lost, x_lost, bins=nbins, density=False, cmap=plt.cm.jet, cmin=2, vmax=nmax)\n",
    "a1 = ax1.hist2d(brem_z_lost, brem_x_lost, bins=nbins, density=False, cmap=plt.cm.jet, cmin=1, vmax=nmax)\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(r\"\\bf{}lost\")\n",
    "#ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
    "\n",
    "plt.suptitle(r\"$B^0\\rightarrow K^{\\ast 0}e^+e^-$, $p>5$GeV, Bremsstrahlung Vertices\")\n",
    "plt.colorbar(a1[3], ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1280x550 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#energyloss in abh von der energie der elektronen\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(12.8,5.5))\n",
    "\n",
    "a0=ax0.hist2d(energyloss_found, energy_found, bins=(np.linspace(0,1,70), np.linspace(0,5e4,70)), cmap=plt.cm.jet, cmin=1, vmax=10)\n",
    "ax0.set_ylim(0,5e4)\n",
    "ax0.set_xlim(0,1)\n",
    "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
    "ax0.set_ylabel(r\"$E_0$\")\n",
    "ax0.set_title(\"found energyloss wrt electron energy\")\n",
    "\n",
    "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=(np.linspace(0,1,70), np.linspace(0,5e4,70)), cmap=plt.cm.jet, cmin=1, vmax=10) \n",
    "ax1.set_ylim(0,5e4)\n",
    "ax1.set_xlim(0,1)\n",
    "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
    "ax1.set_ylabel(r\"$E_0$\")\n",
    "ax1.set_title(\"lost energyloss wrt electron energy\")\n",
    "\n",
    "fig.colorbar(a1[3],ax=ax1)\n",
    "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, only photons w/ brem_vtx_z$<9500$mm\")\n",
    "\n",
    "\"\"\"\n",
    "we can see that high energy electrons are often found even though they emit a lot of their energy through bremsstrahlung\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x550 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot residual energy against energyloss and try to find a good split (eg energyloss before and after the magnet)\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(12.8,5.5))\n",
    "\n",
    "a0=ax0.hist2d(energyloss_found, residual_found, bins=(np.linspace(0,1,80), np.linspace(0,6e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15)\n",
    "ax0.set_ylim(0,6e4)\n",
    "ax0.set_xlim(0,1)\n",
    "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
    "ax0.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
    "ax0.set_title(\"found energyloss wrt residual electron energy\")\n",
    "\n",
    "a1=ax1.hist2d(energyloss_lost, residual_lost, bins=(np.linspace(0,1,80), np.linspace(0,6e4,80)), cmap=plt.cm.jet, cmin=1, vmax=15) \n",
    "ax1.set_ylim(0,6e4)\n",
    "ax1.set_xlim(0,1)\n",
    "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n",
    "ax1.set_ylabel(r\"$E_0-E_\\gamma$\")\n",
    "ax1.set_title(\"lost energyloss wrt residual electron energy\")\n",
    "\n",
    "fig.colorbar(a1[3],ax=ax1)\n",
    "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, only photons w/ brem_vtx_z$<9500$mm\")\n",
    "\n",
    "\"\"\"\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ist die shape der teilspur im scifi anders? (koenntest du zum beispiel durch vergleich der verteilungen der fit parameter studieren,\n",
    "#in meiner thesis findest du das fitmodell -- ist einfach ein polynom dritten grades)\n",
    "z_ref=8520 #mm\n",
    "\n",
    "def scifi_track(z, a, b, c, d):\n",
    "    return a + b*(z-z_ref) + c*(z-z_ref)**2 + d*(z-z_ref)**3\n",
    "\n",
    "def z_mag(xv, zv, tx, a, b):\n",
    "    \"\"\" optical centre of the magnet is defined as the intersection between the trajectory tangents before and after the magnet\n",
    "\n",
    "    Args:\n",
    "        xv (double): velo x track\n",
    "        zv (double): velo z track\n",
    "        tx (double): velo x slope\n",
    "        a (double): ax parameter of track fit\n",
    "        b (double): bx parameter of track fit\n",
    "\n",
    "    Returns:\n",
    "        double: z_mag\n",
    "    \"\"\"\n",
    "    return (xv-tx*zv-a+b*z_ref)/(b-tx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "scifi_found = found[found[\"scifi_hit_pos_x_length\"]>3]\n",
    "scifi_lost = lost[lost[\"scifi_hit_pos_x_length\"]>3]\n",
    "#should be fulfilled by all candidates\n",
    "\n",
    "scifi_x_found = scifi_found[\"scifi_hit_pos_x\"]\n",
    "scifi_z_found = scifi_found[\"scifi_hit_pos_z\"]\n",
    "\n",
    "tx_found = scifi_found[\"velo_track_tx\"]\n",
    "\n",
    "scifi_x_lost = scifi_lost[\"scifi_hit_pos_x\"]\n",
    "scifi_z_lost = scifi_lost[\"scifi_hit_pos_z\"]\n",
    "\n",
    "tx_lost = scifi_lost[\"velo_track_tx\"]\n",
    "\n",
    "xv_found = scifi_found[\"velo_track_x\"]\n",
    "zv_found = scifi_found[\"velo_track_z\"]\n",
    "\n",
    "xv_lost = scifi_lost[\"velo_track_x\"]\n",
    "zv_lost = scifi_lost[\"velo_track_z\"]\n",
    "\n",
    "\n",
    "\n",
    "sf_energy_found = ak.to_numpy(scifi_found[\"energy\"])\n",
    "sf_eph_found = ak.to_numpy(ak.sum(scifi_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "sf_vtx_type_found = scifi_found[\"all_endvtx_types\"]\n",
    "\n",
    "\n",
    "sf_energy_lost = ak.to_numpy(scifi_lost[\"energy\"])\n",
    "sf_eph_lost = ak.to_numpy(ak.sum(scifi_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "sf_vtx_type_lost = scifi_lost[\"all_endvtx_types\"]\n",
    "\n",
    "\n",
    "\n",
    "#ak.num(scifi_found[\"energy\"], axis=0)\n",
    "#scifi_found.snapshot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>[101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 0]\n",
       "------------------\n",
       "type: 11 * float32</pre>"
      ],
      "text/plain": [
       "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float32'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.num(scifi_found[\"energy\"], axis=0)\n",
    "scifi_found[\"all_endvtx_types\"][1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "scifi_fitpars_found = ak.ArrayBuilder()\n",
    "vtx_types_found = ak.ArrayBuilder()\n",
    "\n",
    "for i in range(0,ak.num(scifi_found, axis=0)):\n",
    "    popt, pcov = curve_fit(scifi_track,ak.to_numpy(scifi_z_found[i,:]),ak.to_numpy(scifi_x_found[i,:]))\n",
    "    scifi_fitpars_found.begin_list()\n",
    "    scifi_fitpars_found.real(popt[0])\n",
    "    scifi_fitpars_found.real(popt[1])\n",
    "    scifi_fitpars_found.real(popt[2])\n",
    "    scifi_fitpars_found.real(popt[3])\n",
    "    #[:,4] -> energy \n",
    "    scifi_fitpars_found.real(sf_energy_found[i])\n",
    "    #[:,5] -> photon energy\n",
    "    scifi_fitpars_found.real(sf_eph_found[i])\n",
    "    scifi_fitpars_found.end_list()\n",
    "    \n",
    "    vtx_types_found.begin_list()\n",
    "    #[:,0] -> endvtx_type\n",
    "    vtx_types_found.extend(sf_vtx_type_found[i,:])\n",
    "    vtx_types_found.end_list()\n",
    "    \n",
    "\n",
    "scifi_fitpars_lost = ak.ArrayBuilder()\n",
    "vtx_types_lost = ak.ArrayBuilder()\n",
    "\n",
    "for i in range(0,ak.num(scifi_lost, axis=0)):\n",
    "    popt, pcov = curve_fit(scifi_track,ak.to_numpy(scifi_z_lost[i,:]),ak.to_numpy(scifi_x_lost[i,:]))\n",
    "    scifi_fitpars_lost.begin_list()\n",
    "    scifi_fitpars_lost.real(popt[0])\n",
    "    scifi_fitpars_lost.real(popt[1])\n",
    "    scifi_fitpars_lost.real(popt[2])\n",
    "    scifi_fitpars_lost.real(popt[3])\n",
    "    #[:,4] -> energy \n",
    "    scifi_fitpars_lost.real(sf_energy_lost[i])\n",
    "    #[:,5] -> photon energy\n",
    "    scifi_fitpars_lost.real(sf_eph_lost[i])\n",
    "    scifi_fitpars_lost.end_list()\n",
    "    \n",
    "    vtx_types_lost.begin_list()\n",
    "    #endvtx_type\n",
    "    vtx_types_lost.extend(sf_vtx_type_lost[i,:])\n",
    "    vtx_types_lost.end_list()\n",
    "    \n",
    "\n",
    "\n",
    "scifi_fitpars_lost = ak.to_numpy(scifi_fitpars_lost)\n",
    "scifi_fitpars_found = ak.to_numpy(scifi_fitpars_found)\n",
    "\n",
    "vtx_types_lost = ak.Array(vtx_types_lost)\n",
    "vtx_types_found = ak.Array(vtx_types_found)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>[101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 101,\n",
       " 0]\n",
       "------------------\n",
       "type: 11 * float64</pre>"
      ],
      "text/plain": [
       "<Array [101, 101, 101, 101, 101, ..., 101, 101, 101, 0] type='11 * float64'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vtx_types_found[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x550 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#b parameter des fits [:,1] hat für lost eine breitere Verteilung. Warum?\n",
    "#evtl multiple scattering candidates (lost); findet man einen gewissen endvtx_type (mult scattering)\n",
    "#steiler velo winkel (eta)? vertex type? evtl bremsstrahlung?\n",
    "\n",
    "#isolate b parameters for analysis\n",
    "b_found = scifi_fitpars_found[:,1]\n",
    "b_lost = scifi_fitpars_lost[:,1]\n",
    "\n",
    "brem_energy_found = scifi_fitpars_found[:,5]\n",
    "brem_energy_lost = scifi_fitpars_lost[:,5]\n",
    "\n",
    "\n",
    "bs_found, vtxs_types_found = ak.broadcast_arrays(b_found, vtx_types_found)\n",
    "bs_found = ak.to_numpy(ak.ravel(bs_found))\n",
    "vtxs_types_found = ak.to_numpy(ak.ravel(vtxs_types_found))\n",
    "\n",
    "bs_lost, vtxs_types_lost = ak.broadcast_arrays(b_lost, vtx_types_lost)\n",
    "bs_lost = ak.to_numpy(ak.ravel(bs_lost))\n",
    "vtxs_types_lost = ak.to_numpy(ak.ravel(vtxs_types_lost))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#Erste Annahme ist Bremsstrahlung\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1,ncols=2,figsize=(12.8,5.5))\n",
    "\n",
    "\n",
    "n_bins = (np.linspace(-1,1,100), np.linspace(0,1e5,100))\n",
    "\n",
    "h0 = axes[0].hist2d(b_found, brem_energy_found, bins=n_bins, cmap=plt.cm.jet, cmin=1,vmax=15)\n",
    "axes[0].set_xlim(-1,1)\n",
    "axes[0].set_ylim(0,1e5)\n",
    "axes[0].set_xlabel(\"b parameter [mm]\")\n",
    "axes[0].set_ylabel(r\"$E_{ph}$\")\n",
    "axes[0].set_title(\"found photon energy wrt b parameter\")\n",
    "\n",
    "h1 = axes[1].hist2d(b_lost, brem_energy_lost, bins=n_bins, cmap=plt.cm.jet, cmin=1,vmax=15)\n",
    "axes[1].set_xlim(-1,1)\n",
    "axes[1].set_ylim(0,1e5)\n",
    "axes[1].set_xlabel(\"b parameter [mm]\")\n",
    "axes[1].set_ylabel(r\"$E_{ph}$\")\n",
    "axes[1].set_title(\"lost photon energy wrt b parameter\")\n",
    "\n",
    "fig.colorbar(h1[3], ax=axes[1])\n",
    "\n",
    "\"\"\"\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x550 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12.8,5.5))\n",
    "\n",
    "a0=ax[0].hist2d(bs_found, vtxs_types_found, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n",
    "ax[0].set_ylim(0,110)\n",
    "ax[0].set_xlim(-1,1)\n",
    "ax[0].set_xlabel(\"b\")\n",
    "ax[0].set_ylabel(\"endvtx id\")\n",
    "ax[0].set_title(\"found endvtx id wrt b parameter\")\n",
    "#ax[0].set_yticks(np.arange(0,110,1),minor=True)\n",
    "\n",
    "a1=ax[1].hist2d(bs_lost, vtxs_types_lost, bins=110, density=True, cmap=plt.cm.jet, cmin=1e-20,vmax=2)\n",
    "ax[1].set_ylim(0,110)\n",
    "ax[1].set_xlim(-1,1)\n",
    "ax[1].set_xlabel(\"b\")\n",
    "ax[1].set_ylabel(\"endvtx id\")\n",
    "ax[1].set_title(\"lost endvtx id wrt b paraneter\")\n",
    "#ax[1].set_yticks(np.arange(0,110,1), minor=True)\n",
    "\n",
    "\"\"\"\n",
    "vtx_id: 101 - Bremsstrahlung\n",
    "B:\n",
    "wir können nicht wirklich sagen dass bei den lost teilchen jegliche endvertex types überwiegen, im gegensatz zu den found \n",
    "\"\"\"\n",
    "fig.colorbar(a0[3], ax=ax, orientation='vertical',)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, figsize=(12.8,8))\n",
    "\n",
    "ax0.hist(scifi_fitpars_found[:,0], bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$a_x$ found\")\n",
    "ax0.hist(scifi_fitpars_lost[:,0], bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$a_x$ lost\")\n",
    "ax0.set_xlabel(\"a\")\n",
    "ax0.set_ylabel(\"normed\")\n",
    "ax0.set_title(\"fitparameter a der scifi track\")\n",
    "ax0.legend()\n",
    "\n",
    "ax1.hist(scifi_fitpars_found[:,1], bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$b_x$ found\")\n",
    "ax1.hist(scifi_fitpars_lost[:,1], bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$b_x$ lost\")\n",
    "ax1.set_xticks(np.arange(-1,1,0.1),minor=True)\n",
    "ax1.set_xlabel(\"b\")\n",
    "ax1.set_ylabel(\"normed\")\n",
    "ax1.set_title(\"fitparameter b der scifi track\")\n",
    "ax1.legend()\n",
    "#evtl multiple scattering candidates (lost); findet man einen gewissen endvtx_type (mult scattering)\n",
    "#steiler velo winkel (eta)? vertex type? evtl bremsstrahlung?\n",
    "\n",
    "\n",
    "ax2.hist(scifi_fitpars_found[:,2], bins=500, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$c_x$ found\")\n",
    "ax2.hist(scifi_fitpars_lost[:,2], bins=500, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$c_x$ lost\")\n",
    "ax2.set_xlim([-3e-5,3e-5])\n",
    "ax2.set_xticks(np.arange(-3e-5,3.5e-5,1e-5),minor=False)\n",
    "ax2.set_xlabel(\"c\")\n",
    "ax2.set_ylabel(\"normed\")\n",
    "ax2.set_title(\"fitparameter c der scifi track\")\n",
    "ax2.legend()\n",
    "\n",
    "ax3.hist(scifi_fitpars_found[:,3], bins=500, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$d_x$ found\")\n",
    "ax3.hist(scifi_fitpars_lost[:,3], bins=500, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$d_x$ lost\")\n",
    "ax3.set(xlim=(-5e-8,5e-8))\n",
    "ax3.text(-4e-8,3e8,\"d negligible <1e-7\")\n",
    "ax3.set_xlabel(\"d\")\n",
    "ax3.set_ylabel(\"normed\")\n",
    "ax3.set_title(\"fitparameter d der scifi track\")\n",
    "ax3.legend()\n",
    "\n",
    "\"\"\"\n",
    "a_x: virtual hit on the reference plane\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env1",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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