{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bad value in file '/home/lhcb/cetin/.config/matplotlib/matplotlibrc', line 267 ('font.size:    large'): Key font.size: Could not convert 'large' to float\n"
     ]
    }
   ],
   "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": [
      "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": 5,
   "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",
       "-------------------------------------------------------------------------------\n",
       "type: {\n",
       "    energy: float64,\n",
       "    photon_length: int64,\n",
       "    brem_photons_pe: var * float64,\n",
       "    brem_vtx_z: var * float64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {energy: 4.62e+04, ...} type='{energy: float64, photon_length: int6...'>"
      ]
     },
     "execution_count": 5,
     "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",
    "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.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",
    "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.end_record()\n",
    "\n",
    "brem_l = ak.Array(brem_l)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "brem_f[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
    "\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.end_record()\n",
    "\n",
    "acc_brem_lost = ak.Array(acc_brem_lost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9056"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.num(acc_brem_found,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "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": 25,
   "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(0,100050,1000):\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": 26,
   "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(0,100050,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([0,10100])\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": 11,
   "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",
       "}</pre>"
      ],
      "text/plain": [
       "<Array [{energy: 2.58e+04, ...}, ..., {...}] type='1430 * {energy: float64,...'>"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "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": 13,
   "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'$E_{ph}/E_0$')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 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=(20,6))\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 2000x600 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=(20,6))\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": {
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TcerUqa330Nfr9VhdXY1Lly5tBfxuBkhuBihOBYgGgGFiXggw2MxbABgo/X6n4p0uXbrUOHny5NY7Y7NizTTfddu0srLSGBsb2/b+3n5t1+62AINgfX19613eee8up3zN2G5xyzvwJyYmbnvH+8rKym3peTHaRo1zFmA0mRcCeYwDB4d5Sz7nK0BvDFwMr42NjRgbG4uZmZlYXl6OS5cubfvLt7m5uThz5szW49JNk5OTERFx8eLFvm7X7rYAg+LMmTPbvrvzXev0XvMd+Hl/NX7rX1TuFs5ZgNFkXgjkMQ4cPOYt2ZyvAL0xcAteTbOzs7G0tNRyYnPo0KEYHx+PS5cu3fZ9M/jjxYsXY2Jiom/btdNGAAAAWjMvBAAAdmpPvxvQrlqtFhsbGy0nBc3vnn/++b5t104bAQAAaJ95IQAAcKe7elHJ5cuX48CBA10p68KFCxERMT4+vi2t+V29Xu/bdu20MeWNN96I3/md34kf/dEfjR/6oR9Kbpuyb9++2LdvX8f5AQCAYq5duxbXrl3rOP/3vve9+PM///P4+Z//+XjHO97RxZb1lnlh9rZZzAsBAMo16mP1P/3TP4033nij1Dre8Y53xI//+I+XWsdOdX3B6z/7z/6zuHDhQrz3ve+NRx55JD7wgQ9EvV6PtbW1+A//w/+wcPkbGxsREbe9/7yp+V29Xu/bdu20MeV3fud34u/8nb+T3AYAANg9vvCFL8Tf/tt/u9/N2BHzQvNCAIDdZBDH6n/6p38a7/6Jn4jvl1zPvn374o/+6I8GYtGr6wteKysrMTc3FysrK3Hs2LF48803Y2pqKtbW1uLVV18tXH6rycKdmgGO+7FdxM7bmPKjP/qjERHxX/1X/1X81E/9VG55WZp/yXfs2LF44YUXOi7nTt0sb1DLeuutt+L9739/fP3rX4977rmncHmOQfscg/6XNcjHoJ/Hc3JyMWeLL0XEI5mpFy/OtlH+tYj4QkT8nYjYl5t/cvKPkmW/853/YCSOQS/Lch2UW1bqerp4cbbr/d9O23pdVrfL61ZZjkHxsor+1egf/uEfxt/9u393a44wDMwLB2Ne+Lf+1t+K3/iN3+j4Ca8i10PRa6nT/N24Zw3jfhfNW7TfBm2/88YX3ajbueZc61V+55pzrVf5d+u5du3atXj00UfjN37jNzqqd5DH6m+88UZ8PyL+ekSU9ezZGxHxW9euxRtvvDEaC153vpZifn4+Dh8+HE888URE3PyLtZdeeil3IL9TzUDFrcprfletVvu2XTttTGm+ruKnfuqn4q/+1b+a3HYn7r777q4GQ+5meYNa1uXLlyMi4j3veU9XXr3iGLTPMeh/WYN8DPp7PH8sJ/2u5Db59dya93s/+N9/JyJ+aAf50z/to3MMeleW66DsstLXSrf7v7229basbpfXrbIcg8EoKyIKvdKubOaFgzkv/OEf/uFC88ki53DR87/T/N24Zw3jfhfNW7TfBm+/dz4Wd671Nu/onWvl53euOdd6lX83n2tFx0wRgz1W/3ci/1+yOtWTmFltKNyekydPxtmzZ+ORRx6JI0eOxJEjR+LChQvx2muvxXve857bJgXd0CxrbW1tW1rzu4mJib5t104bAQAARoF5oXkhAAD0W+EFr8nJyThy5Ei8+uqr8dnPfjZmZma2JjPVajX+wT/4B/ETP/ET3WhrRNycNIyNjbV813mtVouIiCNHjvRtu3baCAAAMArMC80LAQAYTHdFxF8osexBUrg9jzzySBw8eDCOHTsWERFvvvlmrK6uxsrKSqysrMTi4mJMTk7Go48+Gh//+McLNzgi4vjx43HmzJlt71BfWVmJarUaU1NTfd2u3W0BGC6NxpPJ9Erlv46IhxPpT+XUcGvetyLiVyPir0XEPT/I/3oi74vJkr/5ze8k68/bN+g25xyMBvNC80IYJLt5fJE11u9Fn5hnANBve4oWcPDgwW3/fezYsfj1X//1+Na3vhXr6+sxNzcXb7zxRlvlNl/x0Op95/Pz81GtVmNubm7ru9XV1VhdXY2zZ8/2fbt2twUAABhm5oXmhQAADKa9cfPJpzI+e3u4HztRyhNnzQB3Bw4ciIMHD8b09HRMT0/vKG+9Xo/l5eVYXl6OiIi5ubmYnZ3dlv/SpUsxOzsbs7OzW/kuXry47R3o/dqu3W0BAABGiXlh+9sCAACd6/qC1z/4B/8gzpw5E5VKJarVaiwtLcUHPvCBHeevVqtx8uTJOHnyZO62i4uLOyqzX9u1u22ZTpw4MbDlDWpZ3eYY9J9j0H+DvJ/dLe/RLpbVbT/btZIG+Ri4DvpfnmPQ//Icg/6XN8jHoGzmhZ1v201Fz8Ei+ftZd1HDut/D2mdF8zvXhq/uIoZ5v4e134a5z4sY5v0e1n4b5j4fdLsphlel0Wg0ulngu9/97q3B/MWLF2NpaSlmZmbi9OnT3axm5P3e7/1evP/974+vf/3r8Vf/6l/td3N2pcuXL8fBgwfjzTffjAMHDvS7ObuSY9B/jkFn0jG2IvLibG2P4fUfRMR/H80YXsXKTvNu/e1cB/2l//vPMei/YZwbmBd2xzAe+35zz+qMfmvfoPbZoMfwGtR+G2T6rDP6rX36rDODPF6r1WoxOTkZvxQRf6mkOv7niPjPIwbmDQaFY3jdaXZ2Nh588MF48MEH4+TJk/Gtb30rxsfH47d+67e6XRUAAAADyLwQAAAGgxheBRw+fDh+67d+K/76X//rW9/90i/9UjzzzDPdrgoAhtTD6eRD9739/xuXIzYiYuz/FFHZwV9YredtkH4CLPVXmRGeAANgZ8wLobf6+VQPg6Wfx9z5BkC/dX3B6z/9T//TePPNN2NiYiIeffTRmJmZiZ/4iZ+Iw4cPd7sqAAAABpB5IQAADIbdFMOr6680PHXqVHzta1+LBx98MH7zN38z3vWud8W9994bs7Oz8U//6T+NP/7jP/YaCwAAgBFmXggAAPRa1xfgfumXfikiIh588MGt71ZXV2N1dTX+8T/+x/HEE09EpVKJzc3NblcNAADAADAvBACAwdCM4VVW2YOkJ0+cTU1NxdTU1NZ//8Iv/EIvqgUAAGBAmBcCAABl2tGC1969e6NarcbU1FTMz8/HgQMHClX667/+64XyA0CZsoJ+d8/D6eT112/5j7du/s/G/xAR99z8/4fuS2R+MVl02YGk8/pOIGuA4WVeCIPLGItB9/Y84XsREXHw4OmI+CHnLkAP7KYYXjtqT6PRiJWVlXjnO9+Z3O6ll16Kb3/72/HII48Unvzsdvv27bvtf+m9/fv3x5NPPhn79+/vd1N2Lceg/xyDQbAvIn7hB/9LP7gO+kv/959j0H+DMjcwL+y9QTn2w8Q9qzP6rX36rFN3RcT7Y/D+iXRwOdc6o9/ap886Y7w2WCqNRqORt9G73/3u+Na3vrWjAp955pn4hV/4hZiZmYnf/M3fLNzA3apWq8Xk5GRcvHgxJiYm+t0cgF2l70945Uk94bXe3yesPOEF0H2DMjcwL+y9QTn2AEVlzRPMD4BhN8jjtWbbTkfEuwqU87sR8bWMtGsR8Z2Igdn/Hf05RbVave2/X3jhhXj++efjZ3/2Z2Nqaire8573bKU98cQTceHChfgn/+SfmNgAAACMCPNCAADYfX7uB59Wvh0Rp3rYljw7WvCqVCq3/fexY8fiwQcfjPHx8ZicnIypqak4derU1usq5ufn45lnnul+awEAAOgL80IAABg+YnjtwNjYWFSr1XjppZe2vZe9mQbAaEu9vm60X52X90rCFwuWn5N/vfOSi/abVxb2h34HBpV5IQA7MWzjVa9gBBhOe3ay0crKSnziE5+I/+6/++9u+/7QoUOZQYjHxsYKNw4AAIDBYF4IAADDp/mEVxmfoX3Ca35+Pubn5yMi4siRIzE1NZXc/t577y3WMgAAAAaKeSEAADCodrTgNTY2Fo888ki8+uqr8Qd/8Afxta99LVZWViLi5gRmamoqjhw5Eo888sjWX/atra2V12oAAAB6yrwQAACGz94o70msvSWV26kd7eepU6fil37pl7b++6WXXoovfelL8dJLL0W9Xo+zZ8/G8vJyzM7ORrVajWq1GrVarbRG7ybHjh2Lu+++u2XaiRMn4sSJEz1uEQAA0G0LCwuxsLDQMu3KlSs9bk1r5oX9Y14IANA/wzBW56ZKo9FoFCngzTffjC996UuxsrISq6ursbGxcbPgSiU2Nze70cZdqVarxeTkZFy8eDEmJib63RyArssKAtzUz2DARdtWGc+pYD1dfhzK2fdk/oeTWRuN+5LplcrrhfID0H3DMDcwLyzHMBx7YPjlzX/u1M+5GsCgGeTxWrNt/4+I+D+UVMcfRcT/NWJg9n9P0QIOHjwYTzzxRHzpS1+KtbW1uHTpUvzqr/5qN9oGAADAEDAvBAAA+q3rr25817veFSdPnoxKpdLtogEAABgC5oUAADAYdlMMr8JPeGW59d3uAAAA7D7mhQAAsHvV6/U4cuRI1Ov1HW0/OzsbMzMzHddX2oIXAAAAAAAA/XNXRPyFkj5ZT47V6/WYm5uLw4cPx+rq6o7auby8HEtLSx3s4dvKepINAJLyAh3nBU4e6EDJ66+n0x/LaftzOUGjv5jI/5G8gNP3JVMbjXQ6AADQO3nzojt1Mk8a6LkVDKjUtemagojx8fGYn5+Per0ey8vLudtvbGzE888/X7heT3gBAAAAAACMoGYMrzI+WTG8xsbGIuLmwtdOzMzMxDPPPNPmnm1nwQsAAAAAAICem5ubi7m5ua1FsiK80hAAAAAAAGAENWN4lVV2EbVaLTY2NmJqamog2gMAAAAAAMCQufaDT6euFMi7sbERc3NzsbKyUqCU21nwAmAglR3ktcwAs43GfTl1v56Tv/P6Kx/pOOvOys8Jmp3X9n4G9i3adgAA6DVjVBhMrk2GSTPeVivPRMSv97Att3riiSdicXGxq2Va8AIAAAAAANhl/pOI+FsF8v+LiPiPO8i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      "text/plain": [
       "<Figure size 1800x600 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=(18,6))\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": [
    {
     "ename": "ValueError",
     "evalue": "cannot convert to RegularArray because subarray lengths are not regular (in compiled code: https://github.com/scikit-hep/awkward/blob/awkward-cpp-24/awkward-cpp/src/cpu-kernels/awkward_ListOffsetArray_toRegularArray.cpp#L22)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/work/cetin/Projektpraktikum/B_rework.ipynb Cell 26\u001b[0m line \u001b[0;36m3\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Blhcba2/work/cetin/Projektpraktikum/B_rework.ipynb#X33sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m fig, ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots(nrows\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, ncols\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m, figsize\u001b[39m=\u001b[39m(\u001b[39m18\u001b[39m,\u001b[39m6\u001b[39m))\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Blhcba2/work/cetin/Projektpraktikum/B_rework.ipynb#X33sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m a0\u001b[39m=\u001b[39max[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39;49mhist2d(bs_found, vtx_types_found, bins\u001b[39m=\u001b[39;49m\u001b[39m110\u001b[39;49m, density\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, cmap\u001b[39m=\u001b[39;49mplt\u001b[39m.\u001b[39;49mcm\u001b[39m.\u001b[39;49mjet, cmin\u001b[39m=\u001b[39;49m\u001b[39m1e-20\u001b[39;49m,vmax\u001b[39m=\u001b[39;49m\u001b[39m2\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Blhcba2/work/cetin/Projektpraktikum/B_rework.ipynb#X33sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m ax[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mset_ylim(\u001b[39m0\u001b[39m,\u001b[39m110\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Blhcba2/work/cetin/Projektpraktikum/B_rework.ipynb#X33sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m ax[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mset_xlim(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m,\u001b[39m1\u001b[39m)\n",
      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1462\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m   1463\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(ax, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m   1464\u001b[0m     \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m         \u001b[39mreturn\u001b[39;00m func(ax, \u001b[39m*\u001b[39;49m\u001b[39mmap\u001b[39;49m(sanitize_sequence, args), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1467\u001b[0m     bound \u001b[39m=\u001b[39m new_sig\u001b[39m.\u001b[39mbind(ax, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m   1468\u001b[0m     auto_label \u001b[39m=\u001b[39m (bound\u001b[39m.\u001b[39marguments\u001b[39m.\u001b[39mget(label_namer)\n\u001b[1;32m   1469\u001b[0m                   \u001b[39mor\u001b[39;00m bound\u001b[39m.\u001b[39mkwargs\u001b[39m.\u001b[39mget(label_namer))\n",
      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/axes/_axes.py:7191\u001b[0m, in \u001b[0;36mAxes.hist2d\u001b[0;34m(self, x, y, bins, range, density, weights, cmin, cmax, **kwargs)\u001b[0m\n\u001b[1;32m   7098\u001b[0m \u001b[39m@_preprocess_data\u001b[39m(replace_names\u001b[39m=\u001b[39m[\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mweights\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m   7099\u001b[0m \u001b[39m@_docstring\u001b[39m\u001b[39m.\u001b[39mdedent_interpd\n\u001b[1;32m   7100\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mhist2d\u001b[39m(\u001b[39mself\u001b[39m, x, y, bins\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m, \u001b[39mrange\u001b[39m\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, density\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, weights\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m   7101\u001b[0m            cmin\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, cmax\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m   7102\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   7103\u001b[0m \u001b[39m    Make a 2D histogram plot.\u001b[39;00m\n\u001b[1;32m   7104\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   7188\u001b[0m \u001b[39m      `.colors.PowerNorm`.\u001b[39;00m\n\u001b[1;32m   7189\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 7191\u001b[0m     h, xedges, yedges \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mhistogram2d(x, y, bins\u001b[39m=\u001b[39;49mbins, \u001b[39mrange\u001b[39;49m\u001b[39m=\u001b[39;49m\u001b[39mrange\u001b[39;49m,\n\u001b[1;32m   7192\u001b[0m                                        density\u001b[39m=\u001b[39;49mdensity, weights\u001b[39m=\u001b[39;49mweights)\n\u001b[1;32m   7194\u001b[0m     \u001b[39mif\u001b[39;00m cmin \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   7195\u001b[0m         h[h \u001b[39m<\u001b[39m cmin] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mhistogram2d\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/highlevel.py:1445\u001b[0m, in \u001b[0;36mArray.__array_function__\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m   1431\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__array_function__\u001b[39m(\u001b[39mself\u001b[39m, func, types, args, kwargs):\n\u001b[1;32m   1432\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1433\u001b[0m \u001b[39m    Intercepts attempts to pass this Array to those NumPy functions other\u001b[39;00m\n\u001b[1;32m   1434\u001b[0m \u001b[39m    than universal functions that have an Awkward equivalent.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1443\u001b[0m \u001b[39m    See also #__array_ufunc__.\u001b[39;00m\n\u001b[1;32m   1444\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1445\u001b[0m     \u001b[39mreturn\u001b[39;00m ak\u001b[39m.\u001b[39;49m_connect\u001b[39m.\u001b[39;49mnumpy\u001b[39m.\u001b[39;49marray_function(\n\u001b[1;32m   1446\u001b[0m         func, types, args, kwargs, behavior\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_behavior\n\u001b[1;32m   1447\u001b[0m     )\n",
      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/_connect/numpy.py:94\u001b[0m, in \u001b[0;36marray_function\u001b[0;34m(func, types, args, kwargs, behavior)\u001b[0m\n\u001b[1;32m     91\u001b[0m unique_backends \u001b[39m=\u001b[39m \u001b[39mfrozenset\u001b[39m(_find_backends(all_arguments))\n\u001b[1;32m     92\u001b[0m backend \u001b[39m=\u001b[39m common_backend(unique_backends)\n\u001b[0;32m---> 94\u001b[0m rectilinear_args \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39;49m(_to_rectilinear(x, backend) \u001b[39mfor\u001b[39;49;00m x \u001b[39min\u001b[39;49;00m args)\n\u001b[1;32m     95\u001b[0m rectilinear_kwargs \u001b[39m=\u001b[39m {k: _to_rectilinear(v, backend) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems()}\n\u001b[1;32m     96\u001b[0m result \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39mrectilinear_args, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mrectilinear_kwargs)\n",
      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/_connect/numpy.py:94\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     91\u001b[0m unique_backends \u001b[39m=\u001b[39m \u001b[39mfrozenset\u001b[39m(_find_backends(all_arguments))\n\u001b[1;32m     92\u001b[0m backend \u001b[39m=\u001b[39m common_backend(unique_backends)\n\u001b[0;32m---> 94\u001b[0m rectilinear_args \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39m(_to_rectilinear(x, backend) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m args)\n\u001b[1;32m     95\u001b[0m rectilinear_kwargs \u001b[39m=\u001b[39m {k: _to_rectilinear(v, backend) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems()}\n\u001b[1;32m     96\u001b[0m result \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39mrectilinear_args, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mrectilinear_kwargs)\n",
      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/_connect/numpy.py:70\u001b[0m, in \u001b[0;36m_to_rectilinear\u001b[0;34m(arg, backend)\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[39m# Otherwise, cast to layout and convert\u001b[39;00m\n\u001b[1;32m     68\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     69\u001b[0m         layout \u001b[39m=\u001b[39m ak\u001b[39m.\u001b[39mto_layout(arg, allow_record\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, allow_other\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m---> 70\u001b[0m         \u001b[39mreturn\u001b[39;00m layout\u001b[39m.\u001b[39;49mto_backend(backend)\u001b[39m.\u001b[39;49mto_backend_array(allow_missing\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m     71\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(arg, \u001b[39mtuple\u001b[39m):\n\u001b[1;32m     72\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mtuple\u001b[39m(_to_rectilinear(x, backend) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m arg)\n",
      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/contents/content.py:1077\u001b[0m, in \u001b[0;36mContent.to_backend_array\u001b[0;34m(self, allow_missing, backend)\u001b[0m\n\u001b[1;32m   1075\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1076\u001b[0m     backend \u001b[39m=\u001b[39m regularize_backend(backend)\n\u001b[0;32m-> 1077\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_to_backend_array(allow_missing, backend)\n",
      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/contents/listoffsetarray.py:2060\u001b[0m, in \u001b[0;36mListOffsetArray._to_backend_array\u001b[0;34m(self, allow_missing, backend)\u001b[0m\n\u001b[1;32m   2058\u001b[0m     \u001b[39mreturn\u001b[39;00m buffer\u001b[39m.\u001b[39mview(np\u001b[39m.\u001b[39mdtype((\u001b[39m\"\u001b[39m\u001b[39mS\u001b[39m\u001b[39m\"\u001b[39m, max_count)))\n\u001b[1;32m   2059\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 2060\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mto_RegularArray()\u001b[39m.\u001b[39m_to_backend_array(allow_missing, backend)\n",
      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/contents/listoffsetarray.py:277\u001b[0m, in \u001b[0;36mListOffsetArray.to_RegularArray\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    272\u001b[0m _size \u001b[39m=\u001b[39m Index64\u001b[39m.\u001b[39mempty(\u001b[39m1\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mindex_nplike)\n\u001b[1;32m    273\u001b[0m \u001b[39massert\u001b[39;00m (\n\u001b[1;32m    274\u001b[0m     _size\u001b[39m.\u001b[39mnplike \u001b[39mis\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mindex_nplike\n\u001b[1;32m    275\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_offsets\u001b[39m.\u001b[39mnplike \u001b[39mis\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mindex_nplike\n\u001b[1;32m    276\u001b[0m )\n\u001b[0;32m--> 277\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_backend\u001b[39m.\u001b[39;49mmaybe_kernel_error(\n\u001b[1;32m    278\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_backend[\n\u001b[1;32m    279\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39mawkward_ListOffsetArray_toRegularArray\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    280\u001b[0m         _size\u001b[39m.\u001b[39;49mdtype\u001b[39m.\u001b[39;49mtype,\n\u001b[1;32m    281\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_offsets\u001b[39m.\u001b[39;49mdtype\u001b[39m.\u001b[39;49mtype,\n\u001b[1;32m    282\u001b[0m     ](\n\u001b[1;32m    283\u001b[0m         _size\u001b[39m.\u001b[39;49mdata,\n\u001b[1;32m    284\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_offsets\u001b[39m.\u001b[39;49mdata,\n\u001b[1;32m    285\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_offsets\u001b[39m.\u001b[39;49mlength,\n\u001b[1;32m    286\u001b[0m     )\n\u001b[1;32m    287\u001b[0m )\n\u001b[1;32m    288\u001b[0m size \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mindex_nplike\u001b[39m.\u001b[39mindex_as_shape_item(_size[\u001b[39m0\u001b[39m])\n\u001b[1;32m    289\u001b[0m length \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_offsets\u001b[39m.\u001b[39mlength \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m\n",
      "File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/awkward/_backends/backend.py:72\u001b[0m, in \u001b[0;36mBackend.maybe_kernel_error\u001b[0;34m(self, error)\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[39mreturn\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 72\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mformat_kernel_error(error))\n",
      "\u001b[0;31mValueError\u001b[0m: cannot convert to RegularArray because subarray lengths are not regular (in compiled code: https://github.com/scikit-hep/awkward/blob/awkward-cpp-24/awkward-cpp/src/cpu-kernels/awkward_ListOffsetArray_toRegularArray.cpp#L22)"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
    "\n",
    "a0=ax[0].hist2d(bs_found, vtx_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, vtx_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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, figsize=(15,10))\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",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
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 },
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}