{
 "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": [
       "10522"
      ]
     },
     "execution_count": 2,
     "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": 3,
   "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": 36,
   "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": 36,
     "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": 37,
   "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",
    "    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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9056"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.num(acc_brem_found,axis=0)"
   ]
  },
  {
   "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": [
    "\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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample size:  322\n",
      "eff (cutoff =  0 ) =  0.9379 +/- 0.0135\n",
      "sample size:  322\n",
      "eff (cutoff =  50 ) =  0.9379 +/- 0.0135\n",
      "sample size:  322\n",
      "eff (cutoff =  100 ) =  0.9379 +/- 0.0135\n",
      "sample size:  433\n",
      "eff (cutoff =  150 ) =  0.9423 +/- 0.0112\n",
      "sample size:  529\n",
      "eff (cutoff =  200 ) =  0.949 +/- 0.0096\n",
      "sample size:  644\n",
      "eff (cutoff =  250 ) =  0.9519 +/- 0.0084\n",
      "sample size:  757\n",
      "eff (cutoff =  300 ) =  0.9498 +/- 0.0079\n",
      "sample size:  846\n",
      "eff (cutoff =  350 ) =  0.9433 +/- 0.008\n",
      "sample size:  919\n",
      "eff (cutoff =  400 ) =  0.9467 +/- 0.0074\n",
      "sample size:  1019\n",
      "eff (cutoff =  450 ) =  0.949 +/- 0.0069\n",
      "sample size:  1117\n",
      "eff (cutoff =  500 ) =  0.9418 +/- 0.007\n",
      "sample size:  1184\n",
      "eff (cutoff =  550 ) =  0.9409 +/- 0.0069\n",
      "sample size:  1268\n",
      "eff (cutoff =  600 ) =  0.9416 +/- 0.0066\n",
      "sample size:  1342\n",
      "eff (cutoff =  650 ) =  0.9404 +/- 0.0065\n",
      "sample size:  1416\n",
      "eff (cutoff =  700 ) =  0.9407 +/- 0.0063\n",
      "sample size:  1484\n",
      "eff (cutoff =  750 ) =  0.942 +/- 0.0061\n",
      "sample size:  1557\n",
      "eff (cutoff =  800 ) =  0.939 +/- 0.0061\n",
      "sample size:  1628\n",
      "eff (cutoff =  850 ) =  0.9373 +/- 0.006\n",
      "sample size:  1690\n",
      "eff (cutoff =  900 ) =  0.9385 +/- 0.0058\n",
      "sample size:  1745\n",
      "eff (cutoff =  950 ) =  0.9381 +/- 0.0058\n",
      "sample size:  1796\n",
      "eff (cutoff =  1000 ) =  0.9365 +/- 0.0058\n",
      "\n",
      "cutoff energy = 350MeV, sample size: 846\n",
      "eff =  0.9433 +/- 0.008\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 = ak.ArrayBuilder()\n",
    "\n",
    "\n",
    "\n",
    "for cutoff_energy in range(0,1050,25):\n",
    "\tnobrem_f = acc_brem_found[ak.all(acc_brem_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "\tnobrem_l = acc_brem_lost[ak.all(acc_brem_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "\n",
    "\n",
    "\n",
    "\tprint(\"sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
    "\tprint(\"eff (cutoff = \",str(cutoff_energy),\") = \",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.all(acc_brem_found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "nobrem_lost = acc_brem_lost[ak.all(acc_brem_lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eff =  0.8535 +/- 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: 1418 * {\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='1418 * {energy: float64,...'>"
      ]
     },
     "execution_count": 41,
     "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.any(acc_brem_found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\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.any(acc_brem_lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\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": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean energyloss relative to initial energy (found):  0.4107345449771658\n",
      "mean energyloss relative to initial energy (lost):  0.7300783757368142\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": 43,
   "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=200, 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(0,10,1), 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": 58,
   "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(0,1.5e5,105)), cmap=plt.cm.jet, cmin=1, vmax=7)\n",
    "ax0.set_ylim(0,1.5e5)\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(0,1.5e5,105)), cmap=plt.cm.jet, cmin=1, vmax=7) \n",
    "ax1.set_ylim(0,1.5e5)\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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dfvvtbh8vJkn58uVTnz599MUXX+j48eN64403dPjwYbVp00YREREpxlWrVi1NnTpV9913n+666y716dNHX3/9tYoWLarnn3/ebbl16tSRr6+v8zVp0iRJ0qVLl7R+/Xp17txZefLkcWmre++9V5cuXdL27dtd8urXr5/+/PNPLVu2TJIUHx+vTz/9VHfddZfKlSvnNpabpec7dONaPpJUvXp1Xbp0SSdPnkyxnB49eqhw4cIuj2SaNm2aChUqpO7du1uK9aGHHnJ5361bN/n4+Gjjxo1J0qYWY3rqfLPMarc77rhDM2fO1JgxY7R9+3bFxcWlGkei5s2b65dfftHhw4d16dIlffXVV2rTpo2aNWumdevWSZKio6Pl7+/vfByidO26ZfUxXVLajsHN+abnfLf7en2jvn376uLFi1qwYIFz24wZM+Tv76+ePXtayqNbt2568skn9dxzz2nMmDF66aWX1LJlyzTFcaOBAwdq5cqVWrhwoWrXrp1smv379ys8PFydOnXSn3/+qbffflsnTpzQ8uXL9dBDD7mcjyl5+umndfDgQQ0cOFC///67jh07pieeeMLZhl5e///ni7Re/xwOR4rl3viZ1XSJMnouZOa5BAAAMobF4QEAQLYQExOjvXv3SpL8/f2TTZMvX74sjOj/FS1a1OX96dOnZYxJsl2Scx2Jv/76y2V7/vz5Xd77+fkpT548CggISLL97NmzbmNKXJi9Y8eOLovZNmnSRCEhIfrwww91//33J9kvODhYH374odv816xZo0WLFqlTp04qUKBAsuUHBATo448/liS9+OKLqlChQpI/0maU1fMis86fXbt2ydfXN8kz8SXpjz/+cFm3wYqzZ8/qzJkzio2NlTFG+fLlk4+P9S57vnz51L59e73//vu6ePGiChYsqMDAwGT/4DZ37lz9888/On78uMtx+euvvxQfH69p06Zp2rRpyZZz84BQly5d9PTTT2vGjBl64IEHtGrVKp04cULjx4+3HPuN0vMduvk8TDzOFy9eTLEcf39/9e/fX5MmTdLEiRMVFxenzz77TM8++2yK58nNwsPDXd77+PioQIECSeJzF2NcXFya63yzzGq3BQsWaMyYMfr3v/+tESNGKG/evOrcubMmTJiQpP43atGihaRrgyOlS5dWXFyc7rnnHp04cUKvvfaa87PGjRsrMDBQkrRjxw4dPXo0TQMnaTkGN7dNes53u6/XN6pSpYrq1aunGTNm6PHHH1dCQoI+/fRTderUKUm5qenbt6/ee+89+fn5adCgQWmK4UZjxozR+++/r48++kht2rRJMZ2vr69CQ0P1999/KzY2VrGxsTp//nyarqt9+/bVqVOnNGbMGL333nuSpIYNG2rYsGEaP368ihUrlur+N1//Es+plM6Fv//+W9L/H0+r6W6U0XMhM88lAACQMQycAACAbOHYsWOSri1MfuMvk29040K/WenmX6GGhYXJy8sr2YVs//jjD0lSwYIFMzWmPXv2qFq1ai6DJtK1P255e3un+Mdkf39/Pfroo6nmvXLlSq1YsUJdunTRvHnzkv3D/p49e1SjRg35+Pjoww8/dC68/NVXXyW7YH16WT0vfvrpJ0vp0mrXrl0qWLBgkj9yffPNN/rll180YsQIt3kcO3ZMCxcu1Pz587Vz504VK1ZM3bt314cffqi6deumOSZjjKRr56W3t7fuuecerV27VsePH3f5o3HlypUlKclCz2FhYc4ZCk899VSyZZQuXdrlfWBgoHr06KEPP/xQx48f18cff6zg4OBkB5SsyMrv0JNPPqk33nhDH3/8sS5duqT4+Hg98cQTlvePiYlx+YNufHy8/vrrr2QHFFNjR50zq90KFiyoqVOnaurUqTp69KiWLVumF198USdPntSaNWtS3O+2225T+fLlFR0drVKlSqlu3brKly+fmjdvrgEDBuibb77R9u3bNXr0aOc+ixYtUvny5VW1alXL8aXlGCR3vU7r+Z7Z+vTpowEDBuiHH37QL7/8ouPHj6tPnz6W979w4YIiIyNVvnx5nThxQo8++qiWLl2a5jhmzpypESNGaNSoUerbt2+qaStVqqRffvlF27Zt09y5c/XGG29o6NChaty4sbp3764uXbqkOsiW6IUXXtCQIUN06NAhBQcHq2TJkurfv7+CgoIsDUTfeP1LVK1aNc2bN0/x8fEu/17997//lSTnuWY1HQAAyB14VBcAAMgWEn+V6XA4VLdu3WRfYWFhtzjKa4KCglS/fn19/vnnLgMUV69e1aeffur8Y2JmiY2N1S+//JLsAMXSpUt16dIl3X333enOf+LEierQoUOKgyaJ5Sc+0sXf31+LFy9W/vz51bFjR8XExKS77JtZPS8y6/zZtWuXTp06pTNnzji3JSQk6IUXXlCpUqVSfLTOuXPnNG3aNN15550qWbKkXn/9ddWsWVMbNmzQ0aNHNWnSpHQNmpw+fVorVqxQzZo1nYM5w4cPV0JCgp544glLj1jKkyePmjVrpr1796p69erJtlVyf5Du16+fEhISNHHiRK1atUoPPvig8uTJk+Y6SFn7HSpatKi6du2qd999V++//746dOigEiVKWN5/zpw5Lu8/++wzxcfHq2nTpmmKI611Tm5mSFa0W4kSJTRw4EC1bNlSe/bscZu+RYsW2rBhg9atW+d8XFT58uVVokQJjRw5UnFxcc6ZKdK1gZO0zDaRMnYM0nu+Z6YePXooICBAM2fO1MyZM1WsWDG1atXK+bm72VRPPPGEjh49qs8//1wfffSRli1bpilTpqQphjVr1uixxx5T3759FRUVZXm/hg0batq0afrjjz+0Zs0alS1bVi+//LKKFSume+65Rx988EGqs8Cka/WrWrWqSpYsqaNHj2rBggV67LHHnDNIUpLc9U+SOnfurPPnz2vRokUu6WfNmqWIiAjVr18/TekAAEDuwIwTAACQLZQtW1bNmjXTK6+8ovPnz6t+/foyxuj48ePauHGjevXqleY/VGamcePGqWXLlmrWrJmGDRsmPz8/vfvuu/ruu+80b968VJ+jnlF79uyRMUZBQUHOZ/OfPn1aX3/9taZMmaLq1atr2LBh6c5/+fLlCgwMTPERUonl3/jr4PDwcC1dulR33nmnOnbsqM2bN7v9I5gVVs+LtJ4/DodDTZo00aZNm1Is+/Dhw/rrr79UokQJde3aVUOHDtWlS5f09ttva/fu3dq0aZP8/PyS3Xf37t168cUX1bFjRy1ZskRt27aVr69vmures2dPlShRQnXr1lXBggV16NAhTZo0SSdOnNDMmTOd6Ro3bqx33nlHTz/9tGrXrq3HH39cVapUcc5MSPwjYUhIiHOft956S3feeafuuusuPfnkkypVqpTOnTunn376ScuXL9eGDRuSxFO3bl1Vr15dU6dOlTFG/fr1SzZuK20rZe13aPDgwc4/is6YMSNN+37++efy8fFRy5YtdeDAAY0YMUI1atRQt27d0hxHWupcrVo1SdeOVa9eveTr66sKFSrY3m6xsbFq1qyZevbsqYoVKyo4ONi5ZlJyj/u7WfPmzfXuu+/qzz//1NSpU122z5gxQ2FhYc5rxb59+/Tzzz+neeAko8cgPed7ZsqXL586d+6smTNn6syZMxo2bJjL+h4pHfvg4GD9+9//1qeffqoZM2aoSpUqqlKligYOHKgXXnhBjRs31h133OG2/MOHD6tr164qU6aM+vTpk2SNl1q1arl9lJ23t7datWqlVq1a6f3339fKlSs1d+5cDRkyRPXr1092YP+7777TokWLVLduXfn7++vbb7/VG2+8oXLlyjkf7ZbI6vVPktq2bauWLVvqySef1NmzZ3X77bdr3rx5WrNmjT799FPnzEyr6QAAQC5xS5akBwAASIfY2FgzfPhwU758eRMQEGDCwsJMjRo1zNNPP21Onz6dqWVv3LjRSDILFy50bouKijKSzKlTp5Ld58svvzT33HOPCQoKMoGBgaZBgwZm+fLlLmlSyqNXr14mKCgoSZ5NmjQxVapUSTXWN99800hyeQUFBZlatWqZ119/3Vy4cMFqtdMlsfw9e/Yk+WzhwoXG4XCYrl27mqtXr9pSntXzwmq6c+fOGUnmwQcfTLXczz77zEgyX3/9tYmMjDQhISEmODjYdOrUyXz//fduYz5//nxGqm3GjRtnatasaUJDQ423t7cpVKiQ6dy5s9mxY0ey6fft22f69OljSpcubfz9/U1AQIC5/fbbzSOPPGLWr1+fJP3hw4dN3759TbFixYyvr68pVKiQadSokRkzZkyKMb311ltGkqlcuXKyn1tt20QZ+Q7NmDHDSDKHDx9O9v3NSpUqZSpVqmQprhvL3b17t+nQoYPJmzevCQ4ONj169DAnTpxIV4xW65xo+PDhJiIiwnh5eRlJZuPGjZbzsBrTpUuXzBNPPGGqV69uQkJCTGBgoKlQoYKJioqydC05ffq08fLyMkFBQebKlSvO7XPmzDGSzP333+/c9sorr5iSJUu6zfPmOmTkGCSycr5nxvU6JWvXrnVevw8ePJjk8+SO/f79+01gYKDp1auXS9pLly6ZOnXqmFKlSln6tzLx37uUXil9h6xI7dr3448/mrvvvtvkz5/f+Pn5mdtvv9288soryaZP6/Xv3LlzZtCgQSY8PNz4+fmZ6tWrm3nz5qU7XUbPhaw8lwAAQPo4jLn+EFAAAAAAWrVqldq3b69vv/3W+cvu5Dz//PN69913FRsbyy+RLbLatllt//79qlGjht555x0NGDDA0j6jRo3S6NGjderUqUxfsyi3qFy5stq2batJkybd6lAAAACQy/GoLgAAAOAGGzdu1IMPPuj2D/u7du1S7dq1GTRJA6ttm1V+/vln/frrr3rppZdUtGhR9e7d+1aHlKt9//33tzoEAAAAQBIDJwAAAICLiRMnuk1jjNGePXvUt2/fLIgo57DStlnptdde0+zZs1WpUiUtXLgw3YvZA+7Ex8e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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(0,1e5,80)), cmap=plt.cm.jet, cmin=1, vmax=20)\n",
    "ax0.set_ylim(0,1e5)\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(0,1e5,80)), cmap=plt.cm.jet, cmin=1, vmax=20) \n",
    "ax1.set_ylim(0,1e5)\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": [
    "#try to find a split between energy lost before and after the magnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
    "brem_vtx_type_found = scifi_found[scifi_found[\"endvtx_type\"]==101]\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",
    "brem_vtx_type_lost = scifi_lost[scifi_lost[\"endvtx_type\"]==101]\n",
    "\n",
    "\n",
    "\n",
    "#ak.num(scifi_found[\"energy\"], axis=0)\n",
    "#scifi_found.snapshot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ak.num(scifi_found[\"energy\"], axis=0)\n",
    "scifi_found[\"all_endvtx_types\"][1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vtx_types_found[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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, vtx_types_found = ak.broadcast_arrays(b_found, vtx_types_found)\n",
    "bs_found = ak.to_numpy(ak.ravel(bs_found))\n",
    "vtx_types_found = ak.to_numpy(ak.ravel(vtx_types_found))\n",
    "\n",
    "bs_lost, vtx_types_lost = ak.broadcast_arrays(b_lost, vtx_types_lost)\n",
    "bs_lost = ak.to_numpy(ak.ravel(bs_lost))\n",
    "vtx_types_lost = ak.to_numpy(ak.ravel(vtx_types_lost))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#Erste Annahme ist Bremsstrahlung\n",
    "\n",
    "fig = plt.figure(figsize=(18,6))\n",
    "axes = ImageGrid(fig, 111,  # similar to subplot(111)\n",
    "                 nrows_ncols=(1, 2),  # creates 2x2 grid of axes\n",
    "                 axes_pad=1,  # pad between axes in inch.\n",
    "                 cbar_mode=\"single\",\n",
    "                 cbar_location=\"right\",\n",
    "                 cbar_pad=0.1,\n",
    "                 aspect=False\n",
    "                )\n",
    "\n",
    "\n",
    "h0 = axes[0].hist2d(b_found, brem_energy_found, bins=200, cmap=plt.cm.jet, cmin=1,vmax=30)\n",
    "axes[0].set_xlim(-1,1)\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=200, cmap=plt.cm.jet, cmin=1,vmax=30)\n",
    "axes[1].set_xlim(-1,1)\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(h0[3], cax=axes.cbar_axes[0], orientation='vertical')\n",
    "\n",
    "\"\"\"\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": []
  }
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