Projektpraktikum/B_rework.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10522"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "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": 32,
   "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": 32,
     "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": 33,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9056"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.num(acc_brem_found,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 35,
     "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": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cutoff =  100 MeV, sample size:  322\n",
      "eff =  0.9379 +/- 0.0135\n",
      "cutoff =  200 MeV, sample size:  481\n",
      "eff =  0.948 +/- 0.0101\n",
      "cutoff =  300 MeV, sample size:  627\n",
      "eff =  0.949 +/- 0.0088\n",
      "cutoff =  400 MeV, sample size:  739\n",
      "eff =  0.9513 +/- 0.0079\n",
      "cutoff =  500 MeV, sample size:  860\n",
      "eff =  0.9477 +/- 0.0076\n",
      "cutoff =  600 MeV, sample size:  973\n",
      "eff =  0.9424 +/- 0.0075\n",
      "cutoff =  700 MeV, sample size:  1106\n",
      "eff =  0.9412 +/- 0.0071\n",
      "cutoff =  800 MeV, sample size:  1188\n",
      "eff =  0.9411 +/- 0.0068\n",
      "cutoff =  900 MeV, sample size:  1288\n",
      "eff =  0.9387 +/- 0.0067\n",
      "cutoff =  1000 MeV, sample size:  1387\n",
      "eff =  0.9416 +/- 0.0063\n",
      "\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 = ak.ArrayBuilder()\n",
    "\n",
    "\n",
    "\n",
    "for cutoff_energy in range(0,1050,100):\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\tcontinue\n",
    "\n",
    "\tprint(\"cutoff = \",str(cutoff_energy) ,\"MeV, sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
    "\tprint(\"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": 62,
   "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": 62,
     "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": 63,
   "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": 65,
   "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(0,10,1), 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": 77,
   "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(0,1.5e5,70)), cmap=plt.cm.jet, cmin=1, vmax=15)\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(0,1.5e5,70)), cmap=plt.cm.jet, cmin=1, vmax=15) \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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      "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": []
  },
  {
   "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": 69,
   "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": 70,
   "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": 70,
     "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": 71,
   "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": 72,
   "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": 72,
     "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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x600 with 4 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, 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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 3 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": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WqOQCAwNttu3evdv8/J577tFXX32lmTNn6uabb1aNGjVkMpnUt29fqyKOJJt3l4qOjtbChQv15JNPqnv37qpVq5aqVKmisWPH2uyjdu3aFs89PT2LbL98+bKqVaumP/74Q4ZhKCAgwOb+5i8dKcoff/yhM2fOmPsuqOD16652N618eXl5ioyM1O+//66ZM2fqhhtukK+vr/Ly8tS5c2ebx+HvTp48qZCQEKv2gq/hH3/8IUkaMmRIoX2dOnXKYjmIvfuwYMECNWjQQKtWrdKLL74ob29v9e7dWy+//LKuv/568/JUV7vwtmR7H+fPn6/HHntM48eP1zPPPKO6devKzc1NM2fOtPhHx59//qng4GBVqXL1776+//57ZWRk6LnnnnPJ4wAAqDzI86yVVp6Xz9bc8l+Hv19+pKD8nxV8zdzd3VWnTh2Ltj/++EOffvppoYW5ku5Dt27d9NFHH2nBggUaOXKksrKy1KZNGz311FMaMWKEpL9yokaNGtnVX1mytY+pqanq2rWrWrRooVdffVVNmjSRt7e3vv/+ez388MPm92Zx8tfs7GytWrVKbdq0MRdNgbJEEQ+o5NLT02225ScLZ8+e1WeffaZZs2Zp2rRp5pisrCydOnXKZp+2vsHKv0bb888/b9GekZGhmjVrXsMeWKpbt65MJpO2bNli81ob9lx/o27duqpTp47Wr19v8+fVq1e3eG7vHdn27t2r3bt3a9myZRo1apS5/ddff7Vr+zp16hT6ev1d/rVrXnvtNas7g+UrmGDauw++vr6aPXu2Zs+erT/++MN8Vt6AAQN04MAB893FfvvtN7v6K0uFvS8jIiK0ePFii/aC156pV6+etm7dqry8vKsW8oYNG6bAwEA99dRTysvLM9+wAwCAskaeZ7uP0sjz8uV/mfp3+a9DwWLc3+X/LD09XfXr1ze35+bmWhX/6tatq3bt2um5556z2VdwcLDF8+Lsw8CBAzVw4EBlZWXp22+/1dy5c3XPPfeoSZMmCgsLU7169cpNnvfRRx/pwoULWrNmjXnliyTt2rXLIq44+Wv+jUF69+6t22+/XevXr1etWrWubfJAMbCcFqjgvLy8ijzDa8WKFTIMw/z86NGj2rZtm/mC/iaTSYZhWCVFb731ll132cpnMpms+vj8889tnqZ/Lfr37y/DMHT8+HF17NjR6nHDDTeYYws7Nv3799fJkyd15coVm30UvDizvfKTi4LH4Y033rBr+x49euinn36y+PZckj788EOL57feeqtq1qypffv22Zx/x44dC/32uTgCAgI0evRojRgxQgcPHtTFixfVvHlzNWvWTG+//baysrKueYziuNp73RZb78sff/xRycnJFm19+vTR5cuXtWzZMrv6nTFjhuLi4vT0008rJiamWHMCAMBe5Hmuk+flO3funD755BOLtg8//FBVqlRRt27dCt0u/zX54IMPLNr//e9/W9xxNn8f9u7dq2bNmtnch4JFvJLw8vJS9+7d9eKLL0qS+a7Gffr00c8//2xxs4uykP/+Kk6uZyv3NgxDb775pkVceHi4/Pz8tGTJEovfl8J06NBBSUlJ+u233xQREaETJ07YPSfgWnEmHlDB3XDDDVq5cqVWrVqlpk2bytvb2yLBOXHihAYNGqRx48bp7NmzmjVrlry9vc2Fhxo1aqhbt256+eWXVbduXTVp0kRJSUmKj48v1jer/fv317Jly9SyZUu1a9dOO3bs0Msvv+zw5Ya33nqrHnjgAd13333avn27unXrJl9fX6WlpWnr1q264YYb9NBDD5mPzZo1a7R48WKFhoaqSpUq6tixo4YPH64PPvhAffv21aOPPqpbbrlFHh4e+u2337Rp0yYNHDhQgwYNKvbcWrZsqWbNmmnatGkyDEO1a9fWp59+qoSEBLu2nzx5st5++23169dPzz77rPnutAcOHLCIq1atml577TWNGjVKp06d0pAhQ8x3Yt29e7f+/PNPqzPP7NWpUyf1799f7dq1U61atbR//369//77CgsLU9WqVSVJCxcu1IABA9S5c2dNmTJFjRo1Umpqqr788kurxNSRrvZet6V///565plnNGvWLHXv3l0HDx7UnDlzFBISYpEwjxgxQu+8847Gjx+vgwcPqkePHsrLy9N3332nVq1aafjw4VZ9P/roo6pWrZoeeOABnT9/XgsWLCj2t/kAABSFPM918rx8derU0UMPPaTU1FQ1b95c69at05tvvqmHHnqoyGWorVq10j//+U/FxcXJw8NDt99+u/bu3Wu+o+zfzZkzRwkJCQoPD9ekSZPUokULXb58WUeOHNG6deu0ZMmSEh37p59+Wr/99pt69uypBg0a6MyZM3r11Vfl4eGh7t27S/orH121apUGDhyoadOm6ZZbbtGlS5eUlJSk/v37q0ePHsUe1x757+tXX31Vo0aNkoeHh1q0aGF15uTf9erVS56enhoxYoSmTp2qy5cva/HixTp9+rRFXLVq1fTKK69o7Nixuv322zVu3DgFBATo119/1e7du23eVbhVq1basmWLbr/9dnXr1k0bN27kMiooG865nwaAa1Gcu5YdOXLEiIyMNKpXr25IMt+lK7+P999/35g0aZJRr149w8vLy+jatauxfft2iz5+++03Y/DgwUatWrWM6tWrG3fccYexd+9eo3HjxhZ3ysq/u9QPP/xgNefTp08bY8aMMfz9/Y2qVasaXbp0MbZs2WJ159HC9q2wvgu7o+nbb79tdOrUyfD19TV8fHyMZs2aGSNHjrTYt1OnThlDhgwxatasaZhMJotjl5OTY8ybN89o37694e3tbVSrVs1o2bKl8eCDDxq//PKLOa5x48ZGv379rPa3MPv27TN69eplVK9e3ahVq5bxj3/8w0hNTbW6g9fVtvf29jZq165tjBkzxvj4448t7k6bLykpyejXr59Ru3Ztw8PDw6hfv77Rr18/i2Nb2PErzLRp04yOHTsatWrVMry8vIymTZsaU6ZMMTIyMizikpOTjT59+hh+fn6Gl5eX0axZM4u7rZXG3Wmv9l4v+J4yDMPIysoyHn/8caN+/fqGt7e3cdNNNxkfffSRMWrUKKs72l26dMl4+umnjeuvv97w9PQ06tSpY9x2223Gtm3bzDH6/3en/bsVK1YY7u7uxn333WdcuXLlqvsBAKjcyPOs97m85Hndu3c32rRpYyQmJhodO3Y0vLy8jKCgIGP69OlGTk7OVbfPysoyHnvsMcPf39/w9vY2OnfubCQnJ1u9FoZhGH/++acxadIkIyQkxPDw8DBq165thIaGGk899ZRx/vx5wzAKvyNsYT777DOjT58+Rv369Q1PT0/D39/f6Nu3r7FlyxaLuNOnTxuPPvqo0ahRI8PDw8Pw9/c3+vXrZxw4cMAcUzC3vda70xqGYcTExBjBwcFGlSpVLHLfol6nTz/91Pw6169f33jiiSeML774wmbuvG7dOqN79+6Gr6+vUbVqVaN169YWd5/9+91p8/32229Gy5YtjSZNmhiHDh266j4A18pkGHacLwqgwklMTFSPHj30n//8p8gbIAAAAKB8Ic8DgIqJa+IBAAAAAAAALo4iHgAAAAAAAODiWE4LAAAAAAAAuDjOxAMAAAAAAABcHEU8AAAAAAAAwMVRxAMAAAAAAABcnLuzJ2DLokWL9PLLLystLU1t2rRRXFycunbtWmh8UlKSoqOj9dNPPyk4OFhTp07V+PHjLWJWr16tmTNn6tChQ2rWrJmee+45DRo0qFjjmkwmm+O/9NJLeuKJJ+zat7y8PP3++++qXr16of0BAAD8nWEYOnfunIKDg1WlCt/BuiryPAAAUFzFyvMMF7Ny5UrDw8PDePPNN419+/YZjz76qOHr62scPXrUZvz//vc/o2rVqsajjz5q7Nu3z3jzzTcNDw8P47///a85Ztu2bYabm5vx/PPPG/v37zeef/55w93d3fj222+LNW5aWprF4+233zZMJpNx6NAhu/fv2LFjhiQePHjw4MGDB49iP44dO1aC7AplhTyPBw8ePHjw4FHShz15nsvdnbZTp0666aabtHjxYnNbq1atdNddd2nu3LlW8U8++aQ++eQT7d+/39w2fvx47d69W8nJyZKkYcOGKTMzU1988YU55o477lCtWrW0YsWKEo0rSXfddZfOnTunr776qtD9ycrKUlZWlvn52bNn1ahRIx07dkw1atS42uEAAABQZmamGjZsqDNnzsjPz8/Z00Ehzp49q5o1a5LnAQAAuxUnz3Op5bTZ2dnasWOHpk2bZtEeGRmpbdu22dwmOTlZkZGRFm29e/dWfHy8cnJy5OHhoeTkZE2ZMsUqJi4ursTj/vHHH/r888/17rvvFrlPc+fO1ezZs63aa9SoQXIHAACKhSWari3/9SHPAwAAxWVPnudSF1XJyMjQlStXFBAQYNEeEBCg9PR0m9ukp6fbjM/NzVVGRkaRMfl9lmTcd999V9WrV9fdd99d5D7FxMTo7Nmz5sexY8eKjAcAAAAAAAAKcqkz8fIVrD4ahlFkRdJWfMF2e/oszrhvv/227r33Xnl7exc6L0ny8vKSl5dXkTEAAAAAAABAUVyqiFe3bl25ublZnf124sQJq7Pk8gUGBtqMd3d3V506dYqMye+zuONu2bJFBw8e1KpVq4q3gwAAAAAAAEAJuFQRz9PTU6GhoUpISNCgQYPM7QkJCRo4cKDNbcLCwvTpp59atG3YsEEdO3aUh4eHOSYhIcHiungbNmxQeHh4icaNj49XaGio2rdvX/KdBQCglBmGodzcXF25csXZU8FVuLm5yd3dnWveAQAAu5DnlR+OzPNcqognSdHR0YqKilLHjh0VFhampUuXKjU1VePHj5f01zXmjh8/rvfee0/SX3eiff311xUdHa1x48YpOTlZ8fHx5rvOStKjjz6qbt266cUXX9TAgQP18ccfa+PGjdq6davd4+bLzMzUf/7zH73yyitlcDQAACiZ7OxspaW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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}