Projektpraktikum/B_rework.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "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": [
       "9056"
      ]
     },
     "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)\n",
    "#ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8606728758791105"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "t_eff(found, lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample size:  32\n",
      "eff (cutoff =  0 ) =  0.96875\n",
      "sample size:  32\n",
      "eff (cutoff =  100 ) =  0.96875\n",
      "sample size:  65\n",
      "eff (cutoff =  200 ) =  0.9692307692307692\n",
      "sample size:  129\n",
      "eff (cutoff =  300 ) =  0.9457364341085271\n",
      "sample size:  169\n",
      "eff (cutoff =  400 ) =  0.9408284023668639\n",
      "sample size:  227\n",
      "eff (cutoff =  500 ) =  0.920704845814978\n",
      "cutoff energy = 350MeV\n",
      "sample size: 150\n",
      "eff =  0.9533333333333334\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 = 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",
    "\n",
    "\n",
    "\n",
    "for cutoff_energy in range(0,550,100):\n",
    "\tnobrem_f = found[ak.all(found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "\tnobrem_l = lost[ak.all(lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "\tprint(\"sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
    "\tprint(\"eff (cutoff = \",str(cutoff_energy),\") = \",str(t_eff(nobrem_f,nobrem_l)))\n",
    "\n",
    "\"\"\"\n",
    "we see that a cutoff energy of 350MeV is ideal because the efficiency drops significantly for higher values\n",
    "\"\"\"\n",
    "cutoff_energy = 350.0 #MeV\n",
    "\n",
    "\"\"\"\n",
    "better statistics: cutoff=350MeV - sample size: 150 events and efficiency=0.9533\n",
    "\"\"\"\n",
    "nobrem_found = found[ak.all(found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "nobrem_lost = lost[ak.all(lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "\n",
    "print(\"cutoff energy = 350MeV\\nsample size:\",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n",
    "print(\"eff = \",t_eff(nobrem_found, nobrem_lost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8593328191284226"
      ]
     },
     "execution_count": 5,
     "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 = found[ak.any(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",
    "energyloss_found = eph_found/energy_found\n",
    "\n",
    "brem_lost = lost[ak.any(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",
    "energyloss_lost = eph_lost/energy_lost\n",
    "\n",
    "t_eff(brem_found,brem_lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean energyloss relative to initial energy (found):  0.6551043170507098\n",
      "mean energyloss relative to initial energy (lost):  0.8273131179948844\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": 7,
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 4 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=200, cmap=plt.cm.jet, cmin=1)\n",
    "ax0.set_xlabel(\"energyloss\")\n",
    "ax0.set_ylabel(r\"$E_0$\")\n",
    "ax0.set_title(\"found energyloss wrt electron energy\")\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=200, cmap=plt.cm.jet, cmin=1) \n",
    "ax1.set_xlabel(\"energyloss\")\n",
    "ax1.set_ylabel(r\"$E_0$\")\n",
    "ax1.set_title(\"lost energyloss wrt electron energy\")\n",
    "plt.colorbar(a1[3],ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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": 11,
   "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": 11,
     "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": 12,
   "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",
    "    #[:,6] -> 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": 13,
   "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": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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JuLVdNLV/NNWF7GC6Hhm/5CcKkVh6U4jkxEjRGoXsRsS0tqOgdpi928qdcaO871ryejmqXy/SXM0zj2qD3Y2XOv/50vFXq5HyadNKAQAVZd2VdUVrpOh9WckFZXGG+EhZfgvtlOWKNT3r91tVqqzbMmSyuo9bpf19JE8SJr2J3irVNamhjHauM+lx/VEMHs8FqZDOCIkk6fmVRz9ohmXLowEuSOvl+RD8rxk3ovfNytvhzgicwCMaQjNE3rce7kbVN7U5CIiIiIiIiCh28Ut+IiIiIiIiIiIiIoohlxGZ1DmM5CciIiIiIqImwvqkqbGRnieQcMzVYj5STh45qm1TtyYptTrZq51jhGICVadpYOw9b2p7m49a1KbAMXtu7DC7FvTnmfIyGh3s+5iWnbQ3bqTysUM7KhoQYoi07M7zHaoJhK0ez63UVVaFIx1ZNI2Kjaa6EDnFL/mJgtSUJ16MJk4nO7Wfdsd4nRltahe9baWJcjUfBOQ8NPoT7+rmzhwsfXiQU+Asu+BXFICa9gZA5jR1Yt3hKAAArBg3T1m3pHimsvwcHlGWK070VJa7o1xZ7opTyvLQafv8Dj28+DlleSPUiXc1aYGOS29Xq+U0Pm5raONq/VRLMo+mGkvU9R55vXFe0/qyzid3dXrNm3/os8/KFzNqiip7KYkCCSaFlhv3a97/iYiIiIiIIoU5+WX8kp+IiIiIiIhCwPzHbitiaTLEcNfNaSS7HeFpf+PrxK0f1M3bqlgqN8TnMbOIaf35meot0S0XaJ+aeaHgO3LC+sS35oJ/Xfoe315EvBxwYjSxlj/zY/jOURZ8UFAwIxfcmNzXvREn5EQ4gnmcv4YYaET6+CU/UZCa2k01XJNbmnW2gz2O3WhivYlyA0fmAxhsdMz5DfuQJ2g16uzK0ftStP9gqU1KXvN7PK1Yndi2YoIaYS9PyDtjsxph/xbuVpaLhqgR9EW5DcvLpNqveUE9zjT1OJ9szlKWbxxyQN1AGkmw94n6MPe4A0JZt+W4NPHuaOkX8WPqYtpSg/PJb/hAlaS2JaqlD1njpfX50CVfX+rzMF8qoTexMYASeViw/GFJHpXhv63RhzUrw98Dvf6MovQDTQJthfHIgOCH2Ft5bbkR7e+2pnb/JyIiIiIiih61iExO/kgcMzB+yU9ERERERESuMwuKcCOa1Uyg4zX2SMhQtZsb+/ENSnB6nbgRBetfN+vzHGgtMXksEOkYVdpHPJ7j0rJv3YxSeQKmEfJJ0nlU+waIaKP8zUYSGJXzPb7vdm7cF3y3tTOqJRSR9HZy5LuRTz8c7RYJoQjwCcfcAqFg1haRrhtFL37JT+Qip/mh3ZtwKLyTfYWSlej9wFHNgSN47UUe63fsDT8YlBjlPt/cUFbufMsdaqnDLuXK1xQpkaPVG+r1obqqYogU7S5F4WO6eg7l2KCW366WH1esrt+yRoqy95Ki7eXtlo2cqyzPKF6qLMujBOLmNUTw91Z3N26adLzt0vHuUBfvxltqvTd3l86hfrmobKha+LgUvX+rwXMgtatnjX4RPdoPJUajL+RI/glSee2EYL7r7E46qH//MP5AFaxAI12C24dRu1rfXzD5/vW2DeW9LpT36mj84BaNdSIiIiIioljHSH4Zv+QnIiIiIiIiIiIiohjCiXdlHiGECFyMQqWqqgopKSk4f/48kpOTI12dJidaoguDyXUdTPSqnXoFOobdc7CXJ1sOYTePVA5Fbm91n0bDXuX6TdAvMliKLvfuptp3gi4vgxzw8vrBUtR3D52i30D/8Wuk5RnqG1Nm+j5leW/VjcrysORPAADDUaCs645yZXkjJinLc6UhA8ugRvXLUfbe4xTNU/P+y/n2h0Kth3ycAgxXlidhI3zJ9Shao+7bMK+/p1LaWoq89+b21wxdlp9T+bnR2c5vWz36+zMavRLoNW4353yg69+t+6Kd+4fbx4j2KPzg7kfujgCj4LEvF10i8XxES38ykFiawDbcQjFpptPjuVU2mPQ1Vo9nhxupVcxS6WhHUgK+6Wusfg7xT61ifYShlvFnFt/RnfrzQ/nvR5uex3c7s36X70S71ie7NWP1dWInlY/ZpLxupaSx+pgv7Tld8HmslW9x2/X0P0bs3LNj5X3Qjmg+p2iuW7Cs9uO85YA1ABLDVj/VTwCmRV3/n5H8RERERERERERERBRDLiMyqXMYyU86GP3lTlSinV8yo+lXz2iMyLQTGRva6H2jiaR8I1G8x5nScAyj6I/AowG09dNGytQziOK2RNrWO9lVtXS8JKl95armSssPSeUHL9Qv47VVWn5efQNakj5HWZ6//QVlWXTwKMuev6hvC+uXjgegjcw/8Jka6b900AxlWc6bL7u6rEzdX/o9ALSR9/IoASu8efgBNcJfjt7/elpXZfkU1OUbJxxQd5IvRd8k6UTePCQtfyQtl/jn1a9nJarfe93pX8NaxlFb/tTXh1HUlLWc98f99iGL9L1T7/hG56jdTn7O9NvK7BhWj0NNF/ty0SXano9Q3DvDEd0Z6Xu+mVBMtmmnj2p9YkzfPoP5e5BVoZiYUhaq51t7zPk+x2xlUA4wm/hWW9Y8Wt08slzu1xuPADDjG9ltNlrTrZEEZn0S82h142h5s/q49TqxHi3v/HhWzzFUI15C8RpzYwRCMCJ9/FCIpvfoWBq5EUr2I/lXAnA2kiY4FwA8HDX9TS9G8hMRERERERERERFRDGFOfhm/5CciIiIiokYp0hHpocivbB4ha5bP2/p+3IjCDbRdKM7f6f7ttKnVa8o/evsdadmdnPxO29ROtLjzHPlmx9dGXZpH5PczKOe7T98o/ws+f5tFhcuR/Nrje6SnUfgMzjAaLei7H4/HaL6thv06fE7No9Xl4zudC818BIaW9hydjoI0nwNBWzfz/ZrP46Yez85cBvKxrUeym7WpW/cp96LQjZ+3cBxfW5dwRLYbv07cGuVhVk4+hlsjs5rqCACqxy/5yTG3PjS5cROK9huZvXRC9jpEdifd1KuH0xQ9xqlzjPatnzLH3nYyvZQ5Rml09CfE9R9aq0enY54k9fg1E+ge0S/TS1ouaXiOV0vtu0raRYm0v63Sh6C5UvneUvnRDb8iD1Bv6ZlVe5RlOWWNPIGtbPyg9cryjEFLleXJ+VsAAPdmvqms8+yTsrz9RV185IkVyvI9yer+Pkkf5ne8LWV3K8uT0tWJdO+u2qIsP5espgKS671l+2S1riPr61p0TE3Xc/X2U8rykpEz1YNKkw5nSp/S5EmAM5fu8VuHLdKv9Ful53G0tF5qe02qn2V6190SaVn+sGT+AbCeen17PyB71ExLJil6jNLQFPuVN37t6b8O5deQ0aRj6nECpxYy/lDWy6+8tcm1jxisl+sX6D4aeII6vX0Ek/ooUNqzaEmZREREREREFBm1iMxX25GYByAwfslPRERERESNUjT9IBWOuljN0R0cqznS3YlStMP5KAPjERDuRXMat5tbzIKFrAYSBXOdWN2vb3sbBSDUM57LQN6PeQQ4oA1W8A0K0pk7y6tEPobvPo3m/9Iyey7q/w4+17l/ObP5ncwCS8zmHfPdzmw/6vH9nxtrIz78aYNnrObdd35NG8/rEJi117u9kTJO6+J7TLNofWf3JqvXsFsjF5wKVdS71dEioTinaOrnUOTxS35yzO7NJFoiAIOZINZ5vQNHvKvrjCaPNHqTN+9AWGl3K+elRs6aTVplXj/9jrDREFyjyUb1O5NqXYw6tJvNKqlD6uAOHuK/69vkslL08odSpPcWqYi87dyG50mO3pcsEY8qy/PLlusX2qrevr2T6c7brUbSX67S32xOshpFvn7keGX53jI1Un9cujqBbun4NABAGsqVdY9Me05ZlifQLUeaspy//F5lec+cTGXZO4GvqIxT1mWlf6JWUJqzRp6Qd16+em5Z49WJd72T8C5ZqkbsP1Kllp0jR83P0M+b543eB4BJqB9VMHypeuy3oI46qEBPdcNx0luoPJpjmXTMuTqvLXkEh/zy6CFdO/nyByz5mlevY/WDplx2gvS4/CF5vrReP1JfHUpu8MErSY6al/d9RFovX+jqMb0pC+Th6tr7hzxCIfCHC71RB3bv7fr338D3SDfeHyIx+s2N92B+iCAiIiIioujBnPyyZpGuABEREREREREREREROcNI/iYqElH14TqO3rnZjbx0o65OI0mN6Ufsa4e+mu/bKILXaH96w1q10bcGUcNyLnp5yGu1XvSvHKUs5ydXo6G17ScVGSznufdGAhsMG9XUQ4q6HqyfQxw9/FelFZcqyxVlajS5JqL7C+kX3eNStP00NdK8YFp9fnk5t/y4kRt0qyFH1S/HHGW5Z6cKZXkP6iPlSwepkfQ9P1Mf3zBonLLsjXwHgO5SdL6cN/+G/CJleen4+rz4p2Zcra5boebKP5B/o1pZtQhmzlGfPzkiPz+/PsL/NWm07V1Q9zH5WXW95241z52AGvmvd8w9g9TRAi3UpwmTBqn5/l8oUq+v0nSprcZKbfX2OADaOQC6Qs3xr4nkvxUS6TrqIb327pCK/HfD/3LO/tXQd1I9X4yTrvNcaXluw//y9ZwkX88G80aMl+qX77+tkEaBaO4H1Ub5/uUh7tK+pde7//B2QL5/yLn8rdyn7HB6LzZ6PJg8+25sp5eTP5Tv6cHMH0AUbTg5nXVuzPlUvx9n6WPM0nBYn/hVu51/ihhr7yvWRq76cz7ZJ6B9/zNL32In7YlZ+hSzFC2+7ehsgkmrz43/tfCObjl/vvMgLdEtVc/3fKX6VPumCzIjjxo2TgHk1xZJPtdGtbNrzOq16TsJtNxH9N+H8QTCvp+ztP1B33nO5FHUvm0j/+37PMmfB7Ujsf0nbDa7buXrwXdEt9lcS8bHN29vo1HozoWyPxnsfuxMum01BZGdsv73RXmeMd9Jz91KVRf7nL4P2jtGuD8XXEZk8uNHZyQ/v+QnIiIiIiIiIiIiohjCdD0yjxBCRLoSTVlVVRVSUlJw/vx5JCcnB94gCuhFEeo9blbGzXoEcxxrEYrWJqky25/Rr/6BcuhrywZuby3vr+lyFIMafSH/wi1HXmgjauVIFm9Uh1GUghz1IUVAyJHF1XqRRwYTVg2W2kaa8EoTkS8HDJQ0nMN46Xijpce36tUZwIfS8jJp+Zi03Lv+v7Rpaoj4I1Dz0s9PfkEtu0Xa7r/VxbTN0iiA7fXR4J+MzFLWyRH29ybnK8vrq9S8+XJ0uXx8r57XqxHplz/xexgAcE/yemXZm38eAO6u2qJTGkhP/hqANpIfZeri+LfV/b3eRs3DX/yDGlmvNzJANu8zNYf+hRvU9fI+5Nz/cr297Sa3nzxyQI7C716lto/cDvKIitqs+pDzuD+ob41yvn/5OHI9ZMuUEHvt6Iur59XXZdxSddTGljI13788z4KGfJ33UjsTmen7AACn0FVZVzFPGmnQW9pOvp4/kpb1Xn5Gc8iVyPcDozkDAtwH5FEMy6T7wWDp/lZiFCUl3cu8+6vWu0dZu59r743eKDL5/uYbyea/b+N7vrdeVuoU+J5v5/3O/TllYlM4o4hisS/XmIXq+bDTHzTeh9kEi9aj60I1GaNVTu9Jbk3GaDUq09790iwi3jjq13m9ffvT+u85+mXl45tFrJpNtuob9a5GLNuZTNmXeTStb6S3Ud3M2kLLdMLeJJ/odYM+Qz2rz7+2TbWfp8xGR/hGnPtElmtGHNtpb2sTz/qfn/7nw3q+bWMc2a7lc3x5XqqPtA+hxOl1ajzRr//1ZtRvBbTn4dtuZhM0W782zUaumH9PIB9TezzrkfW+z7fZtWGH+lyZ3XucTnIeiFmUvxsT39qZBNneqDWnE8Lrf0fjX65xjFJUz6MGwOKA/Thvfw9YBCAhDDX0VQNgYdT1/5mT3wUrV65Ez549kZCQgMzMTOzevTvSVSIiIiIiIiIiIiJqpGoj+C/6MF1PkDZu3IjZs2dj5cqVuOGGG7B69WrccccdOHz4MNLT0yNdPSIiIiIiCpIbeWvNouvs7D/SUXp2jm9+zs7Ow63zd2MksJ0ISvOITTsRu69Jy2aRxcaP+ec2l/P1B5O/Ws6fbydaXo1eNouQ9Y3sNs2RXm2WP963sMFIRADanPG+56RGM5vPK2EeAS7PhwRYzzVuPK8aYDavkfkIAO3zpL1WzCKUfZ7fZVaPYWfkgM9+kkxGcmj2a3YtmkW9G9fbzgh/32No2994tIDz0U/GIwdCxeox3MoB79b7kNVygfZhdl7O3+uMo/fd2H+08Z5HfYT+4gjXJnbxS/4gLV++HA888AD+9V//FQDw/PPP44MPPsCf//xnPP300xGuXWgEvsHFzkS+VvbhxoeuUH5wC2aYtLpeTtFjddgnYGnIoqZPI6fk8HbEpM76R9Ib2ThpMzl1T67BcbbWbztj81Jl1Yp589THp6uLmSP3KMtFE6T8MKOlvGqrzG+PcvqYtKpS3TLLR6qpWuZgufrA+/X/3TPyTWXV3VAn2B1XpaZzubdMLfO1wQ+HV5fV588Z97m6jzer1NQ5zyWrKXLkyXbv3a6mBVomtYmsrKo+TY9nnvpLtTdNjO/+Eg9Lv2ZLKX3wndSWDZ9NZhSrz1PWIDW3UNG16vOxHmqqohUT1Oey+2b1mPPX1KdKGjdNbbP589T0STOWqscpT1ZT7ezDULXMyKXS+voUQXJKHfkcn8Mj6v6k1D3ysuweqM8fZtRfX3J6IO+1AEAzlHlcsZTSZ4haPq1YrYs3TY9mEmh5ol8ptQ96Sc+BURof7yTActqqudKy/CFETsPVS54AG/plvEPklxncM0qMPuzIH9ClD9nV9TcW7b3Vd1K5etZSbOjdG/X3JzO+p3q3db4Pu2WclG3M2A5ERERERI0dc/LL+CV/EC5duoSioiIsWLBAs37UqFEoKCjQ3ebixYu4ePGi8vf58+cB1P9aRRQaNcqStevsJ91tVT8EeBwApONcMRrG5N2PVPZHqaw8W8gVadkonWfDppeqpDpdlPct7a5K+qNWKlMt3aivSLfHhpdsXVW1VCV1H3VQ18t+koZwaco01Eve3yWpLWvl56C6Slqs0z2Ot0xtlbqd/FTX4JLucfCjWkjTJhJlP9X6ZY3qqnFBasuG51J+nq5onxxl8acq6XqQnqeaKvV8cMH/3OXnXT6OXFf5+ZDb5MeGCspljZ5HuYzmHIx42+dH/2urYSfqvuXzkdpEvmb89uu7b/l6ltfLryH5+ELncc2sPdIDQnpu5NenfH/QhKV51xvdM+T7zg8G62v81tu9p/G9lkLBe11xmqvIYN+aws/ovSyY6067T/P9BOqrB+a/f+v7Ma+b2X5+MnlMfe/337+8ndlj5rT7/cHnUbne1tvGrefJfD9mbSPzPYZZ/8esbto2tX5Ms+fC7Bi+j1UZlNMpq9vXNNqv0fF9Wdsu8GvdatsY19vsdWrnNcz3Q4ol9vvVFwMXCYlIHdccJ94NwqlTp3DVVVdhz549GD5cnRDzqaeewiuvvIKjR4/6bZOXl4dFi4IZAklERERE0ebEiRO4+uqrAxckV7FvTURERNS4BOpX19TUoGfPnjh9+nQYa6WVmpqK0tJSJCREYuJfffySPwjeL/kLCgqQnZ2trH/yySfx6quv4ssvv/Tbxjfa6Ny5c8jIyEBZWVnDzNBkRVVVFbp3747y8vKomsk6FrDtnGG7OcN2c45t5wzbzRm2m3Pnz59Heno6Kisr0bZt20hXp8lh39odvAc4w3Zzjm3nDNvNGbabc2w7Z9huztjpV9fU1ODSpUumZUIpPj4+qr7gB5iuJygdO3ZE8+bN/X45OnPmDLp06aK7TcuWLdGyZUu/9SkpKXzhO5CcnMx2c4ht5wzbzRm2m3NsO2fYbs6w3Zxr1qxZpKvQJLFv7S7eA5xhuznHtnOG7eYM2805tp0zbDdnrPSrExISou5L9kjjp5EgxMfHIzMzE9u2bdOs37ZtmyZ9DxERERERERERERFRKDCSP0hz5szB1KlTkZWVhezsbKxZswZlZWXIzc2NdNWIiIiIiIiIiIiIqJHjl/xBmjRpEs6ePYvf//73qKiowIABA/Dee+8hIyPD0vYtW7bEwoULdYcZkzG2m3NsO2fYbs6w3Zxj2znDdnOG7eYc2y668Plwhu3mDNvNObadM2w3Z9huzrHtnGG7OcN2Cw4n3iUiIiIiIiIiIiIiilHMyU9EREREREREREREFKP4JT8RERERERERERERUYzil/xERERERERERERERDGKX/ITEREREREREREREcUofskfYk8++SSGDx+OxMREtG3b1tI2Qgjk5eWha9euaNWqFW6++Wb89a9/1ZS5ePEiZsyYgY4dO6J169b45S9/iW+//TYEZxAZlZWVmDp1KlJSUpCSkoKpU6fi3Llzptt4PB7df0uXLlXK3HzzzX6PT548OcRnE15O2u7+++/3a5dhw4ZpyvCa06qtrcWjjz6KgQMHonXr1ujatSvuu+8+nDp1SlOuMV5zK1euRM+ePZGQkIDMzEzs3r3btPyuXbuQmZmJhIQEXH311Vi1apVfmU2bNuG6665Dy5Ytcd111yE/Pz9U1Y8YO+22efNm/PznP0enTp2QnJyM7OxsfPDBB5oyL7/8su49r6amJtSnElZ22m3nzp26bfLll19qyjWF6w2w13Z67wMejwf9+/dXyjSFa+7jjz/GXXfdha5du8Lj8WDLli0Bt+E9LrzYt3aGfWvn2Ld2hn1ra9ivdo59a2fYt3aOfWv72LcOM0Eh9bvf/U4sX75czJkzR6SkpFjaZvHixSIpKUls2rRJHDp0SEyaNEmkpaWJqqoqpUxubq646qqrxLZt20RxcbG45ZZbxKBBg8Tly5dDdCbhNXr0aDFgwABRUFAgCgoKxIABA8SYMWNMt6moqND8W7dunfB4POLEiRNKmREjRogHH3xQU+7cuXOhPp2wctJ2OTk5YvTo0Zp2OXv2rKYMrzmtc+fOidtuu01s3LhRfPnll6KwsFAMHTpUZGZmaso1tmtuw4YNIi4uTrz44ovi8OHDYtasWaJ169bi5MmTuuW//vprkZiYKGbNmiUOHz4sXnzxRREXFyfeeustpUxBQYFo3ry5eOqpp8SRI0fEU089JVq0aCH27t0brtMKObvtNmvWLPHMM8+ITz/9VHz11VfiscceE3FxcaK4uFgp89JLL4nk5GS/e19jYrfdduzYIQCIo0ePatpEvk81hetNCPttd+7cOU2blZeXi/bt24uFCxcqZZrCNffee++J//zP/xSbNm0SAER+fr5ped7jwo99a2fYt3aOfWtn2LcOjP1q59i3doZ9a+fYt3aGfevw4pf8YfLSSy9Z+iBSV1cnUlNTxeLFi5V1NTU1IiUlRaxatUoIUX+ziIuLExs2bFDK/O1vfxPNmjUTW7dudb3u4Xb48GEBQPMCLSwsFADEl19+aXk/Y8eOFSNHjtSsGzFihJg1a5ZbVY06TtsuJydHjB071vBxXnPWfPrppwKA5o2+sV1zP/vZz0Rubq5mXd++fcWCBQt0y8+fP1/07dtXs+6hhx4Sw4YNU/6eOHGiGD16tKbM7bffLiZPnuxSrSPPbrvpue6668SiRYuUv62+r8Qyu+3m/SBSWVlpuM+mcL0JEfw1l5+fLzwej/jmm2+UdU3hmpNZ+SDCe1zksG9tHfvWzrFv7Qz71tawX+0c+9bOsG/tHPvWwWPfOvSYrifKlJaW4vTp0xg1apSyrmXLlhgxYgQKCgoAAEVFRaitrdWU6dq1KwYMGKCUiWWFhYVISUnB0KFDlXXDhg1DSkqK5fP7+9//jnfffRcPPPCA32OvvfYaOnbsiP79+2Pu3Lmorq52re6RFkzb7dy5E507d8a1116LBx98EGfOnFEe4zVnzfnz5+HxePzSBzSWa+7SpUsoKirSXAcAMGrUKMN2Kiws9Ct/++2348CBA6itrTUt0xiuLcBZu/mqq6tDdXU12rdvr1n/ww8/ICMjA926dcOYMWNQUlLiWr0jLZh2Gzx4MNLS0nDrrbdix44dmsca+/UGuHPNrV27FrfddhsyMjI06xvzNecE73HRj31r9q2Dwb61M+xbB8Z+tXPsWzvDvrVz7FuHD+9zwWkR6QqQ1unTpwEAXbp00azv0qULTp48qZSJj49Hu3bt/Mp4t49lp0+fRufOnf3Wd+7c2fL5vfLKK0hKSsKECRM066dMmYKePXsiNTUVX3zxBR577DF89tln2LZtmyt1jzSnbXfHHXfgnnvuQUZGBkpLS/HEE09g5MiRKCoqQsuWLXnNWVBTU4MFCxbgV7/6FZKTk5X1jema+/7773HlyhXd+5NRO50+fVq3/OXLl/H9998jLS3NsExjuLYAZ+3m69lnn8WPP/6IiRMnKuv69u2Ll19+GQMHDkRVVRX++Mc/4oYbbsBnn32G3r17u3oOkeCk3dLS0rBmzRpkZmbi4sWLePXVV3Hrrbdi586duOmmmwAYX5ON5XoDgr/mKioq8P777+P111/XrG/s15wTvMdFP/at2bcOBvvWzrBvHRj71c6xb+0M+9bOsW8dPrzPBYdf8juQl5eHRYsWmZbZv38/srKyHB/D4/Fo/hZC+K3zZaVMJFltN8D//AF757du3TpMmTIFCQkJmvUPPvigsjxgwAD07t0bWVlZKC4uxpAhQyztOxJC3XaTJk1SlgcMGICsrCxkZGTg3Xff9fswZ2e/kRaua662thaTJ09GXV0dVq5cqXksVq85M3bvT3rlfdc7uefFGqfnuH79euTl5eHtt9/WfGAeNmyYZhK/G264AUOGDMGKFSvwwgsvuFfxCLPTbn369EGfPn2Uv7Ozs1FeXo5ly5YpH0Ts7jOWOT3Pl19+GW3btsW4ceM065vKNWcX73HBY9/aGfatnWPf2hn2rd3HfrVz7Fs7w761c+xbhwfvc87xS34Hpk+fjsmTJ5uW6dGjh6N9p6amAqj/9SotLU1Zf+bMGeWXqtTUVFy6dAmVlZWa6I8zZ85g+PDhjo4bDlbb7fPPP8ff//53v8e+++47v1/r9OzevRtHjx7Fxo0bA5YdMmQI4uLicOzYsajuFIar7bzS0tKQkZGBY8eOAeA1Z6a2thYTJ05EaWkptm/frok00hMr15yejh07onnz5n6/kMv3J1+pqam65Vu0aIEOHTqYlrFzzUYzJ+3mtXHjRjzwwAN48803cdttt5mWbdasGf7xH/9Red3GumDaTTZs2DD85S9/Uf5u7NcbEFzbCSGwbt06TJ06FfHx8aZlG9s15wTvce5g39oZ9q2dY9/aGfat3cN+tXPsWzvDvrVz7FuHD+9zwWFOfgc6duyIvn37mv7zjXKxyjv0UB5ueOnSJezatUvp8GVmZiIuLk5TpqKiAl988UVUdwqttlt2djbOnz+PTz/9VNl23759OH/+vKXzW7t2LTIzMzFo0KCAZf/617+itrZW86EvGoWr7bzOnj2L8vJypV14zenzfgg5duwYPvzwQ+VNx0ysXHN64uPjkZmZ6Tccetu2bYbtlJ2d7Vf+//7v/5CVlYW4uDjTMtF8bdnhpN2A+iij+++/H6+//jp+8YtfBDyOEAIHDx6MyWtLj9N281VSUqJpk8Z+vQHBtd2uXbtw/Phx3bzbvhrbNecE73HuYN/aGfatnWPf2hn2rd3DfrVz7Fs7w761c+xbhw/vc0EK7by+dPLkSVFSUiIWLVok2rRpI0pKSkRJSYmorq5WyvTp00ds3rxZ+Xvx4sUiJSVFbN68WRw6dEjce++9Ii0tTVRVVSllcnNzRbdu3cSHH34oiouLxciRI8WgQYPE5cuXw3p+oTJ69Ghx/fXXi8LCQlFYWCgGDhwoxowZoynj225CCHH+/HmRmJgo/vznP/vt8/jx42LRokVi//79orS0VLz77ruib9++YvDgwY2m3YSw33bV1dXi3//930VBQYEoLS0VO3bsENnZ2eKqq67iNWfSbrW1teKXv/yl6Natmzh48KCoqKhQ/l28eFEI0TivuQ0bNoi4uDixdu1acfjwYTF79mzRunVr8c033wghhFiwYIGYOnWqUv7rr78WiYmJ4pFHHhGHDx8Wa9euFXFxceKtt95SyuzZs0c0b95cLF68WBw5ckQsXrxYtGjRQuzduzfs5xcqdtvt9ddfFy1atBB/+tOfNNfWuXPnlDJ5eXli69at4sSJE6KkpET8y7/8i2jRooXYt29f2M8vVOy223PPPSfy8/PFV199Jb744guxYMECAUBs2rRJKdMUrjch7Led169//WsxdOhQ3X02hWuuurpa6asBEMuXLxclJSXi5MmTQgje46IB+9bOsG/tHPvWzrBvHRj71c6xb+0M+9bOsW/tDPvW4cUv+UMsJydHAPD7t2PHDqUMAPHSSy8pf9fV1YmFCxeK1NRU0bJlS3HTTTeJQ4cOafZ74cIFMX36dNG+fXvRqlUrMWbMGFFWVhamswq9s2fPiilTpoikpCSRlJQkpkyZIiorKzVlfNtNCCFWr14tWrVqpXmz9iorKxM33XSTaN++vYiPjxfXXHONmDlzpjh79mwIzyT87LbdTz/9JEaNGiU6deok4uLiRHp6usjJyfG7nnjNaduttLRU97Utv74b6zX3pz/9SWRkZIj4+HgxZMgQsWvXLuWxnJwcMWLECE35nTt3isGDB4v4+HjRo0cP3S8K3nzzTdGnTx8RFxcn+vbtq+k4NhZ22m3EiBG611ZOTo5SZvbs2SI9PV3Ex8eLTp06iVGjRomCgoIwnlF42Gm3Z555RlxzzTUiISFBtGvXTtx4443i3Xff9dtnU7jehLD/Wj137pxo1aqVWLNmje7+msI1t2PHDtPXHu9xkce+tTPsWzvHvrUz7Ftbw361c+xbO8O+tXPsW9vHvnV4eYRomMGAiIiIiIiIiIiIiIhiCnPyExERERERERERERHFKH7JT0REREREREREREQUo/glPxERERERERERERFRjOKX/EREREREREREREREMYpf8hMRERERERERERERxSh+yU9EREREREREREREFKP4JT8RERERERERERERUYzil/xERERERERERERERDGKX/ITEYXBzTffjNmzZ0e6Gk1CXl4ePB4PPB4Pnn/++YjUoUePHkodzp07F5E6EBERETVG7FeHD/vVRESxg1/yExFRWN1///0YN25cSI/Rv39/VFRUYNq0aSE9jpH9+/dj06ZNETk2ERERETUN7FcTEZEXv+QnImpiamtrI10FV1y6dMnwsRYtWiA1NRWJiYlhrJGqU6dOaN++fUSOTUREREThwX516LFfTURkDb/kJyIKk8uXL2P69Olo27YtOnTogMcffxxCCMPyeXl5+Id/+AesXr0a3bt3R2JiIu655x7NMNX9+/fj5z//OTp27IiUlBSMGDECxcXFmv14PB6sWrUKY8eORevWrfFf//VfuHLlCh544AH07NkTrVq1Qp8+ffDHP/5Rs503Muipp55Cly5d0LZtWyxatAiXL1/GvHnz0L59e3Tr1g3r1q3TbPe3v/0NkyZNQrt27dChQweMHTsW33zzjXJOr7zyCt5++21l2O3OnTsDbifX5+mnn0bXrl1x7bXX2mp/j8eD1atXY8yYMUhMTES/fv1QWFiI48eP4+abb0br1q2RnZ2NEydO+D0H69atQ3p6Otq0aYN/+7d/w5UrV7BkyRKkpqaic+fOePLJJ23VhYiIiIicY7+a/WoiItLil/xERGHyyiuvoEWLFti3bx9eeOEFPPfcc/if//kf022OHz+ON954A++88w62bt2KgwcP4re//a3yeHV1NXJycrB7927s3bsXvXv3xp133onq6mrNfhYuXIixY8fi0KFD+M1vfoO6ujp069YNb7zxBg4fPozf/e53+I//+A+88cYbmu22b9+OU6dO4eOPP8by5cuRl5eHMWPGoF27dti3bx9yc3ORm5uL8vJyAMBPP/2EW265BW3atMHHH3+MTz75BG3atMHo0aNx6dIlzJ07FxMnTsTo0aNRUVGBiooKDB8+POB2Xh999BGOHDmCbdu24X//939tPwd/+MMfcN999+HgwYPo27cvfvWrX+Ghhx7CY489hgMHDgAApk+frtnmxIkTeP/997F161asX78e69atwy9+8Qt8++232LVrF5555hk8/vjj2Lt3r+36EBEREZF97FezX01ERD4EERGF3IgRI0S/fv1EXV2dsu7RRx8V/fr1M9xm4cKFonnz5qK8vFxZ9/7774tmzZqJiooK3W0uX74skpKSxDvvvKOsAyBmz54dsI4PP/yw+Od//mfl75ycHJGRkSGuXLmirOvTp4/4p3/6J83xWrduLdavXy+EEGLt2rWiT58+mvO8ePGiaNWqlfjggw+U/Y4dO1ZzbKvbdenSRVy8eNH0PBYuXCgGDRrktx6AePzxx5W/CwsLBQCxdu1aZd369etFQkKCZl+JiYmiqqpKWXf77beLHj16+LXL008/rTnejh07BABRWVlpWl8iIiIiso79avariYjIHyP5iYjCZNiwYfB4PMrf2dnZOHbsGK5cuWK4TXp6Orp166bZpq6uDkePHgUAnDlzBrm5ubj22muRkpKClJQU/PDDDygrK9PsJysry2/fq1atQlZWFjp16oQ2bdrgxRdf9Nuuf//+aNZMfavo0qULBg4cqPzdvHlzdOjQAWfOnAEAFBUV4fjx40hKSkKbNm3Qpk0btG/fHjU1NZrhur6sbjdw4EDEx8cb7ieQ66+/XnMu3n3K62pqalBVVaWs69GjB5KSkjRlrrvuOr928bYBEREREYUW+9XsVxMRkVaLSFeAiIis836Y8f5///3347vvvsPzzz+PjIwMtGzZEtnZ2X6TZ7Vu3Vrz9xtvvIFHHnkEzz77LLKzs5GUlISlS5di3759mnJxcXF+x9dbV1dXBwCoq6tDZmYmXnvtNb+6d+rUyfC8rG7nex52yXX3tqHeOu/5+D7uLWPWBkREREQU/divZr+aiKgx4Zf8RERh4ptb0pvrs3nz5obblJWV4dSpU+jatSsAoLCwEM2aNVMmx9q9ezdWrlyJO++8EwBQXl6O77//PmBddu/ejeHDh+Phhx9W1plFBFk1ZMgQbNy4EZ07d0ZycrJumfj4eL8oKyvbEREREREB7Fd7sV9NREReTNdDRBQm5eXlmDNnDo4ePYr169djxYoVmDVrluk2CQkJyMnJwWeffYbdu3dj5syZmDhxIlJTUwEAvXr1wquvvoojR45g3759mDJlClq1ahWwLr169cKBAwfwwQcf4KuvvsITTzyB/fv3B32OU6ZMQceOHTF27Fjs3r0bpaWl2LVrF2bNmoVvv/0WQP0w3c8//xxHjx7F999/j9raWkvbEREREREB7FezX01ERL74JT8RUZjcd999uHDhAn72s5/ht7/9LWbMmIFp06aZbtOrVy9MmDABd955J0aNGoUBAwZg5cqVyuPr1q1DZWUlBg8ejKlTp2LmzJno3LlzwLrk5uZiwoQJmDRpEoYOHYqzZ89qoo+cSkxMxMcff4z09HRMmDAB/fr1w29+8xtcuHBBiSR68MEH0adPHyVv6Z49eyxtR0REREQEsF/NfjUREfnyCCFEpCtBRET+8vLysGXLFhw8eDDSVYkp0dJuO3fuxC233ILKykq0bds2onUhIiIiasqipX8Ya6Kl3divJiIKjJH8RETU6Bw6dAht2rTRRGeFU//+/XHHHXdE5NhERERERG5hv5qIKDZw4l0iImpUZs6ciV//+tcAgE6dOkWkDu+99x5qa2sBgMOiiYiIiCgmsV9NRBQ7mK6HiIiIiIiIiIiIiChGMV0PEREREREREREREVGM4pf8REREREREREREREQxil/yExERERERERERERHFKH7JT0REREREREREREQUo/glPxERERERERERERFRjOKX/EREREREREREREREMYpf8hMRERERERERERERxSh+yU9EREREREREREREFKP+HwD8EWK7nOh+AAAAAElFTkSuQmCC",
      "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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": []
  }
 ],
 "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
}