CMResults_NEXT100_halmazan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import numpy  as np\n",
    "import tables as tb\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import math\n",
    "\n",
    "#the line below makes the plot as a pop-up, that can be saved\n",
    "#matplotlib.use('TkAgg')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.colors as clrs\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "from matplotlib.colors import Normalize\n",
    "\n",
    "from scipy.stats import norm\n",
    "from scipy.stats import exponnorm\n",
    "from scipy.stats import skewnorm\n",
    "\n",
    "from IC.invisible_cities.evm.event_model        import Cluster, Hit\n",
    "from IC.invisible_cities.types.ic_types         import xy\n",
    "from IC.invisible_cities.reco.paolina_functions import voxelize_hits\n",
    "\n",
    "from IC.invisible_cities.core.exceptions import NoHits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import functions.histo_functions as myhf\n",
    "import functions.efficiency_functions as myef\n",
    "import functions.recovstrue as myrvt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = '/Users/halmamol/NEXT/files/NEXT100/beershebaFS/'\n",
    "max0nubbf = 999\n",
    "maxselectron = 999\n",
    "blobR = 21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Opening TRACK Files - Name "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File /Users/halmamol/NEXT/files/NEXT100/beershebaFS/0nubb/tracks/21mm/next100.0nubb.723.skel_tracks.R21mm.h5 not good\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['event', 'trackID', 'energy', 'length', 'numb_of_voxels',\n",
       "       'numb_of_hits', 'numb_of_tracks', 'x_min', 'y_min', 'z_min', 'x_max',\n",
       "       'y_max', 'z_max', 'r_max', 'x_ave', 'y_ave', 'z_ave', 'extreme1_x',\n",
       "       'extreme1_y', 'extreme1_z', 'extreme2_x', 'extreme2_y', 'extreme2_z',\n",
       "       'blob1_x', 'blob1_y', 'blob1_z', 'blob2_x', 'blob2_y', 'blob2_z',\n",
       "       'eblob1', 'eblob2', 'ovlp_blob_energy', 'vox_size_x', 'vox_size_y',\n",
       "       'vox_size_z'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#0nubb files\n",
    "track_list_0nubb = []\n",
    "\n",
    "filepath_0nubb_tracks = filepath+f'0nubb/tracks/{blobR}mm/'\n",
    "\n",
    "#loop over all the files, to read 'Tracks' information\n",
    "for nfile in range(1,max0nubbf):\n",
    "    \n",
    "    filename_0nubb = filepath_0nubb_tracks+f'next100.0nubb.{nfile}.skel_tracks.R{blobR}mm.h5'\n",
    "    #the following lines are added because some of the files aren't there (I had to check Paola's notebook)\n",
    "    try:\n",
    "        readfile_tracks = pd.read_hdf(filename_0nubb, 'Tracks')\n",
    "    except:\n",
    "        print('File {} not good'.format(filename_0nubb))\n",
    "        continue\n",
    "        \n",
    "    track_list_0nubb.append(readfile_tracks)\n",
    "    \n",
    "\n",
    "#'concat' command means: 'concatenate pandas objects along a particular \n",
    "# axis with optional set logic along the other axes.' -> following line \n",
    "# is needed to keep an order between the files\n",
    "\n",
    "tracks_0nubb = pd.concat([dt for dt in track_list_0nubb]) \n",
    "tracks_0nubb.columns  \n",
    "\n",
    "#these are all the elements inside the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['event', 'trackID', 'energy', 'length', 'numb_of_voxels',\n",
       "       'numb_of_hits', 'numb_of_tracks', 'x_min', 'y_min', 'z_min', 'x_max',\n",
       "       'y_max', 'z_max', 'r_max', 'x_ave', 'y_ave', 'z_ave', 'extreme1_x',\n",
       "       'extreme1_y', 'extreme1_z', 'extreme2_x', 'extreme2_y', 'extreme2_z',\n",
       "       'blob1_x', 'blob1_y', 'blob1_z', 'blob2_x', 'blob2_y', 'blob2_z',\n",
       "       'eblob1', 'eblob2', 'ovlp_blob_energy', 'vox_size_x', 'vox_size_y',\n",
       "       'vox_size_z'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#same for selectron files\n",
    "track_list_selectron = []\n",
    "filepath_selectron_tracks = filepath+f'/e-_roi/tracks/{blobR}mm/'\n",
    "\n",
    "#loop over all the files, to read 'Tracks' information\n",
    "for nfile in range(1,maxselectron):\n",
    "    \n",
    "    filename_selectron = filepath_selectron_tracks+f'next100.e-_roi.{nfile}.skel_tracks.R{blobR}mm.h5'\n",
    "    #the following lines are added because some of the files aren't there (I had to check Paola's notebook)\n",
    "    try:\n",
    "        readfile_tracks_selectron = pd.read_hdf(filename_selectron, 'Tracks')\n",
    "    except:\n",
    "        print('File {} not good'.format(filename_selectron))\n",
    "        continue\n",
    "        \n",
    "    track_list_selectron.append(readfile_tracks_selectron)\n",
    "    \n",
    "\n",
    "#'concat' command means: 'concatenate pandas objects along a particular \n",
    "# axis with optional set logic along the other axes.' -> following line \n",
    "# is needed to keep an order between the files\n",
    "\n",
    "tracks_selectron = pd.concat([dt for dt in track_list_selectron]) \n",
    "tracks_selectron.columns  \n",
    "\n",
    "#these are all the elements inside the file:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking variables\n",
    "\n",
    "So far, I'm interested in selecting the events in/with:\n",
    "- **Fiducial**: fiducial cut around the detector volume, of 2cm from the border\n",
    "- **1Track**: in this case, considers the track with highest deposition\n",
    "- **NoOverlap**: defined blobs in one trace should not overlap\n",
    "- **ROI**: energy cut in the region of interest, between [2.445,2475]MeV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial amount of events for 0nubb: 9759 +- 98.78765105011861\n",
      "Initial amount of events for selectrons: 9767 +- 98.82813364624468\n"
     ]
    }
   ],
   "source": [
    "#Checking total amount of events, prior any selection\n",
    "nevents_0nubb_total = tracks_0nubb.event.nunique()\n",
    "Enevents_0nubb_total = np.sqrt(nevents_0nubb_total)\n",
    "print(f'Initial amount of events for 0nubb: {nevents_0nubb_total} +- {Enevents_0nubb_total}')\n",
    "\n",
    "nevents_selectron_total = tracks_selectron.event.nunique()\n",
    "Enevents_selectron_total = np.sqrt(nevents_selectron_total)\n",
    "print(f'Initial amount of events for selectrons: {nevents_selectron_total} +- {Enevents_selectron_total}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking variables: Fiducial\n",
    "\n",
    "Test the position of the track end point, to see if it's withing the fiducial limits. To do so, I need the information about the NEXT100 dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dimensions for the NEXT100 detector\n",
    "veto_N100 = 20 # mm\n",
    "#rmax_N100 = 400\n",
    "rmax_N100 = 492 - veto_N100\n",
    "#rmax_N100 = 200\n",
    "zmin_N100 = veto_N100\n",
    "zmax_N100 = 1205 - veto_N100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_0nubb_zmin = tracks_0nubb.z_min\n",
    "track_0nubb_zmax = tracks_0nubb.z_max\n",
    "\n",
    "plt.hist(track_0nubb_zmin, bins = 50, range = (0, 1400), color='blue', histtype='step', label = 'zMin')\n",
    "plt.hist(track_0nubb_zmax, bins = 50, range = (0, 1400),color='green', histtype='step', label = 'zMax', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('position (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('0nubb')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nocheck_tracks_0nubb = tracks_0nubb[~((tracks_0nubb.z_min > 0) & \n",
    "                                     (tracks_0nubb.z_max < 400))].event.unique()\n",
    "\n",
    "#Then I apply the selection into the matrix of elements\n",
    "check_tracks_0nubb = tracks_0nubb[(tracks_0nubb.z_min > 0) & \n",
    "                                     (tracks_0nubb.z_max < 400)]\n",
    "\n",
    "# I get a list of true elements if event ID is not in the above list 'nonfiducial_list'\n",
    "evt_fid_sel_0nubb    = ~check_tracks_0nubb.event.isin(nocheck_tracks_0nubb) \n",
    "\n",
    "# And finally I select the events with an asigned True from the previous line \n",
    "check_tracks_0nubb = check_tracks_0nubb[evt_fid_sel_0nubb]\n",
    "\n",
    "check_track_zmin = check_tracks_0nubb.z_min\n",
    "check_track_zmax = check_tracks_0nubb.z_max\n",
    "\n",
    "plt.hist(check_track_zmin, bins = 50, color='blue', histtype='step', label = 'zMin')\n",
    "plt.hist(check_track_zmax, bins = 50, color='green', histtype='step', label = 'zMax', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('position (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.legend(loc=1);\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "check_track_energy = check_tracks_0nubb.energy\n",
    "\n",
    "plt.hist(check_track_energy, bins = 50, color='blue', histtype='step', label = 'energy')\n",
    "\n",
    "plt.xlabel('energy (MeV)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qZMiCpCnAPwLTyQblARAR5brPNkyhUIjOzs5Gh2FWOcm9oQbK/+2qRtLiiCj0LK/0MdS/kY132Aq8C7gG+GH1wjMzs2ZWabLYIyLuIquJPJ0WRHp37cIyM7NmUmmbxYuSdgEel3QW8Aywb+3CMjOzZlJpzeIcsik+Pg0cCnwUOLlWQZmZWXOpdFDer9PuC8AptQvHzMyaUZ81C0nzJb2ll2PDJX1C0kdqE5rZIOUJA60FlatZfBf4YkoYy4CNZF1npwAjgSvZMWWHmVXCEwZaC+ozWUTEEuBDkvYCCsBY4I/AiohY2de1ZmY2eFTaZvECcHdtQzEzs2a1M3NDmZlZm3CyMKsVN2TbINLviQTT4Ly9IuL5GsRjNni4IdsGkYpqFpKukzRS0nDgUWClpL+vbWhmZtYsKn0MNT3VJI4D7gAmAh+rWVRmZtZUKk0WQyUNJUsWCyNiSw1jMjOzJlNpsvhXoAsYDtwjaRLwXK2CMjOz5lJpsrg1IsZFxNFp3ezfAJ/o6wJJV0raIGlZrmy0pEWSHk8/R+WOnS9ptaSVko7MlR8qaWk69u20vKpZ83CvJ2sDlSaLH+dfpISxoMw1VwFH9Sg7D7grIqYAd6XXSJoOzAUOStd8t7gmN9miS/PIphiZUuKeZo1V7PXUc+vqanRkZlXTZ9dZSW8m+wJ/naQP5A6NJLe8aikRcY+kjh7Fx5Ktyw1wNdmo8HNT+YKIeAl4StJqYJakLmBkRNyb4rmGrN3E63CbmdVRuXEWBwLvA14PHJMr3wycNoD32y8i1gFExDpJxQWUxgH35c7rTmVb0n7P8pIkzSOrhTBx4sQBhGdmZqWUm0hwIbBQ0juKf93XSKl2iOijvKSImA/MBygUCh4NZWZWJZWO4F4t6XNAR/6aiOizkbuE9ZLGplrFWGBDKu8GJuTOGw+sTeXjS5SbmVkdVdrAvRB4HXAncHtu669b2LEc68npvsXyuZJ2lzSZrCH7gfTIarOk2akX1Em5a8zMrE4qrVnsGRHn9ufGkq4na8zeR1I38GXga8CNkk4l6377QYCIWC7pRrKpRLYCZ0bEtnSr08l6Vu1B1rDtxm2rrY6OrIdTpdxF1tqAooKJziT9A/CriLij9iFVR6FQiM7OzkaHYa1I8gSArcb/z6pG0uKIKPQsr/Qx1NnAbZJelPS8pM2SPOusmVmbqHSlvBG1DsTMzJpXpVOUS9JHJX0xvZ4gaVZtQzMzs2ZR6WOo7wLvAP46vX4BuKwmEZmZWdOptDfU2yNipqSHACLi/0narYZxmZlZE6m0ZrElTewXAJLGANtrFpVZPXi2WLOKVVqz+DZwM7CvpIuAE4Av1Cwqs3rwGtlmFau0N9S1khYDR5DN13RcRKyoaWRmZtY0KkoWkv4ZuCEi3KhtZtaGKm2zeBD4Qlqt7uuSXjO6z8zMBq+KkkVEXB0RRwOzgFXAxZIer2lkZmbWNCqtWRS9CXgz2VTlj1U9GjMza0qVjuAu1iQuBJYBh0bEMWUuMzOzQaLSrrNPAe+IiGdrGYxZTfQ25bjHU5hVrNLHUPOBoyR9CUDSRM8NZS2jOJ6i59bV1ejIzFpGpcniMrK5oU5MrzfjuaHMzNpGpcni7RFxJvAiZHNDAQOaG0rSgZKW5LbnJZ0j6QJJz+TKj85dc37qtrtS0pEDeV8zMxu4StssqjY3VESsBGak+wwBniGbSuQU4NKI+Eb+fEnTgbnAQcABwJ2SpuaWXTXreylUt02Y7bRKaxY954b6JfDVKrz/EcATEdHXgsfHAgsi4qWIeApYTTbew9pRb5P/Qel2CbdNmFVFo+eGmgtcn3t9lqSTgE7gM+lx1zjgvtw53ansNSTNA+YBTJw4sQrhWdPx5H9mDVHxoLyIeCwiLouIf6lGokjrYbwf+I9UdDnwRrJHVOuAbxZPLRVOLzHOj4hCRBTGjBmzsyGamVnS3xHc1fRe4MGIWA8QEesjYltEbAe+z45HTd3AhNx144G1dY3UzKzNNTJZnEjuEZSksbljx5ONFAe4BZgraXdJk4EpwAN1i9LMzCruDVVVkvYE/hz4ZK74nyTNIHvE1FU8FhHLJd0IPApsBc50T6g24FHXZk1FMUgbCwuFQnR2djY6DBsoyQ3ZVjn/vlSNpMUR8ZplKBr5GMrMzFqEk4WZmZXlZGFmZmU5WZiZWVlOFmZmVpaThZmZleVkYWZmZTlZmJlZWU4W1li9TTnukdrWH5Mmlf496uhodGSDRkOm+zB7hacct2robc0SlZq02gbCNQurD9cgzFqaaxZWH65BmLU01yzMzKwsJwszMyvLycLMzMpysjAzs7IakiwkdUlaKmmJpM5UNlrSIkmPp5+jcuefL2m1pJWSjmxEzNZDb72b3K/dbFBqZM3iXRExI7ci03nAXRExBbgrvUbSdGAucBBwFPBdSUMaEbDlFHs39dxKLYVqZi2vmR5DHQtcnfavBo7LlS+IiJci4ilgNTCrAfFZJXobSevxFGYtrVHJIoCfSVosaV4q2y8i1gGkn/um8nHAmty13ansNSTNk9QpqXPjxo01Ct361NVVusbR2whbM2sJjRqUd1hErJW0L7BI0mN9nFtqvH7J0V0RMR+YD1AoFDwCzMysShpSs4iItennBuBmssdK6yWNBUg/N6TTu4EJucvHA2vrF62ZmdU9WUgaLmlEcR/4C2AZcAtwcjrtZGBh2r8FmCtpd0mTgSnAA/WN2sysvTXiMdR+wM3KZoPcFbguIv5L0q+BGyWdCvwG+CBARCyXdCPwKLAVODMitjUgbjOztlX3ZBERTwJvK1G+CTiil2suAi6qcWhmZtaLZuo6a2ZWXV4UqWo8Rbn1raOj9EA7j5uwVuBFkarGycL65nUozAw/hjIzswo4WVjGy56aWR/8GMoyftxkZn1wzcLMzMpysjAzs7KcLMzMrCwni8HKK9mZWRW5gXuw6q3B2oORzGwAnCzaTXH6g1LlZma9cLJoN16xzswGwG0WZmZWlpOFmbWf3majdSeQXvkxlJm1n74ex7oTSEmNWFZ1gqSfS1ohabmks1P5BZKekbQkbUfnrjlf0mpJKyUdWe+YzczaXSNqFluBz0TEg2kt7sWSFqVjl0bEN/InS5oOzAUOAg4A7pQ01UurJl5vwszqoBHLqq4D1qX9zZJWAOP6uORYYEFEvAQ8JWk1MAu4t+bBtgJPAGhmddDQBm5JHcAhwP2p6CxJj0i6UtKoVDYOWJO7rJu+k8vg09tobE8hbmZ10rBkIWkv4MfAORHxPHA58EZgBlnN45vFU0tcXvJPaUnzJHVK6ty4cWMNom6QYu2h1OZxE2ZWBw1JFpKGkiWKayPiJoCIWB8R2yJiO/B9skdNkNUkJuQuHw+sLXXfiJgfEYWIKIwZM6Z2H8DMrM00ojeUgB8AKyLiklz52NxpxwPL0v4twFxJu0uaDEwBHqhXvGZm1pjeUIcBHwOWSlqSyj4HnChpBtkjpi7gkwARsVzSjcCjZD2pznRPKDOrmb7mT2vjx76KQdqTplAoRGdnZ6PDqA7JPZ7MGq1N/h1KWhwRhZ7lnu6jmfTW68k9nsyswTzdRzPxmAkza1KuWZiZWVlOFmZmVpaTRSO4bcLMWozbLBrBbRNm1mJcszAzs7KcLMzMrCwnCzMzK8vJwszMynKyMDOzspwsasldZM1skHDX2VpyF1kzGyRcs6gG1yDMbJBzzaIaXIMws0HONYv+cA3CrH0VF0XquXV0NDqyunDNoj9cgzBrX72tkldqVb1BqGVqFpKOkrRS0mpJ5zU6HjOzdtISyULSEOAy4L3AdLL1uqc3Niozs/bREskCmAWsjognI+JlYAFwbM3ezW0TZlapNmnLaJU2i3HAmtzrbuDtPU+SNA+Yl16+IGnlAN9vH+DZ15Q+/XQrPJ8sHXtraNXYWzVucOy10/f3RTPHXvKv4lZJFqX+i7+mpTki5gPzd/rNpM6IKOzsfRrBsddfq8YNjr1RWjH2VnkM1Q1MyL0eD6xtUCxmZm2nVZLFr4EpkiZL2g2YC9zS4JjMzNpGSzyGioitks4CfgoMAa6MiOU1fMudfpTVQI69/lo1bnDsjdJysSs8yMzMzMpolcdQZmbWQE4WZmZWlpNFTrNPKSLpSkkbJC3LlY2WtEjS4+nnqNyx89NnWSnpyMZE/UosEyT9XNIKScslnZ3Kmz5+ScMkPSDp4RT7V1ol9hTLEEkPSbotvW6VuLskLZW0RFJnKmuV2F8v6UeSHku/8+9oldh7FRHesnabIcATwBuA3YCHgemNjqtHjO8EZgLLcmX/BJyX9s8DLk7709Nn2B2YnD7bkAbGPhaYmfZHAKtSjE0fP9k4n73S/lDgfmB2K8Se4vlb4Drgthb7nekC9ulR1iqxXw38TdrfDXh9q8Te2+aaxQ71nVJkACLiHuB3PYqPJfvFJP08Lle+ICJeioingNVkn7EhImJdRDyY9jcDK8hG5jd9/JF5Ib0cmragBWKXNB74S+CKXHHTx92Hpo9d0kiyP+x+ABARL0fE/9ICsffFyWKHUlOKjGtQLP2xX0Ssg+wLGdg3lTft55HUARxC9hd6S8SfHuUsATYAiyKiVWL/FvBZYHuurBXihiwh/0zS4jSVD7RG7G8ANgL/lh7/XSFpOK0Re6+cLHaoaEqRFtKUn0fSXsCPgXMi4vm+Ti1R1rD4I2JbRMwgmz1glqSD+zi9KWKX9D5gQ0QsrvSSEmWN/J05LCJmks02faakd/ZxbjPFvivZ4+LLI+IQ4Pdkj51600yx98rJYodWnVJkvaSxAOnnhlTedJ9H0lCyRHFtRNyUilsmfoD0OOFu4CiaP/bDgPdL6iJ7rPpuSf9O88cNQESsTT83ADeTPZpphdi7ge5U+wT4EVnyaIXYe+VksUOrTilyC3By2j8ZWJgrnytpd0mTgSnAAw2IDwBJInuGuyIiLskdavr4JY2R9Pq0vwfwHuAxmjz2iDg/IsZHRAfZ7/N/R8RHafK4ASQNlzSiuA/8BbCMFog9In4LrJF0YCo6AniUFoi9T41uYW+mDTiarJfOE8DnGx1PifiuB9YBW8j+GjkV2Bu4C3g8/RydO//z6bOsBN7b4Nj/lKxq/QiwJG1Ht0L8wFuBh1Lsy4AvpfKmjz0Xz+Hs6A3V9HGTPfd/OG3Li/8eWyH2FMsMoDP9zvwnMKpVYu9t83QfZmZWlh9DmZlZWU4WZmZWlpOFmZmV5WRhZmZlOVmYmVlZThZm/SDpU5JOSvsfl3RA7tgVkqZX6X2Ok/Slatyrx33fIumqat/XBj93nTUbIEl3A38XEZ01uPevgPdHxLM1uPedwCci4jfVvrcNXq5ZWFuQ1JHWFrha0iNprYE907Ej0oRvS5WtGbJ7Kv+apEfT+d9IZRdI+jtJJwAF4Nq03sIeku6WVEjnnZjut0zSxbk4XpB0kbK1Me6TtF+JWKcCLxUThaSrJF2ubD2QJyX9WYpzRb6WkO59cZp4705Js1JMT0p6f+4tbiUb0W1WMScLaycHAvMj4q3A88AZkoYBVwEfjoi3kE0Cd7qk0cDxwEHp/H/I3ygifkQ2QvcjETEjIv5YPJYeTV0MvJtsJO+fSCpORz0cuC8i3gbcA5xWIs7DgAd7lI1K9/s/ZF/2lwIHAW+RNCN377sj4lBgc4r5z9PnuDB3r05gTpn/Vmav4mRh7WRNRPzftP/vZFOQHAg8FRGrUvnVZGsRPA+8CFwh6QPAH/rxPn9C9qW9MSK2AtemewK8DNyW9hcDHSWuH0s2xXXerZE9M14KrI+IpRGxnWwqjOI9Xgb+K+0vBX4REVvSfv59NgAHYNYPThbWTno20AWlp4cmfcnPIpsl9zh2fAlXouQ9ky2xo6FwG1lNpqc/AsN6lL2Ufm7P7RdfF++Rv/cr56Wkkn+fYek9zCrmZGHtZKKkd6T9E4Ffks0e2yHpTan8Y8Av0robr4uIO4BzyB4n9bSZbInYnu4H/kzSPpKGpPf6RT/iXAG8qexZAzeVbEJEs4o5WVg7WQGcLOkRYDTZ4jQvAqcA/yFpKdlf5N8jSwK3pXN/QdZW0NNVwPeKDdzFwshWQTsf+DnZrKkPRsTCEtf35h7gkDStey28C7i9Rve2QZMvNaYAAABgSURBVMpdZ60tKFvK9baI6GuFu6Yh6Z/J2inurPJ9dydLfn+aHrWZVcQ1C7Pm9FVgzxrcdyJwnhOF9ZdrFmZmVpZrFmZmVpaThZmZleVkYWZmZTlZmJlZWU4WZmZW1v8H3489u3cAbmMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_0nubb_rmax = tracks_0nubb.r_max\n",
    "\n",
    "plt.hist(track_0nubb_rmax, bins = 50, range = (0, 650), color='red', histtype='step', label = 'rMax')\n",
    "\n",
    "plt.xlabel('position (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('0nubb')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EzizKsbSDTKs392JHpSp3sl5nD1FnKH2b+liwZsHI4xXzV5DNZLEe0+9ZpI4oWUhRUTWf2tqqM1z4woUZeGhXUliag6UAubjjwpYtVTm0iJRBl6FkXP1L+iNrQB38oeoMF962aGbk/tsOaGP+qi7cYf6qLsgZ5Iyt59Vd0WGRVNCZRTnWd0cOC02jsocLr+gv63e35bItI/f7Fu+qN2WG/g1EEqAzi3KszyUdwaTrWNkxqjMfoHV5K20HtCUTUKaJZ8uL1DGdWZRjWSvjlKZqSlGdzBWN/lraQWQNyHI7yruWEixvIiKTScmiHNOHko6godWyo1xEakuXoWRStB3QRvcPc7hDy7WtIx3W1eooF5Ha0plFOQbnJR1BwyrssJ7QJMZVa8vu4G5vD1b2G0vDcEVKpzOLcvTmk45Ahso/E4lbAjYqgYhINCWLcnSpYzVxy7SqrkgSlCzKkVmZdAQiIolQshARkaKULKSxxMzLaL++fdR6GoWVgVncNZkRijQlc2/Oyp4dHR3e399f1X3a/oP4M61V3aeUxyzonN6tfZwqtbGviWkXSTMzy7t7x9h2nVmUo0WjoRKXjR4NtXbR2vjX6MxCZMKULMrx9gXFt5Haao0uYHjhwgxmRN6Ys25yYxRpQkoW0hTiSp0Plzu3nqDWSG++d1dfxmXtyQY9Rnt7dLJrb086MpESZnCb2TRgPnAy0Ar8Ebgf+Ja7P1Db8ETG2FH+eueFs8ezmSzZTDBfxub0wSeqFdjEDU8eHCuqppbIZBv3zMLMcsB/Aa8Dfg6sAG4DdgIfNbM7zOwvah1k3ehbkXQEsryKVX9jZoPrG77I7oqdWfzC3XMxz11nZocCR1Y3pDqW1wzuxHXmiFrLu+xS5xDOBt/9q7y+4YvsbtwzC3f/VpHnn3D3ssenmtn7zewBM7vfzFab2TQzOyg8U3kk/HlgwfZXmdlmM9tkZmeUe7yqyenTInGdPdHtfRWUOn8q6MvI9gVfAjK9mbrsyxCpBxVXnTWzrLuX/T/UzGYClwDHufsfzew2YBFwHHCnu3/UzK4ErgSuMLPjwuePJ+gz+YGZHePuL1UauzShbAYoc2jz9VtGnUHks8Hr7QPBXJrc+hw9dxUkp5Z+QCXVJZ0mMhpqIl+z9wT2NrM9gX0Ilp87G7g5fP5mYGF4/2xgjbs/7+6PApuBEydwbGlGMUNqKxL2i+Q6c3i3j9xE0qziZOHuFfX2uvs24FrgN8AQ8LS7fx84zN2Hwm2GgEPDl8wEHivYxUDYthszy5pZv5n1b9++vZLwxrdpfvX3KeVZUd1Z+WVZutuk1oq0Lm8dGb6b6Q3OVLJ9WV3+krpW0mUoM/vnqHZ3/0i5Bwz7Is4GZgNPAV81s3eO95KoQ8fE0wv0QlDuo9zYilrdB6uqvlcp0/CcCYDuU7rJdeYqGlKblJd9ahDCtTQ2ALYUoFd9YlLXSj2z+N+C20vA3wDtFR7zjcCj7r7d3V8EvgH8JfC4mbUAhD+fCLcfAI4oeP0sgstWk09lI5I3lBl1aSjXmQvaKxhS29YWPUS2ra28/QwXMRzcMUjfpr6Rs4b269sjt986Oxc5ebASw53y1mO0Lt/V1yJSde5e9g2YCnyvwte+FniAoK/CCPonLgY+DlwZbnMl8K/h/eOBe8NjzgZ+DUwpdpxMJuPVRo6q71PKQ8w/QVx7VY/d2R3dniNy7vhBp6+I3T7Ktme2+dqH1gb7C28r+lfEvrcla5eUtX+RUgD9HvGZWuloqH2AoypMTj83s68RnIHvBH5JcOloP+A2M7uAoD/j78PtHwhHTD0Ybn+RayRUag2fDUS119z6HK3LWxl6dgiAeS3zghFU+SUx8zLKm5fTOr2V1jmtu3WmL80ZUVdeV25YSW9XBUOGRSpQap/Ffez6a50CzADK7q8Y5u7dQPeY5ueB02K2vxq4utLjSfPYsiXZ4w8ui7jcFTfHI+ZDXqQRlXpmUTgMaCfwuLvvrEE89S3nu6c4SY2qndWs6K/p31H/kgRHjEnTKqmD2923Fty2pTJRAGR0yp9mW7ZEV7Wt+dlOzJDtbZdvq/GB0ylqsELhKoxpNZEZ3OvcPV0TD7qWAqoPJSWKm5eztIOyLk/FDNnOD+ZpnbP7yo0dKzs0iXCC4vqP0mwiM7grqNwmkiKr+6qzn5gh2wvWLIhe7ElqonB+TxpNZAb3UDUDEWk61ZqXc9h9uxZsYtcCTjzVVrX5GrLLknn6HhylpGRhZkeb2dfM7EEz+/XwrdbB1Z1V46zzLDJW3HKu68vr3W775pZgcEXOMYOlHVnIedBehf3LaBqOHK3UM4svAp8jGAl1KvAl4Mu1CqpuxSyWI1KWMmdYl92xrhncEzJcr2us+cekq4t2rFKTxd7ufidg4YioHPCG2oWVrLiV0oLFckQmaNnundJJ77+9Hayjd9ToH5vTx6xjk6msk6QNQ9EVjPsWV6kPqkGVmiyeM7M9gEfM7H1mdg67qsI2neGV0nQtWCYkF/NHM73G3X3ThyK/7Iy3NOzW8wzvz46qu+Wbuti2SF+QhnWtTndtuFKTxWUEJT4uIVj95Z3AubUKSqQpJDgvJ+rLjnvwRaga4uYgNMOIoZb9oisYr3s4pg8qJUqaZ+HuvwjvPgucV7tw6ptGSUhZ4ublDM6r6WHntdR2/wAXLszAQ7vOnJbmYClELo/eaCJLusj4ZxZm1mtmr4x5bl8zO9/M3lGb0OqPRklIVfSWufxrmfLZPNm+7Khv/sMzkmMXWIqbQJiP/oK0bdHMpr1UqxLv0czH+Rc2s7nAh4BXAvcD24FpwNHA/sAXgBvc/fnah1qejo4O7++vrEaOWfQffqY3M7JOs0gxe36gnZf6PhWMoisYHLHfpiXsWJXMFw/rschZyXF/87Htcft5exe+qrE7guPeW1qYWd7dd1sWctzLUO5+D/BWM9sP6ABagD8CG919U00irWNxoyREouy8dkuwgDBQN9Vnn2qjN99LNpMd3b+weD4Q8SGfzQBlfEFq4tUkh39vaVVqn8WzwPrahiIiNXf9FrKfCO4Wfns2I/pDvjXmC1LM5alg1npjn1nEWbpuqZKFlCZulIRIo6hamfW4NTziZq03EJV4jzaRQoKpo1ES0ujKng2+I+YLUjaZagZxE2YLO/OHO6hbl7fGroMu5Ss7WZjZHma2fy2CqXcaJSGpszzmC1Lc5anQcLHD4VvfpupcmoqdMLuif2QyYa4zBwRf7rY+Xf7Eko6Vu/XtArB2Ubprw5VaSHCVme1vZvsSrIW9ycz+T21Dqz89d/UkHYLIpDrg7Fx5ZdDDWevZzOjZ4JnWZM5EKp1zEvWeL1yY7tpwpZ5ZHOfuzwALgW8DRwL/ULOoRKQuPP3qnshv8nH9d8N9ImNvM6+rcdmQpdFnA+MNdc/0ZkbOfFqXB/W0cutzsaXf0176pNRksZeZ7UWQLG539xdrGJOI1Lm4/ru4PpE4cX0QcTWsypXtix69lO3Lks/mR858ht9PrjMH12+pzsGbTKnJYgWwBdgX+JGZtQFP1yqoeqVREpJG+cE8+cF8ZCfyRMX1QVSrhtXKDSvLapd4pQ6d7XP3Tw4/MLPfAOfXJiQRqReFczEmNKs5bl5GBTK9mZEJsi37tQRnBeu7odZrPlXxPTSiUs8svl74wIMaIWuqH059ixslISJFxM3LuKw9unJtXA2rrujLR22PltkRX833kBLjnlmY2SuA44EDzOxvC57an6BGVEXM7OXAjcAJBHUQzgc2AbcC7QSXvN7q7v8Tbn8VcAHwEnCJu3+v0mOLSALiyoZ8+1P4ptHrRHi3x5c6z6wEdv/QjpsnYvtvizzj2Hb5tnHDjVRu6ZMmU+zMYg4wH3g50FVwmwdM5Jzs34DvuvsrgFcBG4ErgTvd/WjgzvAxZnYcsIggaZ0JfNbMpkzg2CIy2eLmZcQtVRxXBbdcLdEf7vnBCj70i8wtaXbFCgneDtxuZq9z959W44DhhL6/Bt4dHuMF4AUzOxvoDDe7maAW1RXA2cCasLLto2a2GTgRqEo85eg+pdYXRUVSZtlMIossVqsg4dsXRO5/wZoFqa4sW4lSO7g3m9mHCC4RjbzG3Svp5D6KoNT5F83sVQTndZcCh7n7ULjfITMbXrZ1JvCzgtcPhG27MbMs4WozRx55ZAWhjW94ZqiIlCmubEiMvc/vwmz3Wd8zXxF9WakScX0asXWyynwPzabUDu7bgQOAHwDfKrhVYk+Cy1ifc/dXA/9LeMkpRtQ/aeRXAnfvdfcOd++YMWNGheHFG564IyJliisbEuOPR66LHFL7uX+vXp9B3NKzsXWyynwPzabUZLGPu1/h7re5+9eHbxUecwAYcPefh4+/RpA8HjezFoDw5xMF2x9R8PpZQCL/akPPDiVxWJHGF3dWHjMcte2AtpFO7sI6Uxd/5+Lyjtu3orz28aT8ysK4K+WNbGT2L8BP3P3bVTmo2Y+Bf3T3TWaWI5jsB/Cku3/UzK4EDnL3D5rZ8QRXL08EWgk6v49295fGO0YtVspL+wpaIpUqd4W+qh23zBUAx91XSv7/V7RSXoFLgQ+Z2QvACwSXhtzdK60+ezFwi5m9DPg1cB7BWc5tZnYB8Bvg7wkO8oCZ3UZQwHAncFGxRFErlRYlE5EYtR6OmjMir1rHtUusUlfKm17Ng4bLtUbNcDstZvurgaurGUMltP62SJXVejjqU8HlrPnHzKdvcR9dq7tY9/A6eKrc1Z6k1BLlZmbvNLP/Gz4+wsxOrG1o9SeuKJmIjO/wtf21n2Ed5foteLfTtzgYWdW3uC+4lFRJscAV6a4NV2oH92eB1wFvDx8/C3ymJhHVMRUfE6nMuj6CSz/hrfuHuXFLnUv9KbXP4rXuPs/Mfgng7v8T9jeIiBSVac1Edg431FLFSztIcz9HqWcWL4YlNhzAzGYAf6pZVCIiUldKTRafBL4JHGpmVwP/CVxTs6jqVEXFx0REmkBJycLdbwE+CPw/YAhY6O5frWVg9aii4mMi0hzWp7s2XKmjof6NYJLcZ9z90+6+scZx1aUFaxYkHYKIlCFuTfDY+k/jqdLqgI2q1MtQG4B/MrPNZvZxM9MqQCJS9+LWBI+t/zSeZemuDVfqZaib3f3NBCU3HgY+ZmaP1DSyJC3uGrXeMAT1adoO0EQekdSanu7acKUOnR3258ArCEqVP1j1aOrF6r7d6sZkM1myGU3KE5F0KrXPYvhM4iPA/UDG3buKvKxxLW7etyYiFRpMd224Us8sHgVe5+6/q2UwdWPOuqQjEJF605uHCiqbN4tSO7h7gTPN7J8BzOzINNaGEpEU60r3ZehSk8VnCGpDLQ4f7yCFtaFEJMUy6a4NV2qyeK27XwQ8B0FtKKB5a0Pl0lv/RUQkimpDRcn0Jh2BiEhdUW2oKF1Lk45AROrN8nTXhit1pbxbzCxPsJKdEdSGSmXJDxFJqZY8kN5Z3CVPynP3h4CHahiLiEj9evsCtJ6FjLZqbdIRiIjUFSWLKEOZpCMQEakrShZRls1MOgIRqTd9KZ6+jZKFiEhp8prBLSIixeQs6QgSlViyMLMpZvZLM1sXPj7IzO4ws0fCnwcWbHtVuPDSJjM7o+bB5ZfU/BAiIo0kyTOLS4HCuRpXAne6+9HAneFjzOw4YBFwPHAm8NlwNnnt9GkGt4hIoUSShZnNAs4CbixoPhu4Obx/M7CwoH2Nuz/v7o8CmwlW7KudrEZDicgYm+YnHUGikjqzuB74IKPrSx3m7kMA4c9Dw/aZwGMF2w2Ebbsxs6yZ9ZtZ//bt2yuPrnVD5a8Vkea0ui/pCBI16cnCzOYDT7h7vtSXRLRFTqN0915373D3jhkzZlQco4jIblK+gma5a3BXw+uBBWb2ZmAasL+ZfQV43Mxa3H3IzFqAJ8LtB4AjCl4/CxisaYQ7Wmq6exFpQClfQXPSzyzc/Sp3n+Xu7QQd1//h7u8E1gLnhpudC9we3l8LLDKzqWY2GzgauLumQS6vbS4SEWk09TTP4qPA6Wb2CHB6+Bh3fwC4DXgQ+C5wkbu/VNNIOnM13b2INK7efC/WYyO3vk3p6Msw9+asotjR0eH9/f0VvdZ6DO9uzt+LiFTGDKI+Lgd3DNI6vS5/Wd0AAAxjSURBVHlKl5tZ3t07xrbX05mFiEjDmXldOmrJKVmIiEhRShZRVlR2+UpEpFkpWYiITMCSeemoJadkEWXpbn07IiKRervSUUtOyUJEZAIyvemoJadkISIyARuG0lFLTskiyvrupCMQEakrShZR1ueSjkBEGkTLfumoJadkEWVZ88zGFJHaGlyWjlpyShZRpg8lHYGINIhcSq5EKFmIiExAz109SYcwKZQsogzOSzoCEZG6omQRpbfURfxERNJBySJKVzbpCESkQfQvSUctOSWLKJmVSUcgIlJXlCxERCagY2U6askpWYiISFFKFlGWb0s6AhGRuqJkEaVFo6FEpDTdp6SjlpySRZS3L0g6AhFpELnOXNIhTAolCxGRCWhdno5ackoWIiITMPRsOmrJKVlE6VuRdAQiInVl0pOFmR1hZj80s41m9oCZXRq2H2Rmd5jZI+HPAwtec5WZbTazTWZ2Rs2DzGsGt4iUZl5LOmrJJXFmsRNY5u7HAicBF5nZccCVwJ3ufjRwZ/iY8LlFwPHAmcBnzWxKTSPMWU13LyLNI59Nx+jJSU8W7j7k7hvC+zuAjcBM4Gzg5nCzm4GF4f2zgTXu/ry7PwpsBk6c3KhFRKJl+9JxJSLRPgszawdeDfwcOMzdhyBIKMCh4WYzgccKXjYQtkXtL2tm/WbWv3379lqFLSIyYuWGdNSSSyxZmNl+wNeBy9z9mfE2jWjzqA3dvdfdO9y9Y8aMGZUHt2l+5a8VEWlCiSQLM9uLIFHc4u7fCJsfN7OW8PkW4ImwfQA4ouDls4DaLnq7uq+muxcRaTRJjIYy4PPARne/ruCptcC54f1zgdsL2heZ2VQzmw0cDdxd0yAXd9V09yLSPLZdno5ackmcWbwe+AfgDWZ2T3h7M/BR4HQzewQ4PXyMuz8A3AY8CHwXuMjdX6pphHPW1XT3ItI88oPpGA2152Qf0N3/k+h+CIDTYl5zNXB1zYISEanQgjUL8O7IbtSmohncIiJSlJJFlFzzf0sQESmHkkWUTG/SEYhIg1gxPx215JQsonQtTToCEWkQ2YxmcIuISBHWk45ackoWIiJSlJJFlFVrk45ARKSuKFlEGcokHYGINIj5x6SjlpySRZRlkUVtRUR207c4HbXklCxERCaga3U6askpWYiITMC6h9NRS07JIkp+SdIRiIjUFSWLKH2awS0iUkjJIkpWo6FEpDRpqDgLShbRWjckHYGINIjefDquRChZiIhMwNJ16aglp2QRZUdL0hGIiNQVJYsoyweTjkBEpK4oWUTpzCUdgYg0iLWL0lFLTskiSmdP0hGISJ1pawOz3W8XLkzH6EklCxGREmzZAu6737YtSkctOSULEREpSskiyor+pCMQEakrShYiIhORklpyDZMszOxMM9tkZpvN7MqaHmxpR013LyJNJCW15BoiWZjZFOAzwN8AxwGLzey4ZKMSESE1teQaIlkAJwKb3f3X7v4CsAY4O+GYRERSU0tuz6QDKNFM4LGCxwPAa8duZGZZIBs+fNbMNlV4vEMsZ7+r8LWT7RCgUWKFxoq3kWKFxoq3kWKFIvFaziYxlKIm+rtti2pslGQR9S+xW11gd+8FJnwB0cz63b0hOi4aKVZorHgbKVZorHgbKVZorHhrFWujXIYaAI4oeDwLUAEnEZFJ0ijJ4hfA0WY228xeBiwC0lGQRUSkDjTEZSh332lm7wO+B0wBvuDuD9TwkI00Fq6RYoXGireRYoXGireRYoXGircmsZp7OpYEFBGRyjXKZSgREUmQkoWIiBSlZFFgUkuKlMjMjjCzH5rZRjN7wMwuDdsPMrM7zOyR8OeBBa+5KnwPm8zsjARinmJmvzSzdQ0Q68vN7Gtm9lD4O35dvcZrZu8P/wbuN7PVZjatnmI1sy+Y2RNmdn9BW9nxmVnGzO4Ln/ukmVV9EkNMrB8P/w7+28y+aWYvr4dY4+IteO4DZuZmdkhN43V33YJ+mynAr4CjgJcB9wLH1UFcLcC88P504GGCkif/ClwZtl8JfCy8f1wY+1RgdviepkxyzJcDq4B14eN6jvVm4B/D+y8DXl6P8RJMTH0U2Dt8fBvw7nqKFfhrYB5wf0Fb2fEBdwOvI5hf9R3gbyYp1jcBe4b3P1YvscbFG7YfQTDwZytwSC3j1ZnFLnVZUsTdh9x9Q3h/B7CR4IPjbIIPOsKfC8P7ZwNr3P15d38U2Ezw3iaFmc0CzgJuLGiu11j3J/hP+HkAd3/B3Z+q13gJRi/ubWZ7AvsQzDWqm1jd/UfA78c0lxWfmbUA+7v7Tz34dPtSwWtqGqu7f9/dd4YPf0YwnyvxWOPiDX0C+CCjJynXJF4li12iSorU1RJYZtYOvBr4OXCYuw9BkFCAQ8PNkn4f1xP88f6poK1eYz0K2A58MbxsdqOZ7VuP8br7NuBa4DfAEPC0u3+/HmMdo9z4Zob3x7ZPtvMJvnlDncZqZguAbe5+75inahKvksUuJZUUSYqZ7Qd8HbjM3Z8Zb9OItkl5H2Y2H3jC3fOlviSibTJ/53sSnNp/zt1fDfwvwaWSOEn+bg8k+MY4G2gF9jWzd473koi2uvl7Jj6+xOM2sw8DO4FbhpsiNks0VjPbB/gw8M9RT0e0TTheJYtd6rakiJntRZAobnH3b4TNj4enlYQ/nwjbk3wfrwcWmNkWgst4bzCzr9RprMPHH3D3n4ePv0aQPOox3jcCj7r7dnd/EfgG8Jd1GmuhcuMbYNfln8L2SWFm5wLzgXeEl2qgPmP9M4IvDveG/99mARvM7HBqFK+SxS51WVIkHK3weWCju19X8NRa4Nzw/rnA7QXti8xsqpnNBo4m6NSqOXe/yt1nuXs7we/vP9z9nfUYaxjvb4HHzGxO2HQa8GCdxvsb4CQz2yf8mziNoP+qHmMtVFZ84aWqHWZ2Uvg+31XwmpoyszOBK4AF7v6HMe+hrmJ19/vc/VB3bw//vw0QDIT5bc3irUXPfaPegDcTjDb6FfDhpOMJY/orglPF/wbuCW9vBg4G7gQeCX8eVPCaD4fvYRM1Gp1RQtyd7BoNVbexAnOB/vD3++/AgfUaL9ADPATcD3yZYLRL3cQKrCboT3kx/PC6oJL4gI7wPf4K+DRhpYlJiHUzwbX+4f9nN9RDrHHxjnl+C+FoqFrFq3IfIiJSlC5DiYhIUUoWIiJSlJKFiIgUpWQhIiJFKVmIiEhRShYiZTCz95jZu8L77zaz1oLnbjSz46p0nIVmFjU7d6L7faWZ3VTt/Urz09BZkQqZ2XrgA+7eX4N9/4RgctjvarDvHwDnu/tvqr1vaV46s5BUMLP2cK2Cm8P1Cr4W1tfBzE4LCwneF64bMDVs/6iZPRhuf23YlgvXD3gLwQSnW8zsHjPb28zWm1lHuN3icH/3m9nHCuJ41syuNrN7zexnZnZYRKzHAM8PJwozu8nMPmfBuia/NrNTwjg3Fp4lhPv+mJnlzewHZnZiGNOvw6Jzw/oIZtiLlEzJQtJkDtDr7n8BPAO818ymATcBb3P3VxIUF7zQzA4CzgGOD7f/l8IdufvXCGZ+v8Pd57r7H4efCy9NfQx4A8EM8deY2XAp6H2Bn7n7q4AfAUsi4nw9sGFM24Hh/t5P8GH/CeB44JVmNrdg3+vdPQPsCGM+PXwfHynYVz9wcpHflcgoShaSJo+5+3+F979CUEplDkGBvofD9psJ1rh4BngOuNHM/hb4w9idjeM1BB/a2z1YH+GWcJ8ALwDrwvt5oD3i9S0EpdML9Xlwzfg+4HEPagP9CXigYB8vAN8N798H3OVB0cH7xhznCYLKtSIlU7KQNBnbQRdXtpnwQ/5Egmq/C9n1IVyK8ZaqfNF3dRS+RHAmM9YfgWlj2p4Pf/6p4P7w4+F9FO57ZLswqRQeZ1p4DJGSKVlImhxpZq8L7y8G/pOgMF+7mf152P4PwF3h+iEHuPu3gcsILieNtYNgqduxfg6cYmaHmNmU8Fh3lRHnRuDPi25VuWMIismJlEzJQtJkI3Cumf03cBDBokfPAecBXzWz+wi+kd9AkATWhdveRdBXMNZNwA3DHdzDjR6Ugr4K+CHBWsgb3L2c0tU/Al4dlpGuhVOBb9Vo39KkNHRWUsGCJWnXufsJCYdSEjP7N4J+ih9Ueb9TCZLfX/mu9aZFitKZhUh9ugbYpwb7PRK4UolCyqUzCxERKUpnFiIiUpSShYiIFKVkISIiRSlZiIhIUUoWIiJS1P8HBZbh/D0xI6cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_selectron_zmin = tracks_selectron.z_min\n",
    "track_selectron_zmax = tracks_selectron.z_max\n",
    "\n",
    "plt.hist(track_selectron_zmin, bins = 50, range = (0, 1400),  color='blue', histtype='step', label = 'zMin')\n",
    "plt.hist(track_selectron_zmax, bins = 50, range = (0, 1400),  color='green', histtype='step', label = 'zMax', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('position (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('e-roi')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_selectron_rmax = tracks_selectron.r_max\n",
    "\n",
    "plt.hist(track_selectron_rmax, bins = 50, range = (0, 650), color='red', histtype='step', label = 'rMax')\n",
    "\n",
    "plt.xlabel('position (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('e-roi')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAX VALUE\n",
      "0nubb\n",
      "- Rmax = 578.1803351896361\n",
      "- Zmin = 1208.861\n",
      "- Zmax = 1212.693875\n",
      "e-roi\n",
      "- Rmax = 579.0306554924359\n",
      "- Zmin = 1209.804125\n",
      "- Zmax = 1212.660875\n",
      "AVG VALUE\n",
      "0nubb\n",
      "- Rmax = 356.06102536753826\n",
      "- Zmin = 518.4037550698541\n",
      "- Zmax = 531.7356071357618\n",
      "e-roi\n",
      "- Rmax = 354.1783942401423\n",
      "- Zmin = 527.7259281704931\n",
      "- Zmax = 543.6584567551021\n"
     ]
    }
   ],
   "source": [
    "print(f'MAX VALUE')\n",
    "print(f'0nubb')\n",
    "print(f'- Rmax = {np.amax(track_0nubb_rmax)}')\n",
    "print(f'- Zmin = {np.amax(track_0nubb_zmin)}')\n",
    "print(f'- Zmax = {np.amax(track_0nubb_zmax)}')\n",
    "print(f'e-roi')\n",
    "print(f'- Rmax = {np.amax(track_selectron_rmax)}')\n",
    "print(f'- Zmin = {np.amax(track_selectron_zmin)}')\n",
    "print(f'- Zmax = {np.amax(track_selectron_zmax)}')\n",
    "print(f'AVG VALUE')\n",
    "print(f'0nubb')\n",
    "print(f'- Rmax = {np.average(track_0nubb_rmax)}')\n",
    "print(f'- Zmin = {np.average(track_0nubb_zmin)}')\n",
    "print(f'- Zmax = {np.average(track_0nubb_zmax)}')\n",
    "print(f'e-roi')\n",
    "print(f'- Rmax = {np.average(track_selectron_rmax)}')\n",
    "print(f'- Zmin = {np.average(track_selectron_zmin)}')\n",
    "print(f'- Zmax = {np.average(track_selectron_zmax)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "npz_file = '/Users/halmamol/NEXT/NBarrays/tracklimits_FULLSIM'\n",
    "np.savez(npz_file, track_0nubb_zmin=track_0nubb_zmin, track_0nubb_zmax=track_0nubb_zmax, track_0nubb_rmax=track_0nubb_rmax,\n",
    "        track_selectron_zmin=track_selectron_zmin, track_selectron_zmax=track_selectron_zmax, track_selectron_rmax=track_selectron_rmax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For the evaluation of the events out of fiducial limits it's necessary to do several steps\n",
    "\n",
    "#First, check a list of events where at least 1 trace is out of the limits:\n",
    "\n",
    "nonfiducial_list_0nubb = tracks_0nubb[~((tracks_0nubb.r_max < rmax_N100) & \n",
    "                                     (tracks_0nubb.z_min > zmin_N100) & \n",
    "                                     (tracks_0nubb.z_max < zmax_N100))].event.unique()\n",
    "\n",
    "#Then I apply the selection into the matrix of elements\n",
    "fiducial_tracks_0nubb = tracks_0nubb[(tracks_0nubb.r_max < rmax_N100) & \n",
    "                                     (tracks_0nubb.z_min > zmin_N100) & \n",
    "                                     (tracks_0nubb.z_max < zmax_N100)]\n",
    "\n",
    "# I get a list of true elements if event ID is not in the above list 'nonfiducial_list'\n",
    "evt_fid_sel_0nubb    = ~fiducial_tracks_0nubb.event.isin(nonfiducial_list_0nubb) \n",
    "\n",
    "# And finally I select the events with an asigned True from the previous line \n",
    "tracks_0nubb_fiducial = fiducial_tracks_0nubb[evt_fid_sel_0nubb]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now I do the same for single electron\n",
    "\n",
    "nonfiducial_list_selectron = tracks_selectron[~((tracks_selectron.r_max < rmax_N100) & \n",
    "                                     (tracks_selectron.z_min > zmin_N100) & \n",
    "                                     (tracks_selectron.z_max < zmax_N100))].event.unique()\n",
    "\n",
    "#Then I apply the selection into the matrix of elements\n",
    "fiducial_tracks_selectron = tracks_selectron[(tracks_selectron.r_max < rmax_N100) & \n",
    "                                     (tracks_selectron.z_min > zmin_N100) & \n",
    "                                     (tracks_selectron.z_max < zmax_N100)]\n",
    "\n",
    "# I get a list of true elements if event ID is not in the above list 'nonfiducial_list'\n",
    "evt_fid_sel_selectron    = ~fiducial_tracks_selectron.event.isin(nonfiducial_list_selectron) \n",
    "\n",
    "# And finally I select the events with an asigned True from the previous line \n",
    "tracks_selectron_fiducial = fiducial_tracks_selectron[evt_fid_sel_selectron]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For the evaluation of the events out of fiducial limits it's necessary to do several steps\n",
    "\n",
    "#First, check a list of events where at least 1 trace is out of the limits:\n",
    "\n",
    "track_rmax = tracks_0nubb_fiducial.r_max\n",
    "track_zmin = tracks_0nubb_fiducial.z_min\n",
    "track_zmax = tracks_0nubb_fiducial.z_max\n",
    "\n",
    "plt.hist(track_rmax, bins = 50, color='red', histtype='step', label = 'rMax')\n",
    "plt.hist(track_zmin, bins = 50, color='blue', histtype='step', label = 'zMin')\n",
    "plt.hist(track_zmax, bins = 50, color='green', histtype='step', label = 'zMax', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('position (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now I do the same for single electron\n",
    "\n",
    "nonfiducial_list_selectron = tracks_selectron[~((tracks_selectron.r_max < rmax_N100) & \n",
    "                                     (tracks_selectron.z_min > zmin_N100) & \n",
    "                                     (tracks_selectron.z_max < zmax_N100))].event.unique()\n",
    "\n",
    "#Then I apply the selection into the matrix of elements\n",
    "fiducial_tracks_selectron = tracks_selectron[(tracks_selectron.r_max < rmax_N100) & \n",
    "                                     (tracks_selectron.z_min > zmin_N100) & \n",
    "                                     (tracks_selectron.z_max < zmax_N100)]\n",
    "\n",
    "# I get a list of true elements if event ID is not in the above list 'nonfiducial_list'\n",
    "evt_fid_sel_selectron    = ~fiducial_tracks_selectron.event.isin(nonfiducial_list_selectron) \n",
    "\n",
    "# And finally I select the events with an asigned True from the previous line \n",
    "tracks_selectron_fiducial = fiducial_tracks_selectron[evt_fid_sel_selectron]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------0nubb-------------------------\n",
      "--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---\n",
      "INITIAL    9759        \n",
      "FIDUCIAL   6753                ---      69.19766369505072 +- 1.0953180330103627%\n",
      "--------!--------------!----------------!----------------\n",
      " \n",
      " \n",
      "--------------------------SElectron---------------------\n",
      "--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---\n",
      "INITIAL    9767        \n",
      "FIDUCIAL   6463                ---      66.17180301013617 +- 1.0610468400150082%\n",
      "--------!--------------!----------------!----------------\n"
     ]
    }
   ],
   "source": [
    "#Checking total amount of events, after fiducial selection\n",
    "\n",
    "nevents_0nubb_fiducial = tracks_0nubb_fiducial.event.nunique()\n",
    "Enevents_0nubb_fiducial = np.sqrt(nevents_0nubb_fiducial)\n",
    "nevents_selectron_fiducial = tracks_selectron_fiducial.event.nunique()\n",
    "Enevents_selectron_fiducial = np.sqrt(nevents_selectron_fiducial)\n",
    "\n",
    "ratio_fiducial_0nubb = 100*nevents_0nubb_fiducial/nevents_0nubb_total\n",
    "Eratio_fiducial_0nubb = myef.ratio_error(ratio_fiducial_0nubb, nevents_0nubb_fiducial, nevents_0nubb_total, \n",
    "                                             Enevents_0nubb_fiducial, Enevents_0nubb_total)\n",
    "\n",
    "ratio_fiducial_selectron = 100*nevents_selectron_fiducial/nevents_selectron_total\n",
    "Eratio_fiducial_selectron = myef.ratio_error(ratio_fiducial_selectron, nevents_selectron_fiducial, nevents_selectron_total, \n",
    "                                             Enevents_selectron_fiducial, Enevents_selectron_total)\n",
    "\n",
    "print('--------------------------0nubb-------------------------')\n",
    "print('--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---')\n",
    "print(f'INITIAL    {nevents_0nubb_total}        ')\n",
    "print(f'FIDUCIAL   {nevents_0nubb_fiducial}                ---      {ratio_fiducial_0nubb} +- {Eratio_fiducial_0nubb}%')\n",
    "print('--------!--------------!----------------!----------------')\n",
    "print(' ')\n",
    "print(' ')\n",
    "print('--------------------------SElectron---------------------')\n",
    "print('--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---')\n",
    "print(f'INITIAL    {nevents_selectron_total}        ')\n",
    "print(f'FIDUCIAL   {nevents_selectron_fiducial}                ---      {ratio_fiducial_selectron} +- {Eratio_fiducial_selectron}%')\n",
    "print('--------!--------------!----------------!----------------')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking variables: 1Track\n",
    "To check the track with highest energy deposition: 1st check if there is more than 1track, later remove the ones with energy deposition. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event</th>\n",
       "      <th>trackID</th>\n",
       "      <th>energy</th>\n",
       "      <th>length</th>\n",
       "      <th>numb_of_voxels</th>\n",
       "      <th>numb_of_hits</th>\n",
       "      <th>numb_of_tracks</th>\n",
       "      <th>x_min</th>\n",
       "      <th>y_min</th>\n",
       "      <th>z_min</th>\n",
       "      <th>...</th>\n",
       "      <th>blob1_z</th>\n",
       "      <th>blob2_x</th>\n",
       "      <th>blob2_y</th>\n",
       "      <th>blob2_z</th>\n",
       "      <th>eblob1</th>\n",
       "      <th>eblob2</th>\n",
       "      <th>ovlp_blob_energy</th>\n",
       "      <th>vox_size_x</th>\n",
       "      <th>vox_size_y</th>\n",
       "      <th>vox_size_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>4</td>\n",
       "      <td>-259.5</td>\n",
       "      <td>-367.5</td>\n",
       "      <td>308.425500</td>\n",
       "      <td>...</td>\n",
       "      <td>333.410510</td>\n",
       "      <td>-254.025000</td>\n",
       "      <td>-226.000000</td>\n",
       "      <td>316.151619</td>\n",
       "      <td>0.373302</td>\n",
       "      <td>0.225996</td>\n",
       "      <td>0</td>\n",
       "      <td>4.961538</td>\n",
       "      <td>4.897436</td>\n",
       "      <td>4.904922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>-286.5</td>\n",
       "      <td>-299.5</td>\n",
       "      <td>313.988219</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>0.373302</td>\n",
       "      <td>0.225996</td>\n",
       "      <td>-1</td>\n",
       "      <td>4.961538</td>\n",
       "      <td>4.897436</td>\n",
       "      <td>4.904922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>4.897436</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-256.5</td>\n",
       "      <td>-282.5</td>\n",
       "      <td>334.031875</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>0.373302</td>\n",
       "      <td>0.225996</td>\n",
       "      <td>-1</td>\n",
       "      <td>4.961538</td>\n",
       "      <td>4.897436</td>\n",
       "      <td>4.904922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>-158.5</td>\n",
       "      <td>-408.5</td>\n",
       "      <td>335.988812</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>0.373302</td>\n",
       "      <td>0.225996</td>\n",
       "      <td>-1</td>\n",
       "      <td>4.961538</td>\n",
       "      <td>4.897436</td>\n",
       "      <td>4.904922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.953390</td>\n",
       "      <td>100.009867</td>\n",
       "      <td>465</td>\n",
       "      <td>16014</td>\n",
       "      <td>10</td>\n",
       "      <td>433.5</td>\n",
       "      <td>-201.5</td>\n",
       "      <td>85.224500</td>\n",
       "      <td>...</td>\n",
       "      <td>113.939352</td>\n",
       "      <td>464.083333</td>\n",
       "      <td>-187.703125</td>\n",
       "      <td>109.401530</td>\n",
       "      <td>0.217124</td>\n",
       "      <td>0.089520</td>\n",
       "      <td>0</td>\n",
       "      <td>4.928571</td>\n",
       "      <td>4.911765</td>\n",
       "      <td>4.649504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9978</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>4.961538</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>-212.5</td>\n",
       "      <td>-382.5</td>\n",
       "      <td>525.002687</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>0.475703</td>\n",
       "      <td>0.430197</td>\n",
       "      <td>-1</td>\n",
       "      <td>4.933333</td>\n",
       "      <td>4.961538</td>\n",
       "      <td>4.806589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9978</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>4.933333</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>-164.5</td>\n",
       "      <td>-345.5</td>\n",
       "      <td>523.072188</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>0.475703</td>\n",
       "      <td>0.430197</td>\n",
       "      <td>-1</td>\n",
       "      <td>4.933333</td>\n",
       "      <td>4.961538</td>\n",
       "      <td>4.806589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9978</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-205.5</td>\n",
       "      <td>-454.5</td>\n",
       "      <td>521.100312</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>0.475703</td>\n",
       "      <td>0.430197</td>\n",
       "      <td>-1</td>\n",
       "      <td>4.933333</td>\n",
       "      <td>4.961538</td>\n",
       "      <td>4.806589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9979</td>\n",
       "      <td>0</td>\n",
       "      <td>2.388946</td>\n",
       "      <td>130.676430</td>\n",
       "      <td>749</td>\n",
       "      <td>25591</td>\n",
       "      <td>2</td>\n",
       "      <td>-258.5</td>\n",
       "      <td>-21.5</td>\n",
       "      <td>838.367000</td>\n",
       "      <td>...</td>\n",
       "      <td>875.780332</td>\n",
       "      <td>-250.055556</td>\n",
       "      <td>22.142857</td>\n",
       "      <td>844.359355</td>\n",
       "      <td>0.770772</td>\n",
       "      <td>0.485058</td>\n",
       "      <td>0</td>\n",
       "      <td>4.875000</td>\n",
       "      <td>4.769231</td>\n",
       "      <td>4.534525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9979</td>\n",
       "      <td>1</td>\n",
       "      <td>0.069214</td>\n",
       "      <td>31.579639</td>\n",
       "      <td>59</td>\n",
       "      <td>1218</td>\n",
       "      <td>2</td>\n",
       "      <td>-129.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>840.218313</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>0.770772</td>\n",
       "      <td>0.485058</td>\n",
       "      <td>-1</td>\n",
       "      <td>4.875000</td>\n",
       "      <td>4.769231</td>\n",
       "      <td>4.534525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37122 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    event  trackID    energy      length  numb_of_voxels  numb_of_hits  \\\n",
       "0       0        0  2.475314  180.973722             739         27246   \n",
       "1       0        1  0.000343    0.000000               1            13   \n",
       "2       0        2  0.000104    4.897436               2             4   \n",
       "3       0        3  0.000052    0.000000               1             2   \n",
       "0       1        0  0.953390  100.009867             465         16014   \n",
       "..    ...      ...       ...         ...             ...           ...   \n",
       "5    9978        5  0.000226    4.961538               2            11   \n",
       "6    9978        6  0.000226    4.933333               2            11   \n",
       "7    9978        7  0.000020    0.000000               1             1   \n",
       "0    9979        0  2.388946  130.676430             749         25591   \n",
       "1    9979        1  0.069214   31.579639              59          1218   \n",
       "\n",
       "    numb_of_tracks  x_min  y_min       z_min  ...         blob1_z  \\\n",
       "0                4 -259.5 -367.5  308.425500  ...      333.410510   \n",
       "1                4 -286.5 -299.5  313.988219  ...  1000000.000000   \n",
       "2                4 -256.5 -282.5  334.031875  ...  1000000.000000   \n",
       "3                4 -158.5 -408.5  335.988812  ...  1000000.000000   \n",
       "0               10  433.5 -201.5   85.224500  ...      113.939352   \n",
       "..             ...    ...    ...         ...  ...             ...   \n",
       "5                8 -212.5 -382.5  525.002687  ...  1000000.000000   \n",
       "6                8 -164.5 -345.5  523.072188  ...  1000000.000000   \n",
       "7                8 -205.5 -454.5  521.100312  ...  1000000.000000   \n",
       "0                2 -258.5  -21.5  838.367000  ...      875.780332   \n",
       "1                2 -129.5   17.5  840.218313  ...  1000000.000000   \n",
       "\n",
       "           blob2_x         blob2_y         blob2_z    eblob1    eblob2  \\\n",
       "0      -254.025000     -226.000000      316.151619  0.373302  0.225996   \n",
       "1   1000000.000000  1000000.000000  1000000.000000  0.373302  0.225996   \n",
       "2   1000000.000000  1000000.000000  1000000.000000  0.373302  0.225996   \n",
       "3   1000000.000000  1000000.000000  1000000.000000  0.373302  0.225996   \n",
       "0       464.083333     -187.703125      109.401530  0.217124  0.089520   \n",
       "..             ...             ...             ...       ...       ...   \n",
       "5   1000000.000000  1000000.000000  1000000.000000  0.475703  0.430197   \n",
       "6   1000000.000000  1000000.000000  1000000.000000  0.475703  0.430197   \n",
       "7   1000000.000000  1000000.000000  1000000.000000  0.475703  0.430197   \n",
       "0      -250.055556       22.142857      844.359355  0.770772  0.485058   \n",
       "1   1000000.000000  1000000.000000  1000000.000000  0.770772  0.485058   \n",
       "\n",
       "    ovlp_blob_energy  vox_size_x  vox_size_y  vox_size_z  \n",
       "0                  0    4.961538    4.897436    4.904922  \n",
       "1                 -1    4.961538    4.897436    4.904922  \n",
       "2                 -1    4.961538    4.897436    4.904922  \n",
       "3                 -1    4.961538    4.897436    4.904922  \n",
       "0                  0    4.928571    4.911765    4.649504  \n",
       "..               ...         ...         ...         ...  \n",
       "5                 -1    4.933333    4.961538    4.806589  \n",
       "6                 -1    4.933333    4.961538    4.806589  \n",
       "7                 -1    4.933333    4.961538    4.806589  \n",
       "0                  0    4.875000    4.769231    4.534525  \n",
       "1                 -1    4.875000    4.769231    4.534525  \n",
       "\n",
       "[37122 rows x 35 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loop to study the number of tracks in the event, to take the one with max energy deposition\n",
    "#Normally it would be asked that: \n",
    "\n",
    "#tracks_0nubb_1track = tracks_0nubb[tracks_0nubb.numb_of_tracks == 1]\n",
    "\n",
    "#tracks_0nubb_1trackfid = tracks_0nubb_fiducial[tracks_0nubb_fiducial.numb_of_tracks == 1]\n",
    "\n",
    "#But I'm going to take the traces with highest deposition. Checking this:\n",
    "tracks_0nubb\n",
    "\n",
    "#I've seen that the 1st track is the one with the highest energy deposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Therefore, I'm going to take the 1st 'trackID'\n",
    "tracks_0nubb_1track = tracks_0nubb[tracks_0nubb.trackID == 0]\n",
    "tracks_0nubb_1trackfid = tracks_0nubb_fiducial[tracks_0nubb_fiducial.trackID == 0]\n",
    "\n",
    "tracks_selectron_1track = tracks_selectron[tracks_selectron.trackID == 0]\n",
    "tracks_selectron_1trackfid = tracks_selectron_fiducial[tracks_selectron_fiducial.trackID == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've represented the plots after selecting the 1st track, otherwise I was double (multiple) counting the events (taking several times the same track, because the variable numb_of_tracks is repeated for each track in 1 event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check the number of tracks with numb_of_tracks\n",
    "ntracks_0nubb = tracks_0nubb_1trackfid.numb_of_tracks\n",
    "maxtracks_0nubb = np.amax(ntracks_0nubb)\n",
    "\n",
    "plt.hist(ntracks_0nubb, bins = maxtracks_0nubb, range = (0, maxtracks_0nubb),density=True, alpha=0.6, color='r')\n",
    "plt.xlabel('number of tracks')\n",
    "plt.ylabel('fraction of events')\n",
    "plt.title('0nubb')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check the number of tracks with numb_of_tracks\n",
    "ntracks_eroi = tracks_selectron_1trackfid.numb_of_tracks\n",
    "maxtracks_eroi = np.amax(ntracks_eroi)\n",
    "\n",
    "plt.hist(ntracks_eroi, bins = maxtracks_eroi, range = (0, maxtracks_eroi), density=True, alpha=0.6, color='b')\n",
    "plt.xlabel('number of tracks')\n",
    "plt.ylabel('fraction of events')\n",
    "plt.title('eroi')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ntracks_0nubb, bins = 9, range = (0.5, 9.5),density=True, alpha=0.4, color='r', label = '0nubb')\n",
    "plt.hist(ntracks_eroi, bins = 9, range = (0.5, 9.5), density=True, alpha=0.4, color='b',label = 'e-roi')\n",
    "\n",
    "plt.xlabel('number of tracks (MeV)')\n",
    "plt.ylabel('fraction of events')\n",
    "plt.title('Tracks in each event')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAX VALUE\n",
      "0nubb\n",
      "- #tracks = 37\n",
      "e-roi\n",
      "- #tracks = 36\n",
      "AVG VALUE\n",
      "0nubb\n",
      "- #tracks = 3.2749888938249665\n",
      "e-roi\n",
      "- #tracks = 3.4338542472535973\n",
      "1-track events\n",
      "0nubb\n",
      "- 1 track = 17.98339993851829\n",
      "e-roi\n",
      "- 1 track = 20.149482952800245\n"
     ]
    }
   ],
   "source": [
    "print(f'MAX VALUE')\n",
    "print(f'0nubb')\n",
    "print(f'- #tracks = {np.amax(ntracks_0nubb)}')\n",
    "print(f'e-roi')\n",
    "print(f'- #tracks = {np.amax(ntracks_eroi)}')\n",
    "print(f'AVG VALUE')\n",
    "print(f'0nubb')\n",
    "print(f'- #tracks = {np.average(ntracks_0nubb)}')\n",
    "print(f'e-roi')\n",
    "print(f'- #tracks = {np.average(ntracks_eroi)}')\n",
    "print(f'1-track events')\n",
    "print(f'0nubb')\n",
    "print(f'- 1 track = {100*(tracks_0nubb[tracks_0nubb.numb_of_tracks == 1].event.nunique())/(tracks_0nubb.event.nunique())}')\n",
    "print(f'e-roi')\n",
    "print(f'- 1 track = {100*(tracks_selectron[tracks_selectron.numb_of_tracks == 1].event.nunique())/(tracks_selectron.event.nunique())}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "Name: numb_of_tracks, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracks_0nubb_track_evt = tracks_0nubb_1track[tracks_0nubb_1track.event == 100]\n",
    "tracks_0nubb_track_evt.numb_of_tracks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount of events with 1 track 0nubb = 21.516363097882422 +- 0.6222335713523952\n"
     ]
    }
   ],
   "source": [
    "tracks_0nubb_1track_cut = tracks_0nubb_1trackfid[tracks_0nubb_1trackfid.numb_of_tracks == 1]\n",
    "frac_1track_0nubb = 100*tracks_0nubb_1track_cut.event.nunique()/nevents_0nubb_fiducial\n",
    "\n",
    "Efrac_1track_0nubb = myef.ratio_error(frac_1track_0nubb, tracks_0nubb_1track_cut.event.nunique(), nevents_0nubb_fiducial, \n",
    "                                             np.sqrt(tracks_0nubb_1track_cut.event.nunique()), np.sqrt(nevents_0nubb_fiducial))\n",
    "print(f'Amount of events with 1 track 0nubb = {frac_1track_0nubb} +- {Efrac_1track_0nubb}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount of events with 1 track eroi = 21.445149311465265 +- 0.6348009175164367\n"
     ]
    }
   ],
   "source": [
    "tracks_selectron_1track_cut = tracks_selectron_1trackfid[tracks_selectron_1trackfid.numb_of_tracks == 1]\n",
    "frac_1track_selectron = 100*tracks_selectron_1track_cut.event.nunique()/nevents_selectron_fiducial\n",
    "Efrac_1track_selectron = myef.ratio_error(frac_1track_selectron, tracks_selectron_1track_cut.event.nunique(), nevents_selectron_fiducial, \n",
    "                                             np.sqrt(tracks_selectron_1track_cut.event.nunique()), np.sqrt(nevents_selectron_fiducial))\n",
    "print(f'Amount of events with 1 track eroi = {frac_1track_selectron} +- {Efrac_1track_selectron}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(tracks_0nubb_1trackfid.energy, bins = 50)\n",
    "plt.xlabel('energy (MeV)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------0nubb-------------------------\n",
      "--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---\n",
      "INITIAL    9759        \n",
      "FIDUCIAL   6753                ---      69.19766369505072 +- 1.0953180330103627%\n",
      "1TRACK.    6753        100.0+- 1.7209435422324602% 69.19766369505072 +- 1.0953180330103627%\n",
      "--------!--------------!----------------!----------------\n",
      " \n",
      " \n",
      "--------------------------SElectron---------------------\n",
      "--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---\n",
      "INITIAL    9767        \n",
      "FIDUCIAL   6463                ---      66.17180301013617 +- 1.0610468400150082%\n",
      "1TRACK.    6463        100.0+- 1.7591299384021004% 66.17180301013617 +- 1.0610468400150082%\n",
      "--------!--------------!----------------!----------------\n"
     ]
    }
   ],
   "source": [
    "#Checking total amount of events, after fiducial selection\n",
    "\n",
    "nevents_0nubb_1track = tracks_0nubb_1track.event.nunique()\n",
    "Enevents_0nubb_1track = np.sqrt(nevents_0nubb_1track)\n",
    "nevents_0nubb_1trackfid = tracks_0nubb_1trackfid.event.nunique()\n",
    "Enevents_0nubb_1trackfid = np.sqrt(nevents_0nubb_1trackfid)\n",
    "\n",
    "nevents_selectron_1track = tracks_selectron_1track.event.nunique()\n",
    "Enevents_selectron_1track = np.sqrt(nevents_selectron_1track)\n",
    "nevents_selectron_1trackfid = tracks_selectron_1trackfid.event.nunique()\n",
    "Enevents_selectron_1trackfid = np.sqrt(nevents_selectron_1trackfid)\n",
    "\n",
    "ratio_1trackfid_0nubb = 100*nevents_0nubb_1trackfid/nevents_0nubb_fiducial\n",
    "Eratio_1trackfid_0nubb = myef.ratio_error(ratio_1trackfid_0nubb, nevents_0nubb_1trackfid, nevents_0nubb_fiducial, \n",
    "                                             Enevents_0nubb_1trackfid, Enevents_0nubb_fiducial)\n",
    "\n",
    "eff_1trackfid_0nubb = 100*nevents_0nubb_1trackfid/nevents_0nubb_total\n",
    "Eeff_1trackfid_0nubb = myef.ratio_error(eff_1trackfid_0nubb, nevents_0nubb_1trackfid, nevents_0nubb_total, \n",
    "                                             Enevents_0nubb_1trackfid, Enevents_0nubb_total)\n",
    "\n",
    "ratio_1trackfid_selectron = 100*nevents_selectron_1trackfid/nevents_selectron_fiducial\n",
    "Eratio_1trackfid_selectron = myef.ratio_error(ratio_1trackfid_0nubb, nevents_selectron_1trackfid, nevents_selectron_fiducial, \n",
    "                                             Enevents_selectron_1trackfid, Enevents_selectron_fiducial)\n",
    "\n",
    "eff_1trackfid_selectron = 100*nevents_selectron_1trackfid/nevents_selectron_total\n",
    "Eeff_1trackfid_selectron = myef.ratio_error(eff_1trackfid_selectron, nevents_selectron_1trackfid, nevents_selectron_total, \n",
    "                                             Enevents_selectron_1trackfid, Enevents_selectron_total)\n",
    "\n",
    "print('--------------------------0nubb-------------------------')\n",
    "print('--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---')\n",
    "print(f'INITIAL    {nevents_0nubb_total}        ')\n",
    "print(f'FIDUCIAL   {nevents_0nubb_fiducial}                ---      {ratio_fiducial_0nubb} +- {Eratio_fiducial_0nubb}%')\n",
    "print(f'1TRACK.    {nevents_0nubb_1trackfid}        {ratio_1trackfid_0nubb}+- {Eratio_1trackfid_0nubb}% {eff_1trackfid_0nubb} +- {Eeff_1trackfid_0nubb}%')\n",
    "\n",
    "print('--------!--------------!----------------!----------------')\n",
    "print(' ')\n",
    "print(' ')\n",
    "print('--------------------------SElectron---------------------')\n",
    "print('--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---')\n",
    "print(f'INITIAL    {nevents_selectron_total}        ')\n",
    "print(f'FIDUCIAL   {nevents_selectron_fiducial}                ---      {ratio_fiducial_selectron} +- {Eratio_fiducial_selectron}%')\n",
    "print(f'1TRACK.    {nevents_selectron_1trackfid}        {ratio_1trackfid_selectron}+- {Eratio_1trackfid_selectron}% {eff_1trackfid_selectron} +- {Eeff_1trackfid_selectron}%')\n",
    "print('--------!--------------!----------------!----------------')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_0nubb_zmin = tracks_0nubb_1track.z_min\n",
    "track_0nubb_zmax = tracks_0nubb_1track.z_max\n",
    "\n",
    "plt.hist(track_0nubb_zmin, bins = 50, range = (0, 1400), color='blue', histtype='step', label = 'zMin')\n",
    "plt.hist(track_0nubb_zmax, bins = 50, range = (0, 1400),color='green', histtype='step', label = 'zMax', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('position (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('0nubb')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_0nubb_rmax = tracks_0nubb_1track.r_max\n",
    "\n",
    "plt.hist(track_0nubb_rmax, bins = 50, range = (0, 650), color='red', histtype='step', label = 'rMax')\n",
    "\n",
    "plt.xlabel('position (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('0nubb')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_selectron_zmin = tracks_selectron_1track.z_min\n",
    "track_selectron_zmax = tracks_selectron_1track.z_max\n",
    "\n",
    "plt.hist(track_selectron_zmin, bins = 50, range = (0, 1400),  color='blue', histtype='step', label = 'zMin')\n",
    "plt.hist(track_selectron_zmax, bins = 50, range = (0, 1400),  color='green', histtype='step', label = 'zMax', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('position (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('e-roi')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_selectron_rmax = tracks_selectron_1track.r_max\n",
    "\n",
    "plt.hist(track_selectron_rmax, bins = 50, range = (0, 650), color='red', histtype='step', label = 'rMax')\n",
    "\n",
    "plt.xlabel('position (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('0nubb')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking variables: **NoOverlap**\n",
    "The variable ovlp_blob_energy is defined for this purpouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ovlp_blob_energy = tracks_0nubb_1trackfid.ovlp_blob_energy\n",
    "\n",
    "plt.hist(ovlp_blob_energy, bins = 50)\n",
    "plt.xlabel('overlap blob energy')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting ovlp variable of the tracks, and selecting events with no ovlp\n",
    "\n",
    "tracks_0nubb_ovlp = tracks_0nubb[tracks_0nubb.ovlp_blob_energy == 0.]\n",
    "tracks_0nubb_ovlp1trackfid = tracks_0nubb_1trackfid[tracks_0nubb_1trackfid.ovlp_blob_energy == 0.]\n",
    "\n",
    "tracks_selectron_ovlp = tracks_selectron[tracks_selectron.ovlp_blob_energy == 0.]\n",
    "tracks_selectron_ovlp1trackfid = tracks_selectron_1trackfid[tracks_selectron_1trackfid.ovlp_blob_energy == 0.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------0nubb-------------------------\n",
      "--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---\n",
      "INITIAL    9759        \n",
      "FIDUCIAL   6753                ---      69.19766369505072 +- 1.0953180330103627%\n",
      "1TRACK.    6753        100.0+- 1.7209435422324602% 69.19766369505072 +- 1.0953180330103627%\n",
      "OVLP       6038        89.41211313490301+- 1.5836298500471826%   61.871093349728454 +- 1.013038349793247%\n",
      "--------!--------------!----------------!----------------\n",
      " \n",
      " \n",
      "--------------------------SElectron---------------------\n",
      "--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---\n",
      "INITIAL    9767        \n",
      "FIDUCIAL   6463                ---      66.17180301013617 +- 1.0610468400150082%\n",
      "1TRACK.    6463        100.0+- 1.7591299384021004% 66.17180301013617 +- 1.0610468400150082%\n",
      "OVLP       6222        96.27108154108+- 1.7098539078161492%   63.704310433091024 +- 1.0333177106039988%\n",
      "--------!--------------!----------------!----------------\n"
     ]
    }
   ],
   "source": [
    "#Checking total amount of events, after overlap selection\n",
    "\n",
    "nevents_0nubb_ovlp = tracks_0nubb_ovlp.event.nunique()\n",
    "Enevents_0nubb_ovlp = np.sqrt(nevents_0nubb_ovlp)\n",
    "nevents_0nubb_ovlp1trackfid = tracks_0nubb_ovlp1trackfid.event.nunique()\n",
    "Enevents_0nubb_ovlp1trackfid = np.sqrt(nevents_0nubb_ovlp1trackfid)\n",
    "\n",
    "nevents_selectron_ovlp = tracks_selectron_ovlp.event.nunique()\n",
    "Enevents_selectron_ovlp = np.sqrt(nevents_selectron_ovlp)\n",
    "nevents_selectron_ovlp1trackfid = tracks_selectron_ovlp1trackfid.event.nunique()\n",
    "Enevents_selectron_ovlp1trackfid = np.sqrt(nevents_selectron_ovlp1trackfid)\n",
    "\n",
    "ratio_ovlp1trackfid_0nubb = 100*nevents_0nubb_ovlp1trackfid/nevents_0nubb_1trackfid\n",
    "Eratio_ovlp1trackfid_0nubb = myef.ratio_error(ratio_ovlp1trackfid_0nubb, nevents_0nubb_ovlp1trackfid, nevents_0nubb_1trackfid, \n",
    "                                             Enevents_0nubb_ovlp1trackfid, Enevents_0nubb_1trackfid)\n",
    "\n",
    "eff_ovlp1trackfid_0nubb = 100*nevents_0nubb_ovlp1trackfid/nevents_0nubb_total\n",
    "Eeff_ovlp1trackfid_0nubb = myef.ratio_error(eff_ovlp1trackfid_0nubb, nevents_0nubb_ovlp1trackfid, nevents_0nubb_total, \n",
    "                                             Enevents_0nubb_ovlp1trackfid, Enevents_0nubb_total)\n",
    "\n",
    "ratio_ovlp1trackfid_selectron = 100*nevents_selectron_ovlp1trackfid/nevents_selectron_1trackfid\n",
    "Eratio_ovlp1trackfid_selectron = myef.ratio_error(ratio_ovlp1trackfid_selectron, nevents_selectron_ovlp1trackfid, nevents_selectron_1trackfid, \n",
    "                                             Enevents_selectron_ovlp1trackfid, Enevents_selectron_1trackfid)\n",
    "\n",
    "eff_ovlp1trackfid_selectron = 100*nevents_selectron_ovlp1trackfid/nevents_selectron_total\n",
    "Eeff_ovlp1trackfid_selectron = myef.ratio_error(eff_ovlp1trackfid_selectron, nevents_selectron_ovlp1trackfid, nevents_selectron_total,\n",
    "                                             Enevents_selectron_ovlp1trackfid, Enevents_selectron_total)\n",
    "print('--------------------------0nubb-------------------------')\n",
    "print('--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---')\n",
    "print(f'INITIAL    {nevents_0nubb_total}        ')\n",
    "print(f'FIDUCIAL   {nevents_0nubb_fiducial}                ---      {ratio_fiducial_0nubb} +- {Eratio_fiducial_0nubb}%')\n",
    "print(f'1TRACK.    {nevents_0nubb_1trackfid}        {ratio_1trackfid_0nubb}+- {Eratio_1trackfid_0nubb}% {eff_1trackfid_0nubb} +- {Eeff_1trackfid_0nubb}%')\n",
    "print(f'OVLP       {nevents_0nubb_ovlp1trackfid}        {ratio_ovlp1trackfid_0nubb}+- {Eratio_ovlp1trackfid_0nubb}%   {eff_ovlp1trackfid_0nubb} +- {Eeff_ovlp1trackfid_0nubb}%')\n",
    "print('--------!--------------!----------------!----------------')\n",
    "print(' ')\n",
    "print(' ')\n",
    "print('--------------------------SElectron---------------------')\n",
    "print('--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---')\n",
    "print(f'INITIAL    {nevents_selectron_total}        ')\n",
    "print(f'FIDUCIAL   {nevents_selectron_fiducial}                ---      {ratio_fiducial_selectron} +- {Eratio_fiducial_selectron}%')\n",
    "print(f'1TRACK.    {nevents_selectron_1trackfid}        {ratio_1trackfid_selectron}+- {Eratio_1trackfid_selectron}% {eff_1trackfid_selectron} +- {Eeff_1trackfid_selectron}%')\n",
    "print(f'OVLP       {nevents_selectron_ovlp1trackfid}        {ratio_ovlp1trackfid_selectron}+- {Eratio_ovlp1trackfid_selectron}%   {eff_ovlp1trackfid_selectron} +- {Eeff_ovlp1trackfid_selectron}%')\n",
    "print('--------!--------------!----------------!----------------')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking variables: ROI\n",
    "Getting events with an energy inside the region of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Limits of the Energy ROI \n",
    "\n",
    "emin = 2.4\n",
    "emax = 2.54\n",
    "\n",
    "#UPDATE: The initial values are not the used ones. Due to some spread in the reconstructed\n",
    "#energy, and some non-linearities, the ROI needs to be broader. Values are increased as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Getting energy of the tracks\n",
    "energy = tracks_0nubb.energy\n",
    "\n",
    "plt.hist(energy, bins = 50)\n",
    "plt.xlabel('energy (MeV)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Selecting events in the ROI\n",
    "tracks_0nubb_ROI = tracks_0nubb[(tracks_0nubb.energy >= emin) & (tracks_0nubb.energy <= emax)]\n",
    "tracks_0nubb_ROIovlp1trackfid = tracks_0nubb_ovlp1trackfid[(tracks_0nubb_ovlp1trackfid.energy >= emin) & (tracks_0nubb_ovlp1trackfid.energy <= emax)]\n",
    "\n",
    "tracks_selectron_ROI = tracks_selectron[(tracks_selectron.energy >= emin) & (tracks_selectron.energy <= emax)]\n",
    "tracks_selectron_ROIovlp1trackfid = tracks_selectron_ovlp1trackfid[(tracks_selectron_ovlp1trackfid.energy >= emin) & (tracks_selectron_ovlp1trackfid.energy <= emax)]\n",
    "\n",
    "\n",
    "energy_0nubb = tracks_0nubb_ROIovlp1trackfid.energy\n",
    "energy_selectron = tracks_selectron_ROIovlp1trackfid.energy\n",
    "\n",
    "plt.hist(energy_0nubb, bins = 50, range = (emin, emax), color='red', histtype='step', label = '0nubb')\n",
    "plt.hist(energy_selectron, bins = 50, range = (emin, emax), color='blue', histtype='step', label = 'e-roi', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('energy (MeV)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('Energy ROI')\n",
    "plt.legend(loc=1);\n",
    "plt.show()\n",
    "\n",
    "#mirar resolucion de la energia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit results: mu = 2.4647074115655525,  std = 0.0140157074810724 \n"
     ]
    }
   ],
   "source": [
    "energy_0nubb_zoom = tracks_0nubb_ROIovlp1trackfid.energy\n",
    "\n",
    "mu, std = norm.fit(energy_0nubb_zoom)\n",
    "\n",
    "# Plot the histogram.\n",
    "plt.hist(energy_0nubb_zoom, bins=25, density=True, alpha=0.6, color='r')\n",
    "\n",
    "# Plot the PDF.\n",
    "x = np.linspace(emin, emax, 100)\n",
    "p = norm.pdf(x, mu, std)\n",
    "plt.plot(x, p, 'k', linewidth=2)\n",
    "plt.title('0nubb')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(f'Fit results: mu = {mu},  std = {std} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit results: mu = 2.477382259117032,  std = 0.018896946587306666 \n"
     ]
    }
   ],
   "source": [
    "ae, loce, scalee = skewnorm.fit(energy_0nubb_zoom)\n",
    "\n",
    "# Plot the histogram.\n",
    "plt.hist(energy_0nubb_zoom, bins=25, density=True, alpha=0.6, color='r')\n",
    "\n",
    "# Plot the PDF.\n",
    "x = np.linspace(emin, emax, 100)\n",
    "p = skewnorm.pdf(x, ae, loce, scalee)\n",
    "plt.plot(x, p, 'k', linewidth=2)\n",
    "plt.title('0nubb')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(f'Fit results: mu = {loce},  std = {scalee} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit results: mu = 2.4664848016117755,  std = 0.01584796333200225 \n"
     ]
    }
   ],
   "source": [
    "energy_selectron_zoom = tracks_selectron_ROIovlp1trackfid.energy\n",
    "\n",
    "mu, std = norm.fit(energy_selectron_zoom)\n",
    "\n",
    "# Plot the histogram.\n",
    "plt.hist(energy_selectron_zoom, bins=25, density=True, alpha=0.6, color='b')\n",
    "\n",
    "# Plot the PDF.\n",
    "x = np.linspace(emin, emax, 100)\n",
    "p = norm.pdf(x, mu, std)\n",
    "plt.plot(x, p, 'k', linewidth=2)\n",
    "plt.title('e-roi')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(f'Fit results: mu = {mu},  std = {std} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------0nubb-------------------------\n",
      "--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---\n",
      "INITIAL    9759        \n",
      "FIDUCIAL   6753                ---      69.19766369505072 +- 1.0953180330103627%\n",
      "1TRACK.    6753        100.0+- 1.7209435422324602% 69.19766369505072 +- 1.0953180330103627%\n",
      "OVLP       6038        89.41211313490301+- 1.5836298500471826%   61.871093349728454 +- 1.013038349793247%\n",
      "ROI        4465        73.94832726068235+- 1.459579487618239%   45.75263859001947 +- 0.8266346895858732%\n",
      "--------!--------------!----------------!----------------\n",
      " \n",
      " \n",
      "--------------------------SElectron---------------------\n",
      "--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---\n",
      "INITIAL    9767        \n",
      "FIDUCIAL   6463                ---      66.17180301013617 +- 1.0610468400150082%\n",
      "1TRACK.    6463        100.0+- 1.7591299384021004% 66.17180301013617 +- 1.0610468400150082%\n",
      "OVLP       6222        96.27108154108+- 1.7098539078161492%   63.704310433091024 +- 1.0333177106039988%\n",
      "ROI        4242        68.17743490838959+- 1.3574979656337907%   43.43196477935906 +- 0.7986327547158496%\n",
      "--------!--------------!----------------!----------------\n"
     ]
    }
   ],
   "source": [
    "#Checking total amount of events, after ROI selection\n",
    "nevents_0nubb_ROI = tracks_0nubb_ROI.event.nunique()\n",
    "Enevents_0nubb_ROI = np.sqrt(nevents_0nubb_ROI)\n",
    "nevents_0nubb_ROIovlp1trackfid = tracks_0nubb_ROIovlp1trackfid.event.nunique()\n",
    "Enevents_0nubb_ROIovlp1trackfid = np.sqrt(nevents_0nubb_ROIovlp1trackfid)\n",
    "\n",
    "nevents_selectron_ROI = tracks_selectron_ROI.event.nunique()\n",
    "Enevents_selectron_ROI = np.sqrt(nevents_selectron_ROI)\n",
    "nevents_selectron_ROIovlp1trackfid = tracks_selectron_ROIovlp1trackfid.event.nunique()\n",
    "Enevents_selectron_ROIovlp1trackfid = np.sqrt(nevents_selectron_ROIovlp1trackfid)\n",
    "\n",
    "\n",
    "ratio_ROIovlp1trackfid_0nubb = 100*nevents_0nubb_ROIovlp1trackfid/nevents_0nubb_ovlp1trackfid\n",
    "Eratio_ROIovlp1trackfid_0nubb = myef.ratio_error(ratio_ROIovlp1trackfid_0nubb, nevents_0nubb_ROIovlp1trackfid, nevents_0nubb_ovlp1trackfid, \n",
    "                                             Enevents_0nubb_ROIovlp1trackfid, Enevents_0nubb_ovlp1trackfid)\n",
    "\n",
    "eff_ROIovlp1trackfid_0nubb = 100*nevents_0nubb_ROIovlp1trackfid/nevents_0nubb_total\n",
    "Eeff_ROIovlp1trackfid_0nubb = myef.ratio_error(eff_ROIovlp1trackfid_0nubb, nevents_0nubb_ROIovlp1trackfid, nevents_0nubb_total, \n",
    "                                             Enevents_0nubb_ROIovlp1trackfid, Enevents_0nubb_total)\n",
    "\n",
    "ratio_ROIovlp1trackfid_selectron = 100*nevents_selectron_ROIovlp1trackfid/nevents_selectron_ovlp1trackfid\n",
    "Eratio_ROIovlp1trackfid_selectron = myef.ratio_error(ratio_ROIovlp1trackfid_selectron, nevents_selectron_ROIovlp1trackfid, nevents_selectron_ovlp1trackfid, \n",
    "                                             Enevents_selectron_ROIovlp1trackfid, Enevents_selectron_ovlp1trackfid)\n",
    "\n",
    "eff_ROIovlp1trackfid_selectron = 100*nevents_selectron_ROIovlp1trackfid/nevents_selectron_total\n",
    "Eeff_ROIovlp1trackfid_selectron = myef.ratio_error(eff_ROIovlp1trackfid_selectron, nevents_selectron_ROIovlp1trackfid, nevents_selectron_total,\n",
    "                                                   Enevents_selectron_ROIovlp1trackfid, Enevents_selectron_total)\n",
    "    \n",
    "print('--------------------------0nubb-------------------------')\n",
    "print('--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---')\n",
    "print(f'INITIAL    {nevents_0nubb_total}        ')\n",
    "print(f'FIDUCIAL   {nevents_0nubb_fiducial}                ---      {ratio_fiducial_0nubb} +- {Eratio_fiducial_0nubb}%')\n",
    "print(f'1TRACK.    {nevents_0nubb_1trackfid}        {ratio_1trackfid_0nubb}+- {Eratio_1trackfid_0nubb}% {eff_1trackfid_0nubb} +- {Eeff_1trackfid_0nubb}%')\n",
    "print(f'OVLP       {nevents_0nubb_ovlp1trackfid}        {ratio_ovlp1trackfid_0nubb}+- {Eratio_ovlp1trackfid_0nubb}%   {eff_ovlp1trackfid_0nubb} +- {Eeff_ovlp1trackfid_0nubb}%')\n",
    "print(f'ROI        {nevents_0nubb_ROIovlp1trackfid}        {ratio_ROIovlp1trackfid_0nubb}+- {Eratio_ROIovlp1trackfid_0nubb}%   {eff_ROIovlp1trackfid_0nubb} +- {Eeff_ROIovlp1trackfid_0nubb}%')\n",
    "print('--------!--------------!----------------!----------------')\n",
    "print(' ')\n",
    "print(' ')\n",
    "print('--------------------------SElectron---------------------')\n",
    "print('--CUT---!----EVENTS----!---REDUCTION---!---EFFICIENCY---')\n",
    "print(f'INITIAL    {nevents_selectron_total}        ')\n",
    "print(f'FIDUCIAL   {nevents_selectron_fiducial}                ---      {ratio_fiducial_selectron} +- {Eratio_fiducial_selectron}%')\n",
    "print(f'1TRACK.    {nevents_selectron_1trackfid}        {ratio_1trackfid_selectron}+- {Eratio_1trackfid_selectron}% {eff_1trackfid_selectron} +- {Eeff_1trackfid_selectron}%')\n",
    "print(f'OVLP       {nevents_selectron_ovlp1trackfid}        {ratio_ovlp1trackfid_selectron}+- {Eratio_ovlp1trackfid_selectron}%   {eff_ovlp1trackfid_selectron} +- {Eeff_ovlp1trackfid_selectron}%')\n",
    "print(f'ROI        {nevents_selectron_ROIovlp1trackfid}        {ratio_ROIovlp1trackfid_selectron}+- {Eratio_ROIovlp1trackfid_selectron}%   {eff_ROIovlp1trackfid_selectron} +- {Eeff_ROIovlp1trackfid_selectron}%')\n",
    "print('--------!--------------!----------------!----------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tracks_0nubb_sel = tracks_0nubb[tracks_0nubb.energy > 2.4]\n",
    "tracks_0nubb_fiducial_sel = tracks_0nubb_fiducial[tracks_0nubb_fiducial.energy > 2.4]\n",
    "tracks_0nubb_1trackfid_sel = tracks_0nubb_1trackfid[tracks_0nubb_1trackfid.energy > 2.4]\n",
    "tracks_0nubb_ovlp1trackfid_sel = tracks_0nubb_ovlp1trackfid[tracks_0nubb_ovlp1trackfid.energy > 2.4]\n",
    "tracks_0nubb_ROIovlp1trackfid_sel = tracks_0nubb_ROIovlp1trackfid[tracks_0nubb_ROIovlp1trackfid.energy > 2.4]\n",
    "\n",
    "plt.hist(tracks_0nubb_fiducial_sel.energy, bins = 50, color='blue', histtype='step', label = 'fiducial')\n",
    "plt.hist(tracks_0nubb_sel.energy, bins = 50, color='red', histtype='step', label = 'no cut')\n",
    "plt.hist(tracks_0nubb_1trackfid_sel.energy, bins = 50, color='orange', histtype='step', label = '+ 1-track', linestyle='dashed')\n",
    "plt.hist(tracks_0nubb_ovlp1trackfid_sel.energy, bins = 50, color='green', histtype='step', label = '+ overlap', linestyle='dashed')\n",
    "#plt.hist(tracks_0nubb_ROIovlp1trackfid_sel.energy, bins = 50, color='purple', histtype='step', label = '+ ROI', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('track energy (MeV)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('0nubb')\n",
    "plt.legend(loc=2);\n",
    "#plt.xlim((2.2,2.6))\n",
    "#plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VnS4iQWACsM7d9DpVfcLd51rgfGAHcImqPpWs+Dps8USnmymFDj30UEpLSxkzZkyT7qWNGzfyn//8B4ANGzbwyiuvICLcdddd/PGPf2TatGncdNNN9OrVizfeeCO8XYO1a9dSWlrK7373O4455piUnlNWmUu427H8uQlMvLscgu07xPyxrYwdjFMC4yBwF4CTxmLEtALqomy6/ukAzaew6jigIvrUVmOaS2afw3bgClVdKiI9gZCI/Nt978+q2qS/RET2B8YCg4AC4BkR+aGqdi6hT/f8Tu3eEQcddBDbtm1jy5YtfPbZZwwdOhSAW265heOOOy5hn3P66aeHn9fW1nL66adTX1/P119/HV7x7Zlnngkn4QPC6zt88803HH300cyYMYMjjzwyYTHlooJJa6jb4Ew1bfjyDi6KPvtoWP4wZ7vKALOWNo4prLk8+lTSaKksIHaLI1b6C2PilbTuI1WtV9Wl7vPNwAqglflynAjMU9VtqroaeA8Y0elATo52PZVcr776KsuWLeOuu+6itLSUZcuWsWzZsnZVCNdffz1Dhw4NVyjR7LrrruHnF198MZMnT+aNN95g5syZbabf7tKlC36/n6eeSr/GWKaJloQuODIYddtQwNm2vKQcnaLhR0HP6PcvlIes28ekVkrGFETEBxwIvOoWTRaR5SJyj4g0LE3WD4icfV1LlEpERAIiUiUiVevWrWv+dkvLgx0PPJr/174pgh01derUcGUC0LNnTzZv3hxz+8gU2nPmzAmXH3vssfztb38Lv27oPhIR7rnnHt555x1uvvnmZJxCzqi8srRFWcG0xNykNnHBxKjlDS2OeLV3aqvJXUmvFESkB/AQcJmqfg7cCXwfGArUA9MaNo2ye4t2sKqWq2qxqhb37du37QDeLGsy6MZnIecRWdZQcTzSuMQhC90UBa8Gmm77XX/Mj+qIJUuWUFhYyIMPPsjEiRMZNGhQ1O3Gjh3LrbfeyoEHHsj777/f4v1gMMhpp53GEUccQZ8+fcLlv/nNb9iwYQM//vGPGTJkCM8//3z4vby8PObNm8fzzz/PHXfckdDzynUdGlBuh4YWR3Njfjgmanljyg1jWpfU1Nki0hVYADylqn+K8r4PWKCqP3YHmVHVP7jvPQUEVfXlWMe31NmpYT/TNkRJb52ovn3f7T7+evxf8Rf46fenxobzhGETKC9pR9dSrNTc4KS5iHZXc1GRcxe0yTqtpc5O5uwjAe4GVkRWCCKSr6oNl1EnA2+6z+cDFSLyJ5yB5n2BxcmKz5hEkVWE7zNo+LJub/dOLNWXVYefx1PJlNxXQuUZLe+rqd1QRGGFhGdHVf3Oz167r6dwUnXsL/42loI12SmZs48OA8YDb4jIMrfsOuAMERmK0zVUDUwEUNW3ROQB4G2cmUuTOj3zyJhUmLYG/bzpGEKs7p1ke2PtG4hbQekUpTxUzsQFEynqVUR186mtFfalb1pKWqWgqv8j+jjBE63sMxWYmqyYjEkGJwldekwDjWxZAAT8AQL+QLiiiFS3IZ8k52w1GSj772g2JskyIQldtJvj+k1O/XRtk/6sUjAmB/gLWs4+mvKzYOoDMWnPKgVjckDkzKUGwVPKPIjEpLucqBQClYEmeerrNtdRubKySVnDnaPN89mDM6MjsjwTzJ49m8mTJ3sdRm6onOl1BG0q6lWElAmBSif/kb/cj2+1x0GZtJT1+Zb95X5CgVCLOd0FPyqIOsUvWlm0KX7xiCd1djJs3749pZ+X6+aPrCTdk801H4AOBUIZc4FjUivrWwpL6xObOjvRuWhqamo4+uijGTx4MEcffTQffvghmzZtwufz8e233wLw5Zdf0r9/f7755hvef/99Ro8ejd/v54gjjuCdd94B4JxzzuHyyy9n1KhRXH311U0+I1Za7WAwyPjx4znqqKPYd999mTVrFqb9SnsuaHujNNT1yz5tb2RyTtZXCokWKxdNR02ePJmzzjqL5cuXc+aZZ3LJJZfQq1cvhgwZEk6LXVlZyXHHHUfXrl0JBAL89a9/JRQKcdttt/HLX/4yfKx3332XZ555hmnTpjX5jMMPP5xXXnmF1157jbFjx/LHP/4x/N7y5ct5/PHHefnll7nxxhupq7MZKbnigAee9DoEk4ayvvsov0d6p85++eWXefjhhwEYP348V111FeCkxb7//vsZNWoU8+bN45e//CVbtmzhpZde4rTTTgvvv23btvDz0047jby8vBbxxEqrDXDiiSfSvXt3unfvzqhRo1i8eDEnnXRS4n4Y2SRWOohgqgNJjJLLi0mX+ytM+sj6SqHuCm9SZ0PHxhQa0lyXlpZy7bXX8tlnnxEKhTjqqKP44osv2H333cOZU5uLTKUd6eKLL+byyy+ntLSURYsWEQwGW3xerNc5J9YXPzi5gKLlChuXmWsol32WsfWZSaKs7z4KLgom9HitrpDVAYceemh4EZy5c+dy+OGHA9CjRw9GjBjBpZdeypgxY8jLy2O33XZjwIABPPjgg4CzVsLrr7/e5mfESqsN8Nhjj7F161bWr1/PokWLGD58eCJPL/PU1Dhf/NEeMXIETfg4MysFY6LJ+kqh7D9lTaaThupChOpCTcoaKo6CaQXhMn+5c7NP8+ms0W4C6oy//OUv3HvvvQwePJh//OMfTJ8+Pfze6aefzj//+c8mK6zNnTuXu+++myFDhjBo0CAee+yxNj8jVlptgBEjRvDTn/6Ugw8+mBtuuIGCAkt80F71JyR2nMkYLyU1dXayWerszgkGg/To0YMrr7yy1e1y6mcqEr2LqLVdMnQJTCkIoXWtXOR04GdhMkNrqbOzvqVgjIlu4dWjvQ7BpKGsH2g2sUUOOJvcc/zGT23ukWkhK1sKmdwllm7sZ9m2/NvXeB2CMQmTdZVCt27dWL9+vX2ZJYCqsn79erp16+Z1KGkt+PuWyeaMyVRZ131UWFhIbW0t69at8zqUrNCtWzcKCwu9DiOtTfwk3TMfRdfrtSmUXxAgcFRjepOCSWs4/qAQdz9p02xzVdZVCl27dm1yx64xJrqNjwbdZ435vOrGARXps5KcSb2s6z4yxsSnYJrdk2JaskrBmM6qSOxd7qlSvyUD1hE1KWeVgjGdtHD8dV6H0CFFvYqi37m/yuPAjKey7o5mYzolh+5ojkV2q0M/L7A7mrOY3dFsjInb/Ntsim0us0rBGNNEqQ015DSrFIxppnaGj/ILAohAaKofKgQqhLV/jzFbJzQhtQEak0RJqxREpL+IPC8iK0TkLRG51C3/roj8W0RWuf/2jtjnWhF5T0RWishxsY9uTPIU9q4hcFc5quC/PgTjFMYpe18UfcGmKV1taqfJHslsKWwHrlDVgcDBwCQR2R+4BnhWVfcFnnVf4743FhgEjAbuEJGWa0sak2S+1c5sHAB/uT88K6fH9T2jbl85vCyV4SVf5UyvIzAeSlqloKr1qrrUfb4ZWAH0A04EGpb/mgM0LAh8IjBPVbep6mrgPWBEsuIzJpaa7VBe4tzlGwqE0CmKTlFW5O8R7kryDwjhHxCCCmHptjYOmGHmj6z0OgTjoTbTXIhIN2AMcARQAHwFvAk8rqpvxfMhIuIDDgReBfZS1XpwKg4R2dPdrB/wSsRutW6ZMWnhrkHnEBwZBCA0rqFUoSy71rUu7bnAklzksFZbCiISBF4EDsH5Qp8JPIDTNXSzOyYwuI1j9AAeAi5T1c9b2zRKWYvfTREJiEiViFRZ0juTDGtipM4q+0/0bqL8HvlJjMaY1GqrpbBEVYMx3vuTe5W/T6ydRaQrToUwV1UfdovXiki+20rIBz5xy2uB/hG7FwItRvZUtRw3g1dxcbFd0JiEC211msTxqrsi+gC0MZmo1ZaCqj7exvufqGrUW4pFRIC7gRWq+qeIt+YDZ7vPzwYeiygfKyK7iMgAYF9gcdunYExitTZPP1QXIlQXakwJUSYEFwVTFltKrBzjdQTGQx1OnS0iAfeqPZbDgPHAGyKyzC27DrgZeEBEzgc+BE4DUNW3ROQB4G2c7qlJqrqjo/EZk2iRqSyyKa1FcxM+trUUclln1lNodXRNVf/XyjZHx9hnKjC1EzEZYzqp/oSJZOayQSYROjwlVVVtMrPJSjP3bHubbLbgC/dJUZGTFC/aw+fzMkSTRHG1FETkt9HKVfXGxIZjjPcCvbyOIE1UV8d+T7JrGq5pFG/30RcRzxvuW1iR+HCM8Z6sssUoTe6Kq1JQ1WmRr0XkNpzZQsaYLJN/+xqY4nUUxisdHVP4DvC9RAZijEkPwd9bIoFcFu+Ywhs0tqjzgL6AjSeYrDR8e26PNE/8xOYe5bJ4xxQi72bZDqxV1e1JiMcYzy0e+EnbGxmTpeIdU6hJdiDGpIuSOrA8oSZXdfg+BRFZkMhAjEkXC75oe5ts1veZ+U1WnKubUYAI3H5+0OvQTAp05o5mW4PQmCy07Ak/BT1D4dcFgE4CKsqAoEdRmVTpzB3Ntry3MVmo359s9lEui6tSEJF9ReRfIvK2iHzQ8Eh2cMZ4Qff1OgJvFfUqQsqkxZKkvtUeB2ZSIt7uo3txbmf5MzAKOJc2EuIZk6nKN+X2lMzqy6qbvA4FnK6kna/u60E0JtXi7T7qrqrPAqKqNe7CO0clLyxjvDPRZqRGdcADT3odgkmBeFsKW0VkJ2CViEwG1gC5fYePMTmm5PJiLCtU9ou3pXAZTmqLSwA/8AsaV08zxuSAss+8jsCkQrw3ry1xn27BGU8wJmvNz/c6AmO802pLQUTKReSAGO/tKiLniciZyQnNmCTy+aIvHlNUhL+b18EZ4522Wgp3ADe4FcObwDqc9RT2BXYD7gHmJjVCY5KhpgY0ev94vzKxnvNoZlZZSu0c0GqloKrLgJ+LSA+gGMgHvgJWqOrKFMRnjEkTC68ejXNdaLJZvGMKW4BFyQ3FmCTw+ZxWQXNFRSkPJdMdv/FTa0HlgM7kPjIm/bXSTRTLybJHkoIxJv11OPeRMdnq4R+s9zoEYzzT7kpBRHYSkd2SEYwx6cD/odcRpKlFNsqcC+JNiFchIruJyK7A28BKEfl1ckMzxhtLt3kdQXqa+f06r0MwKRBvS2F/Vf0cOAl4AtgHGJ+0qIwxaSfom+V1CCYF4q0UuopIV5xK4TFV/aatHUTkHhH5RETejCgLisgaEVnmPk6IeO9aEXlPRFaKyHHtPhNjEqTHNz28DiEt1e/wOgKTCvFWCjOBamBX4AURKQI2tbHPbGB0lPI/q+pQ9/EEgIjsD4wFBrn73CEieXHGZkxCbd5/i9chGOOZeCuFSlXtp6onqKoCHwLntbaDqr4AxJtC60RgnqpuU9XVwHvAiDj3NSahgjb5KKoua6NmvDFZJt5K4aHIF27FMK+DnzlZRJa73Uu93bJ+wEcR29S6ZS2ISEBEqkSkat06u7vSJJ5lA41u9CtTvQ7BpEBbCfH2E5FTgF4i8rOIxzk4OZDa607g+8BQoB6Y1vBRUbaNeseRqpararGqFvftaytBGZMq+eNLvQ7BpEBbLYUfAWOA3YGSiMcwYEJ7P0xV16rqDlX9FphFYxdRLdA/YtNCwOa/GZNGZn0OVAiBo8oRFCoEKoTaGT6vQzMJ1GqloKqPqeq5wBhVPTficYmqvtTeDxORyEz1J+NkXgWYD4wVkV1EZABOFtbF7T2+MdHUzvBBhVB+QQARCE31h7/Q6mYUIALBU4Lhsqr+bR0xh41Typ8LoAiMUxinvLbaxhqyiWgceWFEpC9Oy8BHRL4kVY052Cwi9wEjgT7AWpykuyNxuo4UZzbTRFWtd7e/HmfwejtwmaoubCuu4uJiraqqajN+k8NEnOTu4+LPfxSqC+Ev8CcvpgwlZYJOcX+OIuGcUhFPTYYQkZCqFkd7L96EeI8B/wWeAeKarayqZ0QpvruV7acCNpJlEq5g0hrqxrUs993u4+sdX1N3RR3BRUHK/lMWfi/85WfC1ly+Jmr5/CtKgMrUBmOSJt6WwjJVHZqCeNrFWgqmTSJIsPHlhGETKC8px1/uZ/2X66m+rNqryDJO5cpKSn5U4ryIbB5USGNLrLVU5dXVqQjTxCERLYUFInJCw81mxmSSNQOg4KymFz+hQMijaDLXxQsvpnSeM5ChzgEAABcySURBVANp5jHfJeBWuEVdoFrcCYRFRdH7kiTaBEOTjuKtFC4FrhORr4GvcaaQqqpatlST9kJbocDrILJAtFaV4ow12KBC9oh35bWeyQ7EmGQprY9x04tJjJVjvI7AJFC8qbNFRH4hIje4r/uLiKWhMMYw4eMSr0MwCRRvmos7gEOAhjkcW4AZSYnIGJNR6k+Y6HUIJoHiHVM4SFWHichrAKq6QUR2TmJcxiTMzD29jiC7LfjC6whMIsXbUvjGTWWtEL6Z7dukRWVMAgV6eR2BMZkj3krhL8AjwJ4iMhX4H/D7pEVlTALJKq8jMCZzxFUpqOpc4CrgDzjZTU9S1QeTGZgxJjMM/9eacN6oyBxTb9xqqUIyUVxjCiIyHbhfVW1w2aSn1u6kJUq5SZgLZi8Af+Ok38A4CNwFVCz1LijTYfF2Hy0FfuOuoXyriES9PdoYz9TUODdQNX9UVzN8u400J9PEBTb7KJvE2300R1VPwFn/4F3gFhHrqTWZYfHAT7wOIasV9SpCyoTKlZXUba5DygQpE/q/b8usZ6J4p6Q2+AGwH04K7bcTHo0xSVBSZzk8k6l5+ouGDLNSGvAgGtNZ8d7R3NAyuBFnYRy/qtptjCYj2Dx6b0zpahmnMlG8YwqrgUNUdbSq3quqG5MZlDEm81UOL2t7I5N24q0UyoHRIvJbABHZx3IfGWNas3Sb1xGYjoi3UpiBk/uoYTW1zVjuI5MhdF+vIzAmc8RbKRykqpOAreDkPgIs95HJCOWbvI4gN3X9so/XIZgOsNxHJutNtBmpnjjggSe9DsF0gOU+MlnD54PyCwJNUi4U9K7zOqycVXK53eOaiSz3kckaw87rw8T+s5BVThK8upI1zHwlRFGvIq9Dy0lln3kdgemIuG9eU9V3gHeSGIsxnfLwD9bDuKYLbxb8qICSH9ktNcbEK97uI2PSg88HIi0fRUX4P/Q6OGMyn1UKJrO0kvjO5sWnmZlVXkdgOiBplYKI3CMin4jImxFl3xWRf4vIKvff3hHvXetmYV0pIsclKy5jTGosvHq01yGYDkhmS2E20Py34hrgWVXdF3jWfY2I7A+MBQa5+9zhToE1Jm49vunhdQgmwvEbP/U6BNMBSasUVPUFoPn8gxOBOe7zOcBJEeXzVHWbqq4G3sNJ021M3Dbvv8XrEIzJeKkeU9hLVesB3H8bVj/pB3wUsV2tW9aCiAREpEpEqtatW5fUYE1mCa73OgJjMl+6DDRLlDKNUoaqlqtqsaoW9+3bN8lhmUxi8+LTzKIpXkdgOiDVlcJaEckHcP9tSEBQC/SP2K4QsFtRjclgM79vf8KZKNWVwnzgbPf52cBjEeVjRWQXERkA7AssTnFsxpgECvpmeR2C6YBkTkm9D3gZ+JGI1IrI+cDNwDHuKm7HuK9R1beAB3CW+HwSmKSqO5IVm8lOVf3b3sakTv0OwvmnSg6cH85HVTvD17hRrJsRfb7oBzVJ1941muOmqmfEeOvoGNtPBaYmKx5jjAfGKXXjAOkXTkEy47Rybp4MOlfCaTWfWTGGn9wUsZK2RBtmNKmQtErBmFQr/ijG7ATjiWH5wwAIVAaYFQTKnC/6NU/BoT+ciKyK2HhhCXpTykM0UVilYIxJilAgBEB5STnlJeWNb0yBAppW4JWbS4BAKsMzMVilYDJSlyt97OhZQ34e1H0PrqjtQd5mS5GdqUp7LrBWXpqwSsFkpH67fEHNlMavkWnuwxjTOely85ox7VIzyPLqGJMMVimYjBRY63UEJqFWjvE6AuOySsFkpFmfex2BSaQJH9vqeOnCKgVjjOfqT5jodQjGZZWCMcZzC77wOgLTwCoFk5HWDPA6AmOyk1UKJiOFtnodgTHZySoFk5FK672OwCRS/u1rvA7BuKxSMMZ4Lvj7qAstGg9YpWCM8dzET9rexqSGVQomI83cs+1tjDHtZ5WCyUiBXl5HYEx2skrBZKQmufhN5quY73UExmWVgjHGcwvHX+d1CMZllYIxxnPH73gTKoS6GQWIQPBnU1qu52xSwtZTMBlp+HYbac4649RZkW0S4JsND0Ph3Jqm6zUXFUF1tTfx5QhrKZj05POBCLXTC6FCCBxV7nw3zAUqhIf37u5xgCaRinoVEah0luP0l/uRc2uQIOz8eh9QbXzU1HgbaA6wloJJTzU1oMrhU4XrDp9J+XMBZpVJeIB5zA8PoNLbCE0CVV9WHX7esLYzgH9ACG7xIKAcZpWCSWs12yHgd64gdYqt4ptrSi4vBlu9OaWs+8gYk7bKPvM6gtxjlYLxljt20OJRVATA/HxvwzMm13jSfSQi1cBmYAewXVWLReS7wP2AD6gGfq6qG7yIz6SQO3YQi79bCmMxxnjaUhilqkNVtdh9fQ3wrKruCzzrvjY5rt9qryMwnppZ5XUEOSeduo9OBOa4z+cAJ3kYizEmDSy8erTXIeQcryoFBZ4WkZCIBNyyvVS1HsD91+5OMibHHb/xU69DyDleTUk9TFXrRGRP4N8i8k68O7qVSABgn332SVZ8Jk2cLHt4HYIxOcWTloKq1rn/fgI8AowA1opIPoD7b9RlN1S1XFWLVbW4b9++qQrZeOThH6z3OgSTToqKos9W8/m8jixrpLxSEJFdRaRnw3PgWOBNYD5wtrvZ2cBjqY7NpB//h15HYDy1aErT19XVTdNeWPqLhPOipbAX8D8ReR1YDDyuqk8CNwPHiMgq4Bj3tclxS7d5HYHx0twhdVAh4UdB7zpKhlVa9tQkSvmYgqp+AAyJUr4eODrV8Rhj0teV/Rdwppvvalj+MOo2FBCoDHL48hqqPY0se1nuI5MeKgT/9c6c9NDU4nDxbtt7eBWRSQN1V9S1KCsvKUeWzvIgmtyQTvcpmBwW2grlL8LSc4qRVc5ym8GCKWy6abPXoZlMYAPQCWMtBZMWij8CLfBbJlQTn2lrIHIMOtbCO5EL9Ji4WEvBGJNx5t/WL74NrQXRbtZSMMZknNL6OFdZsBZEu1lLwaSFy7vbgLKJX+FOeUiZIMXlVF5Z4jwvE3xTBREIHFXeZCrr+aNtnb54WUvBpIVphVu8DsFkkI9u2O48mQIQCLcaSu4roVqh5L5K5N3G7fNf9ac4wsxllYJJCwUfQMvJh8a0T+UZlU3+bVC+p2DLesbHuo9MWqjf4XUEJptNjJpJzURjlYIxxpgw6z4yieXzRU9OVlQE1dVU/aWQ4RvWhIsn/AnKKwTf9j6pi9EYE5NVCtmojS/mpIq15rI7BbC+25qmN6iJgCq26qZJqor5TW92MzFZ91E2avhiTqf0wu5NRKX1NL2JqKjIu5hMzlg4/jqvQ8gYVimY1GjIgw9NK6pkt1yMAY7f8abXIWQMqxQymc8X/Rb+WFffsW75b+0RKx1Aez/bGI/VzShABIKnBGEujTe3mSZsTCGTxeq/j6UjV+Wx0gG097NdM/dsfwjGJELBpDp0EkAQpAxU8Q8IERrncWBpJrdbCrGudpOdLMurz+2IWK2LDrYIAr0SHJ8xcZgwbAIA/nK/kxIjCAXTCrwNKk3ldkuhjZkyWfe5HZHgPn9ZZfeVmtQrLykHIBQIOQUioHUEd7Y7nZvL7ZZCoiTqyj/N0/x+55oCpEwI/tXpi+35m550udLndVjGdFjZZ15HkH5yu6UQS8OXc7TyaFfOibryT1KaX58P9hy/F0u6RNzrH1RGT/gFC8v/Gfdxvupe3+Qeg82AlKVh68aYtrh/44VlTrZVZlax8OrRHL/xUwDy2Zn64DZmnh8gcFTE0p/jsr9VYZVCNFmWg33P8Xux+Ka1TQunQP+b5iFlcwEY/spvqHnjZD6Z4Kdwp7zGLJQRqvq3PPawXdoXy/DtNtJs0oD7N/5Rw+spAOvCHUnBRUGCClDuPEQo2H0NdTkwKG2VQiZoreUShyYthAjRvvhB23X1/1jPIn52Yx8e0fXhsjUDnDWXSwItr6oWD7TMZCb9BUcGKZhWQP2WegCGTd4Z/4shkGYrvqUiS0CKWaXQSc/cUMIxKPOvLKFk2IJwue86pTpRH9Lsl27ADX1ZPfBTAmtrmBXxBb5mABS4/6OBu2Yy6/kAOrf9rZvDt+wXtbz4o5ZDcoWTqnk4yrb9yiTq8F1JHdhyJyYT1F3RNJl74M8CS5v9Vmdo70FrrFJoZu+b8lj77bcA7Ed33gl+yeFXDqR6t1V8FOWK/YLfwZjzvkPJbV9Scl8JC951K4ZzBe7t3BV+LNVdPoVx2tCwDatcWUm/eaXOiyMnMvNWwK/Qzn7/jx5cQWh3P/4BSwGo25BPv8l1EIz/GD227omUCTP3dKahyiqnvMh+40yGWrglr7EVPW0N82/rR2kQCm/qEqPVnZlEO3ADUrooLi7Wqqqqjh/ATcbWRpFTXiZNE7m1UU6FJG1QKuZnJoi/3M/SeqdCyO+RT90VdQQXBZm9bDbVl1Un7XONyTg+H3JuDRqM8l4ady2JSEhVi6O9l9PXbWN886kfECI0tfFnc9yvjgQWdfrY5ZsgEKW8y5U+SnfbwsM/WI//Q1i6zSnfbXsPNt20ucm2T0zP56cbPw6/vrx7D6YVbmHPnfI6HV9rwnO5IwRHBgmODCb1c43JONXVfPfYciqvqKS0Z2P3MSvHcM3Hh/AH7yLrsLRrKYjIaGA6kAfcpao3x9q2sy0FKROqJjj7F89yKoaiXkVRr4aHDl/Kzn0eYcnBvwuXHf7umXz04j+jXgz0v6kLtd86y4nNzwd/N+i3Gvrs6MG6Gze32L52ho+7vq1pMm+6qj/4z0uv/x9jTHyOmdqdZ7ZvDb/Ov30Nwd/346Z1eXz0W2+7m1prKaRVpSAiecC7wDFALbAEOENV3462fSIqhWR2wxhjTHP9b+xCre6AivksHH9dOINrrKngyZBJ3UcjgPdU9QMAEZkHnAhErRSMMSbTfHRPobu2SSnc0DijT0rOa99spiSNWaRbpdCPiPtJcFoLB0VuICIBGrvrt4jIyg5+Vh/gUwlmxZSyPsCnXgeRANlyHpA955It5wHpfi6Vs4jz28g5j5qazkyJjTkFMt0qhWhn2KR/R1Wbz8Ts2AeJVMVqPmWabDmXbDkPyJ5zyZbzgOw5l2SfR7olxKsFIpMpFAJ1MbY1xhiTYOlWKSwB9hWRASKyMzAWmO9xTMYYkzPSqvtIVbeLyGTgKZwpqfeo6ltJ+rhOd0GlkWw5l2w5D8iec8mW84DsOZeknkdaTUk1xhjjrXTrPjLGGOMhqxSMMcaEZV2lICL9ReR5EVkhIm+JyKWtbDtcRHaIyKkRZaNFZKWIvCci16Qm6qixdfg82rNvKnT2/8QtzxOR10RkQax9ky0Bv1u7i8i/ROQd9xiHpCbyqPF19lx+5e73pojcJyLdUhN5i9jaPA8RGSkim0Rkmfv4bcR7afH37sbS4XNJ6N+8qmbVA8gHhrnPe+Kkzdg/ynZ5wHPAE8CpEWXvA98DdgZej7ZvBpxHXPtmwrlEvHc5UAEsyNTzAOYAF7jPdwZ2z8RzwbnJdDXQ3X39AHBOup4HMDLa7006/b0n4FwS9jefdS0FVa1X1aXu883ACpxf4uYuBh4CIpcCC6fZUNWvgYY0GynXmfNox74p0cn/E0SkEPgpcFeSQ21VZ85DRHYD/h9wt7v/16q6MelBx9DZ/xOcmYvdRaQL8B08up+ok7/rafP3Dp07l0T+zWddpRBJRHzAgcCrzcr7AScDf2+2S7Q0G559mTbowHm0ua9XOngutwNXAd8mOby4deA8vgesA+51u8HuEpFdUxBqm9p7Lqq6BrgN+BCoBzap6tOpiLU1bfyuHyIir4vIQhEZ5Jal5d87dOhc4t23TVlbKYhID5wrnMtU9fNmb98OXK2qO5rvFuVQns7Z7eB5xLNvynXkXERkDPCJqrZc5MEjHfw/6QIMA+5U1QOBLwBP+7Chw/8nvXGuqAcABcCuIvKLVMQbSxvnsRQoUtUhwF+BRxt2i3Ioz+fod/Bc4tk3Pl71nyXzAXTFuQHu8hjvrwaq3ccWnKbxScAhwFMR210LXJtp5xHPvplyLsAfcK7gqoGPgS+Bf2bgeewNVEdsdwTweIb+n5wG3B2x3VnAHel6HlG2r8ZJKpdWf++dOZeO7BvzmF7+AJL0QxXg/4Db49x+No0DaF2AD3CugBoGngZl4Hm0a990Ppdm5SPxdqC5U+cB/Bf4kfs8CNyaieeCk7n4LZyxBMEZQL84Xc/DrZAbbtQdgdPtJen0956Ac0nY33xapblIkMOA8cAbIrLMLbsO2AdAVWP2v2tq02y0pcPnEWtfVX0iWcG2oTPnkk46ex4XA3PFyev1AXBusgKNQ2f+Tl4VkX/hdGVsB17DuxQS8ZzHqcBFIrId+AoYq863ajr9vUMnzkVEDo+2b0f+5i3NhTHGmLCsHWg2xhjTflYpGGOMCbNKwRhjTJhVCsYYY8KsUjDGGBNmlYJJW25W0V8m8HhBEbkyUcdLNRG5TETOcp/PFpEvRaRnxPvTRURFpE8rx5gtIhOblZ0kIk+IyM4i8oKbz8jkKKsUTDrbHYhaKYhIXopj6ZTOxut+UZ+Hkym2wXu4CdxEZCdgFLCmjUPdh7P2eaSxwH3qJIV7Fji9M7GazGaVgklnNwPfd/PG3+rmkn9eRCqANwBE5FERCbk55AMNO7p58pe6icOebX5gEZngJhTr3qy8r4g8JCJL3MdhbnlQRO4RkUUi8oGIXBKxzy9EZLEb58yGCkBEtojIjSLyKk4Ss/NF5F33GLNE5G8i0lNEVotIV3ef3USkuuF1hKOApaq6PaLsPhq/wEcCL+LcTNZaXM8A+4lIvrvNd4Cf0JhD51HgzDb/Z0zWskrBpLNrgPdVdaiq/totGwFcr6r7u6/PU1U/UAxcIiJ7iEhfYBZwijqJw06LPKh7F2sJTp6or5p95nTgz6o6HDiFpum69wOOc2OYIiJdRWQgzhfzYao6FNhB45fqrsCbqnoQzh3MNwAHA8e4x0KdNMeLcFKDg3PV/pCqftMsrsOA5kkBVwF93QR1Z+Ckfm44x6hxqZPc7mHg5+6mpcDzbhwAbwLDMTnL+g5NplmsqqsjXl8iIie7z/sD+wJ9gRcatlPVzyK2H4+TYO+kKF+84Fw17y8STqC5W0S//eOqug3YJiKfAHsBRwN+YIm7T3ca1x7YgZOxEpyK5D8NsYjIg8AP3ffuwkkN/ihO6osJUeLKx8mR39zDOBXJQUDkWEFrcd0H3IpTAY7FyZkDgKruEJGvRaRnREVhcohVCibTfNHwRERG4nyJH6KqX4rIIqAbTnKwWPlb3gSGAoU4WUCb28k9XpMWhPvFui2iaAfO348Ac1T12ijH2qqNaaejpWkGQFVfFBGfiBwJ5Knqm1E2+wrn3Jqbh5ODaI6qfhtRmbUW14tAvogMAQ6l5RjDLsDWWPGa7GbdRyadbcZZWjCWXsAGt0LYD6drBuBl4EgRGQAgIt+N2Oc1nCvq+SJSEOWYTwOTG16IyNA2YnwWOFVE9mz4LBEpirLdYjem3u6g8SnN3v8/nCv4e2N8zgrgB80LVfVD4HrgjnjjcpPBPYCT3fQJVQ1XACKyB7AuRivK5ACrFEzaUtX1wIviLA5/a5RNngS6iMhy4CbgFXe/dUAAeFhEXgfub3bc/wFXAo9Hmb55CVAsIstF5G3gwjZifBv4DfC0G8e/cbp6mm+3Bvg9zmpYzwBvA5siNpkL9MapGKJZiLOcZ7QYZqrq++2M6z5gCBHjEK5ROOsxmxxlWVKNSRER6aGqW9yWwiM4qZofcd87FThRVce3sv8jwFWquiqJMT6Ms9DMymR9hklvNqZgTOoEReQnOGMDT+NOAxWRvwLHAye0sf81OFf7SakUxFnn4VGrEHKbtRSMMcaE2ZiCMcaYMKsUjDHGhFmlYIwxJswqBWOMMWFWKRhjjAn7/yeZbc4GmRpeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tracks_selectron_sel = tracks_selectron[tracks_selectron.energy > 2.4]\n",
    "tracks_selectron_fiducial_sel = tracks_selectron_fiducial[tracks_selectron_fiducial.energy > 2.4]\n",
    "tracks_selectron_1trackfid_sel = tracks_selectron_1trackfid[tracks_selectron_1trackfid.energy > 2.4]\n",
    "tracks_selectron_ovlp1trackfid_sel = tracks_selectron_ovlp1trackfid[tracks_selectron_ovlp1trackfid.energy > 2.4]\n",
    "tracks_selectron_ROIovlp1trackfid_sel = tracks_selectron_ROIovlp1trackfid[tracks_selectron_ROIovlp1trackfid.energy > 2.4]\n",
    "\n",
    "plt.hist(tracks_selectron_sel.energy, bins = 50, color='red', histtype='step', label = 'no cut')\n",
    "plt.hist(tracks_selectron_fiducial_sel.energy, bins = 50, color='blue', histtype='step', label = 'fiducial')\n",
    "plt.hist(tracks_selectron_1trackfid_sel.energy, bins = 50, color='orange', histtype='step', label = '+ 1-track', linestyle='dashed')\n",
    "plt.hist(tracks_selectron_ovlp1trackfid_sel.energy, bins = 50, color='green', histtype='step', label = '+ overlap', linestyle='dashed')\n",
    "#plt.hist(tracks_selectron_ROIovlp1trackfid_sel.energy, bins = 50, color='purple', histtype='step', label = '+ ROI', linestyle='dashed')\n",
    "\n",
    "\n",
    "plt.xlabel('track energy (MeV)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('Single Electron')\n",
    "plt.legend(loc=2);\n",
    "#plt.xlim((2.,2.6))\n",
    "#plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cut efficiency')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energybin = []\n",
    "events_energybin_notcuts_0nubb, events_energybin_notcuts_selectron= [], []\n",
    "events_energybin_fiducial_0nubb, events_energybin_fiducial_selectron= [], []\n",
    "events_energybin_all_0nubb, events_energybin_all_selectron= [], []\n",
    "\n",
    "cutefficiency_tot_0nubb, cutefficiency_tot_selectron= [], []\n",
    "cutefficiency_fiducial_0nubb, cutefficiency_fiducial_selectron= [], []\n",
    "\n",
    "nenergybin = 2.40\n",
    "steps = 0.01\n",
    "\n",
    "\n",
    "while nenergybin < 2.51:\n",
    "    energybin.append(nenergybin)\n",
    "    events_energybin_notcuts_0nubb.append(tracks_0nubb[(tracks_0nubb.energy > nenergybin) & \n",
    "                                (tracks_0nubb.energy < nenergybin+steps)].event.nunique())\n",
    "    events_energybin_fiducial_0nubb.append(tracks_0nubb_fiducial[(tracks_0nubb_fiducial.energy > nenergybin) & \n",
    "                                (tracks_0nubb_fiducial.energy < nenergybin+steps)].event.nunique())\n",
    "    events_energybin_all_0nubb.append(tracks_0nubb_ROIovlp1trackfid[(tracks_0nubb_ROIovlp1trackfid.energy > nenergybin) & \n",
    "                                (tracks_0nubb_ROIovlp1trackfid.energy < nenergybin+steps)].event.nunique())\n",
    "    events_energybin_notcuts_selectron.append(tracks_selectron[(tracks_selectron.energy > nenergybin) & \n",
    "                                (tracks_selectron.energy < nenergybin+steps)].event.nunique())\n",
    "    events_energybin_all_selectron.append(tracks_selectron_ROIovlp1trackfid[(tracks_selectron_ROIovlp1trackfid.energy > nenergybin) & \n",
    "                                (tracks_selectron_ROIovlp1trackfid.energy < nenergybin+steps)].event.nunique())\n",
    "    nenergybin = nenergybin + steps\n",
    "\n",
    "\n",
    "events_energybin_all_0nubb=np.array(events_energybin_all_0nubb, dtype=np.float)\n",
    "events_energybin_fiducial_0nubb=np.array(events_energybin_fiducial_0nubb, dtype=np.float)\n",
    "events_energybin_notcuts_0nubb=np.array(events_energybin_notcuts_0nubb, dtype=np.float)\n",
    "\n",
    "\n",
    "events_energybin_all_selectron=np.array(events_energybin_all_selectron, dtype=np.float)\n",
    "events_energybin_notcuts_selectron=np.array(events_energybin_notcuts_selectron, dtype=np.float)\n",
    "\n",
    "cutefficiency_tot_0nubb = events_energybin_all_0nubb/events_energybin_notcuts_0nubb\n",
    "cutefficiency_tot_selectron = events_energybin_all_selectron/events_energybin_notcuts_selectron\n",
    "cutefficiency_fiducial_0nubb = events_energybin_fiducial_0nubb/events_energybin_notcuts_0nubb\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(energybin,cutefficiency_tot_0nubb, 'bs', label='0nubb')\n",
    "ax.plot(energybin,cutefficiency_tot_selectron, 'g^', label='single electron')\n",
    "\n",
    "leg = ax.legend();\n",
    "plt.xlabel('energy in ROI (MeV)')\n",
    "plt.ylabel('cut efficiency')\n",
    "plt.title('Cut efficiency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'signal efficiency (0nubb)')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(1-cutefficiency_tot_selectron,cutefficiency_tot_0nubb, 'rs')\n",
    "plt.xlabel('background rejection (1-efficiency)')\n",
    "plt.ylabel('signal efficiency (0nubb)')\n",
    "#plt.title('Cut efficiency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 - Representing TRUE vs RECO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'm going to see if I can represent for an event the comparison between the reconstructed track and the true information from the MC simulation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "File(filename=/Users/halmamol/NEXT/files/NEXT100/beershebaFS/0nubb/hits/next100.0nubb.1.deconv.h5, title='', mode='r', root_uep='/', filters=Filters(complevel=4, complib='zlib', shuffle=True, bitshuffle=False, fletcher32=False, least_significant_digit=None))\n",
       "/ (RootGroup) ''\n",
       "/DECO (Group) ''\n",
       "/DECO/Events (Table(254988,), shuffle, zlib(4)) 'Deconvolved hits'\n",
       "  description := {\n",
       "  \"event\": Int64Col(shape=(), dflt=0, pos=0),\n",
       "  \"npeak\": UInt64Col(shape=(), dflt=0, pos=1),\n",
       "  \"X\": Float64Col(shape=(), dflt=0.0, pos=2),\n",
       "  \"Y\": Float64Col(shape=(), dflt=0.0, pos=3),\n",
       "  \"Z\": Float64Col(shape=(), dflt=0.0, pos=4),\n",
       "  \"E\": Float64Col(shape=(), dflt=0.0, pos=5)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (1365,)\n",
       "  autoindex := True\n",
       "  colindexes := {\n",
       "    \"event\": Index(6, medium, shuffle, zlib(1)).is_csi=False}\n",
       "/MC (Group) ''\n",
       "/MC/configuration (Table(41,), shuffle, zlib(4)) ''\n",
       "  description := {\n",
       "  \"param_key\": StringCol(itemsize=300, shape=(), dflt=b'', pos=0),\n",
       "  \"param_value\": StringCol(itemsize=300, shape=(), dflt=b'', pos=1),\n",
       "  \"file_index\": Int64Col(shape=(), dflt=0, pos=2)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (107,)\n",
       "/MC/event_mapping (Table(10,), shuffle, zlib(4)) ''\n",
       "  description := {\n",
       "  \"event_id\": Int64Col(shape=(), dflt=0, pos=0),\n",
       "  \"file_index\": Int64Col(shape=(), dflt=0, pos=1)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (4096,)\n",
       "/MC/hits (Table(5803,), shuffle, zlib(4)) ''\n",
       "  description := {\n",
       "  \"event_id\": Int64Col(shape=(), dflt=0, pos=0),\n",
       "  \"particle_id\": Int64Col(shape=(), dflt=0, pos=1),\n",
       "  \"hit_id\": Int64Col(shape=(), dflt=0, pos=2),\n",
       "  \"x\": Float32Col(shape=(), dflt=0.0, pos=3),\n",
       "  \"y\": Float32Col(shape=(), dflt=0.0, pos=4),\n",
       "  \"z\": Float32Col(shape=(), dflt=0.0, pos=5),\n",
       "  \"time\": Float32Col(shape=(), dflt=0.0, pos=6),\n",
       "  \"energy\": Float32Col(shape=(), dflt=0.0, pos=7),\n",
       "  \"label\": StringCol(itemsize=100, shape=(), dflt=b'', pos=8)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (455,)\n",
       "/MC/particles (Table(286,), shuffle, zlib(4)) ''\n",
       "  description := {\n",
       "  \"event_id\": Int64Col(shape=(), dflt=0, pos=0),\n",
       "  \"particle_id\": Int64Col(shape=(), dflt=0, pos=1),\n",
       "  \"particle_name\": StringCol(itemsize=100, shape=(), dflt=b'', pos=2),\n",
       "  \"primary\": BoolCol(shape=(), dflt=False, pos=3),\n",
       "  \"mother_id\": Int32Col(shape=(), dflt=0, pos=4),\n",
       "  \"initial_x\": Float32Col(shape=(), dflt=0.0, pos=5),\n",
       "  \"initial_y\": Float32Col(shape=(), dflt=0.0, pos=6),\n",
       "  \"initial_z\": Float32Col(shape=(), dflt=0.0, pos=7),\n",
       "  \"initial_t\": Float32Col(shape=(), dflt=0.0, pos=8),\n",
       "  \"final_x\": Float32Col(shape=(), dflt=0.0, pos=9),\n",
       "  \"final_y\": Float32Col(shape=(), dflt=0.0, pos=10),\n",
       "  \"final_z\": Float32Col(shape=(), dflt=0.0, pos=11),\n",
       "  \"final_t\": Float32Col(shape=(), dflt=0.0, pos=12),\n",
       "  \"initial_volume\": StringCol(itemsize=100, shape=(), dflt=b'', pos=13),\n",
       "  \"final_volume\": StringCol(itemsize=100, shape=(), dflt=b'', pos=14),\n",
       "  \"initial_momentum_x\": Float32Col(shape=(), dflt=0.0, pos=15),\n",
       "  \"initial_momentum_y\": Float32Col(shape=(), dflt=0.0, pos=16),\n",
       "  \"initial_momentum_z\": Float32Col(shape=(), dflt=0.0, pos=17),\n",
       "  \"final_momentum_x\": Float32Col(shape=(), dflt=0.0, pos=18),\n",
       "  \"final_momentum_y\": Float32Col(shape=(), dflt=0.0, pos=19),\n",
       "  \"final_momentum_z\": Float32Col(shape=(), dflt=0.0, pos=20),\n",
       "  \"kin_energy\": Float32Col(shape=(), dflt=0.0, pos=21),\n",
       "  \"length\": Float32Col(shape=(), dflt=0.0, pos=22),\n",
       "  \"creator_proc\": StringCol(itemsize=100, shape=(), dflt=b'', pos=23),\n",
       "  \"final_proc\": StringCol(itemsize=100, shape=(), dflt=b'', pos=24)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (112,)\n",
       "/MC/sns_positions (Table(3644,), shuffle, zlib(4)) ''\n",
       "  description := {\n",
       "  \"sensor_id\": UInt32Col(shape=(), dflt=0, pos=0),\n",
       "  \"sensor_name\": StringCol(itemsize=100, shape=(), dflt=b'', pos=1),\n",
       "  \"x\": Float32Col(shape=(), dflt=0.0, pos=2),\n",
       "  \"y\": Float32Col(shape=(), dflt=0.0, pos=3),\n",
       "  \"z\": Float32Col(shape=(), dflt=0.0, pos=4)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (564,)\n",
       "/MC/sns_response (Table(1505175,), shuffle, zlib(4)) ''\n",
       "  description := {\n",
       "  \"event_id\": Int32Col(shape=(), dflt=0, pos=0),\n",
       "  \"sensor_id\": UInt32Col(shape=(), dflt=0, pos=1),\n",
       "  \"time_bin\": UInt64Col(shape=(), dflt=0, pos=2),\n",
       "  \"charge\": UInt32Col(shape=(), dflt=0, pos=3)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (3276,)\n",
       "/Run (Group) ''\n",
       "/Run/events (Table(10,), shuffle, zlib(4)) 'event info table'\n",
       "  description := {\n",
       "  \"evt_number\": Int32Col(shape=(), dflt=0, pos=0),\n",
       "  \"timestamp\": UInt64Col(shape=(), dflt=0, pos=1)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (5461,)\n",
       "/Run/runInfo (Table(10,), shuffle, zlib(4)) 'run info table'\n",
       "  description := {\n",
       "  \"run_number\": Int32Col(shape=(), dflt=0, pos=0)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (16384,)\n",
       "/Summary (Group) ''\n",
       "/Summary/Events (Table(10,), shuffle, zlib(4)) 'Event summary information'\n",
       "  description := {\n",
       "  \"event\": Int32Col(shape=(), dflt=0, pos=0),\n",
       "  \"evt_energy\": Float64Col(shape=(), dflt=0.0, pos=1),\n",
       "  \"evt_charge\": Float64Col(shape=(), dflt=0.0, pos=2),\n",
       "  \"evt_ntrks\": Int64Col(shape=(), dflt=0, pos=3),\n",
       "  \"evt_nhits\": Int64Col(shape=(), dflt=0, pos=4),\n",
       "  \"evt_x_avg\": Float64Col(shape=(), dflt=0.0, pos=5),\n",
       "  \"evt_y_avg\": Float64Col(shape=(), dflt=0.0, pos=6),\n",
       "  \"evt_z_avg\": Float64Col(shape=(), dflt=0.0, pos=7),\n",
       "  \"evt_r_avg\": Float64Col(shape=(), dflt=0.0, pos=8),\n",
       "  \"evt_x_min\": Float64Col(shape=(), dflt=0.0, pos=9),\n",
       "  \"evt_y_min\": Float64Col(shape=(), dflt=0.0, pos=10),\n",
       "  \"evt_z_min\": Float64Col(shape=(), dflt=0.0, pos=11),\n",
       "  \"evt_r_min\": Float64Col(shape=(), dflt=0.0, pos=12),\n",
       "  \"evt_x_max\": Float64Col(shape=(), dflt=0.0, pos=13),\n",
       "  \"evt_y_max\": Float64Col(shape=(), dflt=0.0, pos=14),\n",
       "  \"evt_z_max\": Float64Col(shape=(), dflt=0.0, pos=15),\n",
       "  \"evt_r_max\": Float64Col(shape=(), dflt=0.0, pos=16),\n",
       "  \"evt_out_of_map\": BoolCol(shape=(), dflt=False, pos=17)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (492,)\n",
       "  autoindex := True\n",
       "  colindexes := {\n",
       "    \"event\": Index(6, medium, shuffle, zlib(1)).is_csi=False}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#0nubb files\n",
    "hits_list_0nubb = []\n",
    "filepath_0nubb_hits = filepath+'0nubb/hits/'\n",
    "\n",
    "\n",
    "filename_0nubb_hits = filepath_0nubb_hits+f'next100.0nubb.1.deconv.h5'\n",
    "h5file = tb.open_file(filename_0nubb_hits)\n",
    "h5file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The true information is stored in MC, more concretely inside the 'particles' group. To check the true position is going to be more complicated than to retrieve the info from a variable. I'm going to use the information from \"plot_blobs_with_true_extremes_beersheba.py\" as a baseline to construct the true information. I'll start with 0nubb, and I'll extrapolate the information to SE later on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['event_id', 'particle_id', 'particle_name', 'primary', 'mother_id',\n",
       "       'initial_x', 'initial_y', 'initial_z', 'initial_t', 'final_x',\n",
       "       'final_y', 'final_z', 'final_t', 'initial_volume', 'final_volume',\n",
       "       'initial_momentum_x', 'initial_momentum_y', 'initial_momentum_z',\n",
       "       'final_momentum_x', 'final_momentum_y', 'final_momentum_z',\n",
       "       'kin_energy', 'length', 'creator_proc', 'final_proc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#0nubb files\n",
    "true_list_0nubb = []\n",
    "filepath_0nubb_true = filepath+'0nubb/hits/'\n",
    "\n",
    "#loop over all the files, to read 'HITS' information\n",
    "for nfile in range(1,max0nubbf):\n",
    "    \n",
    "    filename_0nubb_true = filepath_0nubb_true+f'next100.0nubb.{nfile}.deconv.h5'\n",
    "\n",
    "    try:\n",
    "        readfile_true_0nubb = pd.read_hdf(filename_0nubb_true, 'MC/particles')\n",
    "    except:\n",
    "        print('File {} not good'.format(filename_0nubb_true))\n",
    "        continue\n",
    "        \n",
    "    true_list_0nubb.append(readfile_true_0nubb)\n",
    "    \n",
    "#'concat' command means: 'concatenate pandas objects along a particular \n",
    "# axis with optional set logic along the other axes.' -> following line \n",
    "# is needed to keep an order between the files\n",
    "\n",
    "true_0nubb = pd.concat([dt for dt in true_list_0nubb]) \n",
    "true_0nubb.columns  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inside of MC/particles you can find all the information about the created partiles. Right now, I need to check the primary particles created in the simulation: the two electrons. To do so, I need to check the information about the 'primary' variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "      <th>particle_id</th>\n",
       "      <th>particle_name</th>\n",
       "      <th>primary</th>\n",
       "      <th>mother_id</th>\n",
       "      <th>initial_x</th>\n",
       "      <th>initial_y</th>\n",
       "      <th>initial_z</th>\n",
       "      <th>initial_t</th>\n",
       "      <th>final_x</th>\n",
       "      <th>...</th>\n",
       "      <th>initial_momentum_x</th>\n",
       "      <th>initial_momentum_y</th>\n",
       "      <th>initial_momentum_z</th>\n",
       "      <th>final_momentum_x</th>\n",
       "      <th>final_momentum_y</th>\n",
       "      <th>final_momentum_z</th>\n",
       "      <th>kin_energy</th>\n",
       "      <th>length</th>\n",
       "      <th>creator_proc</th>\n",
       "      <th>final_proc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>e-</td>\n",
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       "      <td>-239.914810</td>\n",
       "      <td>-238.046280</td>\n",
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       "      <td>-200.825760</td>\n",
       "      <td>...</td>\n",
       "      <td>2.549860</td>\n",
       "      <td>0.109969</td>\n",
       "      <td>0.684162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.180298</td>\n",
       "      <td>235.106247</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-239.914810</td>\n",
       "      <td>-238.046280</td>\n",
       "      <td>316.365936</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-240.744125</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013486</td>\n",
       "      <td>-0.226322</td>\n",
       "      <td>-0.556109</td>\n",
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       "      <td>-0.0</td>\n",
       "      <td>0.277532</td>\n",
       "      <td>17.443966</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>452.146729</td>\n",
       "      <td>-166.560150</td>\n",
       "      <td>96.130058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>457.095093</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.406067</td>\n",
       "      <td>1.022711</td>\n",
       "      <td>0.653254</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.866930</td>\n",
       "      <td>63.153664</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>452.146729</td>\n",
       "      <td>-166.560150</td>\n",
       "      <td>96.130058</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.590900</td>\n",
       "      <td>56.679573</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>157.780060</td>\n",
       "      <td>-127.197853</td>\n",
       "      <td>787.558777</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.998825</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.259671</td>\n",
       "      <td>1.266991</td>\n",
       "      <td>-0.398611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.935620</td>\n",
       "      <td>58.765797</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>9977</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>86.457176</td>\n",
       "      <td>212.692535</td>\n",
       "      <td>616.078308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.176163</td>\n",
       "      <td>...</td>\n",
       "      <td>0.299439</td>\n",
       "      <td>-0.555810</td>\n",
       "      <td>-0.178559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.320621</td>\n",
       "      <td>22.073187</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>9978</td>\n",
       "      <td>2</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-132.795700</td>\n",
       "      <td>-376.634216</td>\n",
       "      <td>468.702148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-122.611801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.832997</td>\n",
       "      <td>-0.515719</td>\n",
       "      <td>2.435654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.163582</td>\n",
       "      <td>155.077499</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>9978</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-132.795700</td>\n",
       "      <td>-376.634216</td>\n",
       "      <td>468.702148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-132.453995</td>\n",
       "      <td>...</td>\n",
       "      <td>0.608110</td>\n",
       "      <td>-0.092649</td>\n",
       "      <td>0.094456</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.294248</td>\n",
       "      <td>19.843086</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>9979</td>\n",
       "      <td>2</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-183.630951</td>\n",
       "      <td>2.984318</td>\n",
       "      <td>860.086365</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-171.902847</td>\n",
       "      <td>...</td>\n",
       "      <td>0.471986</td>\n",
       "      <td>0.953077</td>\n",
       "      <td>0.835562</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.934827</td>\n",
       "      <td>77.938103</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>9979</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-183.630951</td>\n",
       "      <td>2.984318</td>\n",
       "      <td>860.086365</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-241.135818</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.673994</td>\n",
       "      <td>-0.850358</td>\n",
       "      <td>-0.592184</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.523003</td>\n",
       "      <td>156.851212</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19598 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     event_id  particle_id particle_name  primary  mother_id   initial_x  \\\n",
       "9           0            2            e-     True          0 -239.914810   \n",
       "10          0            1            e-     True          0 -239.914810   \n",
       "14          1            2            e-     True          0  452.146729   \n",
       "16          1            1            e-     True          0  452.146729   \n",
       "19          2            2            e-     True          0  157.780060   \n",
       "..        ...          ...           ...      ...        ...         ...   \n",
       "223      9977            1            e-     True          0   86.457176   \n",
       "259      9978            2            e-     True          0 -132.795700   \n",
       "260      9978            1            e-     True          0 -132.795700   \n",
       "294      9979            2            e-     True          0 -183.630951   \n",
       "297      9979            1            e-     True          0 -183.630951   \n",
       "\n",
       "      initial_y   initial_z  initial_t     final_x  ...  initial_momentum_x  \\\n",
       "9   -238.046280  316.365936        0.0 -200.825760  ...            2.549860   \n",
       "10  -238.046280  316.365936        0.0 -240.744125  ...           -0.013486   \n",
       "14  -166.560150   96.130058        0.0  457.095093  ...           -0.406067   \n",
       "16  -166.560150   96.130058        0.0  469.336273  ...           -0.455509   \n",
       "19  -127.197853  787.558777        0.0  165.998825  ...           -0.259671   \n",
       "..          ...         ...        ...         ...  ...                 ...   \n",
       "223  212.692535  616.078308        0.0   83.176163  ...            0.299439   \n",
       "259 -376.634216  468.702148        0.0 -122.611801  ...            0.832997   \n",
       "260 -376.634216  468.702148        0.0 -132.453995  ...            0.608110   \n",
       "294    2.984318  860.086365        0.0 -171.902847  ...            0.471986   \n",
       "297    2.984318  860.086365        0.0 -241.135818  ...           -1.673994   \n",
       "\n",
       "     initial_momentum_y  initial_momentum_z final_momentum_x final_momentum_y  \\\n",
       "9              0.109969            0.684162              0.0              0.0   \n",
       "10            -0.226322           -0.556109             -0.0              0.0   \n",
       "14             1.022711            0.653254             -0.0             -0.0   \n",
       "16             0.328598           -1.959947             -0.0              0.0   \n",
       "19             1.266991           -0.398611              0.0              0.0   \n",
       "..                  ...                 ...              ...              ...   \n",
       "223           -0.555810           -0.178559              0.0             -0.0   \n",
       "259           -0.515719            2.435654              0.0              0.0   \n",
       "260           -0.092649            0.094456              0.0              0.0   \n",
       "294            0.953077            0.835562             -0.0             -0.0   \n",
       "297           -0.850358           -0.592184             -0.0             -0.0   \n",
       "\n",
       "     final_momentum_z  kin_energy      length  creator_proc     final_proc  \n",
       "9                -0.0    2.180298  235.106247          none  Scintillation  \n",
       "10               -0.0    0.277532   17.443966          none  Scintillation  \n",
       "14                0.0    0.866930   63.153664          none  Scintillation  \n",
       "16                0.0    1.590900   56.679573          none  Scintillation  \n",
       "19                0.0    0.935620   58.765797          none  Scintillation  \n",
       "..                ...         ...         ...           ...            ...  \n",
       "223               0.0    0.320621   22.073187          none  Scintillation  \n",
       "259               0.0    2.163582  155.077499          none  Scintillation  \n",
       "260               0.0    0.294248   19.843086          none  Scintillation  \n",
       "294               0.0    0.934827   77.938103          none  Scintillation  \n",
       "297               0.0    1.523003  156.851212          none  Scintillation  \n",
       "\n",
       "[19598 rows x 25 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_0nubb_primary = true_0nubb[true_0nubb.primary == True]\n",
    "true_0nubb_primary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The primary particles are separated by 'particle_id' 1 and 2, and this information will allow us to differenciate between blob1 and/or 2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_0nubb_primary_part1 = true_0nubb_primary[true_0nubb_primary.particle_id == 1]\n",
    "true_0nubb_primary_part2 = true_0nubb_primary[true_0nubb_primary.particle_id == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "      <th>particle_id</th>\n",
       "      <th>particle_name</th>\n",
       "      <th>primary</th>\n",
       "      <th>mother_id</th>\n",
       "      <th>initial_x</th>\n",
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       "      <th>initial_z</th>\n",
       "      <th>initial_t</th>\n",
       "      <th>final_x</th>\n",
       "      <th>...</th>\n",
       "      <th>initial_momentum_x</th>\n",
       "      <th>initial_momentum_y</th>\n",
       "      <th>initial_momentum_z</th>\n",
       "      <th>final_momentum_x</th>\n",
       "      <th>final_momentum_y</th>\n",
       "      <th>final_momentum_z</th>\n",
       "      <th>kin_energy</th>\n",
       "      <th>length</th>\n",
       "      <th>creator_proc</th>\n",
       "      <th>final_proc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>14743383</td>\n",
       "      <td>gamma</td>\n",
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       "      <td>-256.607605</td>\n",
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       "      <td>-198.084488</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.001354</td>\n",
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       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.003986</td>\n",
       "      <td>0.231452</td>\n",
       "      <td>eBrem</td>\n",
       "      <td>phot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14854918</td>\n",
       "      <td>e-</td>\n",
       "      <td>False</td>\n",
       "      <td>14743383</td>\n",
       "      <td>-198.084488</td>\n",
       "      <td>-256.718475</td>\n",
       "      <td>311.067047</td>\n",
       "      <td>0.219402</td>\n",
       "      <td>-198.084457</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006188</td>\n",
       "      <td>0.003036</td>\n",
       "      <td>0.001210</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>phot</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>14854917</td>\n",
       "      <td>e-</td>\n",
       "      <td>False</td>\n",
       "      <td>14743383</td>\n",
       "      <td>-198.084488</td>\n",
       "      <td>-256.718475</td>\n",
       "      <td>311.067047</td>\n",
       "      <td>0.219402</td>\n",
       "      <td>-198.084488</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001329</td>\n",
       "      <td>0.006434</td>\n",
       "      <td>0.002097</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>phot</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>14854916</td>\n",
       "      <td>e-</td>\n",
       "      <td>False</td>\n",
       "      <td>14743383</td>\n",
       "      <td>-198.084488</td>\n",
       "      <td>-256.718475</td>\n",
       "      <td>311.067047</td>\n",
       "      <td>0.219402</td>\n",
       "      <td>-198.084488</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000592</td>\n",
       "      <td>-0.006582</td>\n",
       "      <td>-0.001970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>phot</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>14854915</td>\n",
       "      <td>e-</td>\n",
       "      <td>False</td>\n",
       "      <td>14743383</td>\n",
       "      <td>-198.084488</td>\n",
       "      <td>-256.718475</td>\n",
       "      <td>311.067047</td>\n",
       "      <td>0.219402</td>\n",
       "      <td>-198.084656</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015960</td>\n",
       "      <td>0.015451</td>\n",
       "      <td>0.008377</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000551</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>phot</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>14854914</td>\n",
       "      <td>e-</td>\n",
       "      <td>False</td>\n",
       "      <td>14743383</td>\n",
       "      <td>-198.084488</td>\n",
       "      <td>-256.718475</td>\n",
       "      <td>311.067047</td>\n",
       "      <td>0.219402</td>\n",
       "      <td>-198.084427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007276</td>\n",
       "      <td>0.001941</td>\n",
       "      <td>0.010619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>phot</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>14854913</td>\n",
       "      <td>e-</td>\n",
       "      <td>False</td>\n",
       "      <td>14743383</td>\n",
       "      <td>-198.084488</td>\n",
       "      <td>-256.718475</td>\n",
       "      <td>311.067047</td>\n",
       "      <td>0.219402</td>\n",
       "      <td>-198.085205</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038018</td>\n",
       "      <td>-0.038454</td>\n",
       "      <td>-0.014558</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003059</td>\n",
       "      <td>0.013139</td>\n",
       "      <td>phot</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>29046889</td>\n",
       "      <td>e-</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>-196.871140</td>\n",
       "      <td>-288.876251</td>\n",
       "      <td>321.973602</td>\n",
       "      <td>0.430943</td>\n",
       "      <td>-197.021393</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.211537</td>\n",
       "      <td>-0.017156</td>\n",
       "      <td>0.079031</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.047936</td>\n",
       "      <td>1.375517</td>\n",
       "      <td>eIoni</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>56290415</td>\n",
       "      <td>e-</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>-199.225845</td>\n",
       "      <td>-347.134033</td>\n",
       "      <td>334.436646</td>\n",
       "      <td>0.793654</td>\n",
       "      <td>-199.606995</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204526</td>\n",
       "      <td>0.016255</td>\n",
       "      <td>-0.331930</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.131957</td>\n",
       "      <td>5.372118</td>\n",
       "      <td>eIoni</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-239.914810</td>\n",
       "      <td>-238.046280</td>\n",
       "      <td>316.365936</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-200.825760</td>\n",
       "      <td>...</td>\n",
       "      <td>2.549860</td>\n",
       "      <td>0.109969</td>\n",
       "      <td>0.684162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.180298</td>\n",
       "      <td>235.106247</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-239.914810</td>\n",
       "      <td>-238.046280</td>\n",
       "      <td>316.365936</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-240.744125</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013486</td>\n",
       "      <td>-0.226322</td>\n",
       "      <td>-0.556109</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.277532</td>\n",
       "      <td>17.443966</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    event_id  particle_id particle_name  primary  mother_id   initial_x  \\\n",
       "0          0     14743383         gamma    False          2 -198.271820   \n",
       "1          0     14854918            e-    False   14743383 -198.084488   \n",
       "2          0     14854917            e-    False   14743383 -198.084488   \n",
       "3          0     14854916            e-    False   14743383 -198.084488   \n",
       "4          0     14854915            e-    False   14743383 -198.084488   \n",
       "5          0     14854914            e-    False   14743383 -198.084488   \n",
       "6          0     14854913            e-    False   14743383 -198.084488   \n",
       "7          0     29046889            e-    False          2 -196.871140   \n",
       "8          0     56290415            e-    False          2 -199.225845   \n",
       "9          0            2            e-     True          0 -239.914810   \n",
       "10         0            1            e-     True          0 -239.914810   \n",
       "\n",
       "     initial_y   initial_z  initial_t     final_x  ...  initial_momentum_x  \\\n",
       "0  -256.607605  311.145691   0.218630 -198.084488  ...            0.003226   \n",
       "1  -256.718475  311.067047   0.219402 -198.084457  ...            0.006188   \n",
       "2  -256.718475  311.067047   0.219402 -198.084488  ...           -0.001329   \n",
       "3  -256.718475  311.067047   0.219402 -198.084488  ...           -0.000592   \n",
       "4  -256.718475  311.067047   0.219402 -198.084656  ...           -0.015960   \n",
       "5  -256.718475  311.067047   0.219402 -198.084427  ...            0.007276   \n",
       "6  -256.718475  311.067047   0.219402 -198.085205  ...           -0.038018   \n",
       "7  -288.876251  321.973602   0.430943 -197.021393  ...           -0.211537   \n",
       "8  -347.134033  334.436646   0.793654 -199.606995  ...            0.204526   \n",
       "9  -238.046280  316.365936   0.000000 -200.825760  ...            2.549860   \n",
       "10 -238.046280  316.365936   0.000000 -240.744125  ...           -0.013486   \n",
       "\n",
       "    initial_momentum_y  initial_momentum_z final_momentum_x final_momentum_y  \\\n",
       "0            -0.001909           -0.001354              0.0             -0.0   \n",
       "1             0.003036            0.001210             -0.0              0.0   \n",
       "2             0.006434            0.002097             -0.0             -0.0   \n",
       "3            -0.006582           -0.001970              0.0              0.0   \n",
       "4             0.015451            0.008377             -0.0             -0.0   \n",
       "5             0.001941            0.010619              0.0              0.0   \n",
       "6            -0.038454           -0.014558              0.0              0.0   \n",
       "7            -0.017156            0.079031             -0.0              0.0   \n",
       "8             0.016255           -0.331930              0.0             -0.0   \n",
       "9             0.109969            0.684162              0.0              0.0   \n",
       "10           -0.226322           -0.556109             -0.0              0.0   \n",
       "\n",
       "    final_momentum_z  kin_energy      length  creator_proc     final_proc  \n",
       "0               -0.0    0.003986    0.231452         eBrem           phot  \n",
       "1                0.0    0.000048    0.000024          phot  Scintillation  \n",
       "2               -0.0    0.000047    0.000050          phot  Scintillation  \n",
       "3                0.0    0.000047    0.000024          phot  Scintillation  \n",
       "4               -0.0    0.000551    0.000985          phot  Scintillation  \n",
       "5                0.0    0.000166    0.000317          phot  Scintillation  \n",
       "6                0.0    0.003059    0.013139          phot  Scintillation  \n",
       "7                0.0    0.047936    1.375517         eIoni  Scintillation  \n",
       "8               -0.0    0.131957    5.372118         eIoni  Scintillation  \n",
       "9               -0.0    2.180298  235.106247          none  Scintillation  \n",
       "10              -0.0    0.277532   17.443966          none  Scintillation  \n",
       "\n",
       "[11 rows x 25 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_0nubb_event0 = true_0nubb[true_0nubb.event_id == 0]\n",
    "true_0nubb_event0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>4465 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    event  trackID    energy      length  numb_of_voxels  numb_of_hits  \\\n",
       "0       0        0  2.475314  180.973722             739         27246   \n",
       "0       2        0  2.445390  102.110604             714         25383   \n",
       "0       4        0  2.489506  133.095559             742         26827   \n",
       "0       6        0  2.474666  120.271862             705         25458   \n",
       "0       7        0  2.463006  134.762404             745         25910   \n",
       "..    ...      ...       ...         ...             ...           ...   \n",
       "0    9967        0  2.455036  107.755452             730         24909   \n",
       "0    9968        0  2.468142  108.756741             589         21463   \n",
       "0    9970        0  2.468087   87.366642             730         25580   \n",
       "0    9973        0  2.485147  105.241057             685         25228   \n",
       "0    9975        0  2.465953  113.156646             626         22246   \n",
       "\n",
       "    numb_of_tracks  x_min  y_min        z_min  ...      blob1_z     blob2_x  \\\n",
       "0                4 -259.5 -367.5   308.425500  ...   333.410510 -254.025000   \n",
       "0                1   99.5 -143.5   782.350812  ...   831.606715  162.409091   \n",
       "0                1 -452.5  -77.5   641.252813  ...   706.292077 -415.750000   \n",
       "0                2   23.5 -413.5   825.211250  ...   888.536990   34.888889   \n",
       "0                2 -306.5  149.5   402.317125  ...   471.493537 -294.576923   \n",
       "..             ...    ...    ...          ...  ...          ...         ...   \n",
       "0                2  213.5 -209.5   471.402625  ...   474.246864  237.673913   \n",
       "0                3 -183.5  -69.5   204.451219  ...   224.712634 -170.107143   \n",
       "0                2 -290.5    0.5  1024.213125  ...  1028.447045 -273.676471   \n",
       "0                1    3.5 -459.5   792.247750  ...   829.549634   16.961538   \n",
       "0                2  342.5 -102.5   537.364125  ...   547.167441  415.000000   \n",
       "\n",
       "       blob2_y      blob2_z    eblob1    eblob2  ovlp_blob_energy  vox_size_x  \\\n",
       "0  -226.000000   316.151619  0.373302  0.225996                 0    4.961538   \n",
       "0  -134.000000   788.508176  0.749118  0.455082                 0    4.833333   \n",
       "0   -52.666667   649.386413  0.658333  0.657268                 0    4.909091   \n",
       "0  -379.000000   831.376995  0.893941  0.536796                 0    4.950000   \n",
       "0   172.444444   408.390072  0.543604  0.185250                 0    4.812500   \n",
       "..         ...          ...       ...       ...               ...         ...   \n",
       "0  -158.921053   532.475840  0.452511  0.388492                 0    4.846154   \n",
       "0   -18.941176   229.502284  0.864094  0.851099                 0    4.967742   \n",
       "0     6.590909  1070.311348  0.479654  0.270002                 0    4.800000   \n",
       "0  -444.000000   810.160705  0.776549  0.611494                 0    4.882353   \n",
       "0   -41.923077   560.333897  1.052334  0.545863                 0    5.000000   \n",
       "\n",
       "    vox_size_y  vox_size_z  \n",
       "0     4.897436    4.904922  \n",
       "0     4.727273    4.940760  \n",
       "0     4.857143    4.744351  \n",
       "0     4.750000    4.900092  \n",
       "0     4.850000    4.953477  \n",
       "..         ...         ...  \n",
       "0     4.857143    4.804290  \n",
       "0     4.947368    4.385363  \n",
       "0     4.785714    4.821277  \n",
       "0     4.769231    4.835000  \n",
       "0     4.961538    4.423828  \n",
       "\n",
       "[4465 rows x 35 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracks_0nubb_ROIovlp1trackfid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'm going to do a loop over all the events in both reco and true, and I'm going to obtain the difference for each of them. \n",
    "\n",
    "Several things need to be taken into account at this stage:\n",
    "- After all the selection process, not all the events are contained in 'tracks_0nubb_ROIovlp1trackfid', need to select only the events in the loop that are included in 'tracks_0nubb_ROIovlp1trackfid'\n",
    "- For 0nubb, the true end points of the track are the variables **final_x/y/z** from the primary particles. **initial_x/y/z** correspond to the original position of the desintegration. \n",
    "- The true/reco information from the tables are given in terms of arrays. First, in order to get the value from a variable, it's necessary to ask for \"values\". Later on, when doing the comparison, it's necessary to force the term to turn into float before assign them into the 'diff' element. \n",
    "- Last but definately, not least, what it's defined as blob1 or blob2 is not necessarily the information about particle 1 or 2 respectively. Blobs are tagged as the deposited energy in the detector. So when the reco vs true comparison is made, it's important to re-define what's 1 or 2 for the blobs. In the following boxes I've done some tests in order to see how I can tag each of the events. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_rt_11, d_rt_12, d_rr_12, d_tt_12, D_tt_12, x_t_1, y_t_1, x_t_2, y_t_2, r_t_1, r_t_2, z_t_1, z_t_2 = [], [], [], [], [], [], [],[], [], [],[], [], []\n",
    "\n",
    "for nevt in range(1,true_0nubb.event_id.nunique()):\n",
    "    \n",
    "    if nevt not in tracks_0nubb_ROIovlp1trackfid.event.unique():\n",
    "        continue\n",
    "        \n",
    "    true_0nubb_x_p1 = float(true_0nubb_primary_part1[true_0nubb_primary_part1.event_id == nevt].final_x.values)\n",
    "    true_0nubb_y_p1 = float(true_0nubb_primary_part1[true_0nubb_primary_part1.event_id == nevt].final_y.values)\n",
    "    true_0nubb_z_p1 = float(true_0nubb_primary_part1[true_0nubb_primary_part1.event_id == nevt].final_z.values)\n",
    "\n",
    "    true_0nubb_x_p2 = float(true_0nubb_primary_part2[true_0nubb_primary_part2.event_id == nevt].final_x.values)\n",
    "    true_0nubb_y_p2 = float(true_0nubb_primary_part2[true_0nubb_primary_part2.event_id == nevt].final_y.values)\n",
    "    true_0nubb_z_p2 = float(true_0nubb_primary_part2[true_0nubb_primary_part2.event_id == nevt].final_z.values)\n",
    "    \n",
    "    true_0nubb_r_p1 = math.sqrt(true_0nubb_x_p1*true_0nubb_x_p1+true_0nubb_y_p1*true_0nubb_y_p1)\n",
    "    true_0nubb_r_p2 = math.sqrt(true_0nubb_x_p2*true_0nubb_x_p2+true_0nubb_y_p2*true_0nubb_y_p2)\n",
    "\n",
    "    reco_0nubb_x_blob1 = float(tracks_0nubb_ROIovlp1trackfid[tracks_0nubb_ROIovlp1trackfid.event == nevt].blob1_x.values)\n",
    "    reco_0nubb_y_blob1 = float(tracks_0nubb_ROIovlp1trackfid[tracks_0nubb_ROIovlp1trackfid.event == nevt].blob1_y.values)\n",
    "    reco_0nubb_z_blob1 = float(tracks_0nubb_ROIovlp1trackfid[tracks_0nubb_ROIovlp1trackfid.event == nevt].blob1_z.values)\n",
    "    \n",
    "    reco_0nubb_x_blob2 = float(tracks_0nubb_ROIovlp1trackfid[tracks_0nubb_ROIovlp1trackfid.event == nevt].blob2_x.values)\n",
    "    reco_0nubb_y_blob2 = float(tracks_0nubb_ROIovlp1trackfid[tracks_0nubb_ROIovlp1trackfid.event == nevt].blob2_y.values)\n",
    "    reco_0nubb_z_blob2 = float(tracks_0nubb_ROIovlp1trackfid[tracks_0nubb_ROIovlp1trackfid.event == nevt].blob2_z.values)\n",
    "    \n",
    "    reco_0nubb_r_blob1 = math.sqrt(reco_0nubb_x_blob1*reco_0nubb_x_blob1+reco_0nubb_y_blob1*reco_0nubb_y_blob1)\n",
    "    reco_0nubb_r_blob2 = math.sqrt(reco_0nubb_x_blob2*reco_0nubb_x_blob2+reco_0nubb_y_blob2*reco_0nubb_y_blob2)\n",
    "    \n",
    "   \n",
    "    d_tt_12.append(math.sqrt((true_0nubb_x_p2-true_0nubb_x_p1)**2+(true_0nubb_y_p2-true_0nubb_y_p1)**2+(true_0nubb_z_p2-true_0nubb_z_p1)**2))\n",
    "    d_rr_12.append(math.sqrt((reco_0nubb_x_blob1-reco_0nubb_x_blob2)**2+(reco_0nubb_y_blob1-reco_0nubb_y_blob2)**2+(reco_0nubb_z_blob1-reco_0nubb_z_blob2)**2))\n",
    "    \n",
    "    x_t_1.append(true_0nubb_x_p1)\n",
    "    x_t_2.append(true_0nubb_x_p2)\n",
    "    y_t_1.append(true_0nubb_y_p1)\n",
    "    y_t_2.append(true_0nubb_y_p2)\n",
    "    r_t_1.append(true_0nubb_r_p1)\n",
    "    r_t_2.append(true_0nubb_r_p2)\n",
    "    z_t_1.append(true_0nubb_z_p1)\n",
    "    z_t_2.append(true_0nubb_z_p2)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NLmvoZt6GdJ5zTqNrMAkDA0n9hyfWmyrLtBVxUDBrclmjjxYurO15CxPlCo81a5JhsFk/npt9RnB3d/k0HVnZVpuVZzSbNbmsGb6NWiwsa+Wythkx9fIHNelM50atvGZmzWCMtNYdHVt+5Zf8Ulb/uNrY166t8eewqnCaC7Mml5UdtOJFasZIa716dZmkc0O3AzFin4svrvC8lksOCmZNLqtJZtLLR7a3p4EhRq57MEquiaw29qw1kZvdeLOt5p2bj8yaXFYHbtZM57GONdQatHqA/r5I0juUWc6yrPb2JH6MY03kZjfVPpc7ms2a3KQ7mtM3trVVZyGZrPNmpcJuau5oNrOmlXYod2gACbqUrDwzb9vlSLDttrU9vddEbg7uUzCbokbMdE47lFcP/ZLvArqoduvHMcdU+YBWV75TMGtyWS2oWU01te4YbfZ1HMZrqo22clAwm6KyRh/VumM0ax2HqTZKp2CqzWh2UDBrclkpnZcuLV++YEHt6gLZ6zhMtVE6BcNH6za7mgUFSZdIWi/prqKy8yTdK2m5pJ9IelFa3iHpSUnL0se3a1UvM6utoekNJE1YhVGpZ5zR2HpZZWp5p3ApcNSwsuuB/SJif+BPwNlF2/4cEXPSx2k1rJdZS8ia6VxrAwNbhqR2dY09tcHypWZBISJuAh4eVnZdRGxOX94KzKrV+c1aRVZK6qw5B1OtY7TRptpoq0b2KXwI+HnR6z0l3S7pN5Le0KhKmTWbrBnNWeVTrWO00abaaKuGBAVJ/wJsBi5Pi9YCL42IA4FPAFdI2jFj3y5JfZL6NmzYUJ8Km+VYW1v58nPPLV8+1TpGGy1rtFWzqntQkHQycAzwvkhzbETE0xHxUPq8H/gz8PJy+0dET0R0RkTn9OnT61Vts/xJZyivXcuUXyIyz7JGWzWrus5olnQUcBbwxoh4oqh8OvBwRDwraS9gNnB/Petm1nQKKa9FabIh3wrYJNRySOqVwC3AKyQNSjoFuBDYAbh+2NDTw4Dlku4AfgicFhEPlz2wmZUYkZI6HRPax7wRmUph6nWMWnU5S6pZ3nV0lF8gvr191HGe/f1VWFPBRteoNU8nyVlSzZpZoZkoY02DrNFEWTOdp1rHaKNNtXTgvlMwy7sxfo2Odz2FJv1xm09eT8HMzKayMYOCpGmS3iPpAkk/kPQ9Sf8sad96VNDMJiZrprNVV3t7MoFtzZrSfv1mnSQ46pBUSd3AfOBG4A/AemAayRyCL0uaBnwyIpbXtppmliUrJXXWjOZma+rIu+K+/qlwbce6U/hjRMyLiE9GxBUR8cuIWBIR/x4R84H3ATVexM/MRpOVkjprpvNU6xjNq2Yd+eWOZrO8c0dzfo1yMfN8nWvS0SypSVvMzMwsy2RGH3kuvVmOjZjpbHXVqPUsJmvCQSEinJXdLAey1kfo709GwBSPiFmzBhYvrm/9WlXWehZ5V1GfgqTPlyuPiC9UvUbj4D4Fawlp4/T8+cnQx/nzSzNz5rXduiWM0nHQ3Z09AqzRqtGn8Peix7PA24GOqtTOzCpSCAS9vaXZLiyfstazyLuKUmdHxNeLX0v6GuCbUDOzKWaifQrbAXtVsyJmZtZ4FQUFSXdKWp4+7gZWAhfUtmpmTSJdAW3EY4IroK1ZkzQRDR2KoKfHTUXNplm7Oytdea14WY7NwLqI2FyD+pg1n0Jq6+EmuALazJnD+gsk6HJEsPqo6E4hIlYXPR50QLBW1ta25Vd8IZVBV5eXRrZSWetZ5N2E12iWtCQivLCftZwR48+V5BPy0sg2FUxmRvOpVauFWROp9djzU/1/ljXQZGY0r61mRcyaRdb486wU1iOM0THtLKZTQ7OuZ1Hp6KPZkn4o6R5J9xceta6cWTPJSmE9Qtaay6tXA82bctlKdV/aUdVRafVS6Z3C/wYuIhl5dDjwPeA/R9tB0iWS1ku6q6jsxZKul7Qq/btz0bazJd0naaWkt43/o5g11oIF1TnO0qXVOY7VQXt7+S9+ibbBP4wa/POq0qDwgoj4FUmupNUR0Q28eYx9LgWOGlb2aeBXETEb+FX6GkmvAk4A9k33+ZakrSusm1ldNev4c6uBgYHyX/wRrH1290bXbkIqDQpPSdoKWCXpdEnHAruNtkNE3AQ8PKz4ncBl6fPLgHcVlX8/Ip6OiAeA+4CDK6yb2dSQ/uqcwbDFftvbG10zm4D29tIhy8WTEfOs0qDwMZLUFmcC84B/BE6ewPl2L3RQp38LgWUm8Nei9w2mZSNI6pLUJ6lvw4YNE6iC2eRkjT/PSmFdsfRX55poK/3VWbwIsDWNgYEt/UyFGekR8CAZ66TmRKWT1/4YEY9HxGBEfDAi/iEibq1iPcqN8C4bTiOiJyI6I6Jz+vTpVayC2eR0VWktwrymW7bq6CffIwlGDQqSeiS9OmPb9pI+JOl94zjfOkkz0v1nAOvT8kFgj6L3zQKadIkKa1XVmrzWrCmXrTIL6G10FUY11p3Ct4DPSVoh6QeSvpWOKvot8HtgB+CH4zjfYrY0O50MXFNUfoKk50vaE5gN3DaO45rVTdb488JAlPnzk9fzWTzqCMTi6Qr9/cnDXQjWaJWuvPZCoBOYATwJrIiIlWPscyXwJmBXYB1wDvBT4GrgpcBfgOMi4uH0/f8CfIhk2OvHIuLnY9XLK69ZLmStvpWWj9icFuR5ZS6rnVEWa6tjHbJXXqsoKOSVg4I1QlvbsPxHYwSFisutJfSoi65o7LT1aizHadb0urtLR3oWN9mMx9pxJnjJSlvRlu9BKFYjXSxqdBVG5TsFa3nj/eGe1RyU9cas9/uGoTXl4b97Ve8UJG0lacfJV8usvqr1y3zu3OocxyyPKk2Id4WkHSVtD9wDrJT0T7Wtmll1ZTX7ZI0m6u1N+g6Km5y61EP/0mF5biY4XMjBxfKo0juFV0XEoyRpKa4lGT30/prVyqyOskYALViQ3F0MTS5G9ETXyDw3Y8w4Xrx4WHAh6OoaR1ZVm1KOyfk8hUpXXttG0jYkQeHCiHhGXmbKmkzWL/MRo4mqrDBvoWTN5R53JrSqXhaQkbAhFyq9U7gYGAC2B26S1A48UqtKmdVC1i/z8Y4mMpuM+SxudBVGVWlQ6I2ImRFxdCTDlf5CMtHMrGmMNzfRpBPcmZWxhPmNrsKoKg0KPyp+kQaG71e/Oma1syhjeHhWs9KkE9xlLcDiPBaWY6P2KUh6JcnCNztJenfRph2BabWsmFlNdXQMrYDVD2Xz9IogijeM98vcKa+tCY3V0fwK4BjgRVByz/MYcGqtKmVWc4V1kknuCMrOOhaNn2VkU07yQyO//65GDQoRcQ1wjaRDI+KWOtXJLFvRL/wS7e1j/jJ/8MHy5YsWZaeiMKu2Hk6lSktv1ESlQ1Lvk/QZoKN4n4hwZ7PVV9Ev/BIVDJHu7x/frOZjjhlHvcwqtJCeXAeFSjuarwF2An4J/KzoYdY0FiwY3/t78z3HyJpUOwNII2fMZ627UW+V3ilsFxFn1bQmZg2S1aw0f74Dg1XfAHuW3O0WnlZrOdfJqvROYYmko2taE7MGyZrUtmRJfethrS0v/VqVBoWPkgSGpyQ9KukxSY/WsmJmVVG05uXFdJWdK3DGGVuKC/9jejqB1du8eY2uQcLrKVhz8Spn1uwy/k3W85/qpNdTUOIfJX0ufb2HpIOrWUmzWnMOR7OxVdp89C3gUOC96evHgW/WpEZmZi1oxoxG1yBR6eijQyJirqTbASLi/0nadiInlPQK4Kqior2Az5PMmj4V2JCWfyYirp3IOczMmk0t07ePR6V3Cs9I2pp0brak6cBzEzlhRKyMiDkRMQeYBzwB/CTdfH5hmwOCVZsno1meZS32VG+VBoX/RfLFvZukLwE3A/9WhfMfAfw5IsrkLTCrLs85sDw799xG1yBRUVCIiMuBfwb+J7AWeFdE/KAK5z8BuLLo9emSlku6RNLO5XaQ1CWpT1Lfhg0byr3FrKz5+U5jb5YLFQ1JlXQBcFVE/L5qJ076JNYA+0bEOkm7AxtJmqj+FZgxVm4lD0ltQZMYkurRqZYLU2FIKrAU+Kyk+ySdJ6nswcbp7cDSiFgHEBHrIuLZiHgOWAR4yKuZTT0Ziy/1vSQfnV6VNh9dFhFHk3xR/wn4iqRVkzz3iRQ1HUkqHpB1LHDXJI9vZpY/AwPJLcHwx//Nx2Lhld4pFOwNvJIkhfa9Ez2ppO2AtwI/Lir+qqQ7JS0HDgc+PtHjW3ManjVSGj1JWPH7ujkHSFJjZ2WbdNOR5VknGUm46qzSPoWvAO8G/kwyx+AnEfG3GtdtTO5TmFpK2lSLFtOZRx/9pC2WRYvp9PcX5YvJ6jsoetHTk59MlGbD5aVPodLJaw8Ah0bExupVy2wURYvpLB3nsphz55YvX7jQQcFsLJU2H/UAR0n6PICklzr3kdVLe/uWFdO6u7c0GXVmDHfISoVtlmfn0N3oKgCVB4VvkuQ+OjF9/RjOfWRVdvHF5csHBrakAOjuLu2bK6erKEO2CNasSfornArb8qybfMxeq7RPYWkh91FEHJiW3RERB9S8hqNwn8IUNt4GVqfOtibXpjWsiXEsIj4J1ZinULXcR2ZZnNraWtla6hMQxtLo3EdmZpYjjc59ZFNYR8eWIaMl7fy+IzAbYW5O5ilUPHktIu6NiG9GxIURsaKWlbLmUjKJrDspK4wWKowE6unZ0jn84IPlj+PU1tbKhubiNFil8xTMMvX1jVx0fLQFQ/r7twSNYpNKbV3IJ1Ou3KwJdHExPY2uBONPc2E2aQsWFL3o6Bi6zZiv3i23HOP9Ms/KJ5POfjbLu0XkY2al7xRs0jo7xzfqs/Cj/uKLoWv1apQMakviwICHj5o1koOC1V3Jj/eFnkZgliduPjIzy4EHZx5cdp2FzLS/NeKgYJN2zjmNroFZ8+u/6Lby/WKr67uEvYOCTVphGKqZTVzJAIwGclCwSSsZXlo0mqiRt8BmNjHuaLZJW1u8imDROgglPI3ZrCk4KFh9eHKZ2aiyUsfXm4OCTVrWSmclPInMbFR5WRXQfQo2MUV9B/1LNfGZyGYG5KeFtSFBQdKApDslLZPUl5a9WNL1klalf3duRN2sQoW+gwi6TnVaCbOpopF3CodHxJyi1X8+DfwqImYDv0pfWxNYtKjRNTCzaslT89E7gcvS55cB72pgXczM6iovqeMbFRQCuE5Sv6RC98ruEbEWIP27W7kdJXVJ6pPUt2HDhjpVd4rImkPgeQRmDTep1PFV1Kig8LqImAu8HfiIpMMq3TEieiKiMyI6p0+fXrsaTkVF/QATnUrf05PGEQIp+YectWiOmVVu/vxG1yDRkCGpEbEm/bte0k+Ag4F1kmZExFpJM4D1jaibZZOS+NHVVfTCzKpiyZJG1yBR9zsFSdtL2qHwHDgSuAtYDJycvu1k4Jp6183MrNU1ovlod+BmSXcAtwE/i4hfAF8G3ippFfDW9LXVS2HGsVTS1TB/u18Cya2tpyCY1d5QE2366KW+PdCKJm4C6OzsjL6+vkZXo3lMpMknax83H5nVxRq10RajLHo+AZL6i6YDlMjTkFTLiZ48rB5uZgDMpLoBYSwOCjbCwoWNroGZNYoT4tnonN3UrKX4TsFGNzBQfl6DcxyZ1cWp1Lc910HBRli8uNE1MLOCHurbnuugYCPMm9foGphZwTzqO8LSQaGZVbgesgT9/dDP3JK3dXeXP+zMmbWuuJlVain1/ZXmoNDMhuUy6mgPRNA2+AeQ6FZ3su4NA8zrFPPaHyrpFsgKCmbWuhwUppBddkm+7Nds3h0i6I7upE84Osp2Dre1bblrKDQZdXV5YJFZnsyo8zwFD0mdQvr7x/f+NWX+rXnimlm+rGEmyWoD9eE7hSkkLwt/m1n1dHNOXc/n3EfNbFj+IacjMpt6avH/tXMfmZlZRRwUmlC51c/K9Q+YmY2Xg0IOFKYbrFmTfMGPmG4wbD5C10IRiGjvICJZ66CtzU1HZlNRX53nKXj0UQ4UphtA6Zf70Eig4jfgvgMzqx3fKeTAqaeWL/doIjPrZJxjzSfJQSEHsuYGDLUYEcyfn5R5WUwzqyU3H+XAvHnlJ54NNRFJ0Ju86O2tX73MrPX4TqERhgZ5X6cAAApiSURBVHUcL11K2UR2Zmbn0F3X89U9KEjaQ9INklZIulvSR9PybkkPSlqWPo6ud93qZlgiOyD5u3p1Y+tlZrnTzbl1PV8jmo82A5+MiKWSdgD6JV2fbjs/Ir7WgDo11IwZja6BmeVVGw/WNSVe3YNCRKwF1qbPH5O0AmjpDP6eeGZmWdbSVtfzNbRPQVIHcCDwh7TodEnLJV0iaeeGVazOvK6BmeVFw4KCpBcCPwI+FhGPAhcBLwPmkNxJfD1jvy5JfZL6NmzYULf61tK59W0yNLMmMrcV5ilI2oYkIFweET8GiIh1EfFsRDwHLAIOLrdvRPRERGdEdE6fPr1+lTYza4B+yiYzrZlGjD4S8F1gRUT8e1F5cXfrscBd9a5brXV3lyay6+8fNj+hvb38msuerWbWsrq4uK7nq/t6CpJeD/wWuBN4Li3+DHAiSdNRAAPAwrRTOlPu11Po6Cg/zLS9fcTSmGZm5dR7PYVGjD66GVCZTdfWuy41NyyRXVubRxqZWb55RnM1DJuhnNXss3bU+x4zs8Zz7qNqGHZHYGZWLQ/SBnWcvuY7hTqaO7fRNTCzZtNf50V2HBTqqFwmVDOz0SygvqmRHRTqyIvmmFneOSjUQKHfubc3GW1U6He+7rpG18zMbHTuaK6B4f3O7oM2s4m6mC4gY3nGGvCdwiTNn79lhrLS2RcX13cCoplNYV0sKhnpXrw0by34TmES5s9Pl8ccNuXQfQdmVk3lWhuWLKnNuXynMAm1+o9iZtYoDgrjMXzmMjhhnZlNKW4+Go/hPcjCvchmVluF7MnDRHs7Se7Q6vKdwiQ4HphZzQ0MJF82wx49q4+syekcFCahp36jxMzMSiys0TBVB4VJWLiw0TUwM6suBwUzMxvioFBOhesjmJk1ymJqM3vNQaFIIRZ0r/4ARNA2IxBBR3vauTNsCc3FixtRSzMzmEdt0i47KBQpjDjt5lwgSWYXAbvskmzv6iq9cZhX3zTnZmZDZtZo4R0HhSJ9feXLC+sg9PSUjgpra6tf3czM6sFBwczMhuQuKEg6StJKSfdJ+nRNTzasQ7mzE3com1lTOLUV5ilI2hr4JvB24FXAiZJeVbMTFjoRCg8o26FsZpY3PdRmolSuggJwMHBfRNwfEZuA7wPvbHCdzMxyZx4ZnaCTlLeEeDOBvxa9HgQOKX6DpC6gsGLB45JWTvBcuwIbhyeaKpN3aqpLroP5OiR8HRJNcR0m8X2V2Uaet6BQ7iOWpJ2LiB6qsDadpL6I6JzscZqdr0PC1yHh65Bo5euQt+ajQWCPotezoEaDcc3MbIS8BYU/ArMl7SlpW+AEwPOGzczqJFfNRxGxWdLpwH8BWwOXRMTdNTqdE18nfB0Svg4JX4dEy14HhVeKMTOzVN6aj8zMrIEcFMzMbEjLBYW6ptFoMEmXSFov6a6ishdLul7SqvTvzkXbzk6vy0pJb2tMratP0h6SbpC0QtLdkj6alrfUtZA0TdJtku5Ir8O5aXlLXYcCSVtLul3SkvR1S16H4VoqKNQ9jUbjXQocNazs08CvImI28Kv0Nel1OAHYN93nW+n1mgo2A5+MiH2A1wAfST9vq12Lp4E3R8QBwBzgKEmvofWuQ8FHgRVFr1v1OpRoqaBAi6XRiIibgIeHFb8TuCx9fhnwrqLy70fE0xHxAHAfyfVqehGxNiKWps8fI/kimEmLXYtIPJ6+3CZ9BC12HQAkzQLeAXynqLjlrkM5rRYUyqXRmNmgujTK7hGxFpIvS2C3tLwlro2kDuBA4A+04LVIm0yWAeuB6yOiJa8D8A3gn4Hnispa8TqM0GpBYcw0Gi1syl8bSS8EfgR8LCIeHe2tZcqmxLWIiGcjYg5JtoCDJe03ytun5HWQdAywPiIqXc9ySl6HLK0WFJxGA9ZJmgGQ/l2flk/payNpG5KAcHlE/DgtbslrARARfwNuJGkjb7Xr8DpggaQBkibkN0v6P7TedSir1YKC02gkn/fk9PnJwDVF5SdIer6kPYHZwG0NqF/VSRLwXWBFRPx70aaWuhaSpkt6Ufr8BcBbgHtpsesQEWdHxKyI6CD5Dvh1RPwjLXYdsuQqzUWt1TmNRsNJuhJ4E7CrpEHgHODLwNWSTgH+AhwHEBF3S7oauIdktM5HIuLZhlS8+l4HvB+4M21PB/gMrXctZgCXpSNntgKujoglkm6hta5Dllb791CW01yYmdmQVms+MjOzUTgomJnZEAcFMzMb4qBgZmZDHBTMzGyIg4LllqQXSfpwlY95qaQHJJ1WpeOV1FFSm6QfjrHPjZJGXRRe0uWSHpb0noztP5S018RqPep5vybpzdU+rjUPBwXLsxcBZYPCJLNU/lNEfHsS+xfXoaSOEbEmIsp+kY9HRLyPjImVkvYFto6I+yd7njL+gzQ7qLUmBwXLsy8DL5O0TNJ5kt6UrotwBclEtA6VrhXxKUnd6fOXSfqFpH5Jv5X0ynInSO8cvp2+509pXhzSY/9W0tL08dq0vKQOZeo4VKc0+dzXJN0pabmkM8qc/0hJt6Tn+EGan2ks72PLbFskPS7pK+ln/aWkg9O7kfslLUjf8wFJP5XUm94pnS7pE0rWE7hV0osBImI1sIukl1RQD5uCWmpGszWdTwP7pQnckPQmkpTF+0XEA0oynmbpAU6LiFWSDgG+BWQ1i3QAbwReBtwgaW+SvDdvjYinJM0GrgQKTT7D61Bcx+I6dQF7Agems+lfXHxSSbsCnwXeEhF/l3QW8AngC6N8LkhmaF9Z9Hp74MaIOEvST4AvAm8lWTPkMrbccexHkiF2Gkn657Mi4kBJ5wMnkWQOBVianuNHY9TDpiAHBWs2t6U57TOlv7ZfC/wgSXsEwPNH2eXqiHgOWCXpfuCVwAPAhZLmAM8CLx9PHVJvAb4dEZsBImL42havIfni/l1az22BWyo47gxgQ9HrTcAv0ud3Ak9HxDOS7iQJeAU3pOtJPCbpEaC3aJ/9i963HmiroB42BTkoWLP5e9HzzZQ2gU5L/24F/K3w670Cw3O9BPBxYB1wQHq8pzLqMBqVOfbw7ddHxIkVHq/gSbZ8VoBnYku+mudIVlgjIp6TVPz/+NNFz58rev0cpd8F09JzWAtyn4Ll2WPADqNsXwfsJmkXSc8HjgFI10p4QNJxkGRJlXTAKMc5TtJWkl4G7AWsBHYC1qZ3EO8nSaA43jpeB5xW+GIe3nwE3Aq8Lm2uQtJ2kl7O2FYAe1fwvol6OXDXmO+yKclBwXIrIh4iaVq5S9J5ZbY/Q9L+/gdgCUka6IL3AadIugO4m9GXXV0J/Ab4OUk/xFMkfRAnS7qV5Euy7N3BGHX8Dkm2zeVpPd47bN8NwAeAKyUtJwkSZTvEh/kZSfbbqlOy7sTeQF8tjm/55yyp1lIkXQosiYgflnudJ1l1U7IWwg3A66qdwlnSscDciPhcNY9rzcN3CtZqHgH+tVqT12pF0uUkI6KeGr4tIp4kWRujFusEPw/4eg2Oa03CdwpmZjbEdwpmZjbEQcHMzIY4KJiZ2RAHBTMzG+KgYGZmQ/4/xmsNRJ8f/hEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(r_t_1, bins = 50, color='red', histtype='step', label = 'r particle1')\n",
    "plt.hist(r_t_2, bins = 50, color='blue', histtype='step', label = 'r particle 2', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('true[particle] (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('0nubb')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean distance between true particles 61.41822268818963\n",
      "Mean distance between reco particles 69.126148513877\n"
     ]
    }
   ],
   "source": [
    "plt.hist(d_tt_12, bins = 50, color='red', histtype='step', label = 'd_12 (true)', linestyle='dashed')\n",
    "plt.hist(d_rr_12, bins = 50, color='blue', histtype='step', label = 'd_12 (reco)', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('true[particle] (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('0nubb')\n",
    "plt.legend(loc=1);\n",
    "plt.show()\n",
    "\n",
    "print(f'Mean distance between true particles {np.mean(d_tt_12)}')\n",
    "print(f'Mean distance between reco particles {np.mean(d_rr_12)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(z_t_1, bins = 50, color='red', histtype='step', label = 'z part1 (true)', linestyle='dashed')\n",
    "plt.hist(z_t_2, bins = 50, color='blue', histtype='step', label = 'z part2 (true)', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('true[particle] (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average, the distance between particles is around 30mm, so I'm going to look for blobs around the position of part1/2 + 30mm (approximately)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total events = 4375\n",
      "Events 12 = 2196 (50.19428571428571%)\n",
      "Events 21 = 2179 (49.80571428571429%)\n",
      "Events NA = 0 (0.0%)\n"
     ]
    }
   ],
   "source": [
    "diff_b1_x_0nubb, diff_b1_y_0nubb, diff_b1_z_0nubb = [], [], []\n",
    "diff_b2_x_0nubb, diff_b2_y_0nubb, diff_b2_z_0nubb = [], [], []\n",
    "\n",
    "reco_0nubb_e = []\n",
    "\n",
    "diff_b1_x_0nubb, diff_b1_y_0nubb, diff_b2_x_0nubb, diff_b2_y_0nubb, reco_0nubb_e = myrvt.blobassignation(true_0nubb, tracks_0nubb_ROIovlp1trackfid, 'signal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X8O/Aa4FfA0uB60j+0H9e0i2SDivlgJLOIkk+p6eXloiIpyNie/q8D/g98HKSmkT2UtVscN3eGsOSMqSKjlETRZvVRqGaxZqI6Bpj2T9LehFwQLEHk3Q8cDHwhoj4c6a8BXgsInZKeglJQ/aDEfGYpMclvYYkWZ0JfL3Y45mZWXmMmywi4scFlm8FtuZbJmkFcDSwn6QBYDFJ76fnArekPWD/I+35dBTwaUk7gJ3AByIi1zh+PknPqueRtHFk2znMzKwKCrZZjEVSZ9pGkFdEnJan+NtjrPt94PtjLOsFDp1QkGY11Es7lDBNeT6LF5cnFrPJmkxvKA+OM6swj+C2ejHhZBERS8sZiFmz6ZhkrQKGpv0wq7WiLkNJuiRfeUR8urzhmFnWli21jsAsUWybxZOZ57lxFw+UPxwzM6tHRSWLiPhy9rWkfwJ812CzcSRD8rqK32DOnGTK2Yz59ELb26G/v4yRmZVuom0WzwdeUs5AzJpNFyVOGdvfz65pZ9NHX7TDpk0Vic+sFMW2WdzL0DQbuwMtgNsrzMbRyuZJTzfQ2Ynvt2d1odg2ixMzz3cAj2RmjzWzPLYw+a5My5Y5WVh9KLbNwvVgM7MpbMLjLCStKmcgZs1mfhnGWZjVi8mM4PYt5s3G0cfkp4zdvLkMgZiVwWRGcHu4kNk4Opn8JAd9rpxYnSgqWUiaK+kGSfdLejD3qHRwZo1sGZ2T3sfChWUIxKwMiq1Z/CtwBUlPqGNI7oX9nUoFZWZm9aXYZPG8iLgVUERsSm+I9L8qF5aZmdWTYpPFU5J2A34n6UJJpwAvqmBcZg1vcxnGWSz13M5WJ4pNFh8mmeLjg0A7cAZwVqWCMmsGfbRPeh+dk2/2MCuLYgflrUmfPgG8r3LhmDWPhfTsmiNnoiQmvQ+zchi3ZiGpW9Irxli2p6SzJZ1emdDMzKxeFLoMdTnwKUkPSLpe0uWSrpT0S+BXwF7ADfk2TNfbKum+TNk+km6R9Lv0596ZZR+XtFHSBknHZcrbJd2bLrtMkm/namZWZeMmi4hYFxHvBF4FfBP4Jcl9LN4fEYdHxNci4ukxNr8KOH5E2ceAWyNiLnBr+hpJhwCLgHnpNpdL2j3d5gqgE5ibPkbu06wuLS3DOIsTTyy8jlk1FNXAHRFPRMQvImJFRPwwIjYUsc0dwGMjik8ClqfPlwMnZ8qvjYinI+IhYCNwpKSZwIyIWB0RQTK+42TMGkAnyya9j56eMgRiVgaTmRtqIl6cmyYk/ZnrfjsLeDiz3kBaNit9PrI8L0mdknol9W7btq2sgZuVSmVoml6woAyBmJVBtZPFWPK1Q8Q45XlFRHdEdERER0tLS9mCM6uVVZ7b2epEyclC0m6SZkzweI+kl5ZIf25NyweA/TPrzQYG0/LZecrNzKyKip1I8BpJMyTtCdwPbJD00QkcbyVDg/nOAn6UKV8k6bmSDiRpyP5NeqnqcUmvSXtBnZnZxqyunYgbHKx5FFuzOCQi/kTSuPwT4ADgPeNtIGkFsBo4SNKApHOAzwNvkvQ74E3payJiPXAdSSK6CbggInamuzof+BeSRu/fAz8t/vTMaqeHyU8ZGx6RZ3Wi2HtwT5M0jSRZfCMinik03CEiThtj0bFjrH8pcGme8l7g0CLjNKsbC1g56bpFdzdl6IBrNnnF1iyWAv3AnsAdkuYAf6xUUGbNYBWT78p03nllCMSsDIpNFj0RMSsiTkjHO/wBOLuCcZmZWR0pNll8P/siTRjXlj8cMzOrR+O2WUj6G5IpOF4o6W2ZRTOA6ZUMzKxhtLXBpk2jimPOHJKrtxO3ciWUoZ3cbNIKNXAfBJwI/BUMuwD7OHBupYIyayibNuXttlSOxun2yd8Sw6wsFEX0zZP02ohYXYV4yqajoyN6e3trHYZNBVLeZDFGcem7pgw7MiuSpL6I6BhZXmzX2Y2S/hFoy24TEW7kNjObAopNFj8imZ78Z8DOAuuamVmTKTZZPD8iLq5oJGZNZuXKye/j3HOhDDOdm01asV1nV0k6oaKRmDWZcjROd3dPfh9m5VBssvgQScJ4StKfJD0u6U+VDMys0c0a884rxXNvKKsXRV2Gioi9Kh2ImY22dm2tIzBLFDtFuSSdIelT6ev9JR1Z2dDMzKxeFHsZ6nLgtcC709dPAN+sSERmTeLcMgxbnTlz8vswK4die0O9OiLmS7oLICL+W9JzKhiXWcMrR+P04CD5by5sVmXF1iyekbQ76f2vJbUAz1YsKrMmUI7G6a6uye/DrByKTRaXATcCL5J0KXAn8NmKRWXWBMrROL1kyeT3YVYORSWLiPgucBHwOWALcHJEXF/JwMwahQikZB6nXE2gtRXmzKlpWGZlVVSbhaSvAd+LCDdqm43QSzvt0TesbHCwRsGYVUixl6HWAp+UtFHSlySNmpGwWJIOkrQu8/iTpA9L6pK0OVN+Qmabj6fH3iDpuIke26zReOJkqxfFDspbDiyXtA/wduALkg6IiLmlHjAiNgBHAKSN5ptJ2kPeB3wlIv4pu76kQ4BFJDdhagV+JunlEeEJDa0udNCHJxC3ZldszSLnZcDfkExV/tsyHP9Y4PcRMfo2Y0NOAq6NiKcj4iFgI+ABgTYldEy4Dm9WXsWO4P6CpN8BnwbuA9ojYkGBzYqxCFiReX2hpHskXSlp77RsFvBwZp2BtCxfnJ2SeiX1btu2rQzhmZkZFF+zeAh4bUQcHxH/GhH/M9kDp4P6FgK5XlVXAC8luUS1BfhybtU8m+et9UdEd0R0RERHS0vLZEM0K8piumodglnFFZssuoHjJV0CIOmAMswN9RZgbUQ8AhARj0TEzoh4lmQG/9z+B4D9M9vNBtzXxOpGF5UbDLF4ccV2bVaSYpPFN0nmhjotff04k58b6jQyl6AkZWfBOYXkchfASmCRpOdKOhCYC/xmksc2K5tWNlds3x7BbfWiJnNDSXo+8CbgvEzxFyUdQXKJqT+3LCLWS7oOuB/YAVzgnlBWT7bQWrF9t7a6Gm31odhkUda5oSLiz8C+I8reM876lwKXTvR4Zo1qy5ZaR2CW8NxQZpM0n77CK5k1OM8NZTZJfVRuMMT8+cnPzk52zT8leToRq76iB+VFxG8j4psR8Y2IeKCSQZk1kk6WVmzffWmlpbsbIoYefa7MWJWVOoLbzEZYRmfVj7lwYdUPaVOck4WZmRXkZGFmZgU5WZhN0uYKjrMAkrsoZVu3JZbSCW1tlT2uWUax4yzMbAx9tFc2XfT3jyrqhCRxmFWJaxZmk7SQnqof03nCqs3JwszMCnKyMDOzgpwszCZpaQ3GWZx4YtUPaVOck4XZJHWyrOrH7Kl+M4lNcU4WZpOk/DdurKgF5bipsVkJnCzMGtCqVbWOwKYaJwszMyvIycJskk6swTgLs2pzsjCbpB6qPwVsVL+ZxKa4miQLSf2S7pW0TlJvWraPpFsk/S79uXdm/Y9L2ihpg6TjahGz2VgWsLLqx+zurvohbYqrZc3imIg4IiJytxn7GHBrRMwFbk1fI+kQYBEwDzgeuDy9H7hZdbW1jZrQD4lVVL9r0nnnVf2QNsXV02Wok4Dl6fPlwMmZ8msj4umIeAjYCBxZg/hsqtu0afjt6nIPsymgVskigJsl9UnKDX99cURsAUh/vigtnwU8nNl2IC0bRVKnpF5Jvdu2batQ6GZmU0+tksXrImI+8BbgAklHjbNuvvk1836di4juiOiIiI6WlpZyxGlWUC0qFyur30xiU1xNkkVEDKY/twI3klxWekTSTID059Z09QFg/8zms4HB6kVrNr5aNDa3t1f/mDa1VT1ZSNpT0l6558CbgfuAlcBZ6WpnAT9Kn68EFkl6rqQDgbnAb6obtdnYatHYPCvvhVizyqnFnfJeDNyo5O4tewDXRMRNktYA10k6B/gD8A6AiFgv6TrgfmAHcEFE7KxB3GZmU1bVk0VEPAgcnqd8O3DsGNtcClxa4dDMzGwM9dR11qzudXYOH2YxOFibxuZzz63+MW1qUzRpP/GOjo7o7e2tdRjWTKT6GldRb/FYU5DUlxksvYtrFmYlqJdeSPUSh00dThZmJVi7ttYRJOolDps6nCzMzKwgJwuzEsycWesIEvUSh00dThZmJRisk7kDBgehi8XDemb19dU6KmtmThZmJejqqnUEQ7pY4slvrWqcLMxKsGRJrSMYW8eozo5m5eNkYWZmBTlZmJlZQU4WZiWoq0kB5swZNvfIYrqS521ttY7MmlAtZp01s3Lo7x/2siv3r/LdL8xsclyzMCtBPTcit7bWOgJrZk4WZk1iy5ZaR2DNzMnCzMwKcrIwK8HixbWOYGzz59c6AmtmThZmJainEdwjeboPqyQnC7MS1HMjcmdnrSOwZlb1ZCFpf0k/l/SApPWSPpSWd0naLGld+jghs83HJW2UtEHScdWO2SynnhuRly2rdQTWzGoxzmIH8JGIWCtpL6BP0i3psq9ExD9lV5Z0CLAImAe0Aj+T9PKI2FnVqM3MprCq1ywiYktErE2fPw48AMwaZ5OTgGsj4umIeAjYCBxZ+UhtymprGzYyetdjzhw3ItuUVdM2C0ltwCuBX6dFF0q6R9KVkvZOy2YBD2c2G2CM5CKpU1KvpN5t27ZVKGpreps2MWzu79yjv7+uG5E3b651BNbMapYsJL0A+D7w4Yj4E3AF8FLgCGAL8OXcqnk2zzt7f0R0R0RHRHS0tLRUIGqbKtraoKcnuclQtnJRz43I9ZzIrPHVZG4oSdNIEsV3I+IHABHxSGb5MmBV+nIA2D+z+WygTu5XZs1q0yZYsCB53ig3Flq4sHFitcZTi95QAr4NPBAR/5wpz95V+BTgvvT5SmCRpOdKOhCYC/ymWvGamVltahavA94D3CtpXVr2j8Bpko4gucTUD5wHEBHrJV0H3E/Sk+oC94SySlu6tNYRmNWXqieLiLiT/O0QPxlnm0uBSysWlNkI9dw2MZZdCS53n4t85swZNbW5WTF8PwuzPKTGu/6/K8GNlwx8rwubIE/3YdYksr22co3zCxb4xnlWHq5ZmDWJfDWhnh5XJqw8XLMwy+PEE2sdgVl9cbIwy6Onp9YRlE+jtb1YfXKyMMsjd82/GXR31zoCawZOFjZ1jTNh4KpVBbduGJ/97NCp9XDirilM3PBtpXADt01duQkD82miRuFhPWm1ClqT03bDt5XCNQtrfuPUIHK9hXKPpr9kkxuwJ3Eu3UMn7mqGFeCahTW/TA2iszNzR7lNsLk9f+WiaRuFM9WMJC+mI/lczbACXLOwKaO9Pak5ZG9RUc/31K609vZaR2CNxMnCmsNYl5rSy00Aa9fWNsR64/fDSuFkYc1hrLvbpXe4s9HmzBmqWXW98CvD8muf2ulT+1CThvrHTsbZh9s+mpaThdWVXAVhzLvUtbXRrr7Rf8DS2gMkd4zLbtfVlZTPnJnngFNYf3/yHgN0/c+Hh+XX9uijPfp2vX7v4jaIoHVmIJJH+/w8iXnTplqeklWQoklb8jo6OqK3t7fWYViJds322taW/w9PZort1tbkj11XFyxZMrRKb6+vx1dLZ+eIHmSNOF2vDSOpLyI6Rpa7ZmG1NaKtYSmdQz1zClxS2vWtuGv4Kk4U1XPzzUO/vsHBZNBf3itRY7Up+bJVw3CysOoY648FDPtL3xndbmdoIP39w3uWLZhzb1IhLPB73vUgz7pOInXJycImJs8f/051DysaHByaIlub+hFB99Lkj4TI3/js7v4Nrr+fOXMY9nsWgTb1559vK5ttnETqmpOFTUye3kfd0UnMacv9eaB1lliwMP1zMaeNiKG7ueUGEmdv0jOindoaVH//0O85+xHp6Rn6PWcrHN3dw8t6emDwV/1OInWmYZKFpOMlbZC0UdLHah3PVJGvG6WUdLWE5NJDrqy9nbG/KY6oQeRWy00F3tPjq09TQe73nP3b39k5vGzBApg1a0QvOJLPV66G2jozWblrcfLVpG3gTieRCmuIZCFpd+CbwFuAQ4DTJB1S26ga07CuqEq+0uW6okrQqqS/ape6km/6uw+M6kYZkXS1hORSU66sr6/GJ2dNI/tZy/W26usbKhvZuWHTztn09QZ9vZlLX0TyOZVo1eDQlxr1OQnASc4AAAnGSURBVIlMQEMkC+BIYGNEPBgR/w+4FjipxjGVJF+//9bWsT+zw673p49uJT2FsmULlKy0QD3Dq/cS3eocXr3XAjbPOnLoP2EkX+n6on3oP2EkU5J2RVfyTX/H7Cq9Q2YTl+sF197O6C81EQxG69CXmminc/tnh7evqZUeLZj0/7dmrtU0xDgLSacCx0fE+9PX7wFeHREXjlivk10zo3EQsKGqgVbHfsCjtQ6iCqbCefocm0OzneOciGgZWdgos87m6yMzKstFRDe5yTSblKTefANmms1UOE+fY3OYCucIjXMZagDYP/N6NjBYo1jMzKacRkkWa4C5kg6U9BxgEbCyxjGZmU0ZDXEZKiJ2SLoQ+Ddgd+DKiFhf47Bqpakvs2VMhfP0OTaHqXCOjdHAbWZmtdUol6HMzKyGnCzMzKwgJ4s6JukdktZLelZSx4hlH0+nPtkg6bhMebuke9Nll0mNMzWfpC5JmyWtSx8nZJblPd9G1KxT10jqTz976yT1pmX7SLpF0u/Sn3vXOs5SSbpS0lZJ92XKxjyvZvqsZjlZ1Lf7gLcBd2QL06lOFgHzgOOBy9MpUQCuIBmYODd9HF+1aMvjKxFxRPr4CRQ834YyBaauOSb93eW+3HwMuDUi5gK3pq8bzVWM/n+U97ya6bM6kpNFHYuIByIi3yj0k4BrI+LpiHgI2AgcKWkmMCMiVkfSc+Fq4OQqhlwpec+3xjFNVMNPXVOik4Dl6fPlNODnMSLuAB4bUTzWeTXTZ3UYJ4vGNAt4OPN6IC2blT4fWd5ILpR0T1r1z1XtxzrfRtRM5zJSADdL6kun3gF4cURsAUh/vqhm0ZXXWOfVtL/fhhhn0cwk/Qz46zyLPhERPxprszxlMU553RjvfEkuoX2GJObPAF8GzqYBzqsEzXQuI70uIgYlvQi4RdJvax1QDTTt79fJosYi4o0T2Gys6U8G0ucjy+tGsecraRmwKn3ZTNO9NNO5DBMRg+nPrZJuJLn88oikmRGxJb1MurWmQZbPWOfVtL9fX4ZqTCuBRZKeK+lAkobs36TV4cclvSbtBXUmMFbtpO6k/+lyTiFp4Icxzrfa8ZVJU05dI2lPSXvlngNvJvn9rQTOSlc7iwb6PBYw1nk102d1GNcs6pikU4CvAy3AjyWti4jjImK9pOuA+4EdwAURsTPd7HyS3hvPA36aPhrFFyUdQVJt7wfOAyhwvg2liaeueTFwY9pTew/gmoi4SdIa4DpJ5wB/AN5RwxgnRNIK4GhgP0kDwGLg8+Q5r2b6rI7k6T7MzKwgX4YyM7OCnCzMzKwgJwszMyvIycLMzApysjAzs4KcLKymJF0l6SFJH0hfn1zOifUkPVHCuj+X9MTIGX4LbNMm6d0Ti27Mff4inbF0YZn2NyxGSR2SLiuwTb+k/QqsM+b7pcRtkmZMPPIxj3utpLnl3q+Nz8nCSpb+ISjnZ+ejEfGt9PnJJLOx5jtuRccFRcQxQG+Jm7UBeZPFJOM9PSImPVgvjaGNTIwR0RsRH5zsvgu8XycAd0fEnyZ7nDyuAC6qwH5tHE4WVpT02+kDki4H1gL7S/qopDXpxH9LMuuemZbdLek7adkcSbem5bdKOiDPMf4WWAh8Kb0nwkvTb9mflXQ78CFJCyT9WtJdkn4m6cXpti+Q9K9K7qdwj6S3j9j3fpJWS3qrpJmS7kiPcZ+k10/irfk88Pp0X/8g6b2SrpfUQzKp3tGSctOWIOkbkt6bPm+XdLuSiff+bcQI9mzsv5D0VUm/SuM9Mi0/Mi27K/15UFo+LIY8Me6KqdD7lq5zhqTfpNsvVXFTbp9OOqo5/ez8VtK/pPF/V9IbJf27kvtB5M6nS9JySTenNZu3SfpiGttNkqal+/4l8MZKf3mwESLCDz8KPki+nT4LvCZ9/WaSG9WL5EvHKuAoknn8NwD7pevtk/7sAc5Kn58N/DB9fhVwauY4I1//Arg883pvhgaTvh/4cvr8C8BXs+ulP58gGV38a+BNadlHSCZqhGQU9V4jjtdRwvtyNLAq8/q9JPMD7TPG8m+k60wDfgW0pOXvIhnNPSqG9PWy9PlRwH3p8xnAHunzNwLfLzKGXa/Hed/6gf2Ag9Pf3bS0/HLgzELvF7Ap976SfHZ2AK8g+az0AVeSfHZOynwWuoA70/fmcODPwFvSZTcCJ2f2fwvQXuv/F1Pp4cxspdgUEf+RPn9z+rgrff0CknlwDgduiIhHASIidx+A15LcyAngO8AXSzju9zLPZwPfS7+FPwd4KC1/I8k8S6TH/e/06TSSm9NcEBG3p2VrgCvTb6o/jIh1JcRSjFsy5z2Wg4BDSWZnhSRpbRln/RWQ3FtB0gxJfwXsBSxPr98HybmWEgOM/b7lHAu0A2vSOJ9HcZMB7hMRj2dePxQR9wJIWk9y46CQdC9JMsn5aUQ8k5bvDtyUlo9cbyvQSpJ4rAp8GcpK8WTmuYDPxdBd7V4WEd9Oy4uZQ6aUeWayx/068I2IeAXJ3FHTM/Hk2+cOkj8ou25vGcnNbI4CNgPfkXTmeAeXdIqGbvVaTON3Nt4dDP9/lo13feb9e0VEvHmcfY48t9w07j+PiEOBBZl9j4xhPIV+XwKWZ+I8KCK6itjvjhHtWk9nnj+bef0sw+eoexogIp4Fnom0GpFnvenAX4qIw8rEycIm6t+AsyW9AEDSLCX3MbgVeKekfdPyfdL1f8XQN9jTSS435PM4yTfmsbyQ5I88DM36Ccm1+QtzLzR046Qguez1N0rvdy1pDrA1IpYB3wbmj3eiEXFj5o/lyAbdQvFuAg5RMgvpC0m+qUNyqa5F0mvTmKZJmjfOft6Vrvd3wB8j4o8Mfy/eO86248U41vuWcytwavq7zd17es44x8rZALykiPUm6uVAM0zA2DCcLGxCIuJm4BpgdXrJ4AaSa9TrgUuB2yXdDfxzuskHgfdJugd4D/ChMXZ9LfDRtNH2pXmWdwHXS/ol8Gim/P8Ce6cNqHcDx2Ri3UmSqI6R9Pck1+zXSboLeDvwtZLfgCH3kHyLvlvSP4xcGBEPA9el632X9LJdJLdUPRX4QhrvOuBvxznOf0v6FfAt4Jy07IvA5yT9O8klm4nEOOb7lsZ5P/BJksb6e0jaCvI2xI/wY5L3ueyUdGr4S6R3qrPq8KyzVlOSriJpbL2h1rFA0vMI+D95ahA1i6EeYhrLWLGlbUpXR8SbKnDMfwD+lF72tCpxzcJq7Y/AZ5QOyqslST8nuXTyTI1DeQy4SmUalFcp471f6bf+ZarAoDzgf4DlFdivjcM1CzMzK8g1CzMzK8jJwszMCnKyMDOzgpwszMysICcLMzMr6P8DlsXXJvnagCsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(diff_b1_x_0nubb, bins = 50, color='red', histtype='step', label = 'x blob1')\n",
    "plt.hist(diff_b1_y_0nubb, bins = 50, color='blue', histtype='step', label = 'y blob1', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('reco[tracks] - true[particle] (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('0nubb')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(diff_b2_x_0nubb, bins = 50, color='red', histtype='step', label = 'x blob2')\n",
    "plt.hist(diff_b2_y_0nubb, bins = 50, color='blue', histtype='step', label = 'y blob2', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('reco[tracks] - true[particle] (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('0nubb')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x12fe4a910>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(diff_b1_x_0nubb, diff_b1_y_0nubb, bins=(30, 30), range=((-50, 50), (-50, 50)), cmap='viridis', cmin=0.001)\n",
    "plt.xlabel('blob-1 reco_x - true_x (mm)')\n",
    "plt.ylabel('blob-1 reco_y - true_y (mm)')\n",
    "plt.title('0nubb')\n",
    "#plt.xlim((0.0,1.5))\n",
    "plt.colorbar(label='events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x12f8f4bb0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(diff_b2_x_0nubb, diff_b2_y_0nubb, bins=(30, 30), range=((-50, 50), (-50, 50)), cmap='viridis', cmin=0.001)\n",
    "plt.xlabel('blob-2 reco_x - true_x (mm)')\n",
    "plt.ylabel('blob-2 reco_y - true_y (mm)')\n",
    "plt.title('0nubb')\n",
    "#plt.xlim((0.0,1.5))\n",
    "plt.colorbar(label='events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['event_id', 'particle_id', 'particle_name', 'primary', 'mother_id',\n",
       "       'initial_x', 'initial_y', 'initial_z', 'initial_t', 'final_x',\n",
       "       'final_y', 'final_z', 'final_t', 'initial_volume', 'final_volume',\n",
       "       'initial_momentum_x', 'initial_momentum_y', 'initial_momentum_z',\n",
       "       'final_momentum_x', 'final_momentum_y', 'final_momentum_z',\n",
       "       'kin_energy', 'length', 'creator_proc', 'final_proc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#single electron files\n",
    "true_list_selectron = []\n",
    "filepath_selectron_true = filepath+'e-_roi/hits/'\n",
    "\n",
    "#loop over all the files, to read 'HITS' information\n",
    "for nfile in range(1,max0nubbf):\n",
    "    \n",
    "    filename_selectron_true = filepath_selectron_true+f'next100.e-_roi.{nfile}.deconv.h5'\n",
    "\n",
    "    try:\n",
    "        readfile_true_selectron = pd.read_hdf(filename_selectron_true, 'MC/particles')\n",
    "    except:\n",
    "        print('File {} not good'.format(filename_selectron_true))\n",
    "        continue\n",
    "        \n",
    "    true_list_selectron.append(readfile_true_selectron)\n",
    "    \n",
    "#'concat' command means: 'concatenate pandas objects along a particular \n",
    "# axis with optional set logic along the other axes.' -> following line \n",
    "# is needed to keep an order between the files\n",
    "\n",
    "true_selectron = pd.concat([dt for dt in true_list_selectron]) \n",
    "true_selectron.columns  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "      <th>particle_id</th>\n",
       "      <th>particle_name</th>\n",
       "      <th>primary</th>\n",
       "      <th>mother_id</th>\n",
       "      <th>initial_x</th>\n",
       "      <th>initial_y</th>\n",
       "      <th>initial_z</th>\n",
       "      <th>initial_t</th>\n",
       "      <th>final_x</th>\n",
       "      <th>...</th>\n",
       "      <th>initial_momentum_x</th>\n",
       "      <th>initial_momentum_y</th>\n",
       "      <th>initial_momentum_z</th>\n",
       "      <th>final_momentum_x</th>\n",
       "      <th>final_momentum_y</th>\n",
       "      <th>final_momentum_z</th>\n",
       "      <th>kin_energy</th>\n",
       "      <th>length</th>\n",
       "      <th>creator_proc</th>\n",
       "      <th>final_proc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>263.843140</td>\n",
       "      <td>282.704956</td>\n",
       "      <td>1200.066284</td>\n",
       "      <td>0.0</td>\n",
       "      <td>224.406219</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.144866</td>\n",
       "      <td>-1.164350</td>\n",
       "      <td>1.615185</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.459877</td>\n",
       "      <td>180.497162</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>152.977859</td>\n",
       "      <td>-331.856018</td>\n",
       "      <td>3.190071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225.966461</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.728068</td>\n",
       "      <td>-1.545893</td>\n",
       "      <td>2.373892</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.458235</td>\n",
       "      <td>278.690399</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>443.712219</td>\n",
       "      <td>153.831985</td>\n",
       "      <td>1106.283813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>391.517029</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.393338</td>\n",
       "      <td>-2.532633</td>\n",
       "      <td>0.353849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.445680</td>\n",
       "      <td>200.310394</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-348.382904</td>\n",
       "      <td>-172.308670</td>\n",
       "      <td>717.329773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-234.460526</td>\n",
       "      <td>...</td>\n",
       "      <td>2.436209</td>\n",
       "      <td>1.410585</td>\n",
       "      <td>-0.786198</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.456170</td>\n",
       "      <td>243.929733</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-307.496674</td>\n",
       "      <td>-335.867310</td>\n",
       "      <td>101.232605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-354.965210</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.537453</td>\n",
       "      <td>-1.247277</td>\n",
       "      <td>2.158215</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.461958</td>\n",
       "      <td>63.263073</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>9974</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-21.775333</td>\n",
       "      <td>-267.992004</td>\n",
       "      <td>591.497620</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.656631</td>\n",
       "      <td>...</td>\n",
       "      <td>1.784612</td>\n",
       "      <td>-2.010802</td>\n",
       "      <td>-1.137944</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.452816</td>\n",
       "      <td>221.954559</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>9975</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>272.629181</td>\n",
       "      <td>-176.767624</td>\n",
       "      <td>548.630493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324.627625</td>\n",
       "      <td>...</td>\n",
       "      <td>1.047669</td>\n",
       "      <td>1.488665</td>\n",
       "      <td>-2.274985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.447110</td>\n",
       "      <td>228.639877</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>9976</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>422.564270</td>\n",
       "      <td>228.389816</td>\n",
       "      <td>1193.669189</td>\n",
       "      <td>0.0</td>\n",
       "      <td>473.581726</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.076460</td>\n",
       "      <td>-0.900715</td>\n",
       "      <td>2.790703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.466630</td>\n",
       "      <td>209.802994</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>9977</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>11.710984</td>\n",
       "      <td>44.878563</td>\n",
       "      <td>695.600647</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-37.622673</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.835248</td>\n",
       "      <td>-1.775122</td>\n",
       "      <td>-1.454311</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.471505</td>\n",
       "      <td>195.483353</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>9978</td>\n",
       "      <td>1</td>\n",
       "      <td>e-</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>144.205170</td>\n",
       "      <td>287.871674</td>\n",
       "      <td>525.010681</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.512154</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.702252</td>\n",
       "      <td>-2.281809</td>\n",
       "      <td>-1.709306</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.469380</td>\n",
       "      <td>254.425491</td>\n",
       "      <td>none</td>\n",
       "      <td>Scintillation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9767 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     event_id  particle_id particle_name  primary  mother_id   initial_x  \\\n",
       "23          0            1            e-     True          0  263.843140   \n",
       "53          1            1            e-     True          0  152.977859   \n",
       "127         2            1            e-     True          0  443.712219   \n",
       "131         3            1            e-     True          0 -348.382904   \n",
       "149         4            1            e-     True          0 -307.496674   \n",
       "..        ...          ...           ...      ...        ...         ...   \n",
       "166      9974            1            e-     True          0  -21.775333   \n",
       "236      9975            1            e-     True          0  272.629181   \n",
       "292      9976            1            e-     True          0  422.564270   \n",
       "358      9977            1            e-     True          0   11.710984   \n",
       "362      9978            1            e-     True          0  144.205170   \n",
       "\n",
       "      initial_y    initial_z  initial_t     final_x  ...  initial_momentum_x  \\\n",
       "23   282.704956  1200.066284        0.0  224.406219  ...           -2.144866   \n",
       "53  -331.856018     3.190071        0.0  225.966461  ...           -0.728068   \n",
       "127  153.831985  1106.283813        0.0  391.517029  ...           -1.393338   \n",
       "131 -172.308670   717.329773        0.0 -234.460526  ...            2.436209   \n",
       "149 -335.867310   101.232605        0.0 -354.965210  ...           -1.537453   \n",
       "..          ...          ...        ...         ...  ...                 ...   \n",
       "166 -267.992004   591.497620        0.0   63.656631  ...            1.784612   \n",
       "236 -176.767624   548.630493        0.0  324.627625  ...            1.047669   \n",
       "292  228.389816  1193.669189        0.0  473.581726  ...           -0.076460   \n",
       "358   44.878563   695.600647        0.0  -37.622673  ...           -1.835248   \n",
       "362  287.871674   525.010681        0.0  117.512154  ...           -0.702252   \n",
       "\n",
       "     initial_momentum_y  initial_momentum_z final_momentum_x final_momentum_y  \\\n",
       "23            -1.164350            1.615185              0.0             -0.0   \n",
       "53            -1.545893            2.373892             -0.0              0.0   \n",
       "127           -2.532633            0.353849              0.0              0.0   \n",
       "131            1.410585           -0.786198             -0.0              0.0   \n",
       "149           -1.247277            2.158215              0.0              0.0   \n",
       "..                  ...                 ...              ...              ...   \n",
       "166           -2.010802           -1.137944             -0.0              0.0   \n",
       "236            1.488665           -2.274985              0.0              0.0   \n",
       "292           -0.900715            2.790703              0.0              0.0   \n",
       "358           -1.775122           -1.454311              0.0             -0.0   \n",
       "362           -2.281809           -1.709306             -0.0              0.0   \n",
       "\n",
       "     final_momentum_z  kin_energy      length  creator_proc     final_proc  \n",
       "23               -0.0    2.459877  180.497162          none  Scintillation  \n",
       "53               -0.0    2.458235  278.690399          none  Scintillation  \n",
       "127              -0.0    2.445680  200.310394          none  Scintillation  \n",
       "131               0.0    2.456170  243.929733          none  Scintillation  \n",
       "149              -0.0    2.461958   63.263073          none  Scintillation  \n",
       "..                ...         ...         ...           ...            ...  \n",
       "166               0.0    2.452816  221.954559          none  Scintillation  \n",
       "236              -0.0    2.447110  228.639877          none  Scintillation  \n",
       "292              -0.0    2.466630  209.802994          none  Scintillation  \n",
       "358              -0.0    2.471505  195.483353          none  Scintillation  \n",
       "362               0.0    2.469380  254.425491          none  Scintillation  \n",
       "\n",
       "[9767 rows x 25 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_selectron_primary = true_selectron[true_selectron.primary == True]\n",
    "\n",
    "true_selectron_primary_part1 = true_selectron_primary[true_selectron_primary.particle_id == 1]\n",
    "true_selectron_primary_part2 = true_selectron_primary[true_selectron_primary.particle_id == 2]\n",
    "true_selectron_primary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total events = 4155\n",
      "Events 12 = 406 (9.77135980746089%)\n",
      "Events 21 = 3749 (90.2286401925391%)\n",
      "Events NA = 0 (0.0%)\n"
     ]
    }
   ],
   "source": [
    "diff_b1_x_selectron, diff_b1_y_selectron, diff_b1_z_selectron = [], [], []\n",
    "diff_b2_x_selectron, diff_b2_y_selectron, diff_b2_z_selectron = [], [], []\n",
    "\n",
    "reco_selectron_e = []\n",
    "\n",
    "diff_b1_x_selectron, diff_b1_y_selectron, diff_b2_x_selectron, diff_b2_y_selectron, reco_selectron_e = myrvt.blobassignation(true_selectron, tracks_selectron_ROIovlp1trackfid, 'bkg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.05089856276408"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.hist(diff_b1_x_selectron, bins = 50, color='red', histtype='step', label = 'x blob1')\n",
    "plt.hist(diff_b1_y_selectron, bins = 50, color='blue', histtype='step', label = 'y blob1', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('reco[tracks] - true[particle] (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('Single Electron')\n",
    "plt.legend(loc=1);\n",
    "plt.show()\n",
    "(np.mean(diff_b1_x_selectron)+np.mean(diff_b1_y_selectron))/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "-0.7306703486954833"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.hist(diff_b2_x_selectron, bins = 50, color='red', histtype='step', label = 'x blob2')\n",
    "plt.hist(diff_b2_y_selectron, bins = 50, color='blue', histtype='step', label = 'y blob2', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('reco[tracks] - true[particle] (mm)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('Single Electron')\n",
    "plt.legend(loc=1);\n",
    "plt.show()\n",
    "\n",
    "(np.mean(diff_b2_x_selectron)+np.mean(diff_b2_y_selectron))/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x13733c370>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(diff_b1_x_selectron, diff_b1_y_selectron, bins=(30, 30), range=((-50, 50), (-50, 50)), cmap='viridis', cmin=0.001)\n",
    "plt.xlabel('blob-1 reco_x - true_x (mm)')\n",
    "plt.ylabel('blob-1 reco_y - true_y (mm)')\n",
    "plt.title('Single Electron')\n",
    "#plt.xlim((0.0,1.5))\n",
    "plt.colorbar(label='events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x136965e80>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(diff_b2_x_selectron, diff_b2_y_selectron, bins=(30, 30), range=((-50, 50), (-50, 50)), cmap='viridis', cmin=0.001)\n",
    "plt.xlabel('blob-2 reco_x - true_x (mm)')\n",
    "plt.ylabel('blob-2 reco_y - true_y (mm)')\n",
    "plt.title('Single Electron')\n",
    "#plt.xlim((0.0,1.5))\n",
    "plt.colorbar(label='events')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 - BLOB energy distributions for SIGNAL and BACKGROUND"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After properly asigning blob1 and 2 tags, it's straigh forward to represent the energy distributions for the signal and background events:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(reco_0nubb_e[1], bins = 50, range = (0, 2.55), color='red', histtype='step', label = 'energy blob1')\n",
    "plt.hist(reco_0nubb_e[2], bins = 50, range = (0, 2.55), color='blue', histtype='step', label = 'energy blob2', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('energy (MeV)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('0nubb')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1222fc490>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = myhf.get_weights(reco_0nubb_e[1], True)\n",
    "\n",
    "\n",
    "plt.hist2d(reco_0nubb_e[1], reco_0nubb_e[2], weights= weights, bins=(30, 30), range=((0, 1.5), (0, 1.5)), cmap='viridis', cmin=0.001)\n",
    "plt.xlabel('Blob-1 candidate energy (MeV)')\n",
    "plt.ylabel('Blob-2 candidate energy (MeV)')\n",
    "plt.title('0nubb')\n",
    "#plt.xlim((0.0,1.5))\n",
    "plt.colorbar(label='proportion of events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(reco_selectron_e[1], bins = 50, range = (0,2.55), color='red', histtype='step', label = 'energy blob1')\n",
    "plt.hist(reco_selectron_e[2], bins = 50, range = (0,2.55), color='blue', histtype='step', label = 'energy blob2', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('energy (MeV)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('Single Electron')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x13892da30>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = myhf.get_weights(reco_selectron_e[1], True)\n",
    "\n",
    "plt.hist2d(reco_selectron_e[1], reco_selectron_e[2], weights=weights, bins=(30, 30), range=((0, 1.5), (0, 1.5)), cmap='viridis', cmin=0.001)\n",
    "plt.xlabel('Blob candidate 1 energy (MeV)')\n",
    "plt.ylabel('Blob candidate 2 energy (MeV)')\n",
    "plt.title('Single Electron')\n",
    "plt.colorbar(label='proportion of events')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 - Efficiency Values and fom estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all I'm going to check how blob energies are distributed acording to the track energy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x138cb0550>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = myhf.get_weights(reco_0nubb_e[0], True)\n",
    "\n",
    "plt.hist2d(reco_0nubb_e[0], reco_0nubb_e[2], bins=(30, 30), range=((2.4, 2.5), (0, 1.8)), cmap='viridis', cmin=0.001)\n",
    "plt.xlabel('track energy (MeV)')\n",
    "plt.ylabel('Blob-2 candidate energy (MeV)')\n",
    "plt.title('0nubb')\n",
    "#plt.xlim((0.0,1.5))\n",
    "plt.colorbar(label='proportion of events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x13a6d0a60>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = myhf.get_weights(reco_0nubb_e[0], True)\n",
    "\n",
    "plt.hist2d(reco_0nubb_e[0], reco_0nubb_e[1], bins=(30, 30), range=((2.4, 2.5), (0, 1.8)), cmap='viridis', cmin=0.001)\n",
    "plt.xlabel('track energy (MeV)')\n",
    "plt.ylabel('Blob-1 candidate energy (MeV)')\n",
    "plt.title('0nubb')\n",
    "#plt.xlim((0.0,1.5))\n",
    "plt.colorbar(label='proportion of events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(reco_selectron_e[0], bins = 50, histtype='step', label = 'energy track')\n",
    "plt.hist(reco_selectron_e[2], bins = 50, histtype='step', label = 'energy blob2', linestyle='dashed')\n",
    "\n",
    "plt.xlabel('energy (MeV)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('Single Electron')\n",
    "plt.legend(loc=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total events 4155\n"
     ]
    }
   ],
   "source": [
    "plt.hist(reco_selectron_e[0], bins = 50, histtype='step', label = 'energy track')\n",
    "plt.xlabel('track energy (MeV)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('Single Electron')\n",
    "plt.show()\n",
    "\n",
    "print(f'Total events {len(reco_selectron_e[0])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total events 4155\n"
     ]
    }
   ],
   "source": [
    "plt.hist(reco_selectron_e[2], bins = 50, histtype='step', label = 'energy track')\n",
    "plt.xlabel('blob-2 energy (MeV)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('Single Electron')\n",
    "plt.show()\n",
    "\n",
    "print(f'Total events {len(reco_selectron_e[0])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total events 4155\n"
     ]
    }
   ],
   "source": [
    "plt.hist(reco_0nubb_e[2], bins = 50, histtype='step', label = 'energy track')\n",
    "plt.xlabel('blob-2 energy (MeV)')\n",
    "plt.ylabel('events (a.u.)')\n",
    "plt.title('0nubb')\n",
    "plt.show()\n",
    "\n",
    "print(f'Total events {len(reco_selectron_e[0])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BLOB ENERGY = true vs reco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['event_id', 'particle_id', 'hit_id', 'x', 'y', 'z', 'time', 'energy',\n",
       "       'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#0nubb files\n",
    "true_list_0nubb_hits = []\n",
    "filepath_0nubb_true_hits = '/Users/halmamol/NEXT/files/NEXT100/beershebaFS/0nubb/hits/'\n",
    "#loop over all the files, to read 'HITS' information\n",
    "for nfile in range(1,max0nubbf):\n",
    "    \n",
    "    filename_0nubb_true_hits = filepath_0nubb_true_hits+f'next100.0nubb.{nfile}.deconv.h5'\n",
    "\n",
    "    try:\n",
    "        readfile_true_0nubb_hits = pd.read_hdf(filename_0nubb_true_hits, 'MC/hits')\n",
    "    except:\n",
    "        print('File {} not good'.format(filename_0nubb_true_hits))\n",
    "        continue\n",
    "        \n",
    "    true_list_0nubb_hits.append(readfile_true_0nubb_hits)\n",
    "    \n",
    "#'concat' command means: 'concatenate pandas objects along a particular \n",
    "# axis with optional set logic along the other axes.' -> following line \n",
    "# is needed to keep an order between the files\n",
    "\n",
    "true_0nubb_hits = pd.concat([dt for dt in true_list_0nubb_hits]) \n",
    "true_0nubb_hits.columns  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['event_id', 'particle_id', 'hit_id', 'x', 'y', 'z', 'time', 'energy',\n",
       "       'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#0nubb files\n",
    "true_list_selectron_hits = []\n",
    "filepath_selectron_true_hits = '/Users/halmamol/NEXT/files/NEXT100/beershebaFS/e-_roi/hits/'\n",
    "#loop over all the files, to read 'HITS' information\n",
    "for nfile in range(1,max0nubbf):\n",
    "    \n",
    "    filename_selectron_true_hits = filepath_selectron_true_hits+f'next100.e-_roi.{nfile}.deconv.h5'\n",
    "\n",
    "    try:\n",
    "        readfile_true_selectron_hits = pd.read_hdf(filename_selectron_true_hits, 'MC/hits')\n",
    "    except:\n",
    "        print('File {} not good'.format(filename_selectron_true_hits))\n",
    "        continue\n",
    "        \n",
    "    true_list_selectron_hits.append(readfile_true_selectron_hits)\n",
    "    \n",
    "#'concat' command means: 'concatenate pandas objects along a particular \n",
    "# axis with optional set logic along the other axes.' -> following line \n",
    "# is needed to keep an order between the files\n",
    "\n",
    "true_selectron_hits = pd.concat([dt for dt in true_list_selectron_hits]) \n",
    "true_selectron_hits.columns  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "b1_true_0nubb, b2_true_0nubb  = [], []\n",
    "for j in range (0,len(reco_0nubb_e[3])):\n",
    "    evt_number = reco_0nubb_e[3][j]\n",
    "\n",
    "    #print(evt_number)\n",
    "    \n",
    "    evt_hits = true_0nubb_hits[true_0nubb_hits.event_id == evt_number]\n",
    "    evt_hits = evt_hits[evt_hits.label == 'ACTIVE']\n",
    "\n",
    "    xt = evt_hits.x\n",
    "    yt = evt_hits.y\n",
    "    zt = evt_hits.z\n",
    "    et = evt_hits.energy\n",
    "    \n",
    "    true_0nubb_evt = true_0nubb[true_0nubb.event_id == evt_number]\n",
    "    true_0nubb_evt_primary = true_0nubb_evt[true_0nubb_evt.primary == True]\n",
    "    part1 = true_0nubb_evt_primary[true_0nubb_evt_primary.particle_id == 1]\n",
    "    part2 = true_0nubb_evt_primary[true_0nubb_evt_primary.particle_id == 2]\n",
    "    \n",
    "    x_e1 = part1.final_x.values[0]\n",
    "    y_e1 = part1.final_y.values[0]\n",
    "    z_e1 = part1.final_z.values[0]\n",
    "    x_e2 = part2.final_x.values[0]\n",
    "    y_e2 = part2.final_y.values[0]\n",
    "    z_e2 = part2.final_z.values[0]\n",
    "\n",
    "    bx = np.array([x_e1, x_e2])\n",
    "    by = np.array([y_e1, y_e2])\n",
    "    bz = np.array([z_e1, z_e2])\n",
    "\n",
    "    b1_true_i, b2_true_i = 0.0, 0.0\n",
    "    for i in range(1,len(xt)):\n",
    "        R1 = np.sqrt((bx[0]-xt.values[i])**2+(by[0]-yt.values[i])**2+(bz[0]-zt.values[i])**2)\n",
    "        R2 = np.sqrt((bx[1]-xt.values[i])**2+(by[1]-yt.values[i])**2+(bz[1]-zt.values[i])**2)\n",
    "        if R1<blobR:\n",
    "            b1_true_i = b1_true_i + et.values[i]\n",
    "        if R2<blobR:\n",
    "            b2_true_i = b2_true_i + et.values[i]\n",
    "    b1_true_0nubb.append(b1_true_i)\n",
    "    b2_true_0nubb.append(b2_true_i)\n",
    "    #print(b1_true_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4375"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(reco_0nubb_e[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4375"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(b1_true_0nubb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x139c7aeb0>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = myhf.get_weights(reco_0nubb_e[1], True)\n",
    "\n",
    "plt.hist2d(reco_0nubb_e[1], b1_true_0nubb, weights= weights, bins=(30, 30), range=((0, 1.5), (0, 1.5)), cmap='viridis', cmin=0.001)\n",
    "plt.xlabel('RECO energy (MeV)')\n",
    "plt.ylabel('TRUE energy (MeV)')\n",
    "plt.title('0nubb - Blob 1')\n",
    "#plt.xlim((0.0,1.5))\n",
    "plt.colorbar(label='proportion of events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x13bc49670>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = myhf.get_weights(reco_0nubb_e[2], True)\n",
    "\n",
    "plt.hist2d(reco_0nubb_e[2], b2_true_0nubb, weights= weights, bins=(30, 30), range=((0, 1.5), (0, 1.5)), cmap='viridis', cmin=0.001)\n",
    "plt.xlabel('RECO energy (MeV)')\n",
    "plt.ylabel('TRUE energy (MeV)')\n",
    "plt.title('0nubb - Blob 2')\n",
    "#plt.xlim((0.0,1.5))\n",
    "plt.colorbar(label='proportion of events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "b1_true_selectron = []\n",
    "for j in range (0,len(reco_selectron_e[3])):\n",
    "    evt_number = reco_selectron_e[3][j]\n",
    "\n",
    "    #print(evt_number)\n",
    "    \n",
    "    evt_hits = true_selectron_hits[true_selectron_hits.event_id == evt_number]\n",
    "    evt_hits = evt_hits[evt_hits.label == 'ACTIVE']\n",
    "\n",
    "    xt = evt_hits.x\n",
    "    yt = evt_hits.y\n",
    "    zt = evt_hits.z\n",
    "    et = evt_hits.energy\n",
    "    \n",
    "    true_selectron_evt = true_selectron[true_selectron.event_id == evt_number]\n",
    "    true_selectron_evt_primary = true_selectron_evt[true_selectron_evt.primary == True]\n",
    "    part1 = true_selectron_evt_primary[true_selectron_evt_primary.particle_id == 1]\n",
    "    \n",
    "    x_e1 = part1.final_x.values[0]\n",
    "    y_e1 = part1.final_y.values[0]\n",
    "    z_e1 = part1.final_z.values[0]\n",
    "\n",
    "    bx = np.array([x_e1])\n",
    "    by = np.array([y_e1])\n",
    "    bz = np.array([z_e1])\n",
    "\n",
    "    b1_true_i = 0.0\n",
    "    for i in range(1,len(xt)):\n",
    "        R1 = np.sqrt((bx[0]-xt.values[i])**2+(by[0]-yt.values[i])**2+(bz[0]-zt.values[i])**2)\n",
    "        if R1<blobR:\n",
    "            b1_true_i = b1_true_i + et.values[i]\n",
    "    b1_true_selectron.append(b1_true_i)\n",
    "    #print(b1_true_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4155"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(b1_true_selectron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4155"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(reco_selectron_e[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x147dc5520>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = myhf.get_weights(reco_selectron_e[1], True)\n",
    "\n",
    "plt.hist2d(reco_selectron_e[1], b1_true_selectron, weights= weights, bins=(30, 30), range=((0, 1.5), (0, 1.5)), cmap='viridis', cmin=0.001)\n",
    "plt.xlabel('RECO energy (MeV)')\n",
    "plt.ylabel('TRUE energy (MeV)')\n",
    "plt.title('e-_roi - Blob 1')\n",
    "#plt.xlim((0.0,1.5))\n",
    "plt.colorbar(label='proportion of events')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Efficiency Values and FOM Estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following boxes I'm going to define the functions I'm going to use to do the full efficiency/fom analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "blobcut_energy=[]\n",
    "#in MeV\n",
    "minEcut = 0.010\n",
    "maxEcut = 0.600\n",
    "stepsEcut = 0.010\n",
    "\n",
    "blobcut_energy=myef.blobthreshold(minEcut,maxEcut,stepsEcut)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'm going to take the information from reco_selectron_e[][], to see how many events with each cut I get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "blobcut_energy_nevents_0nubb, blobcut_energy_nevents_selectron = [], [] \n",
    "\n",
    "blobcut_energy_nevents_0nubb = myef.nevents_afterthreshold(blobcut_energy, reco_0nubb_e)\n",
    "blobcut_energy_nevents_selectron = myef.nevents_afterthreshold(blobcut_energy, reco_selectron_e)\n",
    "\n",
    "blobcut_energy_nevents_0nubb = np.array(blobcut_energy_nevents_0nubb, dtype=np.float)\n",
    "blobcut_energy_nevents_selectron = np.array(blobcut_energy_nevents_selectron, dtype=np.float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create now arrays containing the statistical uncertainty (sqrt from total amount of events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "blobcut_energy_nevents_0nubb_error, blobcut_energy_nevents_selectron_error= [], []\n",
    "\n",
    "blobcut_energy_nevents_0nubb_error = myef.sqrterror_array(blobcut_energy_nevents_0nubb)\n",
    "blobcut_energy_nevents_selectron_error = myef.sqrterror_array(blobcut_energy_nevents_selectron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'blob-2 energy threshold (MeV)')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SZ50FO+0Exx4Ljz2WZq/t06dsoZnVpdRpqe2prq6OmhoPMLdWZtdd4Ykn0nTnd93lhGEtTtLMiKiuW97UEdxTix9lZuutZ08YPhyefhoOPBCWLnWjt1WEprakfaVZozCzdXr2hKlT1yUMr/NtFaCpI7iXNHcgZsb72zFuvhk+9rE0NmPXXcsWkhnk6w01WNK1kuZKeqZ2a4ngzNq1Aw6AW25Js9XOmgXPPlvuiKwdy1MN9WfSQLrVwAHA5cAVpQzKzDKjR6fV9latgr32gkceKXdE1k7lSRZdI+JOUs+p57MFkQ4sbVhm9l89esCIEWkQ3/77w623utHbWlyeZPGupCpggaSTJR0JbFHiuMys0CabpJlqd9ghtWO89FK5I7J2Jk8D97dJU3x8EzibVBV1fCmDMrMChY3e99wDRx8Nt9+e2jIiUgO4WYnlmUjw4YhYERGLI+ILETEuIh5sieDMrI7u3VMvqS23hOefh2OOgRUrip9ntoEaTBaSLpL0oQb2bSLpi5I+V7rQzKxenTqlqUG22w6uvx723jv1lHI7hpVQY3cWFwA/kjRP0jWSLsjWn/gn8C+gG3Bti0RpZu8nwYABqWvtokVQXQ2vvVbuqKwNa7DNIiJmAcdI2hSoJk0M+A4wLyLmt1B8ZlafwnaMhx9Oa2LMng3bb1+2kKxty9NmsSIipkfE3yLiRicKswqzww6pp1Tv3mmKkDPOKHdE1gZ5lRWztqBbtzRT7VZbwZlnOmFYs2vS3FBmVoEk2HFHOOywlDAi1q37XVhtZdYE65UsssF5m0bEmyWKx8w2hASXXJIezzoLttkGBg4sd1TWBhRNFpL+CnwNWAPMBHpIOjciflXq4MxsPRTePVx8cUoYl16aXnvwnm2gPG0WO2d3EmOBW4BtgGNLGpWZbZiqKrjootSG8cIL8Cv/bWcbJk+y6CSpEylZTI4IryZv1hpUVaU2jD594JRT4Moryx2RtWJ5ksWfgOeATYB7JW0LvFHKoMysmUgwZEhaG+MLX0hzSpk1QZ5kcVNE9IuIwyMigBeALxY7SVIXSTMkPSZpjqQzs/Jekm6XtCB77FlwzmmSFkqaL+nQgvI9JD2e7TtfcuWrWW5VVXDDDalr7VFHpTUxPDWIrac8yeK6whdZwpiU47yVwIERsSswAhgjaRRwKnBnRAwG7sxeI2lnYDwwDBgDXCCpQ/ZeFwITgMHZNibH55vZ9Olp69EDpk2DXr3g8MPhnXfKHZm1Mo1NJDhE0jhS76ejCrYTgC7F3jiS2ukwO2VbAEcAE7PyiaS2ELLySRGxMiKeBRYCIyX1BbpHxANZorq84Bwzy2vrreEf/4D33oPHH0+PZjk1dmexE/BxYDPgEwXb7sBX8ry5pA6SZgFLgdsj4iFgy4hYApA91i6k1A9YVHD64qysX/a8bnl9nzdBUo2kmmXLluUJ0ax9GToUbroJVq6EJ57w9OaWW2MTCU4GJkvaKyIeaMqbR8QaYISkzYAbJA1v5PD62iGikfL6Pu8i4CKA6urqeo8xa/f22ScljTlzUhvG1Kmw0UbljsoqXJ4R3AslfR8YWHh8RBRt5C449nVJ00ltDS9L6hsRS7IqpqXZYYuBAQWn9QdezMr711NuZk3Vu3daE+P22+H44+Gqq1JDuFkD8iSLycA/gTtIo7hzkdQHWJUliq7AwcAvgCmkZVl/nj1Ozk6ZAvxV0rnA1qSG7BkRsUbS8qxx/CHgOOD/8sZhZvWoHe39y1+mMRh9+sBjj3keKWtQnmSxcUSc0oT37gtMzHo0VQFXR8RUSQ8AV0v6Eqkb7qcAImKOpKuBucBq4KSsGgvgROAvQFdgWraZ2Yb63vfg5Zfh3HPTHFLbblvuiKxCKXUwauQA6SfAvyLilpYJqXlUV1dHTU1NucMwq3xr18IJJ8AVV8DgwfDUU+WOyMpI0syIqK5bnqeS8lvAVEnvSnozqxLyrLNmbUVVVZpwcPPNYcGCNAmhWR1Fq6EioltLBGJmZdSpUxrhPWcOTJiQ2i6+/OVyR2UVJM8U5QI+BwyKiLMlDQD6RsSMkkdnZi2nqgqGDUur7n3lK+n15ZenfW70bvfyNHBfAKwFDgTOBlYAfwD2LGFcZtbSahPCu+/CkUemO4sdd0zTnFu7l6fN4sMRcRLwLkBEvAZ4BI9ZW9WlS5p48JBDYP58eOmlckdkFSBPsliVdX8N+O/4ibUljcrMyqtLF7jxRujZMyWMv/+93BFZmeVJFucDNwBbSDoHuA/4aUmjMrPy69o1tWH06AGf/zxMnlz8HGuz8vSGukrSTOAg0jxNYyNiXskjM7Py69ABhg+H1avhmGNgyhQ49NDi51mbk6c31HnA3yPiDy0Qj5lVktpG79degwMPhLFj05xSm23mHlLtTJ5qqEeAH2ar1P1K0gdG9plZG9ezJ9x2GwwalNbCeMMrK7c3RZNFREyMiMOBkcBTwC8kLSh5ZGZWWfr0gTvvhM6dU8J49NFyR2QtaH3mJN4BGEKaqvzJkkRjZpWtb1/YZRfo2BE++lGY5+bL9qJospBUeydxFvAEsEdEfKLkkZlZZerSJSWMDh3g4IPhmWfKHZG1gDwjuJ8F9oqI/5Q6GDNrBWobtp94AvbfHw46KI3y7tzZjd5tWJ5qqIuAMZJOB5C0jaSRpQ3LzCre8OFw663wyitp4aT33it3RFZCeZLFH4C9gM9kr5dnZWbW3lVXw803w8qVMHt2ShzWJnluKDPbMPvum0Z6v/12qpL6j2us2yLPDWVmG65Xr1Qt9eSTThhtlOeGMrPm0atXmg7kqadSwthnHxg9utxRWTPx3FBmtuEKe0FNmQKf/GTqWrvrrmULyZpXrkF5EfFkRPwhIn6fN1FIGiDpbknzJM2R9K2s/AxJ/5Y0K9sOLzjntGxakfmSDi0o30PS49m+87PV+8ysEh1yCNx0E7zzTuol9dpr5Y7ImsH6jOBeX6uB70bEUGAUcJKknbN9v42IEdl2C0C2bzwwDBgDXJC1lQBcCEwABmfbmBLGbWYb6uCDUxvG22/DuHHuVtsGlCxZRMSSiHgke74cmAf0a+SUI4BJEbEyIp4FFgIjJfUFukfEAxERwOXA2FLFbWbNpGfPNEPt3XfD178OEeWOyDZAKe8s/kvSQGA34KGs6GRJsyVdJqlnVtYPWFRw2uKsrF/2vG55fZ8zQVKNpJply5Y14xWYWZNsuSX86Edw6aXwq1+VOxrbAA02cEtaTtZdtj4R0T3PB0jaFLgO+HZEvCnpQuDs7L3PBn4DfJHUeP6Bj2mkvL6YLiKNOKe6utp/xpiVU22jdwQsWACnnAI77ADnn//+/dYqNJgsIqIbgKSzgJeAK0hf3J8DuuV5c0mdSIniqoi4Pnvflwv2XwxMzV4uBgYUnN4feDEr719PuZm1BhL8+c/w/PNpedYhQ6B7rr81rYLkqYY6NCIuiIjlEfFmRFwIjCt2UtZj6VJgXkScW1Det+CwI0kz2QJMAcZL6ixpEKkhe0ZELAGWSxqVvedxgBcDNmtNunSBG29M1VJz5qSGb2tV8iSLNZI+J6mDpCpJnwPW5DhvH+BY4MA63WR/mXWDnQ0cAHwHICLmAFcDc4F/ACdFRO3nnAhcQmr0fhqYth7XaGaVYIst0jxSEfDIIzDZf/O1JooiPRSyxunzSF/+AdxPan94rsSxbZDq6uqoqakpdxhmVteoUTB3LixfDj/4AZx5ZhrxDW7HqACSZkbEB5bPzjOC+zlSt1Yzsw3XpQuMGJG61Z5zDtTUwKpV0KlTuSOzRhRNFpJ2JA2K2zIihkvaBfhkRPyk5NGZWdtTePcwciScfDJUVaVBfFax8rRZXAycBqwCiIjZpJHWZmYb5itfgX/+E9auTethvPBCuSOyBuRJFhtHxIw6ZatLEYyZtUMjR6Y1vdeuhTFj4NVXyx2R1SNPsviPpO1Zt57F0cCSkkZlZu3LJpukBZSefho+8Yk0CaFVlKJtFsBJpFHRQyT9G3iWNDDPzKx51LZjXHstHHMMfPaz6Q5Dcg+pCpHnziIi4mCgDzAkIj6S8zwzs/Vz9NFw3nlpAN+CBZ58sILk+dK/DiAi3spmjwW4tnQhmVm79o1vpHmkliyB555zwqgQDSYLSUMkjQN6SDqqYDsB6NJiEZpZ+/Ozn8FWW6XeUd//vhNGBWiszWIn4OPAZsAnCsqXA18pZVBm1s5JsOOO6fHnP08N3r/9bXptZdHYrLOTgcmS9oqIB1owJjOzlBgGD4ZPfxp+97uUMJ580o3eZZKnN9TLkm4iLY0awAPAdyLimZJGZmbtW+F6GBtvDD/9aZq1dqedyhpWe5WngfuvpNlg+wJbA9cAfytlUGZm/yWlOaTOPhtefhnmzYM1eSa+tuaUJ1koIq6IiNXZdiWNrKBnZlYSP/whDBoEy5bBiSe60buF5amGulvSqcAkUpL4NHCzpF4AEeGx+WbWMrbZJt1VXHwx9OqVGr+tReRJFp/OHr9ap/yLpOSxXbNGZGbWmIED4WMfg1/8Anr2TGMyrOTyrGcxqCUCMTMrqrbRe+1aeP11OPVUuPRS2Hpr95AqsTzrWWwM/D9gm4iYIGkwsFNETC15dGZm9amqgokT4Y034JZboGOeShLbEHkauP8MvAfsnb1eDHjhIzMrr06d4JproEePNP7illvKHVGblidZbB8Rv2Td4kfvAEWHUUoaIOluSfMkzZH0ray8l6TbJS3IHnsWnHOapIWS5ks6tKB8D0mPZ/vOlzyM08xI4y+GD09TnI8bB3ffXe6I2qw8yeI9SV1Zt57F9sDKHOetBr4bEUNJA/pOkrQzcCpwZ0QMBu7MXpPtGw8MA8YAF0jqkL3XhcAEYHC2jcl3eWbW5nXsmBZP2m67tBbGgw+WO6I2KU9F34+BfwADJF0F7AOcUOykiFhCtkhSRCyXNA/oBxwBjM4OmwhMB07JyidFxErgWUkLgZGSngO61045IulyYCwwLdcVmlnbVtuwvWQJ7LsvHHYYbL89bLqpG72bUdE7i4i4HTiKlCD+BlRHxPT1+RBJA4HdgIeALbNEUptQtsgO6wcsKjhtcVbWL3tet7y+z5kgqUZSzbJly9YnRDNr7fr2hTvvhG7d0nreb79d7ojalKLJQtKRwOqIuDnrAbVa0ti8HyBpU9KaGN+OiDcbO7Sesmik/IOFERdFRHVEVPfp0ydviGbWVmy7LdxxR3r+2GOweHHjx1tuedosfhwRb9S+iIjXSVVTRUnqREoUV0XE9Vnxy5L6Zvv7Akuz8sXAgILT+wMvZuX96yk3M/ugHXdMbRhr1sAnPwlvvVXuiNqEPMmivmPyjM8QcCkwLyLOLdg1BTg+e348MLmgfLykzpIGkRqyZ2RVVcsljcre87iCc8zMPmjTTWHo0HR3cdxxaRCfbZA8yaJG0rmStpe0naTfAjNznLcPcCxwoKRZ2XY48HPgEEkLgEOy10TEHNLstnNJDeonRUTt1JInApcAC4GnceO2mTVm+nR4/HH49a/h+uvh9NPLHVGrpygyc6OkTYAfAQdnRbcB50RERd/bVVdXR01NTbnDMLNyioAJE+CSS+DKK+Fznyt3RBVP0syIqP5AebFk0Vo5WZgZAO+9Bx/9aBp/MXRoGvHtLrUNaihZ5OkNdbukzQpe95R0a3MHaGZWEhttBNddB/37w5w57lLbRHnaLHpnPaAAiIjXWDc2wsys8m2+Odx0U3r+6KNw//3ljacVypMs1krapvaFpG3xSnlm1toMHQq77ZYmIDzoILj22nJH1Krkme7jB8B9ku7JXu9HmqfJzKx16do1JQyAY45JvaUmT07rfLsdo1F5Fj/6h6TdSZMBCvhORPyn5JGZmTW32oTwzjtp/MV3vwv9+qW5pKxReaqhANaQRlq/Aewsab/ShWRmVmJdu8Lf/56Sxb//DfPmwapV5Y6qouXpDfVl4F7gVuDM7PGM0oZlZlZiVVWpGmq77WDZMhg/PnWztXrlubP4FrAn8HxEHECaPdZTuppZ2zBgQKqGuv56OPpoWJlnuZ72J08D97sR8a4kJHWOiCcl7VTyyMzMWkJtO8aFF8LXvw5jx8KKFdChgxu9C+RJFouzQXk3ArdLeg3P+mpmba03HKsAABEYSURBVM2JJ6YBfF/5ShrlPXx4uSOqKHl6Qx2ZPT1D0t1AD9JEf2ZmbcuXvpQSxnHHwRNPpF5TXbuWO6qKkLc3FAARcU9ETIkItwKZWdt07LEwZAi8/jqMG+c2jMx6JQszs3Zhyy3TIkrTpsFnPgOrV5c7orJzsjAzq2v6dJg/H847D264AY4/Pq28147laeA2M2ufvvnN1G5x6qlw113pbuOee4qf1wY5WZiZNeaUU9K05medlV6/+y506VLemMrA1VBmZsWccQZssw289BLsvTcsWFDuiFqck4WZWTESDBqUxl48/zzsvjv89a/ljqpFOVmYmeUxfTo8/jjMmgUjRqT1vPv2hX33LXdkLaJkyULSZZKWSnqioOwMSf+WNCvbDi/Yd5qkhZLmSzq0oHwPSY9n+86XpFLFbGZW1IABcPfd8IMfpGqpRx+Fl18ud1QlV8o7i78AY+op/21EjMi2WwAk7QyMB4Zl51wgqUN2/IWkxZYGZ1t972lm1nI6doSf/AQ+9KHUW+qAA1LiaMNKliwi4l7g1ZyHHwFMioiVEfEssBAYKakv0D0iHoiIAC4HxpYmYjOz9dSrV0oYL7wAo0fDi2132rxytFmcLGl2Vk3VMyvrBywqOGZxVtYve163vF6SJkiqkVSzbJlnUTezEps+PbVhTJuWFlEaPTo9tkEtnSwuBLYHRgBLgN9k5fW1Q0Qj5fWKiIsiojoiqvv06bOhsZqZ5bPvvnDrrakqaocdYNSockfU7Fo0WUTEyxGxJiLWAhcDI7Ndi4EBBYf2J02Dvjh7XrfczKyy7L033HZbWp71scdg0aLi57QiLZossjaIWkcCtT2lpgDjJXWWNIjUkD0jIpYAyyWNynpBHQdMbsmYzcxyGzUKdtklJYwDDmhTVVIlm+5D0t+A0UBvSYuBHwOjJY0gVSU9B3wVICLmSLoamAusBk6KiNpZu04k9azqCkzLNjOzytS9e0oYCxfCgQemdo2+fYueVumUOhm1PdXV1VFTU1PuMMysvbr/fjj00DRNyN13p2nPWwFJMyOium65R3CbmZXCPvvALbek6UG22y61abRiThZmZqWy335w001pptrZs2HJknJH1GROFmZmpXTggTBsWBrpXV0NDz5Y7oiaxMnCzKzUevWC3XZL62Dstx9cfHG5I1pvXvzIzKzUpk9Pj6++Cp/9LEyYADU1MG8eVFWt21/BfGdhZtZSevWCm29Oy7RedFEavLdyZbmjysXJwsysJXXoAD/7GVx9NaxYke4wrr223FEV5WRhZlYOn/oU7LEHdO2ann/hC7B8ebmjapCThZlZucyYAa+8khZSuvzytALf7run2WsrjJOFmVk5deqUFlKaPh3WrEkr773wAlTY7BpOFmZmlWDffVODd58+8OyzMH48vPVWuaP6LycLM7NK0aMHDB0KgwbBNdekKUOee67cUQFOFmZmleWee+CZZ9K8Us89l0Z977pr2dsxnCzMzCrRmDHw8MOwxRZpXqnFi8vajuFkYWZWqQYPTnNJbb45PP00nHBCmmOqDJwszMwqWffuaSLCbbdN3Ws/8pHUW6qFOVmYmVW6e+5J7Rc33ZRW4NtjjxZvx3CyMDNrLT7+8dSO0adPi7djOFmYmbUmO+4IDz0EvXundoxx4+D110v+sSVLFpIuk7RU0hMFZb0k3S5pQfbYs2DfaZIWSpov6dCC8j0kPZ7tO1+SShWzmVmr0K0b7LxzWq71ppvSFCE1NSX9yFLeWfwFGFOn7FTgzogYDNyZvUbSzsB4YFh2zgWSOmTnXAhMAAZnW933NDNrf+65J91Z/POfaZqQvfdOvaf2378kH1eyZBER9wKv1ik+ApiYPZ8IjC0onxQRKyPiWWAhMFJSX6B7RDwQEQFcXnCOmZmNGpXmkzr00NT4PXcuvPFGs39MS7dZbBkRSwCyxy2y8n7AooLjFmdl/bLndcvrJWmCpBpJNcuWLWvWwM3MKlavXjB5cqqWKtF8UpXSwF1fO0Q0Ul6viLgoIqojorpPnz7NFpyZWcWrqoIBA9L0ID16NPvbt/Qa3C9L6hsRS7IqpqVZ+WJgQMFx/YEXs/L+9ZSbmVldJVzLu6XvLKYAx2fPjwcmF5SPl9RZ0iBSQ/aMrKpquaRRWS+o4wrOMTOzFlKyOwtJfwNGA70lLQZ+DPwcuFrSl4AXgE8BRMQcSVcDc4HVwEkRsSZ7qxNJPau6AtOyzczMWpCiwlZjai7V1dVRU+J+x2ZmbY2kmRFRXbe8Uhq4zcysgjlZmJlZUU4WZmZWlJOFmZkV5WRhZmZFOVmYmVlRbbbrrKRlwPM5Du0N/KfE4bSUtnQt4OupZG3pWqBtXc+GXsu2EfGB+ZLabLLIS1JNfX2KW6O2dC3g66lkbelaoG1dT6muxdVQZmZWlJOFmZkV5WQBF5U7gGbUlq4FfD2VrC1dC7St6ynJtbT7NgszMyvOdxZmZlaUk4WZmRXVLpKFpDGS5ktaKOnUevZL0vnZ/tmSdi9HnHnluJ4hkh6QtFLS/5QjxvWR43o+l/1eZkv6l6RdyxFnHjmu5YjsOmZl68V/pBxx5lXsegqO21PSGklHt2R86yPH72a0pDey380sSaeXI8688vxusmuaJWmOpHs26AMjok1vQAfgaWA7YCPgMWDnOsccTlpUScAo4KFyx72B17MFsCdwDvA/5Y65Ga5nb6Bn9vywSv395LyWTVnXVrgL8GS5496Q6yk47i7gFuDocse9Ab+b0cDUcsfajNezGWlBuW2y11tsyGe2hzuLkcDCiHgmIt4DJgFH1DnmCODySB4ENsvWCK9ERa8nIpZGxMPAqnIEuJ7yXM+/IuK17OWDvH9d9kqS51pWRPY/F9gEqOQeJnn+7wB8A7gOWNqSwa2nvNfSWuS5ns8C10fEC5C+FzbkA9tDsugHLCp4vTgrW99jKkVrijWP9b2eL1G5S+vmuhZJR0p6ErgZ+GILxdYURa9HUj/gSOCPLRhXU+T9d7aXpMckTZM0rGVCa5I817Mj0FPSdEkzJR23IR9YsjW4K4jqKav711yeYypFa4o1j9zXI+kAUrKo1Hr+XNcSETcAN0jaDzgbOLjUgTVRnuv5HXBKRKyR6ju8YuS5lkdI8yKtkHQ4cCMwuOSRNU2e6+kI7AEcBHQFHpD0YEQ81ZQPbA/JYjEwoOB1f+DFJhxTKVpTrHnkuh5JuwCXAIdFxCstFNv6Wq/fTUTcK2l7Sb0johInsctzPdXApCxR9AYOl7Q6Im5smRBzK3otEfFmwfNbJF3Qyn83i4H/RMRbwFuS7gV2BZqULMreUNMCDUEdgWeAQaxrCBpW55iP8f4G7hnljntDrqfg2DOo/AbuPL+fbYCFwN7ljrcZrmUH1jVw7w78u/Z1pW3r828tO/4vVG4Dd57fzVYFv5uRwAut+XcDDAXuzI7dGHgCGN7Uz2zzdxYRsVrSycCtpB4El0XEHElfy/b/kdSL43DSF9LbwBfKFW8xea5H0lZADdAdWCvp26SeEm82+MZlkvP3czqwOXBB9hfs6qjAGUJzXss44DhJq4B3gE9H9j+70uS8nlYh57UcDZwoaTXpdzO+Nf9uImKepH8As4G1wCUR8URTP9PTfZiZWVHtoTeUmZltICcLMzMrysnCzMyKcrIwM7OinCzMzKwoJwvbYJIGSqq3S1421UCj3VwlrcjxGQMk3S1pXjaD5reaGm85SBoraeeC10V/Lk34jAZ/D42c85f6ZorNZiud2sA5u0m6JHt+gqSQdFDB/iOzsgZnoM3O+1udst6SlknqLGmSpEodPd0uOVlYa7Ea+G5EDCUNnDyp8Mu31CR12MC3GAusV7ySKnUc1PeB/yt4/TjwmYLX40mDxBpzPXCIpI0Lyo4GpkTESuBC4H+bIVZrJk4W1lw6SpqYrdVwbZ0vAQAkfUbS45KekPSLOvt+I+kRSXdK6lP33IhYEhGPZM+XA/Oof5K+PpKuk/Rwtu2TlZ8h6bLsL/pnJH2z4JzPS5qRzfv/p9rEIGmFpLMkPUSaYO5Lkp7K3uNiSb+X1E3Ss5I6Zed0l/Rc7eusbG/gk8Cvss/YPtv1qexzn5K0b3bsCZKukXQTcJukTbK4H5b0qKQjsuOGFcQ8u+Cv8A5ZbHMk3Sapa3b8CEkPZsfeIKlnPT+7MZKelHQfcFR9v2RJ3YBdIqIwGfwTGCmpk6RNSaPUZxWcs4eke5Qms7tVUt9sgOi9wCcK3mc88LeC9zy4ghNmu+NkYc1lJ+CiiNgFeBP4euFOSVsDvwAOBEYAe0oam+3eBHgkInYH7gF+3NgHSRoI7AY8VM/u84DfRsSepNHSlxTsGwIcSprK4cfZl9tQ4NPAPhExAlgDfK4grici4sOkqRV+RLqrOSR7r9rENZ00ZQykL7zrIuK/08NHxL+AKcD3ImJERDyd7eoYESOBb9e55r2A4yPiQOAHwF3Z9RxASjibAF8DzstiribNAwRp4rs/RMQw4PXsZwBwOWnCv11IdwLv+xlL6gJcTPry3pc09UV9qknTRhQK4I7sZ3tEdq2179uJdBdydETsAVxGWmcFUmIYnx23NWmW1Luzn9la0owKFbvQVXvjZGHNZVFE3J89v5IPzgy7JzA9IpZFxGrgKmC/bN9a4O+NnPtf2V+u1wHfbmD6koOB30uaRfrS6p79NQxwc0SsjDQx3FJgS9KMnHsAD2fnHERaUAZS4rguez4SuCciXs0SwTUFn3kJ66aI+QLw54bir+P67HEmMLCg/PaIeDV7/lHg1Cy26UAX0lxZDwDfl3QKaabUd7Ljn42I2r/qZwIDJfUANouI2pXSJrLuZ19rSHbugmyKiysbiLkvsKye8kmkL/7CuwNIf0QMB27PruGHrFuPZCrwEUndgWOAayNiTcG5S4GtG4jDWphv8ay51J03Js808A2+l6QBwE3Z6z9mc151In15XxUR1zdwbhWwV8GXZ/rwNKfUyoKiNaR//wImRsRp9bzXuwVfXg3GHxH3KzUu7w90WI/5d2rjqY2l1luFoQPjImJ+nXPnZdVjHwNulfRl0t1P3WvsmjMWyDfV/TukhPX+EyNmSBoOvBMRT2nddOUC5kTEXvWc847S3EVHkpLMd+oc0iX7PKsAvrOw5rKNpNovhM8A99XZ/xCwf9bjpUN2TO1fulWkxk1Iq3vdFxGLsiqbEVmiEHApMC8izm0kjtuAk2tfSBpRJO47gaMlbZEd30vStvUcNyOLv2dWjz6uzv7LSX9RN3RXsRzo1sC+xtwKfCO7fiTtlj1uBzwTEeeT7qB2aegNIuIN4LXadhHgWNb97Gs9CQwqaE/5DPWbR2qTqM9ppMbvQvOBPrX/NrKqv8JFhf4G/D/SXd6Ddc7dEZjTwGdZC3OysOYyDzhe0mygF6k3y39FxBLSl8ndpJ4yj0TE5Gz3W8AwSTNJbRpn1fP++5C+5A7MGnVnKS1QU9c3geqsIXcuqW6/QRExl1Q1clsW++2kqpa6x/0b+Ckp6d1BWtv4jYJDrgJ68v4qmEKTgO9ljdTbN3BMfc4GOgGzlbrFnp2Vfxp4IqvaGUJKVo05ntTeMZvUZvS+n3FEvAtMAG7OGrifr+9NIuJJoEdB1V7hvmkRcXedsvdIfwj8QtJjpIbvvQsOuY1U1fT3whleJW1JuktZUuS6rIV41lmznCRtGmkVtY7ADaRpoW/I9h0NHBERx5Y1yBYg6TvA8oi4pOjBG/YZb0bEpaX6DFs/brMwy+8MSQeT6tJvIy27iaT/Aw4jrYnSHlwIfKrEn/E6cEWJP8PWg+8szMysKLdZmJlZUU4WZmZWlJOFmZkV5WRhZmZFOVmYmVlR/x9+R0HBVYyqSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.errorbar(blobcut_energy,blobcut_energy_nevents_0nubb, yerr=blobcut_energy_nevents_0nubb_error, fmt = '-r')\n",
    "plt.ylabel('accepted events (a.u.)')\n",
    "plt.xlabel('blob-2 energy threshold (MeV)')\n",
    "#plt.title('Cut efficiency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "fom_error, e_error, b_error = [], [], []\n",
    "\n",
    "e, b, fom, e_error, b_error, fom_error = myef.efficiencyterms(blobcut_energy_nevents_0nubb, blobcut_energy_nevents_selectron, \n",
    "                            blobcut_energy_nevents_0nubb_error, blobcut_energy_nevents_selectron_error,\n",
    "                            reco_0nubb_e, reco_selectron_e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'background rejection')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.errorbar(1-b,e, xerr=b_error, yerr=e_error, fmt = '-r')\n",
    "plt.ylabel('signal efficiency')\n",
    "plt.xlabel('background rejection')\n",
    "#plt.title('Cut efficiency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'f.o.m.=e/sqrt(b)')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.errorbar(blobcut_energy,fom, yerr=fom_error,fmt = '-r')\n",
    "plt.xlabel('blob-2 energy threshold (MeV)')\n",
    "plt.ylabel('f.o.m.=e/sqrt(b)')\n",
    "#plt.title('Cut efficiency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best fom is {2.052841670582836}, that corresponds to:\n",
      "- signal efficiency of 49.44%\n",
      "- background acceptance of 5.80%\n",
      "- energy threshold of 0.46 MeV\n"
     ]
    }
   ],
   "source": [
    "myef.best_fom(e,b,fom,blobcut_energy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "npz_file = f'/Users/halmamol/NEXT/NBarrays/ntracks_FULLSIM_R{blobR}mm'\n",
    "np.savez(npz_file, ntracks_0nubb=ntracks_0nubb, ntracks_eroi=ntracks_eroi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "npz_file = f'/Users/halmamol/NEXT/NBarrays/recovstrue_FULLSIM_R{blobR}mm'\n",
    "np.savez(npz_file, diff_b1_x_0nubb=diff_b1_x_0nubb, diff_b1_y_0nubb=diff_b1_y_0nubb, diff_b1_z_0nubb=diff_b1_z_0nubb,\n",
    "          diff_b2_x_0nubb=diff_b2_x_0nubb, diff_b2_y_0nubb=diff_b2_y_0nubb, diff_b2_z_0nubb=diff_b2_z_0nubb,\n",
    "         diff_b1_x_selectron=diff_b1_x_selectron, diff_b1_y_selectron=diff_b1_y_selectron, diff_b1_z_selectron=diff_b1_z_selectron,\n",
    "         diff_b2_x_selectron=diff_b2_x_selectron, diff_b2_y_selectron=diff_b2_y_selectron, diff_b2_z_selectron=diff_b2_z_selectron)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "npz_file = f'/Users/halmamol/NEXT/NBarrays/ereco_FULLSIM_R{blobR}mm'\n",
    "np.savez(npz_file, reco_0nubb_e=reco_0nubb_e, reco_selectron_e=reco_selectron_e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "npz_file = f'/Users/halmamol/NEXT/NBarrays/trackpos_FULLSIM_R{blobR}mm'\n",
    "np.savez(npz_file, track_0nubb_zmin=track_0nubb_zmin, track_0nubb_zmax=track_0nubb_zmax, track_0nubb_rmax = track_0nubb_rmax,\n",
    "        track_selectron_zmin=track_selectron_zmin, track_selectron_zmax=track_selectron_zmin, track_selectron_rmax = track_selectron_rmax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "npz_file = f'/Users/halmamol/NEXT/NBarrays/fom_FULLSIM_R{blobR}mm'\n",
    "np.savez(npz_file, e=e, b=b, fom=fom, e_error=e_error, b_error=b_error, fom_error=fom_error, blobcut_energy = blobcut_energy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test of true information \n",
    "Now I'm going to do a small test checking the true information, and which true events are removed with the selection cuts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['event_id', 'particle_id', 'hit_id', 'x', 'y', 'z', 'time', 'energy',\n",
       "       'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#0nubb files\n",
    "true_list_0nubb_hits = []\n",
    "filepath_0nubb_true_hits = filepath+'0nubb/hits/'\n",
    "\n",
    "#loop over all the files, to read 'HITS' information\n",
    "for nfile in range(1,max0nubbf):\n",
    "    \n",
    "    filename_0nubb_true_hits = filepath_0nubb_true_hits+f'next100.0nubb.{nfile}.deconv.h5'\n",
    "\n",
    "    try:\n",
    "        readfile_true_0nubb_hits = pd.read_hdf(filename_0nubb_true_hits, 'MC/hits')\n",
    "    except:\n",
    "        print('File {} not good'.format(filename_0nubb_true_hits))\n",
    "        continue\n",
    "        \n",
    "    true_list_0nubb_hits.append(readfile_true_0nubb_hits)\n",
    "    \n",
    "#'concat' command means: 'concatenate pandas objects along a particular \n",
    "# axis with optional set logic along the other axes.' -> following line \n",
    "# is needed to keep an order between the files\n",
    "\n",
    "true_0nubb_hits = pd.concat([dt for dt in true_list_0nubb_hits]) \n",
    "true_0nubb_hits.columns  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "evt_number = 100\n",
    "\n",
    "evt_true_0nubb_hits = true_0nubb_hits[true_0nubb_hits.event_id == evt_number]\n",
    "evt_true_0nubb_hits = evt_true_0nubb_hits[evt_true_0nubb_hits.label == 'ACTIVE']\n",
    "\n",
    "xt = evt_true_0nubb_hits.x\n",
    "yt = evt_true_0nubb_hits.y\n",
    "zt = evt_true_0nubb_hits.z\n",
    "et = evt_true_0nubb_hits.energy*1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9l89+9rO0tLTw4x//mFWrVnHq1CluvvlmhodTsqkPfehDAH8IPEOKXN8I/KzU95RKC1R3QFAsauTKfI9VKSV9fX2Mj49TX1/Pli1biiZWKC4tIKVkbGyMc+fOsW7dOiePViyKHfNS7eRaKgrlb2dmZhgYGCAYDNLV1cWyZcuWnL+18UqmBZYCl8uF2+1m5cqVfPvb3+a6667jH//xHzl+/HiGv62N66+/nv7+/ozXHnzwQZ544gkA3v/+93PDDTfw2c9+lr179zrb7Nq1i3g8TiKRYHp6mmAwiJTyaQAhxD8Db2YR5Aq1gtarBtkqgMnJSXp6epwxK11dXSU/4i9ErrZzlW056Ha7GRkZKYkEC6kFpqamOHv2LD6fz5lPVcmZWkvBUteVnr+18+DPP/8869atIxwOO/pbYFH5WxvVGLnmg2maGce9/PLLWb26+Eft8fFxJ8Jtb29nYmL+JJTvf//77N27F4/Hw/DwcPbxh4DLF7l8Xi2xwG8suWZ7rMbjcTo7O1FVNWPMymIdq3Ltk+5ctWPHjgy5UqnnyZV6SG802Lp1K4Zh0N/fz8TEBGNjY06LZlNTk9Mx9GqFXVlfav7WRqVSDZWo4tvkan8/yr3u06dP8/GPf5xHH30UyFswXTRF1iLXSxTZ0irLsujr62NycpJt27ZltDRCKu9XauSqquo8okx3rjp8+PC8KKjUx/f07aWUDA0Ncf78ebZs2cKKFSsc85Dm5mbnMTGZTBIIBDI6hvx+v0O2xTw+XyqtqtnHXGz+Nt0/odzrNE3T8SAo93GXEhG3tbUxOjpKe3s7o6OjGcqYoaEh3vKWt/DP//zPbNq0CYDVq1czNDSUfojVwMhizq1LnYnE2KLXXk34jSHXXB6rFy5coLe3Ny/hweIjVzt/G4vFOHPmDKqqsn///ryDB0s9j50WCIVCdHR00NTUlKEysMk3nYTdbvc81/50T9P09lU7wvX5fFWZTlgIxaw5V/7Wblm187e2f4J9YypH/tZGJXKukCqSqqrqVPVLxW233cY3vvEN7r77br7xjW9w++23A6mmlltuuYXPfOYzXHvttc727e3tNDQ0IIQ4AjwLvA/48mLWrgk3y92rFrNr1eE3glxtUj19+jQ7d+4kGo3S2dmJx+PJsOrLhcVErrYU69y5c4yNjbFt27YFDVZKJVcpJRMTE4yOjrJz586Clob5ImJbmJ4tf7KjOXvqqj2LqqmpCdM0C0bYhm4w2j8JQPv6FjRX4a9YNRXb8rWsRiIRAoEAY2NjhEKhRetvs1GpnKudHw6FQguaB73rXe/iiSeeYHJyktWrV/PXf/3X3H333dxxxx38wz/8A2vXruV73/seAF/5ylfo7e3lnnvu4Z577gHg0UcfpbW1la9+9ascPHjw/5CSYv2MRRazgLJMf60GvKrJNTsFMDMzQ3d3NzMzMxljVgphMZFrJBJhcHCQdevWceTIkaKik1LSAhcuXODs2bM0NjZy4MCBghd2qRe9qqrz5D2JRIJAIMDMzAzT09MEAoGM3K09IiUUiPDJ372PkZ5xUBSWrW3i//s/f8Tl61cUPGc1R8b2DcjlcrFt2zYgf/42nXAL5W9tVIpc7XUXQ67f/va3c77++OOPz3vtk5/8JJ/85Cdzbn/gwAGklLtLX2kmambZVY5cBivj4+NEo1H8fj9bt24t+oJWVZVEIlHUtnZBKRqNsmLFCjZu3Fj0moshcbvoBrBp06YMU498KIcUy+Px0NraSmtrK6Zp0tbWhqqqGe2ZiqLwzb96jMGnukHTQFGYmgrwZ3s+zldOfZ5Va5bnPHa1qhgKYaH87fj4OLFYrGD+FiqXFrARiUQKNhBUK2oFrSpFtrQqEolw5swZ6uvrqaurY82aNSUdr1jN6vnz5x3nKk3TGB8fL/k8haa5Dg4OMjg4yNatW1mxYgXj4+NFkX6xBi+lIN1W0G7PNAyD//Gf94HHjdA0sCS4NKSq8Kev/Tif/tF/Z+eVua3kLjVyzYVi87f2FNrGxkYMw6gouYbD4Yp4ClcaNXKtMmSnAAzD4OzZswSDQXbs2EFjYyNPP/10yXrFhXKuuZyrZmdnFzXNNdc+dsFq2bJlGa23pUSkr0ReU9M0ECJFrKYFAjAlQtWQ0zr/fP/j3PzWLbStXO6kE+rq6qoq55oPi1ljofytHd3Ozs5y6tSpjOjWnr22WKQbzIRCoUsuctUtg5FYaYFJteKSJ9dcKYCRkREGBgZYv34927dvzxivUSq55otcbeeqWCzGFVdckREhlGOaq31zmJ2dZefOnfMuklfSWyAe1zn14iCTF0JEYzM0NiwnvUs3kTAY7r9Q8Bgqbtat3UJLqy/DfMSWg9ma00rN6loKypW6SC8gQuqxfefOnU6Eu5T8rY30PG4kErnkIldNuGj1tC+84SWAS5pc841ZaWpqyjlpVVVVDMMoSbidHbkW41y1GHJNj1wvXLhAd3c3a9asyZsfLoe3wMxslL7eCSLhBCta6tmwuRWPN/MzSyYNHvzuMTpPDhEOx4lGY3SdnOZdv/ca1m9cweDAJF/81EOMDs+AlEjDRKhqqs1GEUjTBCmpq3Pj83kcwrDR19eHYRhEIhFGR0dJJBJOrrKpqYmGhoaLbpdXie4s+7hutxuv11tS/rahoSGvxCr9+72QaUu1opYWuIjINWalt7eXaDSaM8qzsdRuK/sRfSHnqsWeJ5FI8MILL6AoyoISsaWQq2VZPPd0L//5n72EZ6N4vS4ua6lnYjzI1ddvxeV6uYI9NDDFi8/2EQlFSeom8bhOf/8U3/rGk9x2x37+6cuPMzIwifS7UPZtxzreiZwraGFZYBisf+u1HHzNVlpXzpeLqaqKz+dj5cqVQKZbv62KkFJSX1+fkU54JaPbSpFrvoh4Mflbe4JBegNBKBQq2giomlD9iaLicEmRq50CGBgYcAoq9piVjRs3snPnzoIX3WI1q7quc+bMmYz87UL7lKpZDQQCDA8Ps3PnTkfkv9A5FpNzlVJy/NfdPPbYaYIXoiQCEbw+D+GJIMlAjG07V9HW/vIFOT40zcxkkHgsiWFJEookZiko0wH+/wceITEcRCoCS4DidSF3bEIoAsWt4fZqHL52C799x0HWb25FVecTVPZ7yHbrh5en3eZqXbUJtxKdTjZeaXLNRqH8bfYEA4/Hg67rDA4OEgqFCnoKlGI3ODU1xdvf/naee+457rzzTr7yla84xzl27Bh33nknp06d6mUJdoMvfzC1yPUVRXoKIBqNkkwm6ejoyDlmJR9KJVcpJVNTU0xPT7N9+/aM/G0hlEKuwWCQjo4OFEVh06ZNRRErFGeWnevinZkM8+QjpwhHksyOB0BK4gkTX72XydEAU6OzGeSajOkkIkliSQNTgaRPxRNMEluuESaOQIJuIjwapioQfhdeRbBqXQuvvXknN715Py73wt60haAoyjzpU/q01eHhYZLJJD6fj6amJgzDWHILaDoqMYVgqUjP39omK6ZpMjo6ysTEBP/4j//Id7/7Xerr6zl27Bjvec972LdvX8YxSrEb9Hq93HPPPZw6dcohYhsf+tCHuP/++7nmmmu2sAS7QYCkZTAUm28Ucymi6sk122BF13UmJyeRUnLVVVeVZNNXCrnaEi63201DQ0NJrkLFkKthGPT29hIMBtm1axdTU1MlXcCFJFbJZJLe3l7HODl9u2ce72Skb5JknQdDN0gmTISmoLg0EnGDREzPOJZpWliRGKqmYaCAaSFNC6Vew79Mw9riQj8ZwgwnQVPwNfrZ/5pNvOnWq9iytW3BiG+xxaJc01btSQaGYfDCCy8AmU5Yfr9/UeeqhFE2lF+CpqoqXq+XZcuW8alPfYpgMMitt96K1+tdst1gXV0d1113Hb29vRnbj46OEgwGufrqq5FSyqXaDboUFyu9Kxeza9Whask1O69qa0mHh4cdn85SiBWKI9ds5yr7zl8KFiJX28Rl7dq1bNu2DSEEMzMzJXsLZEeuUkpGR0fp6+tz9Lzj4+OEw2EmJ6cY7Ary60d6kQkD1ZJYUpI0JT63is/rxpRgmJlrSMR1kAKvZSKkhelSEULBY7px14OyE9RVHmTUYs/GNdxywwFWrmpGUV7ZSE8IQV1dHT6fj+HhYQ4cOJAxidZ26l+MM1gl0wLlRnpBKxKJ0N7ezoEDB4revxi7wXSU224Qyms5KIRQgeeBYSnlrUKI5cB3gPVAP3CHlHKmfGd8GVVHrtkGK0IIpqen6e7uZsWKFRw5coSxsTHHGKUULESuuZyrLMtaVOU/14UTj8cdE5fsglWpo2GyzxGNRuno6MDr9XLo0CHgZUu7wf4xnvnFECePniMR11EVgUsaYFq4FKhzKxhJHSOpMDUZyfxMxoJITcECPEIgdQut0UuDx4M17sW90sS9UWPn6jW8Zf8+XCU+ipe7Qyv9eLkm0dqFoVKcwSpBrpXqTEtPh4TD4QXrA0tFue0Gl7zzfHwEOAPYH8TdwONSynuFEHfP/fzx8p4yhaojV8AhzmQySWdnJ1JK9uzZ4wxgU1WVeDxe8nHzkWsh56pSCkc2cs10GhgYYGRkhK1btzqPstn7LMZyMP3Y27dvdywT7c8wHIzz0399kenpJKql4PG4EQp4vW5MNYHbMLE8KlFDB8vi10+cZMfey7hsxXJmJ+N0nRzB63NhGBZujwuRSPC6129n06619A1Oo7hVtmxtY9fW9pKJtRJYiLQ8Hk9RzmDpwv5KtKlWKho2TdOJxhcjxSpkN5gL5bQbBNtboDw3HSHEauAW4H8AH517+Xbghrn//wbwBL8p5GqTRn9/P2NjY44/aTo0TXPSBaUgm1wty3LOU4xz1WIQCAQ4c+aMU3jLV2RZjFl2Mpnk2WefzXts0zB5/IEXGRsMgKYhhERYEkWouDSVTZvbmJiKoCcMLNPEApIxi7HhWcZGL/Djfz3BzGwM3Ap+rwtVEdQvb2T/tRvZvmsDR5bywcyhkpFrMcjlDGYP/rOdwexJBranQC6fgFJRzoJbvuMuhlzz2Q3mg203+Mwzz3D11VcLlmA3aKMEcm0RQjyf9vP9Usr7037+IvAxIP1DaJNSjgJIKUeFEIXvHktA1ZErwMmTJ2loaMjrKLUYSZW9n20iPTU1RVdXlzPRtRKPfWfOnCEUCrF79+4FO2VKiVxN06Svr49gMMihQ4fyXkAnnjlH5wtDqQvO5cbCQloWikdj0442bn3nIf7X5x9jKpZA97oQCswaFrMBGOqdJRlXcLs9qBokEiaWKlm5zk8sOcvw8LCT965kf3ypKEdEmD34b3Jykunpaerr6+f5vGY7gxWLSpm2pJNrPB4vOPutFLtBgPXr1xMMBkkmk/zoRz/i0UcfZefOnXz1q1/lzjvvBOhliXaDSUtnMFq0WmBSSpkzoSyEuBWYkFIeE0LcsNj1LAVVSa5XXXVVQaJZSuSaSCQ4ceIEpmly1VVXOamGcsH2WY1EIqxbt67s8i37ptDa2up0MOXCxFiAn//oBQwp0RBI3cAUoPnc3HDrHm5+81X46zxs27WKXz13DtWyMGYTWJbkB//8JB5VRVUEbreGbljU1/tYuWoZr3njOlpbW5wCo605tUmmqampJIPmix25FgO7k8p2BrNfs9MJ6c5g6emEQm2rlZyfZRe0FlI5lGI3CMxTFtg4cOCALc/aVMpac8ElXKzyti31MADXArcJIX4b8AKNQoh/AcaFEO1zUWs7UDHdV1WS60JR3GIiVyklk5OTjI2NsXv37gVzSYuBnbvVNA2/31+SfGsh96pkMklXVxfJZJK9e/eiKAqzs7M5t03EdX76g+PMzsZQVRUdA69LRUHwO+89zGt++0pn2+17V3Oqf4Lw8CyWS0UPxwnPJnG5VJqX1yHjBqpHpaHBw23vOoSlhvD7/TQ3Nzvvz9acBgIBhoaG0HV9SVHdUlApcs1ev6IoOZ3B0ttW4/E4Ho8nQ51gE1+lcq72FIJLwRAnH8qRc5VS/gXwFwBzkeufSynfK4T4HPB+4N65/z645JPlQdWSayFomlYSudrOVfX19axYsWJRxFroorUsi4GBAUZHR53c7VNPPVXS8fMVzmTaCO50L4NIOEo8pmOaVkbnUygQ4zvffGp59N8AACAASURBVIoXnusHwwLDQojUDWn3oXVcc9OujONvv2I1bU/2kJyMYEgDVQi8PheqppJIGNT73Xg8Lt74jgNs2r6Snp7QvDXm0pza7v3pUV16dGsrJS6VyLUYItQ0jeXLlztFRTk39jsYDDI1NUVfXx+WZVFXV4fL5cI0zbKTbHpawB6tfqmhwveFe4HvCiE+CJwH3lGpE1UluS4E24BlIWQ7V0kp6evrK/l89iN7rsc4m7htmVj6hVLKhZ4rLRCLxejo6MDtdjsjuCcmgrz04nn6zo4zNTXJ+IjCkWu3cPnq5VwYn+Vfvv5r+rrGAYnq0TAUEKbCssubueVdR1C1zPfg97n5nduu4ltjv8KaiYHuxpvUMV0aLo8L1a1yzW9tY9/h4o2/04tEdlRnm5EEAgFGRkZIJpP4/X6SySQej4dly5aVhWQuJrlmI33sd1tbm3OscDjM2NgYkUiE559/PmMK7VKdwWxytSPYSw+i7MYtUsonSKkCkFJOATeW9QR5cEmS60L5yXzOVdFodFGFsFzkqus6PT09RCKReZaD+fYphOxprnbDRLqKoe/cBf795ycZHpwmaVpMTswSnD7HS8cGeN2NO3jmF52MToQwEjouj0ZSt3C5NVRF4aa37GV5S+6i2sZNbdz+tgM89MAxAkIgogmEkLh9Gtt2X85Nt+9dclNALjOSaDRKd3c309PTjI2NZeQs7ei2VJKpBLmW85j2e0wkErhcLjZs2DBvCq3tDGZH+qU4g9nkGggESm6yqRZcugmNTFQluRYzuiQfCjlXLcatCl7O8bpcLqSUjI+Pc/bsWdavX8+OHTtyrqdUcrW3TzfHTpdXRcJxHvzOs8xMRwgGY0RiOtFInAtJSSSc4PSTPfgbPHgb/bjdKnpMx+N34/GobNrRzFUH1hU8/5WHNtCysokn/r2D8bEg9XUeNm9t5chrt+H2Lk12lAt2R1V9fT0tLS00Nzc7OUt7GGAikcDn82XYDy70eVbCByBdO1rOY9rRcK4bz2Kdwewi1qVolA2QsAwGwpMXexllQVWS62JgGAY9PT0FnasWqzKwiS8ajXLmzBk8Ho/zmL7QPqUgEAg4E2rT129ZFj9/4DkGTo3g8qhEZ6MkgETcQNHcoJsoQiClwDItdB0UTUVzq1zzum2s3uDO6UiVjVVrl/PuD1xHMmlgmhYej6vibazpUWGunGUsFiMQCDjOT0BGdJtdka+ED0AljlnoxlusM5jdypueTrBxqXq5uhWNNf6KSU9fUVzy5Jpe8FlI+rTYyFVRFAYGBpypsfbFv9A+xZ5renqajo4OhBAcOnRo3oX85M9P8sy/n0FaFpGwgdfnYnY8gKGqoApcAlSfG7empApSqkJDk483v+8aNm27LK+EJh/cBVysyjHwsFikk0y685P9CN3T00MsFsuIbisRuVaisr+YiRjFOIPF43Gef/55jh8/ntFpmI1S7AYBPvOZz/AP//APqKrK3/3d33HzzTcDL9sNxmIxzp49+3cs1W7wVYTqUX+nodiLIxwOc+zYMaampjh48CCrV68uuO9iWllnZmaYnJzEsiyOHDlSFLHa51qIXHVd59SpU5w7d45du3bNm58kpeSph1/iZ998imQwQnI2jE9TUBSVVe3LaFnuwedx4Wnw4a934/ao+FwqzY1errlxLd76ONPT04uK1l8plJrPtEd/r1u3jiuvvJJDhw6xZcsWvF4vFy5coLe3l/HxcTo7OxkdHSUSiSz5ZlAJci1Hh5at0ti4cSNXXXWV8zQVj8d5/PHHefTRRzl8+DBf+MIX5u1755138sgjj2S8ZtsN9vT0cOONN3LvvfcC0NHRwb/9279x+vRpHnnkEf74j//YqV3YdoM9PT0AW0jZDS4eMjXbsph/1Y5LMnI1TZNkMslLL73Ejh07nLtrOaHruqM0aGlpYdWqVWWZvQVk5G03bNhAe3s7iURi3vbP/aqTRx44Sjxpousmvjo3RiyOq6mO19+2n4SYYWJYoffcBHo4Tp1HY/llftbv8LNz/3rcbjdTU1OEQiGOHj3qaE+bmpqqrrNqsUivyK9cuZLp6WkmJydpa2sjEAhw7tw5YrFYXr1pMagUuVYifaFpGkeOHGF6epr9+/fzl3/5lzknEZdiN/jggw/yu7/7u3g8HjZs2MDmzZs5evSo07F19dVX24dYkt3gy29kSXtXDS45crWdq1RVrViHlZ1m2LBhAzt37qS7u3tREwxykWs8HqejowNN0zLyttnbd3cM85MHjhFMmOiKgntZHUYkjqYq7L5qLb91yx6eP/Yct95+NYFglFAoxuBAP263ZPcVu3G5XE6LZjKZZPfu3Y72dHBwkHA47HRW2f+W2i+/GFRC52p7AKQ/QufSmxY7OqZSqYZyS6WyfQXq6+vx+XysX7++qP3z2Q0ODw9z5MjLThKrV69meHgYl8tVst2gEMK2KItLKee5LyUsg/7IdFHrrXZUJbnm+iJnO1fZblnlRLZtn002S529BamLfnBwkMHBQbZt2zbPGSs9ZTHUf4F/+rvHmZlITQpweVwkJbg0la1XruFtH7get8dem0BPRhg838OGjakoWAjhpALSJxJka0/TO6vOnz+PYRgO4djRbaVF6OX+G+YjwuwxKbbeNBAI0N/f73i9pjc62H//ShW0yn3MdG1rOQtauf5GBXLv814UQmwC9pEyUFkJuIELQohjwIvpJOtWNNbUzXeNuxRRleSajnzOVYut/MP8aMmyLPr6+piYmGD79u3z0gyLnb1lk2s4HOb06dM0NTXlHUljt79Ojgb48n//IeOjAYSQaJoLicDjdbH1ytW896NvpK7R56z7xIkTSCnzDjQsRI7ZnVW5CMftdjtkWylv0HJHrsWQVrqm1jYWt71eZ2dnM242kUiEWCw2Lye+FFTCFSvdVyAcDjuNC8Uin93g6tWrGRwcdLYbGhpi1apVRdkNCiG2AB8A2oEo0A2E5rZ9G+AVQnxRSvldZ6daWqDyKORctVhnrGz96czMDJ2dnbS1tTkG2fn2KfU89iiXCxcusHPnzoKTOBVFIRnXuf/enzAyOI2iqUgLDMNEMRU27L6cd3/oddQ1+pzURTQaZcuWLTkvomx5UrFrzkU4gUDAeZyOxWJEIhESiQRNTU34fL4lkWO5I9elpBmyvV5tc5aOjg7Gxsbo6+tbkklNOipdJLPTAqUgn93gbbfdxrvf/W4++tGPMjIyQk9PD4cOHUJVVcdu8PDhw5DbblAAj0kp/yPXOecs/94khFgnpRyoRIfWxUJVkquUkpdeegnDMPLmVUv1F7Bhk7JpmnR3dxOPxzOMuAvtUwoSiQRdXV2sWbMmL2ln4+gv+uk6PYYQc9sKgWWYNC5fxh3vu5Zly+ucnK3L5aKurm7B6GSp0imPx5PhBtXT04PX60XXdc6ePUs0Gs2QQjU2NpYckVWrt4BtzuLxeNi2bRtutzuvSY39/os1qalU5Oo0nUQiBdMCpdgN7tq1izvuuIOdO3eiaRr33Xefcx7bbjAWiwGcJauYJaXsJhWtIoTwAa2ACcQBnZRt4Dcy9ynDh1EGzBH/tcAqIAacAp6XUhYVaVUluSqKwtq1azPGc2SjWH+BXMceGRlheHg4ozV2oX2KHStjGAZdXV3MzMywdu3aoosJLz7bx5kXLoCigjQxrdT92+PSeMed19G+upmhoSEGBgbYunUrK1asKMocpty6VEVR8Pv9Gd1E8XicQCDAxMQEZ8+eBXDIZrFtrItFpbwF7GMuxaQmHZVQC5RilF2q3eAnPvEJPvGJT8x7Pc1uEOBPs38vhFCklJYQYh2pKQA7SElALaAZ+CbwUyGEcPSxF3m0thDit0iNf1kOvEDKltBLSgmxSQjxAPAFKWWw0HGqklwBmpuby247aF8EqqpmFKwWQrFjZewuovXr1+P3+4s+/sxUmId+8iJxv4bmVyGQRDFMNEVw45v3svvQWo4dO4bf7y96jHg6KqnpzpZCwctO/ultrH6/P6ON1SaWS8EVq1Aet5BJTTAYzDCpSX//r5RaoApg/zE+TEoHe9/czy5gGSmDbWxiTZgGfeGpV3qN2fht4A+klOezfyGE0IBbgTcA3y90kKol14WgaVrR0WR6waqpqYkNGzaUJDtaKOdqDx5UFMUpLJ0/f77oPO3jvzjN+MA4WiSOWl+PsdyLFkhy9Wu2cOim9bz44ouL1vO+EhHjUN8Fjj3Zy9jILE3Nfq7Yt5ZtV6511mubtNiOWOnRXTweJ5lMls1kpBo6tPKZ1AQCAUZHR+nu7iYSiXD27Fknwi1krF0sstUClR5OWCTsO3sA+KaU8ueFNnYrGuvqyj9uqUR8Xko5XxwMSCkN4EfFHKRqybUYw+xiosnp6Wk6Oztpb2/n8OHDdHV1lRzx5ouSpZQMDQ1x/vx551HdRrHTXEf6LvDsT1+CqI5MmqiRGOqyOtbtXsn2I40Yhl5w9tZCqHS76snj/Tz4nWcJTEcxgwks4Pgz57j2+q3c+t6rUVXFMWmpq6tz5lTZ0d3k5CTnzp3DNM2yNDnYOtdyYynEl+v9Hz16lJaWFoLB4KJNarJhmqbT8lot5JqWn/wp8MdzQwOPkVIOJIEeKWUsc6+LXtA6IYQ4CXwb+L6UMrCYg1QtuS6EhXKu6c796UWxpcqqbITDYTo6OmhoaMj5qF5MnlZPGjz0L08Tno0hVIGQAk1zIYTFzkPLufKqKwoqDGDhx+BKkuuFySA//P4xZoIxIkkDQxW4AnF0Cb/82SkaVjTwurSpB+mwo7vR0VE2btyIz+cjHA4TDAadJod8utNCqERaoBIQQuQ0qbGnGNgmNQ0NDc5nsJAyIz0tEI1Gy95gsxjYOVfgILAR2A28i1RaYD3wO8CvM3a6+AWty4HXA78LfEYI8TQpon1o/o0gPy5Zcs2nFpBSMjIyQn9/P5s2baKtrS3jC7kYck3fJz3FsGPHjrxFt2LkWyef7+Ns1yhul0rCtEATJBJx9l+9idtvf92CkUt6g8ArDcu0ePj7x5kZnCGZMNDDcZJuFUNaeGI6UoH/+PFJtl+xmlVr8vsx2OsXQswbm5JMJgkEAhm6U5tsmpqa8Pv9OceYXwrkmo1cTljpJjW9vb0ZJjX2v/Sbejq5ViqCXwTsP8Z7gLuklKcX2uFic6uU0gR+DvxcCOEG3kSKaL8khHhcSvmeYo5TteS6mFEv4XCYM2fOUFdXl7fws5RuK3vqQGtr64LyqoXOYxgmTz7WgWWBoVvo8QSKabJqdSvvuvMNRV0YxUSllYpcX3ruHB3HzyNUFQsDKUAPRDFVFQ9gCY3Z6TAP/9tRPvD/3rwo60K32z1Pd2o3OfT19RGNRud5Blyq5JoLtklNeu7abuO10ylSSueGE4/HF2VOVGHYF8Fp4FohRJKUrCkOJLMr7knToC9YPe2vUsqkEKIDOAPsB3YWu2/VkutCSE8LmKbJuXPnmJycLBhNZu9XLKSUzM7OkkgkuPLKK4sqvixErhdGA1wYniYZTxANRXG5NSxF5aa3H2T5iuJyZfmIM5lM0tHRQSQSobGxEV3XicfjBS3oSsGF0SAPf+cYRjiOblp4vS6EomBYEreioKgKUlHAknS+MEj3ifNs35vbrLvUUTjZTQ7pngHnzp0jHo9TX1+PaZplaXKoBBZLfrnGxpim6dxwgsEgJ06c4NOf/jTJZJLHHnuMw4cPL5ha+tKXvsTXvvY1pJT8wR/8Af/lv/wXTpw4wV133UU4HGb9+vV861vfcnK4+ewHF8AM8OekquyzpEi3TgjxZ1LKGXsjt6qxvv7it78KIdYC7ySVwqgD/g24XUp5pthjXNLkapqm08W1atWqosT6qqoWrTKAlFFMV1cXqqpy8ODBJc3ESsf53jECgRAxPYmvqQ4MC3+dm8M3Fn1jzBmljI2NcfbsWTZt2kRDQwPhcJgLFy7Q1dXlSKLsx+pSp7JalqTvzAV++ZMuZqZiKJqCx+smFk2QtGDD1nYmRwKY8SSYEqlY6Lrgmcc72bpnbd7odSnkl+0Z0NPT44j90x+l7fe8mEJRuVHO6DrdpMb2G77vvvt4z3vew6OPPsoXvvAFfvazn+X9O586dYqvfe1rHD16FLfbzRvf+EZuueUWfv/3f5/Pf/7zvPa1r+XrX/86n/vc57jnnnsy7AdHRkZ4/etfT3d3d97PNM3b9bvAP5EiqjrAAzQC4bJ8EGWEEOIpUnnX7wF/KKV8fjHHqVpyXejLZ1kWgUCAgYEB9u7di8/nK+q4xaoMEokEZ86kblL79u3j1KlTJV0Qhch1amqK5555CUOoNFy2DEM3sZIGqzc2omnFk1165GqvV1EUDh48iKZpJJNJmpub8Xg87Nmzx5EEzc7OMjQ0lFE0sv9pmoZlSSZGZuk+OcTwwBR6zKDt8kb89V6e/o9eAqEEiiJQLIgndAKhJIoqmL4QJp40ScR1GrwuLNNCIhganCYRT+LzzxfTl/sR1s7dLlQoSi+UFYroK/GIXYnurPTjtra20tLSwuc///kF9zlz5gxHjhxxil+vfe1r+eEPf0hXVxfXX389AG94wxu4+eabueeee/LaD6bZDubDCVKP1QK4QMpfICqlnB/pXPysxl8Av1qq6XfVkms+pA8f1DSNffv2lbT/QgWt9ONv2bKF1tZWLMsqOU+rquq8fXRddyLI5pY23I0xdN3C0lSkqlDX7CvpYrbNXuxo1V4vkHO96ZKg7KLR1NQUL5zsorNnkpHTM4RGo2AKVMvC5VE4qaoEggkMxcLr96TSALqJZUoMw8Lr9RAIRUlGDcyEgakbuD0qDY0+onGd6akwidEgmiZYubLJcfWy11UuZEeFuQpF2bO64vF4hgwsPaKvVMdXJbx0bXINhUJF64Z3797NJz7xCaampvD5fDz88MMcOHCA3bt389BDD3H77bfzve99zzFuyWc/WAT+BrgOaCMVuXpJuWPNy+FdbG6VUv4SQAixFfgq0Cal3C2EuBK4TUr56WKOc0mRa7b86ejRoyUfoxC52iYd2QWxxRQJbOKzYfvQ2ubYnaeexDAkZnIu/6spqD5XSSQupeTUqVMZo7dLRcr1qpkXXxznFy9OMts7iR5KYBk6xE1I6oiYiuZxE4nrJE2TUERHURQUBEJKkkgS0TgkDIQJSIkR0bHCEA8lmZ6J86k/fwCVlH1iW1sjR67fwsGrN18U45Zcs7pskb/dwqqqKo2NjdTX15d9jZWKXCH1vVvIVyAdO3bs4OMf/zhveMMbqK+vZ8+ePWiaxte//nU+/OEP8zd/8zfcdtttzncrn/3gAmvyAX8kpVxRcENIMevFZteX8TXgvwH/G0BK+ZIQ4l+BS5tc0/9gdsFqamqKHTt2LJigL4Rc5Jpua1iuyQZ2WiCZTHLmzJl5toBCEZiWlSr8AKqiYJiyaHIdHR1ldnaWbdu2OcWdxSAUjPHD7zzHiXNjxPUkUpcoUkEoKrgEwgSpKOhJE900kQkDIcFAINwqHq+GKUGJ6aSe+AToeirn6nWBArqUJMJxNK8LQ0/SNz3K1EiA537Zy7ptfrZt277o9WdjMZFmviaHQCDAzMwMsViMo0ePZphr19fXLzqirVTkaqPU1tcPfvCDfPCDHwTgL//yL1m9ejXbt2/n0UcfBaC7u5uf/vSnQH77wSJwvxBiO6l0gG3akpBSJtI3Slgm56pHLeCXUh7N+jsXXQ2vWnK1MTk5SXd3t1OwWuojWja5BgIBOjo6WLFixTxbw6VACEE0GuW5555j8+bN89yrNKGQFBLpU1CEABPMpLVglJRIJOjo6EBV1YzoazEYGZrhgW8+Td+5SfQ6hYQ0UEwTqQgEAqEIDEsiLROXS0OxJMyRv4oAQ8z9rIAlQAWQKVujub+TFALFpYApU5smdKSApGkxORHg2Nce5gf/7fuozRr/dOJvqWtcmvC9XO2vLpeLlpYWGhoaiEajXHHFFY43xfnz553pq+let8W2VFcycoXUePlSyHViYoLW1lbOnz/PD37wA55++mnnNcuy+PSnP81dd90F5LcfXAA60A/8APghL8uzJsiyKPQoGhsaLnr7q43JOaNvCSCEeDswWuzOVUuu9owsy7LYt29f3qLDYgbcmabpjOIOhUJcccUVZTW5iMfjnD59mmQyyXXXXZfzooubBkZCh1kdA9CafCja/DytjfTxM3Zu1TbKLhWJhM5Tv+zm8R+/RCAQQ2gCM2hiLVeR7pQrl7fOTbPPw7rDLSiK4FzXOJGuCEnDAlVBkRJpGviafcQME6lIHL24ECAlUgWhCTRFwbJAWCaGaYKqEJ2cZfxYF8LjSVkrRiT/z9o/Y/87ruGOP72JzVcWHjaZD+WeGmBHmbb9YENDgzPaxDbXnpmZob+/P2N0TL4mh/RjlhPp34NSpxC87W1vY2pqCpfLxX333UdzczNf+tKXuO++lMfKW9/6Vn7v934PKGw/WACbSD1ef5FU5OcilXeNAWQ4YlUX/gS4H9guhBgG+kg1QxSFqiVXIQTt7e1OgSYXbKIsxSVKVVVisRjPPvssa9asKTiKu1RkF8P6+vryRjOhQBQR1TGTBtKwSEQSmFe25CTL9Gg1e/xMqd/JieEZvv+tZ+jpHEfXTRAC05K4NQUtKLEavGw7uJI3vHY761a34K9LpTFGhqb5woe/xdh4CFMKpJqKWk3dwq9AsE5DjZpIFXBrIHQ0IfH5XKjCwvBomKZECHAJCDzXifB6UmM8596DcLs59t0nmZoI8Yd//Ra2X5VbG1sI5S5AFSLCXObathtYepODHdnaHVWViFzT11kquf7nf/7nvNc+8pGP8JGPfCTn9vnsBwtAI2WY/fe5flmlxArQLKV8vRCiDlCklCEhxO8AA8XsXLXk6na7CxIrvDzqpVhytcX18Xica6+9tmRRfaELNxqNcvr0aacYpqqq422aEwkDRTcxw3GwJIZuMN41Pm/uVna0mo7solk2stc6NjDJt+57nJHxMJaUSMNCCnB5XEgL6v1ubnrTlVz/uh2oaiahrFy1jH2v2cbzT/cQQWDMcWL7mmbchsnJ3lGsBg0sE+F24U9o7Du0hZtuvRLN4wIhiUbDjI1N8tA/HQchSIt1U+9XAEIwfO4Cxx7vrHpyzYbt9GXXBKSUziQHu6MKUt9b+yZfDjcsqE67wbSI1A1cKYT4C+AJUhFrCLiQ0xO1eqj2a0KI90spTwIIIX4X+K/Aj4vZuWrJtRgU6xOQ7TcQjUZLJtZ8ffxSSgYGBhgZGZlXDCt0Q25f0YQViSNUBVNTUDWVsb4A0+NBGhsb80arudZUDBKxJD/9l6e5MBYEITB0C4lEVVSEhBWtDbzl3YfYuSd3cUxRFNbvWMVLp88jNA1TB0UV6Cbs2LOeid5JAvEklkvBFTa4rNHPHXdew+r1md02p587i09zgZw/zEPMfWZW0iQRT7AYVMIfdrGP8EIIp8khvaOqv7+fcDhMT0+PM5vLjm4X2+SQPYVgKUXOciEtInUDPuDtwAdJmWWvJtVY8F4hhDrXzz+3Y9V01L0deEAI8R5SMrL3ATcVu3PVkmsxF0gxo17sia4+n88hKTuCKAU2kadfaPbgwWXLlpVsC7jn4AYe+eZTJFSBsCTSlMQSkn//6UlueaeaN1pNRynkevr4AAM9E5hJk6RMFZos08Lj97DrqrX8zjv207qysApj54H1PP7EKYKhJJqmkIgaWAmd55/sAY+Ly9wqlmHib/Gxbtsq2lZlShjf2vSB1KSF5iZkUwOEIgi3y6l/yaSOaKhHeF3UtyscO3Yso8GhGKlZJSLXch5PVVWHcC+//HKnycHW3Pb09DhtvumTHBZCNUauNqSUzwIHCvzeuYgTpkFfYOlqASGEF/gVqU4wDXhASvlXQojlwHdIOXL1A3ekt99mrevcXLT6I2AQuOk3whULCvsEWJbFwMAAo6OjbN++fUlVdcjsuEp3xlpo8GA+bNjRzsbdq+jsGkdN6liaSjKh84uHXiAeC/OBD9+Kv65w11mx5GoYJs8+3knSsLASeiodoKn469y8486rOXD1ZjRt4RtDc0s9+65Zxy8ePUs8oqMqMDMVIXIhBKaF1+OmocmL8LhZvXkFLvfLX693rbsLhAJIiEYRdX6sZQ0o0wHk3PtQGhtQfB7e+5E38Du/9xpH7D87O8vg4KAzjXXZsmWvmCtWpQYJ2jeK9CaH9vZ2ILPJYWRkhEQikbfJIf2Y6ZNfyzVWeykQQhwgFfH9GuiVUs7Ova6QIj0B+El1akUBPKrGhsalXatzSACvk1KGhRAu4NdCiJ8BbwUel1LeK4S4m9Q4l49nrfskmcmJ5aS0MM/OXXO5fTSzUNXkWoxhdq7I1ZZXtbS0lE1eZZ8rGAxy+vTpopyxCkFRFG5577UM/c+HiZJqJhCWhRUzOf5IL5HhB3jLH93Alt35q+aFPh9d15mdnaWxsZHJsQgjwzNYmoKuqHh8KoqEO37/eg6+ZltJ696xq43ZWZ3nf9VHLBQHw0IRIFQVxaOmimRSsmVXpvYxMZsERQEEJHQwwiiqgu7z4l3diiYEK1Yt57/+z3ewYVuqkyrb0T/bFSsSieD1ejPkUOUmw0qQ60LHzNXkYMvA7LZlewqt/b6zpxBUA7mSkmCtImXXNyKEGCNl2qICa4BbSFXgXya3MjURzKUkbN8C19w/SWqO1w1zr3+DVA7441m737r0FVQ5uS6E7LSAaZr09vYyOzvL7t27y/oFE0Jw7tw5IpFI2aRbO/etY8+RTTz1i9OIhEQVYq7ryaLrWB9f+W9jXHHNFm546wHWb12ZEQnaa8pV0JqcnKSrq4v6+np6e3s59cIowWAYUPA3eLGkZPX6Fq66dkvJa1YUwXWv2cBo9zTDhomeMPDMtbK6VBWXT8Pf4GHNxnzNOHNlLNMAU5DwmGx99x4++2e3obkKR8/Zrli2BZ89HLG3srV9UwAAIABJREFUt5doNMq5c+dobm5m2bJlix59baPSI7CLQa45XelTaM+fP088HkfTNE6cOMHs7GzRRtm5HLFefPFF7rrrLueYf//3f+9oWYt1xJorZp0g5eq/D7iNVBS7jJRD1rPAh2SOOVXlyrkKIVRSUw82A/dJKZ8VQrRJKUcBpJSjIjXhNRtTUsqChjJCiPqFtrmkyTU9LWATypo1a9i6dWtRxi/FXjQzMzNMTU1x+eWXc+jQobI8dkopmZgYZ+0ON+MjbZzvmCBpmkhDYhkGqqYRjhn0dT1P3cO/5MSTrWjeLWy5Yh+79m0E5kux7Mmz8Xic/fv3I4RgYmSW0fPHUFGxhEU4EkF1KazaXMfk5MSibPnq6r3ccNNOHvnhC5iGRSxpoLhUhCLwel1ceWA9DU2ZKY0PP/g+/u7N33SaCwAMy0K87SDLG+sWJNZcSLfgs30Dnn/+eZYvX04oFGJ4eBhd1zO0p3V1dSW914sRuRaD7Cm0IyMjRCIRjh8/TldXF+9617tYvXo1X/7yl9m2LffTST5HrI997GP81V/9FW9605t4+OGH+djHPsYTTzxRkiOWlFLaagEp5XHg+JLecG60CCHSHavul1Len7YGE7hKCLEM+KEQYneRx31QCPEi8CBwTEoZARBCbAR+C7iDVGvsA4UOUtXkWkxaIJFI8NJLL2EYBvv37y9KBWCbqiz0BbcbDcLhMCtWrJg31WCxsJUAmqbxuje8hoMH43zzy49x4ledSNVEkSqGEFzWEmfP/kl+/et6+h7pAM9ZhPoom6/dxt9840NIS86ZykjGRsd56cXTrF27hm1bN6JpLnRd55c/O0kyYdLQUI8Z0/G43bRc3szh63eh67pjy+f3+51cZjFWhHuv3kQ0GOfM6WF6OkeJzsZYeXkzVx5YxxveMt9M54YbbmD8vlm+88cPAQKJRPuj69Bm42xqK0uOzcFll12WoT21H6n7+/sztKf2I3WhKLISAw8rpXOtq6vjrrvu4qGHHuKhhx4iHo8XrAfkc8QSQhAMphRSgUDAaW8t1RHLVgvMRZCClx/6Zfrv05EwTfpmc9aXcmFSSpm3UJa2jlkhxBPAG4FxIUT7XNTaTqpLLHv7G4UQvw38ESmD72ZSzQ9dpGaBvV9KObbQeauaXAtBSkkoFGJ0dJSdO3eWRHzFNB/YkfDatWvZvn073d3dJY+HsR/b0x2WRkdH6evryxhouHyFiw998ja+87/cHP+PbqIzcYSisHJVnIlJL30/G0M0NqQq7UDv8UE+/NtfYNmaRtQ6P0nTxEgkafD5UbRBFAFuj4alSM4PTmHoBjHLwlXnQkjBrj1rWLsuVTxZu3ZthnFJuhWhTbbZ40QANJfKtTfvYteBdZiGhaIquD0q/nrvvPSFjXe+581cvu0K/u2BZzEsC2mBskzhyFUbSvpcCyG7oJWrs8pOJVy4cMHRIjc2NjrvN706X+6OL6hMNGwYhkOSkUiE+vr6BT0y8jliffGLX+Tmm2/mz//8z7Esi6eeegpYvCNWhsxqAXhUlQ2NS/f2EEKsAPQ5YvWRmon1WeAh4P3AvXP/fTDPmh8GHl7KGi5Jco3FYnR0dCClpL293XkkLBaF9LHptoDpkfBSxsMoipIRrebSrbrdGtfffgV7rttE97PDHP2PLkyinHpyBNxupKIikKBpoAgmJ2bRfW7kTByXpuHWVGaSMbQ5pyojYRDwgBLXkYaBEo4j6j00N/q55vrMx8RcxiW5xO9NTU0kk0mHfDSXSssC8q1sXLNvIwI40TGCW1No9sdZt7q8ketCN9ls7en/Ze/Lw5uq0+/PTdIkXem+b5RCd6Eb4AJSFUFUHEEddQRGFMEZl0HckQFXlMEF16/MqIg6Ij91RBlxaFHUcVSWgt33fV+SNGmbZr2/P8rn401ImputpLXnefrQliw3bXPue9/3vOeYT+dJ1DeJTnF10J87KlfuY/LdWrTmiPXGG2/gxRdfxMqVK7F//37cdtttKCoqcsgR68xt8gC0k17nme/5AFC7cTsrCsC7Z6pmAYD9LMseZEbDBvczDHMbgBYA17vp+T2bXMcS7KekpEAgEKCzk7ePAoU1cjW3BeQ+vyV/Vlsg8do9PT1nVavWbh8Y6ovr7rwMFy6bg++/OoH//qMBEHM2mQSC0dUooQgsDPASSSEWCcEaR/9G9QYDGN3o+pRI6gUvLyF0QyMQ+ogh9ZEgtyAFwRG2B30SiQTh4eFUZ0sIqKmpCa2trWhra6PZTYGBgbz7tgIBgwvzZmB+zmi1Wnzy5DmPYbE0nSeqBJlMhu7ubvT09Ji0EuxZuTaHO4dk9nKVJUesRx55BLt27QIAXH/99bj99tsB2O+Ixfya/PocgC6GYZ5if41JeQnADgB1Z93PrldgGSzLlgDItvD9fgCXuuApbMKjyZULlUqF8vJyBAcHU8G+Uqm0+1IdOJtcyVosABNbQC4IUdqL0tJSSCQSq1tWXHCn/1EJobhh/VL86/WPoWo0gNVqR3fxCREZDBBLxGCEAEvaWWRNnx29TPc2MtBPk8BLbIQ02BtxaVE4/5J0h8iMEJBSqYSPjw9CQ0MpAdXX19MoZ0K2tvq2Qjda7jkLhpNESwZiAQEBZ1XyXKG/PRt/7q5cmTNpunxgyRHrlVdewbfffotFixbh66+/xsyZo6oSBx2xgFGeMQB4gGGYf7As+z8AaWe+N2nh8eRqMBhQX18PmUyGjIwME3kVnw0tSyDkyu2B2tqGsodcyeMODAwgNTWV9vpsQSAQnJXv9e7x17Ei8Db6uIzRCIiESMqIh4ZlYRQJIfX1hhAMWINhdNPJYIR+RA8vLQsxI8CwnxRJaVFYvjwPAb62t334Hqu5LEqtVkOhUKC9vR0qlQoikYj2MUmEzEQDqTLNK3mieVYoFOjq6qL5ZOT1+vr6Wj25eFLlaskR6+9//zvuvfde6PV6SKVS7N49OoB3wBGLHMw0lmVXMwxzM4BnGIa5D6NJBENj3uscg2GYnQDeYXnEgVuCR/+1KxQKlJaWWvVydSTJldxPrVbj1KlTEIvFvKpKvsGGGo0G5eXlVAA/VhKtOSz1dcViMd5q2Inbpm8C9HqwajWe/d9j8PUOQHNTGxihAFEx0ZD6iOHrI4bRyEI9rIG8TwlZlxIsy2JguAeLlsyHrx+/nDFHwN00IpeKJEJGJpOhsbERgPXBkafC2saXpdhrokpobW21mk8GuJdcyRUEX1hyxLroootw8uRJi7e3xxGL009tZhjGn2XZfzIM04RRD9fpGDVvMYFGb0CTXMHv4N2PKoyafIsAvAPgQ5ZlB/je2aPJ1cvLa8zwQb7GLVyQflp7ezsyMzOpTtAWiOxrrMc1VwKUlpba1ac1l55xe8wfdL12lqzGy88Ig8GAhISz+15x08Og1WrBMMzoSUQy/r9qsVhsYsnHrfaIBnVkZATt7e0IDAy06n96LsGXCC0J/clQUCaTUb9Xsk3lSkcsANQdTiaTeZSvwBncd8auj2FZ9n8Mw1wL4HaMJhKYQCIUYvo059UCrgDLsv8A8A+GYVIA3AqghGGYHwD8nWXZb2zd36PJ1c/Pb8zK1F5yJbaARqMR06dP502swNhtAW61au63ag+5cm9PjjUgIMCqKQxfbwF7DF7cCfNqz2g04tixY9SrYWhoCN7e3rSy9ff3d2scCh84U2VaayX09vaipqZmzGBER4/Tg1ZfKViWrT/zL9G39gB45pweFE+cURuknvnow2iK7X0Mw6xnWfbGse7r0eRq66zO96xvbgs4NDRkd8VriSit6VbHus9YIAOt1tZWtLa22szzmmjkag6BQAChUIi4uDiTdVaFQoHOzk66/UOGZOeib+vKS3hychGLxSZR5+b6YkeiY4DR37OnOWJNZDAM8wJG13aPAHiGZVmSiPocwzDVtu7v0eTqChBbwKCgIFoBjoyMQKvV2vU45lXyyMgIKioqxuzZ2kuuer0e3d3dCAsLw9y5c20SiSNJBK6Au56Tu85KHKJI35YbpcLt29rry2sv3NEfJUWBJX2xpdfLlbzZaiXYm5/lkfCcOqAMwGPEscsMNmUSk5ZcubaAGRkZCAgIoP/nSK+WEKWtatXSfWyBPGZdXR38/f2Rnp7O65isVaQsy6KlpQUtLS3w9/eHRqPB0NAQxGKx0z2+8e6JWuvbDgwMoLOzkwr+Cdm6mvhdvf5q6+/B0usl0THm5tqWWiee2BawG55Drqcxmp/F/d4AgGY+gy2PJld7Lvu5t7VlC+gIuQqFQmi1WrsUBnzIVavVory8HCKRCBkZGXYtRVhyxSLhiN7e3sjOzoZGo0FlZSVaWlpQW1trt4eAp8HSlJ7obYl3wC+//GKyuuvMa3T1+qvRaLRL4yoUChEYGEhVJ9xWQkdHB5W8aTQaVFdXo6enh3flaskR6/e//z2qq0eveBUKBQIDA3H69GkA/B2xnIFWb0CTnLe3gLvxOoAcACUY3W3IPPN5CMMwG1iWPTzWnT2aXPmA6xNANLFyuXxMW0B7L9dZlkVvby9kMhlmz549ZrVqz/N0d3ejrq6OamxVKpVT6oKuri7U19cjJSUFQUFB0Gg0kEqlEIvFSE5Ohre3NzQaDRQKBe3xicViEyJyZ+SzO8AV/MfExOD48eOYNWsWBgYG0N3dTZ39uXpbe/qYnuYPa6mVoFarUVJSgmPHjuGNN96ASqVCc3MzVq9ejQULFlh8HGuOWB999BG9zaZNm6hCxR5HLGcgFgmRGOgZagGMJhXcRnSuDMOkYzTF9kmMxoT/NshVpVKhsrIS0dHRNm0BSbAhH5DeqlAoREBAAG9iBayTq06nQ2VlJYxGI/Lz801c6e25rCW3J4/Hsizy8/Pp8zIMQ8lFLBbDaDTCy8sLYWFhCA8Pp0sLCoWC+qGe6wGSMyBVprkNoU6nw8DAABQKBZqbm2nfltvHtAZXk6s7trMEAgGkUilWrVoFuVyOsLAwZGVljal3teaI9eCDDwIY/Vnu378fX3/9NQD7HbEcBgswntMWSOUuELAsW8EwTDY7Gv9i884e/c7ht6suoEYrc+bM4SWg5lO5mvdWAwMDcerUKd7Hbu15iNtWUlISHdrYc1xcMAyD4eFhHDt2DNOnT0dERATtCyuVSlRVVSE+Pp76JJD/I/+STLDg4GCEhIRQslWpVHSgwrIsHSDZsxBxLmBN8O/l5WXifUpOxgqFgv7tEElUYGCgieerp1WulsAl7OHhYQQFBdkkPWuOWATff/89IiIi6Oqro45YExw1DMO8AWDfma9/f+Z7EoymLIwJjyZXYOxqrq+vD3K5HPHx8cjKyrLbctAaLCkBjEajQ0MwstXFNbIey7+Ab+VqMBjQ0tKCgYEBzJs3D2KxmB5fY2MjFAoFZs+ebbKAQd7U5I1IiJysAhOyJZfPhOzJQKW9vR1DQ0Pw8fGB0WjkNb0eT/AdPlnqY5LtqubmZgwNDVHPV71eb3efdCy421dApVLxGmhZc8Qi+PDDD3HTTTfRrx11xHIEHlS5rgHwJwB/wWjP9b8A7scosRbYurPHk6sl6HQ6VFVVQafTITw8HKGhoXb9oq2RK8uJ4E5JSTFZMnBEK0rISSaTobKyEgkJCUhPt26cYi22xRxKpRJlZWUICgqCSCSCSCSC0WikVoxhYWE0icDW8XH/BUBPIqTCBUB7mvHx8WhpaaGtiNraWmrJR8jKXqd/V8LR4ZOl7Sri+arX61FcXExPOqRdYk/flgt3V672qAUsOWIBo4XAp59+arICa68jlqPQGgxokp37gdaZ5YEvWJa9DMDzFm4yZsQLMAHJlQyBkpKSEBkZidraWrv9BSyRK5myW3OwcpQwenp60N/fj5ycHKtrvAS2KleWZdHQ0IDe3l5aqRcXF6OkpAQMw2BoaAgZGRkOpdFyj8GcbMmHXq+HSqVCWFgYQkJCKBERwxYyrZdKpedky8paW8AREM/XlpYW5OfnQ6fT0dXdlpYWGAwGu/SnBO6oXLnhhMQomw8sOWIBQFFR0VmGQ044YtkFsVCIRBsm3+MBlmUNDMMMMwwzzR4/AS48nlxJxUhsARmGMRkCOapZJSQ2VrXqDIgu0dvbG3l5eXxNha1WrsPDwygtLUVwcDDy8/PBsixYlkVOTg7KysogEAjg5+eHiooKSKVSGtDnrBSJkO3g4CAqKioQHh6OyMhI2rdlWRYSiQQREREmwn+FQkGlQtxUg2nTprlNkeBKcjWHpSRa0rflVvBcy0VLx+KuypUbq83VdI8FS45YALBv3z6TlgDgkCPWZMAIgFKGYQrBcfBiWfYePnf2eHIl5GfNFtCeyb85uNXqvHnzXDIZNxqN1CJxxowZUKlUvN/w1lZs29ra6DosiY9mGAa9vb1oaGjArFmz6JseGK0k5XI52tvbUVVVRcktKCjIbnJjWRbt7e1ob29Henr6WZec3CEZ+VwkEiE0NBRhYWEQCATQ6/VQKBTUC5VhGEpCrhT9u5NczcHtTZPnHh4ehkKhoK5YYrGYvk4ic3N3z9WetoAlRywA2LNnj8Xv2+OI5RQ8p+f67zMfDsHjyZX0Vq2J9h11xtJqtTh58qRLq1WVSoWysjJERkZi7ty5UCgUGBjgf0Vh3tfVaDQoKyuDt7c38vPzaWVrMBhQXV0No9GIvLy8s34uRIrEjWyRy+Xo6emh2kRCtoGBgVZPKlqtFpWVlRCLxcjLy7NICpaGZOaKBIZhEBQUhODgYEq2pOpTq9U4fvy4iSLBUSvC8SRXc3D1p1xXLIVCQbO6GIaBUCiEVCqFVqt1OvabgFu5Dg0NTfwNLQ8By7LvnsnfimdZ1qaXgDk8nlxtxWST7RS+INWqwWDARRdd5LJqtbGxEb29vcjMzKR/3I5Iqwi4CwYhISH0cQYGBlBdXY2EhISzpFzWIJFIEBkZaaL7lMvlkMlkJvlYhGzFYjH6+/tRU1ODGTNmjGkibg4+igSGYajRdn9/P+bMmUO3rMhKq5+fHyVbvhEy7khqdQakXcLN6qqrq4NWq0VZWRn0ej38/f1py8TeiHMCg8FAT0h6vd5lpP1bB8MwVwPYCUAMYDrDMHMAPMGy7HI+9/d4crWVNsC3cjXvrWo0Grsvz0jlaL7LXV5ejpCQEMydO9fk/xwJNWRZFqWlpTAYDLRaJFVgXV0dVCoV5syZ45RhiZeX11n5WMQspLm5GcPDw2AYBtOnT+fdv7MGa4oEnU6HxsZGqksm03rijkVWPLkRMoRsrfUz3bGq6kqIRCJ4e3sjKCiIapIHBwehUCgcjjgHTFMIPNH9zB5o9QY09557tcAZbMOoQctRAGBZ9jTDMLyjij2eXG2BT9SLpd5qXV2d3fpFbporsTEk0d6WJvT2kqtMJsPQ0BASExMRERFB3yxDQ0OorKykom5XV2cikQghISGQSqWQyWSIi4tDYGAgFAoFysvLodVqERAQQHf6ndW2knZHWFgYUlJSTHq25Ofl7e0NqVRKWxvEipD0M4kOlTu0c3VbwNVkDZj2R7lROY5GnAO/GmUTeFL1bi8kQiESQ869WuAM9CzLDpj9PHmfvTyeXG39oYwV9TKWEsCR4QK5D7msmzZtGubOnWv1MfiSq9FoRE1NDVQqFXx8fOg0HgBaW1vR3d2N9PR0t1nJkZ9Ta2sr0tPTabVK0lDJZFwul6OqqgojIyPw8/OjbQR7tK3d3d1oaGhAWloaFfEzDHNWZUtaCOQEIxaLqVKBYRiqSOjq6kJtbS3tZep0urPIxlG4Y7I/1mNa8g0gaQb9/f0mLRxyYiHLI6RyncjESuBBSwRlZ3K/hAzDzARwD4D/8b2zx5OrLVhrC9hSAjgq4Wpvb0dXV5dNI2tye1vkSoZgUVFRdF+7rKwMfn5+6OnpQVBQEPLy8tymFdXpdKioqICXlxfy8/OtDq3IGzoxMZE6UcnlcpPLdkK2/v7+Z73JyRBOp9NZHMKZPx9geUhGPoRCIUJCQhAaGkqHZB0dHejp6aEuTlzRvyN9SHcL/vnAWsQ52ZgjJxOJRAKZTMZ7GGjJEQsAXnnlFbz66qsQiUS48sorsWPHDgDj44jlgbgbwGYAGgAfAvgPRk1beGHCk6u5FIuvbtXeqGyysSMUCnkZWZPnsEauLMuisbER3d3dyMzMpCuleXl5aGpqQnNzM7y9vdHX1weNRkMvyR0deliCTCZDdXW13UMrrhMV93JWLpejpaUFKpXKRGsrEAhQWVmJmJgYxMTE2H38fBUJpF+ZnJwMvV5P+5mtra1U9M8dHtnCeFeufEAizrlXFcXFxVAoFHj88cdRW1uLFStW4IorrsC6dessPoY1R6y2tjYcOHAAJSUlkEgk6OnpATB+jlgUHlK5njHJ3nzmw254PLnyaQsQkiTVqlQqtalb5RvLzTVw8fPzw/Tp03lfcloj1+HhYZSVlSEwMBBz586lRKHX61FVVQWBQIALLrgAXl5eYFmWXpLX1NRArVbTS/KgoCCHQv2IFlepVCI7O9tpN3/u5SzZ6iFa29raWiiVSvj5+VFJmLOLBJbIVi6XU5MdQrbkBJCQkEB71wqFAjU1NdSshQzJLP0c3aE+cLXOVSAQgGEYzJkzBy+//DL++te/YseOHWhqarJ6H2uOWCdOnMDDDz9Mq19ywh03RywCDyFXhmFmYdRLIBEcrmRZ9hI+9/d4crUF0nNta2tDc3Mzb92qvUbWc+fORW1trV3VrvlzEEF+S0sLUlNTMW3aNPoGJuSZlJREpTsATGRLhCTIJXldXR2Gh4fh6+tLydZW/5MEH4aGhiInJ8etG039/f3w8fFBTk4OXSTo6ekx8Vi1pbW1BTJYlMlkyMnJoScKSx4JJPqbnADI8Mg8HJEoEtwx0HJnNaxSqRAQEIDk5GQkJydbvb01R6yamhp8//332Lx5M6RSKXbu3In8/PxxdcTS6g1o7vMYtcD/A/B/AP4BwL4eIiYBuWo0GqjVauoOxfdNaqvnam5kDTinWyUJsWKx2GQhgFSRQ0NDvKpIS5fkQ0NDkMvlaGhooK5VhGyJbIlU4C0tLUhLS3PKf8AWBgYGqFEN0eIKhUITzSfxkR1La2sLZLDo7++P7Ozss2Rw1jwSyO9QKpVCIpHQYySi//b2dqhUKqpAkMvlLjMSd8eGFjD6d6FSqeDr62vzttYcsfR6PeRyOX766SccP34cN9xwAxoaGsbVEUssEiIx1KPUAm84emePJ1drv0RSBTY3N0MsFiMjI8Oux7VGrtaMrMe6jy2Qai05ORmhoaG0Vzg0NISKigpER0fbXJawBq6jE1cjSvxYiWyJpBLk5OS4TWROsru6u7tx3nnnjemtS0y7ifk4V2vb0tICvV5vQrbmJx2FQoHKykokJyfzMjC3RLbmigSRSISwsDBERESAYRj09fWho6MDvb29qKurc4kzljsqVwJ7trMsOWJVVlZixYoVYBiGarb7+vrGzRGLwkPaAgC+YBjmTwD+hdGhFgCAZVkZnzt7PLlagnlv9dixY7bvZAZLREny5C0ZWQOOpbmq1Wq0t7fThQDSD2xubqYbXXyqDb4w738Su8Pg4GAYDAacPHkSEomEVrbOGrsQkBaKj4+PQ+oGorUlHgkkiJB4JOh0OjqQIiePOXPm8BpMWQIfRYJOp4OPjw+SkpLoANQ80YBLtnwm9e4kV3tMWyw5YgkEAnz99ddYtGgRampqoNVqERoaOm6OWABGidVzyHXNmX8f4HyPBZDE584Tily51WpqaqqJWYm9Gj8uuRIja41GY9XI2vw+tiCXy2k8TFZWFq2ONBoNKioqEBgY6FaJldFoRENDAxQKxVl2h5aMXQjZEpNse0BUB3yrSD4wDyIkQyviqSAUClFXV+eQ1tYSuGTLsixaW1vpggjDMPT3TsiUkC1RJJifAMZaZ3VXmuzg4CDvE7UlR6y1a9di7dq1yMzMhFgsxrvvvguGYX6rjlhgWZb3NpYleDy5kj/EsZQA3JBCvhAKhdDpdLSyS0xMRHR09Jh/+HwqV6PRiNraWgwMDCA7OxvFxcXo6elBYGAg+vv76TDLnZEpZGgVEhJi0TTb3NhlZGQEcrkcnZ2dqK6u5u2iRfxl5XK5S1QHY0GlUtHWSnh4uMlgz7zXbE1rywcGgwGVlZUQCAQmZjWWPBKAX9d2uZI07jorV5HgyisUAu6W4eDgIG8TIkuOWGKxGO+//77F24+XI5ZWb0Bzj/MDLYZh4gDsBRAJwAhgN8uyuxiGCQbwEUYVAE0AbmBZVm523wdZlt1x5vPrWZb9f5z/e4Zl2Uf5HIPHkyupVpuams6qVgkcIVeGYdDV1YXe3l5eRtbkeUhsiyVwXbHy8vJgMBiQlpaGrq4uVFZWAgAiIiKg1Wqh0+kcdrMfC52dnWhubrZraCWVShEVFXXWYIf0is1dtIRCIUZGRmgaAp/UA0dBqsiuri6TPi5frS05bj7tDyKRI3pcLvimNpATF9dInBsfo1ar0dzcTE8Azl65mHu5Tp/uVLF1ziEWCZEQ5pKBlh7AJpZlixmG8Qdw8owv6x8BHGFZ9lmGYR4G8DCAh8zueyOAHWc+fwSjqgGCpQAmB7kCo0OmsZQAfDWrBKSysMfIGrBeubIsi6amJnR1dSEjIwO+vr60t0omsOnp6QgJCYFCoaAGKSSHilz+OkO2JPqGYRjk5eU5tf5p7uZEVk37+vpQV1dHV4ATEhIQFxfnNmIl22MSicRmC8Wa1paYdtvyte3p6UFDQ4PJ+q8t8FEkSCQShIWFUUeyEydOQCwW06sEkUhEK1tH9L/cFILBwUG3rUiPJ1yx/sqybCeAzjOfqxiGqQQQA+AaAIvO3OxdjJqymJMrY+VzS19bhceTK3FnGsvtZyx/AS6MRiPq6uogl8uRnJyMgYEBp7O31Go1ysrKEBAQYLIQQNoDIyMjyMnJoX1c86GNK8hWoVCgqqoKiYmJ9E3glSwiAAAgAElEQVTsSpC9/tDQUNTW1mJwcBCJiYkYHBxEcXExAJhUtq6oyJVKJSoqKpx6TaSKtFaRE62tWq2GTqdDbm6uU8duS5EwNDQEgUCA0NBQhIeH05OvQqGg3gFE10wI19bxcKVdk8bLlT+5hjIMc4Lz9W6WZXeb34hhmEQA2QB+BhBxhnjBsmwnwzCWVhNZK5/bdXQeT66A7XBAPoMmpVKJ8vJyamStVCohk/FSVFBwK1fumi25BCcLASqVCpWVlYiNjUVqaqpVAif78Y6SLfGRlcvlZyW9uhrkkjkiIuIs2RghCG4cN5FR2VuRkzZQR0cHsrKyXNqnNK/Ih4aGUFpaSgnx1KlTdmttxwJ3SEYq47S0NGojSf5euEbiRqORKhLI2m5AQIBJVhcXjqYQTBL0sSybN9YNGIbxA/AJgL+wLKvkWUzNZhhGidEq1fvM5zjzNe/BwoQgV1sYK+rFmpG1I5pVrisWd3OLLAQQvwCZTGZT52nt8fmSrbe3N6qrqxEUFIScnBy3hgDa6uOSWBcyTDH3hyWSJUK21khLr9ejsrISQqEQubm5bp1IDwwMoKKiAjNnzrR43Hy0tnxAhn4DAwNnVcaWPBIAUDIligSytltVVWViJE6ivycbubqqycQwjBdGifUDlmU/PfPtboZhos5UrVEAeszvx7KsS/7wJgW5WiPKsYysHXXFGhoawvHjx89aCCCx1mRC7wqyMydb8uZvaWmBXC6HVCqFXq9Hf3+/yy7HuTAYDKiqqqLG3Xz7uJY0q4S0WltbTUgrKCgIEokEg4ODKCsrsythwRGQTLLOzs6zdLJjaW07Ojqg0Wiory2fhAS9Xo+ysjL4+voiOzv7rNvySW0AQHvJcXFxAEAVCQ0NDVAqlRCJRPj++++pO5ktWHLE2rZtG/7+979TKd0zzzyDZcuWARhfRyytzoDmLpeoBRgAbwGoZFn2Bc5/fY5R/eqzZ/494PSTWcGEIFdbpbwlZyxbRtb2kqter6c76PPnz4eXlxd9M3R0dKCtrc3ta6XAaBUpEomwYMECMAyDgYEByGQyk8vx4OBgp8lWpVKhoqICsbGxNiVqtiAUCk2cnMwXBIaHh2EwGJCQkGDTxtEZEJkVwzC8KmNLWltLJjrkaoKrtSUtB3t6xnwVCVKpFJGRkYiOjkZHRwf0ej1KSkrQ3NyMK664AlFRUXj//fctyrKsOWIBwMaNG3H//feb3H68HbHEIiESwl3yN3AhgFUYTW89feZ7j2KUVPczDHMbgBYA17viySxhQpCrLXCJkvQGp02bhnnz5lmtIO0hV4VCgYqKCkRERNAel9FopAF+UqnUqheqq2BpXx+AxcrWGbLlpr1mZGS4ZfpMSCsgIABqtRpisRgxMTH0Up1bIZIWiLMgfxfR0dFUTWAvLCW+kiUCrtbWy8sLcrkcWVlZLonJsaZIIFcEwcHBWLNmDf7xj3/g1KlT6OjosHqSsuaIZQ3j7ojlIrAs+19Y7zBcOh7HMCnIlYQUtra20ghqVxhZc2OyiblFV1cXTp06BbFYjIGBAaSkpLhsK8kSSB+3v7/fZh/X/LLWXrIlvgoikchq2qurMDQ0hLKyMpPKODg4GNOnT7eYfODv7++wpy3xBrB2FeMouFrbuLg4mighk8ng7+9Pl17s0draAiFbvV6PiooK+Pr6IioqCrt378bg4CCMRuOYJw9rjlghISF49dVXsXfvXuTl5eH5559HUFDQuDpiUXjO+qtTmBDkauuNZDAY0NbWhrCwMN5G1rYek/QAw8PDkZ+fT3urubm5qKqqwvDwMKZNm4a6ujq0tbXRia+jm0GWoFarUV5ejsDAQIf6uPaQrUAgQG1tLaZPn25ieegOkAFZRkaGxQGMpeQDRzxtWZalvrW5ubluTUXV6/UoLy+Ht7c35s+fT39X9mht+UKtVqOkpATx8fEICQnBfffdh5GREaqbHQvWHLHuvPNObNmyBQzDYMuWLdi0aRPefvvtcXXEAkZLzYkfVDOKCUGu1kBs9Orr6+Hv74/09HSXPGZzczM6OjroZTFZCFAqlaiqqkJ8fDwyMzPpH5larYZMJqObQd7e3ggODjax/LMXJGsqNTXVZX1IS2SrUCjQ1NQEpVIJHx8fDAwMUP2nOwZkNTU1NOqF74DMEU9bnU5H20OWhkmuBKnCExISzuqv8tXa8vW1JdV8eno69Ho9VqxYgcWLF+Ohhx7iffK15IjFPaGuW7cOV111FQCMvyMWMFW5nmsQAxQvLy9kZma65FJlZGQEpaWl8Pf3p64/pGIlJiiW9KRk3TEmJoYqB0h1ODg4CB8fH0q2tgxGiIkMmdC7Y0WWwGg0oqWlBdOmTUNOTg6MRuNZelVXLQeQnmdUVBRiY2OdIjtbnrZKpRJarRaRkZF2xdc4gt7eXtTX11utws1hrrU197U1/5lzq22icsjOzkZDQwNuv/12bN26Fddee61dx2zJEauzs5OeAP71r38hMzMTAMbXEesMPCig0ClMCHI1fyN2dXWhvr6eGlkPDQ055LPKRUdHBxobG6mpCunHkkvzsLAwXjv0JMuJON5z3/gkzM/X15eSLfeSlgyt4uPjERUV5dZqq7+/HzU1NSY6T7I9xNV9uoJsu7u70djYaNdqqT0gnrbkxKVWq5GWlga1Wk1PcN7e3rSydUXrhvTC5XK5Ux65fHxtAwICMDIyApZlkZOTg6KiImzbtg179+7F7Nmz7X5OS45Yq1atwunTp8EwDBITE/Hmm28CwG/WEcsVYMbafIKHFOjEW5MMXFiWRVpaGv2D1mg0KCsrQ25url2P+7///Q+5ubnUBSk1NZUOuhiGoZVCWlqay0iBe0krk8lo/9BoNEKtVju0fGAPiBXhwMAAMjMzeaeFAqZkq1AobJItdwU4PT3drVU40eQCQGpqqgkBkKsJuVwOuVxODcS5ZGtPP5vbX01OTnbrAodGo8Hp06chEolQVFSEd999FxqNBg899BCuv/76swxmzjGcrgYSklPZh3e8xeu2f1p50UlbG1rnEhOicgV+NbKeMWPGWX0tvt4C5jAajTh+/DhNPyUtAGIY4uvr6/KpufklLRlOCIVCSCQS/PLLL1SGFBwc7FIbP1KFBwcHO5SfZWkTy1plK5VKUVVVhfDwcIdTFviCK7OylC7LvZrgulXJ5XK0tbVBpVJBLBbzMhAfHh5GaWkpvbpwJ4hWNikpCdOmTcNbb72FhQsXYsOGDfjpp59QUlLiaeTqPNiptsC4Ynh4GG1tbVaNrO1dCDAYDCbm2KRyZBiG7oDPmjXLor2hK0GGVikpKSZRySqVCjKZjGo+yTZTcHCwXZUmFz09Paivr3f5gMwS2ba3t6O/vx9SqRQajQZ9fX1uGZABv/Y87V3gsOZpO9ZUn7iCuau9wUV/fz9qa2uRkZGBkZER/O53v8Py5cuxceNGmg48Bc/GhCBXsj5oDfZURQMDAygvL0dsbCyCgoKg0+moIJu43Lt7kESei0zNuc/FlSERzScxmSkrK4NOp6PyqbH29LnPRS7N3f26BAIBZDIZjEYjLrroIggEArcNyLgyK1fkgtnytNVqtQCAWbNmucX0moD41/b09CAnJwc1NTVYt24dnn76aTrBn8zQ6gxo6fSY9FenMCHI1RUgvUYixvf29oaXlxcaGxsxPDwMvV6PyMhIJCUluZWAiJVeXFwcr7VSItUhyQXENUkmk9E9/cDAQEq23GMfGhqiTmApKSluvTQn5tkhISGYM2cOfS5rbYTGxkYAjlkVktTXgIAAt8msyFQ/JCQE5eXl8Pf3R3BwMD1RCAQCk2gcZ/xzCYxGI+0b5+Tk4NChQ3j66afxwQcf0On9ZIfYS4j4CI9Jf3UKE4JcnX3zkN5VWFgY8vLy6J42Ma82Go2Ii4vD4OAgSkpKYDAYKGG56nKW6Gd7enqcstLjvqkB685ZwGi2VUZGhtsvYfv6+lBbW2uz5TBWz5Yv2RJFxYwZM9y6GQdY7q+Sfr9Op4NcLkdfXx/q6+sBgB53UFCQ3WSr1WpRUlKC8PBwxMTE4KWXXsI333yDoqIi3tEtkwVTSwQeCPOQQnKJ1dbWhvT0dPj7+9Pe6vDwMPULIBIroj0khCWTyeibnvQ8ScyJPRgZGUFFRQX8/f1dHkpo7pxFssY0Gg2EQiGqqqooGfMRqdsDZzeg7CXb7u5udHR0uF1RAcBmf9XLywvh4eFUR0v0qmQpwx5PW5VKhfLycsycORM+Pj5Yv349/P39cejQIbdulXksxlYwTRhMGHLlY5jNDWsjl6m+vr4mCwEA0NLSgu7ubqSnp1s0JjEnLPLGIW84rsuTrX1xMkgajwEZeZPGx8fTQQ2Jmenv70d9fb1J5evIiYKAyN+CgoJcdmlujWz7+/tRXl4OAIiKisLQ0BDEYrFLTxQEJLJHJpPZ1csdS686lqctGaBmZWVBpVJh+fLl+P3vf4+7777brW2cKbgfE4ZcbYHIsYRCITo7O+kUPigoiJIq2eoKCAiwq4I0f+NotVrIZDI6WRaLxZRsiUCdrHpqNBq377VzQ/zMWw4ikcjk2MnlLDEz4drqkchoWyBR2u4+YYhEIvj6+qKhoQHJycmIiIighGVe2TpyKW4Og8GA8vJySCQSZGdnO3WFYcvTlqxUa7VaxMTEoLa2Fhs2bMDf/vY3t/qlTgRMllPKpCJXjUZDBwLEApC0Abq6utDc3IxZs2ZR2ZOjEIvFiIyMpP03opkk3gJeXl5Qq9WIjo5GSkqKW0XmOp2Oui/x8Sg1v5zVarWQy+Xo7u5GTU0NvLy8aAvEvCo330pyVBbGF5bcrPi2EewlW9JfJYNGV4N7tUNInGVZsCyLm266Ca2trbj88ssxODjo8ueeSNBpDWhtn1ILjCtstQX0ej1Onz5NKxzieanX61FVVQWBQOB0AJ01cM05iEk3uXz9+eef4efnR99Yrsy5ItEfSUlJDu/Qi8Vik113jUZjovck4np/f380NzdTIxR3njDMo1GsVf329GzHIluyCuxqS0JLIP4VUVFRiI6Oxo4dOxAZGYnDhw+jvr4eTU1Nbn1+T4eXlxBxUVNqAY8AufweGhpCamoqwsLCaLUqk8lQU1ODpKQkt9voaTQalJeXw9fX18Skm6y7ymQy6k0aEBBApVOO5jI1NTWhr6/P5cGEEonEpCofGRlBW1sbSktL4eXlBYFAgJaWFpfbKxI4I7Oyl2yFQiGam5vR19fn9tYN8GtuV2pqKiQSCf74xz8iMjIS//73v+Hl5YXIyEhceOGFbj2GCYHJMc+a2ORKFgJiYmIQGRkJjUZDIzHq6+sxNDSE7Oxsl66QWgK5fLXUg+SuuyYkJJhsYJWXl0On09E3fHBwsM3KmpB4QECAy7K6rIFlWXR1dUEul+P888+HVCp1i70iAdEAu0pmZYtsh4eHIZVKkZSU5NafIzDqYdvS0oI5c+ZALpfjuuuuw+rVq7Fhw4apwZU5psh1fMH9A+QmupKFAKVSiZaWFjQ3N0Or1SIkJASzZs1ya1+QbD+p1WrelY+lDSwi+2ppaYHRaDSRfXEvZYmedDyUB8QP1dfX14TEze0Vh4eHKVmRmBO+9opctLe3o62tza0yK0K2vr6+kMvlmDFjBry9veliAACXy9bIiX5wcBC5ubk4ffo07rrrLrz44ou49FLXpo1s2bIFBw4cgEAgQHh4OPbs2WPSP25paUF6ejq2bdtGs7JOnjyJP/7xj1Cr1Vi2bBl27dp1Tsl+MpllTwhXLGC06iAxw2QTaPr06XQoAICK9GfMmEErrOHhYbpd40ojFBLgRzKZXPUHSaormUwGhUIBhmEQGBgItVoNnU5nt5OVIyBCfXt7uVx7RfKzt2avSEDcrIjTmbvt7Mbqr3IrW7l8dKjiDNly019nzJiBjz/+GK+++ir27duH5ORkl70mAqVSSTW5L7/8MioqKvB///d/9P9XrlwJgUCAefPmUXKdO3cudu3ahfnz52PZsmW45557cMUVVzh6CE6/CaJjk9nb733B9g0BPPngNVOuWK4Ay7JoaWmhKasBAQEmEisSh5Kfn0+rrLi4OLAsS3fzuZfhltZF+R5Ha2srOjs73RLgZ34pq1Qqab/TaDSirKyMHrsrMpm4IK+tu7vboV4u8VX18/OjP3tir0gqfJKFRRQbZLjjyhOUJZC/n97eXqtKB0ttBHKiaGhoAMCfbNVqNVUfhIeH46mnnkJpaSmKioroBp2rwV12GBoaMvl5fvbZZ0hKSjKR6XV2dkKpVNKwwdWrV+Ozzz5zhlydhlgsQvzUQGt80dbWhsHBQboQQHSCXV1daGlpoSbX5mAYxuQynOgNSVIAwH/7SqvVUh9Pdwf4Ab+aTGdkZNDXptFoIJPJ0N7eTpNnSVXuTM+T2CxKJBKX9XItJQaQfnNJSQkGBwcRHBxMZXTu6o0bDAZUVFRAJBIhJyeH92sz1wjzJVtuFItQKMSqVaswY8YMHDhwwC2LD1xs3rwZe/fuxbRp0/DNN98AGCXa5557DoWFhdi5cye9bXt7u0mY4biED/6GMGHIleT8kGqVSKy8vLzsymPi6g0BU1F9bW0tvLy8TLavCFmRfifXud9dIAoIrVZ7lnxMIpGYuDdZi5QJDg62GtxnDjJIcnc4ISHb3t5eiEQiXHTRRRgZGaH2ilqt1sTL1hXtD1JBkj6xM+BDtiKRCGq1GjNmzIBSqcSqVauwfv16rF271iWV+WWXXYaurq6zvv/000/jmmuuwdNPP42nn34a27dvx6uvvorHH38cW7duxcaNG8+6yhrv8EFeYOFBzUjnMGHI9fnnn4fBYEBBQQFqa2shkUgwf/58pzOSzEX1pDJsa2uDUqmEVCqlCoTxUB4MDg5SBYQl42dzmA+YSM+TBPeRfjOJpOaCZVmatuCMmQxfkCGZn58fNeuWSCQus1c0B9kkc5d+lUu2RqMR1dXVGBwchFQqxbJly9DX14fLL7+c1++RL4qKinjd7uabb8aVV16Jxx9/HD///DM+/vhjPPjgg1AoFBAIBJBKpVi5ciXa2trofcYlfJAPpsh1fHHjjTfi4MGDWL16NYxGI3JyctDf34+CggIkJCS47I+XWxmqVCqUlpbCz88PLMvi1KlTbhmOAaNE19HRYWIyYy8s9TzJZXhlZSWtDElVXl9fD6FQyGuzy1mQ6nisIZkte0WDwWBCttb65Xz6q66ETqdDaWkpAgMDkZKSgg8//BABAQHYv38/2traUFJSgqVLl7r1GADQKysA+Pzzz5GamgoA+P777+lttm3bBj8/P9x1110AAH9/f/z000+YN28e9u7di7vvvtvtx2kLU0kE44zExERIJBI89NBDWLt2LUpLS1FYWIj77rsPnZ2dyM/PR0FBARYuXIjg4GCnyJZUdB0dHcjKyqJExyUrchlL3ux8NKrWoNfraY6XK3u53EjqxMREWhl2dnbS5NywsDD09/c7NNzjCyKzsrc65muvSMhWJBLBYDCgsrISQqHQrv6qo+BGsYSEhGDr1q2or6/HkSNH4O/vj/POOw/Lli1z6zEQPPzww6iuroZAIEBCQoKJUsAa3njjDSrFuuKKK87pMAsAtFo9Wttl5/QYXIUJI8UaCxqNBj/++CMKCwvxzTffQK/XY8GCBSgoKMD5559v19Rbq9WioqICUqkUM2fOHJPouBpVuVwOlmXttiYksqeEhAS3ZzIBvxJdZmYmpFKpifTIkeMfC9x0B3fIrMylU0ajEVqtFuHh4UhOTnb78IgbxcKyLG6//XZkZWXhqaee+q0mpDp9+Zg4PZXdsnU3r9vefuvFHi3FmhTkygXLshgYGMDRo0dRWFiIH3/8EYGBgVi0aBEKCgowe/ZsmzvmycnJDm0IcQccCoUCIpEIQUFBCAkJOSthlFy6dnd3IzMz0+3+pKSiA2CV6Ii1Ijl+gUBAq/Jp06bZVQWSQdJ4yKwA0PXi6OhoaDQaDAwMgGEYl9grmoMbxXLeeeeho6MDq1evxr333otbbrnFpa91rMWAkpISrF+/HkqlEgKBAMePH4dUKj2XiwFT5MrBpCNXc7Asi/b2dhQWFuLIkSM4ffo0Zs6cSck2KSkJGo2GVlgZGRku69GR4ZhMJoNSqaSrov7+/mhsbISvry9mzpzp9ktXMiSLjY21a2JOHLNkMhkGBgaoiYu5ksIcRFmRlpbmNk0nAVebm5WVZdIHJ0oQcvzEXtGRkwUBiWIhSw//+9//cP/99+PNN9+kelFXwtpigF6vR05ODt577z3Mnj0b/f399ATi4sUAe+A8uSamsH/lSa63rV3k0eQ6YXqujoJhGMTGxuLWW2/FrbfeCqPRiIqKChQWFuLRRx+lkqcbb7wR69evd6l5B3c4xrIs1Go12traUF9fD5FIBC8vL3R1dbl8OMZFZ2cnmpubkZGRYfeQzNwxi8imiJKCnCyCg4NpL5VYEo6HEQqpxonjmTlZWrNX7OrqQnV19Zj2ipbAjWKJjY3F3r178d577+Hf//434uLi3PIarS0GHD58GOeddx5mz54NAHQd2hMXA+zGhC/pRjHpydUcAoEAmZmZyMzMRF5eHjZu3Ig777wTLS0tWL16NUZGRnDBBRegoKAAF154oUvlSZ2dnVCpVDj//PMhkUjcMhwjIP1OvV5vlw54LEilUkRHRyM6OpqeLIjGc3BwEDqdDv7+/khJSXFryCMwSvQlJSV0/ZgPLNkrmpueE7I1b+OQlIfk5GQEBgbikUceQWdnJ4qKitwuYbO0GFBTUwOGYbBkyRL09vbixhtvxIMPPji1GMABwzBvA7gKQA/LsplnvhcM4CMAiQCaANzAsqxbDGR/c+TKRW5uLr777jva73z88cehVCrx3XffobCwEE899RR8fX1pCyEnJ8chkiK5VoGBgVTfCeCsST6RHbW0tDg1XCKpr+7sdzIMAx8fH/j4+CAgIADl5eVITEwEwzCoq6uDWq02WQhwZWVONqCcbTuYL2SYV+Zk+41lWXR2duK8886DTqfDDTfcgHnz5mHXrl0uaek4shig1+vx3//+F8ePH4ePjw8uvfRS5ObmWsz7OueLAXZApzWgvcVlaoE9AF4FsJfzvYcBHGFZ9lmGYR4+8/VDrnpCLiZ9z9UZEMu9oqIiFBUVobi4GNOnT6dky6dfSuwIU1JS7EpAMB+O8c3tIiuz1oL1XI2Ojg60trYiMzPTpILjWivKZDKnFwKAsfur7sDw8DBqa2uhUChQXV2N/fv3o7e3F+vWrcOmTZvGXRHQ3NyMK6+8EmVlZdi3bx+++uor7NmzBwDw5JNPQiqV4pZbbkFBQQFN5Pjwww9x9OhRvPnmm+NxiC7puW59jN+xrl1XYLPnyjBMIoCDnMq1GsAilmU7GYaJAnCUZdkU547aMn7TlastMAyDqKgorFq1CqtWrYLRaERNTQ0KCwvx+OOPo76+HrNnz8aiRYtwySWXICIiglYJRqMRtbW1GB4edjgZlbtqSRICuJ4CISEhtN/JsqxJZpe7L8vJYMdgMFjU5lqyVuRW5pY0qmOBuGcBcLuPLXm++vp6SCQSLFiwAAaDAXq9Htdffz1OnTqFe++9F6+++qpbjwGwvhiwZMkS7NixA8PDwxCLxfj222+xceNGREVFeeRigF3gX9KFMgxzgvP1bpZlbU3DIliW7QSAMwTr3IrnGJiqXJ2AXq/HiRMnqBJBqVTiggsuQFpaGr755hs8+eSTiI+Pd/llGbffKZPJoFKpoNPpEBISguTkZJcmE1iCWq1GWVkZIiIiEBcX59DrMxgMVJ9K7P0I0Zq3QUg0SmRk5LjIurhRLDExMXjrrbfw0UcfYf/+/U77E9iLlStXnrUYQI7h/fffx/bt28EwDJYtW4YdO3YAAE6cOGGyGPDKK69MHClWQgq7dTPPynW9Q5WrgmXZQM7/y1mWdYsN1xS5uhCDg4N46qmnsGfPHqSkpMBoNGLhwoUoKChAXl6eW6bnJLo7ISGBptKSNdeQkBCHL8GtgWiBXS2z4sqmiEaYDPZaW1uRlpZGN7XcCRLFkpKSAn9/fzz44INQKpV4++233X7SmgSYCOQ61RaYiPD19UVQUBBqamqo+9ORI0ewb98+bNq0CTExMbSFkJqa6tSlrdFoRF1dHYaGhpCXl0fbAJaGY9x0A5IdZS/cnfxqLpsaGRlBfX09Wltb4eXlhcbGRgwMDLgtuwsATQieM2cO1Go1VqxYgYKCAjz66KNub0NMYRQ6rR7tLf3ufIrPAawB8OyZfw+464mmKtdxAsuyqKurQ1FREY4cOYKqqipkZWVRso2OjuZNGOSyNSwsjJdpjaPDMQKum9WMGTPcTjRGo5Fuk6WmpkIoFNI2iFwuh0qlcjhOxhK4USyZmZmoq6vDbbfdhsceewwrVqxwOZFb27rS6XS4/fbbUVxcDL1ej9WrV+ORRx4B4HlxLFbg9AFNT0hhtz76Bq/b3rrh0jErV4ZhPgSwCEAogG4AWwF8BmA/gHgALQCuZ1nWLWYGU+R6jmAwGHDq1Cnar+3v78e8efNQUFCABQsWYNq0aRbfPGT7KTU11eHLZNI+IJtLYxluE32nu71eCfj0V0l2F3kNw8PDDseX6/V6lJeXw8fHB8nJyThy5Ai2bNmCPXv2IDs725UvjcLa1tU///lPfP7559i3bx+Gh4eRnp6Oo0ePIjEx8VxuXdkD15DrwzzJ9U9jk+u5xlRb4BxBKBQiLy8PeXl5eOSRR6BWq/HDDz+gsLAQL730EhiGoeYz8+bNA8MwOHHiBKRSqdPbT2Kx2CQ+W61Wo7+/n4YMEqLS6XRU9uRuoTwAKBQKVFZW2jxxMAwDX19f+Pr6msTJcOPLp02bZtN0mxvFEhERgTfeeANffPEF/vOf/9CfjTtgbeuKYRgMDQ1Br9dDrVZDLBYjICBgcmxd2QGPq8cdhMeS686dO/HAAxgT9vwAABeYSURBVA+gt7eXOv9v374db731FoRCIV5++WUsWbLkHB+l6+Dt7Y3LLrsMl112GViWhUwmw9dff43PPvsMmzZtwuDgIC6++GLceeedLtdXent7IzY2FrGxsTRzrLq6GiMjI/Dy8kJzczOtCt210kosHh0xJLcUX25uum2em8aNYvH29sY999wDvV6P//znP27XzwKWt66uu+46HDhwAFFRURgeHsaLL76I4OBgnDhxgvfWFcuyntgu+E3CI8m1tbUVhYWFiI+Pp9+rqKjAvn37UF5ejo6ODlx22WWoqamZlNZuDMMgJCQE119/PRYuXIglS5bg6aefxvDwMF566SWUl5cjLS0NBQUFKCgocKnca2RkBDU1NYiMjDQJeOzv70dbWxsMBoPV6G9HQPSyRqPRZabd5qbb3Ny05uZmaDQaGAwGGAwGyGQy3HXXXbjiiitw//33u6yf7MjW1bFjxyAUCtHR0QG5XI4FCxbQk605rP2+zSPoJ+QgbuxW5YSBR5Lrxo0bsWPHDlxzzTX0ewcOHMCNN94IiUSC6dOnIzk5GceOHXOLE5EnISIiAt9++y2NKbnjjjtgNBpRUlKCwsJCbNy4EV1dXcjPz8eiRYtw8cUXIygoyCGytSSzItHe5Gtu9DdJMiC2ivam0ZL+qjN6WT4gA7zAwECaQhsVFYUnn3wShw4dQkhICIaGhtDd3e0yT11H4lj++c9/YunSpVQ5ceGFF+LEiRNYsGABrziW/fv34+DBgygoKMCKFSswbdq0CVfJajV6tDX1nevDcAk8jlw///xzxMTEULcfgvb2dsyfP59+/VsypDDPfxIIBJgzZw7mzJmDBx54gJqFHz58GK+99hoMBgPt186fP9/mgIdlWTQ1NUEmk9mUWZnHT5PhGDE/kUgkvNJoSX/V3rVgR8GNYpk1axYOHTqE4uJiHD58GNHR0Th69Oi4VXnWtq7i4+Px9ddf45ZbbsHw8DB++ukn/OUvf+G9dXXDDTcgKioKR44cwVVXXYUvv/zSobigcwmxWIjY+JBzfRguwTkh17EumZ555hkcPnz4rP/zyKRKD4FEIsGiRYuwaNEisCwLhUKBo0eP4t///jcee+wxBAUF0RbC7NmzTS69dTodnZZnZ2fbTTCWhmPcNFpfX18EBwcjJCSEkjzpr86ZM2dchPncKJbQ0FDs2rULhYWFKCwspOvFK1eudPtxEFiLY/nzn/+MW2+9FZmZmWBZFrfeeivOO+88ANbjWMj7grwXFixYQCvdO+64Azt37hz3rTKnMUnaAh4lxSotLcWll15KXarI5c+xY8fwzjvvAADV/S1ZsgTbtm2z2RZ44IEH8MUXX0AsFmPGjBl455136CXuZB6QEZA8MCL5KikpoWbhYWFhKC4uxp/+9Ce3yKy4U3yZTIaRkREYjUaIxWJkZmaOC7GSVkdmZia8vLxw7733QiKR4PXXX3e73+x4oqqqCgzDICXl12WjW265BREREXj++efH6zCcl2LFz2K33f8ar9v+8d7LPVqK5VHd7qysLPT09KCpqQlNTU2IjY1FcXExIiMjsXz5cuzbtw8ajQaNjY2ora3F3LlzbT7m4sWLUVZWhpKSEsyaNQvbt28HYDog++qrr/CnP/0JBoPB3S9x3MEwDOLi4rB27Vp88MEH+OWXX/D444/jp59+wj333IPvv/8eTzzxBD7++GP09vZavEJw5rnJBD89PR1CoRChoaEIDg5GeXk5fv75Z1RXV6O3txd6vd5lzwv8GqPT2NiI3NxcDA8P45prrkFubi52797tNmLduXMnGIZBX9+vfcPt27cjOTkZKSkp+M9//kO/f/LkSWRlZSE5ORn33HOPXT97o9FIP9+zZw9WrFiBm2++GW+99RY6OjoAAK+88go+//xzfPLJJy54ZVOwFx7Xc7WGjIwM3HDDDUhPT4dIJMJrr73Ga7J8+eWX08/nz5+Pjz/+GMBvd0AmEAgQHx+PwMBANDY2QiwW46effkJRURF2794NjUaDCy+8EAUFBbjgggtcom+11l8l5i0ymQyNjY1gGMbhzC4uuFEsOTk5KCsrw/r16/Hss8+6VRtqr8rlzjvvxO7du+liwFdffcXr+LgqALLk8eOPP6K0tJSeOJYtW4aQkBC88sorOHz4MK688spxkZi5BpOjLeDR5NrU1GTy9ebNm7F582aHH+/tt9/G73//ewBTA7LXX3+dfr1w4UIsXLgQTzzxBJRKJb799lsUFhbiiSeegL+/Py6++GKHzcLb29vR3t5usb9KKlnucEwul6OzsxPV1dUQi8W0XzvWcIwLrVaL0tJShIaGIi4uDgcPHsSOHTuwb98+pKWl2XXs9sIelUtiYqJDiwGEWPV6Pa666ir4+vqipKQETzzxBC666CI0NzfjyJEjmDZtGpYsWYLIyEiIxeIJQ6xajR7tDb3n+jBcAo8mV76wpSkkn4tEIvzhD38AMDUgs4aAgABcffXVuPrqq6kDf1FREd5++23cfffd1Cz8kksuQXJystXq0mg00pgZvvpV8xgWa8Ox4OBgi2m5g4ODKCsrQ3JyMoKDg7Fz50669UYyptwFe1UuXl5eDsWxCAQC9Pf345VXXsH8+fOxbt06bNiwAXfccQfee+89/OEPf0BzczM+/fRTzJ8/H3PmzMHJkyfR2dk5LtHtzkIsFiEmIfRcH4ZLMCnI1Zam8N1338XBgwdx5MgRSqCxsbFobW2lt7GmHfwtg2EYREdHY/Xq1Vi9ejUlzMLCQmzduhUNDQ3Izs6mSgViFq5Wq1FeXo6wsDCnFhy8vb0RExODmJgYsCyLoaEhyGQyuj1GbBWDg4OhUCjQ0NCArKwsCAQC3HbbbQgNDcWXX37pMuNwV6pc7Dm5k2qVZVmwLIudO3eisLAQmzdvRkxMDPbs2YObbroJW7ZswZNPPkmDN4nD2Nq1a6cKh3OASUGuY+Grr77Cc889h2+//dak2lm+fDluvvlm3Hfffejo6OA9ICOPee+998JgMOD222/Hww8/7K7D9ygIBAKkpaUhLS2NroseO3YMRUVFWLt2LVQqFVJSUlBSUoKPPvoICQkJLntuhmHg5+cHPz8/xMfH0xXX/v5+1NXVQavVorGxEeXl5Xj//fexZs0a3HnnnS4lFWsn8dLSUjQ2NtKqta2tDTk5OTh27JjVk3hsbCyvxQCWZenVQXNzMxITE/HAAw9ALpejrKwM+fn5iI2Nxa5du7B06VLMnj0b1113HWbNmkUfY8IR6ySRYnmUWsAduOuuu6BSqbB48WLMmTMHGzZsAGA6IFu6dCnvAZnBYMCf//xnHDp0CBUVFfjwww9RUVHh7pfhkRCJRLjgggvw17/+FUePHsX69etRXFyMiy66CLfeeiuWLFmC7du348cff4ROp3PpcwsEAvj7+0OtViM0NBQLFiyAUCjEW2+9BZVKhQ8//NBkMu9OOKJy4S4GsCyLvXv3mvRqAVOfgD//+c9Yvnw57rnnHlRVVeGhhx5CVVUVDh8+DJlMhrS0NHz66ae4+uqrx+U1uw/sKLny+fBwTPrKta6uzur/OTIgO3bsGJKTk5GUlAQAuPHGG3HgwAGkp6c7dZyTATNnzkRxcTF8fHzAsix6enpw5MgRfPDBB9i4cSPi4uJouKOzZuEajQYlJSU0iuXTTz/FW2+9hf3792PWrFno7+93OaE7grFULtYWA4BfWwFGoxHFxcUICgrCwYMHUVRUhO3bt+Oll17CXXfdhRdeeAE+Pj5YuXIlcnNzAYwWABPVc2N0oNVzrg/DJZj05OpqtLe3Iy4ujn4dGxuLn3/++RwekeegoKCAfs4wDCIiInDzzTfj5ptvpmbhhYWF2L59O2pqakzMwqOionhfviqVSpSXlyMlJQWBgYF45plnUFxcjCNHjtAFEXcPsMYCX5VLXl4eysrK6Nekp8owDAQCAWpqarB582YolUpceOGFiI+Px7XXXguFQoHNmzfj73//O373u98hPDzcpK88UYkVODPQSpwaaP0mMaUycAwMw2DmzJmYOXMmXdgoLi5GYWEh1q9fD7lcbmIWHhAQYPHnSqJYZs+eDZZlsWbNGsTFxeGLL75w2qHrXMNoNFJiPH78OJ5//nmsWLECp0+fxo8//ojm5mYkJCTg5ptvRn19PbZs2YKXXnrpHB+1O+D5l/x8MOl7rq6Gq1QGra2tKCgoQFpaGjIyMrBr1y4AgEwmw+LFizFz5kwsXryYJqNONgiFQuTn5+PRRx/FkSNH8MMPP2DFihU4ceIErr32WixevBhPPPEEvv/+e2oR+MMPP6CzsxO5ubmQy+W4+uqrsXTpUrz44otuJVbzravCwkLk5uYiKysLubm5+Prrr+ltHd26Onz4MNatWwdgdGX3hRdeQGtrK2666SY899xziI2Nxd/+9jf09fUhMjISDz74IB577DHXv1hPAMvzw8MxRa52Ij8/H7W1tWhsbIRWq8W+ffuwfPlyux9HJBLh+eefR2VlJX766Se89tprqKiowLPPPotLL70UtbW1uPTSS/Hss8+64VV4Hry9vbF48WLs2LEDP/74Iz7//HPk5OTg008/xcKFC5GdnY3du3dDKBTi5MmTWLlyJZ577jmsW7fOrVcOlrauQkND8cUXX6C0tBTvvvsuVq1aRf+PbF3V1taitrYWX331Fa/nycvLQ0xMDFQqFfz9/bFhwwao1WrqqfHSSy+hpaUFO3bsgFarRUJCAkJDQ03WYKfgWZgiVzshEonw6quvYsmSJUhLS8MNN9yAjIwMux8nKioKOTk5AAB/f3+kpaWhvb0dBw4cwJo1awAAa9aswWeffebS458IYBgGoaGhuOGGG/Dyyy9TsiHke/PNN+Ozzz7DxRdf7PZjIVtXXALPzs6mVysZGRkYGRmBRqMxiWNhGIZuXfEB8batrKyEWCxGfn4+HnvsMXzyySf45ptv4Ovri+3btyMpKcnEF2FCmmGPCRZgjfw+PBwTu0l1jrBs2TIsW7bMZY/X1NSEU6dOYd68eSaGzVFRUejpmRyTU0chkUjwySef0J/JHXfcMW4G0Na2rrj45JNPkJ2dDYlEgvb2doe2rgAgOjoaS5cuxZo1a3Dq1Cn4+Pjg4osvRl9fH5566inExMQgIyPDoRP5RIJWo0d7/eT4m58i13OMwcFBrFy5Ei+99JJJcN0UfoX52qYridWRrSuC8vJyPPTQQ/Q2fIed5icHIrvasGEDTp06hZtuugn/+te/EBISgquuugr9/f2/mcv/UbVA2Lk+DJdgsl1TTCjodDqsXLkSf/jDH7BixQoAo7EunZ2dAIDOzk66wjgF96CoqAhlZWVnfSQlJdGtq8TERLp1RYi4ra0N1157Lfbu3YsZM2YAAO+tK0KsP//8MwYHB+lqKwDs2rULg4ODuO2226DT6RAdHY1HHnmEphX8NjA5JlpT5HqOwLIsbrvtNqSlpeG+++6j31++fDneffddAKOeCOZbO3xgMBiQnZ2Nq666CsBvR4HgSoy1daVQKHDllVdi+/btuPDCC+l9+GxdEZSWluLll18+y/dAKpXi0KFDGBkZwbZt26j/8G8Kk2RDa4pczxF++OEHvPfee/j6669pHtaXX36Jhx9+GIWFhZg5cyYKCwsd8i3YtWuXib3eb1WB4C68+uqrqKurw5NPPkl/d6Q3/sYbb+D2229HcnIyZsyYcVYcC/k3KysLjY2N9ERKqlmDwQCRSITdu3dj/fr1CAwMRENDw3i/xHOLyVG4elbMyxScR1tbG9asWYPNmzfjhRdewMGDB5GSkoKjR48iKioKnZ2dWLRoEaqrq8/1of7mQNQgycnJuPzyy3Ho0CF8++232LJlC3x8fCbDMorzMS8xyey2O3fwuu0ft6z06JiXqYGWnfD0qOK//OUv2LFjB1QqFf3elALBMyAWi2EwGLBp0yY89thjUCqV6O3thUQioTaEnvy3NR7QanRorz17wDgRMdUWsBPmU15XZk45i4MHDyI8PJwaeEzBMizlXAFAS0sL/Pz8sHPnTvo9Z3KuzBEWFoa7774br7/+Oqqrq/Hdd9/hnXfewZtvvglgao0aOKMWSArn9eHpmKpc7UBdXR1qa2txxRVXmPhsegp++OEHfP755/jyyy8xMjICpVJJE0CJE/1vXYFgaeOKYOPGjWfFrDiaczUWFixYgPPPPx91dXVgGAb9/f0ATLOxftvwnILFGUz9Ju1AQ0MDtm3bBmBUJvXkk09CrVZbvO2GDRugUCjG8ehGU0bb2trQ1NSEffv24ZJLLsH777/vtAJBoVDguuuuQ2pqKtLS0vDjjz9OWAWCpY0rAPjss8+QlJRkItJ3ZuPKFgQCAVJTU/HAAw/g4MGDUCqVU8R6BsQdzNaHp2Pqt2kHFi5ciMWLF+Oee+7BM888g87OTkgkEgC/ToF7enrwt7/9DYGBgdT+DhitSs6VENxZBcK9996LpUuXoqqqCr/88gvS0tImpALB2sbV0NAQnnvuOWzdutXk+85sXNkCIVI/Pz8MDAygt3dyhPK5BJNELTDVFuAJg8EAqVSKlpYWHDp0CHv27MEVV1xBBeDELu6f//wnGhoa8PzzzwMABgYGLMZEGwwG6tvpDpBcK2DU2/TIkSMOPY5SqcR3332HPXv2ABgdyojFYhw4cABHjx4FMOqBsGjRIjz33HMuOHLn4MjG1datW7Fx40b4+fmZfH887CX7+/uxc+dOuojwW4d2RIu26smRwjxFrjxBIkTq6+sREBCAZcuW0TcawzDUh3P//v3YtGkTfHx80NHRgfvvvx9GoxFBQUG49tprcckll0AkEp1laGw0GsEwjMcNNRoaGhAWFoZbb70Vv/zyC3Jzc7Fr1y6PVSA4knP1888/4+OPP8aDDz4IhUIBgUAAqVSKlStX8tq4cgZ5/7+9ewu1qorCOP4baYFJpfQQGpGWZplYiRSZQiSCb/kQEVREEEGEgm89dLGHokioiIh6ysJKsEIJjVII7CFJQozUCErreCm8FV3Iy/l6WGvjQYo8uT1q7j9s5mXNPedcbPjWWGOMtdf00zaT6JRw4cUXmH33rOMa+9iyk7yZE+Tf8lx7oKrOwxOYisdxLz5JsvKYcWOxBtOT/F5VU/AxFqIfD+JN3IhheDLJrr9Zr5KkU57EU/tXqmo6PsMtSdZX1Yv4BfOTjBowbn+S0adqn4OlqrZpfqc9x/Qvwq9JFrftzzEf67EKLyVZNbS7PXuoqg9xvK8i2JNk7sncz4nQs1yPj0N4H8uSbKqqObgLK6sxNStJP27Az62wnotJ+CzJO1BVz+EqvIxnMQPvVtXD+Bbbk2zuCGqnrKphSY5U1agkB4ZYdPvQl6TzLpvleAQ/VtWYJLuqagxOD9O1+zyE1zECq9tPj5PE6SyWg6UnrsdBK2QboKrOwWu4oqrOS3Kw7aOxSDsiNBLXYnP7vVnYiFeS9FXVCsysqvcwR2PJ/llV03ArJmBnkq1JjrRzPl9Vbyf5579q6jJJdlfVD1U1KcnXmN2e02bch2facsVg566qhXhAE574EvfjfCzDOGzDnUm6noqQZNw/9C86pr0BU7q9fo//P71sgUGSpD/JgSRfJDnY9nXErw9b2/pFuM5RsZ2hsU53V9VIjXh8h8txGEuT3IGleBW34a2qemrA8stxzQAxHyrmY2lVbcL1eFojqnOq6hvNxWFQ6QJVdSkWaG7Np2guLndprOK1SSZibdvu0eOMoyeu3WUNbm5dBYewQ+OvpBGgTUkOY7TGMl2HmzSi+2k7bjT2JXlUIywDrabfMDdJfw1h5CvJxiTTk0xNMi/J/iR7k8xOMrEt9/2HqYdjRFUN11isO3E7lrTHl2Bed86iR4+hpSeu3WU/VqdhZ5IFSX6qqmH4wFGhvRKX4StM09z+7m2PTcYbA+pbqqrj4J+J7W399EorGCRJdmAxvscuja/6I1zSCfK15dn7OFmPM5qeuHaR1l3QCV7VgP4jSV5IsqXt6tMESfpxNXYl+aOqxmss13XtuAka10HnMbDJWjdDG0A7Y6mq0RordTzGYmRV3XNqd9WjR/f4C76G6jgGeXSyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "p = ax.scatter(xt, yt, zt, c=et, alpha=0.3, cmap='viridis')\n",
    "cb = fig.colorbar(p, ax=ax)\n",
    "cb.set_label('Energy (keV)')\n",
    "\n",
    "ax.set_xlim([-40, 80])\n",
    "ax.set_ylim([-450, -330])\n",
    "ax.set_zlim([950, 1020])\n",
    "\n",
    "ax.set_xlabel('x (mm)')\n",
    "ax.set_ylabel('y (mm)')\n",
    "ax.set_zlabel('z (mm)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "      <th>particle_id</th>\n",
       "      <th>hit_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>time</th>\n",
       "      <th>energy</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-238.977295</td>\n",
       "      <td>-237.883667</td>\n",
       "      <td>316.665863</td>\n",
       "      <td>0.003390</td>\n",
       "      <td>0.005771</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-238.066254</td>\n",
       "      <td>-237.623596</td>\n",
       "      <td>316.943878</td>\n",
       "      <td>0.006744</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-237.118423</td>\n",
       "      <td>-237.311584</td>\n",
       "      <td>316.944092</td>\n",
       "      <td>0.010135</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-236.159500</td>\n",
       "      <td>-237.030731</td>\n",
       "      <td>316.971466</td>\n",
       "      <td>0.013532</td>\n",
       "      <td>0.007584</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>-235.207092</td>\n",
       "      <td>-236.733841</td>\n",
       "      <td>316.930054</td>\n",
       "      <td>0.016925</td>\n",
       "      <td>0.009441</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5951</th>\n",
       "      <td>9979</td>\n",
       "      <td>1</td>\n",
       "      <td>204</td>\n",
       "      <td>-241.135529</td>\n",
       "      <td>12.792623</td>\n",
       "      <td>841.066162</td>\n",
       "      <td>0.572172</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5952</th>\n",
       "      <td>9979</td>\n",
       "      <td>1</td>\n",
       "      <td>205</td>\n",
       "      <td>-241.135300</td>\n",
       "      <td>12.791782</td>\n",
       "      <td>841.065735</td>\n",
       "      <td>0.572198</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5953</th>\n",
       "      <td>9979</td>\n",
       "      <td>1</td>\n",
       "      <td>206</td>\n",
       "      <td>-241.135757</td>\n",
       "      <td>12.790796</td>\n",
       "      <td>841.065796</td>\n",
       "      <td>0.572228</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5954</th>\n",
       "      <td>9979</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>-241.135971</td>\n",
       "      <td>12.789834</td>\n",
       "      <td>841.065918</td>\n",
       "      <td>0.572256</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5955</th>\n",
       "      <td>9979</td>\n",
       "      <td>1</td>\n",
       "      <td>208</td>\n",
       "      <td>-241.135818</td>\n",
       "      <td>12.788076</td>\n",
       "      <td>841.066528</td>\n",
       "      <td>0.572308</td>\n",
       "      <td>0.003666</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5850923 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      event_id  particle_id  hit_id           x           y           z  \\\n",
       "0            0            2       0 -238.977295 -237.883667  316.665863   \n",
       "1            0            2       1 -238.066254 -237.623596  316.943878   \n",
       "2            0            2       2 -237.118423 -237.311584  316.944092   \n",
       "3            0            2       3 -236.159500 -237.030731  316.971466   \n",
       "4            0            2       4 -235.207092 -236.733841  316.930054   \n",
       "...        ...          ...     ...         ...         ...         ...   \n",
       "5951      9979            1     204 -241.135529   12.792623  841.066162   \n",
       "5952      9979            1     205 -241.135300   12.791782  841.065735   \n",
       "5953      9979            1     206 -241.135757   12.790796  841.065796   \n",
       "5954      9979            1     207 -241.135971   12.789834  841.065918   \n",
       "5955      9979            1     208 -241.135818   12.788076  841.066528   \n",
       "\n",
       "          time    energy   label  \n",
       "0     0.003390  0.005771  ACTIVE  \n",
       "1     0.006744  0.006734  ACTIVE  \n",
       "2     0.010135  0.003000  ACTIVE  \n",
       "3     0.013532  0.007584  ACTIVE  \n",
       "4     0.016925  0.009441  ACTIVE  \n",
       "...        ...       ...     ...  \n",
       "5951  0.572172  0.000354  ACTIVE  \n",
       "5952  0.572198  0.000041  ACTIVE  \n",
       "5953  0.572228  0.000093  ACTIVE  \n",
       "5954  0.572256  0.000016  ACTIVE  \n",
       "5955  0.572308  0.003666  ACTIVE  \n",
       "\n",
       "[5850923 rows x 9 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_0nubb_hits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total amount of events = 9799\n",
      "Total amount of events = 9799\n",
      "Events in the fid. limits = 9286\n",
      "True events removed by fiducial cut = 94.76477191550158\n"
     ]
    }
   ],
   "source": [
    "base_vsize = 2 #mm\n",
    "totTrueEvents = true_0nubb_hits.event_id.nunique()\n",
    "nEventsLimits = 0\n",
    "\n",
    "print(f'Total amount of events = {totTrueEvents}')\n",
    "\n",
    "for nevt in range(1,true_0nubb_hits.event_id.nunique()):\n",
    "    #nevt = 10\n",
    "    #print(f'Event {nevt}')\n",
    "    this_evt = true_0nubb_hits[true_0nubb_hits.event_id == nevt]\n",
    "    the_hits = []\n",
    "\n",
    "    xs = this_evt.x\n",
    "    ys = this_evt.y\n",
    "    zs = this_evt.z\n",
    "    es = this_evt.energy\n",
    "\n",
    "    if xs.size == 0: continue\n",
    "    for x, y, z, e in zip(xs, ys, zs, es):\n",
    "        h = Hit(0, Cluster(0, xy(x,y), xy(0,0), 0), z, e*1000, xy(0,0))\n",
    "        the_hits.append(h)\n",
    "\n",
    "    voxels = voxelize_hits(the_hits,\n",
    "                       np.array([base_vsize, base_vsize, base_vsize]), False)\n",
    "\n",
    "    vsizex = voxels[0].size[0]\n",
    "    vsizey = voxels[0].size[1]\n",
    "    vsizez = voxels[0].size[2]\n",
    "\n",
    "    min_corner_x = min(v.X for v in voxels) - vsizex/2.\n",
    "    min_corner_y = min(v.Y for v in voxels) - vsizey/2.\n",
    "    min_corner_z = min(v.Z for v in voxels) - vsizez/2.\n",
    "\n",
    "    x = np.array([np.round(v.X/vsizex) for v in voxels])\n",
    "    y = np.array([np.round(v.Y/vsizey) for v in voxels])\n",
    "    z = np.array([np.round(v.Z/vsizez) for v in voxels])\n",
    "    e = np.array([v.E for v in voxels])\n",
    "    \n",
    "    r = []\n",
    "    r2 = x**2+y**2\n",
    "    for ri in r2: r.append(math.sqrt(ri))\n",
    "\n",
    "    r_min = int(min(r))\n",
    "    z_min = int(min(z))\n",
    "\n",
    "    r_max = int(max(r))\n",
    "    z_max = int(max(z))\n",
    "    \n",
    "\n",
    "    if((r_max < rmax_N100) & (z_min > zmin_N100) & (z_max < zmax_N100)):\n",
    "        #print(f'nEventsLimits = {nEventsLimits}')\n",
    "        nEventsLimits = nEventsLimits+1\n",
    "\n",
    "\n",
    "print(f'Total amount of events = {totTrueEvents}')\n",
    "print(f'Events in the fid. limits = {nEventsLimits}')\n",
    "print(f'True events removed by fiducial cut = {100*nEventsLimits/totTrueEvents}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing selected events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2212"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_noaccept = []\n",
    "for i in range(len(reco_0nubb_e[2])):\n",
    "    if reco_0nubb_e[2][i] < 0.46:\n",
    "        events_noaccept.append(reco_0nubb_e[3][i])\n",
    "len(events_noaccept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'deco_0nubb' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-112-091f6a8218aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mbase_vsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;31m#mm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mthis_evt_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeco_0nubb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeco_0nubb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevent\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mthe_hits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthis_evt_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'deco_0nubb' is not defined"
     ]
    }
   ],
   "source": [
    "base_vsize = 5 #mm\n",
    "this_evt_df = deco_0nubb[deco_0nubb.event == 6]\n",
    "the_hits = []\n",
    "\n",
    "xs = this_evt_df.X\n",
    "ys = this_evt_df.Y\n",
    "zs = this_evt_df.Z\n",
    "es = this_evt_df.E\n",
    "\n",
    "for x, y, z, e in zip(xs, ys, zs, es):\n",
    "    if np.isnan(e): continue\n",
    "    h = Hit(0, Cluster(0, xy(x,y), xy(0,0), 0), z, e*1000, xy(0,0))\n",
    "    the_hits.append(h)\n",
    "\n",
    "voxels = voxelize_hits(the_hits,\n",
    "                       np.array([base_vsize, base_vsize, base_vsize]), False)\n",
    "\n",
    "vsizex = voxels[0].size[0]\n",
    "vsizey = voxels[0].size[1]\n",
    "vsizez = voxels[0].size[2]\n",
    "\n",
    "min_corner_x = min(v.X for v in voxels) - vsizex/2.\n",
    "min_corner_y = min(v.Y for v in voxels) - vsizey/2.\n",
    "min_corner_z = min(v.Z for v in voxels) - vsizez/2.\n",
    "\n",
    "x = [np.round(v.X/vsizex) for v in voxels]\n",
    "y = [np.round(v.Y/vsizey) for v in voxels]\n",
    "z = [np.round(v.Z/vsizez) for v in voxels]\n",
    "e = [v.E for v in voxels]\n",
    "\n",
    "x_min = int(min(x))\n",
    "y_min = int(min(y))\n",
    "z_min = int(min(z))\n",
    "\n",
    "x_max = int(max(x))\n",
    "y_max = int(max(y))\n",
    "z_max = int(max(z))\n",
    "\n",
    "#print(f'X min = {x_min}, X max = {x_max}')\n",
    "#print(f'Y min = {y_min}, Y max = {y_max}')\n",
    "#print(f'Z min = {z_min}, Z max = {z_max}')\n",
    "\n",
    "VOXELS = np.zeros((x_max-x_min+1, y_max-y_min+1, z_max-z_min+1))\n",
    "#print(VOXELS.shape)\n",
    "\n",
    "# sort through the event set the \"turn on\" the hit voxels\n",
    "cmap = cm.viridis\n",
    "norm = Normalize(vmin=0, vmax=max(e))\n",
    "\n",
    "colors = np.empty(VOXELS.shape, dtype=object)\n",
    "for q in range(0,len(z)):\n",
    "    VOXELS[int(x[q])-x_min][int(y[q])-y_min][int(z[q])-z_min] = 1\n",
    "    colors[int(x[q])-x_min][int(y[q])-y_min][int(z[q])-z_min] = cmap(norm(e[q]))\n",
    "\n",
    "# and plot everything\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "ax = fig.gca(projection='3d')\n",
    "#a,b,c is spacing in mm needs an extra dim\n",
    "a,b,c = np.indices((x_max-x_min+2, y_max-y_min+2, z_max-z_min+2))\n",
    "a = a*vsizex + min_corner_x\n",
    "b = b*vsizey + min_corner_y\n",
    "c = c*vsizez + min_corner_z\n",
    "\n",
    "# a, b, c are the corners of the voxels\n",
    "ax.voxels(a,b,c, VOXELS, facecolors=colors , edgecolor='k',alpha=0.8)\n",
    "\n",
    "ax.set_xlabel('x (mm)')#,fontsize=16)\n",
    "ax.set_ylabel('y (mm)')#,fontsize=16)\n",
    "ax.set_zlabel('z (mm)')#,fontsize=16)\n",
    "\n",
    "sm = cm.ScalarMappable(cmap=cmap, norm=norm)\n",
    "sm.set_array([])\n",
    "cb = fig.colorbar(sm)\n",
    "cb.set_label('Energy (keV)')\n",
    "\n",
    "plt.show()"
   ]
  }
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