{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Differential Sensitivity\n",
    "\n",
    ".. contents:: :local:\n",
    "\n",
    "In this tutorial, we'll compute a differential sensitivity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import histlite as hl\n",
    "import csky as cy\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mkovacevich/updated_csky_2/csky/csky/plotting.py:92: MatplotlibDeprecationWarning: Support for setting the 'text.latex.preamble' or 'pgf.preamble' rcParam to a list of strings is deprecated since 3.3 and will be removed two minor releases later; set it to a single string instead.\n",
      "  r'\\SetSymbolFont{operators}   {sans}{OT1}{cmss} {m}{n}'\n"
     ]
    }
   ],
   "source": [
    "cy.plotting.mrichman_mpl()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "timer = cy.timing.Timer()\n",
    "time = timer.time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll work with MESC 7yr because things are fast with a low-stats dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up Analysis for:\n",
      "MESC_2010_2016\n",
      "Setting up MESC_2010_2016...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/IC86_2013_MC.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/IC79_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/IC86_2011_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/IC86_2012_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/IC86_2013_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/IC86_2014_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/IC86_2015_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/IC86_2016_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/GRL/IC79_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/GRL/IC86_2011_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/GRL/IC86_2012_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/GRL/IC86_2013_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/GRL/IC86_2014_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/GRL/IC86_2015_exp.npy ...\n",
      "Reading /data/ana/analyses/mese_cascades/version-001-p02/GRL/IC86_2016_exp.npy ...\n",
      "Energy PDF Ratio Model...\n",
      "  * gamma = 4.0000 ...\n",
      "Signal Acceptance Model...\n",
      "  * gamma = 4.0000 ...\n",
      "Done.\n",
      "\n",
      "0:00:11.353483 elapsed.\n"
     ]
    }
   ],
   "source": [
    "with time('ana setup'):\n",
    "    ana = cy.get_analysis(cy.selections.repo, cy.selections.MESEDataSpecs.mesc_7yr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we'll examine $\\delta=-60^\\circ$ so that we can compare the results with pre-unblinding calculations [here](https://wiki.icecube.wisc.edu/index.php/Cascade_7yr_PS_GP/Analysis_Level_Performance#Differential_Sensitivity)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = cy.sources(0, -60, deg=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In IceCube analyses, when we refer to differential sensitivity, we are talking about the sensitivity to signal events in relatively narrow energy bands.  Just like ordinary (all-energy, or energy-integrated) sensitivity calculations, we're looking for the flux that yields greater than the background-only median TS in 90% of trials.\n",
    "\n",
    "Therefore, we start by finding the background-only TS distribution and extracting that median value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing 10000 background trials using 10 cores:\n",
      "      10000/10000 trials complete.   \n",
      "\n",
      "0:00:11.512436 elapsed.\n"
     ]
    }
   ],
   "source": [
    "with time('background estimation'):\n",
    "    allE_tr = cy.get_trial_runner(ana=ana, src=src)\n",
    "    bg = cy.dists.Chi2TSD(allE_tr.get_many_fits(10000, mp_cpus=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mkovacevich/new_venv/lib/python3.7/site-packages/numpy/core/_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
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fpOfs9G8gw/9hjuda4waDYcB5V9XV1TAajQOeE96DM7jJCDcW/Q+zDd4rYTabUVJSMuRQXG1tbdTPyxpur2JNTc2Yg0OKiorGtTdlpEOBk/nsw83w4D2g/Q3+zAYPvBlruRjMZrMw4GAy34fBnE4ncnJyUFpail27dsFgMERl4mY5Mh3rOZEul924xvgq3COPPMIDEH7EnGbC5XLxu3bt4l0ul2jvQeRHObM1ZUtxcTFvMBgm/LzhpmyxWq28wWAYMMdXQUGBsNxmsw2YDoLn+6ZoCU+nUVVVxRsMhiFz8FVVVY04TYTZbOZzc3MHzDWHQVN0hOefC79XYWHhgHUOv+9gw9U7muLi4gFzDPI8L3weYpvoZ2+z2YTpORwOB+9wOITPxeFw8DabjTcYDMLy8PyI4Tnbxlo+3HsOp6CgYMhnBmDAZ2a1WgfMH1hcXCxkzvN9+YVfY7zfh/DjbTYbD0Cos6GhgTcYDANqGlxjeNoSsXOdzPY01nMiXU7z9MUIKebpI4QVrDR9NTU1wtxsE/0PbKQJja1WK280Gof8x8nzffPCDf57PTxHHgDebDYPW8dwzwsL/8cEgM/NzRXqGjwvW3gSaIPBwBcUFAz4T2ukpm+09x2O1WodUH+4oZDKRD97m83GFxQU8AaDQfhc+n9uDQ0NwnKj0ThkTr6xlo/n85tM0zd4Xc1ms/CHwni+D4Pfs7CwkAcgfG6D5x+Uq+kbvJ7jyXSs50S6XM6mj+N5mlMiLDx6t6mpCYsWLZK7HELi2sGDBwFA0dtSeORrYWEh8vPzhRPzxyt8eDQWpnIQS/jybBMdjRpWWVmJqqqqSU8DE+8i/fxI7HE6ncjIyBgwGn4kY/0enWjfQvP0SayrqwtvvPEGurq65C6FiIhyZkN1dbVw3o7FYpn0SfBKtm/fPqxfv37Szx/P+XxKFunnR0h/1PRJTKfTYcmSJTSVh8JRzsoXHp0Ztm7dOtjtdmHAhNQjXmOV2WyOaETz4JG8rIn08yOkP2r6JKbRaDB79mxoNBq5SyEiopyVrbGxcch8d+HpOjZu3DjqVTcGC1+6TOxLUsWrqqqqiOeMIyRWhK/OIxeaskViPT09aG1txaxZs5CYmCh3OUQklLOyjdSEFBYWTmiiYrPZTJfqGwM1fERJxJpwerxoTx+kvfau3+9Hc3Mz/H6/6O9F5EM5E0IIiTXU9KGv6eN5Hk1NTaK/V2pqKq677jqkpqaK/l5EPpQzIYSQWENNHyGEEEIIA6jpk5jL5cKOHTvgcrnkLoWIiHImhBASa6jpk5hWq8X8+fPpeqwKRzkTQgiJNTR6V2KJiYkwmUxyl0FERjmLq83tx9b3jgm3N1w5B9kp1GATQshoqOmTWCAQwKlTpzB9+nSaw03BKGdxdXh68MSuZuH2DZdMp6aPEELGQId3Ie2ULT6fDx9//DF8Pp/o70XkQzmTWGWxWFBUVCR3GcjLy5vQ5dVipW5C4hk1fZB2ypa0tDSsWbMGaWlpor8XkQ/lrHxOpxPV1dUjHsYvLy+HyWRCRkbGsM2K2MtjXVFRUVzWTUg8o6aPEEImqKSkBDk5Odi8eTPsdvuQ5ZWVlaioqEBVVRVaWlpgt9sH7NUSe7kcGhsbJ3R5qcLCwiGXsiOEiIuaPom53W7s3r0bbrdb7lKIiChnZbNarXA4HCgtLR1xudVqRW5uLgwGA6qqqlBbWwun0ynJckIIGQ41fRLTaDSYMWMGndyvcJQzu+x2O+x2O8xms3CfwWCA0WjE9u3bRV8+Hh0dHSgpKUFGRgYyMjJQXl4+5DHhC8ObTCYUFRUJh5KHY7FYkJeXB6fTCY7jwHEcGhsbAfQdxi0vL0dtbS1MJhMqKyuF5/Q/vFtdXY28vDxwHAeTyYTq6upR16GkpAQmk0l4fPh1CSEjo9G7EtNqtVi4cKHcZRCRUc6Ra3P70eHpGXbZ0TbPqLfDMvWJko/qDR/uNRgMA+43Go2w2WwwGo2iLh+P6upqVFVVobS0FNu3b0dRURGMRqNwuDXcjLW0tKC2thYWiwUOh2PIe4aF9zSGH9dfR0cH9u3bh82bN8NqtWLdunXDvsaHH36I0tJSFBQUoLa2Fvn5+WhoaEBubu6QxzY2NqKyshItLS0wGAxobGxER0fHuNadEJZR0yexQCCA9vZ2ZGVl0V4gBaOcI7f1vWMDpmUZTeHWhmHvf2DVfDyUvyCaZSlCQUGB0OAVFhaipqYGFRUVwn3bt29HVVUVDAYDCgoKYDAYYLfbh23AxqOxsXFAwzocq9Uq/NtsNiM3Nxe1tbXDvqfdbkdmZqZwe7J1EcIaOrwrMZ/Phw8++ICm8lA4ypkMNtaeKLGXjyY/Px/79u2b9PPHYjabR234hpOZmTninsvwoe2MjAzk5+fToV1CxomaPomlpqbixhtvRGpqqtylEBFRzuwKNzeDB1U4nU6YTCbRl09W/z1n69atg9VqhdPpRHl5OTIzMyPamzaehs/pdKKkpAR5eXkwmUyora0d8bEGgwE2mw0VFRUwGAw0/Qsh40SHdyXGcRzUarXcZRCRUc6R23DlHNxwyfRhlx1t8ww4pFu5IQ9zs/VDHpepTxStvpEYjUYYDAZs374dhYWFAPoamvDgC7GXT0ZNTc2Aps5ut8NgMCAnJwdGoxE1NTWRfCRjcjqdyMnJQWlpKXbt2gWDwTCuKWgKCwtRWFiI6upqbNy4ERUVFaLWSUi8o6YPfZMzb9q0SZL38ng8aGxsRG5uLvT6of9JEWWgnCOXnaId9yCMudl6LJgaO3tVS0tLUVJSgqVLl8JoNMJisaCgoEDY4yX28rFUV1ejtrYWZrMZ5eXlqK6uRkPDf5rojo4OFBUVCefZ9d8LOBKj0Qin0ymM2g2PKB6Pjo4OOJ1OoaFtbGzEvn37Rnx+dXU17HY7CgoKkJmZiZqamgkfPiaERXR4F9JekUOtVsNgMNBeIIWjnJWtsrISHMfBYrEAgDBNSVhxcTFKS0thsViEvWVVVVWSLR+N0WhEYWEhqqqqkJGRIUzy3H9PX7iRNJlMwlQt+fn5o84DaDQakZubi1WrVmHjxo0TmjMwXFN4mpiKigqYzeYRRwuH9z7m5eUhIyMD+/btG/f6E8Iyjud5Xu4iYsXBgwexePFiNDU1YdGiRXKXQ0hcO3jwIACIsi0dPuPC6sf/Idze+dCXYmpPXzwLT73S0NAg7D1rbGzExo0bsXTpUjqESoiExvo9OtG+hfb0Say3txft7e3o7e2VuxQiIsqZxKvGxkbhsHFYbm4u1q9fP+wl5wgh8YOaPol5vV68++678Hq9cpdCREQ5k3hlNptRW1uLyspK4RBtbW0tKioqhMPZhJD4RAM5JJaSkoLVq1dDq5X2KgFEWpSzuDL1iXhg1fwBt0l05ObmoqGhASUlJSgpKYHT6URubi5KSkqE0cKEkPhETZ/EVCoVdDqd3GUQkVHO4spO0dKVNkSUm5sr+jQthBDp0eFdiXm9Xrz//vt02E/hKGdCCCGxhpo+iXEcB41GM2B6B6I8lDMhhJBYQ4d3JabT6ZCXlyd3GURklHPfIe6enh7wPE/NLyGETBDP8+jt7UViYvTOWaY9fRILBoPo6upCMBiUuxQiIsoZSE9PR29vL44dO0ZT1xBCyAT0/92Znp4etdelPX0S83g8qKurw4oVK5CWliZ3OUQklDMwZcoUuFwutLe3o729HRqNhq5QQgghYwgGgwgEAgD6ZoKYMmVK1F6bmj6J6fV6rFixgq7HqnCUM5CQkIAFCxbA4XDA4XCgp6cHoVBI7rKiKhQKQaWiAyZKRhmzIZZy1mg00Ov1yMjIQEZGRlRPj6GmD33X3t20aZMk76VWq5nd88MSyrkPx3HIzMxEZmam3KUQQgjzYqOtlVlZWRl4nkdTU5Po7+Xz+dDQ0ACfzyf6exH5UM7KRxkrH2XMBpZypqZPYjzPIxAIgOd5uUshIqKclY8yVj7KmA0s5UyHdyWWnJyMZcuWyV0GERnlrHyUsfJRxmxgKWfa0yexUCgEn8+nuBPayUCUs/JRxspHGbOBpZyp6ZOY2+3Gzp074Xa75S6FiIhyVj7KWPkoYzawlDM1fRJLTk7G8uXLkZycLHcpRESUs/JRxspHGbOBpZzpnD6JJSQkICsrS+4yiMgoZ+WjjJWPMmYDSznTnj6JdXd348CBA+ju7pa7FCIiyln5KGPlo4zZwFLO1PRJLBgMwul0Mn1NVhZQzspHGSsfZcwGlnLmeBYmphmngwcPYvHixWhqasKiRYvkLocQQgghZEQT7VtoT5/EeJ5HMBhkYhJIllHOykcZKx9lzAaWcqamT2IulwtvvvkmXC6X3KUQEVHOykcZKx9lzAaWcqamT2I6nQ5XXHEFdDqd3KUQEVHOykcZKx9lzAaWcqYpWySm0Wgwbdo0ucsgIqOclY8yVj7KmA0s5ayIPX3l5eXIz89HXl4eSkpK5C5nVH6/H59++in8fr/cpRARUc7KRxkrH2XMBpZyjvumz+l0AgBqamrQ0NCAyspKNDY2ylvUKAKBAE6ePIlAICB3KURElLPyUcbKRxmzgaWcFTVli91uh8lkgsPhgMFgmPDzY2nKlpDPBxUD5xcQQgghZHLidsoWp9OJ6upqmEymYZeXl5fDZDIhIyMDRUVFQ5bn5eXBZDKhpqZmUg1frOB5HqcffRRHb7sdfE+P3OUQQgghRCFioukrKSlBTk4ONm/eDLvdPmR5ZWUlKioqUFVVhZaWFtjtduTn5w94TENDA2w2G4qKimL68G5XVxfeeOMNdHV1Dbuc4zioMzLh//e/0fZ0hcTVkWgZK2cS/yhj5aOM2cBSzjHR9FmtVjgcDpSWlo643Gq1Ijc3FwaDAVVVVaitrRXO5wszGo0wm82oqIjdZkmn02HJkiWjDg3PLiqE9oIL0FZZie5PPpGwOhIt48mZxDfKWPkoYzawlHNMNH2jsdvtsNvtMJvNwn0GgwFGoxHbt29HdXU1amtrhWW1tbXIy8uTo9Rx0Wg0mD17NjQazYiP4RITMf2XvwB4Hid/+CPwDJxcqjTjyZnEN8pY+ShjNrCUc1w0fQCGnKdnNBphs9lQUFCAmpoa5OXlIS8vDwUFBSgsLBzzdc+ePYuDBw8O+Dly5AgAwOv1Co/r6uoSRvT09PQMmLHb5XKh5/Pz7gKBwIBdw263Wxj+HV7G8zx6enrwySefCI/t7e1FV1cXQqGQ8N4+nw+6RYuQee+98H/yCc5VVAIAfD6fUFsoFEJXVxd6e3sBAN3d3fB4PAD6zgvsX7ff74fb7RZtnQDA4/Ggu7t71HUC+i5s3dXVJVzYWqnr1NnZCZvNhu7ubsWskxJzimSdenp6YLPZ0N7erph1UmJOkaxTT08PDh06JDxWCeukxJwiXafw72ufzxeX6zQRMd/0jYfVakVDQwMaGhpgtVrH9ZwtW7Zg8eLFA37Wrl0LADhw4IDwuD179uDUqVMAgNbWVuzdu1dYtnfvXrS2tgIATp06hT179gjL6uvr0dLSAgBob29HXV0dQqEQ/H4/mpub0dzcDADo7OxEXV2dEOz+/ftx6NAhAEDSXRvQ9YUvQH3VFwEAhw4dwv79+wH0fanq6urQ2dkJAGhubhbOZQyFQqirq0N7ezsAoKWlBfX19aKtEwA0NjaOa508Hg/q6uqEjUWp63TgwAE0NzfD7XYrZp2UmFMk6xTelv/5z38qZp2UmFMk6+T3+3HkyBFh54MS1kmJOUW6Tp9++imam5vhdDrjcp0mhI8hVVVV/OCSampqhtzH8zyfm5vLFxcXT/q9zpw5wzc1NQ34ef3113kAfH19vfC4zs5Ovqenh+d5nvf7/XxXV5ewrKuri/f7/TzP83xPTw/f2dkpLHO5XHx3d/eAZaFQiOd5nne73bzP5+N5nucDgQDf2dnJB4NBnud53uPx8F6vl+d5nu/t7eU7Ozv53t5enud53uv18h6Ph+d5ng8Gg3xnZycfCAR4nud5n8/Hu91unud5PhQKDai7u7ubd7lctE60TrROtE60TrROtE4KWqempiYeAN/U1MSPR0zN01ddXQ2LxYL+JY00957JZEJJScm4DuWOVyzN0zdY1992orftHDK/+lW5SyGEEEJIDIjbefpGYjQaYTAYsH37duE+p9M5ZHBHvHC5XNixY8eAY/pj4QMBnHvySZx9zAr/ZHbnEslNJmcSXyhj5aOM2cBSzjHf9AFAaWkpSkpK0NjYCKfTCYvFgoKCAhiNxqi8fllZGTiOw+LFi6PyeqPRarWYP38+tFrtuJ/DaTSY8Yufgw8GcfJHPwb/+YmkJHZNJmcSXyhj5aOM2cBSzjHR9FVWVoLjOFgsFgB9ExRzHCcsLy4uRmlpKSwWC3JycmA0GlFVVRW19y8rKwPP82hqaoraa44kMTERJpMJiYmJE3qe7tJLkXn33ejevx8dzz8vTnEkaiabM4kflLHyUcZsYCnnmGj6CgsLwfP8kJ/+iouLYbPZ4HA4Ynry5bEEAgEcP358Uhd2Pu8730bi3Lk49+T/wj/MlUtI7IgkZxIfKGPlo4zZwFLOMdH0scTn8+Hjjz8W5jqaCFVSEqb/8pfgEhLgbz4iQnUkWiLJmcQHylj5KGM2sJRzTI3elUtZWRk2bdok3I7F0bv9Bbu6oE5Lk7sMQgghhMhIcaN3pSDlOX3REG74AmfOoOfECZmrIYQQQkg8oKZPYm63G7t37x5wiZbJ6HU4YL/pZpws+QH4z2foJrEjWjmT2EUZKx9lzAaWcqamT2IajQYzZsyI+MLOCRkZSF+zBr6GBjhe/FOUqiPREq2cSeyijJWPMmYDSzlT0wfp5+lbuHBhVOYDmvLdh6CZORNnH38cPcePR6E6Ei3RzJnEJspY+ShjNrCUMzV9kPacvkAggNOnT0dlaLgqORnTf/5z8D4fTv3ox3SYN4ZEM2cSmyhj5aOM2cBSztT0Sczn8+GDDz6I2tBw/bIrYLj9Nng//BDuurqovCaJXLRzJrGHMlY+ypgNLOVMU7b0M9Ghz5PB8zxCoRBUKtWAq45EIuj2wP33vyPthuuj9pokMmLkTGILZax8lDEb4jnnifYtCRLURPrhOA5qtTqqr6lO0SP9xhsAoO8Q76DL2BHpiZEziS2UsfJRxmxgKWc6vAtpB3J4PB6888478Hg8UX/twJkzOP7fd8O5bXvUX5tMjJg5k9hAGSsfZcwGlnKmpg/SDuRQq9UwGAyi/FWh0uvRc+IEzpaXI/DZZ1F/fTJ+YuZMYgNlrHyUMRtYypmaPoklJSXh4osvRlJSUtRfW52SgumPPoqQ14tTP/kp6HRN+YiZM4kNlLHyUcZsYClnOqdPYr29vejs7ER6ejoSEib/8be5/ejw9AxdsGAJuOvXwPPWG/j3Y78B97WNI57fl6lPRHaK8uclkkO0ciaxizJWPsqYDSzlrOy1i0FerxfvvvsuVqxYgbTPr6E7GVvfO4YndjUPuyyZuxzW9EbM++OzeOJjJ96eu2zYxz2waj4eyl8w6RrIyKKVM4ldlLHyUcZsYClnavoklpKSgtWrV4s687dXo0PJ8vtw56c7sXvmF0R7HzIyKXIm8qKMlY8yZgNLOdM5fZB29K5KpYJOp4NKJe5H79XoUHnxzfAn9H2Jv9zaiKRev6jvSf5DqpyJfChj5aOM2cBSzjQ5cz9STM7s9Xqxf/9+XHLJJUhOTp7064x4Th8Ah7cHb3x8Uri9ljsNfcl3gAsXQfXY4+AMBgB0Tp+YopUziV2UsfJRxmyI55xpcuYYx3EcNBpNxJMnZ6doR23YrsjJEv7N84txrqUI7U9XIOGh+zD72WegmTEjovcno4tWziR2UcbKRxmzgaWcaU9fP1Ls6ZNTxwsv4MwvNyNh2jTMfu5ZaE0muUsihBBCyCRNtG9R/gHsGBMMBtHV1YVgMCj5e2fedRdm/KocvW1tOH7PvQj56Rw/sciZM5EGZax8lDEbWMqZmj6JeTwe1NXVyXa5l/Q1azBry+8w7ac/gYqBkUpykTtnIj7KWPkoYzawlDOd0ycxvV6PFStWQK/Xy1ZDype+JPw78Nln8B1oQtp118pWjxLFQs5EXJSx8lHGbGApZ2r6JKZWq2Nq8seTP/4xvO9/gN5zP0TmhjvlLkcxYi1nEn2UsfJRxmxgKWc6vAtp5+nz+XxoaGiAz+cT/b3GY8YvfoHEuXNx5he/wLknn6Tr9UZJrOVMoo8yVj7KmA0s5UxNH/qaPp7n0dTUJPp78TyPQCAQM82VZsYMzPnzn5B08cVo2/IUTpdtAs/Ayaxii7WcSfRRxspHGbOBpZxpypZ+lD5ly2hCHg9OfPs78Pzzn5hSUoKsr90td0mEEEIIGQVNzhzjQqEQ/H4/tFqt7Jd8GXxVD/7RX4F7+UW0rboRR1raB1zVY82SGchIThzyGnRVj+HFUs5EHJSx8lHGbGApZ2r6JOZ2u1FXV4cVK1bIfuLo1veO4YldzYPuNQFbPgAA3GDfi/enL0a7Lh0vvn982Nd4YNV8PJS/QORK408s5UzEQRkrH2XMBpZyVnZLG4OSk5OxfPnymL++38KOY/jW/tfwu7rfYOmZT+QuJ+7ES85k8ihj5aOM2cBSztT0SSwhIQFZWVlISIjtnayfZs7Bzy7/b6j4EH723nO4t+kNJIR65S4rbsRLzmTyKGPlo4zZwFLOyl/DGNPd3Y3m5mbMnz8fSUlJstay4co5uOGS6cMuc3h78MbHs7Hry5fhqpefQMGRPSjQtEH1yC/ATZ8hPC5TP/Q8PxJbORNxUMbKRxmzgaWcqemTWDAYhNPpjIlr/GWnaEcdhHFFThYAgN/wJZx78kk4/vRnzE3TQDs1VaoS41Ys5UzEQRkrH2XMBpZypilb0DdP36ZNm4TbLE7ZMh69bW1IyM4GAPiaDkI7zwSVwv8qIoQQQmLVRKdsoXP6IP3kzMFgMC4ngQw3fIEzZ3D8nntwdN16+G02mauKTfGcMxkfylj5KGM2sJQzNX0Sc7lcePPNN+FyueQuZdISsrOR9bW74T9yBC0FFjhfeZWJjWUilJAzGR1lrHyUMRtYypnO6ZOYTqfDFVdcAZ1OJ3cpk8ap1ci+/34kX3YZWr/3ME796Ec4/fd3wH23BFyyHkB4IMjYkzsDypzgWQk5k9FRxspHGbOBpZyp6ZOYRqPBtGnT5C4jKpKXLkXNQ/+D5N8+hitrduBX7Qbsmr102MeONLkzoMwJnpWUMxkeZax8lDEbWMqZDu9KzO/349NPP4Xf75e7lKgI6NPw6BV345Fl92DXrDwAgC7QDTB+uFdpOZOhKGPlo4zZwFLO1PRJLBAI4OTJkwgEAnKXEj0ch/ppFwEcBxUfwqb3f4+ffPA8Unq8clcmG0XmTAagjJWPMmYDSznTlC39THToMwHa3H50eHqE23x3N/jyn4PftROe9Czsvf0BnJu7kLlz+gghhBCxTbRvoXP6SESGTvCcCv7/fovOV17B6Z//Aquf2YSse76G7Ou+SXP6EUIIITKiw7sS6+rqwhtvvIGuri65SxENx3EwFBQgp2o7ki64AO3PPItzTzwpd1mSYiFn1lHGykcZs4GlnGlPn8R0Oh2WLFnCxNBw7fz5mLt9Gzq2voj0tTcDAPhAAHxPD1R6vczViYulnFlFGSsfZcwGlnKOStN39OhROJ3OIfdfeuml0Xh5RdFoNJg9e7bcZUiGS0hA1tfuFm63P/ccnNurMO3RR5Gy/Cr5ChMZazmziDJWPsqYDSzlHFHT98wzz+C+++4DgCFXZOA4jomLF09UT08PWltbMWvWLCQmDj+wQcnUmZkIOp1o/frXkX7LLZhaUgx1errcZUUd6zmzgDJWPsqYDSzlHNE5fVarFRs3bsSRI0fgcDgG/HR0dESrRkXx+/1obm5mYj6g4WSsWwfjG3+FfvlydL76Kmw33ghXba3cZUUd6zmzgDJWPsqYDSzlHNGULZmZmTh69CjS0tKiWZPkysrKsGnTJuE2TdkiPp7n0fn6X3Dmsceg0ifD9NZbNLqXEEIImYCJTtkS0Z6+wsJCVFVVRfISMaGsrAw8z6OpqUnuUpjBcRwMX1kL05tvYOYTTwoNn+/gwSGnChBCCCEkchEP5CgsLMRjjz2G3NxcZGZmDlj21FNPRfryiuNyubB3715cddVVSE1Nlbsc2SWcdx6cujS0nnGB/+QgQt+4F7hsGVQPl8KZkoE3Pj4pPDaeJnimnJWPMlY+ypgNLOUcUdPndDpx6623Aug7XNfe3h6VopRMq9Vi/vz50Gpjp0GR29b3juGJXc1I7fGg8PwvwPzBP+G+vQB/XHQD3p67DDzXt0P6xfePj/gaD6yaj4fyF0hV8pgoZ+WjjJWPMmYDSzlH1PQ9/fTT0aqDGYmJiTCZTHKXEZNciXr8Ou927Jl5Kb79r1fw7Y9fxTUn/oXffmEdTqVky13ehFDOykcZKx9lzAaWcqYrckgsEAjg+PHjTFzYebL2Tb0Q9698GG/mXIlFHUeR3uOWu6QJo5yVjzJWPsqYDSzlPKE9fddeey2Kiopwyy23AADuv//+UR9P5/QN5fP58PHHH8NgMECj0chdTkzYcOUc3HDJ9CH3H227Cl9/eidO67MAAH+8VIVpOg7csqGTOmfqY2tuJcpZ+Shj5aOM2cBSzhNq+trb2wfMv0fn8E1cWloa1qxZI3cZMSU7RTvsIIxMfSLW37Ss7wbPY8b/laD38L+RsmoVppaWInHm+RJXOn6Us/JRxspHGbOBpZwn1PTt27dvwO3t27dHtRhC+stO0Q4YnBG45GmcLS9H11tvw/Puu8gqKkTWvfdCpdWize1Hh6dnyGs4vD1xOwKYEEIIiaaoXHuXjJ/b7UZ9fT0uv/xypKSkyF1OXNFMm4bzf/MbGNatw+mf/RxtT/4vXG+/jZzXXhNGAI9FqhHAlLPyUcbKRxmzgaWcIx7I8eqrr+Laa69FVlYW5s+fj/Xr1+Pjjz+ORm2KpNFoMGPGDMWfNyAm/bJlML72KqZ8/2Gk3XQTuIS+v100wV6ZK/sPyln5KGPlo4zZwFLOETV9v/rVr1BQUID09HQ89thjKC4uRigUQm5uLl577bVo1agoWq0WCxcuZGI+IDFxiYnIuvdeZG/cCADQeFx4tvYx3P5pDTRB+UdgUc7KRxkrH2XMBpZyjujw7ubNm1FRUYGNn//HCwAbN25EeXk5iouL8ZWvfCXiApUmEAigvb0dWVlZTPxVIZWCnCQ4zjPgrk//hrtcB6H6zvfALbsKR9s8KNzaIDyuckMe5mbrh32NaI4AppyVjzJWPsqYDSzlHPHh3fXr1w+5r7i4mEb2jsDn8+GDDz6Az+eTuxRFmbZkERa++RdM+f7D4BwdCJU8BN2jpVii8eKBVfOFn9w5GVgwNXXYn2gO4qCclY8yVj7KmA0s5czxEVzd/gc/+AGuuOKKIXv0du/ejaqqqribp+/gwYNYvHgxmpqasGjRIlHeg+d5hEIhqFQqcBwnynuwLnD6tDDKd1rZI8i47TbJa6CclY8yVj7KmA3xnPNE+5YJHd4dPBlze3u7cF5ff9XV1SgsLJzIS0ekvLwcFRUVAICioiIUFxdL9t4TxXEc1Gq13GUoWniUb8add0K3ZAkAoKe1Fd2ffopUs1mSjZpyVj7KWPkoYzawlPOEDu+2t7cP+AGAW2+9FTzPD/i59dZbYTAYxKh3iNraWthsNthsNjQ0NKCiogK1tbWSvPdkeDwevPPOO/B4PHKXonjJubngPt+Qz/3v/+Kzb38Hx+/6b/gOHpzQ67S5/Xi85rDw0+b2j/kcyln5KGPlo4zZwFLOE9rTF4uTMS9duhRLly4FABgMBpjNZjQ2NsJsNstc2fDUajUMBgMzf1XEiqnFxVBpk+B85RUcLbAgfe1anPfgg9BMnSI8ZqQJno+2eQbMAbhoRtqog0GyU7SUMwMoY+WjjNnAUs4RndMXTU6nE7W1tSgpKYHNZhuyPHwIt6OjA+vWrRMO5w5mMplQVVWF3NzcCdcgxTl9RF7d//43zjz2GLzvvQ8uORnGv/4FiTNnAgAerzk8rgmeRxPNCZ4JIYSQ0Uy0b4l49G40lJSUICcnB5s3b4bdbh+yvLKyEhUVFaiqqkJLSwvsdjvy8/OHfZ2ioqJJNXxS6e3tRXt7O3p7Y2ciYZYkXXABZv/+95i5ZQvSb75JaPh6OzqAUChq70M5Kx9lrHyUMRtYyjkmmj6r1QqHw4HS0tIRl1utVuTm5sJgMKCqqgq1tbVwOp3CY0pKSmAymWJ6EAcAeL1evPvuu/B6vXKXwiyO45C6cgWmP/IIAIAPhdB6//1Y9ptiXNh+NCrvQTkrH2WsfJQxG1jKOeavvWu322G32weco2cwGGA0GrF9+3YUFhaiqKgIFoslZs/j6y8lJQWrV69mYubveMEHAtAtWQLDn17Cb479H7iV+eAKvwlu+gwAmNQEz5Sz8lHGykcZs4GlnGNiT99owod7B48GNhqNsNlsqK6uRmVlJSwWCzIyMpCRkYHy8vIxX/fs2bM4ePDggJ8jR44AwIBuv6urC4FA32W9enp64HK5hGUulws9PX0n/gcCAXR1dQnL3G43/H7/gGU8z0OlUiEUCgnP6+3tRVdXF0KfH1r0er3CBJHBYBBdXV0IBoMA+iaQDNcWCoXQ1dUl7I7u7u4WRh7xPD+gbr/fD7fbLdo6AX2jn7q7u+NynQIApv3wh5j92itIuvpq8LtrwN+1DvoXnsastATkzsnAt1eYcN/y2fjOynnInZOBmalqzEpLwIKpqTBlJ2OajocpOxnZKVr4fD50d3dDp9MJtVFOylsnlUoFnU7X9x1SyDopMadI1kmlUiEYDArPU8I6KTGnSNfJ7/dDp9MJtcXbOk1EzDd9YykoKADP83A4HMLPeA7xbtmyBYsXLx7ws3btWgDAgQMHhMft2bMHp06dAgC0trZi7969wrK9e/eitbUVAHDq1Cns2bNHWFZfX4+WlhYAfVPd1NXVIRQKwev1Ys+ePTh06BAAoLOzE3V1dUKw+/fvF5Z5PB7U1dUJX6xDhw5h//79APq+VHV1dejs7AQANDc3o7GxEUDfl7Ourk6YVqelpQX19fWirRMANDY2orm5Oa7XyZWWhv033oDzn30GiXPn4uzhwzhy5AiyU7S4e+l5uDBox/1Xz0Z2inbMdfroo4/w/vvvw+l0Uk4KXSev14v3339fUeukxJwiWSev14u///3vOHz4sGLWSYk5RbpOBw4cwPvvvy88L97WaUL4CDzzzDN8Z2dnJC8xQFVVFT+4pJqamiH38TzP5+bm8sXFxZN+rzNnzvBNTU0Dfl5//XUeAF9fXy88rrOzk+/p6eF5nuf9fj/f1dUlLOvq6uL9fj/P8zzf09Mz4LNwuVx8d3f3gGWhUIj3er38+++/zzscDp7neT4QCPCdnZ18MBjkeZ7nPR4P7/V6eZ7n+d7eXr6zs5Pv7e3leZ7nvV4v7/F4eJ7n+WAwyHd2dvKBQIDneZ73+Xy82+3meZ7nQ6HQgLq7u7t5l8sl2jrxPM+73W7e5/MpZp1Cvb1819mzvM/n44Pd3fyxovv4MzW1416n9vZ2ft++fbzb7eY7Ozv50w43/5ud/+bL3zrIW//fAf6cq5tyivN18nq9/L59+/izZ88qZp2UmFMk6zT497US1kmJOUW6Th0dHfy+fft4l8sVd+vU1NTEA+Cbmpr48YhoypZ58+ahtLQU995772RfYoDq6mpYLBb0L8lut8NkMsHhcAw4xGsymVBSUhLVK3/QlC1kON4PP8Txe78OvqcH+uXLMeXh7yFp4cJhHzvaXH8TOS8wmtcBJoQQokyiXoZtsKeffhrr16+H2WzGnDlzInmpERmNRhgMBmHQBtA3p9/gwR2RKCsrw6ZNm6LyWmMJBoPweDzQ6/VMTASpBMmXXQbjW2/h3JNPoOuNN9Gydy/S1tyI877zABJnnj/gsVvfOzauuf76N4CD0Vx/8YG2ZeWjjNnAUs4RndP30UcfIScnB0ajEddeey3uv//+AT/RUlpaipKSEjQ2NsLpdMJisaCgoABGozEqr19WVgae59HU1BSV1xvN4PMgSHxInHk+zi8vR86rr0B/1VXo+usb6Hrjr3KXRWRE27LyUcZsYCnniPb02Ww2GI1GofkKn8Q4UZWVlSgqKhJucxwHAMJh3vDADIvFMuYVOWKdXq/HihUroNcPf2iPxLakCy/E7Gefgef996G75BIAfRM7d776KjLuvFPm6oiUaFtWPsqYDSzlHDOXYYsFdE4fmYxzTz6Jti1PIWHqVCQV3g/vimvBDTpEQOf0EUIIiTbJL8O2e/durFu3DgsW/OccpPvvvx+7d++O9KUlU1ZWBo7jsHjxYtHfy+fzoaGhQZjriMS/rPvuw9QfloL3++H+WRkSCu9E1kd74Wr9tzCX3+AGb262Hgumpg77Qw1ffKBtWfkoYzawlHNETd+vfvUrFBUVoaioCG1tbcL9q1atgtVqjbg4qUh5Th/P8wgEAqAdrMqhSkxE5l13wVSzE1n3FSFw4jOcffAh8PsahJwz9Yl4YNV84Sd85Q4Sv2hbVj7KmA0s5RzR4d2srCw0NDRg7ty5yMrKGnBO3+Db8YAO75JoCJw5C2dVFbK/cT84lQo9ra3gA73QGnPkLo0QQoiCSHp4l+d5ZGVlDbm/paUFGRkZkby0YoVCIfh8PmFWbaI8mqlTkPWN+9Ht9yMUCuHM5sdgX7MGpx4pQ+DsWbnLI1FC27LyUcZsYCnniJq+jRs3oqCgAEePHhXuO3r0KNatW4f77rsv0toUye12Y+fOnQOuy0eUp3/OWV//OnRLlsC5bRtsq6/F2V//BkGnU+4SSYRoW1Y+ypgNLOUc8ehdi8WCV155BUDfVTLsdjuKi4uxefPmqBQohcGTM4t5eLe3txednZ1IT09HQkJEM+aQGDY4Z57n4d69G+d++wT8zc1QpabC+P/ehGbKlHG/Zpvbj63vHRNub7hyDg36kBFty8pHGbMhnnOe6OHdqEzZYrfb8dFHHwEAzGYz0tPTI31JWdA5fURsfDCIrrfegrexEdMfeQQA0HPiBBKmTIEqcfTBHYfPuLD68X8It3c+9CUsmJoqar2EEEJil6SXYQvrP0EzGV13dzeam5sxf/58JCUlyV0OEclIOXNqNdLXrEH6mjUAAD4UwonvfAdBpxPnffObSL/5ZrR3B0e8fu9ot8Nonj9p0LasfJQxG1jKOeKm79VXX0VFRQX27duHzMxM5Obm4oc//CGWLFkSjfoUJxgMwul0IhgMyl0KEdG4cw4GkX7jGrRXVuLUj36M9meeRf0KC37YcR54bvRTbke6fi9du1catC0rH2XMBpZyjnievoKCAvA8j8ceewzFxcVwOBzIzc3Fa6+9Fq0aFUWv1+Pqq69m4nIvLBtvzpxGg6x7vgZTbQ2yv/Ut9J47h0v/8Cs8vud/oQop/xdQPKNtWfkoYzawlHNETd/mzZtRUlKCnTt3YuPGjdi4cSN27tyJhx9+WLhebjyQ8oocPM8jGAwyMQkkyyaaszolBed965sw1dagZdVaNGXlIKTqu5Rbut8lZqlkkmhbVj7KmA0s5RzRQI558+ahtrYWc+fOHXB/Z2cncnJy0NHREWl9kpJiIEdXVxfq6uqwYsUKpKWlifIeRH6R5Nzm9gvn9PHd3QjdcQtgNOF0wdfwtff+M6XASNfvHe2cPhoBHD20LSsfZcyGeM5Z0oEcBQUF+Oijj4Y0fQ0NDVi/fn0kL61YOp0OV1xxBXQ6ndylEBFFknN2ilZoxHodvThz1ZXoeuNNTPvwA/xo+sXYeuG1OJ42Tbh+70R0eHrwxK5m4fYNl0ynpm+SaFtWPsqYDSzlHPFAjoKCAhQUFAy4r7q6GmazeUjjt23btkjfLu5pNBpMmzZN7jKIyKKVc0JGBs4vL0fW17+OlvLfYPm7e/DFU014w3gVgC9FXiiZNNqWlY8yZgNLOUfc9N16661DjoPfeuutAMDE8fGJ8vv9aGlpQU5ODrRa2sOiVNHOOWnBApz32yfw15dqMO+tP2PhAhMy9X3z+gVdLqhT/7PHr//h4cFo2pfooW1Z+ShjNrCUc0RN32OPPRatOpgRCARw8uRJzJw5U/FfLpaJkXN2ihb3bLwR2Hgj+FAInEqFwNmzsP/X9Ui7/r+Qfd990Jx/Pra+d2zAIdzR0LQvk0fbsvJRxmxgKeeIRu8qhZSjd1NSUrBy5UqkpKSI/l5EPmLnzKn6Nt2Q2w3thQvhrKrGkev+C6c2bYLW0SbKe5KBaFtWPsqYDSzlTE0f+po+nufR1NQkdymETIjWaMScrVsx+/fPQXfRRXC+9DKuebQItzb/Xe7SCCGExJj4urKwAnR1dWHPnj245ppr4m5oOBk/KXPmOA76L34RyVdeCc877+DUb5/AxoIrUbSyb6AH73GD0/f9BXu0zTPgkO5o076Q0dG2rHyUMRtYypmaPonpdDosWbKEiaHhLJMjZ47jkPKlL2He1VcDPA9OpUL3J5/g6FfvRMZX70DmPfcAgxq8yUz7QvrQtqx8lDEbWMqZmj6JaTQazJ49W+4yiMjkzJnjOIDjAAAhnw+J55+P9meeheNPfwZfcBtSeubCnZgsS21KQtuy8lHGbGAp54jO6Xv22Wexe/du4fb999+PrKwsXHbZZTh69GiktSlST08PbDYbenqGn1KDKEOs5Jycm4ucv7yO83/zayRMmwb+hd/jz3VWbDacwgOr5tNh3AjESsZEPJQxG1jKOaKmz2q1IjMzEwDwzDPPoLKyEpWVlcjNzYXFYolKgUrj9/vR3NwMv98vdylERLGUM6dSIe3662F846+YUW5F8pQsrP2vy/BQ/gJk6RMR7OqSu8S4FEsZE3FQxmxgKeeIrr2rUqkQCoUAAKtXr0ZGRga2bdsGp9OJrKwsBIPBqBUqprKyMmzatEm4Lea1dwmRGx8MglOrAQCuXbtwsuQHyLzrLmT+911Qp6fLXB0hhJDxmui1dyPa02c0GuFyudDZ2Yna2lrk5+cDABwOBwwGQyQvLSmasoWwJNzwAQCn0SAhKwttW7bgyCozzj35JIJOp3zFEUIIEU1ETd+tt96KuXPnYunSpTAajfj6178OoO/au3l5eVEpUGlcLhd27NgBl8sldylERPGSc8qXvgTj/3sTM8qtSMjORtuWp3BklRmu3XVylxbz4iVjMnmUMRtYyjmi0btWqxXr169HS0sLzGazcL/RaITVao24OCXSarWYP3++4i/1wrp4yplLSED6TTch7YYb0PXWW2h/5lkkLbwAABDy+8H7fFDH0Z57qcRTxmRyKGM2sJRzROf0Kc1Ej40TokQ8z/dN+wKg44WtOPfEE8i4805k3v3fSMjIkLk6QgghYRPtWya0p+/++++fUDFPPfXUhB7PgkAggFOnTmH69OnQaDRyl0NEEs85hxs+ANDMPB8J06ahvaICjq1b+5q/r91NzR/iO2MyPpQxG1jKeUJNX3t7u1h1MMPn8+Hjjz+GwWBQ/JeLZUrJOXXlSqRccw26duxA25an0F5ZCceLL2Lm009Bf/nlcpcnK6VkTEZGGbOBpZzp8G4/dHiXkJHxwSBcf/sbHH9+CbMqnoZKr0evwwH09iLhvPPkLo8QQpgj6ZQthBB2cGo10q6/HnNe3AqVvu8avu1PV+CIOR+nf/FLBM6ckblCQggho4n42rv/+te/UFtbC5vNNuB+g8GAzZs3R/ryiuN2u1FfX4/LL78cKSkpcpdDRMJKzsnLroDnw3o4tm6F8+WXkX7rLcjeuBGa888f9Xltbj+2vndMuL3hyjnITomvkXOsZMwyypgNLOUc0Z6+V155Bbm5uXj55Zdhs9lQWVkJm82Gbdu2wW63R6tG0ZWVlYHjOCxevFj099JoNJgxY4bizxtgHSs5p65YgZxXXsHMp5+C9qIL4Xx5G45cex18Bw6M+rwOTw+e2NUs/HR44u+al6xkzDLKmA0s5RxR0/eDH/wANTU12LdvH3bu3ImcnBzs3LkTtbW1wjV544GUV+TQarVYuHAhE/MBsYylnDmOQ+qXv4y5L7+MWc89i/QbbkDSRRcBAHqOHoXf3iJzheJgKWNWUcZsYCnniA7v2mw2rFq1SrhtMBjgcrmQm5uL7du305QtwwgEAmhvb0dWVhYTf1WwisWcOY5DylVXIeWqqwD0HcI99wsr8O4ecCvM4DbcA85oAgAcbfMMeO7g22GZ+sSYPezLYsasoYzZwFLOETV9ubm5eO211/CVr3wFAGA2m1FRUQGj0QgnXb9zWD6fDx988AFWrFih+C8XyyhnYOt7x7ATi3H7eadw2e4a8LtrsHf6Yrx0QT5shoHn/BVubRj2NR5YNR8P5S+QotwJo4yVjzJmA0s5R9T0lZaW4oMPPhCavtLSUuTk5KCzsxOFhYVRKVBpUlNTceONN0KlooHTSqbknCcyCOOTrLn46Rc3YoHjOG7/dy2uOtWEq041YeOqYpxInSJZHWJQcsakD2XMBpZyjqjpu/XWW3HrrbcKt9PT09HS0oKOjg7k5OREXJwScRwHtVotdxlEZErOOTwII+yGS6aP2WwdzpiNTcvugdH5GS4/84nQ8C1qb4GKD+FAlhHodyUQseqIJiVnTPpQxmxgKeeIp2wZLD09Henp6dF+WcXweDxobGxEbm4u9J/PdUaUh3Lu2/N2wyXTh122ss2Dwq0N+HrTG1joOA7/wsXQ3bMRuHzZgMvAZeoTpSp3wihj5aOM2cBSzhNq+q699loUFRXhlltuATD2tXhpIMdQarUaBoOBmb8qWEU5A9kp2jH3vJXn3YF1zbtx3ZFGhIofQNKiRcgqKkSq2Qwuxg+1UMbKRxmzgaWcJ3zt3Y6OjgG3ycQkJSXh4osvlrsMIrJ4z7nN7R9x7rxojrw9lZKNJ76wDtf/z0+Q/pdtcFZV4bPvPYx5NTuhmTZNsjomI94zJmOjjNnAUs4Tavr27ds34Pb27dujWgwLent70dnZifT0dCQkRP3oOokR8Z7z1veODThfbjTRGHnLTZmKaT/+EbLvK4K3oRGaadMAALVP/hF/39+KXbOWIqAe/XOUegRwvGdMxkYZs4GlnCM6fvLss8+iq6srWrUwwev14t1334XX65W7FCIiynlyErKzkXbtagAAz/Mw1r6KB/5Vjd/XbMbNtneg7Y2dK3dQxspHGbOBpZwjavoee+wxVFVVRasWJqSkpGD16tWKv74f6yjnyHEch/cfegzPLroBKj6E+w78Bc/v/AXWHd4Fba9f7vIoYwZQxmxgKeeI9mM+/fTTWL9+PcxmM+bMmROtmhRNpVJBp9PJXQYRWbznPNrI26Ofj7wNq9yQh7nZQ0e8jTbyNlOfiAdWzR/zsXesuBAdlz8C3t8N/q03YHhpK752Yi/u/XUJjnn5iOuIRLxnTMZGGbOBpZwjavo++ugj5OTkwGg0wmw2w2g0DlgeL6N3y8rKsGnTJkney+v1Yv/+/bjkkkuQnJwsyXsS6cV7zuMZeRs2N1uPBVNTJ/z64znP7j91pAL33QP+njvht9uRlDMV3BkXNhzaAW2wB6/Nu2ZSdUQi3jMmY6OM2cBSzhFfe9doNArNXryO5i0rK0NZWRkOHjyIxYsXi/peHMdBo9EMmIuMKA/lLA4uMRFJCxcCAPhQCF8414wLHcdwU8tehDQH0PPt+5Ao0VEHylj5KGM2sJRzxId3ycTodDrk5eXJXQYRGeUsPk6lwne/9C1cfuYTrD+8Cxe9+Tpsb/0Vadddh2llj0Cdlibq++t0Osy5YDGefle+S8ERcdF2zAaWco6o6fvXv/6FSy+9dMj9R48ehdPpHHYZ64LBIDweD/R6PRMTQbKKcpYIx6F+2kWon3oh/rYyFbrtW9H9739D9fkJ2aHubqiSkkR562AwiONnOmS9FBwRF23HbGAp54hG747UGTscDmzcuDGSl1Ysj8eDuro6eDzDTyRLlIFylhjHgbs0F7OfexZzt70sXM3js4cfxrENd8H9zrvgeT6qb+nxeFBfXx/V1ySxhbZjNrCUc0R7+kb7JWq32yN5acXS6/VYsWKF4q/vxzol5zzekbdy1aH+fC8fHwxCnZ4O99/3wLtxI5IuughZhYVIzTeDi8Jf83q9HpdffjnwcWPEr0Vik5K3Y/IfLOU8qaYvMzMTHMeB4zhkZWUNWe5wOJg5Pj5RarUaaSKfa0Tkp+ScxzvyVu46OLUaM37xC5z3zW+i/fd/gLO6Gp89+CASc3Iwd/s2qFMjG+mrVquZmNeLZUrejsl/sJTzpJq+Xbt2ged5LF26dNhLsRmNRuTk5ERcnBL5fD4cOnQIF110ETPzArGIco4dmhkz+i7xdv996HhhK3qOHRMavp6jR5EwdSpUk8jI5/Ph4MGD0S6XxBDajtnAUs6Tavq+8IUvAAAKCgqwatWqqBakdDzPIxAIRP38IhJbKOfYk5CVhSkPPSjc5nkeJ777XfSeOo3MuzYg4447oE5PH/a5bW4/OjwDLwHn83XjhHPglUGOto18TlCmPpEGecQZ2o7ZwFLOHM/CWo5TeJ6+pqYmLFq0SO5yCCEi4oNBOF5+GR3P/R6BkyehSk6GYf16ZN59NzRTpwx47OM1hweM0p2MB1bNj4nD4oQQ5Zho3xLR6F0ycaFQCD6fD6FQSO5SiIgo59jHqdXI/OpXYfrbDsz4VTk055+Pjj/8Afbrr0fQrfxRfGRstB2zgaWcqemTmNvtxs6dO+F2u+UuhYiIco4fnEaD9DVrkPPXv2Dm008h+1vfgjqlbxSfa3cdfE103h6raDtmA0s5RzRlC5m45ORkLF++XPHX92Md5Rx/OI5D6pe/DHy57zbf24vTZWXoPXsWay6/AtfftgHIvUy4VFNvbxCftJ7D914/LLxG5YY8zM0eftoHuaa2IZNH2zEbWMqZmj6JJSQkDDvNDVEWyjn+cQkJmPVMJdqffQ5db70F1H+ApMWLkbVxI1LNq8Cp1UhIUAP4T9M3N1uPBVMjmwqGxA7ajtnAUs50eFdi3d3dOHDgALq7u+UuhYiIco4PbW4/Hq85LPy0uQeOxk264AKc/6tymHa8jYw7boe/uRmny8rA9/Sgu7sbhw8fHuGViRLQdswGlnJWzJ6+6upqbNu2DVVVVXKXMqpgMAin04lgMCh3KURElHN86PD0jOvauYmzZmHaT3+K7G98A/4jNqh0OgQ9HiS/sg23tPJ4e+4y+DTiXOOXyIe2YzawlLMimr6ioiJ0dHTA6XTKXcqY9Ho9rr76arnLICKjnJUpITsbCdnZAIBkrRbTD3yEjZ+dwG2Hd+GNnKvAOxYDdHhXMWg7ZgNLOSvi8G5FRQXWr18vdxnjwvM8gsEgE5NAsoxyZoBaDTz3Ip64tABdiXrccbgWoXU34/SjjyLk9cpdHYkC2o7ZwFLOMdP0OZ1OVFdXw2QyDbu8vLwcJpMJGRkZKCoqkri66HG5XHjzzTfhcrnkLoWIiHJWPpfLhT3vvY8dc5eh0FyMX1y2AcgxwlNfDy6p71BvqKdnjFchsYy2YzawlHNMNH0lJSXIycnB5s2bYbfbhyyvrKxERUUFqqqq0NLSArvdjvz8fBkqjZxOp8MVV1yh+Ov7sY5yVj6dTodLLrkEABDiVHj3/CVQVTyPOS+8AE7V96v1swcfwvF77oHnn/9kYi+C0tB2zAaWco6Jc/qsViusViuqq6thsVhGXJ6bmwsAqKqqQkZGBpxOJwwGg8TVRkaj0WDatGlyl0FERjkrn0ajwYI5M/DAqoBwX1aKFgkpaQD6LvOm0uvR9fe/w/PP95C0aBGyvn4vUlevBqdWy1U2mQDajtnAUs4x0fSNxm63w263w2w2C/cZDAYYjUZs374dhYWFMlY3cX6/Hy0tLcjJyYFWSxdfVyrKOXa0uf3o8Ax/mPVom2fU22GZ+sQho3r9fj/aTrTgG18aPmNOrcb5vyrHeQ98Bx2//wOcr76Kzx76LjRzZiPnlVegTkmZ5BoRqdB2zAaWco6Lpg/AkD16RqMRNpsNAGCxWITmMD8/f8BewZGcPXsW586dG3DfkSNHAADefidhd3V1QafTQaPRoKenB36/H6mpfaPzXC4XtFotEhMTEQgE4PP5kJbW91e+2+2GRqOBVqsVlqWmpiIQCODEiRM477zzoNVq0dvbC6/Xi5SUFKhUKni9XnAcB51Oh2AwCI/HA71eD7VaDZ/PB57nkZycjFAoBLfbjeTkZCQkJKC7uxvBYBB6vR48z8Plcgl1+/1+BAIBpHz+n0y014njOHg8HqjVaiQlJdE6+Xxwu904efIkZsyYAb/fr4h1itec/vBOC3635+iovw/CCrc2DHv/fctn43vXLhywTgBw8uRJGAwGZGVljbxOU6Zg2k9/AkNRIdq2bgV3+gzUKSlwu93gjx5D8qyZCCUnM59TLK5T+Pf1lClTFLNOSswp0nXyer04efIkpk+fDr/fH1frNFExcU5fpKqqqtDQ0ACHw4GampoxGz4A2LJlCxYvXjzgZ+3atQCAAwcOCI/bs2cPTp06BQBobW3F3r17hWV79+5Fa2srAODUqVPYs2ePsKy+vh4tLS0AgPb2dtTV1SEUCiElJQVarRYnT54EAHR2dqKurg5+f9+ksPv378ehQ4cAAB6PB3V1dfB4+vY+HDp0CPv37wfQ95dJXV0dOjs7AQDNzc1obGwE0Hfx6Lq6OrS3twMAWlpaUF9fL9o6AUBjYyOam5tpnT5fJ5vNhpUrV0Kj0ShmneI1p8F/3E3G0aNHh6xTSkoKVq5ciQ8//HBc63TW78dHF1yA8//nV33r9MEHOPHwwziyYiVaf/YzvPPa60znFIvrFG4Mzp49q5h1UmJOka7TsWPHsHLlSnAcF5frNBEcH0NnF4fP6etfUm1tLfLz84ecBJ2Xlwez2Qyr1Tqp9xppT9/atWtRX1+Pyy67DAD9JUXrROsU7+v0uz3j39M3kuH29EW6Tq6uLvhratH1/PPwNzcDCQlIX3Mjsu65B70zZjCXE60TrROt08TXyWazYfHixWhqasKiRYvG/F0W802f3W6HyWSCw+EYcIjXZDKhpKQkquf0HTx4cEIf3mR0dXVhz549uOaaa4RgifJQzrFjrHP6+h/SrdyQh7nZ+iGPG+6cvmhlzPM8PP/4B9qffQ7eDz8Ep9Vi/jv/gJq+N7Kj7ZgN8ZzzRPuWmD+nz2g0wmAwDBi04XQ6hwzuiBc6nQ5LlixhYmg4yyjn2JGdoh320mrDmZutx4JxXlEjWhlzHIeUa65ByjXXwLd/P7o/+VRo+DrfeBNcYiJSzatoxK8MaDtmA0s5x8U5faWlpSgpKUFjYyOcTicsFgsKCgpgNBqj8vplZWXgOA6LFy+OyuuNRqPRYPbs2dBoNKK/F5EP5ax8YmSsu+QSZKxfBwDgQyGce+IJfPbAA7Bdfz0cL29DiIELwscS2o7ZwFLOMdH0VVZWguM4YY4+juPAcZywvLi4GKWlpbBYLMjJyYHRaERVVVXU3r+srAw8z6OpqSlqrzmSnp4e2Gw29NBM/YpGOSuf2BlzKhXmvvwSsu6/D0FnJ06XleHIKjPannqKLvMmEdqO2cBSzjHR9BUWFoLn+SE//RUXF8Nms8HhcKCiokKmSiPn9/vR3NwsjBwiykQ5K58UGSdkZ2PKAw9g/u5dmPrDH4LTJqLjxT8Bn1/xI4ZOyVYk2o7ZwFLOMX9On9Kkpqbiuuuuk7sMIjLKWfmkzFil1yPzrg3IuP029Bw9CtXn1/Y988vNCDocyLr3HiRdeKEktbCEtmM2sJRzTOzpk5uU5/QRQshkcRoNtPPnA+jbyxdobUXXm2+i5Su34Pi9X6dr/BJCRkVNH6Q9p8/lcmHHjh1wuVyivxeRD+WsfHJnzHEcZj39FOa+Uo2066+H5733cPyee9Fy663obWuTpSalkTtjIg2WcqamT2JarRbz589X/PX9WEc5K1+sZKxbtAjn/+bXMP1tBzLuvBOcRgN1VhYAIHDyJILu4a8nTMYWKxkTcbGUM53TJ7HExESYTCa5yyAio5yVL1oZt7n92PreMeH2hivnjHtewQH1zJqFaT/+EfhQSJj94PSjP4O3oQEZt92GjA13QjNlSsT1ii1an0c00HbMBpZypj19kPacvkAggOPHjwsXbCfKRDkrX7Qy7vD04IldzcLPSFcPGS9O9Z9f6ymrViIhKwvtzzwD2yozTv7oR/DbbBG9vtii/XlEgrZjNrCUMzV9kPacPp/Ph48//hg+n0/09yLyoZzjQ6Y+EQ+smi/8ZOoTx/3ceMg4w2KB8a3/h5m/+z8kXXwxOl95FfYb1yDw2WdylxYX4iFjEjmWcqbDuxJLS0vDmjVr5C6DiIxyjg/ZKVo8lL9gUs+Nl4w5lQqpq1YhddUqeD/6CN4P6qE5/3wAgPuddxFyu5Canw8ugf47GCxeMiaRYSln2soJIYQRyV/4ApK/8AXhdtv//R98H38MzcyZyLz7bhhu+QpUyckyVkgIERMd3pWY2+3G7t274Xa75S6FiIhyVj4lZDzzqS3I/uY3EXK7cebnP8eRFStx7sknEezslLu0mKCEjMnYWMqZmj5IO5BDo9FgxowZTFzYmWWUs/LFUsZtbj8erzks/LS5x3c5qYTMTJz37W9hXt1uTP3pT6BKS0P7s8+B7+0FQJd5i6WMiXhYypmaPkg7kEOr1WLhwoVMzAfEMspZ+WIp40hHvKp0OmTecQdMO97GnJf+jITP5/lrf+ZZtN7/DXg//JDJBjCWMibiYSlnavokFggEcPr0aSaGhrOMclY+JWbMqdXQLVok3A6c/Azuv/8dxzbchaPr1qPr7beFvYAsUGLGZCiWcqamT2I+nw8ffPABE0PDWUY5Kx8LGU8vK4Pp7bdguP02+A8fxmcPfRe2a6+Dv7lZ7tIkwULGhK2cafSuxFJTU3HjjTdCpaJ+W8koZ+WbaMZtbv+wh12PtnlGvd1fpj5R8qtTJM6di+mPPILzvv1tOF56Ca4df4Nm9mwAQODMWQCAZurEr/QR6echxWdB2zEbWMqZmj6JcRwHtVotdxlEZJSz8k00463vHcMTu8beQ1a4tWHEZQ+smj/peQUjlZCZifO++U1kf+MbwmXe2p7aAucrryL9xhuR+bW7kbRg/LVF+nlI8VnQdswGlnJWfls7DlKO3vV4PHjnnXfg8dBF0JWMclY+VjMON3wAkLpiBXRLLkHna6+h5aabcXxjITzvvaeYQR+sZswalnKmpg/Sjt5Vq9UwGAzM/FXBKspZ+ShjIOWaazD3xRcxd9vLSL3uOnj27sXxr90DX8PIeyvjCWXMBpZypsO7EktKSsLFF18sdxlEZJSz8k004w1XzsENl0wfcv/RNs+AQ5iVG/IwN1s/7GtM5NrAE9Xm9mPre8eE2xuunDPuc+Z0S5Zg5m8fx5lPbXj/mZewoyMVqDmM25IdSPy0CYZ166BOTR3wnEg/DzE/izDajtnAUs7U9Emst7cXnZ2dSE9PRwJd61KxKGflm2jG2SnacTVRc7P1WDA1dczHRVt4rr+wGy6ZPuGBEp0ZU/CA5gvA7iMAgP/y7AJf8zbafrcFBksBMjbchcSZfdf9jfXPA6DtmBUs5UyHdyXm9Xrx7rvvwuv1yl0KERHlrHyU8di44h9h+i9/Cc3Mmej44wuwrV6NEw89BH9Li9yljQtlzAaWcqamT2IpKSlYvXo1UlJS5C6FiIhyVj7KeGxcYiIMt3wFOX95HbOefRb6K6+Ea8ffgPBl3np7wYdCMlc5MsqYDSzlrOz9mDFIpVJBp9PJXQYRGeWsfHJkHK9z/XEch5TlVyFl+VXoOfGZcIi388030f7U08i8+7+RvnYtVDG2zdB2zAaWcqamT2Jerxf79+/HJZdcguTkZLnLISKhnJVPjozjfa4/AELDBwB8dzd6nU6c3vQozj3xJDLuuB18/k2y1TYYbcdsYClnOrwLaefp4zgOGo1mwFxXRHkoZ+WjjCOXcdttmL97F6b++MdQpaaibctTCK2/GctOiT991nhQxmxgKWdq+iDtPH06nQ55eXnM7EpmFeWsfJRxdKj0emTe+VWYdryN8598Alh0MT7JnAMASO3xgN9XL9tkz5QxG1jKmQ7vSiwYDMLj8UCv1zMxESSrKGflkyPjWJ/rLxKcWo201atxesmV6Hz8HwCAG1reQ+h7O9DyzMK+8/6uvx5conT103bMBpZypj19EvN4PKirq2Pici8so5yVT46Ms1O0WDA1dcjP4AYvPLfdcD9SD+KIxN9nfgFcwW3oOX4cp35QiiOrzGirqETQ6ZTk/Wk7ZgNLOVPTJzG9Xo8VK1ZArx/+r3CiDJSz8lHG4jutz4Lq29/F/L/XYcr3HwbUapx7/HG4du2S5P0pYzawlDMd3pWYWq1GWlqa3GUQkVHOykcZS0edloase+9F5l13oWvnTqSazQAAv70FZ3/za2TdfTd0eXnCifiRXFJuwPtSxkyYaM7R+n7JgZo+ifl8Phw6dAgXXXQREyeNsopyVj7KWHqcRoP0G24Qbnvefw/u2l1w1+5C0sUXI/Pu/0batddG5ZJyAGXMionmHK3vlxyo6ZMYz/MIBAKyjUYj0qCclS9eM47GBM8jkXqS6Mw77oB+2ZXoeOGP6HztdZz83sM4++tfg7//QQCRn5AfrxmTiWEpZ2r6JJacnIxly5bJXQYRGeWsfPGacTQmeL4iJxMftHRE9BrRmiRaa8zB9LIynPfAA3C+/DI6/vRnQJcMwA/wPLJ9nZN+7XjNmEwMSznTQA5IOzlzKBSCz+dDKIavN0kiRzkrH2UcWxIyMpB9//2Yt3sXsPRyAMCStiN4fucvEPzpD+D96KMJvyZlzAaWcqamD9JOzux2u7Fz50643W7R34vIh3JWPso4NqkSE4UBHd6EJDROWQDs2Y1jt9+Bo+tvQ9eOHeB7e8f1WpQxG1jKmQ7vSiw5ORnLly9X/PX9WEc5K1+8ZhyNCZ5HEmuTRDdnzMJPv7gRf1t7PtL+3yvo/Mtf8dmDDyHznnswtfj7Yz4/XjMmE8NSztT0SSwhIQFZWVlyl0FERjkrX7QyztQn4oFV8wfcFlN2inZcAyjCEzxHYjKvIcbnweWYMP1nP8N5Dz4Ix0svI+2/rgMA8D09OPfUU8goKIDm/POHPI+2YzawlDM1fRLr7u5Gc3Mz5s+fj6SkJLnLISKhnJUvWhlnp2ijMqBBKcT8PBKysnDet74p3HbtrkP7U0+jvfIZpK7O75vvb8kSYTltx2xgKWc6p09iwWAQTqcTwWBQ7lKIiChn5aOM41/qtasx69lnob/ySrje3oGj62/D0dtuh3vPHgCUMStYypn29ElMr9fj6quvlrsMIjLKWfko4/jHcRxSll+FlOVXofvwYXS88AK6/voGfE1NSLnmGuj1eixftgycRiN3qURELG3LtKdPYjzPIxgMMjEJJMsoZ+WjjJUlacECzPj5zzGvbjcyN2wAAPR2ONC8YiVOP/YYAp99JnOFRCwsbcvU9EnM5XLhzTffhMvlkrsUIiLKWfkoY2VKyMqC+vPrsDrtNnhVKjie/yOO5K/GiQcfmtR8fyS2sbQtU9MnMZ1OhyuuuIKu46hwlLPyUcbKl75kCaZuexnTn34K+i9+Ea4dO3Ds9jtwsvSHcpdGooilbZnO6ZOYRqPBtGnT5C6DiIxyVj7KWPk0Gg2mT58OTJ8Ow5e/DH9zMzpeeAHJl10mPKZrx9+g/+KVwt5BEn9Y2pZpT5/E/H4/Pv30U/j9frlLISKinJWPMla+wRlr58/H9J/9DOk33dS33G7HZw8+iCNfXoHTv/glelpb5SyXTBJL2zLt6ZNYIBDAyZMnMXPmTGi1Y0+QSuIT5ax8lLG82tx+dHh6hl12tM0z6u2wTH3iqBNVj5WxZuZMTP/lL9Hxxz/CsXUrHC++iFTzKmTefTeS8/ImsDZETiPlPNJ3bLzfL2Ds75jUqOmTWEpKClauXCl3GURklLPyUcby2vreMTyxq3lcj+1/abj+Hlg1f9SJoMfKWJWYCMMtX0H6V9bC+8EH6PjD83DV1CLU7cfsZyrHVRuR30g5j/c7NtL3Cxj7OyY1avoAlJWVYdOmTXKXQQghkyL1pdxY0Ob2Y+t7x4TbG66cM+IeG47joF+2DPply+C3t4DvDQDomwqk9b77kJybh4z166A2GKQonZAR0Tl96Gv6eJ5HU1OT6O/V1dWFN954A11dXaK/F5EP5ax8sZRx+NJl4Z9YOpwUrzo8PXhiV7PwM9Kh5MG0xhwkLejbs9N75gz8n3yKc48/juYvr8CpsjL47S1ilk0mIZa2ZbHRnj6J6XQ6LFmyhImh4SyjnJWPMpbXhivn4IZLpg+77GibZ8Aht8oNeZibrR/yOLH3iGqmTcO82hp0/e1v6Hj+j3C+vA3Ol7chdfVqnP/Eb8FxnKjvT8ZnpG15pO/YeL9fQOztdaemT2IajQazZ8+WuwwiMspZ+ShjeWWnaMe9R3Nuth4LpqaKXNHwuMREpK9Zg7Qbb4SvoQEdf3wB6owMoeHr/vRTJObkQEWDgWQz0rY83u+YnN+viaKmT2I9PT1obW3FrFmzkJgYW38BkOihnJWPMiYTwXEckpcuRfLSpeBDIQBAqKcHxzduBIIhZNx2GzLuuB0J2dkyV8oelrZlOqdPYn6/H83NzUzMB8Qyyln5KGMyWZzq8/96QyFk3XsvVDod2rZswZEVK3Gy9Ifo/ve/5S2QMSxty7SnT2Kpqam47rrr5C6DiIxyVj7KmERKlZSErLvvRuaGDXDt2oWOP76Aztdeg/uddzC/bjc4jUbuEpnA0rZMTR8hhBAiI06tRtrq1UhbvRq+A03oPXNaaPjann4aqtRUGL7yFaiSk2WulMQ7OrwrMZfLhR07dsDlcsldChER5ax8lDERg+7ixUg1mwEAfCAAx8vbcOZnP0fzl1fg7P/8DwInT8pcofKwtC1T0ycxrVaL+fPn02WbFI5yVj7KmIiN02hgevstTCt7BAnZ2Wh/9jkcyV+NEw89hKDbLXd5isHStkyHdyWWmJgIk8kkdxlEZJSz8lHGRAoqnQ4Zt90Gw7p18Ozdi47n/4gemx0qfd+8cL3nzkFtMND5fxFgaVumPX0SCwQCOH78OAKBgNylEBFRzspHGRMpcSoVUq6+GrOfexZzX35JmOfv1I9/giP5q9H2zDMIOp3yFhmnWNqWqemTmM/nw8cffwyfzyd3KURElLPyUcZELuEBHTzPQ5ebCwSDOPfr36B5xUqc2rQJfrtd5grjC0vbMjV9EktLS8OaNWuQlpYmdylERJSz8lHGRG4cxyG7qBDzdtVixq/KoTUa4XzpZdhvuBGBU6fkLi9usLQt0zl9hBBCSBwbcKm3jz6Ct/5DaKb3XTPWtbsOvW3nkH7TTVAlJclcKZGbIvb0VVdXw2QywWQyoby8XO5yRuV2u7F79264aeSVolHOyqe0jDP1iXhg1XzhZzIXio/Ga5DJ4zgOybm5yL6vSLiv/ffP4fRPH8GRL6/A2cd/i8CZMzJWGJuUti2PJu739DmdTpSUlMBmswEA8vLyYDabkZubK3Nlw9NoNJgxYwY0NNJK0Shn5VNaxtkpWjyUv0D21yDRNevpCnS+9ho6tm5Fe0UF2p97DmnXXYcp330Imhkz5C4vJihtWx5N3O/pq62thfnziSwBYP369di2bZuMFY1Oq9Vi4cKFTMwHxDLKWfkoYxIP1Cl6ZG64E6a338LMLVuQvHQpXDU14D4/1Bvq7gbf2ytzlfJiaVuOmabP6XQKh2mHU15eDpPJhIyMDBQV/WfXtd1uh8FgEG4bDAbYY3jkUiAQwOnTp5kYGs4yyln5KGMSTzi1GqkrV2DO83+AaedOJGRmAgA6nn8eR1avRvtzzyHY2SlzlfJgaVuOiaavpKQEOTk52Lx587ANW2VlJSoqKlBVVYWWlhbY7Xbk5+cP+1qZn3+RY5XP58MHH3zAxNBwllHOykcZk3ilmTpF+LcqJRV8TwBnf/U/aP7yCpx+9FH47S0yVic9lrblmGj6rFYrHA4HSktLR1xutVqRm5sLg8GAqqoq1NbWwul0DtmzZ7fbYTQapSp9wlJTU3HjjTciNTVV7lKIiChn5aOMiRJk3vlVzNu9CzOsjyExZy4cf34J9uuvh/udd+UuTTIsbcsxP5DDbrfDbrcPOG/PYDDAaDRi+/btWLduHUpKSoQGcNu2bXjmmWdkrHh0HMdBrVbLXQYRGeWsfJRx/Gtz+9Hh6Rl22dE2z6i3wzL1ichOie9zwVSJiUi/+Wak3XQTfA0NcFa/guTLLwMA+O0t8H74IdJvWgOVTidzpeJgaVuOi6YPwIDz9gDAaDTCZrMJe/5WrVoFACgqKhrXyN2zZ8/i3LlzA+47cuQIAMDr9Qr3dXV1QafTQaPRoKenB36/X/hrwOVyQavVIjExEYFAAD6fT5jc0e12Q6PRQKvVCstSU1Ph9Xrx4Ycf4pJLLkFmZiZ6e3vh9XqRkpIClUoFr9cLjuOg0+kQDAbh8Xig1+uhVqvh8/nA8zySk5MRCoXgdruRnJyMhIQEdHd3IxgMQq/Xg+d5uFwuoW6/349AIICUlBRR1onjOHg8HqjVaiQlJdE6+XzweDz45JNPcOmllwrrF+/rpMScIlknjuPQ2NiIBQsWIDMzUxHrpJSchjPcOm19/xSe2NU85v8XAFC4tWHY+795TQ6+/18XKSYn/sILkVn2CFRaLXp7e3Hm+efh2b4d537zG+hvuQVpt61H6pw5cbVOY333fD4fDh06hCVLlgDAmOs0GM/zA+qWcp0mKiYO70bKbDajoaEBDQ0NKCwsHNdztmzZgsWLFw/4Wbt2LQDgwIEDwuP27NmDU5/PbN7a2oq9e/cKy/bu3YvW1lYAwKlTp7Bnzx5hWX19PVpa+s6LaG9vR11dHUKhENRqNbq7u3Hs2DEAQGdnJ+rq6uD3+wEA+/fvx6FDhwAAHo8HdXV18Hj6/sI8dOgQ9u/fDwDw+/2oq6tD5+cn3jY3N6OxsREAEAqFUFdXh/b2dgBAS0sL6uvrRVsnAGhsbERzczOt0+frdPjwYRgMBgSDQcWskxJzimSd1Go1DAYD6uvrFbNOSsppsNHWKRIdHe2SrZMcOf1r8SJklhRDZUhH1x/+gNbrb8Bn3/0enPsPxO06Dc4pPCC0p6dn3OvUn9zrNBEcz/P8hJ8lkurqalgsFvQvqba2Fvn5+RhcZng+PqvVOqn3GmlP39q1a1FfX4/LLuvbtR3vf/HGyl9StE60TrROtE5SrVNLRzdWP/4P4Xf7zoe+hGk6fsg6PTuBPX0jUdqevpFy4gC0/20nXC+9hO76epz/TCWwZAmSk5Oh5nl09/bG3TpNNid7u2/A9+tvD16N6cmQZZ1sNhsWL16MpqYmLFq0aMzva8w3fXa7HSaTCQ6HY8AhXpPJhJKSknHv2RuPgwcPTujDm4ze3l50dnYiPT0dCQkxf3SdTBLlrHyUcew6fMY1pOlbMHXoSfpjndPX/5Bu5YY8zM3WD3mcEs7pmyi/zYZEo7Gv+XjvPZz8QSky7rgDhnUWJGRkyF3ehE10Wx7v90sKE+1bYv7wrtFohMFgwPbt24X7nE7nkMEdkSgrKwPHcVi8eHFUXm80Xq8X77777oDzBonyUM7KRxnHv+wULRZMTR32Z3CDNzdbP+zjWGv4AEBrMoHjOABAsLML4Hmce/xxHPnyCpz6yU/hn8RhRzmxtC3HfNMHAKWlpSgpKUFjYyOcTicsFgsKCgqiNjVLWVkZeJ5HU1NTVF5vNCkpKVi9erWw25coE+WsfJQxIUDadddi3q5azPif/4F24QVwVlXBvuYmtD//vNyljRtL23JMHJOorKwccJWN8F8Q4cO8xcXFAACLxYKOjg6sW7cOFRUV0hcaBSqVasTRZUQ5KGflo4wJ6cNpNEi/8Qak33gDfP/6Fzpe2IqU5csBAKGeHnS+8grS1twEdcrQw+OxgKVtOSb29BUWFoLn+SE//RUXF8Nms8HhcMRtwwf07UZ+//33mdiNzDLKWfkoY0KG0l16Kc7/za+hnTcPAOB6+22c3vQojnz5yziz+TH0nDghc4VDsbQtx0TTJzcpz+njOA4ajUbYm0mUiXJWPsqYkLGlrl6NaZs2IWHaVHT88Y+wrb4Wrd/6FryNH8ldmoClbZmaPkh7Tp9Op0NeXh4zu5JZRTkrH2VMyNhUOh0y1q+D8Y03MPv3zyHlS1+Cu3YXvB+8LzyG/3zuObmwtC3HxDl9LBk8DxBRJspZ+ShjQsaP4zjov/hF6L/4RfQcPQr151Ow9TocaLn1VqTffDMybrsdmqlTJK+NpW2Z9vRJbPDM7ESZKGflo4wJmZzEuXOFpi9w4gQ4dQLan3oaR1atwmcPfx++z6+CIRWWtmVq+iSm1+uxYsUK6PWxOYqJRAflrHyUMSGR0118MUw73sbMLb9Dcl4eut58E0fXrcfJH/5IshpY2pap6YO0AznUajXS0tIUvwuZdZSz8lHGhEQHp1YjdeVKzPnj88j5y+tIL7gV+mVXCMs73/x/6HU4RHt/lrZlavog7UAOn8+HhoYG+Hw+0d+LyIdyVj7KmJDoS7rgAsz4+c+RftNNAAC/vQUnH37486t9/ATd/z4c9fdkaVumpk9iPM8jEAgMmYeQKAvlrHyUMSHiS5w1s9/VPqrRcvPNOHb31+Cqq4vae7C0LdPoXYklJydj2bJlcpdBREY5Kx9lTIj4Blzt4+OP0fHCVnT97W/gtIlIXbECQF/TFskceyxty9T0SSwUCsHv90Or1UKloh2tSkU5Kx9lTIi0dEuW4PxfL8GU4u8j5Om7egbP8zj21TuRdNFFyLzzq0icO3fCr8vStqzstRsnKQdyuN1u7Ny5E263W/T3IvKhnJWPMiZEHpqpU6E15gAAgg4HQl4vHC++CNt1/4XjRUVwv7t3QodqWdqWaU8f+pq+srIyHDx4UPTGLzk5GcuXL0dycrKo70PkRTkrH2UcuzL1iXhg1fwBt4kyJWRmIue1V+H98EM4tr4I165d8Oz5B5Iuughzq6vAjWPP3US35Xj+flHTJ7GEhARkZWXJXQYRGeWsfJRx7MpO0eKh/AVyl0EkwnEc9JdfDv3ll6PnxGdw/PnPffd/3vC59+5F4py5SJx5/rDPn+i2HM/fLzq8K7Hu7m4cOHAA3d3dcpdCREQ5Kx9lTEjsSZx5PqYWfx9Ti78PAOB7enDyBz+AbfVqnPj2t+Gprx9y6JelbZmaPokFg0E4nU4Eg0G5SyEiopyVjzImJA4kJGDGLzdDv/wquGpqcfyu/0bLV26B85VXwff0AGBrW6bDuxLT6/W4+uqr5S6DiIxyVj7KmJDYx6lUSLl6OVKuXg6/vQWOF1+E8/XXcfa3jyN9zY0A+s7pY2Vbpj19kHb0Ls/zCAaDTEwCyTLKWfkoY0Lii9aYg2k//Qnm/70Os/73f8El9g3AOP3oz3Dioe/C2/iR4rdnavog7WXYXC4X3nzzTbhcLtHfi8iHclY+ypiQ+KROS4Pu0ksB9P3x1n3iBFxvv41jd9yBo+vWo/OvfxUO/SoNNX0S0+l0uOKKK6DT6eQuhYiIclY+ypiQ+MdxHGZu+R3S/vB7pBUUwN/cjJPFJWhetQqBkyflLi/q6Jw+iWk0GkybNk3uMojIKGflo4wJUQaNRoPzr7wSuPJKBB/+HpzV1fB8+CESpk8HAHR/8gn43iB0F4t/CpjYaE+fxPx+Pz799FP4/X65SyEiopyVjzImRBn6b8tqgwFZX/86ZldUCNfzPfvb3+KoxYKjt9+BrrffBh8IyFzx5FHTJ7FAIICTJ08iEMdfGjI2yln5KGNClGGsbXnKgw8i/ZZb0H3wID576Ls4kr8abRWVCHZ2Slxp5Kjpk1hKSgpWrlyJlJQUuUshIqKclY8yJkQZxtqWky68EDN++QvM+3sdznvwASAUwrnf/paavngl5ZQthBBCCIk/CZmZyL7vPszbVYvZzz+PxNmz5S5pwqjpg7RTtnR1deGNN95AV1eX6O9F5EM5Kx9lTIgyTHRb5jQa6K+4XOSqxEFNn8R0Oh2WLFlC0zwoHOWsfJQxIcrA0rZMU7ZITKPRYHYc7hImE0M5Kx9lTIgysLQt054+ifX09MBms6FHobN9kz6Us/JRxoQoA0vbMjV9EvP7/Whubqa5vRSOclY+ypgQZWBpW6bDuxJLTU3FddddJ3cZRGSUs/JRxoQoA0vbMu3pI4QQQghhADV9EnO5XNixYwdcLpfcpRARUc7KRxkTogwsbcvU9ElMq9Vi/vz50Gq1cpdCREQ5Kx9lTIgysLQtU9MHaa/IkZiYCJPJhMTERNHfi8iHclY+ypgQZWBpW6amD9JekSMQCOD48eN0kXaFo5yVjzImRBlY2pap6ZOYz+fDxx9/DJ/PJ3cpRESUs/JRxoQoA0vbMk3ZIrG0tDSsWbNG7jKIyChn5aOMCVEGlrZl2tNHCCGEEMIA2tPXT3g27iNHjoj2Hl6vFwcOHMDFF1+M5ORk0d6HyItyVj7KWNmOtnnQc+6YcLv5008QaNPLWBERSzxvy+F+ZbxXE6Gmr5/W1lYAwNq1a+UthBBCSEy58fdyV0DIyFpbW5Gbmzvm4zie53kJ6okLTqcTe/bswaxZs0Sbr+fIkSNYu3YtXn/9dcybN0+U9yDyo5yVjzJWPsqYDfGcs9/vR2trK6655hoYDIYxH097+voxGAy4+eabJXmvefPmYdGiRZK8F5EP5ax8lLHyUcZsiNecx7OHL4wGchBCCCGEMICaPkIIIYQQBlDTRwghhBDCAGr6JHbeeefhkUcewXnnnSd3KURElLPyUcbKRxmzgaWcafQuIYQQQggDaE8fIYQQQggDqOkjhBBCCGEANX2EEEIIIQygpo8QQgghhAHU9BFCCCGEMICaPgmVl5fDZDIhIyMDRUVFcpdDoszpdMJisSAjI4MyZoTFYgHHcbDb7XKXQkRQVFQEk8kEjuNgsVjkLoeIoKSkRPidbbFY4HQ65S5JVNT0SaSyshIVFRWoqqpCS0sL7HY78vPz5S6LRNGqVatgNBrR0NCAqqoq1NbWIi8vT+6yiEgaGxsV/x8Eq5xOJ0wmEwwGAxoaGsDzPKxWq9xlkSgrKipCY2MjWlpa4HA4YDQaFf87m+bpk4jJZILVakVBQQGAvl8qGRkZcDgcMBgM8hZHIhZu4m0224D7TCYTGhoaJnRBbBIfwtu0xWKBzWaD0WiUuyQSJUVFRejo6EBVVZXcpRARcRw3ZNvNyMjArl27FPs7m/b0ScBut8Nut8NsNgv3GQwGGI1GbN++XcbKSLQYjcYh/0GEf5Hs27dPjpKIiMrLy2EwGIQ/4oiyVFZWIj8/Xzhdw2QyobGxUe6ySJSNtMNFyTtiqOmTQPh8n8FfJKPROGDPEIlvg/8yDOe+dOlSOcohInE6nSgpKcEzzzwjdylEBOHt1mq1orS0FC0tLcjNzcWqVatkroxEW2lpKYqKimC324XteunSpYrea58gdwGEKFVRUREKCgoUe5iAVRs3bkRhYSHlqlD9m75wxlVVVeA4DtXV1bR3V0GKi4sB9J2qAQBmsxk1NTVyliQ62tMno46ODrlLICIJj9ylc4KUpbGxEdXV1XRSv4KF9/IMbuqNRiM+/PBDOUoiIikpKUFNTQ1sNhscDgdyc3MVP5CDmj4JhH+JDB7pFx4hRpQl3PAp/S9GFm3btg1A38neHMeB4zgAfXsKlP6fBStG+n1tMBiQlZUlQ0VEDI2NjSgvL0dFRQWMRiMMBgOsVivsdjvKy8vlLk801PRJIPyF6j9ow+l0DhncQeKfxWKByWRCRUWF3KUQEVitVvA8L/yEz8m12WxoaGiQuToSLbm5uaitrR1wX2NjIx3SZ0BmZqbcJYiKmj6JlJaWoqSkRJjby2KxoKCgQNEnjLImPz8fRqMRBQUFwojt8AnChJD4UVpais2bNw/4fW02m+mPdAXJzc2F2WxGUVGR8Du6pKQEHR0dKCwslLc4EdFADomETxi1WCzo6OjAunXraG+QgtjtdtTW1qK2tnbIoYGKigpF/xIhRGkKCgrQ0dEh/L5m4QR/FtXU1KCoqAg5OTkA+mZaaGhoUPSULTQ5MyGEEEIIA+jwLiGEEEIIA6jpI4QQQghhADV9hBBCCCEMoKaPEEIIIYQB1PQRQgghhDCAmj5CCCGEEAZQ00cIIYQQwgBq+gghhBBCGEBNHyGERKC6uhoZGRlj/hQVFQ14Tl5eHjiOQ0ZGBiwWCxobG2VcC0IIC+iKHIQQEgGn04mOjo4Bt/Py8mC1WlFQUDDgsUajEZWVlSgpKYHVaoXZbIbdbhcu8WW1WiWtnRDCFmr6CCEkipxOJzIyMlBVVTWk6QOAjIwMWK1Wuh4zIURydHiXEEIk5HQ6kZmZKXcZhBAGUdNHCCESMpvN2LhxI2pra+UuhRDCGGr6CCFEQlVVVVi6dCny8/PBcRzy8/OpASSESIKaPkIIkZDBYEBNTQ0aGhpgtVrR0dGB/Px8lJeXy10aIUThaCAHIYRE0VgDOYZTUlKC8vJy0K9jQoiYaE8fIYTIrLS0FABorj5CiKio6SOEEAnZ7fYh9+3btw9A3zx+hBAiFmr6CCFEIo2NjTCZTCgqKkJtbS3sdjuqq6thsVhQXFwMg8Egd4mEEAWjpo8QQiSSm5uLhoYGdHR0oKioCCaTSbg6B12NgxAiNhrIQQghhBDCANrTRwghhBDCAGr6CCGEEEIYQE0fIYQQQggDqOkjhBBCCGEANX2EEEIIIQygpo8QQgghhAHU9BFCCCGEMICaPkIIIYQQBlDTRwghhBDCAGr6CCGEEEIYQE0fIYQQQggDqOkjhBBCCGEANX2EEEIIIQygpo8QQgghhAH/Hw9+JR4nR1p5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "#The warning indicates that a bin is empty\n",
    "h = bg.get_hist(bins=30)\n",
    "hl.plot1d(ax, h, crosses=True,\n",
    "          label='{} bg trials'.format(bg.n_total))\n",
    "\n",
    "x = h.centers[0]\n",
    "norm = h.integrate().values\n",
    "ax.semilogy(x, norm * bg.pdf(x), lw=1, ls='--',\n",
    "            label=r'$\\chi^2[{:.2f}\\text{{dof}},\\ \\eta={:.3f}, \\text{{median}}={:.3f}]$'.format(\n",
    "                bg.ndof, bg.eta, bg.median()))\n",
    "\n",
    "ax.set(xlabel='TS', ylabel='trials per bin')\n",
    "ax.legend()\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differential Sensitivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready for the main calculation.  We start by defining energy bins and corresponding trial runners:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# you could also use np.logspace()\n",
    "Ebins = 10**np.r_[3:7.1:.25]\n",
    "trs = [\n",
    "    cy.get_trial_runner(\n",
    "        ana=ana, src=src,\n",
    "        flux=cy.hyp.PowerLawFlux(2, energy_range=(Emin, Emax)))\n",
    "    for (Emin, Emax) in zip(Ebins[:-1], Ebins[1:])\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the `energy_range` argument here.  This defines a flux that is zero except for within the given range (which is *always* specified in GeV to match the `true_energy` values in our MC).  To drive the point home, consider a flux defined in the energy range 10-100 TeV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flux = cy.hyp.PowerLawFlux(2, energy_range=(1e4, 1e5))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "E = np.logspace(3, 7, 1000)\n",
    "ax.semilogx(E, E**2 * flux(E), lw=1)\n",
    "ax.set(\n",
    "    xlabel=r'$E$ [GeV]',\n",
    "    ylabel=r'$E^2\\cdot\\Phi(E) [\\text{GeV}\\,\\text{cm}^{-2}\\,\\text{s}^{-1}]$',\n",
    ")\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sensitivity can be computed in the usual way with `tr.find_n_sig()`.  However, to convert to a flux we must be careful to evaluate at an `E0` where the flux is nonzero.  Otherwise, where $\\Phi(E_0)$ appears in the n $\\to$ flux conversion, the flux will evaluate to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log10(E) bin: (3.0, 3.25)\n",
      "Start time: 2021-07-28 13:52:12.259510\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 0.44000\n",
      "  n_sig = 15.000 ... frac = 0.52000\n",
      "  n_sig = 25.000 ... frac = 0.44000\n",
      "  n_sig = 35.000 ... frac = 0.70000\n",
      "  n_sig = 45.000 ... frac = 0.78000\n",
      "  n_sig = 55.000 ... frac = 0.96000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00   22.00   44.00   66.00   88.00  110.00  |  n_sig(relative error)\n",
      "500      |         33.2%   51.6%   81.0%   94.6%   99.6%  100.0%  |     56.278 (+/-  3.0%) [spline]\n",
      "End time: 2021-07-28 13:52:24.277669\n",
      "Elapsed time: 0:00:12.018159\n",
      "log10(E) bin: (3.25, 3.5)\n",
      "Start time: 2021-07-28 13:52:24.280796\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 0.48000\n",
      "  n_sig = 15.000 ... frac = 0.84000\n",
      "  n_sig = 25.000 ... frac = 0.88000\n",
      "  n_sig = 35.000 ... frac = 0.96000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00   14.00   28.00   42.00   56.00   70.00  |  n_sig(relative error)\n",
      "500      |         37.0%   70.8%   95.0%   99.4%   99.8%  100.0%  |     23.511 (+/-  2.9%) [spline]\n",
      "End time: 2021-07-28 13:52:35.519420\n",
      "Elapsed time: 0:00:11.238624\n",
      "log10(E) bin: (3.5, 3.75)\n",
      "Start time: 2021-07-28 13:52:35.523987\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 0.58000\n",
      "  n_sig = 15.000 ... frac = 0.92000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    6.00   12.00   18.00   24.00   30.00  |  n_sig(relative error)\n",
      "500      |         36.6%   63.8%   86.4%   96.0%   99.0%   99.8%  |     13.856 (+/-  3.2%) [chi2.cdf]\n",
      "End time: 2021-07-28 13:52:44.704011\n",
      "Elapsed time: 0:00:09.180024\n",
      "log10(E) bin: (3.75, 4.0)\n",
      "Start time: 2021-07-28 13:52:44.709084\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 0.78000\n",
      "  n_sig = 15.000 ... frac = 1.00000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    6.00   12.00   18.00   24.00   30.00  |  n_sig(relative error)\n",
      "500      |         36.6%   80.4%   97.4%   99.8%  100.0%  100.0%  |      8.548 (+/-  4.1%) [spline]\n",
      "End time: 2021-07-28 13:52:54.388315\n",
      "Elapsed time: 0:00:09.679231\n",
      "log10(E) bin: (4.0, 4.25)\n",
      "Start time: 2021-07-28 13:52:54.391100\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 0.82000\n",
      "  n_sig = 15.000 ... frac = 1.00000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    6.00   12.00   18.00   24.00   30.00  |  n_sig(relative error)\n",
      "500      |         36.6%   88.2%   99.2%  100.0%  100.0%  100.0%  |      6.434 (+/-  7.0%) [chi2.cdf]\n",
      "End time: 2021-07-28 13:53:04.244045\n",
      "Elapsed time: 0:00:09.852945\n",
      "log10(E) bin: (4.25, 4.5)\n",
      "Start time: 2021-07-28 13:53:04.248489\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 0.86000\n",
      "  n_sig = 15.000 ... frac = 1.00000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    6.00   12.00   18.00   24.00   30.00  |  n_sig(relative error)\n",
      "500      |         36.6%   90.0%   98.8%   99.8%  100.0%  100.0%  |      5.984 (+/-  6.5%) [chi2.cdf]\n",
      "End time: 2021-07-28 13:53:13.761301\n",
      "Elapsed time: 0:00:09.512812\n",
      "log10(E) bin: (4.5, 4.75)\n",
      "Start time: 2021-07-28 13:53:13.764916\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 0.86000\n",
      "  n_sig = 15.000 ... frac = 1.00000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    6.00   12.00   18.00   24.00   30.00  |  n_sig(relative error)\n",
      "500      |         36.6%   88.4%   98.8%  100.0%  100.0%  100.0%  |      6.430 (+/-  5.8%) [chi2.cdf]\n",
      "End time: 2021-07-28 13:53:23.017035\n",
      "Elapsed time: 0:00:09.252119\n",
      "log10(E) bin: (4.75, 5.0)\n",
      "Start time: 2021-07-28 13:53:23.022595\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 0.92000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    2.00    4.00    6.00    8.00   10.00  |  n_sig(relative error)\n",
      "500      |         34.8%   69.2%   84.6%   94.0%   97.2%   98.8%  |      4.900 (+/-  3.6%) [chi2.cdf]\n",
      "End time: 2021-07-28 13:53:31.938334\n",
      "Elapsed time: 0:00:08.915739\n",
      "log10(E) bin: (5.0, 5.25)\n",
      "Start time: 2021-07-28 13:53:31.941476\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 0.96000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    2.00    4.00    6.00    8.00   10.00  |  n_sig(relative error)\n",
      "500      |         34.8%   77.0%   89.6%   97.0%   98.6%  100.0%  |      3.770 (+/-  6.8%) [chi2.cdf]\n",
      "End time: 2021-07-28 13:53:41.079282\n",
      "Elapsed time: 0:00:09.137806\n",
      "log10(E) bin: (5.25, 5.5)\n",
      "Start time: 2021-07-28 13:53:41.081609\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 1.00000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    2.00    4.00    6.00    8.00   10.00  |  n_sig(relative error)\n",
      "500      |         34.8%   84.0%   94.0%   98.8%   99.6%  100.0%  |      2.809 (+/-  5.6%) [chi2.cdf]\n",
      "End time: 2021-07-28 13:53:49.995212\n",
      "Elapsed time: 0:00:08.913603\n",
      "log10(E) bin: (5.5, 5.75)\n",
      "Start time: 2021-07-28 13:53:49.997556\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 1.00000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    2.00    4.00    6.00    8.00   10.00  |  n_sig(relative error)\n",
      "500      |         34.8%   86.2%   97.2%   99.6%   99.8%  100.0%  |      2.400 (+/-  6.5%) [chi2.cdf]\n",
      "End time: 2021-07-28 13:53:58.915065\n",
      "Elapsed time: 0:00:08.917509\n",
      "log10(E) bin: (5.75, 6.0)\n",
      "Start time: 2021-07-28 13:53:58.918187\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 1.00000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    2.00    4.00    6.00    8.00   10.00  |  n_sig(relative error)\n",
      "500      |         34.8%   85.8%   97.0%   99.0%   99.6%  100.0%  |      2.504 (+/-  6.1%) [spline]\n",
      "End time: 2021-07-28 13:54:07.787412\n",
      "Elapsed time: 0:00:08.869225\n",
      "log10(E) bin: (6.0, 6.25)\n",
      "Start time: 2021-07-28 13:54:07.789804\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 0.98000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    2.00    4.00    6.00    8.00   10.00  |  n_sig(relative error)\n",
      "500      |         34.8%   87.0%   97.2%   99.6%   99.8%  100.0%  |      2.333 (+/-  5.9%) [chi2.cdf]\n",
      "End time: 2021-07-28 13:54:16.430840\n",
      "Elapsed time: 0:00:08.641036\n",
      "log10(E) bin: (6.25, 6.5)\n",
      "Start time: 2021-07-28 13:54:16.434587\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 0.98000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    2.00    4.00    6.00    8.00   10.00  |  n_sig(relative error)\n",
      "500      |         34.8%   87.2%   97.4%   99.6%   99.8%  100.0%  |      2.305 (+/-  6.1%) [chi2.cdf]\n",
      "End time: 2021-07-28 13:54:25.533501\n",
      "Elapsed time: 0:00:09.098914\n",
      "log10(E) bin: (6.5, 6.75)\n",
      "Start time: 2021-07-28 13:54:25.536389\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 1.00000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    2.00    4.00    6.00    8.00   10.00  |  n_sig(relative error)\n",
      "500      |         34.8%   89.4%   97.8%   99.6%  100.0%  100.0%  |      2.079 (+/-  6.3%) [chi2.cdf]\n",
      "End time: 2021-07-28 13:54:33.434755\n",
      "Elapsed time: 0:00:07.898366\n",
      "log10(E) bin: (6.75, 7.0)\n",
      "Start time: 2021-07-28 13:54:33.438495\n",
      "Using 10 cores.\n",
      "* Starting initial scan for 90% of 50 trials with TS >= 0.000...\n",
      "  n_sig = 5.000 ... frac = 0.98000\n",
      "* Generating batches of 500 trials...\n",
      "n_trials | n_inj    0.00    2.00    4.00    6.00    8.00   10.00  |  n_sig(relative error)\n",
      "500      |         34.8%   89.4%   97.8%   99.6%   99.8%  100.0%  |      2.070 (+/-  6.1%) [chi2.cdf]\n",
      "End time: 2021-07-28 13:54:41.156132\n",
      "Elapsed time: 0:00:07.717637\n",
      "\n",
      "0:02:28.899687 elapsed.\n"
     ]
    }
   ],
   "source": [
    "senss = []\n",
    "\n",
    "with time('differential sensitivity'):\n",
    "    for (Emin, Emax, tr) in zip(Ebins[:-1], Ebins[1:], trs):\n",
    "        print(f'log10(E) bin: {np.log10(Emin), np.log10(Emax)}')\n",
    "        sens = tr.find_n_sig(\n",
    "            bg.median(), .9,\n",
    "            n_sig_start=5, n_sig_step=10, batch_size=500, max_batch_size=500,\n",
    "            seed=1, mp_cpus=10)\n",
    "        # N.B. must set E0 to a value within the energy_range\n",
    "        # of the flux for this trial runner\n",
    "        sens['E2dNdE'] = tr.to_E2dNdE(sens, E0=Emin)\n",
    "        senss.append(sens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can pack the per-energy-bin sensitivity fluxes into a histlite Hist for easy plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hist(16 bins in [1000.0,10000000.0], with sum 1.86320295957662e-08, 0 empty bins, and 0 non-finite values)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluxs = [s['E2dNdE']/1e3 for s in senss] # convert GeV->TeV\n",
    "diffsens = hl.Hist(Ebins, fluxs)\n",
    "diffsens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "color = 'mediumvioletred'\n",
    "hl.plot1d(ax, diffsens, color = color)\n",
    "ax.loglog()\n",
    "ax.set(\n",
    "    ylim=1e-11,\n",
    "    xlabel=r'$E$ [GeV]',\n",
    "    ylabel=r'$E^2\\cdot dN/dE~~[\\text{TeV}\\,\\text{cm}^2\\,\\text{s}]$',\n",
    ")\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compared to the original estimates [here](https://wiki.icecube.wisc.edu/index.php/Cascade_7yr_PS_GP/Analysis_Level_Performance#Differential_Sensitivity), these are less optimistic.  That seems to be because the final MESC dataset has approximate systematics baked into the signal MC.  The impact is significant at lower energies because many events are smeared, and at the highest energies because the per-event smearing is larger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hist(16 bins in [1000.0,10000000.0], with sum 5.887992153145907e-07, 0 empty bins, and 0 non-finite values)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fluxs = np.array([s['E2dNdE']/1e3 for s in senss]) # convert GeV->TeV\n",
    "diffsens3 = hl.Hist(Ebins, (Ebins[:-1]/1e3) * fluxs)\n",
    "diffsens3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "hl.plot1d(ax, diffsens3, color = color)\n",
    "\n",
    "ax.loglog()\n",
    "ax.set(\n",
    "    ylim=1e-11,\n",
    "    xlabel=r'$E$ [GeV]',\n",
    "    ylabel=r'$E^3\\cdot dN/dE~~[\\text{TeV}^2\\,\\text{cm}^2\\,\\text{s}]$',\n",
    ")\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Note"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After computing the differential sensitivity, we tend to show it as a function of the central 90% energy range. The central 90% energy range can be calculated by:\n",
    "1. Restricting the lower limits of the energy range that is used to inject MC signal events. \n",
    "2. Compute the sensitivity using this restricted range\n",
    "3. Repeat until sensitivity changes by 5% \n",
    "4. Repeat steps 1-3 but restrict the upper limits of the energy range\n",
    "5. After finding which upper and lower limit cause the sensitivity to change by 5%, we now have the central 90% energy range\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ana setup                | 0:00:11.353483\n",
      "background estimation    | 0:00:11.512436\n",
      "differential sensitivity | 0:02:28.899687\n",
      "-----------------------------------------\n",
      "total                    | 0:02:51.765606\n"
     ]
    }
   ],
   "source": [
    "## Timing summary\n",
    "print(timer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remarks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Differential sensitivity (or discovery potential) calculations are not very different from energy-integrated ones.  The main pitfall is in the n $\\to$ flux conversion.  By passing an in-range `E0` argument to `to_E2dNdE()`, we can ensure that the trial runner's `flux` instance evaluates to non-zero in that calculation."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}