{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "57790656",
   "metadata": {},
   "source": [
    "===================== Import Packages ===================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6fb97792",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os, pdb, glob\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.table import Table, join, MaskedColumn\n",
    "from astroquery.vizier import Vizier\n",
    "import warnings\n",
    "from astropy.logger import AstropyWarning\n",
    "warnings.filterwarnings('ignore', category=AstropyWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "381042a6",
   "metadata": {},
   "source": [
    "===================== Define Functions ==================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d60ff663",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(catalog, join_key='Name', join_type='inner'):\n",
    "\n",
    "    \"\"\"\n",
    "    PURPOSE:    Get data from literature with Vizier\n",
    "\n",
    "    INPUT:      catalog = ctalog name on Vizier (str)\n",
    "                join_key = column header to join tables, if multiple (str; optional)\n",
    "                join_type = way to join tables, if multiple (str; optional)\n",
    "\n",
    "    OUTPUT:     t = data table (AstroPy Table)\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    ### GET FULL CATALOG (ALL COLUMNS, ALL ROWS)\n",
    "    viz = Vizier(catalog=catalog, columns=['**'])\n",
    "    viz.ROW_LIMIT = -1\n",
    "    tv = viz.get_catalogs(catalog)\n",
    "\n",
    "    ### IF MULTIPLE TABLES, JOIN THEN\n",
    "    for i, val in enumerate(tv.keys()):\n",
    "        if i == 0:\n",
    "            t = tv[val]\n",
    "        else:\n",
    "            tt = tv[val]\n",
    "            if join_key in tt.columns:\n",
    "                t = join(t, tt, join_type=join_type, keys=join_key)\n",
    "\n",
    "    return t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cb5eec0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def spt_coding(spt):\n",
    "\n",
    "    \"\"\"\n",
    "    PURPOSE:    Translate spectral type (e.g., M7) into numerical value\n",
    "                that can be used for plotting a histogram\n",
    "\n",
    "                Scale is 0 at M0, -1 at K7, -8 at K0, -18 at G0\n",
    "                (K8 is counted as M0)\n",
    "\n",
    "    INPUT:      spt = numpy array of spectral types (str, masked)\n",
    "                (masked values are unknonw spectral types)\n",
    "\n",
    "    OUTPUT:     spt_num = numpy array of numerical spectral types (obj, masked)\n",
    "                (returns -99. if unknown spectral type)\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    spt_num = np.empty(len(spt), dtype=object)\n",
    "    for i, val in enumerate(spt):\n",
    "\n",
    "        if val == '':\n",
    "            spt_num[i] = -99.\n",
    "\n",
    "        else:\n",
    "\n",
    "            if val == 'M1+M2':\n",
    "                val = 'M1.5'\n",
    "            if val[0] == 'M':\n",
    "                spt_num[i] = float(val[1:])\n",
    "            if val[0] == 'K':\n",
    "                spt_num[i] = float(val[1:]) - 8.\n",
    "            if val[0] == 'G':\n",
    "                spt_num[i] = float(val[1:]) - 18.\n",
    "            if val[0] == 'F':\n",
    "                spt_num[i] = float(val[1:]) - 28.\n",
    "            if val[0] == 'A':\n",
    "                spt_num[i] = float(val[1:]) - 38.\n",
    " \n",
    "    return spt_num"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "025efb7f",
   "metadata": {},
   "source": [
    "========================== Code =========================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8bc26ef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "### LOAD IN LUPUS DATA\n",
    "T = get_data(\"J/ApJ/828/46\", join_type='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e82d59db",
   "metadata": {},
   "outputs": [],
   "source": [
    "### FLAG UNDETECTED SOURCES\n",
    "T.add_column(MaskedColumn(name='Detected', data=T['F890']/T['e_F890'] > 3.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "24ab8c38",
   "metadata": {},
   "outputs": [],
   "source": [
    "### GET SPECTRAL TYPE CODING FOR PLOTTING (ALL, NON-DETECTIONS, UNOBSERVED)\n",
    "Spt_All = spt_coding(T['SpT'])\n",
    "Spt_Ndt = spt_coding(T['SpT'][np.where(~T['Detected'])])\n",
    "Spt_Obs = spt_coding(T['SpT'][~T['F890'].mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f64fb2a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 25.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### SETUP PLOT\n",
    "mpl.rc('xtick', labelsize=15)\n",
    "mpl.rc('ytick', labelsize=15)\n",
    "mpl.rc('xtick.major', size=3, pad=7, width=1.5)\n",
    "mpl.rc('ytick.major', size=3, pad=7, width=1.5)\n",
    "mpl.rc('axes', linewidth=1.5)\n",
    "mpl.rc('lines', markersize=5)\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xlabel('Spectral Type', fontsize=17)\n",
    "ax.set_ylabel(\"Number of Sources\", fontsize=17)\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "nxticks    = np.arange(-33, 9, 1)\n",
    "ticklabels = np.array(['A5', '', '', '', '', 'F0', '', '', '', '', 'F5', '', '', '', '', 'G0', '', '', '', '', 'G5', '', '', '', '',\n",
    "                        'K0', '', '', '', '', 'K5', '', '', 'M0', '', '', 'M3', '', '', '', 'M7', ''])\n",
    "ax.set_xticks(nxticks)\n",
    "ax.set_xticklabels(ticklabels)\n",
    "ax.set_xlim(np.min(nxticks), np.max(nxticks))\n",
    "ax.set_ylim(0, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1eb5b0cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12ac62c10>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### PLOT DIFFERENT SAMPLES\n",
    "bins_spt = np.arange(-33, 9, 2)\n",
    "ax.hist(Spt_All, bins_spt, align='left', lw=2, histtype='step', color='black', label='All Sources')\n",
    "ax.hist(Spt_Obs, bins_spt, align='left', facecolor='lightblue', hatch='//', lw=2, histtype='stepfilled', label='Observed', edgecolor='black')\n",
    "ax.hist(Spt_Ndt, bins_spt, align='left', color='red', lw=2, histtype='step', label='Undetected')\n",
    "ax.legend(loc='upper left', prop={'size': 16}, edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aaadd125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5IIyrV6/inXfeAZA97MDV1dWwrl27dtBoNPjjjz+wZ88ew3IhBObMmYO1a9fK7ot+VooPPvjA6H7OnTuXK5C/++678Pb2xtdff42vvvrK6Pjgq1evYtmyZYbvd+7ciS1btuSa21hf/++//w4AqFixouw+EBGVBAzFtsdgTIqwZ88e/Pvvv3BxcckVNI2pXbs2GjRogIyMDCxZssQi9SQnJ2Pu3LmoW7cuQkJC0LVrVwwaNAidOnVC+fLl8eqrr0Kn0+G9994z3IgHAC4uLggPD4evry9++OEHVKtWDf3790fXrl3xxBNP4OrVq+jYsaMhrOqVL18eY8aMgU6nQ/v27fHMM8+gT58+CA0Nxfjx4zFp0iTZfenVqxc++eQTpKamIiwsDLVq1UL//v3Ro0cP1K5dG7Vr18bhw4cN24eEhOC3336Dj48Pxo8fj/Lly6N9+/YYPHgwunXrhmrVqqFKlSqYO3euoc3p06fRtWtXlClTBs888wwGDRqE3r17o3Llypg/fz68vLwwffp02X0gIlI7hmJl4BhjBYqJicn153mlMueUXPphEd26dYOPj0+h2w8ZMgTHjx/HggULDFdhzWny5Mlo3rw5duzYgcjISJw4cQKxsbFwcXFB+fLl0aVLF4wcORLNmzfP07Zx48Y4efIkPv/8c2zduhXr16+Hi4sL6tevjyFDhmDkyJFwdMz7o/fNN9+gQoUKmD9/Pg4ePAgPDw+0bNkSq1evxsOHD/HZZ5/J7s/777+Pdu3aYc6cOdi7dy/WrVsHT09PlC9fHhMmTEC7du1ybd+6dWucPXsWc+fOxebNm3H06FGkpaWhbNmyqFChAgYPHow+ffoYtu/WrRsePHiAffv24dKlSzh06BDc3NxQvnx5TJo0Ca+//rpdvKeJiGyBoVg5JFGUwZSUR1JSEjz+e9NptdpC794/f/48Bg8ejGXLlqFmzZpGtwkJCTHrFF7WEhwcjJs3b9q6DFK5ovwMERHZCwcHB+h0OkgaDarXbWC1UBz2RAiETgeNRoOsrKzidMEumJrXeMVYQYw9tcwe2GvdREREtiZ0Ol4pVhAGYwU5duyYrUsgIiIiK2MoVg7efEdERERkI5KkYShWEAZjIiIiIluRTG/CUGw5DMZEREREdoKh2LIYjImIiIjsgDlCMScjKxiDMREREZHCmSMUh/8wC2AwLhCDMREREZGCmSsUr5j9BSDJGNRcgjAYExERESmUOUPxgLETIDEYF4jBmIiIiEiBzB2Kw0aPM3+RKsNgTERERKQwDMW2wWBMREREpCAMxbbDYExERESkEAzFtuVo6wLokUaNGuH27du2LsNkgYGBOHbsmK3LICIismsMxbbHYKwgt2/fRnR0tK3LUAQhBFavXo1Vq1bh6NGjiI2NhZubGypWrIjnnnsOb7zxBipUqJCn3bRp0/DRRx9h6tSpmDZtmvULVxn93cucEJ6IyLIYipWBwViBNBoNvP0DCt0uMyMdifH34eDoCE8fX0iS6SNjUpISkaLVws3DA27unia1TYi9A51OZ/IxC3Pr1i306tULR44cgSRJaNiwIVq2bInk5GRERETgyy+/xJw5c/DVV1/h9ddfN/vxiYiIrImhWDkYjBXI2z8A8/ZEFriN/oeoer2GNvshGtm6PuLv3jG5XUHi4+PRqlUrXLlyBfXr18fSpUtRu3Ztw/rMzEzMnj0bEydOxBtvvIGsrCy8+eabZq2BiIjIagQYihWEwdgOKeE3yxStFonx901uV5g33ngDV65cQeXKlbFz5054e3vnWu/o6Ih33nkHrq6ueOONNzB+/Hh06NABtWrVMnstREREliaEziaf5zqdDg4ODiYfzxxcXV2RlJRkk2MXhrNS2BmlhOIZowYiKzPT5LYFuXLlClauXAkAmDlzZp5QnNNrr72GunXrIiMjA1988YXRbaKiojBkyBAEBQXB1dUV1atXx7Rp05CSkmJ0+zVr1uDZZ5+Fn58fnJyc4OfnhyeeeAKjRo3C6dOnjbYJDw9Hp06d4O/vD2dnZwQHB2Pw4ME4d+5cnm2vXbsGSZJQqVIlZGVl4euvv0b9+vXh4eEBSZKQkJAANzc3ODg4FDjWPCwsDJIkYfbs2cWqRy8iIgKdO3eGt7c3PDw80KhRI/zyyy/5bk9EROZlq89znU5nk6/U1FSTa7UWBmM7oqRQfOPSBXj6+JrcviCbNm2CTqeDt7c3unfvXuC2kiThxRdfNLR7/Oawq1evomHDhvjzzz/RqlUrdOjQAbdu3cJHH32EDh065PmhnD59Ovr164c9e/bgySefRN++fdGsWTM4ODhgwYIF2LlzZ67tMzMz8cILL6Bv377YvXs3qlevjp49e8Lf3x/Lly9Ho0aNsG3bNqO1CyHQu3dvvPfee/Dz80P37t1Rp04deHt7o1evXtDpdFi6dKnRtnFxcdi0aROcnZ0xePDgYtezZs0atGrVCtu2bUP58uXRvXt3uLm5YeTIkXjnnXcKfA2IiKj4JI3Gqp/nzs4ukDQaSJpHEVCSNIZlJn3luLepKNvbAw6lsBNKC8VTflmJL8aMNHkfBYmMzB5XXb9+fTg6Fv7WbNy4MYDssHjt2jVUrlzZsG7JkiXo0aMHVqxYATc3NwDAzZs30b59exw4cAAfffQRPv30UwBAWloaPvvsM3h4eODYsWOoUaNGruNERUXluco8depUrF69Gk2bNsWKFStyHTs8PBz9+/fHwIEDceXKlTxXvq9fvw6dToe///4b1atXz7VuxIgRWLFiBRYvXoxJkybl6fPy5cuRnp6OPn36wM/Pr1j13L59Gy+99JLh6vVbb71laLNjxw48//zzxk88ERHZVHE+z389eTnP53lonfom1yAnl4Q9EQJhgZv2zck+4nsJp8RQLOeHqDCxsbEAgICAwmfkeHw7fVs9Nzc3/Pjjj4ZQDAAhISH46quvAADff/+94arxw4cPkZKSgipVquQJxQBQsWJF1KxZ0/D9/fv38c0338DV1RVr167NFUKB7KEOr7zyCuLj47Fs2TKjtf/vf//LE4oBoH379qhYsSLOnz+PiIiIPOsXLlwIABg+fHix61mwYAESExPRrFmzXKFYX8crr7xitHYiIrIdJXyemyOXKBWDscKVlFAsR0Fz6z733HMIDAzMs/z555+Hn58fHj58iOPHjwMA/P39UalSJZw+fRrvvPNOgeNxAWDXrl1ISUlBy5YtERwcbHSbtm3bAgAOHjxodH2fPn2MLpckCUOHDgUALFq0KNe6kydP4uTJkwgKCkKnTp2KXc/u3bsBAIMGDTLaRl8HEREpgxI+z9UcigEGY0UraaG4TJkyAIA7d4o2Bdzdu3cN/+/v759r3eNXTXOqVKkSgOyhFXpLlixB2bJl8fXXX6N27drw8/NDly5d8M033+DevXu52l+5cgVA9nADSZKMfvXr1w9A3ivZAFC2bFmUKlUq3/qGDx8OSZKwatWqXEM49FeLhwwZkutOYrn16Puf37kq6BwSEZF1KeHzXO2hGOAYY8UqaaEYABo2bIhly5bh+PHjyMzMLHSc8ZEjRwAAfn5+hrBripxXnFu1aoVr165h8+bN2LNnDw4ePIg//vgDW7duxdSpU7F+/Xq0b98eAAwPNalWrRpatmxZ4DFyDsHQyzm8w5hKlSrhmWeewc6dO7F+/XoMHDgQGRkZ+PXXXwHkHkZhjnqIiEjZlPB5bo5cYg9PUWUwVqDMjPQSF4oBoFu3bnjnnXfw4MED/Pbbb/kONwCyf7j0Mzc8//zzhkcX6129ejXftteuXQOQPeY4Jzc3N4SFhSEsLAxA9tXVDz/8ED///DNGjBiBqKgoAED58uUBADVq1Mgz3MFchg8fjp07d2LhwoUYOHAgNm3ahHv37qFFixZ5xkHLrSc4OBjnz583nI/H5beciIisRwmf5+a6WAc7CMYcSqFAifH3S1woBoCqVasa/uT/7rvvIiEhId9tv//+e5w+fRqOjo54991386z/888/cw210NuyZQvi4uLg6emJhg0bFliPv7+/YY7k69evIz4+HkD2jWnOzs7YvXu30WOYQ58+feDl5YWdO3fixo0bRm+605NbT5s2bQBkz3RhzJIlS2RUTkRE5qKEz3Nz/gUbj13EUiIGYwVycHQscaFY77vvvkOlSpVw9epVtGvXDmfPns21PjMzE19//TXGjh0LAPj8889zPTJaLyUlBaNHj841RvfWrVuGuXlfffVVuLq6Asiejm3+/Pl4+PBhnv1s2rQJAODj44PSpUsDyJ4NY8yYMUhKSkK3bt3w999/52mXlpaGjRs34vz583JOA9zc3NC/f3/odDp8/vnn2LZtG0qVKoUXXnghz7Zy63nppZfg4eGBiIgIzJkzJ9f2u3fvxo8//iirdiIiKj4lfJ6be1jn43/dVSIOpVAgTx/fEhmKAcDX1xf79+9Hz549cezYMTz11FNo1KgRqlatiuTkZERERCA2NhbOzs746quvDAH5cUOGDMHvv/+OKlWqoFWrVkhNTcXOnTuRlJSE5s2b46OPPjJsGx8fj1GjRuG1115DvXr1DDedXbp0CSdOnIAkSfjyyy9z3fD22WefISYmBr/++ivq1auHunXrokqVKnB0dMTNmzdx8uRJJCUlYevWrbLH9Q4fPhw//fQTvvvuOwDAwIED4enpaXRbOfWUK1cO8+bNw+DBgzF27FjMnz8fTz75JKKjo7Fv3z6MGzcO33zzjazaiYhIPiV8nlviXqeV3840eR/WxmCsQA/uxWJUm4L/zP+4lKREpGi1cPPwwB8rl+KPlcafnJYfIXRIjL+PrMxMePr4FunhHQmxRZs9wlTBwcE4fPgwVq9ejZUrV+Lo0aM4deoUXF1dUbFiRQwZMgRvvPFGgTfcVa5cGceOHcMHH3yAnTt3Ij4+HhUqVMDAgQMxceLEXDfAVa1aFbNmzcKePXtw5swZbNmyBUIIBAcHY8iQIXjzzTfzDLtwdHTE8uXLMXjwYMyfPx+HDx/GmTNn4O7ujqCgIHTr1g3du3dH69atZZ+Hpk2bonbt2oar5saGURS3nv79+yMkJAQff/wxIiIicPnyZdSoUQM//vgjXn75ZQZjIiIrU2sotheSsIdbBBUoKSkJHv+9UbRaLdzd3Qvc/vz58xg8eDCWLVuW7xXEkJAQREdHm71WSwsODs419RmRJRTlZ4iIyF44ODhAp9NB0mgQfi77M1TtoVj/5DuNRoOsrCyT9yuHqXmNV4wVxNgDKeyBvdZNRESkFGoPxfaCwVhBjh07ZusSiIiIyMoYipWDs1IQERER2YgQgqFYQRiMiYiIiGxFCIZiBWEwJiIiIrIVSWIoVhAGYyIiIiIbkfPQC4Ziy2EwJiIiIrITDMWWxWBMREREZAcYii2PwZiIiIhI4RiKrYPBmIiIiEjBGIqth8GYiIiISKHUEopTtFoInc7kdtbGYExERESkQGoKxTNGDTS5nS0wGBMREREpjNpC8Y1LFyBJyo+djrYugHJo1Ai4fdvWVZguMBA4dszWVRAREamCGkPxlF9W4r3+3QBh8m6sisFYSW7fBqKjbV2FTV27dg2VK1cGAFy9ehWVKlXKd9tKlSohKioKCxcuxLBhwyxa17Rp0/DRRx9h6tSpmDZtmkWPVVK0bdsWe/bswa5du9C2bVtbl0NEpAhqDcVy+mELDMZKpNEAQUG2rqJwMTGAHQykVyKlhEL9LyIVK1bEtWvXbFYHERExFCuBXQTj5ORk/Pnnn9i0aRP279+PqKgoODg4oFq1aujTpw/efvtteOTzwi9atAjff/89zp07B2dnZzRr1gwffvghWrRoYeVemCAoCLh509ZVFC4kpMRf4SYiIjIHhmJlUP4oaAC//vorevXqhV9++QUODg7o3r07WrVqhatXr2Lq1Klo3Lgx7t69m6fduHHjMHz4cJw5cwbPPvssmjRpgu3bt6N169bYsGGD9TtCRERE9BiGYuWwi2Ds5OSEl19+GefOncO5c+ewevVqbNu2DRcuXED9+vVx/vx5jBs3Llebv/76C7Nnz4afnx9OnTqFDRs2YNu2bdi7dy8cHBwwfPhwJCQk2KQ/ZDlt27aFJEnYvXs3Tp48id69e6NMmTJwcXHBE088ga+++gpCGB/5n5KSgmnTpiE0NBQuLi4ICgrC0KFDcf369UKPGxkZiUGDBqFChQpwcXGBr68vOnbsiC1btuTabvfu3ZAkCXv27AEAPPPMM5AkyfC1aNGiXNvHx8dj6tSpqFevHjw9PVGqVCk89dRT+Pjjj5GcnFxgPUOHDkXlypXh6uoKX19f1K1bF++++y6ioqIAAMOGDTOM546KispVhyRJsvuY040bNzBixAgEBQXB1dUVoaGh+OCDD5CSklLoOSUiKikYipXDLoZSDB06FEOHDs2zPCgoCN999x1atGiBdevWIT09Hc7OzgCAr7/+GgDw4YcfIjQ01NCmefPmePXVVzFnzhwsWLAA77zzjnU6QVb1xx9/4Ouvv0bVqlXRoUMHxMTEYP/+/Rg/fjxu3LiBWbNm5do+OTkZ7du3x6FDh+Du7o7nnnsObm5u+OOPP7B582Z07do132PNnj0bb7/9NnQ6HerVq4emTZvi9u3b2L17N/7880989NFHmDJlCgAgMDAQQ4cOxbZt23Dnzh107NgRgYGBhn1Vq1bN8P/nzp1Dp06dcOPGDQQFBeHpp5+Gk5MTjhw5gsmTJ2Pt2rXYvXs3vLy8ctXz5ZdfYtKkSdDpdKhevTp69OiBlJQU/Pvvv5g5cyZq166NYcOG4emnn4ZWq8XatWvh7u6OsLAws/RR7/z582jTpg3u3r2LoKAgdO/eHUlJSfjmm2+wa9euQl9DIqKSQOh0DMUKYhfBuCB169YFAKSlpSEuLg5BQUFISUnBzp07AcDoh31YWBjmzJmDTZs2MRir1GeffYYff/wRr7zyimHZzp078eyzz2Lu3LkYP348QkJCDOumTp2KQ4cOoWbNmtixYwfKlSsHIDswDxw4EEuWLDF6nD/++ANvvfUW/Pz8sHbtWrRu3dqw7u+//0aXLl0wdepUtGnTBm3atEHNmjWxaNEitG3bFnfu3MGkSZOM3nyXkpKC7t2748aNG/jwww8xefJkwy99ycnJGDlyJFasWIG33noLv/zyi6Hdxo0bMWHCBLi6umLx4sXo169frv2eO3fOcDV45MiRePbZZ7F27VqUKVMmz9VquX3UGzJkCO7evYt+/fph8eLFcHV1BQBcv34d7dq1w+XLl40ej4iopGEoVg67GEpRkCtXrgDIHm7h6+sLALhw4QLS0tLg7++fK/zoNWjQAABw+vRp6xVKVtW7d+9coRgA2rVrh44dOyIrKyvXFcuUlBT89NNPAIBvvvnGEIoBoFSpUvjxxx8Noe5xU6dOhRACP/74Y67ACABPPfWU4S8X3377rUn1L168GJcvX8bzzz+PGTNmGEKxvqaff/4ZZcuWxdKlSxEfH5+rHgD45JNP8oRiAHjiiSdQq1Ytk2qR08cDBw7g6NGjcHd3x/fff5/r/FWoUAEzZ840qQYiIrWSJA1DsYLYfTCePXs2AKBTp05wcXEBAMOYUGOhGADc3d3h7e2N+Ph4JCYmmnS8pKQkwxcpV7du3Ywu14fC6ByzaRw/fhyJiYkoU6YMOnXqlKdNYGAgnnvuuTzL7927hyNHjsDNzS3f4+mvBh88eNCk+jdv3gwAeOGFF4yu9/DwQKNGjZCZmYmjR48CAG7fvo2TJ09Co9HgpZdeMul4+ZHbx927dwPI/rn08/PL06ZHjx55hoAQEZVIeW/pKBRDseXYdTDesmULFixYACcnJ8yYMcOwXKvVAsi+spYfd3d3ADA5GHt4eMDDwwMBAQEyKqbC5LzpK7+b5B5fb+xGsQoVKhhtU7p0aQBAamqqYdnN/6bGK+hhIvqb1HK6evUqhBBISUmBi4tLnpvXJElC2bJlAQCxsbEF9uVx+r+EvPjii0b3K0mS4aY3/b71vxAGBQWZLXTK7aP+nBo7b0D2a1bQ+SYiIuMYii3LbscYnz9/HoMHD4YQAl9++aVhrDHZN/0vLAAKvSqv/wXI2BzWGo3lf+fT/fdwEw8PD/Tp08ci++7UqVOhv4RVrFjRrMc2Vocl+khERKZhKLY8uwzG0dHR6NSpE+Lj4/H2229j7Nixudbrg1JB01npQ5enp6dJx9aHsaSkJF41tgBfX194eHhAq9Xi33//xZNPPml0u/v37+P+/fsA8r86XFTBwcEAUOCT34ytK1++PIDsq5+//PKLWcN4+fLlcf78ebz00ksFzhaRk/48xMTE4MGDB2a5aiy3j0U5p/pp44iIqHAMxdZhd0Mp7t+/j+eeew5RUVEYPny40Zt49AHhZj5Pj0tKSkJCQgJ8fHxMDsbu7u6GLzI/jUZjmNlg7dq1+W4XHh4OAPDx8UG9evWKdcyGDRvCw8MD9+7dw59//pln/Z07d4wuL1euHOrUqYPExERs27bNpGPqb6bLzMw0ur5z584AgNWrVxd5n4GBgahbty50Ol2umSqKU4fcPupfw23bthl+gclp48aNnEeciKiIVBOKCx4hqQh2FYy1Wi06d+6Mc+fOoXfv3pg3b57R8aU1atSAi4sLYmNjc91kpXf8+HEAQJ06dSxeM5luwoQJkCQJy5cvx4IFC/Ksj4iIwPvvvw8AeOedd+Dk5FSs47m5ueHll18GALz11luIiYkxrEtJScHo0aPzfSDFxx9/DAAYPnw4Nm3alGe9EAKHDx/OE6z1N4aePXvW6H5ffvllVKxYEWvWrMHEiRONjoW/ffs25s2bl2uZflaKDz74wOgvFufOncM///xj+N7f3x/Ozs64ffu20QArt4+tWrVCgwYNoNVq8frrryMtLc2w7saNGxg/frzRYxERUW5qCcWXTp+AEDqT21mb3QTjtLQ09OjRA0eOHEHHjh2xYsUKODg4GN3Wzc0N7dq1AwCsWbMmz3r91cb87rIn22rdujVmzZoFjUaDkSNHomrVqujbty8GDBiAxo0bo2XLloiLi0P//v0xadIksxxz+vTpaNKkCc6dO4fq1auje/fu6NevH6pUqYK9e/diyJAhRtt169YNs2fPxv3799G9e3eEhobi+eefx6BBg/Dcc88hMDAQzZo1M8yrracfrzthwgR069YNL730EkaOHGmY2cHd3R2bN29GpUqV8MUXX6BChQpo06YNBg0ahF69eqF27dooV64cJk+enGu/vXr1wieffILU1FSEhYWhVq1a6N+/P3r06IHatWujdu3aOHz4sGF7JycndO/eHVlZWahXrx4GDhyIkSNHYuTIkcXu49KlS+Hv74+VK1eiSpUqeOGFF9CtWzfUrFkTfn5+aN68ufwXjIioBFBTKJ4+or/J7WzBLsYYZ2VlYcCAAdi5cydatWqFdevW5ZrX1Zi3334bW7duxccff4yuXbsann4XERGBn376Cd7e3mab0srsYmKAfKaaU5QcV1bN7c0330Tr1q0xd+5c7N27F1u2bEFmZib8/f3Ro0cPDB8+HN27dzfb8dzd3bFr1y589tln+PXXX/HHH3/Ax8cHzz77LD7++ON8H36hr7Vdu3b49ttvsWvXLuzYsQMajQaBgYGoX78+unbtmufGta5du2LevHn44YcfsHPnTsN4+KeffhotWrQAANSuXRunT5/Gjz/+iPXr1+P06dOIiIhAmTJlEBISgvHjx6NXr1556nn//ffRrl07zJkzB3v37sW6devg6emJ8uXLY8KECYZfGvV++ukn+Pn5YevWrQgPD0dGRgYAYP78+cXq4xNPPIFjx45h6tSp2Lp1KzZs2ICQkBCMGTMGU6ZMQZcuXYr+AhERlTBqC8XlQ2vg4qnjEDplXzWWRGFzYinA7NmzMW7cOADZV8T0U249bubMmShTpozh+3HjxmH27NkoVaoUOnTogPT0dGzfvh1CCISHh6Nnz56ya0pKSjLc5KfVagsdc6yfRWPZsmWoWbOm8Y1CQgAjQz8ULzgYyGc8N5G5FOlniIjITjg4OECn00HSaBB+LvdnqBpD8eR5v+LFJjUhdDpoNBpkZWWZvD85TM1rdnHFOOeTvdavX5/vdtOmTcsVjGfNmoV69eph7ty52L59O5ydnfHss89i8uTJhqtyihIYaOsK5LHXuomIiBRGraFYTj9swS6C8bRp0zBt2jRZbYcNG4Zhw4aZtR6LOXbM1hUQERGRjTAU257d3HxHREREpFYMxcrAYExERERkQwzFysFgTERERGQrAgzFCsJgTERERGQjQugYihWEwZiIiIjIhhiKlYPB2Er0j67WKXxiayKl0v/sGHsMPBGRvZI0GoZiBWEwthJPT08AwP37921cCZF9evjwIQCgVKlSNq6EiMi2GIoth8HYSoKCghAYGIj9+/fbuhQiu3Tq1CmULl0awcHBti6FiMhmGIoti8HYSiRJQocOHbBlyxacO3fO1uUQ2ZXExERs3rwZTZo0gUbDf7aIqGRiKLY8u3jynVqMHDkSp06dwquvvorOnTujVatW8PX15Qc9UT6ysrJw9epVLF++HFqtFm+88YatSyIisgmGYutgMLYid3d3fPvtt5g/fz62b9+OtWvX2rokIrvQpEkTvPfeewgJCbF1KUREVqeWUCyEMLmNtTEYW5m7uzvGjh2LN998EzExMdBqtZypgigfGo0Gfn5+8PPzs3UpREQ2oZZQHP7DLIDBmPIjSRLKlStn6zKIiIhIodQUilfM/gKQJMWHYw5uJSIiIlIYtYXiAWMn2MU89AzGRERERAqixlAspx+2wGBMREREpBAMxbbFYExERESkAAzFtsdgTERERGRjDMXKYNFgnJaWBq1Wa8lDEBEREdk1hmLlkB2Mr1+/jp9//hkbN27Ms+7cuXNo2bIlPDw84OXlhaeffpqPQSYiIiJ6jBCCoVhBZAfj+fPnY/To0YiMjMy1/OHDh2jfvj0OHTqErKwsCCFw8OBBtGvXDvfu3St2wURERESqIQRDsYLIDsZ//fUXAOCFF17ItXzevHm4c+cOKlWqhL/++gsHDhxAvXr1EBsbi6+//rp41RIRERGpiSQxFCuI7GB848YNSJKE0NDQXMvXr18PSZLwxRdfoF27dmjevDnmz58PIQQ2b95c7IKJiIiI1ELOQy8Yii1HdjCOjY2Ft7c3nJycDMtSU1Nx5MgRuLi44Pnnnzcsb9CgAZydnXHlypXiVUtERERUgjEUW5bsYOzk5ISHDx/mWnbo0CFkZmaiSZMmcHFxybXOw8MDWVlZcg9HREREVKIxFFue7GBcrVo1ZGVl4eDBg4Zl4eHhkCQJbdq0ybVtWloaEhISEBgYKL9SIiIiohKKodg6ZAfjHj16QAiB4cOHY82aNZgzZw7mz58PAOjbt2+ubY8dOwadTofKlSsXr1oiIiKiEoah2Hoc5TZ85513sGrVKly4cAH9+/cHkD0X38iRI/Hkk0/m2lZ/Jblt27bFKpaIiIioJFFLKE7RaiF0OpPbWZvsYOzp6YlDhw5h1qxZOHLkCEqXLo1OnTph6NChubbLyMjA8ePHUadOHXTt2rXYBRMRERGVBGoKxTNGDTS5nS1IQghh6yLsUVJSEjz+e3NptVq4u7vbuCIiIiKyFw4ODtDpdJA0GoSfu5lnvdpC8Y1LF5CSlAQhdNBoNFabkMHUvCZ7jDERERERmZ8aQ/GUX1YCpk/ZbHWyh1LkdOzYMezYsQM3btxASkoKFixYYFiXnp6O27dvQ5IklC9f3hyHIyIiIlIltYZiOf2whWIF49u3b+PFF1/Ezp07AWTffCdJUq5grNPp0Lx5c9y5cwdHjx5F/fr2cWKIiIiIrImh2PZkD6XQarVo164dduzYgaCgIIwYMcLouA1XV1eMHj0aOp0Oq1evLlaxRERERGrEUKwMsoPxrFmzcP78eTRr1gxnz57FvHnzDIObH9e7d28AwN69e+UejoiIiEiVGIqVQ3YwXrt2LSRJwpw5c+Dl5VXgtrVq1YKTkxMuXrwo93BEREREqsRQrByyg/G///4LZ2dnNGzYsNBtJUlC6dKl8eDBA7mHIyIiIlIdodMxFCuI7GAshIBGo4EkFT73hk6nQ2JiYr5DLYiIiIhKKoZi5ZAdjCtVqoTU1FRcv3690G337NmD9PR01KxZU+7hiIiIiFRHkjQMxQoiOxh36dIFADBnzpwCt0tNTcWECRMgSRK6d+8u93BERERE6iPjoRcMxZYjOxiPHz8epUuXxqxZs/Dpp59Cq9XmWp+RkYGtW7eiefPmiIyMREBAAF5//fViF0xERERUUjEUW5bsYFy2bFls2LAB7u7u+PDDD1GmTBnExcUBACpUqABPT088//zzOHXqFHx8fLBhwwZ4enqarXAiIiKikoSh2PJkB2MAaNOmDU6dOoX+/ftDCIHMzEwIIXDz5k2kp6fDwcEB/fr1Q2RkJJo0aWKumomIiIhKFIZi65CEEMIcO0pJSUFkZCRiYmKg0+kQEBCARo0aqXYmiqSkJEPftFqt0af+ERERERnj4OAAnU4HSaNB+LmbBW6rllAcVisEQuig0WiQlZVlcns5TM1rjuY6sJubG55++mlz7Y6IiIioxFNLKL50+gSE0JncztqKNZSCiIiIiCxDTaF4+oj+JrezBdnBeNeuXahSpQpGjRpV6LYvvvgiqlSpgn379sk9HBEREVGJobZQXD60BiSN8q/Hyq5wyZIliIqKKtLcxM8//zyuXbuGpUuXyj0cERERUYmgxlA8ed6vJre3BdnB+PDhw5AkCe3bty902+effx4ajQYHDhyQezgiIiIi1VNrKJbTD1uQHYxv3LgBb29vlCpVqtBt3d3d4e3tjejoaLmHIyIiIlI1hmLbkx2MNRoNkpOTi7x9cnKy1abmICIiIrInDMXKIDsYV65cGWlpaTh06FCh20ZERCA1NRWVKlWSezgiIiIiVWIoVg7ZwbhLly4QQmD8+PFIT0/Pd7v09HSMHz8ekiShS5cucg9HREREpD4CDMUKIjsYv/XWW/Dy8kJERARatmyJP/74AxkZGYb1GRkZ2LZtG5o3b46IiAiULl0ab7/9tlmKJiIiIlIDIXQMxQoi+8l3/v7+CA8PR69evRAZGYkuXbrAyckJAQEBAIA7d+4gIyMDQgi4u7sjPDzcsI6IiIiIsjEUK0exZlpu3749jh07hp49e0Kj0SA9PR03btzAjRs3kJ6eDgcHB4SFheH48eNFmtaNiIiIqCSRNBqGYgWRfcVYr3r16li3bh20Wi2OHTuGu3fvAgACAgLQsGFDeKjgJBEREREpBUOx5cgOxg0aNIAkSVizZg2qVKkCDw8PtG3b1oylEREREVFODMWWJTsY//PPP3ByckKVKlXMWQ8RERERGcFQbHmyxxiHhIRAp9OZsxYiIiIiMoKh2DpkB+Pu3bsjJSUFu3btMmc9RERERJSDWkKxEMLkNtYmOxh/+OGHqFixIkaOHImLFy+asyYiIiIignpCcfgPswA7CMayxxhv2rQJo0ePxowZM1CnTh106dIFzZo1g7+/PxwcHPJtN2TIELmHJCIiIiox1BSKV8z+ApAkxYdjSci8rq3RaCBJkuGyuCRJRWqXlZUl53CKk5SUZJiKTqvVwt3d3cYVERERkb1wcHCATqeDpNEg/NzNPOvVFooHjJ2Ald/OhNDpoNForJYHTc1rsq8Yt27dushhmIiIiIiKRo2hOGz0OKz8dqbJ+7A22cF49+7dZiyDiIiIiNQaiu1FsR4JTURERETmwVBsewzGRERERDbGUKwMDMZERERENsRQrByyxxgXNCVbfiRJQmZmptxDEhEREamKEIKhWEFkB2M5s7zZwxNPiIiIiKxGCIZiBZEdjK9evVrg+gcPHuDYsWOYNWsWbt26hV9++QV169aVezgiIiIi9ZEkhmIFkR2MK1asWOg2derUwYsvvoiuXbvipZdeQmRkpNzDEREREamOnGdCMBRbjsVvvnNycsK3336LuLg4TJ061dKHIyIiIlIthmLLssqsFDVq1ICXlxf+/PNPaxyOiIiISHUYii1P9lAKU6SkpECr1SI1NdUahyMiIiJSFYZi67DKFeMffvgBWVlZRRqXnJ/IyEh89tln6N27N0JCQiBJUoHjcqZNm2bYxtjXpEmTZNdCREREZC0MxdYj+4rx3r17C1yfmpqK6OhobNy4Eb///jskScLgwYPlHg4zZszAb7/9ZnK7li1bolq1anmWN2zYUHYtRERERNagllCcotVC6HQmt7M22cG4bdu2RbqTUj93cffu3TFx4kS5h0Pz5s1Rp04dNG7cGI0bN0alSpWQlpZWaLuRI0di2LBhso9LREREZAtqCsUzRg00uZ0tyA7GFSpUKDAYOzo6wtvbG0899RTCwsLQpUsXuYcCgGKFaiIiIiJ7orZQfOPSBUiSBkIo+6qx7GB87do1M5ZBRERERIA6Q/GUX1bivf7dAIU/BNkqs1LY0s6dO3Hy5EmkpqYiJCQEnTt3Ltb44qSkpFz/JSIiIjIXtYZiOf2wBdUH46VLl+b6fvLkyejTpw8WLVoEDxlvFDltiIiIiArDUGx7ZgnGhw8fRnh4OE6cOIHY2FgAgL+/P+rXr4+wsDA0bdrUHIcxSbVq1TBz5kx07twZFStWRHx8PPbu3YsJEyZg7dq1yMrKwvr1661eFxEREdHjGIqVQRL6aSNkiIuLw7Bhw7BlyxYAj2agMOz8v5vzunbtioULF8LPz68Ypebm6uqKtLS0PMcsTExMDJ566inExcUhIiICzZo1M6l9zqEUAQEBAACtVgt3d3eT9kNEREQll4ODA3Q6HSSNBkuPnC8RoTjsiRAInQ4ajQZZWVkm71+OpKQkw1/7i5LXZF8xTktLQ4cOHXDq1CkIIVCvXj0888wzhod4REVFYdeuXTh58iQ2b96M5557DhEREXB2dpZ7SLMICgrC8OHDMXPmTGzbts3kYMwATEREROZUEkKxvZAdjL/99lucPHkSZcqUweLFi9G5c2ej223ZsgVDhw7FyZMnMXfuXLz99tuyizWX0NBQANlXj4mIiIhsReh0DMUKIvuR0KtWrYIkSViwYEG+oRgAunTpgvnz50MIgRUrVsg9nFnFx8cD4NVfIiIisj2GYuWQHYwvXrwIV1dXdOvWrdBtu3fvDjc3N1y4cEHu4cxGCGG46a5BgwY2roaIiIhKMknSMBQriOxgnJWVBQcHhyJtK0mSYZC5NcTGxuK7775DYmJiruVarRajR4/G4cOHERgYiN69e1ulHiIiIiKj8n+IcL4Yii1H9hjjypUr49y5czh48CBatGhR4LYHDx6EVqtF7dq15R4OmzdvxowZMwzfp6enA0Cum+cmT56Mrl27IikpCW+88QYmTZqExo0bIygoCLGxsTh+/Dji4uLg7e2N8PBwlCpVSnY9RERERNbGUGxZsoNxjx49cPbsWQwfPhzbtm1D5cqVjW535coVjBgxApIkoWfPnnIPh9jYWBw+fDjP8pzL9HMo+/n5YeLEiTh06BAuXryIgwcPwsHBAZUrV8awYcPw1ltvITg4WHYtRERERNbGUGx5sucxfvDgAZ566incvHkTbm5uGDBgANq2bYvg4GCkpaXh+vXr2L17N8LDw5GZmYny5cvj9OnT8PLyMncfbMLUefGIiIiI9HLOYxx+7mah26shFNvDPMbFesDHxYsX0bt3b5w7d87wMI/HCSFQu3ZtrFu3zjBNmhowGBMREZFcpgRjNYRiAAirFQIhlB2Mi/VI6OrVq+PEiRNYsWIF1q5di+PHj+PevXsAcj8Sun///nBycirOoYiIiIhKHLWE4kunT0AI60zCUBzFumJckvGKMREREclVlCvGagrF00f0R7I2e7YwJV8xlj1dGxERERFZhtpCcfnQGpA0yo+dxRpKkZ+TJ0/i0qVLcHZ2Rt26dVGpUiVLHIaIiIhIddQYiifP+xUvNqlp8j6srcjBODExEZs2bQIAhIWFwdnZOc82Z8+exaBBg/D333/nWt6lSxcsXrwYvr6+xSyXiIiISL3UGorl9MMWinxNe/v27Rg8eDBmzZplNBTfvXsXzz77LP7++28IIXJ9bdmyBc8//zw4nJmIiIjIOIZi2ytyMN61axckScKQIUOMrv/4449x584daDQa/O9//0NMTAwePHiAH374AU5OTjh8+DBWrVpltsKJiIiI1IKhWBmKHIyPHj0KAOjcuXOedTqdDsuXL4ckSXjllVcwadIkBAQEwNPTE6+88greeecdCCGwevVq81VOREREpAIMxcpR5GAcExMDFxcXVK1aNc+6kydPIj4+HgAwatSoPOtfeeUVAMCJEyfk1klERESkPgIMxQpS5GB8584dlCpVyui6Q4cOAQC8vLxQt27dPOsrVKgAZ2dn3L17V2aZREREROojhI6hWEGKHIxdXFwQHx+P9PT0POsOHz4MAGjQoEG+7d3d3ZGZmSmjRCIiIiL1YihWjiIH4ypVqgAA9u7dm2u5EAI7duyAJElo0aKF0bbp6elISEiAj49PMUolIiIiUhdJo2EoVpAiB+P27dtDCIHJkycjOTnZsHz+/Pm4desWAKBbt25G2x4/fhxCCISGhhazXCIiIqKSjaHYcor8gI8333wTP/74I44cOYLQ0FC0adMGt27dwr59+yBJEpo0aYLGjRsbbbthwwZIkoSmTZuarXAiIiKikoah2LKKfMW4QoUKWLJkCZydnRETE4NVq1Zh7969EELAz88PCxcuNNouLS0NS5YsAQB07NjRPFUTERERlTAMxZZX5CvGANC7d2+cOXMG8+bNw5kzZwAADRs2xOjRoxEYGGi0TWRkJFq2bAknJye0a9eu+BUTERERlTAMxdYhCT6nWZakpCR4/PeG0Gq1cHd3t3FFREREZC8cHByg0+kgaTQIP3ezwG3VEor71AoGhIBGo0FWVpbJ7eUwNa8VeSgFEREREVmXWkJx+A+zADu4FstgTERERKRAagrFK2Z/AUiSyW2tjcGYiIiISGHUFooHjJ0AicGYiIiIiEyhxlAspx+2wGBMREREpBAMxbbFYExERESkAAzFtsdgTERERGRjDMXKUKRgPGLECLz99tuWroWIiIioxGEoVo4iPeBDo9EgMDAQt27dyrUsKCgI0dHRFi1QqfiADyIiIpJL/4APSBIgRIkIxWFPhEDodPb/gA+NRoPMzMw8y/nQPCIiIqJiKCGh2F4UKRj7+/vj/v37uHv3rqXrISIiIio5JImhWEEci7JRmzZtsHr1arRp0wbdunXLdUl6+vTpJh1wypQppldJREREpEJyHnrBUGw5RRpj/O+//+Lpp5/G3bt3DS+gEMKkF1O/vbXGlFgaxxgTERGRXPoxxpJGg/BzN4vczp5DsT2MMS7SFeNq1arhn3/+wYoVK3D27FmkpKRg0aJFcHV1Rb9+/YpfNREREREVyJ5Dsb0o0hVjY4zNVFGS8IoxERERyWXqFWM1hGLVXDE2ZsiQIfD29pbbnIiIiIiKQA2h2F7IDsaLFi0yYxlERERE9Di1hOIUrRZCpzO5nbXJDsY5paamYseOHThx4gRiY2MBZE/xVr9+fbRv3x6urq7mOAwRERFRiaGmUDxj1ECT29lCsYPxV199hf/9739ISEgwut7b2xsffPABHylNREREVERqC8U3Ll2AJGkghLKvGhcrGA8fPhxLliyBEAIuLi544oknULFiRQBAVFQUzp07h/j4eLz77rv4+++/sXDhQrMUTURERKRWagzFU35Ziff6dwMU/tDkIj35zpjw8HAsXrwYkiTh3XffRVRUFCIjI7Fu3TqsW7cOkZGRiIqKwvjx4yFJEpYsWYK1a9eas3YiIiIiVVFrKJbTD1uQHYznzZsHSZLw2Wef4fPPP0fZsmXzbFO2bFl88cUX+N///gchBH7++ediFUtERESkVgzFtid7HmN/f388fPgQDx48KPTmupSUFHh5eaF06dK4d++erEKVhvMYExERkVyPz2NcEkKxPcxjLPuKcWJiIjw8PIo044Sbmxs8PDyQlJQk93BEREREqlQSQrG9kB2MAwMDkZCQgKioqEK3jYqKQkJCAgICAuQejoiIiEiVGIqVQ3Ywbt++PYQQeO2115CRkZHvdpmZmXj99dchSRKeffZZuYcjIiIiUh2h0zEUK4jsYDxp0iS4uLhg27ZtaNSoEZYtW4abN29CCIHU1FRcvHgRP//8M5588kls3boVzs7OmDhxojlrJyIiIrJ7DMXKIXse49DQUKxYsQKDBw/G33//jaFDhxrdTgiBUqVKYfny5QgNDZVdKBEREalXo0aNcPv2bZsdPzAwEMeOHbP6cSVJw1CsIMV6wEfPnj1x6tQpfPzxx9iwYQMePHiQa72Xlxd69+6N999/H1WrVi1WoURERKRet2/fRnR0tK3LsD7J9CYMxZZT7EdCV61aFQsXLsTChQtx+fJlw3Rs/v7+qFKlSrELJCIiopJDo9EgKCjIaseLiYmBTqfsxxTnxFBsWcUOxjlVrVqVV4aJiIhItqCgINy8edNqxwsJCbGbK9UMxZYn++Y7IiIiIrIOhmLrYDAmIiIiUjDVhGJZz1q2LgZjIiIiIoVSSyi+dPoEhFD+WG4GYyIiIiIFUlMonj6iv8ntbIHBmIiIiEhh1BaKy4fWgKRRfuxUfoVEREREJYgaQ/Hkeb+a3N4WGIyJiIiIFEKtoVhOP2xBdjD28fGBn58frly5Ys56iIiIiEokhmLbkx2M09PTodPp+HQ7IiIiomJiKFYG2cG4cuXKSE1NtavHKBIREREpDUOxcsgOxn379kV6ejo2btxoznqIiIiISg4BhmIFkR2MJ06ciAYNGmDUqFHYvXu3GUsiIiIiKhmE0DEUK4ij3IZffPEF2rdvj++//x7t27dH3bp10axZM/j7+8PBwSHfdlOmTJF7SCIiIiLVYShWDtnBeNq0aZAkCUJkP/j65MmTOHXqVL7bCyEgSRKDMREREdF/JI2GoVhBZAfjIUOGQJIkc9ZCRERERIVgKLYc2cF40aJFZiyDiIiIiArDUGxZfPIdERERkR1gKLY8BmMiIiIihWMoto5iB+N///0Xb7zxBmrVqgUPDw84OuYenZGQkIDp06djxowZyMjIKO7hiIiIiEoUtYRi/YQNSiZ7jDEArF69GsOHD0dqaqqhs4/fkOft7Y1du3Zh7969qFWrFsLCwopzSCIiIqISQy2hOPyHWYAdBGPZV4zPnDmDF198ESkpKXj11Vexb98+lClTxui2L7/8MoQQ2LRpk+xCiYiIiEoSNYXiFbO/AOxgNjPZwfiLL75ARkYG3n33XXz33Xdo2bJlvg/2aN++PQDg6NGjcg9HREREVGKoLRQPGDvBLqb5lR2Md+/eDUmSMH78+EK3LVu2LDw8PHDjxg25hyMiIiIqEdQYiuX0wxZkB+M7d+7A09MT/v7+RdreycmJN98RERERFYCh2LZkB+PSpUsjKSmpSGE3ISEBCQkJ+Y5BJiIiIirpGIptT3YwrlOnDnQ6HQ4dOlTotsuWLYMQAk2bNpV7OCIiIiLVYihWBtnB+MUXX4QQAu+99x5SU1Pz3W7//v147733IEkShg0bJvdwiIyMxGeffYbevXsjJCQEkiQVaRD3okWL0KRJE3h4eMDX1xddunTBwYMHZddBREREZE4Mxcohex7joUOHYvHixdizZw9atGiBV155xTCsYteuXYiKisLWrVuxfv16ZGZmolu3bujWrZvsQmfMmIHffvvNpDbjxo3D7Nmz4ebmhueeew6pqanYvn07/vzzT4SHh6Nnz56y6yEiIiIqLiEEQ7GCyA7GkiTht99+w+DBg/H777/jtddeM6x79tlnATx6wknPnj2xdOnSYhXavHlz1KlTB40bN0bjxo1RqVIlpKWl5bv9X3/9hdmzZ8PPzw8REREIDQ0FAERERKBt27YYPnw42rZtC29v72LVRURERCSbEAzFClKsJ9+VLl0aGzduxPbt27F48WIcOnQIMTEx0Ol0CAgIQLNmzTBs2DB06tSp2IVOnDjRpO2//vprAMCHH35oCMVAdsB+9dVXMWfOHCxYsADvvPNOsWsjIiIikkWSGIoVpFjBWK9Dhw7o0KGDOXZlFikpKdi5cycAGH0EdVhYGObMmYNNmzYxGBMREZHNyHnoBUOx5ci++U7JLly4gLS0NPj7+yMkJCTP+gYNGgAATp8+be3SiIiIiGRjKLYss1wxBoCzZ8/ixIkTiI2NBQD4+/ujfv36qF27trkOUWTXr18HAKOhGADc3d3h7e2N+Ph4JCYmwtPTs8j7TkpKyvVfIiIiImtgKLa8YgfjVatWYdq0abh48aLR9TVr1sTUqVPRr1+/4h6qyLRaLQCgVKlS+W7j7u6OhIQEk4Oxh4w3EBERERXs7t27AICYmJh8L2xZQkxMDAAgOjraqsfV6XQmbc9QbB3FCsZvvPEGfvjhB8PsExUqVEC5cuUAALdu3cL169fxzz//YMCAAdi3bx++/fbb4ldMREREqpOVlQUgOzBGR0fbpAZbHbcwDMXWIzsYL126FN9//z0A4OWXX8akSZNQqVKlXNtERUXh888/x48//ojvv/8ezZo1w6BBg4pVcFHor+omJyfnu41+KIQpV4uBR1ejk5KSEBAQILNCIiIiyo9vQBBSkhKRotXCzcMDbu6mfVYDgBA6JMbfR1ZmJjx9fOHo5Gx0u/t3sq8YazQaBAUFFatuU+hn8dJoHArcTi2hOEWrhTDxKrktyA7Gc+fOhSRJ+OCDDzB9+nSj21SsWBHff/89/P39MWPGDMydO9cqwbhChQoAgJs3bxpdn5SUhISEBPj4+JgcjN3d3YtdHxERERknaTTo2P9Fszz0Ii05GVOWFxwmR7VpiPt3YhAUFJRvbrCEkJAQREdHw8uvTL7bqCkUzxg10OR2tiB7VoqzZ8/CwcEBEyZMKHTbd999Fw4ODjhz5ozcw5mkRo0acHFxQWxsrNE/ixw/fhwAUKdOHavUQ0REREWjhCfBKYHaQvGNSxcgScqfDE12hW5ubvD09CzSzWgeHh4oXbo0XF1d5R7OJG5ubmjXrh0AYM2aNXnWh4eHA0CxHlFNREREFmDjJ8EpgRpD8ZRfVgKmT9lsdbKDcePGjZGQkFCkgeo3b95EQkICmjRpIvdwJnv77bcBAB9//DEuXbpkWB4REYGffvoJ3t7eeOmll6xWDxERERWBDZ8EpwRqDcX28nrIDsbvvfceNBoNXn/9dcOdpMZkZWVhzJgx0Gg0eP/99+UeDps3b0azZs0MX+np6QCQa9nmzZsN2z/77LMYO3Ys4uLiUK9ePfTs2RNdunRB69atkZmZiYULF8Lb21t2PURERGR+tnoSnBIwFNue7JvvWrVqhRUrVmDUqFFo1KgR3nnnHbRu3TrXdG179+7F119/jWvXrmHlypVo2bKl7EJjY2Nx+PDhPMtzLtM/XERv1qxZqFevHubOnYvt27fD2dkZzz77LCZPnowWLVrIroWIiIiUwZ5DWE4MxcogCf0kxAVwcCh4KpEiH0ySkJmZaZZ92VpSUpJhfLVWq+VsFURERMXg4OAAnU4HSaNB+LmizQ5hjhA2vMVTeHg/DsHBwTaZlcI3IAhzNu8pEaE47IkQCJ0OGo2mwNEG5mRqXivSFeMiZOciMdd+iIiIqGQz1xXWxPj7Fqiu6ITQlYhQbC+KFIyvXr1q6TqIiIiIisScww4cHB2RmZFhgSqLJjH+fvZ8ywzFilCkYFyxYkVL10FERERUKHOPxb0bfQPxd+9YoNKiycrMLPQhJPlhKDY/5c+0TERERATL3KBm64dOePr4MhQrCIMxERERKZ5SZm0wN0cnZ5PbMBRbjuzp2vT+/vtvrF+/HmfPnkVCQgIyChinI0kSduzYUdxDEhERUQmi1lAsB0OxZckOxpmZmXjttdewYMECAEWbcULOpN1ERERUcjEUP8JQbHmyg/Gnn36K+fPnAwDat2+P9u3bo2zZsmab85iIiIhKNobiRxiKrUN2MF64cCEkScLnn3+O8ePHm7MmIiIiKuGsHYpjYmIQEhJSnJJNEhMTU+RtVROK7eBxFrKDcUxMDBwdHfHmm2+asx4iIiIq4WxxpVin0yE6OlpuyRajllB86fQJCKEzuZ21yQ7GwcHBiI2NhbOz6XdTEhEREeXHmqHYp4y/0eUpSYlI0Wrh5uEBN3dPk2sQQofE+PvIysyEp49vvrNP5Hd8QF2hePqI/ia3swXZwbhPnz6YOXMmjh49isaNG5uzJiIiIiqhhE5n1SvFX6zdlmeZucJkWnJyiX94R85+XDx1HEKn7KvGsucxfu+991CzZk2MGDEC169fN2dNREREVIKV9Mcjq7Uf9kD2FWNvb2/s3r0br7zyCqpXr45+/frhySefRGBgYIHthgwZIveQREREpHKSpGGYZD9splgP+Lhw4QJu3LiB9PR0LF++vEhtGIyJiIgoXzIeecAw+Yha+mErsoPxwYMH0aFDB6Snp0OSJISGhnIeYyIiIrIqhslH1NIPW5IdjCdPnoy0tDS0bt0ay5cvR3BwsDnrIiIiIioQw+QjaumHrcm++e7YsWOQJImhmIiIiKyOYfIRtfRDCWQHY2dnZ5QuXZqhmIiIiKyKYfIRtfRDKWQH46ZNmyIxMRH37983Zz1ERERE+WKYfEQt/VAS2cH4ww8/hIODAyZPnmzOeoiIiIiMYph8RC39UBrZwbhZs2ZYs2YNfv31V3Tq1Ak7duzAnTt3zFkbEREREQCGyZzU0g8lkj0rRc5p2bZv347t27cX2kaSJGRmZso9JBEREZVADJOPqKUfSiU7GAshrNKGiIiISi6GyUfU0g8lkx2Mr169as46iIiIiHJhmHxELf1QOtnBuGLFiuasg4iIiMiAYfIRtfTDHkYOyL75joiIiMgSGCYfUVM/wGBMREREZAIBhsn/qK0fkCST21qb7KEUI0aMMLmNJElYsGCB3EMSERGRygmhY5iEOvux8tuZih9OIQmZFWo0GkiSVGAHpRy/GQghIEkSsrKy5BxOcZKSkuDx3xtEq9XC3d3dxhURERHZLwcHB+h0OgDAsmMXGSZV2I+wJ0IgdDpoNBqr5UFT85rsK8ZTp04tcP2DBw9w7Ngx7N+/H76+vhg9ejQcHWUfjoiIiEoASaNhmGQ/bMZiwVjv4MGD6NmzJyIjI/H777/LPRwRERGRUQyT2dTSD1uy+M13LVq0wE8//YRt27bhm2++sfThiIiIqARhmMymln7YmlVmpejevTtcXFywcOFCaxyOiIiISgCGyWxq6YcSWCUYOzg4wMHBAVeuXLHG4YiIiEjlGCazqaUfSmGVYHzo0CEkJycb7gokIiIikothMpta+qEkFg3GWVlZ+O233zBgwABIkoT27dtb8nBERESkcgyT2dTSD6WRPStFlSpVClyfmpqK2NhY6HQ6CCHg5+eHGTNmyD0cERERlXAMk9nU0g8lkh2Mr127VqTtXFxc0K1bN3z66aeoWrWq3MMRERFRCcYwmU0t/VAq2cF4165dBe/Y0RHe3t6oXr06nJyc5B6GiIiISjiGyWxq6YeSyQ7Gbdq0MWcdRERERHkwTGZTSz+UziqzUhARERGZimEym1r6YQ8YjImIiEhxGCazqakfQqczuZ21FXkoxZIlS8xywCFDhphlP0RERKRODJPZ1NYPeyAJIURRNtRoNJAkqdgHzMrKKvY+lCApKcnwwBKtVgt3d3cbV0RERGS/HBwcoNPpAEkChGCYVGE/UpKSIIQOGo3GannQ1LxW5GBcrVo1WcFYp9Ph6tWr2QeTJAZjIiIiysMQjAGGSQv245k+neB6L7ZI+0hOSkSKVgs3Dw+Ucvc0uQYhdHgYfx9ZmZko7eOLB/fjAAC3ATQqWvwsNlPzWpGHUvz7778mFSKEwOrVq/HRRx9BkiQIIeDj42PSPoiIiKiEkSTFhUlTKDkUA4DrvVi43Ykp0n7c9P+j1WZ/yVBK/z/342B6tLY+2dO1FWTdunX46KOPcObMGQgh4OXlhXHjxuGtt96yxOGIiIhIJeT8dZqh+JGi9kNoNEj1DzC6ztxXih2dnAEAzndi4GDy3qzLrMF448aNmDZtGk6dOgUhBDw9PfHmm2/inXfegbe3tzkPRURERMRQnIMp/Uj1D8DWPZF5lputH8nJmLI8dz+a1CyHEJP3aF1mCcZbt27F1KlTERkZCSEE3N3d8cYbb+Ddd9+Fr6+vOQ5BRERElAtD8SNq6YetFSsYb9++HVOnTsXhw4chhICbmxtee+01TJw4EWXKlDFXjURERES5MEw+opZ+KIGsYLxr1y5MnToVBw4cgBACrq6uePnll/Hee+8hIMD4eBUiIiIic2CYfEQt/VAKk4Lxvn37MHXqVOzZswdCCDg7O2PkyJF4//33Ua5cOUvVSERERASAYTIntfRDSYocjDt06ICdO3dmN3J0xIgRI/Dhhx8iODjYYsURERER6TFMPqKWfihNkYPxjh07DFOotGnTBllZWfjoo49MOpgkSfjpp59Mq5CIiIhKPIbJR8zRj+SkRJv3Q4lMGkqhf0jejh07cn1fGP0DPhiMiYiIyFRKCZNqCcX6WhiK8ypyMB46dKgl6yAiIiLKQylhUi2hODkpEW4A3Dw8GIqNKHIwXrhwoSXrICIiIspFKWFSLaE4/IdZaPLfo53lPNFO7aEYADS2LoCIiIjocUoKk2oJxStmf1GsoRdqD8UAgzEREREpjNLCpFpC8YCxE2x2pfjS6RMmt7EFBmMiIiJSFKWFSbWEYlv3wx4wGBMREZFiCJ2OYRLq7Ic9YDAmIiIiRWGYVGc/7AGDMRERESmGJGkYJtkPm2EwJiIiIuWQTG/CMPmIWvphKwzGREREZLcYJh9RSz9sicGYiIiI7BLD5CNq6YetMRgTERGR3WGYfEQt/VACBmMiIiKyKwyTj6ilH0rBYExERER2g2HyEbX0Q0kYjImIiMguMEw+Utx+CKFTRD+UxtHWBRAREREVRglhUi2hGAAext/HjeRkhuLHMBgTERGRoikhTKolFAuhAwBkZWZiynKG4sdxKAUREREplhLCpFpCcYpWi4fx9wEApX18GYqNYDAmIiIiRVJKmFRLKJ4xaiCyMjMBAI5OzibvQ+2hGGAwJiIiIgVSUphUSyi+cekCSvv4mtweKBmhGFB5MG7bti0kScr3a9u2bbYukYiIiB6jtDCpllA85ZeVNrtSHP7DLJPb2EKJuPmuT58+8DDyIgYHB9ugGiIiIsqXgOLCpFpCsa378YXJLa2vRATjmTNnolKlSrYug4iIiAohhI5hEursB2YrPxqreigFERER2R+GSfbDVhiMiYiISDEkjYZhkv2wmRIxlGLBggWIi4uDRqNB9erV0bNnT1SoUEHWvpKSknL9l4iIiGyLYTKbWvphSyUiGH/88ce5vh8/fjwmT56MyZMnm7wvYzfxERERkW0wTGZTSz9sTdVDKVq3bo2lS5fi8uXLSE5OxoULF/DJJ5/A0dERU6ZMwezZs21dIhEREcnEMJlNLf1QAkkIIWxdhLX9+eef6NixI7y9vXHr1i24ubkVuW3OoRQBAQEAAK1WC3d3d4vUSkREVBI4ODhAp9NB0mgQfu5modszTGYztR+d2zSE250YpAQEYeueSKv2o0nNcggBcBNAiJXiZ1JSkuGv/UXJa6q+Ypyf5557Do0aNUJCQgIOHz5sUlt3d3fDFxEREVkfQ3E2tfRDSUpkMAaA0NBQAEBMTIyNKyEiIqKiYpjMppZ+KE2JDcbx8fEAwCu/REREdoJhMps5+pGZkW7zfihRiQzGsbGx2LdvHwCgQYMGNq6GiIiICqOEMKmWUAwAD+PvMxQbodpgfPDgQWzYsAFZWVm5ll+7dg29evVCUlISunfvjpCQEBtVSEREREWhhDCpllCcmZEOAHBwdGQoNkK18xhfvHgRw4cPR2BgIBo0aABvb29ERUUhMjISqampqF27NubNm2frMomIiKgASgiTagnFl06fQNP4+/AEUNrHl6HYCNUG46ZNm2L06NE4fPgwjh49ivj4eLi7u6NevXro27cvRo8ebdI0bURERGRdSgmTagnF00f0x1BHRyAjA5Jk+qABtYdiQMXBuFatWvj+++9tXQYRERHJoKQwqZZQXD60BkpH3wDu3jF5H+bohz1Q7RhjIiIisk9KC5NqCcWT5/1qkyvF+n7YAwZjIiIiUgwhhOLCpFpCsa37YQ8YjImIiEg5hGCYVGk/7AGDMRERESmHJDFMsh82w2BMREREiiFJksltGCYfUUs/bIXBmIiIiOwWw+QjaumHLTEYExERkV1imHxELf2wNQZjIiIisjsMk4+opR9KoNoHfBARERWXu7s7UlNTbXZ8V1dXJCUl2ez4SqWGMPlMn05wib2Dh/H30TYzE6V9fOE4ZqTJNWRmpKNp/H0MdXRE6egbkLq2KXB719i8D/dgKH6EwZiIiCgfqamp0Ol0Nj0+5aaGUAwALrF3UOruHZTSL7gfZ/I+9DwBICPDZk+0U0soBhiMiYiIikSSNIDpEyYAAhAiO1xLmqKNYBQ2DONKppZQnKLV4mH8fZQCoJM0SCsbYPI+MjPS8TD+PhwcHVHax9fkJ9qllvFnKDaCwZiIiKgQkqRB+D83TW4nN4SFPRHCcPwYNYXiGaMGom1mJgAgrWwAtu6JNGkfhn7Ua2jzfqgpFAO8+Y6IiKhwMq4UmyOEUTa1heIbly6gtI+vye0B5fVDTaEYYDAmIiIyO4Zi81FjKJ7yy0o4OjmbvA8l9kNNoRhgMCYiIjIrc4QXIYQFKrM/ag3FJbkfSsdgTEREZCbmCi9gMGaYzEEt/bAHDMZERERmYM7wAknO9BfqwTD5iJr6YQ8YjImIiIrJ3OFFKuHBmGEym9r6YQ8YjImIiIpBCeFFTYROxzAJdfbDHjAYExERyaSE8KJGDJPq7Ic9YDAmIiKSQQnhRY0kScMwyX7YDIMxERGRiZQQXlTLRg9TUUuYVEs/bIXBmIiIyARKCC/0iBJeD6WESbX0w5YYjImIiIpICeGFHlHC66GUMKmWftgagzEREVERKCG80CNKeD3MESYzM9JV0Q81hGKAwZiIiKhwAjYPL/SIWkIxADyMv2/3/VBLKAYAR1sXQEREpHRC6EpEKG7UqBFu375tk2PrdLoibaeWUJyZkQ4AcHB0tOt+qCkUAwzGRERERaL2UAwAt2/fRnR0tK3LyJdaQvGl0yfQNP4+PAGU9vG1636oKRQDDMZERESFkjQa1YfinDQaDYKCgqx6zJiYGOh0Omg0DkbXqykUTx/RH0MdHYGMDEiS6aNaldQPNYVigMGYiIjIIuw1FANAUFAQbt68adVjhoSEIDo6Gl5+ZfKsU1soLh9aA6WjbwB375i8D6X1Q02hGODNd0RERGZnz6FYadQYiifP+5VXihWKwZiIiMiMzBFeRBFvRFM7tYbiktwPpWMwJiIiMhNzhRdimMxJLf2wBwzGREREZmDO8CLnz+xqwjD5iJr6YQ9K9k8eERGRGZg7vEAyf432gk+Ce0Rt/bAHDMZERETFoITwoiaJfBIcAHX2wx4wGBMREcmkhPCiNnwSHPthSwzGREREMighvKiRZwl/Ehz7YVsMxkRERCZSQnhRq5I8vy/7YXsMxkRERCZQQnihR5TweighTKqlH7bGYExERFRESggv9IgSXg8lhEm19EMJGIyJiIiKQAnhhR5RwuthjjCZnJSoin6oIRQDDMZERESFEkLYPLzQI2oJxfpa7L0fagnFAIMxERFR4YRgKFYItYTi5KREAICbh4dd90NNoRhgMCYiIiqcJDEUK4BaQnH4D7OQotUCAEq5e5rcXkn9UFMoBgBHWxdARESkdJJk+jOa7TEU3717FwAQExODkJAQqx47JiamwPWWCsXP9OkE13uxRdpHZkY6msbfx1BHR5SOvgGpaxuT60hOSkQTrRblJAkQwuT2DMWWxWBMRERkZvYYigEgKysLAKDT6RAdHW3jah6x5JVi13uxcLtTcCjPyRMAMjKAu3dMrgMA3PT/w1CsSAzGREREZmSWUGx6ZjI7n7IBSIy/j6zMTHj6+MLRydnkfWRmpCMx/j4cHB3h6eNbpId3+JTxz/W9tYZPCI0Gqf4B+fbj4X/9KF3EfjwuOSkRKVot3Dw8cg2fSH2sv8XtR0EYigvHYExERGQm5govQugsUF3RSRoNygaXR1pyMqYsL14Iq16voV3M75vqH4CteyLzLDeESTvpR34YiouGN98RERGZgTnDi60Jnc7uQ5hawqSa+mEPGIyJiIiKydzhRdLY/uPZ3kOYWsKkmvphD2z/k0dERGTHLBFebE2SNHYfwtQSJtXUD3vAYExERCSTUsKL2Zk+O53iQphawqSa+mEPGIyJiIhkUEp4UQIlhjC1hMmS3A9bYDAmIiIykVLCixKoJYRlZqSroh9qeT1shcGYiIjIBEoJL0qgphD2MP6+3fdDTa+HrTAYExERFZFSwosSqCWEZWakAwAcHB3tuh9qeT1sjcGYiIioCJQSXpRALSHs0ukTeBh/HwBQ2sfXrvuhhtdDCRiMiYiIikAJ4UUJ1BLC9P1wcMx+CLCcxzwrqR/2/nooBYMxERFRIZTwJDglUEsIy9mP0j6+JrcHlNcPe349lITBmIiIqAgYitURwh7vB68UMxTnxGBMRERUCFs+CU4J1BLC2I9HlNAPJWIwJiIiKoyNngSnBGoJYezHI0roh1JJQghh6yLsUVJSEjz+ezNqtVq4u7ub1N7d3R2pqamWKK1IXF1dkZSUZLPjExGZwlb/Zup0OgCApNEg/NzNIrcrbngJeyIEQqeDRqNBVlaWSW2L45gkIfC///cLCEJmRjoext+Hg6MjSvv4yhp2kJyUiBStFm4eHijl7mlyeyF0eBh/H1mZmSjt4wtHJ2eT91FQP1xj70DS6ZASEISteyLz3YcSwqS9h+ImNcshBMBNACFWip+m5jVHaxRFeaWmphr+wbXV8YmI7IWt/800hTnCi62uWQUCCNF/cycGAOAJABkZwN07svbppv8frTb7S4ZS+v+5HyerPVC8fjAUZ1PzlWI9BmMFkDTGfwMX+isVkkbWn/Egsn/TznkMYScfLERE+cn330whACEASYIkyflHM/9/d52dXYrU3lzhBTb+Y24WgNuSpOorxY9LLeNvdLkSwiRDsfUwGNuYsT/PWfKHSP/nOSIie5TfkAYlfOibM7xAkmwajmMAPFuvoe1DWHIypiwv5uuhhH4o5H1l6340MbmV9fHmO4VRyg8REZG9UMqHvjnDi9wr3uZk7yFMLWFSTf2wBwzGCqKUHyIiInuhpA99W4YXS7D3EKaWMKmmftgDBmOFUMoPERGRvVDah76aQrEcfD2ysR+PPN4Pe8BgrABK+SEiIrIXSvzQZyjm68F+PGKvuYTBWAFK6puPiEgOtXzoMxRnU8vrwX48Ys+5hMHYxoROV2LffEREphJCqOJDn6E4m1peD/bjEXvPJQzGClBS33xERCYTwu4/9BmKs6nl9WA/HlFDLmEwtjFJ0pTYNx8Rkckkya4/9BmKs6nl9WA/HlFLLlF9ME5JScGUKVNQvXp1uLq6oly5chgxYgSio6NtXVo2GVNVquXNR0RkKjnz+yrlQ5+hOJtaXg/24xE15RJVB+PU1FS0a9cOM2bMgFarRY8ePVC+fHksXLgQ9evXx5UrV2xdosnU9OYjIrI0pXzoMxRnU8vrwX48orZcoupg/PHHH+PQoUNo3rw5Ll68iFWrVuHw4cP46quvEBsbixEjRti6RJOY480nbPiIUSIia1LKhz5DcTa1vB7sxyNqC8WAioNxeno65s6dCwD47rvv4JHjxXr77bdRp04d7NmzB5GRkbYq0STm+iECgzERlQBK+dBnKM6mlteD/XhEjaEYUHEwPnDgAB48eICqVauifv28L3hYWBgAYNOmTdYuzWTm/CGCjPF5RET2RCkf+gzF2dTyerAfj6g1FAMqDsanTp0CADRo0MDoev3y06dPW60mOcz9QyTnxhUiInuhlA99huJsank92I9H1ByKAcDR1gVYyvXr1wEAISEhRtfrl0dFRZm036SkJACAVqvNs8wU+rG+QgikJicb3ebymVP4dPRQhFQNxbtz5kPSaPLdNj8b5s/Fmu9noe9r4/D80JexYs6XhuPKqZuIyBaK8m9mSpIWn78+HDcvX8J7PyxG+Wo1TP430xL/7praPiVJC6HTAbD+v9XJAJL+++/KOV8Uux9qeT3Yj2zF7UfO95e13tc5j1Ok+6yESo0aNUoAEB988IHR9ZcuXRIARGhoqEn7BcAvfvGLX/ziF7/4xS87+7pz506hOU+1QymIiIiIiPSKcpVatUMp9LNQJOdziV9/cjw9PU3ar34IRWJiIoKCggAAt2/fzjXrRWGSkpIQEBAAALhz5w7c3d1V3daWx2ZbvsZsa562tjw22yq/rS2PzbZ8jQui1WoRGBgIAChTpkyh26s2GFeoUAEAcPPmTaPr9csrVqxo0n6NvRgeHh4mv7ly7q8ktbXlsdnWOm1teWy2tU5bWx6bbZXf1pbHZlvrtLXlsYvTVqMpfKCEaodS1K1bFwBw/Phxo+v1y+vUqWO1moiIiIhIuaT/bihTnfT0dJQtWxYPHjzAiRMnUK9evVzr69ati9OnT+PYsWNo2LChbYokIiIiIsVQ7RVjZ2dnvPHGGwCA119/PdeA66+//hqnT59GmzZtGIqJiIiICICKrxgDQGpqKtq2bYvDhw8jKCgIrVq1QlRUFA4fPgx/f38cOnQIVapUsXWZRERERKQAqg7GAJCSkoJPP/0Uv/76K27cuAFfX1906tQJM2bMyPfhH0RERERU8qg+GBMRERERFYVqxxgTEREREZmCwZiIiIiICAzGREREREQAGIyJiIiIiAAwGBMRERERAWAwLtCRI0cgSRIkScL06dPz3W7atGmG7Yx9TZo0yYpVW19BfZckCW3bts3TJiUlBVOmTEH16tXh6uqKcuXKYcSIEYiOjrZ+B6zM1PPVtm3bArfftm2bbTpiBcnJyZgzZw6ee+45BAUFwcXFBZ6ennjiiScwbNgwbNy4EVlZWXnaZWVl4ZtvvsFTTz0FNzc3+Pv7o1+/fvjnn39s0AvrkXO+hg0bVuD768cff7RRbyxL3z9jMjMz8cILL0CSJFSrVg03btzItf7s2bPo27cv/P394ebmhqeeegqzZs2CTqezRulWJ+dcXbt2rcD3VWBgoEVrlSQJERER+W63evVqw3aVKlUyuk18fDzGjh2LihUrwsXFBRUrVsS4ceOQkJCgyLozMzMxbdo0dO3aFVWqVIGnpydcXV0RGhqK1157DVFRUWavWy5z9HfRokWFfp5KkoQlS5aYVJujnA6VFEuXLjX8//LlyzFlypQCt2/ZsiWqVauWZ3lJebre0KFDjS6vWbNmru9TU1PRrl07HDp0CEFBQejRoweuXbuGhQsX4vfffy8xD14p6vnS69OnDzw8PPIsDw4ONmtdSnHgwAH07dsXMTExcHV1RePGjVGuXDmkpaXh8uXLWLx4MRYvXownnngCZ8+eNbTT6XTo27cv1q9fD29vb3Tt2hX37t1DeHg4Nm/ejF27dqFJkyY27JllyD1feh07djQaVmrUqGGN8hUjMzMTAwYMQHh4OEJDQ7Fr165cP2MRERFo3749UlJS0KRJE1SqVAl79+7FW2+9hYMHD2LVqlX5hki1KexcAUBAQAA6deqUp62Xl5fF61u+fDmaN29udN2yZcsKbHvv3j00b94c//77L6pUqYKePXvi7NmzmD17NrZu3YqIiAj4+vpaomzZdaempuKjjz6Ch4cH6tSpg4YNGyI9PR0nT57EDz/8gOXLl2PHjh1o1KiRReqWS25/q1Wrlu/n6IMHD7BhwwYAwNNPP21aQYKMSk9PF2XKlBEARGBgoAAgDh06ZHTbqVOnCgBi4cKF1i1SIQAIU95KH3zwgQAgmjdvLhITEw3Lv/rqKwFAtGnTxgJVKoep56tNmzYCgLh69arlilKYyMhI4eLiIgCId999Vzx48CDPNtevXxfjxo0Trq6uuZbPmzdPABChoaHi9u3bhuXh4eECgKhWrZrIyMiweB+sqTjna+jQoQKA2LVrl5WqVQZjP4fp6emid+/eAoCoUaOGiI6OzrO+cuXKAoD4+uuvDcsTExNF8+bNVfs5IOdcXb161Sb/ngMQDg4O4qmnnhJlypQx+rN+79494eTkJBo0aCAAiIoVK+bZZtCgQQKA6N27d659jBkzRgAQQ4cOVVzdGRkZYv/+/XnaZmZmikmTJgkAomHDhmatWy5zvU75+f777wUA0bJlS9NrM7lFCbFx40bDSZ0+fboAIF5//XWj2zIYFz3opaWlCS8vLwFAHD9+PM/6OnXqCADi2LFj5i5TMRiMC5aVlSVq1aolAIgZM2YUuv3j7xV92/Xr1+fZtnv37gKACA8PN1e5Nlfc88VgnC09PV307NlTABC1atUSMTExedqsWrVKABB169bNsy4yMlIAEE8++aQly7YJOefK1sH4s88+EwDE77//nmeb7777zvDLjbHAdevWLaHRaISzs3OuX66FECI1NVX4+/sLBwcHcefOHUXVXZCMjAzh6uoqAIiEhASz1S2XpfvbokULAUD8+OOPJtfGMcb50F++Hzx4MAYPHgwAWLVqFTIyMmxZlt07cOAAHjx4gKpVq6J+/fp51oeFhQEANm3aZO3SSCG2bNmCf/75BxUqVMB7771X6PY5hypdvXoV//zzD9zc3NC1a9c826rx/VWc80XZ0tPT0bdvX2zYsAG1a9fG7t27jQ4r2bx5M4BH76OcGjRogCpVquDMmTO4du2apUu2maKeK1sbOHAgJEky+qf4ZcuWwcPDAz169DDadtu2bdDpdGjVqhUCAgJyrXNxcUG3bt2QlZWFLVu2KKrugkiSBAcHB0iSBGdnZ3OUahaW6O/Vq1dx8OBBODs7o1+/fibXxDHGRjx48AAbN240nFRfX1+0aNECBw8exLZt29CtWzej7Xbu3ImTJ08iNTUVISEh6Ny5Mz+EHnPq1CkA2R8ixuiXnz592mo12YsFCxYgLi4OGo0G1atXR8+ePVGhQgVbl2V2W7duBQD07dsXDg4OJrXVv7+efPJJODk55VmvxvdXcc5XTuvWrcPatWuRlZWFypUro1u3bvmOd1eT9PR0hIWFYdOmTahTpw7++usv+Pv7G922KP9+XblyBadPn873hi57Zsq50rtz5w6mTp2KmJgYeHl5oWnTpujevbvFw1n58uXRunVrbNy4EVqt1nB/xpUrVxAREYEXX3wRpUqVMtq2KK/zL7/8YpF/R4pTd36EEPj888+RlJSEdu3awc3Nzex1y2WJ/upDdteuXeHj42NyTQzGRoSHhyM1NRU9evQwDK4fPHgwDh48iKVLl+YbjHPerAcAkydPRp8+fbBo0SKjN02VRNevXwcAhISEGF2vX66ku2eV4uOPP871/fjx4zF58mRMnjzZRhVZhv5DydhfFApTEt9fxTlfOX377be5vp84cSJGjx6N2bNnw9FRvR8VvXv3xubNm1GvXj389ddf8PPzy3fbkvj+ysmUc6V3/vz5PLM6VahQAWvWrLH4TbCDBw/Gnj17sG7dOgwZMgRA9o1e+nX5sfXrLLfunCZOnIg7d+7g4cOHOH36NC5fvoxatWph/vz5Fqm5OMzR35z0wfjFF1+UVQ+HUhihD7g5X5B+/frByckJmzZtwoMHD3JtX61aNcycORNnz56FVqvFjRs3sHz5cgQHB2Pt2rWyXxx7k99UKTn/rKjVagEg398A3d3dAQCJiYkWr9fWinK+AKB169ZYunQpLl++jOTkZFy4cAGffPIJHB0dMWXKFMyePds2HbCQuLg4AECZMmWMrn/ppZcwbNiwXF/79+8HUDLfX8U5X0B2oP7xxx9x8eJFJCcn48qVK/juu+/g7e2N77//Hu+++65V+mErmzdvhiRJWLp0aaFBryS+v3Iy5Vy5uLhg9OjR2L17tyGgRUREoEuXLrh+/To6duxo8V8gwsLC4OLiYghZQHbgCgoKQvv27fNtZ+vXWW7dOa1duxaLFy/G+vXrcfnyZdSpUwerVq1C5cqVLVJzcZijv3pHjhzBxYsX4evra3Q4XZGYPCpZ5aKiooQkScLb21ukpqbmWtejRw8BQMybN69I+7p165bw8/MTAERERIQlylUE/HdjxtChQ41+xcbGGrYdNWqUACA++OADo/u6dOmSYUYBtTLlfBXkjz/+EACEt7e3SE5OtnDV1lOzZk0BQGzbts3oegcHB8M51H/pb3z95JNPBAAxaNAgo20zMjIEAOHk5GSp8q2uOOerIGfOnBHOzs7C0dFRXL9+3cxV257+XLRs2VIAEE888UShP3tOTk4CgLh06ZLR9foZdz755BNLlGwzcs5VQQYOHCgAiJdfftmMVWbDfzd16fXu3Vs4ODiImJgYceTIEQFAvPXWW0IIIWJiYoze1NWhQ4cCP+u3b98uAIgOHTooqm5jYmNjxbZt20TDhg2Fk5OTWLRokdlqLg5L9Vc/a8irr74quzZeMX7M8uXLIYQw/AaTk/4KcmHzH+oFBQVh+PDhAKDqhzDoLVq0yOhXzitZ+iElycnJRveRlJQEAPD09LR8wTZWlPNVkOeeew6NGjVCQkICDh8+bOFqrUd/JerevXtG12dmZkJkz6iDV155Jde6kvj+Ks75Kkjt2rXRvXt3ZGZmYseOHWapVYk2b96MBg0a4Ny5c+jUqRMePnyY77Yl8f2VkynnqiDvv/8+AOCPP/4wZ3lGDR48GFlZWVi5cmWum+oLooTXWU7dxpQpUwYdO3bEjh07EBgYiNGjR+d5YI0SmKO/mZmZWLVqFQD5wygADqXIQz+MYvfu3Xj66adzfX3xxRcAgL179xb5T0ChoaEAgJiYGMsUbGf0N4vdvHnT6Hr98ooVK1qtJnumxvdX3bp1AQAnTpwwuW1JfH8V53wVRo3vr8d5eXnhjz/+wBNPPIHIyEh07do130BUEt9fOZlyrgpizfdVly5d4O3tjSVLlmDVqlWoVatWvjfV6SnhdZZTd0G8vLzQrVs3pKSkYPv27Was1DzM0d8///wTd+/eRZUqVdCiRQvZtTAY5xAZGWl4ZOy///6LAwcO5Po6evQogOw7PHOOhSlIfHw8gEdjkko6/Yf48ePHja7XL69Tp47VarJnanx/de7cGQCwZs0ao497Loj+/XXmzBmjUyuq8f1VnPNVGDW+v4wpU6YM/vrrL1StWhX79+9Hr169kJ6enmc7/vtV9HNVEGu+r1xcXNC3b1+cOHECd+7cKdJVSCW8znLqLoz+r5GxsbHF3pe5maO/xbmynhODcQ76kzp+/HjDnx4f/9q9e3eubQsihMD69esB5D/tS0nTsmVLeHl54fLlyzh58mSe9eHh4QCQ78wf9EhsbCz27dsHQF3vry5duqBWrVq4fv06Pv30U5PaVq5cGbVq1UJKSophztmc1Pj+Ks75KkhaWprhHKrp/ZWfoKAg/PXXXwgJCcGff/6J/v375/lFQ38zj/59lNOJEydw5coVPPnkk6qcqi2nopyrgqxduxaA9d5XL774Ivz8/FCmTBkMGjSo0O07deoEjUaDffv24e7du7nWpaWlYdOmTXBwcECXLl0sVTIA0+suzJ49ewAAVatWLfa+LKE4/dVqtfjtt98AFD8Y8+a7/2RmZoqAgAABQERGRua7XVZWlggODjY8ne3u3bti7ty54uHDh7m2S0xMFK+88orAf4+UTkpKsnQXbAYyHwndokULodVqDcv5SOi8Dhw4INavXy8yMzNzLb969arhRpju3btbokybOnbsWK5HHBt7UtO9e/dE27Zt89xMlvOR0DmfTLV27VoBlT4SWu75+ueff8SSJUvy3Gh89+5dw5PN6tatK3Q6nTW6YVX5/RyeP39elC1bVgAQgwcPztX3/B4JrdVqS9wjoYUo+Fz9/PPP4p9//snTZu3atcLT01MAEOvWrbNIrTlv6ipIQTd16R8J3adPn1z/Xrz55puGm6fNyRx1//777+LAgQN5tk9KShLvv/++IY8kJiaao+RiMdfrpLd48WIBQDRr1qz4tRV7DyqxZcsWAUBUr1690G3ffvttAUCMHTvW8NhLDw8P8cwzz4iBAweKDh06GGaj8Pb2Fvv377dCD2zH1GCckpIimjZtKgCIoKAg0a9fP8P3/v7+4vLlyxas1vZMOV8LFy40/GPWpUsXMXDgQNGyZUvDoz1r165t1seSKsm+fftEYGCgACBcXFxE69atRf/+/UXPnj1Fo0aNDDME1KxZU/z999+GdllZWaJXr14CgPDx8RFhYWGibdu2QpIk4ebmJg4dOmTDXlmOnPO1a9cuw3nq0KGDGDhwoGjbtq0huISEhIgLFy7YuGeWUdDP4cmTJ4WPj48AIEaPHp1r3YEDB4Sbm5sAIJo2bSr69esngoKCBAARFhZWon6JECL/c6V/lH2dOnVEWFiY6N27t2EGFf0vcJaq1RyBKzY2VlStWlUAEFWrVhUvvPCCePLJJw2/dMfFxSmu7qlTpwoAIjg4WHTt2lUMHDhQtGvXTvj6+goAwsvLS+zdu9esdctl7mCsn0nku+++K35txd6DSgwYMEAAEFOnTi1026NHjwoAomzZsuLhw4di4sSJok2bNiI4OFi4uLiIUqVKidq1a4t33nlH3Lx50/LF25ipwVgIIZKTk8XkyZNF1apVhbOzswgMDBTDhg0TN27csFCVymHK+Tp37pwYPXq0aNCggfD39xeOjo7Cy8tLNGvWTHz11VeqmqbNmKSkJDF79mzRvn17ERAQIJycnISHh4eoUaOGGDRokFi/fr3Rq7+ZmZniq6++ErVr1xaurq7Cz89PhIWFibNnz9qgF9Zj6vmKjo4W48aNE82aNROBgYGG7Rs0aCCmTp0q7t+/b8PeWFZhP4eHDh0SHh4eRkPcmTNnRJ8+fYSfn59wdXUVtWvXFl9//bXIysqydNk2IedcLVu2TISFhYlq1aqJ0qVLCycnJ1GuXDnRu3dvsX37dovWaq7AFRcXJ8aMGSPKly8vnJ2dRfny5cWbb74p4uPjzVfwf8xR96lTp8Tbb78tGjduLMqWLSscHR2Fp6enqF+/vnjvvffErVu3zF63XOZ8nW7duiUcHByEk5OTuHfvXrFrk/4rkIiIiIioROPNd0REREREYDAmIiIiIgLAYExEREREBIDBmIiIiIgIAIMxEREREREABmMiIiIiIgAMxkREREREABiMiYiIiIgAMBgTEZEZtG3bFpIkYdGiRbYuhYhINgZjIlI1rVaLr776Cq1atYKfnx+cnZ0REBCAevXqYdCgQViwYAFu3rxp6zJlO3nyJKZNm2Z3gbRSpUqQJMnkr7Zt29q6dCJSMUdbF0BEZCkXLlxAx44dERUVZVjm6emJ5ORknDp1CqdOncKvv/6Kl156CfPnz7dhpfKdPHkSH330Edq0aYNhw4bZupwi8/f3R2pqap7l9+/fR0ZGBlxdXeHl5ZVnva+vrzXKI6ISisGYiFQpIyMDPXv2RFRUFIKCgjB9+nT07dvXELZu376NXbt2YcWKFXBwcLBxtSXP0aNHjS5v27Yt9uzZgxdeeMHuroITkf1jMCYiVfrrr79w/vx5AMCmTZvQsGHDXOsDAwMxYMAADBgwwOiVSyIiKnk4xpiIVOnMmTMAgICAgDyh+HGurq55lunHwO7evRtRUVEYMWIEgoOD4erqiurVq2PatGmFBurdu3ejb9++CA4OhrOzM8qUKYPOnTtj06ZNBbZ7+PAh/ve//6Fp06bw8fGBm5sbQkND0a9fP6xfvz5XjcOHDwcA7NmzJ8943N27dxu21S+7du0azpw5g0GDBiE4OBiOjo4YN26cYbs9e/ZgzJgxaNy4MQIDA+Hi4oJy5cohLCwM+/fvL7BuS0lJSYGXlxckScJff/2V73YJCQlwc3ODJEk4fPiwYXnOGwPj4uLw5ptvolKlSnB1dUXFihUxbtw4JCQkFFjDyZMnMXToUFSsWBEuLi7w8fFB27ZtsWTJEuh0OnN1lYhsTRARqdAXX3whAAgnJyeRkpJicvuKFSsKAOLnn38Wfn5+AoDw9PQULi4uAoAAIJo0aSISExPztNXpdOLtt982bAdAlC5dOtf3Y8aMMXrckydPipCQEMN2Tk5OwtfXVzg4OBiW6TVq1MiwXycnJxEQEJDr68CBA4Zt9W0XLVok3NzcDDU5OzuLsWPHCiGESExMzFOzu7u74XtJksSPP/5otO42bdoIAGLhwoUmn2tj+xk6dGiu5a+88ooAIAYNGpRv2x9++EEAELVq1TK6z5kzZ4oqVaoIAKJUqVKiVKlShr5VrVpV3Lp1y+h+v/nmG6HRaAzbenp65vq+V69eIjMzs1j9JiJlYDAmIlXasWOHIbiMGDFCaLVak9rrg7GXl5eoUaOGiIiIEEIIkZGRIZYvXy48PDwEAPHyyy/nafvVV18JACIkJEQsWbJEPHz4UAiRHTx//vlnQ5hdsmRJrnZ3794V5cqVEwBEzZo1xdatW0VGRoYQQoiUlBSxfft20adPn1xtFi5cKACINm3aFNgf/bnw8PAQ7dq1E//8848QQojMzExx9epVIYQQSUlJol+/fuL3338XCQkJhrbR0dFiwoQJAoBwdnYWly9fzrN/SwfjI0eOGALtgwcPjLZt2rSpACA+//xzo/v08vISgYGBYuvWrUKn0wmdTie2bt0qAgMDBQDRsWPHPPtcu3atACB8fHzE7NmzRVxcnBAi+/UIDw83vF7Tp08vVr+JSBkYjIlIlXQ6nXj66adzBcLu3buL//3vf2LHjh0iKSmpwPb6YOzm5iauXLmSZ/2vv/4qAAiNRiNu3LhhWB4fHy/c3d2Fu7u7IXw+btWqVUavbL711lsCgAgODhb37t0rUj9NDcbVqlWTdQVdCCFGjRolAIgPP/wwzzpLB2MhhHjqqacEADFv3rw8686fPy8ACAcHhzxXfvX7lCRJHDx4ME/bAwcOCEmSBIBcV9kzMzNFhQoVhCRJYs+ePUbrjYiIEJIkCW9vb5GWlmZib4lIaTjGmIhUSZIkbNq0CQMHDoQkSdBqtdi4cSPef/99tG/fHt7e3ujduzdOnDhR4H5eeOEFVK5cOc/yAQMGoFKlStDpdLnG/YaHhyMpKQldu3ZFzZo1je6zd+/ecHFxwT///IOYmBjD8uXLlwMA3n//ffj5+cnpdqFef/11o2Oqi6JLly4AgEOHDpmzpCJ76aWXAMDobBX6ZR07dkRQUJDR9m3atEHz5s3zLG/RogVat24NAFi7dq1h+a5du3D9+nU0btzYsP5xzZo1Q5UqVZCQkIDIyEhTukNECsRgTESq5e3tjeXLl+Py5cv48ssv0aNHDwQHBwPIns5t/fr1aNKkiSGQGtOmTZt81+nDUs5wHRERAQDYsmULAgMDjX6FhIQgIyMDAHDjxg0AwNWrV3H37l0AQOfOnYvR64I1a9aswPXp6en4/vvv0a5dO5QtWxZOTk6GG/d69eoFALnCvDUNHjwYzs7OOHDgAP7991/Dcp1Oh6VLlwKA4WZEYwp6LfXrjL2Wf//9d76vZWBgIK5fvw7g0WtJRPaL07URkepVrlwZ48ePx/jx4wEAUVFRWLlyJT799FM8ePAAI0eORJs2bRASEpKnbbly5fLdr35dbGysYZk+NGq1Wmi12kJrS05OBgBDKAaA8uXLF6FX8vj7++e77uHDh3j22WdzzTHs7u4OX19fSJKE9PR0xMfHIykpyWL1FcTPzw89evTAmjVrsGjRInz88ccAgO3btyM6Ohp+fn7o3r17vu3lvpYpKSlISUkptD79a0lE9otXjImoxKlYsSImTpyIzZs3Q6PRIDU1FatXrzbLvvVTd33wwQcQ2fdxFPhl7UccF/QwkxkzZuDo0aPw8vLC8uXLERcXB61Wizt37uD27dtYs2YNAEAIYa1y89APp1i6dKnhXC9evBhA9vAWZ2dnsx1Lv/9BgwYV6bW0pycPEpFxDMZEVGK1bNkSoaGhAIBLly4Z3ebWrVv5ttevy3kVNiAgAAAMf14vKn07OW3NJTw8HAAwa9YsDBw4MM/jl3Ne1baVDh06oHz58rh+/Tp27tyJBw8eYMOGDQAKHkYBWO+1JCL7xWBMRCVaqVKlACDfK4179+7Nt+2+ffsAAPXr1zcs04/h/euvv5CZmVnkOipVqmQIYtu2bStyO40m+59xc1zFjY6OBgA0atTI6Ppdu3YV+xjFpdFoDAF40aJFWLVqFVJSUvDUU0+hQYMGBbYt6LXUrzP2Wh45cgRxcXHFLZ2I7ACDMRGp0pkzZ3Dnzp0Ctzl37hxOnz4NAKhbt67RbVatWoWoqKg8y1evXo2rV6/CwcHBcFMaAPTt2xfu7u6IiYnB559/XuDx4+Pjc33/4osvAgD+97//FTmIlS5dGgAKfXKbKfu6cOFCnnUXL17EsmXLin0Mcxg+fDgkScK6devwww8/GJYVZvfu3bmeiKd3+PBh7NmzBwAQFhZmWN6+fXuUL18eaWlpeO+99wrc9+OvJRHZJwZjIlKl3bt3o3Llyhg2bBi2bNmSKzjev38fP/30E5599llkZWUhMDAwVyDKycnJCZ06dcKRI0cAAJmZmVi5ciVGjhwJIHvMa86b9sqUKWO4KezDDz/Em2++iatXrxrWa7VabN++HS+++CL69u2b61gTJ05EuXLlEB0djVatWuGPP/4wXHVOTU3Fli1b0LVr11xtateuDSA75BsLfaZo3749AGD8+PGIiIiAEAI6nQ7btm1Dhw4d4ObmVqz9m0ulSpXQrl07pKSk4OTJk3BycsLgwYMLbVe6dGn06tULf/75p2Fc8J9//olevXpBCIGOHTvmms7N2dkZs2fPBgDMmzcPAwYMwNmzZw3rU1NTsX//frz++uto2bKl+TtKRNZnvSmTiYis58cff8z1eGP89yjfnI84BiACAgLEkSNH8rTP75HQrq6uhT4SWgghpkyZYnhohL6tt7d3rmVt27bN0+7kyZOGp6nhvyfN+fn5GX0ktF7r1q0N63x9fUXFihVFxYoVDU/rE+LRAz70T7kz5vz588LLy8uwbalSpQz9LVeunFi8eLEAICpWrJinrTUe8JGT/gErAESPHj2KtM8vv/xS1iOhf/75Z+Hk5JTrvPj4+OR6LHSlSpVk9piIlIRXjIlIlV555RUcO3YM06dPx3PPPWf4k3h6ejrKli2LZ555Bl9++SUuXLiAxo0b57uf0NBQREZGYtiwYfDw8IBOp0O1atUwZcoU7NmzBx4eHkbbffTRRzh+/DhGjBiBKlWqIDMzE8nJyQgJCcHzzz+Pb7/91jDLQ05169bFuXPnMG3aNNSvXx/Ozs5ISUlBlSpV8MILL+C3337L02bdunV47bXXULlyZWi1WkRFRSEqKgqpqakmnbMaNWrg8OHD6Nu3L3x9fZGZmYmgoCC88cYbOHHiBCpUqGDS/iype/fuhvHVRRlGAWRfzT9y5AjGjBmDMmXKIDMzE+XLl8fYsWNx9OjRfB8MMmrUKPzzzz8YM2aM4aEtWq0WAQEB6NChAz7//HPDeHMism+SEDacd4eISKEqVaqEqKgo7Nq1y+pTqlHhtm3bhs6dO8Pf3x/R0dFwcnLKd9u2bdtiz549WLhwIadUI6IC8YoxERHZnZ9//hkAMHTo0AJDMRGRKRiMiYjIrmzcuBEbNmyAo6MjXnvtNVuXQ0QqwkdCExGRXahUqRKSk5MNj21+4403ULlyZRtXRURqwmBMRER2ISoqCpIkoXz58hgyZAimTp1q65KISGV48x0RERERETjGmIiIiIgIAIMxEREREREABmMiIiIiIgAMxkREREREABiMiYiIiIgAMBgTEREREQFgMCYiIiIiAsBgTEREREQEgMGYiIiIiAgA8H9pz+6YQRxRSwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### SAVE FIGURE\n",
    "fig.savefig('../output/figure_01.png', bbox_inches='tight', dpi=100)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6a6fba48",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}