JSON2CSV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bdc3042e-2403-4ba7-af1d-b010efe6d446",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import ast\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "63725c2b-54d2-4e5f-b708-880ff7a3e7e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_files = os.listdir('SimulationData/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "3e5ca787-3a92-4875-af6f-3ca409b2915b",
   "metadata": {},
   "outputs": [],
   "source": [
    "generic = [f for f in all_files if 'all_generic_obs_phi' in f]\n",
    "generic_2x_spot_area = [f for f in all_files if 'all_gen2xspot_area_ob' in f]\n",
    "generic_2x_spot_num = [f for f in all_files if 'all_gen2xspot_num_obs' in f]\n",
    "generic_both = [f for f in all_files if 'all_gen_both' in f]\n",
    "pole_spot_cont = [f for f in all_files if 'pole_spot_contrast' in f]\n",
    "eq_spot_cont = [f for f in all_files if 'eq_spot_contrast' in f]\n",
    "pole_butterfly_lat = [f for f in all_files if 'pole_butterfly_lat' in f]\n",
    "eq_butterfly_lat = [f for f in all_files if 'eq_butterfly_lat' in f]\n",
    "minima_uniform = [f for f in all_files if 'all_minima_uniform_obs_phi' in f]\n",
    "minima = [f for f in all_files if 'all_minima_obs' in f]\n",
    "maxima_uniform = [f for f in all_files if 'all_maxima_uniform_obs_phi' in f]\n",
    "maxima = [f for f in all_files if 'all_maxima_obs' in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de34856b-4748-4c0e-bfb5-1c80619c1864",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recover_data(content):\n",
    "    '''\n",
    "    Recovers corrupted data\n",
    "    '''\n",
    "    regex_pattern = r'\"(.*?)\": (\\[.*?\\]|-?\\d+\\.\\d+|true|false|null)'\n",
    "    match = re.findall(regex_pattern, content)\n",
    "\n",
    "    xs_std = np.float64(match[0][1])\n",
    "    ys_std = np.float64(match[1][1])\n",
    "    \n",
    "    xs_std_err = np.float64(match[2][1])\n",
    "    ys_std_err = np.float64(match[3][1])\n",
    "\n",
    "    xs_std_ci_lower, xs_std_ci_upper = ast.literal_eval(match[4][1])\n",
    "    ys_std_ci_lower, ys_std_ci_upper = ast.literal_eval(match[5][1])\n",
    "\n",
    "    return [xs_std, ys_std, xs_std_err, ys_std_err, xs_std_ci_lower, xs_std_ci_upper, ys_std_ci_lower, ys_std_ci_upper]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "83e66d49-0446-44cf-b80a-a75bba3ebd1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pole_butterfly_lat0.0.json',\n",
       " 'pole_butterfly_lat10.0.json',\n",
       " 'pole_butterfly_lat15.0.json',\n",
       " 'pole_butterfly_lat20.0.json',\n",
       " 'pole_butterfly_lat25.0.json',\n",
       " 'pole_butterfly_lat30.0.json',\n",
       " 'pole_butterfly_lat35.0.json',\n",
       " 'pole_butterfly_lat40.0.json',\n",
       " 'pole_butterfly_lat45.0.json',\n",
       " 'pole_butterfly_lat5.0.json',\n",
       " 'pole_butterfly_lat50.0.json',\n",
       " 'pole_butterfly_lat55.0.json',\n",
       " 'pole_butterfly_lat60.0.json',\n",
       " 'pole_butterfly_lat65.0.json',\n",
       " 'pole_butterfly_lat70.0.json',\n",
       " 'pole_butterfly_lat75.0.json']"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pole_butterfly_lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "8727e809-df5d-4119-a243-e98bf614b300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obs_phi</th>\n",
       "      <th>xs_std</th>\n",
       "      <th>ys_std</th>\n",
       "      <th>xs_std_err</th>\n",
       "      <th>ys_std_err</th>\n",
       "      <th>xs_std_ci_lower</th>\n",
       "      <th>xs_std_ci_upper</th>\n",
       "      <th>ys_std_ci_lower</th>\n",
       "      <th>ys_std_ci_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.231608</td>\n",
       "      <td>0.137210</td>\n",
       "      <td>0.002430</td>\n",
       "      <td>0.001537</td>\n",
       "      <td>0.226866</td>\n",
       "      <td>0.236353</td>\n",
       "      <td>0.134207</td>\n",
       "      <td>0.140216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.215108</td>\n",
       "      <td>0.127828</td>\n",
       "      <td>0.002089</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>0.211047</td>\n",
       "      <td>0.219191</td>\n",
       "      <td>0.124687</td>\n",
       "      <td>0.131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.219966</td>\n",
       "      <td>0.127475</td>\n",
       "      <td>0.002255</td>\n",
       "      <td>0.001247</td>\n",
       "      <td>0.215535</td>\n",
       "      <td>0.224446</td>\n",
       "      <td>0.125057</td>\n",
       "      <td>0.129860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.233443</td>\n",
       "      <td>0.134951</td>\n",
       "      <td>0.002322</td>\n",
       "      <td>0.001332</td>\n",
       "      <td>0.228810</td>\n",
       "      <td>0.237980</td>\n",
       "      <td>0.132356</td>\n",
       "      <td>0.137546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.228604</td>\n",
       "      <td>0.128759</td>\n",
       "      <td>0.002406</td>\n",
       "      <td>0.001341</td>\n",
       "      <td>0.224005</td>\n",
       "      <td>0.233395</td>\n",
       "      <td>0.126142</td>\n",
       "      <td>0.131494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87.0</td>\n",
       "      <td>0.206043</td>\n",
       "      <td>0.205341</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>0.002076</td>\n",
       "      <td>0.201889</td>\n",
       "      <td>0.210100</td>\n",
       "      <td>0.201252</td>\n",
       "      <td>0.209429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88.0</td>\n",
       "      <td>0.200372</td>\n",
       "      <td>0.200069</td>\n",
       "      <td>0.002171</td>\n",
       "      <td>0.002161</td>\n",
       "      <td>0.196272</td>\n",
       "      <td>0.204683</td>\n",
       "      <td>0.195795</td>\n",
       "      <td>0.204252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89.0</td>\n",
       "      <td>0.190421</td>\n",
       "      <td>0.190349</td>\n",
       "      <td>0.001896</td>\n",
       "      <td>0.001889</td>\n",
       "      <td>0.186714</td>\n",
       "      <td>0.194093</td>\n",
       "      <td>0.186640</td>\n",
       "      <td>0.194127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.226594</td>\n",
       "      <td>0.133237</td>\n",
       "      <td>0.002228</td>\n",
       "      <td>0.001455</td>\n",
       "      <td>0.222197</td>\n",
       "      <td>0.230959</td>\n",
       "      <td>0.130405</td>\n",
       "      <td>0.136138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>90.0</td>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.002029</td>\n",
       "      <td>0.002027</td>\n",
       "      <td>0.197289</td>\n",
       "      <td>0.205221</td>\n",
       "      <td>0.197324</td>\n",
       "      <td>0.205270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    obs_phi    xs_std    ys_std  xs_std_err  ys_std_err  xs_std_ci_lower  \\\n",
       "0       0.0  0.231608  0.137210    0.002430    0.001537         0.226866   \n",
       "1       1.0  0.215108  0.127828    0.002089    0.001673         0.211047   \n",
       "2      10.0  0.219966  0.127475    0.002255    0.001247         0.215535   \n",
       "3      11.0  0.233443  0.134951    0.002322    0.001332         0.228810   \n",
       "4      12.0  0.228604  0.128759    0.002406    0.001341         0.224005   \n",
       "..      ...       ...       ...         ...         ...              ...   \n",
       "86     87.0  0.206043  0.205341    0.002092    0.002076         0.201889   \n",
       "87     88.0  0.200372  0.200069    0.002171    0.002161         0.196272   \n",
       "88     89.0  0.190421  0.190349    0.001896    0.001889         0.186714   \n",
       "89      9.0  0.226594  0.133237    0.002228    0.001455         0.222197   \n",
       "90     90.0  0.201243  0.201243    0.002029    0.002027         0.197289   \n",
       "\n",
       "    xs_std_ci_upper  ys_std_ci_lower  ys_std_ci_upper  \n",
       "0          0.236353         0.134207         0.140216  \n",
       "1          0.219191         0.124687         0.131205  \n",
       "2          0.224446         0.125057         0.129860  \n",
       "3          0.237980         0.132356         0.137546  \n",
       "4          0.233395         0.126142         0.131494  \n",
       "..              ...              ...              ...  \n",
       "86         0.210100         0.201252         0.209429  \n",
       "87         0.204683         0.195795         0.204252  \n",
       "88         0.194093         0.186640         0.194127  \n",
       "89         0.230959         0.130405         0.136138  \n",
       "90         0.205221         0.197324         0.205270  \n",
       "\n",
       "[91 rows x 9 columns]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fix maxima\n",
    "df = pd.DataFrame(columns=['obs_phi',\n",
    "    'xs_std', 'ys_std', 'xs_std_err', 'ys_std_err', \n",
    "    'xs_std_ci_lower', 'xs_std_ci_upper', \n",
    "    'ys_std_ci_lower', 'ys_std_ci_upper'\n",
    "])\n",
    "\n",
    "for i in range(len(maxima)):\n",
    "    with open('SimulationData/' + maxima[i], \"r\") as file:\n",
    "        content = file.read()\n",
    "        match = int(re.search(r'_phi_(\\d+)', maxima[i]).group(1))\n",
    "        # match = np.float64(re.search(r'_lat([\\d.]+)', eq_butterfly_lat[i]).group(1)[:-1])\n",
    "        df.loc[len(df)] = [match] + recover_data(content)\n",
    "\n",
    "\n",
    "df.to_csv('maxima.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "f2009c13-874d-4e0f-89d5-2081ea7a1c06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x19daefceee0>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r6293UeTvd1f8frdYLGJUVJQIQJw1a5bD8TfffFNMSkoSQ0JCRLlcLrZt21acMGGCePHixVvGvbZ67N/+9rfb5vDBBx+I3bt3F+Vyuejv7y+OGjVKPH78uN05tf232rdvn5iYmCiq1WoxNDRUfO6558QjR46IAMT169fbznvqqadEjUZz2zyIXEUQRVFs7IZBIqohCALmzp2LefPmuTsVIiIiIiK6Df5+JyJ34px2REREREREREREHoaNdkRERERERERERB6Gw2OJiIiIiIiIiIg8DHvaEREREREREREReRg22hEREREREREREXkYNtoRERERERERERF5GJm7E2gOrFYrrly5Aq1WC0EQ3J0OEVGzJ4oi9Ho9WrduDYmEz49YZ4iIXIt1xhFrDRE1hKu+Vw0GA6qrq12Wl1wuh1KpdFk8T8NGuzq4cuUKoqKi3J0GEZHXyc7ORmRkpLvTcDvWGSKixsE68xvWGiJyhYZ8rxoMBsS0C0be1UqX5RMREYELFy54bcMdG+3qQKvVAqj5x+nn5+fmbIiImj+dToeoqCjb92tLxzpDRORarDOOWGuIqCFc8b1aXV2NvKuVyPrlSfhp5Q3PSV+Ntt0/QXV1NRvtWrJr3cf9/PxY4IiIXIjDc2qwzhARNQ7Wmd+w1hCRK7jie9VPK3dJo11LwEY7IiIiIiIiIiJqGuKvmyvieDk22hERERERERERUdMQxZrNFXG8HJdSIiIiIiIiIiIi8jDsaUdERERERERERE2Co2Prjo12RERERERERETUJERRgCg2fEELV8TwdBweS0RERERERERE5GHY046IiIiIiIiIiJqGKKnZXBHHy7HRjoiIiIiIiIiImoRVFGB1wdBWV8TwdN7fLElERERERERERNTMsKcdEREREREREVELYLVaUZh/EbqyAkgkUoSEtoVfQFiT5iBCAtEFfchcEcPTsdGOiIiIiIiIiMjLlZbk4eihr1BWlg/RagEASGVyhLeKxZ2974FcoWqaREQArhjaKjY8hKdjox0RERERERERkRerrCjFoR+3oaKiFEqVFjKZD0RRhMlkQE5WJixmE/r+7lFIJN7fe605YaMdEREREREREZEXy7rwCyoqSqDxDYQg1DTMCYIAuVwFiSBFwdULKMy/hLCImEbPRRQFiC7oaeeKGJ6OjXZE5HEsFgusVqvDfolEAqlU6oaMiIjIm7DOEBFRS3M5KxNSqY+twe56Mh85DFV6XM0920SNdhKIogvmtHNBDE/HRjsi8jiVlZXQ6XQoLCwEAISEhEAQBGi1Wmi1WjdnR0REzR3rDBERtTRmkxESyS0eTAmA2VTddAlRnbDRjog8jlqthkKhgMVSMzlqaGgoBEHg/ApEROQSrDNERNTS+GqDUVSQDYVS43BMFGtWdFD7BjRJLiJcs4ZEC1iHgo12ROR5pFIpJBIJZLKarygfHx8IgvfPV0BERE2DdYaIiFqatjHxKC7MhtlUDZmP3O6Y0VAOuY8KbaI6N0kuHB5bd95/hURERERERERELVibtl0R0aYjjIZyVFWUwWwywlRtQIW+BKIoomPXZPhqg9ydJt2AjXZE5BHKjWbk6wx2W0lFNUoqqh325+sMKDea3Z0yERERERFRsyCVytC734Po0n0IVBp/mM0mWC1mBIdEole/BxDbsW+T5XJt9VhXbHXVrl07CILgsL300ku/5iRi3rx5aN26NVQqFQYPHozjx4831l9BnXF4LBF5hIzLZThwvsj22myx4tjZSwCAbh0MkEntnzH0bx+MxNjgJs2RiIi8Q7nRjAqDCSUVNRNuS3UGCIIAtUIGXwV/HhMRUfNWXFSIEyeO4fLlSxBFIDIyCp06d0NISBjiOieifcc+MFTqIUgkUKn9mnyKCFGs2VwRp64OHTpkm8sWAI4dO4Zhw4bhscceAwAsX74cK1aswIYNG9CxY0csWrQIw4YNw6lTp9y6SBV/lRCRR4iP9Eds6G+TohrNFmwoq2nE+0PfKChk9isdqXlTRURETsq4XIbUMwUOD4f4QIhcZdWqVfjb3/6G3NxcdO3aFSkpKRgwYMBt3/fjjz9i0KBB6NatG9LT0+2Obd26FbNnz8a5c+cQGxuLxYsX4+GHH26kKyCi5ursmVP4/rtdqKqqtM3dWpCfhxOZxzBw0N24o1MXSKUyaLSBbs60aYWGhtq9fv311xEbG4tBgwZBFEWkpKRg1qxZeOSRRwAAH330EcLDw7Fp0yZMnDjRHSkD4PBYIvIQvgoZwvyUv21aJVRyKVRyKcK0Svtjfkr2hCAiIqfFR/rjD32j0CHUFx1CffGHvlF4ol9bxEf6uzs18gJbtmzB1KlTMWvWLKSlpWHAgAEYOXIksrKybvm+srIyjB8/HnfddZfDsf3792P06NEYN24cjh49inHjxuHxxx/HwYMHG+syiKgZKi0twfff74TJVIXAAD9otVpotX4ICAiE2WzCDz98jaKiQneniZqmKFdtgE6ns9uMRuMtP726uhqffPIJnn32WQiCgAsXLiAvLw/Dhw+3naNQKDBo0CDs27fPhdddf2y0IyKPdCK7EJnZhUg7dwUf7EpDZnaBu1MiIiIv4auQQV9lxuniahzONeB/x/KgM5j4QIhcYsWKFZgwYQKee+45dO7cGSkpKYiKisLq1atv+b6JEydi7NixSExMdDiWkpKCYcOGYebMmejUqRNmzpyJu+66CykpKY10FUTU3IiiiGPp38LP5woigwsRoLgAX9ll+Eh0EATA11cLg8GAUyfdP0+bq+e0i4qKgr+/v21bunTpLT//888/R2lpKZ5++mkAQF5eHgAgPDzc7rzw8HDbMXdhox1RC2KxWGAymRy268f2e4LM7AJs3vML9OV6WMxmnLyUi4+/PcqGOyIicomz+Xqs33cReWVVEKurcOjsVXyw9wLO5uvdnRo1c9XV1Th8+LBdbw0AGD58+C17a6xfvx7nzp3D3Llzaz2+f/9+h5gjRoy4ZUyj0ejQ+4SIvJMoisg6mwpd/k9QKaqBXxu0fCSV0MjyoJIWQhAAmVSKKznZ7k7X5bKzs1FWVmbbZs6cecvz161bh5EjR6J169Z2+2+c208UxSaf7+9GfJxI1IJUVlZCp9OhsLCmS3RISAgEQfi127T7Jte80d7j2aisMkChVEMQgMjwIFwpKEVqZja6RIXePgAREbmNxWKB1Wp12C+RSCCVSmt5R9Pbe6YQZfoKtPapglQ0o71Ggyy9BKlnC9EhzHPqITU/hYWFsFgs9eqtcebMGcyYMQN79+61zT91o7y8vHr3AFm6dCnmz59fzysgouaovOwKss6lApDAbJHZ6q1FlEKAGQppCUxWDURRBNzcCAW4fiEKPz8/+Pn51ek9ly5dwtdff41t27bZ9kVERACo+a5t1aqVbX9+fr7Dd29TY087ohZErVYjNDQUgYGBCAwMRGhoKEJDQ6FWq92dmp2rJXqolIrr6okAlUKOqyXO94BoLr0MiYiau8rKSuTn5yMzMxOZmZnIz89HQUEBKisr3Z2aTU5JFfwUAqSiGSapErCYoVXU7Cdyhbr21rBYLBg7dizmz5+Pjh07uiTmNTNnzrTreZKd7X29a4ioxpVLR2CsKocgSCGKVgC/tYiJkEKACB+JDharFW3btnNbnracXDw8tj7Wr1+PsLAw3HfffbZ9MTExiIiIwO7du237qqursWfPHiQlJbnkmp3FnnZELYhUKoVEIrE9xfXx8XF7d9/ahAdqUVCiw28PgkRUGasR2zrE6ZjNpZchEVFzp1aroVAobA9FQkNDIQgCJBLPeVbcJlCFrPxSWAQZfCwGQOoHfRXQJdD5h1jNoYchNb6QkBBIpVKHHnA3662h1+vx888/Iy0tDVOmTAEAWK1WiKIImUyGXbt2YejQoYiIiKhzzGsUCgUUCoULroqIPJUoijh9Ig2nTxyAIFbBKkohwAqz2QipVAZBkAEQIEIALOXQqINwxx1d3J2221itVqxfvx5PPfWUXc9mQRAwdepULFmyBHFxcYiLi8OSJUugVqsxduxYN2bMRjsi8kADukbhfG4R8koLIZVIkZNfAo1aieQukU7HbA43kURE3qA5PCAaEBeCzCs6pOeroJZYcaFSiUA/NZI7BDsdkw+HCADkcjl69+6N3bt34+GHH7bt3717N0aNGuVwvp+fHzIyMuz2rVq1Ct9++y3+9a9/ISYmBgCQmJiI3bt3449//KPtvF27drm9BwgRudfZ0xn4+eC30MhFyGUSCIIMgiCB2WyCxWKCACsgSACJBYJUgaF33wP/gEB3pw3xWkOiC+LUx9dff42srCw8++yzDsdeffVVVFVVYfLkySgpKUG/fv2wa9cut9dwNtoReblyoxmVRrPttSiKKKmoBgBIdQaHGym1Qub21fO6RIVizKDuePs/h2Csrkan6AgMjo9u0Hx2zeEmkoiImkaHMC2eSWqHpYXF0But6NMhDIPvCGvQfHZ8OETXTJs2DePGjUNCQgISExOxdu1aZGVlYdKkSQBqhq3m5ORg48aNkEgk6Natm937w8LCoFQq7fa/8sorGDhwIJYtW4ZRo0bhiy++wNdff43U1NQmvTYi8hwWsxnHfzkAURQh8QkEcBWACIlUArlEAZOpGoIgwMdHAZlMivad70a7drHuThuA+xrthg8fXjOvXy0EQcC8efMwb968BuflSmy0I/JyGZfLcOB8ke212WLFsbOXAADdOhggk9rfTPRvH4zEWOd7GjjrxsbF4ABfxITXPAV6oH9nKGRS5OsMtuOe0LhIRESOyo1mVBhMDg+IPO17OzbMF3dGqAAA4/q3g8KnYUNY+XCIrhk9ejSKioqwYMEC5Obmolu3btixYweio6MBALm5ucjKyqpXzKSkJHz22Wd47bXXMHv2bMTGxmLLli3o169fY1wCETUD+fk5KNeXQaXSwAoBIkogoBoipIAggUzmA6togUIBaP3boG37Pu5OmZzgOb+cfrVq1Sr87W9/Q25uLrp27YqUlBQMGDDgtu/78ccfMWjQIHTr1g3p6el2x7Zu3YrZs2fj3LlziI2NxeLFi+26qxN5s/hIf8SGamyvjWYLNpTVNOL9oW8UFDL7mxR1PW+oXDWHz42Ni6IoQiWvef8/f77scOPjrsZFIiK6tYzLZUg9U+DwgKgh39uuni+u3GhGSYURVdU1veLy9QYoZFKPa1ik5mvy5MmYPHlyrcc2bNhwy/ferKfHo48+ikcffdQF2RGRNzBVG2G1WiCRSgFBgFlsDRlyIaAagOXXucGtUKqC0KXXY5ArNLeJ2HScXUSitjjezqN+lWzZsgVTp07FqlWrkJycjDVr1mDkyJHIzMxE27Ztb/q+srIyjB8/HnfddReuXr1qd2z//v0YPXo0Fi5ciIcffhjbt2/H448/jtTUVD6ZohbB94YbkPTzV3HhagmM1dX49/4TGNK9YcNOXTWHz42Ni6IoojC/ZvLkkLDwWofx3k5zHBpMTYMPiIgaT3ykPyIDlQ4PiOr7UOh6rp4vLuNyGfafK3R4ONSQhsXm0sOQiIi8g8bXD1KZD0ymasjlCoiCHCaxLSSohIAqmC1mmCxSJCc8AV+/MHena08UajZXxPFyHvULYsWKFZgwYQKee+45AEBKSgp27tyJ1atXY+nSpTd938SJEzF27FhIpVJ8/vnndsdSUlIwbNgwzJw5E0DNHBJ79uxBSkoKNm/e3GjXQuSJMrMLsHnPL9CX6yGVSHHyUi4uF5Zi3NA7nW64c9UcPjc2LoqiCEuFHAAQ5qd0aohRcxkaTE2LD4iIGpevQoaswgqcLq6G3mhFxLE8DOkUhjA/pdMxXT1fXHykP9qHqB0eDjWkYbExehgSERHdTFBwOEJDWyE3Nws+PvKa+yVBgBUaiKIausoSRLXtgMAgD2uwo3rxmEa76upqHD58GDNmzLDbP3z4cOzbt++m71u/fj3OnTuHTz75BIsWLXI4vn//frtVlgBgxIgRSElJuWlMo9EIo9Foe63T6ep4FUSebe/xbFRWGaBQqiEIQGR4EK4UlCI1M9vpRjtPnsOnsYcGU/PEB0REjetsvh7r911EXlkV1BIrDp29inMFFXhuQIzTCz24utb4KmTQyKUNfjh0vcboYUhERHQzgiCgV98h+P7rbdDpiqFQqCCVymAxm2GsroKvNgA9Ewa6O83auWh4bEvoaecxy1kVFhbCYrEgPDzcbn94eDjy8vJqfc+ZM2cwY8YMfPrpp7YfcTfKy8urV0wAWLp0Kfz9/W1bVFRUPa+GyDNdLdFDpVTgt/sSASqFHFdL9O5My4HFYoHJZILZbIbZbIbJZILJZLL1sKgrX4UMYX5K21ZUWo4LV0twMvsq/r3/BArL9HbHOXzJ+117QDR8+HC7/XV9QDR37txaj+/fv98h5ogRI24a02g0QqfT2W1E3mLvmUKU6SvQ2qcKwZIKtNcYodNXIvVsobtTa1S+Chn0VWacLq7G4VwD/ncsDzqDibWFiIgaTUhoKwwd/hjax3YFAFRXGyFIBHTo2B13jXjMY3vZiS7cvJ3H/Yq48SmnKIq1Pvm0WCwYO3Ys5s+fj44dO7ok5jUzZ87EtGnTbK91Oh0b7sgrhAdqUVCigyji14Y7EVXGasS2DqlXnMaeK66yshJ6vd7WGH9tDiNn5y4CGmdoMDU/DXlAtHfvXpc9IFq6dCnmz5/vxBUQeb6ckir4KQQUi2aYpErAYoZWUbPfWa6eL85iscBiscBsrqllJpPJNtzWmYUtgMbpYUhERHQ7QcFhGDDkQVRVVcBoqIJSqYJS5TmLTlDDeEyjXUhICKRSqcMNTn5+vsONEADo9Xr8/PPPSEtLw5QpUwAAVqsVoihCJpNh165dGDp0KCIiIuoc8xqFQgGFQuGCqyLyLAO6RuF8bhHySgshlUiRk18CjVqJ5C6R9YrT2HPFqdVqKJWOcx85O3cR0DhDg6n5cvcDIj4cIm/WJlCFrPxSWAQZfCwGQOoHfRXQJVDtdExXzxfXGA+Hru9hKBXNaK/RIEsvQerZQjbaERFRo1OpNFA1k8Y6ES5aPRbePzzWYxrt5HI5evfujd27d9uttrd7926MGjXK4Xw/Pz9kZGTY7Vu1ahW+/fZb/Otf/0JMTAwAIDExEbt377ab127Xrl1ISkpqpCsh8lxdokIxZlB3vP2fQzBWV6NTdAQGx9d/9dgb54o7ll2AjBNGGKtNgKEMfTpFoWPrINvx+s7nI5VKne7pcDPXhgaXGq/19PDMocHUuDzlAREfDpE3GxAXgswrOqTnq6CWWHGhUolAPzWSOzi/GIOr54trjIdDjdHDkIiIyBuJLprTziXz4nk4j2m0A4Bp06Zh3LhxSEhIQGJiItauXYusrCxMmjQJQE3PhJycHGzcuBESiQTdunWze39YWBiUSqXd/ldeeQUDBw7EsmXLMGrUKHzxxRf4+uuvkZqa2qTXRuQuNw5lDQ7wRUx4IADggf6doZBJka8z2I7XZajR9Su9ZmYXYOdPJ1FVVQGpRIqsK/koLtMjyMOGnbpqaDA1b3xARNT4OoRp8UxSOywtLIbeaEWfDmEYfEdYg3qbuXpF2sZ4ONQYPQyJiIioZfOoRrvRo0ejqKgICxYsQG5uLrp164YdO3YgOjoaAJCbm4usrKx6xUxKSsJnn32G1157DbNnz0ZsbCy2bNmCfv36NcYlEHmcG4eyiqIIlbzmRuWfP192GL5X36FGzWXYqauGBlPzxwdERI2r3GiGViVDx6CalVlHdI2AQiZFudHs9KIMzWG+uMboYUhEROSNRNE1veTEFrAShUc12gHA5MmTMXny5FqPbdiw4ZbvnTdvHubNm+ew/9FHH8Wjjz7qguyImp8bh7KKoojC/JpheSFh4bUuGlEfzWXYqauGBlPzxwdERI0r43IZ9p8rdHhA5Oz8c0DzmC+uMXoYEhEReSNXrfzaAtrsPK/Rjohcy/eG4a6iKMJSUdP7IcxPecuVlOvCk4edNsbQYPIOfEBE1HjiI/3RPkTt8IDI2fnngOYzX1xsmC/ujFABAMb1bweFj2uH4BIREVHLwrtTImoQTx522thDg4mIyJGvQgaNXOrSB0TNYb64cqMZJRVGVFVbAAD5eoNtsQw+ECIiIrqOKNRsrojj5fgLgqgFsVgssFgsMJtrep+ZTCYIggCJROL0hNyePOy0sYcGExFR02gO88U1xrBgIiIib8TVY+uOd6hELUhlZSX0ej1kspr/9QsLCwEAWq0WWm3d59xpLsNOG3toMBEROWqMB0QR/io82KMVsvOKUF5tQafIICTFhiDCX+XK1BukMYYFExERUcvGXxFELYharYZSqXTYL5FI6hWHw06JiOhmXPWA6HoZl8tw6EIJEiJrhsOKIrDvXBGsIjymvjTGsGAiIiJvVLN6rGvieDs22hG1IFKp1OleDtdrjsNOG6PnBxEROXLVA6LrNYdebKwzREREdSNCgAgXDI91QQxP5zm/dIio2WiOw04bo+cHERE5ctUDous1h15srDNERETkamy0I6IWoTF6fhAREV3DOkNERESuxkY7ImqQ5jIcqDF6fhARUdNoDrWGdYaIiKhuuHps3bHRjogahMOBiIiosbHWEBERUUvERjsiahAOByIiosbGWkNEROQ92NOu7thoR0QNwuFARETU2FhriIjI21gs1ajUX4YoWqBUhUKuDHB3Sk1GFGs2V8Txdmy0IyIiIiIiIiJqAqJoRcHlH1F45QCqq8sAUYREqoB/SGe0bjcCPgo/d6dIHoSNdkRERERERERETeDKhZ0ouJwKCFLIfHwhQAKLxYDivCMwVOQjNv5pyHw07k6zUYkQIMIFw2NdEMPTcSIQIiIiIiIiIqJGVlWRh6IrP0EiVUCu8IdE4gNBIoXMRwMfRQAq9Tkoyjvs7jQb3bXhsa7YvB0b7YiInGCxWGAymRw2i8Xi7tSIiIiIiMgDlRVmwmIxQCpTOxyTSGQQBCmKrx5xQ2bkqTg8lojICZWVldDpdCgsLAQAhISEQBAEaLVaaLVaN2dHRETewGKxwGq1OuyXSCRcmIOIqBkyV5cDAASh9mGdEqkPzNXlEK0WCBLv/Z7n6rF1x0Y7IiInqNVqKBQKW8+60NBQCIIAiYQdmImIyDX4gIiIyLtIfWp62ImiWGvDndVqgkIZ6NUNdgAA8dfNFXG8HBvtiIicIJVKIZFIIJPVfI36+Pjc9IkZERGRM/iAiIjIuwSEdEXB5VRYzJUOi02IVgtEqxlB4b3clB15IjbaERERERF5ID4gIiLyLkpNBIIieqPgyn6IVktNw50gwGI2wGKuhMq3NYIiEtydZqPj6rF1x0Y7IqI6KjeaUWk0216LooiSimoAgFRncLiRUitk8FXwa5aIiJxTbjSjwmByqDWsL0REzZMgCGjT/l7IfDQozP0JpuoyiKIIqUyBgNB4tGl/L3zkvu5Os9Gx0a7uWO2JiOoo43IZDpwvsr02W6w4dvYSAKBbBwNkUvvhSv3bByMxNrhJcyQiIu+RcbkMqWcKHGoN6wsRUfMlSKSIiB6K0DZJqNBlQbSaoVCHQakOcXdq5IHYaEdEVEfxkf6IDf1t7gmj2YINZTWNeH/oGwWFzH7CWDV7QRARUQPER/ojMlDpUGtYX4iImj+pTAm/oI7uTsMtRLFmc0Ucb8eKT0RUR743DEdKP38VF66WwFhdjX/vP4Eh3aPRJSrUjRkSEZE38VXIkFVYgdPF1dAbrYg4lochncIQ5qd0d2pERETO4+qxdcalp4iInJCZXYDNe36BvlwPi9mMk5dy8fG3R5GZXeDu1IiIyEuczddj/b6LyCurglhdhUNnr+KDvRdwNl/v7tSIiIioCbDRjojICXuPZ6OyygCFUg0fhQKR4UGorDIiNTPb3akREZGX2HumEGX6CrT2qUKwpALtNUbo9JVIPVvo7tSIiIicJwoQXbBB9P6FKNhoR0TkhKsleqiUCvy2YKwAlUKOqyXs/UBERK6RU1IFP4UAqWiGSaoELGZoFTX7iYiIyPux0Y6IyAnhgVpUGYzXTX4qospYjYhAP3emRUREXqRNoAo6owiLIIOPxQBIZdAbgchAtbtTIyIicprows3bsdGOiMgJA7pGQa1SwmiohMloRE5+CdQqBZK7RLo7NSIi8hID4kLgr9XgikmFIqsGFyqV8NOqkdwh2N2pEREROc0VQ2NtQ2S9HBvtiIjqqNxoRr7OgHydASH+Wozo2wkqlS9EQYqoVqG4r18XhPhrbeeUG83uTpmIiJqxDmFaPJPUDhH+KghyFfp0CMNzA2LQIUzr7tSIiIianZycHDz55JMIDg6GWq1Gjx49cPjwYdtxURQxb948tG7dGiqVCoMHD8bx48fdmDEb7YiI6izjchk+PZhl29JzKtG9XSj6dGwDqToAh7LL7Y5nXC5zd8pERNSMlRvN0Kpk6BgkR+9WSozoGgE/pQ8fCjUDq1atQkxMDJRKJXr37o29e/fe9NzU1FQkJycjODgYKpUKnTp1wltvvWV3zoYNGyAIgsNmMBga+1KIiFzPDeNjS0pKkJycDB8fH3z11VfIzMzEm2++iYCAANs5y5cvx4oVK/Duu+/i0KFDiIiIwLBhw6DXu2/ecpnbPpmIqJmJj/RHbKjG9loURRTmKwAAIWHhEAT77tlqBb9iiYjIeRmXy7D/XCFUcikA4J8/X4YgCOjfPhiJsRwi66m2bNmCqVOnYtWqVUhOTsaaNWswcuRIZGZmom3btg7nazQaTJkyBd27d4dGo0FqaiomTpwIjUaDF154wXaen58fTp06ZfdepVLZ6NdDRORq1l83V8Spq2XLliEqKgrr16+37WvXrp3tz6IoIiUlBbNmzcIjjzwCAPjoo48QHh6OTZs2YeLEiS7IuP54R0lEVEe+Chl8r2uIE0URlgo5ACDMT+nQaEdERNQQ8ZH+aB+idnhAxIdCnm3FihWYMGECnnvuOQBASkoKdu7cidWrV2Pp0qUO5/fs2RM9e/a0vW7Xrh22bduGvXv32jXaCYKAiIiIxr8AIqJmRqfT2b1WKBRQKBR2+/79739jxIgReOyxx7Bnzx60adMGkydPxvPPPw8AuHDhAvLy8jB8+HC7OIMGDcK+ffvc1mjH4bFERERERB7IVyFDmJ8SgRo5AjVyhPkpEeantHuARJ6luroahw8ftrvpA4Dhw4dj3759dYqRlpaGffv2YdCgQXb7y8vLER0djcjISNx///1IS0u7ZRyj0QidTme3EZHrZGVl4csvv8Q777yDd955B//+979x6dIld6fVLLh6IYqoqCj4+/vbttoekJw/fx6rV69GXFwcdu7ciUmTJuH//u//sHHjRgBAXl4eACA8PNzufeHh4bZj7sCKT0TkBIvFAovFArO5Zl4hk8kEQRAgkUgglUrdnB0REXkD1prmp7CwEBaLxambvsjISBQUFMBsNmPevHm2nnoA0KlTJ2zYsAHx8fHQ6XRYuXIlkpOTcfToUcTFxdUab+nSpZg/f37DL4qIHBw5fBjpP/8XaoUOEQEylFWocfy4HqdPn8bgwYORkJDg7hQ9nPDr5oo4QHZ2Nvz8/Gx7b+xlBwBWqxUJCQlYsmQJgJpezsePH8fq1asxfvz43yLeMHpKFEW3jqhiox0RkRMqKyuh1+shk9V8jRYWFgIAtFottFqu6kdERA3HWtN8OXPTt3fvXpSXl+PAgQOYMWMGOnTogDFjxgAA+vfvj/79+9vOTU5ORq9evfDOO+/g7bffrjXezJkzMW3aNNtrnU6HqKgoZy+JiH6Ve/kE9Lnr0TOuElJpTbORVZRAV+mL9DOhtqGXrVq1cneqLYafn59do11tWrVqhS5dutjt69y5M7Zu3QoAtukH8vLy7P7b5efnOzyIaUpstCMicoJara518meJhLMOEBGRa7DWND8hISGQSqUOverqctMXExMDAIiPj8fVq1cxb948W6PdjSQSCfr06YMzZ87cNF5tczoRUcNYzJUovLQBARo9TFYlTNU138dSiRUBvjr0vsOM1KPhOHbsGBvtbkEEINZj5ddbxamr5ORkh8V8Tp8+jejoaAA138ERERHYvXu3bZ7R6upq7NmzB8uWLWt4sk5iox2RB7NYLLBaHdfE4bAY95NKpfxvQETNHuuMZ2OtaX7kcjl69+6N3bt34+GHH7bt3717N0aNGlXnOKIowmg03vJ4eno64uPjG5QvEdVPZWkaBGsRKqvlkEp++362WKUwVAvQqirROqQSubm5bszS810/H11D49TVH//4RyQlJWHJkiV4/PHH8dNPP2Ht2rVYu3YtgJoe0lOnTsWSJUsQFxeHuLg4LFmyBGq1GmPHjm1wrs5iox2RB6usrIROp7MNhwkJCYEgCBwW46V480xETY11pmVhnWka06ZNw7hx45CQkIDExESsXbsWWVlZmDRpEoCaYas5OTm2yc/fe+89tG3bFp06dQIApKam4o033sDLL79sizl//nz0798fcXFx0Ol0ePvtt5Geno733nuv6S+QqAWrKP0FACBaBYdlPUWxZkdYoB4FVfxO9TR9+vTB9u3bMXPmTCxYsAAxMTFISUnBE088YTvn1VdfRVVVFSZPnoySkhL069cPu3btcutvIo/rW79q1SrExMRAqVSid+/e2Lt3703PTU1NRXJyMoKDg6FSqdCpUye89dZbduds2LABgiA4bAaDobEvhajB1Go1QkNDERgYiMDAQISGhiI0NBRqtdrdqVEjqKysRH5+PjIzM5GZmYn8/HwUFBSgsrLS3al5HdYaohqsMy0L60zTGD16NFJSUrBgwQL06NEDP/zwA3bs2GEbgpWbm4usrCzb+VarFTNnzkSPHj2QkJCAd955B6+//joWLFhgO6e0tBQvvPACOnfujOHDhyMnJwc//PAD+vbt2+TXR9SSWS1V8PG5NuzccXCmVRQgk1oQGxvbtIk1M6Louq0+7r//fmRkZMBgMODEiRN4/vnn7Y4LgoB58+YhNzcXBoMBe/bsQbdu3Vx45fXnUT3ttmzZgqlTp2LVqlVITk7GmjVrMHLkSGRmZqJt27YO52s0GkyZMgXdu3eHRqNBamoqJk6cCI1GgxdeeMF2np+fn8PY5drmByHyNFKpFBKJxDYBtY+Pj1tXrqHGpVaroVAoYLFYAAChoaG2VQLJdVhriH7DOtOysM40ncmTJ2Py5Mm1HtuwYYPd65dfftmuV11t3nrrLYcHRkTU9OTKcPj4XIBMJoPZbPq1fl6rm1ZAtMBs9UPXrl3dmSZ5EY9qtFuxYgUmTJhgW948JSUFO3fuxOrVq7F06VKH83v27GmbIBAA2rVrh23btmHv3r12N1KCINhWAiHydOVGMyqNZttrURRRUlENAJDqDA43U2qFDL4Kj/pfmZzEm+emwVpD9JtyoxkVBpNDnWFt8U6sM0REDaMJTEBFSRqCgwQUl1TAZDLjWo87ucwCUZCh050PcYoJchmP+TVWXV2Nw4cPY8aMGXb7hw8fjn379tUpRlpaGvbt24dFixbZ7S8vL0d0dDQsFgt69OiBhQsX2t2A3choNNpN/KrT6epxJUQNk3G5DAfOF9lemy1WHDt7CQDQrYMBMqn90/D+7YORGBvcpDmS67CRtml5Sq1hnSFPkXG5DKlnChzqDGuLd2IjLRFRwyi1HeEbkgR9wV6EBMlRbVLCZDJBIlRDJpMjIHwoQqP7uztNjyeifiu/3iqOt/OY6lxYWAiLxeKwFHp4eLjDkuk3ioyMREFBAcxmM+bNm2frPQEAnTp1woYNGxAfHw+dToeVK1ciOTkZR48eRVxcXK3xli5divnz5zf8ooicEB/pj9hQje31sewCZJwwwlhtAgxl6NMpCh1bB9mOq/kju1ljI23T8pRawzpDniI+0h9mqwWHz+SgvNoKqURAUmwwurbxd3dq1AjYSEtE1DCCICCozUOQK1tBX/gjpMYCqJQ+kCnbwS/kd9AE9WUP5jpwx+qxzZXH3e3f+A9cFMXb/qPfu3cvysvLceDAAcyYMQMdOnTAmDFjAAD9+/dH//6/tXQnJyejV69eeOedd/D222/XGm/mzJmYNm2a7bVOp0NUVJSzl0RUL77XPe3OzC7Azp9OoqqqAlKJFFlX8lFcpkfQ0DvRJSrUzZmSK9zYSGs0W7ChrKYR7w99o6CQ2a88xUZa13B3rWGdIU+RV1aFf6fnoqjCCLXEipOXi5CvNyLYV44OYRza423iI/0RGah0qDOsLUREdScIEmhDEuEb3A8WUxkAQOrjD0Hg/KDkeh5ToUNCQiCVSh16OuTn5zv0iLhRTEwMACA+Ph5Xr17FvHnzbDdSN5JIJOjTpw/OnDlz03gKhQIKheKmx4mayt7j2aisMkChVEMQgMjwIFwpKEVqZjYb7byE7w1DktLPX8WFqyUwVlfj3/tPYEj3aP63diFPqTWsM+Qp9p4pRJm+Aq19qiAVzWiv0SBLL0Hq2UI22nkhX4UMWYUVOF1cDb3RiohjeRjSKQxhflw0h4iovgRBApk80N1pNEvOrPx6szjezmOaguVyOXr37o3du3fb7d+9ezeSkpLqHEcURbt5gmo7np6ejlatWjmdK1FTuVqih0qpwG8dgASoFHJcLdG7My1qJJnZBdi85xfoy/WwmM04eSkXH397FJnZBe5OzWuw1hDZyympgp9CgFQ0wyRVAhYztIqa/eR9zubrsX7fReSVVUGsrsKhs1fxwd4LOJvP3xVERNSUBBdu3s1jetoBwLRp0zBu3DgkJCQgMTERa9euRVZWFiZNmgSgZjhRTk4ONm7cCAB477330LZtW3Tq1AkAkJqaijfeeMNuyfT58+ejf//+iIuLg06nw9tvv4309HS89957TX+BRPUUHqhFQYkOoohfG+5EVBmrEds6xN2pUSNgz8qmwVpD9Js2gSpk5ZfCIsjgYzEAUj/oq4AugWp3p0aNgD0riYiImheParQbPXo0ioqKsGDBAuTm5qJbt27YsWMHoqOjAQC5ubnIysqynW+1WjFz5kxcuHABMpkMsbGxeP311zFx4kTbOaWlpXjhhReQl5cHf39/9OzZEz/88AP69u3b5NdHVF8DukbhfG4R8koLIZVIkZNfAo1aieQuke5OjRrBtZ6VpcZrPVzYs7IxsNYQ/WZAXAgyr+iQnq+CWmLFhUolAv3USO7ARQm80bWelcXsWUlERG7E4bF1J4hiS7jMhtHpdPD390dZWRn8/PzcnQ55uXKjGZVGs+31sewCbNiVDmN1NRLuiERiLavH+nICaa+w5n9HcOx8DkoMVggC0K1tCK4UlKJbTGs8P6Knu9NzKX6v2uPfB7lTZk4Zlv77CPRGK+7v3Q6D7whjrysvtf7HC9h/KhfGklxIRTM6RoUhq0qFfh0j8FRSO3en51L8XnXEvxOiWxNFERcvZuH4iZMoL6+An1aLbt06IyqyDVeEhWu+Q67F2PPVMvhqGj6fanmFAYNG/sWrv9d4p0/kYTIul+HA+SLba1EU0b1dzdBIiToAh7LLcSi73Ha8f/tgJMayR4Q3YM9KImpq5UYztCoZOgbJAQAjukZAIZOi3GjmAyEvxJ6VRES1M5nM2P7Ff3Ds+AmYTCYIECBCxMGffkbPnt3xwH33QCqVujtNaoH4a4zIw8RH+iM2VGN7LYoiCvNrVpkMCQt3eMqj5k1Vs3Z9z8oQfy1G9O1k61kZ1SoUiZ2iEOKvRb7OAIA9K4nItTIul2H/uUKo5DU3Iv/8+TIEQeADIS/VIUyLZ5LaYWlhMfRGK/p0CGPPSiIiAN98+z3S03+BWqOBn1YLQRAgiiKqqgz46dARBPj7Y/Cg37k7Ta8h/rq5Io63c/md37lz5wAAsbGxrg5N1CL43tAoI4oiLBU1PSDC/JTsmu1l2LOy/lhniFwnPtIf7UPUDg+H+EDIO7FnJRGRo/LyChxOOwq5XAGV8rchm4IgQK1WwWQy4adDh5GU2BdyudyNmXoRttrVmcuqc3FxMcaPH48OHTpAFEWcPXsWn3zyCQIDA131EUQezWKxwGq1OuyXSCROd6W2WCywWCwwm2t6YplMJgiC0KCY5FnYs7LuWGeIXM9XIYNGLuXDoRaCPSuJiBxlX85BRUUlAvwDaj2u1qih15cjJycXMTHRTZsctXguu/ubOXMmpk2bhqFDhwIAvvnmG0yfPh3r1q1z1UcQebTKykrodDoUFhYCAEJCQiAIArRaLbRa54adVFZWQq/XQyar+V/1WuyGxCTPwp6Vdcc6Qy0dHw5RQ7FnJRGRI9FqBUTxpr+7Jb8Ola2tBpNz2NGu7iSuCnT8+HEMHToUCxYsQHFxMe666y6cOHHCVeGJPJ5arUZoaCgCAwMRGBiI0NBQhIaGQq1WNzjmjVtDYpLnslgsMJlMMJvNMJvNMJlMMJlMsFgs7k7NI7DOUEtXWVmJ/Px8ZGZmIjMzE/n5+SgoKEBlZWWDYhYWFkImk0Emk6GwsLDBMclz+SpkCPNTIlAjR6BGjjA/JcL8lBwaS0QtWnh4GBRKBQyGqlqPV1VVQalUIiwstIkz816i6LrN27m8QovX/a2xJZpaEqlUColEYusV5+Pj0+BeUlKplD0dWhD2rKwb1hlqqdRqNRQKha0hPzQ01NYrriExldfN33NNQ2KS52LPSiIiR8HBQbgjrgOOZhyDXC63/RYHalaVNRgM6N+vD7RaXzdmSS2VyxrtkpKSsGnTJsydOxcAsHnzZiQnJ7sqPJFHun7lT6CmMaGkohoAINUZap2PjE+z6WZ483xrrDPU0lWZRVQYLNAbaxqrFVWWX4c2CvB1sr2FD4daFj4cIiKq3b33DkdxSSkuX86BIJHAR+YDk9kEURTRvn0Mht09xN0pepWaXnINnwaIPe3qYf78+ZgyZQq2bt0KAPD398eqVatcFZ7II9248qfZYsWxs5cAAN06GCCT2je2cKJnuhXePN8a6wy1dBmXy5B6psChzrC2UF3x4RARUe38tFo889RYpB/NQHp6BnTl5WjlH46ePeJxZ/duUCgU7k7Ru3BSuzpzWaOdSqXCunXrYDAYIIoiVCqVq0ITeawbV/48ll2AjBNGGKtNgKEMfTpFoWPrINtxTvRM5DzWGWrp4iP9YbZacPhMDsqrrZBKBCTFBqNrG393p0bNBB8OERHdnEqlQmL/vkjs39fdqRDZuLwFoband0Te6vqVPzOzC7Dzp5OoqqqAVCJF1pV8FJfpETT0TnSJ4qSlRK7COkMtVV5ZFf6dnouiCiPUEitOXi5Cvt6IYF85OoRxaCMRERE1FwJENHx4LFwSw7OxLzyRi+w9no3KKgMUSjV8FApEhgehssqI1Mxsd6dGREReYO+ZQpTpK9DapwrBkgq01xih01ci9Wyhu1MjIiIiqjPRhZu3c6rR7tKlS9i1axdyc3NrPX7lypUGJUXUHF0t0UOlVOC3tScEqBRyXC3RuzMtomaJdYbIUU5JFfwUAqSiGSapErCYoVXU7CciIiIi71PvRrvNmzejQ4cOuOeeexAbG4uPP/4YQM0N1uuvv45+/fqhbdu2Lk+UyNOFB2pRZTBet4KNiCpjNSIC/dyZFlGzwzpDVLs2gSrojCIsggw+FgMglUFvBCID1e5OjYiIiKjOalaPdc3m7erdaLdw4UK8/PLLyMjIwLBhw/Diiy9i1qxZiI2NxYYNG9C3b19s27atMXIl8mgDukZBrVLCaKiEyWhETn4J1CoFkrtEujs1omaFdYaodgPiQuCv1eCKSYUiqwYXKpXw06qR3IErxxIREVEzIgqu27xcvReiOHfuHF555RVER0fjvffeQ9u2bbF//35kZGSgc+fOjZEjkccqN5pRaTQDAEL8tRjRtxM27EqHsboaUa1CkdgpCiH+WuTrDABqVo/15QqyRLfEOkNUuwh/FR7s0QrZeUUor7agU2QQkmJDEOHPlZSJiIiIvFG9Ww9MJhNUqpofh5GRkVCpVHjjjTd4I0UtUsblMhw4X2R7LYoiurerWSlWog7AoexyHMoutx3v3z4YibHsEUF0K6wzRLXLuFyGQxdKkBBZMxxWFIF954pgFcHaQkRERM2GVazZXBHH2znV5WfTpk2455570KlTJ0gkEgQGBro6L6JmIT7SH7GhGttrURRRmK8AAISEhUMQ7LvrqtnLjqhOWGeIHMVH+qN9iNqhzrC2EBEREXmnes9p97vf/Q5z585F165dERISAoPBgJUrV+If//gHMjMzYTabGyNPIo/kq5AhzE+JMD8lgjU+CFRJoVVIoFVIEKiSIlAlRbDGx3YOh8YS3R7rDFHtrtWcQI0cgRo5awsRERGRl6v3r7wffvgBAHDmzBkcPnwYR44cweHDh7Fx40aUlpbCx8cHd9xxB3755ReXJ0vkySorK6HX6yGT1fxvVVhYCADQarXQarXuTI2oWWGdIaqdxWKBxWKxNVybTCYIggCJRAKpVOrm7IiIiIjqRvx1c0Ucb+f0o9m4uDjExcXhD3/4g23fhQsX8PPPPyMtLc0lyRE1J2q1Gkql0mG/RFLvDq1EBNYZohvx4RARERF5A1Gs2VwRx9u5dDxFTEwMYmJi8Nhjj7kyLFGzIJVK2dOBqJGxzlBLxodDRERERC2L0412paWlWLduHfLy8hATE4MePXrgzjvvhEajuf2biYiIboN1hsgeHw4RERGRNxBFAaIo3P7EOsTxdk432j3yyCPIyMhAnz598NVXX+H06dOwWq1o3749evTogX/84x+uzJOIiFoY1hkiIiIiIi/ESe3qzOlGu4MHD2LPnj1ISEgAABiNRhw/fhxHjx7F0aNHXZYgERG1TKwzRERERETUkjndaNetWze7OVQUCgV69eqFXr16uSQxIiJq2VhniIiIiIi8Dzva1Z3TMxcvW7YMs2fPhsFgcGU+REREAFhniIiIiIi8kQjBZZu3c7qnXUxMDPR6PTp37owxY8agX79+6NmzJ9q2bevK/IiIqIVinSEiIiIiopbM6Z52v//975GdnY0hQ4bgp59+woQJExATE4Pg4GAMHTrUlTkSEVELxDpDREREROR9RNF1m6cyGo0uieN0T7vMzEwcOHAA3bt3t+3LyspCWloa0tPTXZEbERG1YKwzREREREReyoMb3Jyxc+dObN68GXv37kVWVhasVivUajV69eqF4cOH45lnnkHr1q3rHdfpnnZ9+vRBeXm53b62bdti1KhRmDt3rrNhiYiIALDOEBERERGRa8ybNw+CINhtERERtuOiKGLevHlo3bo1VCoVBg8ejOPHj9827ueff4477rgDTz31FCQSCf785z9j27Zt2LlzJ9atW4dBgwbh66+/Rvv27TFp0iQUFBTUK2+nG+2mTp2KefPmoaSkxNkQREREN8U6Q0REzdWqVasQExMDpVKJ3r17Y+/evTc9NzU1FcnJyQgODoZKpUKnTp3w1ltvOZy3detWdOnSBQqFAl26dMH27dsb8xKIiBqNu4bHdu3aFbm5ubYtIyPDdmz58uVYsWIF3n33XRw6dAgREREYNmwY9Hr9LWMuWbIEb7zxBq5cuYIPP/wQkyZNwgMPPIC7774bjz/+OBYsWIDvvvsO586dg5+fHzZu3FivnJ0eHvv73/8eABAXF4cHH3wQ/fv3R8+ePdG9e3coFApnwxIREQFgnSEiouZpy5YtmDp1KlatWoXk5GSsWbMGI0eORGZmZq2LKWk0GkyZMgXdu3eHRqNBamoqJk6cCI1GgxdeeAEAsH//fowePRoLFy7Eww8/jO3bt+Pxxx9Hamoq+vXr19SXSETUIK5a+bW+MWQymV3vOlscUURKSgpmzZqFRx55BADw0UcfITw8HJs2bcLEiRNvGvOnn36q02e3adMGy5cvr1e+QAN62l24cAHbt2/H//3f/6G0tBTLli1D//79odVq7eYfIiIicgbrDBERNUcrVqzAhAkT8Nxzz6Fz585ISUlBVFQUVq9eXev5PXv2xJgxY9C1a1e0a9cOTz75JEaMGGHXOy8lJQXDhg3DzJkz0alTJ8ycORN33XUXUlJSmuiqiIg8l06ns9tutgjEmTNn0Lp1a8TExOAPf/gDzp8/D6DmviMvLw/Dhw+3natQKDBo0CDs27fP6bwsFgvS09MbNHLI6Z520dHRiI6OxqhRo2z79Ho90tPT8csvvzidEBEREcA6Q0REzU91dTUOHz6MGTNm2O0fPnx4nW/80tLSsG/fPixatMi2b//+/fjjH/9od96IESNu2WhnNBrtblx1Ol2dPp+IqLGJcM06FNdiREVF2e2fO3cu5s2bZ7evX79+2LhxIzp27IirV69i0aJFSEpKwvHjx5GXlwcACA8Pt3tPeHg4Ll26VOd8pk6divj4eEyYMAEWi8XW6KdWq/Gf//wHgwcPru8lOt9oVxutVosBAwZgwIABrgxLREQEgHWGiIg8W2FhISwWS603ftduCm8mMjISBQUFMJvNmDdvHp577jnbsby8vHrHXLp0KebPn+/EVRARNTIXt9plZ2fDz8/Ptru2qXRGjhxp+3N8fDwSExMRGxuLjz76CP379wcACIL9cFtRFB323cq//vUvPPnkkwCAL7/8EhcuXMDJkyexceNGzJo1Cz/++GOdY13j9PBYIiIiIiIicuTMjd/evXvx888/4/3330dKSgo2b97coJgzZ85EWVmZbcvOzq7nVRARNQ9+fn52W13mv9ZoNIiPj8eZM2ds89zd+CAkPz/f4YHJrRQWFtpi7dixA4899hg6duyICRMm2C16UR8u7WlHRERERETUUoWEhEAqlTp14xcTEwOgpgfI1atXMW/ePIwZMwYAEBERUe+YCoWCCzdRsydaTTCV/4Rq/T5YTfmQSLXw0faDjzYJEqnW3emRk5xZ+fVmcZxlNBpx4sQJDBgwADExMYiIiMDu3bvRs2dPADXTHezZswfLli2rc8zw8HBkZmaiVatW+N///odVq1YBACorKyGVSp3K0+N62nF5dCIiamysNURE1Bjkcjl69+6N3bt32+3fvXs3kpKS6hxHFEW7+egSExMdYu7atateMYmaG9FqQOXV1ajKXwdzZSZEix4W4yUYCjajIudvsJoK3J0iOelao50rtrqaPn069uzZgwsXLuDgwYN49NFHodPp8NRTT0EQBEydOhVLlizB9u3bcezYMTz99NNQq9UYO3ZsnT/jmWeeweOPP45u3bpBEAQMGzYMAHDw4EF06tSpvn9NAJzsaWcymTB8+HCsWbMGHTt2dOqDa8Pl0YmIXMtiscBqtTrsl0gkTj/taQqNVWcA1hoiImpc06ZNw7hx45CQkIDExESsXbsWWVlZmDRpEoCaYas5OTnYuHEjAOC9995D27ZtbTd0qampeOONN/Dyyy/bYr7yyisYOHAgli1bhlGjRuGLL77A119/jdTU1Ka/QKImYiz9H8zlRyDIAiCRKG37RdEMi/ESKvM/gqb1n+o15xi1XJcvX8aYMWNQWFiI0NBQ9O/fHwcOHEB0dDQA4NVXX0VVVRUmT56MkpIS9OvXD7t27YJWW/cenfPmzUO3bt2QnZ2Nxx57zNbbWSqVOixQVFeCKDrXoTA0NBT79u1DXFycUx9cm379+qFXr152y6F37twZDz30EJYuXVqnGI888gg0Gg0+/vhjAMDo0aOh0+nw1Vdf2c655557EBgY6DBPxM3odDr4+/ujrKzMbnJDIiJPp9frodPpUFhYCKBm2I4gCNBqtfUqQK5Wl+/VxqgzgGfWGtYZImquPPXhkLu/V1etWoXly5cjNzcX3bp1w1tvvYWBAwcCAJ5++mlcvHgR33//PQDgnXfewZo1a3DhwgXIZDLExsbi+eefx8SJEyGR/DYw6l//+hdee+01nD9/HrGxsVi8eDEeeeSROufk7r8TovoQrQbos2ZBtJRDIgt0PG6phCga4dtmJqTKdk2fYAvkiu+QazG2frYCGrWqwTlVVFbh93+Y5pHfawaDAUql8vYn3obTw2PHjx+PdevWNTiBa64tjz58+HC7/c4sjz5o0CDbvv379zvEHDFixC1jGo1G6HQ6u42IqDlSq9UIDQ1FYGAgAgMDERoaitDQUKjVanendluurjOA59Qa1hki8haVlZXIz89HZmYmMjMzkZ+fj4KCAlRWVro7NbeaPHkyLl68CKPRiMOHD9sa7ABgw4YNtgY7AHj55Zdx7NgxVFRUoKysDEeOHMGLL75o12AHAI8++ihOnjyJ6upqnDhxol4NdkTNjdV0FaJFB0Fyk9+sEhVE0QhLdVbTJkZ0CxaLBQsXLkSbNm3g6+uL8+fPAwBmz57t9H2N0wtRVFdX44MPPsDu3buRkJAAjUZjd3zFihX1isfl0YmIXE8qlUIikUAmq/m69/HxaTZDCFxdZwDPqTWsM0TkLdRqNRQKBSwWC4CaXtKCIDg0OBER1Y8EgADgFgMDxWvnEXmGxYsX46OPPsLy5cvx/PPP2/bHx8fjrbfewoQJE+od0+lGu2PHjqFXr14AgNOnT9sda8gNobPLo5eXl+PAgQOYMWMGOnToYFtpyZmYM2fOxLRp02yvdTodoqKi6nMZRERuU240o9Jotr0WRRElFdUAAKnO4PD9p1bI4KvwvMXEG6vO1Pb+pq41rDNE5C2a88MhIvJcEnkrSHxCYa2+CkHiOMRQtJZDkKohU7l27mNqGp6wemxj2LhxI9auXYu77rrLNo8pAHTv3h0nT550KqbTd2nfffeds2+tFZdHJyJyjYzLZThwvsj22myx4tjZSwCAbh0MkEntn0j2bx+MxNjgJs2xLlxdZwDPqTWsM0TkLcqNZlQYTA4Phzz1gRAReS6r1YoTp84hLf0YiovL0KWdFt3bXYJC0EGQam0PBESrAaKlHHK/AZD4hLk5a3KKKEAUXfCAxxUxXCgnJwcdOnRw2G+1WmEymZyK2aC+pHv37sWTTz6JpKQk5OTkAAA+/vhjp1Yx4vLoRESuER/pjyf6tbVtvaM0EE1GGCrKAUMZ+kT52h2Pj/R3d8o35co6A7DWEBG5WsblMmzcfwnLd57C8p2nsHH/JXx6MAsZl8vcnRoRNSMmkwkfb9qOv3/4GQ4eOorzF7PwxTdV2HNYjZKSYliq82A1XYWlOg+ipRw+mp5QhYx1d9pEdrp27Yq9e/c67P/nP/+Jnj17OhXT6cdfW7duxbhx4/DEE0/gyJEjtpsXvV6PJUuWYMeOHfWOyeXRiYgazve63g2Z2QXY+dNJVFVVQCqRIutKPorL9Agaeie6RIW6OdNba4w6A7DWEBG5UnykP8xWCw6fyUF5tRVSiYCk2GB0beO5D4SIyPPs+iYVPx/JgK9WA5Xyt9EIRy/44uSlfAxL1iC+cygEqRY+mp6QqbtAENy3QjU1jLcOj507dy7GjRuHnJwcWK1WbNu2DadOncLGjRvxn//8x6mYTjfaLVq0CO+//z7Gjx+Pzz77zLY/KSkJCxYscCrm6NGjUVRUhAULFtiWR9+xYweio6MBALm5ucjK+m11GKvVipkzZ9otj/76669j4sSJdvl89tlneO211zB79mzExsZiy5Yt6Nevn5NXTkTUfOw9no3KKgMUSjUEAYgMD8KVglKkZmZ7fKNdY9QZgLWGiMiV8sqq8O/0XBRVGKGWWHHychHy9UYE+8rRIUzr7vSIqBkwGIw48FMafHxkdg12ACCXK6AvD8K2bwR07PkkggL5QIA81wMPPIAtW7ZgyZIlEAQBc+bMQa9evfDll19i2LBhTsUURNG5tkm1Wo3MzEy0a9cOWq0WR48eRfv27XH+/Hl06dIFBoPBqYQ8kU6ng7+/P8rKyuDn5+fudMgFLBYLrFarw36JRAKplE9syHss2LwH5VVG5JZVAQC6tQ1Fia4CGpUcs/8wyG151eV7lXWGmjvWGmoJ1v94AftP5cJYkgupaEbHqDBkVanQt2M4nk6KcVte/F51xL8T8lTnzmfhndUfQavVwMfHsV+R1SqisKgYE556HD3v7OqGDAlwzXfItRj//PQtqNWqBudUWVmFx574o1d/rznd065Vq1Y4e/Ys2rVrZ7c/NTUV7du3b2heRI2qsrISOp0OhYWFAGompxcEAVqtFlotnwqT9wgP1KKgRAdRBGrm7hVRZaxGbOsQd6d2W6wz1Nyx1lBLkFNSBT+FgGLRDJNUCVjM0Cpq9hMRuYIgAHDRcEryDOKvmyvieJrS0lL861//wvnz5zF9+nQEBQXhyJEjCA8PR5s2beodz+lGu4kTJ+KVV17Bhx9+CEEQcOXKFezfvx/Tp0/HnDlznA1L1CTUajUUCgUsFgsAIDQ0FIIgQCJp0NosRB5nQNconM8tQl5pIaQSKXLyS6BRK5HcJdLdqd0W6ww1d6w11BK0CVQhK78UFkEGH4sBkPpBXwV0CVS7OzUiaiZatwqDr68alZVV8Pd3fKhVUVkFlUqJtlGt3ZAdNQZvndPul19+wd133w1/f39cvHgRzz33HIKCgrB9+3ZcunTJNmd2fTjdaPfqq6+irKwMQ4YMgcFgwMCBA6FQKDB9+nRMmTLF2bBEjabcaEal0Wx7LYoi9MaaYUuKKsuvS4hbANQsxay+bjJ/oubk+n/rIf5ajOjbCRt2pcNYXY2oVqFI7BSFEH8t8nU1w0s99d866ww1d1VmERUGi0OtUSsE+HJ0LHmJAXEhyLyiQ3q+CmqJFRcqlQj0UyO5Q7C7UyOiZkKlUqJfQg/s+mYvDEYjlIrf5rUzmUyorKhCvz49EBIc6MYsiW5v2rRpePrpp7F8+XK7URUjR47E2LHOrXbcoLu0xYsXY9asWcjMzITVakWXLl3g6+vbkJBEjSbjchkOnC+yvc4v0SHjzEWIFgvCQgIRGR6CIL/f/v32bx+MxFj+4KTm58Z/66Ioonu7mkUnJOoAHMoux6HscttxT/63zjpDzVnG5TL8Oz0HP52+DKMFaBeRh5gQX9wb38pj/58jqq8IfxUe7NEK2XlFKK+2oFNkEJJiQxDh3/C5ioio5Rh+9wBczS9ExvFT0OsqIJNJYTZbAEFAx7gYPPzgcHenSC4kigJEUXBJHE9y6NAhrFmzxmF/mzZtkJeX51RMpxvtsrKyEBUVBbVajYSEBIdjbdu2dTY0UaOIj/RHbKgGAHD6SjG2XjwPuWiC1EcChbUKlaUFGNE1Ah1bBwGo6X1E1Bxd/28dqGm0K8yveWIZEhb+a6/S33jqv3XWGWru1Aopyo0mQAQCZBbIYIbOYIJawW525D0yLpfh0IUSJETWDIcVRWDfuSJYRbBxmojqTKGQ4+lxj+JY5ikcTjuGoqJS+Pn5olePrujRvTPkcrm7UyS6LaVSCZ1O57D/1KlTCA0NdSqm03dqMTExyM3NRVhYmN3+oqIixMTE2OZvIfIUvtcNAdy+Lw9mkwlqtRqCAMS0CsGVglJkXsrD7zpxrgRq3nxvGO4qiiIsFTU/dML8lA6Ndp6KdYaauyOXSmA0GBGpqKpZVVPri6wqI9KySnBnZIC70yNyifhIf7QPUTs8HPLUB0JE5LlkMil6dO+CHt27uDsVIqeMGjUKCxYswD/+8Q8AgCAIyMrKwowZM/D73//eqZhOz4QsimKtN37l5eVQKpXOhiVqEldL9FApFfjtn7AAlUKOqyV6d6ZF5HIWiwUmkwlmsxlmsxkmkwkmk6lZNHixzlBzd21VTSlX1SQv5quQIcxPiUCNHIEaOcL8lAjzU3rkXKlEROQZri1E4YrNk7zxxhsoKChAWFgYqqqqMGjQIHTo0AFarRaLFy92Kma9q+m0adMA1LQYzp49G2r1bytDWSwWHDx4ED169HAqGaKmEh6oRUGJDqL46xLiEFFlrEZs6xB3p0bkUpWVldDr9ZDJar7uCwsLAQBardZuclRPwjpD3oKralJLYLFYYLFYYDbXLIBkMplsqyRLpRwKTkREjsRfN1fE8SR+fn5ITU3Ft99+iyNHjsBqtaJXr164++67nY5Z70a7tLQ0ADU9IDIyMuzGlsvlctx5552YPn260wkRNYUBXaNwPrcIeaWFkEqkyMkvgUatRHKXSHenRuRSarW61l5pEonTHa0bHesMeQuuqkktQXN8OERERORqZrMZSqUS6enpGDp0KIYOHeqSuPVutPvuu+8AAM888wzefvtth2IsiiKys7NdkhyRK5Ubzag01jwFDvHXYkTfTtiwKx3G6mpEtQpFYqcohPhrka8zAKiZnJ9DO6i5k0qlza6nA+sMeQuuqkktQXN8OERERO7lqqGtnjQ8ViaTITo62uXTEDndIrFx40YsW7bM4WaquLiYE4STR8q4XIYD54tsr0VRRPd2NSu4SNQBOJRdjkPZ5bbj/dsHc9UzIjdinaHmjqtqUkvQHB8OERGRm3np+NjXXnsNM2fOxCeffIKgoCCXxHS60U68SZMmJwgnTxUf6Y/YUI3ttSiKDiudXY+rnhG5F+sMNXdcVZOIiIio5Xj77bdx9uxZtG7dGtHR0dBoNHbHjxw5Uu+YDVqIYs6cOZwgnJoN3+uGu16bNNmoqBm6EaiSctJkIg/BOkPeQiUTIFdJa6k1jqsiExEREbUUIgSIaPjvIVfEcKWHHnrI5TG5EAW1SJw0mchzsc6Qt2CtISIiInLkjXPaAcDcuXNdHrNBC1GsXLkSfn5+Lk+KqLFx0mQiz8U6Q96CtYaIiIiIGsLpSVXWr1/vyjyImhQnTSbyfKwz1Nyx1hA1T+fOnQMAxMbGujkTIiJqTgIDAx3mygdqpv1RKpXo0KEDnn76aTzzzDN1jlmvRrtp06Zh4cKF0Gg0tjmHbmbFihX1CU1ERMQ6Q0REblNcXIzx48ejQ4cOEEURZ8+exSeffILAwEB3p0ZE5FW8dXjsnDlzsHjxYowcORJ9+/aFKIo4dOgQ/ve//+Gll17ChQsX8OKLL8JsNuP555+vU8x6NdqlpaXBZDLZ/nwztbUsEhER3Q7rDBERucvMmTMxbdo0DB06FADwzTffYPr06Vi3bp2bMyMiouYgNTUVixYtwqRJk+z2r1mzBrt27cLWrVvRvXt3vP32243TaHdtnqEb/0xEROQKrDNEROQux48fx9ChQ7FgwQJMmTIFd911F2bPnu3utIiIqJnYuXMnli1b5rD/rrvuwp/+9CcAwL333osZM2bUOSZnQiYiIiIiIvqVeN14K6vV6sZMiIi807Xhsa7YPElQUBC+/PJLh/1ffvklgoKCAAAVFRXQarV1jlnvhSgeeeSR2weVyRAREYFhw4bhgQceqO9HEBFRC8Y6Q0RE7pCUlIRNmzZh7ty5AIDNmzcjOTnZzVkREVFzMXv2bLz44ov47rvv0LdvXwiCgJ9++gk7duzA+++/DwDYvXs3Bg0aVOeY9W608/f3v+05VqsVZ86cwQcffIDp06djwYIF9f0YIiJqoVhniIjIHebPn48pU6Zg69atAGrq0apVq9ycFRGR9/HWhSief/55dOnSBe+++y62bdsGURTRqVMn7NmzB0lJSQBgGyZbV/VutFu/fn2dz/3vf/+LF198kTdTRERUZ6wzRETkDiqVCuvWrYPBYIAoilCpVO5OiYjIK4m/bq6I42mSk5Nd2ku7Uee0S05ORkJCQmN+BBERtWCsM0RE5GpKpZINdkRE5JRz587htddew9ixY5Gfnw8A+N///ofjx487Fa9RG+0CAgKwbdu2xvwIIiJqwVhniIiIiIjIE+zZswfx8fE4ePAgtm7divLycgDAL7/8Ypsvtb64eiwRERERERERETUJb109dsaMGVi0aBF2794NuVxu2z9kyBDs37/fqZhstCMiIiIiohbl0qVL2LVrF3Jzc2s9fuXKlSbOiIiImruMjAw8/PDDDvtDQ0NRVFTkVEw22hERERERUYuxefNmdOjQAffccw9iY2Px8ccfA6hpyHv99dfRr18/tG3b1s1ZEhF5L2/taRcQEFDrw6C0tDS0adPGqZhON9otXboUH374ocP+Dz/8EMuWLXM2LBEREQDWGSIiahwLFy7Eyy+/jIyMDAwbNgwvvvgiZs2ahdjYWGzYsAF9+/blfKlERI1IhIsa7dx9ITcYO3Ys/vKXvyAvLw+CIMBqteLHH3/E9OnTMX78eKdiOt1ot2bNGnTq1Mlhf9euXfH+++87G5aIiAgA6wwRETWOc+fO4ZVXXkHXrl3x3nvvobKyEvv370dGRgZOnjyJd955Bw8++KC70yQiomZm8eLFaNu2Ldq0aYPy8nJ06dIFAwcORFJSEl577TWnYsqcTSYvLw+tWrVy2B8aGnrTuSGIiIjqinWGiIgag8lkgkqlAgBERkZCpVLhjTfeQOfOnd2cGRERNWc+Pj749NNPsWDBAqSlpcFqtaJnz56Ii4tzOqbTjXZRUVH48ccfERMTY7f/xx9/ROvWrZ1OiIiICGCdISKixrNp0ybcc8896NSpEyQSCQIDA92dEhFRiyHCNUNbPW147DWxsbGIjY11SSynG+2ee+45TJ06FSaTCUOHDgUAfPPNN3j11Vfxpz/9ySXJERFRy8U6Q0REjeF3v/sd5s6diz/96U8IDAyEwWDAypUrkZSUhG7duqFjx46QyZy+TSIiomZg6dKl+Otf/4pXXnkFKSkpAABRFDF//nysXbsWJSUl6NevH9577z107dr1pnGmTZtW589csWJFvfN0uhq9+uqrKC4uxuTJk1FdXQ0AUCqV+Mtf/oKZM2c6G5aIiAgA6wwRETWOH374AQBw5swZ/Pzzz0hLS8Phw4exceNGlJaWwsfHB3fccQd++eUXN2dKROSdXLXyq7MxDh06hLVr16J79+52+5cvX44VK1Zgw4YN6NixIxYtWoRhw4bh1KlT0Gq1tcZKS0uze3348GFYLBbccccdAIDTp09DKpWid+/eTuXq9EIUgiBg2bJlKCgowIEDB3D06FEUFxdjzpw5zoYkIiKyYZ0hIqLGFBcXhzFjxmD58uX45ptvUFxcjLNnz+KTTz7B/fff36DYq1atQkxMDJRKJXr37o29e/fe9Nxt27Zh2LBhCA0NhZ+fHxITE7Fz5067czZs2ABBEBw2g8HQoDyJiNzCFSvHOjnGtry8HE888QT+/ve/202NIIoiUlJSMGvWLDzyyCPo1q0bPvroI1RWVmLTpk03jffdd9/ZtgceeACDBw/G5cuXceTIERw5cgTZ2dkYMmQI7rvvPif+ohrQaHeNr68v+vTpg27dukGhUDQ0HBERkR3WGSIiagylpaV488038ec//xmrVq3C/v37ER4ejsceewxLlixxOu6WLVswdepUzJo1C2lpaRgwYABGjhyJrKysWs//4YcfMGzYMOzYsQOHDx/GkCFD8MADDzj03vDz80Nubq7dplQqnc6TiMhb6HQ6u81oNN703Jdeegn33Xcf7r77brv9Fy5cQF5eHoYPH27bp1AoMGjQIOzbt69Oebz55ptYunSpXWNgYGAgFi1ahDfffLOeV1WjXsNjp02bhoULF0Kj0dx23K4zY3WJiKhlY50hIqKm8sgjjyAjIwN9+vTBV199hdOnT8NqtaJ9+/bo2bMntmzZ4lTcFStWYMKECXjuuecAACkpKdi5cydWr16NpUuXOpx/bS6la5YsWYIvvvgCX375JXr27GnbLwgCIiIinMqJiMibRUVF2b2eO3cu5s2b53DeZ599hiNHjuDQoUMOx/Ly8gAA4eHhdvvDw8Nx6dKlOuWh0+lw9epVhznw8vPzodfr6xTjRvVqtEtLS4PJZLL9+WYEQXAqGSIiatlYZ4iIqKkcPHgQe/bsQUJCAgDAaDTi+PHjSE9Pd3o+u+rqahw+fBgzZsyw2z98+PA699SwWq3Q6/UICgqy219eXo7o6GhYLBb06NEDCxcutGvUu5HRaLTrbaLT6epxJUREjcfVc9plZ2fDz8/Ptr+20TnZ2dl45ZVXsGvXrlv2Ur7xPkMUxTrfezz88MN45pln8Oabb6J///4AgAMHDuDPf/4zHnnkkTrFuFG9Gu2+++67Wv9MRETkCqwzRETUVLp16waJ5LfZghQKBXr16oVevXo5HbOwsBAWi6XWnhrXenHczptvvomKigo8/vjjtn2dOnXChg0bEB8fD51Oh5UrVyI5ORlHjx5FXFxcrXGWLl2K+fPnO30tRETNhZ+fn12jXW0OHz6M/Px8uwUhLBYLfvjhB7z77rs4deoUgJoed61atbKdk5+f7/CdfjPvv/8+pk+fjieffNLWEUEmk2HChAn429/+Vt/LAuDknHYmkwlDhgzB6dOnnfrQW+GkrURE1Jh1BmCtISIiYNmyZZg9e3ajfFc721Nj8+bNmDdvHrZs2YKwsDDb/v79++PJJ5/EnXfeiQEDBuAf//gHOnbsiHfeeeemsWbOnImysjLblp2d7fwFERG5kOjCra7uuusuZGRkID093bYlJCTgiSeeQHp6Otq3b4+IiAjs3r3b9p7q6mrs2bMHSUlJdfoMtVqNVatWoaioCGlpaThy5AiKi4uxatUqaDSaemT7m3r1tLvGx8cHx44dc/nwpGuTtq5atQrJyclYs2YNRo4ciczMTLRt29bh/GuTti5ZsgQBAQFYv349HnjgARw8eNCuq7ifn5+t1fQaTtpKROS5GqvOAKw1RERUIyYmBnq9Hp07d8aYMWPQr18/9OzZs9ZaUFchISGQSqUOverq0lNjy5YtmDBhAv75z386TJB+I4lEgj59+uDMmTM3PUehUHABJyLySK4eHlsXWq0W3bp1s9un0WgQHBxs2z916lQsWbIEcXFxiIuLw5IlS6BWqzF27Nh65aXRaNC9e/d6vedmnF49dvz48Vi3bp1Lkrjm+klbO3fujJSUFERFRWH16tW1np+SkoJXX30Vffr0sf2FxsXF4csvv7Q779qkrddvRETk2RqjzgCsNUREVOP3v/89srOzMWTIEPz000+YMGECYmJiEBwcjKFDhzoVUy6Xo3fv3nY9NQBg9+7dt+ypsXnzZjz99NPYtGkT7rvvvtt+jiiKSE9PtxvCRUREDfPqq69i6tSpmDx5MhISEpCTk4Ndu3ZBq9Xe9D2TJk2qc0/mLVu24NNPP61XTk71tANqugl+8MEH2L17NxISEhy6+tV3VT9O2kpERNdzdZ25FtMTag3rDBGR+2VmZuLAgQN2vSGysrKQlpaG9PR0p+NOmzYN48aNQ0JCAhITE7F27VpkZWVh0qRJAGqGrebk5GDjxo0Aahrsxo8fj5UrV6J///62XnoqlQr+/v4AgPnz56N///6Ii4uDTqfD22+/jfT0dLz33ntO50lE1NJ9//33dq8FQcC8efNqXXn2ZkJDQ9GtWzckJSXhwQcfREJCAlq3bg2lUomSkhJkZmYiNTUVn332Gdq0aYO1a9fWK0enG+2OHTtmm6T1xjmHnBnOxElbiYjoeq6uM4Dn1BrWGSIi9+vTpw/Ky8vt9rVt2xZt27bFqFGjnI47evRoFBUVYcGCBcjNzUW3bt2wY8cOREdHAwByc3ORlZVlO3/NmjUwm8146aWX8NJLL9n2P/XUU9iwYQMAoLS0FC+88ALy8vLg7++Pnj174ocffkDfvn2dzpOIyF3cMTy2sSxcuBAvv/wy1q1bh/fffx/Hjh2zO67VanH33Xfjgw8+wPDhw+sdXxBF5y4zKysLkZGRdisuATVdtbOzs+s9F8SVK1fQpk0b7Nu3D4mJibb9ixcvxscff4yTJ0/e8v2bN2/Gc889hy+++OKWc0BYrVb06tULAwcOxNtvv13rObX1gIiKikJZWdltVyQhIqLb0+l08Pf3v+X3qqvrDOA5tYZ1hoiocdWlzmzfvh2rV6/Gli1bEBgY2MQZNr26/J0QEd2MK75DrsVY9d5KqFSqBudUVVWFyS+94lHfa6Wlpbh06RKqqqoQEhKC2NjYBs3T7XRPu5iYGOTm5tqtagQAxcXFiImJgcViqVc8TtpKRETXc3WdATyn1rDOEBG53+9//3sAQFxcHB588EH0798fPXv2RPfu3fkdTS2KxWKFsboaCrkcUqnT094T1Zk39bS7UUBAAAICAlwWz+lGu5t10CsvL3dqtbzrJ219+OGHbft37959y+7pmzdvxrPPPovNmzfXa9LW+Pj4eudIRERNx9V1BmCtISKi31y4cAHp6ek4evQo0tPTsWzZMly8eBFSqRSdOnXCL7/84u4UiRpVcYkO3/1wCD8eOIoqgxEqlRLJ/bpj6KA+CAzwjF5LRC1dvRvtpk2bBqBmPqE5c+ZArVbbjlksFhw8eBA9evRwKhlO2kpERI1ZZ67FZ60hIqLo6GhER0fbPbTR6/VIT09ngx15vfyCYqSs2ozLV65CLpdDLpdBp6/A5zv24HD6SfzxpbEIDfH+YeNEnq7ejXZpaWkAanoRZGRkQC6X247J5XLceeedmD59ulPJcNJWIiJqzDoDsNYQEdHNabVaDBgwAAMGDHB3KkSN6rOtu5CdcxVhoYGQSqU1OzU1D0izc65iy7bdmPLC47cOQuQk8dfNFXG8ndMLUTzzzDNYuXKlx0z215g4aav7WSwWWK1Wh/0SieS3IkNEzUZdvldZZ6gpsc4QeRd+rzri3wldc/XqeXz1n+XoHFMEtVLE1WJ/HL8QiZyCIAACyssrYbVaMf+vkxAeFuTudMlDuHIhinffcd1CFFNe9qyFKFzN6Tnt1q9f78o8iG6psrISOp0OhYWFAGomkxcEAVqtFlqt1s3ZEVFjYJ2hpsQ6Q0RELYFovgiVcRHuTb4AQZBCFCVoE1qCbrGXceBYB+zL6AiVSomiolLk5Rex0Y7IzRq0NMzevXvx5JNPIjExETk5OQCAjz/+GKmpqS5Jjlq2cqMZ+ToD8nUGlJslEBVaQK4B5BqICi1EhRblZslv5xjN7k6ZiFyMdYaailqtRmhoKAIDAxEYGIjQ0FCEhobazalIRETUnIliNaBPgUJWgOIyFUr1augqVCjWaWC1CkiKP4OOUXmwWq2QSAT4yNjTnBrHtdVjXbF5kqtXr2LcuHFo3bo1ZDIZpFKp3eYMp3vabd26FePGjcMTTzyBtLQ0GI1GADWTty5ZsgQ7duxwNjQRACDjchkOnC+yvc4v0SHjzEWIFgvCQgIRGR6CID9f2/H+7YORGBvsjlSJWpSmGkbIOkNNqcos4thlHb7LKMDVchPaRxjQr30wurbxhy/vWYiaVFPUGZPJhOHDh2PNmjXo2LGjS2ISebzqw4AlGzJ5OKTSfJgtFvjIZAAEVBoUCNBW4M64i9h/VI2gQH/ExkS6O2PyUt46p93TTz+NrKwszJ49G61atYIgCA2O6XSj3aJFi/D+++9j/Pjx+Oyzz2z7k5KSsGDBggYnRhQf6Y/YUA0A4PSVYmy9eB5y0QSpjwQKaxUqSwswomsEOrau6bKtVjj9z5mI6qGphhGyzlBT+l9GHj5MPY/i0lIoBQuO5+qxM/MqnkmKwaMJvGkhakpNUWd8fHxw7Ngxl9xQETUb5nOAaIVEqkBgoB/yC0pgtlgg+7Ux3FgtQ3hQMSRCNYYP7Q+FQn6bgER0vdTUVOzduxc9evRwWUynWzlOnTqFgQMHOuz38/NDaWlpQ3IiAgD4KmTw/bUhbvu+PJhNJqjVaggCENMqBFcKSpF5KQ+/69TazZkStSxqtRoKhQIWiwUAEBoaCkEQIJE0aMYFB6wz1JQKKwwI85UgqNoIGSxo18oP2UYJiiqM7k6NqMVpqjozfvx4rFu3Dq+//rpL4xJ5tpqG6qBAP1gsVpSU6lBtMgEAfGQWCJDgvuG/w91D+rozSfJyoijCyTVRHeJ4kqioKJfn5HSjXatWrXD27Fm0a9fObn9qairat2/f0LyI7Fwt0UOlVKDUWPXrHgEqhRxXS/RuzYuopSg3mlF53byRp3KK8EP6ORSXG9AmLA9974j8tddrzY8+9XWN7s5inaGmVKivRohKimKJFSapCkqJFSFqKQrL2WhH1NSaarh6dXU1PvjgA+zevRsJCQnQaDR2x1esWOG6DyPyBLI4QJAAYjUEQY6wkEAE+PtCX14Js9kCraoMgiIBD905wt2ZEjVLKSkpmDFjBtasWeNwD+Msp++oJk6ciFdeeQUffvghBEHAlStXsH//fkyfPh1z5sxxSXJE14QHalFQooMoAjWjGERUGasR2zrE3akRtQjXzzFZrCvHmfNZ0FeUA4IEWYV6/HQ2F3HtomzzTLpijknWGWpKbQJVyMovhUWQwcdiAKR+0FcBXQK5EAVRU2uq4erHjh1Dr169AACnT5+2O8Zhs+SV5L0AaVvAfB6QhAOCDHIfHwQH+AFiKQAtoL3f3VkSNVujR49GZWUlYmNjoVar4ePjY3e8uLi43jGdbrR79dVXUVZWhiFDhsBgMGDgwIFQKBSYPn06pkyZ4mxYIpvre/Z0bReBk9n5qCwugVQqwcXcQqhVSnSODke+zgDANT17iKh2188x+en3vyBAJUAQNBAEAR1bByG/uBRBPmY80a8tANfMMck6Q02pV3QgjlwqxjmjCiqpBaLeB36+CvRoG+Du1IhanKYarv7dd9+5NB6RpxMEH4jaqYDuDcByGTVDZSUATICgBtSjAXkf9yZJLYKrVn71sNGxSElJcXnMBt1VLV68GLNmzUJmZiasViu6dOkCX1/f27+RqA5uXD1WExiKgCozjNXVMEnVUAcE41B2OQ5llwPg6rFEjen6OSb1FVXwU6tQYaoZrq6Sy+CnVqG8sgphfkqXfi7rDDWVSqMF/io5WgeqoDdaYRV84Kf0QaXR4u7UiFocDlcnajyCrC3EgMWA8Ueg+mdANACyGEAxCIJPB3enRy2Gd64f+9RTT7k8ZoMa7QwGA44dO4b8/HxYrVbk5eXZjj344IMNTo5atut79gA1k0wW5tc0yoWEhTsMW+DqsURNoymHq7POUFOJj/RH+xA1CvNrVqa8VmdYW4iaXlMOV9+7dy/WrFmDc+fO4V//+hfatGmDjz/+GDExMfjd737n8s8j8gSCRAuo7qnZiMilLBYLPv/8c5w4cQKCIKBLly548MEHIZU6Nymr079E//e//2HcuHEoKipyOCYIgm21JyJnXd+zx2KxwGKxwKioWTUsUCW1rSLm7D9+Iqo7dwxXZ52hpqSSCZCrpLXUGc5rRdTUmmq4+tatWzFu3Dg88cQTSEtLg9FY05NPr9djyZIl2LFjh0s/j4iIanjr8NizZ8/i3nvvRU5ODu644w6IoojTp08jKioK//3vfxEbG1vvmE6vmz5lyhQ8/vjjyM3NhdVqtdt4I0WuVllZicLCQshkMshkMhQWFqKgoACVlZXuTo2oRci4XIZPD2bh04NZOJRdXjNc3U8LuY/Pr8PVQ3Eou9x2TsblsgZ/JusMNSXWGSLPcf1wdZlC3WjD1RctWoT3338ff//73+0mC09KSsKRI0dc+llERPQbURRdtnmS//u//0NsbCyys7Nx5MgRpKWlISsrCzExMfi///s/p2I63Q0iPz8f06ZNQ3h4uLMhiOpMrVZDqXScK0sicbrdmYjqwR3D1VlnqCmxzhB5jqYarn7q1CkMHDjQYb+fnx9KS0td+llEROT99uzZgwMHDiAoKMi2Lzg4GK+//jqSk5Odiul05Xv00Ufx/fffO9W9j6i+pFIph8ESuZE7hquzzlBTYp0h8hxNNVy9VatWOHv2LNq1a2e3PzU1Fe3bt3fpZxERkfdTKBTQ6/UO+8vLyyGXy52K6XSj3bvvvovHHnsMe/fuRXx8vF2XcgBOd/0jIiLPVllZCb1eD5mspoQUFhYCALRaLbRarcs+h3WGiKhlaqo6M3HiRLzyyiv48MMPIQgCrly5gv3792P69OmYM2eOyz6HiIjseefascD999+PF154AevWrUPfvn0BAAcPHsSkSZOcXkTP6Ua7TZs2YefOnVCpVPj+++/thkYJgsCbKSIiL9VUwwhZZ4iIWqamqjOvvvoqysrKMGTIEBgMBgwcOBAKhQLTp0/HlClTXPpZRETk/d5++2089dRTSExMtHU4MJvNePDBB7Fy5UqnYjrdaPfaa69hwYIFmDFjBud7ISJqQZpqGCHrDBFRy9RUdSYrKwsLFy7ErFmzkJmZCavVii5dukCj0SArKwtt27Zt9ByIiFoib109NiAgAF988QXOnDmDkydPQhRFdOnSBR06dHA6ptONdtXV1Rg9ejRvpIiIqFGwzhARUWOKiYlBbm4uwsLCkJCQYNtfVFSEmJgYrlRORNRYvHV87K/i4uIQFxfnklhON9o99dRT2LJlC/7617+6JBEiIqLrsc4QEVFjEm/SRaO8vLzW4blEREQ3mjZtGhYuXAiNRoNp06bd8twVK1bUO77TjXYWiwXLly/Hzp070b17d4cJwp1JhoiI6BrWGSIiagzXbqoEQcCcOXOgVqttxywWCw4ePIgePXq4KTsiIu/nTR3t0tLSYDKZbH92Nacb7TIyMtCzZ08AwLFjx+yOXT9ZOBERkTNYZ4iIqDFcu6kSRREZGRmQy+W2Y3K5HHfeeSemT5/urvSIiLyfF01q991339X6Z1dxutGuMZIhIiK6hnWGiIgaw7X68swzz2DlypXw8/Nzc0ZEROQNnn32WaxcuRJardZuf0VFBV5++WV8+OGH9Y7J2b2JiIiIiKjFmT9/vsON1TVZWVlNnA0RUcshunDzJB999BGqqqoc9ldVVWHjxo1OxXS6px0AlJaWYt26dThx4gQEQUDnzp0xYcIE+Pv7NyQsERERANYZIiJqPNevHns9rh5LRNS4vGh0LABAp9NBFEWIogi9Xm+3mJHFYsGOHTscak1dOd3T7ueff0ZsbCzeeustFBcXo7CwEG+99RZiY2Nx5MgRZ8MSEREBYJ0hIqLGJYpirXOkcvVYIiKqj4CAAAQFBUEQBHTs2BGBgYG2LSQkBM8++yxeeuklp2I73dPuj3/8Ix588EH8/e9/h0xWE8ZsNuO5557D1KlT8cMPPzgbmoiIiHWGiIgaxfWrx86ePZurxxIRNTkRotesH1szV6ooihg6dCi2bt2KoKAg2zG5XI7o6Gi0bt3aqdhON9r9/PPPdjdSACCTyfDqq68iISHB2bBEREQAWGeIiKhxcPVYIiI3c9WEdJ7RZodBgwYBAC5cuICoqChIJK5bPsLpRjs/Pz9kZWWhU6dOdvuzs7NvOqErERFRXbHOEBFRY+DqsURE1Biio6MBAJWVlcjKykJ1dbXd8e7du9c7ptONdqNHj8aECRPwxhtvICkpCYIgIDU1FX/+858xZswYZ8MSEREBYJ0hIqLGtX79enenQETUcnlILzlXKigowDPPPIOvvvqq1uPOLHDkdKPdG2+8AUEQMH78eJjNZgCAj48PXnzxRbz++uvOhiUiIgLAOkNERK43bdo0LFy4EBqNxja33c2sWLGiibIiImpZvGx0rM3UqVNRUlKCAwcOYMiQIdi+fTuuXr2KRYsW4c0333QqptONdnK5HCtXrsTSpUtx7tw5iKKIDh062E3kSkRE5CzWGSIicrW0tDSYTCbbn2+mtlVliYiIbuXbb7/FF198gT59+kAikSA6OhrDhg2Dn58fli5divvuu6/eMZ1qtDOZTBg+fDjWrFmDjh07Ij4+3pkwREREtWKdISKixnBtPrsb/0xERE1HdNHqsa5ZgdZ1KioqEBYWBgAICgpCQUGB7V7myJEjTsV0akkLHx8fHDt2jE+giIioUbDOEBERERF5J1F03eZJ7rjjDpw6dQoA0KNHD6xZswY5OTl4//330apVK6diOj08dvz48Vi3bh3nFSIiokbBOkNERI3hkUceue05MpkMERERGDZsGB544IEmyIqIiJq7qVOnIjc3FwAwd+5cjBgxAp9++inkcjk2bNjgVEynG+2qq6vxwQcfYPfu3UhISIBGo7E7zolbiYioIVhniIioMfj7+9/2HKvVijNnzuCDDz7A9OnTsWDBgibIjIiohXDDShSrV6/G6tWrcfHiRQBA165dMWfOHIwcObImlChi/vz5WLt2LUpKStCvXz+899576Nq1a50/44knnrD9uWfPnrh48SJOnjyJtm3bIiQkpO7JXsfpRrtjx46hV69eAIDTp0/bHeNwJiIiaijWGSIiagzr16+v87n//e9/8eKLL9a70W7VqlX429/+htzcXHTt2hUpKSkYMGBAredu27YNq1evRnp6OoxGI7p27Yp58+ZhxIgRdudt3boVs2fPxrlz5xAbG4vFixfj4YcfrldeRESewB2rx0ZGRuL1119Hhw4dAAAfffQRRo0ahbS0NHTt2hXLly/HihUrsGHDBnTs2BGLFi3CsGHDcOrUKWi1WqfyU6vVtvsZZzndaPfRRx8hMjISEon9tHiiKCI7O7tBSREREbHOEBGRuyUnJyMhIaFe79myZQumTp2KVatWITk5GWvWrMHIkSORmZmJtm3bOpz/ww8/YNiwYViyZAkCAgKwfv16PPDAAzh48CB69uwJANi/fz9Gjx6NhQsX4uGHH8b27dvx+OOPIzU1Ff369XPJtRIRebMbpzpYvHgxVq9ejQMHDqBLly5ISUnBrFmzbFMofPTRRwgPD8emTZswceLEOn2GxWLBhg0b8M033yA/Px9Wq9Xu+LffflvvvAVRdG7qPqlUitzcXNvKGNcUFRUhLCwMFovFmbAeSafTwd/fH2VlZfDz83N3OkREzV5dvldZZ4iIyFnu/F7t168fevXqhdWrV9v2de7cGQ899BCWLl1apxhdu3bF6NGjMWfOHADA6NGjodPp8NVXX9nOueeeexAYGIjNmzfXKSZrDRE1hCu+Q67FWLTkb1AqVQ3OyWCowmt//TOys7PtclIoFFAoFDd9n8ViwT//+U889dRTSEtLg1KpRGxsLI4cOWJ7WAIAo0aNQkBAAD766KM65TNlyhRs2LAB9913H1q1auUwOuitt96q5xU6uXosUNPToTbl5eVQKpXOhsWqVasQExMDpVKJ3r17Y+/evTc9d9u2bRg2bBhCQ0Ph5+eHxMRE7Ny50+G8rVu3okuXLlAoFOjSpQu2b9/udH5ERNQ0GqvOAKw1RETUOKqrq3H48GEMHz7cbv/w4cOxb9++OsWwWq3Q6/UICgqy7du/f79DzBEjRtwyptFohE6ns9uIiDyB6MINAKKiouDv72/bbvaAJCMjA76+vlAoFJg0aRK2b9+OLl26IC8vDwAQHh5ud354eLjtWF189tln+Mc//oEtW7YgJSUFb731lt3mjHoPj502bRqAmvmE5syZA7VabTtmsVhw8OBB9OjRw6lk2JWciIgas84ArDVERNR4CgsLYbFYGnTj9+abb6KiogKPP/64bV9eXl69Yy5duhTz58+vR/ZERM1TbT3tanPHHXcgPT0dpaWl2Lp1K5566ins2bPHdvzGnnGiKNZrLm25XG6bM89V6j08dsiQIQCAPXv2IDExEXK53C7Bdu3aYfr06YiLi6t3MuxKTkTUMtzqe7Ux6wzgmbWGdYaIyLXc9b165coVtGnTBvv27UNiYqJt/+LFi/Hxxx/j5MmTt3z/5s2b8dxzz+GLL77A3Xffbdsvl8vx0UcfYcyYMbZ9n376KSZMmACDwVBrLKPRCKPRaHut0+kQFRXFWkNETnHl8NiFLhweO/uvf3Y6p7vvvhuxsbH4y1/+4pLhsW+++SbOnz+Pd99912UL59W7p913330HAHjmmWewcuVKl33hX+tKPmPGDLv9ruhK/sc//tHuvBEjRiAlJeWmcWorcERE1DQaq84AnlNrWGeIiLxTSEgIpFKpQw+4/Px8h55yN9qyZQsmTJiAf/7zn3YNdgAQERFR75i3m9OJiMht3LF8bG1vF0UYjUbExMQgIiICu3fvtjXaVVdXY8+ePVi2bFmd46WmpuK7777DV199ha5du8LHx8fu+LZt2+qdo9Nz2q1atQoy2W9tfpcuXUJKSgp27drlVDxP60p+/XjoqKioelwJERG5gqvrDOA5tYZ1hojIO8nlcvTu3Ru7d++22797924kJSXd9H2bN2/G008/jU2bNuG+++5zOJ6YmOgQc9euXbeMSUREv/nrX/+KvXv34uLFi8jIyMCsWbPw/fff44knnoAgCJg6dSqWLFmC7du349ixY3j66aehVqsxduzYOn9GQEAAHn74YQwaNAghISF2v/f9/f2dyrvePe2uGTVqFB555BFMmjQJpaWl6Nu3L+RyOQoLC7FixQq8+OKLTsV1dgzx5s2bMW/ePHzxxRcOKw3WN+bMmTNtcyoBv3UlJyKiptNYdQZwf61hnSEi8l7Tpk3DuHHjkJCQgMTERKxduxZZWVmYNGkSgJoakJOTg40bNwKoqS3jx4/HypUr0b9/f9sDH5VKZbvJe+WVVzBw4EAsW7YMo0aNwhdffIGvv/4aqamp7rlIIqIGEMWazRVx6urq1asYN24ccnNz4e/v///t3Xd8VFX+//H3pE16gAAJJcQA0pYiTQjdrwJiAcHCigqsKD8WEAGVhQXXgK4oImBDwRWiqxRdwbKLKxEQEBQxJHQpUgKYGIGQXmfu7w9kljEBUiYzk+T1fDzu4+GcuXPmMwecD/O559yj9u3b67///a/69+8vSZo2bZpyc3M1fvx4paWlqVu3blq/fr2CgoJK/R7Lly8v60e4pnLPtNu1a5d69+4tSfrXv/6l8PBwnTx5Uu+9955effXVMvfniKnkH374ocOmkgcHB9sdAADncnSekdwn15BnAKD6Gj58uBYtWqQ5c+bohhtu0JYtW7Ru3TpFRkZKkpKTk5WUlGQ7f8mSJSoqKtKECRPUoEED2/H444/bzunRo4dWrVql5cuXq3379oqNjdXq1avZ7AhAlWQYhsOO0nrnnXd04sQJ5efnKzU1VV999ZWtYCddvAAfExOj5ORk5eXlafPmzWrbtm1lfPwyKfdMu5ycHFvFcf369Ro2bJg8PDzUvXt3nTx5ssz9XT6VfOjQobb2uLg4DRky5IqvW7lypR5++GGtXLnyqlPJL7/XEFPJAcD9OTrPSOQaAIBzjB8/XuPHjy/xudjYWLvHX3/9dan6vOeee3TPPfdUMDIAgCN16tRJGzZsUO3atdWxY8errt7ZtWtXmfsvd9GuefPm+uSTTzR06FB9+eWXth8qqamp5Z4xwFRyAMAllZFnJHINAAAAAMcYMmSIbdOfu+66y+H9l7to97e//U0jRozQlClTdPPNN9u2NF+/fr3dFrllMXz4cJ07d05z5sxRcnKy2rZtW+qp5BMmTLC1jxo1ynYF69JU8lmzZunpp59Ws2bNmEoOAFVAZeQZiVwDAAAAuJKbbB7rEM8880yJ/+0oJqMsi4B/JyUlRcnJyerQoYM8PC7eHu/7779XcHCwWrVq5bAgXS0jI0MhISFKT0/nvkMA4ACl/V4lzwAAyoPv1eIYEwAV4YjvkEt9xDw7T76+fhWOKS8vVzFPT6vW32vlnmknXbzxdnh4uF3bjTfeqMTExIp0CwCAJPIMAAAAUN24YvfYylK7du2r3sfucufPny9z/xUq2l0uPT1dH3zwgf7xj39o9+7dslgsjuoaAADyDAAAAFANVKflsYsWLarU/itctNu4caOWLVumNWvWKDIyUnfffbfeeecdR8QGAAB5BgAAVFnZOXn69oeDit99RHl5+YqMCFPfHu0V1ST82i8G4PZGjRpVqf2Xq2h3+vRpxcbGatmyZcrOztZ9992nwsJCffzxx2rTpo2jYwQA1DDkGQAAUNWdST6rl17/SMeTLu487+nhofjdR/TfjT/oj0P7acit0aVeVgdUK9Vpfexl1q1bJ09PTw0cONCuff369bJYLBo0aFCZ+/Qo6wtuu+02tWnTRgcOHNBrr72mn3/+Wa+99lqZ3xjVl8ViUWFhYbGDpWwASoM8g2shzwAA3F2RxaKFb63RTyeSVb9uLTUMD1VY/dpqGB4qw2rog39t1M7Ew64OE4ADTZ8+vcR/j1qtVk2fPr1cfZZ5pt369es1adIk/fnPf9b1119frjdF9ZaTk6OMjAydPXtWklS3bl2ZTCYFBQUpKCjIxdEBcHfkGVwLeQYA4O527zumYyeTVS80RF5enrZ2k8mkOrWD9HPKOX258Qfd2LGlC6ME4EhHjhwpcVVQq1atdPTo0XL1WeaZdlu3blVmZqa6dOmibt266fXXX9evv/5arjdH9ZGVX6TUjDylZuQpq8hDhjlI8gmQfAJkmINkmIOUVeTxv3Pyi1wdMgA3RZ7BtVg9fUrMM1ZPH1eHBgCAJOnHI6dksVhlNnuX+HxggK9+PHJKeXkFTo4McD3DgYc7CQkJ0bFjx4q1Hz16VAEBAeXqs8wz7aKjoxUdHa1XXnlFq1at0rJlyzR16lRZrVbFxcUpIiKCq9w10N7T6fru2Dnb49S0DO09ckKGxaL6dWurcVhd1QkOtD3fvWmoopuFuiJUAG6OPINrOZCcpc8Sz+j7w6eVb5GuCz+rqLqBuq1dA0U3M7s6PAAAdO1ywsV72Vnd7J5cgDNU01vaafDgwZo8ebLWrl2rZs2aSbpYsHviiSc0ePDgcvVZ5pl2l/j7++vhhx/WN998o7179+qJJ57QCy+8oPr165c7GFRd7RqH6IFuTfRAtybqGhGovPSz8jEK5e8tma25yrnwq7pGBNrOadc4xNUhA3Bz5Blcib/ZU1n5hZIh1fKyyEtFysgrlL/Z89ovBgDACZpGNpCHh0kFhSWvMMrOyVNUZLj8fJklDlQXL730kgICAtSqVStFRUUpKipKrVu3VmhoqObPn1+uPstdtLtcy5YtNW/ePJ0+fVorV650RJeoYgLNXqof7Kv6wb7afyJFRYWF8vf3l6+vr6Ia1FVRYZEOnEyxnRNoLtfGxQBqKPIMLrfrZJry8/LV2Jyr+l45ahVUqMK8fCUkpbk6NAAAJEmdO1yvxg3r6dezF2S1Wu2ey8jMlqeHhwb268zusaiRLs20c8ThTkJCQrR9+3b95z//0fjx4/XEE09ow4YN2rhxo2rVqlWuPh1aOfH09NRdd92lu+66y5Hdoor5JS1Tfr5mXcjP/a3FJD+zj35Jy3RpXACqPvIMJOlMWq6CzSadN4pU6OkrWYoUZL7YDgCAO/Dx8dbk/zdM815breTUNHl5esjT00MF+UXy8fHS4EHR6tW9ravDBFzDUTekc7OinXRxs5kBAwZowIABDumP6U5wiKz8IuX8trlEUICffj53QUUWq0wmk3ILipSRk6vwuiFKzciTJPmbvZhtBwAol7pBPjqaYlGR1UNe1lzlWQN1Nt+ipg25nx0AwH00u66B5s4ao6+37daOXT8qL79AUU3CdVOvG9S+TRSz7IBqaMOGDdqwYYNSU1OLzbJdtmxZmfujagKHuHwjivOFXrqQZ8hSkCdPD08dOPGLPHx8dK7ASx/sSJLERhQAgPKrG+CrX7OtKrAGyN/Dqp+TDXmbrQoNoGgHAHAvdWoHadgdvTTsjl6uDgVwGxcn2lV8mpy7TbSbPXu25syZoy5duqhBgwYOKcxTtINDtGscomb1Lm5hbLFY9OPpUH2z56jSsvPUsH4ddWneSC0bhcrT8+JNwv2ZZQcAKKf+beopPMhLm/efUmpWoaLCa6vrdXXUtnEtV4cGAACAa6iuq2PfeustxcbG6qGHHnJYn+WqnBQWFmrAgAFasmSJWrRo4bBgUHUFXrbcNTMzUy1DvdXyptb253hZFRQU4IrwAFQx5BlcjYelQE2DrGravdFlrRZ5WAokMdsOAAAAzldQUKAePXo4tM9yFe28vb21b98+1uCjRJd2jf09Dw+HbFYMoAYgz+BqyDMAAABVl6N2fnW33WMfeeQRrVixQk8//bTD+iz3GsWRI0fqnXfe0QsvvOCwYFA9eHp62pbBAkB5kWdwJeQZAAAAuJu8vDwtXbpUX331ldq3by9vb2+75xcsWFDmPstdtCsoKNA//vEPxcXFqUuXLgoIsF/2WJ5gAAC4hDwDAAAAoKrYs2ePbrjhBknSvn377J4r7wqichft9u3bp06dOkmSDh8+7JBgAAC4hDwDAAAAVEPVdCeKTZs2ObzPchftKiMYAAAuIc8AAAAAqMnKXbS75MCBA0pKSlJBQYGtzWQy6c4776xo1wAAkGcAAACA6sRBG1G4y0y7YcOGleq8NWvWlLnvchftjh07pqFDh2rv3r0ymUwyfhvxS0uWLBZLebsGAIA8AwAAAFRD1W11bEhISKX1Xe6i3eOPP66oqCh99dVXatq0qb7//nudO3dOTzzxhObPn+/IGAEANRB5BgAAAIC7W758eaX1Xe6i3bfffquNGzeqXr168vDwkIeHh3r16qW5c+dq0qRJSkhIcGScAIAahjwDAAAAVD/VbaZdZfIo7wstFosCAwMlSXXr1tXPP/8sSYqMjNShQ4ccEx0AoMYizwAAAADVkOHAo5or90y7tm3bas+ePWratKm6deumefPmycfHR0uXLlXTpk0dGSMAoAYizwAAAACoycpdtJs1a5ays7MlSc8995zuuOMO9e7dW6GhoVq9erXDAgQA1EzkGQAAAKD6MRy0e6xDdqB1c+Uu2g0cOND2302bNtWBAwd0/vx51a5d27azHwAA5UWeAQAAAKof7mlXeuUu2pWkTp06juwOAAA75BkAAAAANUWZinZTp04t9bkLFiwoczAAgJqNPAMAAABUbyyPLb0yFe0SEhJKdR7LlgAA5UGeAQAAAICLylS027RpU2XFAQAAeQYAAAAAfuPQe9oBAAAAAAAAV8JGFKVXoaLdhQsX9M477+jgwYMymUxq3bq1xowZo5CQEEfFBwCowcgzAAAAQPViyEH3tKt4F27Po7wv/OGHH9SsWTMtXLhQ58+f19mzZ7Vw4UI1a9ZMu3btcmSMAIAaiDwDAAAAoCYr90y7KVOmaPDgwXr77bfl5XWxm6KiIj3yyCOaPHmytmzZ4rAgAQA1D3kGAAAAqH7YPbb0KjTT7i9/+Yvth5QkeXl5adq0afrhhx8cEhwAoOYizwAAAABwhLlz56pr164KCgpS/fr1ddddd+nQoUN25xiGoZiYGDVs2FB+fn7q16+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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "plt.errorbar(df['band_lat'], df['xs_std'], yerr=df['xs_std_err'], fmt='o', color='tab:blue',\n",
    "             alpha=0.5, capsize=4, label='1 sigma (std dev)', markersize = 4)\n",
    "\n",
    "plt.errorbar(df['band_lat'], df['xs_std'],  yerr=[df['xs_std'] - df['xs_std_ci_lower'], df['xs_std_ci_upper'] - df['xs_std']], \n",
    "             fmt='o', color='gray', alpha=0.2, capsize=2, label='95% CI', markersize = 4)\n",
    "# plt.ylim([0.08, 0.18])\n",
    "plt.xlabel('Inclination (degrees)')\n",
    "plt.ylabel(r'Solar Astrometric Jitter $mR_\\odot$')\n",
    "plt.title('Equatorial')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.errorbar(df['band_lat'], df['ys_std'], yerr=df['ys_std_err'], fmt='o', color='tab:blue',\n",
    "             alpha=0.5, capsize=2, label='1 sigma (std dev)', markersize = 4)\n",
    "\n",
    "plt.errorbar(df['band_lat'], df['ys_std'], yerr=[df['ys_std'] - df['ys_std_ci_lower'], df['ys_std_ci_upper'] - df['ys_std']], \n",
    "             fmt='.', color='gray', alpha=0.2, capsize=2, label='95% CI', markersize = 4)\n",
    "plt.xlabel('Inclination (degrees)')\n",
    "plt.ylabel(r'Solar Astrometric Jitter $mR_\\odot$')\n",
    "plt.title('Polar')\n",
    "\n",
    "# plt.ylim([0.08, 0.18])\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "\n",
    "plt.scatter(df['xs_std'], df['ys_std'], c = df['band_lat'].values, cmap = 'cividis', alpha = 0.75)\n",
    "plt.xlabel(r'Jitter $mR_\\odot$')\n",
    "plt.ylabel(r'Jitter $mR_\\odot$')\n",
    "plt.title('Equatorial vs Polar')\n",
    "\n",
    "plt.colorbar(label = 'Inclination (degrees)')\n",
    "# plt.xlim([0.11, 0.18])\n",
    "# plt.ylim([0.08, 0.16])\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}