V1298Tau_Paper_Calculations_and_Plots.ipynb 1.4 MB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this notebook you can reproduce the results from the paper <br> **X-ray irradiation and evaporation of the four young planets around V1298 Tau - Poppenhaeger, Ketzer, Mallonn (2020)**\n",
    "."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "# Planet Classes\n",
    "sys.path.append('../platypos_package/')\n",
    "from Planet_class_LoFo14_PAPER import planet_LoFo14_PAPER # this is the code with fixed step size\n",
    "from Planet_class_LoFo14 import planet_LoFo14 # this is the code with variable step size\n",
    "from Planet_class_Ot20_PAPER import planet_Ot20_PAPER # this is the code with fixed step size\n",
    "from Planet_class_Ot20 import planet_Ot20  # this is the code with variable step size\n",
    "import Planet_models_LoFo14 as plmo14\n",
    "import Planet_model_Ot20 as plmoOt20\n",
    "from Lx_evo_and_flux import  Lx_evo, flux_at_planet_earth\n",
    "\n",
    "# functions for evolving more than one planet at once\n",
    "sys.path.append('../population_evolution/')\n",
    "from evolve_planet import evolve_one_planet, evolve_ensamble\n",
    "from create_planet_chunks import create_planet_chunks\n",
    "from create_summary_files import create_summary_files_with_final_planet_parameters\n",
    "from read_in_PLATYPOS_population_results import read_results_file, read_in_PLATYPOS_results, read_in_PLATYPOS_results_dataframe\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams.update({'font.size': 18, 'legend.fontsize': 14, 'axes.linewidth':1.1}) #set values globally\n",
    "from matplotlib.ticker import ScalarFormatter, FormatStrFormatter\n",
    "import matplotlib.ticker as ticker\n",
    "from astropy import constants as const\n",
    "from PyAstronomy import pyasl # to fetch Exoplanet.eu cataloge\n",
    "from sklearn.neighbors import KernelDensity\n",
    "\n",
    "p = \"../supplementary_files/\"\n",
    "# Tu et al. (2015) - model tracks\n",
    "blueTu15 = pd.read_csv(p+'Lx_blue_track.csv')\n",
    "redTu15 = pd.read_csv(p+'Lx_red_track.csv')\n",
    "greenTu15 = pd.read_csv(p+'Lx_green_track.csv')\n",
    "                    \n",
    "# Jackson et al. (2012) - Lx sample\n",
    "jack12 = pd.read_csv(p+\"Jackson2012_Lx_clean.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Present V1298 Tau parameters, $L_x$ evolutionary tracks, and planet models\n",
    "First we need to define all the necessary system parameters. <br>\n",
    "This includes the host star parameters, parameters to set the shape of the assumed future $L_x$ evolutionary tracks, and the planets themselves. <br>\n",
    "To model the radius evolution of the planets we use the results from *Lopez & Fortney (2014)* and *Otegi et al. (2020)*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>radius</th>\n",
       "      <th>a</th>\n",
       "      <th>period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>planet_c</th>\n",
       "      <td>5.59</td>\n",
       "      <td>0.0825</td>\n",
       "      <td>8.24958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>planet_d</th>\n",
       "      <td>6.41</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>12.40320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>planet_b</th>\n",
       "      <td>10.27</td>\n",
       "      <td>0.1688</td>\n",
       "      <td>24.13960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>planet_e</th>\n",
       "      <td>8.74</td>\n",
       "      <td>0.3080</td>\n",
       "      <td>60.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          radius       a    period\n",
       "planet_c    5.59  0.0825   8.24958\n",
       "planet_d    6.41  0.1083  12.40320\n",
       "planet_b   10.27  0.1688  24.13960\n",
       "planet_e    8.74  0.3080  60.00000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stellar Parameters:\n",
    "# -------------------\n",
    "# (David et al. 2019, Chandra observation)\n",
    "L_bol, mass_star, radius_star = 0.934, 1.101, 1.345 # solar units\n",
    "age_star = 23. # Myr\n",
    "Lx_age = Lx_chandra = 1.3e30  # erg/s in energy band: (0.1-2.4 keV)\n",
    "Lx_age_error = 1.4e29\n",
    "# use dictionary to store star-parameters\n",
    "star_V1298Tau = {'star_id': 'V1298Tau', 'mass': mass_star, 'radius': radius_star, 'age': age_star, 'L_bol': L_bol, 'Lx_age': Lx_age}\n",
    "\n",
    "\n",
    "# Lx evolutionary tracks:\n",
    "# -----------------------\n",
    "# create dictionaries with all the values necessary to define the evolutionary paths (this is done by the function Lx_evo)\n",
    "# this includes: starting age, age until Lx is saturated, two fixed ages at 1 & 5 Gyr which are set by the Tu et al. (2015) model tracks, \n",
    "# and (if wanted) a time interval in which and factor by which Lx drops (if you want a to mimic a track which drops fast early and then levels out).\n",
    "Lx_1Gyr, Lx_5Gyr = 2.10*10**28, 1.65*10**27  # Lx value at 1 and 5 Gyr from Tu et al. (2015) model tracks\n",
    "\n",
    "# use dictionaries to store track-parameters\n",
    "track1 = {\"t_start\": star_V1298Tau[\"age\"], \"t_sat\": 240., \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 0., \"Lx_drop_factor\": 0.}\n",
    "track2 = {\"t_start\": star_V1298Tau[\"age\"], \"t_sat\": star_V1298Tau[\"age\"], \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 0., \"Lx_drop_factor\": 0.}\n",
    "track3 = {\"t_start\": star_V1298Tau[\"age\"], \"t_sat\": star_V1298Tau[\"age\"], \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 20., \"Lx_drop_factor\": 16.}\n",
    "list_tracks = [track1, track2, track3]\n",
    "\n",
    "# these are the tracks which use the upper and lower value of the current Lx\n",
    "track1_lower = track1.copy()\n",
    "track1_lower[\"Lx_max\"] = Lx_age-Lx_age_error\n",
    "track2_lower = track2.copy()\n",
    "track2_lower[\"Lx_max\"] = Lx_age-Lx_age_error\n",
    "track3_lower = track3.copy()\n",
    "track3_lower[\"Lx_max\"] = Lx_age-Lx_age_error\n",
    "list_tracks_lower = [track1_lower, track2_lower, track3_lower]\n",
    "\n",
    "track1_upper = track1.copy()\n",
    "track1_upper[\"Lx_max\"] = Lx_age+Lx_age_error\n",
    "track2_upper = track2.copy()\n",
    "track2_upper[\"Lx_max\"] = Lx_age+Lx_age_error\n",
    "track3_upper = track3.copy()\n",
    "track3_upper[\"Lx_max\"] = Lx_age+Lx_age_error\n",
    "list_tracks_upper = [track1_upper, track2_upper, track3_upper]\n",
    "\n",
    "# additional tracks could look like this (different t_sat)\n",
    "#track2_2 = {\"t_start\": star_V1298Tau[\"age\"], \"t_sat\": 70., \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 20., \"Lx_drop_factor\": 5.}\n",
    "#track2_3 = {\"t_start\": star_V1298Tau[\"age\"], \"t_sat\": 100., \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 0., \"Lx_drop_factor\": 0.}\n",
    "\n",
    "\n",
    "# Observed planet parameters \n",
    "# --------------------------\n",
    "# radius R, semi-major axis a and period P from David et al. (2019)\n",
    "pl_params = pd.read_csv(\"../supplementary_files/V1298Tau_planet_parameters.csv\", index_col=0)\n",
    "\n",
    "R_c, R_d, R_b, R_e = pl_params.loc[\"planet_c\"].radius, pl_params.loc[\"planet_d\"].radius, pl_params.loc[\"planet_b\"].radius, pl_params.loc[\"planet_e\"].radius\n",
    "a_c, a_d, a_b, a_e = pl_params.loc[\"planet_c\"].a, pl_params.loc[\"planet_d\"].a, pl_params.loc[\"planet_b\"].a, pl_params.loc[\"planet_e\"].a\n",
    "P_c, P_d, P_b, P_e = pl_params.loc[\"planet_c\"].period, pl_params.loc[\"planet_d\"].period, pl_params.loc[\"planet_b\"].period, pl_params.loc[\"planet_e\"].period\n",
    "pl_params.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot current V1298 Tau $L_x$ & evolutionary tracks used in the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SpRk0aJClQxJJJDEUQggh8kmpUqU4fPgwX3zxBVFRUVSrVo1PP/2UcePGWTo0i/rll1/o1KkT1apVY/ny5Tg4OFg6JJFEEkMhhBAin9SoUYN9+/ZZOoxCp2PHjshQtsJJJp8IIYQQQghAEkMhhBBCCJFEEkMhhBBCCAFIYphrso6hEEIIIYoLmXySS4sWLZJ1DIUQQghRLEiLoRBCCCGEACQxFEIIIQqln376CU3TCAkJASAkJARN0/jpp58sG1ge2LlzJwsWLEhz3NfXl+bNm2errpkzZ5psXxccHMzMmTN58OBBruME+Oabb9i4cWOe1JVVlvxaS2IohBBCFAGVK1fm6NGjtG3b1tKh5Jq5xHDatGn4+/tnq66RI0eyY8cO4+vg4GBmzZpVpBNDS5IxhkIIIUQRUKJECVq1amXpMPJVrVq1sn2Nu7s77u7u+RBN9sTExGBvb2/pMHJNWgyFEEKITCR3cW7ZsoX69evj4ODASy+9RHh4OBcvXqRTp06UKlWK5s2b88cff5hcazAY+OSTT6hduzYlSpSgbt26LFu2zKSMUoqZM2fi6upK6dKlGTp0KBERESZl0ute1DSNL774wqRc6q5Vf39/NE3j119/pWPHjjg4ONC4cWN+/fVXoqKi8PPzw9nZmZo1a7J69epM34t58+bh5eWFs7MzFStWpHfv3ly8eDFNuQ0bNtCiRQvs7e1xcXHhxRdfJDQ0lJkzZzJv3jxCQ0PRNA1N0/D19TV5nwEuX76Mpmls3brVpN7ExEQqVarEtGnT0jzv/v376d27N6DvOqNpGtWrVyc8PJySJUum+77XqFGDsWPHpvusHTt25JdffmHZsmXGWJNbNKtXr864ceN4//33cXd3x8nJCYCjR4/Sp08fqlSpQqlSpWjcuDErV65MU3doaCiDBg2ifPnyODg40KhRI1atWmX2fd+/fz+lS5fmvffeM1smL0hiKIQQQmTBlStXmD59Oh988AHffPMNP//8M6NGjcLHxwcfHx/WrVtHQkICPj4+Jtu9jR49mg8++IBRo0axZcsW+vXrx/Dhw00SvIULFzJ79mxGjRrFunXrsLe3Z+LEiXka/7Bhwxg0aBCBgYEopfD29mbEiBFUqVKFdevW0bJlS4YOHcq1a9cyrOfatWu8/fbbbNq0iW+//ZbExETatGnDw4cPjWVWrFjBK6+8Qq1atVizZg1Lly6lbt263Llzh5EjRzJ48GAqVarE0aNHOXr0qDHJS6lGjRq0aNGCgIAAk+MHDhzg9u3bDBw4MM01TZs2Ze7cuQCsX7+eo0ePsmHDBsqVK0e/fv1YunSpSfn9+/cTEhKCn59fus+6ePFiPD09efHFF42xvvTSS8bzq1at4sCBAyxevNgYZ2hoKG3atOG7775j8+bN9O/fHz8/P5OkOywsjOeff54TJ04wd+5cNm/ezIgRI7h69Wq6cezYsYMXX3yRCRMm8NFHH6VbJs8opeQjBx9AaUBFREQoIYQQ5p09e9bkdWJioop8FGnRj8TExGw9w7Bhw5S1tbW6ePGi8diECRMUoJYtW2Y8tmXLFgUYn/nChQtK0zTl7+9vUt/rr7+umjdvrpRSKiEhQVWuXFm98cYbJmW6du2qAHX58mWllFKXL19WgNq8ebOxDKAWLVpkct2MGTOUi4uL8fXSpUsVYBJDcpx+fn7GYw8ePFA2NjZq8eLFWX5fEhISVHR0tHJ0dDS+D4mJiapKlSqqX79+Zq8bN26cqlatWprjw4YNU82aNTO+nj9/vnJyclKxsbHGY6NGjVL169c3+7ybN282ed+S7dq1S2mapi5dumQ89vrrr5vcLz3NmjVTw4YNS3O8WrVqqlKlSiomJsbstQaDQcXHx6tRo0apTp06GY9PmjRJOTg4qBs3bqR7Xcqv9aZNm1SJEiXUZ599lmGcSqX9XkspIiJCAQoorTLIb2SMoRB56ORJ+PlnSEyExo2hUyfT8+fPQ926oGmWiU+IwiA6Jpqly/5n0Rj8hg3HsZRjtq6pXr26yRi42rVrA9C5c+c0x65fv069evXYs2cPVlZW9OvXj4SEBGO5Ll26sHr1ahITE7l69So3b97k5ZdfNrnfK6+8wu7du7P9bOZ06dIlw9idnZ2pUKEC169fz7CeY8eOMW3aNH799VfCw8ONx4ODgwE4f/48N27cMNsKlx0DBgxg3LhxbN++nZdffpmEhATWr1/Pv/71r2zX1aVLF6pVq8ayZcuYNWsWkZGRrF+/nk8//TTH8XXp0oWSJUuaHLt//z4zZsxg06ZNXL9+ncTERADc3NyMZfbu3UuPHj2oXLlyhvUHBgayatUq5s+fz1tvvZXjOLNDupJzSXY+ESnVrg23b8O5c3Drlum5U6dg/nz46iuIjrZMfEKInCtTpozJazs7uzTHk4/FxsYCcPfuXRITE3F2dsbW1tb44evrS0JCAjdv3uRW0g8LV1dXk/pTv87L+NOLPfl4cuzpuXLlCt27d0cpxddff82RI0c4ceIErq6uxuvu3bsHkGnSkxVubm60bdvW2E27Z88e7t69i4+PT7br0jQNPz8/li1bhlKKNWvWkJCQwODBg3McX8WKFdMc8/X1JSAggAkTJrBz505OnDjB8OHDTd7Xe/fuZen9+fHHH43d4AVFWgxzSXY+ESmVKQNDhsBPP0GKsd8oBcnjp4ODISoKHBwsE6MQluZg74DfsOEWj6EglCtXDhsbG44cOYKVVdq2GFdXV2NLYlhYmMm51K/TU6JECR4/fmxyLGUrXl7bvn070dHRbNq0iVKlSgGQkJBgck8XFxcAbt68mSf3HDhwIJMmTSImJoaAgACaNGlCnTp1clSXn58fs2bNYt++ffj7+9O3b1/Kli2b49i0VN0/sbGxbNmyhS+++II33njDeNxgMJiUc3FxydL7s2jRIubPn0+3bt04ePCg8b3NT5IYCpELsbFw/TqkXGGhfn39IyVNg3fegWXLoHVrqFChYOMUojCxsrLKdjduUdW5c2cSExN5+PAh3bp1S7dM1apVqVSpEps2baJHjx7G4+vXr8+0fnd3d/766y/ja4PBwN69e3MfuBkxMTFYWVlhY/MkfUhueUvm4eGBm5sby5YtM84QTi2zlsmUXn31Vf7973+zYcMGNmzYwOTJkzMsn7rVNqWqVavSvXt3ZsyYweHDh9m+fXum989OrHFxcSQmJlKiRAnjscjISH788UeTJLJLly4sXLiQ27dvp9vqmMzJyYkdO3bQoUMHXnjhBfbu3Wuc/ZxfJDEUIoeiomDhQj0xHDNG70bOiIMDvPFG2vGFBgOk05AghCgGPDw8eOONN/Dx8WHixIk0b96c2NhYzpw5Q3BwMN999x3W1tZMnDiR8ePHU758edq1a0dgYKBJwmdOv379+PLLL2nSpAk1a9bku+++S7PMTV5KTnT9/PwYMWIEZ86cYe7cuSZd0lZWVsyZM4chQ4YwZMgQBg0ahKZp7N27l0GDBtG8eXM8PT25ffs2/v7+NGjQgPLly1O9evV07+nq6krHjh0ZP348Dx48YMCAARnG6OHhAcDXX3+Nj48PDg4ONGzY0Hh+xIgRvPrqq7i7u5tN1lPy9PRkx44d7NixAxcXF2rUqGG25c7Z2RkvLy9mz56Nk5MTVlZWfPLJJzg7O5t8Xd555x2WL19Ou3btmDJlClWrVuWvv/4iKioqzWx0FxcXdu3aRbt27ejVqxfbt2/HIR+7nOTXkRA5FBICoaEQHw8HDmTtmtRJ4c2b8OGHkGrZMyFEMfLll18ybdo0li9fzosvvoivry9btmyhffv2xjJjxozhvffeY8mSJfTv359Hjx4xZ86cTOueMWMGr776KlOnTsXX15fGjRszfHj+ddM3bNiQpUuXcvz4cXr16sWqVatYu3Ytzs7OJuUGDx5MYGAg586dw9vbm6FDh3Lu3DkqJHWXDBgwAF9fXyZOnIiXlxczZ87M8L4+Pj7cvHmTVq1amU0gk1WrVo25c+eyfv162rRpk6bVslevXtjY2DBs2LB0u/dTmzp1KvXq1WPAgAF4eXllOq9g1apV1KhRg6FDh/Lvf/+b/v37M3ToUJMyFSpU4MiRIzRp0oQxY8bQq1cvvvnmG5555pl066xcuTJ79uwhJCSEV155Jc3wgbykqRRrLYms0zStNBAREREhYwyfEvfv661+KXoIOHoUzp4FX1+wts5effHxMGUKPHwIpUrBjBmQ6merEMXCX3/9Rb169SwdhhAAbN26lV69ehEcHGycnV1cZPS9FhkZmdwN7aSUijRXh7QYCpGJqCj4+mt47z04ftz03PPPw/Dh2U8KAWxt4dVX9VbEDh1A/r4QQoj8c+PGDQ4cOMCkSZN48cUXi11SmFckMRQiE/b2epexwQD79ukzjFPKzZqEXl56S+HLLz8ZZ2gw6F3T9+/nvF4hhBCmvvnmG+O6g4sWLbJ0OIWWdCXnkHQlF1/x8XoLYMqhJ3v2wLVr0LEjVKuWv/e/cAGSdnTijTegSZP8vZ/IWwkJcPeunuCXKWO6LNH9+08S/po1Ta8LCdGvLV0aUk5SjI7W/+8BuLub1nfjhj4Uwc7OdGY8QFgYODrqf9hYekF16UoWomDkRVeyzEoWIoULF2DFCmjbFrp3f3I8xYYB+S55IqKmpZ3pvGuX3gVdr55p8pCRhAS9O1zTIOUqB7duwcGD8OgR9OoFKdfS3boVfvtNT1JGjzat7+uv9boaN4YWLZ4cv3dPf/9AP5dyM4AzZ/QExskJGjR4cjw2Fk6c0D+vW9f0mUJD4dIlPf6UXwuAlSshMhI8PEx3lwkJgeTtSAcPNk3iN2/WJ/k4O8Pbb5vW9/77+r/t2+vd+slOnYJNm/Qkb+xYPdFLtnAhXLyof41SbsIQHq63AgP4+UGrVk/OHToEW7bo79+SJaYxLFmiJ42tW8OwYU+OX7kC//mP/vm4cfr7lGzHDjh2DFxcIOX2qUrB7Nn6HznduoG3N0IIkSWSGAqRxGCA77/Xdy758Udo2tR0keqC0rs3PPecnhCkbIxWCrZt05M8Ly8YOfLJuePH9e7nx49h8mTTMY8zZ8KdO2kTjshIvSUU9LGSKRPDu3f1xCzVpggopSeMBoNpedCTuOT96T/80DQx3L5dX9i7Th3TxDAqSn/PAYYONU0M//xTT+Y0TU9uUrZ6nT6tJ2CpdqLi8WM9OQSIizM9d++e/p6mt8pEcotc6lU+YmL0meOgJ6gpxcfr90g9OTBlS3OqNW2N8rujJi5Ojw9k7KoQInskMRQiiZUVvPaa3hLUuzeUK2eZODRNb+lK3WV9//6TpCNpmS6jiAg9MYMnXeHJktZ65dEj02scHfXEytExbQLj5qYncI6p1iBOTIRnntFb+lInjRmt/2quKzMrXZxK6UmZre2TY5Ur67GnjsHREZ59Vv889TJfVavqiWh6idJzz+n/Vqpkerx8eWjWTI8z+X1M1qyZ/jVKnSCXLq0n7VZWkHpVjVat0nb5JhsxQn/O1DPTn3lGbykEvSs5pRdf1Fu3U743oH/9R4zQ/1+k3CAiMhL279cT7dRJtRBCgIwxzDEZY1j0XbsGf/+tdx+mFBWlLx9TGCmlt2A5O5vGeOqU3mJoZ6cvnZMyKTpyRE8WK1UCT8/8iy02Vk88NA3KljVNTiMi9BhsbU27sw0GvYtZ0/SxcCmXAoqN1RNROzuwsbH8OLmi7to1+OQT/evg7a0nhwVFxhgKUTBkjKEQObRvHwQE6MlGnTp6C1SywpoUgh5vlSppjzdpYn6SSps2+RtTspIlzbdCmdvBycpKTyLN1Sfyjpub3o1+65beulyQiaEQouiQxDCXRo8eTf/+/c3uBykKp5TdaxcvmiaGQhRHmgY+PnrrqyzfJoQwRxLDXFq0aJF0JRdyCQn6RIbGjZ90R7q7w6BB+gxPSQrF00J6c4UQmZEFrkWxduGCvmPJkiVPllJJ1qGDJIVCiKzx9fWlefPmlg4jx9asWYO/v3+a4x07dsQ7m+sZpX4vgoKCMt3rODvmzJnD/v3786y+rNi/fz+apnH69OkCvW9hJIlhHggO1tdHi4mxdCQitUqV9AWCQZ+EkR1Xr17N+4CEKCRu39aXP5L5h08Hc4nIiWpnAAAgAElEQVTh4sWL+fjjj7NV17Rp00zqCgoKYtasWbmM8AlLJIbiCelKzgM7dujrqrm6PlkoVxSsuDj4/Xd9du7w4U+W7yhdWl96xslJX/svqy5evIiHhwfBwcHUMre+iBBF1PHj+pqTSulrRzZtaumIhKXUr18/29cUlp+JMTEx2NvbWzqMYkdaDHNJKX3JE0g7oPvuXfjgA1i7Vt+eCqXSrqAr8sTp0/Df/8Kvv+qfp/TCC/oCzjbZ+DMoMDAQg8FAYGBg3gYqRCHQoIG+pJGm6dvqiZz57bff6NKlCw4ODpQtW5YhQ4Zw+/Zt4/n27dszatQo4+sdO3agaRpjx441HgsMDMTOzo7o5K6NdEyaNImGDRvi6OiIu7s7Q4YM4datW2nKffvttzRs2JCSJUtSsWJFvL29efjwIb6+vgQGBnLgwAE0TUPTNGPXb8qu5H379qFpGmfOnDGp9/79+9jZ2fHf//4XMO1K9vf3Z3TS9kjJdXfs2JEzZ86gaRoHDhwwqevRo0c4OjqycOHCdJ+1evXq3Lt3j1mzZhnrS2491DSN+fPnM2bMGCpUqEDDhg0B2LJlC926dcPV1RUnJydatWrFzp0709T9xx9/0Lt3b8qUKYOjoyMtWrRg165dZt/3H374ATs7O5ak3qaomJMWw1zSNH1tsIsX0y4GHBwMV6/qHy1bAkePwg8/QJ8+BLt1ooSDNe7upuu9iYzFxcG5c/ouFp07PzneqJG+Bp61ddqFnHMiMHA1XbrUJDDwByZOnJj7CoUoREqV0te7LFtWX/i7oKnERAwpEihLsKpYES0XP3zv3LlDx44dqVevHqtWreLRo0dMmjSJbt26cfLkSezs7Gjfvr3JH5cHDx6kZMmSHDp0yORY06ZNcUi9InsKYWFhvPfee1SpUoU7d+4wb948OnfuzJ9//ol10jN88MEHTJ8+nTfffJPPPvuM6OhotmzZwqNHj5g2bRpXrlzhwYMHLF68GAD31KulAx06dKBy5cqsWbPGpGt4w4YNAPTr1y/NNS+99BLjxo1j3rx5HD16FAAnJyfq169Pq1atWLp0KR1S7DO5du1a4uPjGTx4cLrPumHDBjp16oS3tzcjk7Z3Stmq+dlnn9G+fXtWrFiBIWll/suXL9O7d2/Gjx+PlZUV27Zto2fPnhw8eJA2Set1nTt3jjZt2uDh4cGSJUtwcXHh5MmTZocM+fv7M2rUKL755ht8fX3TLVNsKaXkIwcfQGlARUREKHNOnlTqk0+UeucdpRKjY5UaO1apUaOUmjhRfTgtRo0apdR//mN6TWKiUgaD2SqfeitW6G/hW28pFRdneu7KFaXi43N/j5CQEGVra6MuXRqrbG1tVGhoaO4rFeIpdvbsWZPXCdevq+tg0Y+E69ez9QzDhg1TzZo1M75+9913lbOzs3r48KHx2PHjxxWgVq1apZRSavv27QpQYWFhSiml2rVrp9566y1lbW2tIiMjlVJKNWnSRI0fPz7LcSQkJKhr164pQB04cEAppdT9+/eVvb29euedd8xe179/f9WhQ4c0xzt06KD69+9vfP2vf/1LeXh4mJTp3r27eumll4yvU78XixYtUno6Yerbb79VpUqVMj6rUvp7kPJ+6XFxcVEzZsxIcxxQjRs3zvDaxMREFR8fr7p37678/PyMx318fJSbm5uKjo5O97p9+/YpQP3555/qq6++UnZ2dmr16tUZ3qswSv29llJERIQCFFBaZZDfSFdyPmrWDN59F+bMASv7Evo+Wa6uPH75Va6E6av3pt72LDgY/v1vmDv3yR6tTxul9Mk8a9fq+/KmlNRzAMD166bnqlbNXnexOevXr6dz5zrUrFmOTp3qsH79+txXKkQhFxUlE1GyIygoiO7duyfvJAFAixYtqF69OocPHwagdevWWFtbc/jwYeLi4ggKCmLkyJGUL1+eo0ePEhERwR9//EG7du0yvNe2bdto3bo1zs7O2NjYGFv7goODATh69CgxMTH4+fnl+rkGDhzI+fPn+f333wG4e/cue/fuZeDAgdmuy8fHB9BbCQEuXbrE4cOHcxXnSy+9lObYtWvXGDZsGG5ubtjY2GBra8vOnTuN7w9gfIbMxiQuXLiQMWPG8MMPPxjjf9pIV3IBMCYr9erBjBnYWlkzuzZcvqzvRsDBg/r5du0IDdWIi9OXVkm9A0dgIDx+rG9rZm6Xi6IoISFtQvf99/pWaXfv6usPJvP0hLfe0v9NvXdtXgkM/IFhw+oC0L9/XVas+IExY8bkz82EKARu3IAvvoDmzeGVV/L/flYVK1Ix9V92BcyqYsVcXX/z5k2eTd6YO4WKFSsSHh4OQOnSpWncuDGHDh2ifPny2Nvb06hRI9q2bcuhQ4dISEjAYDAYuzvTc+LECfr06UO/fv2YNGkSrq6uaJpGq1atiE3aoPzevXsAVM6D9beef/55nnnmGQICAnjuuecIDAzExsaGvn37ZrsuR0dHBgwYwNKlS/Hz88Pf359KlSrRo0ePHMdXMdXXzWAw0KdPHyIjI5k9eza1a9emVKlSTJ8+nbCwMGO5e/fuZen9CQwMpHbt2nTt2jXHMRZ1khgWNBsbNPQZzK6uwP37sG6dPnguJIRqLYbSrp1+OPU2YseO6XNXoqNNE8MbN2DLFr2+du2gXLmCfKCsU8p0v9tz52DVKrhzBz7+GMqU0Y9rmp74nTihJ8IprytRQh9PmF9u3rzJ8eO/sGGDPq6wb996vPXWT9y6dYtKlSrl342FsBClYMUKfdzujh36xJS6dfP3npq1Ndbp7e1YhFSuXNkk8Uh2+/ZtmjVrZnzdrl07Dh06hIuLC23atMHKyop27dqxceNG4uPjqV+/Pi4uLmbvs2HDBipUqEBAQABa0g/C0NBQkzLJ19+8eZPy5cvn6rk0TWPAgAEEBATw0UcfERAQQM+ePXO8kcPIkSNp06YNFy5cYPny5QwdOtQ4LjKn8aV08eJFTp06xbZt20wSzphU68e5uLhwMwvdcCtXruSf//wnvXv3Ztu2bU/lrGdJDC3t4UN9emBcHDRrhqennhSlFhcH5cvrayU+84zpuWvX4ORJ/fPUS7L4++vna9fWt8NKZjDoe6aWKQP29qYJW048fqw/SlSU3j2esr7Fi/XW0UaN4PXXnxwvUUJfSw3g/PmkCTpJ+vWDwYPzfr/cffv2Zbg+1tmzZ2nXrjYVKujNta6ujrRtW4vRo0dnuKxDx44d6dSpU94GK0QB0DQYMUKfRNeli+l2kcK8li1b8tVXXxEZGWlMmk6cOEFISAht27Y1lmvXrh2LFi3Czs6Ol19+GdBnK0+ePJmoqKhMu5FjYmKwtbU1SYhWrlxpUub555/H3t6eZcuWMXfu3HTrsbOzM7YwZsbHx4e5c+fy008/ceDAAVavXp1hebuk7pvY2FhKpvqh3bp1azw9PRk+fDhXrlzJ0kSO7MSanACWKFHCeCw0NJQjR47QKEUrQpcuXVizZg0ffvhhmhhTcnd3Z8+ePbRr1w5vb282btyIbfL6Z08JSQwtrXp1mD5d37MtdbdEiqayEiX08YoGAyQmmhYzGPSkMTxc/zela9f0WdGpWx/v3YPkSWe+vvpyLsn27tXXAyxbVl8TMKWFCyE+Htq2NU3kjh/Xu39BHx+Z8o/LqCi9pTP1H2uVK+vdxJUr62uppVS2LPni2rWrfPTRh5QubYe3d0OsrEwzYhcXePvttibHZs5sx+rVp7l923T2msGgWLv2Tx49ekzt2oVjXS8hcqJ8eZg9W/8bVWTN2LFj+eqrr3jhhRd49913jbOSGzZsSP/+/Y3l2rVrR2JiIj///DPz5s0D4LnnnsPW1pYTJ05kOkylW7duLFiwgDFjxtC7d29+/vlnvk/+YZukTJkyTJs2jSlTpvD48WNefPFF4uLi2LJlCzNmzMDNzQ1PT082bdrExo0bcXd3p0qVKlQx02rbrFkzateuzahRo7C3t6dXr14ZxuiZ1Jrx+eef07lzZ5ycnPDw8DCeHzFiBBMmTOD55583ls2svi1bttCjRw8cHR3x8PAw22Lp6emJu7s748aN4/333ycyMtL4zCnNmDEDLy8v2rdvz7hx43BxceHUqVO4uLgwPNUvupo1a7J7927at2/Pa6+9xurVq7GyeoqmZGQ0M0U+cjcrOVdu31Zq9myl/v47y5ekNyN33TqlFi5UavNm0+PBwfrs3lGjlDpzxvTc99/rx8eNS1vfW2/p59atMz3+yy9P6rt82fTctm1KLVum1K5dWX6UfBUUFKRq1aqmXnjBU926NUkp9UG2P27efFd17+6patWqpoKCgizyHELkJ4Mh7cz/nMpopmRRkXomrlJK/frrr6pTp07K3t5eOTs7q0GDBqlbt26ludbT01M5ODiox48fG4/16NFDAVla9eDTTz9V7u7uysHBQXXp0kUFBwcrQC1atMik3JIlS1S9evWUnZ2dqlixonr11VeNs6bv3Lmj+vbtq8qWLasA46zf1LOSk02ZMkUBysfHJ9P3wmAwqAkTJqjKlSsrTdPSzH6+cOGCAtS3336b6bMqpdTJkydVy5YtlYODgwLUvn37lFIq3WdWSv+Z7uXlpUqWLKlq166tli5dmu7X6/fff1c9e/ZUjo6OytHRUbVo0ULt3r1bKWU6KznZr7/+qpydndXw4cOVoYgsF5IXs5I1JdPQckTTtNJARERERI7HXphlMOhTmS9f1rfwmDYtbZNaLkVE6GP8HjyAFi2ejO8D2LlTnxVsbQ3vvGN63YIF+mSR556Dbt2eHL9/X6+vVCmoVSvtxJnCJiIigjff/Ae7dm1j+fK+vPBC1vvPduy4wKBB63F2akm9euOxsZFmFlF0NPcqx7Rp9dOM1UopPl4fhhITA2+/DbltLPnrr7+oV69e7ioRRdbixYuZOHEiN27cMJnFLfJeRt9rkZGRye+/k1Iq0lwd0pVcGGmavtZNSAh06JA0SyVvOTnpCWF6unfXP9JjrtejbFnT7ujCzsnJie+/X82KFSt49dV/8o9/NOPDDztjZ2f+W+Lx4wTefXc3X355EkPiSB5Ftuf69QhAdrMRRcf27bco42zLv/5tfobJTz89Gbe8dStk0pMoRLpCQkIIDg7mo48+wtfXV5LCIuKpazHUNG0x0BtwBiKBtcBEpdTjpPM2wDzgdfQtA9cBbyml4lLVk38thsmuXdPXs8ntzBCRoQsXLuDj442DQxSHDplfX6tVq++4dcuOfv0WUL58NbPlhCisTp26z4+bbmBjo7FvXydatkp/NmxcnN5p4eAAb7yR+x4AaTF8Ovn6+rJq1So6dOjAmjVrKJtfg8eFUV60GD6NiWF9IFQpFaVpWgVgDbBXKfV+0vnpwCvAi0A88CNwVCk1NlU9+Z8YpqaU3rcjI8Tz3IoVK1i4cDonTow0W+a5576icdMu+Pn60qxp84L7uguRRx4/NtCp4z5OnAinalV7gk50w8WlRLplIyP1FQvyYtF4SQyFKBh5kRg+RdNsdEqps0qpqJSHgNopXo8EPlBK3VBK3QFmAn6apln2vTIY9MXGPvtMX8hQ5KnAwAD69894nOHAgfX5849T/HnmT5avXMb+A/uIjDT7vSVEoWNnZ8XKVa0oW9aWq1djGO4XhMGQfuNA6dJpk8KnrB1BiKdSoU0MNU2brGnaWk3T/tY0TWmaFpJBWStN097RNO2cpmmxmqZd1TRtnqZp6XaAaJo2SdO0SCAMaAQsSjpeBqgKnEpR/Fcg+bjl/PwzHDmir2adtL2QyBuPHj1ix47d9O//ZLmgoKBrvNx7JUFB14zHvL2f5a+z57Gzs8NgMEiCKIqk6tVL8b+l+gDjbdtu8dln57J03bFjMH++PvlMCFF8FdrEEPgI6AxcAu5nUvY/wHzgLDAafdzgv4DN6bX0KaU+UUqVBuoD3wC3kk4l9w0+TFH8QapzltG6tb4lQc2akM5ekSLntm7dSp06FahTpzwGg4E5cw7RqZM/zmWb0andd3z6yQEMBgN165andm1XnB3L0LVzV5ycnCRBFEXSSy9VYfwEfZ25GdNPc/DgnQzLnzwJS5fqe7mvWZOzez5tw5aEKGh59T1WmGcl11JK/Q2gadppwDG9QpqmPYueDK5XSvVPcfwysBDwAVald61S6i9N034D/IGu6JNRQJ+Ycjfp8+SFXCz7G9/KCkaO1DcIzsV2QiKtdet+wNvbg5s3Ixk6dCMhIXEcPHiYZs2a8c8OHRny9hvs3hrMijU+eHvXZcOGtfj4+FC3jgfBF85z4uQJHkY85M8zf3LmrzPUr/cszWUMoijkZs9uwLGj9zh8+C6vDTnGiZPdqFgx/R0hGjeGGjX07SubNs3+vWxtbYmJicFBxkcLkW+Sd8nJrULbYpicFGbBIEADFqQ6/i0QDbyWyfXWQJ2kez4ArgKNU5xvgt5qeDXtpQXM3l6SwjwWExPD1q3bcXCw5bnnvsLNzYtff/3DuNfp8yNGsLdVW8oeu0qjegspVcqOLVu2ERMTg7W1NfU86/Pa4Nfp2rkrzk7OGAwGTie1IO6TFkRRiNnYWLHi+1ZUqFCCW7diGfr6cRIT029xsLGBf/wDpk5Nf8vOzLi6unL9+nWio6Ol5VCIPKaUIjo6muvXr+OaB8vbFYlZyckthkqp6umc24He2ueQzpIyR4C6SqkKSa8dgVeBDejdxQ2AAOCwUmpUUpnpQF+gF/qs5E3AsUIxKzm1P/+E0FBZZCwXNm7cSL9+/XByKsXixV8zZMiQNGXiDh3iXvv2rAemONoT8SiGjRs3Gvc9TWYwGDgffJ4TJ4N4GKGPRrCyspIWRFGo7dlzmxd7HkQpeG9KPWbObJAv94mIiCAsLIz4+Ph8qV+Ip5mtrS2urq4ZrhVZrJarySQx/BNwVUql2RpE07Q16IlgCaXU46TJKBuBZoAd+uSTQGCGUio66Rob9PGKyesYrgXeVkrFpqrbsonhnj1PBvu8/DK8+GLBx1AMTJr0Lnv37mL16rXUqmV+v+O7nTvzeN8+brRvz5sxkXTu3I1PPvk03bKSIIqi5v33z/D+7LNoGmz+qR3du1fK9JqwMDhwALy9ZalVIYqCpykxvATYKqWeSefccvQEr2xSN3FexlQaiGjcuDHWSd27o0aNYtSoUXl5G/PCwvQVaOPiYPJkMLMZushYfHw8NjY2GW4PBhC3fz/3OnUCwOXYMayaNs10LIfZBNGzPs2aNceptOwCIAqHxERFr5cOsmdPGOXL2xF0ohvu7ubHA/79N3z+OcTGQp8+Mh9OiKLgaUoMs9ximMcxWb4r+f59ffmaZ5/NvKzItbsdOvD44EFK9OqFy+bNWb7OmCD+EsTDh5IgisIpLCwWr+a7uHkzltatXdi1uyO2tukPQ4+JgY8+0v8+7dkT+vYt4GCFENn2NCWGWR5jmMcxWT4xTM+lS3D3LrRsaelIip24vXu516ULAOVPnsQuaYJKVkmCKAq7w4fv0K3rARITFWPH1uWTT58zW/bGDT0xbNzYbBEhRCHyNO18cgL9OVqkPKhpWkn02cUn8/Pmo0ePZnM2Wo/yVUQEfP01+PvD6dOWjqbYsevUCbs2bQB49P772b7eysqKep71eG3Q63Tt3A1n56RZzGdPs2Llcvbu30tEZERehy1ElrVtW4H3P9Ann8yfH8yPP143W7ZKFUkKhSiOikOLYUPgd2BDqnUMR6OvY/i6Uur7fIip8LUY/v03zJunb03Qu7fMVs4Hsbt2Ed69OwAVTp3CNhe/Gc21INbzrE9zaUEUFmIwKF555Qhbt9ykTBlbjgd1o0aNdDeRMpGQAKdOgZdXAQQphMi2It+VrGna60C1pJej0WcRz0t6HaqUWpGi7CLgbfRlaLYC9dB3PjkCdFZKGfIhvsKXGAL89hvcuwdJXZ4ibymluNu6NfHHjlGyXz/KrV+f6zoNBoNxoewHD/U5UpIgCksKD39MC69dXLkSTbNmZdl/oBMlSphfQ/XBA72z4u+/YehQSGpYF0IUIsUhMdwPdDBz+oBSqmOKstbAGGAUUB1915IAYLpS6lE+xVc4E0OR72K3bye8Z08AKvz+O7aNGuVJvZIgisLkRFA4HTvuJT5e8c9/1uLzhea3PHnwAD74ACIjoU4dGDdOlrARorAp8olhYVdkEsP4ePjvf/Wpg9WqZV5eZEopxd1WrYgPCqKktzfl1q7N0/rNJ4j1aN7UK8MFTIXIS19+cYF33vkNgO9XtmLAgKpmywYH63sqDxig75QihChcJDHMZ8mJ4bBhw+jfvz+9e/e2dEjp+/57OHRI/0k9dixksIizyLrYLVsITxrDWeHPP7FtkPe7RUiCKCxNKcWgQcdYH3gNR0cbjh3vSt26hfgPYSGEWZIY5rMi0WKolL5DSmAg1Kih9+/IXst5QinFXS8v4n/5hZIDB1Luhx/y7V56ghjMiZNBkiCKAvfwYTzPt9rNxYuPaNDAmSM/d8HePms/R8LCIA+2bhVC5AFJDPNZkUgMk126BOXKQdmylo6kWIn98UfCX34ZNI0Kp09jW79+vt7PmCD+EsSDB5IgioLz228PaNd2D3FxBnx9q/PNtxlPPTYY4McfYft2ePttyIcGdSFENj1N6xiKzNSqlTYpDAqCO3csE08xUaJ3b2ybNAGlePThh/l+PysrKzw9PBni8xrdunSnTJkyGAwGzpw9w4pVy9mzb49x6z0h8lLjxmX4/PMmAPj7h7B8WUiG5cPDYe9evdNi1SpITCyAIIUQeUJaDHOoyIwxTM+ePbBmDZQvDxMmQJkylo6oyIrZuJH7/fqBlRWuZ89i4+FRYPc2GAxcuBhM0EnTFkRPj3o0b9YcZyfnAotFFH9KKYb7nWDlylDs7a058nMXGjQw/3/s5EnYsgXeeAMqptmwVAhR0KQrOZ8Vqa7k1DZuhG3bwN4exoyB6tUtHVGRpQwG7jRpQsIff2D/2muUXbEi84vymCSIoqBERSXQuvUe/jobQV2P0hw71hVHR/NTkBMTZVizEIWFJIb5rEgnhkrpA4CaNQN3d0tHU+TFBAZy39tbbzU8dw6bOnUsEof5BNGT5s28JEEUeeLs2QhaP7+b6OhEBvpUZfnylmiyaKEQhZ4khvmsSCeG5iglq9LmgDIYuPPccyScPo39sGGU9fe3aDySIIr8tnJlKH6+QQB88UVTRv0j82Wwzp/XOyrefBPs7PI7QiFEapIY5rNilxjeuAErVsDIkeDiYuloipyYNWu4P3AgWFvjev48NoVgvUg9QbyQlCDeB1IkiE29cHaWBFHk3Jv//IXvvvsbOzsrDh3qTJOm5lc9OHcO/vMf/fNOncDHp4CCFEIYyaxkkXVRUfDFF/pGp598AtHRlo6oyCnp7Y1N/fqQmMijjz6ydDiAngR61PVgiM8Qund9gTJlymIwGDj711m+X72CPft28/ChzGIWOTP/P4157rkyPH5sYNCgozx48Nhs2bp19cUR7OygcuUCDFIIkW3SYphDRXpWcmoGg74I9u7d0Levvn2eyLbo1at5MHgw2NjgGhyMTY0alg7JRHotiJqmUc+jnt7FLC2IIpsuXnxEyxa7iIxMoM/LVVi7trXZ8YZ37+qTUWSGshCWIV3J+azYdSWDPgiobl3TcYYRESALJ2eJSkzkzrPPknD+PA7/93+U+eYbS4eULoPBwMVLFwg6EcR9SRBFLq1ffw2fgUcB+Oyz5/j3mLoWjkgIkR5JDPNZsUwMU7t/HyZPhtq14ZVXoGZNS0dU6EV//z0PXn9dbzW8eBGbatUsHZJZkiCKvDJu7G8sWnQBGxuNvXs70er5zMcpR0XB9ev636JCiPwnYwxF7h07ps9UvnBBFiPLInsfH6zr1IGEBB598omlw8mQlZUVdet4MNhnCC90e4GyZcqilOLsubP6Tip7ZQyiyJqPP2mEl1c5EhIUgwcf5e7duAzLBwfD7Nnw5Zdw714BBSmEyBJJDIV5TZpA9+5Qrx4884zpudOn4bH5weZPK83GhtJTpgAQ/d//knj1qoUjylxmCeLuvbt5+PCBpcMUhZidnRWrVreibFlbrl2LYbhfEAaD+d6ouDh48ABiY+HIkQIMVAiRKelKzqGnoivZnCtX4OOPoVw5GDFCuphTUQkJhHl6knjpEg5vvkmZL7+0dEjZoncxXyTo5HHu33/SxezpUQ+vZs1xdpYtFEX6tm69Sd+XDwMw+/0GTJpUz2zZdev0iSht28ryqUIUBBljmM+K1azk7Fq+XP8z38oKpkyR3VPSEb10KQ+GDwc7Oyr+/TfWbm6WDinbJEEUOTFlyp98NuccVlawY2cHOnRwtXRIQggkMcx3T3WLocEA+/ZBfDz06GF6TnZPAUDFxxPm4UHi5cuUGj0a54ULLR1SjkmCKLIjIcFA924HOHz4LpUqlSToRDcqVSqZ6XVK6R9WMsBJiHwhiWE+e6oTQ3POnIGdO8HbG6pWtXQ0Fhf13Xc8/L//gxIlqHj5MtZFfGVf8wmivtVeGUkQRZIbN2Lwar6LO3fi6NixAtu2d8Da2vwfjFFR+sZLZcrIrihC5BeZlSwKlsEAa9fqe1/Nm6ePLn/KOQwdivUzz0BcHI/mzLF0OLmmT1Kpy+CBQ3ihWw/KltUnqfx17i++X7WC3Xt38UAmqQigShV7lq9oiabB/v13mD37TIblV6yAU6f0joizZwsoSCFEuiQxFHkjPl6fvWxlpe+cUqKEpSOyOM3ODsf33gMgaskSEm/dsnBEeSN1gliubDlJEEUaXbpUZNr0ZwH4+KO/2LHD/P///v31HxlNmkhngxCWJl3JOSRdyWbcvq3PVra1fXLs11/hzh19+mGpUpaLzQJUXBxhdeqQePUqpcaNw3nuXEuHlOeSu5hPnAwi/H44oHcxe9T1xKu5dDE/zRITFb17HWL37tu4uNgRdKIbVas6pFv2zh0oX4vvCqMAACAASURBVF6GKAuRX2SMYT6TxDCLHj+GadP0Rctq1IBJkywdUYGLWryYh2+9hWZvj2tICNauxXOWplLKuJOKJIgiWVhYLC28dnHjRizPP+/C7j0dsbWVziohCpqMMRSFQ1gY2Nvrn7drZ3ru8WN9GmIx5zBiBFZubqiYGKLmzbN0OPlG0zTq1K7LYJ8h9Oj+pIv53Hm9i3nXHulifhq5upZk5crnsbbWOHr0HlOn/JnpNUrpGy9FRBRAgEIIE5m2GGqatjeP7qWUUl3yqC6Le6rXMcwupfRJKbVrm3Yxr10Lv/wCXl76IKNi7NGiRUT8619opUrprYbly1s6pHwnLYgipXnzzjN50h8ArAtsTZ8+6a/tmZAA33wDv/8OjRrBm29K97IQeSHPupI1TTPkUUxKKVVsNtyVruRciouDd9+FmBj9p/9bbz05l5Cgf5TMfO2zokLFxnK7Zk0MN2/iOGkSTh9/bOmQCoz5BNEDr2YtKFNGEsSngcGg6N//CFt+uomzsy3Hg7pSs6ZjumW/+w5OnABnZ5g8GcqWLeBghSiG8jox3A58mot4JgHdJTEURnFx8PPP8Ntv0KoVPP/8k3N//KE3GfTrB507F5vmgkeff07EmDFojo5UDAnBysXF0iEVKD1B1NdBDA+XBPFpFB7+mJYtdhEaGk3TpmXZf6ATJUum/bUQFQUbN0Lfvk/dfDUh8k1eJ4b+SqnhOQ1G07SlwFBJDEWWJG+5p2n6lnvFZP0KFRPD7Ro1MNy+jeOUKTh98IGlQ7IISRCfbieCwunYcS/x8Yo33qjFwkVNLR2SEE+FvJx88jtwJZfxXAX+yGUd4mnRtKned/Tyy8UmKQTQ7O1xnDgRgKiFCzEk7R7ytNEnqdRh8MAh9Ojek3LlkiepnOP71SvYtWcnDx7IJJXiyqtFOT77rDEAS5ZcIiAga79eYmLyMyohRDJZriaHpMUwn0VFgYODaTeywVDkN1I1REURVqMGhjt3cJw+HadZsywdksWZa0GsW8cDr+ZelC0jA8yKG6UUgwcfI3DdNRwdbTh6rCseHun/HFUKDh+Gdetg7FioVq2AgxWimJB1DPOZJIYF7PFjmDsXWrSALl2K9LjDR599RsTEiWjOzvpYQ+k6BfRk4dLfFwk6EcS98HuAJIjFWUREPK1a7ubixUc8+6wTR37ugoODTZpyd+7AjBmQmKh3IEyZUqS//YWwGFnHUBQvq1dDaKi+xM3vv1s6mlxx+Oc/sSpfHvXwIVELF1o6nEJD0zRq16rDoIGD6flCT1zKuaCU4nzwOVau/p6du3dy/8HT2f1eHDk52bL6h+cpWdKKM2ci+Pe/TqVbrkIF6NEDXFz0+WiSFAqRv/K0xVDTtDpAIyBUKXUyzyouhKTFsICFhsJXX+nb7U2YUOR/O0R+8gmRkyejlSmjtxo6O1s6pEJHWhCfDkv/d5l//EP/dfHtt80Z5lsjTZmEBL3FULZgFyLn8q0rWdO0V4CRwCyl1PEUx6cCM4Hk39irlVKvZTPuIkMSQwuIioLYWL3pIFl0NISHg7u75eLKAUNkJLerV0eFh1P6/fcpPXWqpUP6f/buOzzKKu3j+PdkZpIQIARIQm+h99ACAkoXG74uCOqKsK6oa0HXuqsi6trALrr2tQBiQRHEgggKokAIVVroPSEhkJCeTDnvHydtIKEMmcwkuT/XlYvJ85yZuWeFzS+n+i0TEPewJi72lIDYjj69Yqgrm9xValprbvl7HLNnH6BGDQu//zGMrl3lFyUhyps3g+E84FIgUmudXXCtC2bVsQNYDXQGwoCxWut5Hn0CPycnn/iJWbPMfogjR8JVV4H19DlK/irj2WfJmDIFVbcuDQ4cIEB+wTgjCYhVV1aWg/79l7J9Wzrt2tdm1aph1K5tK7N9UpL56tatAosUopLzZjDcByRorQeUuDYNeAi4WWs9UykVBWwDftVaX+7JB/B30mPoB44fhylTzGrlNm3MkkVL5dkq05WebnoNU1Op/dxz1H7kEV+XVClIQKyatm9Pp/9FS8jKcjLuumbMmtUXVcqUkZUrYc4c80996lT3AQQhRNm8ufikPnDklGuDgExgDoDWei/wO9DRg9cX4tzUrw+PPgpdusCtt1aqUAgQEBpKrfvuAyDr5ZdxZWb6uKLKwSxSaVOwSOWKEotUdvDp57NZ/PNPpFbTPSIrs44dQ3nr7d4AfPnFId57d2+p7UJDwW43hydt316RFQpRPXjSY5gHfKu1HlvwfSBwEliutb6sRLvZwBitdY1yrNdvSI+hn9uyBTp18vt9D11paabX8ORJak+fTu2CDbDFuSvqQVy7huPHU4CCHsQ27ejTW3oQK5u77lzH++/vJTAwgN9+G0rPXqf/91u4EDp0gLZtfVCgEJWUN4eS9wNZWuvOBd8PBxYDT2itny7Rbj5wkda6wfmX7/8kGPqxdevMWcsdO8Itt4Cf//dJf/JJMp96ioCICCL37SNADof1yJkCYu/efahXt56PKxTnIjfXycUDf2HTpjRatarJ6tjh1K0b6OuyhKj0vDmUvBzooJR6WCnVDXga0MCiU9p1AQ578PpCeE5rWLzYPD5wwIw3+bla996LCg3FdewY2e+84+tyKq2iIeZxN5gh5vrhZoh51w7mfP4pi3/+iROpJ3xdpjiL4GALn39xEaGhVvbty2LSpDjO1oHhcpkvIcSF86THsB0QB9QqvAQs0VpfekqbeOAdrfWd5VSrX5EeQz+Wl2dWK/ftC127+rqac5L++ONkPvMMAZGRptcwJMTXJVV6Wmv27ttLbFxsUQ8iULQPovQg+rdvvjnMdeNWAfDCC935533tSm13+DDMnGn+uQ8bVpEVClG5ePVIvILtae4HIoE1wIta65wS9+8AbgMe01r/cN5vUAlIMKyE/PisZdeJEyS1aIHOzCT01Vep9c9/+rqkKkMCYuX14AMbmTFjF1arYunSwVzUP9ztvtbw1FOQmAiBgebovPDwMl5MiGqu3IKhUmog8IeWQ5XdSDCsZFJS4PXX4frroXNnX1dTqvRHHyXz+ecJaNiQBnv3ompUyXVbPlMYENfExZIiAbFSyM93MXTIr6xZc4KmTWuwJm4E4eHux5/s3AmvvgqXXmq2MrWVvf2hENVaeQZDF5AMzC/4WqK1dpRjrZWSBMNKxOEw3QrJycWbnzVs6OuqTuNMSSG5ZUt0Vhahr79OrXvu8XVJVZIExMrl4MFsYvr8zIkT+Vx6aQO+XXgxAQHu+xumpoIsPhfizMpz8ckbQC5maPh74JhSapZSaoxSSiZCCf9ntcLo0ebP4cOhgX8ulLeEh1PzrrsAyJw+HZ2b6+OKqialFK2jWnP9uBu44rIrCa9vxh537trBp5/N5qefF3HihCxS8RfNm4fw0ccxACxenMT06fGntZFQKET5Oec5hkqp3sAY4BqgPWYlci5mq5p5wEKtdZqX6vQ70mNYCSUmmp7CUk5T8BfOY8dMr2F2NnXefLMoKArvKbsH0ZykUq+e9CD6gylTNvPC9HgCAmDRT4MYPDiy1HYOB8TGQv/+fv1PXYgK5+3FJx0xIXE0EI0JiQ7MVjbzgAVa60QP6q40JBhWAU4n/PorDB7sV2csn3zoIbJeeomApk1psHs3Kijo7E8SF6woIK6NJSVFAqK/cThcjLx0OStWpNCgQRBr4kbQqJH7PNykJHjnHUhIgAkTYMCAMl5MiGrIq8HQ7QWUakFxSOyHGZ52AbGYkDhfa73ngt7ED0kwrOS0NntcrFwJvXvDpEl+073gTEoiuVUrdE4Odd5+m5r/+IevS6pWygqIbdu0I6Z3H+rVk8N5fSUxMYc+vX8mOTmPSy6JYNFPl2C1Fs+Iys4204nT0qB9e7jvPr/5Zy2Ez1VYMHR7MaUigb9gQuJgwIbpTXxIa/1Kub2RH5BgWMnl5ZmljPv2QWQkPPywX52QcvL++8l69VUszZsTuWsXKlBOfqhoWmv27Tfb3EhA9B+//prMZSOXozX8+5GO/Oc/Xdzub9kCBw/CyJGV7vh0IbzKJ8HQ7YWVqgNcjQmKK7XWL3nljXykMBhOnDiRMWPGMGrUKF+XJM5XZibMn28WpvjZhtLOxESSoqIgN5c6771HzVtv9XVJ1ZYERP/z7LPbeOrJrQB8u3Agl13WyMcVCeH/fB4MqzrpMRTedvLee8maMQNLy5ZE7tyJkg3afEoCov9wuTSjrlrBzz8nUb9+IGviRtCsWdm/3DkcfjWNWAifkGDoZRIMq6gDB6BxY7/YJdeZkGB6DfPyCPvf/wj5+999XZKgMCDuY01cLMdSjhVdb9umLX16x1BfAmKFOHYsj5g+P3PkSA59+9Zj6S9DCAx034HNbod582DvXjNbRIaWRXVWnvsYulFKOc/xK1cpdVgptUApdc2FfBghvE5rWLYMpk2D2bPN9z5madyYkEmTAMh49lm03e7jigSYfRCjWkVx3djrufLyq4gIjwBg1+5dzPn8UxYt/pHjJ477uMqqLyIiiNmz+2GxKGJjTzDlsc2ntfnlF/O1fz/8UCUPZxWi/HlycKw6x69AoDEwCvhaKfVReRQshFdoDXFx5jzltWvNnod+oPa//w2BgTj37iXn0099XY4oQQKi7w0YGM6zz3UF4LXXdjJ//hG3+8OGQdOmEBUFffv6okIhKh9P9zF8AfgH8BYwBziA2aKmJfBX4E7gfeA1YAjwIhAB3KS1nlMehfuaDCVXQRkZ8N//wrhx5ieJn0i74w6y33kHS5s2RG7fjpLJUn5Jhph9Q2vN6NF/8P13idSpYyN2zXCiomoV3U9Lg9BQCPCkG0SIKsRrcwyVUjcD7wKXaK1Xl9GmL7ACuFNr/YFSqh+wEnPO8qXn9YZ+SoJhFaW132185jh4kOQ2bcBuJ2zmTEJuusnXJYkz0Fqz/8A+YuNiOXasOCC2ad2WmD4SEL0hNTWfvjE/s39/Nj16hLH8t6EEB8uEQiFK8mYwXAuc1FoPO0u7pUCY1rpXwffrgOZa64jzekM/JcGwmrDbzQkpwcE+LSPt9tvJfu89LO3aEbltG0pm0fs9CYgVa23cCQYP/pX8fBe3396aN97sWWq7XbvM1qV16lRwgUL4mNcWnwAdgKPn0O4o5kzlQnuBUA/eTwjf2LcPnn0WPvvM15VQ65FHwGrFuXMnOV984etyxDlQStGqZRTXXXs9V11xFRER5nfi3XvMHMQff/qR48dlDmJ56d2nHi++2B2Ad9/dw+efH3S773TCRx/BSy/5zfoyIfySJ8EwD3M+8tlEF7QtFAiUmVCF8Ds//GAWoaxeDfHxPi3F2rIlIRMnApD59NNop9On9Yhzd8aA+IUExPL0jztac+3YpgDc8Y+1xMenF92zWMwAAMCePVCiE1cIUYInQ8kLgKuAp7TW/ymjzRTgP8C3WutrCq79WfB+XS+sZP8gQ8nVQFoaPPOMWdo4cqTPZ6879u4luV07cDqp+/nn1LjuOp/WIzxjhpj3syYuluRjyUXX27RuQ0zvvtSvL0PMFyI93U6/fkvYvSuTTp1DWblyGCEhZsFWdjbMmgVjxkB4uI8LFaKCeXOOYXdgFRAE7AS+wKxK1kALYBxmuDkP6K+13qiUag7sB97SWt993p/GD0kwrCby8iAoyNdVFEm9+WZyPv4Ya+fORPz5J0qWWlZaEhC9588/0xg4YCm5uS4mTGjJB//r4+uShPA5r558opQaCswGGmICodttIAmzNc2SgvYRQDcgXmt9hCpAgqHwBcfu3SS3bw8uF3W//JIaY8f6uiRxgSQgesfHH+3jttvWAvD++72Z+LdWpbZLSTHb2QQGVmR1QlQ8rx+Jp5SqAVwLDAKaFFxOAH4D5mqtsz164UpCgmE1lJcHCxfCpZeanyQ+kjphAjmzZmHt2pWIjRul17CKOFNA7NM7hvD6MvZ5PrTWTLoljlmzDhAcHMDvfwyjW7cwtzZbtsD//gddusDf/+53O1UJUa68OZQcCugzvWh1IMGwmsnKguefNzPWu3eHO+7w2U8Rx44dJHfqZHoNv/6aGqNH+6QO4R0SEMtPVpaDAQOWsm1rOm3a1mL16uGEhppz0LWG11+H7dtN23vvhU6dfFisEF7mze1q0oAlnhbmS0qpIKXU+0qpvUqpTKXULqXUP09p01op9b1S6oRSKkkp9bxSSrpkqruaNaFFC/M4O9v0HvqItX17alx/PQAZ//kPnvb6C/9kVjG3Yty113HVFaOIjIgEYPee3Xz2xRx+/OkHUo6n+LjKyqFmTSuffXYRNWta2L0rkzvuWFf070UpuOUWaNYMbr0VOnb0cbFC+AlPegxPAgu11uO9U5L3KKVqAv8GPsHsq9gN+Am4R2v9hVLKAvwJfAdMBSILHs/RWk8/5bWkx7C6ycqCNWtg8GCfjznZt2/nWOfOoDV158+nxv/9n0/rEd4jPYgX7rPPDjJxQiwAM2b04B93tCm654eHHQnhFd4cSl4N5GqtB19QhX5CKfUhkKm1vkcp1QkTDGtqrfMK7v8NeEJr3eqU50kwFD6VesMN5Hz+ObYePQhftw4lP92qNK01Bw4eIDYuluTkpKLrraNaE9O7L+Gy/8oZ3X3XOt57by+BgQEsXz6EXr3r+bokISqUN4eS3wcGKqV6eVrcuVBKPaKUmlsw7KuVUvvP0DZAKXWfUipeKZWrlDqklHq5oIfwTO9hBQZiwiCYFdWnNQNaFsytFMJv1JoyBZTCvmEDed9/7+tyhJcppWjZoiXjxoxj1JVXExnZAIA9e/fw2Zdz+GHR96SkyBBzWV56OZro6DDy813ccMMqUlPzT2uTlgaLFsmpKKJ683S7mhnAeGA68A1woLCHrdwKU0oDJ4D1QC8gXWvdsoy2rwP3FNTyI9ARmAysAIZrrV1lPO8toA8wQGudXxAUtwILgCeABsB8oDvQTGt9uMRzpcewOktLMyejhIXBFVf4rIwT48aRO3cutt69CV+zRnoNqxHpQTx/e/Zk0q/vEk6etHPVqMZ8/XX/on8z27fD22+b6cMTJsCAAT4uVohy5rUeQ6WUE7gLqAM8B2wHspVSzlK+HB7WD9Baa11faz0Csw1OWfV0xoTAeVrr0Vrr97XW9wP3A0OA68t43suY3sLLtdb5AFprBzAK6ILZtHsR8Clmr8bUC/gsoqr54ANYvtx0L6T67q9G7ccfB8C+di15ixb5rA5R8U7tQWwgPYhn1bp1Ld7/wGx2/d3CBF57bWfRvebNi/eyP3rUF9UJ4R88mWNYau9bWbTWF7yiVym1BahVWo+hUuoZ4DHgEq31ihLXg4HjwHKt9RWnPOc1YBgwVGt9xhMzlVJ3ARO01n1PuS49htVZfDy8+ip07Qp//SvU8918pRNjxpA7bx62vn0JX7VKeg2rqcIexDVxsSSd0oPYp3cMEeERPqzOvzz04EZef30XFoti6dLB9B9geld37oTMTOjZ08cFCuEFXt/guiKdJRj+BAwHQk4dzlZK/QG001pHlLg2AxgKDCktFCqlumFWLOdiehxnYoLhz6e0k2BY3R04ULyFjQ/ZN27kWI8eANRbtIjgkSN9XJHwJQmIZ5ef72LY0F+JjT1BkyY1WBM3gogI/zn6UghvqE7BcDMQqbVuUMq9L4GxQFDBHMIWmDOb84CSw9wrtNaXFzznaeBOIBiIB6ZqrU+b2S/BUPiTE9dcQ+6CBdguuojwP/6QXkMhAfEsDh7MJqbPz5w4kc+IEQ1Y+N3FBAS4/7txuSAhAZo29VGRQpSj6hQM9wA2rXXzUu7NBG4C6mqt08q5ptpAenR0NBaLBYDbbruN2267rTzfRlQmJ09CnTo+eev89etJ6WU2Cqj/888EDR/ukzqE/9Fac/CQWaSSlFQcEKNatSamT/UOiIsWJXL1qN8BePKpzjz6aPHRJ8ePw0cfwcGDMHUqyFoeUdlVSDBUStXBrOqNwKxMXunxi535fcqlx7Cca5IeQ2EcOwZz58Lu3fD00+aUFB84PmoUed99R+DAgdT/7TfpNRRuJCCW7vHHNzN9WjwBAfDjokEMGWJOmtm6FWbMMG369YObb/ZhkUKUA2/uY4hSqk7BxtDJmJNDZgOTSty/UymVoJTq58nrn6cEIFwpVdoEkSZASnmHQiHcHD4MmzaZk1F8uJ9g7SeeACD/99/JX7bMZ3UI/6SUokXzlowdPY6rr7qaBg3M79J79+3h8y8/4/sfv+dYyhnX4lVJTzzRmUsuicDlgpvGryYxMQeAzp3NIUeDBpn1ZUJUF55sV1MTWAb8DbOFy4+cvjH0IqAhcM2FlXdO4jCfI6bkxYJVydHA2gqoQVRn0dHQoQP07w8XuvDDbodt20wPpN3ufm/bNli5EhITS31qYO/eBBXsqZjx1FMXVoeosiQgurNaA5g1uy8NGgSRnJzHTeNjcTjM5hvXX29CYZCsSxHViCc9hg9iNnyeDURpra86tYHWei+wE7P619u+wOwz+M9Trt8KhGD2IfSayZMns3DhQm++hfB3SsE998DEiRc+x3DzZnj9dViyxOydUdJvv8Enn5j7Zag9dSoA+cuXk7d8+YXVIqo094D4f6UExO+qTUBs1KgGs2b3IyAAfvvtGE89tRUo/QzlSjAtX4gLYvXgOWMxw7e3nuW0k4NAZ4+qApRSNwGFe4FEAIFKqSkF3x/QWs8C0FpvVkr9F7hbKTUP+AFz8sk9wHJgjqc1nIs33nhD5hgKKFiAdME6doSAgt/Xjh4141mFEgr2eW/Vyv05mZkwfTp07kzgoEEEjRxJ3k8/kfGf/xC0dGn51CWqLBMQW9C8WXMOHjpIbNxqkpKS2LtvL3v37SWqVRQxffpW+TmIgwdHMvWJzjz5xFamT4unf/9wLr+8UdF9l6t4P/sbb/RhoUJ4mScbXGcDP2mt/1Limgv4WGv99xLX5gCjtdbBHhWm1DJgUBm3l2utB5doa8H0GN4GtARSMD2JU7XWmZ68/znUJ4tPROm0Nj9B6tWDvn3P3HbPHhP0Akp03u/YYY5hqFHDvW3h3hngvn/G6tVm+STAAw+Qn5JCSsF5XqHTphF05ZVYO3ZElVd4FVWaWaRykDVxsRxNKj4CJKpVFDG9Y4iIiPRhdd7lcmmuHrWCxYuTqFcvkDVxI2jePASAr76Cnwt2s739dtkEW1Q+XluVrJQ6Caws3Pev4FppwfA3oJPWukou8pdgKMo0ezasWAE2G/zrX9CsWentdu2CV16B9u3h1ls9X828ZYv5iXXkiOk5tFhIGTGC/CVLipqooCCsPXsS2KsXtp49sfXsibVTJ5TN5tl7iipPa82hQweJrWYB8dixPGL6/MyRIzn07VuPpb8MITAwgNRU+M9/IDQUJk0q+5+1EP7Km8FwDdAcaKm1zi245hYMlVJ1MRtJr9daD/HoE/i5wmA4ceJExowZw6hRo3xdkvAX27ebeYD168Odd0KTJqW3e+0109ZqhYcegpYtL+x9HQ7zWoBj1y7SH34Y+5IlODPL6DS3WrFFRWEbOBBbv34mMHbpgpKZ9qKEsgJiq1ZR9K2iAXHVyhSGDVuGw6G59962vPhSNGAOO2rUCAIDfVygEB7wZjB8GJgGzNBa/7Pg2qnB8G3MsO7dWuu3PfsI/k16DMUZbdgA7dq59wLa7aYXsVBeHsycaeYVDhxY/jXk5cHatTjXrsUeEoI9JQX7+vXY16/HuXdv6c+x2bA1boytSRNsffpgu+EGbN26oWrUMMHT4YBgj2aHiEquugXEV1/dwb8e/hOAL768iL/8RY4/EZWbN4NhCGaLmA7AKmAe8BJmC5u5mMUpg4DNQExV3UNQgqE4by+8YELVyJFm+BjMfEQfbETtmjcP+5dfYk9MxN64Mfnr1+M8dRV0IYsFa8eO2Nq2xZaUZILjY48REB1d3GbrVnPyS926JuiKKqu6BEStNddeu5KF3yYQGmolds0IWreu5dZm1y7IyYFu3XxUpBDnwasnnyilmmBCYD/MVjGq4E8KHq8DrtFaHznvF68kJBiK87JvH0ybZh5ffjlcUxFbfJ6DvLyiTdpc6enYN2zA/vrr2PfswZ6WhuPwYbPo5VRKYW3fvmi+ou3QIWwnTxLQqpU5P6yQ3Q6PP24mZl16KfTuXXwvIcF81axpeldlcUylorXm0OFDxK5ZXWUDYmpqPn1jfmb//myio8P4bcVQgoPN39Ply+Hzz82w8qOPQoPTzt4Swr+cazD0ZLsaCgJff6XUZcAVQBRgAQ5hNryeryvDIcxCVJQ6dWDIEIiNNccp+IsS8wkDQkMJGjSIoEHFmwG4srJw/Pkn9nXryP/9d+xxcTgOHACnE0d8PI74eHLmFO8IZalfH9vWrUWBMbBlSwJSU80eH3mn7G61cSMsWGAev/mmezB85RVzkkx0NJScv5uVZfZ3DA01czdlWNtnlFI0b9acZk2buQXEffv2sm/fXlq1bEVMn75EVuKAWLduIJ99dhGDBv3Kxo1pPPjARt78rzmTPCzM/M7kcJh1XxIMRVVxQWclV2ey+ER45NR5hpWQzs3Fvnmzma+4bp35c/NmyC991oilfn0zZ3HYMGwjRmDr2RNLgwbmdJclS0yXyxtvuD/pwQchIwMuvhjGjy++Hh8Pr75qHj/wgOlpLLR0qVkd0LAhFJwAYwrWkJICISFmC6AAj04CFWdR2IO4Ji6WxKPFp/NUhYD4ztu7ueeeDQB8MrMvN9zQHDB/fdu2hRYtzvRsIfyDV4eShQwlC1GSzs/HsXUr+QWLW+zr12PfuBFyc0ttH9C4MbboaGwdOhDYvj22K68koHFjVOF8y9mzIT0dunSBSy4pfuKaNfC//5nHTz5plogWevNNc3JM06Zm+LpQdjbcd595PHYsDB9efO/oUXPEYIMGJlBKaLxgVTEgaq0ZPz6WuV8eomZNCytXDadjx1BflyXEeSm3YKiUt81cCgAAIABJREFUuh/YorVe7GkxSqlLgS5a61c8fQ1/I8FQiDPTDgeO+Hj3nsUNG9BZWaW2D4iMNEPQJfZatLRoURwWwQTNY8dMaGzXzr339ZNPTI9is2Zmm6BCKSnw2GPm8cSJ5kzrQj/+CPPnm8evv+4+NF24F2XTpu4biotzcsaA2LsvkZGVKyBmZNjp128Ju3Zm0qlzKH/8MYyaNd1nY2VlwYkTsseh8E/lGQxP27z6fCmlPgImaK2rzOxyCYZCnD/tdOLYtau4V7EgMOr09FLbq3r1zFzFEoHREhWFOp+evZwc2LTJ/NTu1Mm9l/Hjj2HVKjNn8cUX3Z93770miA4eDDfcUHx9715YuNCswL7ySrNfZdEH9M0qc39WlQLi5s0nGdB/Cbm5LsaPb8H/PuxT9IvLwYPwzjtmzuGUKeavlBD+xKuLT4QQwhPKYsHWoQO2Dh3gr38FQLtcOPftK+5VXL+e/HXr0CdOoE+cIH/JEvdTXEJDi1dDFwRGa9u2ZR/5V6MG9OtX+r1x40zwO7UXMy+veBg8LMz9XmIibNtmHpecywgmGRw+bLbsKTk3UmvzVQ2Hqk9dpFIYEPft38e+/fsqVUDs2rUOb7zRk1tvXcvs2Qe4+OIIbv67Obt8/344fty0W7HC/M4gRGV0rj2GmZjzhz0VDtSsij2GsvhEiPKntcZ58KB7z+K6dbiSk0ttr2rWNHMWSx7517EjynoBv/vm5UFamgmWJbt/4uLMEYSpqfD880WnzQBm3mNiInTvfvpw9hNPmLmMY8dW670etdYcPnKY2DWrK20P4qRb4pg5cz/BwQGs+H0Y3buHobWZGtuwoZnGKh3Hwt+U91ByedBVMRjKULIQFUNrjSsx8bSeRdeRMrZLDQ7G1r178dY5vXph7dwZ5c3zzL7/3uzNGBUFw4YVX//zT/jvf83jBx80S1kLzZtntuCJijI9mNVEWQGxZYuWxPTpS4NI/93/JTvbwYD+S9m6NZ02bWuxevVwQkNtMpNA+LXyDIblthBfa32gvF7L1yQYCuEfnElJZmPuEoHRuX9/6Y1tNmxdu7r1LNq6dUN5ez/Ew4fNXMbERLjlFvejEl96yRyh0aKF2Sm5kN0OH35oVjL06OE+N7IKKQqIcbEkJiYUXff3gBgfn07/i5aSmelgzLVNmTOnn/tCKczsgexs9//cQviKbFfjZRIMhfBfruPHsW/YYLbPKQiMzt27S29ssWDt3LmoV9HWsyfW7t0JqKif5vPnFwfDkj2GBw7Ac8+Zx5MmQZ8+xfe2bzcT2po0gZYtq0Q3VWUMiJ9/fpAJN8UC8NprPbjzrjZF9wqPQj98GB55RPZiF74nwdDLJBgKUbm4Tp40PYsl5i06duww3TqnCgjA2qGD2wIXW3Q0ARW51HT7dpg1ywTAU/ds/OADM9cxLAymT3d/Xny82V6nlvu5vpVFZQuIk+9ez7vv7sFmUyxfPpTefeoB8Msv8MUXps2QIXD99T4sUggkGHqdBEMhKj9XZib2TZvcts5xbNsGTmep7S1t2xb1Ktp69sTWowcB9ep5t8jsbNPdVHJF87PPmv1ROneGe+4pvp6RYeYwgtlix5+OXzxPZwyIvfvSwE/OoMvNdTLokl/YsCGNFi1CWBM3grp1A3G5zJ7rWpvZA5U0p4sqRIKhl0kwFKJq0jk52P/8031F9JYtZs5fKSytWrn3LPbsiSUiwrtFulxmo2+HwwwnFyq5yOW++6BDh+J7331nToZp0wauvbbSDD8XBsQ1cbEk+GlA3Ls3k74xSzh50s6VVzVi3rwBKKXIzTUnPlbDXYqEH5Jg6GWyXY0Q1YfOy8O+davbAhf7pk1mIlkpApo2de9Z7NULS0UsHsnNNRtw798PQ4e6T2x75RXYscMEyalTi69rDcuWmVXRzZr5bYrRWnMk4TCxa/wzIC5YcISx164E4Plp3XjggfaltnO5/PZ/YlHFSTD0MukxFKJ603Y7ju3bi3oV89evx7FxIzo7u9T2AQ0bnn7kX7Nmp61k9ZoFC8z8wxYt3Ce8JSTAU0+Zx+PHw8UXV0w9HiorILZo3pKYPjE0bNDQZ7U9/NAmXnttJxaLYsmSwQwYGO52/48/4Lff4P77ISjIR0WKakuCoZdJMBRCnEo7nTh27HAfht6wAZ1R+v8HB4SHn3aKi6VVq4oLi2DSysyZ5vGpi1zmzjXLart0gREjKq6mc1AUEONiSUjwj4Bot7sYNnQZq1cfp3HjYOLWXkpEhEmAGzfC22+bdr17w623Vnh5opqTYOhlEgyFEOdCu1w49+wp2pC7MDTq1NRS26uwMGw9ehT1LAb27ImlbdvzOx/6vArU5mSWPXugb1/3uYdTp0JSkpmreN99xdcdDjh61AxL+3iuor8FxEOHsonp8zPHj+czYkQDvl14MRaLwuWCN94wa4buuMNM9RSiIkkw9DIJhkIIT2mtce7ff/qRfymlnzyqatUyYbHEULS1ffsLO/LvbFwu+OwzMy+xXz/3c6F37DBzFuvUMSmnVSvv1XEezCrm1T4PiD/9dJRRV60AYOoTnZkypRNgFphnZ0N4+JmeLYR3VHgwVEq11lrvOcP9oVrrX8rlzfyABEMhRHnSWuM6csStV9G+bh2uxMRS26saNbBGRxf1Ktp69cLaqRPKZiv/4k5dMfHNN7BokektfPll96M91q+HunV9uvF26QGxBTF9+lZYQJw6dQvTnt+OUvDjoksYOrT0xTFyjJ6oKL4IhhnA/Vrr90+5HgRMB+7WWnvx19uKJcFQCFERnImJpx/5d/Bg6Y0DA7F16+Z+5F/XrqjyXumQkgJbtpgtc8aOLb7ucsFDD0FmpullvPnm8n3f81S4zc2RhOLztCsqIDocLi6/7DeWLz9GZGQQa+JG0LhxDbc2iYnwySfmYBvpRRTe5otg+AJwP/ADMElrnayU6gnMApoA92qtPymXN/MDEgyFEL7iTEkp7lUs6Fl07t1bemOrFWuXLqZXsbBnsVs3AkJCyr+wQ4fgmWfM4wkTYMCA4nu7d5v7nTtDZGT5v/cZ+CogJibmENPnZ5KS8rj44nB+WjwIq9X0vJ48CU88ATk5Zr3PI4/ISmXhXT6ZY6iUugT4BAgB5gK3AiuBiVrrMn7FrZxkH0MhhD9xpaZi37jRrWfRsXNn2Uf+dezo3rMYHU1AefySm5ZmNtKOjoaSrzdzplkBHRgIr74K3pwfWQZfBMRly5K5bORy05n6cAeefbZr0b2vv4bFi2HUKLjyShlSFt7ls8UnSqloYBUQBKwH+mmtHeX6Jn5AegyFEP7OlZFhwmLJI/+2bzdDvqdSCmu7du57LfboQUBYWPkU8/zzZuPtrl3h7ruLr9vt8OGH5np0NHijJ/MUpQXE5s1b0Ld3DA0blv9G5M8/v50npm4BYP6CgVxxhXkPl8t0pLZrV+5vKcRpfNVjeAvwCrAX+Ap4FNgM3KS13lVub+QHJBgKISojV3Y2jsLzoQu20HFs3Wq2oCmFpXVr9yP/evTA4smEOK3NZtpOJzRvXny95DF+t90GvXp58Kk8U1EB0eXSXD1qBYsXJ1G3ro01cSNo0aLm2Z8oRDnyxRzDb4ErgJeAx7XWdqVUB2A20AF4SGv9drm8mR+QYCiEqCp0bi72LVvcj/z780/Izy+1vaV5c/dh6F69sHh6JN3KlWZMNS/PrHAuOdHuxx8hLAx69vTqBLwjRw6zZu0aDh85XHStvANiSkoeMX1+5vDhHGJi6vHLr0MIDHTfm9JuN4tRevSo0HwsqglfBMN9wASt9YpTrluBJ4GHtdaB5fJmfkCCoRCiKtP5+Ti2bTO9igVD0Y5Nm9A5OaW2D2jcuCgoFp4THdCkybmd4uJymQ2zGzcuvma3wwMPmMBYQUeFeDsgrlqZwrBhy3A4NJMnt+XlV6Ld7r/5ppmeabWajx4VdcFvKUQRXwTD2iXfSCnVAAgqXHSilOqntV5dLm/mByQYCiGqG+1wmCP/SvYsbtiAzswstX1ARMTpPYstWpxbWDxyxCxSyciAO++E7t2L7x09ChYLRESU0yc79a1LCYjNmhPTpy+NLjAgvvbqTh5+eBMAn39xEaNHNy26t22bOR0lKsp85Joy2izKkc9PPlFKrQL6VKW9C0uSYCiEEAVH/u3aVdSrWHTk38mTpbZXdeu69SraevbE0rp16Uf+OZ2wdSt06uS+ivm992DdOrP1zeTJXlvO642AqLXm2mtXsvDbBEJDrayOHUGbNrWK7sfHQ+vW4I19ykX15i/BMEZrbfHKG/iYBEMhhCid1hrn3r3uR/6tX4/r+PFS26vQ0OIj/wr3WmzXDmUp5cdHbq4ZZ3U4Tt9Eu3C1dTmfK30k4Qhr4mLLLSCmpeXTN2YJ+/Zl0b17GCt+H0pwcNk/KuV0FFEeJBh6mQRDIYQ4d1prnIcOuQVF+7p1uJKSSm2vQkKwRke79SxaO3Uy50MnJsKqVWZ4uXXr4ietXQtffQWDB8OQIeW+YKU8A+L6dalccskv5Oe7mDQpirfeLn21yerVpnP0H/8wo+dCeEqCoZdJMBRCiAvnTEhw61nMX78e1+HDpTcODjZH/pXca7Fz5+Ij/954wxzVFxwM06ebP72gvALiu+/sYfLk9QB89HEMN97Ywu3+unVm1BzgkkvgxhsvvHZRfUkw9DI5+UQIIbzDmZx8+pF/+/eX3thmw9a1qwmJoaHY0tOxDR6MKpmi7HbYtw/ati3XMdkyA2LvvjRqdPaAqLXmppti+fKLQ4SEWFi5ajidOoUW3S/cwSc52ewJ3qZNuZUuqiEJhl4mPYZCCFFxXCdOYN+wgfwSC1ycu8o4N8FiwdqpU3HPotWKLS6OgIYN4aGHoG7dcq3tSMIRs0jl8KGia+caEDMy7Fx00VJ27sigY6dQVq4cRs2a1hL3IT0dmjQp15JFNSTB0MskGAohhG+50tOxb9jg1rPoiI8v/XxowFq/PrbLLisOjNHRBKSkQKtW5bJgpbSA2KxpM/r26XfGgLh580kGDlhKTo6TG29swYcf9Tnjlj4Oh0+OmhaVXLkFQ6XUJR7W8DbQQYKhEEKIiuLKzMTx559uPYuOrVvN1jelsISGYmvWDNvllxM4cqTZmLtevQuqwZOAOPOT/UyaFAfAO+/04u+3lL67dXIyzJgBo0ebA2GEOFflGQxdgCfdigrQEgyFEEL4ks7Jwb55s/uK6M2bzdzDUlhatnRf4NKzJ5bIyPN+34TEBNbExXLolIAY06cvjRs1Pq39rZPi+OST/QQFBbDi92FER4e53Xe54IknTDi0WGDKFPfDYoQ4k/IMhvvxLBgCoLVu5elz/ZkEQyGEqLx0fj72tWuxf/kl9o0bsefkYN+0yaz4KEVA06YElthn0dazJwGNGp3TKS7nGhCzsx0MHPALW7acpE2bWqyOHU5oqPtO19u3m6PzLr0Urr5a9jcU587ncwyrOgmGQghRtWi7HUd8PPZffsH+wQfYs7KwHz1a9vnQDRqc3rPYvHmZYfFcAuKOHRlc1G8JmZkORo9pymef9Tvt9ZKTwYMOTFHNSTD0MgmGQghRRX3wAcTFgVLop5/GkZLiforLhg3o9PRSnxpQv77bCS62nj2xREW5hbuzBcQvvzzE+BtXA/Dqq9HcdXfbM5arNZw8CWFhZ2wmqjkJhl4mwVAIIaqo1FRYtMjsE3P77cXXtYZ330W3bYszIgL79u3Y168vOidap6aW+nKqTp3isFhwTrSlbVsSk46WGRCnPXeUd97Zg82mWLZsKH1iSl8Q43LBxx/Djh1mJ57w8PL8H0JUJRIMvUyCoRBCVDO7dsFLL5nH48bBsGFFt7TWOA8cOP3Iv2PHSn0pVasWtuhobL16caJ7NzYG2TicVhwsmzRuykf/s/LTojxatAghds0I6tULPO11Sp6O0r073Hln+X1cUbVIMPQyCYZCCFHNbNoEX3wBaWnwwgtQq1bxPbsdbO4LRbTWuI4ccetVtK9fjyshodSXP96xA9tHX0NS4+ItbfbtCeT772rTsWML5s0bQECA+3xDrWH+fNi6Fe67D2rWLL+PK6oWCYZeJsFQCCGqIa0hMdF9nxinE5580myUfcUV0LDhGV/CefSo+5F/69fjPHCg6H5KVCu2X3EZyR07FF1zxR8jVDXj5tdvK7Wk/HwoPDJaiNJIMPQyCYZCCCEAWLXKTPQDuOEGGDz4vF/CmZJSfIpLQc9issvJtisuI7lTx6J2tbck8JepD1On4Zk34d6yBRo1gvr1z7sUUUVJMPQyCYZCCCEA04P43XewZw88/bT7kHJmpvuQ83lwpaVh37CBg+vWs+JoCllRZog5+GgK3ToNpO/1l5b6vI0b4d13zZHQDzwg4VAYEgy9rDAYTpw4kTFjxjBq1ChflySEEMKX8vLcx3Nzc+HRR80Q8zXXQLNmHr+00+nkq4deIqW5BVdgIAF2O/X25TLu5X9jsbgfMPbNN2ZRtdUKd98NHTuW8aKiWpFg6GXSYyiEEOKMfvoJ5s0zj2+/vVwON1779TI2blhKTuMIAGruPMLwm2+jeffWRW20hgULoG1b6Nz5gt9SVBESDL1MgqEQQogzOn7cdN3t3296DkueYJKS4vGmgxnHT/L149PI6GKGlgNPZtDC1pzLHrypHIoWVZUEQy+TYCiEEOKcuFwQEFD8fVoaPPYYtG4N114LzZt79LLfP/chhyxHsdc2cxhDtxxl7POPEVLn9D1r0tNh5kyzNkbmHFZP5xoMA8q6IYQQQohyEHDKj9qffwaHwxxXkp/v8cte+ejfGTHkGkJ2m30R07s0ZM6059m48A+3drm58MorsHkzvPiiCYlClEWCoRBCCFGRRo6E4cOhWzdo08b93smT5/VSrWM6MXH6M9TbnoZyOMhpFsnK3SuZ+/DLOJ1OwKyH6drVtO/cGWSQS5yJDCV7SIaShRBCXBCt3ecdJiaa7W5iYswq5rCw83q5Pz75gW371pAbafY4rLX9CFfcO5kGbZqitdnGpnv30zswRfUgQ8lCCCGEP1Pux9uxYIE5RWX1asjOPu+XGzDxCsZMuotaW44AkNmxCQs++5Bf3/oKpaBHD/dQqDUcPXohH0BURRIMhRBCCH9wzTUmvfXv737kHpg5ieegXtMIbn5rOk0TFNbsHPLqh7HFdZhZd00lLzu3qJ3W8NVX8MwzZu6hEIVkKNlDMpQshBDCK05dxbxrF3zwAYwaZULjOY4Fb/tlHasWfU12S3N2c8i+owy4ahwdBkVz5IgZtdbaZNDHH5ch5qpOhpKFEEKIyujU8d5588wWN59/fl5LijsN7cX4J6cStuUYuFxkt2rIr3E/Mf+Jt2jSBO64AyIjYfJkCYWimPxVEEIIIfzZxRebg4+HDnVfkHIOI35BIcHc9NbTdFZNCDqRhiOkBocaOPjorn/TPCKFJ5+EevW8V7qofGQo2UMylCyEEKLC2O1miLnkWcwbNsDvv8OYMafPSSxF4s5DLHrjTTI7NgEgOPkEnaNi6D/hiqI28fEQGws33mjOWhZVhwwlCyGEEFWFzeYeCh0OM8S8ZYvZvdpuP+tLNGrXjAmvPUfDfXlY8vLJjazH+rR45tzzDA67g0OH4K23YOVKeO+9c+qQFFWQBEMhhBCisnE4oEMHs+XN5Zeb4HgOLBYLY198iIui+lLjcDLaauV4h1A+eXgKGQfiadTIvGRMzOm76YjqQYaSPSRDyUIIIXwuMREiItzHfWNjzVF7AwaccVVJVmoGXz32POldzKplW3omTVyNaH7lzXTv7u3CRUWToeRSKKWClFLvK6X2KqUylVK7lFL/PKVNY6XU10qpFKXUcaXUAqVUU1/VLIQQQpSpUSP3UJibC3PnwuzZ8PbbZ3xqzbq1mfjWc7TJCCUwPQN7aC32h2Ww8Z1HyEotzg0ZGSZ/iuqhWgVDwAocBS4FQoGxwCNKqetKtHkLsAGtgGZAFvB+BdcphBBCnL9jx4qHlWNizukpl//rb1x26Vhq7koAIL1rIz576QXWfbOcvDx480144QXYvdtbRQt/Uu2HkpVSHwKZWut7Cr7/E5iutf604PsrgXe11k1PeZ4MJQshhPA/drsZTh4wwH2i4Pbt0KIFhISU+jSnw8GXD0zjRFQILpuVgPx8asS7WO18ABVg4eKLYfz4CvoMotxV+qFkpdQjSqm5BcO+Wim1/wxtA5RS9yml4pVSuUqpQ0qpl5VSNc/yHlZgIPBnicuvAOOUUmEF4e8m4Pty+EhCCCGE99lsMHCgeyg8edIMLU+ZAuvWlfo0i9XKDa9PoVd4Z4KTjuMKDCSrWzAxQY/SsfEBbrihguoXPuW3wRB4DhgK7AFSz9L2VUyg2wZMBuYC9wALlVJn+owzgJPAzBLX/gDqASeANKAd8G8P6hdCCCH8w8qVkJcHWVlQq9YZm/a7cSRjb7+H2lvM0HJOpybUsM7k1/9+UdRGa9nOpqry52DYWmtdX2s9Akgoq5FSqjMmDM7TWo/WWr+vtb4fuB8YAlxfxvNexvQWXq61zi+4FgD8DKwCagG1gSXAj+X3sYQQQogKdtllcPvtMHgwtG/vfu/48dOahzWuz9/emkbzJBvWrGzy69Zhe0AiM+96nNzMHBYsgA8/NLvmiKqlUswxVEptAWpprVuWcu8Z4DHgEq31ihLXg4HjwHKt9RWnPOc1YBgwVGt9rMT1cOAY0ExrfbjgWjPgIBChtU4p0VbmGAohhKjcjh6Fp56C6Ghzgkp4+GlNdqzYxO8LPic7qhEANfYfZeuB0WSH9WbAAJgwoaKLFp6o9HMMz0MfwAWsKXlRa50LbCy4X0QpNQMYzimhsOA5KcBu4C6lVHBBuJwMHC4ZCoUQQogqYcECc9Tehg1mmLkU7S/uzk3PPEXdrcdRTic5LRvSLuYXWh6fweWXV3C9wuuqQjBsDKRorfNKuXcECFdKBQIopVpggl4bYF/BXoaZSqmSQ8X/B/QseG4C0Bu42psfQAghhPCJa6+Fvn1h0CCzYrkMgcGBjP/vU3QJbEnQ8TQcNYKpNRS+e/JfHD+QVIEFC2+rCkPJewCb1rp5KfdmYlYV19Vap5VzTbWB9OjoaCwWCwC33XYbt912W3m+jRBCCOF9WruvYt6/HxYtgrFjoX59t6bJexL4/tXXyezUBICgYyfo1KwXYTGjyMyEfv0qsG5xzs51KLkqBMPNQKTWukEp977EbGIdVLjApBxrkjmGQgghqh6XC6ZNgwMHzJ6Hzz8PwcFuTZxOJ9889jrJkU6cwUEopxP77+nEhz7GyMutjB7to9pFmarTHMMEzHBxUCn3mmCGmcs1FAohhBBVlt1ujtoDs4r5lFAIYLFYuHba/QzseDE1DiahLRasg+rSq+YUQrK3Vmy9olxVhWAYh/kcbmf/FCwciQbWevPNJ0+ezMKFC735FkIIIUTFCQqCm2+GBx/ktNUlBw9CWvHMrG6X9+OvjzxKnS1J4HKR164x+xO+5btn5STZyqoqDCV3BTYB32itx5S4PhmzgfVNWuvZXqhJhpKFEEJUHw6H2drm5EkYPdr0Jpaw+JVP2Ze3n/w65mdi7S2JjHjwXxxKDpN5h37gXIeSrRVX0vlRSt0EFC6RigAClVJTCr4/oLWeBaC13qyU+i9wt1JqHvAD0BFz8slyYE7FVi6EEEJUQZs3Q3KyeVzKztaX3n8jhzfvZfEH75LVvgkZXRrxw4evsCN+EAfHDePaayGgKoxTVnF+22OolFoGDCrj9nKt9eASbS3AP4HbgJZACvAFMFVrneml+qTHUAghRPWyfTv89htMmgQFO3IAZsFKQepzOp3MffAFjrcIxBVoIyDfDuvt3P7hQ1itljJeWHhblVqV7I8kGAohhBBAbi489xwMHAhDh4LVDEbGfbmUjZuXk9vInKZSc8cRLp10O027Rvmy2mqrOq1K9ilZfCKEEKJa+/57SEqCr7+GTZuKLvcZN4zrJt9P7S2JAGS1b8L3C+ew+NU5HD7sq2LF2UiPoYekx1AIIYTADC9/9hnUqgUPPeS+UXaBhU+/z5GgFOy1aoLLhV51jC4TH2PopSE+KLh6kqFkL5NgKIQQQhRwOMxZy3XqFF/LyIA1a8zqZYuF3Su3sPzrT8lubfZIDD6QRO+LR9HjKlmyXBEkGHqZBEMhhBDiDD7+GFatgqZN4YEHICQER76dWfc8R1bnULTFgiU3j8hkC3959t6i42WFd8gcwwoicwyFEEKIU+Tlwd695nG9elCjBgDWQBs3v/ME3UJaE3QsFWdwEInNrcy891EObEnwYcGikPQYekh6DIUQQogzcDhg8WLo2xfq1y++npcHgYEcP5jMty+8QmbnJgAEpaRSy9mdvz51jY8KrtpkKNnLJBgKIYQQ50lrePttEw5vugnCw/nqkRkcC8/FUSMY5XQSui2N6199jMDgQF9XW6XIULIQQggh/MumTeYrPh6++w6Aa5+/hzbhQwjefxRtsXCya31mTXmCHSs2neXFhDdIMBRCCCFExWjXDi65BGrWhDFjii6PmNiHMQ9OIWxLMrhcZEc14pfVPzJv6js+LLZ6kqFkD8lQshBCCOGhrCwTDgtpDStWQL9+LHnnG/Zk7CS/rtn6JmhDAtc++TD1mtTzUbFVgwwlVxBZlSyEEEKcp5KhEMy2Np9+Ck89xfAxAxk1egLBW44AkNejMV+/9zqr5/zkg0KrH+kx9JD0GAohhBDlZMYM2LrVbJD91FNQowZHjzqZ88+XCeoXgCswkAC7g3p7cxj3yr9lz0MPyKpkL5NgKIQQQpQTlwuWLTPb2nTvXnTZnudi3fzf2PLnUnKaRAAQsiuBIeNvIapXWx8VWzlJMPQyCYZCCCGEly1dChs3knH5/zHvjf+R3tUcp2dLz6ChvSnXPD7RxwVWHjLHUAghhBCV14kTsGAB7NxJ7bmfMvGt5wjdUwuX88mHAAAYFElEQVRbRib20Nocqn+ST+54lKzUMjOO8IAEQyGEEEL4n5AQ6N8flILrrgOl+Ou0v2NlNME7zPF56V0b8tmL09mw4HcfF1t1yFCyhwqHkidOnMiYMWMYNWqUr0sSQgghqp5jxyAiwu1SRuxmFs7+hhNta6GtVix5+YQd1Fz30v2yMKUMMsfQy2SOoRBCCOEDR4/C009DWBgrIzqyNXUXuQ3MWcw1thzhynvvplH7Zj4u0v/IHEMhhBBCVD3LloHDAceP0/+qflz217uxrjVDyzldmrBw7sf88tZXvq2xEpMeQw9Jj6EQQgjhA4Vb22RlQcE0rvh4mD/1TerEZOEIqQEuF2HbTzDuxUcJCgn2bb1+QoaSvUyCoRBCCOE/HPsPs2vuD6w8tp/slg0BqLHvKP0uu5Yuw3r6uDrfk6FkIYQQQlQPDgfWWR/Rcfc6xteqSdjWFHC5yGnVkBUbfubLf7/p6worDQmGQgghhKjcMjLMEDMQ1L0LN/33P+htTQk8cRJHSA2Smrv46K5/ceLwMR8X6v9kKNlDMpQshBBC+BGHw8w9HDwYrFbsdpjz7mHyt75BbtcmAAQnn6BzVAz9J1zh01J9QYaSK8jkyZNZuHChr8sQQgghqjerFYYPN38CNhtMvL0BN4dZabjqIJa8PHIj67E+LZ6P//E0DrvDxwX7J+kx9JD0GAohhBB+7vvv4dtvAdjYJoa1qfvIaRoJQI2dCQwb/zda9e7gyworjPQYCiGEEKJ6u/hiiImBZs2IfuBm+o55BFYeBSCnXWN+WjqP75/70MdF+hfpMfSQ9BgKIYQQlUReHgQFAXDoEMx/8iNsbROwh5qf37W3JDLm6X9Tu34dX1bpVdJjKIQQQggBRaEQoFkzmDw8mMv/PEDN+EMAZHRpxOevvMSauct8VKD/kGAohBBCiOojKwvWraNFbcXEJhGE78wgwG4nt0kEcUfWMuef03E6nb6u0mdkKNlDMpQshBBCVFLp6fDll3DNNdjrhPPiPxYT3uQP8hrWB6Bm/BFG3v4PmnRu5eNCy48ciedlEgyFEEKIqsHhgLkzT5D7xzNk9moJQGBaOq1rtmb4vTf4trhyInMMhRBCCCHOgdUKN/Q7ys3WLFos3ootK5v8sFC2W5L4361TyMnM9nWJFUaCoRBCCCEEQFgYV7cIZljXSwjZkwgBAWT3iOTTp59ly+I1vq6uQshQsocKh5InTpzImDFjGDVqlK9LEkIIIcSFyM2FgwehXTviVufz+6vPYRtYB22xYMnJpcHxIMY8d6+vq/SIzDH0MpljKIQQQlRtqamw/Jl3Sa5zlLzwugDU2nqEKx+4j8iohj6u7vzIHEMhhBBCiAtQV6Vxjd7BNbsPU2vDPgAyOzdh/qx3WTTjWx9X5x0SDIUQQgghShMaCoMHExli4ebbbyRynwtLbh55EXXZrfYw6+4nyc/N93WV5UqGkj0kQ8lCCCFENZGUBA0aYLfDR8/GonK+JbdFAwBC9iYyaPSNtOnfxcdFnpnMMfQyCYZCCCFE9bRrWzar3nyOk53CISAAa1Y2jbLqcc2Tt/m6tDLJHEMhhBBCCC9o2ymECW89Qwd7JIFp6ThqhnAoMpf3Jv2btKNpvi7vgkiPoYekx1AIIYQQhzfv5cd33yW3UxMAgo+m0L3TJcRcP9zHlbmTHkMhhBBCCC9r2jWKkQ89h/23bALy7eQ2DCcuaROf3fc8TqfT1+WdN+kx9JD0GAohhBCikN0Oa75YyrYdy8ltFA5AyI4j9Bt7G50HtvZxdbL4xOskGAohhBDiVOnJacx7cjoZXRoBEHgyg5onWzB+2nif1iVDyUIIIYQQFSw0Moy/vfU84QdqYMvMIr9ObVKbn+CTOx8j+2SWr8s7KwmGQgghhBDl7Ibpt9KqydUE70wAIL1LA+ZMe45N3688p+e7XC7mzp2Ly+XyZpmnkaFkD8lQshBCCCHOxp7v4MsHniO1XW20xYIlLw/rdgs3vHwvtWtbynzesmXLGDJkCMuWLWPQoEEXXIcMJQshhBBC+Jgt0MqNb0ylR622BB07gTMoiLxoK3MefJQ//0go83lff/21258VRXoMPVTYYzhx4kTGjBnDqFGjfF2SEEIIIfzY8QNJfP3MK+T1MHseBh1Po11kVwbf/he3di6Xi2bNmtGpUye2b9/OwYMHCQi4sL48WZXsZTKULIQQQojzpTV8dPcb5EXl4KgRjHI6qbMjjXEvPkZQjUAAVq1axaBBg9i5cydt27ZlxYoV9OvX74LeV4aShRBCCCH8jFLw9/9OZlCPYYQcOIq2WEjrVJ9Zjz/BjEc3kp5uho+HDRtGy5YtGTZsWIUOJ0swFEIIIYSoYJ2G9mL8E1Ops/UYuFzkRDXCVu8nZj/wFl999RVjxowBYMyYMXz11VdU1AivDCV7SIaShRBCCOGp9u3bs2vXrtOua62xWq0kJCQQERHBsWPHaNy4MQ6HA6XUae3btm3Ljh07zvp+5zqUbD2vTyGEEEIIIS7YjBkzmDBhAjabjTfeeIMmTZoU3atbty4REREAREREsG3bNlJTU4vuHzlyhMmTJ2O325nx/+3dfZQU1ZnH8e+DEVDZCMYEA0lAzQmoGyUBjBKPDgZl1agEBZQT1CC6GxZcknX3RI/muOTlD3cVVzF7RF1edDXGHVBZQdREWNFEJa4OShIVARUEX0IUhAGEZ/+4t6Fsqme6h67unp7f55w6PX3vrVu3qHuGZ6rq3nvzzWVtl+4YtpHuGIqIiMi+WL9+PRdffDHLli1j5syZnHPOOa3u8+CDDzJ+/HgGDx7M7Nmz6dmzZ1HH0uATERERkRp22GGHsXDhQq666irOP/98Jk+eTHNzc2rZrVu3MmnSJEaNGsXVV1/NggULig4KS6FHySIiIiJV0qlTJ6688kpOOeUUzjvvPLZv385tt922V7kpU6awcOFCnn76aQYNGpRdezKrWURERESKMnjwYLp3707//v1T8/v160ePHj0yDQpBgaGIiIhI1b3yyissX76ckSNHArBx40amTZu2e9DJyJEjaWpqSh3JXE4KDEVERESqrLGxkUGDBtGnTx+WLl3KcccdxzXXXMOAAQN46qmn6Nu3LwMHDsx8smsFhiIiIiJV1tjYyIgRI5g6dSpDhw5lzJgxbNiwgdGjR9PQ0MDUqVMZMWJE5oGhpqtpI01XIyIiIuWwatUqjjjiCPr06UNzczOzZ89m+PDhu/MXLVrERRddxAEHHMCaNWtYtWoVffv2LekYmq5GREREpB2YN28eAP379+fFF1/8RFAIMHz4cJqamnYPTJk7d25mbelwgaGZdTGz283sdTPbbGavmtmUvDKb87YdZtZUrTaLiIhI/erfvz/Tp09vcW7Cnj17smDBAqZPn15w5HI5dLhHyWZ2EPAjYDbwOnAssAi4wt3vK7BPE/BLd/95Ik2PkkVERKRd0KPkAtz9I3e/1t1fc/dd7v4C8DDwzbTyZnY8cDQwq4LNFBEREam4mg4MzewqM7s/PvZ1M1vdQtlOZvYDM/ujmTWb2ZtmdkO8Q9jSMT4FnAQUelR8KbDQ3de1+URERERE2oGaDgyBnwOnAiuBja2UnQbcCKwAJgP3A1cA882spfO8GfgAmJOfYWYHAhcAd5TcchEREZF2ptbXSj7S3V8HMLOXgG5phczsGEIwONfdz0ukryIEfhcA96TsdwPhbuGp7r49perRwBbCo2YRERGRulbTdwxzQWERLgQMuCkv/XZCYPfd/B3M7CbgdOBb7v5egXonALPd/eMi21E3ZsyYUe0miJSF+rLUC/Xljqca17ymA8MSDAZ2Ac8mE929GXgh5u9mZjcDwwh3Ct9Nq9DM+gFDgP9s7eDz589vU6NL3a/Y8sWUa61Mvf4Cauu1qvXjl6PeWuvHxZZVX66vY9djXy5XOfXl9nPsctV7/fXXV/z4tf4ouVi9gPfcfVtK3lpgiJl1dvftZtaH8Nh5G7DKzHLlnnT3MxL7XRrTXmnpwJs2baKxsZGGhoaSG13qfsWWL6Zca2V27tzJpk0FR7O3W229VrV+/HLUW2v9uNiy6sv1dex67MvlKqe+3H6OXa56N27c2KZrnnb8YutpN/MY5t4xdPe+KXkrgf3d/UspeXOAcUAPd/9LGdvzeUAjlUVERKQ96eXubxfKrJc7hluAzxXI65ooU07rCXcqN5e5XhEREZEsdCPELwXVS2C4DjjazLqkPE7uTXjMnDbquM083GotGHGLiIiI1JhWnyfXy+CT5wjncnwy0cy6AgOAZdVolIiIiEh7Ui+B4X2AA1Py0i8DDgT+q+ItEhEREWlnajowNLNxZnaNmV0DfBY4OPfdzMblyrn7cuBWYKSZzTWzCXHy6huBJaRMbt2RlbLUYCz/DTN73Mw2mdmHZvaImQ2oUHNFMu2zZtbLzOaY2btmttXMlpnZqExORDqMWumzZtbFzKaa2Soz22ZmK+P/ofuX4TQlIV7ntG2vsQhm1s/MHjCzjWb2kZk9aWanFqj3YDO7xczWxiV/Xzaz71tiWpVE2TYtD/yJOmp5VLKZLQZOKZC9xN0bEmX3I9wxvBzoC7xHuJP4Y3fXAJEEM3Pgz8DzwEDgw7TR3rHsCcBiwrQ/02PyJMJgnyExKBfJVFZ91swOIbxq8jnCH5JvAWMJv3fGu/vMDE5HOoBa6bNm9gBwLmFO3t8CJwLjCYs3XLLvZyo58Zo/CeRPOLnD3e9LlDuSMO/yx4SFOT4gPOH8a+AMd388UbYzsBT4GnAL8AfgDOA7wL+4+3V5bfh3wnLA84CFwFGEKfqeBIa5+65WT8TdtXWwDTgi8fNLwOoWyj4LfAj0TqT1jmmPVvtctHWMLas+C1xPeA3l7ETafrGO9wlTZFX9/LW1v60W+ixwZix7Q14dN8T0IdX+d6qnLf6bziqi3K+AncCARFo3YA3wJ+JNu5g+MdY7Oa+ORmA70CeRdgxhsY/GvLKTYx1jizmPmn6ULNnwIpcaNLMvE1aNud/d1yb2XwvcDwwzs8OyaaXIHhn22bHASnefnyi7k/CX+SGE/1hFSlYjfXZs/MxfLjb3fa/lYmXfmVlnM+tWIO8g4Bxgsbu/kEv38GTzDuArfHK1trGE6fZuz6vqJmB/YEwireTlgdMoMJSW5Drnb1PyfkfogAMr1xyRVhXdZ+Mk9b1jelrZZH0iWcmyzw4G1rr7m8mC8fs61L+zcD4hCNtkZu/EdwMPTuQfC3Sh8PWGeF3MrBPwdeD/PCzxm/Qs4e5g/vUuenngQuplHkPJRq/4uTYlL5fWu0JtESlGKX1W/VtqQZZ9thewosBx1wJfKLKNUpxnCXd5XwM+Tbh7Owk4xcyGxLuCpVzDHsABaWXdfZuZvc/e17uo5YFbOgkFhtKSA+NnWidrzisjUgtK6bPq31ILsuyzBxYomyuv/l1G7v6NvKQ5ZtYE/Az4h/hZruudK1/K9c6VaTEw1KNkaUluGcEuKXlZLTUosi9K6bPq31ILsuyzWwqUzZVX/87evxICsbPi93Jd71z5Uq53ss6CFBhKS9bFz7THabm0tNvhItVSSp9V/5ZakGWfXVegbK68+nfG3H0H4TocGpNKuYYbga1pZc2sC/AZ9r7eh8a8tLqLWh5YgaG05Ln4eWJK3gmE4e+/r1xzRFpVdJ9197cJv1RPKFAWtJymZC/LPvsc0NvMvpgsGL/3Qv07c3Fp3i8AG2LScsLj3kLXG+J18TDn4PPA11KCveMJMVz+9d7n5YEVGEpB7v4aoSONMrPcC7PEn0cBv3H39dVqn0i+NvTZe4EjzezsRNn9CPN+/QVYUJGGS4eVcZ+9N37mLxeb+67lYsvEzD5TIOsnhPEc82H3tDTzgQYzOy6xfzdgAvAqnxxVfC/hvcDL8+qdQpgg+1eJtLIsD1zTK59INiwsJ9gnfp0MdCZMeAqwxt3vSpQdAjxBmF3/lsQ+PYFvuvuLFWm0dGhZ9dn4y/z3hEcyNxLuxlwINAAT3P3OjE5J6lyt9Fkzmw98G7iTPSufXArc7e7jkLIws2mEO35PAG8QJqw+ExgKPAMMdfetseyXCcHfDmAaYSLzy4CvAme5+6JEvZ2Bp4HjgJsJK5+cSVj55Kfufm1eO24hjISeR/gj4SjCSihPAae6Vj7RlrYRll7yAtvilPInAr8GNgObgEXA16t9Hto6zpZlnyW8e3MXYRnNZsKjmzHVPmdt7XurlT5LGHTwU2A14RHm68C1wP7V/jeqp42w7OAiQqDeDHxEmDvwaqBrSvmjgAcJd3m3EJa9G1ag7u6EpRLXxWu4ghD8WUrZ/YB/JKygsi2250ZKWMVJdwxFREREBNA7hiIiIiISKTAUEREREUCBoYiIiIhECgxFREREBFBgKCIiIiKRAkMRERERARQYioiIiEikwFBEap6ZeRu2WXHfhvh9cXXPom3M7JKUcxuUV+a6RN5bcYm0QvWdlldX36zPIR53Ssp5VOTYIlK8T1W7ASIiRZidknYYMJywwsB/p+QvzbRFlbeSPef0XgvlegOnAY8UyL+kjG0qxQr2XMfzgYOq1A4RaYECQxGpee5+SX6amTUQAsP30vITniUsP7Uli7ZV0NJWzhNgGTCIEPztFRia2acJa6z+AfgSFQzO3P1R4NHYjoZKHltEiqdHySJS19x9i7v/0d3fqHZbKuAZQtB3rpl1T8m/ADgAmFXJRolI+6HAUETqWqF3DM2sb0xfbWadzOyHZvaymW2N7+ndaGYHxrI9zOymWHabmb1qZj9s4ZhmZheY2aNm9l7c5w0zu70C79XNAroSgsB83wN2AnflZ8Q2vxr/TU4oVLmZzY1lJibSFse0BjM72cwejue9y8xG7PspiUilKDAUEYF7gKnAKsLjzoOAHwCNZnYI4U7cGOA5wnt+fYEbzOzq/IrMbH/CO4/3AicR3q17iPAu5ATg+fzBI2V2FyH4+15eu/oBJwCPuPvb+Tu5uwO3xq8T8/NjHb2Bs4FNpASXwCjgCcJj6seAXwM72nQWIlIVCgxFpKPrAwwAvuLu33b3c4FjgfeBvwGWAC8Ch7v7KHf/FpC7C/aj3F3FhJ8AI4H/jXWeHPc7CpgM9AB+aWaZvOMdg75FwPFmdlQiKxcozmph95nAZmC0mR2akv+3hHfT57j7ppT8icD33f2r7n6hu5/m7g+XfBIiUjUKDEVE4Ap3X5f74u5vAnfHr30IwU5zIv9hoAn4K8JgDwDi3cUrCMHVKHd/K3kQd58OPAwcCZyRzakAIcCDOAI5Tl8zDvgz4e5lKnf/gHAnsAswPpkX74ReFr/+okAVj7n7jDa3WkSqToGhiHR0O4DfpKS/Fj+XuXva9DCvxs9eibShhMEdS9z9nQLHWxI/Tyy1oSV4iBAEjotB4emEdt7j7ttb2Xd6/Pw7M0v+HzGSMEXQYndfUWDfufvQZhGpAQoMRaSjW+/uH6ekb46fb6XkJfO7JtKOiJ9nFZp4G7g+lvnsvjW7sBj83QN8njClT+4x8syCO+3ZdwXwOHA44VF6Tu69w1v32mmPNSU3VkRqiuYxFJGObtc+5iflVhz5E/C7Vso+U0K9bTETmARMAU4Gmtz9+SL3vQUYRggGF5jZMbGOdcADLey3te3NFZFaoMBQRKR83oyfy4uYjDpT7v68mTURVkGB0uYu/B/CCO0z4vQ6fx/TZxS4uyoidUKPkkVEyudxwjuLwwpMMF1pMwijq99hz2CaVrn7LsIAk07APwHfBT6O9YlIHVNgKCJSJu6+gfAOXnfgITPrn18mTpY9wcx6VqA9t7r7oe7e093fLXH3OwnLCE4kjL6elzb/oYjUFz1KFhEpr38mjAAeDbxkZi8QHst2Bb5IWLe5c/zcUK1GtsbdN5rZ3cDlMamlQSciUid0x1BEpIzcfYe7jwHOJbyr1yv+PITwx/g9wHeAlVVrZPEei58vu/uSFkuKSF2wsAqSiIjUIjO7hDDCeHalB7SY2TzCKi8T3f0/yljvasLE4Ye7++py1Ssi+06PkkVE2oeTzGxW/Pm6rAMqMxsInEMYvDKnDPWdDoyNX9OW2xORGqDAUESkfTgybhBWJ1mdxUHM7A6gG3Am4XWjH7v7R2Wo+mjg4jLUIyIZ0qNkERHZLa7Osouwiskv3P3fqtwkEakgBYYiIiIiAmhUsoiIiIhECgxFREREBFBgKCIiIiKRAkMRERERARQYioiIiEikwFBEREREAPh/1gbj4DkbmIkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "# plot Tu15 tracks (for a Sun-like star!)\n",
    "ax.plot(blueTu15[\"time\"], blueTu15[\"Lx\"], marker=\"None\", linestyle=\":\", color=\"blue\", linewidth=2.5, alpha=0.6, label=\"__nolabel__\")#, label=\"fast rot. (solar model)\")\n",
    "ax.plot(redTu15[\"time\"], redTu15[\"Lx\"], marker=\"None\", linestyle=\":\", color=\"red\", linewidth=2.5, alpha=0.6, label=\"__nolabel__\")#, label=\"slow rot. (solar model)\")\n",
    "#ax.plot(greenTu15[\"time\"], greenTu15[\"Lx\"], marker=\"None\", linestyle=\":\", color=\"lime\", linewidth=2.5, alpha=0.5, label=\"__nolabel__\")#, label=\"interm. rot. (solar model)\")\n",
    "\n",
    "# plot approximated tracks\n",
    "step_size, t_track_start, t_track_end = 1., star_V1298Tau[\"age\"], 5000. # Myr\n",
    "t_arr = np.arange(t_track_start, t_track_end+step_size, step_size)\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track1) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:royal blue\", ls=\"-\", zorder=2, label=\"high activity track\", lw=2.2)\n",
    "#####\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track2) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:grey\", zorder=3, lw=2.2, alpha=1., label=\"medium activity track\")\n",
    "#####\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track3) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:red\", zorder=2, label=\"low activity track\", alpha=1, ls=\"-\", lw=2.2)\n",
    "\n",
    "# plot current X-ray luminosity of V1298 Tau as measured with Chandra & the assumed X-ray luminosity at 5 Gyr\n",
    "ax.scatter(star_V1298Tau[\"age\"], Lx_chandra, marker='*', c='xkcd:pale yellow', edgecolors='black', linewidths=1.1, s=500, alpha=1, zorder=4, label=\"__nolabel__\")#, label=\"today\"\n",
    "ax.scatter(5000., Lx_5Gyr, marker='*', c='white', edgecolors='black', linewidths=1.2, s=350, zorder=4, label=\"__nolabel__\")#,  label=\"at 5 Gyr\"\n",
    "\n",
    "ax.loglog()\n",
    "ax.set_xlabel(\"Time [Myr]\", fontsize=22)\n",
    "ax.set_ylabel(\"L$_\\mathrm{x}$ [erg/s]\", fontsize=22)\n",
    "ax.set_xticks([10, 100, 1000, 5000])\n",
    "ax.set_yticks([10**27., 10**28., 10**29., 10**30., 10**31.])\n",
    "ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.0f}'))\n",
    "ax.set_xlim(left=4.9, right=6500)\n",
    "ylim = ax.get_ylim()\n",
    "ax.set_ylim(abs(ylim[0]), ylim[1])\n",
    "ax.set_ylim(10.**27, ylim[1])\n",
    "ax.tick_params(direction=\"in\", which=\"both\", labelsize=18)\n",
    "ax.legend(loc=\"best\", fontsize=15)\n",
    "#plt.savefig(\"./Plots_PAPER/Activity_tracks_v1298Tau_largelabels.jpg\", dpi=300)\n",
    "#plt.savefig(\"./Plots_PAPER/Fig8_largelabels.jpg\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MqlWrMBqNtkdqairp6em2fYKDg+s8fsSIEcydO5dHH30UPz8/Hn74YfLz8+vc/6OPPrJNcTEajRw+fJjs7Ow622pMX+ypWV96ejrBwcG11nkPCQnh1KlTttd+fn625x4eHhe9Pj/C3lK1ueQ7LS2N3bt3YzKZCAsLw2QysXv3bknAhRBCCEFwcDApKSmXvDjQy8sLgOLiYtu2M2fO2J77+/vz3nvvkZ6ezt/+9jfmzJlT5wonwcHBLF68mNzcXNujuLiYqVOn2va51M1dHn/8cX7++WeOHDnCiRMnWLlypd3jkpOTeeihh3jrrbfIyckhNzeXG264AU3T6myrMX2xp2Z9gYGBpKamUlVVZduWkpJCx44dG1xfS9fmku/4+HhCQkIwmUzodDpMJhMhISHEx8c7OjQhhBBCONigQYMICAhg4cKFFBUVUVpayu7duy/az2w207FjR9avX09lZSXvv/9+rXnOmzdvtg3smUwmlFI4OTkB1pHdxMRE274PPfQQq1evJi4uDk3TKCoq4quvvqKgoKBBMe/bt4+4uDjKy8ttU2XqaquoqAilFGazGYAPPviAw4cP11t/fX1prMGDB+Pl5cWKFSsoLy9n+/btxMTEXNa8+paqzSXfFosFHx+fWtt8fHywWCwOikgIIYQQzYWTkxMxMTEkJCTQqVMngoKCbCuSXOi9995j5cqV+Pr6cuTIEW655RZb2b59+xg8eDB6vZ4777yT119/nc6dOwOwdOlSZs2ahdFo5OOPP+amm27ivffeY+7cuZhMJrp168batWsbHHN+fj4PPfSQbUDR19eXp556CoAHHniAo0ePYjQaGT9+PL169WLBggUMGTIEPz8/4uPjCQ8Pr7f++vrSWK6urnzxxRds2bKF9u3bM2fOHD766COuv/76y6qvJVI1v2ZoTZRSBiA/Pz8fg8Fg275lyxZMJhMmk8m2zWKxYLFYiIqKckCkQgghRNty7Ngxevbs6egwhLgs9s7fgoICvL29Abw1Tav3K4s2N/Ldp08fkpOTsVgsVFVVYbFYSE5Opk+fPo4OTQghhBBCtHJXdilvCxQUFER4eDjx8fEkJiZiMpkIDw8nKCjI0aEJIYQQ4hLS0tKIj4/HYrFgMpno06eP/A4XLUqrTL6rb7Izoa7yoKAg+Y8qhBBCtDDnVywLCQkhNDSUvLw8du/eLYNookVpldNO5CY7QgghROsjK5aJ1qBVJt9CCCGEaH1ayopl27dvb9Uj8ffddx/PPvtsg/ePjY1l/Pjx1zCihpk/fz6rV692dBiSfAshhBCiZTCZTOTl5dXalpeXV2sFs6shIiKCNWvWXNU6m5Om7t8zzzzDwoULAcjMzGTq1KkEBgbi4+NDeHg4cXFxtfaPjo4mJCQELy8vxo8fz9mzZ21lb731FjfddBNubm7cd999tY7bs2cPo0aNol27dpjNZiZOnMjp06dt5U8//TQvvfQS586du3adbQBJvoUQQgjRIsiKZS3Pvn37yMvL4+abbwagsLCQgQMH8vPPP3P27FlmzZrFmDFjbLeMP3LkCLNnz2bdunVkZGTg6enJnDlzbPUFBgby7LPPcv/991/UlsVi4eGHHyYpKYnk5GQMBgN/+MMfbOUBAQFcf/31fPHFF9e41/WT5FsIIYQQLcL5FcssFgv79+/HYrFc04stLRYLY8eOxWw2YzKZGDt2rO1OjwBnz57lD3/4A4GBgZhMpjqnVrzxxhv06tWLtLQ0srOzGTt2LEajkXbt2nHrrbfabrV+7NgxIiIiMBqN9O7du1aSeN999/Hoo48yZswYDAYDgwcPrnVHzQvt2bOHW265BaPRSL9+/di+fTsAixcvZteuXcydOxe9Xs/cuXPtHj9x4kT8/f3x8fFh2LBhHDlypLFvH2C9v8rw4cNtr7t06cL8+fMJCAjAycmJhx9+mHPnznH8+HEANmzYwLhx4xg2bBh6vZ4XX3yR//u//7Pd7XPChAmMHz8eX1/fi9qKiopi4sSJeHt74+npydy5cy+6O2lERARfffXVZfXlapHkWwghhBAtRlBQEFFRUUybNo2oqKhrOre6qqqKP/zhDyQnJ5OSkoKHh0etZHXGjBkUFxdz5MgRMjMzefLJJy+q48UXX2Tt2rXs2LGDoKAgVq1aRVBQEFlZWWRkZLB8+XKUUpSXlzNu3DgiIyPJzMzkzTffZPr06bakFGDjxo0sWbIEi8VCt27dWLx4sd24T506xZgxY3j22Wc5e/Ysr776Kvfccw9ZWVm89NJL3Hrrrbz11lsUFhby1ltv2a0jKiqK3377jczMTPr378/06dMv6z2Mj4+nR48edZYfOHCAc+fO0a1bN8A68t2vXz9bedeuXXF1deXEiRONbnvnzp307t271raePXty8ODBRtd1NUnyLYQQQghhh6+vL/fccw+enp4YDAYWL17Mjh07ADh9+jRbtmxh9erVmEwmXFxcao3waprG/Pnz+fbbb9m2bRtmsxkAFxcXTp8+TXJyMi4uLtx6660opdizZw+FhYUsXLgQV1dXRowYwdixY9m4caOtzgkTJjBo0CCcnZ2ZPn06Bw4csBv3+vXrueOOO7jjjjvQ6XSMGjWKm266ia+//rrBfb///vsxGAy4ubmxdOlSDh48eNF8+4bIzc2tdafxmvLz85kxYwZLliyxXUhbWFho96La8yPfDXXo0CH+/Oc/s3LlylrbDQYDubm5jarrapPkWwghhBDCjuLiYmbPnk1ISAje3t4MGzaM3NxcKisrSU1NpV27dnVe7Jmbm8u7777LokWLaiWTTz/9NN26dSMyMpIuXbrw8ssvA5Cenk5wcDA63X9Ts5CQEE6dOmV77e/vb3vu6elpmyd9oeTkZDZv3ozRaLQ9fvjhh1oXH9ansrKShQsX0rVrV7y9vQkNDQUgOzu7QcfXZDKZ7CbOJSUljBs3jptvvplFixbZtuv1evLz82vtm5+fX2cCb09CQgJRUVG8/vrr3HrrrbXKCgoKMBqNjezF1dUqb7JzKXJ3LCGEEEJcyqpVqzh+/DhxcXH4+/tz4MABwsLC0DSN4OBgzp49S25urt1kzmQysX79eiZNmsSnn35KeHg4YB15XbVqFatWreLIkSPcdtttDBw4kMDAQFJTU6mqqrIl4CkpKVx33XWNjjs4OJgZM2bw3nvv2S1XStV7fHR0NJ9//jlbt2613czIZDKhaVqjY+nbt+9FU0bKysoYP348HTt25G9/+1utst69e9eaFpKYmEhZWVmD34fk5GRGjhzJc889x4wZMy4qP3bsWK1pLY7Q5ka+z98dy2QyERYWhslkYvfu3bUuoBBCCCGEKCgowMPDA6PRyNmzZ3nhhRdsZQEBAURFRTFnzhwsFgvl5eXs3Lmz1vERERFs2LCBu+++27ac3pdffklCQgKapuHt7Y2TkxNOTk4MHjwYLy8vVqxYQXl5Odu3bycmJoYpU6Y0Ou57772XmJgYvv32WyorKyktLWX79u22XMfPz4/ExMR6++3m5oavry/FxcU888wzjY7hvDvuuMM2VQegvLyc3//+93h4ePDRRx/VGukHmD59OjExMezatYuioiKef/55JkyYYBv5rqiooLS0lMrKSlvfKioqAOtc9xEjRvDoo4/yyCOP2I1nx44dREVFXXZ/roZWmXxX317+DXtlcncsIYQQQlyKUop58+ZRUlJC+/btufnmmxk9enStfdatW4eLiwvXX389HTp04LXXXruonlGjRvHBBx9w55138vPPP/Pbb78xcuRI9Ho9Q4YMYc6cOURERODq6soXX3zBli1baN++PXPmzOGjjz7i+uuvb3TswcHBfP755yxfvhyz2UxwcDArV660raryxBNP8Mknn2AymXj88YtvCD5z5kxCQkLo2LEjvXr1si0TaE9KSgp6vZ6UlBS75f3798fHx8f2x8e///1vvvzyS2JjYzEajej1evR6Pbt27QKsI9+rV69m+vTpdOjQgYKCAt5++21bfcuWLcPDw4OXX36Z9evX4+HhwbJlywBYs2YNiYmJvPDCC7Z69Xq97djTp09z9OhRh9/wR13OVwgtgVLKAORfOE8oOjqasLCwWn9pVVVVsX//fqZNm+aASIUQQoi25dixY/Ts2dPRYdSpf//+PP/88w5P0lqL2NhY3n77bT777DOHxrFgwQK6du1aa93wy2Hv/C0oKMDb2xvAW9O0eq8ObXNzvs/fHavmBRLX4u5YQgghhGh5jhw5wrFjxwgLC3N0KK1GZGQkkZGRjg6DVatWOToEoJVOO6mP3B1LCCGEEPb86U9/IjIykldeeYWQkBBHhyNaqTY37QRktRMhhBDCkZr7tBMh6iPTTi5DUFCQJNtCCCGEEKLJtcnkW0a+hRBCCCGEI7S5Od+yzrcQQgghhHCUNjfyHR8fj4eHB4cPH6agoACDwUCHDh2Ij4+X0W8hhBBCCHFNtbnkOyEhgdTUVIqLi223cPX09CQ4ONjRoQkhhBBCiFauzU07OXbsGKdPn8ZgMNC+fXsMBgOnT5/m2LFjjg5NCCGEEM3A8ePHCQsLw2Aw8MYbdm+Y3exs2LChwWtp9+7dm+3bt1/bgK4SpRQJCQmODuOqanPJd2pqKj4+PphMJsxmMyaTCR8fH1JTUx0dmhBCCCGagRUrVhAREUFBQYHd2683VEREBGvWrLmKkVklJSWhlKKiosK2bfr06cTGxjbo+CNHjhAREQHA0qVLuffeey87lmvVx9aszSXfRUVFdOjQAZ1OR1FRETqdjg4dOlBUVOTo0IQQQgjRDCQnJ9O7d29Hh9Eq1PwDQVi1ueTbaDRy5swZXFxcaN++PS4uLpw5cwaj0ejo0IQQQgjhYCNGjGDbtm3MnTsXvV7PiRMn+OqrrwgLC8Pb25vg4GCWLl1q27+0tJR7770XX19fjEYjAwcOJCMjg8WLF7Nr1y5bPXPnzrXb3sSJE/H398fHx4dhw4Zx5MgRW1lJSQkLFiwgJCQEHx8fhg4dSklJCcOGDQOsOY1er+fHH39k7dq1DB06FIBHHnmEp556qlY7d911F3/5y18ACA0NZevWrXzzzTcsX76cTZs2odfr6devH5s3b2bAgAG1jl21ahXjx4+/KPa6+qiU4n//93/p3r073bt3B+CJJ54gODgYb29vBgwYwK5du2z1VFZWsnz5crp27YrBYGDAgAF2ZyT88MMPBAcHs23bNvsfXgvR5i64HD58OPv27SMxMREnJycqKyvJz89n+PDhjg5NCCGEaHMqKzUyMkqbpC0/P3ecnFS9+/zrX/8iIiKCe++9lwcffBCA9PR0PvroI3r37s3hw4cZNWoUN954I+PHj+fDDz8kLy+P1NRU3NzcOHDgAB4eHrz00kvs3r27Vj32REVF8f777+Pq6sqf/vQnpk+fzoEDBwB46qmnOHLkCP/+97/x9/cnLi4OnU7Hzp076dy5M7m5uTg7W1O548eP2+qcNm0a06dPZ+XKlSilsFgsxMbG8s4779Rqe/To0TzzzDMkJCSwfv16AMrKypg9e3atuziuX7+eZ5999qLY6+vjZ599RlxcHB4eHgAMHDiQ559/Hh8fH15//XUmTpxIUlIS7u7u/OUvf2Hjxo18/fXXXHfddRw6dAhPT89a9X377bc8+OCD/POf/2TQoEH1fobNXZtLvu+55x6Ki4vJyMiguLgYd3d3evfuzT333OPo0IQQQog2JyOjlNCQL5ukraTksQQGejT6uPPzowH69u3L1KlT2bFjB+PHj8fFxYWcnBwSEhLo27fvRaPGl3L//ffbni9duhSTyUReXh4Gg4H333+fPXv20LFjRwBuueWWBtV56623opRi165dDBs2jE8++YQhQ4YQGBh4yWPd3NyYPHky69ev56WXXuLIkSMkJSUxduzYRvVr0aJFtGvXzva65rzyBQsWsGzZMo4fP06/fv1Ys2YNK1asoEePHgD069evVl2bN29m9erVfP311/Tp06dRcTRHrXLaiVJqHGD38uSgoCBmzJjB7bffzrBhw7j99tuZMWOGrPEthBBCCLvi4uK47bbbMJvN+Pj4sHr1arKzswFsOcWUKVMIDAzkj3/8I+Xl5Q2qt7KykoULF9K1a1e8vb3+w7MQAAAgAElEQVQJDQ0FIDs7m+zsbEpLS+natWuj41VKMWXKFDZu3AhAdHQ006dPb/Dxs2bNIjo6Gk3TWLduHZMmTcLNza1RMVy4hPOqVavo2bMnPj4+GI1G8vLybO9hampqvf187bXXmDRpUqtIvKGVjnxrmhajlNoO3GevPCgoSJJtIYQQohnw83MnKblxo6pX0tblmDZtGnPnzmXLli24u7szb948W+Lo4uLCkiVLWLJkCUlJSdxxxx306NGDBx54AKXqn+ISHR3N559/ztatWwkNDSUvLw+TyYSmabRv3x53d3dOnjx50UjwpeoFmDp1KpGRkSxcuJC4uDg+/fRTu/vZq+vmm2/G1dWVXbt2ER0dTXR0dJ3t1BVLze27du3ilVde4fvvv6d3797odDpbP8GaqJ88eZIbbrjBbl2bN2/mgQceoGPHjsybN6/OWFqKVpl8CyGEEKJlcHJSlzUVpCkVFBTQrl073N3d2bt3L9HR0bY1tbdt20b79u3p1asX3t7euLi44OTkBICfnx+JiYn11uvm5oavry/FxcU888wztjKdTsf999/P/PnzWbduHX5+fuzdu5f+/ftjNpvR6XQkJiZy3XXX2a07LCwMs9nMgw8+yO23317nwhJ+fn589913thsPnjdz5kzmzp2Ls7Oz7ULOuo6vr4/n++ns7IzZbKaiooKXX36Z/Px8W/mDDz7Ic889R69evejWrRvx8fF07NgRX19fAAIDA/n++++JiIjA1dWVOXPm1Ntec9cqp50IIYQQQlwtb7/9Ns8//zwGg4E///nPTJo0yVZ25swZfv/73+Pt7U3Pnj0ZPny4bX7zE088wSeffILJZLK7XvjMmTMJCQmhY8eO9OrVi5tvvrlW+auvvkqfPn0YOHAg7dq1409/+hNVVVV4enqyePFiwsPDMRqN7Nmzx27cU6dOZevWrUybNq3Ovk2cOBEAX19f+vfvb9s+Y8YMDh8+zIwZM+p9by7VR4Dbb7+dqKgorrvuOkJCQnB3d681LWX+/PlMmjSJyMhIvL29eeCBBygpKalVR6dOnfj+++955ZVXWvy64ur8kH9ro5QyAPn5+fkYDAZHhyOEEEKIajVX0hDNU0lJCR06dOCXX36xLRcorOydvwUFBXh7ewN4a5pWUN/xMvIthBBCCCFqeeeddxg4cKAk3teAzPkWQgghhBA2oaGhaJrGZ5995uhQWqU2mXynpaURHx+PxWLBZDLRp08fWf1ECCGEEAJISkpydAitWpubdpKWlsbu3bsxmUyEhYVhMpnYvXs3aWlpjg5NCCGEEEK0cm1u5Ds+Ph6DwUBiYiKFhYXo9XqMRiPx8fEy+i2EEEIIIa6pNpd8JyYm4uLigr+/P35+fhQVFZGWltbgu1EJIYQQQghxudrctJPc3Fw8PT3R6/UopdDr9Xh6epKbm+vo0IQQQgghRCvX5pJvb29vcnJyyMvLo6qqiry8PHJycs6vzSiEEEIIIcQ10+aS727dutG5c2dycnKIj48nJyeHzp07061bN0eHJoQQQohWbunSpbY7YKakpKDX66msrHRwVI0TFRXFhx9+eMn9du3aRY8ePZogoiu3du1ahg4d2iRttbnku0+fPpSUlHDDDTcwevRobrjhBkpKSujTp4+jQxNCCCFEG9KpUycKCwtxcnJydCh1qvnHwnlbtmxh1qxZlzz21ltv5fjx47bXoaGhbN269bLiSEpKQilFRUXFZR3fnLS55DsoKIjw8HAsFgv79+/HYrEQHh4uK50IIYQQQrRgLSUxb3PJN1gT8KioKKZNm0ZUVJQk3kIIIYSwCQ0NZeXKlfTt2xcvLy8eeOABMjIyiIqKwmAwMHLkSCwWi23/PXv2cMstt2A0GunXrx/bt2+3lf3nP/9h+PDhGAwGRo0aRXZ2tq3swtHcC0eGa446n9/3gw8+IDg4GJPJxOrVq9m3bx99+/bFaDQyd+7cOvu0d+9ehgwZgtFoJCAggLlz53Lu3Dlb+ZEjRxg1ahTt2rXDz8+P5cuX880337B8+XI2bdqEXq+nX79+AERERLBmzRrKysowGo0cPnzYVk9WVhYeHh5kZmayfft2W441Y8YMUlJSGDduHHq9nhUrVjBmzBjefPPNWnH27dvX7p01hw0bBoDRaESv1/Pjjz+ydu1awsPDefLJJ2nXrh1Lly7l5MmTjBgxAl9fX9q3b8/06dNrLaqRmprKhAkTMJvN+Pr61vmePf300wwdOpS8vLw639PL1eaWGgTYt28fsbGxZGVlYTabiYyMZODAgY4OSwghhGhzKrVKskqzmqQts7sZJ9WwKR7//Oc/+e6776ioqCAsLIz9+/fz97//nV69ehEVFcUbb7zBkiVLOHXqFGPGjGHdunWMHj2a77//nnvuuYdff/0Vs9nMtGnTGDJkCLGxscTFxTFmzBjuuuuuy+5DXFwcv/32Gzt37uTOO+9k9OjRbN26lfLycsLCwpg4cSLDhw+/6DgnJyf++te/ctNNN5GWlkZUVBRvv/028+bNo6CggJEjR/LUU08RExNDeXk5R48eZfDgwTzzzDMkJCSwfv36i+p0c3NjwoQJbNy4kZdeegmAjz/+mOHDh9OhQweOHj1q23fdunXs2rWLNWvWMHLkSMD6x8aqVat47LHHADh48CCnTp3ijjvuuKitnTt30rlzZ3Jzc3F2tqavx48fJy4ujilTppCZmUl5eTmnTp1i0aJFDBs2jPz8fO655x6WLl3Ka6+9RmVlJWPHjmXEiBGsW7cOJycnfvrpp1rtVFVVMXv2bFJSUoiNjcXT0/MyP6m6tbnke9++fWzevJm+ffsyaNAgMjIy2Lx5M4Ak4EIIIUQTyyrNom/sgCZp61Dkz/h7+Ddo38ceeww/Pz/AOne5Q4cOhIWFAXD33Xfz/fffA7B+/XruuOMOW8I4atQobrrpJr7++mtuu+029u3bx9atW3Fzc2PYsGGMGzfuivrw3HPP4e7uTmRkJF5eXkydOpUOHTrY4ty/f7/d5HvAgP++x6GhocyePZsdO3Ywb948vvzyS/z9/VmwYAEA7u7uDB48uEHxTJs2jYcfftiWfEdHRzN79uwGHXvXXXfxyCOP8Ntvv9G9e3fWrVvH5MmTcXV1bdDxAIGBgbbk3dnZmW7dutkW0TCbzcyfP58XXngBsI7+p6ens3LlSlsCX/Miy/LycqZOnUpFRQUxMTGNiqMx2lzyHRsbS9++fQkMDASw/RsbGyvJtxBCCCEAbIk3gIeHx0WvCwsLAUhOTmbz5s3ExMTYysvLy7nttttIT0/HZDLh5eVlKwsJCSE1NfWax3WhEydOMH/+fH766SeKi4upqKiwJeSpqal07dr1suIZMWIEJSUlxMXF4e/vz4EDB7j77rsbdKybmxuTJk1i/fr1LFmyhI0bN/LJJ580qv3g4OBarzMzM3n88cfZtWsXBQUFVFVVYTKZAGs/Q0JCbIn3hRISEjh48CB79+69Zok3tMHkOysri0GDBtXa5ufnd9HXDkIIIYS49szuZg5F/txkbV1twcHBzJgxg/fee++isuTkZCwWC0VFRbYEPCUlBaWU3bq8vLwoLi62vT5z5sxVi/N//ud/CAsLY+PGjRgMBl577TVbohscHMzGjRvtHldXrOfpdDomTZrExo0b8fPzY+zYsRgMhgbXNWvWLGbMmMHQoUPx9PRkyJAhjYrjwu2LFi1CKcWhQ4fw9fXls88+s83rDg4OJiUlhYqKCrsJeM+ePXn00UeJioriX//61zVbJrHNXXBpNptJTEwkISGBw4cPk5CQQGJiImbz1f8PKYQQQoj6OSkn/D38m+TR0PnejXHvvfcSExPDt99+S2VlJaWlpWzfvp20tDRCQkK46aabWLJkCefOneOHH36oNUJ+oRtvvJF//OMflJeX89NPPzV6FLg+BQUFeHt7o9fr+fXXX3nnnXdsZWPHjuXMmTO89tprlJWVUVBQQFxcHGAdoExKSqKqqqrOuqdNm8amTZvYsGED06ZNq3M/Pz8/EhMTa20bMmQIOp2OBQsWMGPGjDqPNZvN6HS6i46310+9Xo/RaOTUqVOsXLnSVjZo0CACAgJYuHAhRUVFlJaWsnv37lrHT506leXLlzNy5EhOnjxZb1uXq80l3/369WPbtm0UFBRgNpspKChg27Zttit4hRBCCCEaKjg4mM8//5zly5djNpsJDg5m5cqVtmQ1OjqauLg42rVrxwsvvMDMmTPrrOvFF1/k5MmTmEwmlixZUm8i21ivvvoq0dHRGAwGHnroISZPnmwrMxgMfPfdd8TExODv70/37t3Ztm0bABMnTgTA19eX/v3726178ODBeHl5kZ6eTlRUVJ0xLFq0iGXLlmE0Gnn11Vdt22fOnEl8fPxF64nX5OnpyeLFiwkPD8doNLJnzx67+y1ZsoRffvkFHx8fxowZw4QJE2xlTk5OxMTEkJCQQKdOnQgKCmLTpk0X1TFr1iyef/55RowYQVJSUp0xXS6ladpVr/RqUEr1BJ4A2gPfa5r2jlKqC7AY8NE07feXON4A5Ofn59f6+mPLli0cOXKEvXv3kpeXh4+PD4MGDaJ37971njBCCCGEuDqOHTtGz549HR2GaCY++ugj3n33XX744QdHh9Ig9s7f898sAN6aphXUd3yTzvlWSr0PjAUyNU27ocb20cDrgBOwRtO0lzVNOwY8opTSAe8BaJqWCDyglLrs72ESEhJwcXFh6tSpGAwGCgoKSElJISEh4Uq6JoQQQgghGqm4uJi3336bOXPmODqUJtPU007WAqNrblBKOQH/C0QBvYCpSqle1WV3Aj8A31+tAPLz83FxcSEtLY1ffvmFtLQ0XFxcyM/Pv1pNCCGEEEKIS/j2228xm834+fld1Sk2zV2TJt+apu0Ezl6weRCQoGlaoqZp54B/AHdV7/+Fpmm3ANOvVgw6nY6UlBRcXV0JCQnB1dWVlJQUdLo2N/1dCCGEEMJhbr/9doqKivj888/rXP6vNWoOGWdHoOaCl2lAR6VUhFLqDaXU34CvAZRSvkqp1UCYUmqRvcqUUg8rpX4Cdtgrr6qqonPnziilyMjIQClF586d672KVwghhBBCiKuhOfyZYW/hRk3TtO3A9gs25gCP1FeZpmnvAu+ev+DywnJvb29ycnJwd3dHp9NRWFhIaWnp+UnyQgghhBBCXDPNIflOA2renigISL9WjZlMJlJTUykqKqKsrAw3Nze8vLzo1KnTtWpSCCGEEEIIoHlMO9kHdFdKdVZKuQJTgC+uVWM5OTmkpKRQWVmJm5sblZWVpKSkkJOTc62aFEIIIYQQAmj6pQY3AhFAe6VUGrBE07S/K6XmAt9iXWrwfU3TjlxhO+OACfbKDh06REFBAWfPnqWyshInJydcXFw4dOjQlTQphBBCCCHEJTX1aidTNU0L0DTNRdO0IE3T/l69/WtN067TNK2rpmkvXYV2YoDH7ZUdPXoUd3d3+vfvz7Bhw+jfvz/u7u4cPXr0SpsVQgghRCsQGhrK1q1bHR1Gg0RFRfHhhx9ecr9du3bRo0ePJojoyq1du5ahQ4c6OoxrpjlMO2lSxcXFODk5kZaWxrFjx0hLS8PJyYni4mJHhyaEEEIIUaelS5dedAv2LVu2MGvWrEsee+utt3L8+HHb6yv5AyMpKQmlFBUVFZd1fFvXHC64bFIVFRWcPXsWg8GATqejrKyMgoICOYGEEEIIIa6SioqKNrV2d2O0ypHv6jnfb9gr0zSNiooKdDqd7VFRUYGmaU0cpRBCCCGau7KyMubNm0dgYCCBgYHMmzePsrIyAIYPH84///lPAH744QeUUnz99dcAbN26lRtvvNFunXv37mXIkCEYjUYCAgKYO3cu586ds5UfOXKEUaNG0a5dO/z8/Fi+fDnffPMNy5cvZ9OmTej1evr16wdAREQEa9asoaysDKPRyOHDh231ZGVl4eHhQWZmJtu3bycoKAiAGTNmkJKSwrhx49Dr9axYsYIxY8bw5ptv1oqzb9++fPbZZxfFP2zYMACMRiN6vZ4ff/yRtWvXEh4ezpNPPkm7du1YunQpJ0+eZMSIEfj6+tK+fXumT59Obm6urZ7U1FQmTJiA2WzG19eXuXPn2n2/nn76aYYOHUpeXl49n1TL0SqT7/rmfHt4eODs7Iynpyc+Pj54enri7OyMh4dHE0cphBBCCK2yksqMjCZ5aJWVjY7vpZdeYs+ePRw4cICDBw+yd+9eli1bBliT7+3btwOwc+dOunTpwo4dO2yvhw8fbrdOJycn/vrXv5Kdnc2PP/7I999/z9tvvw1AQUEBI0eOZPTo0aSnp5OQkMDvfvc7Ro8ezTPPPMPkyZMpLCzk4MGDtep0c3NjwoQJbNy40bbt448/Zvjw4XTo0KHWvuvWraNTp07ExMRQWFjIH//4R2bNmsX69ett+xw8eJBTp05xxx13XBT/zp07AcjNzaWwsJAhQ4YAEBcXR5cuXcjMzGTx4sVomsaiRYtIT0/n2LFjpKamsnTpUgAqKysZO3YsISEhJCUlcerUKaZMmVKrnaqqKh566CEOHTpEbGwsPj4+dX9QLUib/D7A398ff39/nJycqKysRNM0LBaLo8MSQggh2pyq7GyywsKapC3z/v04+fk16pgNGzbw5ptv2hLYJUuWMHv2bF588UWGDx/Ok08+CVgT0kWLFrFmzRoAduzYwRNPPGG3zgEDBtieh4aGMnv2bHbs2MG8efP48ssv8ff3Z8GCBQC4u7szePDgBsU6bdo0Hn74YV56ybp2RXR0NLNnz27QsXfddRePPPIIv/32G927d2fdunVMnjwZV1fXBh0PEBgYyGOPPQaAs7Mz3bp1o1u3bgCYzWbmz5/PCy+8AFhH/9PT01m5cqVtekrNiyzLy8uZOnUqFRUVxMTENCqO5q5VjnzXR9M03N3dcXd3x8PDw/Zcpp0IIYQQ4kLp6emEhITYXoeEhJCebr0X4JAhQzhx4gQZGRkcOHCAmTNnkpqaSnZ2Nnv37rVNz7jQiRMnGDt2LP7+/nh7e/PMM8+QnZ0NWKdidO3a9bJiHTFiBCUlJcTFxZGcnMyBAwe4++67G3Ssm5sbkyZNYv369VRVVbFx40ZmzJjRqPaDg4Nrvc7MzGTKlCl07NgRb29v7r333lr9DAkJqXNeeEJCAp9//jlLlixpVYk3tMGRbzc3NyoqKsjOzkYpZZsD7ubm5ujQhBBCiDZH17495v37m6ytxgoMDCQ5OZnevXsDkJKSQmBgIACenp4MGDCA119/nRtuuAFXV1duueUW/vKXv9C1a1fa19He//zP/xAWFsbGjRsxGAy89tprfPLJJ4A1ga05daQmpVT9/dPpmDRpEhs3bsTPz4+xY8diMBgaXNesWbOYMWMGQ4cOxdPT0zadpKFxXLh90aJFKKU4dOgQvr6+fPbZZ7Z53cHBwaSkpNR5YWbPnj159NFHiYqK4l//+leLWSaxIVrlyHd9F1z6+fmRm5uLTqfD1dUVnU5Hbm4ufo38GkoIIYQQV045OeHk59ckD+Xk1Oj4pk6dyrJly8jKyiI7O5s///nPtZb7Gz58OG+99ZZtfndERESt1/YUFBTg7e2NXq/n119/5Z133rGVjR07ljNnzvDaa6/ZVmSLi4sDrDlMUlISVVVVddY9bdo0Nm3axIYNG5g2bVqd+/n5+ZGYmFhr25AhQ9DpdCxYsKDeUW+z2YxOp7voeHv91Ov1GI1GTp06xcqVK21lgwYNIiAggIULF1JUVERpaSm7d++udfzUqVNZvnw5I0eO5OTJk/W21ZK0yuS7vgsu+/XrR25uLllZWWRmZpKVlUVubq7tqmEhhBBCiPOeffZZbrrpJvr27UufPn3o378/zz77rK18+PDhFBQU2KaYXPjanldffZXo6GgMBgMPPfQQkydPtpUZDAa+++47YmJi8Pf3p3v37mzbtg2AiRMnAuDr60v//v3t1j148GC8vLxIT08nKiqqzhgWLVrEsmXLMBqNvPrqq7btM2fOJD4+/qL1xGvy9PRk8eLFhIeHYzQa2bNnj939lixZwi+//IKPjw9jxoxhwoT/3nzcycmJmJgYEhIS6NSpE0FBQWzatOmiOmbNmsXzzz/PiBEjSEpKqjOmlkS11rnOSikDkJ+fn1/rK5c333yT+Ph4UlNTKS0txd3dneDgYPr06WO7SEAIIYQQ186xY8fo2bOno8MQdnz00Ue8++67/PDDD44Opdmyd/6e/zYD8NY0raC+49vcnO/8/HxuvPFG+vTpQ0lJCR4eHjg5OclqJ0IIIYRo04qLi3n77beZM2eOo0Np1dpc8q3T6fjmm2/IzMy0TfLv0KFDnRcVCCGEEEK0dt9++y0TJkxg5MiR9c4VF1euzSXfP//8M2fOnKFLly54eHhQUlJCYmIiP//8s6NDE0IIIYRwiNtvv52ioiJHh9EmtMrku3q1kwn2yo4ePcoNN9xA165d8fT0pLi4mKqqqlq3YxVCCCGEEOJaaHOrnVRVVeHv7092djYnT54kOzsbf3//epftEUIIIcTV1VoXfBCt29U4b1tl8l2fyspKMjIy8Pb2JiAgAG9vbzIyMqisrHR0aEIIIUSb4OLiQklJiaPDEKLRysvL67wrZ0O1ueTb1dWVzMxMysrK0Ol0lJWVkZmZ2epuXSqEEEI0Vx06dODUqVMUFxfLCLhoMaqqqsjIyMDHx+eK6mmVc77r4+3tjbOzM4cOHULTNJRSuLi4nF+bUYgmk5aWRnx8PBaLBZPJRJ8+fQgKCnJ0WEIIcc2d/52bnp5OeXm5g6MRouG8vLxo3779FdXR5pJvJycn/P39cXV1pbKyEicnJ86dO8d//vMfR4cm2pC0tDR2795NSEgIoaGh5OXlsXv3bsLDwyUBF0K0Cd7e3jLwJdqkNpd8+/r6UlZWRo8ePfD29iY/P59Dhw7h6+vr6NBEGxIfH09ISAgmkwnA9m98fLwk30IIIUQr1uaS75tvvpmjR4/y008/UV5ejouLCwaDgbCwMEeHJtoQi8VCaGhorW0+Pj4kJiY6JiAhhBBCNIlWecFl9Trfb9grCw4OxmAw4ObmZnsYDAaCg4ObOErRlplMJvLy8mpty8vLs42ACyGEEKJ1apXJd33rfP/222/89ttvmEwmunXrhslksm0Toqn06dOH5ORkLBYLVVVVWCwWkpOT6dOnj6NDE0IIIcQ1pFrrEj9KKQOQn5+fj8FgsG0fM2YMVVVVFBYWUlFRgbOzM3q9Hp1Ox1dffeW4gEWbI6udCCGEEK1DQUHB+QuIvTVNK6hv3zY35zs5OZlOnToxYMAADAYDBQUFnDhxguTkZEeHJtqYoKAgSbaFEEKINqbNJd/l5eUEBASg1+sB0Ov1BAQEcPLkSQdHJoSoad++fcTGxpKVlYXZbCYyMpKBAwc6OiwhhBDiirS55NvZ2Zns7GyysrJsN9lRSl3xrUKFEFfPvn372Lx5M3379mXQoEFkZGSwefNmAEnAhRBCtGhtLuM8d+4cpaWldOnSBS8vL4qKikhMTOTcuXOODk2IJtWc55zHxsbSt29fAgMDAWz/xsbGSvIthBCiRWuVq53Up7KyknPnzpGbm0tWVha5ubmcO3eOyspKR4cmRJM5f4dNk8lEWFgYJpOJ3bt3k5aW5ujQAMjKysLPz6/WNj8/P7KyshwUkRBCCHF1tMqR7+p1vifYK9PpdLi6ulJaWkpZWRmapuHq6opO1+b+DhFtWHO/w6bZbCYjI8M24g2QkZGB2Wx2YFRCCCHElWuVGWd963w7OzsTEBBAjx496N69Oz169CAgIEDmfIs2xWKx4OPjU2ubj48PFovFQRHVFhkZyaFDh0hPT6eyspL09HQOHTpEZGSko0MTQgghrkibyzh9fX3JycnBxcUFd3d3SktLycnJwdfX19GhCdFkzt9hs+YdNZvTHTbPz+uOjY3lp59+wmw2M3HiRJnvLYQQosVrc8m32Wzm3LlzZGRk2LZVVlbK19miTenTpw+7d+8GrCPeeXl5JCcnEx4e7uDI/mvgwIGSbAshhGh12lzy7eLiQnl5Od27d0ev11NYWMivv/6Ki4uLo0MToskEBQURHh5OfHw8iYmJmEwmwsPDm8V8byGEEKI1a3PJt7u7Ox06dODIkSOUlZXh5uZG165dyc/Pd3RoQgBNtwSg3GFTCCGEaHqt8oLL+lRUVHDmzBkCAgLo2rUrAQEBnDlzhoqKCkeHJkSzXwJQCCGEEFemzSXfRUVFFBUV4eHhgdFoxMPDw7ZNCEeruQSgTqfDZDIREhJCfHy8o0MTQgghxFXQ5qadlJSU0K5dOzIzM6msrMTJyQkfHx/Onj3r6NBEG2NveonFYiE0NLTWfj4+PiQmJjomSCGEEEJcVW0u+S4vL0cphZ+fH66urpw7d46cnBzKy8sdHZpoQ85PLwkJCSE0NJS8vDx2795NRUVFs14CUAghhBBXps0l35qmAWAwGHBzc6OsrIycnBzbdiEaKjcXSkvB2Rnat2/csXXdYTIhIYHk5GSg+S4BKOxrqgtlhRBCtGytcs539e3l37BXVlVVhU6nIy8vj+zsbPLy8tDpdFRVVTVxlKKl27ABliyB1asvLrvUJQR13WFSp9MRHh6OxWJh//79WCwWWQKwBZALZYUQQjRUqxz51jQtRim1HbjvwrLy8nJKS0upqKiwJeIVFRUy7UTUq6wMXFxAV+PP1fPPL/y77fRpeOUViIqCyEhQ6uL66rvDpCwBaNWSRpLr+iYjPj6+2cYshBDCMVpl8l2f8vJyTp06hclkwtnZmYqKCiwWiyw1KOpUVARvvAEBATBr1n+T6ZEj4aabwNPzv/tqGnzwAZSUwKefQo8ecMH1k0DLuMOkI9U1J/5KvgW4lsm8XCgrhBCiodpc8n3u3Dk8PDwwm814enpSXFxMYWEhxcXFjg5NND/UhS4AACAASURBVFP/+AckJVkf3brB0KHW7d27X7yvUjBjBrz7rjUxt5d4g/UGN126dCE2NpasrCzMZjORkZEEBQW1qBHfa+VqjyRfi2S+pvq+yRBCCCFqanPJt4uLCz179sTFxYXKykoMBgM9e/bk559/dnRoopmaNAmSkyEkBIYMufT+wcGweDG4uta9T1paGomJifzud7+zjXyfHyVNTEy8ZkliS3G1R5L/P3tnHh5Vef3xz50lM5PMJJnsIQlZCIQAEQKSCFQ2lU3FVql119Yu1rq21V/tprZa21r3ql3c9wWtIgIiIJsiBAwhgUDInglZJ5NMZsms9/fHJSsJkJgNcj/PkyeTu73nzk0m5z3vOd8z1Gkh8kqGjIyMjMzpclYWXJ4Mg8FAQEAATqcTm82G0+kkICAAg8Ew0qbJjBLa2qC1tfNngwHuuQd+9CNQKk/vGlpt9/xwjwfefRfMZunnvprpbNy4EYPBQGlpKTt27KC0tBSDwTDmmuy0R5K78m0iyX0VuFoslgHb2JX4+Hi5UFZGRkZG5rQYc5HvpKQkioqKGDduHEFBQbhcLoqKik6IssmMPUQR3n4bvv4a5s6Fq67q3Pdt5mZut1SAaTJBaankyFssFgwGA/v27cNms6HX6xk/fjwVFRVERkYSExNDdHQ0drsdk8k05gqCBzuSPBxpIXKhrIyMjIzM6TDmIt9RUVEIgoDb7cbpdOJ2uxEEgaioqJE2TWaEEQRoaJCUTXbtkr4PBgEBnfnhKpUUWff7/Wzbto2GhgacTicNDQ1s27YNq9VKYGAger0eQRDQ6/UEBgbS3Nw8OMacIQx2JDkjI4OKigosFgt+vx+LxUJFRQUZGRmDbLmMjIyMjMzJGXOR78LCQnQ6HS6Xi7a2NgRBQKfTUVhYONKmyQwjbjds2ADTpkFKSuf2hQul1JIFCyRpwcFi1SqIjpauq1CA2WymoqKS5OQ5JCVpsViaqKysxOfzYTabCQoKwmAw0NraitlsJjg4ePCMGYO0O/P5+fmUlpZiNBrltBAZGRkZmRFhzDnfpaWlpKenExsb2yE1WFNTw9GjR0faNJlhwuGAhx+GxkYp0t3V+Z4+XfoabFQqWLSo8+f6+nomTVrA++9PISCghfPOa2X27Nls27aN5ORk6uvrKS8vx2AwkJycjHowZwJnAEOhTiKnhcjIyMjIjAbGnPPtcDiwWq1ERESgVCrxeDxYrVZZanAMERgoOdyNjVIBpCj23ghnKPH7/VRWSvnGDkcQOp0Lvz8Ao9GIyWTCZAoiIEAH2HC73axcuXJI7KipgZYWKTWm6yTE74fDh6XXUVEQEdG5z+WSVg60WmlSMRTv3anUSfx+yQaPR3qeXQth6+vBZpNWLhISut/TkSOd9xQe3rnPbIbaWul1Wpp0X+2Sj4WFHrRaI5mZqcyZE9txjsMhvUd+P0yYAF3Tx4uKoLJSem8uuKD7vb36qpR6lJEh1Ra0U1ICa9ZIr6+9VrKxnY8/huJiiIyEG27o3O5ywTPPSL/DixZJ8pbt7N0LmzdLr++8U3pe7bz0EtTVSelQq1Z1bm9rgx07IDhY2hcWhoyMjIzMIDPmnG+FQoHBYKClpQWLxdLxs0Ix5tLfxwyiKDkVOl3ntiuvlBym+fOH3/EGiI6OJidnA7NnL8brjSEqCnbv3k1CQgIKhYLc3AxsNi1JSQ3Mm9eZEuX1djq/PWsFCwqk5j7h4d0d6eJiyXmz2eCWW6T0l3Y+/BAOHJC2/elPndt9PnjqKen1974Hy5Z17tu7F157TXr9l790d2Jfekly6FNT4Qc/6NxeXw///rf0etUqSE/v3Ldxo5Rjr1bDb38rbWuXGvzvf8NwOgUyM53Mm+fvkBrcv7/zen/4A3QNaK9eDXl50rY//KFzu9sNTz7ZacNFF3Xuy8uT1GgAHn8cams7I+8VFSlUVIjs3WsiIcHXET1vauq04ZZbuj+P3FzYskX6nevpfOfmSs8pNLT7dputc8LTs97g2DHJoe8ZI/D7oX3RLjOz+76WFqnAt/24nterqoIeAjCYzdL7B/CTn8jOt4yMjMxQMOacb1EU8fv9Hd8FQej4Webso64O3nhDyrO+665OR9tgkPK7R4qIiAgmT25Fqy1CFI/gdAokJSVhs9lITp5FcHAKwcGwcKGBqVM1HRHftWth/Xrpfp5/vvs1X38dmpth3rzuzrfHIzluAFZrd+c7KKj/tre1db7uGk0FKXpcWXmiU+f1SmovIDmeXbFaJWdQo+nc1q5O0tgYjdOpoKVF2U2dpKuGek8hmPYouM93+vfUcwLWXQpSQKMJICwsrJsueNf5es+Pj3b7ehOpiY6WnOueafxBQdKEsOv57YwbJ3VajYzsvl2hkCL1cKKjHBbWOcnpGVuYMEEaf/z47tut1s7XcpmBjIyMzNAw5pxvv99PU1MTqamp6PV6bDYbNTU1+HuGhmTOCr74otPx3LMHsrNH1p52FAoFixYtorKyspvU4OrVq0lI0POLXzRSVhZAaqq7W3OZdqfM75ecy67pFu373O7uY4WGSpFovb67gwuwdKmU+tBzu0oF994rve7p1KWlSTKMbW0nOt+pqZLjnZjYfbtWK+XSC8KJjnl8PMycKY3ZTkZGBmvXrkUQHKjVSmpqmtm/v4ZLLrkEgNhYuOIK6Z572rdsmTQB6brS0f7+3HOP9LprtB5g1qxOm7Xa7lKQsbF+kpIMJCfHdNMFj4yEP/5Rcmx7rkKsWAHLl/detHvffSduA+m9a3/Pe3LZZb1v12jgl7/sfV9m5onR8Hauvrr37ZMnwxNPSE643JxTRkZGZmgYc863RqPBaDTi9XppaWlBFEWMRiO17QmfMmcVl10mLfPPmAHnnDPS1nRiNBpRqVTMmjWrY5vFYiEyMhKrtYXoaCPR0c7j2zsjvlOmSM5hb90zb71Vcm57RrNjYzudzp7ExkpfPRGEzihsT+Lju6d5dOXKK3vfHhYm2dcb550nffWktLQUi2XD8YZYOhSKSR37wsNhyZLer9fT8W9HoZAc3N4IDu4e6fX7/eTk5DBhwgRmzQrCbrdTUvIVkV1Cz2o1xMV1ntOeI26xWDAajWRkZJxxBZ6CIOXQBwZ2brPZpFSjBQtGJkVLRkZG5mxjzDnfgYGBeDwePB4PgiAgiiIej4fArv9tZM5Iiopg2za4+ebOZXadDh588MQI7UjTVxOZJUuWkJOTg9vtxu/3o1AoCAgI6Ci4TEqSvnqjNyf6TGX16tWYzWYuvPBCIiIiaGxsJDc3l9WrV3PXXXcN+fh+vx+Xy4XP50MURXw+Hy6Xq88VMpPJdDxSL32mtLa2UlFRwSWXXHLGOeBdOXgQ/vUvaTUlOrp7rr6MjIyMzMA4K51vQRAuBS7vbZ9arcbn8+Hz+VAoFPj9fnw+35iTcjvbyM2VnASQVBq65nOPNscbJNm7lJQUNm7cSENDA5GRkSxZsoTY2FgUCgU6nQ5RFDucuaFgNEdq9+zZQ2ZmJn6/H5PJREBAAJMnT2bPnj3DMr5KpSI7O5uSkpIOycfs7Gxqamp6PX7r1q04nU4mTJhAUFB7pLyErVu3ct111w2LzUNB14leXp7sfMvIyMgMBmel8y2K4ieCIGwFburrGI1G06HzLXPmk5EhyeFZrf0rtBspTCYTpaWlXHDBBR2R79LSUgoLC5kxY0a3tucWi6Vbod9gjT/YOtqDid1up6mpCUEQ8Pl8KJVKRFHEbrcPy/hGoxGdTsf555/fsa19ktIbhYWFzJ49G71eD4Beryc5OZmcnJxhsXeoCAqSUokiIzsLO2VkZGRkvh1npfN9MjQaDT6fj6NHj+L1elGpVOj1ejQ9K85kRiWiKEmrbdkC110nFRGCVKz34x9LxYVnQqFYXzrWH3/8MbNnz+52bNeCy6Eef7Cd/IEiCAImk4kpU6Z0FEYfOnQIYZiSjjMyMlizZk2f6T898fv9tLS0UF9fT1tbG1qtFo1Gc1YUcneZf8jIyMjIDAJnvfP99deSwzZ5siSr5fF4UKvVzJ49G6PRiMVioaCgAE9vmmAyo46yMnjsMel1QgJcfHHnvuTkvs8TEamnHhUqwgnv+8Bhol3HuishISEoFIpuknrACT8P5fiD7eQPlNDQUGw2G9XV1Wi1Wtra2nA6nYT2FMceQvqT/hMTE8NXX33F9OnTiYqKoqmpiX379jFlypRhs1dGRkZG5szgrHe+d+yQtJ5TUuD//g+8Xi+xsbEolWrs9jZUKhWxsbEcaW99JzOqaGzs3l0xOVlyumtrJe3o08GPn+d5ngYaOJdzuYRLhsbYftCuY93TyU5PT6eiogLoXog5b968YRl/sJ38gRIcHNwR8W5sbESr1RITEzNszbDy8/P7lf5jNBoxGo3U19dTW1uLQqHo2HY2UVsrKZ9cMvJ/QjIyMjJnLGe9893eEW7ScZWygIAA1Go1hw+7OHp0MXp9PZMm+QgIKBs5I2VOoLQU3n5bathy//1SkxGQpM5uuknSijYYTu9aChSEEEIDDRzhCBdzMQIjq5nWqWMtdIustutY5+fnU1paitFoHJI87L7UVgbbyR8ogYGBmM1m3G43brcbhUKBy+UivKdA9xDR35UBlUrF0qVLKSkpobW1FYPBwIQJE/os0DwT+eYbeOEFqaYiIUHSbZeRkZGR6T9nvfN9//2STFbXgFl9fT1O52RAi802HotlXbdzcj6tJ+zgDsKuvxhj7CiUyjgL8Xi6NyQJDpYcb4CcnO5NRgbih2aRxQQmMJnJI+54t+P3+0/IKR4u4uPjmTdv3pA7+QPF4/FQU1NDenp6x+SgsLCQ4GFqu9jflYH+FmieiUycKDX1cTqlbqWy8y0jIyMzMM565xu6d7Pzer20trYyfrwHo7GFxkaRtrbaDtUT0S9S/fi7OI8VYNuyB+NHnSLRbrfUWXA0StedqezeDZs3S90SH3yws4lHRATMni1pC2dlfftxJjHp1AcNI/n5+cycOfOEtIatW7eiVquHRYUkPj5+1DjbPWlubiY7O5vAwEDcbjcxMTEEBwdTXl4+LOP3d2XgVCsZZwMGA9xwg9Qwqa9GRjIyMjIyp2ZMON9dUSgUpKSk0Ny8F6/3a8LCVISGpnTkfLc2uvDbpc6CyunTunnaeXnSsmtUFNx5Z/dcZJlTY7dLedpd24vbbHA8xZmqKqkotp0f/3h47RtO+kprKCwsJDs7m4KCgo70haioqCFRIcnJyTlBZ7yn0spIoVQqUavViKLY7btSqRyW8fvSYT/ZMxjJlYzhoq929TIyMjIyp8+Yc77dbjcul4uJEyei0+lwOp2YTCbcbjcAwVFaLt1+D3Wf7CFodnelgpoDDQR4AmlsDOrmQAL85z/S98xMKWIr04kowj/+ASUlsHhx9xbkGRmwa5f0fbiajIqI+PGjZHgcud7oK62hpaWFsrIyxo8fT1JSEq2trZSVlQ16oWFOTg6vvPIKkZGRxMTE0NbWxiuvvAIwKhzwsLAwLBZLN3WT5uZmwsLChmX8vnTYY2Nje3XA+1rJGC3SjUOFzSalosg9ymRkZGROnzHnfHu9Xux2O/X19R1Lw3a7vVuzHXWAQPwV2d1PFEXmHX6RqU31lJz7A9Tqzv0+nxQV93olnemuvovJJKkDxMZKOZJnc8qK2y0VSh45AitWdP5DFgQp514UIT+/u/MdFQW///3w2OfHzyY2UUgh05nOQhYOz8C90FdaQ2BgIOHh4YQcn92FhIQQHh7eoYAyWKxevZqQkBCSk5M7JqFut5vVq1ePCuc7JiaG/Px8EhISOtrLFxcXk5GRMSzj91cH3WKxoNVqu61YTJgwAYvFMiz2wvB3LK2thWeekbpg/vjHnSljMjIyMjInZ3h0u0YRXq+XxsZGWlpacDgctLS00NjYeOpOlwcPEm4tY0K0nSWp3ZVRHA6YOlVq7tI1bQIkR3T9enjpJamosCuffAIffig5pGcSLS1w+LDU7KYrpaXwxBOwbp30uitz5sDSpXDttZITPhIoUFBKKRYsHObwyBhxnPaCR4vFQm5uLhaLhXnz5pGYmIjD4cBmsyGKIjabDYfDMej61qWlpaSmphIYGIggCAQGBpKamjpqdL6joqKYNWsW27dv5+WXX2b79u3MmjWLqKioYRnfYrF0TIDaCQkJ6dOZ9nq9bNmyBavViiAIWK1WtmzZMmwddNs7lhqNRjIzMzEajXz55ZeYTKYhG/PDDyUp0L17JSUUGRkZGZnTY8w5306nE61Wi0KhwOfzoVAo0Gq1OJ3Ok584dSr87GdSyf/ll3fbZTDArbfCX/8K2T0C5larFBHSaDq7Mbazcyd89hns29d9e3m5pNLy5JNS5LwrFgs0N0uFn0OJ2QxffQVr10rFkF15+mnJyV6/vvv2mJjO18eOdd83d670tk2ePLIRsilMwf18PdvuL0JE5Le/PcCOHQ20tflYctFWANauPcbf/lYIwN135bJvbxNNTW6+e9lOAN57r4pnnpZmHj/76V4OHbJSXe3kqqt2AfDqK2W8+ILkxF5//deUl9s5erSVH/1wDwDPP1fMW29VEB8fz4svBnPBBZczbtwc/vpIHSkpKeTsiWLbVjslJSU8cL+T8LA43O5E7r0nD4CHHjrEZ5/VArBo4Rf4fCJbttRx//0FAKd1T3Z7NA0NTh59VHpfdu2CDetFNBrNt7ongFWrvqSuro28vGZu+4X0y/3YY0f46KNqAC5esZ3WVg9f7zL3eU9ut5fcXA8K4UruueceRP+VHDjgxun0DstzMhqNPP30YR577GvWrVvHddfuIyeniObm4F7v6e9/V9DYaKe+LoQvtowjKCiILZtD2P2181s9p9O9p+uu3UNiYiJbt4p8+KEU+X71FT2bNuV96+fk8fT+YXPjjVJB9KWXwsyZvR4iIyMjI9MLygceeGCkbRgSHnzwQQ1w33333detdfxTTz1FcnIyoaGhaLVatFotgiBQV1fHXXfd1fcFBUHKHZkzR+pl3o7fL4V6ExJArT7BsUxPlyK+WVndnW+/X2oA5HRK+c6TJ3fuKy6G7dulqNKCBZLsXjuvvgqvvy5FmhYu7NzucMC//iWlvwQGQmRk574DB2DTJinCfs453e177DH49FOpGDItrXP7oUNStL6oSEqj6WpDUZHkXPt8cOGFnds1Ginyf9VV3a81mhjPeGI+2U5CbBiTzj2fouDtfGfiFAx6DWmTgolPCCQkRE1Kip7wcA3x8YEkJQURFKRi0iQDcXE6jMYAUlKCMBoDGD8+kKSkQIKCVKRNMhA7Tkd4uIbklCBCQwNITgoiMTEQvV7NxEkGYmK0REZqSE4OIjhYTUqyvnP/RAOJiaE0NZUxc2YCM2ZMJjZWQFA0MXduJpkzxxEZqSEmRkty8nGbJhoYPz4Qg0FNaqqBiAgN48bpOmzu655cLhPFxQeZMsVIdHQAbreFxoZDZGVP5ftXLh7Ue4qO1hIdLdlsMKhJnaBnfGIgeoOa1ImGXu+puaWEY8dKcbaVcfDgbnz+RvT6NsaPj2bVqnlD/py02jb27fuCadOimDo1haAgO+Xle5k371zmz590wj3l5HzCnDmT8PvtaHXNGAwiKRPCcTgrWbZs0YCf0+neU0N9HjMykwgzqohP0GAwKEmI19DmqiY7e8a3ek433bSHsLAAkpODuv0tBQRIk+opU+SUExkZGRm3280jjzwC8MgDDzzgPtmxwslaJp/JCIJgAKxWqxVDl24sqampZGZmEhERgUql6khDyc3Npbi4uP8DrVsHH38s6W/deWf38O9p4PdL6Shd5gccOQJffAFNTdIlg7r8z/vb36SUjsmT4e67O7ebzfDb30qvb7xR+qfYzpo1koMN8O9/dx//N7+Roulz50rntVNaKo0FcMcdUuC/nbIyadIQGyul2pyptDosPFD0CH+f/gjVzmNEa6LQKDWnPnGIGY7cXZPJxHPPPUd5eTltbW1otVqSkpK49dZbR0WB4EMPPURVVRXjxo3ryEk/duwYCQkJ/H4YigTWr19PfX09hw8fprm5mdDQUCZPnkxUVBTLly8/4fj77ruP2bNnn1AgmpOT0/5hPOT29uyoabFYsFgsvdrbH8xmF2FhknKLcAovWxShtbX7ZF1GRkZmLNDa2treiyJYFMXWkx075gouRVHE7/cTHByMRqPB5XJRX1/PgCYhotiZ3KzRdBcUP00Uiu6ON0hR474ixytXQl1d790dExPB5ToxvUWrlY5vL3rs+v/znHOkqHlKSvdzEhLgz3+WnOueSgbJyad3b6ORXbuKeOrLX/DdPTHENamZtOp3vPB1OesN/yTJPZ001zz8eFEx0jJxki55qxUqK1xAyaBe3dJs4dChcTQ3B+L1eFGpVTgcobzxRi3GUNegjjUQNmzwEhs7E8ROdZMmSwwHD9YQGTG470VvbN1ajV8UCQs7F61GS3NzG+vWNaEQqqmsOHH82tppvPFGLdHRIlqNljZXG3V1dYSETOPf/xp6ey3NkZSUlBAeZkOn0+J0tmFuMjNhwoRe7e0PU6cFM2GCnhuu382GzxagVPbugHs88Npr0kfib35z+h1oZWRkZMYaYy7ynZGRQWpqKsHBwSgUCvx+P1arleLiYvIHUvkoilKOSFKS3HniDOCuexbw1neKEXzg2ZmJ48Ol+Ks7VytUU48SsHwHjn/8aAStlJEZXfzznzNZsDCKtLS+PeqvvpLS4kBKs7v55mEyTkZGRmYU0J/I95hzvr/zne+QmJiIWq3G7XYTEBCAx+OhoqKCnTt3Dp4BTifodIN3PZlB4cF/Pcpa7+NUJByvNfZD0JHZGL9ciaZBkqrxq10IXjXHrvo70R/fisoxNGvobo8bp8OJz+dFqVShC9QRoB6eiLvJZEKj0eDz+fD5fSgVSpRKJS6Xa1SknZhMJnx+H2qVukMS1OP1oFQoh8W+mppjeDxetFptR3paW1sbarWK2NhxvZ4zks/T7XFja7WhUChwuVyoVCr8fj8BAQH4fD70Bv2AbGlp8VBcbCMgQMEXXyyi8LAVv0/kph+euPwlivDKK1Ktys9/fuIKnIyMjMzZjOx807fzff3111NcXExCQgJBQUHY7XaqqqpITU3l9ddfH5zBDx6UWmH+7GfdKyllRgVNt/yMj6o+4N/f01IW2fn7vzxmGb9Mu5PpoVJVaqH1MOnBk/nQ9BEh6mAuiF48aDa0S8MlJiae0L58OJzLO+64A+CEFSCAp59+esjHPxXPPPMM+/fvx2AwdNjX2trKjBkzuP3224d8/IcffhidTtch9RgYGIher8fpdPK73/1uyMfvL+053wUFBR068TabDZvNRkpKyoBzv9vafCyYv4Xc3GYSEnS88+4cYmJ0JCT03hHL65WccLnpjoyMzFijP873mJMaBKirq+PAgQN8/fXXHDhwgLq6usG7uM0mVTW2y484HIN3bZlBIfiXv2LZ/gD+90cfL6puY7p6IgDrazdw0bblXPP19ext2kd6sDRxSglKZpxuHHavg7cq3hkUG/Lz89HpdBQUFLBhwwYKCgrQ6XTk5+djMplYv349b731FuvXrx8SrWafz4fT6SQ8PJzx48cTHh6O0+nE5/MN+lgDJSYmBo1Gg1qtRqPRENPPYuZvg0KhoLi4GIvFgsfjwWKxUFxcPOidRgeLdl3y9gY/AEFBQdhstpPqk58KrVbJO+/OxWhUU1Xl5P4/HiQqSsM9v95PS4vnhONVqhMd755SpTIyMjJjndH5n2QIycvLIzExkQULFrBs2TIWLFhAYmIieXl5gzOAXi/JhqjV0vfh6pkuc9oc8GmpzfwOCgTS7vuElx4ReDPzJZI8UvfETXVbWLFjJau+uoon1n5KpGMSiapUXn6vgEZXI/n5Lby5fQduv5uPP66mvr4Nq9XDu+9WApD7jYV9e5sAWL26CovFjdns4sMPJSd6z24z27eVU1BQwPZtXtxuBSaTjQ8+KGPfvn38+U/v8tprm9m0aROPPZbDiy++xu7dJR062Fu31nP0qDSpfunFUvx+kZISG1u2SJPIzz+vpbzcjtfr55WXpYZQhw9b2bGjAYB162poawskLCyG7dt9lJaWcviwHa83GZVKNeB7ystrBuDttyux2bzU1DhZu1YSfP9yZyOHDkmR9ddeK8fl8lFZ6ejznlqarYSHpxMVtZilS5cSEbGIyMh0mi3WPu+putqJw+HlzTclHev8/Ba+3mUG6Pc9mUwmKirUNDUZUCqVFB+Norm5jaKi+gHf00Ce0+neU36+npaWFiyWEPbvtx6/vhVB0FNZ2URhoWHAzyk5OYhXX8tGEGDTpjoeeaSQWeeGERBw6n8fu3dLKkxVVac8VEZGRmbMMOacb6fTyeTJk4mOjiY0NJTo6GgmT5586iY7/WHWLPjLXyAzc/CuKTNo2P5wC1+F1IFCgbLyKNbv38L5UReQve1hPpz7HtNU5wKwvWEHj/h+yk0Hr2Jb/Q7Kv1Fzx6TbqK9v483Gl6hyVJFXWEdzq5O2Nh/5+S0AHKtxYqqWfp/y81toa/Nht/s6nJoqk5PSMgsul4vm5hAiI2PR6yOoqVGwc+dOCgrqgBDi4uKwtoRRUHCIDz9cT9ERyZErK7VjbpQkRHNyJOfR0uSmpNgGQPFRGxaLG59P5JtvpIinudFNWakdgCNHrLjdCkBJkzkEg8GAz6entVWDKIocOmTFbvf1+55qa6UQZ15eM263H5vNS2GhtL+8wk5Dg7Q/9xsLPp9IS4unz3sKCQ3FbHZRVuZAFEVKS52YzS6CQ0L7vCer1YPXK7I/V9pfX99GRaW0v7/3VFNTg9Wqoq7ORVNTE1UmBS6Xl+pq84DvaSDP6XTvyeGIoKKiAjCyb18ZGzduZMvmoxQXV7A3Jx+XK2LAz+mZp4+SkBDIb383BYC/PFxIcLCa6mpnx0SmN+rr4eWXpR4C6NZqXAAAIABJREFU//qXlJIiIyMjIzMGc74nT57MihUrCA4Oxu/3o1AosFqtrFu3jsOHh7DleE+NP5kRw9/cjBASQtPtt1Lz9ouEZMwkQNAQ/p8XUE2UUlC+Nu/h8SNPsrVhW8d5s4wz+VXaXVwQtbhD7/jNircpsZXyx6m/QxTFU+ogt3PHHXcQGRnJ+PHjO3SsKysref/99zn//POJjY1FqVTi8/moqanh2LFj/O9//xu09+Dee+/F5/MxZcoU9Ho9NpuNQ4cOoVQq+fvf/z5o4wyU9evXk5+fT15eXkfO9fTp08nIyPjWutWnw0UXXYRWqyUoKKgj59xut9PW1sbnn38+5OMPBJPJxJYtW9i8eTMqlYrAwECCgoIICwvjmmuuGXAtwbp1NZxzTgixsTouW7mDjRvrCA1Vs3r1PIqLW/nRzSl9nrtxI2zYAD/9qVz+IiMjc3ZzVuh8C4KQDtwJRACbRVF8XhCEIOA5wA1sFUXxzf5eV6fTUVpaSkxMDGq1Go/HQ21tLbqhVCaxWODFF2HxYrkP8yhAERqKa9s2vKlxaHQ62ooOsvans4jRHuR7SM73eeFZvDf3Lb6x5PL4kSfZWLeJfZZvuObrG5gecg53p93JspglXJt4NR6/B4vbwve/upoN89eiUpz6zyooKEiKbFut1NXVodPpiIuLw+v1EhoaisFg6HC+nU4nBQUFg/oeJCQkYDabqaqq6piEGgwGwgegVT8U1NXVsXnzZrKzs4mLi6O6uprNmzcTFRU1LOO73W7GjRtHXFxcx3Oorq6mvLx8WMYfCPHx8SgUCmbMmMGECRM6CspLSkrYunUr11133YCuu2JFLA6HlyeeOMLLr2Qx57zNVFY6+PWv97Nt+2I+/7yWWbPCOhrxdOWii+C88+SmOzIyMjJdGda0E0EQXhIEoV4QhIIe25cJgnBEEIRiQRB+AyCKYqEoircAVwLnHj/0cmC1KIo/AVYOxIagoCBKSkqoq6ujubmZuro6SkpKCAoKOvXJA8Hvh8cfh6NHpQ4UZvPQjCPTLxQREQRnzqVi1TQAFnxRz4LtLRTvXsfP93Wqacw0ZvLGea+yecFnXBy7AoC8lgPctOdmFm9dwprqT1AKSowBRl7NfhGVQsWjhx/nG0vuScdPT0+nvr6e+Ph4Zs6cSXx8PPX19ahUKpxOZ0fTJ1EUcTqdg17ol5qa2jGu0WjssCM1NXVQxxkoO3bsICsrC1EUKSoqQhRFsrKy2LFjx7CM7/f7cblcWK1WbDYbVqsVl8uF3+8flvEHSmFhIcnJyej1egRBQK/Xk5ycTGFh4be6rlqtQBAEQkICeOedOQQEKNi/v5k77/iGvXstVFX1XlguCCc63mfpYquMjIzMaTPcOd+vAMu6bhAEQQk8CywHpgBXC4Iw5fi+lcBOYPPxw+OB9tKdAckyOJ1OtFothYWF5OTkUFhYiFarHdyc764oFHDFFdLr2bPlENAoQT11Ktq55xO2fBVaZRDGoxaUBw4xThPNL1JvAeDhQ49QYZdyWjNCp/Fy1n/Ztmgz34u7DAGBQ9ZCfrz3FuZvWczqqg+J1kQDsDBqPklBiZTYSlhd9WGv4y9evJjg4GAqKio4cOAAFRUVBAcHk5aWRk1NDbW1tZjNZmpra6mpqSFxkBs4ZWRk4HQ6mTZtGsuWLWPatGk4nU4yMjIGdZyBYjabiYmJQaVSodFoUKlUxMTEYB6myavD4cDr9VJaWkpBQQGlpaV4vV4co1y9yO/3nzBB6G1bf1GrFfzqV2lUVNixNLt58kmpnuWVV8qJidaSkRHCnt2nfjb19fDXv8IQCPjIyMjInDEMq/MtiuJ2oKnH5iygWBTFUlEU3cA7wGXHj18jiuJc4Nrjx5qQHHAYoO0WiwWVSsWMGTOYO3cuM2bMQKVSDViK67SYMQN+9zu49lpZAHcU4T18mOQ3viF41dUICHi++grtpHTS26TitHNCzyFKG8l+Sx6f124CID14Mv8+9zm+XLyVKxNWoRSUFNmOcus3tzNvywLerniXGaHTCQsIw+v3oRSUAGyt347H3ynNFh8fz8qVK0lLSyMmJoa0tDRWrlzJpZdeSmRkJI2NjZSUlNDY2EhkZCSLFw+exnj7+PPmzcNisZCbm4vFYhk2jfHTQRAEDhw4QFBQELGxsQQFBXHgwIHTzqn/tjidTmpra5k4cSLnn38+EydOpLa29qST9OGQiDwV6enpFBUV0dLSgt/vp6WlhaKiItLT0wfl+i3NHqpNTm7+cTLXXy9NCO+44xs2b6rj0X8cwefrO6ztcEiOd3k5PP+8rMIqIyMzdhn2gktBEJKAtaIoTjv+8ypgmSiKPz7+8/VANrAaKc1EAxwQRfHZ4znf/wTagJ295XwLgvBT4KdIznlmz4LLxMREzj33XKKjozsKqerq6ti7d+9xtYBhoq0N6urklvSjAE95OU3z5yN6vWgvuQRlYiKGLo1UvrHk0uhq5KLoC1lb8ynLYpaiVkiTqDJ7Oc8cfZZ3Kt/DK0pyDuMDE7h94i+4KuFKNEoNbr+b27+5iydmPIbNa8MYENpxfk9MJhNr1qzB7XZ35GIHBASwcuXKUeMYDwfXX3891dXVTJ06ldDQUJqbmzl48CBxcXGD1wzrJMyaNYvk5GR0Oh0+nw+lUonT6aSsTJKD7MlIN03qaserr75KdXU1bW1taLVa4uLiuPHGGwfVjk2b6pg6NZhLL9nBgQMtJCUF8vXui1CpBDweP+Hhml7P27AB/vc/WLoUvvtdaWFQRkZG5mzgTCu47C2UJYqiuBXY2mOjHfjhyS4miuJ/gP+0q5303K/T6dDr9YSEhKDVamlra8Nutw9tweWJRsLrr0NuLnz/+7BwoayEMkL4W1tpvuIKNJdfjvO9d2kryCP6uee6HTPTKC2x2zw2ttZvZ0XsckpsJURrokkOSuLxGY/yy0l38czRZ3mz8m0qHVXck/cbnjjyFLdNvJVrE6/m3+dK13z66DOMDxzPNYlX4fV7TyjObI+I5+fnY7FYMBqNZGRkjCnHG0Cj0RAbG8uhQ4dwu90EBAQQGxuLRtO7UzfYREREEB4ejk6n62hv73Q6aW3t/fM0Pz+fxMREjEYjQMf3/Pz8YX924eHhREREdKjvDEWA5auvGomO1vLue3M5L3sT5eUOfnjTbs4/P5KgIBU/v7X32oGlSyE1VfqSkZGRGauMBufbBCR0+TkeODZUgwUHB3fkcrb/U1epVO2zleGhuhq++UYqxszPl5xvmRFBYTAQ+sFqvlTuYtz/XiWg/Ci2xx/Hd+QIoS++2O1YvVrPYzMkGb4PTB+RETKNJTEX0uJpIT4wjr9N/wt3TbqdZ4uf57XyNznWVsNv8//Ak0XPcGvqz7gx6QZ+k34voiiS31LAgwcfYvXcEztmxsfHjzlnuyeiKDJz5kzGjx/fsa2yspJDhw4Ny/gzZszAZrOhVqs7nFiPx8OMGTN6Pd5isZCUlNRtW0hICKWlpcNgrUR71Fun0xETE0NSUhLh4eFYLJZBnwT88Y9T8ftFdu5s5OVXsrj8e1+yfn0ts7PC+PU9k2lp8RAScuLqjiDIjreMjIzMaFj0ywEmCoKQLAhCAHAVsGaoBjMajZSVlaFUKgkPD0epVFJWVtYRqRoW4uPhnnsgJQV+9CM56j3CqBKTUK7eQOn30vDgxfbh+wTedttJz7l38q9YHruU/OYCfrHvTgA8fg+xulgeyvgTey/6ml+k/pxAZSD1rnoeOPhnzv08m6eKnsHmtZERMo3/nvs8oijyk5xbqHQMfwvA0ZCj3Bepqak0NDTQ1NSE3++nqamJhoaGYVNjWbVqFTabjYaGBurr62loaMBms7Fq1apejzcajbS0tHTb1tLSMmyfK+1pLyAV0+r1eg4dOoTZbP5W7eVPhsXi5vnni1m6NIb/+40k4v3nPx1i/foaFi/6grq6U/eV93rhvffg2JCFW2RkZGRGH8MtNfg2sAtIEwTBJAjCzaIoeoHbgM+AQuA9URQPfstxLgWe7m2fTqfDaDTi8/lobW3F5/NhNBqHN+0EJMf73nuldvQyI4ogCCQHTKL1hqXo1AaEymq8+fk43njjlOfOME7njfNeQRRFLt5xGeV2qW4gShvJ/VN/z76LdnP3pDswqAyY3U08XPhXZn1+Ho8efpyaYzVs2LCB5JJE8rce4L2Dq/msduNQ3y7Q6awZjUYyMzMxGo18+eWXo8YBnzFjBhkZGdTX17N3717q6+vJyMjoM/I82MTGxpKQkIBSqUQURZRKJQkJCcTGxvZ6fEZGBhUVFVgsFvx+PxaLhYqKimFTj2lPe4mOjsZms6HX64mKiqK8vHzIJgHh4RrefnsODoePm25MZvHiKEQRfnjTHt5+5zyio7V4vX2rrPj98MQTsHmzXIB5MkbzJFlGRmZgjLkOl0uWLCEhIYGoqKiOJjv19fVUVVWxcePwOD598s03UFws5YHL0fBhx2+x0PrwwzjfegtFfDwB2dmEPP44wmkq1LR4WghRh/Ba+RvYvXZ+nvqzbvv+W/oS/yl5gWZPMwBaUctK/cVcG3MNgg12VO5kyjlTWDppCWuOreW7cStRCEMzP16/fj1Go7GbU2axWLBYLMPSQfJUDEUBo8lkOu1c+jfeeIOKigr0XSbHNpuNxMTEPpvV9Of6g81bb71FZmYm9fX15OXlkZCQgF6vJz8/n7i4uCEt/HznnUrKy+3cfHMK2VmfYzI5mTXLyP0PTOWtNyt57fXsPs/99FNYswaSkuDWWyEkZEhMPGMZLYW8MjIyp6Y/BZejIe1kWBEEgdjYWEJDQ9FoNISGhhIbGztsEmZ9sm8f/Oc/Uhho7dqRtWWMYv3d71BNm4agVuM3mQiYMwexH+G4ELXkOXwv7jJWxl2Cw+vk1n234/V7CVGH8Ou0u9l30df8Pv0+DOhpE9p4z/4BV5Vey9ue95gWNw1FmUCT28L+5jwEBEpsJd0kCgcLi8VCSA9PZ6jSEwbCYEsh9jfSn5OTg9frxePxdHx5vV5ycnK+zW0NGUajkaNHj3L06FHMZjM7duxg06ZNOJ3OIXfUrrpqPL/5TToOh5c33zwPtVpg3z4LH39UzRNPzjhpweeKFXD11fDrX8uOd290LeRVKBQYjUYSExPJz88fadNkZGS+BWPO+Y6Pj6empgalUklUVBRKpZKampqRjyJMmABhYaDRyBVJI0TIs88SdNNN6K6/HhGR1scfo3HRIrxlZf26jkFtIE4Xh1JQcEX891ApVLxXtZqDLYcwqA3cMek2fm+/j1sjbyFMGYZTbOPtpnf5qflWXrS/gk/08qdp9yMIAk8V/ZO85gO4/e5BdcJHOkd5uOmvE1NdXY3L5UKv12M0GtHr9bhcLqqrq3s9fqTTeCIiIti5cydarZbvfOc7zJw5E1EUWbJkybB9tv3qV/vRBar4xz+k1KAXXyzj009ruOVn+/hyZ2Ov5wiCVG8utz/ondE+SZaRkRkYo0HtZNA5nvN9eW/7Fi1axJ49e8jNzcXlcqHRaAgPDycrK2uYrexBaCjcdRfY7ZCcPLK2jFEEQcD6/lscEYoI1rShO1ZB+MOPohrg89AoNVwQLTXH0Sl16JQ6Gl1mcpr2EmOMJrwxjOj6CHYKX7HHuJcWpZXt6p3M3jSXa8Zfxe0Tf8HTM58A4OPqNWxv2NmhtvJtycjI6CjQ67mcPRroutyelJRES0sLX3755YCjuBaLBa1WS0FBAa2trRgMBiZMmNCnE+P3+2lubiY6OhqVSoXL5aK5ubnPTpH5+fnodLpu14+Kiho2qcHGxkamT59OaWkpeXl5BAcHM336dBobe3d6h4L335+LIAiMG6dl1y4z77xTyW2/2Mcbb55HVnbYaV8nLw+ioyEmZgiNPUNonyR3nRSfzZNkGZmxwlkZ+RZF8RPgjt72paWl4fP5mDhxIueeey4TJ07E5/ORlpY2zFb2QlTUiY53WRkMU0ttGQicO58DP8ug6IZz8ODB/s9/0vqXv+DaufNbXffScReTok+m0dVImb2MiIgI/rf/IwK1gdw0/gZ+5/g/5hRnEa2Mwu1380r5a2Rvmsfdub+mzF7OZXEreSjjQRxeJyu2r8Th7bvT4ukw2jtcDvZyu9frZffu3YSHh5ORkUF4eDi7d+/G6/X2evy4ceNQKBQd8oaVlZUoFArGjRvX6/HFxcWUlZV1u35ZWRnFxcUDsre/lJaWYrfbmT59OsuWLWP69OnY7fZhlToUBIHqaieXrdzJs8/NZOrUYNra/PzfvXmUltj44U17TtoBE+CTT+C556SvtlOLpZz1jHQhr4yMzNBwVka+T0ZjYyNZWVkcPnyYuro6QkNDycrKGtYI0WljtUr/hbxeuOkmmD59pC0661HFxTOr7jyaJzai1Zbjr6lBdDpRDVIq0OTgNCYHp7Fu3Toq0k3k5ueybc92wg1hXD7pu4QpwrBlOHjy6NOU2ct5s/Jt3ql6j8vjvsvdk+4g1ZDKU5mPE6jS8WLpK0wNmcJ54QNbtRnNeuIWiwWDwcC+ffs61DvGjx8/4OV2hUKBRqNBqVQiCAJKpRKNRoOijxaLWVlZ7N69G7PZ3NEpMigoiOzs3osHrVYrSqWS7du3d0S+o6Oj8fl8A7K3vzQ3N5OYmNhRIKrX6wkMDBzerr1AXJyO7TsWo1AIvPJqNosXfUFJiZ3f3HeAW29NRak8vdqalhapHcKECUNs8CinfZKcn59PaWkpRqNxVE2SZWRkBsaYc75LS0upqKjoyOm0Wq34fD5sNttIm3Yi+/dLDjjAaLTvLGWaIxVnSRjc+CPs//43rk8/RXf99fjNZtRTpw7KGCUlJVzYuhBtqJbcxDzKhAqM1UaarE3cveJuvp9wBR9Vr+HJoqcpsh3lfdMHrDZ9yGVxl3L3JElXfFrIVMbpYmlwNbKtfjurEnrNtOqTkVTnOBV+v5+cnBwmTJhAdHQ0drudnJwcIiMjB3Q9hULB7NmzqayspLa2Fr1e3/Fzb6Snp7N7926SkpI6OuE2NDSQnp7e6/FWq5XKykqmTZtGeno6DQ0N5OXldWsSNJQEBwdjNpsJCgrCYDDQ2tqK2Wwe3uZhx1GrFTz3bDHmJhcvvDibH1y5i0/X1jBnTgROpw+lUuDii3tfQbjkEklycMECOe2kndE8SZaRkRkYY875zsnJ4dixY4SFhaHRaPD5fOzfv5/6+vqRNu1E5s+XdMCPHIG5c0famjGDKjkZw/334ykrw/Haa/hqa7E//jgB2dmD5nxXV1ej0WhISEjgUs3FtLnayPcV8HLE61xRdQUF+QW4m9u4L/Qe6lMaebXxdQ5ZC/moeg0fVa/h4tgV/HLSnSQExlNiK8HqlSZpe5v2MSN0+glt63sy2DnVg43f78flcuHz+RBFEZ/Ph8vl6jPn+lQYjUZUKhWzZs3q2NY+6eiN/q6QNTY2MmHCBJxOJ0ePHkWj0TBhwgRqamoGZG9/SU1N7ZBN3b9/P62trajVaoKDgzGZTMP+TG/+cTIKhRTl/uWv0nj8sSP88Q/5PPHEDM6bE9HneYIAP/jBcFkpIyMjMzKclTnfJ+PgwYOo1WoSExOZOnUqiYmJqNVqDh78Vn19ho6ZMyUtrq5SiH4/DNNy9ljF39xM07Jl6G64ARER9+7d6K6+GtEzOIojDocDlUrV4UyKfpFgDMzbms2ur3bxqvJNSBMIDwsn8JCW11Jf4tWsl5gecg4An9as44JtS7nu6xtp8Vj5UfJN+EU/zxx9DounmRZPC15/7/nMMPolzFQqFdnZ2ZjNZvLz8zGbzWRnZ6NSDSxekJGRwf79+9m6dStffPEFW7duZf/+/X3mzhYXF9Pc3ExWVhZXXHEFWVlZNDc395nD7XK5cDgchIeHk5aWRnh4OA6HA5fLNSB7+0tGRgYmk4mqqipqamoIDg4mKiqKzMzMEWmepNEoUakEli/bznXXjWf+/Ej8fnjooULCwgJ49p9HcbtPbyLV2gpNTUNssIyMjMwwclY63yfrcOlwOEhLS0OpVGKxWFAqlaSlpeE4U9qreb3w3//CSy9JTrjMkKAIDcV7YD2f3hZJS6AbX10t1vvvp+mKKwbl+kFBQcTFxWG1WikvL8dqtRIXF4fX7iUxMZE7429jauAUjmlr2B6+k4MFB1keu5SNC9bx9nmvc65RiuBurNvEsu2XcOVX17CnaS+vZr9IpCaCV8vf4D+lLwDgF0/8PRntEmZGo5F9+/axceNGNmzYwMaNG9m3b9+3Unnw+/04nU4cDgdOp/OkUXSr1Up4eDghISEoFApCQkIIDw/H2p4G1gONRkNISAhtbW1UVVXR1tZGSEgIGo1mwPb2F4VCQUtLS8ekSqVSERoaOmKTKkEQeOfdOUybFsrrb2QTG6ulocHFtdfswtLsweHoe3LYzsGD8Kc/SR958sedjIzM2cJZ6XyfTO1Eo9FgNps5duxYx5fZbB7Wf5LfijVrpE6Ye/fCunUjbc1ZjSdAIOo3L1B85RQ8eGhbv57Q//53UK6dnp5OSUkJNpsNv9+PzWajpKSE0NBQydFThaFVaBkfkMCF4RdgsVh4pPBvWDwWLohezKfnf8zque8wN3wOAFsbtrFy5/f43per2NGwk9tTb+WnKT+m3F7Byp2Xn9DoZLTrfNfV1fHBBx/gcDgICgrC4XDwwQcfUFdXN6Dr5efnM3PmTJYvX87FF1/M8uXLmTlzZp9OaWhoKA6HA5vNhiiK2Gw2HA4HoaGhvR6fmJhISUlJN1WKkpISEhMTB2Rvf8nPz2fGjBkkJiaSkpKCy+Xi2LFjfPLJJzidzhGbVEVEaCgpsXHTjXt4883zUKkE9uyx0NjgwusV2b694aTnFxRIZS+lpdLHnoyMjMzZwJjL+Y6Li+Pw4cPExcUREBCA0+mkurqauLi4kTbt9Fi2TAoHBQTA4sUjbc1ZTaKQxIHvL0I3ZTrq9x9EbGzE+cYb+M1mDA8//K26oqanp7Nr1y58Ph9qtRqPx0NLSwtpaWndnGCD0kCsKwZzqJlxOh/BqmD2NOWgElTMjzyf+ZHns6vxax4repLtDTv4snEXXzbuYnbYufxq0l0silrIC+f+C0EQ+O2BP3BT8g1MMkwc9Trf77zzDpGRkUyePJmgoCDsdjuHDx/mnXfe4aabbur39fqr852SkoIoijQ3N3cUaMbHx/f5zJOSkjh06BC7d+/G4/GgVquJjY0lKSmp37YOBIvFQlJSEh6Phy+++AKlUokoijidTtasWUNmZuaw2NEbKSlB/Oe/55KYGMRf/3oOv/51Hs89V0xUtBYQmT+/7yLayy+H8nKp/KVLur6MjIzMGc2Yc75BUgaIjo4mMDAQh8NBa2vrSJt0+gQGSs14AgKkbpgyQ4aAwKVL/oE3dz+Oq67B+eKL2F9+maCf/1xaA1cqB3ztxsZGLr30Upqbmzuk9EJDQ2lqauqQh+vpFLcXzbW4W1AqVPhFP9sadrAwcj6r577D3qZ9PF70JJvqtpDTtJervr6OzNAZ3D3pTpZqo7lk3ArGByaQa9lPjbJ2VEuYVVdXs3TpUlQqFTabDbVazaRJk/jss88GdL12ne+0tDSSkpJobW1l9+7dfep2Z2Rk8Prrr1NXV9chNRgdHc3111/f6/Hl5eV4vV6WLFlCZGQkDQ0N5ObmUl5ePiB7+0v7Skb7Sl5CQgJqtRqVSkV1dTVhYaff5GawEQThuOO9l6qqarKy/OzZo+Bvfz3El19dSEFBC6mperTaE/+e1Gq4997uJS8yMjIyZzpnZdrJyairq2PKlCkdS8N+v58pU6YMeDl7RDAYTnS8c3Kk7pgyg4qAQNvatWjOOw/BYEBsakIQBLz5+SekcvQHi8WCTqfD4XBgt9txOBzodDoUCsUpm99cFHMhi6MWUt9Wz4em/wFQYa9kRuh03jrvdT5fsJ7lMcsAyG3ezw17fsjirUswu80EKAJQCAoCFGri4uJQZCr5wdU/YPny5aPG8QbJWXY4HAQGBhIREdExUe6rKc6p6K/Od01NDSaTidDQUJKSkggNDcVkMvWpXlJUVMScOXMwGAxYrVYMBgNz5syhqKhoQPb2l/ZmLAcPHkSv12O1WjGbzTidToKDg8nLyxsWO/pCei9LuPTSCJ58airjE1U4nX6uuHw7zzx9lAN5zX2e29Px/hZ/djIyMjKjgjHnfLvdbjweDwqFouPL4/HgdrtH2rSBk5sLL7wAf/0rnEmTiDMEwx/+gPbii9H96IeIiNiefRbrfffhr64e8DW9Xi+fffYZJpMJi8WCyWTis88+w+v1Eh8fz/Lly7nmmmtO6hTH6GJ4ZuaTCILAo0ceY6/lG9x+N5MNabya/SJfLPycy8ZdioDAQeshbs75GQu/uJASWymLohbS6m1l7TGpbuCY89hJ1VGGm9DQUGpra3E4HPj9fhwOB7W1tX3mXJ8KhUJBYmIiu3btYvXq1ezatYvExMQ+ne+NGzcydepUwsPDUSgUhIeHM3XqVDZu3Njr8S6XC61W222bVqsdNrWT+Ph4UlJSKCsrw+v1EhUVRVJSEuPGjcPj8Qxbp82+kHLSE0lKCuehh2r44x8S0OkEysrasDS7mZ0VRlXVqYveW1ulvmN79w6D0TIyMjJDxFmZdnJc7aTXjiMKhYKCggLS09MxGo1YrVYKCwv7/Cd8RnDggPTdapUlCIeI0ucfoKrpK2KDRYItFrQ//zmKuDhEURxQ7rfFYqGuro6kpCT0ej1Op5O6ujpiY2PJyclh48aNNDQ0EBkZyZIlS5g9e/ZJr/fPmU8hiiKf123i4+pPeHbW00wJTue/s//FPa1HebLoaT40fcTh1iPcsu8XPHr4Me6cdDuPz3gUpaDkyaJnuCj6Ai6IXoxf9J9SJ3yomT9/Pnl5eRQVFaFSqToi4fPnzx/Q9bxeL0VFRWRlZXU0oTly5EifaSfl5eVKETjJAAAgAElEQVS43W4EQehw/kVR5NixY70eHxUVxZ49exg3blxHDv+xY8eIiooakL39xWQyUVpais/no+14X3a/34/P58Nut494al17TrogCPzgB+Gcc04gD/4pnnvvqeKj/1XzwAMH+WJLPVu3LerQB++JKMJjj0FNDRQXS90vR0l98GkxmptaycjIDC9nsMfZNydTOwkMDESpVGI2m6mqqsJsNqNUKgkMDBxmKweRG26QWsP95CfQ1ZmQ12cHjarrstj252zyfzodLz7szz1H809+gmvDhgFdr6SkhKioKJxOJ/X19TidTqKioti9ezfvv/8+iYmJXHzxxSQmJvL++++Tk5NzymsKgsCSmIt4fMajeP1eFm29iGZ3M5MME3lu1jN8dcF2rhl/FSpBRYm9lDty72bO5vm8Xv4mf572ABdGX8DWhu3c9s2dA7qnwWThwoWsWLECg8GAx+PBYDCwYsUKFi5cOKDr9TftpLW1laamJiIiIhg/fjwRERE0NTX16cRGRERQW1tLS0sLTqeTlpYWamtriYjou6HMYNKu265Wq3G73VRVVXH06FHKyspwu90jrubUVV1n5swgao652bvXwiWXSpO8v//tMH/68zR8PhGns/cAgiDAZZdJr6dMObNKXtqbWhmNRjIzMzEajSOivy4jIzM6OCud75Oh1WqZOHEidrudhoYG7HY7EydOPGHJ+IxCEODSS+H/2Tvv+Kbuq/+/r4a1rOG9t/EAbKbZBAiBLLKeJ0mz2owmzXrSrDZNR9qmvzbNapqEktE0SdOQklkaAk0KARzCNnsZvKdsy7LloW1J9/eHsILBBmyMbUBvvXhhS/deH8u2dL7ne87nM3Zsz/s/+gjefBNGiHHKucwU7TwSK7xketOR6Qz42tqQJiaiWLBgQNdraGhAoVCQmppKbm4uqampKBQKSktLSUhIwGg0snHjRoxGIwkJCX22O/SGQqpAJpHx4bSlGEIMvFH+V/5d/znpoWm8POFPbL1kI7enfp8QSQg19hoe3/sE09bO4p3K95gePpXnx/0Rj8/DTVtuw+IeHom6vLw8TCYTEokEtVqNRCLBZDL1aYpzKrrt5bslHa1WKwUFBX0m32q1GqvVitPpRBRFnE4nVqu1z0W62+1mwYIFgTglEgkLFiwYsna2bt32qCi/ckh31dvr9SIIAnFxcUMSR1/k5eWxa9cuvvzyS1atWsXuPesw6Ov5058mMWNGBF6vyPdv28pvf3OAv75Z3ud1JkzwD2Defbd/9vxcYaSbWgUJEmRoOS/bTk6GQqHAZDIRHx+PIAiIoojJZBr2ytCg09UFW7aA0+k35hlg0hLEjwoVPwh/mK7kQrz352B97jmcH31EyPTpCCEhKObN69f1fD4f9fX1WCyWQOtK9/ClxWIhKSmJlJQUrFYrtbW1A6qQxapiAbg6fhEiIq3uVv585BV+N/a3vDDuWR7N+jF/KX2dpdX/pN5h5Of7f8nLJa/y4Kj7+H7ybTyZ81PCQsJYXvc5UYpIZkUNnQzhnj172LdvH/Hx8eh0OhwOB/v27WPPnj0D2qrvbjE7lo6Ojj51zePi4ggNDeXAgQPYbDY0Gg3Z2dlYrdZej5dIJGRmZjJx4sQe1x8qfe3uyvKiRYtYuXIlGo0GpVKJ1Wqlra2NO++8c0jiOBlWq5XW1lY8Hg8ymYzklHDWrLbwwIOZlJdbaWpysXGjmdVr5uD1ikilvbefZGQMceCDQHfbzbHo9XoqKiqGJ6AgQYIMKxdc5TsyMhKPx0NERAQpKSlERETg8XiGbHt4yHA4/GUihQKmT+/5mNUKzSc3twhyIrLoGJQ33oh86lQkBgO+9nacn3+OOAAFDolEQmVlJQ6HA6lUisPhoLKyEp/Ph1arRafTIZFI0Ol0aLXaM1JWiVfFk6BKQCpImR45HUEQ+Lj2U3yiyDP5/4+iBVu4P+Ne1FIVTa4mfn3gaQq+nsYG80asXVbiVHGEK8Kxeewsr/t8wHH0h+XLlzNnzhzmz5/PjBkzmD9/PnPmzGH58uUDul5kZCSFhYX4fD7S0tLw+XwUFhb2+XcfGxtLY2Mj06dP5/rrr2f69Ok0NjYSGxvb6/G5ublUVlb2MOWprKwkNzd3QPH2l261k3nz5nHFFVfQ0NDA3r17aW9v58477xyQNvpgUlhYiFwuZ8aMGVx++eXMmDEDuVxOXf0BcnP1fPDP6UilAlu2tPCLn+9jxvSvMZmcp7yuKMKmTf4aw0hmpJtaBQkSZGi54JJvvV7PmDFjAlbMEomEMWPGnGC1fc6j08Edd8ALL0B+fs/HPv4Ynn4aVq8O9oX3E7G1lc5f/ALvj26hiy7c69YhLyjA20/lE5lMRmJiIna7nfr6eux2O4mJiej1epqbm2ltbcXn89Ha2kpzczOZmZlnHLterueKOL8EYbu7HblERr2jnjp7HU+P/TU7Fmzj4VH/R6gsFLO7hd8feoZJa6ayybyJBFU8ZpeZcpu/UlfccRivePaGe1taWrDb7axYsYJly5axYsUK7HY7LS0tA7qe2WwmJyeHXbt2sWzZMnbt2kVOTg5ms7nX4/V6PaGhobS2tlJZWUlrayuhoaF9vk7MnTsXp9PJunXr+OSTT1i3bh1Op3PAPer9JTExMSBRmZaWxpNPPskXX3zBmjVrhj3xBiguLiY8PJzq6mp27txJdXU14eHheL2HGD1aR1FRK7/+zRgAFi8u454fpRMdffJWQJcLliyBf/wDli0biu9i4HQvjo51QK2urh5wG1WQIEHObS64thOtVktubi52uz1gnqFWqykuLh7u0M4Ox7fTNDTAtm3+j8vKYIA9yxcqkogIJOs+5ePO15n/NwFdq5nOJ59EtNkIe//9U55fRRUePHQZuoiURWIwGAgJCcHtdtPW1obBYCAvL4+KigrKysrQ6XTk5eUNuknKPRk/BGCLeSv72vczKXwiJpeJX+Q+yQOZ9/FWxTu8VfE2lq42njv8Iq+X/ZW70+/i3oy7AXi2+AV+O/YpYhQxKKQhSIWBGw71hs/nY8eOHUycOBGDwUBbWxs7duwY8A5AWVkZPp+P2bNnB9ROampqsPWhjS+TybjkkksoLy8POGJOmzatT51vAKlUilKpRCaTIZPJkJ6BCdNASExMJDExMaCqsWHDhhGjqtHe3k5VVRWpqanExMTgcDioqqo6WgABmVTgrvvS2b3bwr+X1/PET/ehUEgp2t7Kq4sn9nrNkBB/Rx1ASYnf5kCjGcJvqh90L45GqqlVkCBBhpbzMvk+mdRgbm4u1dXVhIaGAuBwOGhubh6y7eFhJy4OfvIT+PRTuPnmoHXcAHCKTi5a9DZlN49l4pJ9ONetI6p7QXMMVqyEEtrjvnWso4YaTAUmJjRMQKvV4na70ev1dCZ0EtYWRmdnJ9HR0URFRSEIAp2dncyZM2fQv4+ioiLWrV5Lc3MzT0f/jm8mbGTFJf9CIkj4Sfaj3JdxD+9U/p03yv9Kq9vCSyUv82b5W9yVdgcvjn+OKEUkb1f8nWaXiSdznxiw7GJvRERE4PP5kMvlSCQS5HI5BoNhwJKgHR0dpKSkBCrXer2eiIiIgJvo8YSFhaFSqZg9e3bgvm6JuN44tq1Co9Fgs9koLy+nsLCQ2267bUAxD4S6ujpWrFiB2+3G5/NhMpmorKzk6quvHtZET61W09HRgcfjQRRFPB4PHR0dqNVqBEHg4UeyMJmcXHVVPAcOtFNWauW554r54IPpfV5TEODOO+GLL/w29CN9ALN7cRQkSJAgp0y+BUE4nZKbTxTFvi3KhhhRFL8QBKEQuOP4x7Kzs9myZQtRUVEolUqcTifNzc0sXLhwyOMcNkaNgief7Jl4i6JfHSU7G8aPDyblJyFeiEf6zofYY9tRfvgYvpYWrC+9hGfvXsKXL0eQSrFhYwlLyCCDhSxEhw4AG/5Ka7w+nopNFURERKBQKDArzWwbu42wG8KoqKrAetga2JlJSEgY9O+hqKiITz75hPz8fKZMmUJTUxO29VaKI4v5r+ZrUtTJfD/1Vh4a9SB3p/+Q96re57WyN2h2NbO4bAl/q3ybH6R+nwcz7iMsJIxml5k7tv+QFbP+NShVcL1eT1ZWFmVlZdTU1KBWq5k6deqAHSO7HSrLy8vxer0BqcG+THvy8vLYtGlTIJb29naqq6uZObP3odPi4mKysrKorq7G4XCgUqkIDw8f8h21devWUV9fT2hoaGCgvLm5mXXr1vGDH/xgSGM5lpSUFDo7O6mrqws8/1FRUWi12sAxDocXo9HBxx/PYNbMtZQcsfL884dJTVFz/Q1JTJhw4sJHr4chXNsECRIkyKBwOmUkI7AD2HmSf/vOVoCDjdlsZsqUKbhcLqqrq3G5XEyZMqXP3s/zluOT6127YP16eOMNWLdueGI6h4jJmMLEla2obrwRAMeyZageehDxqEHIetbjwMEBDmDkO2OWW7mVu7iLGd4ZhIaG0tnZidFopDKiEnmIHCtWFD4FCxYs4JZbbmHBggVIpVLWDfLPZPXq1aSmpvawsU9NTWX16tX8MvdJbk7+Hkc6Srhu0w2EyjQ8mHkfOxZs4Q9jf0esMhaH18mb5W9R8PUMfnPwd7i8LhZPeBmpIOWlIy+zv/3AGcUXFRWFTqdj0aJF3HjjjSxatAidTheQ0usvWq2WpqYm2traaG9vp62tjaamph7J37Ec20Pd/fycrE2gu61Cp9ORmpqKTqcLtFUMJdu3b6ejo4P6+nqqqqqor6+no6OD7du3D2kcx5Oenk5cXFyg2OF0OomLiyM9PT1wTEqKhp/9LBeX08vv/p9fNvWTj2ux271kZIT2dekT6KOTKEiQIEFGDKfTdlIsiuKEkx0gCMLuQYrnrFNRUYFcLmf69OmB7eHGxsag5FNXl79hUhRh2rThjuacQHS7UVxxBY5PP8XX3Mye7e/ibfqSGTe/wEXSi3DgoIsusskOnBPefXOH4031kpGRgUajodPWSWFbIbv27mLClAk92iNGZY1iR9Hg+mmfzMFREARkgoxsXRZ/n/I3AH60434ey3qEezJ+yPdTb2VZzUcsLl1CnaOedyr/zvtVH3BT8o38eNT/MTl8EgmqeMqt5RzuKOHK+Mv7Hd/ChQv55JNPAIiJiaGpqYl9+/Zxww03DOj7bW9vx263ExEREXDMbGlpGbTkWK1WYzabkcvlgeubzeYhN++qqqpCq9Wi0WgQBAGPxzMiHC4jIyNZv349+fn5PX6eo0ePPuHYoqJWxozRc//9Gbz+ejlvvlnOggUx7Nhp4amnRvfZ2iSKUFgIy5fDww+PPEnCoMNlkCBBujmdynffTXf9O2ZE0NbWhlqtDmzLhoaGolaraWsbMV0zw8O0afD738MDD/ScWvL5YOVKv3V9kB6ov/995GPHorrjDly4CXvnvzi2bWZzy0p06LiBG7iRGxE4MVk43vTFbrVzfdL1xFbE9uhrrlfVszZtLXa9fVBjP10Hx/AQf9fZY1mPkBmaQaFpAx/WfMydabez9ZKN/Hn8i6SoU+gSu3i/+gOmrZ3FJ7WfYXFbsHsdOLwOADabt/RLHaWgoICLLrqI9evXs2TJEtavX89FF11EQUHBgL7f0tJSoqOjMRgMaLVaDAYD0dHRlJaW9np8XV0dK1eupKamho6ODmpqali5cmWfeuthYWGBHuvGxkZMJhM+n2/IpeRaW1vxer0kJSWRnZ1NUlISXq+X1tbWIY3jeMxmM3Pnzg1IbEokEubOndvrjuN992cyf34MV1+TQEFBGB6PyP3370CnlZ1UnKm52T/K4nLB3//uf+kaKQQdLoMECXIsp0y+RVF0AgiCcIMgCNqjHz8lCMK/BEGYeOwx5wI6nS5Q8fL5fLS3t9PS0oJOpxvu0IYftdrfD34smzf7J5p+9SswGns/7wLGtngxuN0oYhJQOERkMXFM1s4NaH/L+thcCgsLQyaTMWnSJObMmcOkSZOQyWSkp6cH9KK76GJD6AbKneU0LWzCzeC5JfbXwTFHl41MIiMjNJ18Qx4+0cdfSl/jfxKvZcv8Dfxl4itkhmbgFb18VPsJM9fOZUnp64zVj8HpdfJG+Vu4vG5aXK2nlYTX1dVhs9m45557+OMf/8g999yDzWYbcLJiNpuJjo4mLS2N1NRU0tLSiI6O7rPdrLCwEIfDQVxcHKNGjSIuLg6Hw0FhYWGvx/t8PnQ6HY2NjZSWltLY2IhOp8M3xBlgt3sigPOo+HW3q+Jw0i2BeOzve1paWp8mRG63j7fequD1NyYTGRlCQ4OLVasa2LSxmaLtvS8koqPhuusgIgJ+8AMY5m+5B0GHyyBBghxLf9ROnhJF8RNBEGYBC4EXgdeBqWclsrNEZmYmXV1dmEymwBZtWloacrl8uEMbmXRXBtVq6MNg5EJG89hjCBIJ0qgoxF/9ktHv7se+/XYkDz+MYv78Ps/Ly8vj1VdfpaSkJDCgl5WVxQ033MDOnTtpaGjAI3jQ2DWExIRwa+KthBASON+GDRHxBDWV0yUuLo7o6Giqqqo4fPgwWq2W8ePHYzKZTnpekjqRJHUiDq8DlVSFUqJkW2sRBeGT+fbi9XxhXMmfj7xKcedh/lX/b5bXf86i+Ct4Iudx1DIVzx1+gTH60dyYdD0+0YdE6D1D2r9/PyqVigMHDgSk/qKjo9m/f/+AtuojIyMpLS2lqakJiUSCz+ejo6OjT5Od4uJiCgoKAqpIoaGhpKWlUVRU1Ovxra2t7Nixg8jISLRaLS6Xi82bNzN58uR+x3omxMbG4nQ62b17Ny6XC4VCgV6v79McaKjoNpk5difgZCYzISESPvxwOm63j2f+mM999+7gm2+aWfyXMn70o777SebPh1mzQHlyifAhx2KxoFQqe/w+Z2RkDJkDapAgQUYW/Um+u8tVVwKvi6L4uSAIvx38kM4ueXl5ASmubic6t9vN1VdfPdyhjUzuvBOmTvW7Yh5bSrJa4dVX/e90U6eeqCd+gSBIJLi3bcO9ezey2Di8jY3Ix407aeINfvv0srIycnJyAjrWJSUlNDU1sWjRokBv6KXSS0lISGBs9Nge529jGxvZSCaZ3MiNfVbY+yI3N5fm5mbmzZvXQxrvdCU3VVIV92feC0CZtQxR9JGgiiddk876eWv4suG/vFTiH7z8wriKL4yruCx2IY9mPcw4Qz67LLt56cjLLJ32Xq/XLysro7OzE6VSiUQioaOjA5PJ1OeA5KnIyMigqKgIQRCQSqWBVoy+2lh8Ph9bt27l0KFDWK1WQkNDGT26737j3bt3o1ar0Wq1yGQyQkJCsNvt7N49dOMwdXV1CIJAdXU1o0ePJiIiAqvVyqFDh4bdzKW/6jHd7NzRyratLfz26bH8+qkD/Ht5PTffnMzKlUauuCIOiaTnz0MQRl7iDeDxeNi2bRvZ2dmkpqbS2dnJtm3biI+PH+7QggQJMgz05x27XhCEN4FLgOcEQVAwQh0yT6bzDf6tWZVKFdAlPhPr7guCXoai2LgRqqv9/2Ji/BKFFyiy0aPR/OhHdE2aRMcvfoHjgw+QJiUh0elQ3XRTr+csX76cmTNnkpycHLgvKiqK5cuXs2jRopNWd0VE9rIXHz5s2Hok3iL+3+Xe+syPZe7cuaxcuZKGhobA34FKpRqQI+NtKbcAcLjjCG9XvssrE15icvhE1lz0JWtN63ip5GV2WnbzVeNqvmpczcXR83gs62FeGv8ioijy4K4f86vRPyde9V0iUl9ff4JueHt7Ox0DnD2IiIggNzc3UMkGiI6OJiIiotfjnU4nO3bsIC8vj4iICFpaWti0aVOfleyGhgamTJkS0CL3+XyIojhkKiPdPcVqtZrMzEykUilmsxmlUhn4fDgZqMnM9BmRTJ8RidHoYPNmM1992cgP79rOwkvjmDYtgsjIky/6jUa//fz11w+veqpEIsHhcLBv376A1KLP5zutdqDgoGaQIOcf/UmebwT+C1x2VNM7HPjpWYnqDBFF8Qvgx709tn//fuLj45FKpTgcDqRSKfHx8cHeu/4SGgrh4RAfD1lZPR/7+GPYuvWC0fySaLVI09NBpUIaH4/odOLevRv5hL5FglpaWk6oesXHx5+2ffr1XM9kJlNAz8ptE028yIu8y7u001PJozsxB38ytGjRIpKTk9HpdCQnJ58y6T8VObpsXpnwEgD37niQClsFc6IvYtWsFXw8fRnTIvwdautM61m08Vru3/kgm8ybuSX5JmKVsRSaNlBo2gBAc3MzVqsVhUJBVFQUCoUCq9VKc3PzgGKTSCTk5eXR0dFBXV0dHR0d5OXl9Zn8VFVVkZOTQ3R0NHK5nOjoaHJycqiqqur1+O7eebfbjcPhCOysdfddn226e4qlUikFBQXEx8cTFhaGWq1mzpw5uFyuIYnjZCQmJnL55Zdzyy23cPnll/frd+355w5z660ppKdrsFq9HDncgSBAcXHfi7GDB+EPf4Cvv4ZvvhmM72DgWCwWFAoFWq2W8PBwtFotCoXilG0nwUHNIEHOT07HZGc6sFUURTvwr+77RVFsAPr2Wh6hlJWV0dbWhkwmQxAEnE4nDQ0NfZptBOmDWbNgxgywWHqWlMxmWLvW//E118AVVwxPfEONKOLesAH1Qw/R+fOf4/7qK8SHH8ZdVERIL60NERERGI3GHpVvo9HYZyX2WAQEko7ejmcve7EdvalQ9XjsXd7Fho0xjOFiLg447p1utbw//Gvmx0gECe9VvU+NrZanxvyCudEXsdm8hT8d+TPfmjcF/k0Nn8Jj2Y+glCj98ng+D9XhtcxQ++3cKyoqUKlUgb7tgeDz+SgvL2fcuHE92mz60g23WCxMnToVl8uF2+1GLpeTkZHR59f3eDxUVlYSFxdHSEgIbrfb37ff7X9+lunWaTcYDAFlE5lMhsPhwGw2D1gffaTw55fHIwgCMTFKrr7qWw4e7OC2W7eSO1rHSy/1vshNSwOdzv8SZR9csaB+0+2weuyC22g09umw2s2xg5pA4P+Bzj4ECRJkZHA6bSe3A0sEQSgBvgK+EkWx8eyGdfaoq6tDKpWSnZ2NSqXC4XBw5MgRrFbrcId27iGR+KUFjqWpyT+cabdDfv7wxDUMSEJDMSxZgtdiwb5kCd66OqzPPos0ObnX5Pu6665jyZIlxMfHo1arsdvtGI1GHnzwwTOKI5ZYxjMeO/YeA5oiIiZMOHFip2cmUkMNy1iGDh3/y/8SQ0zgsQYa8OFDhw4tp99v3T1I+YOU27B5bHR2dXLjlpv5fNa/+Gzmx2xvLeKlI6+wzrSeba3b+d6WW5gUNoFHsx6m2dlMR3wnJdtLCE3REqY20OXuoqSkZMC62T6fD5fLhdfrRRRFvF4vLpfrpGokTU1NpKamBnrE+6p6g7+ybrfbcblcgQq43W4fMpWR7oHGrKwsvvrqKyZOnEhUVBT19fVs2LBhSC3u++JM2icEQcDh8PLoI7t54cXxPPR/u1i71sQ11yTQ2OgkKkqBVNpz8ahWw913+yUHjxdxGmoMBgN2ux2r1RpY/Nnt9lMWfboXVcei1+uDvhRBgpzjnDL5FkXxPgBBEHKAy4G/C4KgB9bjT8Y3iWI/BHyHmW5nte43RYlEgk6no6HhnCvij0zGjIE//QkqKuBYW3RRhA8+gClTTmxTOU8QfT5aZs9Gfd99WJ95BndhIZFbtiB6PAiyE//UWlpaaGpqCvQ2y3o5pr+MO3o7Hh8+JjMZM+YTKuYttOA8epPTU/VnLWspo4w44riXewP3u3GzmtXo0ZNFVo+E/VgEQSBU7u+zXjLxVUIkIbxa8hcmh0/iw+lL2W3Zw59LXuGrxtXstOzmtm13kKcfS1x7DK2treyZcoDYvdGEWjS4na4B62bLZDKmTp1KeXl5QOVo6tSpff7dJyUlceDAAVQqFdHR0ZhMJg4cOEBS0om7DQAqlYrs7GyUSiU+ny9gdGMbotar7oFGp9PJrFmzKC0tZe/evSQlJXH55ZcPe893d/tESkoKqamptLe3s2nTptPq++5GpZKyvWgBMpnAjqJW3nuviscf38Py5fX85jdjmDHzROWakWK0k56ejiiKtLW10djYSGhoKImJiX0O8HbTX5WYIEGCnBuc9ru9KIqHgcPAnwVBUAHzgBuAl4Ch1dM6A9RqdUCzt1v+KS4ubshtoM9rJBLIzOx531dfwbff+qef7r0Xxo8fntjOIoJEQtSOHSCV4li6FG9NDZ3PPIP722+J2rUL4Zgq6NKlSxk/fjxjxowJJGkHDx5k6dKlLFq0aNBjkyLlEi7p9bEIIiiggE46T6hud+I33Tn+/g462MGOwGPHJt+72c0RjhBGGAtYgOToaEl6qN9KfF70XKKVUdTZ69nSspV/TH2XA+0H+XPJK6w0/sdvTT8BlMkK4nfHENqqpjndTGe4lfiKgalDhIWFnWAg1NnZ2WcSM2XKFLZt28aGDRtwu92EhISQmprKlClTej1epVIFhuq6B+mkUikqlarX4web7oHG119/naSkJObMmUNqaioRERH4fL4hVV3pjcFqn5DLJXz5ZQMut49Jk8LYudNCSUkHo7K02GweNJqTv6V1dfk354a6Y6N7cZSent4vtZeBqsQECRJkZHPaybcgCI/1cvce4O3BC+fsk5uby5EjRwIOlzabDavVetoSa0EGiE4HMhmEhZ23lW8AQamk8/e/R3nttdhefRXXihWEr1rVI/EGvzrG6NGjaWlpwWg0olAoSExMZG13v/wQknL01hvXcR3ttKOgp6qEEycqVDhwEEbPBLaOOg5zGDVqLuXSHo+9xmsoDUomMIEYRwyJKv/uyJHOEv407nmeyP4Jfy59lX/VLMcZ4aLikhoaO5pJOZiEbmcoVa5qrtt0PcumLUUpPX1NucjISN555x30ej1yuZympiZ27tzJXXfd1evxer2exMREMjMzA8m00+lEr9f3erxGo4Dj2zYAACAASURBVKG5uZnU1NRAG1FVVRWaY91ihwC1Wh3oJS4vLw/ouQ93pXQw2ycuuyyWiy6KoqHBwcwZa6mvd3LjDZuw2bxs3XbJCfKD3RiN8Le/QWcnPP20vy1lqEhMTCQ9PZ3Vq1fT3NxMVFQUCxcuPOXCY6DnBQkSZGTTn33uyUf/fXH08yuBIuBeQRA+FUXx+cEO7myQm5vLli1b8Hq9yOVyurq6aG9vDybfZ5uZM/3lJql0aN/1hoGQ2bORZmTgXLECb1UV9vfeQ5BI0D79NMJREeJum22FQhGQHnO5XMPuRHg8sUdvx5NIIj/jZ7hwnaAxrkFDFFGo6flzduLEhN/EJ4ss4lXxXJ0QjyiKFLZ/w+GYQyilSn6c8yAbnyhEcqWcpvRm7DoHxdNLUI5Volgn46XRL6GUKvl75T/IN+QxMaxvVZluiouLiYyMDDgM+nw+5HJ5wEzneCQSCTExMWRkZPQY0Ozr53PscGP3wKXD4RiUVqLTobutIy4uju3btzNq1KhAr/rGjRu54YYbhiSOvhjM9glBENBoZPzxmWIefiSLp397kE2bWnjs8WwEAXw+sdcEvLoa6uv9H69Z458HHyrq6uooKipCo9EEdkmKioqIi4s7aSJdV1dHRUUF8+fPD1S+KyoqTnlekCBBRjb9eaePACaKovi4KIqP40/Eo4A5wB1nIbazgtlsZtq0aQiCgNlsRhAEpk2b1qfNdJBBJCXlxP3eL7/0t6R4z5mxgVOimDMHvN6Axrfz44+RREcjdnUFjomKisJkMhEWFkZWVhZhYWGYTKZzTpVCgQIpPfuJL+ZiHuRB7uTOHvd78TKe8SST3KNNRRAE/mfMNXTJu9jTto8tpq3IWmRMqM/nf3ddQ7jPgAA4tU7ar7Fyd9G9vFv5HgmqBKIUUZiczawyfnnSOIuLi4mLi8NqtdLY2IjVaiUuLo7i4uJej5dIJBQUFGC1WikvL8dqtVJQUNBn8u1yuTAYDMhkMrxeLzKZDIPBMGQSf91tHT6fj6lTpxISEkJnZycmk4m5c+cO++tbXl4ee/bsobCwkPXr11NYWMiePXvOyPzn+RfG8fOf5/LLX/kLJy/96Qh3313Ekr+U9Xr8tGkwbhxcdRVceeWAv+yAWLduXUDxJD8/n5SUFDo6Oli3bt1Jzwva0gcJcn7Sn7JMMuA+5vMuIEUURYcgCMMvInualJWV4fP5mDJlClqtls7OTmpqagZs3hHkDGhogC++8Cfe9fXwwx8Od0SDhmvNGiQGA9K0NLyVlXgbG/E1NSHIZAhHh/gAHA4HtbW1SCQS0tLShjTGoTbv0KDhWq7t9bE44hjPeHIScriJm3gr4k02jd3G/5RcTaw2Cr1PS3tDJ5bQNuoc9fxs3y+IUkYyIXMc48LzkTtCgMvZ17afsfoxJ9jWm81m3G43ycnJxMfHByrZff3dh4WFIZPJmDRpUuC+7uepN7q6ukhPT0etVuPxeJDJZAEFm6Ggu62js7OTxMTEgEZ6e3s7Op2OmpqaIYnjZPh8Ptxud6CNJyQk5NQnnYSICAUdHV189WUjCxbEsGZNE/9eXs/jj/du+CUIcP/9w2O2073D0t22pNfrycrKoqio6KTnBdVOggQ5P+lP8v1PYKsgCJ8f/fwqYJkgCBrg0KBHdpbo6OggMjKS5uZmamtrUSqVKJXKYa8MXZC43RAdDY2NcEnvw4DnKpq77/Z/oFTS8cgjOD/6CG9NDaGPPkrItGno9XpiY2Opra3FZrOhVCrJyMigsbGRlStXsnz5clpaWoiIiOC6664b9CHMwVCfGExyjt4Cnyfl0LGyA2uCFbPJgq4rlMSdcYxz5ZH1UA7/qHqfZqeZ1QfW8q1iMw9lPEBHVwd/KH6W1yYuZqPkWzwyD+lCOhOYELCq71b9kEqluN3uE4Ywu8nLy2PlypUBB9zu//v6OYSFhWE0GsnKyiIqKor29naMRuOQ9Vp3t3V4vV4OHTpEamoqOp0OhUJBUVHRsO+o7N+/n4kTJ/Z4PiwWyxnrVet0cv65bDo6nZypU9ZQXW3nB9/fRlSUgn+8P42oqJ6zCsPlcimRSE7YNentvuMJqp0ECXJ+clrJt+DXQ/o78B9gFiAA94miuOPoIbeelegGyMns5SUSCYcPHyY8PDxQGWptbQ1UIoMMISkp8KtfQUmJ/+NjqamBpKTh9YQ+Q1xff41z+XJkGRl4ysuRJiQQMm0aoigSGxtLWVkZo0ePxmAw0NbWxqFDh3C73SxdujTgUmg0Glm6dCnAoCbgI928Y9KkSWzfvp3q8mqSKhL89j9ZEpqntfLR2N/y41H/xzNlf+TTyuU4XA6eP/Qn/lb2Dvem34NcIuMf5UsR5SLfy7iB8eJ4QkNDUSgU1LTWYIo3oXFpCFGH9LCbP57KykrKy8txOByoVCoyTqJbl52dTVtbG/X19YGdjJCQELKze6/CDjbdqhidnZ20tLTQ2tqKzWYjMtIvv3c65k1nk25lqQMHDtDZ2YlWqyUjI+OUDo+nQ2qqhi+/bGD27CgaG2vZt6+dK6+MIyJCftLzurpgxQq/HcHZ1gHPzc2lsrKyxwxBZWXlKWeNgmonQYKcn5xWz7coiiLwb1EUd4qi+Iooii8fk3iPOE5mL9/e3o7b7cZoNFJSUoLRaMTtdgelBocLmQxGj+55X1UVPPMMvPHG8FvTnQEhs2djeOMNNI8/DoDjk0/o+PWvsS1ejF6vR6fTYbPZaGhowGazodPpKCkpoaCggOTkZGQyGcnJyRQUFLB8+fJBjc1isZyg3KHX6wclGRoMfD4foiiSmZlJblYOozJGEWbSM6/1IkRR5Of7fsmTGU+wd+EOHst6BJ1MR6vbwh8PP8+kNdNQiiryE8YiWuHGLbeQlpZGaGgolZ5Kdhh2sDVrK2Ks2KPVp4WWgAHRZ599htFoJD09nYkTJ5Keno7RaOSzzz7rNd7s7Gyio6MDO2lKpZLo6OghS767pQYtFgsWi4WOjg5CQkJwuVwIgjDsr28ej4dt27YRERFBXl4eERERbNu2bdAcQGfNiuSpX4/hlVf8w7erVjXw66cO8swzvW/K+nzw7LOwejW8+y44nYMSRp/MnTsXlUpFQ0MDpaWlNDQ0oFKpmDt37knPO/bnunv3biwWy7DtTgUJEmTw6E/byVZBEApEUTx5k9oIx2Qy4XQ6yc/PJzw8nNbWVvbt24fD4Rju0IJ08/nnflOeffv8orxD3As9WAgKBb6ODrr27UM2ahSe0lK8DQ2EPvEEshUrGDt2LNu2baOtrQ2DwcDUqVMpLCzsYUENEB8fz7fffjuosYWFhVFZWUlbWxtWq5XQ0FAMBsOI2c42Go3ExsYSHR0daPmQy+VYa/1tInen30W0Ipr/Nq5hvGEcOxdu5e2Kd3mz/C0sXW2sKVnLlopt3Jl2O78a/XNqW2v4zcHfMTY1l+zsbKydVuo/rOeSm79rd/qcz6mhhmyy2bBhA7GxsWg0GiQSCTKZDJ1Ox4YNG3j44YdPiDcsLIwdO3ZQU1MT0AVPTk5m4sSJQ/acJSYmolarCQsLIzMzE4VCgcvloqysjPpumY9hQiKRoFAokEqlCIKAVCpFoVAMmrqPVitHq5WzpLiDK6+MY9WqBv785yMsXtz78y+R+AWYPvoI9HpwOEB5+sqV/SYxMZFFixYNaMYiMTExmGwHCXKe0Z/kex5wnyAIVYANf+uJKIriOeUh7nQ6ycnJwWaz0dzcjFqtJicnh8OHDw93aEG6uftueO89vx74OZp4dyPR65FGRqJ59FHaH3gA15df4r7pJsK2bmVvfDyzZ88ODP4eOXKEkJAQSkpKCAkJwel0olQqcbvdg942EBkZySeffEJ+fj5paWk0NTVRWFg47JJ03VgsFmJjY2loaKCrqwu5XI5Wq6WxsdGvUBQxFYAEVTxe0YtaqiZUFkrRgi28V7WU18vewOxuYXHpEt6ueIeLxNlMiZ4Mm0TMRS1IYmVcPOviQA+4iEgjjYDfNMhkMjFq1Cj0ej12nZ39KfsRa0TqP+k9id25cyclJSWkpaUFdL5LSkoIDw8fUmt3u91OZ2cnVVVVgcHP0NDQYXe47FaPqampCTg8dn8+mNz0vWRS0zRcftkG9u5t449/LMbU7OJ730smLa2n5vq8eX7V0ylT/Mn42aahoYFdu3YF9LojIyODSXWQIBco/Um+Lz9rUQwhkZGRCIJAZmYmKpUKh8NBTU1NoDcyyAhAo/HLEhyPyQRaLQyRa+BgIISEoHngAdwHDiDLycFz+DCOd95BrtH0WgnMyspi8+bNTJ06lYSEBOrr69m2bRs333zzoMZlNpuZNWtWwDZdq9Uya9asETN4bLVaaWlpQa/XExISglQqpaWlBavV2uO4PMNYAFrdrTi8DnRyHVPCC7huzjWsavgPfyl9nSZXE1+xGplBSv7EPJJsCUgEKTa7ja/r13KpeCkIcCM30kADCSQglUqpq6ujsrKSpqQmmrXNSPVStPKeTp/FFCNDxqbdm0hNSiU6Ohq5XE5oaCgej4etW7cO2XMG0NTUhMfjISkpCaVSidPppLa2dsj0xvuiv+oxA2VyQTitrW5ycrRUVVmprrbz2ad13Hxz8gnHCoJffnAoKCoqCix2p0yZQlNTE5988glArzrzQYIEOb/pz3q/BpgN3C6KYjUgwjFivecI3fbQJpOJqqoqTCZTj/uDjBAEoeewpdMJixfDH/7gH8Y8hxC9Xjoeegj1UQUU1zff0JmTw7S0tBN0pMPDw7nyyiupra1l9erV1NbWcuWVVxITM7h/ahaLhVGjRjF79myuuOIKZs+ezahRo0ZMz7fX66W8vByAuLg4AMrLy/H2oQcfHhLOw1kPAVDY/A2t7lZuTbmFz2d9yrP5f0DjUuOReNkVtodVCV/RENlIib2UjWxGRKTZaSZdTGc2s0knHa1WS1VVFTqdjvS4dCKtkXTWdxLm65ksrmUtH/ABDdMbiImJwWazYTKZsNlsxMTEnLBYONuYzWbUajVxcXEkJCQQFxeHWq0e9kVVXl4e1dXVWCwWfD4fFouF6urqM9L57guDQc7//G8i7/7d/5q+d28bb71VzvvvV53y3MpKf8fbYLN69WqkUikrV67k1VdfZeXKlUilUlavXj34XyxIkCAjnv6UQ14DfMDFwO+ATuAz4Jxats+dOxer1dpDQkyr1Z5y8CXIMLNhg7/yDXDwICSfWMkaqQhSKZHr1yP6fNjffhtPcTGjVq5EYzIR/umngUVGd2KyYMECLr30O1t2n8/H7t27BzWmkS5hJpVKGTduHCaTicrKSjQaDePGjaO2tvaU5/4s5ycAbDJvZnn957w47jk2v7qRlmQLh1KOYJFa2KrYjjBWIKUhiWpbDS+VvMz/JF7H3KiLAL9jZWJiIh6PB98+H+mSdGIdsbhl31kduHDRQgsAEqOEaks1BoPBb+ClNVOWXUabp40OOtChOwvP0okoFAoSExN7tOskJibS0tIyJF+/L4bSJl0iEbjuukQ2bmzmqqvj+WKFkeefO8INNyZx660pvbpfut3w2WdQWAi33QazZw9uTEVFRVitVtRqNVKpFLvdTlFR0UnVdoIECXL+0p/ke6ooihMFQdgNIIqiRRCEM3NJGAYSExOZNGnSkLwJBBlEFizwl6TKyuCyy4Y7mgHRdvfdKC67DE9xMWGHDrHzpz8luq2th4RYbm7ukCTFI13CLDIyEplM1kONxG6396s9bGbkDGZGzsAretkwdTMP2u5lctlE9ikOsCd+H5aQNqoSapix9iKuS7iGBGU8a5q+5j8NX+FwOFCr1VgslkDvtEajod3xnWqIAgVP8ARGjHxU/hHN3mbCwsIIDQ2lIboBi9SCO8+N7LiX2VpqCSccDZrjQz5j4uLiCA0NJS4uLjD42dnZGdg9GC6GwyY9MlLBA/dn4rB7+frrJtZ+3cQ335hIStKQmdkz6fV6Ye9e/8crVvjbUeQnVyrsF1VVVURERGAwGJDL5XR1dWGz2aiqqhq8LxIkSJBzhv4k312CIEjxt5sgCEIU/kr4OcVwvAkEGQQEAS69FBYu7NmOIornjBa49pe/RJKcjHvNGjhwgEm7dtHx8cfsu+cedAkJgcR3KJLioaxEDoRRo0Zx4MABIiIikMlkeDweWlpaGDt2bL+vJRWk/NLzM0orS9kRt4sQdwjX7b6KqsgaDqYW0yQ18Vn9cv5V/2+uir+Su9Lu4NGOR6i8ooZLauZhUBiw2+2Ul5fT1tbW49oqVGSQgdqhJjw2HKfTicPhQNAIRIRE0GZrQ406cLyIyD/5Jw4czGAGC1l4xs/VsVx33XUBnfiEhASMRiP79+8f0qHP3hgOXfmcHB05OTr27LVw6FA7RqOTe+7ewf/7/dgTkm+Vyl/x/u9/4fbbBzfxBgJa8R6PJyCv2D1zFCRIkAuP/iTfrwLLgWhBEP4AXA/86qxEdRYZ6eYiQU7B8Yn3hx/63zmvuWbEJ+GyjAxcGzagWLSIrgMHYO1aLLffjvcYjbNuXd/9+/dTUVFBWFjYWdH1HemLUL1eT3R0NGFhYUgkEnw+H1Kp9ARt8tMlwhDBR7s+IiIqDKlSSrmvgj2x+3lR9iySyTJeOvIyBzsOscK4khXGlaiuVRJTHYW7w83+xAPIumT4fD58vt7rDTKZjNFH9eo9Hg8pYgr5Dfl8s+EbeOq740yYcOBPuCLoqWDTSCOttJJJJiEMbFOx24hp+fLlfPvtt0RERHDbbbcNukNqfxlOm/QwQwiLF0/kppu2UFNjZ8M3zeTlGRg7tufv0tixMGbM2XkZ8Xq9qFQqYmNjAxKQXV1dfc4wBAkS5PzmtJNvURQ/EARhJzAfv8zgtaIoFp+1yM4Sw/kmEGSQ+e9//U2aAAoFXD7yBXlEqxXZmDF4s7Jw79tH5JEjRKWk0BoT08Pe/WwnwCN9ESqTyZgwYQJFRUUBHfSCggKcA3RD2bBhA2FhYXicHuwdduQKOblHRrGpbhMx2XH8ZeIr1DnqeOnIK+xu24Mj20lVdi1ttR0Y6nXom3XI5TJsmb1XKnU6HUajkfDwcARBwOPx0Nrail7XM8GLIII7uINKKsmgp2NmEUXsZCdKlPyEn5zQrnK6LFq0aNiT7eMZzhmDu36Yjs3m4XvfS2bp0mrefruSzZvNrF03j8jIobGfVygUaDQatFotISEhhISEoDmqeBQkSJALj369uouieBg4pwWxR/qgWZB+MGkSfPutvwI+ffpwR3NaKK+4AtHno3rtWhIPHkS+eTMevZ7wlBTElJQhS35H+iLU5/NRXV3N9OnTA3bc5eXlREVFDeh6Bw8eJCMjo4e9d3l5OQcPHuRSw2UkqOKRS+Tcm34PBoWB7394O10pHtqS2mlLaie2NQbDIR12jz/5LreWk65JRziarRkMBmpqahBFMdBOUFtbS2xsbI84ZMhIPXo7FhGRSioBSCKpR+LtwcMe9pBNNlp6Sh32Rl1d3YDMXM4mwz1j0NXlIy5Oyc03J7NsWQ2VlTZqaux4vSIxMb276+zfDzt2wB13nHlSnpaWhsfjwWw2I5VK8Xq9eDyeHjMNQYIEuXA4ZfItCMIuURRPatN2OseMFIb7TSDIIBIVBT/9KXR1gcEw3NGcNq6VKwnZsgVx9GgoLgabDV9aGnqXi4ohkvob6YtQn8+Hy+XC6/UiiiJerxeXy9Vn28ep8Hq9REdHo9X6k1etVkt0dDQlJSXMj7kYgAZnIzKJlIuj56LerCT6QBTtM6yYwpppDG+icVYTIVUyCk0bWFL6Oi+Of44YZTQKiYKGhgY8Hk/A2t3r9dLV1UVDQ8NpxScgcD/3U0EFSnomg5VUspKVrGIVt3IrmWT2eZ26ujree+896uvrAyZNu3bt4vbbbx/WBHyo2qn6wmAI4Q/P5HPoYDvbt7dSXm7l2mu+Ze68GP7xj6knHL95s9/nC/w+X2cqhnXppZdSWFh4Qo/3sapGQYIEuXA4ncp3riAI+07yuAAMrBFzGBjuN4Egg0xvSff+/RAZCcOs8NAXikWLaJbJkFZWEvX008g2bICnn8aanEzYxRcPSQwjfRHabee+bNky2tvb0ev1zJ07d8BmMUlJSVgsFrRabaDybbFYSEpKChyTq8shV5dDl68LT7aX+NJ4tF93YNBqMeWZaUvowJ3q4cYtNzMpbCKl1lJWGf+D0+fEYrEQHh6O0WjE5/MhkUjQ6XS0traedoxy5GSTfcL9Ffh3IyRISCKpx2NllBFFFPqjL8GffvopW7ZsQS6XB+RUa2pq0Gg0PPLIIwN56gaNkWCTXlpm5a4fpvHcs8U0Nrpob3djs3lQq6WBXQyAiRNh5Uqw2fwumGdKt278sWonbW1tgcXgyRiJOxlBggQ5MwTxFI4CgiCknMZ1vKIo1g1OSIODIAhaoKOjo+O0XuCCnCdUVsKf/uSXK/jxj0esPX3d9u2YH30ULaAoLcU+cSK777iDmbNmDdkb60h+U3/55ZfZv38/EyZMIDIyErPZzO7du8nLyxtQErl06VIOHz6Mz+fD6/UilUqRSCTk5OT0qgQyfvx4kpOT8czxoZVqia+JZadjF5VpNbiyv9P6HqfP54HM+3lg4f2E3KtkUellREZE0tLSwq5du6iurqa+vndL+tNFRKSeepppZgITAvd78PACL+DCxWxmM5/5zJ8/H41GQ1ZWVmCRUVJSgs1mY+3atWcUx/nE888V86tfHQAgN1fLa69NZuasnjKW1dV+Q93w8DP/eldffTVtbW1oNN/JS9psNgwGAytWrOjzvLq6OjZt2kRKSsoJi+SR8rcaJEgQP52dneh0OgCdKIqdJzv2lGWko26W5xUjOekIcoaUl/vbUDweqK0dscl3wuTJ8NhjVJSVkfTssyiKiph56aUYPv0UhqhCORIqkX3R2NhIYmIier0eiUSCXq8nMTGRxsbGAV2vN3MtURT7NNfSarUIgkDYLn9FuSq0ivpJDaS8n8DiexbzaulrrGr4D3vb93HvzvuRPCSQ0pyETqdjZ9RukmQJjBo1iqampoE+BQEEBBKP3o6lkkpcuACIOWo23NjYyIIFC1DHq5EjJ0IZQXp6OmvWrDnjOM4XRFGkvNzGXT9M4523Kzl8uBOny4vPJ/Yw4Ek5nbLTaVJZWUlOTg5RUVGBnu/m5mYOHz75CNVIH4wOEiTIwOiPvfw5gyAIV+GXRjyB7kpCWFgYEyZMICwsjE2bNlFXN6IK90EGyiWXwA9+4K96X3TRcEfTJ4JEQsK11zI5NRXd7NkoFArUGzciLzinDGPPGk6nk/z8fBwOB42NjTgcDvLz8wesdtJtrmU2mzl8+DBms5lJkyb1mcBkZWVhMBjQ6XQYDAZiJbHM3DSN7Kxs/lD8HI9kPcQ389ZyXcI1CAj44kUOjyvlo9H/wuxqwdpoxWVwo5jY+zDfYJBJJj/iR8xkJllkAf7Esquri72Re/lyzJdsTtyMu8vNqXY4LyQEQeDNv05m8eIJTJ4chijCLTdvYeaMtfh8fT9PnZ3fmez2F6/Xi0ajISYmhoSEBGJiYtBoNKeUGrRYLCfIa+r1eixDNBsSJEiQs8N5mXyLovgF8OPeHju2kiCRSAgLCyPlqMpEkPOEmTPhqObySKa+vp6y99+nKCcHl8uFY+NGBKkU9+bNwx3asBMVFUVZWRllZWUcOXIk8PFA1U6O1TW/9957mT9/PhUVFX0uuufNm0doaCjNzc3U1NTQ3NyMNlTLvHnzeLfgLfL0YyntLCVNk8qmiwsJ3a8BH1i1Vioyq9g0azvlURUQ5q+k7mzdNegJsIBAPPEsYEFAFzwkJISy6jKaw5uRyqX4PD4qKyoJCfE/3kUXduyDGse5Sn29E4MhhLg4BRZLF11dPrq6eh/o3b0bfvtbeP11/8Zaf9Hr9SiVSiQSCYIgIJFIUCqVp9St7x6MPpaRNBgdJEiQgXFeJt8nI1hJuABxufxDmCOIuro6VqxcybpLLuFIeDhN8fE4XS6af/tbbK+9dsFXKhMSEli3bh1KpZL8/HyUSiXr1q0jISFhQNfr76I7Ozsbm82GXq8nKioKvV6PzWYjOzsbrVyLRJCwIPYSbkm5mVRNKoIUUt9PJK0uBcEnYFVbqc6ro21mJ2+Vv8Nzh1/E6XPS3tV+Vn+2MTExmIwmHG87MP3HhPE/RpqamoiJ8belHOEIL/AC7/Iu7bSf4mrnN6mpGv7z5UW8884UpFLYt6+dyy/bwLvvVJ5wbF0dWK1gNMLOnf3/WmPGjKG1tZVdu3axadMmdu3aRWtrK2PGjDnpeXl5eVRXV2OxWPD5fFgsFqqrq8nLy+t/EEGCBBkxDCj5FgThScFPiCAIzw52UGeTYCXhAsNshueeg9deg1P0Vw4l69ato6Ojg5SUFC5bvhz3rFmIPh/ubdvQPPooDFBS73yhvr6eefPm0dnZSVFREZ2dncybN2/Aw4v9XXQXFxeTlJTEmDFjyM/PZ8yYMSQlJVFc/J2vmEqqIlntVx+JLA9nfOQ4OCgS/0U0kQfCkXgleAwefnngKUo6S/igehm/O/AH/l2/AlEUz1oSLpfJiXXGkl2aTaw1todCzBGOICLSSCOh9LRYN2HCx4X3e7d/fwdXXeVf1G3caMbpOrEV5Mor/Q6Yd98N06b1/2skJibS3t5OTEwMWVlZxMTE0N7efsq+7W51LovFwu7du7FYLMFhyyBBzgMGptsF24EPAR/w/OCFc/YZ6RJrQQYZm83fqOnz+cV7c3KGOyLAn9ylpaVRWlrKgfnzURgMXDxqFLqSEjqffRbvoUNEbt6MZIB26uc6zc3NXHnllUil0sB9Xq+XVatWDeh6/dU1Ly4uJudoO5DT6UStVhMREdEj+e5GJpER3xKHKlnFmOTRKN1KwroM7CzbQ4O3AcvYdozOBn6x/ymiFdGkh6axoXkjS6s/4K2CNwb0/fRFUWKlUQAAIABJREFUt3xidHR0YLBPrVZTW1sLQAEFgaRbynfPrQsXb/ImSpQsZCHjGDeocY1kfvzwKB5+ZBQ33riZz/9t5GdP7GX9ehN//etkDAZ/u45EAv/3fwM323E4HOTm5mIwGPD5fISGhqJUKk/Q/e6NkTwYHSRIkIEx0LaTMYAJ8ACxpzh2RBGsJFxgpKT4BzDnz/db1Y0QbDYbVVVVREREkDVuHHklJVQYDP6BuU2b0L300gWbeIO/5/t4pZCmpqYB93z3d/vebrdTXl5OQ0MDZrOZhoYGysvLsdt775fOz89n3759dO1x46ns4nD9EepaarnWeRXvTnmLe9PvQS1VY3KZePrQ77l35wMkq5PocHfwyO7HaXIOcJLvOGw2G1qtFoPBQFRUFAaDAa1Wi81mAyCZZC49ejuWMsrw4sWGDQ2aHo810xxQVTkf6VY4MdY7ycgMxen0sXVrCx5Pz52J3hLv0928cLlcjB49GrvdTktLC3a7ndGjR+Nynb/Pa5AgQfpmoJXvWlEU/z975x0fRZ3+8ffsbE3vISGNBEJJoQZEelWUA1HUO/DnKZ4NPeshnuXUu+PUO/Xs593ZBVHRAwFFKdIRCNISILT0nmzabnazdX5/DNkkkFCTEGDevPJi+M7sd76zIZNnn3mez+ctQRAeA3oBq9pxTR2Okkm4whg6VP7qQnh5eaHRaNBqtQiCgC0mhkKdjrDiYvxra7F88AHO/fsxzJ6NGBl5sZfb6UyePJkPP/wQf39/jylJbW0tc+bMOa/5oqKiiI+PZ/Xq1VRUVBAaGsrkyZPbvA84HA5ycnKIiopCo9HQ0NBAYWFhm54BoijicDjIzs72SBkGCH6Ik0WWFS3nsd6PcFf8nXyc8ymf5S2iyl7FW8fe5bO8RUwIH49GULOpYjMaQcPwkPOoaziBzWZDr9fjcrmoq6tDFEX0ev0Zg7we9GA60znKUeKIa7FvNaspppjRjGYoQxG4QK/1LoggCGzYOI7jx80Mv2otpSUNzJ69ndmzYrj9t6fKlVZXw5dfwoABZ1eGotPp2Lt3LyqVCpfLhcViYe/evYoHhYLCFcp5Zb4lSVp2YvMRSZJalfRTUOjSZGWdfdqqA4iOjiYgIICqqipKSkrI8/dHGxlJzTDZ6tq+ZQuSzXZR13gxiYiIoMcJjfbGR/M9evQg4jxdS89V7cRisWC1WjGZTFgsFkwmE1artc3M9969e0lISGDo0KFcffXVDB06lISEBPbu3ctbg16nh3cce6r3IiGxe/IOHkt8mABNADWOWr4pXEra2qv5PO8L6hx1ON1OfirfcF414YIgkJubi9lsRqVSYTabyc3NbeHe2BpeeDGQgdzCLaib5WRs2Mgmm3rqKaDgsgy8G9FoVLjdEB0jW1qu/6mctetO1WmXJHjtNVkB5euv5cq2M+F0OsnKykIURcLDwxFFkaysLJxOZ3tfhoKCwiXA+Wa+G7l878QKlyduN3z1FaxfDzfdBJMnX5RlxMfHI0kSNTU1mM1mfHx8iDaZ8HK50I0di23DBhx794JWi6uiAvE8yy0uVTIyMhg9enSLmuzq6urzNhc5V7MSs9mMwWAgNzcXm82GTqcjMDAQs9nc6vwVFRUkJCTQvXt31Go1TqcTtVpNdna255gZUdO5ofs0LC4r/yv6lq3jN/B5/he8d/w/GO1V/K9oGT+UrmZm1I1Y3VbGh42l2FpMhD7ijMFzI+Hh4RiNRqxWqycDb7FYPGon54oWLTdyIxvYwHjGt9jXQAM6dJdVQN6vnx/btk3kheczeeONo3z1ZQGJvXy5f25PgoN1gFx+Mm0avP++rGh6Np+R8vLyiIiI4PDhw57/TxEREeTlXXYedgoKCmfBhQbfV2ZaTuHSpb4e9u6Vt1etkjXBvb1P/5oOoLHxNz4+vqnxVxQZcccdeJWWYtuwAfu2bdTNn4/+mmsw3Hprp6/xYlJdXU1cXFyLMX9//xbB7LnOp9fryczMxGQy4evrS0JCQptqJzU1NUiSxMCBA/Hz86Ouro6DBw+eopTUiFqtxuVy4Xa7kSTJY2PfXGkE5My0t9qL9WPX4K32otxWwYspCyhpKOGdY+9Rbivn07yFGEQ9gZoADtVl8XzSn+jr1wdREM8YhCcnJ7N//36CgoLQ6XTYbDYcDgfJycnn9b4JCCSRRD/6tQiyJSS+5EucOJnIRGJpRzvIi4yPjxpfXw3x8d5kZ9fz2muHGT06jNFjmj4ADxkCYWFn74JZWFhIbGwsSUlJ6PV6TxmTYu6moHBlcsbgWxAEE60H2QJgaPcVKSh0JL6+smzBsmUwa9ZFCbyhqfE3IyOD7OxsAgMDGTFiBN1sNqofegjtuHHY169Hqq5Gf8stF2WNF5NzVSc5E06nkx07dtC7d2/i4uIwmUzs2LGDyDbq6Z1OJ97e3tTW1noCbm9vb4xGY6vH9+jRg7y8PIqLi1GpVLjdbhwOh6d05mS81XJpwx96P4ooiJQ2lHHUdIwk/368dfQdihtK+E/2B2hVWj7LW0SMVzRV9mr+lPT0aa9Tq9WSmJhIRUUF9fX1aDQaEhMTPSY758vJ2e1ssslB1sM+xKHLKvgGePiRRH57RxwjR6yjrMzG00/v5/kXkpkwQX6CIAjnZj9vtVpRq9X4+fmh0+nQarWo1eqzUjtRUFC4/Dhj8C1JktIRonB5ERUlB+BdEDE+nqClS3GXlGBcvx77jh2YX34Zd0UF/q++erGX12m0tySoSqVCp9MhinL2WBRFdDodKlXrbS9arZaGhoYWmXGbzdZmEOvv78+BAweIi4tDp9NhsVgoLCwkJibmtOsK1MofJlSCyOzY3zA4aBBGmxG9qOfTvIXkWwr4OPdT1IKaGd2nk1lzgAWHXuKTYXJgfjJFRUV069aNgQMH4u3tTX19PUeOHDlvffS2iCSSkYxkD3sYxagW+1y4WsgYXor4+2vw8VETGqqjosLGjh1VPPLwHvbtv8ajjtIctxs2boRRo0Ddym9Vg8GA2+2mtLTUIwHpdrsxGJT8lYLClcgV53CpoNAqndzYWFhYyNatWwkMDGTgwIEEBgaydetWioqKUAUGYv3mG7QTJgBg27gR73nzOnV9F5tGdZJ169bx73//m3Xr1hEfH3/eKkUqlYq0tDTMZjPHjx/HbDaTlpbWZvCtUqlwOp0YDAa8vLwwGAw4nc42j8/NzSUoKIiSkhKysrIoKSkhKCiI3Nzcs1qfj9qbwUGDAIj2iubuhLv47+D3eLDnXHp4x+GUnCwp/IZJm6YAEvn1+SzM+5yM2swW8zSWvRQVFXH48GGKioo8ZTDtiQEDE5nIozzaQprQjZv3eZ9lLKOCinY9Z2cjigLf/G8kL76UCsDhwyY++iiHioqWyjFWK7z1FnzxBXz+eeu3kuDgYERRxG63YzabsdvtiKJIcHBwZ1yKgoJCF+NCa74vSQoLC8nIyKC6uprAwEBSUlIU6cErmd274fvv4bHHwMurU055ugbA7tdei9itG/qpU6latw7nvn04tm/HXleH1+23d8r6LjbN1UkaM9/Z2dlERESc189qYGAgarWawYMHe8Yaf/5bw2KxIIoiWq3Wk0G22Wxtqp3k5+cTGhpKjx49PNKIZWVl5Ofnn/NafxMr1/db3VYGBQ7kj32e4MWsl/mxdA1HzcdYV76en37awNCgNCINkZQ1lHOg7iDjw8ai1+spKyvDbrd7Gj8b6907AvVJv0IyyaTkxJ8IIgjl0m4U7tHDm/HjwoiONlBQYOWh3++mrMzGU0/19Ryj0UCjkmNODjQ0wMkJ7bi4OKqqqggPD8dgMGC1WsnJyTmlr0FBQeHK4IrLfLeVcVQaX65QsrLg3/+GggI5bdVJnM7ufNeuXbxeU8Pf/vUvDoeH43A4qH/3XZxXkDJCRkYGBoOBzMxMfvjhBzIzMzEYDGRkZJzXfOdqstPQ0EBYWBh5eXns3r2bvLw8wsLCaGhoaPV4i8VCSEgI8fHx9OzZk/j4eEJCQtoM1s+Gq0OGMzXyOixuC8XWEjaMW8tLqQvo69sHCYkdVTv59c+zmfvLg6wvWw9Alb4KtUaNt7c3er0eb29vj055ZxBGGH3p65EubE4FFZekfX1Ssj+LvxhOYm9fHA6JxYvzyMlpUr1Rq+G++2D0aHjyyVMDb4A+ffoQExNDdXU1ubm5VFdXExMTQ58u4riroKDQuVxxme9zlRxTuMzp3RtSUuD4cdkxo5Noq6HQaDSyd+9eUlNTGbx6NfmjRmH98EPYswffJ5/EVV6OGBbWaeu8WBw7dgy3201MTIynQTInJ6fNso8z0VaDa1s/8263m8rKSlJTUwkICKCmpob9+/e3Wb7h6+uLVqulurra03Cp1WrbxUTFX+PPe0PeASCnPpcn+vwBgNeOvE5GbSabK7eyuXIrx+tzODziGCN2DschONAKWiS3hMFgwGQyXfA6zoZudONWbsWGDS1NNekOHHzER+jRM4lJ9KXvaWbpWqjVKoYODeZXUyN5K+coh7NMDE1by8FD1xIaKj9R8POD2bPbnqNnz57ExsZSXl7uUdsJCwtDo9F00lUoKCh0Ja644Lu9JcwULnEEAX77W3A64TyVNM6HthoKCwoKSE1NJTIykpLnnkMD1O3fj2r3bkwvvYS7sJCQnTtR+fh02lovBnV1dcTGxnqeDvj7+xMcHHxBusjn4mwbFRWFVqvFZDJRX1/vaY5r6/VhYWG4XC5PXXij1GBYO39Q+kvy8wDsrEonXBfGU1d9xquHX2dX9S+sLVsHobBvWCYGu47gmmB6FSeg1+s7rOykLXToWvx7H/uwnPjjwtWpa2kvbvu/WHr08ObBB3dTW+vg80X53HtfAnp9682lRiMEBcm3mJSUFJYvX47dbkeSJE/d97Rp0zr5KhQUFLoCV1zw3d4SZgqXARfB4rmtTOzPP/9MdXU1mzZtwmQyce3x40SEh+PnduPMzMT/ww8v+8AbICAgAIvFgtls9tRcWywWAgICOuX8w4YNIzMzk4KCAtxuNyqVCi8vLwYOHNjq8SEhIVRWVuLv7+9Zr9FoJCQkpEPWNzQojY+Gvo9WpeXvh17lX4Pe5rO8RWwz/kx5iNzoWK+3onKryAvKZ9yB0R2yjrMllVTcuDnEIfrRr8W+TDKJJJIggi7S6s6OpCR/EhN9effdYxw8WMf8+ftYu7aUFStbvrcOh2wh8OOPcMcdkJYmj5vNZqqqqjwGTEFBZ3e96enprF69moqKCkJDQ5k8eTJpjZMqKChcklxxwXd7S5gpXIZYLFBZCWeQibtQWsvE2mw2Nm/ezKBBgwgKCqIuNpY1R45wa0wMaTU1WP75TxqWLMF3/nzUiYkdur6LSXMH0NLSUnx8fIiKijprp8cLxcfHx6P13YjD4cCnjQ8+KpUKtVpNeXm5Z0ytVp93mczZoBPl7PKnV31IuD6cBncDlkP15LsLqOpWTWWQkcogI75VPlgTGvhz5gLuiL+dGK/oDltTW2jRMvTEn+bUU88yluHCxVjGMoYxnb62c0GjUfHsn/rx+j+PsGNHFXv31lBSYiUioqnQ2+GATZvkh2lffw0DB8KGDRuorKz0NO7qdDrcbjcbNmzgtttua/N86enpLFmyhNTUVIYOHUpZWRlLliwBUAJwBYVLmCuu4bIx41hdXc2ePXuorq4+be2nwhXGpk3w9NNyE6bT2emnF0WRkJAQDAaDXL4QEUG8VktpeDgIAo6MDDSpqYjRnR9AdSYpKSmYTCbi4+MZNWoU8fHxmEymNhsk25v09HSMRiOiKKLX6xFFEaPRSHp6eqvHNzQ00Lt3b3x8fBAEAR8fH3r37t0pjY7hetn4ZXK3SQyoTuE+9930rk+kh1l2gTEFmVkRv4qVJd+xr3o/e6v3sa3y5w5f19lwjGM4cSIhEc2l8X/6ppui+euCFLy9RcrKbAwZvJqtW5pkFb28YOZM+bP7/ffLDZnr16+noKAAjUZDUFAQGo2GgoIC1q9ff9pzrV692lOGJooikZGRpKamsnr16o6+TAUFhQ7kigu+QQ7Ap0yZwqxZs5gyZYoSeCs04XY3Zb737On006vVaoYNG0ZtbS3Hjh2jtraWgX36IOl06E/UhzasWIF93z6cx493+vo6i4v9ITkrKwtvb28EQcDlcsm28N7eZGVltXq8Xq/n2LFjnrpwg8HAsWPHOrXWOlQXQqx3LKa6OuLKY7jm8ESuKk4jxiwHtbmWPO7adQ/37LqftWU/4XK72G7cgdTJGvfN6U9/7ud+JjKReOJb7CugoMuqo/Tt68cjj/ZGpYKKCjvLl7c0MRo2DP74R2hsLzp+/DgGgwEfHx/0ej0+Pj4YDAaOn+FnuKKigvDw8BZj4eHhVFRc2hrqCgpXOldc2YmCwmkZORIOHJCt6jopy9qc0NBQRFFk5MiRnrHi4mIqNBq8Jk6kYflynAcPYl20CMMNN6BOSOj0NXYW59Ig2d5YrVYiIiIQBAGbzeZpWmyrMdtgMFBbW4vD4fC4FzaOdya+vr6UlpYyUNMfVZCKIHUQsTkxXN/jWvbFZvKzcTu5ljzePvYuq0p/wEfty3ejlmGymwnSBnZaWU9zwk/8aU4llXzER4QQwjSmEUXXSpCEhel57rkkjh8z88UX+fzzn0f5+ecqNmwch0olIAhyo2UjNpsNq9VKbm6up4fA5XJhs9naPgny/aCsrIzIyEjPWFlZGaGhl7Z+uoLClc4VmflWUGgTtRoeeABSU1v+9uwACgsLWbVqFZ9//jmrVq2isLCQyZMns3//foqLi3G5XBQXF7N//36uS0ykbt489DNmAOA8eBDN2LG4L0BHWqFttFotxcXF+Pn50bNnT/z8/CguLm7TXl4URbp160Z9fb2ntrdbt26IYufarKtUKk+ZjCAIDLT0p5sQjkEwMCZ0FJvHr2dS+ARUqDhuzmZfzT5G/zSeWdv/jw3lG5Ek6aJmwhvZxjbcuKmg4hQjn67E9OmRjBkjB8L799dw9Ki51ePM5giOHu2FVqvFz88PrVZLTU0NzjOUtrV1P5g8eXK7X4uCgkLn0WXvaoIg3ABcD4QB70iStFoQhH7A84ARWCdJ0tcXcYkKCudNo9lTbGwscXFx1NbWsnXrVkaMGMHNN9/M6tWr2bVrF6Ghodx8880MTEtDuvFGXHl5NCxbhvPQIUzz5oHLhf8bb1zsy7nsiI6ORqVSUVhYSEFBAYIgoFariW6j1r6+vh6Hw0F0dLTHwbCwsLDTdZxra2txu90UFhZit9s9wV5AtQ8P9H4At+TG7nawcuQyPsz9mGVFy8muzwFg3v4/Mi50DBISrwx4uVPXfTJTmEIQQZgx041uLfbZsJ0iZXixuGlmNGPHhdE/9UfKy21ce81G/v73VG6+palZe/t2KCn5Dd7evpjNe+jevRxRFNFoNGcMvhubKk++HyjNlgoKlzadGnwLgvAhMBUolyQpudn4tcAbgAi8L0nSS5IkLQOWCYIQCLwCrAamAG9JkrRZEITlgBJ8K3QslZWQng5TprTrtKcze5oyZUrrv1wdDur+8Af006bRsGwZ9l27CF6zpl3X1ZUoLCwkIyPDYwOfkpLSaWUoQUFBlJeXEx4ejo+PD2azmdra2jZ1uy0WC35+fjidTurq6hAEAYPBQF1dXaest5GCggJ0Oh2pqanodDpsNhvHjh2joKAAAJWgYsnViwHYUZ1ON104JpeZxflfkm/J55O8zwjXh5N4vBc/G7fz1sDX8dF0vrSlBg0jGXnKeCGFfMZnjGIUV3FVl8iK+/lpuHZKBF99mU9RkZVFi/KYeXO0p4SnZ0+ABgTBC6PRgkpV4nnCcKbgG+Qyk+zsbIxGIyaTibKyso69IAUFhQ6ns+9cHwNvA582DgiCIALvAJOAQiBdEITlkiQdPHHIMyf2A3wGPCcIwjQguLMWrXCFsm8f/Oc/supJ9+5yKUo7cT5mT4Jej/dDD6GKiqJhxQpcx45h+egj3IWF+P31r+22tq7A6Z4MdEYAXlZWRnx8PCqVCpPJhE6no0ePHm1+f2w2G3V1dRgMBvR6PQ0NDdTV1Z2xpre9sVgsqFQqT9bbbrfjcDhatbl/oOd9uCQXFQ0VbKv8mdGho1iUt5iyhjKeyXyOQE0AC/M/J1QbSoJPPAMC+3fqtZyMhMQP/IANG+tZTxJJBHLx/Rk0GhXvv5/G4MGBPPzQHr7/vpQbZ2xl8RfD0etFQkLAz+8rEhMjiI6WEMUAXC4XDofjjMH3ypUrWbhwIWlpaURGRlJcXMzChQsBmDp1amdcnoKCQgfQqTXfkiRtAqpOGh4KHJMkKVuSJDvwBTBdkHkZWCVJ0u4Try+XJOkB4EmgsjPXrnAF0qMHNJYN5OS069SNZk/NORuzJ93YsbiystBPnw6A9bPP0LRh/HIp0/zJgEqlIjAwkNjYWDIyMjrl/E6nE5fLhUajwc/PD41G43GwbA2bzYYoimRnZ3uMk0RR7PTg29vbm7i4OIxGIwcPHsRoNBIXF9dCr7w5oiDSzdCNFaOW8mLqX7k+cgozut+Al2ig2lHDnzJf4In9f+Trwm8obShjY/mmTr2e5ggIjGMcYYQxnOFdIvBuTtqQIIYMkde0alUJ27Y2/Yry8ytAoynB4XC0+NLpTl8+s3TpUtLS0oiJiUGtVhMTE0NaWhpLly7t0GtRUFDoWC7+MzvoDhQ0+3chMAz4PTAR8BcEoackSe8JghAHPAV4A/9obTJBEO4B7kFpJlW4UPz84P/+DwICoJ1VRS7E7Mm+bRv6mTNp+PZbXDk5SIBtyxZ0I099TH+pcj5PBtqTwMBAKioqMBgMuFwuRFHEarW2+eGotraW2tpaBgwYQGhoKBUVFezdu7dT1tqcvn37UlxcTO/evfH19cVkMnH48GH69u172tcFaWW3xXm9HydcH872ypm8dewd9tdmYHKa+E/2B3yRv4R+fn0ZENifWkcd0YbOMz1qJIEE7uO+Uyzqj3CEgxxkAhPwpfMdawGGpAWxfsM4hg1dw8GDJu64Yyev/XMAM2dGExAQgNPpxGKxIAgCDoeGuroQAgJMp53TaDS2UDoBiIyMZPPmzR15KQoKCh1MVwhQW7t7S5IkvSlJ0mBJku6TJOm9E4O5kiTdI0nSbEmStrQ2mSRJ/5EkaQh0cas0hUuDwYPbPfCGC9Ox9vvb39CNGoX+ppsAqH/lFcwvv4xktbb7Oi8W5/tkoL2Ijo7GZJIDo0a5QJPJ1GbDpdlsJiEhAavVypEjR7BarSQkJGA2t65+0VGMHz8ePz8/8vLy2L9/P3l5efj5+TF+/Pizen28Tw+81V4kByTxx77z+WXSdkaFjMRP7Ueds47tVTsYtHoY0zbfyIHag9jd9k5XR1GhQkNTI6sLFz/yI3vZy3/57ymBeWei04mMHhOGn5+a0tIG/vRsJk6nm8DAQHx8fAgJCUGvTyI39zZKSm7C2/v0P+/BwcEUFxe3GCsuLiY4WKm6VFC4lOkKme9CaGFtFgUUt3GsgsLFRZLaTYLwQnSs6/7wB9QxMQhqNa68PHwefxzO8Aj7UuJCngy0xbk0cDZagNfW1qLRaDwlAm2VkajVatxuN263G61W69lWqzv3FhsVFUVaWhqrV6+moqKC0NDQ86qTD9eHEa4Pw+l2MiF8HP8d8i9eyvoHS4uWUeuow+Q08astM0gNSOHq4OHM7/uHDrqiM2PDRiihGDFyNVcj0rnyjifz5puD+NWvIrn+us0cO2bmnnt24XKJuFw2HA4HDQ1arFYDNpsVozHptHPNmDHDU+PdWPOdnp5+Wkt6BQWFro/Q2VmLE6UjKxvVTgRBUANHgAlAEZAOzJIk6cAFnscXqKurq8PX9+I8hlS4zCgogC++gKlT4QyP8c8KSZLnDA9vGTgXFEBZmWyPFxzcarDvrq9H8PKibv58rAsXIsbFIeh0+L/zDuo+fRBUXeGh1oXRnmonhYWFrFy5EkEQkCTJ8/fUqVNbnXPMmDF069aNXr164e3tTX19PUePHqW0tJSNGzeecvyECRPw8/MjJCQEURRxuVxUVlZSV1fHunXrzmvN50PzRtWTP7RcaKPqt0XLUaEiz5LPW0ffodpRA4BepWdm1AzyLYUsuXrxRTHqAcgll2iiWwTfRziCN950p3unrsVkcjB40Gpyc+VG14SePzNypAmz2YzNZqO0dByBgTYcjjWsXv3jaedauXIlS5cuxWg0EhwczIwZM5RmSwWFLojJZMLPzw/AT5Kk09aUdbbU4GJgLBAiCEIh8JwkSR8IgvAg8COy1OCH7RB4/wq48ULXq6DgwWaDV18Fq1UOwJ95pqkZ83woLIQ33oC6Opg7F/o3U5LYvh3WrgVRhDfflI1/GjmReVd5e2PbsAFVaCiCRoMrNxff555D068fdc88gyo0FJ+HH8by0Ufob7kFVRsNd12Z9nS43LBhg6cUpDGYPn78OBs2bGg1i2g0GomPj0eSJGw2G5Ik4eXlhdFobHX+xMRE8vLyCAoKIjg4GKPRSFFREYmJie2y/rPldBKWF/peTu8+DYBCSxG7q/dwVfAw3jz6NuW2ChbmL0YjaHhy/9P4a/y5NWYmCT6d674aR1yLf9uwsZzlmDEznOFcwzWdthZfXw1Zh6/jhulb+OGHUnKyB+HltZaEBBdarZaoqM3U1NR4nFBPx9SpU5VgW0HhMqNTg29Jkn7Txvj3wPfteJ4VgiBsAO5orzkVrnB0Olnre+lSuOqqlgHx+RAaCo3ybwcOtAy+8/Plv6OiWp7H7YannoKYGBg3DjEqCp1Wi1RVheWTT7B8/DFec+bg+8ILSCcCRse+fRhmz8a6dCm2desIePttbD/9hDopCTG8paX35cyhQ4dIS0vDx0fWrPbx8aFHjx6kp6e3erwkSR6zmkY78Mbx1mgMug8fPszBgwfR6/UMGjSo0+uhO6NRNcqrOx8O/S8AWyrPZS35AAAgAElEQVS3Ee/dg2+LV1BkLeKj3E8QBZHihmJujb4ZURC5OmR4u537XCigACtyH8TJRj2dgUolcOuvY9i6tRKTCQ5kTiI6egUGg/zUxeFweBxTJQlcrgu/rSgoKFwaKD/qCgpny8SJEB8PvXqd+2vNZvBpZlai08G114K/PyQntzz2oYfkzLjd3nI8Jweqq+WvgQNRDx+OFBWFu64O68KFuPLysH79NV6/+Q2ClxcA/q+/DoD++uvRXn01APbNm1F164Zks1F7//0Ef/cdjqwsBC8v1DExXI401mCfaawRjUZDWVkZffr08WTKs7Ky2nSsdLvdpKWltWhsrKmpaTO47ygaG1WbN6Z2ZKPqJ0M/AKB/QAof5XxKSUMpeZY8vir4mq8L/seQwMG8MehVLC4ryX5JnVqS0pOePMAD7GMfqbTU6HfgaNG02VHMnh1Ln96+DB/+I263luPHxzB1ajlut6x6Ul1djdUKJ8q6+d3vTq0yU8pOFBQuP5TgW0HhbBHF8wu8a2pgwQJIS4OZM6GxHvtXv2r9eI1G1hg/GZ0Ohg+XM+VJcqOWq6AA6+LFGPr3x7J+PaaHHqJh6VI0ycmok5PRpKQgxscjaLWeTLfvc88BINls+L3yCgD2jRtRBQUhRkdTNW0agYsXIzU0INXXo46NPfdr7mL07duXI0eOtJDgO3LkSJsSfDExMdTW1lJaWupptLRarcS08eHkXOfvKDqiUfV0NAbTN3SfzqTwiagEFWPWT0AURLLrc9hZnc6IdWMJ0gWy+KqFJHjH46327rQgPIggxjGuxZgZM+/xHoMZzChGdbhL5uAhQfj4VGE2h3E4K4S6WhPDrz5Et27dMBqNfPMN7NolH5ucLP+IN6KY7CgoXJ50esNlZ6E0XCp0OHY7fPUVTJokN022xVtvQWamvH1yfff5cLLiiiThfPBBqhYtwg1wIuvduE+QJNS9eqEZNgz1wIFoUlLkpswTj7xbTO12Y9+4Ed24cTSsWoV982b8/vY36p59FsOsWah79sRdXo7YvXMb2C6UwsJCli9fjt1u95SRaLVapk2b1mot9NNPP43L5eLAgQNYLBa8vLxISkpCFEUWLFhwwfN3JO3ZqHo+ZJtziPWO4bE989hcuYVCa5FnX7Qhmt/F38l9CfcAXJTmzG/4hgxks6bZzKYX5/GB+hwZP34yuTljKSzshUrlZsaMA9Rb9mKxWFi6dC1/+QtER8Mdd0DzX1d33XUX/fr1a/GhLz8/n4MHD/LBBx90+LoVFBTOnnNpuLwsg+9mDZd3KMG3QofgdMLf/w55ebL1/LPPti1BaDTCv/4ll6zMmtXuS3HX11PZvz/Bjz8uW1bb7TgyMnBmZOAqKGiqLffxkbP3gKBWI0ZEoElIQD1kCJoRI1D364eqjZ8V29q1aAYNwl1ZSc3cuYSsXYv1iy9QdeuGbuxY3HV1qOSbTpflXILShQsXsnPnToxGo0d2MDg4mKFDh7Yp83axg96uxlHTMTQqDTuNO/nzwb9Rbiv37EsLHEKAJoBFwz/p9HUVUcQKVhBIILdya6ec89prr0UQgtiwfhoul4hK5Wb06O9QieX88MMPGI0QFHTqLeSGG27g1ltvbSFZ6XQ6+fLLL1m2bFmnrF1BQeHs6LJqJ52F0nCp0OGo1XLpR16ebMJzugxecDDMm9dh3VQqb2+CN21CjIw8ReHY/eWXOBYuxFldjaNvXxwHD8qumE4nzkOHcGZmwnffwQk1FDEuDk1iIhqzGXXPnqhvvRVx4EB0EyfK5/LzI2TNGnm7e3dUgYFITiflvXsTnpOD48ABXLm5GG66CcnpROhCHWTnop7idDrZvn07/v7+iKJIbW0tR48eZdCgQe0y/5VAL9+eAPiofVAJIv4aP57NfJ7s+hzSq+U6i1u3zUIn6nhtwCuE6DrHOKY73bmHe7DTsqeihhpyyaU//RFa9X47f7RaLYGBGsaOW8emjWNwOHRkZQ0jtf9qQL5FtEajyU7zzLdisqOgcOlzWWa+QSk7UegEJAmWLIFRoyAioml8/345KB8/3hPUdgaml1/G++67UQUFtdxhs0F5ufxcG3CbzTgPHMD5zDM4SktxuN24TCYkp1M+3uWSG0QBvL1Rde+OJjVVriM3GtGUlKDq1QvhmWc8p5AkCWHDBhwFBbgA/e23UzlyJP5vvYUqOhrbd9/h9dvfdsK70D5MmzYNq9VKcHAwarUap9OJ0WjEYDCwfPnyi728S5ZXs/5JvE8P/n38fXbX7PGMjwi5mmvCJ5EWNITBQW1/wOkoJCQ+53OOcpQe9OA2bmtXs57rr78ef39/QkJC2P5zD/bu7Q4I+PlVUmm8/5TjN2+Ghgaw2U6t+W402VFqvhUUuhZXfOZbQaFTEAS45ZZTx7//XlYmiYm58Pruc0AMC0Oy27F+9RVotRhuuAHHoUOoe/ZEaGaLrvLxQTtsGNoVK6C4GHQ6pNBQnIcP48jMxLlpE47163GWlSGpVLjLyrCtWYNtzRq5hMXhQOXlhXr//qbGzj59ED//HI1KhWb6dACC168HlQrX99/j/uwzKC6mNj8fzahReM2ahWXRIgy9e8s5xtBQ6EKNnceOHWPw4MHEx8ej1+tpaGggOzubX3755WIv7ZLm8T6PAuCvDeC74u/Jrc9lc+VWtlZuY2vlNvr69eHZvk/jJRq4OrTzJArNmCmlFIAAAtrdJVOj0eB2uwkPD+fWX4t4eVezbWsQdXUhrFpVwpQpTR/eV6yAlSvl28tDD03ltttg6dKlbN68meDgYCXwVlC4DFAy3woK7YnFIpvxFBbKqiYvvABhYZ26BMe+faBSoUlJoXL0aIKWLcN55AgN332H31/+gmPfPlTduyOGhJx2HsnlwpWTI9ePZ2bKf+/ahbumRo4MmrtyShJCfT3qwEA0I0eivu66psbODRvgm28AcL/4Img0CFottQ88gH9MDNYNG3B5e+O7YgW2TZvQ9O+PqvF99PGBX/8a+vRpOtfx41BZKcs0Nh9vR/r27cvUqVMJDQ31jFVUVLBy5UoOHTrUIee8ErG6rIxfP5koryg2VmzyjAdrg3hj4GsMCBhAmD70NDO0Hw00sIlNjGQkXjQ1Lbtxo+LCHGPvvvtudDodpaWl2O12NBoda9fcgNWqRadT8cWXw7n++khAvnW8/DLo9XDffXJVm4KCQtdHabhUGi4VLiaSBLm5co13s4zzxcRVUYErPx/t4MHUPfssumuuQZOSQtW0aQRv2IDz0CFwONCcIVMvSRLu4uKWAXlmJq6SEvm6G5VYTtTAexo7/fxQh4Sgefhh1ElJTY2dTz6Ju6wMKTUV8dFHqZk7F58nnkDKzKR+3jwCRo/GecMNqIYORdVY57pwofxc3tcXTkglenjpJTlqueoq+asRqxXq62UlGIPh9DX6QFpaGsnJycTFxXmkBnNzc8nMzOx07e7LHUmSEASBe9Lv53h9Nhm1mZ593qI3T/adx1097kSt6vwHtRISn/AJEUQwjnFoOVUh6GyYP38+JpMJlUqF0+lErVZTXu7F2jWpmM06+vf3Z/OWCej1csb9wAG5jzsgoD2vRkFBoSO54oNvUDLfCgpnQnI4cGZloUlJwfrtt0hmM16zZ1M5ciQBCxci6HTYt27FMHMmktuNoGo7++c2GuWSlWYBufM0ropiXJycGU9IQBMfjzolBbF376b5jhzBtWQJmuBgTLm5aEaORHfNNVTPmkXg2LFIO3dCt26oXn65aVK7HX7/e3n7hhtkR9JGtm2DT04oa7z0EjQ3nfnqKzk4j4+X6/eBuXPnUlhYSFhYGBqNBofDQXl5OVFRUbz77rtn/yYrnDUuyYVLcrGq5EeeyniWSlslEvLvp276cAYHDub9tPcQhfYtCTkd6aTzHd8BMIYxp2iGny3PP/88e/bsYciQIYSHh1NWVsauXbvw9R3Jkq/ikCRISfFn1y+TLor8ooKCwoWj1HwrKCicEUGjQZOSAoDhRJ02QOCSJahCQ3FlZ+MqkjWaa+bMQX/ddehnzsT8l7/g8+yzciOnRoOgVqMKDkY3Zgy6MWM887jNZpwHD3oCckdGBq4jR5CcTly5ubhyc1usRxUejiYlRQ7Kk5PR3H47UlQUvieCEclux2vOHISxY7FKEq6cHHyBumeewfu++1B5eSH16YOqoeFU3fWamqbt5jroAIcOybXvJpMn+L7zzjv56JVXGL1vHzU6HYcjInAEBnLnnXdewDuucDpEQUQURKZFTiUtaDAmh5nZO35LoaWQ0oYyviv5nhHrxuCj9mXlyKXo1foOX1MccUQRhQkTIzh/oyKbzcbQoUM5evQo+/fvx9vbm6FDh2IyVTF//rW89FIWGRm1fPxRDnfOiT/l9SUlchvJCZNaBQWFSxwl+FZQUGiBeEK5RZ2YiE9iIgAB778vq6DY7aDXI6hU1H/+Oc79+/F/4w1ML76IYdYsxG7dcFdVIUZEyI2dQ4eiHTrUM7dktzc1dmZm4ti/H+fBg0hWq9zYWVaGbe1az/Eqf3/UKSlNjZ3JyUhaLV4PPug5RpOaihAQgCMzE9OaNQR//z2WRYvQCAKagQOR7HaE/v3lZ/gWC5xsLhQaKqu7NAvY09LS0M+YgePVV2morUVKS2PIrFmkpaXJByxaBAcPymVF993Xcr7jx+VzBQR4dNUVzh5BEIg0RIIB3hv8Nj5qH57LfIGNFZvJrs8BYNT68QwPHsY98b8jOSCpw9YSSihzmEMttS1KTty4ySGHBM6uINtiseDv78+ECRM8ZUxFRUVYrRZefDGZbdsq2bSpkgcf3I0oCtz+2yaH28WLM3nzTStms50JEzKYPXtw0/9DBQWFSxKl7ERBQeG8aazXtSxejP6aa3AVF1P3xBMEf/899f/5D2JcHPrJk3Hs2YM6NRWhlWDU09iZmYnzRIbcmZkpN3a2gqDXo05KahGQq/v0QdDpPOtpWL4csVcvxO7dZQ3y4mIcO3ciWSzoxo3zHNcCt1tukm3k0CH49lsoK4PnnmtZgPvmm3JhbmwsPPVU03hDAzz8sLx9441wzTVN+/Lz5fRlaCgkJnaY7vvlyFHTMQ7WZbGpYhOf53+BS3IB0E3fjbt63MmAgFTGhI3utPVsZStrWEMSSUxlKgYMpz1+/vz5WCyWFr+LTCYTXl5evPzyyxw5UseNM7Zy5IiZbt107N13LUFBWtLT0/ngg9UcPz4TrdablJQs3O7V3HzzzUoArqDQxVDKThQUFDqFxgDW6ze/AUAVFETw998DoBkyBJW/P5LLRc0DDxCycSMNa9bg2LMH3z/+Efv27agTE1EFBcmGPj17yrXanL6xU2powPHLLziayf4JajVir15NJSvJyYhRUah8fQnPz0cQBKSGBiSrFYCKAQMIWr4cQaPBsWcP+uuvbxl4A/TtK3/JC2q5r18/WcP9ZMWYqqqm7eZ15SDrv69YIW+//XbLfatXy+ox8fFdpkm3K9HLtye9fHsyIXwcXqIXDredz/I+p7ShlAWHXsRX7cv8Pn9gZMgI+vn37dC1OHGyne0AlFGGBs1Zva6oqIiIiAi8vLywWCyUlJTQq5dsbZ+Y6Meyb0cxoP+PlJbauGbyBn7ePpHVq1czcmQsqal1aDQ1JCSoKC5OZfXq1UrwraBwCXNZZr4VtRMFha6Jq6wMt9GIpl8/ah9/HK8770Tw8aF27lyCv/8e+y+/IOj1aJJaLyVotbEzJ+fU4PgEnsbO5GTP32JICO7aWgRfX5yHDmFbvRqfRx+l5t570d90E7qJE2lYvhzj4MFkZGaem118Q4NcdlJVJQfozZ0IP/4Yfv5ZzqA3bxSVJHjkEfm1o0fD7NlN+8rLm6QvevQAzdkFelcC/8h6lay6w6wpW0eDuwEAtaDm8d6PcGfcbwnSBZ1hhvPHjJlVrGIYw4gh5ozH33XXXURGylKCFosFrxN9B8XFxXzwwQee41544QAL/noQgPlP9sFk+oDrr78esdkTI5fLxXfffcfrr7/enpekoKBwgShqJyhlJwoKlwqS3Y4rNxd1YiLWL75A8PVFd911lPfpQ+j27XLAfeAAhunTW7WsP6WxMzMT1+HDTY6dJ9HY2NkYkGuSk1FFReGuqEDQ68HppOT++9ly443027sXL52OimuvpWrtWlJ//WuievY8vwt1u+XGT7NZNmBqxGSSS1fsdrj1VtkZtZEtW+Czz+TtP/+5ZSNpdrbcPBoWdmrW/gpiU/kWnsp4hiJrMfWuegD0Kh0TwsfzxsDX8NP4ddpadrITL7xIJrnF+O9+9zuioqKIjo7Gx8cHs9lMQUEBhYWFvP/++y2OffTRPbzz9jEArr++jJtmBngCd5AD9uzsAmbO/CP9+nX8NSkoKJwdSvCNEnwrKFzquMrKUIWFyXXgu3fjdccdGKdNw/vBB9GOHEn9P/6B73PP4TabEXQ6hGZZ4VMaOzMycB444Ck7ORmVv3+L7Pg2oxHvfv0IkiRwOJAiIxHvuYfsuXMZGxcnGxb9+c84c3JQhYWh8va+sIuVJDlbrtPJxkKNfPONXJKi0ch15s2D7Oeeg9JS2UV17tymcYdDDuQvdE2XGEZbFePWT8TsqsfsNAPgo/YhytCdb0d+Q6A28AwzXBhFFPEBH+DGfYos4YIFCxBFkbKyMkwmE76+voSHh+NyuXj66adbzGM2O7l6+FqyskzodDD7tn2MHNnHI1G4Y8dRXK67kaQoHn1Ubh9QUFC4+Cg13woKCpc84oksryY1FU1qKgBBS5eCJCHV16M64T5p+c9/kMxmfP/0J+qefBLvxx5D8PZGFRTkqUWHkxo7G5VWTjR2umtrsW/din3rVgDirVY0Pj64ExJwJSbiTkzE+cgjGOvqEOPj0U+bBoD51Vcx3HQTmiFDqJs3j4D33sNdXY1gMMhZ9LNFEFqWqDRy440wdqwcmDcPvB0OuREUoFlWFIDDh+Gtt+Sa89//Xi5Z8bwJ0hkNhi5VgnVB/DJ5BxaXhft/+T1bKrZidprJMh1m8Jqr6Ovbh7/3f5Ek/45JFzfQgB49Nmz0pneLff3792fhwoWkpaURGRlJcXEx6enp3HbbbafM4+OjZu26sQzo/yOVlXY2bhhCePhBdu3aRWhoKJMm3ciPP0bhcsFPPynBt4LCpYiS+VZQUOiSFBYWkpGRcVY11x7VlYULMcyciWP3burfe4/ATz/F9Pe/o736arQjRuD4+Wc0w4d7GkU9jZ0nBeSW3FwElQpVs0DVLUm4BQGf5lrkycmok5JAELCtWYNhxgzMr76K5HLh+8QT1P3pT/g89hiClxeoVKeUzJw3bresnlJYKDdoxsY27fvhB1i6VN5+7bWWGfDXX5cD+QED5MD+MsXpdlLSUMoX+V/x+pE3cUgOAAyigesjpjCj+3QmdZvY7ueto44CCkiiZc/CqlWrOHDgADt37qS2thZ/f3+GDh1KUlISU5qbQTXjvX8d49FH9+BywQ03RPLVkiad8W3b5G//zTcrapYKCl0FpewEJfhWULiUKSwsZOvWrcTGxuLv709tbS15eXmMGDHizE2PJ2HftQsxIgLBz4/qmTMJ/vFHLIsX466sxOf3v8f200+yMotfU21wYUYGGd98Q7TZjHdBAVJWFkJBAVqNpkXzWyMtGjtPSCCqQkKwvPsuXvfei+2HH7B8+ilBX32FZdEitMOGISYkgCSd1jn0vMjPl5s0jUY4ObP6hz/INebDh8MddzSNl5fLAXtcHAwbdtn4mrslN79U7WZ3zR7+nvUKphPlKGpBze1xtzExfDwTwyd06BqKKeapH56i3/F+9Irsha+vLyaTifz8fFQqFb9vdGVthXfePsqjj+4F4K23B3LvvefZb6CgoNDhKME3SvCtoHAps2rVKgIDAwlsJtdXXV1NdXV1m5nCc8FVWopksaCOj6dm7lx8n3oKV3k55hdfJGjJEmybNlEhSWRYLFRXVREYFERKfDxhtbUtGzuPHEFyyFlVl8uF0+nE7XajUqlQR0TgNWhQUy15376IsbFYP/kE7ejRIIpUXXcdYQcOYN+2DfR6tIMGXfC1tYnbDcuXy26e/fvDiGaOjdu3w0cfydvPPNNS7vDYMdDr5fKWS7ixc2P5JvZU7+XD3I8pbZBLdgQE/i92NnN63NEhEoU2bPybf7Ns0zKCVEGM3DcSe70dg8GAKIpUV1efUvPdHLfbzeBBazhwoA5BgJ3pE+nfv/Xa9crKU5UvFRQUOo8rvua7mdSggoLCJUh1dTVxcXEtxvz9/cnOzm6X+cVu3TzbAe++C4AqNBS/V14BwJWdTWhCAtdOmEBZbCzh+fk4DxzAVVGB15w5SDabbOpzorGzbONGCtetI6SyEl1eHpLVir24GCoqEE927ExOxpWXhzolhYDPPpNr0cvLEby8kCSJiuRkQrZtw11bi6ugAN2I87c1b4FK5dFRPwWHQ85219efWkP+xRdQUCBrnj/ySPus5SIwJmw0Y8JGc13kFN499h5bKreRb8nn07yFfJa3iPFh41iQ/ALxvqfau18IkUQiCAL6DD0GtYGI2AhqamrIycnB39//tK9VqVRs2TqB4cPXknXIxK23bGPL1gmEhDT1E7jd8kOLdevg0UfhhHS4goJCF0bJfCsoKHQ5OjrzfS64q6tRBQZi37kTV0EBhptuonL0aPxefRUxNhbLBx+wZcAAgkSRgIgIEEWEwkKse/bgPHCAHjYbzoyM0zt29uuHJiUFMSkJMSwM3ZgxOH75BXt6Oj4PP0z1nDl4z52LJjkZ+86d6EZ3kJtjfX3LGnG7XXbsdLtlt87mdeLHj8Onn0LPnnDdda03jHZh7C47v95+G0XWInLqcwE5E97TpycfDf0vib7tE8VKSDz2r8fwKfGhIL+A+vp6vL29iY6JRq/Tnzbz3ciGDeVcf90mHA6JlBR/0ndNQqWS+xEKC+FvfwOXCyIi4E9/uqQfUCgoXLIoZScowbeCwqVMe9Z8dwSS2w2Au7IS248/skwUuWrrVggJwTFrFrrnnsM6bx4Zu3dz05QpqLp3b9nYmZGBMyMDV0lJq/Of7Nip8vdHM3IkOByYX3iBgA8+oP7tt1FFRGC46SYc+/ahTkpqv4ZOz4VKspxhTg5ERbXUJ//xR/jf/+Ttl19uWSd+6BD4+8vRYBdXV3FJLp7c9zSrSn+g3FYByEF4d0Mk/x3ybwYHDbzgc8ybN4/s7GwGDBhAeHg4RRVFrAhaQaollY8f//is5li6tJBbb/kZgL8uSOaJJ5rKZLZtkxUpH3xQKT1RULhYKME3SvCtoHCpcy5qJxebFpl6SUK9YgUVI0fiXLuWlOpq/F97jbonn0R/yy1oUlKw79iBbuRI3FVVpyitnK1jpyowUM6YDxhA5ahRhKxbh337dpyZmXg/+CCuoiJU4eHtH5A3kp4OW7dCXZ2cbm1EkuDJJ2VDoWHDYM6cjjl/O+OW3CzO+5InM57G5rZ5xgcFDOTennczo/v08577rrvuIiYmBq1WS0NDA9kp2RSEFFBbW8uX078kkbPTC7xm8gbWr5c/ICz5+mqmT2+SkXQ6oaO+1QoKCmdGCb5Rgm8FBYXO42wy9fadO1EnJCDZbNQ9/jiBixdT//bbCF5eeM2ZQ8PKlWjHjQNJwnnokCcgP7mx82SaO3aqwsIQg4LQ/epXVM+cic+TTyJGRVH/73/j9/zzuE0mBG/v9lVYOVk7vLwcnn1W3r7lFpjQTE0kK0t27ezXDwYPlk2Fuhgut4u15ev4/e5HqHHUesbHhI5masR1/LbH/53znHfffTe9evUiPDwcg8FAjiaHjYEbqc+uZ/2c9ag4u++H0+nmmskb2by5kpAQLTvTJxEV5dXqsVVVstR7F3/woKBw2aAE3yjBt4KCQudyPpl6V0kJuN2I3btTPWcO/m+8gX37dqyLFxP44Yc0rFqFundvxKgoHIcPy5nxxrKVgweRLJZW5/U4diYno4qJQTKZ8H7gAer++EfU8fF43Xsv5r/8BZ/582WhaFH0aJ9fMI2lKkeOyEH2CTMkAL78UnaGUangn/+UVVQa6WKp23qHhW3Gbfzl4N/IMh32jI8KGcmdcbcztfv1Zz3XggULCAwMxOVyYbVaMRgMOPQOampq+PO8P3uOc+JERESg7e9FcbGV3onfY7O56d7dwOEj16HVtgzeDx2C996DSZNg6tRzuGgFBYXzRgm+UYJvBQWFSxPJbsdtNCJGRFD/3ntohw9HjI6mvH9/wvPzsW/dimS1ohs/HueBAzizs5sC8sxM3NXVrc7b2NipTkpC3a8fzt278X35ZRo+/xz77t0EvP02ls8/RzduHGJERMdc3JIlcoFy9+6y5ngjTifMmyePT54MJxxNuwI2l43lxStZlLeYbcafPeODAwfxRJ/HGRs65owfXNLT03n33XfRarWIoojL5cJutzN37lzS0tI8xy1nOWbMTGc63ni3Od/WLZWMH78eSYIHHujJP19vqkt3u+H552UDVEGQK4JOFrBRUFBof5TgGyX4VlBQuLxwm82ofHywbdmCZLWinzSJ8tRUgv73PxAEGr79Fu9HH8W5fz/O/HxcR454AnJXcXGrcwpqNaqePdH07Ytm4EAce/fi/dBDUF9P3fz5BK9ejX3bNlShoajbS8PO7QazGZqZGnHkCLz6qrx9110wdGjTvro6MBhAo2mf858nbsnNu8feY1PFFjZUbPSMRxuiebn/AiaEjW8zCE9PT+edd95BFEWPG6vL5eKBBx7wBN+HOMSXfAlAEknczM2nXc8T8/by+utHAfjHK/15+OGmuvGyMvntvOUWGDLkgi5bQUHhLFGCb5TgW0FB4fKn8f7tLirCnp6OYcYMah97DO1VV6G/6SZqbr+dgE8/xXn0KI7MTKTyco/SyukaO1WxsWh69UKTloYrPx/tiBHor7uOimHDCE1Px1VYiGQ2o0lObkHjlOsAACAASURBVJ8LKS2FTZsgMxPmz28pd/jhh7BnDwwaBHfe2T7nu0A+y13ElwVL2FmV7hnz1/jz+oBXmRJxDSqhZRnIggULsNls5Ofne6QGY2Ji0Ol0HqnBBhpYwQpyyOE+7sMPP06HJEnMmvUz33xdhF4v8PP2SSQlNemG2+2g1bbjRSsoKJyWKz74bmayc4cSfCsoKFyJSHY7DStXYrjxRqxff43z8GF8n36amnvvxefxx8HPD9vKlQiiKDt2ZmSctrFTCAvj/9u77/Coqq2P4989k0mjE3oRld6UIgFEsIBgF0QEu6JXseDF8ioqolcvYkewXhWu5YoKWBAFBEERRXrvSC/BQAikJ1P2+8cZIJEigSSTTH6f58mTmVPXOZnjLLZr7x1Rrx6RnTo5teZuN+WGDGH/rbdSbuhQXDVq4N+8GU/LlgV3EYGAU56Snu4k33fffXhdZqbTYt64cd7a8SI0K3E2j694kj/SNh5aVj2qOv9sdD+3n3ErbuMG4Nprr8Xn81GxYkUiIiLw+Xzs37+fiIgIJkyYcGhfiyWFFCqQd/KdbLKJ4sjOqampXho2mMy+fTlUrRbFmjWXUr780f8Pgdfr/MQevX+miJyiUp98g1q+RUSOJmfePCKaNyeQkEDa669T8a23SP3Xv4ho1ozonj1JGzkSd40a+Nas+fuOneXL46pdm8iOHXFVrkzO3LnOKC6jRuFp1YqoCy/Et2ED7gYNTq5Dp98PS5fCihXQokXeGopFi+C995wOo48+Cn+ZEbUorUlZS//5/2BT+mYszndqvdh6XFf3Wh5s9AAd4jtQv359mjdvTmxsLBkZGaxatYqNGzeycOHC4x57Nav5nu/pRS8a0OCI9cuX76fzeT+SmWnp2asWX3xx7hH3OjUV3n7b6ec6aFDIK3hEwpKSb5R8i4icKH9CAkRE4Kpcmf133EHF0aPJHDcO79y5lH/tNdLffhsTE4M/IcFpIV+16rgdO121a+Np2RJP27akvfQSVZcuJWf2bOy+fcT060cgKQlTufKpjbDyySfOsIUejzNySu6McvFip/Nm9eonf/yT8EfqHwxeMYTZe349lISfUeZ0kicl0Sm9I7Wq1yIyMpKcnBx2797N8uXLWbBgwTGPl046b/AGWWQRQwyDGHTUFvCxY7dy263zAXjkkUY8P/zsPOvHj4cff3Re9+wJRTxJrEipoOQbJd8iIqfCer3YlBRccXGkjRxJ9FVXgd/Pvl69qLp8OZljx+JPSMAYQ86iRfjXrz9ux05TsyYR9eoR1b076SNGUPGTT3BVqkT25MmUue8+bE4OJj9FytnZsGEDJCXB+efnXf7ww06NxSWXQK9ep3gn8m9z2mZGbniTsds+P7QsOiOaemvrUOuPGkQQgcfjYdu2bcycOfO4x1rOcr7jO67hGprQ5JjbDbx/Mf/5z0aMgR9nXEDnzoeHePR6YdQoZ9zvW24pViM6ioQNJd8o+RYRKQw2KwsTHU32zJmYmBg8HTqQeMYZVF28GO/y5WRNnEhEw4Zk//47/k2b8G/ZctSOndZa3LVq4apYkeiePcmaNImYfv2IvfVW0l97jTLBoQjz3Tq+ciW88Ybz+r778g5bmJjojJxSRN8JG1I38sGm0fx300ccnEcnyhtFs01NCPzsJSYihm+//fZvj5NBBrHkLdZOJz3PcIReb4D28dNZuTKFypUjWbK0OzVrxhxan53tdMDUpDsihUPJN0q+RUSKysHh83ybN+PbsIHo7t3Zf9ddxPTti7tpUw7ceScxffqQ/dtv+NatI7Bt2xEdOw9+F7mqVsW4XMTccAO+detwVapE+ZdfJmv8eKJ69MBVocLRQsgrIcGpFe/WLW85yrvvwrJlzuyad95ZkLfguC7oewF7Wu0jqfE+Au7gdXoNNRdU59dhsykTkb9ekAc4wLu8S0ta0p3uROA0ZW/fnkF8u2kkJXmJj6/MTz9fiMdz7Nkz//yzyCtzRMKWkm+UfIuIFAeBjAxyZs8mukcPMj78EJuZSeydd5J0+eXE9OyJb8MGchYsILB79xEdO621YC3uihWx1hLTqxfExOBbtoxK48bhXbAAd716uE9kFpn0dGciH78fzj0Xbr01V5ABp0m4kJqFL774Yjp27Eiibw8L6i4mofpufG4fANGuaO5tcDf3NbiHcp4T+676lE/ZgDPG943cSEMOj8E+a1Yi3S+ehbVw551n8PY7Rw70bS18+y1MnQr33gsFOUCNSGml5Bsl3yIixVnOvHl42rTBt3IlmV99RblnnmF///6469UDj4esSZOwaWkEkpPzlJ5YayEQwFWmDERFEdmuHZHnnUfGmDHE/fwzgYQEcLmIqFcv7wmthU2b4PffoUMHaJBr5JB582DiRGjfHnr0KPChC3v37k2VKlUIBAJ4vV4CsZZlZ68ksXIi/sgAAGUjytK7Ti+ebDqYipEVj3u8ZJL5ki+pQQ2u4Mj544cOXckLw9cA8NnnHendu06e9YmJ8OyzTi14XJzzWnXgIqdGyTdKvkVEShr/7t2YmBiIiCB18GDKjxpF2rBheNesIbJNGzK/+gqbloY/MRE4XA9urQWfDxMdjSlXDnfNmsT060fWV19R/q23cFepQmD/fiJOO+3oJx45Elavdib3eemlAs9EH3zwQRYtWkSrVq2Ii4sjKSmJpUuX0rJ9S2JuLMuEHV+R4Xda/ctFlOWKmpcztPkQ4qIqH/te4cdiD5WcgFMbHkkkbuumd+85fDdpF7GxLn6f242mTfOW6yxbBhMmwMCBUK1agV6uSKmk5Bsl3yIi4cD6fNiMDFzly5P28svE3HYbvmXLOPDww5S5+24yv/6aQGIigb17sT4fuFyHk/JgL0NX5cqYQIDYe+/Ft3Il0X37EnXhhQQSE3FXrw6zZsGcOc5Y4ddff/jk2dnOOH1dusCxEvcTMHjwYFJSUsjJySEnJ4fIyEgiIyMpX748L7zwAqneVN7fNJrX1o8kJ5ADQKw7lutP68uDjf5Jteiqf3MGCBDgYz4mhxz60AfXgbK0afMD27dlUu/0WJYs6UHZsnn/UeHzqcVbpKAo+UbJt4hIOLNeL8bjIeuHH5xRU+rWZU+TJpR77jmyp0/Ht2qVU7aSmQlud96EPCICV9WqBHbtouzgwdiMDNz16xN7ww3Y7GxcB6eBnDULxo51Xj/4IDQ59lB/xzNo0CA6d+5MUlISWVlZREdHExcXx+zZs3n99dcPbZfuy+DjLZ/w0tpXSfenAxDpiuSWejcxsOG91IypecxzLGAB3/M9AO1ox+VczooVB+jQfjper+XaPnX49NMOxx09ZudOqFABypY9qcsUKdVKffKt6eVFREov34YN+BMTiYyPZ9/VVxPZpQv+nTvJnjQJIiIIpKSAMRi3M/27zXFam11xcfi3b6fswIG4ateGJUso4/FAXBxm2DBnishDJznxZuNhw4ZRr149auXqGLpr1y62bt3Kk08+ecT2mf5Mxm79nNfWj2RP9h4A3MbNzfVuZGDD+6gbW+eIffz4mc50NrGJO7mTSJwx00eP3sQ9AxYB8OqrZzPwgUZHjXHTJmcs8GrV4KGHCrzsXSTslfrkG9TyLSIihwVSUvAuW0bkueeS+txz2PR0TPnyZLzxBqZyZWfGTr8fPB6MMc5QiD4frgoVsKmpRPboQdRFFxHYuZOy992Hee45p9PmVVfBXzt3/sWCBQsYP348Z511FtWrV+fPP/9k+fLl9OnTh3bt2h1zv2x/Np9u/Yy3/niH7Zk7ADAYrj+tL/9sNJAzypx+xD455BxKvAEslnsGzmfMu9twu2HGjAs4t9ORZSz/+x/Mnu28vusuZzRGETlxSr5R8i0iIsdnrcW7aBERbdqQPWkSWZMn42nUiPS33oLISAIHDkBmJsTEOGOZWwsZGU7Hzuxs3OXKEfPAAwSsJfaOO3BXrQpZWUdtNl6wYAHTpk1jz549VK1ale7dux838c7NG/AyZvOHjN70X7ZkbAXAhYseNS5mSLMnaFiuwTH3nc1sfsmewyfnNGXT2mwqVoxgxcpLqV49b4x+P7z/vjPsYKdO+biJIgIo+QaUfIuIyMnxJyZiypYlsGMHqcOGEdm+PeljxmCTkrA5OdiUFKwxzvjgZctCSgqmQgVcFSti9u8nNj4e06oVkTfdhKdhw78/4QnyBXyM3fo5b/7x9qEk3GDoUrUzz7Z4mqbl89ak72Mfb/ImAQK4tlflzTa12L/fR5cuVZj6w/lEROSdgMdazYApcrKUfKPkW0RECo71+53OmtaS8uijeM46i+xp08iZPRsTE0PgwAGsy+W0jBuDDQQw5cvjqlYNMjOJHTAAV7VqeFq3xnPWWcft+Ph3AjbANzsn8u/Vw9mRufPQ8ouqXcCTTR+nZcUWh5atYhWTmczN3MyK6XD5ZU5tyf0DG/Daa62Pex6vF375BS66SEm5yN9R8o2SbxERKXw2EAC/n/T33oO0NPy//krGDz9gatfGHjjgJOEREWAMNjsb43Lhql4dm5VF7E03EdG4MRFNmuBp08bZLh8CNsAPu6czdOW/2BpsCQfoWu0iHmn8IG0rtwHAixcPHgBeeGENQ59aCcD4Cedy9dW1j3rsrCx46y1Yvx7OP98ZgVEJuMixKflGybeIiISI14t1ucj+8Ue8q1dDWhrpL7+MiY4m4PdjMzMxUVFYt9sZNcXrdcYbj4oiqksXPOeei6dRIychj4r629NZa5mZ+BPDVr/AypRVh5Z3qdKZR5o8SIe49oeWBQKWC7pPY+6sFCKjYfHiHjRqWP6IY6amwquvQkKCM8Li/feDx1Mwt0ckHCn5Rsm3iIgUE0uWwLvvYq3F16kT2ampkJ1NxmefYVNSCKSmYpOSnBk2DybkGRm4qlfHVakSEU2bEtWtm9NCfvbZuMofmSyDk4TP3vsrr6wdwdx98w4t7xjXnkcaP8R5VTphjeXl5Ld5uW0lUrZH0qBJLAvm9qBMmSNb3ffvh8mToU8fJd4if0fJN0q+RUSkmNi3z8li58+H4cOdJPugOXPwZ2SQtX07ZGaSNWUKvrVrCaSnOwl5dLQznrjPB2lpULUqEdWr465dm6hLLsHTogURLVs6I63k8vveuby87jV+3fvboWVnVziLx5s+StNqTXh92deM6lgVvw+uuaY2n33e8ZTq0EVKOyXfKPkWEZHiY8eOHaxesIC9mZlUqlSJli1bUqdmTXjsMafG4+yz4d57D20fSE8n5/ff8W/eTM68eeT88gs2K4tAUpIzQZDHAz6fM0FQuXK4qlcnompVoi69FE/LlkS0aIG7bl0WJS/m1XUjmJH406FjNy7XiMeaPsLeaU25605nAp433mjDLQNqEU00hmMn4b/8AtnZcPHFhXevREoiJd8o+RYRkeJhx44d/Pbbb9SrV48KFSpw4MABtm7dSpczz6Tm//7n9G687Tbo2PHwTl6v0+KdqzXaer14167Ft3Qp3tWryZ44EX9GBnb/fsjKchJyY7A+H0RF4a5cGVfNmkR17crasyryevnZ/JhxuBylQdn6VPypNz++VAOXcXH/7L2cF1+Ty7n8UAfN3H75BT791Hl93XXQtWth3TGRkkfJN0q+RUSkeJgyZQqVKlWiUqVKh5YlJyeTnJzMpd26wdKlcNZZkLtz5bhxzlAjl10GrVsfc6gRGwg4reMrVuBfuZKsqVPxJyQ4EwSlpDg15JGR4Pdj3G42NCrDe71jmN4kCxs8pGdPDfZ/ejGeP5pz15ItXFflUs7hnCPOtXMnjBjhTMjz0ENQt26B3iaREk3JN0q+RUSkeBg7diytW7fG5To8qU0gEGDJkiXccMMNR+6QmgqPP+60fjdtCoMG5et81loCCQl4V67Et2IF2bNn41u7Fn9SkpOQG8OmM2IYfbmbKZ0iCbicLLzSLg8NfjuL8e99RmxsmaMeOyEBcnKgXr18hSQS9pR8o+RbRESKh+O2fF966ZE7ZGXBjBnOz913Q+PGh9elpEBMzEkNPxJITj6UkOcsXEjOwoVs8e1idA/DpPM8+COcJPzMjW5ebPUB51/UHS9eXLhw4z7mcVNTnYk+1V9TSjMl3yj5FhGR4uFYNd+dOnWiTp06x94xJ8dJsnNntR98AOvWQY8e0K3bKccWSE/Ht2YNm1f9wrNbxzKjxS4CbkN0luXGDZdxzuOXkOzeTx/6UIEKR+z/55/wyivQoQNcc40ScCm9Sn3ybYy5ErgGuE3Jt4iIhNqOHTtYsWIFycnJh0c7OV7ifTSJiTB0KFgL7dtD//4FHuenn/yH4RnPkljDed98aSzn9LuSdo07cB3X5dnWWnj5Zdi40Xk/aJBTJSNSGpX65BvU8i0iImEmEIAFC2DqVPjHP6BWrcPrEhKccpSKFU/5NLt37WLA25cxp90eACrts9yTNYBB9ww9YtukJHjtNWegliuuOOVTi5RYSr5R8i0iImHK2iPrO157zWmCvugi6N27QE7z4quP8X61T0gp75zrwvmn8e7Dk6lUuRLZZBOFMzpLRoaT96vkREqz/CTfruOtFBERkWLmr1nuzp1OHbjP57SOF5DHHn6RCc0m0mxVLAA/xW+j26dn8Z9vXudN3mQrWwGIjc0bkt8Ps2YVaCgiYUXJt4iISElWuzY8+qgzS2b37nnXbdwIu3ef9KFbtW3H9P9bTb9FnfDkWLafFuBZ30v8/tRXfO79HC/ePNsHAjBmDIwd6/z2+0/61CJhS8m3iIhISVe/vjM9fYVcI5JYC598As88c3hqypPg8XgY9ew4/hM5itO2Gbwewy/nbOH7V99l5YIlebbNzIRdu5zXe/Y4jfEikpeSbxERkXC0ebPT6m0txMWd8uGuuPpaZtyykm7zzgBgbdMceq/vyfOvPAJANtmUKQOPPOJ0wHzggbyTdoqIQx0uRUREwtXu3fDTT04nzMjIw8s3bYJq1ZzZcU7Cu/95iZGe10mKc4q92y+M45y7rqZ73cvoQAcMR/a+DATApSY/CVMa7QQl3yIiIkfl9cKQIc5Mmr16wQUXnNRhNm7YwH1jr2JxqxQAqiZCp8jzeerWF6lL3TzbLlwIU6bAwIEFMhqiSLGj0U5ERETk6BYvhv37neQ7d2t4PtVv2JDvh6zk7lVXEpNh2VMNvi33M089eQfpaWmHttu6FUaPhh07YNQopwpGpDRT8i0iIlKaxMc7BdnnnOPMC5/b9u35GiPQ7Xbz3BPvMrbWpzTYEEHAbZgcv4qu7zXj5xnTyCKLOnUtXbpARAT066fxwEVUdiIiIiKQlgZPPgnlyztZcvPm+do9MyODgcN6833bZfjdhuhMy4Xrz6bHkzfT09WLA7tjqFmzkGIXCTGVnYiIiEj+/PyzU4qSmHhSu8fExvLBsCmMyHyOGgmGrBjDlLOX8/6wp/h67fgjEu+9e2HmTJWhSOmjlm8RERFxOmL+8gv88QfcdVfe+pC9e6FKlRM+1O5duxjw9uXMaeck8pWSLQMz7uP+e58EnEb2F1908vyuXaFPH5WjSMmmlm8RERHJH4/HyYTvvvvIxHvoUHjrLfjzzxM6VI1atfjm30t4ZOPNlE+xJFcyPFv7ba57ogNJe/eSnOYlJ8fZ1u1W4i2li5JvERERObZJk5x54pcvd8pS8uHRh17gy+bf0mJlDAA/t9/OxZ+fzZtzHmLA48lcdhlcc01hBC1SfKnsRERERI5t/3747jvIyYH+/fOuy8k5oeEK/X4/Dz97ExNazCIn0uDxWrosa8hHQ6cT6Tm8fyAA6emgr20paTTJDkq+RURECpS1eetDtmyBkSPh0kvhooucsQT/xveTvmTo5n+yvZ6TezReE83rF/6Ptu07Yi188gmsW+eMhFi9eiFdh0ghUM23iIiIFKzcibe18NVXkJEB33wD+/ad0CEuv7I3M29dyUXzTscELOuaZtHnj94Me+khliy1/PabU2L+xReFdA0ixYCSbxEREcm/bt2gRg1nevpq1U54twoVKvL587/x7J4HiUuypJU1jGz8BU983YxzOq+hShW47bZCi1ok5FR2IiIiIifH7wefD6KiDi9btQp+/dXpSVm16nF337xxIwM+uZIlrQ8AUDURBrkG8487BhZm1CIFTmUnIiIiUvjc7ryJdyAAEybA4sXOQN4+33F3P6N+fb59agnXrOpITIZlTzV4qtJwbn2yK+lpaYBzqE8/zdes9yLFmpJvERERKRjZ2VC3rvO6a9cT6oQZ5Y7inSfGM7LOSBqujyDgNkyJX0vX95rxxdipjBnjzP0zenQhxy5SRIpt2YkxpidwOVANeMtaO80YcxrwJrAXWG+tfeE4+6vsREREJBS2boWaNfMOQ7hwodNBs1Mnp8X8KLKzs7n/ud5813ox/ghDVJbl3BldqWg+5IEH3DRvXkTxi+RTsS07McaMMcYkGmNW/mX5JcaYdcaYP4wxgwGstd9Ya/8B3Ab0DW7aCPjeWtsfaFaUsYuIiMgJqlcvb+Lt9cL48U79yKhRx9wtKiqK9//9HSNynqXmLsiONvx0+Uz+qNWUCLOqCAIXKXxFXXbyIXBJ7gXGGDfwFnApTkJ9vTEmd2I9JLgeYAnQzxgzE/ip0KMVERGRU7dnz+HW7vj4v9382utv48Fe/6bNggoArGiVzlXzL2bUm88BkJQEq1cXWrQiharIy06MMacD31lrWwTfdwSesdb2CL5/PLjpC8Gf6dbaH4PrHgHmW2t/McZMsNZee5zzqOxERESkuPD5YN486NgRXLna/tavh9q1oUyZPJtbLEtZyrTXx/F+3EeklHfGGe8y/zQa2+/YlxzHDTdA585FeREiR1dsy06OoTawPdf7HcFlA4FuwLXGmAHBdVOBB4wx7wJbjnYwY8xdxpiFwKxCi1hERETyJyLCqffOnXinpsI778CQIbBgQZ7NDYbWtOaxQcP5uuX3tFgZA8Av8dv4vtbZpBwYS0JCUV6ASMH4+27Ihc8cZZm11o4CRv1l4UrgmK3dwW3eA9472PJdYFGKiIhIwZo/3+mECXmHLPyLlq1aM73lOgY825MpLRaxqy4kXvMIFZaOp1evcXg8niIKWOTUFYeW7x1A3Vzv6wC7QhSLiIiIFJWLLoJ77oEuXaBly7zrvN48b11uF5f/6w56xl5G3S0ufB7DhHbz6fpqExbMnYPX61SwiBR3xaHmOwJYD3QFdgILgBustafUrVk13yIiIiVUUhIMHw7duzsJenC8cItlHvOokhrH08P782O7jViXoWyapfv86yD7dS67DK64Im91i0hhK7Y138aYz4DfgcbGmB3GmDustT7gfuAHYA0wrgAS7yv5S8mKiIiIlBDjxzv14F9+Cdu2HVpsMHSgAw3KNeTT52czLOkRquy1pJU1fHXReDaWb8mi+Vs0G6YUa8V2kp1TpZZvERGREmrNGvj8c2e88P79j7vpwo3zGfzxzSxv40xHXzUR/i/icW67/f6iiFQEyF/Lt5JvERERKX58PsjJgdjYw8t27IClS6FHDwh2stzCFsb5x7H8pZn8Wm85GWUMLr/l4oVNePeJb0lNK0uNGiG6Bik1lHyj5FtERCSsWAuvvgobNkC1ajB06KEEPJ10NrOZlNlJPDr3VtY3cjprnr7JTZOF73Jt38u48kowRxtfTaQAFNuabxEREZGTkpYGmZnO65YtDyXeAGUoQwtacG7n85nxwDp6LWiD22fZcqafn6+6k4+/upU9e/whClwkr7Bs+Q52uLwGuE0t3yIiImEiEIDffoO2bfOWoyQkQPnyh2bJ9OHjsc8H8mPytyTUcjY5a3kZ3rzma5o0bx6CwCXcqewElZ2IiIiUCoEAPPccHDgAvXtDp05YLHOZy+Tdk1j2xvfMjd8LQIX9lvtT7mLAXc8cb04fkXxT2YmIiIiUDkuXwq5dkJ4OKc7E1gZDRzryzxoP8fWwxQze0p/yBywHKhqGnfY+PQfH883XewjT9kcp5tTyLSIiIiWXtU4C/vPPcP/9eWrBsfZQL8tVy5Yy8LtrWdnCqRuvtQPudb3IXffcFIKgJdyo7AQl3yIiIqVaTg689BJ06gTnnw8uF0v9S3ju6UHMO3sDOZGGCK/lqqXtGPnUOKJUhyKnoNSXnWiGSxERkVJu6lTYvt2ZrGfhQgDOdrdi2L//Q//I2zl9sxufx/BVu4V0e70Jv/82O8QBS2mhlm8REREJPytXwmefOaOiPP44uPK2N6ampnLP8CuZ3m491mUok27psqwbHwz/Lx6XO0RBS0mlshOUfIuIiJR6Xq/TCTMu7vCyzExYvhzi48EYRn8wgld5mb1VndrwVgsqMvrO76l7+umhiVlKpFJfdiIiIiKCx5M38Qb4+msYMwZGjIDMTO6480GGt/6MlvPLArC03X56/Hgu/x0zMgQBS2mg5FtERERKh6wsWLbs8PvoaACubns+3z29mtuXX0VshmVvVcPgSi9y4xPnM/nAVLx4QxSwhCOVnYiIiEjpkZkJEyfChRdC9eqHlwcC4HIx97dfePi3W9nQOAeAupvcdGnYi2FXDCeW2GMcVEq7Ul/zrenlRUREJF/GjAG3G3r3Jtvj4e6hvfkhfjH+CENUtqXv6q68+NSHuN3qjClHKvU139baScADoY5DRERESoDVq2HePJgzB778kqioKN577jtuX/gCNXdCdpTh49Yz6T68CatXLMePP9QRSwkWlsm3iIiIyAmrVg1atICoKLjqKgAiI+H54bcw/qIldF5QB4AVLTO4evElPPh6f77ma7LICmXUUkKFZdkJqOZbRERE8sFaSEqCKlXyLvv1V4iPZ8S7z/N2xfc5UNEZkrD1vIr0HzSEvtWuD1HAUpyU+ppvUPItIiIip2jhQnj/fahUCe65hxXJB7j/m96saZ0BQK2d8FSVF+h93c0hDlRCrdTXfIuIiIicskWLnN9eL1SpQstWZzGq51q6fHMhUdmWsAqpXgAAEbZJREFUXbVhoOsx7n7qKrKys0gmObTxSomglm8RERGRozlYdhITA+ecc2hxWqplzuzvGLL+XracEQCg4doI2p93Fdd2up4OdMCl9s1SpdS3fAeHGhwV6jhERESkBDMGOnfOk3gDlF36K91XrGbGpT/SY14jTMCyoYmPr7d+ydvDnieHnBAFLCVBWCbfGmpQRERECkVKCnz5JWzaRLmPP+aTf8+g/7zBVEmE9DKGaS2WcM3T8WzdtCnUkUoxFZbJt4iIiEihiIpyWsNdLrjuOnC5eOZfD3Bv+m+0mlsJgIVtkrh05nmMHj2CjWwkg4wQBy3FiWq+RURERPJr7968wxICvg2beX7CCP5bbwLpZQwmYImfX4OOT1xLv3L9OJMzQxSsFLZSX/MtIiIiUqj+knizdy8Rb4xgqK8qX5R9h0Zro7Euw7wOf/LVB+8wd/LPIQlTih8l3yIiIiKn6ocfIDsbduwgvlFjPrthNR0ntiXCa9l2ZoDH0obw8DM34vdravrSTmUnIiIiIqfK54MpUyAjA/r2BWDXLnjpxU/4ufFgdtV2Nmu5IpbnrhxN9tl+utAFN+4QBi0FRTNcouRbREREQsBaZ4jCg3bsYO/Eidyz8yNmtd8JQIUDlg5729HloSvoT38l4GFANd8iIiIioZA78bYWxo6lyvLljM/uzBOb+lNxv+VABcMP9Rcy8fE3SU5MCl2sEhJhmXxrkh0REREJuexsKFPGed2xI4MefI77kqbSbHEsAPM77KXrl22Y8MXHAFjCsxpB8lLZiYiIiEhhWrECGjaE6GgA5v7u538TbmZi/M9kRxvcPsuVi9sQP7Qn8VHxtKQlBvM3B5XiRDXfKPkWERGRYsrng+HDmbF9M080nc7mMwMA1F/nJv7cK/jHeffSghYhDlLyQzXfIiIiIsXV3LmwYwddjYeZtV6h++9NcPktGxv7mbjtG8YNfzvUEUohUvItIiIiUpQ6dYJ+/aB+fcr07s3w+2Zw+aTBVEmEjDKGd5tN5PKnm7N10yZ8+MgmO9QRSwFS2YmIiIhIKOQaljArC2ZO2cDbC65ifvsUAKrstfS2fah85+n0pCdncEYoo5XjUNmJiIiISHGXa1jC6Gi4LLCc75K6MHDiWZRNs+ytYngvbjwzBn/E5NTJGg0lTCj5FhEREQk1nw/WrgXgqcYX8NkZE2i0KgrrMszvuIcPxwxn2uRJIQ5SCoLKTkRERESKA78fZsyApk0J1K7L1Cle3vvyOuZfNg+fxxCZY+mz8nxeGfo//nD/QX3qE0FEqKMWNNQgoORbRERESr6EBJjz5qP8u9an7KzjLGu2Ipa2V1xGg1ZNuJZrqUrV0AYpqvkWERERCQc1k1fTe+8BZizswfmz4gBY3TKDb5ePZ84r03DjDnGEkl9hmXxrenkREREJC2eeCR06ULl8OcY/MZM75t1Fxf2WAxUMPzScz51PXkzi7t2hjlLyQWUnIiIiIsXdvn1QuTJeL7z3zirGJ/VmdRunuqHGLsOQSs/Rql8ndrObTnRSi3gRU803Sr5FREQkfGUtXMbQUffx2eWbyI42uH2W9gvq0fJfXWkQ1ZBbuAWD+fsDSYFQzbeIiIhIuPL5iB4/lpdimvPx5As5Y6MLf4RhTsdt/DjyY5gZUOJdjCn5FhERESlJIiJgwACoVo0L7xnI5FvW0GFqM1x+y8bGfp7e/SRDn78Xv98PgBdviAOW3FR2IiIiIlISeb1OIm4MKSnw9NC3md5sGInVndVtl1TgtZu/YHL9abSlLZ3prFrwQqKab5R8i4iISOmzY9s27vngCua1TQIgLskSn9mJM+5rzvVcT2MahzjC8KSabxEREZFSqM5ppzHp2eU8sO46yqZZkuIMU2v9xqLHJlP9QLVQhyco+RYREREJO0MeHcGT6V/ScGUk1mWYd+5Oun3YkqnffwNAKqkkkBDiKEsnlZ2IiIiIhKl1a70Mf+86pp87D6/HEJltuXZVF9o+dQWbIzbRmc5cwAUaHeUUqexERERERGjcxMOHr33N267XqL3dRU6UYWyb2fznhaEkzk0imWQl3kVMybeIiIhImLv6mn7M6LeUC+bWBWB9y2ymbhjP2lfnhTiy0kfJt4iIiEgpULlKHJ88M5cbZt1LpWRLSnnD6w3+R68hbUjcvRuLZTKT2c3uUIca1lTzLSIiIlLK/PTjGp6fcw3Lzk4BoGYCXBc5gJzb/biNi7701bCE+aCabxERERE5pgu7NWXqkyu5fVkPojMtCTXhzYrvMO/hKfj3xnA6p4c6xLCl5FtERESkFHK73bw4dAwfVRnDGRvd+N2GxefvYNp/32ThrDmhDi9shWXybYy5EhgV6jhEREREirsLu13ClFtXc/7M5rj8lo2NfNy460aeen4Afr8/1OGFHdV8i4iIiAgAn3z8Li9lPcuf1Z3hB1svqUC/uhO56ZaGeDwhDq4YU823iIiIiOTbzbcM4IfL5tNhYRUAlrQ+wIvmAu4e8DIZGSEOLkwo+RYRERGRQ2rVqcO3zy3jgbXXUzbVsi8Ovrvqde7893kcOLA/1OGVeEq+RUREROQIQx57hfGNvqHxqigAfmy/ma4ftWDypK9CHFnJpuRbRERERI6qbXw8M/9vHdct6ojHa9lWz3JX5v38c2hfdcY8SUq+RUREROSYPB4Pbz47gXciRlBnm4ucKMNnbX+l64tNSdyt2TDzS8m3iIiIiPytq3r2ZcaNy7lw3mkARPoiiKtaNcRRlTxKvkVERETkhFSqXIkvnv+dfyU8wJt9vsTtdoc6pBJH43yLiIiIiJwCjfMtIiIiIlIMKfkWERERESkiSr5FRERERIqIkm8RERERkSKi5FtEREREpIgo+RYRERERKSJKvkVEREREioiSbxERERGRIqLkW0RERESkiCj5FhEREREpIkq+RURERESKiJJvEREREZEiouRbRERERKSIRIQ6gGMxxvQELgeqAW9Za6cZYzoDN+LE3cxae24oYxQRERERyY8ibfk2xowxxiQaY1b+Zfklxph1xpg/jDGDAay131hr/wHcBvQNLpttrR0AfAd8VJSxi4iIiIicqqIuO/kQuCT3AmOMG3gLuBRoBlxvjGmWa5MhwfW53QB8VnhhioiIiIgUvCItO7HW/mKMOf0vi+OBP6y1mwCMMZ8DVxtj1gAvAFOstYsPbmyMOQ04YK1NOdo5jDF3AXcR/IdFampqQV+GiIiIiMgh+ck3i0PNd21ge673O4D2wECgG1DBGNPAWvtucP0dwH+PdTBr7XvAe8aYmsCu2rVrF07UIiIiIiJ5lQWOm4kXh+TbHGWZtdaOAkYdZcXTJ3jc3UAtIA3YDJxx0hE6cTxQyPuc6PYnst0s4Px8nDscnOrfuCCdzOcllOfQ8xHeiuLzmB+F/awW9PWe6vH0fBRvxen5KGnfHXBqz/PJxpKf/Qpr22Mpi5N/HldxSL53AHVzva8D7DrVg1prLZAAYIxJttaedP2JMear/O6f331OdPsT2c4YEziV6y2JTvVvXJBO5vMSynPo+QhvRfF5zI/CflYL+npP9Xh6Poq34vR8lLTvjuDxTvp5PtlY8rNfYW17HCd2LidHLTrBmu/vrLUtgu8jgPVAV2AnsAC4wVq7qgDPudBae05BHa+4K23XC6XzmgtKabt3pe16ixvd/+JNfx/JD31eTk5RDzX4GfA70NgYs8MYc4e11gfcD/wArAHGFWTiHfReAR+vuCtt1wul85oLSmm7d6Xteosb3f/iTX8fyQ99Xk5Ckbd8i4iIiIiUVppeXkRERESkiIR98m2M2WKMWWGMWWqMWRjqeAra0WYNNcZUNsZMN8ZsCP6uFMoYC5Ixpq4x5idjzBpjzCpjzD+Dy8P2mgva0Z6JcLp/+XkmjGNUcHbd5caYNqGLvOQzxkQbY+YbY5YFn89/BZefYYyZF7z/XxhjIoPLo4Lv/wiuPz2U8YergnomjDG3BrffYIy5NRTXIoUvP98R+rycnLBPvoMutNa2CtNOAR/yl1lDgcHADGttQ2BG8H248AEPW2ubAh2A+4wzI2o4X3Nh+OszEU7370NO/Jm4FGgY/LkLeKeIYgxX2cBF1tqzgVbAJcaYDsCLwIjg/U/Gma+B4O9ka20DYERwOyl4H3KKz4QxpjLwNM48HPHA0yX5H+nyt070O0Kfl5NQWpLvsGWt/QXY95fFVwMfBV9/BPQs0qAKkbU24eCMp8EhgdbgTNQUttdcRMLm/uXzmbga+Ng65gIVjTNBl5yE4H1MC771BH8scBEwIbj8r/f/4N9lAtDVGHO0uR/kFBTQM9EDmG6t3WetTQamc2RCL+FLn5cCVBqSbwtMM8YsMs7U86VBdWttAjjJKlAtxPEUiuD/om4NzKOUXHMBOdozEe7371jXd7QZdjUt7ikwxriNMUuBRJwv3I3A/uDIVpD3Hh+6/8H1B4C4oo241MrvM6FnpfTIz3eEPi8noThMslPYOllrdxljqgHTjTFrg60AUoIZY8oCXwKDrLUpaizLlyOeiVAHFEJHnWG3yKMII9ZaP9DKGFMR+BpoerTNgr91/4ufY/1N9LcqPfLzHaHPy0kI+5Zva+2u4O9EnC+C+NBGVCT+PPi/zoO/E0McT4EyxnhwEu9PrbVfBReH9TUXpGM8E+F+/451fYUyw66AtXY/8DNO34yKxplQDfLe40P3P7i+AkeWR0jhyO8zoWellMjnd4Q+LychrJNvY0wZY0y5g6+B7sDK4+8VFr4FDvYsvhWYGMJYClSwHnQ0sMZa+1quVWF7zQXpOM9EuN+/Y13ft8AtwR77HYADB//XquSfMaZqsMUbY0wM0A2nX8ZPwLXBzf56/w/+Xa4FZlpNPlFU8vtM/AB0N8ZUCnac6x5cJmHkJL4j9Hk5CWE9yY4x5kycf7WBU2Iz1lo7LIQhFTjjzBp6AVAF+BOnd/E3wDjgNGAb0MdaGxatScaY84DZwAogEFz8BE7dd1hec0E61jNhjIkjTO5ffp6J4D/m3sTpCJQB3G6tDbshSYuKMeYsnM5YbpzGnXHW2meDn7vPgcrAEuAma222MSYa+ASn78Y+oJ+1dlNoog9fBfVMGGP64/z3FmCYtfa/RXkdUvjy+x2hz8vJCevkW0RERESkOAnrshMRERERkeJEybeIiIiISBFR8i0iIiIiUkSUfIuIiIiIFBEl3yIiIiIiRUTJt4hICWCMiTPGLA3+7DbG7Mz1fk4hnO82Y8weY8wHwfcXGGOsMeaOXNu0Di57JJ/Hfjl4DfnaT0QkHJSG6eVFREo8a20S0ArAGPMMkGatfaWQT/uFtfb+XO9XAH1xJroC6Acsy88BjTFua+3/GWPSCyhGEZESRS3fIiIlnDEmLfj7AmPMLGPMOGPMemPMC8aYG40x840xK4wx9YPbVTXGfGmMWRD86XSCp9oGRBtjqgcn17gEmBI8Zn1jzOJcMTU0xiwKvt5ijBlqjPkV6FOAly4iUuKo5VtEJLycDTTFmTFyE/CBtTbeGPNPYCAwCBgJjLDW/mqMOQ1n2uemJ3j8CTgJ9BJgMZANYK3daIw5YIxpZa1dCtwOfJhrvyxr7XmnfHUiIiWckm8RkfCywFqbAGCM2QhMCy5fAVwYfN0NaOY0XgNQ3hhTzlqbegLHHwd8ATQBPgPOzbXuA+B2Y8xDOOUp8bnWfXES1yIiEnZUdiIiEl6yc70O5Hof4HCDiwvoaK1tFfypfYKJN9ba3YAXuBiY8ZfVXwKXAlcAi4J16gepxltEBCXfIiKl0TTgUEdKY0yrfO4/FHjMWuvPvdBam4VTwvIO8N9TDVJEJBwp+RYRKX0eAM4xxiw3xqwGBuRnZ2vtHGvtN8dY/SlgOVzuIiIiuRhrbahjEBGRYsYYcxtwzl+GGjyR/R4BKlhrn/qb7Z6haIZLFBEpVtTyLSIiR5MJXHpwkp0TYYz5GrgFZzSV4233MnATqgMXkVJILd8iIiIiIkVELd8iIiIiIkVEybeIiIiISBFR8i0iIiIiUkSUfIuIiIiIFBEl3yIiIiIiRUTJt4iIiIhIEfl/Mr7uAMR79osAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax.set_title('$L_x$ evolution evolutionary tracks with Jackson12 sample')\n",
    "\n",
    "# plot Tu15 tracks (for a Sun-like star!)\n",
    "ax.plot(blueTu15[\"time\"], blueTu15[\"Lx\"], marker=\"None\", linestyle=\":\", color=\"blue\", linewidth=2.5, alpha=0.6, label=\"__nolabel__\")#, label=\"fast rot. (solar model)\")\n",
    "ax.plot(redTu15[\"time\"], redTu15[\"Lx\"], marker=\"None\", linestyle=\":\", color=\"red\", linewidth=2.5, alpha=0.6, label=\"__nolabel__\")#, label=\"slow rot. (solar model)\")\n",
    "ax.plot(greenTu15[\"time\"], greenTu15[\"Lx\"], marker=\"None\", linestyle=\":\", color=\"lime\", linewidth=2.5, alpha=0.5, label=\"__nolabel__\")#, label=\"interm. rot. (solar model)\")\n",
    "ax.plot(jack12[\"age\"]/1e6, 10**jack12[\"logLx_cgs\"], ls=\"None\", marker=\"o\", color=\"grey\", mec=\"k\", alpha=0.3, zorder=1, label=\"cluster stars from \\nJackson et al. (2012)\")\n",
    "\n",
    "# plot approximated tracks\n",
    "step_size, t_track_start, t_track_end = 1., star_V1298Tau[\"age\"], 5000. # Myr\n",
    "t_arr = np.arange(t_track_start, t_track_end+step_size, step_size)\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track1) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:royal blue\", ls=\"-\", zorder=2, label=\"fast activity track\", lw=2.2)\n",
    "# 1 sigma errorbars on Lx at 23 Myr\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track1_lower) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:royal blue\", ls=\":\", zorder=2, label=\"__nolabel__\", lw=1)\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track1_upper) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:royal blue\", ls=\":\", zorder=2, label=\"__nolabel__\", lw=1)\n",
    "#####\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track2) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:green\", zorder=3, lw=2.2, alpha=1., label=\"medium activity track\")\n",
    "# 1 sigma errorbars on Lx at 23 Myr\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track2_lower) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:green\", ls=\":\", zorder=2, label=\"__nolabel__\", lw=1)\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track2_upper) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:green\", ls=\":\", zorder=2, label=\"__nolabel__\", lw=1)\n",
    "#####\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track3) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:red\", zorder=2, label=\"low activity track\", alpha=0.9, ls=\"-\", lw=2.2)\n",
    "# 1 sigma errorbars on Lx at 23 Myr\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track3_lower) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:red\", ls=\":\", zorder=2, label=\"__nolabel__\", lw=1)\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track3_upper) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:red\", ls=\":\", zorder=2, label=\"__nolabel__\", lw=1)\n",
    "\n",
    "ax.loglog()\n",
    "ax.set_xlabel(\"Time [Myr]\")\n",
    "ax.set_ylabel(\"L$_\\mathrm{x}$ [erg/s]\")\n",
    "ax.set_xticks([5, 10, 20, 50, 100, 300, 1000, 5000])\n",
    "ax.set_yticks([10**27., 10**28., 10**29., 10**30., 10**31.])\n",
    "ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.0f}'))\n",
    "ax.set_xlim(left=4.9, right=11000)\n",
    "ylim = ax.get_ylim()\n",
    "ax.set_ylim(abs(ylim[0]), ylim[1])\n",
    "ax.legend(loc=\"best\", fontsize=12)\n",
    "#plt.savefig(\"./tracks_v1298Tau.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create planets for evolution calculation\n",
    "\n",
    "The scenarios:\n",
    "* \"fluffy\" planets with a 5 and 10 M$_\\oplus$ core, and H/He envelope to match the current observed radius\n",
    "* \"dense\" planets which follow the empirical mass-radius relation for more mature planets\n",
    "\n",
    "To create a planet object (either from the LoFo14 class or the Ot20 class), a dictionary with the star & planet parameters needs to be passed when initializing the class object.\n",
    "\n",
    "*NOTE*: there are four classes: planet_LoFo14_PAPER, planet_LoFo14, planet_Ot20_PAPER, planet_Ot20 <br>\n",
    "In the paper we originally used a fixed step size for the foward-integration, but later added a variable step-size option, which is much faster.\n",
    "For completeness, both versions are being shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create planet objects using either LoFO14 or Ot20 results:\n",
    "# ----------------------------------------------------------\n",
    "\n",
    "# for fluffy LoFo14 planet, a core mass needs to be specified, in addition to the metallicity (solar or enhanced)\n",
    "# based on the specified core mass and the observed radius, one can estimate the current envelope mass fraction \n",
    "# needed to produce a planet of current size -> the envelope together with the core sets the starting mass of the planets\n",
    "# for the dense Ot20 planets, the current mass is estimated based on the observed mass-radius relation in Otegi et al. (2020)\n",
    "\n",
    "###############################################################################################\n",
    "Mcore5, Mcore10, metallicity = 5., 10., \"solarZ\"\n",
    "# 'fluffy' LoFo14 planets with 5 M_earth core\n",
    "planet_c = {\"core_mass\": Mcore5, \"radius\": R_c, \"distance\": a_c, \"metallicity\": metallicity}\n",
    "planet_d = {\"core_mass\": Mcore5, \"radius\": R_d, \"distance\": a_d, \"metallicity\": metallicity}\n",
    "planet_b = {\"core_mass\": Mcore5, \"radius\": R_b, \"distance\": a_b, \"metallicity\": metallicity}\n",
    "planet_e = {\"core_mass\": Mcore5, \"radius\": R_e, \"distance\": a_e, \"metallicity\": metallicity}\n",
    "\n",
    "# fixed step size\n",
    "pl_c_5_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_c)\n",
    "pl_d_5_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_d)\n",
    "pl_b_5_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_b)\n",
    "pl_e_5_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_e)\n",
    "planet_list_Mcore5_PAPER = [pl_c_5_PAPER, pl_d_5_PAPER, pl_b_5_PAPER, pl_e_5_PAPER]\n",
    "\n",
    "# variable step size\n",
    "pl_c_5 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_c)\n",
    "pl_d_5 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_d)\n",
    "pl_b_5 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_b)\n",
    "pl_e_5 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_e)\n",
    "planet_list_Mcore5 = [pl_c_5, pl_d_5, pl_b_5, pl_e_5]\n",
    "\n",
    "###############################################################################################\n",
    "# 'fluffy' LoFo14 planets with 10 M_earth core\n",
    "planet_c = {\"core_mass\": Mcore10, \"radius\": R_c, \"distance\": a_c, \"metallicity\": metallicity}\n",
    "planet_d = {\"core_mass\": Mcore10, \"radius\": R_d, \"distance\": a_d, \"metallicity\": metallicity}\n",
    "planet_b = {\"core_mass\": Mcore10, \"radius\": R_b, \"distance\": a_b, \"metallicity\": metallicity}\n",
    "planet_e = {\"core_mass\": Mcore10, \"radius\": R_e, \"distance\": a_e, \"metallicity\": metallicity}\n",
    "\n",
    "# fixed step size\n",
    "pl_c_10_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_c)\n",
    "pl_d_10_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_d)\n",
    "pl_b_10_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_b)\n",
    "pl_e_10_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_e)\n",
    "planet_list_Mcore10_PAPER = [pl_c_10_PAPER, pl_d_10_PAPER, pl_b_10_PAPER, pl_e_10_PAPER]\n",
    "\n",
    "# variable step size\n",
    "pl_c_10 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_c)\n",
    "pl_d_10 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_d)\n",
    "pl_b_10 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_b)\n",
    "pl_e_10 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_e)\n",
    "planet_list_Mcore10 = [pl_c_10, pl_d_10, pl_b_10, pl_e_10]\n",
    "\n",
    "###############################################################################################\n",
    "# 'high-density' Ot20 planets\n",
    "planet_c = {\"radius\": R_c, \"distance\": a_c}\n",
    "planet_d = {\"radius\": R_d, \"distance\": a_d}\n",
    "planet_b = {\"radius\": R_b, \"distance\": a_b}\n",
    "planet_e = {\"radius\": R_e, \"distance\": a_e}\n",
    "\n",
    "# fixed step size\n",
    "pl_c_Ot_PAPER = planet_Ot20_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_c)\n",
    "pl_d_Ot_PAPER = planet_Ot20_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_d)\n",
    "pl_b_Ot_PAPER = planet_Ot20_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_b)\n",
    "pl_e_Ot_PAPER = planet_Ot20_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_e)\n",
    "planet_list_Ot_PAPER = [pl_c_Ot_PAPER, pl_d_Ot_PAPER, pl_b_Ot_PAPER, pl_e_Ot_PAPER]\n",
    "\n",
    "# variable step size\n",
    "pl_c_Ot = planet_Ot20(star_dictionary=star_V1298Tau, planet_dict=planet_c)\n",
    "pl_d_Ot = planet_Ot20(star_dictionary=star_V1298Tau, planet_dict=planet_d)\n",
    "pl_b_Ot = planet_Ot20(star_dictionary=star_V1298Tau, planet_dict=planet_b)\n",
    "pl_e_Ot = planet_Ot20(star_dictionary=star_V1298Tau, planet_dict=planet_e)\n",
    "planet_list_Ot = [pl_c_Ot, pl_d_Ot, pl_b_Ot, pl_e_Ot]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evolve planets using a fixed step size (this was used in the paper, but is very slow!)\n",
    "\n",
    "*****\n",
    "**TO DO**: <br>\n",
    "Run the cells below to start the evolution calculation (or immediately read in the results if already available).  <br>\n",
    "**Some of the calculation results are already available in separate folders. If you want to redo them, delete the results-folders. Just remember that this will take some time with the fixed step-size code.** <br>\n",
    "For each planet scenario (\"fluffy\" and \"dense\"), the planets are evolved along three tracks (high, medium and low activity). <br>\n",
    "The output will be four arrays per track: t1_XXX, M1_XXX, R1_XXX, Lx1_XXX (time, mass, radius, Lx evolution). <br>\n",
    "The high activity track is labeled in the variable name with a \"1\", the medium one with \"2\", and the low activity track with a \"3\".\n",
    "\n",
    "Some info: <br>\n",
    "* *create_planet_chunks* - this function creates the right directory structure for saving the results; for each planet in the list of planets which is passed into this function, a folder is created where the results are saved. The function returns the correct path for saving the results, and a a new list of planets (`list_planets` is divided into chunks to avoid problems with multiprocessing; only important if `list_planets` is very long -> can be ignored here!). <br> **NOTE**: the folders created for each planet are **numbered**! \n",
    "So if `list_planets` contains: [planet_c, planet_d, planet_b, planet_e], then the corresponding folders will be [planet_1, planet_2, planet_3, planet_4].\n",
    "\n",
    "* Set the name of the folder for saving the results of one scenario, the evaporation efficiency ($\\epsilon$), the initial step size and the end time of the simulation\n",
    "\n",
    "* Initiate the multiprocessing & call *evolve_ensamble*, which takes care of the rest. This funtion evolves all the planets in `list_planets` (which, if long, is chunked up into smaller pieces -> `planet_chunks`) along all the evolutionary tracks provided in `evo_track_dict_list`; decide here if you want the $\\beta$ and $K$ parameters to be turned on or set to 1.\n",
    "\n",
    "* The results are read in by the function `read_in_PLATYPOS_results`, which returns three things:\n",
    "    1. Dictionary with planet names as keys (so planet_1, planet_2,...) and a corresponding dataframe as value, which has the time, radius, mass and Lx evolution for each track stored (i.e. (4 * number of tracks) columns)\n",
    "    1. Dataframe with all the initial planet parameters\n",
    "    1. Dictionary with planet names as keys, and the parameters of the evolutionary tracks as values <br>\n",
    "    track '1' = intermediate activity track ('track_23.0_23.0_5000.0_1.3e+30_0.0_0.0') <br>\n",
    "    track '2' = low activity track ('track_23.0_23.0_5000.0_1.3e+30_20.0_16.0') <br>\n",
    "    track '3' = high activity track ('track_23.0_240.0_5000.0_1.3e+30_0.0_0.0') <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the paths\n",
    "path_up_to_playpos = os.getcwd().split(\"platypos\")[0]\n",
    "curr_path = path_up_to_playpos+'platypos/example_V1298Tau_github_copy/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/media/laura/SSD2lin/PhD/work/gitlab/platypos/example_V1298Tau_github_copy/'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curr_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LoFo14 planets with M$_{core}\\,=\\,5\\,$M$_\\oplus$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folder results_LoFo14_Mcore5_PAPER/ exists.\n",
      "That took 0.00045972267786661785 minutes\n",
      "CPU times: user 11.9 ms, sys: 12 ms, total: 23.9 ms\n",
      "Wall time: 28.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "folder_name = \"results_LoFo14_Mcore5_PAPER/\" \n",
    "# chunk_size...\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Mcore5_PAPER, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 0.1, 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
    "    # evolve the ensamble (multi-threading)\n",
    "    evolve_ensamble(planet_chunks, t_final, initial_step_size=init_step, epsilon=eps, \n",
    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)\n",
    "    \n",
    "# read in the results as a dataframe\n",
    "#planets_LoFo14_Mcore5_dict, planets_LoFo14_Mcore5_init_df, tracks_LoFo14_Mcore5_dict = read_in_PLATYPOS_results(path_to_results=\"./results_LoFo14_Mcore5_PAPER/\", N_tracks=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LoFo14 planets with M$_{core}\\,=\\,10\\,$M$_\\oplus$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folder results_LoFo14_Mcore10_PAPER/ exists.\n",
      "That took 0.00047680139541625974 minutes\n",
      "CPU times: user 8.72 ms, sys: 15.7 ms, total: 24.5 ms\n",
      "Wall time: 30.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "folder_name = \"results_LoFo14_Mcore10_PAPER/\"\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Mcore10_PAPER, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 0.1, 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
    "    evolve_ensamble(planet_chunks, t_final, initial_step_size=init_step, epsilon=eps, \n",
    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)\n",
    "    \n",
    "\n",
    "# read in the results as a dataframe\n",
    "#planets_LoFo14_Mcore10_dict, planets_LoFo14_Mcore10_init_df, tracks_LoFo14_Mcore10_dict = read_in_PLATYPOS_results(path_to_results=\"./results_LoFo14_Mcore10_PAPER/\", N_tracks=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ot20 planets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Planet:  planet_1_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_4_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Planet:  planet_3_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Start evolving.\n",
      "Planet:  planet_2_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Start evolving.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-10:\n",
      "Process Process-9:\n",
      "Process Process-11:\n",
      "Process Process-12:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"../population_evolution/evolve_planet.py\", line 71, in evolve_one_planet\n",
      "    path_for_saving=path_for_saving, planet_folder_id=folder)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"../population_evolution/evolve_planet.py\", line 71, in evolve_one_planet\n",
      "    path_for_saving=path_for_saving, planet_folder_id=folder)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"../platypos_package/Mass_evolution_function.py\", line 706, in mass_planet_RK4_forward_Ot14_PAPER\n",
      "    Mdot4 = mass_loss_rate_forward_Ot20(times[i]+dt, epsilon, K_on, beta_on, planet_object, R_05k3, track_dict)\n",
      "  File \"../platypos_package/Planet_class_Ot20_PAPER.py\", line 155, in evolve_forward\n",
      "    track_dict=evo_track_dict)\n",
      "  File \"../population_evolution/evolve_planet.py\", line 71, in evolve_one_planet\n",
      "    path_for_saving=path_for_saving, planet_folder_id=folder)\n",
      "  File \"../platypos_package/Planet_class_Ot20_PAPER.py\", line 155, in evolve_forward\n",
      "    track_dict=evo_track_dict)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"../population_evolution/evolve_planet.py\", line 71, in evolve_one_planet\n",
      "    path_for_saving=path_for_saving, planet_folder_id=folder)\n",
      "  File \"../platypos_package/Mass_evolution_function.py\", line 691, in mass_planet_RK4_forward_Ot14_PAPER\n",
      "    Mdot1 = mass_loss_rate_forward_Ot20(times[i], epsilon, K_on, beta_on, planet_object, R, track_dict) # mass M, radius R\n",
      "  File \"../platypos_package/Planet_class_Ot20_PAPER.py\", line 155, in evolve_forward\n",
      "    track_dict=evo_track_dict)\n",
      "  File \"../platypos_package/Mass_loss_rate_function.py\", line 87, in mass_loss_rate_forward_Ot20\n",
      "    rho_p = rho = plmoOt20.density_planet(M_p, radius_at_t) # initial approx. density\n",
      "  File \"../platypos_package/Mass_evolution_function.py\", line 691, in mass_planet_RK4_forward_Ot14_PAPER\n",
      "    Mdot1 = mass_loss_rate_forward_Ot20(times[i], epsilon, K_on, beta_on, planet_object, R, track_dict) # mass M, radius R\n",
      "  File \"../platypos_package/Planet_model_Ot20.py\", line 74, in density_planet\n",
      "    rho = (M_p*const.M_earth.cgs/(4./3*np.pi*(R_p*const.R_earth.cgs)**3)).cgs\n",
      "  File \"../platypos_package/Mass_loss_rate_function.py\", line 87, in mass_loss_rate_forward_Ot20\n",
      "    rho_p = rho = plmoOt20.density_planet(M_p, radius_at_t) # initial approx. density\n",
      "  File \"../platypos_package/Mass_loss_rate_function.py\", line 87, in mass_loss_rate_forward_Ot20\n",
      "    rho_p = rho = plmoOt20.density_planet(M_p, radius_at_t) # initial approx. density\n",
      "  File \"../platypos_package/Planet_model_Ot20.py\", line 74, in density_planet\n",
      "    rho = (M_p*const.M_earth.cgs/(4./3*np.pi*(R_p*const.R_earth.cgs)**3)).cgs\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 768, in cgs\n",
      "    cgs_unit = self.unit.cgs\n",
      "  File \"../platypos_package/Planet_class_Ot20_PAPER.py\", line 155, in evolve_forward\n",
      "    track_dict=evo_track_dict)\n",
      "  File \"../platypos_package/Planet_model_Ot20.py\", line 74, in density_planet\n",
      "    rho = (M_p*const.M_earth.cgs/(4./3*np.pi*(R_p*const.R_earth.cgs)**3)).cgs\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 768, in cgs\n",
      "    cgs_unit = self.unit.cgs\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/utils/decorators.py\", line 744, in __get__\n",
      "    val = self.fget(obj)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 768, in cgs\n",
      "    cgs_unit = self.unit.cgs\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/utils/decorators.py\", line 744, in __get__\n",
      "    val = self.fget(obj)\n",
      "  File \"../platypos_package/Mass_evolution_function.py\", line 696, in mass_planet_RK4_forward_Ot14_PAPER\n",
      "    Mdot2 = mass_loss_rate_forward_Ot20(times[i]+0.5*dt, epsilon, K_on, beta_on, planet_object, R_05k1, track_dict)\n",
      "  File \"../platypos_package/Mass_loss_rate_function.py\", line 87, in mass_loss_rate_forward_Ot20\n",
      "    rho_p = rho = plmoOt20.density_planet(M_p, radius_at_t) # initial approx. density\n",
      "  File \"../platypos_package/Planet_model_Ot20.py\", line 74, in density_planet\n",
      "    rho = (M_p*const.M_earth.cgs/(4./3*np.pi*(R_p*const.R_earth.cgs)**3)).cgs\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1331, in cgs\n",
      "    return self.to_system(cgs)[0]\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1331, in cgs\n",
      "    return self.to_system(cgs)[0]\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 768, in cgs\n",
      "    cgs_unit = self.unit.cgs\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1331, in cgs\n",
      "    return self.to_system(cgs)[0]\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/utils/decorators.py\", line 744, in __get__\n",
      "    val = self.fget(obj)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1312, in to_system\n",
      "    composed = x.compose(units=system)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/utils/decorators.py\", line 744, in __get__\n",
      "    val = self.fget(obj)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1269, in compose\n",
      "    max_depth=max_depth, depth=0, cached_results={}))\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1312, in to_system\n",
      "    composed = x.compose(units=system)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1107, in _compose\n",
      "    cached_results=cached_results)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1269, in compose\n",
      "    max_depth=max_depth, depth=0, cached_results={}))\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1082, in _compose\n",
      "    factored = composed * tunit\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1331, in cgs\n",
      "    return self.to_system(cgs)[0]\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1312, in to_system\n",
      "    composed = x.compose(units=system)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 694, in __mul__\n",
      "    return CompositeUnit(1, [self, m], [1, 1], _error_check=False)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1312, in to_system\n",
      "    composed = x.compose(units=system)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2052, in __init__\n",
      "    bases=decompose_bases)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2119, in _expand_and_gather\n",
      "    new_parts = [x for x in new_parts.items() if x[1] != 0]\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1269, in compose\n",
      "    max_depth=max_depth, depth=0, cached_results={}))\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/media/laura/SSD2lin/PhD/work/gitlab/platypos/population_evolution/evolve_planet.py\u001b[0m in \u001b[0;36mevolve_ensamble\u001b[0;34m(planets_chunks, t_final, initial_step_size, epsilon, K_on, beta_on, evo_track_dict_list, path_save)\u001b[0m\n\u001b[1;32m    220\u001b[0m             \u001b[0;31m# \"join() says that the code in __main__ must wait until all our tasks are complete before continuing!\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m             \u001b[0;31m# Make sure Python waits for the process to terminate and then exits the completed processes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m         \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstarttime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/multiprocessing/process.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_pid\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'can only join a child process'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_popen\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'can only join a started process'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_popen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0m_children\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiscard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/multiprocessing/popen_fork.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m     46\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0;31m# This shouldn't block if wait() returned successfully.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWNOHANG\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0.0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/multiprocessing/popen_fork.py\u001b[0m in \u001b[0;36mpoll\u001b[0;34m(self, flag)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m                 \u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m                 \u001b[0;31m# Child process not yet created. See #1731717\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2119, in <listcomp>\n",
      "    new_parts = [x for x in new_parts.items() if x[1] != 0]\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1107, in _compose\n",
      "    cached_results=cached_results)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1269, in compose\n",
      "    max_depth=max_depth, depth=0, cached_results={}))\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1107, in _compose\n",
      "    cached_results=cached_results)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1123, in _compose\n",
      "    factored = composed * tunit\n",
      "KeyboardInterrupt\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1082, in _compose\n",
      "    factored = composed * tunit\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 694, in __mul__\n",
      "    return CompositeUnit(1, [self, m], [1, 1], _error_check=False)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 694, in __mul__\n",
      "    return CompositeUnit(1, [self, m], [1, 1], _error_check=False)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1107, in _compose\n",
      "    cached_results=cached_results)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1081, in _compose\n",
      "    composed = (u / tunit_decomposed).decompose()\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 654, in __div__\n",
      "    return CompositeUnit(1, [self, m], [1, -1], _error_check=False)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2052, in __init__\n",
      "    bases=decompose_bases)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2120, in _expand_and_gather\n",
      "    new_parts.sort(key=lambda x: (-x[1], getattr(x[0], 'name', '')))\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2052, in __init__\n",
      "    bases=decompose_bases)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2052, in __init__\n",
      "    bases=decompose_bases)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2120, in <lambda>\n",
      "    new_parts.sort(key=lambda x: (-x[1], getattr(x[0], 'name', '')))\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2113, in _expand_and_gather\n",
      "    for b_sub, p_sub in zip(b._bases, b._powers):\n",
      "KeyboardInterrupt\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2115, in _expand_and_gather\n",
      "    scale = add_unit(b_sub, a * b, scale)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2097, in add_unit\n",
      "    if unit in new_parts:\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1958, in __hash__\n",
      "    if self._hash is None:\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "folder_name = \"results_Ot20_PAPER/\"\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Ot_PAPER, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 1., 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
    "    evolve_ensamble(planet_chunks, t_final, initial_step_size=init_step, epsilon=eps, \n",
    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)\n",
    "    \n",
    "# read in the results as a dataframe\n",
    "#planets_Ot_dict, planets_Ot_init_df, tracks_Ot_dict = read_in_PLATYPOS_results(path_to_results=\"./results_Ot20_PAPER/\", N_tracks=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evolve planets using a variable step size (faster & recommended)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LoFo14 planets with M$_{core}\\,=\\,5\\,$M$_\\oplus$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folder results_LoFo14_Mcore5_varstep/ exists.\n",
      "That took 0.00045744578043619793 minutes\n",
      "Total # of planet folders =  4\n",
      "Non-empty folders:  4\n",
      "\n",
      "Total number of planets to analyze:  4\n",
      "CPU times: user 74.8 ms, sys: 27.3 ms, total: 102 ms\n",
      "Wall time: 108 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "folder_name = \"results_LoFo14_Mcore5_varstep/\"\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Mcore5, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 0.1, 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
    "    evolve_ensamble(planet_chunks, t_final, initial_step_size=init_step, epsilon=eps, \n",
    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)\n",
    "    \n",
    "# read in the results as a dataframe\n",
    "pl_LoFo14_Mcore5_dict, pl_LoFo14_Mcore5_init_df, tracks_LoFo14_Mcore5_dict = read_in_PLATYPOS_results(path_to_results=\"./results_LoFo14_Mcore5_varstep/\", N_tracks=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# after code update 18.6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([['planet_3',\n",
       "         <Planet_class_LoFo14.planet_LoFo14 object at 0x7f5bbe4167b8>],\n",
       "        ['planet_4',\n",
       "         <Planet_class_LoFo14.planet_LoFo14 object at 0x7f5bbe4167f0>],\n",
       "        ['planet_2',\n",
       "         <Planet_class_LoFo14.planet_LoFo14 object at 0x7f5bbe419748>],\n",
       "        ['planet_1',\n",
       "         <Planet_class_LoFo14.planet_LoFo14 object at 0x7f5bbe419710>]],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet_chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.59"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet_list_Mcore5[0].radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folder results_LoFo14_Mcore5_varstep_18June2020/ exists.\n",
      "That took 0.000495290756225586 minutes\n",
      "Total # of planet folders =  4\n",
      "Non-empty folders:  4\n",
      "\n",
      "Total number of planets to analyze:  4\n",
      "CPU times: user 63.2 ms, sys: 40 ms, total: 103 ms\n",
      "Wall time: 109 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "folder_name = \"results_LoFo14_Mcore5_varstep_18June2020/\"\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Mcore5, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 0.1, 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
    "    evolve_ensamble(planet_chunks, t_final, initial_step_size=init_step, epsilon=eps, \n",
    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)\n",
    "    \n",
    "# read in the results as a dataframe\n",
    "pl_LoFo14_Mcore5_dict_new, pl_LoFo14_Mcore5_init_df_new, tracks_LoFo14_Mcore5_dict_new = read_in_PLATYPOS_results(path_to_results=\"./results_LoFo14_Mcore5_varstep_18June2020/\", N_tracks=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LoFo14 planets with M$_{core}\\,=\\,10\\,$M$_\\oplus$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folder results_LoFo14_Mcore10_varstep/ exists.\n",
      "That took 0.000504453976949056 minutes\n",
      "Total # of planet folders =  4\n",
      "Non-empty folders:  4\n",
      "\n",
      "Total number of planets to analyze:  4\n",
      "CPU times: user 68.7 ms, sys: 20.2 ms, total: 88.9 ms\n",
      "Wall time: 92.8 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "folder_name = \"results_LoFo14_Mcore10_varstep/\"\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Mcore10, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 0.1, 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
    "    evolve_ensamble(planet_chunks, t_final, initial_step_size=init_step, epsilon=eps, \n",
    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)\n",
    "\n",
    "# read in the results as a dataframe\n",
    "pl_LoFo14_Mcore10_dict, pl_LoFo14_Mcore10_init_df, tracks_LoFo14_Mcore10_dict = read_in_PLATYPOS_results(path_to_results=\"./results_LoFo14_Mcore10_varstep/\", N_tracks=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ot20 planets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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