fitting_hybridCommunity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting Induction data to the Community model using softmax rule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from scipy import optimize\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from tesser import cython_fit\n",
    "from tesser import util\n",
    "from tesser import network\n",
    "from scipy.spatial import distance\n",
    "import importlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data for all subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir= \"/mnt/c/Users/rodv2/Dropbox/tesser_successor/Data\"\n",
    "fitted_path= \"/mnt/c/Users/rodv2/Dropbox/tesser_successor/Figures/fitted_models/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "struct_all = util.load_struct(data_dir)\n",
    "induct_all = util.load_induct(data_dir)\n",
    "n_states =21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get community matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-d132e81e18b8>:2: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  comm = 1 - distance.squareform(distance.pdist(net['comm'][:, None], 'hamming'))\n"
     ]
    }
   ],
   "source": [
    "net = network.temp_node_info()\n",
    "comm = 1 - distance.squareform(distance.pdist(net['comm'][:, None], 'hamming'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit each individual subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating parameters for 100...\n",
      "Estimating parameters for 101...\n",
      "Estimating parameters for 102...\n",
      "Estimating parameters for 103...\n",
      "Estimating parameters for 104...\n",
      "Estimating parameters for 105...\n",
      "Estimating parameters for 106...\n",
      "Estimating parameters for 107...\n",
      "Estimating parameters for 108...\n",
      "Estimating parameters for 109...\n",
      "Estimating parameters for 110...\n",
      "Estimating parameters for 111...\n",
      "Estimating parameters for 112...\n",
      "Estimating parameters for 113...\n",
      "Estimating parameters for 114...\n",
      "Estimating parameters for 115...\n",
      "Estimating parameters for 116...\n",
      "Estimating parameters for 117...\n",
      "Estimating parameters for 119...\n",
      "Estimating parameters for 120...\n",
      "Estimating parameters for 121...\n",
      "Estimating parameters for 122...\n",
      "Estimating parameters for 123...\n",
      "Estimating parameters for 124...\n",
      "Estimating parameters for 125...\n",
      "Estimating parameters for 126...\n",
      "Estimating parameters for 127...\n",
      "Estimating parameters for 128...\n",
      "Estimating parameters for 129...\n",
      "Estimating parameters for 130...\n",
      "Estimating parameters for 131...\n",
      "Estimating parameters for 132...\n",
      "Estimating parameters for 133...\n",
      "Estimating parameters for 135...\n",
      "Estimating parameters for 136...\n",
      "Estimating parameters for 137...\n",
      "Estimating parameters for 138...\n"
     ]
    }
   ],
   "source": [
    "# Using a hybrid model function with the community matrix.\n",
    "fixed = {}\n",
    "var_names = ['gamma', 'alpha', 'tau', 'w']\n",
    "var_bounds = {'gamma': [0, 1], 'alpha': [0, 1], 'tau': [0, 10] , 'w': [0, 1]}\n",
    "results = cython_fit.fit_induct_indiv(struct_all, induct_all, fixed, var_names, var_bounds, split =False,\n",
    "                               n_states=n_states, verbose=False, model_type='comm', model=comm, split_list =[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gamma</th>\n",
       "      <th>alpha</th>\n",
       "      <th>tau</th>\n",
       "      <th>w</th>\n",
       "      <th>log_like</th>\n",
       "      <th>k</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.146458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.572237</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-29.112182</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.992895</td>\n",
       "      <td>0.724412</td>\n",
       "      <td>1.078343</td>\n",
       "      <td>0.942471</td>\n",
       "      <td>-25.312826</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.620687</td>\n",
       "      <td>0.105569</td>\n",
       "      <td>0.621340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-18.923571</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.998635</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>0.999821</td>\n",
       "      <td>-24.368312</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.558111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-27.130966</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            gamma     alpha       tau         w   log_like  k   n\n",
       "subject                                                          \n",
       "100      0.146458  0.000000  9.572237  1.000000 -29.112182  4  42\n",
       "101      0.992895  0.724412  1.078343  0.942471 -25.312826  4  42\n",
       "102      0.620687  0.105569  0.621340  0.000000 -18.923571  4  42\n",
       "103      0.000000  0.998635  0.001407  0.999821 -24.368312  4  42\n",
       "104      0.000000  1.000000  0.558111  1.000000 -27.130966  4  42"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = results.set_index('subject')\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv(f\"./Data/hybrid_results/hybrid_community.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get response probabilities for the fitted models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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2G+tbZKIxJtb9ijbGNHi9iFLKJ7SGaQ0LWBrC1MfAUBE5V6znyPwO6OIxPwooAvJEpDtwe53lD2Bdl+DZvhzr0Hk48NcjbdwY4wTeBf4BxANfAohIsIhcKiIxxphKoABwtWwXa23PhVUI/yIiUSLSG7gV69sx7nm/E5EeIhIH3OWx7D6sgvuoiESLiM190e+kY+2XUqrFtIZpDQtYGsI6OGNMNnA+8DBW0ekPLPZo8gAwCsjHKnZz66ziIeAe96Ht24BXsQ5/7wHWAz80oRtvYH3rfMdd0KpdDmS4Twlch/UN1htuBIqB7cAi9/ZfdM/7N/A5sBrrgty6+3sF1qmA9UAuVvHt6qV+KaWaSWuY1rBAJu6L8ZRSSimllA/pkTCllFJKKT/QEKaUUkop5QcawpRSSiml/EBDmFJKKaWUH2gI66BExIhIP3/3ozEi0ss9lpuOaaaUapa2Xt+UqqYhTB0T9wCzLndg8nx1O5b1GmN2GWsst2N+ro4/uT+fRf7uh1KqZdzP23pdrAHAi0VkqYic4YPtviwif/Z471lfq0Sk1OP9pa3dH9U6NIQpb/jeHZg8X3tba2Ni0d9dpVSrEpF4rOdwVQBDsYYMegx4S0R+6cu+eNZXag86HmmMaWyAcdXG6T9kHdsZIrJdRLJF5B/upyeHiMghEUmtbuQee61URDo1dwMikiEit4nIGhHJF5E5IhLqnrdBRM70aOtw92WUiCS7Tyk43PMWiMhfRGQxUAL0EZHxIrLMvd5lIjLeY10LRORPIrJYRApF5AsRSXTPq173lSKyW0RyReQ6ERnj7meeiDxZZz+ucvc3V0Q+dz+lunqecS+/xT3/KXdQHAw8Cxzv/raa19zPTynVYt6ob7dgPW3/V8aY/caYUmPMm8BfgH+6/85r1Sr3OheIyNUe7xusH+7lHxORg+46tkZEhonINcClwB3u2vG/hnbwaPsjIieJSKaI/MH9OWR4HjVzL/+IiOwSkQMi8qyIhLX4E1fNpiGsYzsHSMd6mvTZwFXGmHLgLWo/2fli4CtjTFYLt3MBMBVIAYYDs9zT3+TwwLcApwPZxpiVjazncuAarGFFCrGefv0EkAD8E/hYRBI82l8CXIk1blswcFud9Y3Derr2hcBs4I9YT70eClwg7qE83N94/wCcC3QCvnP33dOZwBhghHt/TzfGbMB6Snb1kcLYRvZLKeV93qhvpwHvGWOq6kx/G6ueHfW6s6PUjynARGAAEItVi3KMMc8DrwN/d9eOXzS07ibuTxesI3jdgZnA8yIy0D3vb+5tp7n3pTtw79H2SXmPhrCO7W/GmEPGmF1YIaQ6EL0CXCKHT/ldDrx2hPUc5z56VP3aVmf+E8aYvcaYQ8D/sP7gwRpq4ywRCXe/vwSPwW8b8LIxZp17WJApwBZjzGvGGKf72+lGwLNYvWSM2WyMKcUqmml11vcnY0yZMeYLrCFA3jTGHDTG7MEqlCPd7a4FHjLGbHBv+69AmufRMOBhY0ye+7P8poFtKaV8yxv1LRFrQOy6qqc15ezAkepHJdaXykFYI9hscI/v2BxN2Z//M8aUG2O+xfryeoGICPBr4Bb351To7ttFzdy+OgYawjq23R4/7wS6ARhjfsQKJZNEZBDWN6QPj7CeH4wxsR6vvnXm7/f4uQSIdG9nK7AB+IU7iJ3FkUOYZ3+7ufvsaSfWN7kjbtfDAY+fSxt4X92+N/B4dcgEDgHSzG0ppXzLG/Utm4bHVaye1pSzA43WD2PMfOBJ4CnggIg8LyLRTVhnjSbsT64xptjjffVn0QlrgPIVHn37jKYFS+UlGsI6tp4eP/cCPC+mfwXrEPflwLvGmLJW6kP1KcmzgfXuYNYYz4FO92IVN0+9sAbd9bbdwLV1gmaYMWZJE5bVwVmV8g9v1LevgBlS/0agC4BMYBtWAAIr0FTr4vHzEeuHMeYJY8xorMsgBgC3u5drTu040v7EiUiEx/vqzyIb68vmUI9+xbgv/Fc+oiGsY7tdROJEpCdwEzDHY95rWNdUXAa82op9eAvr1OJvOPJRsLo+AQaIyCViXdB/ITAE+KgV+vgscLeIDAUQkRgROb+Jyx4AeohIcCv0SynVOG/Ut8eAaOA/ItJFREJF5GLg/4D7jDFV7muv9gCXiYhdRK4CPM8GNFo/xLoZaJyIBGGFuTKg+rE8B4A+TdzXo+3PAyISLCInYl2/+o77Ord/A4+JSJK7P91F5PQmblN5gYawju0DYAWwCus6gf9UzzDGZAIrsb6NfXeU9VTf/ef5GtOUDrivf/geGE/tInm05XKwisnvgRzgDuBMY0x2U9fRjG29j3UB61siUgCsBaY1cfH5wDpgv4h4vW9KqUYdc31z15kTgFBgPdadkq8C1xtjXvRo+musI1g5WEe0lnis40j1IxorCOVinSbMAR5xz/sPMMR9qnDekXb0KPuz373+vVgX+19njNnonncnsBX4wd23r4CBKJ8RY/RsiWqYiLwI7DXG3OPvviillDe1pL65r9daDLxvjGlTdxE2tD8ichLwX2NMD3/1Sx2Z4+hNVEckIslYt1SPPEpTpZQKKC2tb8aYArGeln+ViHQxxuw/6kI+oPU6cOnpSFWPiPwJ65D5P4wxO/zdH6WU8pZjrW/GmN3GmAfaUADTeh3A9HSkUkoppZQf6JEwpZRSSik/COhrwhITE01ycrK/u6GUagUrVqzINsa06wdHag1Tqn1qav0K6BCWnJzM8uXL/d0NpVQrEJG6IyK0O1rDlGqfmlq/fBbC3LfPngkcNMYMa2C+AI8DZ2AN+zLrCAM5q3buQEEpi7fm8M2mg4zqFcfJA5NITow4+oJtWe4u2PY17PgOUk6EvpMhrpe/e6WaQOuXAqAkF3YugvUfQuIAGPwLMC7Y8BEc2g5DzoLeEyAoHDKXwtq5YA+BYedAl+Gwd6U1zWaHYTOsdhs/hpwt0Hs8lBdC99HQYyw4PJ7vvG81bPgf5O+BoedA7+MhJMp/n4PyGl8eCXsZa4ysxp5OPA3o736NA55x/1d1MOVOF098vZXXf9wFwP9W7+Pd5Zm8dOUYkqJD/dy7FirNh49/D1u/sN6vew8GTINznoOwGP/2zc+MMVgZpk17Ga1fas2b8Nndh98L8MMzUJLjnv8WTH8MOg+GV86E6hvflj0Pl8yB188HU2VNC42GZS9AySHr/c/vwIm3wbtXwrn/hj4nWdMPrIeXz4TyAuv96jdgxguQ2tRBO1RrO5Ya5rML840xC7EGLm3M2cCrxvIDECsiDQ2cqtq5jOwS3li6q9a0dfsK2HKwyE898oKcLYcDWLXNn0LOkYbKbP+KioqYNm0aX3zxxdEb+5HWL0X+HvjmodrTqpyHA1i1b/4Me1cdDmDV7dbNg1iP4W7FdjiAVVv5Cgz6hRXsqtyjF2UuOxzAarbx1/rLKr+YPXs2v/nNb6iqqmrR8m3pmrDu1B71PtM9bZ9nIxG5BrgGoFev+qdyKisryczMpKystcabDlyhoaH06NGDoKAgf3fliIwxNPTklKqqAH6cSnVBrcs0Mr0DKCoqYvr06SxatIgrr7zS3905Vk2qX6A1zBv8UstMlRWmak1rqFA5G57uqgC7xz+5jS1rd4Cz/PD8hmqEq/LwETXlN7Nnz+aWW25hxowZuFwubLbmH9dqSyGsoWN59X5LjTHPA88DpKen15ufmZlJVFQUycnJgXCKw2eMMeTk5JCZmUlKSoq/u3NEvRLCmZ7ahY9/PvwsxF7xYfTrHOnHXh2jhH7QfQzsWXZ4Ws/jrOkdkGcAe+ONN7jwwgv93aVj1aT6BVrDjpXfall0d5hwMyz46+FpjhAIjoQKj6P0J9wKnetcNihiXcu1xmN4XJsdgiOgovjwtLRLrCNmZz52OLB1Hw2OUHB6hPITfw8Rid7aM9UCngHszTffbPEXgrYUwjKBnh7ve2ANONosZWVlWrwaICIkJCSQlZXl764cVXiwg7vOGMzIXnF8tGYfx/WJ59xRPegaE+bvrrVcRAKc+yz8/C5s+QL6nw6pMyA83t8987l2GMDAS/ULtIYdjd9qmc0Go2dBdFdY+SokDYV+U6DPybD8RcjeDKNmQv/TrGB2yTvw4zNWgBp3HXQbCZe+Bz8+awWwXuOtm3OWv2RdrjBgGjhLrACWfMLh7XYZDjM/gmX/hrzdMOZX0PcU3+67qsVbAQzaVgj7ELhBRN7CuqA13xhT71B+U2jxalggfS4948K5+sQ+zByfTJC9nTxTOKEfnHSX9S3W3rZPCbeWdhrAwIv1CwLrb9Uf/Pb5RHWGUVfAiEusIFXdj24jrUsOPE83DpgC/SZbP9vs1n/7nwp9T649rftoa1mxWacY7XX+WRaBnmOgR3r9bSif82YAA98+ouJN4CQgUUQygfuAIABjzLPAJ1i3d2/FusU74C8SUceu3QQwTxrAAi6Aaf1StTQUlBoKR9VB60jTai17hHrX2DaUz3g7gIEPQ5gx5uKjzDfA9a2xbbvdTmpqas37iy66iLvuuqvZ63n22WcJDw/niiuu8Gb3vGrVqlXs3buXM844w99dUapGIAcw8G/9OprMzEyuv/561q9fj8vl4owzzuDRRx8lJCTEK+ufN28eAwYMYMiQIVx//fUsXryYiooKduzYwcCBAwG45557OO+887yyPaXaotYIYNC2Tke2mrCwMFatWnXM67nuuusanO50OnE42sZHuWrVKpYvX64hTLUZgR7A2jJjDOeeey6/+c1v+OCDD3C5XFxzzTXccccdPP74417Zxrx58zjzzDMZMmQITz31FAAZGRmceeaZXqmrSrV1rRXAoIMP4J2cnMx9993HqFGjSE1NZePGjVRVVZGcnExeXl5Nu379+nHgwAHuv/9+HnnkEQBOOukk/vCHPzBp0iQef/xxvv76a0aOHElqaipXXXUV5eXljW4D4P7772fmzJlMmTKF5ORk5s6dyx133EFqaipTp06lsrISgBUrVjBp0iRGjx7N6aefzr59+2q2f+eddzJ27FgGDBjAd999R0VFBffeey9z5swhLS2NOXM87sRRyg80gLWu+fPnExoaWvOID7vdzmOPPcarr77Kk08+yQ033FDT9swzz2TBggUAfPHFFxx//PGMGjWK888/n6Ii6+6+u+66iyFDhjB8+HBuu+02lixZwocffsjtt99OWloa27Ztq9eHyy+/nA8++KDm/aWXXsqHH37Iyy+/zNlnn83UqVMZOHAgDzzwQE2b//73v4wdO5a0tDSuvfZaXK6O+6gW1ba1ZgCDDhLCSktLSUtLq3l5hpPExERWrlzJb37zGx555BFsNhtnn30277//PgA//vgjycnJdO7cud568/Ly+Pbbb7n++uuZNWsWc+bM4eeff8bpdPLMM880uo1q27Zt4+OPP+aDDz7gsssu4+STT+bnn38mLCyMjz/+mMrKSm688UbeffddVqxYwVVXXcUf//jHmuWdTidLly5l9uzZPPDAAwQHB/Pggw9y4YUXsmrVKv0HT/mVBrDWt27dOkaPHl1rWnR0NMnJyTidzgaXyc7O5s9//jNfffUVK1euJD09nX/+858cOnSI999/n3Xr1rFmzRruuecexo8fz1lnncU//vEPVq1aRd++feut7+qrr+all14CID8/nyVLltQciV+6dCmvv/46q1at4p133mH58uVs2LCBOXPmsHjxYlatWoXdbuf111/38iej1LFr7QAGejqSc889F4DRo0czd+5cAC688EIefPBBrrzySt56661G//Gonr5p0yZSUlIYMGAAADNnzuSpp57i5ptvbnQbANOmTSMoKIjU1FRcLhdTp04FIDU1lYyMDDZt2sTatWs57bTTAHC5XHTtevgh3J7rzcjIaO7HolSr0QDmG40Nl2IaehCo2w8//MD69euZMGECABUVFRx//PFER0cTGhrK1VdfzfTp0znzzDOb1IdJkyZx/fXXc/DgQebOncuMGTNqLs847bTTSEhIAKx6tWjRIhwOBytWrGDMmDGA9SU5KSmpWfutVGvzRQCDDhLCjqT64lW73V7zzfH4449n69atZGVlMW/ePO65554Gl42IsAaUPlLBa2wbntNtNhtBQUE1xdRms+F0OjHGMHToUL7//vtmrVcpf9IA5jtDhw7lvffeqzWtoKCAAwcOkJCQwObNm2umVz+B3xjDaaedxptvvllvfUuXLuXrr7/mrbfe4sknn2T+/PlN6sfll1/O66+/zltvvcWLL75YM71uQBQRjDHMnDmThx56qO5qlGoTfBXAoIOcjmwuEeGcc87h1ltvZfDgwTXf5BozaNAgMjIy2LrVGgfwtddeY9KkScfcj4EDB5KVlVUTwiorK1m3bt0Rl4mKiqKwsPCYt61US2gA863JkydTUlLCq69a44q7XC5+//vfc8MNN5CSksKqVauoqqpi9+7dLF26FIDjjjuOxYsX19SrkpISNm/eTFFREfn5+ZxxxhnMnj275uxBU2rKrFmzmD17NmAFw2pffvklhw4dorS0lHnz5jFhwgQmT57Mu+++y8GDBwE4dOgQO3fu9ObHolSL+TKAQQcJYXWvCWvK4ykuvPBC/vvf/zbpH5HQ0FBeeuklzj//fFJTU7HZbI3eSdkcwcHBvPvuu9x5552MGDGCtLQ0lixZcsRlTj75ZNavX68X5iuf0wDmeyLC+++/z7vvvkv//v1JSEjAZrPxxz/+kQkTJpCSkkJqaiq33XYbo0aNAqBTp068/PLLXHzxxQwfPpzjjjuOjRs3UlhYyJlnnsnw4cOZNGkSjz32GGA90ucf//gHI0eObPDCfIDOnTszePDgemOAnnDCCVx++eWkpaUxY8YM0tPTGTJkCH/+85+ZMmUKw4cP57TTTqu54Ugpf/J1AAOqB0sOzNfo0aNNXevXr683TR2mn49qDYWFhWbixInGZrOZt956yyvrBJabNlBnWvPl7Rq2ePFi06tXL7N8+fIWr6MliouLTZ8+fUxeXl7NtJdeeslcf/31rbZNrWXKmx577DEDmBkzZpiKiopjXl9T61eHvyZMKYCsnM1syd1Cqamkb9wAkhOH+LtLAUOPgLUd48eP9/mpva+++oqrrrqKW2+9lZiYGJ9uWylv8MsRMDcNYarD27t/NXct+ys/HVoPQERQBP8++V+kdh3j5561fRrA1KmnnsquXbvqTZ81axazZs3yfYeUagZ/BjDoINeEKdWoKherD66sCWAAxZXFPLXmecqcZX7sWNunAUwpFcj8HcBAQ5jq6JxlZJbsrzd5c/42iiuL/dChwKABTCkVyNpCAAMNYaqjC45gaNygepOn9ZpMXGicHzrU9mkAU0oFsrYSwEBDmFKkJqVxx7BrCXOEAXBa90lcOOB8bKJ/HnVpAFNKBbK2FMBAQ5hPeQ4A7m15eXk8/fTTrbLu9i4qLoXLhv+a9057kXnT/stfJ/6dXvED/N2tNkcDWMdht9tJS0tjxIgRjBo16qjPJ/SG5ORksrOzAbjqqqtISkpi2LBhrb5d1XG0tQAGGsLaBZfLpSHsGElQCD2ThtE3aQShweH+7k6bowGsY6keb3f16tU89NBD3H333T7d/qxZs/jss898uk3VvrXFAAYawmrM+2kPEx6eT8pdHzPh4fnM+2mPV9b7l7/8hYEDB3LqqaeyadMmALZt28bUqVMZPXo0J554Ihs3bgTgnXfeYdiwYYwYMYKJEycCVsC6/fbbGTNmDMOHD+e5554DYMGCBZx88slccsklpKamctddd7Ft2zbS0tK4/fbbvdJ3pUADWFvXWrWrWkFBAXFx1vWRxhhuv/12hg0bRmpqas2oHAsWLKg14PcNN9zAyy+/DFhHuO677z5GjRpFampqTb3LyclhypQpjBw5kmuvvbbWGLwTJ04kPj7eq/uhOq62GsBAnxMGWEXs7rk/U1rpAmBPXil3z/0ZgF+O7N7i9a5YsYK33nqLn376CafTyahRoxg9ejTXXHMNzz77LP379+fHH3/kt7/9LfPnz+fBBx/k888/p3v37uTl5QHwn//8h5iYGJYtW0Z5eTkTJkxgypQpgDXY7tq1a0lJSSEjI4O1a9fWjPemlDdoAGvbWqt2VQ/1VlZWxr59+2oG8p47d27NEbLs7GzGjBlT84XxSBITE1m5ciVPP/00jzzyCC+88AIPPPAAJ5xwAvfeey8ff/wxzz//fIv7q1Rj2nIAAw1hAPzj8001RaxaaaWLf3y+6ZgK2Xfffcc555xDeLh1euuss86irKyMJUuWcP7559e0Ky8vB2DChAnMmjWLCy64gHPPPReAL774gjVr1vDuu+8CkJ+fz5YtWwgODmbs2LGkpKS0uH9KHYkGsLavtWpX9elIgO+//54rrriCtWvXsmjRIi6++GLsdjudO3dm0qRJLFu2jOjo6COur7qejR49mrlz5wKwcOHCmp+nT59ec7RNKW9p6wEMNIQBsDevtFnTm0NEar2vqqoiNja2wSNWzz77LD/++CMff/wxaWlprFq1CmMM//rXvzj99NNrtV2wYAERERHH3D+lGqIBLDC0Zu2qdvzxx5OdnU1WVlatU4aeHA4HVVVVNe/Lymo/6DgkJASwLvh3Op010+vWR6W8JRACGOg1YQB0iw1r1vSmmjhxIu+//z6lpaUUFhbyv//9j/DwcFJSUnjnnXcA6xqL1atXA9a1YuPGjePBBx8kMTGR3bt3c/rpp/PMM89QWVkJwObNmykurv8Q0aioKAoLC4+pv0qBBrBA0lq1y9PGjRtxuVwkJCQwceJE5syZg8vlIisri4ULFzJ27Fh69+7N+vXrKS8vJz8/n6+//vqo6504cSKvv/46AJ9++im5uble67Pq2AIlgIEeCQPg9tMH1rquAiAsyM7tpw88pvWOGjWKCy+8kLS0NHr37s2JJ54IwOuvv85vfvMb/vznP1NZWclFF13EiBEjuP3229myZQvGGCZPnsyIESMYPnw4GRkZjBo1CmMMnTp1Yt68efW2lZCQwIQJExg2bBjTpk3jH//4xzH1XXVMGsACS2vVruprwsD6ovjKK69gt9s555xz+P777xkxYgQiwt///ne6dOkCwAUXXMDw4cPp378/I0eOPOo27rvvPi6++GJGjRrFpEmT6NWrV828iy++mAULFpCdnU2PHj144IEH+NWvfnVM+6Q6hkAKYADS2OHlQJCenm6WL19ea9qGDRsYPHhws9c176c9/OPzTezNK6VbbBi3nz7wmK6paKta+vmo9q+tBTARWWGMSfdrJ1qZN2pYR6lddWktU3W1pQDW1PqlR8Lcfjmye4coXEo1pK0FMNV0WrvapszCTLbkbsFus9M/tj9dI7v6u0vtWlsKYM2hIUypDk4DmFLetTl3M9d8cQ05ZTkA9IrqxZOTnyQlRu9mbw2BGsBAL8xXbdjegmxW7tvAjty93ltpRQlkbYJDO8DzVLzLCTlbIXsLOCuatKrSChdbDhayM6e40bvG2joNYEp53/tb3q8JYAC7CnexYPcCv/WnPQvkAAZ6JEy1Ucv2rubPP97P9oKtdArrxJ3p9zK594k47PaWr/TQdvjyPtjwIQSFwyn/B2mXgasMfngGvv8XmCoYORMm3gYxjZ/i2ZlTzMOfbuTTtfsJC7Lz+ykDuCC9B9FhwS3vn49pAFPK+5xVTn7O/rne9A05G/zQm/Yt0AMY6JEw1QbtzjvAvd/fzfaCrQBklWZx9+JbWZe1teUrraqCpS9YAQygsgQ+vxv2rYSM72DRP8FVCVUuWPEibPz4CKsyvP7jLj5dux+wHo755483sGp3fsv752MawJRqHQ6bg+kp0+tNP7nnyX7oTfvVHgIYaAhTbVBm0X4yi3bXmlZZVcnOgt2NLNEEJdmw9t3603N3waZP6k//eQ64Gj4tmVtSwYer6p8iXZ2Z1/L++ZAGMKVa18m9TubigRdjFztBtiB+nfprxnQd4+9utRvtJYCBhrBW98QTTzB48GAuvfRSr687IyODN954w+vr9bfYkGjCHeH1pieEHcOwJsFR0Dm1/vSwOOiaVn969zFgb/jUYkSIgyHdoupN7x1fv89tjQYw1RR2u520tDRGjBjBqFGjWLJkSatvMzk5mezsbHbv3s3JJ5/M4MGDGTp0KI8//nirb9vbukR04fYxt/P+2e/z/tnv89u035IYlujvbrUL7SmAgYawVvf000/zySef1DwZ+kg8h/NoivYawgZ3SuH3o/6AcHhIkwv7X8GghAEtX2lwGJx8N4R4hKdex0O3NBgwFeL7Hp4emQSjLm90VaFBdn43uT9RIYcvqRzdO5ZRvdv22HcawFRTVY8duXr1ah566CHuvvtun23b4XDw6KOPsmHDBn744Qeeeuop1q9f77Pte0uQPYiUmBR6R/fGYdPLr72hvQUw0AvzD1vzNnz9IORnQkwPmHwvDL/gmFZ53XXXsX37ds466yxmzZrFd999x/bt2wkPD+f5559n+PDh3H///ezdu5eMjAwSExN5/PHHue6669i1axdg/dJNmDCBb7/9lptuugmwxltbuHAhd911Fxs2bCAtLY2ZM2dyyy23HPPH0Fac2fd0ekenkFmYSafwRAbHDyAhvP7Rp2bpkQ6//sa6OzIoHDoPhajO1ryZH8KB9WBckDQY4pKPuKq0nnF8cMMEth4sIjTIzsAuUXSODj22/rUiDWDtWCvULk8FBQU1g2sbY7jjjjv49NNPERHuueceLrzwQhYsWMAjjzzCRx99BMANN9xAeno6s2bNIjk5mZkzZ/K///2PyspK3nnnHQYNGkROTg4XX3wxWVlZjB07tuYO465du9K1q/VMraioKAYPHsyePXsYMmSI1/bJG6qqDNuyitidW0JSVAgDQ3MJyt0GQRHQaSD7nWFsPVCEzSb0T4qiU1QIlBdC1ibKC7LIC+nGXmcMCWFCZqkDYwumf2Q5SYXrIaIThyJS2FF8iOyyTCJDQhic0J/4sPgG+1JU7mTLgULySipJTgxHgrLJKz5ASkUFkWWF2MPiwFkO4fGQOBCCQmotn1Oaw7b8bbiqXKTEpNAloosvPsJmaY8BDDSEWda8Df/7HVS6B73N3229h2MqZs8++yyfffYZ33zzDQ888AAjR45k3rx5zJ8/nyuuuKJmEO8VK1awaNEiwsLCuOSSS7jllls44YQT2LVrF6effjobNmzgkUce4amnnmLChAkUFRURGhrKww8/XKvwtSfhwaGM6z6ccQz37ooT+1uvumJ6WK9m6NMpkj6dIr3UsdajAawda6XaVT1sUVlZGfv27WP+/PkAzJ07t+YIWXZ2NmPGjGHixIlHXV9iYiIrV67k6aef5pFHHuGFF17ggQce4IQTTuDee+/l448/5vnnn6+3XEZGBj/99BPjxo1r8b60lm+3ZHHdayuocFXx1plhBC2+Gkqsx1K4Bv2Cj6J/w58X5gEwvEcML1wwgKTVT8LixwkBOgeFkXvS8/xpaxdiI0J4Z3kmAzuF8kxaJn2+O5/QSX/kv84Mvtr7HQCjkkbx1xP/SvfI2ndtF5RW8K/5W/n3dzsIcdi4Z0Yo3+x5gb+G9Sdm0WzrUTw2B5z8B1j2H5h4O4y8DOxWiMkszOTu7+5mVdYqAHpE9uBfp/yLfnH9fPExNkl7DWCgpyMtXz94uIhVqyy1pnvJokWLuPxy6xTXKaecQk5ODvn51t10Z511FmFh1oC7X331FTfccANpaWmcddZZFBQUUFhYyIQJE7j11lt54oknyMvLw+HQ/KyOTgNYO9dKtav6dOTGjRv57LPPuOKKKzDGsGjRIi6++GLsdjudO3dm0qRJLFu27KjrO/fccwEYPXo0GRkZACxcuJDLLrsMgOnTp9ccbatWVFTEjBkzmD17NtHR0ce0P962L6+UO95ZQ7mzipP6RDF8y1M1AQzAvvF/TAjbhbivqFiTmY/rwFpY7HF9W2UpA378IwOjyukRF45NYFNWGZ8W9YOoLoR/+xfOjRlU03zlwZUs3be0Xl827C/k39/tAGDq8GjeyXiCy5PG0aU6gAFUOWHRY5B6Pnxym/U8RLfv931fE8AAMosyeW/Le23m2YftOYCBhjBLfmbzprdAQ7/Q4v4LjYiIqJlWVVXF999/z6pVq1i1ahV79uwhKiqKu+66ixdeeIHS0lKOO+44Nm7c6LW+qfZJA1gH4IPadfzxx5OdnU1WVlaj/zA7HA6qqqpq3peVldWaHxJinf6y2+21rn2troF1VVZWMmPGDC699NKaANeW5JVWklVUDsDwRCHs4Mp6bWLKMgl1HH6uYVX+vnptbAW76BdVSW5xBeHB1hfrJQfsENsbgOiK2gG7oWeNHSwor/m5dycbOwq2ElNZVvth1GCdCrU5rEBWfLBm8tqstfXWuWz/Mspd5fWm+1p7D2CgIczS2GmoZp6eOpKJEyfWXJy/YMECEhMTG/x2N2XKFJ588sma99WnLLdt20Zqaip33nkn6enpbNy4kaioKAoLC73WR9V+aADrIHxQuzZu3IjL5SIhIYGJEycyZ84cXC4XWVlZLFy4kLFjx9K7d2/Wr19PeXk5+fn5fP3110ddr2dN/PTTT8nNzQWsL6y/+tWvGDx4MLfeeqvX9sObkqJC6JNo3Q39baaL/F6n1WuTFdaX0kpXzXt7QnK9NpVJqSzPDiYxMoSiciucTu3htK5btTnICgmr1X5059H11tHT467sn3c5SUtM50BQcP27uyOToKLIuh7W4/djXNf6p3pP630aoQ7/XuPaEQIYaAizTL4Xgmr/shMUZk33kvvvv5/ly5czfPhw7rrrLl555ZUG2z3xxBM17YYMGcKzzz4LWL+Qw4YNY8SIEYSFhTFt2jSGDx+Ow+FgxIgRPPbYY17rqwpsGsA6kFaqXdXXhKWlpXHhhRfyyiuvYLfbOeeccxg+fDgjRozglFNO4e9//ztdunShZ8+eXHDBBQwfPpxLL72UkSNHHnUb9913HwsXLmTUqFF88cUX9OrVC4DFixfz2muvMX/+/Jo+fPJJA8/y86OEyBAevSCN7rFhrNpTzI9dL8fVPd2aaXPgmngnKyqtcSJF4ML0Hji6psJZT1ohCKiKTWZV2oOERMaxcpcVQM8aHM0pVUvAVFF45lN8VWwdPbOJjYsGXtJgCBvUJYqHz00lNMjGtxsLmNbtWt7LWcOOKfdBaKzVKLIznHALbPwEzn+51t3gY7qM4cIBF9bcjT6pxyTOSDmjFT61pusoAQxA2sp535ZIT083y5cvrzVtw4YNDB48uPkra+U7jNqKFn8+KiC0pwAmIiuMMen+7kdr8koN6yC1q662UMsOFpSxL7+M2PAgeoVVIHk7rRAc34dip7AzpxibTUhOCCc0yGGdIjy0nYriPA7ZEyly2ekS4iKzMgpjd5AcVk5Y8W4Ii6cksgeZebnkV+4nKjSUlJhehDhCGuyHMYaMnGIKy5x0iwnDHlTCgeIDdKusJNJVgS04AirLIDwBYnvWW77cWc6uwl24jIueUT2JCIpoYCu+0V4CWFPrl17dXW34BR2icKn2qz0FMNUMWrv8Jik6lKSaR9NEQPjhmwsi7DCkW0ztBUQgoS/BCeD5EIhBnm1iOwEQDgxI6gR0Omo/RISURM87tUOIC236cwtDHCH0j2vgrnEfay8BrDl8djpSRKaKyCYR2SoidzUwP0ZE/iciq0VknYhc6au+KRXoNIC1Lq1fSrWujhjAwEchTETswFPANGAIcLGI1H3y3vXAemPMCOAk4FERaXjcGKVUDQ1grUvrl1Ktq6MGMPDdkbCxwFZjzHZjTAXwFnB2nTYGiBLrnuVI4BDQvHF8lOpgNID5hNYvpVpJRw5g4LsQ1h3Y7fE+0z3N05PAYGAv8DNwkzGmqk4bROQaEVkuIsuzsrJaq7+qrXBVuP/rBPdziCpcriMsAFS5rPZgLeP+2VnlxFVVe1mny4kxhkpXFcYYjDE4XU7KnZU4XfV+/doUDWA+47X6BVrDlKrW0QMY+O7C/IaeyFf3tszTgVXAKUBf4EsR+c4YU1BrIWOeB54H684i73dVtQm5u2DDB5C7ExIHwPp5ZKTeyEe5vfly0yFOGtiJs9O61x42yOWE3T/Cj8+COCDtQlg9B1N0gJJRl/Fa2W42lOzlssGXkRKTwg97f+DtTW/TJaIHYxOmExUczpKsD9mWv4XxnafiLBzEgMQunNi/CxEhbeseFg1gPuW1+gVaw5QCDWDVfPUvSybgeV9sD6xvjJ6uBB421jMztorIDqybRuqP06Dat4pi+Ope2PYNjLsWPr2d/MGXcNfSMH7YZQ3PsTozn/kbsnj5qjEkRLpv296zAl79hXUk7NT7Yc5l4KpEgIidi5l+6j28vO9H1mav5fLBl/Poiket5bJ+4pvdX3LdiOuYu/Vda/1Zq5nRZxb/+HwUNpudKUO6+v5zaIQGMJ/rcPXLbreTmpqK0+lk8ODBvPLKK4SHh9drN378eJYsWeKHHqpApgHsMF+djlwG9BeRFPfFqhcBH9ZpswuYDCAinYGBwHYf9U+1JbkZsO596H8qrLVC0Y7Ek/hhV3GtZj/vzWdbVtHhCVs+twJYSDQUHQBXZa32PVe9zbSu45nUYxIvr3u51rwyVxmlzlJscvhP4qOdbzJlRAgvLco4+ilQH9EA5hcdrn5Vjx25du1agoODax4aXc3l/nvQAKaaSwNYbT4JYcYYJ3AD8DmwAXjbGLNORK4Tkevczf4EjBeRn4GvgTuNMdm+6B/Ax9s/Zsq7Uxj+ynCmvDuFj7d/7JX1/vOf/2TYsGEMGzaM2bNnA/Dqq6/WPHW6elDvrKwsZsyYwZgxYxgzZgyLFy8GYOnSpYwfP56RI0cyfvx4Nm3aBMDLL7/Mueeey9SpU+nfvz933HGHV/rbJohYryoX2Kw/UBsNX59lt3mcKbK7j4iZKhB7vbYuexDlVU6cVU4ctvoHgQWpNTaeXew4XRDssGFr8IyUb2kA84+2Xr9aq3ZVO/HEE9m6dSsLFizg5JNP5pJLLiE1NRWAyEjrcoAFCxYwadIkLrjgAgYMGMBdd93F66+/ztixY0lNTWXbtm1e7ZMKTBrA6vPZhS7GmE+AT+pMe9bj573AFF/1x9PH2z/m/iX3U+ayBp3dV7yP+5fcD8D0PtNbvN4VK1bw0ksv8eOPP2KMYdy4cYwZM4a//OUvLF68mMTERA4dOgTATTfdxC233MIJJ5zArl27OP3009mwYQODBg1i4cKFOBwOvvrqK/7whz/w3nvvAda4kj/99BMhISEMHDiQG2+8kZ496z8NOeDE94VRV8LPc+DE38PXD9Jn38dM7f9rPtty+MjXCf0S6Ot5TVj/02DRo9b4aGFxEBxhndp02zXyIr7c+hoRQRFcn3Y9D/7wYM286OBoQuwhGI9Lfc5NuYr/fVfGX88ZhsPu3xG+NID5V1utX61Vu6o5nU4+/fRTpk6dClhfCteuXUtKSkq9tqtXr2bDhg3Ex8fTp08frr76apYuXcrjjz/Ov/71r5ovoapj0gDWsLZ1tbGfPL7y8ZoiVq3MVcbjKx8/pkK2aNEizjnnHCIirCEgzj33XJYvX855551HYmIiAPHx8QB89dVXrF+/vmbZgoICCgsLyc/PZ+bMmWzZsgURobLy8Cm2yZMnExNjPZF5yJAh7Ny5s32EMEcITLoDeo2DrM1w7vNEbl/AvcMqOHnIABZnFDIuJYGJAzoRG+7xKKZuI2HWJ7D+AyjOgQtfhx0LMcVZlAycyndlmVw06CJO630aKdEpJIUn8XnG53QK60ZK2Dgig0P47Ygb2J63nbSECRTn9+Khc7swLiXBf58FGsBU41qrdlWPHQnWkbBf/epXLFmyhLFjxzYYwADGjBlD167WtZN9+/ZlyhQrk6ampvLNN9+0uC8q8GkAa5yGMGB/8f5mTW+qhsblFBGsRwnVVlVVxffff09YWO3BeG+88UZOPvlk3n//fTIyMjjppJNq5oWEHB5HzG6343S2o8cSRXeFERcdfj/8QroBFwIXHt/IMiLQI916Vet7snVhPnBFneaTek5iUs9JtaadyvBj7ro3aQBTR9Jatav6mrC6qr9QNsSzHtlstpr3NputfdUm1SwawI7Mv+dY2oguEV2aNb2pJk6cyLx58ygpKaG4uJj333+f0aNH8/bbb5OTkwNQczpyypQpPPnkkzXLVhfA/Px8une3Hkn08ssvH1N/VGDRAKaOprVql1LeoAHs6DSEATeNuolQe2itaaH2UG4addMxrXfUqFHMmjWLsWPHMm7cOK6++momTJjAH//4RyZNmsSIESO49dZbAXjiiSdYvnw5w4cPZ8iQITV3I91xxx3cfffdTJgwoeaOJNX+aQBTTdFatUupY6UBrGmkoVNmgSI9Pd0sX7681rQNGzYwePDgZq/r4+0f8/jKx9lfvJ8uEV24adRNXrmwta1p6eejfEcDmEVEVhhj0o/eMnB5o4Z1lNpVl9aytksDWNPrl14T5ja9z/QOUbhU26YBTDWX1i7VlmgAax49HalUG6EBTCkVyDSANV+7PBJmjGnwDsSOLpBPPbd3GsCUJ61hRxaItayksoS9xXsJsYfQI7IHFa4K9hTtwS52ukd1b/AB0oFEA1jLBPb/9QaEhoaSk5NDQkKCFjEPxhhycnIIDQ09emPlUxrAlCetYUcWiLUsIz+Dvy/7O9/t+Y4wRxi/G/k7iiuLeXr109jExqyhs7h8yOXEh8b7u6stogGs5dpdCOvRoweZmZlkZWX5uyttTmhoKD169PB3N5QHDWCqLq1hRxdItayyqpKX173Md3u+A6DUWcrflv2NG0feSJWpospU8cLPLzAofhCnJ5/u5942nwawY9PuQlhQUFCjT3RWqi3RAKYaojWsfTlUdogvd35Zb3phRSHBtmAqqioAWJS5KOBCmAawY6cX5ivlBxrAlOoYIoMi6Rfbr970iKAIKqsOD0M3KGGQL7t1zDSAeUe7OxKm2odDxcVsOlDE7kPllFU6SU4MJj2+gvCD66jK30N5TAqrTDLZlWGEBts5UFBKVJghIaaYnmE2eh/caq2oy3BItArg7t07Wbu/mHKXMDApgkE9OyNBh4daobIM9q+BnC0Q3gm6jgCbHfatgaJ9EJdirS8ksoEeN50GMKU6joigCG4ZfQvXfHkNpc5SAMZ1HUdBeQEG6waDPtF9OKHbCf7sZrNoAPMeDWGqzXG6XCzedogXFu5kdWY+ADNHxXFC6Fuw8hVsQBjQ78SH+NGcyuyvttQsO3lIFJ16fcuvgiPp/8X9ENEJrviQHaVhzJqzjZ255QCEOGy8frkhfWDvwxve8CHM/fXh9/2nQt+T4bM7D0+b+hCMvdYKZy2gAUypjictKY05Z85hR/4Owh3h9I/rT6mzlLFdx+KwOegf25/OEZ393c0m0QDmXRrCVJuz/kAWO7LKagIYwG8HFmN7/5Va7UqqhOcWbq817ev1hdzWfwwLqpbSP6ITFGfBnpX8mDeoJoABlDurePK7PTzbJYrQmHjI2w2f3lG7I1s+gx6jak/76n7odxok9m/2fmkAU6rjSolJISWm9rV+PaIC4+aCahrAvE+vCVNtTnG5k0pXVa1pwZUF9dqV2CIpraw/nqazMoiMsmwIT7AmVJawp6CyXrsdueWUO53uNqVQmlu/M646yznLobyoaTviQQOYUh1Llalib9Fe9hXta/ayRRVF7C7cTX7Z4S+iWaVZ7C7cTbmz/AhLth4NYK1DQ5hqc3rHRxEfEYTN4xFJGdINwuJqtetasoVh3aJrTQsLsmMLzmZyzADr2i6AuN6MS67dDuCitARiYmKsN9HdoO/k2g3sQeCo8yyiTkMgtlez9kcDmFIdS3ZpNs+seoaz5p3FLz/4JS+vfZm8srwmLbshZwO//eq3nDH3DK764ipWHljJt7u/5aKPLuLM98/k/xb/Hzvzd7buDtShAaz1aAhTbU63mBhG9Y7kwbOH0iMuDLtNeGdnGM4ZL2O6poEIzt4nsqXTZH45sjsTByQiAv2TwvnDL2PoEVnG6O0/QnAUTPs79BjLyJ4JPHpmLxIjgwlx2Lh2fFfOHpoADveF+SGRMPVhGPJLEBskDoBL34U+J1kX6ItYIe28FyAiocn7ogFMqY7n293f8uyaZyl3lVPiLOHRFY+y7MCyoy6XXZrNrQtu5aesnwDYnLuZG+ffyMqDKzlYcpAqU8WnGZ/y3JrnqHBVtPZuABrAWpteE6bapBE9kugdX0J6cjRVxkW32FAcYVHQ+R0oyaIiJIp4VxgTncGcOjQSp2sAwUEuQoJcdA4/DnpOtsJUdDcAIsJhxvgkTuyXQGUVdElMwB4cUnujnQbAOc/BaX+yQlm4++nVV3wIZfkQkQjBEU3eBw1gSnU8zion7299v970r3Z+xWm9TzvisnuK9pBZlFlrWkFFAUG22sHnkx2fcMPIG+gW2e3YO3wEGsBan4Yw1WbFhocTGx5ee2JUEkQlEQ70PdLCMQ1c8GqzkdTlKEUrKBTi6pxuDIu1Xs2gAUypjslhczAwbiCrs1bXmt439ogVC7CeKeawOXBWOeut01PXiK6EOcKOvbNHoAHMN/R0pFJepgFMqY7tvAHnER18+DrUTmGdOKXnKUddrnd0b24edXOtaZcPvpyckpya93ax84dxfyAuNI7WogHMd/RImFJepAFMKTU4YTD/PeO/bM7djE1sDIobRM/onkddzmFzcN6A80hNTGVP0R6SwpMYFD+IClcFE3tOJL8inz4xfRgQN6DV+q4BzLc0hCnlJRrAlFLVGnouWFNEBEUwqvMoRnWu/YzCTuGdvNW1RmkA8z0NYUp5gQYwpdq/0qIC8orLiY2JJiw05MiNXU7I2mD93CUVnBXWw6PtQZSUlVERHE9stDUEWn5pJSUVTpKiQrG7n82TX5ZPRVUFFRXhVDgN3WPDCHbYKSgvoNRZSqfwTtjEuqIoq7CUElcucWGRRIfUfxxPU2gA8w8NYUodIw1gSrV/67fv4pH5u/hxVxHHJ0fz+1P7MLhXl4Yb7/kJts+HH5+znjc44RaoKIagMMyaOYRnb8L0mUbmqBvYbrrx8GcbyTxUwnmje3DF+N7sKlnJlxmLiHdOZs73RRgMM8cnM7JPKf/46T4OlBxgRv8ZnJ1yGVuzc1hy8EO+yvyQTmFJ/D799xzXbVy9OyqPRAOY/2gIU+oYaABTqv07sC+Ta97eTGae9bT6rzbnsjFrHXOviSApLqp2Y2c57FkGXz94eNonv4eznoDP70HKrdE/Ija8TUjJPh5z3Mn6vda0FxdnUFReQWbov5kQdy3/+Dy7ZhV//2wTv58eR0ZBBpVVlczZNIfucgZriubxYcYbgPU4ixvmX89/p/2X1E6pTdo3DWD+pXdHKtVCGsCU6hgysotqAli1zNwyMg7m1W9cXgRr36s/fds39R6d49j5Had0rb3euSv3Mb33hSzbItT1/UYYmjAUgLRO6eSVlfD1ng9qtakyVWzN29qEvdIA1hZoCFOqBTSAKdVxRITUDyciDU8HO0Q18DzCqG71x6cNCqPAWXsd8RHBZJfuJ7GBS7s6xUBeeR4AeeU5GCPEhyTW72/Q0R8qrQGsbdAQplQzaQBTqmPp0zmGWaPja027alxX+nSrH4CIiIWRl0KQx4OmQ2Oh81BI6Feraf6EP/LV/toPXf3j9AEsOfAlA3sWEhVy+IqhiGA74wZUkVGQAcCGQ+sZ2iWB81J+g3D4qFmfmD41R8saowGs7RBjjL/70GLp6elm+fLl/u6GaiXOvF2UHdpOYWh39lTE4agqJzw0jzxTTkhVIjYqqZL9xEd2IzK4O7sOlWATF47gQ4QE2ekV1QNH7i4oy6MwpDPbyyKJDQ+md4L7W2KVCw5tt4YkiukJUZ2P2icNYL4jIiuMMen+7kdr0hoWOA5lH+DnPQXsyi2nV0I4qT0TiY9t5E7EylLYvRQOrreGT+vsvj6rNIeqimKcxXkURPWjrFMqhESxJjOf3OIKBnaJIrV7DAdL97IpdxNS0Z19h4JxuWz07xxF907lbDq0jvzyfAbEDWBIwhC2ZxWxu3gLe0q2kxAWQ1rnVHpENTBiiJsGMN9oav3SC/NVm1S5YyFB7/2K7BG3cfc2G99nbEYEpo+IJbLzt2wrXMNpiTfhcNj44sBd/HrQLVz70iEMcMHYWGI6/cD1jnCivrofKkuJiu5G1XFPcO7X8OdzhjGlfwz2NW/C53dbF9LG9oILXoVuIxvtkwYwpTqu+MTOTEo8+hc1AILCoM8k61WHDQgGPI+h9YirPTxbz6CejT7ctXd091rvB3aJYyBjgbFH7ZYGsLZHT0eqtqdgH0Fzr8HYw3i3cAjfZxQCYAx8tCqPrvaJbM3fxKayT/h4hZPRiVN5YdNsTk+NxlVlePOHXIaFTSHq0zutb6QABXsZvvwuzh0Uyk1vrmL73iz4+FYrgAHk7YL/3QyleQ12SQOYUqq5CisKKa4srjc9ryyPcmd5A0scWaWrkryyvHpjSzaFBrC2SUOYanuKDkDhPkq6pPN5RlW92dv3C13Cu7AqZwl9OttwuBJYm7OOIT0O/zrvOuSE4NoXp9oPbSU1ppwKVxV7c0vqb3ffKuthinW7owFMKdUMhRWFfLz9Y6749Aqu+vwqvt71NaXOUvYU7eHpVU9z8ccXc9u3t7Ema02T17kldwsP/vAgF318EX9b+je2521v8rIawNouPR2p2p7wBAiLIyxnHeO72dhysPbsnomGJXuzGZN0Apk7q+jWrYiU6BQyPNp1jXFAZZ2gFd2NHSUh2KScTtGh9bcblwJhtQfF1QCmlGquH/b9wF3f3VXz/uZvbuaVqa/wwdYPmLt1LgCZRZks3b+UN6e/SZ/YPkdc34GSA/xu/u/ILMoE4K1Nb7Emew3PnfocsaGxR1xWA1jbpkfCVNsT25PKs/6FLW8Hl3bdQ6+4w8ODjE6OoDz4Z6KCo0iPO48Jg4JYsP9tbhh6Gx/+lA/AyYOjOGBWUDT+d4fXGRTOxnEP8+KqEu4/ayh9uybACbccnh8cCWc/CRGHr9TQAKaUai5XlYs5G+fUm76rYBfzts2rNa3EWdKkZ3rtzN9ZE8Cqrc9Zz67CXUdcTgNY26dHwlSbFDRwOhVXf023Q9t59eJe7CiOwIGTqIhCsl1DmNzzaWxVwVSRwZSBfyIyqC8vzCzGbncSHHqIkKA4bBFnwtBzqCrOoSisG4dKO/HmNSH07RRBSJAdTrwdBp9lPbsnrnet28c1gCmlWsImNuJD4+tND7YHE+YIq3eNWKi9gaPydYTY649TKUiD06tpAAsMGsJU22SzEdwlleAuqUQCyY02PHwYv1vNHUZdD88OT8AGRAPj6y4aEgHdR9VbowYwpVRLiQgXD7qYL3d9WXMBfZgjjL6xffndyN/x0NKHatr2j+3PgPgBR11nSkwKp/U+jS93flkz7YIBF9A7uneD7TWABQ4NYUp50ACmlDpWI5JG8Oq0V1m2bxkOm4MxXcYwMH4g3SK70Tu6NysPrqRXVC9GJY2iS0Qjg4B7iA6J5s4xdzKl9xS25G5hSOIQ0hLTCHXUP4qmASyw6MNalXLTANa26MNalWoeDWBthz6sValm0ACmlPIlp8sJAg7bsf8zXO4q5+knnubWW2/VABZgfHZ3pIhMFZFNIrJVRO5qpM1JIrJKRNaJyLe+6pvq2DSAqaPR+qW8paSyhPm75nPtl9dy0/yb+GHvD1S6Klu0rszCTP7z83847rrjuPXWWzn9rNM1gAUYnxwJExE78BRwGpAJLBORD40x6z3axAJPA1ONMbtEJMkXfVMdmwYwdTRav5Q3/bjvR2765qaa99/t+Y6Xpr7E6M6jm7WeksoSHl3+KG/9+y32v7mf6PRozCWGQxWH6BzUxOGVlN/56kjYWGCrMWa7MaYCeAs4u06bS4C5xphdAMaYOo/oVMq7NICpJtL6pbyi0lXJa+tfqzXNYPhq51fNXtfuwt21AljP63qyt2wv2/K3eau7ygd8FcK6A7s93me6p3kaAMSJyAIRWSEiVzS0IhG5RkSWi8jyrKz6Q8wo1RQawFQzeK1+gdawjkyQBu9obGja0bz67Ku1Apg4BACH6KXegcRXIUwamFb3tkwHMBqYDpwO/J+I1HuAijHmeWNMujEmvVOnTt7vqWr3NICpZvJa/QKtYR2Zw+7giiFXIB6/UkG2IE7pdUqz1jN79mz++se/MuSkIbUC2LCEYfSN7evVPqvW5avInAn09HjfA9jbQJtsY0wxUCwiC4ERwGbfdFF1BBrAVAto/VJeM6rzKF48/UW+2PkFYfYwJveezLCEYU1e3vMxFI//53F+yv6JJXuXMLzTcCZ0m0BCWEIr9l55m69C2DKgv4ikAHuAi7CuofD0AfCkiDiAYGAc8JiP+qf8qNzporjcRWxYEDabx0GHqiprSKGQKHAEk12ch9NVhtNpIzo8mujQRg7hVxSDswLCdTBu5RVav5TXBNuDSe+STnqX5j8Cr6HngHWP6c6Zfc9shZ4qX/BJCDPGOEXkBuBzwA68aIxZJyLXuec/a4zZICKfAWuAKuAFY8xaX/RP+c/aPfk8s2ArqzPzmTq0C5ce15uUxAjI2QbLX4QNH3LwhDv4JiKcNza9Sog9hOk9Z7J+exJnDOvKif26Hw5urkrYuRi+eQiKD8K438DQX0JkkgYw1WJav1RboA9ibZ/0ifnKb3YfKuGXTy0mp7iiZtqkAZ149oIBhM2dBdu/gaBw5p7zJPctu7/WsjcNfYS/ve/ijWtGMaa3e9iP3cvgxSlgqg43nPZ3ioZeqgEsAOkT81Wgc1W5WJ21mg+3fUhFVQWn9z6drblbWX9oPWf2OZMxXcYQGRxZa5ncslyW7lvKJxmf0C+mH+O6juP/Hv4/vnjiCyZPn8xH731EaEjzL+RXvqVPzFdt3taDRbUCGMC3m7OozHYQtv0bACqGn8/cjPfrLbu58Hu6x05i475CjxD2Y+0ABhQteJzpv3+FRd//qAFMKeVTP2f/zFWfX4XLuAD437b/cVv6bXy16yu+2PkFfznhL5zV96ya9sYY5m2dxz9X/BOA+cxn9uOz2f7qdqLTo8k6N4uN+RtJS0rzx+6oVuCzJ+YrVVdoUP1fvyC7IPYQcA/lYSs5RGxwfL12EY4YiitctdcRElWrTVGFYfp/9mgAU0r5xac7Pq0JYNUW711MamIqAM+seobcstyaeftL9vPs6mdr3md/ns32V7eTNjmNntf1pMpexecZn/um88onNIQpvxnQOYrxfWvfyXPdpL6EdukHE24GwLHxIy5KPrvWs2/CHeF0CRqNQ2BY99jDC/c6DiISAXcAe6OERduKNIAppfyiqs6RebCOdtnE1uB8YwzG/fST7M+za54DduVDV9Y8hqKhdarApacjld8kRIbwj/NGsCzjENuyihjRM5ZRvWIJCgqB434LPdJhzwqOdzl55pR/81P2chwSQrfQoeTmxfHvWTEM7uIR4joNhJkfU7ThK6bf9E8W7d7JG6+9qgFMKeUX01Km8fbmt2sFpwndJzB7xWwArh1xLXGhh+/i7hrRlatTr+a+h++rCWBDfjcEm8MKbTaxcXry6T7dB9W6NIQpv+oeF0b3uLoPHwciEmDgNBg4DTtwHHBcj6Nfo10U3oPp977Dop936hEwpZRfpXZK5T9T/sOcTXOocFVwdr+z2ZizkXFdxnHewPMY22VsrfYiQtbnWex/cz/JJyRz9d+u5pTkU3h/y/tMS57GBQMvILVTqp/2RrUGDWGq3dDHUCil2pIgW1C9Z4Kd0usUjDGI1B+IYfbs2dxzxz3MmDGDN954g+DgYACO73a8z/qsfEtDmGoXNIAppQJFYwFMnwPW8WgIUwFPA5hSqq07UHyAFQdWsOLACvrG9iXEHkJsSCyjOo/ilWdfaXYAK3WWsvrgar7b8x1J4UlM6DaBfnH9fLAnypuaHcJE5DjgX8BgrOE57ECxMSbay31T6qg0gKnm0hqmfK24spjZK2bz0Y6PaqYd3/V4QhwhPPOvZ3jv0feafQRs4e6F3Lbwtpr3/wn5Dy9PfZk+sX283n/VelryiIongYuBLUAYcDVWQVPKpzSAqRbSGqZ8KqMgo1YAA/h+3/fs+3Qf7z36HlPPmtqsAJZXnsfsn2bXmpZbnsvaHB0pK9C06HSkMWariNiNMS7gJRFZ4uV+KXVEGsDUsdAapnzJ6XLWm5b9eTZr31xLdHo0j73wWLOuAXO6nJQ5y+pNL3eWH1M/le+15EhYiYgEA6tE5O8icgsQ4eV+KdUoDWDqGGkNUz7VO6Y3wxKG1byvfhDr4JMG8+u//ZrkuORmrS8xPJErh15Za1qQLYihCUO90V3lQy0JYZe7l7sBKAZ6Aud6s1NKNUYDmPICrWHKp2JDYvnriX9l1tBZsBD2v7mfMaeN4cZHbuSWMbcQ6mj+gNzT+0znvuPvY2DcQE7ucTIvTHmBwQmDvd951apacjryl8aYx4Ey4AEAEbkJeNybHVOqLg1gyku0himfS4lJwfadjbUvruWcc8/hxVdfJDYitsXrSwhL4LwB53FGyhkE2YIIsusjLQJRS46EzWxg2qxj7IdSR6QBTHmR1jDlc7Nnz+bWW29lxowZzHlrzjEFME/hQeEawAJYk4+EicjFwCVAioh86DErCsjxdsdUx7Z933Z2F29kS94WEiWBv97wOMuXruDmR24m6fgknlv9AlGOeNI6pTEkqf4t2duyilixM5fC0gpGJtlJLVxIUFAIJT3HsLY8m3U56+gc3pkRcQPonrUVDm6EhH7QYwxEdfbDHqvWpjVMedvmA4Ws3JlLaaWLUb3iGNY9BrtxUnxwJ6uLHPQo30X8oTU8+/533D77bY4//XhOvH0yn2z/jjhbCqVFVQzrFk0BFWzMXUtmUQb94voxpusIEsMT/b17ygeaczpyCbAPSAQe9ZheCKzxZqdUx7Y35xCf7Z7LMxtewVXmYudjOyndUspfH/8TnSZ14Xff/K6mbfeInjw26SkGd0qpmbbtYBEX/fsHsgqtO4VsAi9N782k737LpxN/y/1rn61pOyh2AP8K6UOXH563JqReANMfhVB9ZFQ7pDVMec2m/QVc+PwP5JVUAuCwCf+9ehzHObbxycF4BpZvoNf8y5m9uIjbPy+n9/ieFJxfwAsbnwbg8v43kBw0lRvmrOM3U4K498fDz/w6t9/53Dn2NsKDwv2yb8p3mnw60hiz0xizwBhzvDHmW4/XSmNM/ftvlWqhvNIM/r3p9ZoAVrK5hB7X9mDStHG8s/mdWm33FO9mfc76WtOW7zxUE8AAqgz8c6Vhz4l/5J+b36jVdmPeZjbF9zg84ee3IXuz93dK+Z3WMOVN323JrglgAM4qw9PfbGWfvQtfrs2i/7aXmb24iFs+L+fkk4cTeVU04jg8XNE721+gXLIRRxAVxVG11j136zvsyM/w1a4oP2r2NWEicpyILBORIhGpEBGXiBS0RudUx1RZVU55SXlNAOt5XU9ixsVQ5iwhrzyvXvtSV2mt9wWllfXa5JRWURYcS1FlUf3lTVWdDpQcU/9V26Y1THlDTlH9Z3IdLCynDAchNsOzH63gls/LmTHYwd0PXlYrgAGUucpwmUqiQ4Mod5p66yp1ltabptoffWK+anOCXfEcejKnVgCLDYmla2QvpiZPrdXWIQ76xfSvNW1krzjqjo87c1gwvda8yFndT6o1PcQeQl+XRwGM7gHxfb25O6rt0RqmjtnEAZ3qTZs5Ppmezt1s++59bn1nGzMGO3hzRhj9q5xEBkXWajs2aQJhksDavfnER9c+ENs7Kpnk6N6t2n/VNrQkhGGM2QrYjTEuY8xLwMne7ZbqqIqKirh25jVkrc/m/HvPps+kPpzcdTxPnfAIT298kZiQGK4adhWdwzszLH44j058klFdaj+gcHiPWF6cNYZh3aPpFhPKHyZ352zzDUEVRVw3+HKuGnYVncI6MbbLWP598hP0z9sPkUkw5JdwyVsQ090/O698RmuYOlYje8Xx/OWjGdQlkh5xYTxw1lCmDOnMk29+wacvPML4kyfzz8ceJSghmd6bvuaZ8X9lfNfxJIYlcmbyBZyXcj0ZByt47qIhREaHc1L3KSSEJjCl9xn8feIjemF+ByHG1D8MesQFRBYCpwIvAPuxLnSdZYwZ4f3uHVl6erpZvny5rzerWkndx1Cc/oszyCs6QHRoLKFh4eSU5uKqMiSGxZNbVkh4UBgJ4Y1fQF9YVkmFs4qEiGAozoKgMAiJospUkVuWS5gjzLrw1VkJpbkQFgOOEB/usToSEVlhjElvhfVqDVNek19aiauqiviIEGbPns0tt9zCjBkzeOPfT3OgoooYWykIVIVFERoSSXZJPmESiaksJzwsnPAw60Gth0qKKa4soktkvD5yoh1oav1qycNaPZ82fQvW06ZntGA9StVo7DlgseGHL1j1vFMoMuTodw1FhXoUssikmh9tYiMhLOHwPEcQRCWhOgytYcprYsKsOuMZwKoH4+7ZQPse0dVPx689UlZ8eATxOnpWh9PsEGaM2Skindw/P+D9LqmORh/EqnxJa5jytoYC2KqDq9iUuwm72BkUP4hhicMoKC9gTdYadhbuJCY4hmGJw0iOSfZ395UfNedhrQLch/XtUQCbiDiBfxljHmyl/ql2TgOY8hWtYao1NBTAlu5byu+++R3FlcUAxIXE8dQpT7ExdyMP/nD4V2100mj+NOFP9Ixu6JiZ6giac2H+zcAEYIwxJsEYEweMAyaIyC2t0TnVvmkAUz52M1rDlBc1FMCqqqqYu2VuTQADyC3PZVfhLv71U+2bcFccXMH6Q+vrrlZ1IM0JYVcAFxtjdlRPMMZsBy5zz1OqyTSAKT/QGqa8pqEABuA0TvYU7anXvsxV1uBzDgsrClu7q6oNa04ICzLGZNedaIzJAvRWDtVkGsCUn2gNU17RWAADCLYHMzVlar1lksKTmNB9Qq1pDpuDvjH6XMKOrDkX5le0cJ5SNTSAKT/SGqaO2ZECWLWJPSaSV57H6+tfx2FzcOWwK+kf158b0m4gzBHGN7u/oWdUT24edTMjknz+ZBTVhjQnhI1oZGgPAUIbmK5ULRrAlJ9pDVPHpCkBDKBnVE+uT7ueacnTsImt5g7ILhFd+NP4P7GveB9RwVF0jujsw96rtqjJIcwYY2/Njqj2TQOY8jetYepYNDWAeeoT26fetIjgCPoF92uNLqoA1JKHtSrVLBrAlFKBrCUBDCC/PJ9NhzaRU5ZDz6ieDIwbSJA9CGMMW/O2siN/B+GOcAbGD6RTeP2xKFX7pyFMtaqmBrADxQfYWbiTCHsclUURFBeX0S2qiriS9VRFJFIZkUxw1lbCHDZCuw6EiJYXrOzCcrZnFxHssBEVWUB22X6ig6NJiUkh1KFnpZRSh7U0gBWUFzB7xWze3fIuAILw8IkPc0afM1hxYAXXfnktFVXWpYijO4/m4RMfpktEl1bbD9U2aQhTraapAWzjoY3c8PUN9I1OJbr4PN5ZmgdAfEQw/5kcysh5F5A7/R9QXk7oxzdR1X00tnP/DQnNv6to28EibnhzJduzivnDuaE8v+geCioKEITrRlzHFUOuIDI48lh2WynVTrQ0gAFszdtaE8AADIa//PgXBicM5tEVj9YEMIAVB1awLnudhrAOqDmPqFCqyZoawMqcZTyz6hkOlBwgLfrMmgAGcKi4gvuXB5PfZzpxn95NRZcBANj2rKBq3Qct6tfclZls2FfImWnRvJPxGAUV1nXaBsMzq59hU+6mFq1XKdW+HEsAAxp8JlhBRQGFFYVk5GfUm5dTltPCnqpApiFMeV1zrgErqChg+YHlAJSW1T8VuHpvMfkJaVCaC8WHi5Rs+6rZ/SqvdPHtliwAuicadhbuqNfmQMmBZq9XKdW+HGsAA+sOSYet9smmIfFD6BHVg2kp0+q11+eFdUwawpRXNfci/NiQWMZ3Gw9AWGhJvfljekYQf/BH6xqwiKSa6WZA/SJ2NCFBdk4bbN0SvvOg0DdmQL023SK6NXu9Sqn2wxsBDKBvbF/+dcq/6Bxu1Zy0Tmk8OOFB4kPjmTV0FlN6T0EQooOj+dP4PzE0cag3d0MFCL0mTHlNS+6CDLYHc83wa9iUu4llue8z68TLeW1xLq4qQ7eYUO4bWULkooXknvkIobuWAuDqcwr2wb9oUR/PTuvOkm05fLo6jz+eexOvVNxPVmkWDpuDW0bdwoC4+sFMKdUxeCuAAdjExgndT+DN6W9SVFlEYlgiUcFRAPSK7sVfTvgLN468kRB7CF0ju3prF1SAEWOMv/vQYunp6Wb58uX+7obi2B9Dcaj0ELsKdxHhiKGyOJzi4lK6RjiJLNmMiehEVWQf7NnbCA0SQpMGQFhMi/uaX1LBjpxiguw2YiJKyCq37o7sFd2LIJuOXtNWiMgKY0y6v/vRmrSGtR3eDGBKNbV++exImIhMBR4H7MALxpiHG2k3BvgBuNAY825DbVTb4o3ngMWHxRMfFu9+4znH46GGsQnH1M9qMeHBpIUHV7+jB/otVB2Z1q/2TQOY8hefXBMmInbgKWAaMAS4WESGNNLub8DnvuiXOnb6IFbV3mn9at80gCl/8tWF+WOBrcaY7caYCuAt4OwG2t0IvAcc9FG/1DHQAKY6CK1f7ZQGMOVvvgph3YHdHu8z3dNqiEh34Bzg2SOtSESuEZHlIrI8KyvL6x1VTaMBTHUgXqtf7rZaw9oADWCqLfBVCJMGptW9I2A2cKcxxnWkFRljnjfGpBtj0jt10rG2/EEDmOpgvFa/QGtYW6ABTLUVvrowPxPo6fG+B7C3Tpt04C0RAUgEzhARpzFmnk96qJpEA5jqgLR+tSMawFRb4qsQtgzoLyIpwB7gIuASzwbGmJTqn0XkZeAjLWBtiwYw1UFp/WonNICptsYnIcwY4xSRG7DuGrIDLxpj1onIde75R72OQvmXBjDVUWn9ah80gKm2yGfPCTPGfAJ8Umdag8XLGDPLF31STaMBTHV0Wr8CmwYw1Vbp2JHqiDSAKaUCmQYw1Zbp2JGqUb4OYK4qF1mlWQhCFVVE2cIoKSsipKyU4KAgwmK7gSMYirLAVQHR3UCEgvICip3FJIYmEmSvXWALSyspLHeSEBlMiMN+xO2XV7rIKa4gKtRBVKgWaqUCnQYw1dZpCFMN8nUA21O0h7mb55IQlsAHWz9gW/42Hhp+AxP3bSNk5UsQFk/5SfcSEhoGn90FpXmYcdexb9Dp3Lz87+wo2MHpyadzderVJMckA7A84xB/+XgD6/cVcNqQztx8an/6JUU1uP0tBwr555ebmb/xIMO6xXDP9MGM7B3XqvuslGo9GsBUINDTkaoeXwewKlPFWxveQkR4fOXjrD+0nvjQeNL2rCXk+yegvBDydhIy71eQtQnydkF5AbLw7zjWziWnPIcyVxkfbPuAR5Y/QmllKduzipj54lJ+2p1HubOKj9bs4673fqawtLLe9vNKKrjtndV8unY/5c4qVuzK5YoXl5KRXdyq+62Uah0awFSg0BCmavHHNWBZJVm8u+VdBKHEWQLA9M5j6bT+f/UbF2dBcGTN26T1H3Fa0uGB6r/N/Jb9JfvZnlVMcUXt52Yu35lLZl5pvVVm5payOjO/1rTCcifbNYQpFXA0gKlAoiFM1fDXRfihjlCSwpOw2w5fs7W3LJfKyC71GwdHgLOs5m1FVGf2l+fVvI8OjibUHkpkSP0z7aFBNsKC6l8XFh5sJ8RR/08hqoF1KKXaLg1gKtBoCFOAf++CjAmJ4fYxt7P50GaO73Y8AF/sX8L29MvAdjgImbgUjD0EqpzWBHsQ+8deyYL9P9a0uX3M7XSN7MrALlFMG1Y7xN01dRC9E8Lrbb93QgS3TRlYa9ovRnSjf+fIem2VUm2TBjAViPSrvmoTj6EY13UcMcEx5JTm8Is+v6C4sphDEd3JveRDHFmbkeAwbJ0GER4eAQl9obIUOg8lPKYbj8V05VDZIVJiUhiSMASAuIhgHjh7KOen9+BAQTkpCREM6xGDe1iZWuw24eKxvRjWPZqMnBI6R4eS2j2a2PBgX38MSqkW8EcA25ldzI6cYiJDHAzpGkV4SBCZuSXszy+jU1QIpeWl7M0vJzRI6BvvICI8jh05JdgFyipdhATZSYoKYW9eKRUuQ4jdRo/wSmIr9uMqzaeiSnDG9iEmsWuD2z9YcpC9RXuJCo6id3RvHLbm/XPudFWxI6eYglInPeLC6Bwd6o2PRTWThrAOri0EMIAgWxCpnVIbntlvQu33if0P/wicHJHU4GJJUaGcMqhphSUy1MHxfRM5vm+Tmiul2gh/BLDlGYe48701bMsqxm4TZo3vzfTUrvzqleV0iQnl96f15/nvMli64xAAU4d15pcji1mzu5wftuewclceN57Sj0Vbs/lpVx4A56R15fb+e7Ht/gTb6jcJMlVUdRpC7vTniEseXmv7a7PXcvM3N3Og5ABBtiB+n/57zu1/LmGOsCb1v6TCydvLdvPXTzZS4aqia0woz142mhE9Y735Makm0NORHVhbCWBKKdUS/ghgh4rL+df8rWzLsm7ccVUZ/rMogx3ZJeSWVHLdxD4s3JJTE8AAPlt7gH35TkrKXazclUfP+DAOFJTXBDCA91ftY3l5L/j5HTBVANiy1hP8w2wO5RfVtMsvz+eBJQ9woOQAAJVVlTy89GE2H9rc5H3YuK+Q+/+3ngqXtZ19+WXcPfdn8koqWvy5qJbRENZBaQBTSgUyf10Dll1YzvKMQ/Wm7y8ow24TYsIc/FznbmuApdvzySoqB2BI1xh+2pVbr82KvWXWQ6g9ROxagLM4u+Z9blkuG3M31lt2T9GeJu/DngbuEl+/r4CcIg1hvqYhrAPSAKaUCmT+vAg/PiKYYd1j6k3vFBWMq8pQVOFiUNf6D4Ue2SuauAirn1sPFjG0W/11DO8SCoX7a00r7ToOe3hszfuYkBiSo5PrLds5onOT96Gh67/6JIYTG643M/iahrAORgOYUiqQ+fsuyMSoUG45dQCdIkNqpv0yrRvJ8ZGEOGy8uCiDif0T6J90+O7q9N6xJCcGkxQVSp/ECLZlFdEvKZJkj7u1T+wbz9jwfdD35MMbi+pK2YTbSIiNrZkUFxrH/ePvJzLIWr8gXDv8WgbG1b7D+0iGdI3ixlP6UX2fUlSIg4dnDCfBY5+Ub4gxxt99aLH09HSzfPlyf3cjYGgAU4FERFYYY9KP3jJwaQ1rHn8HME8b9xewPauYKPfdkXERIezILmZvXilJ0SGUVZSTmVdGaJDQP8FGRHgC27KKCLLbKCpzEh5sJzEqhD25pTirDGFBNvqElRJbvgdTlk+FCyri+xPTObnBu7p3F+wmsyiTmJAY+sT0IdTRvLsbSyucbM0qJr+kgp5x4fROjPDWR6Noev3SuyM7CA1gSqlA1pYCGMCgLtEM6hJda1rfpEj6ehwBG9Gr9jINHWnqnVA3/PQArH+c6z/V8LCe0T3pGd2zGT2uLSzYQWoDp1WVb+npyA5AA5hSKpC1tQCmlLdoCGvnNIAppQKZBjDVnmkIa8c0gCmlApkGMNXeaQhrpzSAKaUCmQYw1RHohfntUKAGsHV785m/8SC5xRWcNCiJysoqvtp4gNTuMZzQrxO9EsIhZztsnw/710KfkyF5PER0AqC00slPO/P4asMB4iOCOWVQEkMaeBaPUqpt0wCmOgoNYe1MoAaw9fsKuODZ7ymucAHw4uIM7po2iLkr9/Dm0t2k9Yzl9Qt6EfHOpXBwvbXQipfgxNvgpLvB7mDRlhx+/erh2/2f+3Y7b193PIO7Rje0SaVUG6QBTHUkejqyHQnUAAbw4/acmgBW7aM1ezmxfyIAq3bnUXVg3eEAVm3J45CbQVFZJbO/qj12WmG5k2U76g8vopRqmzSAqY5GQ1g7EcgBDKC0TgCzplUR4rDXvDeuBsY1q3KCceGqMg2uo8xZf5pSqu3RAKY6Ig1h7UCgBzCA4/omYLfVfir09OFdWLg5C4BusaHYOw+BiMTaC6ZeCLG9iQkP5rpJfWrNctiEscnxrdpvpdSx0wCmOiq9JizAtYcABjCiRyxvXD2O5xduJ6e4nMuO602VMfSID2NscjyXjOtFRJdouHweLPsP7FkOw86DYedCkDVcx5ShXQhy2Hl5yQ46RYbw6xP7kNoj1q/7pZQ6Mg1gqiPTsSMDWHsJYJ4qnVW4jCE0yDoNWVLhJNRhx+Z5lKzKBc4yCG54rLOyShd2EYIceqA3kOnYke2fBjDVXunYke1cewxgAEEOG55lODy4gV9Rm73RAAbUBDilVNulAUwpvSYsILXXAKaU6hg0gCll0RAWYDSAKaUCmQYwpQ7TEBZANIAppQKZBjClatMQFiA0gCmlApkGMKXq0xAWADSAKaUCmQYwpRqmIayN0wCmlApkGsCUapyGsDZMA5hSKpBpAFPqyDSEtVEawJRSgUwDmFJHpyGsDdIAppQKZBrAlGoafWJ+G6MB7LC8kgo2HSikoNRJn8Rw+gYdgqxNYA+CpMEQ1eVwY2cFZG2E/N0Q2QWSBh3xqfpKqdahAUypptMQ1oZoADssu6icv3y8nvd/2gvAq2dG0PeHa6DogNWgaxqc/zLEp4AxsPY9+OA31s8Apz4A466FoDC/9F+pjkgDmFLNo6cj2wgNYLWt31tQE8AGdY5gxL73DgcwgH2rYPs31s+HtsHHtx4OYABf3w9Zm33WX6U6Og1gSjWfhrA2QANYfVmF5TU/94t3EJO9sn6jfWus/5bkQmVJ7XnGQHFWK/ZQKVVNA5hSLeOzECYiU0Vkk4hsFZG7Gph/qYiscb+WiMgIX/XNnzSANSw5Mbzm56WZZezveUb9Rn1Osv4b07329WFgnYaM6916HVQditavxmkAU6rlfBLCRMQOPAVMA4YAF4vIkDrNdgCTjDHDgT8Bz/uib/6kAaxxQ7pG89A5qUQE2zlYWM6yqFNwDTnXmmlzwPjfQe/x1vvobnD+KxDTy3of0QkueA0S+vmn86pd0frVOA1gSh0bX12YPxbYaozZDiAibwFnA+urGxhjlni0/wHo4aO++YUGsCMLC3Zw0diejO+XQEm5i25xodjtT8FJt4M4rAvy7R4Fv9dxcPVX1nVj4QnW0TGlvEPrVwM0gCl17HwVwroDuz3eZwLjjtD+V8CnDc0QkWuAawB69erlrf75lAawphEReid4PmYiGJLqHoDwENXZeinlXV6rX9A+apgGMKW8w1fXhEkD00wD0xCRk7GK2J0NzTfGPG+MSTfGpHfq1MmLXfQNDWBKBRyv1S8I/BqmAUwp7/HVkbBMoKfH+x7A3rqNRGQ48AIwzRiT46O++YwGMKUCktYvNw1gSnmXr46ELQP6i0iKiAQDFwEfejYQkV7AXOByY0y7e8CTBjClAlaHr1+gAUyp1uCTI2HGGKeI3AB8DtiBF40x60TkOvf8Z4F7gQTgaREBcBpj0n3Rv9amAUypwNXR6xdoAGtISYWTYLsNh72RYxkVxWAPAbuDgtIKHDYhPOTYPrcKp4sqA6FB9mNaj2o7xJgGL20ICOnp6Wb58uX+7sYRaQBTqmVEZEV7CjINCYQapgGstqzCMj5fd4A3ftxFckI4V5/Yh1G94w43KNgL6+bBqtdxJQ3hwOArufFbQ2JkCBeP7UV67zgiQ5v3GbqqDMsyDvH8t9vJKipj1vgUJg9OIjY82Ls7p7ymqfVLn5jfijSAKaUCmQaw+t5Zkck989ayfl8Bn6zdz8X//oH1e/OtmS4nfP80fH43HFiL/ee36fbBhVzRv4LP1x3gxjd+4qfdec3e5prMPC594UfmbzrIz3sK+P07q/l87X7v7pjyCw1hrUQDmFIqkGkAq29/fhnPfbu91rRyZxUb9hVabwoyYelztRcqL2CQWE84KSx3snZPPhVOV7O2uyzjEK6q2metnl24nfySiubtgGpzNIS1Ag1gSqlApgGsYQ6bEBpU/5/NYId7mtjBEVJvvlMOX34dZLchDT71pHENXQMWHmzHbmveelTboyHMyzSAKaUCmQawxiVGhXDH1EG1pnWKDGFot2jrTWxPOPkPtea7YnqzrNQawaNHXBgjesQQ5GjeP71jk+OJCql9H93Npw5o9rVlqu3x1XPCOgQNYEqpQKYB7OhOH9KFxCuDmb/pID1iw5k0sBN9OkUebjDiEojrA1u/whXfj32Jx7NxDdx8alfG901kRM/YZm9zUNdo5lx7HN9syuJQcQWTBycxqlfc0RdUbZ7eHeklGsCU8i69O9K3NIAp5T16d6QPaQBTSgUyDWBK+YeGsGOkAUwpFcg0gCnlPxrCjoEGMKVUINMAppR/aQhrIQ1gSqlApgFMKf/TENYCGsCUUoFMA5hSbYOGsGbSAKaUCmQawJRqOzSENYMGMKVUINMAplTboiGsiTSAKaUCmQYwpdoeDWFNoAFMKRXINIAp1TZpCDsKDWBKqUCmAUyptktD2BFoAFNKBTINYEq1bRrCGqEBTCkVyDSAKdX2aQhrgAYwpVQg0wCmVGDQEFaHBjClVCDTAKZU4NAQ5kEDmFIqkGkAUyqwaAhz0wCmlApkGsCUCjwawtAAppQKbBrAlApMHT6EaQBTSgUyDWBKBa4OHcI0gCmlApkGMKUCW4cNYRrAlFKBTAOYUoGvQ4YwDWBKqUCmAUyp9qHDhTANYEqpQKYBTKn2o0OFMA1gSqlApgFMqfalw4QwDWBKqUCmAUyp9qdDhDANYEqpQKYBTKn2qd2HMA1gSqlApgFMqfarXYcwDWBKqUCmAUyp9q3dhjANYEqpQKYBTKn2r12GMA1gSqlApgFMqY6h3YUwDWBKqUCmAUypjqNdhTANYEqpQKYBTKmOpd2EMA1gSqlApgFMqY6nXYQwDWBKqUCmAUypjslnIUxEporIJhHZKiJ3NTBfROQJ9/w1IjKqKevVAKaUam2tVb9AA5hSHZlPQpiI2IGngGnAEOBiERlSp9k0oL/7dQ3wzNHWW1VVpQFMKdWqWqt+ARw4cEADmFIdmMNH2xkLbDXGbAcQkbeAs4H1Hm3OBl41xhjgBxGJFZGuxph9ja10y5YtlJSUaABTSrWmVqlfAJmZmRrAlOrAfBXCugO7Pd5nAuOa0KY7UKuIicg1WN80AcqBtRdddBEXXXSRVzvsQ4lAtr87cQwCvf8Q+PsQ6P2Hhvehtz860gCv1S+oX8Pee++9tcHBwd7rrW+119+9QBLo/YfA34cW1y9fhTBpYJppQRuMMc8DzwOIyHJjTPqxd89/An0fAr3/EPj7EOj9hza/D16rX9C+alig9x8Cfx8Cvf8Q+PtwLP331YX5mUBPj/c9gL0taKOUUr6m9Usp1Sp8FcKWAf1FJEVEgoGLgA/rtPkQuMJ9l9FxQP7RrqdQSikf0PqllGoVPjkdaYxxisgNwOeAHXjRGLNORK5zz38W+AQ4A9gKlABXNmHVz7dSl30p0Pch0PsPgb8Pgd5/aMP70Ir1C9rwfjdRoPcfAn8fAr3/EPj70OL+i3Uzj1JKKaWU8qV28cR8pZRSSqlAoyFMKaWUUsoPAiKEteaQIb7ShH241N33NSKyRERG+KOfjTla/z3ajRERl4ic58v+HU1T+i8iJ4nIKhFZJyLf+rqPR9OE36EYEfmfiKx270NTr0vyCRF5UUQOisjaRua3+b/jltD65X+BXr8g8GuY1q9GGGPa9AvrQthtQB8gGFgNDKnT5gzgU6xn9RwH/OjvfrdgH8YDce6fp7WlfWhK/z3azce6SPk8f/e7mZ9/LNYT0Hu53yf5u98t2Ic/AH9z/9wJOAQE+7vvHv2bCIwC1jYyv03/Hbfi/7c2vd9av/z/CvQapvWr8VcgHAmrGTLEGFMBVA8Z4qlmyBBjzA9ArIh09XVHj+Co+2CMWWKMyXW//QHrOUNtRVP+HwDcCLwHHPRl55qgKf2/BJhrjNkFYIwJxH0wQJSICBCJVcScvu1m44wxC7H61Ji2/nfcElq//C/Q6xcEfg3T+tWIQAhhjQ0H0tw2/tTc/v0KK1G3FUftv4h0B84BnvVhv5qqKZ//ACBORBaIyAoRucJnvWuapuzDk8BgrIeE/gzcZIyp8k33vKKt/x23hNYv/wv0+gWBX8O0fjXCV8MWHQuvDhniJ03un4icjFXETmjVHjVPU/o/G7jTGOOyvsi0KU3pvwMYDUwGwoDvReQHY8zm1u5cEzVlH04HVgGnAH2BL0XkO2NMQSv3zVva+t9xS2j98r9Ar18Q+DVM61cjAiGEtYchQ5rUPxEZDrwATDPG5Piob03RlP6nA2+5C1gicIaIOI0x83zSwyNr6u9QtjGmGCgWkYXACKAtFDBo2j5cCTxsrAsUtorIDmAQsNQ3XTxmbf3vuCW0fvlfoNcvCPwapvWrMf6+2K0JF8M5gO1ACocv6Btap810al8Qt9Tf/W7BPvTCetr2eH/3tyX9r9P+ZdrQha1N/PwHA1+724YDa4Fh/u57M/fhGeB+98+dgT1Aor/7XqePyTR+YWub/jtuxf9vbXq/tX75/xXoNUzrV+OvNn8kzLTukCE+0cR9uBdIAJ52fxtzmjYyqnwT+99mNaX/xpgNIvIZsAaoAl4wxjR4K7I/NPH/wZ+Al0XkZ6xCcKcxJttvna5DRN4ETgISRSQTuA8IgsD4O24JrV/+F+j1CwK/hmn9OsJ63QlOKaWUUkr5UCDcHamUUkop1e5oCFNKKaWU8gMNYUoppZRSfqAhTCmllFLKDzSEKaWUUkr5gYYw5XMiYkTkNY/3DhHJEpGPmrmeDBFJPNY2SinVVFq/lDdpCFP+UAwME5Ew9/vTsB7Mp5RSbZ3WL+U1GsKUv3yK9YRhgIuBN6tniEi8iMwTkTUi8oN7OBREJEFEvhCRn0TkOTzG6hKRy0RkqYisEpHnRMTuy51RSnUoWr+UV2gIU/7yFnCRiIQCw4EfPeY9APxkjBkO/AF41T39PmCRMWYk8CHWUCmIyGDgQmCCMSYNcAGX+mInlFIdktYv5RVtftgi1T4ZY9aISDLWt8hP6sw+AZjhbjff/Q0yBpgInOue/rGI5LrbTwZGA8vcQ6aEAQdbfSeUUh2S1i/lLRrClD99CDyCNR5Xgsd0aaCtqfNfTwK8Yoy526u9U0qpxmn9UsdMT0cqf3oReNAY83Od6QtxH44XkZOAbGNMQZ3p04A4d/uvgfNEJMk9L15Eerd675VSHZnWL3XM9EiY8htjTCbweAOz7gdeEpE1WKPRz3RPfwB4U0RWAt8Cu9zrWS8i9wBfiIgNqASuB3a27h4opToqrV/KG8SYho6OKqWUUkqp1qSnI5VSSiml/EBDmFJKKaWUH2gIU0oppZTyAw1hSimllFJ+oCFMKaWUUsoPNIQppZRSSvmBhjCllFJKKT/QEKaUUkop5QcawpRSSiml/EBDmFJKKaWUH2gIU0oppZTyAw1hSimllFJ+oCFMKaWUUsoPNIQppZRSSvmBhjCllFJKKT/QEKaUUkop5QcawpRSSiml/EBDmFJKKaWUH2gIU0oppZTyA4e/O9BciYmJJjk52d/dUEr50IoVK7KNMZ383Q9v0BqmVMdypPoVcCEsOTmZ5cuX+7sbSikfEpGd/u6Dt2gNU6pjOVL9arXTkSLyoogcFJG1jcwXEXlCRLaKyBoRGdVafVFKqebSGqaUam2teU3Yy8DUI8yfBvR3v64BnmnFviilVHO9jNYwpVQrarUQZoxZCBw6QpOzgVeN5QcgVkS6tlZ/lFKqObSGKaVamz+vCesO7PZ4n+metq9uQxG5BuubJr169fJJ55TytcrKSjIzMykrK/N3V/wmNDSUHj16EBQU5O+uNIXWMKXctH61rH75M4RJA9NMQw2NMc8DzwOkp6c32EapQJeZmUlUVBTJycmINPTn0b4ZY8jJySEzM5OUlBR/d6cptIYp5ab1q2X1y5/PCcsEenq87wHs9VNflPK7srIyEhISOmQBAxAREhISAumbtNYwpdy0frWsfvkzhH0IXOG+w+g4IN8YU+8wvlIdSUctYNUCbP+1hinlIcD+fr2uJfvfaqcjReRN4CQgUUQygfuAIABjzLPAJ8AZwFagBLiytfqilFLNpTVMKdXaWi2EGWMuPsp8A1zfWttXqiP6y1/+whtvvIHdbsdms/Hcc88xbtw4f3crIGkNU8r3OloNC7gn5iulGvb999/z0UcfsXLlSkJCQsjOzqaiouKY1ul0OnE4tEwopVpfR6xhOoC3Uu3Evn37SExMJCQkBIDExES6devG119/zciRI0lNTeWqq66ivLwcsIbPyc7OBmD58uWcdNJJANx///1cc801TJkyhSuuuIIDBw5wzjnnMGLECEaMGMGSJUsA+O9//8vYsWNJS0vj2muvxeVy+X6nlVLtRkesYRrClGonpkyZwu7duxkwYAC//e1v+fbbbykrK2PWrFnMmTOHn3/+GafTyTPPHP3B7itWrOCDDz7gjTfe4He/+x2TJk1i9erVrFy5kqFDh7JhwwbmzJnD4sWLWbVqFXa7nddff90He6mUaq86Yg3TEKZUOxEZGcmKFSt4/vnn6dSpExdeeCHPPfccKSkpDBgwAICZM2eycOHCo67rrLPOIiwsDID58+fzm9/8BgC73U5MTAxff/01K1asYMyYMaSlpfH111+zffv21ts5pVS71xFrWNs9UaqUaja73c5JJ53ESSedRGpqKq+88kqjbR0OB1VVVQD1nm0TERFxxO0YY5g5cyYPPfTQsXdaKaXcOloN0yNhSrUTmzZtYsuWLTXvV61aRefOncnIyGDr1q0AvPbaa0yaNAmwrqdYsWIFAO+9916j6508eXLN4X+Xy0VBQQGTJ0/m3Xff5eDBgwAcOnSInTt3tsp+KaU6ho5YwzSEKdVOFBUVMXPmTIYMGcLw4cNZv349Dz/8MC+99BLnn38+qamp2Gw2rrvuOgDuu+8+brrpJk488UTsdnuj63388cf55ptvSE1NZfTo0axbt44hQ4bw5z//mSlTpjB8+HBOO+009u3T55QqpVquI9YwsR51EzjS09PN8uXL/d0Npbxuw4YNDB482N/d8LuGPgcRWWGMSfdTl7xKa5hqj7R+WZpbv/RImFJKKaWUH2gIU0oppZTyAw1hSimllFJ+oCFMKaWUUsoPNIQppZRSSvmBhjCllFJKKT/QJ+YrFYCcrirmrtzDnOW72ZdXStfYMC5M78mM0T2w26TF67Xb7aSmplJZWYnD4WDmzJncfPPN2GyNf1/LyMhgyZIlXHLJJS3erlKq49D6dZiGMKUCjNNVxQ1v/MRn6/bXTNubX8aKnbnM33iQJy8ZicPesoPcYWFhrFq1CoCDBw9yySWXkJ+fzwMPPNDoMhkZGbzxxhsawpRSR6X1qzY9HalUgJm7ck+tAubps3X7mfvTHq9sJykpieeff54nn3wSYwwZGRmceOKJjBo1ilGjRrFkyRIA7rrrLr777jvS0tJ47LHHGm2nlFJav2rTI2FKBZg5y3cfcf7by3ZzQXpPr2yrT58+VFVVcfDgQZKSkvjyyy8JDQ1ly5YtXHzxxSxfvpyHH36YRx55hI8++giAkpKSBtsppZTWr9o0hCn1/+3deXxU9b3/8dcnk4QsLAECCAQR2WQpskTQui9F1LpVr1urtfVetZZ7a1vb6/X2V/XX1qv29tq63Hr9Vaul1qXKbV2xVsV9Yd8EAVkk4EIChC0kmZnv749zEiZhkkzCnJxM8n4+HnnMmXO+c+Y7h+Ezn/l+v/P9ZphPd1Q1e3xLC8dbq25ps9raWmbOnMnixYuJRCKsXr06aflUy4lI16P41ZCSMJEMM7Aony2V+5o8PqgoP23PtW7dOiKRCP379+fWW29lwIABLFmyhHg8Tl5eXtLH3HXXXSmVE5GuR/GrIY0JE8kwF7fQVH/RUelpyt+6dSvXXnstM2fOxMyorKxk4MCBZGVlMWvWLGKxGAA9evRg165d9Y9rqpyIiOJXQ0rCRDLMBVNKmDHukKTHZow7hAsml7T53FVVVUycOJFx48Zx2mmnMX36dG6++WYArrvuOh555BGOPvpoVq9eTWFhIQATJkwgOzubI488krvuuqvJciIiil8NWV1/aaYoLS11GuQrndHKlSsZM2ZMSmWjsTizF23myXmb2LKjikFF+Vx01BAumHxw8+x0BMmug5ktcM6VhlSltFIMk85I8cvT2vilMWEiGSg7ksVFpUPS9isiEZH2ovi1n7ojRUREREKgJExEREQkBErCREREREKgJExEREQkBErCREREREKgJEwkE8WisHAWPDgd/mucd7twFsQPbmJBM+Pyyy+vvx+NRunXrx9f/epXW3Weww47jPLy8oMuIyKdkOJXPU1RIZJpYlF46kpY+ez+fTvLYNP7sOYluPBhiLTtv3ZhYSHLly+nqqqK/Px8Xn75ZQYPHpyWaouIKH41pJYwkUyz5LGGASzRymdh6eMHdfozzjiD559/HoDHHnuMSy+9tP7Ytm3bOO+885gwYQJHH300S5cuBaCiooLp06czadIkrrnmGhIngf7jH//I1KlTmThxItdcc42WMRLpyhS/GlASJpJpFs1q/vjCFo634JJLLuHxxx9n3759LF26lGnTptUfu/nmm5k0aRJLly7ltttu44orrgDg1ltv5bjjjmPRokWcc845fPLJJ4A3e/QTTzzB22+/zeLFi4lEIjz66KMHVT8RyWCKXw2oO1Ik01RubuF42UGdfsKECWzYsIHHHnuMM888s8Gxt956i6effhqAU045hYqKCiorK3njjTeYPXs2AGeddRa9e/cG4JVXXmHBggUcddRRgLe2W//+/Q+qfiKSwRS/GlASJpJpeg32xlA0ebztC+DWOeecc7jhhhuYO3cuFRUV9fuTrTVrZg1uEznn+OY3v8l//Md/HHSdRKQTUPxqQN2RIplm0uXNH5/cwvEUfPvb3+anP/0pX/rSlxrsP+GEE+qb4+fOnUtxcTE9e/ZssP/FF19k+/btAJx66qk89dRTfPHFF4A3JmPjxo0HXT8RyVCKXw2oJUwk00y8zPsVUbLBrWPOhiMvPXB/K5WUlPC9733vgP233HIL3/rWt5gwYQIFBQU88sgjgDfW4tJLL2Xy5MmceOKJHHrooQCMHTuWn//850yfPp14PE5OTg733XcfQ4cOPeg6ikgGUvxqwJI1z3VkpaWlbv78+WFXQyTtVq5cyZgxY1IrHIt6vyJaOMsbQ9GrxPsGeeSlkBUJtqIBS3YdzGyBc640pCqllWKYdEaKX57Wxi+1hIlkokg2TPqG9ycikkkUv+oFOibMzGaY2UdmttbMbkxyvJeZPWtmS8xshZl9K8j6iIikSvFLRIIWWBJmZhHgPuAMYCxwqZmNbVTsu8CHzrkjgZOAX5lZblB1EunoMm14QLp1lNev+CXSeh3l/29Y2vL6g2wJmwqsdc6tc87VAI8D5zYq44Ae5v02tDuwDYgGWCeRDisvL4+KioouG8icc1RUVJCXlxd2VUDxS6RVFL/aFr+CHBM2GNiUcL8MmNaozL3AM8AWoAdwsXMu3vhEZnY1cDVQ/6sFkc6mpKSEsrIytm7dGnZVQpOXl0dJycHPE5QGaYtfoBgmnZ/iV9viV5BJ2IEzn3nfHBOdDiwGTgGGAy+b2ZvOuZ0NHuTcA8AD4P2yKP1VFQlfTk4Ow4YNC7sa4klb/ALFMOn8FL/aJsjuyDJgSML9ErxvjIm+Bcx2nrXAeuCIAOskIpIKxS8RCVyQSdg8YKSZDfMHq16C13Sf6BPgVAAzGwCMBtYFWCcRkVQofolI4ALrjnTORc1sJvASEAEecs6tMLNr/eP3Az8DHjazZXjN///qnCsPqk4iIqlQ/BKR9hDoZK3OuReAFxrtuz9hewswPcg6iIi0heKXiARNC3iLiIiIhEBJmIiIiEgIlISJiIiIhEBJmIiIiEgIlISJiIiIhCDQX0eKiIh0VZc/+D5l26so6Z3PrKsar3oloiRMREQkEGXbq1hfvifsakgHpu5IERERkRAoCRMREREJgZIwERERkRAoCRMREREJgZIwERERkRAoCRMREREJgZIwERERkRAoCRMREREJgZIwERERkRAoCRMREREJgZIwERERkRAoCRMREREJgZIwERGRNIrG4jw5bxNbdlQBsGVHFU/O20Qs7kKumXQ0SsJERETSJBqLM/NPi/jx00upjsYBqI7G+fHTS/nuowuJxuIh11A6EiVhIiIiaTJ74WbmrPgs6bE5Kz5j9qLN7Vwj6ciUhImIiKTJE/M3NXv8obfWq1tS6mWHXQEREZHOoCYaZ83nu5ots+qzXUz+2cscO6Ivx4/sx/EjiynpXdBONZSORkmYiIjIQXDO8dKKz7ljzip27ou2WL6yqpYXln3GC8u8bsvDiws5fmQxx4/sx9HD+9K9mz6auwr9S4uIiLTRok+2c9sLK5m3YXtK5Y85vC9bKqvYWLG3ft+68j2sK9/DI+9uJDvLmDy0NyeO8lrJxg3qRSTLgqq+hExJmIiISCtt2raXO1/6iGeXbGmw/8zxh7C7Osoba8oPeMyMcYdw39cnE8kyNlbs4c015by5ZivvrK1gV7XXghaNOz5Yv40P1m/jly99RO+CHI4dUcwJI/tx3MhiBhXlt8vrk/ahJEw6tMsffJ+y7VWU9M5n1lXTwq6OiHRxlXtruW/uWh5+ewM1CdNNTBnam5vOHMOUob2JxuLMXrSZ//OX5VRH43TLzuJn543ngskl9a1aQ/sWMrRvId84eijRWJwlZTt4Y7WXlC3etIO6sfvb99by3NJPeW7ppwCM6N+d40d6Sdm0w/tQkKuP8Uymfz3p0Mq2V7G+fE/Y1RCRLq4mGueP723k7lfXsGNvbf3+oX0LuHHGEcwYfwhmXoKVHcniotIh/Hbux6wv38OgonwuKh3S5LmzI1lMGdqHKUP78P2vjKJyby3vrivnjTXlvLF6K2Xbq+rLrv1iN2u/2M3v395ATsQoHdqH40d5SdnYgT3JUtdlRlESJiIi0gTnHHOWf8btc1Y1GMfVKz+Hfzl1JJcfPZTc7PTO9tSrIIcZ4wcyY/xAnHNsqNjLm2u28sbqct79uJw9NTEAamOOd9dV8O66Cu6c8xF9CnM5bkRx/SD/Q3rlpbVekn5KwkRERJJY9Ml2fvH8SuZv3D/oPjeSxTe/PJSZJ4+kV0FO4HUwM4YVFzKsuJArjjmM2licRZ/s8JKyNeUsLduB87sut+2p4ZklW3jGH6c2akD3+mkwpg3rS35uJPD6SusoCRMREUmwadte7pizqn4cVp2vThjIj08/gkP7hjevV04ki6nD+jB1WB9+OH00O/bW8PbaCr+lbCtbKvfVl139+W5Wf76bB99aT24ki6OG9a5PysYcoq7LjkBJmIiICN6g+3tfW8Mj72xsMOi+dGhvbjprDJMP7R1i7ZIrKsjlrAkDOWuC13W5rnwPb67eyptrynl3XQV7/a7Lmlict9dW8PbaCm5/EYq7e12XJ4zqx3EjiunfU12XYVASJtKF6NemIgeqicaZ9d5G7n5lDZVV+wfdH9a3gBvPOILTx+0fdN+RmRnD+3VneL/uXHnsMGqicRZ+sp0313hJ2bLNlfVdl+W7a/jL4i38ZbHXdXnEIT04wZ+b7KjD+pCXo67L9qAkLI30AScdnX5tKrKfc44Xl3/GHY0G3RcV5PC9U0fy9WnpH3TfnnKzszj68L4cfXhffnS6N2bs7bXeLy7fXFPOZzv3d12u+mwXqz7bxQNvrKNbttflecLIfhw/qpjRA3p0mCS0s33OKglLI33AiYhkhgUbt/OL5z9k4Sc76vflRrL41rGHcd3JI+iVH/yg+/bWpzCXs48cxNlHDsI5x9ovdvOGP2Hse+sq2FfrdcFWR+P+RLLl8AL069Gtfm6yY0cU069Ht9BeQ2f7nFUSJiIiXcbGij3cOecjnl/WcND92UcO4senj2ZIn66xmLaZMXJAD0YO6MFVxw2jOhpjwYbt9UnZii0768tu3VXN7IWbmb1wMwBjB/asn5tsytDe6ro8CErCRESk09uxt4Z7Xl3LH97dQG3M1e8/6jBvpvtJHXDQfXvqlh3hyyOK+fKIYm484wjKd1f7XZdeUvbFrur6sh9+upMPP93J/7y+jrycLKYN6+u1lI3qx8j+3TtM12UmUBImIiKdVnU0xqx3N3LPq2uTDLofw+njBihpSKK4ezfOnTiYcycOxjnH6s93189N9v66CqqjXtflvto4r6/eyuurt8LzKxnQs1v9NBjHjSimb/fwui4zQaBJmJnNAH4DRIDfOeduT1LmJODXQA5Q7pw7Mcg6SWaIxuLMXriZLTu85Tq27KjiyXmbuGDK/rXXRIKk+JXZnHO8sMwbdP/Jtv2D7nv7g+4vy/BB9+3JzBh9SA9GH9KDfzz+cPbVxpi/YXt9Urby0/1dl5/vrOapBWU8taAMgPGDe3oD/P2uS13zhgJLwswsAtwHfAUoA+aZ2TPOuQ8TyhQB/w3McM59Ymb9g6qPZI5oLM7MPy1izorP6vdVR+P8+OmlvLrqC+69bBLZEf1HluAofmW2BRu38YvnVx446P64w7jupPYbdF/SO7/BbWeRlxPhuJHFHDeymH8Dvti1j7fXlvPmam+9y/Ld+7sul2/eyfLNO/nvuR9TkBvh6MP71i+rNLxfYZdvhQyyJWwqsNY5tw7AzB4HzgU+TChzGTDbOfcJgHPuiwDrIxli9sLNDRKwRHNWfMbsRZubXQxXJA0UvzLQxoo93DFnFS8saxg/zp04iBumt/+g+84whUIq+vfI4/xJJZw/qYR43LHqs131c5N9sGEbNX7X5d6aGK+u+oJXV3n/VQb1yvO6LkcVc+zwYnoX5ob5MkIRZBI2GNiUcL8MaPyOHAXkmNlcoAfwG+fcHxqfyMyuBq4GOPTQQwOprASrOhqjcm8tO6pq2b6nhh1VtVTurWX7Xm97x94aduytZcfeWhZv2tHsuW6avYzf/H0NeTlZdMuONH+bEyEv27vtlt3w/gG3TZynM3R/qnu31dIWv0AxLGg79tZw9ytrmfVew0H3Uw/rw7+fNYYjhxSFV7kuJivLGDuoJ2MH9eSaE4dTVRPjgw3b6mfx/+jzXfVlt1Tu44n5m3hi/ibMYMLgXvXjySYd2rDrsrPGsCCTsGRXxTW6nw1MAU4F8oF3zew959zqBg9y7gHgAYDS0tLG55B2VBONU1lVS2VVDdv9pGn73poGCVX99l4/uaqqrV86Ix2iccdm/z9ie8iJWIOkrFuDJC2LPD/Ba+m2W4rl6m7T1eWq7t02SVv8AsWwoFRHY/zhnY3c8+oadu6L1u8fVlzIjWccwfSxGnQftvzcCCeO6seJo/oB8PnOff4cZFt5a005FXtqAHAOlpRVsqSskntfW0thboRjhvfl+JH9+PLwvvznSx/x0oef15+3s8SwFpMwM/sq8IJzLt5S2UbKgMQ+oxJgS5Iy5c65PcAeM3sDOBI4IIhJekVjXjK1fa+fUO2pbdgi5SdZlXXbe2qprKpld3W05ZMfhLycLGJx1+DbbGMFuRFKeuezrzZOdTTW4DYItTFHbSxKwjCHdpGdZQckZ7mtSOLycryEcVlZZZfu3m1jDFP86sCcczy39FPufGkVm7bt/0LWuyCH608bxWXTDiUnQz+UO7sBPfO4cEoJF07xui4//HRnfVI2f8P2+jU799TE+PvKL/j7yuZ7+TM9hqXSEnYJ8Bszexr4vXNuZYrnngeMNLNhwGb/PJc1KvNX4F4zywZy8Zr770rx/B1GmM2ksbij0k+e6hIqr3Wqlkp/X+PkaseeWnYFnEzlRrIoKsihd0EuvQpy6F2QQ1F+LkWF/q2/r1d+Lr0T9uXlRHhy3iZ+/PTSJs99yznjkv6Hc85RE4vXJ2XVSZK0A25rY+yLxqmujbPPf0zj2+om9ifeugDaNqJxR7Qmxp40tiIm8+S8TRkbwFLUlhjWZeJXppm/YRs/f35lg2ELudlZfPvYYVx38nB65nW+me47q6wsY/zgXowf3IvvnDScvTVR3l+/jTf9ucnWfLE7pfNkcgxrMQlzzn3DzHoClwK/NzMH/B54zDm3q5nHRc1sJvAS3k+8H3LOrTCza/3j9zvnVprZHGApEMf7Gfjyg39Z7SddXT3xuGPnvv3de43HSdV169UlV3VjqxKb4IOQEzGKCnIpyt+fUBXl59C7MJde/r4if19RQW594pWXk9XmboALppTw6qovkrbezBh3CBdMLkn6ODOv27BbdgRvxoD24ZzXcleflNXGqI7uv61udL+1t9W1yRPIdCV/W9qxazcMbYlhXSV+ZZIN5d6g+xeXN4wL500cxA2nj6akd9eY6b4zK8jN5uTR/Tl5tPdD408rq3hzTTk3/3U5Vc30dGRyDDOXYhQ3s2LgG8D1wEpgBHC3c+6ewGqXRGlpqZs/f357PmWzWmq1ueq4YUwo6bU/uUpIqBK3K6tqA2lNqZOdZRQV5DRMnPzkqn67ruXK39e7IJeC3EgoYyqisTizF23m//xlOdXRON2ys/jZeeO5YHJmD8JMp7rkr7nWvuraOD97/kPWbW16rbXSob156jtfbseat56ZLXDOlR7kORTDMtD2PTXc/eoa/vjexoaD7of14SdnjWFCSVF4lZN2ccFv32HBxu1NHu/oMay5+JXKmLCzgW8Dw4FZwFTn3BdmVoAXyNo1gHU0T8zf1OzxB99an9bnyzLqE6bE1qei/Fyvy68gh14Fufu7//x93btlZ9QA1exIFheVDuG3cz9mffkeBhXlZ2xzc1DMjNxsIzc7ix55TZfbuqu62S8KFx3Vua+rYlhm2lcb4w/vbuCeV9eyK6HF//DiQv7tzDGcNqZ/RsU0abuLS4c0m4RlcgxLZUzYPwB3OefeSNzpnNtrZt8OplqZ49M2NoOaUd8q5d16CVXDlipvX11C1asghx7dsslSS5C0Qlu7dzsRxbAM4pzj2aWfcuecVZRt3x9f+xTmcv1pI7l0qgbddzWdOYalkoTdDNQvN29m+cAA59wG59wrgdUsQwwsymdL5b4mjw/pnc8Pp4/2B6fvH1vVI0/JVJv84TzY8QkUHQpX/CXs2mSESJZx72WTunL3rmJYhpjnD7pf0mjQ/VXHDeM7J2nQfVfVmWNYKknYn4HEztaYv++oQGqUYVpqJv3nU0dy3qTB7VijzinLxWDhLPjkXYjug52bvfsTL4OsSNjV6/C6ePeuYlgHt758D3e8uOqAlo7zJw3mhtNHM7iocy37I63XZAzL8C/mqSRh2c65mro7zrkaM+t6aws0oTM3k3YUEWLcvO+X8My7+3dG98EzM2HNS3DhwxAJdC16yWyKYR3Utj013P2KN+g+Gt8/6H7asD785KyxfKmkV4i1k46ss3wxT+WTa6uZneOcewbAzM4FyoOtVubozM2k7S5aAzW7oXYv1OyFmt0cGVvON7Lf5ITYu8kfs/JZmHsbjL8QunWH3O7QrQdE1G0h9RTDUnT5g+9Ttr2Kkt75ga57uK82xiPvbODe1xoNuu9XyE1njOFUDbqXZnSmL+ap1PJa4FEzuxdvKY9NwBWB1irDNNvVk+FNpQeIx6F2T32SlJgwtX17j3fO+IFznv0aWn6Xvvkr7y9Rdp6fkPlJWW6PhO1Gt/XJW8+GiVxdmexu3i8pJFMphqWobHsV68ubns7kYMXjjmeXbuHOOR81WHqsb2Eu139lFJccNUSD7qVpzpHnqrgq8kLzX8yXPg6TvtG+dWujVCZr/Rg42sy6480r1uQErZIgFoUlj4XTVOocRKv9JGe3l+jU7vGSnYPdjmbIpHjRfd7f3jQ0eGRlN5HINUrwGidv9QlewuNyCjpOQlf3Hn3pJu+9klMAp9+Wcc35LVEM6xjeX1fBbS+sZElZZf2+btlZ/OPxw7j2xOH00KD7ridWC3u3eXF6b4X3t6fc31fRaL93+2KsuuW5uBfO6jxJGICZnQWMA/Lqmoidc/83wHpltCwXg6eu9DLyOsmaSmPR/a1KaUuY/PO0eqnPdmJZkFMIuQWQW9ho279N2L7/3c84Y9+LDM1qZv2wXkNg/AVQvct77dW7oWaXd796t7/PP9YW8ShUbff+DpZlNWqJa651rkejlroeDR+X273NyVLS92j1zoxszk+FYlgbpaElf93W3dz+4ir+lrD4MsDXJg/mhumjGaRB952Dc7Cvcn/S1CCpqkiSbFVAdWXL522LyrJgzhuAVCZrvR8oAE4GfgdcCHwQcL0y2unR1xp+uCVa+Szcfqj3wR5r5xWhWyM7L6UkqfntJMlWdl6rWoKeWDCXj/d045dZDzRd6KQbU/vWU9eVWp+c+bf1yduuRoncbi8xSUzkEhO8tiS6Lu6fc2frH5tMTmGrullPiH7CkCxjes3Hzb9HM6g5vyWKYa2XjkHP2/bU8Ju/r+bR9z9pMOj+mMP78u9njWH8YA2679Bq9x2YUDVIqpL8JRlSctCysqGgb/3f+58bI6qW0pcdTT+mV+b8IC6Vr7pfds5NMLOlzrlbzexXwOygK5bJzqx9ufkCtWkac2ER70O2zUlSM9sdqDvq6dgJnJ23LPkYgDFnw5GXpnairKz9rUkHyzmorUqevFXv2p/gHXB8V/IEL1bT8nMmU+uPp9v9ectlgVvBW2q6toWCGdScnwLFsFY42EHP+2pjPPzOBu57dS27qvd/KA/vV8hNZ47hlCO68KD7sMYIx+NeS359wnRgN1/D/dva3nPQkrxeCUlVsX/bx7stLG6QcFHQ1yuf8H6ZBl58emZm088x+fJg6h6AVJKwuplI95rZIKACGBZclTJfP9fCOKRILhx6zMEnTJHcjjO+KEBxsrg170e8ctqn8PwPvQ+E7Dw461deAhZGwmjmJ78F0L3/wZ8vWt1061yyVrikCZ5/Px3j9jKoOT8FimGt8LVIC79Gfu56GHai9wvkSA5kebdxy+at9ZU88v4WtuyK0p9s+liE7vn5fPuEUZw7ZSjZOd28/79ZOd7/2y4Qv4D0jhF2zht20mQ3X5L9VduDGaIS6eYnTn0aJVV9obBv8mQrHb9cn3iZ94UgWWt+a76YdwCpJGHPmlkR8EtgIeCA/xdkpTLdVitmQHOJ2KDJ8M1n2q9CGayktzdeZFDvfK9lZtlT+79Jdp6WGu8XmNndvMB1sGLRRglckuTtnbubT7QyqDk/BYphrXBxZG7zBRbN8v4ayQJO8P/olnAgDsz1/xqwBkmc95frdT812M71yzWx3eAcKT6+8fMe8PgWHpOV47WspyIWbX6M8Nd+57WKN+jmSxicnqz7L9r0Ki1tZwnJVKO/Bi1UffYnVbmF4STSWRGvRXbp4zDnJq83IKcQZtwW3hfzNmo2CTOzLOAV59wO4Gkzew7Ic84FNJquc3gh5yuMr17VdIEMaioN2wFzFXWGaT6CFsmG/CLvryk5BZ2mOb85imGtN9Aq2umZnNcNH6tpuXu8I7JIaolb1Q7Yvj75OVY+C78YEEz9crsnb6Eq6NMoqfK384syKnkhku19Ec/wL+PNJmHOubg/fuIY/3410IFHk3cML2WfzI8P39Apmkqlk+pEzfnNUQxrvU9dXwY3l4j1Hcmuo3/Ay8vKmL/uc7JcjFyiZBNleN9unDyiiP6FEW/6gVgtxGv9ZCvacDtW499vajvx8QnbQQz+bgsXg2gsoFapRhoNTk/eStWnYbdfjn51mglS6Y78m5ldAMx2zrkWSwtx6zxNpdJJdaLm/BQohrXCE7GTKM1a3eTxV4sv41+e68vu6l54s37AiP7duenMIzh5dDsMuo/H/V+XtyaJSzUJbCLxO5jEcW958+OxsvO8FT+StlD1STo4XTqPVJKwHwCFQNTM9uHNOO2ccz0DrVkGqhu/VNI7v9M0lUon1nXeo4phrdDcr5Ffyzqaf1wykjhea1Rx91y+/5VRXFw6hOz2muk+KwuyciE7Q5b/fHA6bHq/6eMDJ8J597VbdaRjSWXG/DT8nr9rCHKtNRFpG8Ww1mnwa+Q5NxGv2c1e8ri15us8HTuBOFnk5WTxT8cfzjUnDqd7t84zqW8gJl3efBLWScZfStukMlnrCcn2O+feSH91RETSSzGsZdFYnNkLN7PFX8+xrLKGe7dNY/Ggp/n7yv2rVZjBhZNL+OH0UQzspTFHKeki4y+lbVL5CvOjhO08YCqwADglkBqJiKSXYlgzorE4M/+0iDkrPqvfVx2N858vNxwXduyIvtx05hjGDdJM963StcZfSiul0h15duJ9MxsC3BlYjURE0kgxrHmzF25ukIA1NqBnN26/YAInjerXdWe6P1hdZ/yltFJbOvPLgPHproiISDtRDEvwxPxNzR4vKcrn5NFpWBVCRA6Qypiwe/BmmAZvUuSJwJIA6yQikjaKYc37dEfzy1x9WtkO82CJdFGptITNT9iOAo85594OqD4iIummGNaMgUX5bGkm0RpUpAH4IkFJJQl7CtjnnIsBmFnEzAqcc3uDrZqISFoohjXj4tIhLNi4vcnjFx01pB1rI9K1pDK73itA4lehfODvwVRHRCTtFMOaccGUEmaMOyTpsRnjDuGCyZ1qMXeRDiWVJCzPObe77o6/XRBclURE0koxrBmRLOPeyyZx54UT6JbtfSR0y87izgsncN/XJxPJ0i8iRYKSShK2x8wm190xsylA8yM5RUQ6DsWwFmRHsriodEj9+K9BRflcVDpECZhIwFIZE3Y98Gcz2+LfHwhcHFiNRETS63oUw0SkA0plstZ5ZnYEMBpv4dtVzrnawGsmIpIGimEi0lG12B1pZt8FCp1zy51zy4DuZnZd8FUTETl4imEi0lGlMibsn5xzO+ruOOe2A/8UWI1ERNJLMUxEOqRUkrAsS1gwzMwiQG5wVRIRSSvFMBHpkFIZmP8S8KSZ3Y+39Me1wIuB1kpEJH0Uw0SkQ0olCftX4GrgO3iDWhfh/bpIRCQTKIaJSIfUYnekcy4OvAesA0qBU4GVAddLRCQtFMNEpKNqsiXMzEYBlwCXAhXAEwDOuZPbp2oiIm2nGCYiHV1z3ZGrgDeBs51zawHM7PvtUisRkYOnGCYiHVpz3ZEXAJ8Br5nZ/zOzU/HGU4iIZALFMBHp0JpMwpxz/+ucuxg4ApgLfB8YYGa/NbPp7VQ/EZE2UQxrvZLe+QwrLqSkd37YVRHpElIZmL/HOfeoc+6rQAmwGLgxlZOb2Qwz+8jM1ppZk48xs6PMLGZmF6ZacRGRVLQ1hnXF+DXrqmm8dsNJzLpqWthVEekSUpmstZ5zbptz7n+cc6e0VNafEPE+4AxgLHCpmY1totwdeHP5iIgEJtUYpvglIu2hVUlYK00F1jrn1jnnaoDHgXOTlPtn4GngiwDrIiLSGopfIhK4IJOwwcCmhPtl/r56ZjYYOB+4v7kTmdnVZjbfzOZv3bo17RUVEWkkbfHLL6sYJiIHCDIJS/YrJNfo/q+Bf3XOxZo7kXPuAedcqXOutF+/fumqn4hIU9IWv0AxTESSS2XZorYqA4Yk3C8BtjQqUwo87q+tWwycaWZR59xfAqyXiEhLFL9EJHBBJmHzgJFmNgzYjDdz9WWJBZxzw+q2zexh4DkFMBHpABS/RCRwgSVhzrmomc3E+9VQBHjIObfCzK71j7c4jkJEJAyKXyLSHoJsCcM59wLwQqN9SYOXc+7KIOsiItIail8iErQgB+aLiIiISBOUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAiUhImIiIiEQEmYiIiISAgCTcLMbIaZfWRma83sxiTHv25mS/2/d8zsyCDrIyKSKsUvEQlaYEmYmUWA+4AzgLHApWY2tlGx9cCJzrkJwM+AB4Kqj4hIqhS/RKQ9BNkSNhVY65xb55yrAR4Hzk0s4Jx7xzm33b/7HlASYH1ERFKl+CUigQsyCRsMbEq4X+bva8pVwIvJDpjZ1WY238zmb926NY1VFBFJKm3xCxTDRCS5IJMwS7LPJS1odjJeEPvXZMedcw8450qdc6X9+vVLYxVFRJJKW/wCxTARSS47wHOXAUMS7pcAWxoXMrMJwO+AM5xzFQHWR0QkVYpfIhK4IFvC5gEjzWyYmeUClwDPJBYws0OB2cDlzrnVAdZFRKQ1FL9EJHCBtYQ556JmNhN4CYgADznnVpjZtf7x+4GfAn2B/zYzgKhzrjSoOomIpELxS0TagzmXdJhDh1VaWurmz58fdjVEpB2Z2YLOkuAohol0Lc3FL82YLyIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIVASJiIiIhICJWEiIiIiIQg0CTOzGWb2kZmtNbMbkxw3M7vbP77UzCYHWR8RkVQpfolI0AJLwswsAtwHnAGMBS41s7GNip0BjPT/rgZ+G1R9RERSpfglIu0hyJawqcBa59w651wN8DhwbqMy5wJ/cJ73gCIzGxhgnUREUqH4JSKByw7w3IOBTQn3y4BpKZQZDHyaWMjMrsb7pgmw28w+Sm9VA1EMlIddiU5G1zT9MuWaDm3n50tb/ALFMAF0PYOQKde0yfgVZBJmSfa5NpTBOfcA8EA6KtVezGy+c6407Hp0Jrqm6adr2qS0xS9QDBNdzyB0hmsaZHdkGTAk4X4JsKUNZURE2pvil4gELsgkbB4w0syGmVkucAnwTKMyzwBX+L8yOhqodM4d0JQvItLOFL9EJHCBdUc656JmNhN4CYgADznnVpjZtf7x+4EXgDOBtcBe4FtB1ScEGdX1kCF0TdNP1zQJxS9A74100/VMv4y/puZc0iEMIiIiIhIgzZgvIiIiEgIlYSIiIiIhUBLWCmZ2i5ndENC5i8zsuiDO3RGZ2b+Y2UozezSAcx9mZpel+7wimcDMYma22MyWmNlCM/tyOzznBjMr9rcfMrMvzGx50M/bHsK8nmY2xMxe82PlCjP7XtDP3REkXPPlZvZnMytootw77V23dFMS1gH4S6QUAV0mCcN7rWc6577eUkEza+0PSA4DlIRJV1XlnJvonDsS+DfgP9r5+R8GZrTzcwYpzOsZBX7onBsDHA18N8nyWZ1R3TUfD9QA1yYe9D8zcc4FnhAHTUlYC8zs3/1FfP8OjPb3DTezOWa2wMzeNLMj/P3/4GfuS8zsDX9fxMx+aWbz/EV+r/H3n+R/w/kTsAy4HRjuZ/+/DOfVtg8zux84HHjGzH5oZn/xr817ZjbBL3OLmT1gZn8D/mBm/czsaf86zjOzY/1yJ/rXbLGZLTKzHnjX8nh/3/dDe6HtyMx+4L/3lpvZ9f6+K/zrusTMZvn7mrqOU83sHf8avmNmde/1K81stv9+X2Nmd4b2IqUtegLboX7B8V/675FlZnaxv/8kM3uu7gFmdq+ZXelvbzCzW/0WoGUJsa6vmf3Nf7/8DwkT1zrn3gC2td9LbFftej2dc5865xb627uAlXirMnQlbwIjknxmYma7/duTzOx1M3vSzFab2e1m9nUz+8C/zsPDfAHNcs7pr4k/YAreP3YB3n++tcANwCvASL/MNOBVf3sZMNjfLvJvrwZ+4m93A+YDw4CTgD3AMP/YYcDysF9zO17bDXhLTtwD3OzvOwVY7G/fAiwA8v37fwKO87cPBVb6288Cx/rb3fGmXTkJeC7s19iO17LufVroX4MVwLHAR0CxX6ZPC9exJ5Dtb58GPO1vXwmsA3oBecBGYEjYr1l/zb4fYsBiYBVQCUzx918AvIw35cYA4BNgYOP/L8C9wJX+9gbgn/3t64Df+dt3Az/1t8/CWymgOOEcnSaedYTrmXBNPwF6hn1N2uGa7/Zvs4G/At+h0Wdmo3InATv8698N2Azc6h/7HvDrsF9TU39BLlvUGRwP/K9zbi+AmT2D90H0ZeDPZvVf/rr5t28DD5vZk8Bsf990YIKZXejf7wWMxGti/cA5tz7wV9GxHYcXzHDOvep/I+zlH3vGOVflb58GjE245j39Vq+3gf8yb2zZbOdcWUKZruI4vPfpHgAzmw2UAk8558oBnHN1LRNNXcdewCNmNhLvAyAn4fyvOOcq/XN/iLcOWuKaidKxVDnnJgKY2TF4Lcnj8d4njznnYsDnZvY6cBSws4Xz1cWyBcDX/O0T6radc8+b2fb0voQOJfTraWbdgaeB651zLZ2/M8g3s8X+9pvAg3ifu819Zs5z/mTJZvYx8Dd//zLg5ADrelCUhLWs8URqWcCOuv+UDQo6d62ZTcP7JrPYzCbiNSv/s3PupcSyZnYSXlbf1TW3/l7i9ckCjklIyurcbmbP402a+Z6ZnRZAHTu6pq5hskkAk15HM7sHeM05d76ZHQbMTThcnbAdQ3EjYzjn3jVvwHw/kr9PwBt3lDg0Ja/R8bp//8b/9l1ukskwrqeZ5eAlYI8652YnK9MJVTX+jPW/ODb3mZkYp+IJ9+N04JilMWHNewM438zy/daCs/Fmxl5vZv8A9eMCjvS3hzvn3nfO/RRvZfcheDNuf8f/j4SZjTKzwiTPtQvoEfxL6nDeAL4O9YlpeRPf9P4GzKy74ye4ddd8mXPuDryu3iPoetfyDeA8Myvw31vn433LvsjM+gKYWR+/bNLriNcSttnfvrId6iztwB9zFAEq8N4nF5s3TrUfXuvLB3hdzGPNrJvfCn1qCqdO/H97BtA7iPp3NO19Pc3LPB7EGzbwX+l+PRK+DpsddgTOuYVm9gTeeICNeM2i4P1n+a2Z/QSv2+ZxYAnwS787x/DGjS0BluL15S/0/0NtBc5L8lwVZva2eT/rftE596MAX1pHcgvwezNbipfgfrOJcv8C3OeXy8YLWtcC15vZyXjfKj8EXsT75hM1syXAw865u4J9CeHy36cP430AgDfO5G0z+wXwupnFgEV4yVVT1/FOvO7IHwCvtvNLkPRK7Mox4JvOuZiZ/S9wDF5ccsCPnXOfAfhDKJYCa/DeKy25FXjMzBYCr+ONVcI/12N4Y3SKzawMb8zng+l4YSEJ83oeC1wOLEuow03OuRcO+lVJh6Bli0RERERCoO5IERERkRAoCRMREREJgZIwERERkRAoCRMREREJgZIwERERkRAoCZNWsf2r29f93djG81xrZleku37pZGYTzezMsOshIsEzsxIz+6t5a6SuM2/Nx24tPzLl859n/uLbZnafHz8/NLOqhHh6YUvnkc5FU1RIq5jZbudc9wDPn+2ciwZ1/tYwb9HdUufczJbKikjm8udwfB/4rXPu92YWAR7AW5vwe2l6jofx1pR8KmHfYf6+8el4Dsk8agmTtDCzDWZ2q5ktNG/V+iPMLMvfX5RQbq2ZDTCzW8zsBn/fXDO7zby1175nZqea2SL/PA/VfRtN9hz+/lvM7BEz+5tf5mtmdqdfZk7CagVTzOx1M1tgZi+Z2cCE57/DzD4ws9VmdryZ5QL/F29G7MVmdnH7XlERaUenAPucc78H8NeD/D5whZnNNLN76wqa2XPmre6BmU03s3f9mPRn89Z4xMxu91u5lprZf5rZl4Fz8Cb0XmxmwxtXwMxmmdm5CfcfNbNzzOxKv4Vujpl9ZGY3J5T5hh+3FpvZ//jJo2QQJWHSWvmNuiMTk5Ny59xk4LfADc65OPBXvGV0MG9dzQ3Ouc+TnLfIOXcicB/wMHCxc+5LeLO6f6ep50jYPxxvzc5zgT/irYP4JaAKOMtPxO4BLnTOTQEeAn6R8Phs59xU4Hq8Gb5rgJ8CTzjnJjrnnmjldRKRzDEOb6mvev7yaRtoYmUZ89aQ/Alwmh+T5gM/MG+JsPOBcc65CcDPnXPvAM8AP/LjycdJTvk74Fv+uXvhLVhdNzP+VLyVWiYC/2BmpWY2BrgYONZfZzHml5EMomWLpLUOWFg1Qd3isguAr/nbT+AlM78HLvHvJ1O3fzSw3jm32r//CPBd4NfNPAd4Sz3VmtkyvLXd5vj7l+EtGzUaGA+87PU8EAE+baLuhzVRRxHpnIzkC2g3tUg3wNHAWOBtP6bkAu8CO4F9wO/M7HnguVQq4Jx73R8r1h8vtj3tnIv6537ZOVcBYGazgePwFgqfAszzy+QDX6TyXNJxKAmTdKpbtT7G/vfWu8AI8xa4PQ/4eROP3ePfNhf0mnqO+v3OubiZ1br9gx3jfjkDVjjnjmnleUWk81sBXJC4w8x6AgPwFuselXAor64IXnJ0aeOTmdlUvIW7LwFm4nV3pmIWXmvWJcC3E/Y3ThCd//yPOOf+LcVzSwek7kgJlJ8M/S/wX8DKum9zzVgFHGZmI/z7l+MtaHuwPgL6mdkxAGaWY2bjWnjMLqBHGp5bRDq2V4AC83+x7Y+t+hVwL7AemOiPcR2C1zUI8B5wbF2sMrMCMxvljwvr5S+yfT1eFyKkFk8e9h+Dc25Fwv6vmFkfM8vH+zL7tl/nC/2WM/zjQ9v06iU0SsKktRqPCbs9hcc8AXyDprsi6znn9uGNi/iz37UYB+4/qBp7560BLgTuMLMlwGK8MRfNeQ0Yq4H5Ip2b/2XxfLykZg1e61fcOfcLvIRnPd7Qhv8EFvqP2QpcCTxmZkvxkrIj8BKt5/x9r+MN8Ad4HPiR/6OjAwbm++f8HFiJN3wj0Vt4rWSL8bop5zvnPsQbk/Y3/7leBgYe5KWQdqYpKkRERBL4v2Z8DPiac25BS+XT+LwFeMneZOdcpb/vSjRVTqelljAREZEEzrl3nHND2zkBOw1vOMY9dQmYdH5qCRMREREJgVrCREREREKgJExEREQkBErCREREREKgJExEREQkBErCRERERELw/wGwSgD2bHBppgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitted = cython_fit.fitted_results(struct_all, induct_all, results, n_states, False, 'comm', comm)\n",
    "fig = cython_fit.plot_by_question(fitted, split= False,  fig_name='Hybrid Community')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv(fitted_path+\"results/hybrid_community.csv\")\n",
    "fig.savefig(fitted_path+'accuracy_plots/hybrid_community.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add weights by question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating parameters for 100...\n",
      "Estimating parameters for 101...\n",
      "Estimating parameters for 102...\n",
      "Estimating parameters for 103...\n",
      "Estimating parameters for 104...\n",
      "Estimating parameters for 105...\n",
      "Estimating parameters for 106...\n",
      "Estimating parameters for 107...\n",
      "Estimating parameters for 108...\n",
      "Estimating parameters for 109...\n",
      "Estimating parameters for 110...\n",
      "Estimating parameters for 111...\n",
      "Estimating parameters for 112...\n",
      "Estimating parameters for 113...\n",
      "Estimating parameters for 114...\n",
      "Estimating parameters for 115...\n",
      "Estimating parameters for 116...\n",
      "Estimating parameters for 117...\n",
      "Estimating parameters for 119...\n",
      "Estimating parameters for 120...\n",
      "Estimating parameters for 121...\n",
      "Estimating parameters for 122...\n",
      "Estimating parameters for 123...\n",
      "Estimating parameters for 124...\n",
      "Estimating parameters for 125...\n",
      "Estimating parameters for 126...\n",
      "Estimating parameters for 127...\n",
      "Estimating parameters for 128...\n",
      "Estimating parameters for 129...\n",
      "Estimating parameters for 130...\n",
      "Estimating parameters for 131...\n",
      "Estimating parameters for 132...\n",
      "Estimating parameters for 133...\n",
      "Estimating parameters for 135...\n",
      "Estimating parameters for 136...\n",
      "Estimating parameters for 137...\n",
      "Estimating parameters for 138...\n"
     ]
    }
   ],
   "source": [
    "\n",
    "fixed = {}\n",
    "var_names = ['gamma',  'alpha', 'tau', 'w_prim' ,'w_bound1', 'w_bound2']\n",
    "var_bounds = {'gamma': [0, 1], 'alpha': [0, 1], 'tau': [0, 10],\n",
    "              'w_prim' : [0, 1], 'w_bound1' : [0, 1], 'w_bound2' : [0, 1]}\n",
    "results_w= cython_fit.fit_induct_indiv(struct_all, induct_all, fixed, var_names, var_bounds, split =True,\n",
    "                               n_states=n_states, verbose=False, model_type='comm', model=comm, split_list =[\"w\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>gamma</th>\n",
       "      <th>alpha</th>\n",
       "      <th>tau</th>\n",
       "      <th>w_prim</th>\n",
       "      <th>w_bound1</th>\n",
       "      <th>w_bound2</th>\n",
       "      <th>log_like</th>\n",
       "      <th>k</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911399</td>\n",
       "      <td>6.704224</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.294792</td>\n",
       "      <td>-28.489425</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.993999</td>\n",
       "      <td>0.669074</td>\n",
       "      <td>1.023599</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.495415</td>\n",
       "      <td>0.642532</td>\n",
       "      <td>-24.966161</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.625211</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.498288</td>\n",
       "      <td>0.067316</td>\n",
       "      <td>0.198039</td>\n",
       "      <td>0.654610</td>\n",
       "      <td>-18.302686</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.998592</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>0.999810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999027</td>\n",
       "      <td>-24.040362</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.558109</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.613149</td>\n",
       "      <td>-26.791167</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            gamma     alpha       tau    w_prim  w_bound1  w_bound2  \\\n",
       "subject                                                               \n",
       "100      1.000000  0.911399  6.704224  1.000000  1.000000  0.294792   \n",
       "101      0.993999  0.669074  1.023599  1.000000  0.495415  0.642532   \n",
       "102      0.625211  0.000000  0.498288  0.067316  0.198039  0.654610   \n",
       "103      0.000084  0.998592  0.001407  0.999810  1.000000  0.999027   \n",
       "104      0.000000  1.000000  0.558109  1.000000  1.000000  0.613149   \n",
       "\n",
       "          log_like  k   n  \n",
       "subject                    \n",
       "100     -28.489425  6  42  \n",
       "101     -24.966161  6  42  \n",
       "102     -18.302686  6  42  \n",
       "103     -24.040362  6  42  \n",
       "104     -26.791167  6  42  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_w = results_w.set_index('subject')\n",
    "results_w.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_w.to_csv(f\"./Data/hybrid_results/hybrid_community_weighted.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get response probabilities for the fitted models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitted2 = cython_fit.fitted_results(struct_all, induct_all, results_w, n_states, True, 'comm', comm)\n",
    "fig2 = cython_fit.plot_by_question(fitted2,  fig_name='Hybrid Community weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_w.to_csv(fitted_path+\"results/hybrid_community_weighted.csv\")\n",
    "fig2.savefig(fitted_path+'accuracy_plots/hybrid_community_weighted.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'tesser.cython_fit' from '/home/work-machine/Preston_Lab/tesser_successor/tesser/cython_fit.py'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(cython_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>SubjNum</th>\n",
       "      <th>TrialNum</th>\n",
       "      <th>QuestType</th>\n",
       "      <th>Environment</th>\n",
       "      <th>Source</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>Prim</td>\n",
       "      <td>ocean</td>\n",
       "      <td>Data</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>Bound2</td>\n",
       "      <td>desert</td>\n",
       "      <td>Data</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>Prim</td>\n",
       "      <td>desert</td>\n",
       "      <td>Data</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "      <td>Prim</td>\n",
       "      <td>ocean</td>\n",
       "      <td>Data</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>Prim</td>\n",
       "      <td>ocean</td>\n",
       "      <td>Data</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>138</td>\n",
       "      <td>38</td>\n",
       "      <td>Prim</td>\n",
       "      <td>desert</td>\n",
       "      <td>Model</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>138</td>\n",
       "      <td>39</td>\n",
       "      <td>Prim</td>\n",
       "      <td>forest</td>\n",
       "      <td>Model</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>138</td>\n",
       "      <td>40</td>\n",
       "      <td>Prim</td>\n",
       "      <td>ocean</td>\n",
       "      <td>Model</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>138</td>\n",
       "      <td>41</td>\n",
       "      <td>Bound1</td>\n",
       "      <td>forest</td>\n",
       "      <td>Model</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>138</td>\n",
       "      <td>42</td>\n",
       "      <td>Prim</td>\n",
       "      <td>ocean</td>\n",
       "      <td>Model</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3108 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SubjNum  TrialNum QuestType Environment Source  Accuracy\n",
       "0       100         1      Prim       ocean   Data       0.0\n",
       "1       100         2    Bound2      desert   Data       0.0\n",
       "2       100         3      Prim      desert   Data       1.0\n",
       "3       100         4      Prim       ocean   Data       1.0\n",
       "4       100         5      Prim       ocean   Data       0.0\n",
       "..      ...       ...       ...         ...    ...       ...\n",
       "79      138        38      Prim      desert  Model       1.0\n",
       "80      138        39      Prim      forest  Model       1.0\n",
       "81      138        40      Prim       ocean  Model       1.0\n",
       "82      138        41    Bound1      forest  Model       1.0\n",
       "83      138        42      Prim       ocean  Model       1.0\n",
       "\n",
       "[3108 rows x 6 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_list = []\n",
    "for subject in results_w.index.unique():\n",
    "    subj_filter = f'SubjNum == {subject}'\n",
    "    subj_struct = struct_all.query(subj_filter)\n",
    "    subj_induct = induct_all.query(subj_filter)\n",
    "    subj_param = results_w.loc[subject]\n",
    "    param={}\n",
    "    for name in results_w.columns:\n",
    "        if (name == 'log_like') or (name == 'k') or (name == 'n'):\n",
    "            continue\n",
    "        param[name] = subj_param[name]\n",
    "\n",
    "    res = cython_fit.assess_induct_fit_subject_hybrid(subj_struct , subj_induct, param, n_states = 21, split= True, model_type= 'comm', model=comm)\n",
    "    res_list.append(res)\n",
    "fitted2 = pd.concat(res_list, axis=0)\n",
    "fitted2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Tesser",
   "language": "python",
   "name": "tesser"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}