notebooks/11_nonlinear_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Non-linear features\n",
    "\n",
    "This notebook demonstrates how to use YASA to calculate epoch-per-epoch non-linear features of a full-night single-channel EEG recording.\n",
    "\n",
    "Please make sure to install the latest version of YASA first by typing the following line in your terminal or command prompt:\n",
    "\n",
    "`pip install --upgrade yasa`\n",
    "\n",
    "In addition, you will also need to install the [AntroPy](https://github.com/raphaelvallat/antropy) package: `pip install --upgrade antropy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2161058,) [15.797 22.307 39.922 25.657 27.094]\n"
     ]
    }
   ],
   "source": [
    "import yasa\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import antropy as ant\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme(font_scale=1.2)\n",
    "\n",
    "# Download the EEG data\n",
    "filepath = yasa.fetch_sample('full_6hrs_100Hz_Cz+Fz+Pz.npz')\n",
    "\n",
    "# Load EEG data\n",
    "npzfile = np.load(filepath)\n",
    "data, ch_names = npzfile['data'], npzfile['chan']\n",
    "sf = 100.\n",
    "times = np.arange(data.size) / sf\n",
    "\n",
    "# Keep only Cz\n",
    "data = data[0, :]\n",
    "print(data.shape, np.round(data[0:5], 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(720,) Unique values = [0 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "# Load the hypnogram data\n",
    "hypno_filepath = yasa.fetch_sample('full_6hrs_100Hz_hypno_30s.txt')\n",
    "hypno = np.loadtxt(hypno_filepath).astype(int)\n",
    "print(hypno.shape, 'Unique values =', np.unique(hypno))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(720, 3000)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the EEG data to 30-sec data\n",
    "times, data_win = yasa.sliding_window(data, sf, window=30)\n",
    "\n",
    "# Convert times to minutes\n",
    "times /= 60\n",
    "\n",
    "data_win.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate non-linear features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perm_entropy</th>\n",
       "      <th>svd_entropy</th>\n",
       "      <th>sample_entropy</th>\n",
       "      <th>dfa</th>\n",
       "      <th>petrosian</th>\n",
       "      <th>katz</th>\n",
       "      <th>higuchi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.993005</td>\n",
       "      <td>0.615179</td>\n",
       "      <td>0.992649</td>\n",
       "      <td>1.275455</td>\n",
       "      <td>1.027439</td>\n",
       "      <td>2.794894</td>\n",
       "      <td>1.743780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.993249</td>\n",
       "      <td>0.565308</td>\n",
       "      <td>0.858924</td>\n",
       "      <td>1.384108</td>\n",
       "      <td>1.027283</td>\n",
       "      <td>2.593634</td>\n",
       "      <td>1.748034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.993561</td>\n",
       "      <td>0.573424</td>\n",
       "      <td>0.773124</td>\n",
       "      <td>1.406985</td>\n",
       "      <td>1.027411</td>\n",
       "      <td>2.633458</td>\n",
       "      <td>1.721437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.998517</td>\n",
       "      <td>0.495381</td>\n",
       "      <td>0.166400</td>\n",
       "      <td>1.453336</td>\n",
       "      <td>1.028952</td>\n",
       "      <td>2.259923</td>\n",
       "      <td>1.868283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.991045</td>\n",
       "      <td>0.737926</td>\n",
       "      <td>1.332437</td>\n",
       "      <td>1.152280</td>\n",
       "      <td>1.026885</td>\n",
       "      <td>3.484543</td>\n",
       "      <td>1.769742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   perm_entropy  svd_entropy  sample_entropy       dfa  petrosian      katz  \\\n",
       "0      0.993005     0.615179        0.992649  1.275455   1.027439  2.794894   \n",
       "1      0.993249     0.565308        0.858924  1.384108   1.027283  2.593634   \n",
       "2      0.993561     0.573424        0.773124  1.406985   1.027411  2.633458   \n",
       "3      0.998517     0.495381        0.166400  1.453336   1.028952  2.259923   \n",
       "4      0.991045     0.737926        1.332437  1.152280   1.026885  3.484543   \n",
       "\n",
       "    higuchi  \n",
       "0  1.743780  \n",
       "1  1.748034  \n",
       "2  1.721437  \n",
       "3  1.868283  \n",
       "4  1.769742  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import apply_along_axis as apply\n",
    "\n",
    "df_feat = {\n",
    "    # Entropy\n",
    "    'perm_entropy': apply(ant.perm_entropy, axis=1, arr=data_win, normalize=True),\n",
    "    'svd_entropy': apply(ant.svd_entropy, 1, data_win, normalize=True),\n",
    "    'sample_entropy': apply(ant.sample_entropy, 1, data_win),\n",
    "    # Fractal dimension\n",
    "    'dfa': apply(ant.detrended_fluctuation, 1, data_win),\n",
    "    'petrosian': apply(ant.petrosian_fd, 1, data_win),\n",
    "    'katz': apply(ant.katz_fd, 1, data_win),\n",
    "    'higuchi': apply(ant.higuchi_fd, 1, data_win),\n",
    "}\n",
    "\n",
    "df_feat = pd.DataFrame(df_feat)\n",
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lziv(x):\n",
    "    \"\"\"Binarize the EEG signal and calculate the Lempel-Ziv complexity.\n",
    "    \"\"\"\n",
    "    return ant.lziv_complexity(x > x.mean(), normalize=True)\n",
    "\n",
    "df_feat['lziv'] = apply(lziv, 1, data_win)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add classic spectral power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perm_entropy</th>\n",
       "      <th>svd_entropy</th>\n",
       "      <th>sample_entropy</th>\n",
       "      <th>dfa</th>\n",
       "      <th>petrosian</th>\n",
       "      <th>katz</th>\n",
       "      <th>higuchi</th>\n",
       "      <th>lziv</th>\n",
       "      <th>delta</th>\n",
       "      <th>theta</th>\n",
       "      <th>alpha</th>\n",
       "      <th>sigma</th>\n",
       "      <th>beta</th>\n",
       "      <th>gamma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.993005</td>\n",
       "      <td>0.615179</td>\n",
       "      <td>0.992649</td>\n",
       "      <td>1.275455</td>\n",
       "      <td>1.027439</td>\n",
       "      <td>2.794894</td>\n",
       "      <td>1.743780</td>\n",
       "      <td>0.565987</td>\n",
       "      <td>0.696338</td>\n",
       "      <td>0.116689</td>\n",
       "      <td>0.066675</td>\n",
       "      <td>0.020174</td>\n",
       "      <td>0.068901</td>\n",
       "      <td>0.031222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.993249</td>\n",
       "      <td>0.565308</td>\n",
       "      <td>0.858924</td>\n",
       "      <td>1.384108</td>\n",
       "      <td>1.027283</td>\n",
       "      <td>2.593634</td>\n",
       "      <td>1.748034</td>\n",
       "      <td>0.400426</td>\n",
       "      <td>0.802190</td>\n",
       "      <td>0.068789</td>\n",
       "      <td>0.045489</td>\n",
       "      <td>0.015227</td>\n",
       "      <td>0.045956</td>\n",
       "      <td>0.022349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.993561</td>\n",
       "      <td>0.573424</td>\n",
       "      <td>0.773124</td>\n",
       "      <td>1.406985</td>\n",
       "      <td>1.027411</td>\n",
       "      <td>2.633458</td>\n",
       "      <td>1.721437</td>\n",
       "      <td>0.550586</td>\n",
       "      <td>0.877757</td>\n",
       "      <td>0.047858</td>\n",
       "      <td>0.021888</td>\n",
       "      <td>0.007310</td>\n",
       "      <td>0.028541</td>\n",
       "      <td>0.016645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.998517</td>\n",
       "      <td>0.495381</td>\n",
       "      <td>0.166400</td>\n",
       "      <td>1.453336</td>\n",
       "      <td>1.028952</td>\n",
       "      <td>2.259923</td>\n",
       "      <td>1.868283</td>\n",
       "      <td>0.431228</td>\n",
       "      <td>0.697172</td>\n",
       "      <td>0.024946</td>\n",
       "      <td>0.041486</td>\n",
       "      <td>0.025344</td>\n",
       "      <td>0.108747</td>\n",
       "      <td>0.102304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.991045</td>\n",
       "      <td>0.737926</td>\n",
       "      <td>1.332437</td>\n",
       "      <td>1.152280</td>\n",
       "      <td>1.026885</td>\n",
       "      <td>3.484543</td>\n",
       "      <td>1.769742</td>\n",
       "      <td>0.539035</td>\n",
       "      <td>0.690355</td>\n",
       "      <td>0.098202</td>\n",
       "      <td>0.069255</td>\n",
       "      <td>0.023337</td>\n",
       "      <td>0.082457</td>\n",
       "      <td>0.036393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   perm_entropy  svd_entropy  sample_entropy       dfa  petrosian      katz  \\\n",
       "0      0.993005     0.615179        0.992649  1.275455   1.027439  2.794894   \n",
       "1      0.993249     0.565308        0.858924  1.384108   1.027283  2.593634   \n",
       "2      0.993561     0.573424        0.773124  1.406985   1.027411  2.633458   \n",
       "3      0.998517     0.495381        0.166400  1.453336   1.028952  2.259923   \n",
       "4      0.991045     0.737926        1.332437  1.152280   1.026885  3.484543   \n",
       "\n",
       "    higuchi      lziv     delta     theta     alpha     sigma      beta  \\\n",
       "0  1.743780  0.565987  0.696338  0.116689  0.066675  0.020174  0.068901   \n",
       "1  1.748034  0.400426  0.802190  0.068789  0.045489  0.015227  0.045956   \n",
       "2  1.721437  0.550586  0.877757  0.047858  0.021888  0.007310  0.028541   \n",
       "3  1.868283  0.431228  0.697172  0.024946  0.041486  0.025344  0.108747   \n",
       "4  1.769742  0.539035  0.690355  0.098202  0.069255  0.023337  0.082457   \n",
       "\n",
       "      gamma  \n",
       "0  0.031222  \n",
       "1  0.022349  \n",
       "2  0.016645  \n",
       "3  0.102304  \n",
       "4  0.036393  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.signal import welch\n",
    "freqs, psd = welch(data_win, sf, nperseg=int(4 * sf))\n",
    "bp = yasa.bandpower_from_psd_ndarray(psd, freqs)\n",
    "bp = pd.DataFrame(bp.T, columns=['delta', 'theta', 'alpha', 'sigma', 'beta', 'gamma'])\n",
    "df_feat = pd.concat([df_feat, bp], axis=1)\n",
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ratio of spectral power\n",
    "# df_feat.eval('dt = delta / theta', inplace=True)\n",
    "# df_feat.eval('db = delta / beta', inplace=True)\n",
    "# df_feat.eval('at = alpha / theta', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find best features for sleep stage classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6duwIwOzZs0lOTmbx4sVWN8a4u7vz5ZdfcuLECWrWrGmX8xIREZEH0+VpSbfffvuNiIgIOnfubAmMAHXq1KFKlSoP3Xbs2LHs2bPHKjAmJCTg4HDvR/H27duPp2gRERF5JBpplHQLDw8HoEyZMqm+q1ChAtevX3/gtgaDgdjYWObNm8epU6e4dOkSly9fxmg0AvdGMUVERCT7UWiUdDMYDAAkJiam+s5sNj902y1btvDWW2/h6elJQEAAdevWpUqVKly4cIFx48Y9lnpFRETk0Sk0Srr5+PgA957D+E8XL1586LZTp06lZMmSrF271upRPL/++mvmFikiIiKZSnMaJd2qVKlC6dKlWblyJXFxcZblP/30E7/88stDt42OjqZEiRJWgTEmJoaQkBAAy2VqERERyV400ijpZjAY+PDDDxk4cCAvv/wy3bp1IzY2lsWLF+Pl5fXQbRs1asTGjRsZPXo0NWvW5Nq1a3zzzTfcvHkTgPj4+Kw4BREREUknjTRKhjRs2JD58+fj5eXFZ599xrfffsvw4cOpX7/+Q7f78MMP6datG2FhYYwfP55169bRoEED1q9fj5OTE/v378+iMxAREZH0MJht3bkgkoNcnvUeSVfO27sMecxcSpWj9OCJREXFk5xsvzvunZwc8PQsYPc6ciL1LuPUu0ej/qXm5VUAR0fb44gaaRQRERERmxQaRURERMQmhUYRERERsUmhUURERERs0iN3JFdxfqK0vUuQLKDfZxGRrKfQKLmG2WymWLdB9i5DsojJaMRk0sMfRESyikKj5BoGg4GYmASMRj1CIT0cHR3w8HDNcb0zmcwKjSIiWUihUXIVo9Gk525lkHonIiIPoxthRERERMQmhUYRERERsUmXpyVXSctrkMRaSs9yYu80r1FEJOsoNEquYTab8fBwtXcZOVZO7J3ZZCQyKkHBUUQkCyg0Sq5hMBiI+n4RydF/2bsUyQJOhUvg2agPDg4GhUYRkSyg0Ci5SnL0XyTfDLd3GSIiIrlOzpvEJCIiIiJZTqFRRERERGxSaBQRERERmxQaRURERMQmhUaxCA4Opn79+pm2P7PZzKVLlzJtfyIiImI/Co3yWMTFxdG1a1dWrVpl71JEREQkEyg0ymMRHR3N8ePH7V2GiIiIZBKFRhERERGxSaFRUgkLC6N9+/b4+fnRunVrli9fbvX9tWvXGD16NPXq1cPPz4927dpZrXPw4EGaNm0KwNy5c/H19SU8/N4Dty9dusR7771Ho0aN8PPz49lnn6VXr14cPnw4605QRERE0k1vhBErt27dYuDAgXTq1Ilu3bqxefNmxo0bR3R0NIMGDSIiIoKuXbuSlJREUFAQRYoUYd++fYwbN45z584xZswYKlasyOjRo5k0aRKNGzemdevWeHl5ERkZSdeuXXF2diYoKIiiRYty7tw5Vq5cSf/+/dm+fTvFixe3dwtERETkPhQaxcrdu3cZNWoUffv2BSAwMJCePXvy3//+l+7duzN9+nTi4uJYt24d3t7eAPTo0YOJEyeyePFiXn75ZSpXrkyzZs2YNGkSTz75JB07dgTgq6++IjIykm+++QY/Pz/LMcuUKcOHH37IoUOHaN++fdaftIiIiNiky9Nixc3NjR49elg+Ozk50aNHD5KSkti7dy/bt2/H398fNzc3IiMjLb9atGgBwPfff//Afb/yyivs37/fKjAmJSVZ/v/t27cz/4REREQkU2ikUayULl0aFxcXq2VlypQB4MKFC8TGxhIWFkZAQMB9t79y5cpD9280Gpk5cya//PILly5d4tKlS9y9excAk8mUCWcgIiIij4NCo1gxGAw2v2vSpAnBwcH3XadYsWIP3P7IkSO88soruLi4EBAQQLt27Xj66acxmUwMGjTo0QoXERGRx0qhUaz89ddfGI1GHB0dLcvOnTsHQIUKFXB1dSUpKYl69epZbRcZGcnhw4cpW7bsA/f9+eefYzAY2LhxI0888YRl+YYNGzL5LERERCSzaU6jWImJiWHLli2Wz0lJSSxevJgCBQrQoEEDGjZsyP79+zl27JjVdjNmzGDo0KGcPXsWwBI6/37JOTo6msKFC1O0aFGr/X/11VfAvUvXIiIikj1ppFGseHh4MGbMGE6dOkXx4sVZt24dJ06cYNy4cbi7uzNy5EgOHjxInz59CAoKoly5cvzwww+EhobSqFEjGjRoAEDhwoVxcHBg9+7dlC9fnhYtWtCoUSPmzJnDwIEDady4MdHR0axbt87yfur4+Hh7nrqIiIg8hEKjWPHx8eGNN95g+vTpXLp0ifLly/Ppp5/Spk0by/dr1qxhxowZrFu3jtjYWEqVKsWQIUN45ZVXcHC4N3jt6urKiBEjmD9/PhMmTKBMmTIMHjwYk8nEpk2b2LdvH0WLFsXf35///Oc/BAUFsX//fl5//XV7nr6IiIg8gMFsNpvtXYRIZolY+2+Sb4bbuwzJAk5FvHnixVFERcWTnGyfO++dnBzw9Cxg1xpyKvUu49S7R6P+peblVQBHR9szFjWnUURERERsUmgUEREREZsUGkVERETEJoVGEREREbFJd09LruJUuIS9S5Asot9rEZGspdAouYbZbMazUR97lyFZyGwyYjLpARAiIllBoVFyDYPBQExMAkajHqGQHo6ODnh4uObI3plMZoVGEZEsotAouYrRaNJztzJIvRMRkYfRjTAiIiIiYpNCo4iIiIjYpMvTkquk5TVIYi2lZzm5d5rbKCLy+Ck0Sq5hNpvx8HC1dxk5Vk7undlkIjLqtoKjiMhjpNAouYbBYCD2txCM8TfsXYpkIccCRSlYtTMODgaFRhGRx0ihUXIVY/wNjHF/2bsMERGRXCfnTmISERERkSyj0CgiIiIiNik0ioiIiIhNCo0iIiIiYpNCo4iIiIjYpNAoIiIiIjYpNIqIiIiITQqNIiIiImKTQmMOsn//fgIDA/H396dhw4bMnj2bWbNm4evra1nn0KFDDBgwgLp161K1alXq1avHm2++yZUrVyzrHDx4EF9fX3bv3s1HH31E3bp18ff3Z8CAAdy4cYOTJ08SHBxM9erVadKkCYsWLbKqo0mTJrz//vusXbuWNm3aUK1aNdq2bct3331HQkICEyZMoG7dutSuXZthw4YRGRlptX1aahQREZHsRW+EySF2797NG2+8Qbly5Rg6dCi3bt1iwYIFODn977fwwIED9O/fn6pVqzJw4EBcXFw4evQo69ev58yZM2zYsMFqn2PHjqV06dIMHz6ckydPsnLlSgYPHsz58+dp164dbdu2ZfXq1UyaNIlKlSpRv359y7ZhYWHs3LmT3r174+rqypw5cxg2bBjVq1cHYMiQIZw5c4YVK1bg5OTEJ598kqEaRUREJHtQaMwhJkyYQLFixVi9ejXu7u4ANG3alC5duljWWbhwIZ6enixZsgRXV1cAAgMDSU5OZtOmTVy7do3ixYtb1vfw8GDJkiWW4Pnrr7/y008/MWrUKPr27QtA3bp1admyJWFhYVah8dq1a6xevdoSEp2dnRk3bhwxMTF8++23ODjcG8Q+deoUe/fuzXCNIiIikj3o8nQOcOrUKS5evEi3bt0sgRGgWrVqVkHuiy++YOPGjZYwBhAXF0e+fPkAuH37ttV+mzZtajVSWb58eQCaN29uWebj4wNARESE1bYlSpSwBMa/b9usWTNLYEzZPjo6mqSkpAzVKCIiItmDRhpzgAsXLgBQrly5VN9VrFjRMpLn6OjI1atXmTVrFmfOnCE8PJwrV65gNpsBMJlMVtsWLVrU6nNKgPz7ckdHx/tuW6RIEavPKes9aHlKDemtUURERLIHjTTmAMnJyQC4uLik+i5lhA5g0aJFdOrUiT179uDt7U1wcDBLly7l9ddfv+9+UwLdPxkMBps1/X2EMj3bprdGERERyR400pgDlClTBoDz58+n+i5lFDIxMZHPPvsMf39/lixZYhUw169fnyV12pITahQREZH700hjDlC1alW8vb0JCQkhISHBsvzChQvs3r0bgDt37pCQkEDZsmWtwtiVK1fYtm0bAEajMWsL/4ecUKOIiIjcn0YacwAHBwfef/99Bg0aRNeuXXnppZeIi4tj2bJllnUKFSqEv78/GzZswMPDg6eeeoqLFy+yevVqS9CMj4+31ynkmBpFRETk/jTSmEM0adKE2bNn4+zszCeffMKaNWvo168fTZo0sYzaff7557Rs2ZKNGzcyceJEduzYwcsvv8zSpUuBew8Ht7ecUKOIiIikZjCn3LYq2ZbJZCIqKirVnckAr7zyCmfPnuX777/P+sKyoehDX2KM+8veZUgWcnQvQeHarxEVFU9yctbefe/k5ICnZwG7HDunU+8yTr17NOpfal5eBXB0tD2OqJHGHMBsNtOwYUPeffddq+URERH8+OOPPPPMM3aqTERERPIKzWnMARwdHWnXrh1r167F0dERf39/oqKiWL16NQaDgcGDB9u7RBEREcnlFBpziH/961+UK1eOdevWsWnTJlxdXalduzaDBw/mqaeesnd5IiIiksspNOYQ+fLlY8CAAQwYMMDepYiIiEgepNAouYpjgaK2V5JcRb/nIiJZQ6FRcg2z2UzBqp3tXYbYgdlkwmTSgyBERB4nhUbJNQwGAzExCRiNeoRCejg6OuDh4Zqje2cymRUaRUQeM4VGyVWMRpOeu5VB6p2IiDyMntMoIiIiIjYpNIqIiIiITbo8LblKWl6DJNZSepbbeqd5jiIimUuhUXINs9mMh4ervcvIsXJb78wmE5FRtxUcRUQyiUKj5BoGg4HbV3dhTIqydyliZ44unriVbIKDg0GhUUQkkyg0Sq5iTIrClHjT3mWIiIjkOrlrEpOIiIiIPBYKjSIiIiJik0KjiIiIiNik0CgiIiIiNik0PqLg4GDq169v7zLSLTIykri4OHuXISIiIjmEQmMetHv3blq1asW1a9fsXYqIiIjkEAqNedDx48e5deuWvcsQERGRHEShUURERERsynahMS4ujvfff5/GjRvj5+dH48aNGTduHFFR/3vLx6FDhxgwYAB169alatWq1KtXjzfffJMrV65Y1jl48CC+vr7s3r2bjz76iLp16+Lv78+AAQO4ceMGJ0+eJDg4mOrVq9OkSRMWLVpkVUeTJk0YOXIk69evp0WLFlSvXp1OnTqxdetWm+dw7do1Ro8eTb169fDz86Ndu3YsX748wz0xm80sXryYtm3bUq1aNerXr8/777/PjRs3rNbz9fVl5syZLF++nJYtW+Ln50fLli2tjj1q1ChmzZoFQJs2bQgODrYsb9KkCV9//TV16tShZs2afPvttwDExMQwYcIEGjZsiJ+fH02bNuWTTz4hISHBst/w8HB8fX1ZsWIF06dPp169etSsWZP+/ftz8uRJy3pvvfUWVapU4eZN6wdwm81mGjZsSL9+/TLcJxEREXl8st0bYYYPH86PP/5Ir1698PHx4cyZMyxfvpzTp0+zfPlyDhw4QP/+/alatSoDBw7ExcWFo0ePsn79es6cOcOGDRus9jd27FhKly7N8OHDOXnyJCtXrmTw4MGcP3+edu3a0bZtW1avXs2kSZOoVKmS1U0tR44cYcuWLXTv3p3SpUsTEhLC0KFD+fe//02nTp3uW39ERARdu3YlKSmJoKAgihQpwr59+xg3bhznzp1jzJgx6e7JBx98wNdff0379u3p2bMnly9fZvny5fzwww98/fXXeHp6Wtb99ttvSUxMpEePHhQqVIivvvqKcePG4e3tTcOGDenWrRtxcXFs376dt99+m6efftqy7Y0bN/jkk094/fXXiY2N5bnnniMmJoagoCDOnTtHly5d8PX15dixY3z55Zf8+OOPLF68GBcXF8s+vvzySxITE+nVq5cl7Pbo0YNVq1ZRqVIlOnTowMaNG9myZQs9evSwbPfjjz/y119/MWLEiHT3R0RERB6/bBUaIyMjCQsLo0ePHrz55puW5W5ubuzZs4fY2FgWLlyIp6cnS5YswdXVFYDAwECSk5PZtGkT165do3jx4pZtPTw8WLJkCU5O9071119/5aeffmLUqFH07dsXgLp169KyZUvCwsKsQuOVK1eYMmUKHTt2BKBr1660b9+eKVOm0L59e8s+/2769OnExcWxbt06vL29AejRowcTJ05k8eLFvPzyy1SuXDnNPTl8+DBr1qxh9OjR9OnTx7K8devWdOnShTlz5jBq1CjL8oiICLZu3UqpUqUAaNiwIU2bNmX9+vU0bNgQf39/fH192b59O40bN6ZixYqWbRMTE/nggw/o0qWL1fmcPXuWqVOn0qFDBwC6d+9OpUqVmDZtGitWrKB3796W9W/cuMHGjRspW7YsAM2aNaNjx4589tlnzJ49m/r161OkSBE2bdpkFRo3bNiAq6srzZs3T3NvREREJOtkq8vT7u7uuLu7ExoaSkhICDExMcC90ceQkBAKFizIF198wcaNGy2BEe5d0s6XLx8At2/fttpn06ZNrcJd+fLlAazCiY+PD3AvcP1d6dKlLUEJwNXVlaCgICIjIzl27Fiq+k0mE9u3b8ff3x83NzciIyMtv1q0aAHA999/n66epFwOb9KkidX+SpYsSaVKlfjuu++s1q9Ro4YlMAJ4e3vj4eGR6lL2g9StW9fq844dO/D29qZ9+/ZWy3v37o27uzs7duywWt62bVtLYASoVKkSDRo0YO/evSQlJeHk5ESbNm04evQof/31FwB3795l69atNGnShAIFCqSpThEREcla2Wqk0cXFhfHjx/P+++8zevRoPvjgA6pXr06zZs3o3LkzhQsXxtHRkatXrzJr1izOnDlDeHg4V65cwWw2A/eC298VLVrU6nNKgPz7ckdHx/tu++STT2IwGKyWlStXDrg3h++5556z+i4qKorY2FjCwsIICAi47zn+fd5lWly4cAHggSNwzs7OVp+9vLxSrePi4pLq3B6kSJEiVp/Dw8OpXbt2qj64uLjg4+PD5cuXrZY/+eSTqfZZrlw5vv/+e27evEnJkiXp2LEjS5cuJTQ0lH79+rFv3z6io6OtArqIiIhkL9kqNMK9mzMaNGjAzp072bNnD/v372fy5MksWLCAkJAQQkNDmTRpEmXKlKFWrVqWG2bCwsKYM2dOqv2lBMJ/+mcIup/7XX5OCV/3+85oNAL3RgVTbjD5p2LFitk87j+Ply9fPv773/+maX0Hh0cbPP5nv8xmsyWQ36+2f4bWh/UlZd/VqlWjQoUKltC4ceNGPD09ef755x+pdhEREXl8slVoTEhI4NSpU5QqVYoXX3yRF198EZPJxMKFC5kyZQpr1qxh7ty5+Pv7s2TJEqsbMNavX5/p9Vy6dCnVsvPnzwNYXYJN4eXlhaurK0lJSdSrV8/qu8jISA4fPnzf7R6mdOnS7N27lyeffDJV4Ny1axeFCxdO1/7Sy9vbm3PnzmE2m62CdlJSEuHh4VSrVs1q/fv17MKFCxQsWNBqFLN9+/Z8/vnnhIeHs3v37gfOERUREZHsIVvNabx27RqBgYHMmzfPsszBwcESTFxdXUlISKBs2bJWgfHKlSts27YN+N+oVmb4/fffOXDggOVzfHw8K1aswMfHBz8/v1TrOzk50bBhQ/bv359qzuOMGTMYOnQoZ8+eTVcNTZs2BeA///mP1fJjx44xcOBAFi9enK79wf9GIx80gvjP41++fDnVXelLly4lPj6exo0bWy3fsGEDkZGRls+nTp1i7969NG/e3GoUs0OHDhgMBj799FNiYmJSzZkUERGR7CVbDe2UK1eOli1bWgJJjRo1iI6OZtmyZXh6etK+fXu2bdvGhg0b8PDw4KmnnuLixYusXr3a8szA+Pj4TKvHxcWFgQMH0qtXLzw9Pfn666+5du0a//nPfx54eXvkyJEcPHiQPn36EBQURLly5fjhhx8IDQ2lUaNGNGjQIF01NGzYkBYtWrBixQquXr3KCy+8wM2bN1m2bBkeHh4MGzYs3eeVMu9x4cKFNGnSxBJM7+e1115j+/btjBo1iiNHjuDr68vx48dZu3Yt1apVo3v37lbr37lzh27duhEUFMTt27dZsmQJXl5eDB8+3Go9b29vatasycaNGylTpgz+/v7pPg8RERHJOtkqNAJMnjyZChUqsHnzZstd0gEBAQwbNownnniCzz//nH//+99s3LiRO3fuUKJECV5++WVatGhB165d2b9/f6YFkKeffpqgoCBmzpxJZGQkfn5+LFq0iFq1aj1wGx8fH9asWcOMGTNYt24dsbGxlCpViiFDhvDKK69kaM7hp59+yoIFC1i7di2TJk2icOHC1K1bl2HDhlGhQoV0769t27Zs27aN9evXc/To0YeGRg8PD1auXMmMGTPYsWMH33zzDaVKleKNN97g9ddftxrxBSyP0UmZg/n888/z1ltvWT0GKUXHjh05cuQI7dq1S/c5iIiISNYymNNyjTIPatKkCUWLFmX16tX2LiVHCA8Pp2nTprz66quMHDkyTdusWbOGMWPGsHXrVstd6Y8q9sI3mBJv2l5RcjWHfEUoWPYloqLiSU5O25MDMsLJyQFPzwKP/Ti5kXqXcerdo1H/UvPyKoCjo+1BrWw1p1HyjuTkZFavXk2tWrUyLTCKiIjI45PtLk/nBZGRkWm6YcfR0fG+z13MyW7evMm4ceM4f/48p06d4ssvv7R3SSIiIpIGCo128PLLL6d6KPb9lC5dml27dmVBRVmnYMGCHDt2jISEBN5++20aNmxo75JEREQkDRQaH+BxhrWpU6eSmJhoc72UVyPmBN7e3pw+fdrmei4uLuzevTsLKhIREZHMpNBoB88++6y9S8i1HF087V2CZAP6ORARyXwKjZJrmM1m3Eo2sXcZkk2YTSZMJj0cQkQksyg0Sq5hMBiIiUnAaNQjFNLD0dEBDw/XXNc7k8ms0CgikokUGiVXMRpNeu5WBql3IiLyMHpOo4iIiIjYpNAoIiIiIjbp8rTkKml5DZJYS+lZXuyd5j2KiKSdQqPkGmazGQ8PV3uXkWPlxd6ZzSYiI28rOIqIpIFCo+QaBoOBxLjjmIxx9i5FcgAHR3fyuT+Dg4NBoVFEJA0UGiVXMRnjMBtj7V2G5AC6T1xEJH3y3iQmEREREUk3hUYRERERsUmhUURERERsUmgUEREREZsUGsVi1KhR+Pr6kpiYmK7twsPD8fX1Zdq0aY+pMhEREbE3hUYRERERsUmhUURERERseuTnNO7Zs4eQkBBOnjxJTEwMBw4cYP369Vy8eJH+/fvj6pr33jIhIiIikts80kjj2LFjef3119myZQvh4eFER0cD8OuvvzJr1iz69OlDfHx8ZtQpmWDNmjUEBgZSs2ZN/Pz8aNq0KZMnT37gHMaQkBB8fX356aefGDZsGP7+/gQEBPDee+8RGRmZan2j0cjs2bNp0qQJ1apVo0OHDmzZssVqneTkZObPn0+nTp3w9/enWrVqtGrVijlz5mAy6XHLIiIi2VWGQ+PKlStZvXo1LVq0YNu2bQwYMMDy3aBBg3jppZf4+eefWbhwYaYUKo9m5syZjBkzhpIlS/LOO+/w9ttvU7JkSRYsWMDUqVMfuu2bb77JpUuXGD58OG3btmXt2rUEBwenCpsrVqxg7dq19OzZkzfffJNbt24xfPhwjh8/bllnzJgxTJ06lWrVqjF69GhGjBhBvnz5mD59OosXL34s5y4iIiKPLsOXp1euXImvry+ff/45cO+9vykKFSrExx9/zJkzZ9i8eTODBw9+9Eolw+7evcvixYtp3Lgxn376qWV5jx49aNq0KWFhYQ/d3t3dna+++or8+fMDULFiRT766CNWrlxJ7969Levlz5+fNWvWULhwYQD8/Pzo2bMn27Zt45lnnuHGjRusW7eOnj17MmbMGMt2Xbt2JSAggLCwMPr27ZuJZy4iIiKZJcMjjefOnaNBgwYPXadWrVpcvnw5o4eQTOLs7My+fftSPRLn5s2beHh42JxC0LdvX0tgBOjSpQseHh7s2rXLar0XXnjBEhgBqlWrBsD169cBKFq0KEeOHOHNN9+02i4yMhJ3d3du376d7nMTERGRrJHhkcb8+fNz8+bNh65z/fp1q7Ah9uPi4sLevXvZvn07586d4+LFi5Z5iUWLFn3otk8++aTVZycnJ7y9vVP9g6BIkSJWn1N+7+/evWtVx6ZNm9izZw/nz5/n4sWLxMTEAODj45OxkxMREZHHLsMjjc8++yzbt2/n6tWr9/3+/Pnz7Nixg5o1a2a4OMkcZrOZIUOGMHDgQP7880/8/PwYPnw4GzZs4LnnnrO5vbOzc6plRqMRR0dHq2X//PxPSUlJdO/enXfffZeIiAhq1arFqFGj2L59OyVLlkzfSYmIiEiWyvBI46BBg9i7dy9dunShf//+nDt3DoBDhw7xyy+/MHfuXO7evcvrr7+eacVKxvz4449s376dfv368e6771p9d+PGDZvbX7x4kaefftry+e7du1y+fDlNgfPvQkND+fnnnxk7diw9evSwLE9OTiY6OppixYqla38iIiKSdTI80li1alVmzpyJ0Whk8uTJbNq0CbPZTO/evZk6dSrJyclMmzaN6tWrZ2a9kgEpj0L652Xm77//nvPnz5OcnPzQ7ZctW2b1OJxVq1YRFxdHq1atMlRHxYoVrZavWrWKhIQEm3WIiIiI/TzSw70bNmzId999x86dO/ntt9+IjY3Fzc0NX19fmjdvTsGCBTOrTnkENWvWxMPDg6lTp3L9+nWKFCnCzz//zNq1a8mXLx+3b9/GbDY/cPuff/6Z3r1706pVK86cOcOqVauoVasWHTt2TFcd9evXx9nZmffee4/g4GBcXV05cOAAW7ZsIV++fHqmp4iISDb2yG+EyZ8/P23btqVt27aZUY88BkWKFOHLL79k2rRpzJ0713Ijy5gxYzAajYwfP54jR448cPtx48axZcsWpkyZQqFChejXrx9DhgzBwSF9A9WVKlVi1qxZzJgxg88//xxXV1fKlSvH559/bnmm5+XLlylduvSjnrKIiIhkMoP5YUNMaXDu3DkuX75MUlLSA0ermjZt+iiHEDsJCQlh9OjRzJ07lxdeeMHe5aRJwq39mI2x9i5DcgCDY0FcC9UjKiqe5OSMvY3IyckBT88Cj7SPvEq9yzj17tGof6l5eRXA0dH2QFCGRxqjoqIYNGgQP/300wPXMZvNGAwGTp48mdHDiIiIiEg2kOHQOH36dI4ePUqlSpUICAigYMGCVm+FEREREZHcI8OhcefOnVSpUoU1a9bYfD6fiIiIiORsGQ6N8fHx1K9fX4ExF+vcuTOdO3e2dxkiIiKSDWQ4ND711FP8+eefmVmLyCNzcHRH05olLRwc3e1dgohIjpLh0PjGG28wZMgQtm3bRosWLTKzJpEMMZvN5HN/xt5lSA5iNpswmR7pARIiInlGhkPjiRMn8PX1ZdiwYfj4+FCuXDlcXFxSrWcwGJg5c+YjFSmSFgaDgZiYBIxGjTWmh6OjAx4ernmydyaTWaFRRCSNMhwaZ82aZfn/Fy9e5OLFi/ddT3dUS1YyGk167lYGqXciIvIwj3T3tIiIiIjkDRkOjXrVm4iIiEje8cjvng4PD2ft2rWcPn2ahIQEChcuTKVKlWjTpg0+Pj6ZUaNImqXlNUhiLaVn6t2Dae6jiMgjhsYVK1bw8ccfk5ycnOq7WbNm8f777xMYGPgohxBJM7PZjIeHq73LyLHUuwczm01ERt5WcBSRPC3DoXH//v2MGzeOokWLMmDAAJ599lmKFStGTEwMhw8fZvbs2YwfP56KFStSq1atzKxZ5L4MBgOxt38m2Rhv71IkF3FyLEBBt+o4OBgUGkUkT8twaJw3bx4FCxZkxYoVeHt7W5Z7eXlRrlw56taty0svvcT8+fMVGiXLJBvjMZpi7F2GiIhIrpPhSUzHjx+nadOmVoHx73x8fGjatCnHjh3L6CFEREREJJvIcGi8e/cubm5uD13Hzc2NO3fuZPQQIiIiIpJNZDg0VqhQgbCwsAeGwoSEBPbs2UP58uUzXJyIiIiIZA8ZDo1dunTh4sWLDB06lMuXL1t9d/bsWQYOHEh4eDgvv/zyIxcpIiIiIvaV4RthgoKCOHjwIFu3bqVZs2YUL16cggULcu3aNWJjYzGbzbRo0YIePXpkZr3yN2azmfDw8Cx/HubMmTOZNWsWoaGhVKxYMUuPLSIiIvaR4dBoMBj47LPPWLduHd9++y2nTp3ixo0bFChQgNq1a9OpUydefPHFTCxV/i4uLo6+fftSp04dRo4cmaXHbt68OWXKlKF48eJZelwRERGxn0d6uLfBYODFF19UOLSD6Ohojh8/Tp06dbL82JUrV6Zy5cpZflwRERGxnzSHxl69emXoAAaDgcWLF2doWxERERHJHtIcGg8dOpSuHRsMBsxmMwaDId1F5VbBwcEkJyczaNAgpkyZwvnz5yldujS9evUiKCjIst61a9f47LPP2L17NzExMZQrV46goCDL/NCDBw9aQvzcuXOZO3cuO3fu5PLly/Tq1YsJEyawYsUKzpw5Q926dZk7dy4Aa9euZcmSJZw5c4b8+fNTq1Ythg0bhq+vr+XYcXFxTJo0if379xMREcETTzxB48aNGTJkCJ6ensD95zSeOnWKOXPmcPjwYaKionB3d8ff358RI0ZY9h8eHk7Tpk2ZMGEC169f5+uvv+bGjRuUL1+egQMH0qpVq8f/myAiIiIZkubQePjw4TStd+7cOT766CNOnDiBs7Mzr732WoaLy40uXbrEG2+8Qbt27ejatStbtmzho48+4ubNmwwePJiIiAi6du1KUlISQUFBFClShH379jFu3DjOnTvHmDFjqFixIqNHj2bSpEk0btyY1q1b4+XlZbmLfeLEibRu3ZqXXnqJAgUKADB9+nTmzJlDzZo1GTlyJDExMSxfvpzAwEAWL17MM888A8Dw4cP58ccf6dWrFz4+Ppw5c4bly5dz+vRpli9fft9zOnv2LIGBgZQsWZK+fftSsGBBTp48yZo1azh+/Di7du0if/78lvW/+OILHB0d6dmzJ46OjixcuJDhw4ezfv16nnrqqcf8OyAiIiIZkebQWLBgwYd+bzQamTdvHl988QV37tzB39+fCRMm6O7af4iIiGDYsGEMHDgQgMDAQIKDg5kzZw5BQUFMnz6duLg41q1bZ3nbTo8ePZg4cSKLFy/m5ZdfpnLlyjRr1oxJkybx5JNP0rFjR6tjVK5cmYkTJ1o+//HHH8ydO5fnn3+eL7/8EkdHRwA6depEu3btGDt2LGvXriUyMpKwsDB69OjBm2++adnezc2NPXv2EBsbe9+fg+XLl5OcnMzixYspVqyYZbm7uztffvklJ06coGbNmpbliYmJbNmyxbKvp59+ml69erFp0yaFRhERkWwqw89p/Lvjx4/TuXNnPvvsM5ycnBg7diwrVqxQYLyP/Pnz07dvX8tnJycnevfuTVJSEnv27GH79u34+/vj5uZGZGSk5VeLFi0A+P77720eo27dulafd+3ahclk4vXXX7cERgBvb286dOjAyZMnCQ8Px93dHXd3d0JDQwkJCSEm5t47nIcPH05ISMgD/+EwduxY9uzZYxUYExIScHC49+N1+/Ztq/UbNGhgta8qVaoA9wK1iIiIZE+PdPd0QkIC06dP56uvvsJoNNK0aVPGjh2rR7E8hLe3N66urlbLypUrB8Dly5eJjY0lLCyMgICA+25/5coVm8coWrSo1efw8HDg3lt8/ikl2F++fBlvb2/Gjx/P+++/z+jRo/nggw+oXr06zZo1o3PnzhQuXPi+xzMYDMTGxjJv3jxOnTrFpUuXuHz5MkajEQCTyWS1vpeXl9VnFxeX+64nIiIi2UeGQ+Pu3bv517/+xZUrV3jiiSf44IMPLKNh8mBOTqlbnhKWnJ2dAWjSpAnBwcH33f7vo3kPkjLCl8JsNlv97/2+Szl2mzZtaNCgATt37mTPnj3s37+fyZMns2DBAkJCQu57/C1btvDWW2/h6elJQEAAdevWpUqVKly4cIFx48bZrE9ERESyv3SHxsjISCZMmMDmzZsB6Nq1K2+//bbNOY9yT8oI3N8vE58/fx6AsmXL4urqSlJSEvXq1bPaLjIyksOHD1O2bNl0HzNlbuSff/7JE088YfXdn3/+CUCJEiVISEjg1KlTlCpVyvL8TZPJxMKFC5kyZQrr16/nlVdeSbX/qVOnUrJkSdauXYu7u7tl+a+//pruWkVERCR7SteQzzfffEPr1q0JDQ2lXLlyLF26lHHjxikwpkNsbCwhISGWzyk3kLi7u/P888/TsGFD9u/fz7Fjx6y2mzFjBkOHDuXs2bMAltCZlku6TZs2xWAw8OWXX1ouGcO9S93r16+ncuXKlCpVimvXrhEYGMi8efMs6zg4OFCtWjWrY/5TdHQ0JUqUsAqMMTExlvP8+zFFREQkZ0rzSGPv3r0tz2qsUqUKr776Krdu3WLnzp02t23atGnGK8xlnJycGD9+PL///jtly5Zl48aNHDt2jPHjx+Pu7s7IkSM5ePAgffr0ISgoiHLlyvHDDz8QGhpKo0aNaNCgAQCFCxfGwcGB3bt3U758+YdODahYsSL9+vVj/vz59OzZk9atWxMTE8NXX32F2Wzmww8/BO7NrWzZsiVLly4lPj6eGjVqEB0dzbJly/D09KRdu3b33X+jRo3YuHEjo0ePpmbNmly7do1vvvmGmzdvAhAfH5/JXRQREZGslubQePDgQcv/P3HihNUjWR4k5eHeJ0+ezFh1uVDhwoWZNGkSEydOZNWqVVSqVIlZs2bRvHlzAHx8fFizZg0zZsxg3bp1xMbGUqpUKYYMGcIrr7ximQ/o6urKiBEjmD9/PhMmTKBMmTIPPe4777xD+fLlWb58OVOnTrW8I3zw4MFWj7mZPHkyFSpUYPPmzWzcuBFXV1cCAgIYNmxYqkvbKT788EMKFCjArl272LRpE8WLF6dBgwb069ePtm3bsn///gcGThEREckZDOb73R1xH7NmzcrwQQYPHpzhbXOT4OBg/vzzT/bt22fvUnKtqNj9GE0x9i5DchFHBw88C9YjKiqe5GTr6SBOTg54eha473fycOpdxql3j0b9S83LqwCOjrZnLKZ5pFHBT0RERCTv0rNPRERERMQmhUYRERERsemR3ggj6bN06VJ7lyAiIiKSIQqNkqs4ORawdwmSy+hnSkTkHoVGyTXMZjMF3arbuwzJhcxmEyZTmh40ISKSayk0Sq5hMBiIiUnAaNQjFNLD0dEBDw9X9e4hTCazQqOI5HkKjZKrGI0mPXcrg9Q7ERF5GN09LSIiIiI2KTSKiIiIiE26PC25SlpegyTWUnqm3qVfdu+d5mKKSGZSaJRcw2w24+Hhau8yciz1LuOya+9MZhNRkbcVHEUkUyg0Sq5hMBi4dHMnd5Kj7V2KiN3ldyqMT5GmODgYFBpFJFMoNEqucic5mjt3b9i7DBERkVwne07EEREREZFsRaFRRERERGxSaBQRERERmxQaRURERMQmhcY8LDIykri4OABGjRqFr68viYmJmXqMixcvZur+RERExD4UGvOo3bt306pVK65du/bYjtG/f38+/fTTx7Z/ERERyToKjXnU8ePHuXXr1mM9xt69ex/r/kVERCTrKDSKiIiIiE0KjXnQqFGjmDVrFgBt2rQhODjY8t2pU6fo168fNWrUoE6dOowaNYro6Gir7WNjY/n4449p2LAhfn5+NG/enNmzZ3P37l0AwsPD8fX1BSA0NBRfX18OHjwIwM2bN/n4449p3rw5fn5++Pv7061bN3bs2JEFZy4iIiIZpTfC5EHdunUjLi6O7du38/bbb/P000+zYcMGAPr06UOrVq0YPXo0R44c4dtvvyUqKoo5c+YAcPv2bXr27MnFixcJDAykTJkyHDt2jJkzZ/Lbb78xe/ZsvLy8mDJlCu+88w41atSge/fuVKxYkcTERHr06EFkZCTdu3endOnSXL16lZUrVzJ48GC+/vpr/Pz87NkaEREReQCFxjzI398fX19ftm/fTuPGjalYsaIlNL7yyisMGjQIuBcu//rrL8LCwoiPj6dAgQIsWLCAM2fOsHLlSp555hkAgoKCqFq1Kh9//DHfffcdTZo0oWPHjrzzzjuUKlWKjh07ArB582bOnTvHzJkzadGihVU9r7zyCnv37lVoFBERyaZ0eVqsdOjQwepztWrVMBqNREZGArB161YqVKiAt7c3kZGRll+NGzfGYDDw3XffPXDfrVu35sCBAzRt2tSyzGg0YjKZgHujmCIiIpI9aaRRrBQpUsTqc/78+QFISkoC7j138c6dOwQEBNx3+ytXrjx0/46OjixcuJCjR49y6dIlLly4YHk2ZEp4FBERkexHoVGsODo6PvR7o9FI9erVGT58+H2/9/DweOC258+fJygoyBI6mzVrhq+vL6VKlaJLly6PUraIiIg8ZgqNki6lS5fm1q1b1KtXz2p5YmIiO3fupESJEg/cds6cOURFRbF+/Xqeeuopy/KjR48+tnpFREQkc2hOYx7l4HDvt95sNqdru6ZNm3L+/HlCQ0Otli9ZsoQRI0Zw4MABq2P8/ZJzdHQ0Li4ulClTxrLMZDKxZMkS4N4opoiIiGRPGmnMo7y8vABYuHAhTZo0SfN2r7/+uuVRPQcPHqRKlSr89ttvrFmzBj8/Pzp37mx1jCNHjrBq1SoaNGhAo0aN2LVrF/3796ddu3YkJiYSGhrKiRMncHBwID4+PtPPU0RERDKHRhrzqLZt21KvXj3Wr1/PtGnT0rxdoUKFWLVqFV26dOG7775j/Pjx7Nu3j169ejF//nxcXV0t644cORKACRMmcOjQIbp27crbb7/N9evXmThxIgsXLqRIkSKsXr2aKlWqWI1SioiISPZiMKf3+qRINnbm2jfcuXvD3mWI2F1+56JUKv4SUVHxJCdnvycTODk54OlZINvWl52pd49G/UvNy6sAjo62xxE10igiIiIiNik0ioiIiIhNCo0iIiIiYpNCo4iIiIjYpEfuSK6S36mwvUsQyRb034KIZDaFRsk1zGYzPkWa2rsMkWzDZDZhMukBGSKSORQaJdcwGAzExCRgNOoRCunh6OiAh4erepcB2b13JpNZoVFEMo1Co+QqRqNJz93KIPUu49Q7EckLdCOMiIiIiNik0CgiIiIiNunytOQqaXkNklhL6Zl6l37qneZNiuQlCo2Sa5jNZjw8XO1dRo6l3mVcXu6dyWQiKuq2gqNIHqDQKLmGwWDgp0vfEpd4w96liOQJ7vmK4u/TCQcHg0KjSB6g0Ci5SlziDWLu/GXvMkRERHKdvDsRR0RERETSTKFRRERERGxSaBQRERERmxQaRURERMQmhcYscvDgQXx9fVmxYoW9SwHuPZ7m0qVL9i5DREREcgiFxjwoLi6Orl27smrVKnuXIiIiIjmEQmMeFB0dzfHjx+1dhoiIiOQgCo0iIiIiYpNC49/ExcXx/vvv07hxY/z8/GjcuDHjxo0jKiqK0NBQfH19Wb9+fart3n//fapVq0ZMTAxwbyRv7Nix1K9fH39/f4YOHUpERMQj1Xb8+HFeeeUVatasSY0aNejZsycHDhywWmfUqFE0adKEU6dO0adPH2rUqEHt2rUZPXo0UVFRwL25lU2bNgVg7ty5+Pr6Eh4ebplzuWbNGjp37ky1atV49dVXLfteu3atZXmtWrUYOHAgp0+ftjp+cHAwQUFB7N27lw4dOvDMM8/QunVrq3mce/fuxdfXlwULFqQ6x08//ZSnn36av/7Sw7lFRESyG4XGvxk+fDibNm2iffv2fPjhhzRv3pxVq1YxePBgmjZtSsGCBQkNDbXaJikpie3bt9OoUSM8PDxISkqiV69efPPNN7Rp04Y333yTGzdu8MEHH2S4rgMHDtC9e3euX7/O4MGDGTp0KPHx8fTr148tW7ZYrXvr1i169+5NyZIlGT16NI0aNSIkJISPPvoIgIoVKzJ69GgAGjduzJQpU/Dy8rJsP3HiRCpXrsyoUaNo27YtANOnT+fdd98lX758jBw5kl69enH06FECAwNTXea+dOkSb7zxBlWrVuWdd96hSJEifPTRR8yaNQuAgIAAnnjiCTZt2pTqPDdt2kStWrUoUaJEhnslIiIij4deI/j/IiMjCQsLo0ePHrz55puW5W5ubuzZs4ekpCRatmzJunXriImJwcPDA4CwsDBu3bpFx44dAfjmm284ffo0U6dOpUOHDgAEBQXx6quvsn///nTXZTKZGDt2LE899RSrVq3C2dkZgJ49e9KzZ08mTJhAkyZNcHFxAe6Nlr711lu89tprAHTr1o2rV6+yY8cOEhISKFq0KM2aNWPSpEk8+eSTlrpTVK5cmYkTJ1o+//HHH8ydO5fnn3+eL7/8EkdHRwA6depEu3btGDt2LGvXrrWsHxERwbBhwxg4cCAAgYGBBAcHM2fOHIKCgihSpAjt2rVj4cKFXLx4kTJlygBw7NgxLl26xIABA9LdIxEREXn8NNL4/9zd3XF3dyc0NJSQkBDLpebhw4cTEhJCwYIF6dixI3fv3mXbtm2W7UJDQylcuDAvvPACAN9//z0eHh60a9fOso6TkxM9e/bMUF0nT57k4sWLNGvWjNjYWCIjI4mMjCQuLo5mzZoRERHBr7/+arVNmzZtrD4//fTTJCcnEx0dbfN4devWtfq8a9cuTCYTr7/+uiUwAnh7e9OhQwdOnjxJeHi4ZXn+/Pnp27ev5bOTkxO9e/cmKSmJsLAwAEuY/vto48aNG8mXLx8tW7a0WaOIiIhkPYXG/+fi4sL48eNJTExk9OjRBAQE0L17dxYsWGAJW7Vq1aJ06dJs3rwZgISEBHbt2kXLli0tI33h4eF4e3vj4GDd2ooVK2aorgsXLgDw+eefExAQYPXrk08+AeDKlStW2xQpUiTVuQEYjUabxytatKjV55RAWKFChVTrppzT5cuXLcu8vb1xdXW1Wq9cuXJW61WpUoVKlSpZLvUbjUa2bNlCo0aNKFiwoM0aRUREJOvp8vTftGnThgYNGrBz50727NnD/v37mTx5MgsWLCAkJIRixYrRrl075s+fT2RkJAcOHOD27dtWl3gNBgN37txJtW+TyZShmlK2GzhwILVq1brvOk8++aTV538G1vT457Zms9nqf+/3Xcolc7g3svhPKefw9+86dOjAJ598wtmzZ7l+/ToRERGWEUgRERHJfjTS+P8SEhL46aefuH37Ni+++CLTp09n//79vPPOO0RERFjumu7YsSPJycns3LmTzZs34+3tzbPPPmvZj7e3N1evXiUpKclq/xl9+0rp0qWBe5d969WrZ/WrSJEiJCUlpRrZy0ze3t4A/Pnnn6m+S1n29xtXLl++nGpE8/z58wCULVvWsqx9+/YYDAa2b9/Ozp07KVSokOUSv4iIiGQ/Co3/79q1awQGBjJv3jzLMgcHB6pVqwZgmc9XsWJFqlatSmhoKGFhYalGx1q0aEFCQgJLly61LDObzVaf08PPz49ixYqxbNkybt26ZVmelJTEu+++y9ChQ0lOTk7XPlPOJS2jn02bNsVgMPDll19ahcErV66wfv16KleuTKlSpSzLY2NjCQkJsXxOTk5m8eLFuLu78/zzz1uWlyxZklq1arFr1y727NlDq1atLJfRRUREJPvR5en/V65cOVq2bMnSpUuJj4+nRo0aREdHs2zZMjw9Pa1ubOnYsaPlDuN/hsYXX3yRkJAQpk6dyvnz56lcuTI7d+7k5MmTGarL2dmZsWPHMmzYMDp16kTXrl0pWLAga9eu5eTJk4wcORJPT8907bNw4cI4ODiwe/duypcvT4sWLR64bsWKFenXrx/z58+nZ8+etG7dmpiYGL766ivMZjMffvih1fpOTk6MHz+e33//nbJly7Jx40aOHTvG+PHjcXd3t1q3Q4cOjBkzBoBJkyal6xxEREQka2mk8W8mT57MgAEDOHLkCBMmTGD+/PnUrFmTFStW8MQTT1jWa9euHU5OTlSrVo3y5ctb7cPBwYG5c+fSt29fvv/+e6ZMmYKDgwPTp0/PcF3Nmzdn0aJFlC1bli+//JJp06ZhMpmYMmWK1QO408rV1ZURI0Zw48YNJkyYwKlTpx66/jvvvMOECRNISEhg6tSpLFu2jOeee441a9ZQs2ZNq3ULFy7MrFmzCAsLY8qUKdy9e5dZs2bRtWvXVPtt1aoV+fLlo3Tp0laX+EVERCT7MZjvd4eDSAYEBwfz559/sm/fvjStHxcXR/369enTpw8jRozIlBrCzs4l5o7eKCOSFTzyl6DBk68SFRVPcnL6bvZzcnLA07NAhrbN69S7R6P+peblVQBHR9vjiBppFLv59ttvSUxM5KWXXrJ3KSIiImKD5jTaQXx8PLdv307TuoUKFcp1N4h89tlnnD592nIDTMpbYURERCT7Umi0gwULFljexWzLkiVLqFOnzmOuKGslJCSwf/9+6tWrZ3kntoiIiGRvmtNoB5cuXUrzcxurVq1KoUKFHnNFuYfmNIpkHc1ptA/17tGof6mldU6jRhrtwMfHBx8fH3uXkSu55ytqeyURyRT6700kb1FolFzDbDbj79PJ3mWI5CkmkwmTSResRPIChUbJNQwGAzExCRiNutyQHo6ODnh4uKp3GaDegclkVmgUySMUGiVXMRpNmqOSQepdxql3IpIX6DmNIiIiImKTQqOIiIiI2KTL05KrpOWRAWItpWfqXfqpd/+juY0iuZ9Co+QaZrMZDw9Xe5eRY6l3Gafe3buLOirqtoKjSC6m0Ci5hsFgYNvpFUTdvm7vUkTyFE+3YrTwDcLBwaDQKJKLKTRKrhJ1+zoR8ZftXYaIiEiuo4k4IiIiImKTQqOIiIiI2KTQKCIiIiI2KTSKiIiIiE0KjZJKcHAw9evXT9c2M2fOxNfXlz/++MNq+cWLFzOzNBEREbEThUZ5bPr378+nn35q7zJEREQkEyg0ymOzd+9ee5cgIiIimUShUURERERsUmjM43bs2MFLL71E9erVadmyJd9++22qda5du8bo0aOpV68efn5+tGvXjuXLlz9wn+Hh4fj6+gIQGhqKr68vBw8eBODmzZt8/PHHNG/eHD8/P/z9/enWrRs7dux4PCcoIiIimUJvhMnDNm7cyMiRI3n66ad58803iYiI4KOPPsLBwQE3NzcAIiIi6Nq1K0lJSQQFBVGkSBH27dvHuHHjOHfuHGPGjEm1Xy8vL6ZMmcI777xDjRo16N69OxUrViQxMZEePXoQGRlJ9+7dKV26NFevXmXlypUMHjyYr7/+Gj8/v6xug4iIiKSBQmMeZTKZ+Pe//02lSpVYuXIl+fLlA6BBgwb06tXLEhqnT59OXFwc69atw9vbG4AePXowceJEFi9ezMsvv0zlypWt9u3m5kbHjh155513KFWqFB07dgRg8+bNnDt3jpkzZ9KiRQvL+v7+/rzyyivs3btXoVFERCSb0uXpPOq3334jIiKCzp07WwIjQJ06dahSpQpwL1hu374df39/3NzciIyMtPxKCX3ff/99mo/ZunVrDhw4QNOmTS3LjEYjJpMJgNu3b2fCmYmIiMjjoJHGPCo8PByAMmXKpPquQoUKXL9+naioKGJjYwkLCyMgIOC++7ly5Uq6juvo6MjChQs5evQoly5d4sKFCyQmJgJYwqOIiIhkPwqNeZTBYACwBLa/M5vNwL1RQIAmTZoQHBx83/0UK1Yszcc8f/48QUFB3Llzh4CAAJo1a4avry+lSpWiS5cu6T0FERERyUIKjXmUj48PAOfOnUv1XcpbXLy8vHB1dSUpKYl69epZrRMZGcnhw4cpW7Zsmo85Z84coqKiWL9+PU899ZRl+dGjRzNyCiIiIpKFNKcxj6pSpQqlS5dm5cqVxMXFWZb/9NNP/PLLLwA4OTnRsGFD9u/fz7Fjx6y2nzFjBkOHDuXs2bMPPIaDg4PVJefo6GhcXFysLombTCaWLFkC/G9kU0RERLIfjTTmUQaDgQ8//JCBAwfy8ssv061bN2JjY1m8eDFeXl6W9UaOHMnBgwfp06cPQUFBlCtXjh9++IHQ0FAaNWpEgwYNHngMLy8vjhw5wqpVq2jQoAGNGjVi165d9O/fn3bt2pGYmEhoaCgnTpzAwcGB+Pj4rDh1ERERyQCNNOZhDRs2ZP78+Xh5efHZZ5/x7bffMnz4cOrXr29Zx8fHhzVr1tC8eXPWrVvHhAkTOHHiBEOGDOHzzz/HweHBP0IjR44EYMKECRw6dIiuXbvy9ttvc/36dSZOnMjChQspUqQIq1evpkqVKhw4cOCxn7OIiIhkjMGccteDSC6w6qfPiYi/bO8yRPKUJwqUppv/MKKi4klOTttTEJycHPD0LJCubeQe9e7RqH+peXkVwNHR9jiiRhpFRERExCaFRhERERGxSaFRRERERGxSaBQRERERm/TIHclVPN3S/oYaEckc+u9OJG9QaJRcw2w208I3yN5liORJJpMJk0kP4xDJzRQaJdcwGAzExCRgNOoRCunh6OiAh4erepcB6t3/mExmhUaRXE6hUXIVo9Gk525lkHqXceqdiOQFuhFGRERERGxSaBQRERERm3R5WnKVtLwGSayl9Ey9Sz/1LuPS2jvNlRTJPhQaJdcwm814eLjau4wcS73LOPUu42z1zmgyEh2VoOAokg0oNEquYTAYWHZ0Cdfirtm7FBHJBMXdi9OzZi8cHAwKjSLZgEKj5CrX4q5x+Va4vcsQERHJdTQRR0RERERsUmgUEREREZsUGkVERETEJoVGEREREbFJoVHSpUmTJnTt2jXN68+cORNfX1/++OOPx1iViIiIPG66e1oeq+bNm1OmTBmKFy9u71JERETkESg0ymNVuXJlKleubO8yRERE5BHp8rSIiIiI2KTQKBkSHh6Or6/vA3+NGjUKsJ7TePz4cXx9ffniiy9S7e8///mP5j6KiIhkY7o8LRni5eXFlClTUi2fP38+p0+fpkmTJqm+e+aZZyhXrhyhoaG88cYbVt9t2rSJqlWrUrFixcdWs4iIiGScQqNkiJubGx07drRatmbNGk6fPs3rr79OixYt7rtdx44d+fzzzzl79ixPPvkkAKdOneLs2bOMHj36sdctIiIiGaPL05IpfvzxR/71r3/RoEEDhg8f/sD1OnTogMFgYNOmTZZloaGhODo60q5duyyoVERERDJCoVEe2ZUrVxg6dCglS5bkk08+wcHhwT9W3t7ePPvss4SGhlqWbdq0iYCAAIoWLZoV5YqIiEgGKDTKI7l9+zZvvPEGCQkJzJ49m0KFCtncpkOHDpw/f54TJ05w7NgxwsPDU13qFhERkexFoVEyzGw2M2rUKE6dOsXEiRN56qmn0rRd69atcXFxYdu2bWzevBk3NzeaNWv2mKsVERGRR6EbYSTDZs2axdatW3nttddo3bp1mrfz8PCgcePG7Nq1i7i4OJo3b46bm9tjrFREREQelUKjZMju3buZPXs2ZcuWpVq1amzYsAGTyWT5vkCBAg8dPezQoQODBg0CYNy4cY+9XhEREXk0Co2SIcePH8dsNnPhwgWGDBmS6vvSpUs/NDQ2bNiQwoUL4+zsTEBAwOMsVURERDKBwWw2m+1dhEhm+WTPVC7fCrd3GSKSCUoX8uatF94mKiqe5GST7Q3yCCcnBzw9C6gvGaT+peblVQBHR9u3uehGGBERERGxSaFRRERERGxSaBQRERERmxQaRURERMQm3T0tuUpx9+L2LkFEMon+exbJXhQaJdcwm830rNnL3mWISCYymoyYTHrIh0h2oNAouYbBYCAmJgGjUY9QSA9HRwc8PFzVuwxQ7zIurb0zmcwKjSLZhEKj5CpGo0nP3cog9S7j1LuMU+9Ecg7dCCMiIiIiNik0ioiIiIhNujwtuUpaXoMk1lJ6pt6ln3qXcY+zd5oHKfJ4KDRKrmE2m/HwcLV3GTmWepdx6l3GPY7eGU1GoqMSFBxFMplCo+QaBoOBj3d8wcWoy/YuRUTspIxnad5v9gYODgaFRpFMptAoucrFqMucuXHB3mWIiIjkOpqIIyIiIiI2KTSKiIiIiE0KjSIiIiJik0KjiIiIiNik0JhHXbx40d4liIiISA6i0JgHjR07lrffftveZYiIiEgOotCYB+3duxezWc8vExERkbRTaBQRERERm3JlaAwODiYoKIi9e/fSoUMHnnnmGVq3bs2KFSus1rt27RqjR4+mXr16+Pn50a5dO5YvX261TkhICL6+vmzevJmWLVtSrVo1xo4dS3h4OL6+vnzzzTd8+umnNGjQgOrVqxMcHMy5c+cIDw9nwIAB+Pv788ILLzB9+nSMRmOGzic9df7yyy+MHj2aOnXqUL16dfr27cupU6cs6/n6+nL58mV+/vlnfH19CQkJsSyfNm0aQ4cOpVq1ajRq1Ihbt24BcOzYMV555RVq1qxJ9erVCQwMZMeOHVbHnzlzJr6+vvz555/069eP6tWr06BBA/7973+TkJAAQFxcHM888wwDBgxIdY779u3D19eXDRs2ZKhHIiIi8njl2jfCXLp0iTfeeIN27drRtWtXtmzZwkcffcTNmzcZPHgwERERdO3alaSkJIKCgihSpAj79u1j3LhxnDt3jjFjxljt7/3336dbt26UKFGCMmXKWJbPmDEDDw8PBgwYwNWrV1mwYAGDBw8mISGBOnXqMGrUKDZv3sycOXPw9vama9eu6TqP9NY5bNgwfHx8GDp0KNevX2fBggW8+uqrfPfddzg5OTFlyhQmTZpEwYIFGTx4MDVr1rRsu3z5cqpUqcKYMWO4efMmhQoVYvfu3QwcOJDixYvz6quvkj9/ftauXcugQYP44IMP6Nmzp9Xx+/fvj4+PD2+//Ta//vorCxcu5OTJkyxatAh3d3caN27Mzp07uXXrFoUKFbJst3HjRtzc3GjWrFm6+iMiIiJZI9eGxoiICIYNG8bAgQMBCAwMJDg4mDlz5hAUFMT06dOJi4tj3bp1eHt7A9CjRw8mTpzI4sWLefnll6lcubJlf02aNOHdd9+1fA4PDwfg7t27rFixAnd3d+DeXclbt24lODjYEujat29P7dq1CQsLS3doTG+dFStWZO7cuZbPTk5OzJo1i4MHD1K/fn06duzI559/jqenJx07drQ6ltlsZs6cOZZzMRqNfPjhhxQuXJiQkBAKFy4MQPfu3QkKCmLKlCm0atWKokWLWvZRoUIF5s6di4PDvUHsokWLMnfuXHbu3EmzZs3o2LEjW7ZsYdu2bXTp0gWApKQktm/fTvPmzXF1dU1Xf0RERCRr5MrL0wD58+enb9++ls9OTk707t2bpKQk9uzZw/bt2/H398fNzY3IyEjLrxYtWgDw/fffW+2vbt269z1O/fr1LSELoHz58gA0b97csszNzY0iRYoQERGRrnMwmUzprrN169ZWn59++mmANB27atWqVufy22+/cfXqVYKCgiyBESBfvnz079+fxMRE9uzZY7WP1157zRIYAfr16wfArl27AGjQoAGenp6EhoZa1tm9ezexsbG0b9/eZo0iIiJiH7l2pNHb2zvVqFW5cuUAuHz5MrGxsYSFhREQEHDf7a9cuWL1+e+jaQ9b7uTkdN/ljo6OmEymNNcPEBUVle46ixQpYvXZxcUFIE3H/ue2KaOpFSpUSLVuxYoVgXu9/LtKlSpZffby8qJQoUKW9ZydnWnTpg0rV67k5s2bFClShA0bNlC0aFHq1atns0YRERGxj1wbGlPC29+lBCdnZ2fg3iXn4ODg+25frFgxq89/Hz37O0dHx0cp86FSbpxJT50GgyHDx/vnuaQ8lud+j+f5Zy9T3K/vRqPRanmHDh1Yvnw5W7ZsoWPHjuzevZuuXbs+1l6KiIjIo8m1ofHy5csYjUarIHL+/HkAypYti6urK0lJSalGtyIjIzl8+DBly5bNynLvy8vLy651psyh/PPPP1N9l7KsRIkSVssvXryIn5+f5fPNmzeJi4uzqrNGjRqULVuWHTt2ULRoUe7cuUOHDh0exymIiIhIJsm1cxpjY2Mtj5MBSE5OZvHixbi7u/P888/TsGFD9u/fz7Fjx6y2mzFjBkOHDuXs2bNZXHFqTk5Oj6VOBweHNF2urlq1KsWLF2flypVER0dbliclJbFgwQKcnZ1p0KCB1TZLliyx+jxv3jwAWrVqZbW8Q4cOHD58mE2bNlG+fHmqVauW7vMQERGRrJNrRxqdnJwYP348v//+O2XLlmXjxo0cO3aM8ePH4+7uzsiRIzl48CB9+vQhKCiIcuXK8cMPPxAaGkqjRo1ShSF7eRx1enl5cebMGZYvX06dOnV48skn77uek5MTH374IUOGDKFz58507dqV/Pnzs27dOk6cOMGoUaNSzYPcsmULt2/fJiAggKNHj7Jx40bL3eN/16FDB2bOnMnWrVsZOnRous9BREREslauHWksXLgws2bNIiwsjClTpnD37l1mzZpleeSNj48Pa9asoXnz5qxbt44JEyZw4sQJhgwZwueff/7AOYxZ7XHUOWTIEDw9PZk0aRLbt29/6LpNmzZlyZIllC1bljlz5vD5559ToEAB/vOf/1jdnZ5ixowZREdH8+9//5uff/6Zt956i8mTJ6dar0yZMvj7+wPo0rSIiEgOYDDnwpcQBwcH8+eff7Jv3z57l5JnzJw5k1mzZhEaGmq5s9qWoKAggFRv6nkUr68Zw5kbFzJtfyKSs1QqWpY5XSYQFRVPcnL6nliRUzg5OeDpWSBXn+PjpP6l5uVVAEdH24NQ2WM4TfKcU6dO8dNPP/Hyyy/buxQRERFJg1w7pzG7SkpKsrzT2RY3NzcKFCjwmCvKWhs3bmTHjh0cOHCAUqVK0a5dO3uXJCIiImmg0JjFfvrpJ3r16pWmdQcPHsyQIUMec0VZy9HRkT179lC6dGkmT55Mvnz57F2SiIiIpEGunNOYnd26dYvffvstTev6+Pjg4+PzmCvKXTSnUSRv05xGsUX9Sy2tcxo10pjFChUqpNflPUZlPEvbuwQRsSP9GSDy+Cg0Sq5hNpt5v9kb9i5DROzMaDJiMukimkhmU2iUXMNgMBATk4DRqMsN6eHo6ICHh6t6lwHqXcY9zt6ZTGaFRpHHQKFRchWj0aQ5Khmk3mWcepdx6p1IzqHnNIqIiIiITQqNIiIiImKTLk9LrpKWRwaItZSeqXfpp95lnHqXcerdo8np/bPnnF09p1FyDbPZjMFgsHcZIiIij02y0cit6IRMDY56TqPkOQaDgeGrpnA24pK9SxEREcl0Tz7hw2fd3sHBwWCX0UaFRslVzkZc4rcrf9i7DBERkVwnZ17QFxEREZEspdAoIiIiIjYpNIqIiIiITQqNIiIiImKTQmMOExwcTP369R+6zsyZM/H19eWPP+x7Q8ioUaPw9fUlMTHxoeuFhITg6+vLnj17sqgyERERSS/dPZ0LNW/enDJlylC8eHF7l5ImtWrVYsqUKVSuXNnepYiIiMgDKDTmQpUrV85RAczHxwcfHx97lyEiIiIPocvTIiIiImKTQmMOdfDgQQIDA3nmmWd4/vnnmTRpEnfu3AHuP6fxxo0bjB49mnr16uHv78+gQYM4evQovr6+hISEWPbp6+vLihUrrI71xx9/4Ovry8yZM62WHzhwgD59+vDcc89Rp04dXn/9dU6dOpWq1lOnTtGvXz9q1KhBnTp1GDVqFNHR0ZbvNadRREQk+1NozIFu3brFgAEDqFatGu+99x5Vq1Zl0aJFTJky5b7rx8fH06NHDzZu3EjHjh0ZMWIE165d44033shwDVu2bKFfv35cvXqV1157jQEDBvD7778THBzMuXPnrNbt06cPxYsXZ/To0TRs2JBvv/2Wd999N8PHFhERkaynOY050N27d5kwYQIvvvgiAF27dqVVq1Zs3bqVsWPHplp/8eLFnD9/ntmzZ9OsWTMAAgMDCQwMtBrxSyuTycSECRMoU6YMISEhFChQAIAmTZrQunVrlixZwocffmhZ/5VXXmHQoEEAdOvWjb/++ouwsDDi4+Mt24qIiEj2ppHGHMjJyYk2bdpYPjs4OFClShVu3ryJ0WhMtf727dspV66cJTACuLi40L9//wwd/9dffyUiIoIuXbpYhb6yZcvy9ddfM2TIEKv1O3ToYPW5WrVqGI1GIiMjM3R8ERERyXoKjTlQwYIFcXFxsVqWP39+zGYzycnJqdY/f/48ZcuWTbW8YsWKGTr+5cuXAShXrlyq76pUqYKXl5fVsiJFiqSqFSApKSlDxxcREZGsp9CYAzk6OqZr/eTk5FQhE7jvsvsxmUz3/WwwGNK0fXrrFRERkexHoTEPKFu2LOfPn0+1/MKFC1afU8LdP0cAb9y4YfW5VKlSAFy8eDHVPj/55BNmzZr1KOWKiIhINqTQmAe0aNGCM2fOcOjQIcsyo9HI8uXLrdYrWrQoACdPnrRavnHjRqvPfn5+PPHEE4SEhFge8wMQHh7O4sWLuX79emafgoiIiNiZ7p7OA/r168f69et59dVX6dmzJyVLlmTLli388ssvwP8uM5crV45q1aqxdu1a3N3deeqpp9i7dy+nTp3CweF//75wdnbmvffe480336RLly507tzZEkILFCjwSI/yERERkexJI415gLu7O8uWLaNJkyasWrWKTz75BC8vL8vjef4+t3HGjBm0aNGCkJAQJk+ejMFgYOnSpanmL7Zp04a5c+fi7u7OZ599xvz586lWrRorV66kZMmSWXp+IiIi8vgZzGaz2d5FyOMVGRlJoUKFUt2QEhoayogRI1i0aBEBAQF2qi5ztZs1hN+u/GF7RRERkRymaqmKbBw8k6ioeJKTTbY3SCMvrwI4OtoeR9RIYx4wbdo0nnvuOeLi4qyWb968GScnJ6pUqWKnykRERCSn0JzGPKB9+/aEhIQQHBxMp06dcHZ2JiwsjJ07dzJ48GAKFSpk7xJFREQkm1NozAMCAgKYP38+c+bMYfbs2SQlJVG+fHkmTpzISy+9ZO/yREREJAdQaMwj6tevT/369e1dhoiIiORQCo2Sqzz5hI+9SxAREXks7P13nO6ellzDbDan+dWGIiIiOVGy0cit6ARMpsyLb2m9e1ojjZJrGAwGYmISMBoz7zEEeYGjowMeHq7qXQaodxmn3mWcevdocnr/TCZzpgbG9NBIo+QqOfEPgOzA0dFBvcsg9S7j1LuMU+8ejfpnzcHBkKYrdQqNIiIiImKTHu4tIiIiIjYpNIqIiIiITQqNIiIiImKTQqOIiIiI2KTQKCIiIiI2KTSKiIiIiE0KjSIiIiJik0KjiIiIiNik0CgiIiIiNik0ioiIiIhNCo0iIiIiYpNCo4iIiIjYpNAoIiIiIjYpNEqOd+3aNa5du2b5bDab7VhNzhUbG8uhQ4e4deuWvUsRkUdgMpnsXUKOpv49mJO9CxDJCLPZzI4dO/jiiy+4dOkSnp6eVK9enTFjxlCoUCF7l5ejXLx4kRkzZrB9+3acnZ1xc3OjV69edO3aFQ8PD8xmMwaDwd5l5kg7d+4kKSmJatWq4e3tDaB+ppF6l36hoaFs2rSJIkWKULduXV544QXc3d0xmUw4OGiMyBb1zzaDWcMykgP9/vvvDBgwgKeeeoomTZpw6tQpVq9eTfXq1fnoo4+oVKmSvUvM1hITE8mXLx8A48aNY+fOnQwYMICCBQsSFhbG+vXr6dChA5MnT7ZzpTmP2Wxm/fr1TJ06ldu3b+Pk5ISTkxOvvfYaffr0sXd52Zp6lzFJSUl8/PHHhIaGUqVKFW7cuMEff/xBkyZNmDFjBk5OGh96GPUvHcwiOdDgwYPNzz//vPnUqVOWZWvXrjU/++yz5iFDhtixsuzrjz/+MI8fP95cr14988KFC81ms9l88uRJc5UqVcyffPKJ2WQyWdb96KOPzL6+vubvv//eTtXmXBcvXjS3aNHC/MYbb5gPHjxoPnz4sPn11183+/r6mleuXGlOTk62d4nZjtFoNJvN6l1G7dixw1y1alXzrFmzzBEREWaz2WyeM2eO+emnnzZPmjTJHBsbazabzVb/jedlV69eNZvN//u5U//STuOtkuPExsZy+fJlqlevjq+vL0ajEYCWLVvSvn17tm3bxvHjx+1cZfYQFxfHwoULadmyJW3atGHv3r107NiR2rVrA/fmgxqNRlq0aIHBYCApKQmAbt26UalSJRYuXGg1X1T+JzExkbt376Zavm7dOq5fv85rr71G7dq1ee655/joo49o0aIFX3zxBUePHrVDtdnLrVu3+PLLL2nXrh3R0dGWS3/q3YPFxsYSFhbG5cuXU83b3rFjByVKlCAoKIiiRYsC0L17d4KDg1m9ejX79u0DyNOX9m/dusW8efNo0aIFjRo1IiIiwvJzp/6lnUKj5Dhms5no6GjLX9iOjo6YzWby589Pq1at8PT05JtvvrGEybzq1q1bvPzyy0yePJmaNWsya9Ys5s6dy7Bhw6hSpQoA169fx8HBgXPnzgFY/hCtUKECHTt25Mcff1QA/38pf1Hv2bOHPn360KFDB0aOHMmOHTssYRsgIiICT09Pq3/QlChRgldffZW//vqLjRs3Wq2fl2zYsIGgoCDq1KnD8uXLKV++PLdv37Z8r96l9tdffzF69Gjq16/PiBEj6Ny5M5MnT+bq1auWdZKTk3FxccHLy8vy56K7uzu9e/cmMTGRzZs3Ex8fb69TsKsdO3YQGBhInTp1WLRoEXXq1OGTTz7B1dXVso76l3YKjZLjeHh4ULhwYW7evEl4eDjwv7vdKlWqREBAAN9//z0RERH2LNOuzGYzhQoVonDhwtSoUYM33niDZs2a4ePjQ758+az6ZTKZ+PPPPwEsc3dcXFxo0KABbm5u7Nu3j7i4OLudS3ZhMBj47rvvGDt2LM7OzjRo0IDTp08zfPhwPvnkE+De3ChHR0eio6NxcnLC0dERuPf7Ua1aNWrVqsV3333HqVOn7HkqWe63336jdevWvP322xQvXpzx48czf/58Jk2aRKlSpQC4e/euevc3ycnJAKxatYqwsDBGjhzJlClT6NSpE8uWLWPUqFHAvZ85Jycnrly5QkJCAs7OzpjNZkwmE6VKlaJx48bs27ePX3/9FchbT5fYvXs3b7/9NpGRkXz++efMmzePUaNG0bZtW9zd3YF7P3fqX9opNEqO1KBBAy5cuGAJjSkjZF5eXjRo0IBr167x888/27NEu0r5g6127dpWIxIpUvpVo0YNPDw8OHnyJFFRUcD/Anjp0qWpXbs2hw8fJjo6OmsKz2b+/hfErVu3mDlzJgULFmTs2LGMGTOGLVu20LZtWxYvXszu3btxcXHB09OT27dvc/jwYQCMRqOlp4GBgVy/fp0ff/zRLueTlcxms6V/BoMBk8lEq1atmDp1Kp07d+bJJ5+0/MVtNptxdnamUKFCebZ34eHhHD58mMjISODeP+AuXrzIV199xfPPP0+vXr1o0qQJo0aNYvjw4Rw8eJDly5fj4uKCt7c3iYmJlsuof39kTNeuXYmNjeWnn34Ccu8l1n/2D6B48eIUL16cunXr0rJlSypXrkyBAgUALMHQ2dmZkiVLcvfu3Tzdv7RSaJQcqV69esTFxfHrr7+SnJxs+UsJ7o2ePfHEE5Y/APKilFBYuXJlrl+/bhm1OXz4MJMnT2bmzJmWPwRbtmzJTz/9xJkzZ6z24ebmRkBAABcuXODmzZtZewJ2tHfvXqZNmwZY/+Vx584d/vjjDwIDA/Hx8bEsf/3116lUqRJz5swhOjqaZ599FmdnZ3bv3g3c+0smpf8tWrQgX758HD9+PFeO3v6zdyl/wVauXJl8+fKRlJREcnKypR8pUtbz8/Mjf/78eaZ3JpOJ0NBQOnToQKtWrRg8eDBBQUHMnTsXuPczd+vWLZo3bw5guXTasWNH6tSpw7Jly7hy5Qr16tXDycmJPXv2APf++0/5M+CFF17Aw8ODX3/9lZiYGDuc5eNjq3+VK1cG4MaNG6nO3Ww2W3qU8t9sXutfRig0So5UpUoVKlWqxJ49e/jrr7+A/40KFS9enLJly1pCUV7m6emJs7Mzq1evZtSoUQQHB7NhwwbmzZtHcHAwkydPJjg4mFu3bnHo0CHgf4HTwcEBb29vkpOTuXDhApD7L80kJSWxcOFCli5dyp07dyzzZeHe3DKj0WiZa5cyt65ChQoEBgZy9OhRdu/ezXPPPUfx4sU5dOgQt2/ftvQzZVQjICCAs2fP5rogfr/ewb3RQgcHB8qUKcOlS5cswef8+fOsWrWKXbt2WUJgnTp1KFq0aJ7p3Q8//MD48eMpVaoU48eP56233sLLy4tPPvmEb7/9lmvXruHs7MylS5eA/4XrJ554gm7dunHu3Dm+++47atSoQdmyZTl48CCRkZEYDAbLSBpA3bp1+fPPP4mNjbXbuT4OD+vf8uXLAahatSqXLl3CYDBw7do1pk6dSnBwMJMmTbL846Ru3bp4e3tz6NChPNW/jFBolBypYMGCtGvXjiNHjnDkyBHgXsgxm8088cQTuLi44OzsnGcvq6bw9PSkRIkSLFmyhFu3brF8+XJWrFjBt99+S/369Vm4cCGnTp2iRIkSfP/99/zxxx/AvTuD4V6f8+fPbwnmufXSTEowvHr1Kr/99huJiYns3LkT+N/ojrOzMwUKFLDMbXJ2drZs36JFCzw8PNixYwcuLi40atSIs2fPWkYukpOTLQGoTp06/PXXX1bb52QP611KYIR7f/FeuXKFH374gT59+tCqVSs+/vhjhg4dSocOHdi/fz/u7u40bNgwz/Ru+vTpeHh48N5779G+fXu6du3K5MmT8ff3Z9WqVZjNZtzc3Cw/c39/XmCDBg0oVqwYu3btAqBVq1aEh4ezbds24H+9N5lM+Pn58ddff+Hh4ZH1J/kYPah/zz33HCtWrCAyMpKaNWsSHh7O8uXLeemll9i+fTt3797lm2++4fXXX2fBggU4OjrSqlUrLl26lKf6lxEKjZJjde/eHVdXV7755huuX7+OwWCwhJq4uDiKFStG4cKFc/3o2MNUqFABNzc3nnjiCfr378+zzz6Lj48PFSpU4J133qFKlSps376d1q1b88cff7BmzRoAy4O/jUYjd+7coWbNmvY8jccu5efmP//5D9HR0Xh4ePDtt98C//uLukqVKhQuXJhz585ZjUbAvZGfF154gZ9++okLFy7w4osv4ubmxqpVq1Idy2g0EhcXl2vu7n9Y7/7+32SZMmVITEzkv//9Lx4eHsyZM4dFixYxYsQIzGYzH374IadOnaJLly55oncpc7Kff/55ypQpYwnGxYoVo0aNGhw7dozKlStTrFgxfv/9d8ujr1JGvwoWLEiTJk349ddf+eOPP+jQoQMlS5a0jLD9/YrBjRs3cHJyylV3AD+sf9WqVePs2bMkJCRQunRpEhMTWbhwId27d2fp0qUsWrSIJUuWEBAQwMyZMwkLC+Oll16iRIkSeaZ/GaXQKDmWu7s7r776KsePH2fWrFlcv36dGzdusHjxYk6cOEGjRo2A3Ds6lhYuLi6MGDGCt956yxL8UoJOmTJlaNiwIYcPH6Zly5Y0aNCAZcuW8fXXX3PmzBmOHz/O1KlTqVq1KuXLl7fnaWSJJUuWEBoaSs+ePalatSpHjhzh2rVrODg4WC5F16pVizNnzvDLL78A1nMeW7RowY0bN/jtt9/w8/OjTZs2HDhwgLCwMKsRoiNHjlCxYkXc3Nyy9gQfo4f1LiXgOTo6UqxYMeLj43n99dd5/vnnqVmzJv3792fcuHFcunSJNWvWULlyZdq2bZvre5cvXz6io6NxcXGxzK8zmUzkz5/fclNaYmIizZo14/fff7c8o/Lv/whu1KgRt27d4vfff6dMmTJ06dKFM2fOMG/ePMs6V69eZefOndSsWRMvL6+sPcnHKC39i4mJoXDhwlStWpXGjRszcOBAihcvTv78+fHz82PMmDEkJCQQGhpKyZIlCQwMzDP9yyi9G0dytKCgIAwGA9OnT2fbtm2UKVOG8+fP07RpUzp27Gjv8rKFF154wSo4p4yQOTs7kz9/fqKjoylUqBDjxo3jzp07jBkzhpIlS5KYmIiLiwsffvghRYoUseMZZI0ffviBatWqMWbMGFasWMGBAwcIDQ2lb9++lnU6d+7M119/zb59+2jYsKHVDR0VKlSgQIEClvmfPXr04OTJk7z77rv07t2b2rVr88svv3D06FH69++fq3r6sN4ZjUYcHR15+umn6du3L0WKFKFq1aqWbY1GIw0aNKBChQr89NNP3Lp1i759+3LixIlc3buUUa2//4Ms5R8oZ8+excfHh+LFi9OqVSu++OIL9uzZQ+vWra1+5kqWLIm7u7vlKRLdunXj4sWLfPrpp+zevZv69etz7NgxjEYjffr0wcXFJcvP83Gx1b8yZcpQunRpHB0defPNN/H09LTa3mQyUbFiRapWrWqZr9ijRw/OnTuXJ/qXUQqNkqN5eHjw2muv4e/vz759+7h27RpvvPEGjRs3tndp2YbBYOD69eucOXOG6tWr4+7ubgmRp0+f5oknnsDV1ZXChQszffp0Tp06xd69e/Hx8aF9+/Z55g/KUaNGWebA1qxZkxIlSliCT0oPatasSbly5QgLC+PcuXOUL1+e5ORknJyccHZ2xtPTk/PnzwPw5JNP8vHHHzNhwgS++OILFi1aRFxcHK1ateKll16y01k+HmnpnZeXF127drVMfTCbzZanHjg6OvLUU09x8uRJ8ufPT6FChfJE75599lnL/0/px48//sipU6do1KgRLi4u+Pr64u/vz/79+zl+/DjPPPMMSUlJuLi44OTkhIuLi+XSdeHChRk1ahQ+Pj7s2rWLZcuW4e3tzfvvv0/dunXtdZqPja3+pcxBDAgIsNouZb5icnIybm5uxMXFUbBgQQBGjx5NmTJl8kT/MuRxvqNQRLKHDz74wOzr62tetmyZOTk52Xz16lXz0qVLzVWrVjVPnz7d3uVlS8OHDzdXrVrVfPLkSbPZbDYnJiaazWazefHixebq1aubp02bZjab//c+2tjYWHONGjXMM2fOtNrP3bt3zYcPHzavX7/e/Ndff2XhGdjPP3tnNBotfYqLizOfPXvWbDZbv8u3Xbt25qCgIKv3S+el3t29e9dsNpvNI0aMMPv6+pp/+OEHy3ebNm0yP/vss+YRI0ZYbXPy5Emzr6+vedOmTan2d/PmTXN0dPTjLTobeVD/kpOTzXfv3jVv2LDB/O2331ptEx4ebq5Zs6Z5zJgxqd4rndf6l1YaaRTJA3r37s2xY8cYP348ixYtokiRIpw+fZp69erRq1cve5eXbZj//zEbjo6OvPDCC2zbto0NGzZYnvcG0K5dO3744QcWLlxIQEAAtWrVIj4+nqVLl5KQkGB5r3fK/pycnHjuuefscTpZ6mG9M///nLOUS8+3b9/mq6++onDhwkRGRvLNN99w5swZ/vWvf1m9CSYv9c7JyYkDBw6wbds2GjRoQJ06dSwjiik3vCxYsICyZcvSpEkTAD799FPKly9P9erVU+0zL82/s9W/uLg4Pv/8c27cuIGnpyfe3t6W95+7u7vTpUuXVHPf81L/0sNgNufhW0tF8pCbN2+ydetWfvrpJ0wmE23btrX85SP/Y/7/y1zXrl2zzMnbunWr1Xd//PEHo0aN4uTJk9SsWZPChQtz4MABXnrpJd555x3LnZd5zcN6ZzKZcHBwYObMmcyZM4cCBQpQt25dzGYz+/bto1mzZowZM8ZymTAv6tevH4cOHeI///kPL7zwgtV3CQkJfPzxx6xdu5bChQtbHpL+3nvv0bZtWztVnL08rH+7du1i6tSpnDt3jlKlShEfH4/BYGD48OF06dIl1QPn5f4UGkXymJS/2MW2jz76iJUrV7JgwQLq1atnmb8IcPHiRbZs2cIPP/zA3bt3efHFF+nYsaPV3b552T97lzLqA7Bnzx7WrVvH77//jru7Oy+99BIdO3bMNc9fzIi9e/cycOBAAgICmDNnDnDvGaEpT4WoVq0aJpOJM2fOsG/fPooWLUqbNm3yzJxjWx7Uv2vXrhEVFUXlypWJjo7m8OHDHD9+nEqVKuWpOduZRX+6ieQxCoy2pdzx27BhQ9atW8fGjRupV6+eVe/KlCnDa6+9Ru/evS03d8iDe/f3kZwXXniBF154gfj4eMu7gPOqlBHY7777jqSkJDp16sTdu3c5duwYhw4dYsuWLYSHhzNv3jxq1qzJ008/zdNPP23vsrON9PTv2WefpU2bNrRp08beZedYGmkUEXmA+Ph4evfuzYkTJ/jxxx9TPR9Qo7YPZqt38j+RkZH07t2biIgIBg4cyM6dOzl69ChOTk60atWKrl274u/vb+8ysy31L+topFFE5D5MJhMFChTg+eeft8yB+mfwUWC8v7T0Tv7H3d2dixcvkpiYyOTJk6lduzaffvopzZo1s3dpOYL6l3U00igich8po4gaTUw/9S59YmNjmTNnDuXKlaN9+/aa7pBO6l/WUWgUEREREZvy5nMhRERERCRdFBpFRERExCaFRhERERGxSaFRRERERGxSaBQRERERmxQaRURERMQmhUYRERERsUmhUURERERs0msERURyoZCQEEaPHm1zvdmzZ2f569aCg4M5dOgQhw8fxsPDI0uPLSIZp9AoIpKL1a5dm9q1az/w+/Lly2dhNSKSkyk0iojkYrVr12bIkCH2LkNEcgHNaRQRERERmxQaRUTkvgYMGICvry9//vlnqu82bdqEr68vc+fOtSw7cuQIgwcP5vnnn8fPz49atWrRt29ffvjhh4ce5+DBg/j6+vLxxx+n+m7UqFH4+vpy8uRJq+W//fYbAwcOpE6dOjzzzDN07NiRFStWYDabrda7ceMG7733Hs2bN6datWo8//zzvP3221y4cCE9rRARFBpFROQBOnToAMDmzZtTfbdp0yYMBgPt27cHYMeOHQQHB3Ps2DGaNWtG79698ff358CBA/Tv3z9V6HsUu3fvJjAwkB9++IHGjRvTs2dPTCYTH330EWPHjrWsl5iYyKuvvsq6deuoWrUqffr04dlnn2XTpk0EBgYSHR2daTWJ5AWa0ygikosdOnSImTNn3ve7Tp064e3t/cBtmzZtiru7O5s3b2bQoEGW5bGxsYSFhVGrVi1KlCgBwLRp0yhYsCBr166laNGilnXnzp3LtGnT2Lx5M08//fQjn09CQgKjRo2iYMGCrF692lL/yJEjGT58OKtXr6ZZs2Y0bNiQ/fv3c+LECQYNGsTQoUMt+5g/fz5Tpkxh06ZN9OjR45FrEskrFBpFRHKxQ4cOcejQoft+V7t27YeGxnz58tGiRQtCQkI4c+YMlSpVAu6NKiYlJVlGIk0mE2+99RYuLi5WgRGgTp06ANy8eTMzToddu3YRGRnJO++8Y1W7g4MDb731Flu3buWbb76hYcOGmEwmAE6fPk1iYiL58uUDoHv37rRp08YSeEUkbRQaRURyscGDBz/S3dMdOnQgJCSE0NBQhg0bBty7NO3i4kLLli2Be4GtefPmAFy+fJkzZ85w8eJFzp49y8GDBwEsAe5R/frrr8C9OY33G0F1dHTk1KlTANSrVw8fHx927NhBvXr1qFevHi+88AKNGjWiZMmSmVKPSF6i0CgikofFxMSwePHiVMtTLl3XqVOHEiVKWEJjVFQUBw4coEmTJlYP5j59+jQTJkywjGo6OztTsWJF/Pz8OH/+fKobVDIqNjYWuBdcH+TWrVsAuLq6snr1ar744gs2b97Mtm3b2LZtmyXkjhs3jsKFC2dKXSJ5gUKjiEgeFhMTw6xZs1ItT7l07eDgQNu2bZk/fz6nTp3i559/Jjk52XIDDEBcXBz9+vUjNjaWd999l3r16lGhQgVcXFz4+eef2bhx40NrMBgMAPcNlgkJCVaf3dzcAFi0aBEBAQE2z8/Ly4v333+f9957j9OnTxMWFsa6devYunUrDg4OfPbZZzb3ISL3KDSKiORh3t7enD59+qHrdOjQgfnz57Nz505+/PFHPDw8aNSokeX7H374gRs3btCvXz/69etnte0ff/wB3D8QpnB2dgbg9u3bqb67dOmS1WdfX1/g3mXqf4bG6OhoZs+ejZ+fHx07duTw4cNs3bqVXr16UaZMGSpXrkzlypXp2bMn9erV48cff3zoeYuINT1yR0REHqpy5co89dRThIaGcujQIVq2bImLi4vl+5QbTP55s8uVK1cso5jJyckP3H/ZsmVxdHTkhx9+sBpZ/P777/ntt9+s1m3evDnu7u7MmzePc+fOWX03depUlixZwsWLFwGIiIhg6dKlLFiwwGq9GzdukJiYSOnSpdPaAhFBI40iIpIGHTp0YNq0aQBWl6YBnn32WUqXLs26deuIioqicuXKXL16lZ07d5IvXz4MBsNDn4no5eVFs2bN2Lp1K126dKFhw4ZcunSJXbt28eyzz3LkyBHLuh4eHkyYMIGRI0fSqVMnmjVrRrFixTh8+DDHjx+nWrVqltHOZs2a4e/vz4oVK/j999+pUaMGcXFxbN26FcDqMTwiYptGGkVExKb27dvj4OBAiRIlqFWrltV3bm5uLFy4kBYtWvDbb7+xbNkyTpw4QYcOHVi/fj2VK1fmxx9/JD4+/oH7nzhxIsHBwURHR7N06VIuX77MjBkzaNGiRap1W7duzbJly6hbty5hYWEsW7aMuLg4Bg4cyKJFiyhQoAAALi4uzJkzh1dffZXIyEiWL1/Oli1bqF69OkuXLqV+/fqZ2ySRXM5gzqxb2kREREQk19JIo4iIiIjYpNAoIiIiIjYpNIqIiIiITQqNIiIiImKTQqOIiIiI2KTQKCIiIiI2KTSKiIiIiE0KjSIiIiJik0KjiIiIiNik0CgiIiIiNik0ioiIiIhNCo0iIiIiYpNCo4iIiIjY9H9gkTrg7GTEKAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import f_classif\n",
    "\n",
    "# Extract sorted F-values\n",
    "fvals = pd.Series(f_classif(X=df_feat, y=hypno)[0], \n",
    "                  index=df_feat.columns\n",
    "                 ).sort_values()\n",
    "\n",
    "# Plot best features\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.barplot(y=fvals.index, x=fvals, hue=fvals.index, palette='RdYlGn', legend=False)\n",
    "plt.xlabel('F-values')\n",
    "plt.xticks(rotation=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot hypnogram and higuchi\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6), sharex=True)\n",
    "\n",
    "hypno = pd.Series(hypno).map({-1: -1, 0: 0, 1: 2, 2: 3, 3: 4, 4: 1}).values\n",
    "hypno_rem = np.ma.masked_not_equal(hypno, 1)\n",
    "\n",
    "# Plot the hypnogram\n",
    "ax1.step(times, -1 * hypno, color='k', lw=1.5)\n",
    "ax1.step(times, -1 * hypno_rem, color='r', lw=2.5)\n",
    "ax1.set_yticks([0, -1, -2, -3, -4])\n",
    "ax1.set_yticklabels(['W', 'R', 'N1', 'N2', 'N3'])\n",
    "ax1.set_ylim(-4.5, 0.5)\n",
    "ax1.set_ylabel('Sleep stage')\n",
    "\n",
    "# Plot the non-linear feature\n",
    "ax2.plot(times, df_feat['higuchi'])\n",
    "ax2.set_ylabel('Higuchi Fractal Dimension')\n",
    "ax2.set_xlabel('Time [minutes]')\n",
    "\n",
    "ax2.set_xlim(0, times[-1]);"
   ]
  }
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