exoplanet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a1e11ceb-ecb4-e83c-9571-dee02a834580",
    "_uuid": "595424eb4c5c162239022da0cd17e6635986556c"
   },
   "source": [
    "# Exoplanet Detection with Keras\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ricardo Castellanos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "61aec30a-f65c-02dd-77b0-67aec83e8632",
    "_execution_state": "idle",
    "_uuid": "6ce895e9b32b7216d59f64c276b7d69068b287e8"
   },
   "outputs": [],
   "source": [
    "# Import the libraries for linear algebra, graphs, and evaluation of results\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "from scipy.ndimage.filters import uniform_filter1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "d4f7e51a-b964-8eaf-220b-7a924aa7932e",
    "_execution_state": "idle",
    "_uuid": "3dab37c32ac2c1edef4bae4fc3534b92355911ae"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/gpu_cuda10.0/lib/python2.7/site-packages/tensorflow_core/__init__.py:1473: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# import keras modules\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Conv1D, MaxPool1D, Dense, Dropout, Flatten, \\\n",
    "BatchNormalization, Input, concatenate, Activation\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b3da338c-5beb-d12c-9cb4-088c1eb86069",
    "_uuid": "68a1f6dba3b023e7eb59c6983fdde57851c030ce"
   },
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "2763eb6d-1867-7254-7278-099e63ddd562",
    "_execution_state": "idle",
    "_uuid": "1cfdd50387bac260912e23444d1234540d5c1b02"
   },
   "outputs": [],
   "source": [
    "INPUT_LIB = './'\n",
    "raw_data = np.loadtxt(INPUT_LIB + 'exoTrain.csv', skiprows=1, delimiter=',')\n",
    "x_train = raw_data[:, 1:]\n",
    "y_train = raw_data[:, 0, np.newaxis] - 1.\n",
    "raw_data = np.loadtxt(INPUT_LIB + 'exoTest.csv', skiprows=1, delimiter=',')\n",
    "x_test = raw_data[:, 1:]\n",
    "y_test = raw_data[:, 0, np.newaxis] - 1.\n",
    "del raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "a0b077fb1c89e5afe853f137ded82ffbfcb0449f"
   },
   "source": [
    "Normalise all samples for mean and sd."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "17cbb5bb-29d7-9a0c-193c-9c132cdea389",
    "_execution_state": "idle",
    "_uuid": "76dd566fee56a9feaf06b9cff2fa54f5239e33cb"
   },
   "outputs": [],
   "source": [
    "x_train = ((x_train - np.mean(x_train, axis=1).reshape(-1,1)) / \n",
    "           np.std(x_train, axis=1).reshape(-1,1))\n",
    "x_test = ((x_test - np.mean(x_test, axis=1).reshape(-1,1)) / \n",
    "          np.std(x_test, axis=1).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5087, 3197)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.52418682 -0.44002245 -0.85184454 ...  0.23145922  0.39045488\n",
      "  0.45595309]\n"
     ]
    }
   ],
   "source": [
    "print(x_train[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "e2aa36f3133a1fad819e48dc60e120aed6283ce2"
   },
   "source": [
    "Next we add an input corresponding to the running average over\n",
    "200 time steps. This helps the net ignore high frequency noise and instead look at non-local\n",
    "information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "2c006e96-8688-72e4-6abe-65d5d8f22fc6",
    "_execution_state": "busy",
    "_uuid": "f4d354b6d9d6b2fb5c788355c99e61a62699323c"
   },
   "outputs": [],
   "source": [
    "x_train = np.stack([x_train, uniform_filter1d(x_train, axis=1, size=200)], axis=2)\n",
    "x_test = np.stack([x_test, uniform_filter1d(x_test, axis=1, size=200)], axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn-whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r/PEHZT0dNcoq8EjF5s0R0bs3yontwV95BRgxApg6VbB96NaNAoWYzfTp3Zu2BwQAlSqpirymJ0bOrThkCMVA+f13ICtLyEpgCy3CGBfYOK7G3zRjoaFA3braztmyhTRmH35o/1hf/j3DwsKwceNGbNy4EcnJyRg6dKini6QIF8Y4HEscagpasoD7Iv42CGvRogXS09ORkZGBoqIiJCcnIzo6WttFmjShpMYHDlCEQUZuLvDjj/QWZtx9NzntVK9O31mPXbs28NBDwnHikfO5c2SwLcdzz9HInAlS994LtGtnfVyrVkCzZnScOOBHp05Anz5A1aqWU6tq/gjit03LlnTvWbNgZSjPjhNPhQcEkN3DhAnCtkaNLOM8s8iVw4fT7+ghjDyDn5VFcritODAM7ovCMRK+LDwoUVBAc1bp6crHiLteOfNDs1leIy5uq7m5vO16Cj3SdboSfxvjcTyP0wE87GUBl5pKuTZ6j2v5999KAOrj+vXrSEs7Y/NYb35OKYGBgZg0aRKGDBmCkpIS9O7dW7st+pgxwrrS/8VeD2jrvNq16QMIb1hpTy9Vezz8MOXyOm4n4azSG0NNjy0N86907osvWpYRIC2YFKXRQ3CwEKnSAxw4ALRp47Hb2+R2jkx89x0wbZrtY9nPq2aQxl/YHFfj68KY2UzZR3r3tsw8ct99wn45xG1v+3ZalpRQtpThw+maI0YIPmaMf/4h4W3SJHpdDB1aC59+qt/zcNRhdM0YF9I57sYhYYxlAQ8JCbGbBVxqKuWO6D2u4uxZWlauXMnuMxj5OR2J3BMVFeVc2OuAAPvHKI1ulXpGe9vtCX0soTP7rlVIdMaWTfwWEj8He0t16AB06WL/+gbhwQeN+wJj5crNVX+sUZ+F41/4ujC2Zw8waBAp9ZnVuRrkfpf16ylK35kzwKFDtI2lhWT06EGTM8yVautW2z69u3YB4eGAC12PvZ6TJ8ng448/yLBEjt27gZQUwSCE1d+SJcBrr9H69evAE09QphalOUx7zJ5NhiOPPurY+RyOp3BoXoJnAec4hVaVglrhSnq8Elo1Xc5oxpTOVdrOyl6+vP1rc2RhkyZSzp+nQcAHHygHCWA/v68PgjnegVH/h1lZ9NGKuGu+epXccQHgwgX540WuyhYMH2697cYNWubnC12z9FXA2j2bGyxb1vpdUVwM3LpF6506ASpjRWmipISED19g2TIyCV21ityupb+52Qx07AhMngy8/DJtY2aKYuOhn34iV+433nC8LKNHC4I2x7vZts1as+3L2BXGkpKS8Oyzz+L06dPo1KkTvvnmGyQmJmL37t2IjY3Fnj17kJiY6I6ycnwFvcwUlYQ0tWaKau/vjDCm9Zps9CU9z+A2cWJXPk+ycSMQFkY5xffsoW0s/gtAHfzrrwMjR8qfzzVjHCNhVGGsTh0hHpIWWLs6ehSoVo0C7DDkBl533aX9HmXKKHeXN29alkNOGKtfn9yDGc7WwblzQn40gAIKPf20pVmmN8NiXX3zDRAfDyxebLlf3JfOn09Lud9U/Oozm4HERJo44/guubnKXiJdunjU88Lt2DVT5FnAvYu//iJTgJUr1Set9RjOChxKwpWSQONo3HJnDNu1CmNKgqTBMYoi79dfablmjbBt61Zhnbnh6akZ44Ibx1XoIYylpqZi6tSpKC0tRd++fZ2aPE1Pt9YUlZQAc+dSkFiTCdi3j/yxpFk6CgqAfv3kr7ttG4Wn1wNx17l5s/wxrJ+oUMHyBy4uFrR0UhNHR7h8meJRAdRPlJRYTlz9979AUhKwejXQt6/z9/MErC9lWq4XXwT+7/9snyMX2Zb1o5s3W9bhoUM0njFCP2s2O/ZqLioCPvmEJgEDnY7U4Du0akWWLEp1yzVjHK9l8GDg228p+rlhcVTo0us+rvAN04pWzRjDCG8kGWrV8nQJiHPnaLl0qe3jfv2VfmJRRgIAXDPGMRbOCmMlJSWYMmUKFi1ahOTkZGzatAknTpywcw7Qtm0TzJ5tGZGwbl1rQSw2lgaXY8aQn09ODs1mN2hAQWBPnwZeeonaWqVKwN9/O/c8ahBrxpQC9sycScucnEALwYBlSAHID4qRkUGCmtqchdu2AQcPWkZw/fpr64F4UhItn3kGOHxY3bWNhtxvIv7d5fpSW8KYlJUrHSuXMyiVRU16FCkvvEBmmq++KmgGOYSSS4E/woUxDfABmk7YE3rUhNASH+fsdnvlcoWWykc0Y6NG0XLtWiEeiidRq7BnviiiVHgAuM8Yx1g4+845fPgwwsPDERYWhqCgIMTFxSFFnPtQhvPngevXAzB6NFCuHGnDLl+W9+tikQwBCkcvnpSZPh2IiKCADO5kyRIKGKGGU6fKITCQun5p919YKKzXr0+CWrlyQP/+JJgBwJEjFDBEzF9/kYnVAw8A8+YJ25991nZZ3ntPXZmNBhNQgoOFbUeOCOuXLimfA1AAj+JiY42v5MqycaOlsK6WL78k01TAMn0phyPGO0Z8BsPg7jsAjNWxKaLVTFHOM1juPHvaJXeaKSqhVAZ7ZTcY4pneRx7xXDkcRTpTzUPbc4yEs5MCWVlZqCNy7goNDUWWncgbs2ZZfr/nHsBGwGS/Y9UqGpSbTJQ6Mjyc1r/4gpbNmzt23YoVdS2m22CCqfi/KrY4mD3b+hwWIAWgIC7ffquuz/3lFxK2PYHUisIRvGJcxvEIXBhzACM3KK8YILoylDzguAbMnWaKjLAwdfc0aMV6q2kNQ0kYs5VwVoqR+wOOd+MuDW1aWtqdj4KbuCKtWhW4plA6s2fPMXz5ZToAYPz4TCxb9q+u109IcO78jIyrFvXgLTAtl5IJp1jwYoiDKgGkbVTTjz7yCLliyKFnP+yq1y1/V3CU4MIYx/04aqYoRWueMXsmgO40UwTIfuG77yy3delCHtBeYlwuNuUBgB9+8Ew5HEUp/V3HjspBPhgGlY85PoSzwlhoaCgyRfHhs7KyEBoaanVcZGTknY9azGb6HDpUERcuAEOHqi/Xvn3Ajh203r27+vMA++Z+cnz3HdC+fVMMHNgAZjMwbVodPP98OP78Mw1mMzBsmPZrauXGDTL3/Owz+f2pqVUdqgdPwzRj0ncBQyqMbd9uba5XWuq8oOLqiQs9BCkujClz9KiwLo4+akS2bQNOndL3mlwYcwB3D8JOnqRIPD6DkmZMLzNFvTVjrhLGBg6kcEJiypYlJwul0GIG682lYwalaGm2OHdOeKGLuXVLCMjhKr76yvIlLv55Hc0DVFjIfc44+uDs/6hFixZIT09HRkYGioqKkJycjOjoaJvnvPwy0LnzNfzvf9b7du+mNimdqKhTB/j0U9pfq5ZygvVhw0jr3KYN0LkztbfkZAo5P3iwpQ8aY/x4QfAzm8lMsLhYmPh55x1KFiwmJoYiPJaU0DlPPilfHtblz5tneY+xYxV/Hk18+63lvYKD6fc9exbo08fy2Hbt9Lmnu7EX1EIqjMXGyh9n779u7z6OBNdQwpnXrDiSr57X9QRms7zPn95Mn26Z6Lt6dWG9wACK96IiIQIrQHPmDRvqew8ujGlA3JBu3pR/cbiCjh2BESOALVvUn+MVjV6tVKtX/jGt92V4STANTyA1Tbl0iRyd1XLlCoV+lub9Sk8n5/+777aecT17ljroK1dImHIWcXA5cbtR+ze5fJkGXSUl5KhdoYLlQGvhQuXBaWqqMV42HGPibD8eGBiISZMmYciQIejevTu6deuGxo0b2zzns8+AuXPPol8/0uZcvEhCVF4emYnVrWuZh0vMI4/Q8eLBFGPECBJ6pCHvAUrDsmgRCVVMIDp5koKJyEVEDAgAHn+cjps0id7FBQVCIIht24BXXnG8654xw/b+6dOFcqpF3J/Uq0d5ucxmElYSE4EVKxwrq6exJwR9+qm669j7LeV8z5TKUVhIkT0B+h/JmVCuWEH9cmqq9cSbnGCoVL7Vqy0Dxrz4onIZPTUuy80F7r03UvPQZ/58SlPx55/03WQioalZM6BrV/3KN2GC8r5KlfS7D+PkSeVgX/v2CWkaTp0i4fqNN4CHHwZ69nTdRCsfZTqAyUQhaWNjKXytq7l8mZZqzDm8wnRKLyHJXs/mao2ZJ3BxWVJTU9GlSxfExMRggYowaCyJqphevYDfflN3P9bJS013IiOFsLdFRXSf1FTgwAFys4uJodnugQPJYf7CBSHJs1aaNqUOGLD8S8lFzsrIoJl68Yv/ySdJ+AoMFHIIrVtH5T58mAZakZFUPiawLVpE14qKopfNxIl07M6dFI0NIIfx+++nzv/kSTpv8GDqd9wxWylGzu+D4x1ERUVh69at2LFjB4ZqsSUE5Q+sVYuEKCUBTIkpU0jDlJBAeafmzNF2fkSEtqTPFSoomx1rRU6Iq1JFWB8zRljfsEG5v1MzuRMYSINeOSHVkwQFAVOn2j9OzqrBEfLzbe+Xi+apVI4KFeh/O3cu0KgR0Lq1pQnHmTP0n2zfnvrgQYMsr7Vtm/X1lQbh/frRhLkafvvNM31pzZrCunTYtHixvInxwoXCZOexY8I77+hR+i7O3+kI//yj/liTSZ3yIyMDuHlTvqGtXQsMH06/f6NGVOdyQ8i2bSmtxcKFpP3q21cIavTdd8JYQW94+jkHMJuFfCkPPED2z+KO2hX3Y/z+O+VwqV1b/TmGQ29hzNWBPYykGXNhxbKcREuXLkVoaCj69OmD6OhoNGrUSPEcOWEMEExuVqwg4enRR+lle/UqzWBfvUqChvglpvTzV6tmvU0seP31F83WO0Pbttbbbt0i4e+BB+j70aPl8cwztG5v5hygMNiM7GygQwflY6dNU86JVK2a9UBl6VL9TKnU4C4rAI7vIM6x5a0kJpIJ5pQppM1i731x2+7ZU921jDSnp4Zbt4A336SJIiWys/WbGPrPf2zvF/sj79lDWguWXPrWLflySi0uGOyVzgSCAwcs94sFvy1bHM+juWiR5ffkZBJyPTk+e+cd4O23he9DhtBy8mRK+dK6NXD8OP33Gb17y78jN2wg7flzzwH/+582N4WmTbWVOzaWJj179ACef17YXlpKptNhYZSC4rHH6uHHHy3PPX2angGwnDiZM4fS81y7Rm06KEjYJ35+JRYvpusGB5M29soV6i+OHCGBTsvkkIFGmd5Laqr77vXQQ8Ls+/nz1lGJvAq9NGFaEyR7o5miG97kjuQkspcEdcAAoFMnKn6VKjSgadyYfEaM8LPaom5dKifLQfTMM/fYP8kFyM0Y79rl3jLo6Y/B4XgDZjNprJ56ir6LB2pySKP8SXOXeZswZo/0dCA01H0RdcUCU4cOZBVx/TqwaRNpUefOVXedBx+0rqvTp4HNm+WP796dxlxyw4ljx6y3ievZlsmiM2i1YBHzzjskLLz+uuUz3XUXTZAC8v7SLFeamPh4EsQA9YF1Jk92vC2sWUNJtB9+mITx998ngad+fUGw3rlT0Ixcu0aKi4gI4Rpizda6dSTIVa1qOcFii7VrhfUhQ8gkOyyMtLF33UVmqy1b0gSOFnxCM3bzJvDzz+QQrAdvvEGzXUoz2dI/kjQ8tt5IOwEWdpvFeJDrJAzd8WsN4KFV2FLCF8wUXTilJpeT6LDMm1Ycdjk3NwxA5TvfV606jeee84zQ4k989x3cGv7a3kCUw/FVxEF+c3KU28KiRTRTLj1P6bvR6NKFzPPOnBFcI2zhDhcNe1SubP8YKfv3W28zm4G4ONvnyZlJduum/f7O4ogFi5SWLWn5wQfW+y5dEoQyV/Df/zp/jV9/JY3UoUPCNrGm7e23SYAuLLStuf3pJ/JLZ6hpo++/b72NuVQAgnZwyhQqh9p2b/C5aXUkJZHjr7hi1HDoEPD557ReWkpaJrMZmDlTvQ0woJ+duhRbDsLijlDuGG6mqOP9ja7CcTPi8MuBgZZvw2efvUfzf69ZMx0LB5rVa9iQXtTp6fQ3Yf4qqanO2ey/9BL5JqgxYXAlqamOhSN3FDn/OQ7HHxALYzVqqBcA+veXv44Ryc8X/KTq17cO8iuHI5FzvRk5I5HTp91fDkcsWLRgzwXGUYqKaDLj2jX5/bNmCWPel16yfz1b4/133iHhmpkmeooBA9Qf6xOjTDZBzKLMSJVeAAAgAElEQVTnqOX++wXHxXnzSOVqK6KR0iDTVZqxWrXIpEvuvtnZ8uVi6zdv0kvDVphVj6N3wA611zVKnjFHcOEbXW1OIjFiM8XgYNvX37KFBJmXXybzDtbx6q3gqViRIiReuyY4xb/yCpk1PPootVezmbTpf/4p+IAqRVdixMdfweef0+TL/PnUX/TqJdivS0OBFxUpR1EEgO+/p3u/8ILldqmfgRTmg+csWkxdXK3953CMitb0lwCNRZYskb+OK3HUfE2tSZU4HxgP6iOPq+tZzoIlKyvL5jla/bMcoUcP2/u7dlX2vcvPJ98txvjx+pXLk4hTW9jDQKNMx2FjZWe0Qf/+S8vz5+0f6y4zxdxciqJmT/PF1ktKgF9+ofUnniANQd++wPLlrimfw+hlJqh3AA8ljCSMuVDl6UhOotJSIDr6Gs6cUU6COHYs2WV37UqCzGefAU2a6Fv2TZtoRnfcOPn9JhNwj8R6skMHcrIF6GX1/PP0UlCK+ta7t2UmynXrKOLhsmVULdKZ4rJl5UN8M1i/IQ1QUr68sD56tPV5ephjM1OXRYsWITk5GZs2bcIJcYx/Ccw0msPxN1hwLi0D2ho13K9N1tqmxciZqzFu3BDWAwIoRYHYLMtbuHHDPQJxbi4JFp7ONZmWlnbnU1JSiMcfdyyTcnz8FbRuTflXKlRQfqjvvgNWrkzHE09cQ0rKcYv7HzmSZhVUAwC2bj2BPXv+wZkzaRbHFxSkYc+eY9iz5xjefDPT+kQv4ZFHrlk8ly18Yr6TNTClP39xMUWHsWXNw8a4ahqQdDyslzB27Rpps6Qdhhrn+eJi5c5/+XJydjUMeiV9Vnue2uON7DPm6G+jAXFOopKSEvTu3dtuTqKPPgIuXryIsDDLcKLFxRT5aP16cph2NtqhLfLzKTw8m1BxhkqVaLAxfDjlU2Fh5u++G2jT5obtkzXCzJulVScO0Tx4sBBWl8FMq51BbOoC4I6pi5LfgTtmVjkcI9KgAYXxfuQRT5fENlrbtFoqViQBg1nCzZtHHzGhoYAd5YzHcWcfNmeO+iibWlFrwSI2X1++HLhy5Sx+/NGO+YqEZs2AdeuEc/773zJISlI+fsCABgCAlJQq+OMP8k374QflCcTYWPv/zfbt6X2sJc2FUXjssSoW9bBfzmHxNgaa8ldHaamQm4hhz4xg/HiaAT95Uvm6csJYSQmZItmLXKaHMDZsGEV0mT5d3fHiZ502zfYsnHim3VA4G5jDUQHEm33GXOwMqDUnUceOQLNm1vHtAwIEe3o7lo52EVlkAACeeYaiQbVqRdo4VySFLFOG7vHpp8AXX1D+Ei08/riwruR/wfoNaZWKj5cbQKiN+mQLtaYubEYvM1NB7clxOeKZVXcGbeEIxMY6FizCnWht02lpaSgsLMSUKXYSeIE0fX37Ku9PTMzEDz8cd6jc7kJrH+4sTzyhvM+ZNu2IBcvDDwPh4bcU06fI8fvv1i4Eo0cDq1apO79VK2DvXnlBbNgw5bQ4ctSpQ0FlbJn9GxEtUYidGmU6E15Tyu7dNAPz0Ufka3HjBvlF/fGH5XEffgjcdx/5VXz5Jdlkshw4XbvSeLpuXeC112g9OJjOASjR2zvv0Ey6kgZMnKvi0iUK6hEfT7mRGNIxuzSAx+XLwuz2kSN0fK9etpNesqS39rLMM8R5fyZNsn2s4YQxe8KQq8wOfcFMUQkjaO8ksLZbWOjcdbp0sfx+//3UBxw6ZG16qCdlypBPqdSnyxbMD06cE0fs3yX2q1PSjLVurb2sroIFCGnaNML+wRyXIA7U4o5gLRzHadDA0yWwj/i/VL58eUye7LjK4ehR6r+mTKmDxx9vrNo0b/JksgQydKAxF+JMmxZbsHTv3h3dunWza8HCGD+ekrCLWbqUJhsAIYjLvn1k0SLFZKIQ9gUFlJNswgTb92vfXn77J59oj9AbHExm/3v32g/M8eabpL3t1Im02uy9fOgQ8N572u5rC+ZGIA34l5REefNsaRGlODzKdMY+GaAfpkkT8vEwmehhbtwgIapcORLMQkOtByYsYduLL9IgqU8f62tfuEBCHQDk5Vnue/ttsgFnAhpDrhNhAlVuLs2+s2AFW7daDrbmzaPcA3ffTVq7GjWAli0jYTIJIUQ3biQ74g8+sDRDevddyyR0ap1itYQHtZfZ3u3oFdpe7X61xxvZTJGhV5h/N8DMCphflhbeeUdYv3mTzPU6dqSXxOuv61M+d8GqZs4cS1MLJWFMjKv+elqDtfBoihyOffbvF0yb3Y0jAZicYeFCoHlzy20mk/0AREuW0DjMU5pGNvb0ZrRasIiZN48m/VeupPHpCy8IAktMDC3btLF9jQoVaHw+daq2ct91F13fmfdau3YUmO7qVeCrr4Sk1UwwCggwY8oU0r799JMgaAKkrZs4kYJgydGjBwmZYuWLlJkzhfX//peC9Yit565eJflj8WL7Qc3EOCyMORNes6iIzG+OH5eP2S+loIBm11evVk7Mp5U33hDWQ0PlNVLi/AMAabfkYFm4z52jGXtbvP46DWxYQshJk+jPxbh82TpZpLPc0NfVRT+cjaZoT9Ol9XhupqgrTEXvyEB+xAhhvXt3esHv2uUdyaKlsCorU8ay+pgwJp4IqljRPWXSaurC84xxOPapUUPeN11p7KAnjpivOQMbBEsZPFg+svWXX9IYSaqZOXNG3yT2tgQE1o8ZLqiZG6lQARg5kpI1//GHe+eaz5zR71pVqlDo+M8+o3HzjBlA27bAF1/8a/eZzpyhd/E//1AaHIAEp40bSR6oUIEUI5s2WZ/L3ATYGKVGDVoyCzRHU105PKxxJLwmY9QoEsTUUqkS/Tj9+rlGsBCHifdFDGfr7qk8Y+JRsRaMMPrXWzvoBpiJghqfriNHaFY5M5M+rMOrXt1gwWccgAlbJpOlMCbnM+auRLFaTV24Zsy4bNmyBXFxcWjWrBmOHDlisW/+/PmIiYlBly5dsEvPES9HE+54hThjvqaVZ56xvb9GDer3WJ6lr76iflwukFNYGFk97N7tfLnOnbNtOsf8lB56iCb3baUykmPtWrK24mH9tVO3rmsijwcGkiBVrhxZmWkJtNW4MQmlAPDqq9bXlUsEHh9PQv2LL1puZ5PPjgpjLo+mKHVOvHGjUJdoYFqoXLkE+fkuysyswP/+9zeuXq2IBx4oQH5+AB57zDWdohrM5jykpamI2e8uPC04uNtMceZMYfrFj1ixgoLmVKli/1g5jfKCBWQ24e307EkREB95hDRfbGZYzkzRnU0jKioKUVFRqo7lwphxadKkCebOnYvJkydbbD9x4gSSk5ORnJyMrKwsJCQkYOvWrQhwdLTAcRh3GTRoadMA8PnnNR26jz1fIYD6ssWLycfoySftH//II2TeJR0Uixk1CnjrLfI3Y/7CzZpRnkhAEPbUmML17Wvpe2+L4mLrQfb16xSkg6UTUsubb2o73uj07EkapeJi0jjdc4/1bz9smHUETqMQG0t+ZLYCroipX18++EizZjSp7HbNmJbwmuLPli22bZjHjCG/qrg46wz2YhYupMHeli2WJk0NGgghVqtWpbxh164F4MoVmtHQ4msFkG+ZdHJJzmSHqT3Pn6cZoZYtzXjxxfpo06YZoqL0FcSGDyffmW7dgAcesNw3cCANbC9eJNXtihXA2rXVjOkArlb40eon5agWyVVmimPHAk8/7dw1vJBKlQSfSUd48UXvcIi3R7duNGvWujUwaBB12oB8fkRPz1MowYUx49KwYUNERFgHWElJSUFcXByCgoIQFhaG8PBwHD582AMl9F+M2p4Zc+aE3FkX/4U2brR9nlKEWCnlyqkTxBhJSWQhMXOmfBTeWbOAmjXpvcCCMqhNELxunfU2teHS5QbYFSuSNu/tt9VdA6Ax6Lvvqj/eG1i9muI0BAQIArI4p9iMGcYVxAAKsFVaShEn7WHLjWDHDuD77x3X/jk8ynTUPjk11dpmTuw3Nn48BfHYtIlU25GRwNdfAz//TPtPnaIGOGQICWtdu5JjfEkJ8NJLwDffACEhdExentDYqlUj4Wz0aNpnz/ds1Cg67tVXya7UbKYKKykhqdhspsAexcW0zhwC77pLvgPWMhZXssUGyDnwk0/Id2bzZuq4WKdkNpNd9pEjlOk8OJh+I8P5eziaM0urD5jS+b4YTZFjaMR/Iam1rCfMFLViuD6EYxdnXAk4+mBkF19plNuXXxbWQ0Igy+bNtqNC68EDD9D85YkTNJknDnAmx9Wrtvfv2AH075+L+HjrfffdZx2ERIqtaNUmE0WGVENKCo1BfY1y5azTzzz2GCkMPvnEMj6DUVH7nrU1FAwJsY78rAWHzRQdSRB74ACwbZvwb+zfnyK6iGPxi2cgTCbLyES2OrYyZbQlQ+3WjbRHtWvT97vvpuAaAweSWZXcTIg0sEb16urv9/XX1PmpMdmSBg556SVg/nzypVFzvuHRGsLe09EUjSSMGfntzlGFdE5ASRjr2tV9ZbIH14x5lkGDBuHSpUsAgBs3bqBChQoAgNGjR+MJtfY1NpC6ExQWFvpFTjNXP+fZs1UA3I38/KtISzvnsvs4wj//KO9j7b1VK5rYZbHZunVzfbkYlSsL6VFmzwZ27pQ/jkX+kw6Ef/+dhIS77wbq1s0CUEP2/KNHgWPHyKpJTp+gRvN1+TKZSCrFNJg8Wf7avkybNvajMnIEnPIZ02qfLI1YyFSaSsKYq6lVS9AouXoGOjBQfSAN6dh/8mRtgqaezJ07F6tXr0aN2yFjkpKSNNW5TZz10bInmGg1YzRyaHsjlIGjC9K/pTia4rhxlscYRfbmbkae5Ysvvrizvn//frRRMcrREupcar6elpZmLJN2F+Hq52RyXuXKVREZqU0tsn//fheUSEBqQt6li5A2hAljxcWkWfL062fkSPrI0b49+YxJw9XL5clSomlT+XD3M2aoe/bgYIr6rXSsFlNGjnGpX99113brlL/Uzpi94EtKhMbviZe+pzsaKeLfYOZM9XbNrmLQoEHYsGEDNmzYoI8g5ugPrpeZolZNl9H+IByvRslMcfFiQRjjcJwlOjoaycnJKCoqQkZGBtLT09HSGSdOjma86dUhNjUTC2PeQNOmzv/WJpNl2P24OO0mdvv2WW9TmwibY2xWrVIf8MUR3CqMnTxp+V0sjD300O0CGcgizBUoze6IKVOGfNtef51sp30OJTNFR9UAajVajuYZ43B0RPo3fP11cp7v0cP6WP6X5Nhj+/bt6NSpEw4ePIiXXnoJgwcPBgA0btwY3bp1Q/fu3TFkyBBMmjSJR1LkKCK2UGLCmL+FcF+yhIQns1k+x5Q92rShmAKrVtH3U6d4H+4rPPusfGoGvXB5aHsx0gdhgldJCfDdd2Qf7K6kp57iww/tO8AGBGiP+uhKVqxYgfXr1+O+++7DuHHjUK1aNX0u7Gwv5S4zRQ5HR6R/y8hI64kqDkctMTExiFHIATF06FAMHTrUzSXiMIxiZqwGLowRzg4DgoJo4P7ss/qUh+MfuFUY27pVWG/b1lIzVr06RWDxddRMTLpbJhA7h4sZPXo0nnvuOQwbNgwmkwmzZ8/GjBkzMH36dNnriB2hmWN0pMy+oFOn0BBASUkJ/hFtD7xwAY0B3CouxgnR9koZGagPIP/6dWSIttfIykIogJzcXGSLtte9ehXVAJw7dw5XRdsbFxcjEMCJkydxSxRKipXx77//hpllGxZtlzp4S7fbela9CL5wAXcBuJybi0zR9WtdvIjaAC5evIhLfuBw70tw2Z/D4RgFsTDGwnP7ozDG4XgCtwpju3bR8uBByrXDNETiTsDXUWOG6e4w0mLncFv07dsXL4vj30oQO0JLHaMtnKRv/wgBAQGW2ytVAgCUDQy03P7vvwCAyhUrWm6/HX+3Zs2aqCnefjt+bL169VBP5r6NGjcGwsOtyt8sMpLitNp4LrntNp9VL26rlatXr47q4uvfDgdau3Zt1Ja5r6udwDnacTTDAsf/8Iaw0BxlvKmNiyeK/VkzxuF4Ard6aH35JfDmm5lo3RooX95SM8YR6NnT0yUQyM7OvrO+Y8cOu+kLNKFXNEWtyZmUJGJvenNyvJYJE2gpzc1iC38Li8whvMnMjeO9jB5tmVInOJhek+IcsBwOx3W4VTM2cCCQlnYZAI1C2JiYR5sR+M9/KLu8Ufjggw/w999/AyBN05QpU5y/qNY8Y0q4K+mzlKVLgZwc566hldataekPtrw+zpAhthO7Szl2DKhXT31qDG+kWTMKT+0MSUmUKHbjRqBDB2D3bn3K5kn4u5HjKsSC/oABlvvKluWT5ByOO3GrMCaFa8asWbTI0yWw5IMPPtD/okwYkgo09gJuKCVoUhshTK9oioMGqTtOT9q1A7KzhSzlHL9BLv+NJ1i69F8kJFib9zrKQw8Bv/1G63/8IVgIb91qncBVjueeE6KWAcBTTwEdO9L63Ln2hbEXXqCPHlrHli2Bw4edvw6H4y7EbuL2hP4ff3S/+wSH4094NJA8G0P7++xfYCBQoQKwfr2fWMppsc8ClPODMSleyezQ16Ip6iCIbdmyBXFxcWjWrBmOHDlisW/+/PmIiYlBly5dsIs5eHI4t2nXrgBmM+Xf0QPxdZiPCgDExsq6biIlhbSEdeuSsDZmjOV+sX+LGvO+4GDg8ce1lVmMOEghj5zG8TZOnxbWWXv5/nvSLEt57DHgkUfcUiwOxy/xqDBWpQotfdn8xhYVKtAyIYGyt/fq5dnyuA2lCmdSuZLQJdWAKW1X0oDVqye/fcwYqgzpfWNjhbBSPkKTJk0wd+5ctG3b1mL7iRMnkJycjOTkZCxatAjvvPMOSrjKmiPD/Pnqj23XDti5k5r2b79ZpvUQpzFhTbJvX1ru20fniomOJi3huXM0aJR2I9WrC+tqhDG5ORyx34wcLVoAP/wAXLkCfPopWTKsWUP5IFevFo5jwllwsP1ycDieQDzh0agRLbt0kc93yOFwXItHhbEBA8hBdPJkT5bCc+zcSZ3fp596uiRuho28Wra03B4eDrzyCrB5s+X2Dh1oOlz6R2GCknQaXcl8cdEiYNgwIDTUcvtHHwG5udajs3XrgLNn7T+PJ2FRIevXV3V4w4YNERERYbU9JSUFcXFxCAoKQlhYGMLDw3GY211xZKhXD8jMBHr3Fhz8X30VmDgRWLYMuHgRuHCBhKcNG4CoKGrybduSaSEA3HcfMHIkJVllk1B5ecCKFcL+vXuFex4/bl2OyEhg7Vqydk5NFdwqAWG9fHnLc7ZsIaGwenUgMZG27dpF3c4vv1Dg1qFDL1qcM20aPcuAAWSK+PjjAEu1OHgw/Q6BgYIgCQjP+eqrwKxZwIgR1J1cuCCYe737rp0fGncCzHI4usM0u926ATVrerYsHI6/43Gfsddf92QJPMtDD9EMr0+zciUgF4Hx0CFrAaJMGXL2kBIcTNPhUpKSaAQntVcSX09Mu3bW0+0AjRSlozaApu61ZCFv0MD90VdeeIHMPtU42dggKysLrVq1uvM9NDQUWVlZssdK86ixPGu+juefU0il4GlCQ0kjBCj34Skp1ttq1bLUWiUk0Ae4k5HCgvR0asZhYfL3eOopWj76qOX2qCjg/Hmy7L1+nbqJ0lKheebmCsd27Cj4mgHAiBGXMG9ebWRmUtMymYDx4+XvL+XQIaCoCHjwQTI7j4uzVq7/8gvw9dfUfb31lrD9yBH6LapXBzZtojkipa6N4x3oFavKFfzyCy1//92z5eBwOB4WxvyV6dMtX/4+DZsiliIa+DtMxYqAXICRWbNIgIuPd/4eWhAb4bsLkwno2tVikziJ940bN1Dhtj3s6NGj8cQTTzh9S2keNWmeNV/F08/5v//RQF+uDL6aT04mHaBq7rqLltWqCZostZhMwvlaEHdrSmbnDzxAH7OZhLx+/eg5g4MtB8ZqBUCOcYmLA15+GZg0ydMlsebCBfqPHz3q6ZJwOMblhx+AAwdcfx8ujHmAceM8XQIfp04d4PPPPV0KjyFO4r1//360adPG7jmhoaHIzMy88z0rKwuhUnNOjkfp18/TJeDoiclEJpAc36VsWeCzzzxdCnnq1AH++isNoaG+P5HG4TjK4487F+hJLR71GeNwOMYgOjoaycnJKCoqQkZGBtLT09FS6tPH4XA4HA6Hw9EVk9msJu6UY/iq6YwvoEZbogVe18ZFXNfbt2/Hu+++i9zcXFStWhWRkZFYvHgxAOCzzz7Dt99+i4CAAEyYMAFRUVFW1+L1bFx4m/Yf9KxrXs/Ghbdp/4G3af9AqZ5dKoxxOBwOh8PhcDgcDkcebqbI4XA4HA6Hw+FwOB6AC2McDofD4XA4HA6H4wHcJoylpqaiS5cuiImJwYIFC9x1W924cOECBg4ciO7duyMuLg7Lli0DAFy5cgUJCQmIjY1FQkIC8vLyAABmsxnvvfceYmJi0KNHD/z55593rrVu3TrExsYiNjYW69at88jzuBJe1/5R17ye/aOeAV7X/lLXvJ79o54BXtf+Ute8nr2kns1uoLi42Ny5c2fzmTNnzDdv3jT36NHDfPz4cXfcWjeysrLMR48eNZvNZvO1a9fMsbGx5uPHj5vff/998/z5881ms9k8f/5888yZM81ms9m8c+dO8+DBg82lpaXmgwcPmvv06WM2m83my5cvm6Ojo82XL182X7lyxRwdHW2+cuWKZx7KBfC69o+65vXsH/VsNvO69pe65vXsH/VsNvO69pe65vXsPfXsFs3Y4cOHER4ejrCwMAQFBSEuLg4pKSnuuLVuhISEoHnz5gCAypUrIyIiAllZWUhJSUH87eTC8fHx2LFjBwDc2W4ymdC6dWtcvXoV2dnZ+Pnnn9GhQwcEBwejWrVq6NChA3bt2uWx59IbXtf+Ude8nv2jngFe1/5S17ye/aOeAV7X/lLXvJ69p57dIoxlZWWhTp06d76HhoYiKyvLHbd2CWfPnkVaWhpatWqFnJwchISEAABq166NnJwcANbPXKdOHWRlZfncbyHF156P17U8vvZsvJ6V8bXn43Utj689G69nZXzt+Xhdy+Nrz+bL9cwDeGjk+vXrGDlyJCZMmIDKlStb7DOZTDCZTB4qGUdveF37B7ye/Qde1/4Br2f/gde1f+Dr9ewWYSw0NBSZmZl3vmdlZSE0NNQdt9aVW7duYeTIkejRowdiY2MBADVr1kR2djYAIDs7GzVq1ABg/cyZmZkIDQ31md9CCV95Pl7XtvGVZ+P1bB9feT5e17bxlWfj9WwfX3k+Xte28ZVn84d6dosw1qJFC6SnpyMjIwNFRUVITk5GdHS0O26tG2azGRMnTkRERAQSEhLubI+Ojsb69esBAOvXr0fnzp0ttpvNZhw6dAhVqlRBSEgIOnbsiJ9//hl5eXnIy8vDzz//jI4dO3rkmVwBr2v/qGtez/5RzwCva3+pa17P/lHPAK9rf6lrXs/eU88ms9lsdseNfvrpJ0ybNg0lJSXo3bs3hg4d6o7b6sa+ffswYMAANGnSBGXKkAyblJSEli1bYvTo0bhw4QLq1q2LWbNmITg4GGazGVOmTMGuXbtQoUIFTJs2DS1atAAArFmzBvPnzwcAvPzyy+jdu7fHnssV8Lr2j7rm9ewf9QzwuvaXuub17B/1DPC69pe65vXsHfXsNmGMw+FwOO5l/Pjx2LlzJ2rWrIlNmzYBoPwsY8aMwblz51CvXj3MmjUL1apV83BJORwOh8PxT5wSxqKjo1GpUiWUKVMGAQEBWLt2rcX+/fv3O11Ajmto06aNrtfjdW1c9KxrXs/GRa6ef//9d1SsWBFvvPHGHWFs5syZCA4ORmJiIhYsWIC8vDyMHTvW6lxe18aFt2n/gL+n/Qfepv0DpXoOdPbCy5Ytu+M4p+bGaWlpiIyMdPa2hsfIz+mqhiquayM/v94Y+VldUde8TRsPpXpu27Ytzp49a7EtJSUFy5cvB0D5WQYOHCgrjAH+2aaN/py8TeuHkZ+Tv6f1w+jPydu0fhj5OW3Vs9PCmCb27UO5c+cAg/5QHI47OHkSOHcO6NTJ0yVxATk5wO7dQM+eni4JRwGl/CxypKWl3VkvLCy0+O6rqH3OHTsq48EHCxAcXOqGUnk3+/YB5coBt103OBz1bNoEtGsH1K7t6ZJ4BObrFRoaivnz5yMjIwNJSUm4cuUKmjdvjpkzZyIoKMgN5QBWrQL69wfK8KRYuuO0MDZ48GCYTCb069cP/fr1s33w6NGoXbYs0KuXs7flcLyWRo1o6ZPemj17Anv2kFBmQ2POMQb28rOIZxiNPOOoJ2qe88IFYORIIDoaSElxU8Fu440mSG3b0tIn+zyO67h2DejRA3jwQeD33z1Xjlu3gG++AZ57DnBzPqsvv/wSDRs2RH5+PgDgww8/xKBBgxAXF4dJkyZhzZo16N+/v8vLMXcuMGYMcOMG8OKLLr+d3+GUMLZq1SqEhoYiJycHCQkJiIiIQFvW695GPMMYnp8Pc4UKfHaVw/FVTpygZVGRZ8vBUYTlZwkJCbHIz8JRT2EhLU+d8mw5OByf5tYtWrL3ilqOHQOWLgWmT9dHeJoxA5g0CQgMBJ55xvnrqSQzMxM7d+7Eyy+/jC+++AJmsxl79+7FRx99BAB46qmn8Mknn7hFGMvKouWlSy6/lV/ilDDGEqbVrFkTMTExOHz4sJUwZjHDWKkS8s1mPrvqYbxxZpXjJbh51pCjHZaHJTEx0SI/C0c7XNPD4RiQrl2B9HRg+HAgLMz56/35Jy1tmHS7gmnTpmHs2LG4fv06AODy5cuoWrUqAgNp6F6nTh1kMSlJglQZ4KyC4NKl2gBqITs7G2lp7v0dtOCtihCHhbGCggKUlpaicuXKKCgowO7duzFs2DDbJ5UpAxOb6eBwOByOS0lKSsJvv/2Gy5cvo1OnThgxYgQSExMxelf9oo8AACAASURBVPRorFmz5k5+Fo42+JyDd5KRARQXA/fc4+mScFwKs8zQq6F+/bU+19HAjz/+iBo1auC+++7Dr7/+qvl8qTLAWQVBrVq0DAkJQWRkiMPXcTXeqghxWBjLycnB8OHDAZCD4ZNPPolO9iISlCnDpxI5HA7HTXz88cey25ctW+bmknA4nqd+fVryYYiX4GhFsfP0njVx4yzMgQMH8MMPPyA1NRU3b95Efn4+pk6diqtXr6K4uBiBgYHIzMy8Y6HG8W4cFsbCwsKwceNGbSeVKQOU8shTHI5RsZc7kMPhcDgcVfz1F1CxItCggWPnOzpe1FMYE5fBjVL8q6++ildffRUA8Ouvv2LJkiX46KOPMHLkSGzduhVxcXFYt24doqOj3VYmjutwb2j7MmVg4sIYh2No7OUO5HA4BNewcDg2aN6clmYzULUqqSaPHlV3bm4ucPmyY/fVUxj78ENhfd8+56/nJGPHjsWYMWMwa9YsREZGom/fvm65L+/rXIvbhTFeoxyOD8Nefrydc3wY7jPG4Wjk2jUhEIYaatYU1qUNLjMT2LsXiI+XP1dPYWz3bmF9yRJg8WLrY65dA65fB+rUsdz+xRdA585OBxFp164d2rVrB4Cs0tasWePU9TjGw72p27iZIodjeAYPHoynn34aX3vAaZnD8Sb4nAOH4wTXrgFXr2o7p1cv4K67gKeeEkLfSykpoaUeDVQ6Zv3hB+tjGjakMonJzwcSEkgb6AMdBZ+Aci3cTJHD4dxBa+7AsufOoc7Mmfh75kyYy5VD4+JiBAI4fvw4iq9ccXPpXYu3hszlcDgcK27eRK1580ggKl8e+PRTICDAvWWoWpWW9oQVsSQgjlWgdB4TxpwZb5aWAnl51tf46iugShXgoYfoe2oqcPGiULaePS3LAADbtwOxsY6XhePzcDNFVxMTQ5lBT570dEk4HLtozh04bhywfTuqp6eTyUjZsgCAxo0aAXXruqvYbsFbQ+ZyOByOFTNmoPa8ecL3114DqlenMVrt2u4tS14ecOOGtZkfIzcXGDkSmDPHcvumTcCoURQopEoVYXtxMS3tCWNmM3DzJgmjUphgKnn/wWwGhgwRvt9OwAyAtHYAsGULUK2asJ1nSubYgZsp6sWFC0C/foBUG7BjBwljHI7BKSgoQH5+/p313bt3o3HjxrZP4rYLHD/El//2S5YAO3ZU9nQxOK6mfXvL71u3khAWcjuHVF6eINQokZenboKdaY6UCA62NvOTMneu9bbevYGzZ+kj5vZ7DGYzmTKWlpLQJX2ehQuBChWAbdvoe0kJkJJiWd7ff7c854svgMOHhe8//WRdrm7dgEceEb7z/LocO7hdGPNZM8XZs4HVq4GlSz1dEg7HIXJyctC/f3/07NkTffv2RVRUlP3cgQx/03hzOPDNv/3gwcDIkc4FHOB4htTUVHTp0gUxMTFYsGCB7YOlf94RI4T1ihVJQCpblmYeTCZg+XLSQLHvJhMdU6YMrffsCbz0Egkrly5Z+laFiJIE//EH8Pjj8jMaERHA+PEkzMgxY4b89qVLgV9/Bfr2tbxueDgQFITI++4j7VfZsiRAvvgiHffSS3Rcly4kPAUGAk88YVlee6gxxx80SPjNvNRixBf7OiPBzRT1gs3EBLr3J+Vw9MKh3IE8eiKH41N06gTcuHEdQCWHzt+1C6hRQ4hqznEPJSUlmDJlCpYuXYrQ0FD06dMH0dHRaNSokfwJtoSCGzestz3/vO0CfPcdLe0Jga1bK+87fVpZ4AJIUJPjgw/oo4bgYPntv/yi7nxnuXDBPffheBXcTFEvmPo7MBC4cQMVf/3Vs+Xh+DxBQeSS6FGks5uO2m/duAG0awf88w+QkwNMnEimI0OHchMPDseNVKgAFBY6PjTo1Am47z7ny3H+PHDwoPPXcTVmM7nLMks3T3H48GGEh4cjLCwMQUFBiIuLQ0pKivIJt/2DORw1+LJpthHg0RTlOHWK1OUAUFRE0XMGDSJhUgk2YDx4EPj5Z4SvXAk8+qjLi8rxX27dIpdEQyDVjGnVlI0bB/z2G9C0qbBt2jRa9uoFdO3qXPk4HB3x5YFJxYrAkSMVcOIEoKRUcQcREeTmY3Sle3ExsGEDkJysfd7omWfov6RHFpGsrCzUEQXACA0NxWGxb9NtWETYshkZaGgywWT0H9gH4VF5OVLcrxkzesN/8EHKGfHtt6QynzqVjOgDAuTzSzCYZmzhQmDlSlovKHB9ecXcukU20FJnVg7HVehlprhvn/K+F16g6x86JNjdu5oRI4R27G6ys43fT3IA+GY1VaxIS3uxe1zNzZuevb87+OYbcjV3J5GRkYiMjESj2Fj8/eeflpGeX3sNaNBA+eQhQ6zNEPfsAdatsz72669pX5cu9P3//k/YFxcn9OMDBwLff0/mg+KIiJGR9JHSujUFH2nQwDKqYXg4jTGlMwgbN+L8e++R1cXp08L2v/+mQB1Hj9JkoBqSkqjRT50qv/+pp4T1u+4C3niDQt9LYHXAPhyOZ8wUMzKMKzCwENHLl9PU3JQpwr4ePZTPk4s6FBSkb9nssW0bdZQvv+ze+3L8F70Eo3btlPdlZ5OGevJkfe6lhk8+AQYMcN/9GAcPkvnQkiXuvzeHA8uI3Bz1FBerE84/+oiMAPQmNDQUmZmZd75nZWXdSVViE3GwiqpVaRzx1VdCf1upEgkf339Pk83MGbBBA+Czz4CHHyY7zYULafvgwfRDPPMMCU2PPUbb69Wja/7nPxSSvnt32t63LwlsubmUALrSbV/FhQspYIiUHTtIyDt9WrgnQOUrKQGOHwe+/FLY3qMH8p5+mmYXxIJm06ZArVr0PGKLDLOZPqdOkaAmhllHMcspgCw4cnLo3uw606aRne2MGWQhxQKFdOoEPP209TNx/B7PRFOsXx8IM1C0JhaBR6zJkssLZmvg6Slh7N9/aTbr9Gnf9cfjGB/pKGTzZnrhqsXWbCxA7UtrcBFXc+2a/v5sbPBhSwvPMQznzqkLpmaLv/4SYh8YgQoVXHv9wkL5aODeirjru3aNYkxcv658/Guv0dyTjAWhU7Ro0QLp6enIyMhAUVERkpOTER0dbf9E8bimTx8SWgYMICshAKhcGZgwQdBwMWrXpolf6bjIlsn6228DixdbbmfnS60slNxClMZh4uMHDpQ/Ron777feds891sE+atWyLCMAlCtHEWvE91eSyp99lqyuvBBftAIwEp41U8zKcuvtZbl1C1izhta/+krYrlWwEWdbdycNGtBUW8uW9CYAHNJWXLhwAQMHDkT37t0RFxeHZcuWAQCuXLmChIQExMbGIiEhAXl5eToWnuP1FBbSkkXfYv+9xETSJCcmqoseJRe9S0xlSd4jR5Ko37ypX/L1P/6gWeSgIO3mjHl57jdh5uiKuIt94AHnrtW8OUUFNwri/Levvqr/9UeOJGXJ33/rf21PIB7SfPYZKUPee8/+edI0X84SGBiISZMmYciQIejevTu6detmP08kYPlnFpvMSYUk6XYloUtp/KEktNnbvnkzsH278nXsbVdD/fry28UC1qefkpkiYDk+HDpUfRl8wNnUBx7BkLhdGAvKyBC+K2VbdyXvvw+0aCF8FwtRTJUMKAtjS5bQv7GoyHK7nGbMVZqq48eB3bstp1Pz8wWzKgdaS0BAAMaNG4fNmzfj66+/xsqVK3HixAksWLAA7du3x7Zt29C+fXv7uUs4/kVyMi0/+UR+/8KFwPDh9q+j1maf8c47ZAayaxewZYu6cxISyJ+ApaHQglQDJg7PPGCANiEvOFi7Qw4TejmG4/Rpkq2lrwRvRawZ+/hj/a9/5AgtL1/Wdt7Nm8J8oxwbNpD3w+zZdGxQELB2rfLxubnAe++F4tQpYdvZs+SaKsePP1rn/wUsZRLWTLOzaY70zz+pC5Qrtyt84qKiorB161bs2LEDQ8VCgiPYE8bsncewJ3TZ04B160a5v+xhK8CaPdRo24YOpTxlgFD2AQMsG4w99ZEPqJd84BEMifs1Y56ipIQa3LhxZAe8fz9NYSnZZBw7Jr997FhaXr1qud2dwliTJkDHjsrTqQ4IYyEhIWh+2xa8cuXKiIiIQFZWFlJSUhAfHw8AiI+Pxw7DhO/jGAKWV+/332ly5dw562PUaIGaNdN23+XLyQehUyfyPYiKsu+HymZXtWqlVqwAgoJQac8e+v7999bHNGpEo3K5UdelS8I6ExzPn1d//8WLqZ86fZr6Mf429DjSLrZSJaBVK8+URW/EmjEAmDfPsesUFVk3B/FfV7x+4wYZd5w5Q69msaJ83jxqLuXLkzKazZ+WlFDwC7OZmn58PHk/jB4N3HsvzZ/07g18+KF12a5eBWrWBFaurIHOnYXt995LFmsnTgApKTTvCZByPzoaeOghawu43FxhnQ0Dliwhc8T77iPjgAYNaP5FPKwQzwP/+6/sT+ge9NI0KfVLSsIYGx9pvb8awUkrWq9pr+w6YDRrJa4Rcy3uT/qshsJC6tWkpklKbNhAPXFeHvXWjORkElrS061nz5k9tBJaOpaCAvcKY/ZwstWcPXsWaWlpaNWqFXJychBy28G3du3ayMnJUTxPHK61sLDQb8K3an9WMgVx/vfR6zpOIP7fK5kdb91KIypbGjJnbZZSUynp5+zZyscwwVHOpPj552lK/PBhy7bfo8cd37f6Q4aQRnrpUvnrM6fuixepDTL/AoBMoAcMoNEmIyeHRoSMW7dgMU0PAK+8IoyGN2wAxoyh9WPHaFKGYxiManaXmpqKqVOnorS0FH379kViYqLN46WB4l55RbnpFhTQa10qwAEkvOzeTUpsxhdfCK8n5ldVWkpNpaCAAuJJeeUV+jACA6mJvv46ae5OngT697c8R9yMxo4FqlenuBKMiROFdTavKhYexYrrBQss4zV89RV9fvmFnl2ca1jJPDE3lz5Kc04NGtDvYMgBr7OaMa1oNXfU475ahTElrZ6Ovw2zVmrevDny8/PRu3dvdOjQAWvXrkX79u2RmJiIBQsWYMGCBRjLlAQcr8V4wtihQ4IzpdoZYBb15+RJmtoaMYJU2+KoNVLnU0dhZRKXLTzccvZbeqzW65eUCANHAHj3XTIwV6OqB+RtKVRy/fp1jBw5EhMmTEBliTBsMplgstGZiEO0pqWl+U3IVkefVa/fx9Z19rPooK7ioYfUhQZTGtGVlsq3HUeYM4fMkOVGhgClpwDIvDc+npyuL12itrZ8Oe0zmYDPPxfOkQYhURLExNSubb1t6lTSHIojnTJhbdgw4MABYO9eYd/KlaSRE6slmCAGkCZQjS8ex2G2bQPmziUZWMs8otLfzx6VK9Nr6ttvSTt09Srw+OOOXYtRUlKCKVOmYOnSpQgNDUWfPn0QHR2NRjYSiAUHW5sQLllCQfAAekV9+CEwapQQ+A6gpnzihPB9925aioOgsmsAQGwsMH06BbzQivg1tGIFuXDaYsgQyljDEFtV5+ZSiPlnnpE/V0l21dvna+ZMioTudvQSerRqxhwN1OENmjF7v4UKQkJC7kyES62Vlt9+X8XHx2PgwIFcGPMBPG+mmJJCf2jm7ySOaqNkKiiFzXQHBNDba+FC6/ChjviJyMEak3h2XWkw6Yhm7KOPyC55zhwhE+SkSUBMDA3Gpk+3f43z5ymnhkZu3bqFkSNHokePHoiNjQUA1KxZE9nZ2QCA7Oxs1KhRQ/N1Od5DamoqunTpgpiYGHX+geIRjj0KC6n9TJkitMeEBArlfvfdjhVYSoUK1J/UrUvL9evJecRkEkwoX3qJ7lm2LOWCkQpPrkgN0auX8mTKp59aCmIMW1EAPBUwyA4DBtBPfeYM+cvIRZVjGoD331d/3awsutalS7YtPG/eJH8dPejShWRx6TP8849y98qieDvC9euCj9ODD5JmyVkOHz6M8PBwhIWFISgoCHFxcUhJSbF5zttvW28bPJjqbM4ceo2//joFkRNTpox2Za0jgpiUP/9UlzTZlmCrJIi5k1mzPHRje0KPVo2TWp8xR80UlXBGGFM6V2/NmIM4aq2kJ9w63rV4XjPG3j5sJlhMs2Y0MPrkE+Dnn2nq8P77ra/DnOt/+IFsB+TQo5Fcvy6YQYoN15VQEsYKCsh08q67qFzi8Kks7OuoUbTs10/Yp6W3ZlOb58+TKZT0zSnBbDZj4sSJiIiIQEJCwp3t0dHRWL9+PRITE7F+/Xp0FhvYc3wKR2bRNfXQ166RvVBhIU2Xm81CPhi98w4yrZE4CaferFoFPPecumNnzNB+/bfeUt5npNQgIlhgyRUrKBo2QNaY4jkc5no3bhxpAkpKSDZXym/18cfWUf2U/nb/+Q+VQW7/W28BTz4pn9Lu7bfJnG7rVuuflr1ebt0iud2Wi8avv1qex/z9xeTl0bNOmmQ7dSVAQq1SoDc1ZGVloY4oUFZoaCgO24mpbiu0PXstcfTHnueEx1Brpqh1tK42mqK98tjbrgZ3+Yw5UEZHrZWk7gvOuo7k5NQGUAsXL2YjLc09AqAjeKuLjOeFMXEuLjlP4c8/JyFM7IV76xaZFl24QDPgDLEZj5Sff9ZeXjnYm5n5ytgKiSQVxn74gd7AN26QWZKYefPIXkIvxwP2W7doQfe08xbdv38/NmzYgCZNmqBXr14AgKSkJCQmJmL06NFYs2YN6tati1kem77juBrxLDqAO7PoNoUxLVy/7ltRAZ99llQBbdq4/95u1oxdugTce28kNmwgwaptWzJJu/de+fEFE8QAcsdj1p5//005UBkmE6V5/OYb+mtI54xKS+XDq//0E5mmjRpl6SoszjJw/jywbx+N7Xr0IEXje+9RGZo2FfyMdu4UZPcuXSjvl1hZWlRE11i3zrYgJn6myZNJuLt2zdL1efZsCjCxZg1Zn7/7rvX5YoEuPNw9M9LiwUvDhiYAGgPqcJymuPgq0tJkAiC5GlcIN2K0mi/au78RzBTtmVgqobEx27JWCgkJsWmtJHVfcNZ1hLk3h4SEIDIyxPbBHsTILjK23EY8L4yJfaPEXrpipOGQPvjA8m3vTliYpwsXaMrSVqxdqTD2/PPy0eYAdeG/tfDpp6Rly81VlZX0wQcfxDEFs1AWxYfj2zgyi64pnvc991h+N6S3ukacTTLlKFWquPV2LKjD7XkaC6ZNI3MzpXHGjz8K65mZ1vu/+YaW5cuTfLtgAQkwn3+u7OorNgesX9+6i5s3z/J1IrbkGTCANGCrVslfG7C0PP/6axKgtMCCVkycSNo/NmfIrsOs8uV4+GFt97JFaGgoMkU/elZWFkJDQ62OEw9epHJ+//7aU+lNnWoZJMMeFSvS/4RpLZctI8OYsDDL+VbG8OG2ozyuXEnuneL0VHL89Vca7r3XGAO3iIiqiIwUApC53N+XoZcwZk9jppcGTAlXCGPlypGqWDrRzyYoXTgRZ1RrJW6u6Bo87zPG8hRp4aOPnC+Ls7A3pq2Zfum/NtAB2VcuSqMavvhCCMHNAhdwODqQlpZ251OkZBbs45ydNevOb+AJrgUFWdSDq5GmWRMzYQIJNkpdVUEBmSHGx9sPSPG//5Eyf/duimuixiUxL49iNol9xaTzemIL+P37LQNdStm3z9JOT6sgJmbOHMrAYDIBL74obHdFjik5WrRogfT0dGRkZKCoqAjJycmItuOMJn1d2HptrVxJwltpKb3upk2j7U89RRENjx2j8PLR0ZYZJcRp+gB6pT/0kPD9+efp+113CdvE5pP33SdsYwE2ypShMpjNZD28bRv5k9mjoADIyKB5Vnb+66/bP08rtxUbikijWBoGvULc651/TIorhLHAQHL5kPrvPv44pUgS56Z15j4yMGulvXv3olevXujVqxd++uknJCYmYvfu3YiNjcWePXvsRkfVC1+YOzUynteM2RtIhIZah8x2k8OiXRYtoqhmSkg1Y44kE3EmmABz1ObCGEcFjsyi4+GHyX7Lz7i7QweA/Q7vvmvbv8sFVOnc2aIeHJlF1xLy3F7cnv79rfMvidGiyPv3X0tTRjV88oly3nEAGDlS/bWef76BtpurZNEil1zWJoGBgZg0aRKGDBmCkpIS9O7dG401JhxnwtjChYJAuXWrvHDxxhvk5iwOBb9mDS3NZopDtX07CfcnTpCC4f771b3mCgpIcyoO1hEURIYyCxbIa+9YqPx27UjQb9CAhMc33yTz14sXSaCTxg96/32KbmiL7GwgRIO1VoMGyvu6drV0HXcregfwkOJolEUj+IwByv72t/OyyqJDNEWjWStxjZhrca9mTJwdUS1iQczNAx67vPii7fBReuQZYwE9HOHjj2nJhTGOChyZRbfI6+d8AShfmFjbxnxKmTmgdEpdb2zZj4kRt22ts6POcugQRb9wAhasZdGiRUhOTsamTZtwQhyXXIJYq6N8TaeKxHERUVFR2Lp1K3bs2IGhQ4dqPp8FIRFrPh95RP7YMmUsBTExJpOQBrC4mLSeX32lzTiGzeeKM8xUrUpLcawrRqVKtG/vXkEYCgigoMS2tKP2aN9ePoOFLWzF3HHUAEYX3OUz5mqhyxWaMU9dx8D4wSN6BKeEMc1hsB0It36HqlXlvZ2NjDRIh6fgwhhHBeJZ9O7du6Nbt272Z9HVTDjIha8Tc+4c2aRt2kQqEbHTDBM6evSg/GBO5NCzS3Gx4KQktpmSQ+wrx0ZlSuEA9aR3b6BVK+cGHtAe8tygwRs5LmL+fCE5OXt9iE1V7QTnVYRp2ZjwMWCApSmiPaR/e1fO1nfooOxXqJS8WUp8vLD+2mvA0KGkVaxYUdgGGFTroHekQLXXsyekKeGK0PaOotZUk8O5jcNmig6FwXYmfPXVq46f6ylcka/IEfTQ0HH8gqioKETZMr2VomZKd9EicvJISCBfxvvvBw4eJHuj4GDy0Jd642/bZh119Mknafnbb+TI07+/ctAfRwgIEGyF7r7bMpn1zZs4tWULIpKSyBmGBfJhXL5MEShsxQV3lv37ldUOGlEbrIX5o/31V3kA91jt57geT/glPvroddSrR754LOaUWPPpiPszIGjZlHwQBw2y7Z/49NPk/9epE3UpcjnR9IIFYJbLXqF2TC0OFh0QQHG1ABLGCgqE5uxRYUwvIYohfRhHBROjmCnqiSGlbo4RcFgYcygMdrNmjgtkDRvSLLkaexkj4slGuHo1wDO0c1yBdOT08cdAUpLwfe1awdt+6VLy1FeTODwmhpbM619sDty2LUV4AKyFsdxcdddX4sknyfmoa1chByIABAXhZpMm5CcmJ4y5w+HDA5EbmV+aWk2AqzlwwHMBLD2FNEyzu6LssVc1CyRXUkLzAVu3Oj52lWrGpCxdavv8p5+mcwMCgCNHHCuDHjjy/HLn6K2QcQhHhRu1YxpHNV3eaKboqutxfB6H/71yM6tZ0kAbsIy8dtWJP2he06Y4qcWe4f/ZO/PwJqr1j3/TQlksOzSsooBAQUAFvFYQtNAilAqyuXCRVqDiQmVRFFBUBFRQL4pbK4p4f7iBUrxUFKiyiwiCKAZklT2FQstaWsr8/ng5zGQyk8wkk8wkOZ/nyZNkZnLmTN5ZznvezWI4tKR1ChDFZ84ENfMaJ4KQj6rYSLlzZyr0JC+6rFdRuu8+slB5K65cUkKvGjXUt1m2TH0d0zZsNsqbreZyyAJTWrTw3B9P2/zvf2JWA0A9f3sA0ZqshaHn1h2o0N6EBDKqcoILc1MsK6PLe8IE39tSij/zxt69VFdO3h8zMWqsLY+BsxRGJfDQixluikYfYxi7KVryXA0DAj4vEx8ff/VVVVY9XA/VqldHU6nV7ZprDOhd8IhfudK0fVeMjXWRA4djGPJRlfRhY8Tkic1GljBvD7Hy5cWRnqe2lLhwgSoIS1Eb8Q0ZQtY6LcqYJyWrf396v+MOz/3Wk65NBz4la9HAkiXAlClkjPfEtdfSu8L8nQvSRBGrV/vXNzWCbZ1gGf5CBaky5i/eLGNKXH89uSVaCV/OGU+WsZB0U5Qv11tnzNd+BTrhiD8YZU3kRBw+P4b0zqwC8O9EjI52vQN6yqNsRUaNMm/fwSpqw4k85KMqS4wuVFAbQVWs6BrcAbgGxciLznuKC3vsMe/9YNH7paXAypWmxHT6lKxFA7160fvAgcrFehnr1wOffuqua8qTx65bJ9Z+kscpnTsneqtK0Zr1eeNGqlF16RK1f//92n7nCW+5agDPGbFbtfK/D0bDlDEjTlM2H/vww/63ZSZGKfBW0B9UMVqxsLKbotFY8flnEJY+Z0MYn8/eQM2sqhIV5XqxyWM2OOqE8Y2BYzKssDhAigW7UwdLwfjzT2DBAm3bSu8fGzZ43lY68h83Tnt/XnvN+zas8nG5ctQnuUL7r3+RlnLkiPb9+oDelOcVKogyVavBJH1Q79oF7NmjvF2DBuJ82o4d4vKHHwbOnPHcj8xMysJXubJyivUqVUi/VoI5V7zyChlcs7LEPn/+uWvhaIBOY1YfTC1WLT1d/LxhAxUMfuYZYNAg5e27d1deDlCRYl8zFQYKIy1jFSrQo1vLZWJl/I0Zk9c1tuQjOtB1xrxtH8yiz8GCazIcFXw+e32aWfXXMiYt9syVMe34k9CAw9FK167iwyZYo4vWrYEBA7RtK31YezNh+JouTureqHVmV16waPx48t+zWIxsTIwoU+ZlCagb3itXpmxx27ZREk01WrQg5Yjpx9682d96i+pTqVGhgqvxknmLvvcesGYNhf2pJeGsUwdISytA8+ZkpbPZgGHDgMJCSl7BKh5IQxjl4nztNeDVV9X7J9V7t2+nS+XzzwFW3/XECeDAAeXfmkGDBvSut66WGhUrhsa42RMRETPGMCqbojc3Rq3bq2ElRUfrsXI4V/A5myLgQxpsf5Ux5mxfrx5QXOx7W0ZTv37AZ7H9wgoRz5zIwsyHzuTJFLgkRz4CfPhhoE8f5TZ8ZgIdNwAAIABJREFUvWZ8GWW+9x7l62ZI/zsLPbyZMjZ+vGuGfW+hem3a0GvoUKpg8L//uW/jZw3rq/0oLSU9et06YNEiYOJEelR8/71Y8+mLLzy3M358PuLja7ksY/lc1q8HiopofovVoHr+eeDjj7X3U3pqMbdEqYtkbKyokAYoZFAXGRmULFTN0heJ6C2ZpfYbK+kPbnibTJLfm7xNxBlVZywULGMWum9zQoPgnr3+KmN2O3D2LHDwIJCcTMv9KSRtFHK/kmAUf9WDt9ESxzLMm6fu2mVJ5Gm22bkWyHpb3njmGeXl8of4Rx8B99yjvK2vD3YtljE5VatqCzYymfLl6f4ttyrpGVA+/rirZ6sR7NpFro6HDpHou3WjCgQTJ9L6ihVdi+/6Q3S0u6OBmuOBv3kGli51v7zMICqKlEUrjXXNRqsMvXlrW9oyZlRCjlBIbW8UltauOVYmtJQxgLIoRkdToHxBAaAn6FwepK8XNSVLrux48lExA+Zbw7E8aWnK4poxA9i6Nejd8Y48kKZDB+CFFzz7kQUabw9lpUCjQOxbz4M5BB7iTBkLRj6gP/8E/vlH27bNmpGrY1wc3XqD7QigJrrXX1e2JmkV9d13U+1xjvXQOu6XKmMhGzPm63qGmjJmdLHpELiHcjhqhI4yJr/72WzuU5KzZ3tuo6TE9/0D5J+iBHv616lDI+aRI9XbMMNioCXDG8cynDzpvuyZZ6xbY+mfefMo+AWg6/LFF8VAEzNg94py5YKfqdCI2Vk/SoAEktmzD2HYMFcXxUDRurWY/t7qqI0BGzYEvvxS/F5QQG6O3Gs89NF6mXsb8ljBmOMVrUpOsAonh4LSpebCyeGoEDrKmJYnmFknPOvbihVAu3YAAMfvv7u6S91zD5CaCvz1F3D4MClsGzcC+/YFvn/8RhASmDk7Onv2bNxxxx3o06cP+vTpg1XSKqteON+xo7VyckvPd+nn9u2pGPXbbwe/H3pgLthSatVyXxZkmje/iDlzxAHkjTcCTZuK66+/3px+mY1WMdesSYWrOaGJL0q02j09pCxjRiXw8HX/WpcbgafakByOAhcvGjPnGzrKmJYpJLOUDvaElboxli9PPjMsQUB6OvDtt8B111HCj/ffp9zK111H6b2kGD3wConpN4708vjrr+DvPy0tDYsXL8bixYv1JeaxGuXKUe7zZcvo+5gx9F6xIqXTa9/evL5pQek+xiyPFuKPP4Ddu+nz338Dv/1mbn84nEDCyiIAER4zphe92RSN3r9WDhwAFi4M7j59ZPXq1ejRoweSkpKQnZ1tal8cDlN3r4vLl+l0e+ghbadXWRnwww+U3HjPHuD8eXKwO3AAmD6dhhMVK9JEjc1GebicThq/6b2m/cqmqJtQt4wdP06z6g4HkJJC0dWxseSWlZEhZnvUyxdfUC0jFiz022++twWQlUI6mueWsZBAenncdRdd1BwfsNlcY9befJNewWLqVLJuSX3UvBHi16gB9aJDlhAXHccLiYnAjz9SuDpDq5XMm8cxq4dn6eoz8hPcKEuX0TFj/tKokf9tBKHPZWVlmDJlCubOnQu73Y4BAwYgMTERzaSzBTLOnQNatYrHTTe5x57bbMAbb5DD1htviMvXrSNbwg03UDkOVhvwxRfJntCuHc0RDh5My+fOpUid4cOBY8co6XmrVkBentjmn3+SMrNmDfDkkxRTm5FB+3Y6gTvvJFvE99/TcHvIECrDuWoVlRMpLKTUD//8Q14ZVavStbh8OWW3pRIq8W7HX7485dk7e9b92OWw4XO5cu4lQL3x73+7fv/xR7GsqDeCq4z5g5nWnUWLKAamdm3XlNlpaeLnDh3820fVqvR+ww2+B0tER9NZ3LYtZZ7khBTSZ5wRBVb1Mn/+fOTk5ODGG2/Es88+i2oqCWscsqmw4uJit2XhALuls2PTfJz9+gEA4goKILdxlzRsiD0KbTS+cAGVZfsDgBvKylAOwN+7dqFMKZiQYzpcGQtvXnyRBlXScG+tw5GPPhJLICjRsSPVzWMDWkuhV1nSa/nyptTxC0uRbdu2oXHjxmh0RXlMSUlBXl6eR2WsdWt6V0oCJgjA2LHuyzt1ovcTJ4CffxaXX7xIZUrkpKe7fj96lF5SbrzR9ftXX9FLjRdfFD9nZKhv543SUnppgdkx9CpiSnz6qVWVMXbxVawILFkCdO9O3ydNAqZNE7c7dMg9jZQvlrH//IdcA3/6ib5Xrkx2Rr0YlRdZC/JjGDfOdbpCzhdfUBGfc+fozs6oUEFMe2bRhAAcV3x9ZmklLS0NJ06ccFs+evRoPPDAA3jsscdgs9nw1ltv4dVXX8Urr7yi2E58vOvMk8PhcFsWTrBj032cCu7GMeXLK7chGem5rL9y32vevLnHolObrZADPULhY8bwxh/5esvXZbMBmZm+tx9QjI4Zi6TsiAH0O3U6nahbt+7V73a7Hdu2bXPbTjqp988/4ft8tjJ//XUWDsdBTduaEzOWk0PFYD75hOyTU6fSuv/9j5wxGzSgujvvvCP+1hdl7L77XLO66ckNHuxqm2oX7+uv0/QaQAlAlGjVylURA6jSaVkZcOYMKaEcyxNoZeyTTz7BkiVL3F7du3dH7dq1ER0djaioKAwcOBB//PGHMTsNdfyxyCvNVKoJs00b3/fDMZVQHjNytOOtgLMvGNWOPwmYvBKog/XWbijGulvoZhAfH3/1JadGDfXfvf02MGAAfZaW2ZFbvpR49lngv/8lV8Xu3SmuaulSGtbXru267fDh9D54MKkEzGZw993A6tU09NXD9dcDc+YAQ4cWYN8+cnsMdCxmlSrAc8+pW+1uvDHWoxykmGMZYyes3NbZu7f4ecMGem/aFOjZU7SZekLpQmjZUvx8ww3kcFpSAly4QBa6jz4Sz4rHHwfefZc+t2oF5OcHLw+xp9mjhx+m15kz5Lj7wgvAgw9SsKmniz8qilvFQgjpjaOggGqLjR8fnODu/Px8xF2ZgFixYgVuiOQgIEZuruv9Qy+PPELO9lLUhPn224DJgdgc37DQ+IsTAJh8pZcuiypg9OxJg05f2zaCtLQ0DBs2zLgGjU7gYdb+wwy73Y5jx45d/e50OmH3EpaSlQUUFx9C3boN0a4dOaN9/TWFNbP8cVIxjRoFrFxJ0TfSIeTHH7u2W1BA6ytUcF0uj50CKAZMzocfip/PnHFdd8cd5Biml9tvz8d114leKSxpR2mpGKOphnwY/uijlGuPUbs25eVjKsPp0+K6OXNoX//8Q8nTf/+d/netmKuMaeHuu0kpqlPH+7ZK7T77LClyLDvcY48Bs2aJhZqHDSPL0YMPAiNGiMoYI1izM1r+mypVyIqodXtOSPPMM3RKVqkS+H3NnDkTO3bsAAA0aNAAU6SxkZFKr17+/V7p2lQrwC5/mnFCBn4Ljjyeesr1+3ff6TsP5KntLYnRboV627P0n2Mebdq0wf79+3Hw4EHY7Xbk5ubiDU+hLCDLjcNxBsw4w8YUZ85QmoHvv3f/zZ13eu+LBSqueMVmo5e3R6w0GqldO7Ls1avnuo2nWP7cXOC99ygPjB4nPIb1lTFAmyKm1m50tOtZ9eabZHKQWrweeICsctJRL2vLqBuCt9mhSPSr5rgQaDdFT8ycOTPwO4lk4uJoUsnLQ5PD4VgXPfdiT0aqQNzTfUnAxJISyZMVAUDF/ftxPYCLJSXYK12+dy+uB3ChuBj7JcurHTmC+gAKCwtxVLKcJTJyOp04KVl+7fnzuAbAP/v347xkOevL37t2oUwS46zUR63LpcmX1LY3gtrHj6MOgOPHj+OEpP26p06hBoCjx46h0M/9litXDpMnT8bw4cNRVlaG/v376/ZkYUPq0lIqrRZJ5dWeeIKyl06ZQtas4cNpGD1vnrgNU6YuX3adeOneXX3Ifffd9PKV4CpjycmU59Lo6qAJCZTuRasrI7OKSWGK2HvvkfWMEazZGVaQJFhukRzLYaYyxgkwX38NzJxJOYH1wIVvefh8WHjjq3xZFrVatcidy18CkYBJnpTIJa7l3DkAQIWKFV2XX/HNqlSpkuvyK/e26tWqobp0+ZUAJXvdurBLl1+JZW/cuDGgEE/TvEULRdOLWuyNp+VKyZcCknTqipZTp3Zt1FH4D+rVrYt6CvvVm4Cpa9euftUCnTyZzmtpQvBIQPo4fekleu/bV93CJx3+79pFeQU/+ywwfQuuMvbUU/j7ttvQvGlTY9tdt47ebTb6t+vVo0IHvsCmCAYMoCyM/j5plRzOlWDKmNb98UFa2MGVsTCmc2d6+Qof8VsWb6Jp04Ye5JzQxtf78LZtwEFZQjVfLudPqIiSVwYOHIiRI0fq34Ecb53U+4dEgptiiNynY2PFumGRjlZXS5aPK1AiDu7ZHhWFMq0uh3pgTqFG0KQJJfhgEYjBjhnTu78gXfxGV3yXB2tGCocOqa9TerYJAlfGOBwrY7OphwICFMgtr6iyfLlr8DrHurAEBtLEzN6oVQtISqLP9etTcuhAkp+ff/WzYQmYjAqZUHuABbroc8OG+j0RjII/tMOGP/+kUsOBJgSnHoJA+fLGuQ1qjQVj+2PK2M8/U6p/b+0GAVbxfc6cOcjNzcWSJUuwe/dun9oqLKTSb1WrKv8lf/8t6tZKh/j557RuzRpxGVNY1q93r9uyaBH5BF+4QOvU5gKWLAHkrtxSRUipdIrN5p6uldGoET28r3h64O67afsrdRoBAAsWUDHR33+ndVLvWMb06a6BpUeOUAYkaXjj228r94HD4QQem42cM1hZR6X18ntd9+5iRi6OtWnbFpg/3z2TnBzpPfnEieBWx5k5cyZSU1ORmpqKDRs2YMKECcY1rjZ20ap8ycc2/u5XKxs3Aps2+deGXsaMId8/X9IAcixJ69auY7BA2T+C66YYSlStCtx7r3EVGb1J8Lrr6J2lwr7tNs/bBzGboi8V31evvgatWnlv22ajjIGDB1MW8RYtxHVRUcC111KNCgBo3x5grtVdutBv5s93b3P2bHJH//lnqlcBiKXaTpygQM277wYkdROv8txzlNZVLVPsli2UOWvSJPpeUAC0aqXue+6pssCgQa7fP/3UfRt5vgel2dknn6TX+PHc9YDDCTY2G1COP0nDmgcf9L7Niy/SGFxvfSQjCEgCJqY8bd/uXzu+Tmz765UkT4UXDKpWBebODf5+OUGDDbkfesjYdsPzEZKQQCaRSpV8byMqCvjmG//7otWCVbOmPmtXfDxVylPSKAxGb8X3XbtiMHKk9rjADz9Ud9lhihggKmIMJUWM4Ukp8RS0OnWqWD1AiZtvVl9nNjNmAGlpxmeI4nA4HI47P/wgfs7MJM+LwYPN64+hsERrVxJtXEVtnKKmdHmzjGl1Y4yO9pxbnMMJIkY7p4Wnm+L//R/w229A9epm9yRwFqyXXiIft9tvN7ZdP2BVxmvXdlXEpO53zZsD06YB+/apl3Fatgz46y/1/fzyi+v3ceOAjh2peKHT6bqueXMqXgio/1U336z8AO3VC3j+eaqxrcTBg8COHUCHDucwfz65Qn77La3zFl8wbZrr9y++UN9240bX7+XLk9l81ix31xlptfeAZIviBI5ly9xPhG7d6N1btUpOxLJ06VKkpKSgZcuW+OOPP1zWZWVlISkpCT169MAaqW83xxCSk+kFkHV0yJDQzDuhCMs6LVeAmG++PJsfW868fBjs9/I/Rk1JY2ZIedbrZctoxlHOnDn0oOZwggB3U9RD5crWM2EYLcHy5cXcuQFGb8X31q1dv48ZQxUDAGDnTnF5bq74efBgShn6009iWbhLl+h79+7A3r1Aaiq5HlatCqxaRbp227bu+798GVi6lBSidu2As2fJ63PGDHIxnD6dtrvmGorp+u03+v7f/4rPhbVrXSslsDSohw4B1aq5uh9++umBq4pPaiq5MrZqReljmYUuJ4fWSZ87Eye69rtXL3LFnDEDaNqUxt8sDtvTLMzgweQuWbOm+jacEIBF/Ev55BM6+YJR+ZsTkjRv3hyzZ8/GC7JZo927dyM3Nxe5ublwOp1IT0/HDz/8gGhePoWjREqK6/eKFck69vLLrsubNaOsBi1bui5PTaUSHvfc47q8c2cgO5sexlLUlLEPP6R7nrxKb2IiveR4KuhmFUaMAD74QH0GmhNyGG0ZC09lzErcfjsl4mjc2Oye+Izeiu/VqgG5uXvw9ddNkZJC9+577nG3WkmZP9/d7TA6mhQxgJJcSl3Xu3RRb8tmc73nxcaSEgeQOKZPJ3eSI0cocab0d6tXkzukvGRdYiLQr5+2jFo33UTvr7xCzzGlsnZKVKlCCUz0EhNjjns8RyP+1FWsWNE1kJJjGZKTabLebJqqlIrJy8tDSkoKYmJi0KhRIzRu3Bjbtm3DzVabqIxAevakZFRyfcM0zp5170xUFM2CKiGfcQXoAdqvn/vyIUPIwi/PbNilC2Xiki+vXFnMIx4IFi0KfpbFW27hGRbDBDZB7imDri9wZSzQjB9PN6jmzc3uic/4UvH9+utLribNAGjCzAru3jYbKWGVKpFbiTzw/o476CUnL8+3fWlVxDhhyqFD3KoVpuTkAJKM4pbD6XSincQaYbfb4fQ0I8YJGnPnkqu6PBzLNK65JrDtKyk/L70EDB0qjm6DhTQ1Hoejk4QECk8xekjPlbFAExUV0ooYw9+K70qKj1lwKxInaOgpTsQJKSpVCp7DQ1paGk6cOAEAuHDhAipdSU41evRodGfuA37gkNX1KC4udlsWjph9nBHwF6sTHR18RYzDMYBAOKtYZHjM4XA4HA5HiU8++eTq582bN6N9+/Zef6Mn1lee7MfhcEREAiArH+dmefpgDocTttgEIXCOrPxmYl20PMz1wGVtXYyUNZezdeHXdOSgJOshQ4Zg/PjxaNOmDQBg165dGDduHBYuXAin04m0tDQsW7bMLYEHl7N14dd05MCf05GBmpwDqoxxOBwOh8MJHMuXL8fLL7+MkydPomrVqoiPj8dHVwJ233//fXz99deIjo7GxIkT/XI153A4HE5g4MoYh8PhcDgcDofD4ZhAuJQn5HA4HA6Hw+FwOJyQImjK2OrVq9GjRw8kJSUhOzs7WLs1jKNHj2LIkCHo1asXUlJSMG/ePABAYWEh0tPTkZycjPT0dBQVFQEABEHA1KlTkZSUhNTUVGyXFMlatGgRkpOTkZycjEWLFplyPIGEyzoyZM3lHBlyBrisI0XWXM6RIWeAyzpSZM3lHCJyFoLApUuXhG7dugkHDhwQLl68KKSmpgq7du0Kxq4Nw+l0Cn/++acgCIJw5swZITk5Wdi1a5fw2muvCVlZWYIgCEJWVpYwY8YMQRAEYeXKlcKwYcOEy5cvC1u2bBEGDBggCIIgnDp1SkhMTBROnTolFBYWComJiUJhYaE5BxUAuKwjQ9ZczpEhZ0Hgso4UWXM5R4acBYHLOlJkzeUcOnIOimVs27ZtaNy4MRo1aoSYmBikpKQgz5cquiYSFxeH1leqzsfGxqJJkyZwOp3Iy8tD3ytFBPv27YsVK1YAwNXlNpsNN910E06fPo38/HysXbsWnTp1QvXq1VGtWjV06tQJa9asMe24jIbLOjJkzeUcGXIGuKwjRdZczpEhZ4DLOlJkzeUcOnIOijLmdDpRt27dq9/tdjucTmcwdh0QDh06BIfDgXbt2qGgoABxcXEAgDp16qCgoACA+zHXrVsXTqcz7P4LOeF2fFzWyoTbsXE5qxNux8dlrUy4HRuXszrhdnxc1sqE27GFs5x5Ag+dnDt3DpmZmZg4cSJiY2Nd1tlsNthsNpN6xjEaLuvIgMs5cuCyjgy4nCMHLuvIINzlHBRlzG6349ixY1e/O51O2O32YOzaUEpLS5GZmYnU1FQkJycDAGrVqoX8/HwAQH5+PmrWrAnA/ZiPHTsGu90eNv+FGuFyfFzWngmXY+Ny9k64HB+XtWfC5di4nL0TLsfHZe2ZcDm2SJBzUJSxNm3aYP/+/Th48CBKSkqQm5uLxMTEYOzaMARBwKRJk9CkSROkp6dfXZ6YmIicnBwAQE5ODrp16+ayXBAEbN26FVWqVEFcXBw6d+6MtWvXoqioCEVFRVi7di06d+5syjEFAi7ryJA1l3NkyBngso4UWXM5R4acAS7rSJE1l3PoyDloRZ9XrVqF6dOno6ysDP3798ejjz4ajN0axqZNmzB48GA0b94cUVGkw44dOxZt27bF6NGjcfToUdSvXx+zZs1C9erVIQgCpkyZgjVr1qBSpUqYPn062rRpAwBYuHAhsrKyAAAjR45E//79TTuuQMBlHRmy5nKODDkDXNaRImsu58iQM8BlHSmy5nIODTkHTRnjcDgcDofD4XA4HI5IuUA2vnnz5kA2z/GD9u3bG9oel7V1MVLWXM7WhV/TkQO/piMDfk1HDvyajgzU5BxQZUxpxw6HA/Hx8YHerelY+TgDdaFKZW3l4zcaKx9rIGTNr2nrwa9p47D6cfJr2jisfJz8mjYOqx8nv6aNw8rH6UnOPLW9gQweTC+ONZgzB6hf3+xecJxOoHx54JdfzO4JhxM8Ll0C6tQB5s83uyfBpUsXYMIEs3vB0UWDBsCHH5rdCw7HcJ5/Hrj9drN74R2ujBnIZ5/Ri2MNRowAjh41uxecH3+kgemsWWb3hMMJHmfOACdOAE88YXZPgsuaNcCrr5rdC44ujhwBMjJcl50+DRQVmdMfDscgpk4Ffv7Z7F54hytjHA6Hw+FwOByRatWA6tXN7gWHY0m2bwdsNmD3bmPa86qMTZgwAQkJCejdu/fVZYWFhUhPT0dycjLS09NRxGdPOBaG5wu1BlwOnEiEn/ccDocTXnz6Kb1//bUx7XlVxvr164c5c+a4LMvOzkZCQgKWLVuGhIQEZGdnG9MbDocTdthsZveAwwk+/LznhAR8toDDMR2vyljHjh1RrVo1l2V5eXno27cvAKBv375YsWJFYHrH4RgAf9ZwOJxgw+87HA6Hw9GCTzFjBQUFiIuLAwDUqVMHBQUFhnaKw+FwOBwOh8PhcMIdv+uM2Ww22Dz4YzgcDpfvxcXFbsvCB6pt4HA4wvw4Qws+Q83hcIINd1PkcDgcjhZ8UsZq1aqF/Px8xMXFIT8/HzVr1lTdVl58zcoF2YwiPj7e0scZjOrsv/9eESNGAHl5QIUKAd8dh2MIO3YALVrwgTSHw4kQvM1WCgK/IZrA0aNHMX78eBQUFMBms2HQoEEYOnQoCgsLMWbMGBw+fBgNGjTArFmz3EKJOKGHT26KiYmJyMnJAQDk5OSgW7duhnaKYz0SExORmpqKPn36oF+/fl63f/HFeli3DmjfnmpMmQm3jLliVoZUq8th+XIgPl7MksThcDgRz+XL3rf57Tfg11+BggJg797A9ykCiI6OxrPPPovvvvsOX375JT777DPs3r2bJ9CzGEaNa7wqY2PHjsX999+Pffv2oUuXLliwYAEyMjKwbt06JCcnY/369ciQFwvkhCXz5s3D4sWL8c0333jd9uJFmknbvp2sDV27AlfCDDkmE+wMqaEyqfrXX/T+22/m9oPD4XAsQ1mZ923atwduvZXcCpo2DXyfIoC4uDi0bt0aABAbG4smTZrA6XTyBHphilc3xTfffFNx+bx58wzvDCd8KCkRR+DvvQesXm1iZzgudOzYEYcOHXJZlpeXh//+978A6AY/ZMgQPP3002Z0z3SsbsHTw4QJE7By5UrUqlULS5YsAQDu5sLhcLRToQIwcybw1FPet2XJ3G65hc9qGcihQ4fgcDjQrl07zQn0Iitfg4j7cYq5HIykoKAOgNo4fjwfDof/SQz9TuDBiRyGDRsGm82G++67D/fdd5/HbUtKRKPr++8HumeeCafBdaDgGVJDx4Knh379+uHf//43nnnmmavLmBU0IyMD2dnZyM7OjljFOxjw+w/H0mg5QZ9+Wpsyxtiyxff+cFw4d+4cMjMzMXHiRMTGxrqs85RALxLzNQDqx2n0sdeqRe9xcXGIj9fm9uUpXwNXxjia+Pzzz2G321FQUID09HQ0adIEHTt2dNlGOvNQVNRCsZ3gzsyIMyLlywduL+E242R0htTDh6sAaIjTp0/D4ThsVDcN59ixGgDq4uTJk3A4nG7rQ1HO3ArK4XB0c+6c/22UlJBVbc4cYNgw/9uLQEpLS5GZmYnU1FQkJycD0JdAjxM6cGWMowm73Q6AbgRJSUnYtm2bmzImnXkoLVVux4yZmZYt4xETE7j2AzXj9McfQPXqQKNGvrehNXNmIDOkbttG71WqVEV8fFVN/TGDunXpvWbNmoiPdz9+K88s6smQqscKKlU+Q1EZ9QWjjvP06SgALSAIZXA4/va/YxxOoBEE4LnngOnT/W/r5El6nzjResrY008DVaoAkyeb3RNVBEHApEmT0KRJE6Snp19dzhLoZWRk8AR6YQRXxjheOX/+PC5fvozY2FicP38e69atw2OPPeZzewsWAB07AtddZ1wflbDZQttFqG1beg/GMQTyBh+O7n/hgjcrqFT5tLIyaiRGHWdhIb1HRUUb+r8FozQJJ0L58ktjFDFAfHAZ8QD46y+galWgYUNt2xcWAhs2AHffrbz+9dfp3cLK2ObNm7F48WI0b94cffr0AUAJ9TIyMjB69GgsXLgQ9evXx6xZs0zuKccIuDLG8UpBQQEef/xxAEBZWRl69+6NLl26+NSWIACDBgH16gFHjhjZS8/7lDN1KvD882TBKxdhV8HYsWOxceNGnDp1Cl26dMGoUaP4DV5CKCvwWuBuLsEl3M8nTogjPUGZNUsrixZRBkUl6tend6e7y7durmQV1HwxDRhARU7z84E6dfzfvwl06NABO3fuVFzHE+hZB6Pu7xE2DOX4QqNGjfDtt98a0lZxMb0fPWpIcz7zyiv0XlwMyGJiwx6zMqRafVBqVQte587Agw8CfhjZmnB3AAAgAElEQVSjXeBuLsHBqucTh6OK3pNWQ81RU2C+8VpqpOnh8GFg3jzg99+BRx4BEhPdtzl2jHzeL1+mz0wp5YQVRt/ffSr6zOH4CgtPqV7d3H5wOIHm8mVj4uDXrQOuGKZ1w+tEmofVJx84HADAL7+Iny9dCsw+PvooMO0CwJNPursbsqD1AQOATz9F+cOHgZEj3Y/vu+/07athQ2DSJOCrr4Bu3egi/+ILCvC+cIFG6PXq0fE+8wzQoAEwapTvx8axLEbf37lljBNUmGWsYsXg7ZMPiqxBpFkKnnuOLLCnT1OsuBnwOpEcDscjL7wgfs7M9LztN98Ar71G8VhFRdr3MXw41R67+Wbf+qjE6dM0q8se8BcuADfdBAweLCpja9cCa9eiXocOwKZNQFYW0LOn2EZKCv3e6aSYtLZtqRaP00lxZ/HxFNzepIlyH6JU7BnDh4uf33kHePvtyHsARghGiTWslbH164FOnchi3aaN2b3hAKIyFsjshgxPCTz4fTH4hIpSbFQ/r2SPR2GhecoYxzyCeY85evQoxo8fj4KCAthsNgwaNAhDhw7lBb45HrFPm0axVVrp35/ez54FatTQt7NbbjE2hmv1atebNUvKcdddbi4JURcuiF+WLnVtZ8ECCmTv1w/YvRtISnLflyD45/KYkqLfCseJKMLaTfHrr+l92TJz+8ERYcpYhQrB26enwXWoKAihTKgovoHqp9I5tmMH7c+gUExOhBMdHY1nn30W3333Hb788kt89tln2L1799UC38uWLUNCQgKys7PN7irHQtScP1/7xvv3i5+r+lii5N13/XvoLl9Olqu8PDHmQU6DBm6LKm3frt7moEH0/s036tts2SIOXnxh6VK64dtsfEDKUSSslTF/KC4GfvzR7F6oc/w4Xdcff2x2T/QRTMuYJ/QMvB0OIIKTC3J8xNM5xsI02IQRh+MPcXFxaH0l41xsbCyaNGkCp9OJvLw89O3bFwAV+F6xYoWZ3eQEmNWrV6NHjx5ISkoyXvG+/nr/23jpJXLtEwRg1y5KcKGH5GTKZNS9O5CW5n9/tHLPPcA11xjTlq8BwJywJqTcFEeMoGLueidWfJmIefJJIDub4jJvvFH/742msBCoVk0c4O3eTe8ffgg8/LB5/VLC0/+tpowdPkzH1LVrcPuj5dy49VbyynjyydCx8lgRboWksUd8fHDHEZzI4tChQ3A4HGjXrp3mAt/yItf+Fb6OV2zTioRTIfOysjJMmTIFc+fOhd1ux4ABA5CYmIhmzZqZ3TV3pLFWBw8Cf/9NroUvvEDxVgMHkjvjihXKboPB5tAh49pigzdOUDl/Hqhc2Zi28vKAsjL6HJGp7efMUV/XuTO5vkndn/0ZOP/1F72fOuV7G0bhcACtWpHiJY0LtSosdlYJNWXs1lup7piRA3Ym/7VraUJNaZ0Wzp6ld0FQ/l1xsQ1Ll7rGBXM4DOk5vXQpTawwSytX7kODMWOAPn2AO+80uyeeOXfuHDIzMzFx4kTEymp2eCrwLS9K7a3w9e7dgLcxfigUCLdyIXO9xb23bduGxo0bo1GjRgCAlJQU5OXlWVMZk3Klv1d5/HFuPeIYzjXXGDO+zMsjw6zRWNZNUe9ExLp11nYr9Mbhw8DMmconC1MMQyX+01N2XDYxGxMDnDkjLjeiAPTBg3QPZ/tn/2WPHq7bHTnium+tqF3Ir75qR69e5FYeCPLyyNU+FJk3Dxg6lD6HiuLBLXgcObNmUV4AXwjW+VRaWorMzEykpqYi+crsEyvwDcCwAt9ffAHccIN7HgSOuTidTtStW/fqd7vdDqcRBZfDlREjzO4BJwQxYqyqhCWVsZ9+osmSL780uyfBo18/YPx4cqOWEyqDWIYnZYy5Z61ZQzHAOif/PDJ8OPDee3T+eOKWW3xr/++/gZMn3Zfv309mvnvu8a1db3TvDjzxhOdtjh0zpqaV0aSlUcZhwPpKjhHXmSAAf/5Jnw8eVF7PMZ66dYEroVERiSAImDRpEpo0aYL09PSry1mBbwCGFfhm92x2nnNCC4fDAYfDgX1ffGF2V0zFMWaMefu+IgP24nAsqYxt3UrvGzYY055VB0BnzlAtotJSYONGWsb8UKWw/i9a5L7MijBLnhaMtCYxJdBmoyy0aplopZOFhYXkBnr0KJVY+fBD4MrYBQAwe7b4uVUr5RIJGzdSYC+z5q5YoaxUA+S3fOONVHaEIQhUhuHSJcr8W78+0KKFl4OVUa8ecPvt+n7DMZ6336ZzZN06cZmnazXUJlqsitMJLF7sfzv79wOtWsUb6oUQDBlv3rwZixcvxoYNG9CnTx/06dMHq1at4gW+Iwi73Y5jkoQYTqcTdrvdbbv4+HjEx8fj+oEDg9k9yxEfH+85g2KA9y19cUIXXmdMA1Yf6LzwAvCf/7gmKbJ6n7XQsKH2bcuV069Ynj5N7/LsuqydY8eo2K6UIUOASZPcXWsaN1beB2tLXgNTbqJmfWHcf79o0V2/HkhIcF3/4ovA9u2UDGTUKJL3K69Q3+rUoSyZACmHcjIzXZU4RmEhvW/bpnwsVsHKEwhGsWkTve/dq7zen+ub/X/hcI8wgiVLKLbfyDIZLMvlvHlAr17GtBmM875Dhw7YuXOn4jpe4DsyaNOmDfbv34+DBw/CbrcjNzcXb7zxhvoPsrLU140YQTOTarzyCvDAA1QQ2Upcey1w4AB9njgRmD5dXPe//wGpqa7b33uvcft+7z16kEsTDRw5QrOrnLAhUPdzS1rGrEAwHqDnz9N7SYnn7ZQGX2oDstOnQ8u98//+z/X+eOkScPEi/f+bNlEdppwcyrLYtSutq1aNXoWFwL59FM+xebPonjhkCLUr3098PDB2rLZ+2WxAx47K60pLSZk6coT6IUX63ytZqmbOFD9HRdF+Jk2i70wRU0NqpQPI/e+bb/TX3uR4xohrX60Nf9q+4Qbj6qUaxZkzrsnBTpwA9uwJ/H43baL7htbrWS9WmDi4dEl08eVwvFGuXDlMnjwZw4cPR69evdCzZ0/ccMMN6j+Qz2JIrWhyFz65ha1JE5rJHDDAv04bjbTG2LRp4kwlAPTu7frdaIYMcY1jePRRclmRK2NGxmZwLMOGDcCrr/r+e0sqY1Z4EAYT6eBF66y32n80YgRZZ4y2kuipX+Ipm6KcvDwgN1f8Xr48ULEiKSodO1IGs3vvBVavplfFiuK2NWrQM2HMGKBDB9d2d+zQ3gc1mJVDTkwMWagUaku6wdxPWb1Hf+ndW/x/W7UC+vf3v00OYYR89LahZ/s9e9TrnAabvXtpEqlrV1ISGddd5znLXkkJxV76CxvzvPceUKWKuPzUKVE5fOcdIDFRXFdQQGNKo8dCUpfoN98E/vUv+uzvcywlxbhUzJzIoGvXrvjhhx+wYsUKPProo543llu1pO5y8hvTggVAdLR7G7Vr03tKivfOdenifRs9TJpEweeMr79291WWz5bKvwcC9j+99x69y//L8uUD3weOTwgCxd6fOgWsXKm8jVyc7D6fkABMmOD7vi2pjFmBYLgCMSF68iTQ25d//qF3ZnUzAla/ZM6cOcjNzcWSJUuw20OtDE8JPCKNf/1LVMiMIDeX3B+bN6f4lnDm9GlSPg8fDs7+AmkR88TevUDLlvrrn5rFuXNA06ZU31Ae8+kticwjj1A8pFIiHDWKi4G2bYHffqPv338PPPWUuJ6VngCAmjVJObz2WnIDlibz+fFHisnUMntps1Hc58SJousi4K7InThBY6/oaCqvMm6c8vVeWkqK6KFDYnkPJU6epG2PHweWLfPeTw7HZ+SWMelARD7osNk8p7hOSXF10ejXjwYjzK/+8ceBVavowlRDEGgbNeSB1FOn0kUn3ac/rgOe+qbG+++Ln9l/tnWrazyB/L9UUmoVmDBhAhISEtC7d++rywoLC5Geno7k5GSkp6ejqKhIf585qmRnA7GxFNd/112uz7OjRwP7jLakMqZV+diyhbb94w/P2/kyQDLLOic99htu8F7LRQ6boTVSmZTWL4mJiblav0QNPZaxcEGSUdgNNlNuFHfeqZ4gJJyYP5+Uz6lTze6JfqTXn7d7yVtvATt30kS1t3uZmXTv3hQ2Gz2sAJKPHiZPBj75hD6fPUv3CaXx3cKF9P8xj4GZM+l/ad+evvfs6f1/kmayHDeO3BkHDRLbZwlWjh+nyXRBAGbMEBX/r76iCY9XXgFuu01sq0MH8f5WUuI69pMmbenYUbx3nzlD1vQKFShLcKVKdGwvv0yeTNOmkWJrswG1atGY80qdZksTaR4sYUc5WcoAb0pC3bpUjFkJQaCZBFZAsWFDUm7kA5GPPnJtj6WXZy4vLIi7QQOKUWjaVNx+61bgmWc899Ef9J7Qn38OjBzpvvzGG2kmiCH9D2rXdk0S4IF+/fphjqy4bnZ2NhISErBs2TIkJCR49VLylVOngGefjbyJdZa8jeUGkB5//frkdRqo+56pCTw++QRITyfBV6+u//cLF9L7t98qrzdCIVmzhpQiT4NtX/EmVC2F2jdscC1GHIggf6X6JdsU/CBZitadOysAaGJcB0KAH390YPPmShgy5DpT+xHqaXL//psGu61bi8uMvvk5HK4eOQwjrxmtfZa6r168SBag/PzgxIatXr0a06ZNw+XLlzFw4ECPmfa2bweOHIlRXb9+vXtJiQsXSAmJjiaZvvyyuO6nn8QyFydP0rPgyBFSYu67j5YrTUT5IqM333Rf1rkzKWhsXcuW2l2b5QXrlVBzcWZ4mmTbt09bPzgcv5BeTDExroqPzUYX/d13K9foUENrsGxiIsUo/PYbJQpRuiGnplLgNXOFrFiRZi5ee017f/Sg90Fz//36tv/rL+XjVKFjx444JJutysvLw3//+18AQN++fTFkyBA8/fTT+vqhgfHjgTlzgHbtKE+LXubOJRdrdi/Xw549NDGp0YAYUII54WSqMvbYY/Q+ZYryA9NM2H1q0iQquMtmTH/9lbJs7dhBs5j+oFXQ3opZnz4tukKbmXGNpWg10kUyVIiPj7eERVCeJnezhYKFPZ3vo0bRM5+5/CtNMBjBokXkzfLVV54neX1F6bo7f56sLuPGaWsjI8O1jEUgYK7Hc+fOhd1ux4ABA5CYmIhmKloCKzeiRqdOrt+93X+YIgaQa6EZSJ85RsSYRho8q2eII3XZuHjRdZ3NRkHJDRvSjdnXYFi136nNHsu3j5I5bwXypHvgAdcMW0bh7b/QQUFBAeKumM3r1KmDAg9BxPKJ2eLiYs2TtU5nfQDVcODAYTgcp71uL+fhh2kc0ratvsnhf/4pj549m+HRR49j1KgTuvcLKB2nOCbydvxnzzYCEHv1+86dO1G1KquRRO0cPXoYgJgwID8/Hw5HwdX1vk6Im6qMsUxR//mPucpYaal7TKV0QCZNZz59OsUJrF7tf1ZUpUGf0vX6zjue25Her1ib8nuYP2itX8IItlJy/Lg+S8KqVZR4wGhatTK+TT2EgmuTGvJzfMcOY5SxWbNoErZtW/r+++/0vn27ujJmNIMG0aToM8+4JpsAlBO7nD1LVqTrrqMEXYFA6noM4KrrsZoyJo3L4nA4EYLe2V2jt1eKXQsU/s4KaT0Gg7DZbLB5aFs+MetwODTXNGOT+/XrN0B8vIZMZRr74I38fHrfvr0O4uN9cw/xdJze+hMb6/q9RYsWbl579eu7/h92exzi48XBl6d9eJocD+s6Y0o4nRSU1+SKF92ff4qFfLOyyCpupCLjCaNm/JWUMSOvf731S7QqY1WqUDwFY9Ik8kL45Rca/JUvry0BE/NikLNoEf0PP/9M7m9DhgDdulF9MvY/ffABbdOgAW03e7Zrn/Qgd8EPNtLYFiuwYoXrdz3nZHy85zI4WmEZmnftItcwFlMZqGtcGjfEkBZBl59bu3aJsVCMkycpvgoInDKm1/V4w4Y6AFQuNE5AsarrMY8ZC2N8VYK8DUDkJ438uzfLWCAJ9L4MGJTVqlUL+fn5iIuLQ35+Pmqa5VYQpqhlSvS2zAgsp4wVFvo+GFZD+udJY79KSykzF+ORR8TPt9xibB+89Yvhy/WqlCzASGVMWr+krKwM/fv391i/RBr0OGIEZag5cQIYPZpmXDIyyBf58mUgOZlcx2+7jRI1yJM1fPIJKW2xsUCPHq7rHnuMguEBqu/F/JNbtKCECH370vc+fdSPTRp/27s3KYNK/92MGeRHLWXqVOC551yXMYVSjXr1lAs6G4GRxW/1snEjWSelsclJSa7b6L2JseuR/S4tjRIgxMeTVb1bN7IeHTxI+5en+ZdmPb7hBnKH/uAD+i5/7l6+7FpiwVdYsWetWZdWr3Yvy8CyBgLmD3jZLF9Skph8gxNcrOZ6zN0Tw5imTV1r7XhTnuToCZb19F3NxSeQJ1+g2jbQTTExMRE5OTnIyMhATk4OunXr5nebHBFfRGSYUcWYZvzn9GmyiNSoAbzwAi07fNize4w3P39vf+zatYBa7KN0QKREv35i5hU5ly8DWVm1IM06WlZGFrmyMhr0r10rztJLcTiUlzNYgWM1ApFNEdBXv0RqGUtIoPfatanw8rvvkiIG0H12xQo6mX/+WbmtoUPpv77pJvou/c/ffRd4/XX6zGJWVq6kgbnRMSBPP02D+fr1RRM+K9Z8661i/lPmhvbOO5ScRo7e7Jh6kNZgCzb/+pdobVaDJXgoLaW03TVrakvPP2cOJTWYN48U4MGDgeHDSfF7/HFK2sVqj776Kp37M2e6W1UnTxYLa1+6BHzxhauF9Lvv6LPem+vevVR8W4qepF+ertVAKWN6XY8ffND1u6cs11Juu834mofB4tIlmgCSZqnmmD9BwAkiarO73ixfWkwMSsuD6ZYox1/LmDc3RZ3HMnbsWNx///3Yt28funTpggULFiAjIwPr1q1DcnIy1q9f7zHpEie0MM0y9tVXrt+VavEtWEBuhNu3Kz/85QMgOd4eGnfd5Xm9tzb79yfl6oEHaGDHWLIEeOutOBQVUVYZgKw42dk0i5+bS8pe9+7u7d9zD72XlSnvn7ldrV3r3qeiInHgY+bsJbOMxcWJx+MvcXGe5dmggev6qlX921/HjiRHux349FNa9vzz9Dp/XtzXiRPAoUMHAbQEQPKpVImsOuXKAR9/7CqL8uUptkzqumYUbBLDKjRo4FojrLCQlKEXXhDLs3z2GdVy8oaaosfqajJY0UW5FVPOSy/R+6FDlLRLqTbUvHkUu7VwIWV8Vcuo2qoVxb+zSQYjCdTAV6/rsRy5d0zFimSRZjF5AE0+xcTQdTB7NqVK9laHTA/PPUfKee/epLA7HCTP226je+3QoZSV88gRsUD7O+8ATzzhvW32vzPPiaeeolT2wYZZWq0It5BFEL4K29vvvLnyBNNNMdCWMZ28qZJIYd68ef70Rhd84sWdQJ0mpljGfv9de8pLh4NmtD3V4/vzT/FzWZnnuoFGs22bqyIGiEU9mVXvhx9ocACQOx1ArmqesiR6S+upZD2Tpvi32UiJuPHG4BcHvuUWIDn5NPbs8T/jpBrMBTEQXL5MVlqmAMrjdipXBq65hj7XqgXExIh3rJgYIDPTNX4sNVX8/PjjlJHTgxHCZ6SZiX1l9erV6NGjB5KSknyuYXL+PJ1/SsWa4+Jc62TKk3j5gy//6dNPU1pz6fMtK4syOqelkcfOzTdTEhA5r79Oijc7BqkiogdPN3dmyTMaqetxr1690LNnT4+ux1IcDjru48dJxpMnk2v5li2uEwKVK4vXwRNP0P0wP59+xwovjx/vWsC5aVOyQANU40s6GDh0iLb98Ue6Rl9+mdr6+muS/Z13Av/+N1mfZ8wQyyPUr0/tCIJ4/emlqIju61eySnvk8cfJQq+Hnj3dl6Wnay5JZAp8oBYB6LVoqbkXqrWjZgkzIy10oDNw8dkLzVjl3hLMfviljPk6cGMuZ1qRxxHJM5BJlaFrr6WHslIxaKXZb62wBzhLvQ3QgEBpNpwpSgUF1I+77xbXSQceSoNVrUiVMXbCPPSQuGznTpoZ3r49+A/0unWBWbMOu2WmMYrTp8lqGiiUMtz5w+ef07knCORyWbkyDSDVWL1au5XLlxogarB053PmzEFubi6WLFmC3VqK3UkoLBQVVS1MmWLcf80yMRnBDz+4L/v662ro2ZNca202UuTYxIs/LF+uvq5ePfdl3tLMa0WP6zEAlCtHN5rKlel77dqk6Lz0EildNhvw4oue26hTh3730EOkLD32GClODIeDrM+CQJY0gO5hp06RdWvmTPJoYOdM7dra6n5J6dDBNUMuQNk233mHvC1SUorcflOxIsVk/vvfNPmn5gYtCNSO1mytbLvx461RV0cLfEwZgWh1tfOWql4vwbSMyYPSlfBkRQhyNsVgEMJdDxiBUtB8PtN9Hbjl5Cj4IxqI9CHLXEzYnyctq6GXW2/VfmKyAbK8CKqR3Hmn+HnJEve+DRoUuH2bTZUq5mcu1MM115DboxRP8V133CHGQKnBJvF0Zo71iDTdeUxMzNV0597IzhbNnzVqGNcfq/H88/Xx/ffuiUn8Ra9F7eabvce0BoIaNcj/2AiloV49UmoaN6Z719at5LouLzEC0LkuTy/sL3XrUkIhxu+/k0Xr3nuBmTOPqP8QpES2aCF+1+qeunSp+zJpdk8zE8npwSqz1pwA4C1hh97v3pZ7I5jagJZ9KblIaCUELxyzuhyJSqDPt3tfBm633w5MnFjf111yVDDSOsIJDi1bel5/442e1zNrgJEDNqV0506n0+Nv3n0XmDUrhAuchSgseUwwYeeatwf0yZNkydJDu3bBq/sG0MP+P/8xpq3OnZWXy5MSMYuiFJbkp3lzd9fzULGUccIQb4k0tH73tVg0w6ozEnowomBmhBHov2rdOldPOUGgkASteWekGKU4+nym+zJwU8uYFwzkwdeBKPprVQIRn8TxD6krnzw9vhZYghczZpAcDsfV13vvXQh+Bzg4f/6MixyCQd26lCbVm5JQo4bxlqxAsWwZ1SD0B7VrUEvdv/R0euDXreuujFl1HBqJs9YRi95U9qFsGdOCL8dhtWOQce4chVIoYUTXFyxwD9f5+GPjM17roXNn8pQ7cwbYsIHutc2auXucDBggZuxm+KKwaSHgt3utA4by5T3kc/eT/Px8vP666HrSps0FvP++2J833tCYpzlE+fbbnUEfuHG08/LL3reRZt585hn34sW9evnfD63pzuPj46++qlSppKntn392HWzy8ij+celSFRc5BINZsw7jo4+U49hClaQkqkHnD3oGLLVqeT/3WV3EUFFoOaHL3pwc95S0UrQm0jDKMuatXSvjbfbEQpaxkhLK+LxqFd2PHnyQkpbJ0dLlPXsollYp2+yXX4ohM9I47GHDKE5XDU+VE9av994nrUyY4JqUSV4DduVK9/JXlosZ82XgpsSoUfR+6RJ1ZcwYSj5hJHFxcSguFt0jhw2r5NKfsWMb+r0PljrZCKTFao3g1ltbBH3gxjEWaar+li3d68kZ8cySpjsvKSlBbm4uEr34yKslT2DpwytVApxOshLYbGJWuxUrgPnzfe/rP//4/ttgIY0rMprGjQPXthp2+yU8/HDw92t19Fx7J07Qua/EkiVUCmThQso2OXmyMf0zmpkzze4BxyguNm8OSJP3eMt6qDVmzFsdMm+YkU1RDWlNE0/995bAw0LK2HffUYbvO+8UlbAzZ3xra/hwepZLyy1J96OGtB6tHLW/6oMPqKasNHO4P7DMz1bAZ2XMl4GbUpwDm5UUBGDsWODNN8l/3sjzVhBc63Y99phxbW/ZQu3/84/nRDu9e2trb+lSMqHOnm1M/4zI9sYJDNu2aTfVS13DoqMDo4z5ku5cKeECQIPJceNoYiFOJaTs/vuBNm0ocUNhIc2seasBVaEC1V6Slrr46Se6BtWSnnzwgXp7HTvqz8anhcWLSbaXL3suy+Er8jqNHPPwdO3pKd2QkkLnTblyZP2upM3ozOEYjzcFwltKejnBVkQ8ZcgKFCGeTTEQnqRGHzobLxlVf9FC+rHvypgvA7e0NPFzQQG5LkmDmuWauSC4B0e/+SYtV6rLwrj9dvdlzNrWubM4gGSKH0AZygDgjTeofa2ZEFma/uhoSrHfty+ZX6dOJbfIJUsoVfnixTQToOQRcOyYaDFgfrVPPEEp3OfMAR5+mEy8M2cCu3ap90U6sL90iQa3FSpoOw5O8GnTRrSeeHNJkpZEiIpyV8aMii/Rm+5cqYZdaSnFxL3+OtC+vfpvo6JIIR04kIq+ly9PkxBFReS/rURxsXupBlYwXk+5g2bNKHX6V1/RgHnpUkq7rgVWK1BKVha9jxtH1ygrdm6z0UTN4cP0f/zvf6SESpEna2nblmqx7d1LM4tKkzxq/w/HWgRC0eeEFko1QS2PWh0x+Xqtypq39fL2/Hmgbd3q+2hdKbWttK++WMa0/Nai6FGmjDy8YBoZraIr+5UgvGvXruiqIxMGGzTFxAA1a5LrklS5+PBDsTgyg7nsTZsGJCSImUUXLAD27aOBTFkZcOECpTwHqIaTPK6B1XWSpnwfM4ZeAA3M7rtPHBhJU8dLSU4m4ZWUiAMwKYsW0bvDUYRJk1wzR0ZFkUfAjz/SrGdWFhVCVRsEVqlCvrXDhon7BuhE/PVXMrGyfrKTs1kzmmGNjubZuEKJzz/3PMEgzb6opIyZdUOpWdP1ude3r/9lB6pWFe8LSUmiW9cbbyhvL61bOHIkWcKuvZYUmu+/dy1psXOnqAC/8oq4/O67aTJDrUj5559T1tK4OLqHMAXpmWcoADojg15q1K9PihpAVvLOnclF+8gRuv7PnaPrXS7H66+n8+Kzz+gedeAAfWdxRRwOx1zOnVOvrbhnDz2TZ892tfpfvEjjF29ZdYOOr9kJtD6A9CYEYegJMtZaa0IJT4FM3uAo8XkAACAASURBVAghy5hSl5T+ej3ZBI32ZvO0r0DvxwyCWq2J3bCk57vWmUO5i+M114iKU3Q0EBur/sdKXRTV3AUHDSKXJems+8KF7q5PXbr4n1ZaOoPvqysKq1t15Ahw8KC43JPljGNdpJmGlJAq1lFR7tkUzbrfX3stsGmT+H3hQmPb91QQmSE9djahOn48JTXp1cu9+LsaNWtS5lGnkyxliYlU0Pjhh4GmTYHS0v24997rAACZmcDx4+SO6QuPP04vhjQmUImoKNfiyBzroPXas+CYzDDy8mhyVU/BdyMQBMqA5skCH0j++ovqzj31FE0YV6hAEzTsvsAMLaNGuSpjGRnAp5+Sh1DNmsHvtyryQdSECTTz1LSp63qtdS60KijyEb10/YEDVN3d6oRQzJhWZUxPW0q/97eygRrhaBkLavLcypWB2bMPYskScVmg3DikBTaZVQzw/LCQuz9JjX4sE4zVUuLXq0cFqQPF7Nmzcccdd6BPnz7o06cPVq1aFbidcVSRem1YyTImjRnLyTHfGsv+B19dg9jv2rale9O0aeI4pEOHC4iNpc9vvUXWKg5H67VnRtIVAFi9ejV69OiBpKQkZMtdT3Tw/vvKmV/37aNsr2qW4b17XSdELl4k131pWMLSpUBurv4+ffkl0KGD7zGUTmc5HDhA1i1fWLmS3l9/nWLeAeC118gStnOn631IEMj6fvYsKWIAuV0fPUq1+fzBsOe0PE3voEHUcebWJH/wMPyNGZO7KUrbb9TI3ABKaV+U6gR98w3N0oe4MmaFtnzV7QONNPb38OHA7COoljEA6NbtrMv5LC1N1tD/pIZXKSoSP2/dKn7WI8zatWmmv0sXciUqLo7MGKy0tDQMY76SHFMoX55cAJnCI1c2zLpJsZnfnj3FOCkzYR4q0omV1q3Fz1r/J7OVSk74EYhELt4oKyvDlClTMHfuXNjtdgwYMACJiYlopiPo8MABuh7kia+eeYZeX3xB3z/7zDX29dw5ctFjSZczMoDnn6exNaNZM7IiMQ+Uxx+n+O3hw8n746efyALdsCGFBlSqRHXhWrakzwkJ9LtRo8g9PzaW3JAffpiUnuefp7jUceMojfemTeTWnJdHSuKjj7rHuV9zDVmhBw6kNm+7jVyky5Wj3585Q2OD3btdrdvvvit+zsx0/x+VwqCkWZiPH/fPAGTIc9pbzFZqKj2EWrXy3I7WWUI1t8hAPtBSUiiuxRfuvVd5mdJyTsDEaJReKwja+ij1+AlUJuegK2NyjojlvzxmPdOL2sy4XjeK/v3Fz5GoiHGCx6RJ6q7x0dGia6L0M3tmmqWMsaQxsbHWMPenp9NArVMncZnSuMLbBK4VjoXD8Zdt27ahcePGaHRFA0pJSUFeXp4mZeziRbKGsbhqNaQJaaQJqpgVmZGd7R4Tvnu3ayiAVKH59Vf3NqRcuEDx1wCQny9uu3MnKYmAuyWvQwf19hjnzrm6Ef/6q3HZjT0xebLnkl9Bgf2JarNR6emkpbIAfb2p6lmWwxo1PP8+kDdgqWuWFqR9tNno4bJunfbfJySQP6uFBpDe3BT1/P3BdFM0y8iYny9+DlRCHtOVMenNdtMmmrQwArXYMGn2Ro425s+fj5ycHNx444149tlnUY25LMiQFpQuLi6OmALTRh3r4MH0zpr6/XcgNbUpDhyIwY4dDpw+3RBAFRw+fBAlJfUAlEN+/jEAdXHmzGk4HAGyn2vAKt6r5coBd9zh+++5MsbRy/33A//5j9m9UMbpdKKupE6S3W7Htm3bvP5OECiOUamQKycw/PWXf7/X+pz2SG4uBbWruSnZbKIiBoi+t6x+CUtLzYL45G6PXboAs2YBQ4cqtx+IG/Dbb+Pw+fPQXAo2ORnYsIHSWQNkXpWahZcv11eUa8YMch1h/u4GsHr1akybNg2XL1/GwIEDkeEpe5QC3pQxNWVn61byELvtNve2jFCQtLopGqWMffqpawIwNZgLciAxXRkbPlycxdqwwbh2pfcLjmfS0tJw4sQJt+WjR4/GAw88gMceeww2mw1vvfUWXn31VbwiTUMnQVpQ2uFwREyB6UAe66ZNNNMbHx+PDh1I6enYsdHVm1K9ejTQql69KuLj3bNAbN68OSD9kmP1FN4dO7q6K6sRLsrY0qVL8c4772DPnj1YsGAB2rRpc3VdVlYWFi5ciKioKDz33HO4wx/tlRPQmN1gIZ9M6ty5WUQpYn37FiInx0t9EQXq1y/BkSMxaNasGLt3+1fbqlatQjgcR1XXG/Wc9jpp2ru3OCPojXvvRWyNGjjbrBn95vrrUX7ZMpQ2bAg4HKh+5AjqAThVVIRjrM3kZAqUO3oUFffvx/UALhQXY7/DgfKHDqEZgJJLl7BHpQ/sScv63fCuu3AmMRFFan3u3h3FxcU4rbI+9v33gdJSnGXrZ82i/VxxxXTceafbPgFQ9hWttGqlvaioF4xwPfYVpmsrKUOBiqWSojfVvpYKB1rGBfJ2pUyYQJmOpet9GT+YroxJMwlJXRQ4weOTTz7RtN3AgQMxcuTIwHaG40KdOmLpg+nTKTtgx47WcVNkeMsGaDYbN9L77t30rjazlpVFmdG81X2zOs2bN8fs2bPxwgsvuCzfvXs3cnNzkZubC6fTifT0dPzwww+I5kFyYYndbscxFrQFspTZFZIQSCeTBAE4dcq9rX79aHJo2TKxPIQUdg/6v/8j18NTpyjxVVoa8NFHZGx5801aFh9PJStYzFSjRuQOKDHi+cSSJTQxlJxMyT3i4oC77qJ1U6dS/GjfvsDcucCIEcDmzTvQrl1LANXx7be0/W23abuf5uUBiYlsFooUMeaNVr06jW327aPZ982bKc9D+fJi20uW0Hi+cWNKgPLGG9Vx3XXijUc+kWbUc9rwSVPJRM+VHYifn3wS+P571Hj1VdSQZ0gDKLAPQKWKFakfV1z5YmJiPPdr0CBx/Y8/ogqA+upbez5OteVz5wLnz7v8LlCTrnomTX1xPT5yBGjVKh6JicCDD5IRRM7atVQjtGFDUbE6eZLuBxcuuOqeO3dSLpOLF8Vao8OGkb45c6a4HUt8x7jvPldr+48/0t8cF0fJcJjF7fJlV6WmShW6/7Dn9sGDNAa6dIlKPEVFUV/mzwdOnKiBSpXI5feobG4jO1u5NqoevCmd99xD13ZmJiX60orpyhhA19/Fi0CTJmb3hCMnPz8fcVdcEFasWOG1sDcncMTEiHX2rJJNsWtXstZNmGDO/o1mwAD3chahSFMVl5i8vDykpKQgJiYGjRo1QuPGjbFt2zbczKY8OWFFmzZtsH//fhw8eBB2ux25ubl4Q61g3xVGjHD9PnUq8NxzlBRHmplYTmEhhQGwDKt165I3Wp8+lIzPZiPFo2VLGkRJ9f+KFV0T1e3aJRY2V7q3zZoFjB7tuqxDBzHMQTrZ0qQJzZAPGgSwx1d6Or0cDnFDPQmImjZVrkUqz2vRpAnw4ouuyw4epMkr6QRWaan2fSth2ed0zZrAL79o357FkXiqFVZa6l9RaK2kpYmf164ljdoC+OJ6zCY9fvxRjLOUM326+zJpLV4pavXxpIoY4K64yLOeymPkf/uN3tetcxcxy0AKUAynehyn+ozOI4+ortKMNFM7Q3qPYiGJb79N2c6lVjNPWEIZKy4OTLv//OOaTjgvLzD7CWdmzpyJHVfM6w0aNMCUKVNM7hEHEC1j7F2aYj6YLFoEvPhiPh58MM6cDnB04XQ60U4y0LHb7XBKU9rKiMQ4UK3H+f335VGp0mU4HGUA4lG9+iU4HEqFHmk23bVNpWXGU65cOUyePBnDhw9HWVkZ+vfv73Wg3rMnWbIASoAxYgQl0Bs1yvO+1EKUpFZmVhD+0iXXbOXyLlX04vEnHfxs2kSKmFpgfYsWpIx5a1PK9deTVUtO48aUlt+fzJhGZo1mhM1zum5d+oM9TQ6VM2HY2qmTa1aoEEDpfsMJLp9/fh59+mhLv2gJZSxQyL0xlGayOJ6ZKZ/q4FgCpoRdukTvZiVqqlEDGDmyAFFRoaGMme3OaSTSGJILFy6g0pXR7ejRo9G9e3dD9hGJcaBaj1O6yfLlQMuW5dCwofrvlNr05f/UGwfatWtXdNVRIFNqGX7uOXr/9Vddu1SFWcPKysgytGYNuQ737UvL162j2XGpwtK+vatRolUrmuE+cQJ46CExd4Sadf6zz2h8L02p740tW6gvZWU0jvjXv6gPbduaowt4I6ye0zyG1SO+uB63ayeWoZHy2GPuGTw//pisty+8QImJ5JaxqlXF3CYAZRxnFvNp0+ia2baNSlbs2EGeqnv2AFOmkEXt2DFyZd64ERg5Usyizkr3+EKnTmSEveUWoHz5Ixg3rj5sNpqE8VaFQUqVKlSCQxDIvXnuXErwMXmyb/3asqUyoqJEOXi6d1vwtmIc8gFqMCzbHE4wGDeO3IfYOW2hrLlB4+RJUSmNRKQxJJs3b0Z7lsHMA1of5Bx9eNJ9f/vtamhMSHHwoPFt1qtHFqpXX6XvnTvTi3H77fSSsn49TTpdcw0N/FjNH6nxx1N2terV9ddArFaN4nO1tM/hBBNfXI+3blWfZFLL1cAUELk7sBJnzyqXoWjaVDlDutTK/v774mc2uextwuPiRbJcK7lLOhxFqFiRIgjj4+nazcoixS8jgz4rsW8fcN11rhO2LCv788+r96WsjPpTuTLVN/YliSkAhL16wnxQ/a1wz+FYiZdfppvMv/9NZUzGjze7R8GnRg39RVJZsLzU/zySSExMRG5uLkpKSnDw4EHs378fbdu2NbtbYc3NN7tP9n/wAfDEE+b0xxusFlcg3OkqVKBkAA88oP03MTE00CkoAD7/3Pg+cUykbVtyWZKOyDkekboe9+rVCz179jQ9RtBTPUA9lCunzfJcoYJ63JoSgwZRchBP8VvXX++b50x0tBjq6KsiBoS5ZQygByGf1eKEK7Vq0cwxRxvlypFLWbizfPlyvPzyyzh58iQeeeQRxMfH46OPPsINN9yAnj17olevXoiOjsbkyZN5JkUTMCKQPFD8+iuLN7GWS6o08zInTKhQAfjpJ7N7EXLodT2OdGrUAH7+2exeeCbslTEOh8OJNJKSkpCUlKS47tFHH8Wjjz4a5B5xOBwOh8NRwiYIgbMbBavgLEc/WuJL9MBlbV2MlDWXs3Xh13TkwK/pyIBf05EDv6YjAzU5B1QZ43A4HA6Hw+FwOByOMmGfwIPD4XA4HA6Hw+FwrAhXxjgcDofD4XA4HA7HBIKmjK1evRo9evRAUlISsrOzg7Vbwzh69CiGDBmCXr16ISUlBfPmzQMAFBYWIj09HcnJyUhPT0dRUREAQBAETJ06FUlJSUhNTcX27duvtrVo0SIkJycjOTkZixYtMuV4AgmXdWTImss5MuQMcFlHiqy5nCNDzgCXdaTImss5ROQsBIFLly4J3bp1Ew4cOCBcvHhRSE1NFXbt2hWMXRuG0+kU/vzzT0EQBOHMmTNCcnKysGvXLuG1114TsrKyBEEQhKysLGHGjBmCIAjCypUrhWHDhgmXL18WtmzZIgwYMEAQBEE4deqUkJiYKJw6dUooLCwUEhMThcLCQnMOKgBwWUeGrLmcI0POgsBlHSmy5nKODDkLApd1pMiayzl05BwUy9i2bdvQuHFjNGrUCDExMUhJSUFeXl4wdm0YcXFxaN26NQAgNjYWTZo0gdPpRF5eHvr27QsA6Nu3L1asWAEAV5fbbDbcdNNNOH36NPLz87F27Vp06tQJ1atXR7Vq1dCpUyesWbPGtOMyGi7ryJA1l3NkyBngso4UWXM5R4acAS7rSJE1l3PoyDkoypjT6UTdunWvfrfb7XA6ncHYdUA4dOgQHA4H2rVrh4KCAsTFxQEA6tSpg4KCAgDux1y3bl04nc6w+y/khNvxcVkrE27HxuWsTrgdH5e1MuF2bFzO6oTb8XFZKxNuxxbOcuYJPHRy7tw5ZGZmYuLEiYiNjXVZZ7PZYLPZTOoZx2i4rCMDLufIgcs6MuByjhy4rCODcJdzUJQxu92OY8eOXf3udDpht9uDsWtDKS0tRWZmJlJTU5GcnAwAqFWrFvLz8wEA+fn5qFmzJgD3Yz527BjsdnvY/BdqhMvxcVl7JlyOjcvZO+FyfFzWngmXY+Ny9k64HB+XtWfC5dgiQc5BUcbatGmD/fv34+DBgygpKUFubi4SExODsWvDEAQBkyZNQpMmTZCenn51eWJiInJycgAAOTk56Natm8tyQRCwdetWVKlSBXFxcejcuTPWrl2LoqIiFBUVYe3atejcubMpxxQIuKwjQ9ZczpEhZ4DLOlJkzeUcGXIGuKwjRdZczqEjZ5sgCEIwdrRq1SpMnz4dZWVl6N+/Px599NFg7NYwNm3ahMGDB6N58+aIiiIdduzYsWjbti1Gjx6No0ePon79+pg1axaqV68OQRAwZcoUrFmzBpUqVcL06dPRpk0bAMDChQuRlZUFABg5ciT69+9v2nEFAi7ryJA1l3NkyBngso4UWXM5R4acAS7rSJE1l3NoyDloyhiHw+FwOBwOh8PhcETKBbLxzZs3B7J5jh+0b9/e0Pa4rK2LkbLmcrYu/JqOHPg1HRnwazpy4Nd0ZKAm54AqY0o7djgciI+PD/RuTcfKxxmoC1Uqaysfv9FY+VgDIWt+TVsPfk0bh9WPk1/TxmHl41SS89GjRzF+/HgUFBTAZrNh0KBBGDp0KAoLCzFmzBgcPnwYDRo0wKxZs1CtWjXFdvk1bT34NW0cVj5OT3L2msBjwoQJSEhIQO/eva8uKywsRHp6OpKTk5Geno6ioiJjemoBfv0VOH3a7F5wvLF2LXDxotm94Ohl717gSswthxM0Ll8GVq40uxccMykpASxU49UnoqOj8eyzz+K7777Dl19+ic8++wy7d+9GdnY2EhISsGzZMiQkJCA7O9uYHW7YAJw/b0xbHI4ahw4BO3ea3QtT8aqM9evXD3PmzHFZFrAL32QuXgRuvRXo08fsnnA8sWMHcMcdwJNPmt0Tjh7WrgWaNgXuvRc4dcrs3nAiiTfeAO66C1i61OyecMxi/HigSxfg99/N7onvxMXFoXXr1gCA2NhYNGnSBE6nE3l5eejbty8AoG/fvlixYoX/Ozt6FEhIANLS/G+Lw/FEo0ZAy5Zm98JUvCpjHTt2dDN3B+TCtwCXLtH7xo3m9oPjmSuF1vHHH+b2g6OdPXtIgWYsX25eXziRx99/0/uhQ+b2g2Me7Hlx4oS5/TCKQ4cOweFwoF27digoKEBcXBwAoE6dOihgD0l/OHuW3rds8b8tDofjEZ9ixvRc+A6Hw+V7cXGx2zKrcP68DUBLCMJlOBz+mUytfJyhzvHj9M7zgIYODz0kfo6JAX75BRg0yLz+cDgcTqhy7tw5ZGZmYuLEiYiNjXVZZ7PZYLPZVH8rHZd4GqeU378fzQCUlJRgT4iPZfh4jGN1/E7g4e3ClwfSWTm47tw59inK7z5a+ThDPdPOvffSO7NkcqxNSQmwfr34vawMePNNch3jBB5WX8ZutyMrKwsHDx7E2LFjUVhYiNatW2PGjBmIiYkxu5tBgU/gcEL9HCgtLUVmZiZSU1ORnJwMAKhVqxby8/MRFxeH/Px81KxZU/X30nGJx3FKORoexsTEWHYsoxUrj8eA0B+TcfzHq5uiEuzCB+D1wg8lPOiUHE7Yc/ToUQwZMgS9evVCSkoK5s2bZ0i7UpfEzExSxgCgsNCQ5jle+PTTT9G0adOr319//XWkpaVh+fLlqFq1KhYuXGhi74IDv7dzwuEcEAQBkyZNQpMmTZCenn51eWJiInKuZEbKyclBt27djNiZ/21wfCbSkudFOj4pYwG58DkcnfBnhbGoZeryF/YsSUkB3noLuOkm+v700343zfHCsWPHsHLlSgwYMAAADeY2bNiAHj16AADuvfde5OXlmdlFDoejkc2bN2Px4sXYsGED+vTpgz59+mDVqlXIyMjAunXrkJycjPXr1yMjI8O4nYaDFhuCRFLyPI4GN8WxY8di48aNOHXqFLp06YJRo0YhIyMDo0ePxsKFC1G/fn3MmjUrGH3lcFy4fNnsHoQXcXFxV2NBpZm6mjVr5nObousvMGQIvY8ZAwwdCsyZA3z4oT895nhj+vTpePrpp3HuiiBOnTqFqv/P3tnH51T/f/x12dzPPdukocnNSChirazGJmaRm5T41qIpRaIUShG6U6HkJgkVP924jTImFkJEE1dyt9xv2gwzNtvO74/3Ps65ruuc6zrnus51nevm83w8rsc517n5nM85n3PzeX/ed9WrI7jMBCk8PBxZWVlGVpHD4aikffv2OKwQAlwvSwaOd9ChQwectoo4lJaWhq+++goABc8bPHgwXuGjmn6BQ2Hso48+kl3OH3yO0XDNmPuQRupyBRaIKyiINGMAkJQkri8oAKpUcekQHAV++eUX1K5dG3fccQd27drlVBlqnf29nby8cAC1cPbsOZjN9u1jffk8OY7h3w2Or+KvwfOYN58e9fPm87SHywE8OByjCGTN2JEjwLp1wKhR+pdtL1IXoO0lv2FDLQDhaNbsGk6dyry5vHHjSGRmVsRXX51E585XZff1NtS+5AsLTZg+PRQjR15AtWrG3aR//PEHNm/ejPT0dBQWFiI/Px9Tp07F5cuXUVxcjODgYJw/fx5hYWGKZah29vdyatakaf369REVVd/utt5+ntzZn8Ph+FPwPIYe9fPm87T37ubCGMdnCbQRznPngPfeA6ZPp+Sl588DQ4cCs2YBw4eLHU5XkIvUZY2Wl3xuLk27d69ssU3v3sCMGUBWVkN46XvTBrUv+agoSkz+zTe1PXaPyr3kx4wZgzFjxgAAdu3ahYULF+LDDz/EyJEjsWHDBiQmJmLlypWIi4vzTCW9AO7+wuH3AMdX0RI1k+NbOBXAw98JtE6+N7N7N4VGHzNGTPbM8Pd2iogA2rShjj0ADBtGATA2bgSuXKFl69cDEyZQlEJXUYrU5Qp79tBUmvAZIGESABYt0uUwXgVrLwB48UXj6qHEK6+8gi+//BLx8fHIy8tD//79ja4Sh8PhcBzAg+f5L1wzJoGPmHkX6elAbCxw993A3r3kXzRnjrje34Wx06fpFxVF58ryqgkC+WABwPXrNNUjTDyL1NWsWTP06tULAAXwiY2NdbpMFozx3nstl3foQNMTJ5wu2ithYfsZs2aRAG00HTt2RMeOHQEAERERARHOXg5/f2dwHMPvAZXwC2UoPHheYMGFMQn83eNdHDpEU2aBNXeuKHwA2nzGUlOB1auB2bP1q5/ebNoENGwINGtmu056bwoCUK5Mp80END0GEuxF6nKGoiIyU6xWDSjzOb5JgwZASAiQnw9cugTUqKHbYQ1l7lyja8CRgw+0cfg94CT8whkCD54XWHAzRQm//WZ0DThS5IQtqVmbFuG5Wzfgs89crpKuXLkihn7/8UcgPh5o3lx+2+Jiy28i04wxTYw3fi8zM2naqJHtOpNJFDp1lP8MZ+FC22U8uTWHw+FwOBwluDBWxo8/Al27Gl2LwOD8eeDaNcfbOdJ85eZSp94bBRE1VK8O1KlD89Jw73LcuGH5n2nGvFkYY0JW69by61nyZ6YB9Qf++MN2GQvvzzEebv3A4feARvgF43DcDhfGyvA33xVvpn59oHt3x9s5EsakuWp97XvBNGKFhcDDDzveXhAoWAebd4eZot78+SdN775bfn10NE1ZkA9/ZcAAo2vA4XA4HA7HW+HCmAy+1rH3RbZudbyNFp8wJpxkZwOtWomBI7yJb7+lICQAcPCguHztWsf7Sq+FNICHN2vGmFlop07y6++6i6YZGZ6pj7spLLT836QJTS9c8HxdOPJ443PC8Sz8HtAIv2AcjtvhwhjHa3EmqfPy5WT2Jg0yZKRw/c8/FHRk1y7SkIwY4VydpNfCZLIUPr2V//6jabt28utZyi7mW+brSNti0CCgLCAlh8PhcDgcjiI8mmIZfPBHP/btA+66KwpHj4raAWfQKoz98gtF8AOA8uXF5SdPOl8HZ5g9G0hMBKpUoYAcwcGiOSEzh9UqjEmj9AmCeL9Om0ZTb7t/BYG0drVq0XWQo3Jl8ptjQpuvc+6cOP/oo8Dly8bVhSMPt3rg8HtAJfxCcTgewys1Y8XFnu9AS/G2jq2vceYMTQ8ccK0crcJYXJyYcLdCBXE5E4T0xrrc1avJFPGFFyivVliY7XYswIPW79xrr4nzM2aImjGG9X+j+e8/ar+GDe1vd8stFMzF2sTPF3nvPZrWrw/07AlUrSqu4/0aY+HvdA6/B5yEXzgOx+14WReOGDOGwmEbZYLFO06uERlJ09GjXetkX7mifZ8FC2i6ZYu4zJX2LC6W399kIu2btI69e4vBGqRaEimXLtHUXq7GqVPth3vfvNlW+PrhB+XtjYDVn5kiKsFC+R854t76eAImeCYn0/2RmCiu+/xzY+rE4XA4HA7Hu/FKYeznn2l68aKx9eA4R7VqND1xAoiJoY7pvn3A/PlAbKy43U8/USCKrl2BSZNsy5kyxfk67N5Nxz16VLswNnAgBZUoLCSBKyyM6s3C6EsHCpWELnukpJAGTYnXXwe6dLFfhvVgpTP+de6E5exjQTqUYP5kv/7q3vp4AmZuyc65fHngscdo3tty3AUqfKCNw9EIf2g4HLfjlcIYx/tIT09Ht27dEB8fj/nz59vdtmJFcX7vXpredRcwbBiQni6u69GDhIi0NOCtt9yj3WnaFDh1SvwvCCQYSqMZAhRoY/hw4MMPgWXLgDZtgOnTad2FC5b1lmI201SL4KhGS8JMPZU4flz98YyAmWN27mx/u/vuo+nvv7u3Pp7g669pyvKnAcCsWTRlYf45xuDvllbnzp3D4MGD0aNHDyQmJmLx4sUAgLy8PCQnJyMhIQHJycm4xFTzAQyXLTgcjrfh15jrWQAAIABJREFUkjAWFxeHpKQk9OrVC3369NGrToYg/Vjzl7UlJSUlmDx5MhYsWIB169bhxx9/xFE7seOlwpgWnnvO8v/zzztXjjVSLZMgkGB4xx2iluu118hcbs4c4OWXxW1ff91x2b17A1u2hOCNN/Spqyswfzlv4J9/aKqUY4xx7700ZUKtP3DbbeJ8vXrG1YMj4u/v9KCgILz22mtYv349li9fjqVLl+Lo0aOYP38+oqOjkZqaiujoaIcDaRyODf4+ksHheAEua8YWL16M1atXY8WKFXrUxzD8/WPtChkZGWjUqBEiIiJQoUIFJCYmIi0tTXF7Z4WxCxfEZMg5ORRtT29Yfi4pLPCCswwfHuFaATrhyD/Lkxw6RH5twQ7itVauTOZ8//7rmXq5i+vXaVqunK0/X7NmlttwjMNf+5WhoaFo1aoVACAkJASRkZHIyspCWloaevfuDQDo3bs3Nm3aZGQ1vQJfvwfGjRuH6Oho9OzZ8+YyrgHlcHwbHtpeBl9/WetNVlYWwsPDb/4PCwtDhp1MvVlZzh8rJATYuBGIj3e+DI7xaBE8atYkfytpyH5fg5njypllRkWRpvC334AHH/RsvTiWBMKg2+nTp2E2m9GmTRvk5OQgNDQUAFCvXj3k5OTI7mO2Uk1fv37dZpmvc/VqBIAQ/PvvSZjNNOrni+fZp08fDBo0CK+++urNZUwDmpKSgvnz52P+/Pl45ZVXDKwlh8PRgsvC2JAhQ2AymTBgwAAMYKHkJDjzki8qigRQEceOHUNpaZGrVVRFVlYtACRwlJaWwmy2E85OBb74kncVdr6XL5cD0Nzpcrgg5jzecM+xXGpKyZ6tadCAtKJnz9K8L8LyvdWpY7vu3nsp7cHrrwPbt3u2XhzCV4V8rVy9ehUjR47E+PHjERISYrHOZDLBpHAhoqzU6maz2WaZr8MuR8OGDW9aEXjzee5lIzxWdOjQAadPn7ZYlpaWhq+++goAaUAHDx7sujAWCCMXHI6X4JIwtmzZMoSFhSEnJwfJycmIjIxEhw4dLLZx5iXPckQ1adLkZuhrdyNR/MBkKufyC9rdL/kLF4DQUODLL4GnntK2r9JLXomwsDCcP3/+5v+srCyEsSRaErz1oxZIWLeB1rbWA2YJ1aOHuu1btQL27yftklphbPJk4M03gYULKfqlM6axBw4AffoAO3fKC1FaYPsPH267LjERePVVYMcO146hlXPnzmHs2LHIycmByWTCo48+iieffBJ5eXl46aWXcObMGTRo0AAzZsxAjRo1PFs5ju7cuHEDI0eORFJSEhISEgAAderUQXZ2NkJDQ5GdnY3atWsbXEuOO1CrAQUsB+zsDRpXOHYMTQAUFhXhuBcM8rlCIA6Oc3wLl4Qx1iGvU6cO4uPjkZGRYSOMuQIfmFGGBUiYP99SGMvJIR+oadMc++uopXXr1sjMzMSpU6cQFhaGdevW4cMPP3SqrO++A/r3t1z2558U4VCrUKmFRx4hTc3Eie4pXxAo2a9EZg1YWFh7R5EUGR07At98Q+HtH37Y8fYvvihGKXz6afo5866IiyPzyLp16f9ff5FgCADjxlHi+W++UVfWqlU0jY62XXf77drrpgcsqEOrVq2Qn5+Pvn37IiYmBitWrAhYkyZ//aYIgoAJEyYgMjISycnJN5fHxcVh1apVSElJwapVq9DFUc4Mjs9jTwMKWA7Y2R00LiujYoUKPj/Q6s0aUMCYQVOOd+F0AI+CggLk5+ffnN++fTuaNm2qW8WMRBBI2DGZvDf/EcsrxTq+kydT+PWRI4EPPgDWrtXvWMHBwZg4cSKGDh2KHj16oHv37k63db9+ZC4yYwbQujVFMLzzTuDJJ/XrKLVrR0mfT5ygnHWCAKxYAbzxBvmjaUFNR3rrVpKMv/hCe129DS0pDJQ4cICmHTuq2751a5ru2qVueyaISVF7LIYgiHnBGHfcARQVAY0bA+++CyxdCpRZ/jiEJf+WCzoj1dp5Mh8cD+og4u9minv37sXq1auxc+dO9OrVC7169cLWrVuRkpKC7du3IyEhATt27EBKSorRVTUcfxTImQYUgH4aUE9fqLw8+nE4AYjTupOcnBw8XxZ7vKSkBD179kRntUPhKjHyA8qCBS5dCtx/v3H1AIA9e4AOHUiQ+e47Wibt1F25QiZbAGmAAP3fo7GxsYiVZmzWQP36lsmRWcf1xRfVl3H33RT+fNUqoMwCB48+SsmTv/4aqF6dQrtbD/A3bmz5v2tX0niMHy/mhZJj2DAyPWvYEHj2Wft1q1evBADQvTvlEAsNBXr1Un9uaoiJAbZtA0pKSPCePFnf8gExhcGXX36JsLAw9OvXD3Fxcbhdo2onM5MiJKq1fGvYkKYHDjgO4rF+vfxyluQ7Opr8shy9O9q0kV9erRoJZIz//Y/MIOWicGrhjjtI83bqFNCokWtlOYMzQR0A9SZN3s7Fi+EAauH8+fMwmy/a3dYXz7N9+/Y4fFjez5nlHAt0/Fkgd6sG1FMXrlYtmvqLtHziBDB6NHUi3REamuNXOC2MRUREYM2aNXrWxVCk7xtvyTnWty9pkZYsof/ffy+ukwpj0vquXEnTskEyw1i58jjCwyNRvTpwyy3ie9YRN24AubkkCM2aBQwdSh1kOZYvpx8jKUndMSIigEWL7Atjc+eK8/XrqxOuTCaqL0Ad7zvukC/XkXAnZepUun4sNk5QEDBpEv30/kZKUxgAuJnCQIswVlJC7SfjUqgI89fMyyON9G23iX6j1kgssGT57TcKLW/vuV2xQtTeNWkCZGTQM/bcc5aCGOP//g944gnl8phpavXqyttERdE9ceiQ54UxZ4M6ABpMmrycmjVpGh4ejqiocLvbevt5cpMm5/CXPv7o0aOxe/duXLx4EZ07d8aIESOQkpKCUaNG4fvvv8ctt9yCGTNmGF1NzqhRwJo1QGqq/qOzHL/D5TxjrlBaal9o8JeXpxr+/JM617//Li5bsUIUxBh33UUdeqkwJh21Z35iaoUfd9G8eSE6dQJatqSO0MmTgFUAKFmCg0mzFBQEvPSSvCD24IMUuMQVgoLUa5diYuSXV6mivE+rVjQgBojC1z33kMZNC+PHky+dvYG1p54iYWHIEG1lWyOXwiBLY56Co0fp3pQmPnaE9DrGx9sPxsHeF44E2jlz5JdfukSDHIxGjej49tpl2zb7x1q4kKaXLytvw8woHZk9l5aKycj1sNixF9QB0NGkicPheISPPvoI27Ztw8GDB5Geno7+/fujVq1aWLx4MVJTU7Fo0SLUZKMPHOMJpI4sx2kMzTP21lvA228DZ87Q6L+3YMSzw8yvvv2WTBKnT5ffbt8+GsFPTZVf37kzsHmz69Hh9CZCx7zImzfrU84bb9AvKMi+L4+ccNCpE2nW7KRbw+OP02/mTHEfKSUltuZv778PjB2rrv4MVwVTrdhLV7F2bXUADXDrrRdhNmuJZkKaiFOn5I8BAP/9FwSAMig/+6wZc+cqay+GDwfuvddso2Fr2dJyn9zcApjNlHG6RYvb8PfflW6uq1ixFIWF5TB3LjBypFnRfG3nzlsA1FCsNwDUrVsFQCPs3n0ZZvMZxXqvXFkDAL0Ma9UCDh1y3lyOB3UQ8WcTNY46+D2gES5EuAa74fh15KjAUGGMBZk4f15eGDPy5enp52j8eJoyfypHwc2kwsONG+I8i7KYlkb+URzHlJDLFwSBTNysse7QX71KPlHly5OZm1r3Eut7qVw50n5cu0Yd77Nnycft6adJKPN0KHRnUxhIzbo++YSWxcTUQlSU8+pZOTMxprS77z6gTRvHZmR790bh6afF/+zZklKvXpWbxzKbSRObnw8cPw5cuFDupkaroCAKVarIm68xf8h9+5TTOzBNdVpadURFKdszWvuyhYZGqRpYkTNdY0EdmjVrhl5lZjKjR4/mJk0cDofD4XgRHhfGWraMwogR5A/ER6psKS5Wt51UGJOaZDFTwGXLyLRt8GBtJmOBjMlEJmnW/dry5S3/2zNPlOOBB2gqF7a9Rg0x0AVrpzp1KD2Bp9EjhQHTWtrzn5JjyhRKiqzEn38CzGLSWqgdM4ZM8/fvB6pWFZcPGUI+Zuw987//We43cqQ4CMKoWpWEsXLlyKy0Rw/SWs+erTxAkplJ0xYtlOuv1oeODaywoDd16zo/IMSDOnA4HKfhHTR9OXSIfDa8yQzMCM6fpxFwtUlF7fHSSxTRrXt318syGEN8xtjouSMCUbt70X6gr5tIhbGyDAMW/PsvRVh87DF96hUoxMRQJ12K9JvkTFCkNm3oXvZ2TaWrKQxKSgDW91d7HzP69bP8v327OJ+RAbRtK/6/fp2m69aRdn36dNIIywnJO3fS9L//xFxgjI8/thWSKpVZKbLniwmISlrK/HwxRH6lSvLbAHQPsW8P08Rac/y4OH/hgjivZJLM0U4gflM4lvB7gOMRrM2rWrXSRwAxkuvXyZTHFerXB269VZ/6zJhBI6Zq8WRuGY14VBiTXofZs5VNAY0YkDHimIJApmm5ueKyFSvU7VtQIM7bu7927+apO/RE2mH2R2JjY7FhwwZs2rQJzz33nKZ9Dx4U5+1FH5Tj9tvFiHcAmSLOnk3z1kocJoz16AH07Gm/3HvvpenAgZbLO3aUN0ll/oGFheJ2lSuL2i9r/vjD/vGlsO+PdX4zxkcfifM//CAGjunWjXzgOM7DB/k57rgHtmyhn1/iy1LrF1+QAz5HX269VbtpkJ5MmEAPsjNC1YcfkpP+iBH610sHPCqMsQTFAPDCC6JJjrWw4MvvAEc89RTdS4IAvPwyDZRY+4SoCVDx6KPivKP78plnNFeTY8Wrr5ImJtx+VOyA5qefaPrCCxQRUwtBQYB1GrsXXgAGDbIUUgBRGFNLt262yb6VtFORkTRlHbdy5ShFQWEhcOSIbbx99k4ry61sF5Yh4MgR+fVM+KxcmUxa2fUEyBS5aVMx0qLRqSt8DX/+pnDU4Y574MEH6efX+OJIxtChYj4Yjn7YyUnpNrKyyH+kuBiYNo2WBQVZOoGr6TS//DJNP/3UtfqYzRSSuaiIwoQrfdA14lFhzLoDxHL9JCSQ5vPECcv1Wt8BX3wB/PKL8/VjOEo8+8MPdCyAfN+0uNWwUf6gINtOJkNrvlFHwpjG6OQcGd59V5s2PBBhWl0nc4NDLp3ZN9/YLuvTR1u5cmZ+SvnKli6lHHTNm4vLmM9fr15NbLZnqRbVaAJZ3jlHaaKYGXe1amJ6BIDSBjDGjXN8PA6HY4svyhYcH8RboymeOiVfpx9/pBFnd1BSIkaX00p0NHD//TQ6K0WaK6lLF8pbo4S1+poJH84wZAh1dvbupdw4zZpRxDwXMTTPGKO0lDQ9kZGumXQOHQrExcmvEwTLqIOu0K8fHWv7duDFF0WB2xHS+9/e86k2iAdj1iz76x3lNuJwXKWggExiAaB1a+fKeOopcZ4FNbEmOBh45x3lMjIyRAFJiV9/VTb7q1ULePJJy2WJicplHTpEUzXC2N130/Svv+xvJ/Wfe/xxiqwpJSZGfY48DsE74ByGt/WNvRZ+oVzDG186Bw4ADRvKdxqTksjuv6hI/+NOmUIjnFITwbNn1e3LtDTz5lkub9nS8n9hIbBuHar/+KNtGX//bfn/zjvVHVsOJqRI/Ry6dqWoea+8Auza5VSxXiGMASSUA5aCiJ7vgjffpBDlUl8rNZw7B8yfL7/Okb+KNVOnqttu1Cht5Wo12+Jw9EYaHENOw6UGpjkCLH3zbruNIiYCFGXSOjeblNat7T+XDz9MefycrRfzJQNI45yXB4SE0PfNEffcQ1Ophotx8iRN69a1FURfeYXehey3bZvv+4FzOJ7GG/vGHI5HYWZXo0ZRgt+XX7Y1WZN2wr/+mvLtWHfG332XPtJqBSoWAUtqIvjvv5qq7pCwMKBnTzRgiVpjYsSEvQsX2m5vMgGbNmk/Drte1k7nAwfS8Tp1IgFSowDjUWFMjWbKXcFOmFlh1aq2Gsq1a21NA9mLe+1aYNgwS40oQ8u1/vNPZbNEDsfXYaZ18fH2hSW1SIN5NGxI7zhBAEaPdryvyWSr3WKsXi2fxNsedeqIkXN/+EFczvzQ5KKZysGELDmHf6btY0E7OBwOx1D8RXr99VfUUBsZzR14k4ZR6rPy1FPkY7Ntm+U2Uo3T4MEkNN1yi6Uf0bhxlGS1QQNgzx7Hx7XODwTQsZWct11lxQoSAFk+GmaWYk18PNVDbRtdvy6eLxtdlSMyUj5CmB08KoypCQsuvSaFhfQ+kNM6akEQLLVH0nxDp0/TaLk03H5xsa15q5yQKL2P9u8XhbxPPgEOHbKMc922rfZw3xyOL7BtW9WbIeTlEis7Q7lywPLlNO9MJN1Fi/SpB4MFlvz6a3HZ6tU01erDJgfzAbaO+sjRF2/qF3GMgd8DMuTnAzNn2r84Y8aI4Wl9ic6dcYu9JJbOMGIEsHKl/W28SZgVBNRassQybw/TCln7iclprM6fF6NbWdOhA3D5sv3jV7ANfoUffgD697e/n7P07Wv5n3VQ5Hj5ZfVhsrWarTHNnAo8KoxVqya/XGo2JH0XfPklTZOSqHN15oylBvXXX0U/FSkbN9JzMmECaVE//NAyfLwUpY6edVAOOSFXWpd27UQz1JEjgX79xEzL0tF0I3j8cWOPz/Ffli8HRowQc4a4mhPr77/FgSdmiuesGW58vGt1kdK+PU03bqTvTpl5OgDRhFINzOTR2kqA+f8mJLhWT4483tQv4hgDvwfs8Mor1NFcu1ZcZi2YffSRZUjsQObTT9WPwnmD9F+uHMLffVd+3Qcf2C776y9tH15pYkxriouVr8HKlZoEFqfZv9/+ejVtdO6crd+aI7ZuVb1psLaSXUPJBPH338X5qlXF+ZkzxXkW/eyll0Rzv86dadqokaUwL+3Q/P677Wg9a5e8PPV5guRe5HJml99/b7vMOqGtpzl3ztjjc9zD118b7zv0+utAYaE4UqE04KIWaRRD9i5wNsektblk9erOlQNQnkqAvivTp1O6FVavdu3Ul3PbbfSdO3LE1v8YsDTP5HA4HI/AzHauXrVd58tSLDNfMAI3Xrf09HRMnToVpaWl6N+/P1JSUvQ9gNYoXLffTr4+LVrYrrv/fvuaKWZKCND9FxFBnRu9wldLBxiUOHPGsbO7M1o8DakAPKoZY07qrjBrFmnUpSZ/9vwA7YV1r1ULeOwxdce99VbSxEmTv8qZu7pL6+oKctpDju/zxBNi2HWjkA586G2W76owNnEiBcTYsIH+N7GNTK+JiAiKMjVlimi22KePOvNrBvvGSdOiHDtG0zZtXKufryEI5CMnDf7C4bgLb1BQeC3eGobdVXr39vwxi4vJV4VFJczKstREuNgRLikpweTJk7FgwQKsW7cOP/74I47KRYXSwptvurY/AERF2S57/337gpg1x45R5/7VV12vD+Phhx1v88AD9oXnkhIKn66V225zvE0ZLglj6enp6NatG+Lj4zFfKeSghCVLXDkaUVJCo++1a6vbPiNDfrmjcPBydO6s6doCcF9AEi1ojSDJ4ajl00+BZctO4O+/gUce0bdsJow5a6YYHU3WEwkJ9G10FPLeEW+8cR4AvduZYLdggbYymM+v1GeaDdzpff28naIi8rHmuVk5HINhHVGplsIeM2Yod66U6NEDGD9e2z5S5JLRSvnqK+Dzz7WVeeUKmSNs2EBmmocP0/J//hH9UJhpUU6OfU3HmjXkS/bll+Srwj44I0ZY5iKR0z5qICMjA40aNUJERAQqVKiAxMREpLma50qvXCnWSTS1ClXMH8hR/hfG4cP6mgfl5NB9xkaA16+nYBAs2bRWpKZ+DnBaGHNGOtfD0d0Z5ATzF190/3Hvvltd/iEOx1cxmYA2ba5bmBfqhauaMSkvvEDabVfo0KEAlSvTu//IEbK+qFVLWxlsMEdqwj5nDk3t5TPjuAeWLoDj//iytZ1bEQRRyJALVc6SKTIuXyZ/EXuq/BUrLF/ce/YAP/0kho2dOpWWzZkDPPOMcjkssEhJCZnARUbaPrAHD5LPyP/+B6SkKD/QR4+Ko2CXL5Ow9emnlCz4oYfoOC1a0DVo3pwiAG7fTpEEV60iM4u6dS3L37OHTCPOngV69aLy/vvP9tjSZJEuvnCysrIQHh5+839YWBiyZEzAzGbzzZ8rXG3fHsVKiT+tad8e5kOHYDab8Y8zCW4l5pHXW7ZUfGhLK1GAPHNJCY599pn24yhRty5QpQpKa9ak65aYSMEgJk60u1uJgn9Gwd69qtvBaWHMGelci7bSH/jjD+D//s/oWnA4vgkTxho3NrQaN6lYUUBSEsDe/XI+X46IiKDp8ePiwCtLm6EUfZfjHOvWieakcly6RAOx1sGa1DJzJqWU4WgjLU05oJYzfPCB+oF0f0eTtVJuLqJatgTuussy8pIg0CiyNMGiNAqYtGOekUEdZtZxNpmoMfr2JcEmPZ2WSTVKRUXkbNyhAzB8OJkX/PKLuH7VKtpn9mzS2IwaReUx876zZ8VjDR5M9ZT6hyhFQWralEbQrl2jcyhfXt5cQupkz/q0bMQMsEwUOW4cmW506SIuk/NfkWpv5MK8u4GoqKibP1eoev/9CNZg1hZVqRKioqLQjAV1sCY9ne4nB6HfKykJL/fcg3IZGcCyZYiKikKThx8WP8o6Ua6oSN11K7suQV27Wi578EEAQJVRo1S3g9PCmDPS+X//6fgG5mhCr1ESDsdTBAdTWguWz8sbYNYc1aoBH3+sff+aNSnK740bFFwoP180I/b0yL1WM3NrWH+otJSm33yjbX9nB4hPnlT37e3ZE5g7V/mY58nqVLVl040bNFDKknCPGgXs2kX9zNdes4yuK8e2bb6phVu/ntr3zz/l169bR1FQ1XD1KtC1q36uPIIAjB0rRjsNZDRbKzEByDrSXLlywNKllsuURpWZdkzar2DajZMngdhYmn/oIXG9XKLHuDhxntlrv/CCGOlPGohDKhiyXCPS9Y4S+VapIs7LaQikNxOL8CYVVqV5kJi5hfQBkAuTLh19sH4paSQsLAzn2csL1BcPCwuzv5OryT+loYmnTKGpUpCPJk3s2+/ffz/dX87a5W/bRoK1NODDc89RfaTpBuxpXNWg5pv42GPAG29QEmwACA2lAYLNm+nlNHiw6sO5PYCHVCpculSloxdHV2rU0G+UhMPxJImJYiRDb6B5c3rHXr6sLXAHw2SiJNYmE/UfWB/C0xoWZ53ACwps0xewACSDBtluay+4klQwuXZNzLXmiIceAp5/HsjOpv+//+7YFcWeoOtIQMrPp6Tfs2aRYDJ9uuWgbt26wHvvkSaXlXXuHPnC5eeTkDZjBvVBXnvN8fl5AxkZlW4Gv2Lms5Mm0XW3vpY9e8r77kvJzydNJLt3/v6bfiYT8MUX4na7d9v2j3/+WRwE+fZb2ufUKfrPlBCFhTS9cYP8yb/5hq47C94jRa69mzSxLDc1FXj0UXH9+fO0LikJaNXK/rnKUVBAQqM7/bc1Wytp8GfxKpwxf3MWuWiMUmFMLqEly8kkRSqMsXDgTtK6dWtkZmbi1KlTKCoqwrp16xAnFWblYHmXrHnrLdtllSxz5EIQ6GPXpQuweDHljBIE+z6DSoLQ/feL884mA1XSLGZkWI7yfPqpbUJrLQwbprxu2TLKT1a+PL2cQkNpuQu+WE6HtndKOucYgjOdRg6H4x4aNyZBYtky6twD2sLj64G04wbgZsftdgfhfYcOpXpLadZMnJcTev79l77vqamUXqBXLwrexfqJRUXiYPXFi/S+2rsXaNtW7BesWUOBU8qVEy2e+vYF6tUTB0N//pkEuvx8y+PXri1G3x05kn4NGogd45wcqtuNG3TM++9virQ0qgdLZfDzz/SzR1GRreXNt99a/n//fUv3EYCOExpKwhtb168fncebb7ouqGsNg71vH/DYY7chONgyfUv//mJS8u+/pxyD332nXM7DD5PCZM8eGoCQ5oWtVk0U4IYOJbkgNNTS2uuOO0gr3r07/U9NFQOapafToLS0X1ZSQoP2Fy9SzISMDLqeX3wBPP20uB0TxvbtA2JimmPECFGZ0bAhuTNNmED/i4ro/rKOunz1qqUsk5tLGuJ27WjfZ58VU/O8+CL1vz/4gCzaevYkRdDVq9T2cvlwnUHOWilDpsPMrGNq7twJLxrnCihcsVAKDg7GxIkTMXToUJSUlKBv375o2rSp/Z02bMDJFSvQcOBAevjatSONKBMiAPLp696dBJhZs2wj7jnSOKohPV2cDwmhj6E0RLkjtETQq1CBwvX++Se9CN5+WzlxtRaOH6d6SF8KNWvSebjimC44yY0bN4S4uDjh5MmTQmFhoZCUlCT8888/Ftvs2bPH4r/oLs1/nvzdf79gt130wLrMQ4cO6X4Mb0XrubJ28QR6t7VceYHS1nqd51NP2T6jmza5VqbWdv7pp5+E8ePH3/y/cuVKYdKkSYplXrwoCC1aFBj+LgvEnyttXVxcLHTp0sXiO33kyBG75UmPPX+++nq+9pr6bevWdf26/Pyz+m2vXze+HeV+JpMg5OQIwsKFglBS4nw7C4L2Z1rYuFF7hWvXNv6iWf86dLC//t57XSu/Y0d969u5s03befw73b8/1WX5cuVCPv6YtnnxReVt2DkNG6bu3K3p0kXdfpUqqT95pWPl57vedkuWqK+HDPba2WkzRal03qNHD3Tv3t2xdO6AJk3I3CMQsdYM60WjRjyPD8cx7733Hh566CEkJSXh+eefx2XpMDZHV1gExi++EEPk33WXcfWxB/Mz3bDhBP7+m6vYjUDq76t1RN3VMNhacskytwk1yAWc04rUDckR7vq+uoogAHXqkObO1Ry3mq2V5PprUrVtvXo0nTRJXPbVV7b7aImwxHy8pDA1JEB2sAy5Bh41Spz9RqknAAAgAElEQVRn/mrR0eIyuWg8O3bI10XOdE9O86IUaIKpifv2FZc5SjQ7bx6wdav9bTzBuHFAeLilr541avIy/fwzqckFQXkbPSJw6WHfW7WqZaAYZ9DgA6YVl3zGYmNjsWHDBmzatAnP2QtbpZJvvyW7anaPW6MQPdIvcCSEVq4s7084fLj9/U6cUJ+TjRO4xMTE4Mcff8TatWvRuHFjzJs3z+gq+S0s8FKLFqK/ldYQ+a6ituPG/ExbtxY7KYsWkbmV0vBhnTp0Po6GGUtK6HtfWEj9peRk8bidOgGOrN7ffZd+2dlkcfP772RWpnS8K1doWlpKxxYE8lU7e5b+m80UCO7QITMEAcjLI/Oz06fJ5O3IETLR/OQT13KjWbe1tP/JkFrTSP19tfr8ejoMtq9St+4Nxxu5mQ0bXGsHzb5EjRrhnFTQAihoBoOFpl+xQlwmFUzYAyq9gVmeLsDSGZAhl79j6lRxXtoRkgu5KXVMZcKi1PRNblRLKUxtw4a2y+T8mIIl3jzSlxQLfCIVFNq2lT8Ww1scoNu1I8fWunWVt4mJoalSdEoA6NaNhNGePZW3Ybnh5MIPM7t2e9GfFi7UFN2qwN7I5gMPkF26dABALR07at9HCy7p3DSq5KSfxgEDBGH3bjIf2LWLNOZSatYUhHbtBCEujravW1cQjh8XhKlTBWHKFFr2+uuCUKWK/Kf3zjv11SzL/d54w/J/y5ZkatCggfayLl4UhPPnLZdt2ULnCwjC2LG21xCw3QcgS4KFC0kDrKZd1q9fL/To0UNo3ry5kJGRYbFu7ty5QteuXYWEhAQhPT1ddVsHiumaIPifmWJqaqowevRo1eUFSlvrdZ7HjlH7S826XEVrOztjZr5x4z/Cv/86LruoiH6+itZ2PnVKEN56i9px1ixBKC0VhKZN6f+MGbbv59JScf6226iMoiLLbW7cEIQrVwTh6lXb42lpa82ma4Ig7NtnW+eQEOe+ke3b6//dve8+fctj96q7+wstW9pfb/XpderdvWXLFiEhIUHo0qWL8Nlnn9msV/xOHz4sCGx+1y5ByMoShDNnBOGWWwQhO1s0T0xLo/UHDwpCmza07I8/BOG//wSBlb1kiSDs3SsI164JwrvvCsL33wvCwIHiQdkJT5pE03ffpQdl0CDL9SdO0PSHHwThscfEB0N6wQBB2LZNXHbtmuWD9tdflg/crFni/Jw5NDWZxGW5ueL82rU0feAB8QFnL29AEGJjbRtRaju7ZYvterNZtt281p3gyhX12xYW2p5vYSGtO3iQOrnWxMfTdhs2iPsEBVmWoRHzvn10zzpCy8Nrz5xTA/baWfuZunDge+8VhLZtrwpWJutOcfkyPWMJCXStUlMt2660VPTNYO8kpZf4yJGW/zMzLf/Xrk0mtuzdsW0bPVPSZ/zNNwWhoICOM2SI/XY9fFgUMq1NYdk2o0bR/3Pn6J138qTlekAQVq2i9dJlo0eL2yph3S5Hjx4Vjh07JgwaNMhCGDty5IiQlJQkFBYWCidPnhS6dOkiFBcXqyozUDroguB/wtiwYcOEVatWqS4vUNpar/MsLrZ8Zt96y/UyPdpx83P0OE/2fp850/b9LwjiPBPGBIH6tGreDVra+o8//hCefvrpm//nzp0rzJ0712F50vpGRtKy6tXpf1aWIPz+uyCMGUPfwEuX6Hul8MoQSkupHzZvHg2+pqYKwrhx9A396Scq888/Lf26Skvp/4gRtE4Q6Jt/4QLN16lD202cSNsuWSII0dHUn9fSx7rzzoKb9bTnV3b6tPxyNliq5seIibFdt3277XXzKt/uBx6giqalics6dRIbTwt//UUdFyX++09saDnkHhLpsuefF4Rvv7VYfXT1ahL62LZdu9LI+TPPkAB34wYJYtJOnSAIwi+/0PqSEhr5FgTqyE6aJAiLFonbMmFxzx7L/fPzqczGjWnZpUuyp+S1wphWpDf1gQOOt+/Z07YDLx3BadZMcxVUn+e4cdofXhex185OR1N0hhdeAHJycnD77VUcb+wAZrL47bcUDjc+nsyRWSQtk4k0kXv2UGQqgKI3bdtGOWqmTqXgMA0aUFljxpB/FUDTK1dovw0bKOoX0z4rJeKWRgh9/XVg+fISjBgRhLFjKbT81atinZs1o2PPnUtJ46WsW0f1ZtFPw8Mt04BkZFBdMjMpgSzLTxgebpmrUAtNmOOKFWlpaUhMTESFChUQERGBRo0aISMjA+08HfqNowtPPfUU/pNx1hg1ahS6ltnOzZkzB0FBQXj44YcVy7E2obl+/XpAmDfpe56iuVlMzFGYzZ43k4qNjUUsywPE0ZURIyjVzD33iMtq1KDw7gDlNnvmGfrSM5TS9riC1HQtLCwM69atw4cqslx/9tkpnDkTgeBgiugIkNmmIJClWmiomI6penX7OfdMJgo2xvzP4uPFtEUtWlheA+l8xYoU1I1RrZr4DbV+jQ0eTL9r1+i6du1qGQkSIAu3774Tw+AfPw5cu5YJ9ixK019dvEh+Zp9/ThGuK1Sg0PYsaXtyMllPAWJ+Yynp6UDnztQvsI4e/ssvthEU770X3s2iRcC0aXRSjOXL6QJpvXEd5QaoU0dz9ZCZKfo4ffqpzeqipk0pvCdAN3JwMN3IUt8PZj8s9ZV64AFxnpllspCerMMVFka+dNOni6GrX3+dpizk5j//0E1bvbr2c/MltmwhH8P/+z/LSI1KfP45+fnFxZG/4tixYojUGjWA335zX13Hjwfeecd95WvEo8LY448DZnO+4w01UKOG+GJ/4gnLdbffDhw4IP6fPp0ErZQUyhEnpWFDui+YSX5ICAl3y5aJfqJyrFpl68vWuDGwe/c/Fvb9ISGW25hMtnUAyInXniNv69aWCd7Dw4GXXrIM3asXWVlZaCM5eSV/A45vsMhBXo8VK1Zgy5YtWLRoEUx2bLSt/VbMZnNA5K9z13l27Wo/nLwa9u7dq0NNOHrRu7coWMyZQ24rdesCBw/SsuRkGugbO1bcx9W8rHI4FQYbwAMP5NvkDmNJvr2ZypVJXigqoj731auUNuHiRfLD++orEsKCgqgvYD22smsX9dNr1qT/I0aI6269lb6zCxdaxolo3pz6AFeu0P+4OEqnxNq/Tx8SzBnly9Pxpd9xr6dRIwo+IaVhQwoX7mlSU23zV7CRdDXUqGF/vdqbnPnPlZbSTdOgAf3PzbU9Rvny3uMv5k5iYy1vdkeEh4uJvdl1Z76ATzzh3oAHISH0kKani8nJ5di1y311kOBRYcxoqle377c3dKjl/7p1LV/GcvTq5Xq9XMFkUpdDUKoVuXbtGiqXjeBItSKuItUaBIq2BNB+rhUrNkdhYTmvuj7p6elYsGABvv7665v3Bsd9pKaSX/SQIUbXhONunn1WnGfB2oKCbHN0sb6I3pE1A1UDWrOmKFRJg6bYSzUk1WTKMW8eBY2xDsLSvj1pvPLzbSM3tm5tqzzavJkUCL/+ql+esYCBjb4bDftOduhgudzT0Zj8BZbjsmdP0iwqJarWG6apbdeONHsnT4oPbHKy45eCTgSUMGY0hw45HpRxF1KtyN69e3G3UoQhCVpD5Uq1BoGiLQG0n2tmJgX08cT1Uasxefvtt1FUVITksmhRbdq0weTJk91ZtYAmPp46ZFKrHw5nzx598pJy3ENwsDhwL2XlSrJEkyaCtkfnzuT+cP26vvXjeJDq1UlrIhclkKOd3r3JLLFjR8+q4OvUoZC5oaGkwbzjDmrXW25xLYmzRrgw5kF8TTaJi4vDmDFjkJycjKysLGRmZuJOT41W+DHh4fTzJjZu3Gh0FQKOBx80ugYcb0PFGBnHC6lRw1ZBogZvzX/GUYmHtCYBQ6dOxhyXmZgyDGhXLoxxsHHjRrz99tvIzc3FsGHDEBUVhS+++AJNmzZF9+7d0aNHDwQFBWHixIkIcodjA4fD4XA4HA6HE4CYBEEav0hfuFO596LGTFELvK29Fz3bmrez98Kf6cCBP9OBAX+mAwf+TAcGSu3sVmGMw+FwOBwOh8PhcDjylDO6AhwOh8PhcDgcDocTiHBhjMPhcDgcDofD4XAMwGPCWHp6Orp164b4+HjMl2Y99xHOnTuHwYMHo0ePHkhMTMTixYsBAHl5eUhOTkZCQgKSk5Nx6dIlAIAgCJgyZQri4+ORlJSEgyzbJ4CVK1ciISEBCQkJWLlypSHn4054WwdGW/N2Dox2BnhbB0pb83YOjHYGeFsHSlvzdvaRdhY8QHFxsdClSxfh5MmTQmFhoZCUlCQcOXLEE4fWjaysLOGvv/4SBEEQrly5IiQkJAhHjhwR3nvvPWHevHmCIAjCvHnzhPfff18QBEHYsmWLMGTIEKG0tFTYt2+f0K9fP0EQBOHixYtCXFyccPHiRSEvL0+Ii4sT8vLyjDkpN8DbOjDamrdzYLSzIPC2DpS25u0cGO0sCLytA6WteTv7Tjt7RDOWkZGBRo0aISIiAhUqVEBiYiLS0tI8cWjdCA0NRauyTN0hISGIjIxEVlYW0tLS0Lt3bwBA7969sWnTJgC4udxkMqFt27a4fPkysrOzsW3bNsTExKBmzZqoUaMGYmJi8Ouvvxp2XnrD2zow2pq3c2C0M8DbOlDamrdzYLQzwNs6UNqat7PvtLNHhLGsrCyES7LchoWFISsryxOHdgunT5+G2WxGmzZtkJOTg9DQUABAvXr1kJOTA8D2nMPDw5GVleV318Iafzs/3tby+Nu58XZWxt/Oj7e1PP52brydlfG38+NtLY+/nZs/tzMP4KGRq1evYuTIkRg/fjxCQkIs1plMJphMJoNqxtEb3taBAW/nwIG3dWDA2zlw4G0dGPh7O3tEGAsLC8P58+dv/s/KykJYWJgnDq0rN27cwMiRI5GUlISEhAQAQJ06dZCdnQ0AyM7ORu3atQHYnvP58+cRFhbmN9dCCX85P97W9vGXc+Pt7Bh/OT/e1vbxl3Pj7ewYfzk/3tb28ZdzC4R29ogw1rp1a2RmZuLUqVMoKirCunXrEBcX54lD64YgCJgwYQIiIyORnJx8c3lcXBxWrVoFAFi1ahW6dOlisVwQBOzfvx/VqlVDaGgo7rvvPmzbtg2XLl3CpUuXsG3bNtx3332GnJM74G0dGG3N2zkw2hngbR0obc3bOTDaGeBtHShtzdvZd9rZJAiC4IkDbd26FdOmTUNJSQn69u2L5557zhOH1Y09e/bgiSeeQLNmzVCuHMmwo0ePxp133olRo0bh3LlzuOWWWzBjxgzUrFkTgiBg8uTJ+PXXX1G5cmVMmzYNrVu3BgB8//33mDdvHgDg2WefRd++fQ07L3fA2zow2pq3c2C0M8DbOlDamrdzYLQzwNs6UNqat7NvtLPHhDEOh8PhcDgcDofD4YgEu7PwvXv3urN4jgvcfffdupbH29p70bOteTt7L/yZDhz4Mx0Y8Gc6cODPdGCg1M5uFcbkDmw2mxEVFeXuwxqON5+nux5UaVt78/nrjTefqzvamj/T3gd/pvXD28+TP9P64c3nyZ9p/fD28+TPtH5483naa2ce2t5ACguBihWBzZuNrkngUVICPPQQ8MsvRteE46ssWwb8739G14Ljb5w/D9xzD3D2rD7lxcXFISkpCb169UKfPn30KdTDHDwIxMQA+flG14Tj73z8MfDqq0bXgmM0U6YAkyZ57nhu14xxlHnuOaCoCOjSBeCee54lNxfYsAHYuxe4cMHo2nB8kYEDabpkibH14PgXn38O/P478Nln1CHQg8WLF98M/eyLjB0L7NgBbN0KJCYaXRuOPzN6NE3fe8/YenCM5Y03aPrmm545HteMGcjffxtdAw6H421cuAC0awdkZhpdE44RsNylfICOw+FwAgMujHE4HI4XsXQpsH8/mctwAo+y6M26CmNDhgxBnz59sHz5cv0KNQAuoHI4HH/EJTPFuLg4VK1aFeXKlUNQUBBWrFihV70CAv5h8RzXrwOxscCsWUDHjuJy3gYcDsddpKcDLVoAoaHq92GasdJSfeqwbNkyhIWFIScnB8nJyYiMjESHDh0stjGbzRb/r1+/brPMU2zfXhWRkYWoX7/45rKrV28FUA2nTp2C2ayf45iR58nhcDgMl33GfN0W3Vt44w3yDyguBoKCjK6N//Hnn8Du3cDIkcCuXWKHh8PhcNxFbCwQGQkcO+Z425Mn6R2lt5liWFgYAKBOnTqIj49HRkaGjTBmHX3MyIhkLVsCNWsCFy+Ky6pWpWlERAT0rJavRl7jcDj+BTdTNBDpx5Y5ahcUGFMXDofD4ejP8ePqtrv3XqB/f32FsYKCAuSXhSAsKCjA9u3b0bRpU9cLdjN5eUbXgMPhcDyHy5qxIUOGwGQyYcCAARgwYIAedQpoSkqMrkFgwM0TORyOqxw5AjRuDJQv73pZZ87QVE9hLCcnB88//zwAoKSkBD179kTnzp1dL9jDcEsGDofjz7gkjPmaLbonUXOeBQWNAVS2WHb48GFUr66TswDHBi6EAefOncPYsWORk5MDk8mERx99FE8++STy8vLw0ksv4cyZM2jQoAFmzJiBGjVqGF1dDscryc4GmjUDhg0D5s7Vr1w9hbGIiAisWbPG9YK8BP7+5nA4/ohLwpiv2aJ7EjXnWamS7bK//26OJ590U6XKCERbdOuR1UAeaQ0KCsJrr72GVq1aIT8/H3379kVMTAxWrFiB6OhopKSkYP78+Zg/fz5eeeUVo6sbcPAOp2+Qm0tTvRPH6x3Awx8I5Pc1h8Pxf5z2GfNVW3RvQq7T9e23nq8HJ7AIDQ1Fq1atAAAhISGIjIxEVlYW0tLS0Lt3bwBA7969sWnTJiOryeH4BO4SFLhQLsKvReBQWgpcvWp0LTgcz+K0ZsxfbNGN4MIFig4lN/LJRwA5nuT06dMwm81o06YNcnJyEFoWg7tevXrIycmR3YebHjNI8633uWdn1wIQjtzcXJjNWbqWzdEPdwkIPOkzJ1A5fBi47z7gv/9IIKtSRb+yS0qArl2BCRNoyuF4E04LY/5mi+5JQkOBtm3tC17ffQds3gw89xxw552eq1ugEcgdnqtXr2LkyJEYP348QkJCLNaZTCaYFG5Qbnpsid7nXmb9jdq1ayMqSl3akEA0PfZFfvoJqF3bMtehEoH8brLGHwcpb9wAZs6kdCsVKhhdG++gdWu6LoB+wtj69cC334bi3XeBLVuAQ4eALD7GxfEyXI6myHGO/fuBNm1sl7MP8KOP0nTuXMuP8qRJQGIi0L69++voz/jjx10LN27cwMiRI5GUlISEhAQA5PuZnZ2N0NBQZGdn8/yBfk5cXByqVq2KcuXKISgoCCtWrDC6Sj6FM++QHj1oqkbQ4sKYLf50TT79FHjlFTone665SgGXpOzatQvDhw/HrbfeCgCIj4/HCy+8oFtdn34aqF4dmDFDtyJlYYKYniQmAkAdvPMO/Q/0bz/HO+HCmIFoMVOcMAGYNo3m33rLvz5KRhDI108QBEyYMAGRkZFITk6+uTwuLg6rVq1CSkoKVq1ahS5duhhYS44nWLx4MRe6ncTd75BAfkdZo7YDPX48WZTs3One+uhBmcs9Ll+2v51SwKXbb7/dYrv27dtj3rx5bqnrl1/S1N3CmCfgwhjHG+FJnw1E7cf2+nVREOO4hvU1D8QX8969e7F69Wrs3LkTvXr1Qq9evbB161akpKRg+/btSEhIwI4dO5CSkmJ0VTkcr0fvd4g3RVO8dAnYvp3m770XGDLEmHqo/Va+8w6wa5ft8unTgb/+0rdOrqK2nZUCLvkbe/ZY/tfjuZIGAuGDGxxvhmvGDETu5bBune3y+fM9Ux9/RunFHogv6Pbt2+Pw4cOy6xYvXuzh2nCs8eQ9OWTIEJhMJgwYMAADBgywWS8NThK4gVrkOXq0AoAmKCoqhNl8XGEr6yAv9P+bbzJx113XLLY0mVpAEExlHe0w5OZehNl83rmT0Ik+fUjTdOUK8Ntv9PviC/cd7+239S+TmQFOmAAUFgILFgAxMYBWV8+LF4FateTXLV8ODBxI2q7KlW3XX7gAXLsGNGxIQmG9es4FapEGXLJm//79ePjhhxEaGopXX31VMbq1c8+0e4IVSenQwbJBjhz5BxculLhUZm5uEIBmAIB//jkCoClKSm7AbD5qZy+qx+DBuTh8uCIWLz7pUh3uuacZHnroMiZPNvZZ5ng3XBgzEKURMWYSwHCHHbU1gZYIOBA1Yhzfwt336LJlyxAWFoacnBwkJycjMjLSbp7IQA/UYg3rRFesWNHh9tbrBw1qDEEg4WDnTiA2VlzH8nfWrFkLUVG2vX9PBmth2oriYs8cb+JE+eWuPAusnYqKaPrMM0BwsLbv6rffAgMGkNbtnnts148fT9/zs2dJYKtYkSImM8qC1EIQKEhF1aokHErr5wh7AZdatWqFzZs3o2rVqti6dSuef/55pKamypbjyjPtrudf7ho0a9YMu3aRdvbxx7WX2bu3ZQAQJpyWL19e1Xl8/TWZb7t6zvn5wPff18J33ylI8pB/pgOtTxbocDNFA1F6Cau1QCgoAP75R5+6MLv09evXY/ny5Vi6dCmOHj2K+fPnIzo6GqmpqYiOjsZ8rqbjcDyCuzVkrNNfp04dxMfHIyMjw70H5NgwahTwwAOWJnTlyr7K3mCm6K578PBhoEEDEl60kJdHGiqAAmB9843jfUpklCtahcuNG2n655+WywsK6BpJr1OdOkCzZuL21ywVoADIfE5LO8sFXJISEhKCqmXSX2xsLIqLi5HLspL7AKtW2S776y8KvjFwoHNlrl4NLFsm/mdtJAikHb1+3blyPQXvkwUWXBgzEKWXcDmrVlEy3ejVC2jeXJ+6BEoi4EA0S+T4Fp7Q2hYUFCC/LIJAQUEBtm/frmjWxFHPH3+QoKGWgwdpmpsrtnt2Nk3lhAhPkJ0NvP56fVy7Jn6j9K7Lp5+SIPbDD8rb7N5NWhEpTz5JqQEAICMDGDTI8bH0EGpZGSYT8PDDlHImL480XJMmiduxNjx7lnJltW1LkQjlUOszphRwScqFCxcglH3cMjIyUFpailpKNpVeSJ8+tsumTHG+PHvX9Nw50o4+84zz5XuCQOmTcQhupmggaoUx6w8Sw13PoDOJgH0VLpxxApGcnBw8//zzAICSkhL07NkTnTt3NrhWvs/dd9PUmfcK+x6wENxGCWMTJgArVtTEN9+I52FEXTp2JN+ubdts10mv76lTpFkZMUK+HD3qzsooVw5Yu5bmR4+m6axZ8hER33iDpv/3f/JlqvUZYwGXmjVrhl69epUdezTOlqkVH3/8cWzYsAHLli1DUFAQKlWqhI8++kgxT6SUBQvqoE0b4H//c7ipx/n1V+f3ldN8Wl/nr78GlizxDZeFQOqTBSpcGDMQvcxQBEF8oRQVUQ4zObt2NTibCBjwbmf/EycqAbgNhYXXYDZnIi+PHHtLSkpgNrtm6+lt58rhOCIiIgJr1qwxuho+jbsHcow2UxQE+5qx69eB998ngSkuTlunVu21k4uMaL1/z56kJXvkEfltpdfx9Gl1x7WGnX9QkLjs559pyswmAQrUwZg717YcqZ/alSs0dXQt7AVcYgwaNAiD1KgJrfjoI+rUP/QQEBkJTJ4sCplGU1jo3H4zZ4r5/KTIPU+ZmcBtt6krNzOTIopu365+Hz1wtk9m3ScJlH6Kfufp/qA1Urgw5mGko3xKH1tXPsIvvQR89hmZyjC7dbW4mgjYm539WU6XihUrIyoqCmwwKTg4yOV6etu5SvGksz+HE4goCSGuCidGacb+/ZemUl8oubq89x7lvARIwzB4sHx5hYVUTqVK9H2yNkm7eJGO2batcp2sr6X0ejFXx+nT5feVfk937FA+hhyPPAJ07iyWIRXGRo603b5TJ/vlSY/vjsiRzrJsGfmxjRnjvDD2+++kIVSbnjI3F1i4UAxu4oiXXqI8Z/aE11OnyA9z1ix1ZX70EfDBB3RvOmLRIjJxHDwYWLECOHkS6NCBfNvKrAkBAGlpQJMmQM2aQOPG6uqhhCt9Mus+iTf3U/RE7/PUsyx7/THuM+Zh7r9fnFd6qVibKcoh3Vc6//vvNJWO1qnBUSJgALomAjabyfnZU/iCKQKH8/ffxnXCHVFcDBy1FxE6wNBDM8ZMsbxJGGPBKrZuFbUTcnVhA1wAsH69cnkREWK49xkzSLhhmEwUwKRdO+X9jx8H1ChxmfmgNVJhrGJFcT4tDWjZUhyYY9y4IZq5rVpFwgkrQ8232R4PPGC7zBtM5U+cULddVhYJ3nLpDe65B+jaVf0xGzemlANPPqluezUJp1n7HJfJNPHHH7bLPv0UmDpV3fHZPbB9O2mCly+n/+vWidscPkzX4LbbgFtuUXYxUYOn+2QcY+HCmIEoacDUCA4rV8ovlzOnUIMnEwFfvw60bAk89pjLRbmMN3wIOb6NXoL+oUOU+4hpG7xtAGHmzHpo2pTMdXwZpqnRC2fbqW9f++uNFsqXLhXn5eoivYa7dyuXw0z3pN875u9cUiJqtuQQBNIyWCMneEkFpa1byXyyRw/L41aoIM6/9x5N9+4l7ciDD9L/kBAy2Tt3TtxWTjOmF0abowJk2mdNQQEJWFIhJjychKehQ6lt3nmHhHc1BhilpRThsEkTutbMTFMrSs9uaSnw1VfK+8kFCQEcD1xPmUICqPS4hw7Z+vzdfTfQooW4jVwUTS14sk+mBpOJtJMc92C4meKZMzSC4G0dD0/gykt44UJxXvqScPaj4clEwMxu/pdfdC1WE4F4v3G8mzNnaOpsJ8WdCALwww81AVCkOFfNb4zi9GnS1MyeDQwfrm/ZBw5o237FCnF+82bb9UYLY1IcCWNxcdrKYJ8aRx1hpWsg5x8mFcaeeQY4coTmpd/ZY8fEeam2a+xYmi8uJr/rU6csO9Zs27w8+/V1BqmG0ZvYvZssbZTMFiJws0gAACAASURBVGfPpvxqAKUZYJw5Q99463fEp58CL75I83KaK7VIfeSlLFkCjBunvTyWf04JFoiF5YUD6PjWwpic5s0VPNknU8uMGcDHHxtyaL/HUM3Yvn3ArbcC8+YZWQvjcEUzJkVOGHPVnMITGKGV4powjq/gTffql18CeXk0duepBMDugHUkygJJqmL3bnXfqP/+E+ettYc5OfbbU85/yCiNiVy6FGuhaNs2S8Fmzx7gk09s83DZKwOwNBtkIf2dRdqpZoIYYHkdpREX09Joykz7AUoXw5BGSGQm9c8+61od5fjiC/rmM7M3o2H3KdMiKpkmS6+ltN1vvdU2wEVREfmjuRO5iJZqULIysubvv8V5OWGMw3EFQ7vs7ObeutXIWhiHK8KY0iiMNATvxYtkzyyN4OQNGKGV4powDsd5qlQR5119nxQXA8nJ2vJxaeX6dUth4exZ6qydOqW9rI4dbTvhJSX2O3/WndG6dbUPkBmlGatXz3aZ9UD8/feTyRlj/34KaNG2rbKmRyr0MKQdXK2aRWuUzGcdCbVSM0kl3zd7PnF6oRQC39Ow+658eZoyjb0rfPKJfoM4LLDMqFGieeSFC6LWzZny1HDokOV/5is2ezbvX3Bcx1BhTCo4BCKuCGNSe/affrItc+FCSo7Zsyfw+uvO19Ff4KNXHI7zhIeL89JO1dWrQEqKNvOt/fspMtnAgbpVz4a+fYHbbxe1JQ0aUMQzqYAjFcxmzHAcae/yZeCfsiwYLVqQTxIgJm7W25/o11/lzRfdjdy78t131e9fWEgmXXfdZbl80SLbbZcsEedd9bFRwpEwJtXOGYmrmkG9YNeLCWOukJpKz8zLL7teFkMQSOCfOVMMBqNF022N2jRdUs13SQlFUQTUC6v2fCM5HEPFIF8yqXMHepmhPPywbZnSyEMBkFqCw/Fbjh413pRbGvhg7FhRIJs/H/j8c6B7dzKzuvtuMkc6dQr47Tf5jj0TWtS+/558kkJV79unvr5MkyEVvk6ftvTTathQjHb20kskXK1cSRH2NmywLbNWLdGEz9p06/Jl/f2J8vLUhwnXE0caOUf5n/76C5g2jdpLqj2U+jnLkZSkrn5acdQJlpomGonWsPvuQq9B8qIioFs3ebNXVxAEYM4cmmfvku++c73MlBT7pqLSHHLOINUkczjWcGHMQPTyGZMi9yHVY4TL1+FmBBxvoLBQuznWPfe4x1dFC9J3SEaG2Glh77CdOyk66h9/UO6ehg0pQercubZma8w0raSEnPwddYaXLCETpLvu0m4imZ8PfP218vqaNS3NGVnEtSFDxDoy2LnKvUtq1PCeTr2rOBKSHeVkkg4csIiFRtK9u/31SlH2AhV2z7tqVqin+4nUp1IQgFdfpfkrV+z7KarlwgUaVHJnhOctW9xXNsf38QphzB3hYn0BpRFIZwSH+fPJ0dyXhDFuOmgM48aNQ3R0NHr27HlzWV5eHpKTk5GQkIDk5GRcciVBCseG1FR6rrt0Ae6809LM+OpV+QiK7D3AIs4Z+bxINWMAMGgQ0LQpsGCB/f2GDweqVSO/iowM0pQw88QDByjZ75o1wB130LZnz1rm5nnmGcvy9uyhaWkp5fNZsAA4f56uzapV5Mu0a5e4fWiocjJixu23K69zl+mcN+OqxYbUnMtRpDqO98E0dFo00XLoOQA6caI4Hxtruc5esnC1ONL26gH/pHLs4Vc+Y/n5FD7VVzr5ekbLGjaMHM3lkjdad6SMxpu0VL5yr+hJnz59sMCqFz1//nxER0cjNTUV0dHRmD9/vluOXVREJrS+HJHPGZYto+n27TSVmm/VrQtUr+75OqWnp6Nbt26Ij4932N5y75CjRy0DMNjjhRco/HWNGvLrDx4k06MGDUhbxTRg1sLejBm0LiyMouE98wxQvz6lR3nkEYry16mTujrZg/mBeGvYcXfi6neJJY0GvC94lK/i6FktKirCqFGjEB8fj/79++P06dNOH6tbN7rvXTUvdPY+uvtu++t37rRd1q2btmNYJ99Wm3jaFdyRFoHjP3hcGCsqEh9SqZnitWs0wukKo0ZRuFU5e39vROlDH6xz9jdvE8YYRoa29yaB0NN06NABNax6xWlpaejduzcAoHfv3tjEsrLqzIcfkn+Os7Lehg1iFCutXLniHQlWAct7//p17fu4SklJCSZPnowFCxZg3bp1+PHHH3FUKYY1PP8OqVBBXkD99ltaJ9W+AK5/O5Rg5lC+jBahG9D3GZFLJszRhppn9bvvvkP16tWxceNGPPXUU5g+fbrDcu2187Bhrg+YORs4zBlLKUems1KGDweee85ymSdynkrNoTkcazwujLVtG4XERJpnL4OCAgqdXL++a2WzqDjWpiW+pv3Q27bYW80UPUkgC19qyMnJQWhoKACgXr16yFEbYkojbHTQ2cTGDz1EEUK1cvEide7ffNO547qK9f2n5p3kzvdWRkYGGjVqhIiICFSoUAGJiYlIY4mXZDBiQMfo5Nc//GAZ7c8X0Sp0A9y00NtQ86xu3rwZj5Rlwu7WrRt+++03CA5eIExLL8fSpa6nVpBLZWAPptF2ZjBa7bvyoYcozL5c+gYOx0h01sGo4+efybGXhZT95htxnclEo98JCerKKimhCFh9+pDPgDXLl5NT5tGjQJMmrtfdE3z/vb7leaswZqSA5GsCuicxmUww2Wkcs1V4zuvXr9ssUyInpx6AusjOzobZ7IzAFyVbB0dkZpYHcDuWLCnCwIHODVHanmfUzTlH9cnLqw+g5s3/x44dA8B6vVTOyZMnATS8uU1ubi7M5qyb6w8dMuvmX5uVlYVwSbz6sLAwZMiEnWPndeFCEIBm+hzcR+jXz+gaEFrvdSnSjjyAmx352+04ynFhzLtQ86xmZWWhftlodnBwMKpVq4aLFy+idu3aNuWx+6lixXIAlG0Rjx+3fB+5i/Xrj6Fx4yKUlAAzZtRDjx6XsWNHpKYy1qxRt11p6WUcPnwGhYX0PfA0hw6Z+cAwRxaPCmNStfdrrynnLunWzX5nubCQRrrDwykZ5ZAhwGefyW/LQp7u2+c7wpjeeKsw5kmBiAtf9qlTpw6ys7MRGhqK7Oxs2Y84IyoqyuK/2Wy2WaZ8HJqGhoYiKirU6fqqPR6DjbZWqFBB874Me+fpqMyaNS3/N2nSBNa7NGxo2fGpXbs2oqLEdoiKilIUxvay7Kc6w86roMAtxXNUYH1vaWlrtUK3lMaNbUP3c/wHdj/l5trfrkED9wtiANC9u9gxW7AAuHatrtuOVatWdURFVbd59+rFb78B0dHK63fvjsJTT9kud9f7m+M7eNRM0TqHw2uv2d/+1VdJe2KtLu/XTzRpZJHGjhyxX9aVK/SBsfY10JM9e7zHJ0WK3j5oruINI0PeUAdvIi4uDqvKVMurVq1CFyMSHLkRfxDG9TyHsLAwnJc4WmVlZSEsLExxe6XAGxz/wGw23/yFhPBIA0YhbQeGmmc1LCwM58pCtBYXF+PKlSuoVauW3WM5Ck5z9qzl/+XLKVLpgAEqTsQOv/1mf33lyq6Vbw+pEOZqbjI52rSxL+QmJ+t/TI5/4FFhTGsH+P33aWotTPz4I01v3ABCQmhe+mKRvkR++IGmTz9NoZhvu01bHeQ4e1ZUi//1F+XSMZmADh2AqVNdL19vuGaMC19SRo8ejcceewwnTpxA586d8d133yElJQXbt29HQkICduzYgZSUFKOr6Vc44zPmznu2devWyMzMxKlTp1BUVIR169YhLi5OcftIFVZDtWsD48bRgJQgUGASvU2u/QG1Jvh6oFbojoqKuvmrXr2mzXqOZ5C2A0PNsxoXF4eVK1cCADZs2IBOnTrZNTUHxAivSlinlXj0Ucp5aC9vnxqk0U6V8tAxFxY9eOABoHp1GtGXBhXp109dFEXWx1RDuXKUHJ7D0YpHhbFbblG/bY8ejrdJSBAflM8/F5e/8AJFa7Me2QFIaJsxQ36dWjp2pASf3bsDrVtbRuaZPdv5ct2Ft2nGvAl/0Jho5aOPPsK2bdtw8OBBpKeno3///qhVqxYWL16M1NRULFq0CDWt7ep0wqjr7Q/CuJ7XLjg4GBMnTsTQoUPRo0cPdO/eHU2bNlXcXk36kdxcYNo08VpXrAj07Qt88QVw/Li43dSpFMp+9mygf39aHyisX08+0adP20arW7hQ/+NpFboB77TuCGSUntWZM2feDOTRr18/5OXlIT4+Hl9++SVefvllh+Xeeaez9aE+EOD8QO/o0TRVeq/oGXnQZAI2bTqK7GzbSI3x8Y73V5NvrWpVmrJ3tL1B/0OHHJfHCTw82k3XkkDzp5/EeeZnYs2WLRTOXo5hw5TLfuklUrk7UpcrwVJ4/Pyz7bqsLO/LrcKFMREe2j4w8WWh22RyT/1jY2MRa51B1Qn69AFWrFBe//TTNLU+h5YtKcw0QCPW0kTYUlJSyD948mT59evXyw/ejR1LkTenTKGk22p57rkLmDPH+XBr06YB48eL/wcOJIGzYkXxvdOgAU1//ZUSVTdrRiZMbdo4zrOkBWlHvqSkBH379rUrdAOuR9Hj6I/cs/riiy/enK9YsSJmzZqlqUzrQC3W9609Nm+mJMYjRzqn/WZCmNJ7jT0femAyASEhpbIRFO0Jk5GRloNISqxZAzRqRNeBhdiXy/fKaNXKt79HHPfgkmZMa/4Stfl0rGEfbDmcDQPPfM3cQYUKwJIl3qOr3rXL6BoYDxe+vINAbQc9zttbP+CupiQBSCh55x3y4xAE+p07B2zaBMybB0yaRMuKiyl6LuOee8hCAaBE0CwE/4IFZAJ1//3A6tXa6vL887aOxdY5z+wl6X7lFcv/ixZRJ03uHrjvPuq47d5N/++6i0zrWTJtNuLuCrGxsdiwYQM2bdqE56wTLMngL5oxbzXP9xashbFHH1W/r9ZURE2akN8+89lnwpgn7jV77141A9X29j96FEhKIi2j0mARh6MGp4UxZ/KXaNGMSbH3wM6Y4VyZWjs2N24AaWnqXx7vvhvuNZ2njRuNroE8Rl4fb2mbQIObKarHV+7RgQNdL6NJEwroJA0nHx4OWMeRCQoCevcWcxgxU7/8fBJq9u4FPvqIIuwyKlWi0WgAaNuWrmtJCe2Tnw80by6WlZ9vazo1YABpAdSYKwHUwXv8cTJl//dfx0JB48aWAVL69KE67dsn5s70JNJv3M6dnj++XqiQO72KCRM8e7wHH7T8b+99o/T+fPttx8dZswY4cIBcSpiVEyvPaMFfjfm1EmPGKEfo9odk8RzP4vStqDVpKAB8/LFzxzL6gQUoWWzXrtqyw2sxjXEnWrLT+yu+0rENFDwtHHmq/Q8fJn8oKXLJi52pjzfdw3/+Kc4bkRCaCThMGKtalaKw3XEHmaFbs3kz+bls20b/y5WjfapWpeS3v/9O73apJqp3b0qjsnSpuA/D0Tdp6VLKe9nQhejgbdvqG8hALVIzRTsZLtzCggXivKvvCEcd7VOnXCtfb/Q0zVND3bpAhQrqOleJifLLW7RwvG9Cgm2ERNa2nnin2buPlNZNnGi/zJgY+4Lo1Klk4njrrY7rx/FtCgqAuDjAhXSQAFwQxuTyl2RlZdnd599/nTuWO2zY//mHVMxSkxd7OJN3ZeZM7fu4gypVjK6BJd7UqeS4j5IS6gTLocc9cOAAfUyZTxJAAyBq7PzdRYsWJBDk5wOZmVS/6tVtNQzvvac+Kpm3aPWCg8VGq1bNwIqANF3/+58oKDkiNJTaQM7sr04doH17y2WCQN+GChXETr20HfTQBnorUkHT0eCjks+2I6TtIL2X9BTsHQlj3tZRNuI5LyoSL5K94/ftq7xO+v5Vi7cLY8HBlAe3QgUKPPfOO7R89GgKVLRtm/0Q/EFBFMRD0kXm+Cm//EK/MWNcK8ft0RSleTMGDLjgVBmrV1/HoUO2+TdcpWlTMgmR1lHpd+3aJc3l//QT8PLLWTCbzXj//TNISLisuO2kSefQv7+7HNluyOYvCSS8pUPr7wgCOYIfOAA8+yyZmSkJZI64eJHMPayjzgEUfIdFA/vyS3F5t27ypiNq2j8z06lqyjJiBPDNN+J/a23ZV18BgwfbL8OZcPjuRCqMSWFmfp7UkAUHA4sXi+aHnkDaHkof3kWLPFIVtyId/HQkjM2b59wxpFoF6X0tvcaLF4vzZSkQLVi71v4xXDFBM4LQUM8f84knHGR+LsOehnf2bPsdUbl3r6MAHnoyfbr2fQSBIr0WFpLQNXYssGMH8OGH2kLXr13rniipzpKba/x3RA9OnPAeyzO9+pZOv66cyV9SrZpzEaoOH66EDh2i8MknUahVS3vqdGaaooS0jkq//Hz7WU8XLbL1bwCADz8Mw4wZUXj11QbYuFHZ6/vpp+ujUyf3BP2oVKm8bP4SDkdv9u0j34c77xRNjuTSSKh5gb38MuUalIvWJdc5s4ejD9CGDTSSqVci0Kws/V7SnhxFtodUGJOeW6BEa1Xz6vSHQR8lzZhUOGJI/cClUSCVzNoYUkFJGgxFev169RLn5cwlHQn/jRrJL7fWggLUwfbUYILSu+uRRzxzfCm33iqGflYyif3lFzLDUqJSJcdmfdZ4MoCHvRD+SgK7db3KlQOio7UfOzzcexI9nz0bjDp1gA8+cE/5N2547hsVHU0Dr97gwqQXTgtjzuQvkYumuHw5jR5s3Wo/I3xBATBnjrYIPsXFdHPExLgebt46auPp0zTavXUrjdI/+SRF/pJDRaBJhIRoi2akBSNGCHfsqIp69YCrV5W3MaJzaX1Mozu4jhAE26hX3oycFkvuGqu57oWFymU62+lV2m//fpqywBCu4u33lTM4Esb8PSR60P+zd97hUVRfH/9uEkIngUhCFYy0AAGUIj0/gwlCCKCAFZRYUKSIWOiiNEUFQfTFIAioiEpXonQhVFEUIhLpXUggJKEnkJ33j8PNlJ3Znd2d7ffzPPvM7OzsnTt7d2buufec7wkmbwp/h3VwqlTRbtM6dWgpfR6npYmzpO+8I27v3l1cZ677LVuK2xo2FNelYifMEOzShZ7hn34KXL5MsxOHD5PwiTXUVJh37iR3VXZvYWiJpfTpY/0YDHtm1aW/hxRPGPJlylBjN2yo7bb5v//ZLkda98uX5f0ttfPq108UunEltu7DWr+5P96///uPLq4ff1T/fMgQx/+DBQU0mKE3NYKzsIgoTwgcKTFqsNThbrq9SUMBMT+XlD59KBdMx47kR75tm6XKjx6mTCEJ9x9+oM7r7dvyUb2QECAvz/5yBQFQy39bvTrdwDp2lGeUZ6OHUVH2WX/lytHNUBAosNhspnVp3JnWTdwWnpD4nT69Mi5eVI+tkGa0z8wklzZX444H3axZdJzL2t6odvP55zRiqXbtBApGPhi1yjJ69kkQjP/PebqDIL2fKo2xxo3J9TJQ0GoLa5L3vkJREVCnzk0cO0buaR07kjui9JzZbMO0aeK24GAaoLx2TT77FBYmPkP79gUuXADathU/nziRtq1cKc8pGhxM4jerVtGA4qBBFF82fDgZg/Xqqdf/2DFycZYOQjIRq5gYKpfNgiUl5eOpp2gWrVw5fX0PtZkO6bFsKTx70+zpI4/kYepU4I8/nCuHnZPJRG0knS1XO9+6dclg8/TghruMMZYk25v59FPHv3v9Oi1nzzamLrbwFjVOwAuMMcD+/CVKFyCz2fJiaNeOYkwOHAAkXpBWuXYNGDWKcs706UPGh5qve5iGp+Hhw9pl5+fTSy/PPEN+ub/+ql/x4/Zt+c2rRg3xdxk6VEyo6ugMl9ZIiCth9f/xR+sXTMOG1t0IHGXPHus56FzRsZ01i5Z6/7d6WLyYlkePGlemK1F7uKn91no6JNZuct7UoXE13nKuJUqoz4yZTDSg4upRbm/AVltIXet8FbMZKF/ejNKl6Xy3bKHE2+w6rFtXnDErVUp81oaE0LpSMKqoSHRjK1eOVPzYdwEazLzrLsvfLiiI9rd3MPGeeywHUFl9la54H374nyy2c8MGig+yhnTwlSH9XzCD0hNKmPYSEkLna02MQg+sjYYMcb5ODGcNRHtxpZqlrfhgV1NYCJjN9Cd1xfOE3QPcZRyxc3DEG+P4cccmZvSSmemY4J/HQlx/+cX6nyImhpJ4fvWV+ucNGogzYPaoBR45QgaOFLURtn37aLsjroMswFMQbAcZ33ef7SBp9rmjN0xPjD5J29Yow+fiRf3upi1a6BvlNPLG5IrYHlaWt3TIvQWj3RRdMTNmdBmenhnTclPkiPjD76I2SCqlTRux02UyiQOJWs+x27cp3YAgyOOyjh0DMjLk+7ZrJ67r+S2PHrU+6MZgMWy24sKCgmwPekoFgqz1PaQKhAcPyj/buFHeYZO6avoiJUuSR4ha+iJHrwl3zDJL21oa52j0vTYhwdjy7CU8HOjfn4IoXfEccfdMlTPHi46mtCF6MZsp7n3yZOu/3fXrdF03bOhYn9sjxlhqKvDww/r27dePfoCtW8VtTz4J7N+vPQNmjXvvVZecP3mSZuMA6sg3a0YzZmoJk+0JgOzWDVi9Wr6NuSHu2QP8+aftMpKSaOZv1iztoGRvxpkLVBDEUYzKlY2RlJ48WRypcsWNycgymfiMvYIVnkLvzJie38gVM2O23BSNRE+Z0nhAR1N/2MusWbPQoUMH9OjRAz169MCWLVt0fa9pU1GtQS2OLxDwFjEVV1JUBAQFWZ5gjx5A06Yk0CP9bzMXLC0jZtw49e1VqwKxsfJtpUqJQhZ6PEGio4G4OPF9ly7yz8+eBbKzycPgxAl9Zb70kuW2++8X19VC46VGWefOpIz45pviNmU8Vny83KjTkoefOnUqHn74YSQnJ2PQoEG4rOEDHx8fj+TkZPTo0QOPPvqoemEupnx5Y+PTldeYK/KlSv9/0qTbRhsVnh6kkQrtuAJHZ6quXCHxrCpVyLW4a1eaCElNJeGYQ4conpP9FwSB9nNmZgygZ63JBMybZ3vf/v2Bxx8Hxo4lN+ydO+X2CKvL1q2Ui9hR3GqM/fQTMHv2KQwYYP9327enhsnJodwy9hphtqhdm2SSN20iI0mLM2dI5c0ekpLIdS0uDjh3TnRDlN7grREcTDFxERE0o+gL6JkZ09OhmTePZhqZIr+asp69jB0L/Pyz8+UocWVHTSsjwf79FG/hLeg1xhwty1fQe85ScR/lfceV0vb9+/fHqlWrsGrVKsRJe7NWmDjxXPE6N8Y8Ww9XYjarP18rViShm3r1RG+SiAhyRd+yxTKH24ABJNTRuLF9x//2W+osOfKMVw58VqtGg3ilSukfyFQTBpG6Lqo926QuiZUrk8CAdPTd1r1s+HD17e3atcPq1avx008/oXbt2ki1kktg4cKFWLVqFZazuAYvwaiBM3tdgPX8DNWri26zUkPS6GePN6VZkN7DrGU6MpkoPnLDBvJKYowfT5+tWSNuY22l14j97z8xB+fDD9P1sngx9W+bNaO0OPHxJAh0//2i0TR3LimrsueP9HgZGXQdFRTob7yxY8X1W7coPlV5Dl9/LYYqPf88xbt27EjvP/mEUh4YgVv/It26AXFxVuT1bFC3rrrErSNodSbU5OmlOOpXHBVF7hTOJgGMiRGDJX2Fc+ds76MFe7j++68xddGL2UwXvlR1i6lzAnRzWrZM/h1XdtS0Hg6xsfZ3drwBex52Rv6eto7rzmMB8vhCrQeZtxilpUoJxZ3wQDXGGP5sjBUV2f7PTZ5M6okdOlAsNuucSElNBXbvtv/4pUpZz2ulxjPP0NLoTi+7t1arJt+emUmj+uyaDQ6mGT0tkS1BoP6PMkk2EzLR+r3bt2+PkDt+oM2aNZOlE/J3mFLnzJnkQfDVV/Lfz1pKjV9/1Z8qQM3oN/qea3R56enp6Ny5MxISEjBHj1y3BHbvGjuWXOq2b5f3c378UdxnwQJysawsyUo1YQItV66kWWeAjBSAroe8PJo0uXGDDJmuXeVtceAAzWgrsSbkxjyElBM5LVvSca5coVn7jz8GkpOjcekSuUEDNCM+aRLVQ60dCgvpOTx1KtCzJ/0f0tPl+7DrXHo/mzsXePVV8fdwlgDJEGOJI6Nuel0rXU3p0tQZ0uPf7g3Urg2sWEGxevv2idulHZrPP6fRECVGx0zpyTfVpAldjC++SMGe48fTDeS++2hWdMQI8eY0bRoQH68dGFtQQGpho0fbjm387Td6mB89Klec1CqbwW6I3oCjM2OnTtENVZrE150CHkYb0uvXW84UqDF5srhu69hGGgCLFi3CypUr0bhxY4wcORJhWupGCgJFxl4LbzGMXYnZrO6mKCU0lDo33sLChep50Bzl8mW63sqWpdjjDh3knzdoQEvWSQsKUp+JCQ0VXZHV4sc3btQ/uLps2TJ0UfphSnj++edhMpnw+OOP4/HHH9fcL1MyHXLz5k3Ze4CS6c2efRoDB9a02N8+qKx//810+LphoSMsvm7AACAxsQSOHCmJTp2uolEj9eR/UVGZslkfy/MUMZvrACiBI0cOA6Bgn5ycC8jMvKi6vyNkZ4cUl/3aa9nIzHRck72oqAgTJkzA/PnzERUVhd69eyM+Ph51WL4JG2zfTsbHlCn0vn17+ed6ZyBTUy2Tvt++rZ0Ue9gw9RAhxuefa3+2cKE8UTwjJ8eyX3XmTKhMldUa58+rC+0onUX++cdynxdf1HcMvQSsMQaQX7fejuyAAZZ/PE/CjMmKFcmH1tWkp6dj8uTJMJvN6NOnDwZY8TU9exb4+2+52ki/fpRHbudOcZu0czlwoHVjTDqykp5uORJ7/Dj59sbEiDcZgAybSpW01XNu3KAXE0d5+GEatRkxgt5nZckv1v/7P3qAMl5/HWjbtgYeekh8MBcU0E1p/Hhqp8mTKb5x/Hgqr7CQ6pObKz8PptJVrRqdgzSJ6s8/k2roV1/R72Qy4VbaqgAAIABJREFUuWZ03p52VsNRY4y5EEn3ZWWpdVY8PXJ55YrtfZxNTD1zpm2ZbC369++PixctOxPDhg3Dk08+iVdeeQUmkwkzZ87E+++/j/fee0+1HGXHrUmTXBw4UBHZ2WLHxfHOmndireNWUBANoCSOHj0K4F6Lz/3htzCbAyeRtxbly4vrSkNMytq15GqspfgYEWHdM6RUKeDll9Wv1eTkZDS/8xCYPXs2goOD0V1j6m3x4sWIiopCTk4OUlJSEB0djZbSZG4SYiTZyzMzM2XvGX361AQTyFb73B4aNnTu+0q0qnPrFnk2ValiWWet8wTE53t0tKi6MG5cZVStWll1f0eQHrpq1UjExETKPt9jLTZGQUZGBmrVqoWaNclYTkpKwsaNG60aY/370yyXWAfdhzMMa4aYHuydLfc0JpNtMSQpAX3LPXRIPYeYGp984tq6OMr69fKcLkqMUCSydyRGzf+dJfSWzhYoO5/bt5MvcenSNFrzf/+nPpoYF0dGUGgojVomJoqfpaeTQApDWUWletetWzSysm2bONt17JjohqUM8Lx+nQJKpezYUU7mN6wWC/jOO9SxVhqFbduSz7F0qvvKFXWJYDZbppbMdNkySu1w5/7sEI6MuN24QQ/bMWNoin/SJMt9Dh+ma02aBJwZrmazXCHzxg1RmIftO3gwuQtJk4+ypI+MH36Q/59ycshVpUQJiqvTci/u25f80tkMs5qRJQjknnDuXElcuUKzvO+/L98nM9NSMc1etJLes99n8WISF5gyhQR9rLFA+uS1Qp8+ffCy2ijIHZQdt6++qoixY4FGjeqq7uMP6Ou4WRpigHt+C3s6bo5AAh4uPYTf8OCD1lV709PJndOaV4TWtcraefny5di8eTMWLFgAk0bPLioqCgAQERGBhIQEZGRkaBpjelAmxfZWpB41ISEUu2gvkyaRsSJ1xXOFsfLoozR7qjVzpJesrCxUkcS7REVFIUPZsVEgFZzguI+qVfWnOgpoYywsDJg+XTt4lhEf7705Q2y5C9nqtOnB3pGYuDjqHKsh7VArY2SU0+XWYMlG1Qb0HRHnUB570CD7y7CF2uwcM+JYHh5H6d2bls7Mljky4sbac/JkucudlPfftzReRo1S/29qdVqk/xsA+OIL+XulVw4LylZy+LD1kaq5c+mljoqj+x2MkKb+7jvLbdK6MpW30aPp3qVmlOshOzsbkZE0MrthwwbUtUOHt0QJ0ZW0Rg3KqRhING9Ohrd0kOuuu+QB7r4OCXj4cVCcg2zZYn+wfp06FFfiKOnp6Zg7dy6++eYblNbIbXP9+nWYzWaUK1cO169fx/bt2/GKozeHO/hK8vKXXiJjTDqTaS/PPksvV7N4MT231JKGuwLpLH1ExN04elSH3zzHULKy9HtLBLQxBpAf6333aY9u3bjhGklVo2ja1PrnI0c6fwx7R2IeecQ1xgzHtehtZ+nNZfdu/5oV8RUGDQIefNAxl7gPP/wQ/95RxKlevTomOBiBfOyYfwtZqDFnDhnB0hnouDiamVbmr/RV7HGtCSQ6dlQXKnElEydORGFhIVLu9OCbNm2KCRMmICsrC2PHjsUXX3yBnJwcDLrzwC0qKkK3bt3Q0cmKKmOWvZmjR2lwytsJDTWmXxQVFSUTcsnKyiqeGZUinaVfvZrCcjju5d9/gfr1xXaw5tUQ8MaYyUSj7jk5sAj684WORsmSNBPUtSu5urEZFk/UXS0QmONe3BGzIr3JV6hAwe5Kype3dPl7+GGKt7t1ixLHSuMHbREdTdfn778DixZRQPf48fTZAw/QbM3evWLevhIl9CcI10Pp0kD58reweXMJCAKwa5eoIGUPLVvS7Mrnn1Od1QKD9XDtGlCmjL6bvJIP7UmUaAWtOBl/pnRp+u9KiY0lY+yxxzxTJ6PRyjPGcT/r1RKdgjrkX9xxD6hZsyZ+/PFHQ4733XfGpw1yNWrKfP5MbGwsTpw4gdOnTyMqKgppaWmYxlyFNNCrQt6pEwl4MIXUmjXJ2J02jfLmOZLM2BGGDiW3/7Vrqd8gldFX0r69qLZ4/Dhw770CzGZxNOnMGQpTYCqPa9fabxT/8gvNwl6+TH0baShMYSE9C/v0sUy9VL++/mMEvDHGqFSJOkaNGtHDdfNmT9dIP126kE9w27bOu7up4chIzIEDmThxIsarFLfspXZtenn6vzB6NI2wMLWur74iKecePSgvBmPtWsuYFXs66XrbWUpeHvDPP5lo3Fjb+C4sBG7edMz15eRJEjRR6/jb81/PyaGBl0qVxJundPTfbKZO6O3b5IqkluIiM/NI8e/bsCElav3xR2qHbt3U4xsBMtxatJCLIsyeLa4XFtJnyjidGzeo3mFh5HdevbptRU6O+xk7lpJ9tmvn6ZoYA6kperoWHE9gRYTRbv76yzX5PF2Ntw+qhISE4O2338YLL7yAoqIi9OrVy6aruZaBLb3W332XEl8r961XTxSv++QTGox68UUabJ0zR0yC3q0bxUcyVq2ivnRkJIm61a9Pg6WCQAOSzZqRLkB4OB2TGS5qEwk3btCz7403gA8/JDGP9u1JOj84mPKAXbhA/bX9+/9Vjd0ND6fXwIEkyvP778Cnn1KdzGYy+Lp0oecxSz4/fz71CSIiqC8ipaCA3NNZ32TJEqpnQQHFBU6dqt0eanBjTELDhr4xG6YGi3lq29a5LOBqODISA9AfOyuLOr2FhRRb8cMP9GeNiKCLr2FD6qgXFIiuBpmZtK7M7aLkyhXqQN+4QYGSubl0Ud59t3iDycujznCDBnTRhofTBVSjBl3Ic+eKcQBr15KP/6FDNBrD1HCKiqzPAmRmZqKgIAahoXQ+p09Tx3nsWDGm7c8/aduhQ3QjuP9+Chw+fZpubMwwOH+ekiy2aQPcq9AIOHaMjInwcFKnFASKeTx+nGKynHUtcaSdTSbbI6mhofRyBL2JWm0hnfVWqwtLE1GihO1cg1LataPZaaa+qQZTydRC67cpXZr+p4BzMREc1/DAA0DnzvT/9xdDDKDOVfny1wH4SOAQxyXExdm+d1mjWTN54mtfwFf6f3FxcYhT6q/bYP/+TPTqFYNXX0WxUiYAvPACpdRJTrZdBhMWU4qYAeSxsnUrGTJVqsjzn9arJ/9tb92yTM00bZp2uozSpeXfP3VK/nlYmH5XVZOJjMTYWBpQBage7NglS+r7H5QsaSkMVrq0ZV31wo0xP2P7duPLdGQkhqH0U+7Xz3KfUqXkcXl6BclYB5UpYlasaKlUFB5umR+Ofe+558SLEaCOFSA3gvTmcpM+dFg8yZQpcpl9gH4PaxdqlSqk8KeG0h3DZCJVSaNwpp0DlYgIGkzgBB67dnm6Bq5h1iwgMzMXQBWb+3L8F097hHCMJSiIPGyUKMWwHCUsjGbH9KCWOsOWkJ6/w40xji4cGYnh+B68nTkcDofD4XDch0kQXDcx6+p8KBzHaS7NKGwAvK29FyPbmrez98Kv6cCBX9OBAb+mAwd+TQcGWu3sUmOMw+FwOBwOh8PhcDjqcM0kDofD4XA4HA6Hw/EA3BjjcDgcDofD4XA4HA/gNmMsPT0dnTt3RkJCAubMmeOuwxrGuXPn0K9fP3Tt2hVJSUlYuHAhACAvLw8pKSlITExESkoK8vPzAQCCIGDSpElISEhAcnIy/pFkd12xYgUSExORmJiIFStWeOR8XAlv68Boa97OgdHOAG/rQGlr3s6B0c4Ab+tAaWvezj7SzoIbuH37ttCpUyfh1KlTQkFBgZCcnCwcPnzYHYc2jKysLGH//v2CIAjClStXhMTEROHw4cPC1KlThdTUVEEQBCE1NVX44IMPBEEQhM2bNwvPP/+8YDabhb/++kvo3bu3IAiCkJubK8THxwu5ublCXl6eEB8fL+Tl5XnmpFwAb+vAaGvezoHRzoLA2zpQ2pq3c2C0syDwtg6Utubt7Dvt7JaZsYyMDNSqVQs1a9ZEaGgokpKSsHHjRncc2jAiIyPRqFEjAEC5cuUQHR2NrKwsbNy4ET179gQA9OzZExs2bACA4u0mkwnNmjXD5cuXkZ2djW3btqFdu3YIDw9HWFgY2rVrh61bt3rsvIyGt3VgtDVv58BoZ4C3daC0NW/nwGhngLd1oLQ1b2ffaWe3GGNZWVmoUkVMIBkVFYWsrCx3HNolnDlzBpmZmWjatClycnIQeSezceXKlZGTkwPA8pyrVKmCrKwsv/stlPjb+fG2Vsffzo23szb+dn68rdXxt3Pj7ayNv50fb2t1/O3c/LmduYCHnVy7dg1Dhw7F6NGjUa5cOdlnJpMJJpPJQzXjGA1v68CAt3PgwNs6MODtHDjwtg4M/L2d3WKMRUVF4fz588Xvs7KyEBUV5Y5DG8qtW7cwdOhQJCcnIzExEQAQERGB7OxsAEB2djYqVaoEwPKcz58/j6ioKL/5LbTwl/PjbW0dfzk33s628Zfz421tHX85N97OtvGX8+NtbR1/ObdAaGe3GGOxsbE4ceIETp8+jcLCQqSlpSE+Pt4dhzYMQRAwZswYREdHIyUlpXh7fHw8Vq5cCQBYuXIlOnXqJNsuCAL27t2L8uXLIzIyEu3bt8e2bduQn5+P/Px8bNu2De3bt/fIObkC3taB0da8nQOjnQHe1oHS1rydA6OdAd7WgdLWvJ19p51NgiAI7jjQli1bMGXKFBQVFaFXr14YOHCgOw5rGH/88Qeefvpp1KtXD0FBZMMOHz4cTZo0wbBhw3Du3DlUq1YNM2bMQHh4OARBwIQJE7B161aULl0aU6ZMQWxsLABg6dKlSE1NBQC8/PLL6NWrl8fOyxXwtg6MtubtHBjtDPC2DpS25u0cGO0M8LYOlLbm7ewb7ew2Y4zD4XA47mXUqFHYvHkzIiIisHr1agCUn+W1117D2bNnUb16dcyYMQNhYWEerimHw+FwOIGJS42xPXv2uKpojpM0b97c0PJ4W3svRrY1b2fvRa2df//9d5QpUwYjRowoNsY++OADhIeHY8CAAZgzZw7y8/Px5ptvWnyXt7X3wq/pwIA/pwMHfk0HBlrtHOLuA2dmZiImJsbVh/U43nyeahfquXPn8NZbbyEnJwcmkwmPPfYYnn32WbtG0aVt7c3nbzTefK6uuCnza9r70Grnli1b4syZM7JtGzduxNdffw2A8rP069dP1RgDAvOa9vbz5Ne0cXjzebqqQ82vae+DX9PG4c3naa2dXW6McYzl33+BBg2MLzc4OBgjR45Eo0aNcPXqVfTq1Qvt2rXD8uXL0aZNm+JR9Dlz5mh23Dj6uHwZuHoVqFbN0zXhuJrDh4HoaCA42NM1EdHKz6JGZmZm8frNmzdl7/0VT57nf/+FIDy8CGXK8OgBf+TcOaB0aSA83NM14djixg0gOxuoVct9x9QaFJfy22+/4ZVXXkGNGjUAAAkJCRg8eLD7KqmFqzqnAQI3xnyIlSuBRx4BfvgB6NPH2LIjIyOLO2jKLOd6R9E5+oiNBU6dAni0pn9z6BBQvz4wbhwwYYKna6OOrfws0hFGbx5xNBJPnmfDhkCbNsCOHdr7cBck36VaNSAsDMjL83RNOLbo3h3YsMG9z2mtQfE6derI9mvRokWxEIVXsHgx8NRTwOrVQFKSp2vjk3BjzIf4+29aZmQYb4xJ0ZPlXI1AHEUH7D/XU6eooxcov0+gcvYsLdPTPVsPJSw/S2RkpCw/C8c72LnT0zXguJL8fE/XgKOHDRvcf0ytQXGlMeZ1sAGiAwe4MeYg3BjjyHAmy3kgjqIDjp+rO34fPorOUcLysAwYMECWn4XD4XA43oF0UFzJ3r170b17d0RGRmLEiBGoW7euB2oowWymZZBbUhf7JdwY80FcNW1uLcs5H0XncOzDG2bEhg8fjt27dyM3NxcdO3bEkCFDMGDAAAwbNgxLly4tzs/C4XA4HO/A2qB4o0aNsGnTJpQtWxZbtmzBoEGDsG7dOosylJ43rvRWirp4EZUAnL94Ebke9vjxVa8sboz5EFYmpZzGVpZzPorOcSUXLwKVK5Pr+RNPeLo2xvDOO56uATB9+nTV7QsXLnRzTTgcDodjC7VBcSlS4ywuLg7vvvsuLl26ZDFQrvS8cam30h1FmipVq6KKhz2ivNkry5qnEp9T5ACgP8mqVauwa9cu9OjRAz169MCWLVswYMAAbN++HYmJidixYwcGDBjg6apy/IjCQiAri1zNAWD2bM/Wh8PhcLyVc+fOoV+/fujatSuSkpKKB1Xy8vKQkpKCxMREpKSkIJ8HpvkkWoPiUi5cuACWHjgjIwNmsxkVK1Z0ZzUtYW6KZ84Ac+Z4ti4+Cp8Z8yFcqerTokULHDx4UPUzPorOcRUtWojCNAAQwu9IHA6HowpPQePfsEHxevXqoUePHgDI1fy///4DADz55JNYu3YtFi9ejODgYJQqVQrTp0+3GsvvFoqKaPnhh7Ts2RO4I0TC0Qfv+nA4HI8hNcQAoEQJz9SDw+FwvB2egsa/sTYozujbty/69u3rphrphM2MMRo0AC5d8kxdfBRujPkQnh784HAcJSODXBGl8WDz5lnu5wlj7OxZoHp19x+Xw+FwHIWnoNGPY+fJU9DoRmmM5eZSDEJoqGfq44NwY4zD4bgcps4rNcZeeMFyP3cbYzt3Am3bAosWUc5KV8CTe3M4HCPhKWjsw5nz5ClobFBQAMyda7n9zTeBmTPdXx8fhQt4+CC8c8fxFm7cABo2jFG9FzuCyQTcumVMWXpgz8Dt24ELF4DDh2lA79Ah/WWcOgWMHGk5OMhITwc++wzYtk2+XRAosejNm9av6cuXgW++0V8fDofjv1hLQQOAp6DhuJdZs9S3//OPe+vh43BjzIfgboocb+PO8x8vvggMHux8ecuXiy6Dx47Rf145aPjnn5aGjaMUFtIyNBSoWxeoVw8oWRKoX9/yWSIIJL2vNBZr1QKmTgXWrtU+zuDBQIcO8m3ffw8kJAClSwMaCvQAaAaxXz+gSRPg33+B3bvda7BybPPHH2TQcziuxFYKGgA8BQ3HfXz3Hc2AqeGuBNC7d9NIqo/DjTEfgs+IcbwN6X/ys89s75+TQ9+x9l++cAFYswZ45hl6/9VXZDQxwykuTjRsTp8mg+377x2r/82btJwxA1CqQaelkex+Sgpw/Tqwbl15PPUU8N578v3uuouWXbsCW7ZQfU6eVD/eoUPAAw/QbNqTT4rb33gDOH9e/TunT9Py77+BmBj6/vjx9p0nx7W0bAm0b+/pWnD8HZ6ChuNVfP659mdnzgC3b7u+Dg88QC8fh8eMcTgch7F1r83LA956S3yfm0vGS58+1r/XpYu4/sknwNdfk2v6ihXA1aviZxkZtBw1Cnj8cfvqDliPURsxgoynBQuA1q2B/PxgAKJxBABPPy13T/zoI1pu2aJe5uTJNJD37LOWn40dK3e9FwTx/JSwvGwcDidw4CloOF6F1oMOADIzgdGjgQ8+cH09jh+npXTU1sfgM2M+BHdT5HgbSmMsPV3+/pVXgC++EN+nptJyyRL7jpObS7NTynySbIaN3YsZZjOwdy9dM5mZ9H7UKIoJ++UXcvP77DPb6rtM8fH2bSA4mA7GUqr88w/w7bfyMlavtl7ekSO03LzZ8jPlbOHnnwPNmgG7dlnuq4jZ53A4HL9l/37gpZe043JdCfdI0kD5w9SsadlA69e7rz4AULIkYpo1c+8xDYIbYxwOx2GYYcIoVUpcnzSJYqyksJkjR5HOZA0dCiQni+/PnKFldjYQHAzcdx+9nzmTHubvv08xYV270mvwYNqmB7MZ2LSpPABxUITlt1RD6wFurTOh/Gz3bu19y5TR/ozD4XD8ieRkGojTcv/m2MBkQkzDhjQSaRTKmLCMDMsZA1fJI7NYB2kHxNNW8549lh0iO+DGGIfDKSY9PR2dO3dGQkIC5iinoVRQzoyxe/GLLwLjxuk7ZosW+usnnYVSijgNH05ujFFR8u2pqaK0PmPjRv3HBIApU4BffyVjbP58mhVzxCtIbZaLsWABqVMyrBljPH0Lh8MJNDzR3/Z0H99QHn3U+TIWL1Z30woPp+Vvv4nbzp1z/ng//URB1oycHDIEP/lE3gGRCNpg82aaSnU106cDzz9Pv0eLFkCI45FfNo2xUaNGoU2bNujWrVvxtry8PKSkpCAxMREpKSnIV0a+c1yKX90cOF5DUVERJkyYgLlz5yItLQ2rV6/GEeZXp4FS1a9VK7ov2SN13727egyVGuvWaX927pz+Z42915BUXEMQgMaNre/fv7995TPKlAFWrqRZstKltfc7dsyx8jkcDsfX4CEaBmGEgaKWkHPHDnG9VStxnbmrOEP37uplfvmlvAMiHR198EGaSp061fnja5GfD7z+OtVDSpUqojKYHdg0xh599FHMVfSs5syZgzZt2mDdunVo06aNrhF0b2fjRrnxzeEEGhkZGahVqxZq1qyJ0NBQJCUlYaONKSQjYmVff51mhZzFht3oMzzyCM36WRM56d7dffXh2Me5c9SGV654uiYcDocDYNEiWlao4Fw5UvUsxkMPAW3ayLdJXRg3bHD8eMx3PytL3MYsc0GwrSA2cqTjx9aiqIhUvNhMoJKsLIeMCZvGWMuWLREWFibbtnHjRvTs2RMA0LNnT2xw5sf2Eh56CGjY0NO10AcfJfJfbMm+u5KsrCxUqVKl+H1UVBSypDdBFXJynD+uUfFPZcsaU443cOSIdXd7rlztPfz5p/z9O+/Q7Cbr/3A4HN/FLzyRnnoK5tKlgWvXHC/DZALKl7fcvm+f5TaW7wXQlgTWQ0GBej0Aahi1z43m5En5DF/jxsDdd1v/Tv36wKef2nUYhxwcc3JyEBkZCQCoXLkycqz0yDIzM2Xvb968abHNO4jBtWuW9XUUveeZlRWC/ftLoVMnlREHBdnZEQAiceHCRWRm+n6SO44lrVpRAllvfgBI/9d9+9YDEGxQeTFOlXP0qFNf9yps3ccPHvTGe2hg0ry5/L20r8DhcIyDX1OOE3TjBsU4TZum/0uXLgEREdbzeEljxBj//gtUqkTrr79OAd32kpsLfPyx9ueCIA+ydhW1a4vHmzKFzk0PQ4ZQYlCdCdidzjNmMplgsjJVExMj72BlZmZabPMmjKqb3vPs3JlmPPXcZJgwwV133YWYmLus72yFPXv2OPxdjmv54w/Lbey/4eoZ0aioKJyXBEdlZWUhSqmGAfk1cvmyc8eMjBTLmziROrZduzpXpjdy/jy5kqvRsiXw+++2y2jbVnTNV95b+DXtPTAPHd5x5HCMgXsDGYgg6P9Bq1alpZrBBZB0ceXKltsrVnSsboxFi+glVX88fpwMI2ndr19X/36nTqJK16FDJKPsLLduAWPG2PedUaOsK3FJcEhNMSIiAtnZ2QCA7OxsVGIWMMdupAlkORw1One2VJF1BbGxsThx4gROnz6NwsJCpKWlIT4+3qXH3LtXXB87Vp7s2Z+IilLvnP/1Fz3nbCXBZmVwvB/lzNiFC5Yui/v3A3Xq2M5zpxcutMXhuAa/G1SxlQyTsX+/dlB4jx70w6gZYs6ycyfQt6+lDH90NPDEE+J7jZmxoz//LE9Iai0HjS3++09cd0TCuH593bs61MWLj4/HypUrAQArV65EJ53TcBxj8LubQwAzfLjtQSp35U0MCQnB22+/jRdeeAFdu3ZFly5dULduXavfadlSX9k3b6qLdLCBN2+lQQP5e6PHncqVo/Z/5hnb+zLFyR49jK0Dx1iUxljPntS3kD7XJ08mt9q1a405ZqAIbXECG973cZDcXHHdloiHIFAgd2ys9j56DByp1oQ9EvfWlI9++EG8wR44ACQlyT8XBBTWrk2GG5MjduYmKx0tdgQ7DDibxtjw4cPxxBNP4Pjx4+jYsSOWLFmCAQMGYPv27UhMTMSOHTswgEeTczgOYc0l2hPExcVh7dq12LBhAwYOHGhz/1q19JVbsiQZE2lptvfVO3BnFLNnW24bP56WM2cC8+aJ25X3fmeNMyZeokeVskcPek4tX+7cMTmuhfUVmBAYi/2WqjAb3akMFKEtTmDC3RSd5IknUMQeNrZELxYt0o7F+vprIDFRjKOyhlRqvlo1XdUEYPvm+Oqr4rpUYKx6dfl+gwbR0hn3M+UD317sUBWzGTM2ffp01e0LHcl4yjEEfmPieAv2Stu3bk1CSxcvAu+/r75P586UL+x//wOGDnW6ijLuvttSdTY6WlwvUYI6zQMHAt26UR7HGzcoryMABCu0SmrVcszVrEEDigMuV47ed+ig73tsf473opwZs6Zy7Mp7uT1CWxyOL8CTPjvIhx/C1KIFrdvKgcVGIhmXL8tVFPv21XdMpQvHZ5+JBpI1bP3gWul2EhLk70eOBD76SCzT3putvZNMzZsDytjtOnV0f91pAQ8OhxO4KJM+S+nTB1iyBJgwQdxWqRLF0FgjJARYtowGtIw2xn7+2TJhs9TACg2lczKZyBADRIn54GABTZvSDX3JEno+zZpFcV8ApVN56CGxrDVrSPVXzYt7zRpyP2UeI5Urk3GmdIvkeB+jR5Oh3ro1pZxRIjXGNm8Wt//yC9CkCQmxuBtbQlu+o3psLO4/TxLeCYTfluMY6enpmDx5MsxmM/r06WPheVZYWIi33noL//zzD8LDw/Hxxx+jRo0a1gtt0gRXO3RAhU2brMvb5+QAx47Jt6nJ2TvC4MHGGGNqlCpFQedSIiLE9b/+Au6/X395n38OfPGFfXUYPBhISZFv0+FdxODGmA/iFyM1HL9A6s2QkAA8/jjwwgvklXD//WS03OW48CcA8j744APg6afpfc+ewIoVjs0qKL/z+us0A9ehA7B1q+XMF0DG4bvvAo0bH0fPntFo00ZU+k1NpeWXX5LRlZlJara1a9MMX+fO6vWoVYt+Jyks1tdkog7/H3/QrArvt3kWQaCQC+aS+t579Hr4YTKLvaQjAAAgAElEQVSqpZw5I1dTnDJF/MyO57IhMKGtyMhIm0JbvqZ6bBSeOk89x+QKqd6BO/tbRUVFmDBhAubPn4+oqCj07t0b8fHxqCOZYVmyZAkqVKiA9evXIy0tDR999BFmzJhhs+zrDzxAxtjBg9o7OfuwVlK7NnDihH3fceQH/+cfuYsLo0YNuinv26ffGLt8Wf/NOj+fZt8mTgSaNQPWrSM3Toa1ZKEK3KDRxjEK7p7I8Takg2zr1tHA0DPPAKtWkTjJ9OmWRodepJL+zKiJiiJDTIpyIE8v779P99HgYJrVunpVNMaU19rbbwMxMQUICpKnXGnThpasbxVyZ3hLS/1y5Ur5bImS//6jmcNJk6ijf+AADfjpkb7n6GPfPtueOlJmzKBBVpNJHn6gNMQAmu1k/52vv7YtvtO3Lz37L1+Wu7seO0YDGm+/rb+eSrjQFsdfCJS+T0ZGBmrVqoWaNWsiNDQUSUlJ2Khwy9u0aRMeeeQRAEDnzp2xc+dOCDoMmOssIeI//+irzLvv0s3PGY4ft/87jhhjTKxDCQv4fu45/WXNmqV/vwoVgHfeAf7+m4yxhARyfwAsZ8lswGfGOByOw3TrJjcUgoLkcbuvveb8MUwmmpUYM0acHZNyzz1iDJY9jBghroeG0osZU0x8wRavv04DYU2b0vvatYHu3cldXQ1bSohq6pITJ+qrC8c2Fy7QM/OJJ4DFi/V9Ryooc/fd1veVPvOZ+6o1iorkomOMe++l5cSJcjdfLYYPH47du3cjNzcXHTt2xJAhQzBgwAAMGzYMS5cuRbVq1XSNnnM43oy/ewVlZWWhiiQhZVRUFDIyMiz2qXrnQRESEoLy5csjNzfXYuZb6Qp7i7ntLV2q6SYrna/NZPlWnHTNkJWpo6xyp0+jph3lH//hB9zMywPy8gAoXI+rVy8+vl7X4Bilu6OCU3Pn4hrzNWdlBgcXrwelpiJi7lxcHDIEgh2/HTfGfBB/vyFxfIe337aM93UFJhPNFmmxaBHFz6rRtSvFirFyrMFmxtRigdQIChINMYCMuVWr9H3X1zh2LBSXL8tnBhmHDtGspZph4U2wwcrvvgO++sq2F0lGBrBpk+vr5SxcaIvDcQ2+2t9SusL+K4kpiGnQwPrD8PhxxOhRTHSyTqocPSquh4VRvrP//iN3gbQ04JtvZLvfo0jSqeV6HBMRAdwRNHKYDh1wN1Pzskbr1lBz+LTmeszdFA1i1y7LxJ4cDsc93HeftrGmplSoJZTBZsZu3zamXv5Et273onVrWs/LI7dORv36QHi4/WV+8QX1CazFlNuioIDULpmEvBrLltEspjS1QmgosGULra9cSbm/AODAgZI4cYLi9aSGNofDcQ1ms+8aPUYSFRWF8+fPF7/PyspCVFSUxT7n7uTtun37Nq5cuYKKFSvaLFsoWVJ8o/SfFgSgXj3xvQsMMQDAr79qf/bMM/Qw+Owzcdtvv1HcV6tW5M7w9dfA9u00kpaSAkhmEW2ixxVCKpXPeP11IDubZr6sxRg4CTfGDKJNG/2Kn84SKP7TnMCmVClaNmpke1+TidwY1VB7yGs9+Pm1ZZunngIqVhRFtqQGzhtviL+t0mBTg6U3kPQ/irGWNoE9twsLSUTlyy+pXjVqUPjAmjXyOPXevSl+Ucn//kfxY488Iopx9e4djXvuAdq1s153DofjPCxWVyp2o0SZLsJfiY2NxYkTJ3D69GkUFhYiLS0N8fHxsn3i4+Ox4k7g9Nq1a9G6dWurSqnFSAOZlTMH27YBhw87W33bbNum/dnXX9NSGoyrJsrRti3Qrx/d9PUkk546lZa2AngBdfnmd94hueMGDbSDwQ3AK42xkydp1HL/fk/XxH3Yc5Px9xsShwOQR8H69cD33ztXTk2JAzo3tpxHOsB48CDFDTKmTSOPEkBusDFWraI2yMkhI47dy5o3J9dBQSCFzilTKFH44sUkbvHttzT7dvAglc+e2yVLijlAt24Fzp4l46pLF3p2mkxkoFlDK/bb2kwbh8Mxhn37aDlnjvY+nrxvu7O/FRISgrfffhsvvPACunbtii5duqBu3bqYOXNmsZBH7969kZeXh4SEBMyfPx9vvPGG/QdSJkKW5pvRK2Chl9mzxfXcXPu+a4caoSbDh9NSTXFJyYED8vfbt7stuadXxowtX065fubOdV7MxRZFRRRT4gvwjmTgkJ4OdOzo6Vro46uvTqBt29ouKVuat8sRWrYkKXwlfEDDPv7+W327lrvnb7+pb2dtERkpF0nJzweefJJeUp56St/xrHH2rP59mSHJ4XDcQ/v2tDx1yrHcvJ5CEGhy5umnRS8OI4iLi0NcXJxs26tsxAlAyZIl8cknnzh3kD/+oBswm+np1Uv87PJl58pW8uKLolT8xx+ruyiokZxszPFZ7EFRET1orAU2S2eAlixxa1JIr5wZYxfjzJmudxXJzydJbg7HmzD6fuhKWrS4Uaz+5m3s3i0f2NL7oA8EYy09PR2dO3dGQkIC5lgbloZ8cNMWNWqgOLZMC71qle6menVP14DD8U3MZuDZZ6mf7yhNmlCfrEUL78+vuGoVpW0ZN87TNbGD3btpeeUKqRMBNAIldb+zU5LdJmrJO/XAfNiNZO5c7c+Us3aNGxt/fCt4pTEmZccOY8v77DPqkLHOgNYsqNlMs3McjifQYwycOFGs5soxiECJTWDJRefOnYu0tDSsXr0aR44c0dw/NNT5Y0rzaHE4HP/i3DnSVeje3fEy9u8HfvkF2LOHkqqbTPI+4PDhNEv/00/uM9a0ngX5+bTMznZPPQxBKtLBZGKrVxc7xPfc47zioCOoJQvV8h93hjff1P7s44/l73WIohiJx42xtWuB69fddzzmPsoMLa1kqk89ZUwHRC/+3vnzRdistlFlXbwo36Zn1pdJsgPk8mw2i4Na99xD9wuTiaTFOfoJ9OtNT3JRKc4YUiYTDXKyNDccDsc7OH26BKyobduFdCDr2DFSt2X32d27Sdzn3Dlx5uyllygdhhL2nVOnaLlwofh8+/lnmj3r3h1o2FD+vYMHqQ73308xpywWTYvz58V7kyP45DOkQgVxff16y8DYY8ccn8myhloCTSnffit/36kTdXCMonJlWgqCdtJrFojMUPtzuhCPGmP799Pox4AB7j92QQEFenfqpP65s6IBHOsUFgIrVhh3QxMEuvkaMVP08890n2rRguS6Fyyg/wojL080rPbtA/Lz1S+jS5fIaJo8mdyWK1cG5s8XP5eO+L38suhWzc4HAJKSxG1dutB9skIFS8nt+vWB996z/1z9DenAnxq+Eo/gatSSi2apyPpmZmYiMzMTAwY4Z+2PGuXU191Oq1Y2ZCDdCGsD9uJwjKJz5zpo0cKx7968SW56W7eSAA979p4/T3H448aRTsTChZSbcNo0oFo1iuM1mUiwQ21WSakebs2Dulw5evYBABtL+usvUtZt1sx6v5vZB6NGWfeCUuujDBwo9+YTBB8xzkwm0Yd8/Xpgwwb3HFd6HGUMxs2bluIQ77xj7PEHDxbXtdwPT5ww9ph24lFj7N13aalU2XRlh4mVXb++a2ZBvYU//5TPqjiLPfElAHDgQClZXMj+/TQDOmgQEBdHRsajj1Lc6M2b1C4zZgCjR4tJc3/8EShblkbVfv6ZRspat6ab/8iRQP/+ZEy3b08uz82a0UzRPfdQeS++SEuTyfoM15kz9PmePaTylpQEJCYCe/fS5ykp9F9hyY0rViTD6rvv6Jjt2okWADueyUQzAU2bipLZAPDcc+p1SE0FPv9cfD9qlP3XwejR9u3vj+zdqy7YlJamb4CFG2tyYmJiEBMTg7ZtbVi5fka9euV0iW+5A9YG7MXhaGHvc5px9Cig1IRgqSUOHiSj6fffaZ2lgujXD5g3j4Sm/vxT7M+x7wCUUqJ/f/vOwY5q49o1mjUzmahvoaRxYxp4z88PwqJFtN+4cZQaQ0phIc3cmUwkMiR9DpQtS+8TE0npu2dP+bN6yxbqfyhVWz/4gPQIZs2i7//0k/7zcim7dtGyqMj4+DAtpMpLzDC6dQuIjaUOXE6OfP8yZYw9fqVK8vdKw6ugwNjjOYBH1RSXLnX/MdlFppbXxhoHD1Inz1ZguhZFRfT9u+6iEZQTJxyfhdUzAtO8uf59bcHiS+bPn4+oqCj07t0b8fHxqFOnjur+W7cCvXvTya1bR6NQyhlgxooVojH+2mvi9vLlyR0PoFE1KbZUBtl1Jo3V1EpIe+KE/vyGEybIpbqZ8pvZTH8qK15edqNUWOXoo3Rp9UEWppjK3F18YhTThehJLirFhelVvBKTCejcmTqgLVt6ujYcjm3sfU5LYbsw0b6nnrL0HLPFDz9YbtMKA3EnpHRYv/j9pEmW+0hFnthgsJL169X7CidP0vK//2ggV2sQnMXSHT2qnj7LbTzwgLbcrasICgJq1aIf64cfyN3o/Hnt/FX332/s8fv3B4YMEd/fcw8FOPbrR+/dGSulgUcfsVouRe6YGbOXBg0osbOjjBpFsymXLtHITHS0PF+Pns6hp0bt7Y0vOXpUXE9M1DbEGC+8YLmNGWKuxt5E81rxnyaT8zLsHNejV6DD3401PclFjaJJE5cU6xCvvKJvP/Y/keao43C8GXuf09bijO01xDiEHm8kjysPS6cwpUgTRroCduIFBcDEidoPWS33IWcoV87yeM88I7rRqFnobsajxpitmD5/YuVKWl64QDFEAE2L+wJ640sYgTaKzvEdbA1oBIqbolZyUVewbBktW7XS/5169YBvvnHfoIwS5tLqilh2TuCwf7/7VETtjQM9deqweyrGscCjcaCdO6tvl8ZTuALmrgVQTJiWMfbll66th5RKlYAHH9Sf+8yFeLTbnJ4urrP4onbtxKlye7h2jVwB1ZgzB+jTx/4yAe3OmdlMgaJ6YcqM330nbnNHglGjlJL0wG4s5csftb0zxyXwYH/nYK7tRibx9Fbi4uKwdu1abNiwAQOl6jFOUlAgVyirU4fit7dsEQciK1QgZTUleXn0jD54kJKplisHfPopfTZyJC2Vg1gffWRY1WXnAPCBJY5zxMbaNwjhDljsYdWqNPjSrJlj5bz8soGVMhitgW6pPeBJPB4Hqjbl7+qRJzYLwdBSTXH31KFSMcZDuP1Rw4yuo0flhvGWLbTUyit24gSNlN64IW67eJFyTgD00A4JUU9O+tJLYnya9PtaKBX5WDAqK+vFFynu8f77xRx6Sm7fJsPr778p/oep+miJxLz3Hhnn58+Tit/Zs9r1W7aMXG6lXLhAhmOvXhTbxGjRwnmRGL3xJezG8sgj2hfTihXax7nnHgqO1RuXpzSGb9+WzzbfukWdKvYfcQXbtxv3fa24Nnvw+E3eR9AalPvuO8pzE0iz9kYTGmqZFqRTJ0sDVzKIX5zvUW3w65VX6Lnx3nt0TX/4oSiPfd99wOuvW35nwADLGJbgYODffy33TU7WPpfbt7U/4/g+330XblMC3VmOumls0t440NOnafnpp3Q/3LULYJPj0vCarCyKhTp5kvoyTDlw9mxaHj9OcVaCoD4wogxH2buX+mpGYDZTvQRBLtL14Yc0K7l0KW1/8MErmDCB+muLF1Of4Mcf5WVVq0bhVFu20MD+vn00eHTVQWFVLU2EkiUdK89QWOO//TbdKIODjZWSV0OZ1FerY5aY6Np6eCuCC/njjz9k73/9lS7j998XhNat2SUtvr75xnIbIAhFRfL3TZsKwpgxglC6tOXnZcrQsUaNEoRduwTh0CHxs+xs9fKrVhXXBUF9H2uvypXl76dMsb8M9po4kZZjxlj+nmPGyPfdvVsQLlwQhLJlrZf599/W28UWt27dEuLj44VTp04JBQUFQnJysnDo0CGrZWrVJT9fXL91SxDy8mi5caMgmM3i9+fNE4RXX6X9MjPp8xEjBCE3V/z+lSvysgVBEKZOpfVWrcSyMjOt/z5XrgjCli2C8PDDghAUJAiDBwvCQw8JwqZNYhlFRYJw9Kgg3LwpCDk58t/nrbfOC/XqCcITTwjCokX2tbcgCMLAgbT+/ff0fsUKQahQwbH/jxJ729oWauUdOHDA0GPoReucrX125Ahtj462/3ieOk89GN3OamXa8x+cPp3Whw2Tl8mu37AweZklSojXoh727KH9mzVTrxtj+nRBOHxYELZtE4STJwXh8cfVr3/2HHj0UVrfuJG+X1io75w3b9b+rHdvunfk58vvcZs2CUK3btbLve8+2+3iLN50Tbsba/cQV5Rvz/Fc/ZzOzBSEli2vCvn56uUdPy4IZ8/aVYVirl8Xz3XcOMtrMyHB8r/+2Wf67zFFRdRvkHLjhvbv6+z/+exZQUhKsu9ZvHevIAQHy7d16aJevtuv6c6dqUIffUTvpTcmV9Kune0f7uZNpw5hta1//tnxzpQBWGtnp2bG7JVRZep2I0eK6ppS+vZV/55y9nTfPprxZLNca9eKn12/TqOr771HMyzSUZlGjdTLl7rLaLk6WuPCBfl7ZyTGx42jZWEhJS4cOlSUSlfO8rZqRaIg165ZL9PZUShH4kumTROTCUqVAaWKpSEhQFgYLePj5aPizz1HUveCQK5j8fHk+iSdPVIbRWeDL1KxFen5S5Vc2eh9cDApNP7yC7X/rFmknPTgg+K+QUEkulKypKVKav/+l3DwII24sUSU9vzmn3xCg1PMlbZnT8eSTTuaLyaQqFGDPDRmzvR0TfwDLUEbhr0xeEbH7L32GrlJtmsH3H23mJPoiy/EfcqVo+v+7FnyOjhwIBNMy6RECRr5z8kBVq9WP8axY5SuY8oUUSWtalW6l+/YQWqx0dHkmik9vwcfJLlrQaAR+F9/pfVffhHdNf/809jfwxswm8VUJu7k5Ek6rpZanpQ1axyfEfEU9j6nGzQAFi48JcsDLKV2bZotcgSpoi3Lt9uhgyifr5wkmTvXvms/KMjyGcue547W2RrVqlFfRQlTWu3QQdy2dSuJkjVpIrpWs9/A1ZNPumFqhawT7a5g6V9+sf55VpZrpw67dNG3n1SK3004bIwxGdW5c+ciLS0Nq1evxpEjR6x+R5lPzCiYbLUtlEYTQJ2zuDjxvS3Dxl18+CElLpw1y/mylC6NjmBvfEmXLlcQG0vrLF8JQDfQJk2MqVNQELkCREeLbq7sJq/mjhwdLY8NFQSxHKNgrth6/pNPP03LkBAyxJT3w4IC6iDWqkXvf/+dcpcpadWKXD+0XHw5IiVLUr46VwtHBQoffKC+XZpjUAq75rRwdYzW2LFkACkVXIOCtPsjtWvTAEz16paf/e9/Ygdr1CjR/f6//8hdvE0bS5dNNcLDqSwAePhh9Y6fv8C8k+wRsPr9d2qfY8ccPy7LG2lLH+D4ceqzPfusfLsgWE8OzDh50nNpi1wVB+oMAwdSXq5Nm0T3R6khJQjA88/rL09r0DEoiPqYrnoOsnuT9GedP5/qL9U/aN+eBntMJgqbEATgrbfoM6/JbcsqcvOme48rzQ2kRsWKrq+D1sNJitpskYtx+NFnr4yqt9K3rzxZoPIG7A9MneqZ47IBDqkxBtDMphG/M4uRO3pUzD3WqxeNYkkTris7WfPn00wUk7U3sgMYEUEzrXpEBT780PrnoaF0Lnv2UKxLixYUJykIQGYmSRMvX04PuaZNLUcbORxXsnUrLd9/X/QGYDPf7JmrZsAA2oaP3gFaRwdyS5Rw3BCXxrxnZ9N1+OuvjpXl75jN2oY3E4ywZZg/9ZQo/MYMqDVraHblq6/srxN7Htnqf7IBWWV84bx5dE8+dUr7u7du0XNFy8snEAkJoVh7qQFm6/q1NuNuLXfZU0+Jg5dGwzy07r5b3GbrP8zo04fsjAEDjK+XQzBjTI+IgjsxKpjQGnoeHh5QbnL4iPbKqHqrsluvXgdxzz3iXZdJ0PsbnmiHhARa3nWXa8pXu6aqVqXZJKl2hTK3VP/+dHPcvJlm1ow2YqpUkd+wmWuGWl31EBEhulcxGjSgYOtHHgHKlnWsnhyOPUyYcA6rV5Pc/KVLNAIMACNGkAv15MliR+n554HUVPuVce01svR2howgIoL6Lmaz6HbkrxQVkWfGmTOWn924IRe12r7dUpAsOFjdIFETUFGyYAHdwxcvBtato22sb2Q203+KDeadPy+6E/7f/4nlr1pF/6Xffyfj+/p1UUBGzRibNIn2P3VKFLwSBHF982Zg+HBaX7OGZvcKC2l2LzeX6mQyiYPuakJVyjCDQEbtOpdu05px9yRq/XO9959ateieqZVb1+0wY8wTyY61RqqDg93nLqn1YJoyhZYekFN2ufknVXaTzkB5Cy1a1EfDhur+oY895ubKuBBPKOxNnAgcOeI6P2l7R9GVN85q1WgmzRWULCkqtDL3Iw7Hl+ndOw9JSRRjpeZNMnq0GDMZHEyjwMqBzrAwGr3Wir/Si6fywZUqFRi56HbuLIspU2g2cMcOmhUKCaGZn2efpcGga9dI0bZ9e4p/W7OGDDgGSxy8Zo2oXCd9/JjN5Mp54gT9po88QrF5KSlyRd2CAjK02Hek369alWZhL14kD5eYGDIge/akfRITgbQ0qjPz0GCzmtKwCRarXauWqK6ZmUnPrvR0Oj+W8+6ll8jV/ttvgWnTSM2TDbgx76aiIjGNA0OaxsmdgwjeiDLu2heYOBHo3l0u6a/H480r8ZSbIiB3W5LiTtdArZxqo0bRxekBNyOHjTF7ZVQBMV+Mt3DyJC2lohJSZsxQjzMzEnf4ljvjZ+8MwcGuSRnBOkN6Z5LZzJw9fulG8MADtNQKkOZwAg0W19GuHb2/dInusWzWwR1eKozWrYHx4913PE9hr9CWkl27KMauqIji65csoe0ffSTqAAAUZzVlitwIuXiRtvfood7vGz8eePJJWl+5Upw5kM7ISdPBSLdLhaGkRqBUeImlqVm6lFy6AZo9e/55/a6E0phyKUyAZP58cZt04E1qcChToAQFkSG5d6/obh9IfPyx5Tbm/dG2rfb3PClSVb06zbiGh5MxDviwUe1JN0UtgQ53Nq4XxnQ4bIzFxsbixIkTOH36NAoLC5GWloZ4JkFlhZUrLS0DpkAlRWLnAaA8XUxz8tYt9Q5up07i+tat8geFFHYhSV3JlPTpQzfLu+4iY6ZOHe199dCmDY2kXb4sv4BDQymGqkYN699ftYpGKaUPHT3UqOFFCj4GwZQS9Y5Qly9P/xl7fztnmTsX2LlT/j/zx5hEDsdRKlake+z779N90d68o850hnbu1M776C84IrQFAEFB4g8r7a9Jn9Vav5100EuaC1IrJyMzzAEyzpVIE4hLxQGlrpJSG5M935VI3eWlBpSj2JOfjLn0Sjl/nvLkAaKhGCiw/pu0X/Xgg5QbbNs2+b6bNpEAyIULcqEMT6LlbeMoixYtwsMPP4zk5GQMGjQIly9fVt0vPj4eycnJ6NGjBx599FHHD8jc8DwVM6ZX1dBVSB80r7ziuXpIcNgYc0TuHACqVLGUI5I+G5j6TFSUXPSfub/QselGNn063VSZJPmGDWTsXL1KN789e+i7RUV0IZ8/T9PKO3eq+8JLYa6jABkzhw+LsTkff0yznDNnUvkTJ1p+f8yY8zL/+B07aASQBbaPHk03GYAeHloukXffTTLr3buTP/ukSXJ312nTxHWpbDujXDnr5+mL/Pwzjdba03ELCXG/e1Hp0jT6Lj2utQEADofDMRJHhLaGDAEWLBBlW6UePXrUBKXxUtIUEk88ob4/UzksVYpm0JRIBdikXh69e6uXp2XYeLvarLdpKbiaf/+lJMxSGjUSn5eUZoKMtJdeImPaW9QIv/kGePxxFCtGO0tsbCxWr16Nn376CbVr10ZqaqrmvgsXLsSqVauw3BkL3pNuioDod8z47jv3Hp/Nzg0Y4BGxDjWccgqJi4tDnNYcvgYVKlg62ZpM5O/92Wf63VRKl6YcMkrUlDODguSjYmXKWLomli0rl7VXmwlbsoQky+Pj5aNvQ4aIPucAuT88/XQu6tevgsceoxgJJcpg3qFDybf9iSdotCw9XW5oSZkxgxKnZ2eT7/7q1XRTmDGDHoR5eWSgbtxoKeHsKZYtMy5vS1iY6ALoa4wZQ52Pt9/2dE38m3nzSPKfE1i0aAH88Yena+E9qAltZWRkWP0OhRMYM4qXliauX7yovg+zDW/eVM8BxmK1APksmb14u/LlwYOi0mQgoBSlUuLMxI+radzYWPuhSZMmCLnT+W3WrBnWrFljXOFqsARpzD/c3dSuLart5Oe7/2FdogT5U5crR0GfXoAbPfRFVq2yHAGbNIlGhjw1Y3j2rDyhsBpduogJK6WEhdEMGduemirGo33/vb7j16olV5pSGyGUUqmS6JPOZtgAMsq2bgUiI21Lp7sTb76xupOSJcVgdo7reO45T9eA40q03IQ2baJBKo59yFV23SfyZDQREbeRkxOC4GAzioosR7xDQ824dcsEQTAhJETA7dv0RypbtgjXrpGrRblyRbh61dLtIiTEjNu39Y2iV6x4G7m58u5V6dJFuHFD251jwYJsvPRSjq7yOf7LsmXL0MWKG9/zzz8Pk8mExx9/HI8//rhjB4mI8HzAmy1r3NWwDr8/zIw5SvfucuMFoN9l3jxP1IbQKw9uzdXN0/9tgOSA/+//AmuEjcPhBBbK+/C2bTTIWb687byigYReoS2pym5MDCkJKunYUYzZ+eYbUQCjVSvR3ey990iQzBlefplihAAKT2BCHffdJ49BY7RpQ6EHANCzZwjmzQOqVg0qDkUoVYoUEpcsAVq3DsKFC3R+/fubMHcu7dO3bzCYZ9jLLwcXq28/95yY4+zzz4OKPU26diV3+W7d1JVB27QJwc8/y7cJQnDxbzhwIDB7Nm1PTqZE5HFxkYiJiSzef8+ePTp/MY4v0L9/f1xUmR4eNmwYKt6Rp509ezaCgzsjQj0AACAASURBVIPRvXt31TIWL16MqKgo5OTkICUlBdHR0WjJZrkUKNMY3bx502tTTBmJvecZmZsL5pTtyd/HI8aYN+JOFS9XUr06z2fC4XACC6kIBEdEKrQVFRWFtLQ0TNPyf7/D7t2iQfv++8DIkbS+apWY0kDquTFmjPh+xAjRGMvKothvgAyXrl1p/dVX5bFkSo4eFdc7dBCNsaZNRWPsxg0x7KVtW9EYYy6PZ86IoQ+tWgGHDonfq1GDjLHKlYGmTa9j374yqFiRYpCLimjSgCENg5cm/WYq3GFh4rYKFShmHZD3J+66i1w0b94UhbqkAwZsYOHvv0nen+OfLFiwQPOzPXv2YPny5di8eTMWLFgAk8aoPxtIiYiIQEJCAjIyMjSNMWUao8zMTLemNvIUdp+nJGmkq38fawMs3jE/52WoqTtyOBwOh+NLOCK0JRV9SkykWarjx8l75cYNcsGX7pOcTAYRE19o3ZpiqqWJsaXCC0OG0PK778gAAbRd6qVGzXvvievSnKxvvimuS1WJly2jZXo6CUAAlASaKflFRgL79lHw+KpVohhDRISo2Fu7tlie2vn884+4TToIKpWqHzZMXGe519jMH0DhBmPGAM88A06Asm/fPsydOxezZ89GaQ2VkuvXr+PqncD769evY/v27bpE8zg28JLEkX4yH2QsSUmergGHw+FwbOENruHejiNCW4w6dUT5dYCMIKYIm5FBUvQmE81IMXVbNksFALNm0cwUM3S+/ZZyT0rbja0HB5OewLhxJHDUoQPJ51+8SLHXTIeEpQhavJik0KVpbr7+mtSPO3SgWSamT8CMx2HDKAm1kurVSY0ZAM6dE89BKvohVXR++mnggw9EJUhAjBMHSB2ZpTFR87qRKpd36aKtpszxHebOlf8H7IHNhqXckcRu2rQpJkyYgKysLIwdOxZffPEFcnJyMGjQIACUsqJbt27o2LGjUdUPXFjMmDPqQAbAjTEJW7ZQgseHHvJ0TTgc1/DLL7/g008/xdGjR7FkyRLESrR5U1NTsXTpUgQFBWHs2LHo0KGDB2vK4WjDYnylMxcc49ix4xCCgupZjb+TynpLZ6qkDB4srtsynF97jWbNmPHC9v/mGxTHdmVliftLpfKXLSMDrHJlMhDDwqiPlZREroZFRaSg3KsXGVItW1LMW3T0MfToEY116yhv2H33UT0OHaLk5DNnkvF3/jyJL+3aRS6GbEZvxgwy3L7/npSLH3yQ3kt/jz59RFfPV16hmO5588R8bNJE0RzfRZpfz14+/vhjNG/e3GJ7VFQUvvjiCwBAzZo18SNX/zKeqlVpKfVP9gAeNcZ27fKuPFgdO9KNnPnFczhGsXWrGD/hSerVq4dZs2Zh/Pjxsu1HjhxBWloa0tLSkJWVhZSUFKxduxbB9mbhdSMZGUBBgadrwfEE995LHfBOnTxdE/8kPLwInggvUZtFCgkR+wmRkZafA3K1Xunzu149sQzmOtiihWjo1a1bULzerJm4ff584JNPyKgiqX+CpVSJiaHZtHbtSIxjyBASh6tfX4yNy8sjwy0oiAy02FggNJREP7p0oRnD5cu1z8nXOXqUcqNyOF7N4MFkiKnloHIjHjXGvDFXFDfEOK6gfXtP14C49957Vbdv3LgRSUlJCA0NRc2aNVGrVi1kZGTgPqmPkpdhVMJNjm/C02VwXEWJEmLqGDVMJvGeHhqqLiAjFfdo3VpcZ6rljRvTy1+Jjubx9xwfIDhYlIb1IFzAg8PhqCaHzZL6BHE4HA6Hw+FwDMflM2NqUo6Bkj8jUM6ToTzfQDp/bzrXyZMnIz8/v/g9U2caNmwYHjIgIJJf04FDoF7TgXKeDH5NBw78mg4M+DXtW7jUGFMLSOT4J7ytvYfly5fb/R29yWF5OwcOvK0DA97OgQNv68CAt7Pvwd0UORwO4uPjkZaWhsLCQpw+fRonTpxAkyZNPF0tDofD4XA4HL/GJAg8UwuHEyisX78eEydOxKVLl1ChQgXExMRg3rx5AIDZs2dj2bJlCA4OxujRox3OTcThcDgcDofD0YfbZsbS09PRuXNnJCQkYM6cOe46rGGcO3cO/fr1Q9euXZGUlISFCxcCAPLy8pCSkoLExESkpKQUx+oIgoBJkyYhISEBycnJ+Oeff4rLWrFiBRITE5GYmIgVK1Z45HxcCW9r723rhIQEpKenY//+/dixY0exIQYAAwcOxIYNG7B27VpdhhhvZ+9tZ6PhbR0Ybc3bOTDaGeBtHShtzdvZR9pZcAO3b98WOnXqJJw6dUooKCgQkpOThcOHD7vj0IaRlZUl7N+/XxAEQbhy5YqQmJgoHD58WJg6daqQmpoqCIIgpKamCh988IEgCIKwefNm4fnnnxfMZrPw119/Cb179xYEQRByc3OF+Ph4ITc3V8jLyxPi4+OFvLw8z5yUC+BtHRhtzds5MNpZEHhbB0pb83YOjHYWBN7WgdLWvJ19p53dMjOWkZGBWrVqoWbNmggNDUVSUhI2btzojkMbRmRkJBo1agQAKFeuHKKjo5GVlYWNGzeiZ8+eAICePXtiw4YNAFC83WQyoVmzZrh8+TKys7Oxbds2tGvXDuHh4QgLC0O7du2wdetWj52X0fC2Doy25u0cGO0M8LYOlLbm7RwY7Qzwtg6Utubt7Dvt7BZjzN9yGJ05cwaZmZlo2rQpcnJyEBkZCQCoXLkycnJyAFiec5UqVZCVleV3v4USfzs/3tbq+Nu58XbWxt/Oj7e1Ov52brydtfG38+NtrY6/nZs/tzNXU7STa9euYejQoRg9ejTKlSsn+8xkMsFkMnmoZhyj4W0dGPB2Dhx4WwcGvJ0DB97WgYG/t7NbjDG9OYy8nVu3bmHo0KFITk5GYmIiACAiIgLZ2dkAgOzsbFSqVAmA5TmfP38eUVFRfvNbaOEv58fb2jr+cm68nW3jL+fH29o6/nJuvJ1t4y/nx9vaOv5yboHQzm4xxmJjY3HixAmcPn0ahYWFSEtLQ3x8vDsObRiCIGDMmDGIjo5GSkpK8fb4+HisXLkSALBy5Up06tRJtl0QBOzduxfly5dHZGQk2rdvj23btiE/Px/5+fnYtm0b2rdv75FzcgW8rQOjrXk7B0Y7A7ytA6WteTsHRjsDvK0Dpa15O/tOO7stz9iWLVswZcoUFBUVoVevXhg4cKA7DmsYf/zxB55++mnUq1cPQUFkww4fPhxNmjTBsGHDcO7cOVSrVg0zZsxAeHg4BEHAhAkTsHXrVpQuXRpTpkxBbGwsAGDp0qVITU0FALz88svo1auXR87p3LlzeOutt5CTkwOTyYTHHnsMzz77rGyf3377Da+88gpq1KgBgKTRBw8ebLVc3tbe19augLdzYLQzwNs6UNqat3NgtDPA2zpQ2pq3s2+0M0/6HMBkZ2fjwoULaNSoEa5evYpevXrhs88+Q506dYr3+e233/Dll18W/4E5HA6Hw+FwOByOMYS4svA9e/a4sniOEzRv3hyRkZHFajRSyVCpMaYX3tbeS/PmzQ0ri7ez92JkOwO8rb0Zfk0HBvyaDhz4NR0YaLWzS40xtQNnZmYiJibG1Yf1ON58nmoXqlQyVMnevXvRvXt3REZGYsSIEahbt65qudK29ubzNxpvPldX3JT5Ne19uOrhG4jXtLefJ7+mjcObz5Nf08bh7efJr2nj8ObztNbONo0xrbiivLw8vPbaazh79iyqV6+OGTNmICwszNCK6+XyZeD114Hp04Hy5T1SBZ/GmmRoo0aNsGnTJpQtWxZbtmzBoEGDsG7dOtVyMjMzi9dv3rwpe+/PqJ3rsWOhWLIkHG++mY0gnkDC/eTmAiNGADNmAGXKeLo2HCe4dg147TXggw+A8HBP1yZAyc4Ghg4FxowBzpwBjhwBEhOBsDBAkruHw/FWrlwBhg8Hpk0DKlTwdG04ADBhAtC5M/DAA56uieexaYwFBwdj5MiRsriidu3aYfny5WjTpg0GDBiAOXPmYM6cOXjzzTfdUWcLPvoImDsXuPtuYNw4j1TBZ1GTDJUiNc7i4uLw7rvv4tKlS8UyolKkoxHePDphNGrn+sgjwMGDwLhxEbj3Xg9VDAHsrvDuu8AXXwCNGgGvvurp2nBskJND/Xu1h/Ls2dSU4eFkkHHcjNkMMAno779X3+fkSXoAczheyscfUz+xWjV6PAQ6Z84Ad3TZPMb48fTiyhU6pO0jIyPRqFEjAPK4oo0bN6Jnz54AgJ49e2LDhg2urakVeEM6hpZkqJQLFy6AabxkZGTAbDajYsWK7qymT2I20/L2bc/WI2DhNwWf4n//A1q3Vv+MXUtqfPMNTc7cuuWSanEAIDjY9j61agF16wJFRa6vD8cQioqATZvKBcytMlDOUw8//wzUrAn89JOna8Jh2BUzJo0rysnJKRZ/qFy5MnJyclS/o3TfcoX72sWLlQHchQsXLiAz86KhZTuKL7jp7dmzB6tWrUK9evXQo0cPACQZ+t9//wEAnnzySaxduxaLFy9GcHAwSpUqhenTp/t8pnN3wPovBQXGl33gAFCnDhAaanzZfgN/8voU+/drf2atKV99ldzUL18GIiKMr1dAU1QEhCi6CNWrAy++CDz6KN3cBgwA/vqLPjtyRNz/yhVA4fLO8S5mzQJee60mKlcGHnvM07VxH/zRAEyZQss9e4DkZM/WhUPoNsasxRWZTCbNDrrSfcsV7mvsIVy5cmXExFQ2tGxH8WY3Pea61qJFCxw8eNDqvn379kXfvn3dUS2f5Ouvgdu3yyAmBvjwQ+DPP4HFi8U+ydWrxh7v2jXyvOvdG1iyxNiy/RI+cKBJfHw8ypYti6CgIAQHB2P58uWerpIqrPOkpymzsoDTp4EWLWzve/UqULIkUKKEc/XzW6TeLg89BKxbZ9kIf/5JN8FnnpFvL18eCAoSpzWzsmgK02wGSpcGCguB69d5EKAHOXyYltnZrin/99/p9corrinfXvijQGT7dloqx1o4nkOXtIBaXFFERASy71zF2dnZqjFE7oZfbP6D2UzPanexfz/NONnLM88Azz1XCwDw1lvAd99R3dlN7to18s1OSQFu3HC+npcv03LVKlrOnw+kpTlfrt/Bhz91sXDhQqxatcprDTHAPmOscWOgZUt95ZYvD3hRzk/v4+GHxfVFi7QboF8/aqQ7HhXFSP1Lo6KAUqVITMdkIiu4YkVaz8szvu4cm9x/Py1r1XJN+a1aAYMGaX+u/Lu4C3seDW+9Bb8S4Nq1C7gocR7jxpj3YPNvphVXFB8fj5UrVwIAVq5ciU6dOrmulpyA4NlngZEjaf2dd4CyZV0bC3LqlGggxcbSjJMe5s0D0tO1Py8sFN0Ur14lJbgFC4D1652qLgAy7gDRRfG554Bu3Zwvl8PxdqS2gCAAX35pOfMs7Wi88Qb1963BYyY0kPZYW7UC7oQkWKVqVTLARo+271gVKwJxcfZ9h+M09evTstT/t3fe4VFU6x//bhpEIPQsUqQJEqSJ5Yo0CSYIoUm1XJTccBGUq1wRf4CIXlQQQa5YkAAqYEFQmpeIdAhNBUQRiAUFDW2RQCiJISSZ3x9vTubM7szs7O7MtpzP8+TZ2c3szJk9M+ect1f0/7kzMsjj9Ysv/H9uT5g5M7z0eh06AJ07y++FMBY8uBXGWFzRV199hX79+qFfv37Yvn07Ro4ciV27diE5ORm7d+/GyJEj/dFeXcLpoSmPLFkCzJhB2y++SK9WWscaNqSsh54yYgStHbTiwa5eVbop8oLZlSvADz941152DEC4VgnMIS0tDQMGDMAyrSx5QYCaZSwzE0hLI8UHQGPHjh3K7732mjC6eEvMb7/Jb3r2NP5Fmw14+WXqtD/+IOFs925g/Xoa+KpXB9S8aDIzSRsn8BtsXgpEzpWvv6bXvXv9f+7y6kHFxtEff5Q/80YY++47/aRKAu9w2xV6cUWLFy82vUGhQJ8+QJMmwJw5gW5J+MMWW97y55+k1D19mjxkqlYlyxab99ev9+x4vMCvJSg6C2OszFV+PjB0KGkDCwrIU8dTmGVMCGMGKa8zrwGWLl0Ku92OnJwcpKamokmTJrjdycfPf7UDE1zOx3A4agKIR07OOWRl/QkAOHKkMoAGZfs89ZTyO3Qc7WPqnTMUki9ZTRw/MJZmTfaYBqX906GD/Nn58/K2JFG8GRuMlyyhYqEiE4vXeBIHGkhhjBGI4bm8Ku3V+jkyklLLz5xpTPG9dy8Zyl96iUoOhhIlJdT3RpLDBoKwMlL668Feu5ZehTBmPb66KQ4eTK9JSQCbl158EXjwQc+Ow+4tJgwB2oLi1auyn3l+Prlbsm0WOJuXJ4QxS3GecVevphiYQPjkBCn20tpRNWvWRFJSEg4ePOgijPm7dqDa8WuX5mSqVasWEhJqAaDEfUaP467NZiWZWreOclz8978ef9Uj/FE7sAITRtu0Adq2teYkNhsF3SYnk4sjANSqVX5XyyaxePFiQzH8bI4KhJUjEF1c3vVyxcWuP0BUFBVeNsoff9BrKJYv7daNDPCPPUZhKaNHy//bvx9o3Ni9W7uVhFFooiAcKSwEfv2V4r/VJo2iIv3EGH370mtiojwBSJJ+yvmvvpKzNQNKjdLFi/K2lqBYWChPdNeuyRmeL12iRGKAUqjzBNbu8j6xGMZmIzep++4Dxo0LdGuChvz8fFwp9XnNz8/Hrl270KxZswC3Sh1PEngEkl69gNdfD3QrTOCVV1Bl82bafust63/4OnUoUwLDqvR+AgWBtIwF8pn2VRBcuZKScoUab79dy+UzT90Ug30M1oPF+c+dqxTEAODWW9W9p/1JWFjGhCIt9OFDFHgKC+VA43btgIMHlf/v0QPYskX7HmCGkKtXZUGmpERp1XIWqphXDTsmH3fCC3FawtjVq/KgVVgoT3Z5eXJ7vE15z84pScJvWxf+hrhwgV6PHw9IU4KRnJwcPF6a6qy4uBi9e/dGly5dNPc/dQp4+um6+PRT2e3WCiTJdcJXW7j5OuaLZ0eHmjVR9lPz0f5WMmMG8OqrtG23i0ndB9LS0mCz2TB06FAMHTrU5f/MBff33ysAaII//jiBrKzLFrRE202Y1YY9d+4ssrLUa9SaSUFBAf78808AtXHunOzu7B7lNUgSMHBgAurWLcSmTb8aOsI331wHu/0aGjYMXGV6SQIWLvRdGOOPF46cPw/89BOweTPQrx95WlevTome7r3X2oQnYSGMMUJZai+v7N5NworWOrCwEIiLI6sSKwyblUUCWkQECWJ68PcEE6QkSSmMuROMeGGMF8D4Y3z8sby9Y4d8zGvX5PP+9ZdsGbt8ma7p/HmgUSP98/PwwpgVBaXDknCdOXygQYMG+Pzzzw3v/803wBdfVMVPPwG33GJdu8wSxtwJW/6yBmzeDLRsKXvh6bFqFRmimFEqYKSlUTFno+llzWL3buCuu2hb7UYQuMVIHChzwWXPwPXX14eV3sdqLr9ybdh4JCQYyNTpI1lZWahd6u9cu7bs7qxHaRUnAPI1sPI3p07FGHZlbtmSXvXGLKtdj/nxji//pxU/JUlkQbr/fmUIZ7A8kqdOAcuXA2PHmnvc336Th6DnnpM/Z4Wxv/tO9touKKBwEbNi0ISbosAyHnhAOaCp0bGjtiAGkPDB4r4mTiRBp2VL1weAH2yKi4HPP6cBhw06ERH08AD0GS/IsM+14ANbeWFsxAh5+6GH5O1Ro4B9+2i7sFA+15UrsjB25QppWho39kxW4IUxd+0WOBEsM0kIwmIUrSw1oQV7hvncD+5wN0H6yzJ2zz3A3/5mbN8BA9wrl/zC5VIrSVqaf8/LJ/po1cq/5w4T1OJAtRAJPPRRK0Xjb/2EWfDjNj82all6jhwBxozRTnBqtn5z+XI5nt4I/ftTySAtjyqenBwKdTECKxmkRbt2lCzKZqO1XM+ewKefknu6zUb1X/PyyGNLI++hJmEljAkFeHDxySf6tbW0Fnb85MBbn6KjlTFbRUXq2598QibmDz+Uj2WzaVvG3C0w+X0fflje3rlT/3vs2Oy8774ruymuWQPs2eN6fCPHA+gazCgiHfYIAcwU/CWMqY3h7Nl++21g2jR6pj0Z619+2dXbzp8L0Oxsz/YP+DzG3HqrVfP/uT/6iF6PHAmslBCCeBoHyhblIoFH+MOvj/gi1lqJwC5dole+biNg3W84dCjQqZPx/VlYqZH2NG0K3HijseMaSYzGJ2jauBEYMoQEQ4CExMqVKZFTixaeeS+FlTAmCE6uXQMcDqrrxRR1+fnA99+r788LJ4WF8gKwoAD48kv5f1rJNL79ll4dDvlYNpvSMuZ8Dj34B8pTb4KrV5UWLCaMvfGG/JmzUPXRRzQw2e0kWP71F7X/gw+U12mFZSwxMRF9+vRBv379MGDAAPNP4C8CvqINL/xpGTt0iIqkM/g1+bPPkhbVk+6dPNlVcRLMMWPz5tE44Us9Qp9gftmBEMb4NLdvv+3/84cwOTk5ePDBB9G3b18MHjwYXbt21Y0DZYvy558P3PMQzJaxcELLMsbD/y7MOK413gfqN/z9d1qDMeHSSAwXv050h6eljtzx/PPG9xUxYwLLWbWKTLd//EH+tpIEDBpEqaDVcI7LYsLSX38pzc3OwtipU0DduvLiLTJSFnR4y5izm6KWMMbCFnypdfbXX8rvq7kh5edT/Y6ePV2VwQ88IGuMHn4YKM25gOJi69wUjaZGDglstvI5+5qMv4Sxixcp7TBAKe1TUlyficJC9+4k7uCPuWeP0kPOSsaMIaXU+PHa+zz2GL3OmxcgeSSQwhhAtUeeew548kngiScC04YQxNM4ULYoP3oU2LoV6N7dooapIIZk/6JlGeP7Yd06oH59qmaxfDl9xhTbDLbGNtJ/hYVUvmf2bBr3jFidWremPD5adeZZfH28RWGGzMJlFqdPG99XWMYElrNwoXIAALQFMcDVhZAJTgUFyhi0P7mESBs2APXqUTkptQyK7PuAcTfFG26g+LRu3bTb6o4rV5QDoRoTJ1KcmZZXDq/V//lneq1QQcSM6aI2Wwhtjdcw4cfXIuzuePddebt3b3p1fi6ys33X5PPfZwHb/uDtt5VZ3Hmcb8+AWe8CLYxNnixvb93q2XcLCmg1effd1t+sIQ4/Jzu7o1mNWlKekyfp/YoV/m1LeUDLMsaPMSkppCzPzZXH4eHDlccxIoydP09ZCFmIylNP0fxxmUvY+dFHwP/9n+t3Dx2itRA7h9bajH0ezB4OgOu6V3df65rhP4SWJfjgF1AbN8o+vkY00PPmydu8ZaygQCmA8OVo9u6lV14YKyiQhTFeqNNzU+TvpRMnKHOjL1y+rBTG1NKCL1lCVj0jsAEuJkbp3njpEvDjj963kyctLQ0DBgzAsmXLzDlgoBEDhM8EMmbMuYjyc8/5Hk4U6HAktjDhFxxBJ4wFsgIq4957je/Loup/+AHYvp00VkIBowm/KA/UEMlbDljYwsKFgWmLt1y75v/xJDMzEz169EBSUhLmz5/vdn9+DcJ7FG3a5Lov/9g7O8ioPU7XrlGdri+/pHEtPZ3WTkyZxoiLkxOC/P3vciULZypWpLEvIoLWOefOkU5m8WJ5HxbWymLbghVPhDHhpiiwhPx8+S4cMEC2YsXGkiVIDz6lKJ+NkBeuAMqSw2AP5+LF5OUCkLDCFjqFhdqWMV4raHa6+CtXSACLjKQBW0tZa9TKlZhIro5FRcrC0d27UwZHXydVI6mRnevGFBQUqNaSCSR1LlxAdQBnzpzBNUlCAwCXr1zBCR/aGYzX6S/8JYwZFUCWLrXmPFRuwqRcxTrExdGzOm8euSWeOOE6fwVMYAy0ZQygAS4qigZMviaIFnrBvNu2kaVMoICfi6wW/P/6i+bC0uzyZfPUm2/K8dNWF4L25bhZWdBM/x8TA9x2m6wQtpri4mJMnToV77//Pux2OwYNGoTExETcqJOlQmvc5j0R1Jg9Gzh2jApdA3IfrV1LQt3gwZS04ttvKbv0yZP6x1uyBFiwQH+fn39WKgrYPaNGmzbkOfTTT8A//qF/3EDgyT0XVsKYUIAHD9nZclDHddfJQs6WLZ6lb3aOGeOFJf7Bf+89eZutJfLz5UnG2TLGH6dXL3mbT2NvBjt30jqgVi1KKOLOZdEdDRrQa3a2MpzCrHtfLTWyVp0aRlZWluGaKwokiQLh/vlP/foG3lCq3qtz/fXkvwqgSuXK3rWzFK+v0w9YXaeGCWOszo5VGF0UehAao4qzoPPWWzSxd+0KAM39NpcwofLXX4NIGGOq8ypVAtQAKFdj992nzNzkzOnTtBpmvPkmqegnTaL33bpR8bbERGvaGqJouatZQcuWwPHj+vOU1cKY83k8oWVL/e+xUjb+4ODBg2jYsCEalC4GUlJSsHnzZl1hzEgKeC1WraI+OXqUslQzmK6Geeu4E8QYFSp43xY1WEx9fDzVA1u7NsbSunmecOKE8X3Dwk1RWMSCC0rQ0bjsfW6u9+77164p3RS1LGP8OLRhA72+/bZsxs7Pl61PFy9q14DgCxyaxbZtSsWut0UC27ZVZqDkryEhwdWlwFM8TY3sMwUFVH+gRw/zjy1ixkyFCWMvvKAM6pYkYP5839xFfvlFeTweq9bPzoLOv/7FBDHfMSoXb9wo35IlJa6KmoC5KebloaRiRfOqmXoLc79av1452PPk51PWJsbevZQtYOJEpR979+4UBCwog7nzA9bea+vXkyDGozY8s1qdYpjWx+FwoE6dOmXv7XY7HA6Hy35ZWVllf55OsUOGXHD5zGh6+EDBCjP37t207LoPHgysJ0u9eucU/aBHWAhjwiLmPe58jwsLCzF27FgkJSVh8ODBOGFA1D98WPl+7VplHJgn6Lkp8r7P3Nik8K5hZWsuXFB+VyuI3ir4CcbbUIzvv6cK8GpcuWIsW5EenqZGNg0rH2CRTdEUeOFlqxLeFgAAIABJREFU9mx5e+dO4NFHKcunJJG19ttvSbBxzhiYnEzdwQL1hwyh1+bN5X2cF4We5m8wilmLz3Xr6Bp46zoz0nzzjfyZzea6yExOpiL2gHqSILPa6Gl8CfLyUOLOLdAf/POf8vbAga7/P30aqFRJfr9vn9JCVru2XEsFIHcCm03OglTO4ecLK4Wxr76St3fvple1+qNMoeNJnE2ww7sBmh0C4Y6EhISyP0/p0qU6rl71XcFrNvfdB9x5p/uU9rm5CWjSJAFt2/rHROZcx5Ixblwtw/0QRre90Kh4CvM9XrhwITIyMrB27VocPXpUsc+nn36KuLg4bNy4EcOHD8esWbPcHrdFC3mbudV5i56bInNHBJSa+QuuSh1cuOB5jTAz4X22rYiL//57beWxUVhq5M8//xwZGRkYPXq0OY3TwsoHVljGTIV3Lfn6a/Ia+/FHCsQGyAhx6hR5iN16K5CZCcyaRftt307dwRZg9evT66efup7Harm5qIiKQPOZvTylpEQWrpgQ5pwdVpKAv/3N/bH0rtcMN0UjY7wLeXmQeLNJIGEp17ZvV2r0xoxRWsQAuvGcad3atdDQTTcprWblFF4YM9sllp/v+GG3Y0d61VIqOu9vJoEY/keMkLcrViRvWV+x2+04c+ZM2XuHw1EWXuAtRUU0FvFTfkwMrSn+9z/vjtmrFxmuJYn+Tp70zfO5enWKXduzh9aEejXB7rpLafm1msxM189Gjyb3VqO4FcYmTpyIDh06oDeXGiU3NxepqalITk5GamoqLnpSVU0QNPC+xzExMWW+xzxbtmzBfffdBwDo0aMH9uzZA8nNionXWviaMpe3jPFxX4C2MFbqaafgu++8H1R8gf0WfIIO3nL30EPmaAKPHVPP1BgSCMtV0MOve3NyqA5MQgLVDmSoxQL07Ekxk0bvcb3QIDOIjqbM6a1a6e8nSRTjyQttkkTWPi0tKI8nsQJamGGtMDLGuxAsljEAeOUVeXv0aFkK5guwVaum7wcfF+fqR+vj4jUc4OdpM+61I0eoa/buVdbu9hS2nLx8WZkxOBwwQxhr3bo1jh8/juzsbBQWFiIjIwOJHvhzb99OkQHbtpFgc+2a7JGsJrD27k1j37p1pBtp145CvL/8UplsDSBj9vnzZLTOyFCGZ9StS4+hmhJOj3vvpZi38+flz2w28iz44gvPjuUM73I/c6Z3x9i2jV7HjqXXEyfIO2zuXM+O43aKHDBgABY65RqdP38+OnTogA0bNqBDhw7GXB8sRKzlvMOI77HD4cD1118PAIiKikKVKlVwQc30pIGvgykfM3bxonJxxFu6eH3AlStK75VAMWYMJe4AlIJjZKQ8+EVEyBrKzp3lgrfeEJA0rwcOIMY5IMAowlIVUDxxX+MXbmqPf1aWftYro+zZ4/sxtPDkdrt4kVyf4+LombTZ6FmdNUt2tdLDjPAkMypLGI0vUZCXh5Jg0uwsWqT9v7176YZ056NdpQpJHBMmyJ/ZbLDxKWnLGUZS2588Se62RrL9rl1Lr8uXA599Jn/u6TC/fTvN83Fx2hkMfSGQ60UzhN6oqChMmTIFI0aMQK9evdCzZ0+3sd1MUPjyS1mQ6trV1eVv/HgyMA8d6nqMe+8l3ciBA7JAN3UqGZlXrJDjh6tXV4aNODNoEO3766/kWXzyJAlVO3eSQC9JwJkzlHa/sJCEwMaN1Y/VsycJafv30z5Gf98hQyiO8ZZb5M+efpo8mCWJlP7OisHt212PU1IixxrPnk3r1Xr1PLOIMdxmU7z99ttd4oQ2b96MDz74AADQv39/DBs2DOOdAwQCgFjbBRY+QLFRo8aoXFlCXl4Ejh3zPn3Oc8+VoLCQdAbHjwPZ2RIA6mheALtwQSoND7Lh8uUSVKlSjLw8H4OofKSo6BxiYuIAxCiC8x2OAkRGxqC4OAKXLuUiMrIKgEhcvZqHoqIIAN5rpf2een30aNSuWpVGxWBExIyp4k16ZD2ys01uYIDha+QcOhS4dvgLfty44c8/UVKhQvCUcbjjDlSfMAF1eCsZgJ937kRxpUqeFYN8+GHYjx9HjU8+AQC0uP12ZFmdIjRIcZdN8YcfKMMoQElv+TpPaviSDdF5iI6Lo9fff9f/Xr9+tLBmyT8Cic1GVp9Bg7T3MWsq6tq1K7p6kHFo2jSgTp2TSE6up7tfo0aeZ4asXZvKF3lKkybytrPHsd1u3HhdvTr9sYyR332XhYoVE/Dzz0DfvmREf/xxef/Dh7WFJSbTxsS45hXr0oWUBIsWUcInQHmv22zuY9n08OqrOTk5iI+PBwDUrl0bOb4Gq5iEWHN5hhHfY7vdjtOnT6NOnTooKirC5cuXUV0j6IkPUCwsLET79jGoUMF9XQk9mCDGKC6W7/5KlfhaWzZUqMCSfESgpCTw4ZB16tRC5cq0fdttZBIvKQEcjoplE+Hly9XKts+dq4SYGNfjxMWR77W7tPhDhrimnbc65TmKixFhtEiaFlY8uFbGjF24QOZXXwMiA4g36ZHLE7t2BboFxDffAHfc4f33jcaXOI8bVypVCq4yDtOn09+RIyT533wzmrPgQ0/58ENSw5cqmRNatiTNHpMAggSrx27edVhNnuVDC40s0NmQ6+zuZWTYffll9/uo8fnn9BcMwhhAdbf0prNAZUiNjQX69r0Em01fGAsHYmIoLPSmm+S+eOwxc45dubJ15Rd9rjNms9lg03na/FEgNienNoBaOHv2LLKy/CEY0iSldx2hUCCW9z222+3IyMjAa6+9ptgnMTERq1atwi233IL169fjzjvv1O1vxtWrEYiNhUK4qFjReHFjNWw2MkUzDUheHpnD2VqDP7bVxWmNEBUlX3+zZrSOyM9X1jLbuVOerLRqgSQm0iDurrYSb3L3G1FR3kd/+0N7YoW5vGlTEshCWPuj5r52kM88V4pyDAuixXk54W9/A44c8X4eMTLGu3DddbgWrDFVLVt65wPEExlJAl379uRzBQBVq5JPlK8paUMIfmh84w1KDX7PPfJnvLBWXEzz7Xvvkfu987AqScanAWdX5JYt9Y2bnljcJk2iJCEpKa7/85fnlCTR+kNNsRqwchWCoMcrYaxmzZo4e/Ys4uPjcfbsWdTQyX9pWoFY3fbQa3x8PBIS4k09th5618FfZ4cOQFIS+dcGA0zjxvseFxcXY+DAgWjWrBnmzJmDVq1aoXv37hg0aBDGjx+PpKQkVK1aFf/9738NnaOgwIbYWNkV4pZbaBBPS/O+3ZGRFIN14oQcR1ajhiyM1a4N/Pknbd90EwXg87Fa/iYqSp7bIyPl7RtvJMGrpIQGbTbZxMaqx9hVqGBsonv4YXPa7RFRUbD5Kvn6yzJmFh7ETHqMJNFNO28eOfD74vdgAmaM1ffcQ/7/Au/wxdqtNcbrsmoVzv72GyxI+hpc7N+P/PbtcR1L7RcTQ89e1aqBbVeA2LmT3BJLnZ4Ubow//UTFdb/7jubgBx5QfnfECBLU1HAWgu66S/nenc565Ehg4UJjQ/r06fSqti8ThPhanVoYURr/9BOtM5xJT6dcM2qu2yGsvwtbCgo8K6loVR96NdMnJiZi9erVGDlyJFavXo3u3bub3S6vCNaYsa++or9gEcZ41HyPn3zyybLtChUq4I033vD4uMwyxoiO9r3yelERue00bCj7kvMm45tvljPbVK4sC2L33w+Uhgj4lehoWTvGW8latCAt4+nTFGq1ZQsNCElJFMy6fz8F0cbG0gQZHW0sG52z37VfiIry3txp1aj22GM0e/t6nvPnKbKY5W+3ktOn6QaZOpXU1ABFAkdG0s3A14swAU/TI3ti5LfbKa8CS2Kxfn3g6weXZzyNL0GNGihxl+QjHLDZ8PvHHyPhwAHZz61aNfLJU0uTH+b85z/0x4ZL5zmHyawPPigLY+nplNhg6VLt4/o6zDvljzOEJJHX6aRJNB516CBnfl23jmKM9HRq/fq5b0eLFsCLL7ru98479KpWQcLftcYE7vF1XWoWbpd4Tz31FO6//34cO3YMXbp0waeffoqRI0di165dSE5Oxu7duzFy5Eh/tFUQIhQVAUVFNoWbYnS0su7DyZOeHVNLGOGFMX67YkX5fM4hbmb5DwPqWYcYvDWMF8aiomSDxw03yJmCoqLkjHTNmsmfR0cH3ECiTWQkbGYXqfEVNhsyvI0sf+ABYNgw4JdfzGmXHnXrksqZV3w88ggJggkJpvvdepoemQ+2dsfy5cpwOpsNeOEF79vqb+rWJbM7qzP83HNkdebX52radT5RnzcES5xauePBB5XFoW+7TfkcCjQZNUpfEPMXy5dTKCDj5EkK6500ibxxtmyprCj9oucxs2cPsGGD6+d8DXKGc2p3QL6V1ITQuXPdx34LghurjD5uhbHZs2dj586dOHz4MDIzMzF48GBUr14dixcvxoYNG7Bo0SJUsyqizSDC9BtcMENJbKwsjEREKDUQnlpxeIGK17LzDwYff12xony+v/5SFmD1xVWS5+abZbcINSpUULopMmGMF9JsNuXnTOiKipLbHxMTxKEMUVGw+Tq7WPkAO6c78gSmMfA1QYkZvP66qYfzND2yJ9pDZyuYzQY8/zyVXjh3DliyRHaFMgpfPNVK7r8fyMj4DRcuUHpuSSJj5eLFwEcfyfs5L+zS040Vk54wgVysb7oJGDeOjj97NmnRnd23BH6kdWulKePJJ8k/TqCKJ/o3M61BL78sF47mGTqU9GYM57Tk771XU3f437uX3AonTbL+OdQriScIfqxargQ+5ZyJBKubYnmDxT05J/DwxRzMW9X4RBfHjsnbvFtgbKzsyrhokbI2kLchAc4PIV8jTA1eiOLjx3jLWESE3Gb+eLwlLagtY9HR3seMsR/USsuazSYHFQKktmSrA3crZ57Tp1GdV72axf79cp5cPZ55hqotm0jXrl2xfv16bNq0CaNHjzbtuGfPqn9epQrF9w4bRqmWAeDf/9Y/Fos169+f6tKsW0e3TUkJufoOGKCe1JLJ0S+/rB2vlp5OWmpJkv+WLgUqVJBUM2bx8SH8LXvddbRuj4gg92hnl+gff6Q04S+8QMqb6Gj6bNYs+Tdo2pS2rS56LdChaVNg1Sr5/YIFNH6cPh24NlnIuXP6/1dLgsHwJJX5Sy8Z39cdkyfLc7netONsxfr22+tc5u+JE8nCfegQZSy94QZ95aqnbN1q3rEE4U9YCWPCQhYcqAljNptSoPIULUGOz06UmSnvFxsra6DmzJHdHHkhx1dsNldhbOZMuXCzswCmZhnjBTBnyxjbPyZGaW1QW7vHel+azDdiYrz3u+CFpP79Xf20lizxLa83QD8wKzJy8iTQti1lkjl4kEypej42TLvz1FNAt26oM22adspLb7n7buCtt4ztGyIFao0oDtLSZKvQqFHkYnTsGHmEpqdTIgGHA+jencaTlBRylbz3Xvq+zUZC0IoVFAty7hwd7+efKUt53br0ftIkoFs3YMoUOQnfnDmU2XTkSM9j2VjoHi+MOR9j6FCypr3wArXhppuAVq3IOuiOHj2AHTvk961aedY+gY/07+9a3bVuXWNZH0KMS5e0//fVV/rfdZfZ12p4jxKjOK8PX3kFaNdOnq/NRi2eDAiOTM8C7wmYm2IoICxiwQUvjPHCilmBkp06ydu8Bvv22+UBumJFefBt355ed+4k7TrfJhajZRSWlAAgn3RnYaxlSxrgGe5ixnjLGC+M8W6d0dHytUyfLhfi5OHdpvxKdLS2m+KXX9LDyZsveZg5AADWrKGOtdkomD43l2Km9u71rX384MACBXbvllNar1tH7bDZqEPVvrtpE6XOAszNTSxJrufUY84c885tIay2nlHeeYe6vFEjyjQ6ciStfZkroxElDsuo26yZa7moiAhKTPD112Qpe/xxCsPzhqVLqZZQ8+byZ2rzz3PPGRO+1OjUCXj2WbqWLVu8O4bAB7p0oeQ9PO3aWWvBDwB6caAdOrj/Pq808Aeffebb94Nl+GSx4ILQRLgp6iAsYsGFFW6KvN857y3GGzZmzVJaxhhMYOvYkYQpvk1aLlVa/PgjsGwZbf/2m76bIm85c44Z0xLG1FLh88JYTIzyfn//fXq9/nrPrsM01ISxwkJSrfbsSe+/+Yak4Lw8Ssm1fr264z/j449ds654i1bMGPsRP/hAtnYlJCiFox9+cD2eGWkBs7Ppxps927PvzZ7tukgMQoJVOVa5MlnKfOnCdu0oWQBv/bPiel96iax8niqLBCZRvTq5MfM+7b5kjvUTmZmZ6NGjB5KSkjB//nxLz9Wli6WHd4EPNQhlrKyMIrAGX5MzGSFYI1G8IlgXAeUNNWFMknxzU/zb34CVK2m93LCh/HmjRvJ2jRqyMMafy3ldbyQZRm4uWZskiXInMMtTbCy5EgHA/Pmux3JWDGhZxrTcFJk7pbPAxgwyvDA2YgRleTt8OEA1xgBZGGMF3k6dAp54gnxGGRUqkMkjEKSmytvMuvX77+oWrhMnKLBJT7vj7IO3cCEJlzYbrdLdMWcOMHas+/20GDiQfsu5c4M2q4sYhwVhQeXKFKfJP/OxsUGr/S0uLsbUqVPx/vvvw263Y9CgQUhMTMSNHo69jzxiUQN9xEh5F4HACvhYQqvmt7ASxgTBARPGKlY0Zhmz2fTnt5EjyZCycqW+VrtCBXXLGHNhYqj5mletSrEm/HvGM8+47quVe4KfMCRJPYEHL2g5Z1NkD3pEhLohh7eS2Wy038yZrtfjN6KjEXn+vH56POcf0FMqViQrlbtitUbJy9NPqak32vJSb16eMlI8Lw+oVEn/3L4IYgAV0tu2jXzlkpN9O5ZFBEru9jdNmgRPjRqBRURGkqV/6VJZSvm//wNmzAhsu1Q4ePAgGjZsiAalGW1SUlKwefNmj4WxJUusaJ3viNIPgmBAuCnqEKSKKo84edI/5Yz8AQtQve46ebEiSa4Ll7FjKdi9pIS8w/bsUT9eu3ZKgQWgkB+1ooosQ9S5c/T/ZctcLXLOBoWCArkgZI8enrlD8MJhZCRZ8FjikOho+f+8m6KzYKbmvsgfl2WPY99lXoFBkWExOhoR7nL1+npjX72q9E3Nz6cUmYF48FlVccDV1bJyZXXXRisIsPmJueoCSle6wkKqVQ1QyODkyf5tlz/59VdKBCIIc6KjlUqYV18FTMw+ahYOhwN16tQpe2+32+EIo+Ld7pKKCAShTDAs5wQA6ten13AQLLt0ASZOPIP27euUxWSpCWP//a+8XakScOedyveFhSTYOWciBJRJMniYMPbuu+RBxueIYDi7OzCL2h9/AHa751maPvvsN9x+e5Oy5B5TplBsWc+esree8zXwQhcvmPHCG1tvs7TbAH3WrRttDxniWTuNkJmZiZdffhklJSUYPHiw+4Lu/nKV4x+Mp5+mrA/16wP33EOfrV5NUsDtt/unPYB6hrXPP9dOz2Vm8o/MTCApybzjGcBmkyBJdFPy1mZe2cHfDj16yC69AkHIU1goD+Lz5pGrcAj65GZlZXHvvMxkI/AJZR8IQgmRTVGHEBwPw5rYWGDYsAuIjFRaxjyJGTt9WvZKc7YkGaFGDf3//+c/ypIyANUr8ibtfcuWVxVZFlu2pDwV1arJ96ZWzJhWMWjeTZG3jEVE0PElibKimwmLOVi4cCEyMjKwdu1aHFUzP/L4Kox17Wpsv6+/lqNoWd0fvk7YffdRGvzDh31rj69Mnky1w3hycqgegdGbV8tEzGNm8R6DREXJAjH/nGRn+70pAoH/iY5W3ux66QgDgN1uxxmuXIjD4YDdbnfZLyEhoexPEBj4PhD9EFoIN0WBYSZMoIW8mYp4b+ETeDDBrEoVY99jwghfzFlt7X/HHa4ue4MH6x9/yhQqKWM1vDCmlahDLeW9lmXMSviYg5iYmLKYA118dYNhecjT0vR9vjZsUI/T+PVXYPx4+X0wFGZyjpGrVct4LTEgaLVL/DPGC2OPPeb/tggEAaF+fbLMA8Dx4wFtijOtW7fG8ePHkZ2djcLCQmRkZCAxMdG04/sje2KQ5iQSCCwnLNwUw8G1z0xYxuyiIvMKHHsLn5AiMpKSyRnJO8APyryQojZYr1vneg888YR37TUbLaGL/5xPYc8LbLwwxqyKVmZWVos5OHjwoMt+vItFgo/R3mcbN8aVFStQ2LQpJADRGzbgRp0bJCsrC/UvXUIVANknTqDBgAE+nd8K8vLz8UdWFmz5+ai8YwfqG/xeUfXqiLpwAb/m5ID3rv159240v+sul/397eqiZRnTchkWCMKSadOojgpAvu28W0QAiYqKwpQpUzBixAgUFxdj4MCBaGZW0iNQLWwr9UR33gk8+STwwAPWnUMg8BWRTdEAQapQ1qWkxLXd27aRFkorlaskUfHSfv0oPOXxxymI/oUX6P/seMFgGWNCFMvxYFRI4t30oqLka1ETxtRcEgNWBNkJvm/VsibyQlp0tHr9IkmSC+nm5VnbXiOY4laRmgokJyN+0CDE8xedkEBaBI3sJAktWpR1boMff/S9HRZQ6coVJHTqRKuLL74w/L2o994D4uLQNDFRztSybx+a33orJS3hb+pq1RT9sN/ZNdICIiPVhTEWFxawWncCgT/hJ6GGDYNKG9y1a1d0Ner6DSAiQkJJifbCafx4YOJEOTvx6dMUq6xX8PnUKaBuXcNNKKNRI8Bq3dq991JiIYHACC1aUPw/j3BTDCOWLwf27QPee48W448+Kv/v2WcpQcPIkVQOKSGBFuUsg95vv5Gg8txzQJs2lI597lyKgSooAP7+d1nwGTbM/9fmTJs2VOdryhRj+6enAzfdRNu8MMYyNBrNIBhswpgkySFDzpYx9jmffbGkRM5KV60a9Ssg11G2AqMxBwq8jZt46SXg/vvVO1QvtkqSgLVraXvuXP1z1K9PiT7c0acPvZpVYffQISrO7IEghuRkoHdvgLkVsRGfJQOJjVUWzAtANjctN0W2NnUu8yAQhC25ufJ2fn7g2uEj7mp3zZxJww7zzKhTh3IH/fvf2t/xRimzbBnV7fTGk+euu2idwXj2WQon/sc/XPf1phrI/PnmVVURhBZ79gBaDigPPWTuufwqjJ05Axw+7EPlXw2CSDFliKFDKekb83RYsED+37Rp9Pruu6QpYlI5qyfrnB2wWjV5+6WXgI8+kt9/9plc8ytQVKpE69LevY3tP3KkqyYiJkYWxoz6lPN1xgIJb6XkBTDeMsZvs31KSkhLOH06WT5vuYXu85YtrWurVzEHnvqoPfMMaRC8UZ0CxpNgAKSxGDWKfrhFi+ic+/a57teiBb2OG6d7uMseaJw9ZsECpbTDBjV+tcSKVgM+zQRvvvkmOnfujH79+qFfv37Yvn27oe/xbop8U9k2UxgJBGEPX4jSH8HHFtGihXd+76NGKd87z8tqOrpbb5W327ZV/m/IEGOx5M7cdBPVH/v+e0qoa7NRGbjKleUh8sABef+xY6lknCfcfTewZYv2/3nPHCPK0kaNPDu/IHBUqyYvDxhhkU0xIQEYPLix7j4ffwysXy+/Ly6W05UDpITSqjexY4dy33DiwgXv9nnwQfPb4i/YejQmRtZM9eql/52FC0kzx9z6Ag1zr+SFLmfLGIMVcQZk18QJE/x3LXzMQa9evdCzZ0/3MQdGFyKnTpH/7YwZctIOq+ELMj/yCBXzu/VWsqjxAgj70d2YdnLcpfn3hvXraUBzjjvZsIFWKPwNwlvubr7Zp9MOHz4ca9aswZo1awy7NfHCGD8hsYWYEMYE5QqWaXbjxsC2wwfmz/cuFaqzTmz4cOX7r792/Q5f5mLrVu1je1uhpF8/mm+ZUJeYSPMory+02cghwxOio+XSQzzZ2XQL5OTIn2VkACdO6OsM/VWKUmANYeGmyFv2GSxTnM1GGoiHHiK/XrZ/VBStQdgN/+ijQIcOdMMz2MIgIwPo1Mn79vEpxM1Crx6uJ/H3Y8a4l8jVvLb0Br1gh9300dFkEXI4lC6daqSl0bo/WOIH+QLNvACmti1JcrZ255ps/qJr165Yv349Nm3ahNFGXOH0TJW//kp+IQsWkIRspWXJmcOHtW+C0aMpKHP+fGDJEtk35to14LXXNA/5l1r9MDfWNLckJ1OlcGe6dyffnWC5kQF063albFsIY4JyD++msnhx4NrhA9Wqeedb3KQJDZXZ2RTH7Jzolo0PzLN66lRg4ED5/7zH9RtvKL974gTwzTfG2mF0eDxyJEuxiHa3juBhliznWLP69eVbIDMT+OQTak+9espr5bl2zZhydd26dUhJSUGLFi3wg5P0lp6ejqSkJPTo0QM79IL3BCGFTwk8PC4QW8revaSgjo1VCiu8str5IatVi/6Y5ev8eVlbwe/LPHkuXqSbXk9DceiQrCjPzZUz1m7aRAt/m42U68uX61tkmDBZUkIPW4UKgMMRhV27lMp5m819mIvZaCn7Z8yYga1btyI6Oho33HADpk+fjjgVi0ViYiIqVaqEiIgIREZGYuXKlRa32BW2Vo6P9/upfYbdf0VFstDFJydxzprIxla+hFZQoyWMHT1KMzZv5vaGRo2Mp5C++WYSwj77zJg/J3s4d+4EXnyRBKAOHeiGy8mRM+IANNhERZF5PyuL6pqtWkWukGfPAh984OGFBZaPPvoIq1evRqtWrTBhwgRU5d2uOPiMjf/6VwE+/rg6iott+OWXowBuBAAcPfojgBYoLJSQlRWcSVU8oaCgQBRlFRijQwcKLBk+HHj44aBSnhilZk2ldccINhvw1FPyexZGwGDTQqNGtFbj92Ee6vv20RrQF9f7Rx7x7nvz5tFf48by9PLWW6T05uG7U6+AfefO6p/Xq0cOGWrH06N58+Z488038fzzzys+P3r0KDIyMpCRkQGHw4HU1FSsX78ekZ647wuCEq+FMVYg9v3334fdbsegQYOQmJiIG2+80e2gOU9YAAAQtUlEQVR377jDu3PyLoijRpG/8Isvuu63aROQlETbc+fSvpMn08O3aBFpc+Lj5VpUI0Yov3/PPcr3KSnkAsjHZwHAlStU1uj332mffftIiKN1mrp7l79r8ly5ov55x44dMW7cOERFRWHmzJlIT0/HeL5eE8fixYtRw10VZQvg3RRDlb59aZDv3JmyIAN0XWpuipJECVqAELpm5wQct99OFa+dgxs9pXlz4OefSdhJSzP2nT59SBirVcuzc3XqRCsFdi1jxgDffqsUxrKzSQj73/9IBTphAq0yqlZVqnmtZvdubT9tjuHDh+Ocis/22LFj8cADD+Cxxx6DzWbDnDlz8Morr2D69Omqx+EzNmZlZeGGG2w4dgxo3Fge51u3Jqf6MWNsYVHANCsrK6ivwx+ZMwUG2b1bXmH/+9/A668Htj1eoJfE4/vvjR2DVy4C5Im+YoXSUyk6mmLhu3Wj93wMmRp33UU/rxaFhcYTemmxd6/s/X3ffa7C2HffKd8nJZFXqrvfha1dnIUvo8JYU435c/PmzUhJSUFMTAwaNGiAhg0b4uDBg7jllluMHVhgGmbrXby+lfkCsQDKCsTqCWMxMfpue56wZw/9LVrk+j8miAEk/PACUN++3p2venXySc7IkD/jA075z1et8u4cVnHokGst3E7cKNmuXTt8GcT5XkO5EGRSkrzO//hj+owvZxAZKVsvo6IosPnECd/cbf2Kc+cY9S9xR4cOJIxFRJBbobusiG3akC9Mp07euUM6z+rt25NgV6OGUgvTtCml6wLkWXzaNFdfGyN8/rnn3+nQgf7csEhtYFRh8ODBGOUcja8Dn+2TERFB97hQzgrKNXPmhKQwprWo7N9fmaXQyDH4YVQtTb1ahkMtMjNJOJo3T/3/ZqwLatUiS92pU8pYoPffp0Rizte/Zg0ZBUqXvW5x/m3dZa90h8PhQFsu+4ndbofD4fDtoIKgwGthzJsCsZ0718PmzfrB+488koOBA3PRt29TtG+fjwceuIDx4+t520xTWbMm0C3wjunTczB58lnN/69YsQI9ddIApaWlwWazYejQoRg6dKjmfnxfm+Hqc/VqEwAVcPz4r4iONkmKtwCj13rhQjyAmjhz5iwuX64AoCocjlM4daoigBrIzXXgxhttAOJRVPQbsrKuWtxyE7C6mJ0kkSndnTA2YgTNzikp5p3bqP9MpUokAOplJWzVirQiAKW879jRf4lMnDh79iziS31+N23a5FFhWK1cJ75qqAWCkIXFKQCUC17DwyRY0RIQPNH88/HdZhEZSfKtmjBmZpZotTHNOSEJIzbWmCDGfg9eQeVcY5X3Xvjrr78QW5oCeuzYsbjH2T3LC5zXJOXF/drq6zx1Kg5APVy8eBFZWadMO67lU6ieu0fbtmTuTUmRLUs9etREnz41S2/m6wBcVxbLVVJCSulXXrG61ebw9NNk5fjkk8Ccv1694YiKOofDh5XzQ58+fXBrqY/AO++8g8jISPTVMBkuXboUdrsdOTk5SE1NRZMmTXC7RqojZ5cmX119WK2whg2bIoi9hgxfKzOk1KoVj7OlsnGDBnVxqvR5rlfPjscfJ7faFi28rN/lhOUuTStWyNubNpl33CFDKCj+zjvJGpWaqr+/CZOXT2zbRsEHFSu6FtrZsoX8VNlKxcpicQaYOXMmfiytH1GvXj1MnTrV8HfZ4kLUFBMIVHjmGYodc1efMYjw1VoDyOOB2dbxmBhl3NXatRTnVdHECkk9epD7pJlZi9nvMHkyeZW//jrp7Hh474X9+/eXrcn08KQWqPOaJNjdr83C6utk1XGqVq2KhAT1WGst9NZjXgtj3hSI7dkT2LyZPwa9btxIgsusWXL9VTUiIqjuklXC2PbtwJtvAjfeSJ5JEyZ4fgxm8gYoK2T37sDVq4FxXTx5chEAMvUzgRaQb4iVK1di27ZtWLRoEWwaajDWpzVr1kRSUhIOHjyoKYyZzaOPUts5A2xIw9cQ41PeO+/jXNciqOFTrHfvbt5xe/UylkO2qCh4/OO0CsiwIIkgYebMmV5/VwhjAoEKfCHJOnWCa1xyA5v6J0+mWqXewC7VORzCDPhpwEzHB8bcucDEicp6Yb4yZw4db9gw4yHPRkhMTMS4ceOQmpoKh8OB48ePo41RX1JBUOO1TsSbArHZTiUtPv2UXo8do9Soly8b006MGUPFjTdtogGED49w1j7wxMVR+Anj6aeB06fl9PpdulCbpk+nwoGSZCyA9bXX6OEDlDFpLPsfX6s1EPz5p+tnmZmZWLhwId55550y87gz+fn5uFKaASQ/Px+7du3yyKXJVx57jATZEFIy6sJnTeQ1ifznIUe/fvSqVuXTTIYMkbdZBXQgZBY8AEjrxFsSQxCW06SxfrlIgaB8YbMBfJhGVFTIpMRl8w+f+RnwzPoUF0ep370Jg3UHmxf37jX/2ABZ33zNN+WM3U5CnreJuDZu3IguXbrgwIEDePTRR5FWKtE1a9YMPXv2RK9evTBixAhMmTJFZFIME7y2jPEFYouLizFw4EC3C3Xn9KlxccoFqFEz8ZtvyttMGf/ww0BBAfn0usOT9PJt2pCSa9YsbUvZbbdRUeIFC8gdkPk4s/XpkSPax2/fnjIwbtxImSE/+EBeX6oZq9jvtXs3hZ0AJGzl5pIWRi3R2syZrnVAXnzxRRQWFiK11P2rbdu2mDp1KhwOByZPnowFCxYgJycHjz/+OADKntm7d2906dJF+2JMxmYLoayCBuAtY3z9sZEj6Z5hck1IYYaPixGWLaO/K1eCp6K3FocO0UDkPMMH2pXSBAYODFGlgUBgNa1bk5viq6/S+7g4ykLB8p7/9BMwaRJlxhgyJHDFJJ3g61wyJkxQetMYQS/1uy+wdrGU+OWBpKQkJPGZ6DhGjx5trAaoIKTwKWasa9eu6OpB5rKpU4EPP6RtJkiYhc1mTBDzhshIGmP796dwEFaS58oVUtJ37kzn52vz2e3XUKkSxYisX+86UN19N60t+dpZLNseg2mZunaVz8ng18BVqlBWoD17SLg7cIAyGbGSYGvXul7Txo0bVa/VbrdjwYIFAIAGDRrgcytUXeUULcsYE/hDEn+b9YJdEAOUrpsCgaD8MGMGucqwAV5NeblyJWmPAWVq3QDBKwkZGpUuAsKjjwL/+Y9raSGBIJzwk1qb4F1bvvjCn2f2HZuN6poxa54kkUtkaqrrWCpJwNatR8veJyfTZ0VFJMCVlABbt7ovYtynD/2pJV274w4qa/LLL0oFGxtYJ04kS2FJiX7BaoH/4Cc9qwKe/Q6r5CnS6QkEAgFN9LwrtR4REZTlK4DauJo15aYEI88/T9MMS+glEIQjAXn8OnQIWFbngBIZSQKcN4qwZcvI44EREQHMnk3JRnjYsUtKSEgLsNJNwKHmphjywhhTV1oRWS0QCAShyODBwG+/KQvQx8cDGzYAf/+7ct8GDSjTqlk1Gj1kzRqKeW/YMCCnd4vNJnR9gvDH77f43r0/oXXrm/x92pCHz1+gh5r/tyA4YBnP7fYwsozZ7fhl40Y0u/tu/5533DgK1BQIBKrMmDEDW7duRXR0NG644QZMnz4dceVRCxooGjdWz56VlETB4Tt2KN0Yn3mGSmT4mXr1XGtgCQQC/+J3YaxSpZJgiVsNS558EnjoIfOzAwl8JzWVrJUPPUTByBs2UK29UKeoXj3/qy5nzfLv+byhc+eQyajmDXffDXDVTQRBRseOHTFu3DhERUVh5syZSE9Px/gQK0gc1nTuDFy6JLsJ+VBywixefDF43RX9xciRgW6BIJhh+S6GDTP3uML4G2Y8+CD9CYKPqCg5bnvAAGG9DHt4v+IwZOvWQLdAoEenTp3Kttu1a4cvv/wygK0RqFKlSlBNBJMnB7oFgSWIukIQpDRpYs19IoQxgUAgEAjCmBUrVqBnz56a/8/KylK8LygocPksHCkv1ykQCIIbmyRZpwvYv3+/VYcW+Mitt95q6vFEXwcvZva16OfgRTzT5QfW18OHD8e5c+dc/j927FjcU1rX7p133sGhQ4fw1ltvwaaS0Un0c/Ainunyg5inywda/WypMCYQCAQCgSAwrFy5EsuWLcOiRYsQa1UhToFAIBD4hHBTFAgEAoEgzMjMzMTChQvx4YcfCkFMIBAIghhhGRMIBAKBIMxISkpCYWEhqpXWAmzbti2mTp0a4FYJBAKBwBm/JTHNzMxEjx49kJSUhPnz5/vrtKZx+vRpDBs2DL169UJKSgoWL14MAMjNzUVqaiqSk5ORmpqKixcvAgAkScJLL72EpKQk9OnTB4cPHy471qpVq5CcnIzk5GSsWrUqINdjJaKvy0dfi34uH/0MiL4Oxb7euHEjtm/fjjVr1mDNmjWGBDHRz6HXz94i+rp89LXo5xDpZ8kPFBUVSd27d5f++OMP6erVq1KfPn2kX375xR+nNg2HwyEdOnRIkiRJunz5spScnCz98ssv0owZM6T09HRJkiQpPT1devXVVyVJkqRt27ZJaWlpUklJiXTgwAFp0KBBkiRJ0oULF6TExETpwoULUm5urpSYmCjl5uYG5qIsQPR1+ehr0c/lo58lSfR1eelr0c/lo58lSfR1eelr0c+h089+sYwdPHgQDRs2RIMGDRATE4OUlBRs3rzZH6c2jfj4eNx8880AgMqVK6NJkyZwOBzYvHkz+vfvDwDo378/Nm3aBABln9tsNrRr1w6XLl3C2bNnsXPnTnTs2BHVqlVD1apV0bFjR+zYsSNg12U2oq/LR1+Lfi4f/QyIvi4vfS36uXz0MyD6urz0tejn0OlnvwhjDocDderUKXtvt9vhcDj8cWpLOHHiBLKystC2bVvk5OQgPj4eAFC7dm3k5OQAcL3mOnXqwOFwhN1v4Uy4XZ/oa3XC7dpEP2sTbtcn+lqdcLs20c/ahNv1ib5WJ9yuLZz72W8xY+FCXl4ennjiCUyaNAmVK1dW/M9ms6nWcRGEJqKvywein8sPoq/LB6Kfyw+ir8sH4d7PfhHG7HY7zpw5U/be4XDAbrf749Smcu3aNTzxxBPo06cPkpOTAQA1a9bE2bNnAQBnz55FjRo1ALhe85kzZ2C328Pmt9AiXK5P9LU+4XJtop/dEy7XJ/pan3C5NtHP7gmX6xN9rU+4XFt56Ge/CGOtW7fG8ePHkZ2djcLCQmRkZCAxMdEfpzYNSZLw7LPPokmTJkhNTS37PDExEatXrwYArF69Gt27d1d8LkkSvvvuO1SpUgXx8fHo1KkTdu7ciYsXL+LixYvYuXMnOnXqFJBrsgLR1+Wjr0U/l49+BkRfl5e+Fv1cPvoZEH1dXvpa9HPo9LPf6oxt374d06ZNQ3FxMQYOHIjRo0f747SmsW/fPjz00ENo3rw5IiJIhn3qqafQpk0bjB07FqdPn0bdunXx+uuvo1q1apAkCVOnTsWOHTsQGxuLadOmoXXr1gCAzz77DOnp6QCAUaNGYeDAgQG7LisQfV0++lr0c/noZ0D0dXnpa9HP5aOfAdHX5aWvRT+HRj+Los8CgUAgEAgEAoFAEABEAg+BQCAQCAQCgUAgCABCGBMIBAKBQCAQCASCACCEMYFAIBAIBAKBQCAIAEIYEwgEAoFAIBAIBIIAIIQxgUAgEAgEAoFAIAgAQhgTCAQCgUAgEAgEggAghDGBQCAQCAQCgUAgCABCGBMIBAKBQCAQCASCAPD/y/uzhNI+1OoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 49 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "random.seed(42)\n",
    "n_x = 7\n",
    "fig, ax = plt.subplots(n_x, n_x, figsize=(15,10))\n",
    "for rowIdx, row in enumerate(ax):\n",
    "    for colIdx, col in enumerate(row):\n",
    "        n = random.randint(0,300)\n",
    "        y = y_train[n]\n",
    "        col.plot(x_train[n], color = 'red' if y else 'blue' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c90d56a7-dec3-65a3-cac3-32e36c6bf31c",
    "_uuid": "6651259e2b8bab4c98f4649263e583201e494e45"
   },
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 3197, 2)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 3177, 8)           344       \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 794, 8)            0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 794, 8)            32        \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 774, 16)           2704      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 194, 16)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 194, 16)           64        \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 174, 32)           10784     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 44, 32)            0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 44, 32)            128       \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 24, 64)            43072     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1 (None, 6, 64)             0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 384)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 384)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                24640     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 85,993\n",
      "Trainable params: 85,881\n",
      "Non-trainable params: 112\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#functional model\n",
    "from keras import Input\n",
    "\n",
    "# input layer\n",
    "conv_inputs = Input(shape=x_train.shape[1:])\n",
    "\n",
    "# 1st feature extractor\n",
    "conv1 = Conv1D(filters=8, kernel_size=21, activation='relu')(conv_inputs)\n",
    "pool1 = MaxPool1D(strides=4)(conv1)\n",
    "batch1 = BatchNormalization()(pool1)\n",
    "\n",
    "# 2nd feature extractor\n",
    "conv2 = Conv1D(filters=16, kernel_size=21, activation='relu')(batch1)\n",
    "pool2 = MaxPool1D(strides=4)(conv2)\n",
    "batch2 = BatchNormalization()(pool2)\n",
    "\n",
    "# 3rd feature extractor\n",
    "conv3 = Conv1D(filters=32, kernel_size=21, activation='relu')(batch2)\n",
    "pool3 = MaxPool1D(strides=4)(conv3)\n",
    "batch3 = BatchNormalization()(pool3)\n",
    "\n",
    "# 4th feature extractor\n",
    "conv4 = Conv1D(filters=64, kernel_size=21, activation='relu')(batch3)\n",
    "pool4 = MaxPool1D(strides=4)(conv4)\n",
    "\n",
    "# flatten feature extractors\n",
    "flat1 = Flatten()(pool4)\n",
    "drop1 = Dropout(0.5)(flat1)\n",
    "\n",
    "# interpretation layers\n",
    "dense1 = Dense(64, activation='relu')(drop1)\n",
    "drop2 = Dropout(0.25)(dense1)\n",
    "dense2 = Dense(64, activation='relu')(drop2)\n",
    "\n",
    "# prediction\n",
    "output_layer = Dense(1, activation='sigmoid')(dense2)\n",
    "\n",
    "#model\n",
    "f_model = Model(inputs=conv_inputs, outputs=output_layer)\n",
    "\n",
    "print(f_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "5d25c5ad8a6ddd1319af7c56075a05fbfec8749b"
   },
   "source": [
    "The data here is extremely unbalanced, with only a few positive examples. To correct for this use the positive examples a lot more often, so that the net sees 50% of each over each bats. Also, generate new examples by rotation them randomly in time. This is called augmentation and is similar to when we rotate/shift examples in image classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "4ba8b520-7302-747c-7f84-8a816508e651",
    "_execution_state": "busy",
    "_uuid": "0604716b9a92c9113dcd16631b6fc48ff006fd53"
   },
   "outputs": [],
   "source": [
    "def batch_generator(x_train, y_train, batch_size=32):\n",
    "    \"\"\"\n",
    "    Gives equal number of positive and negative samples, and rotates them randomly\n",
    "    \"\"\"\n",
    "    half_batch = batch_size // 2\n",
    "    x_batch = np.empty((batch_size, x_train.shape[1], x_train.shape[2]), dtype='float32')\n",
    "    y_batch = np.empty((batch_size, y_train.shape[1]), dtype='float32')\n",
    "    \n",
    "    yes_idx = np.where(y_train[:,0] == 1.)[0]\n",
    "    non_idx = np.where(y_train[:,0] == 0.)[0]\n",
    "    \n",
    "    while True:\n",
    "        np.random.shuffle(yes_idx)\n",
    "        np.random.shuffle(non_idx)\n",
    "    \n",
    "        x_batch[:half_batch] = x_train[yes_idx[:half_batch]]\n",
    "        x_batch[half_batch:] = x_train[non_idx[half_batch:batch_size]]\n",
    "        y_batch[:half_batch] = y_train[yes_idx[:half_batch]]\n",
    "        y_batch[half_batch:] = y_train[non_idx[half_batch:batch_size]]\n",
    "    \n",
    "        for i in range(batch_size):\n",
    "            sz = np.random.randint(x_batch.shape[1])\n",
    "            x_batch[i] = np.roll(x_batch[i], sz, axis = 0)\n",
    "     \n",
    "        yield x_batch, y_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/gpu_cuda10.0/lib/python2.7/site-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "#Start with a slightly lower learning rate, to ensure convergence\n",
    "f_model.compile(optimizer=Adam(1e-5), loss = 'binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "fe6afcb2-7e0a-501d-bf19-759c0b3fda77",
    "_execution_state": "idle",
    "_uuid": "c8844e546b1d4677b3be321cbc203c8c617ef7c4",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:2741: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "hist = f_model.fit_generator(batch_generator(x_train, y_train, 32), \n",
    "                           validation_data=(x_test, y_test), \n",
    "                           verbose=0, epochs=5,\n",
    "                           steps_per_epoch=x_train.shape[1]//32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "964bdbf2-a325-11da-98c8-4dd56b31c9ad",
    "_execution_state": "busy",
    "_uuid": "6030dfe0cb763066fdecf81a27dff897cce69af4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      " - 3s - loss: 0.6675 - acc: 0.6057 - val_loss: 0.3749 - val_acc: 0.8982\n",
      "Epoch 2/40\n",
      " - 2s - loss: 0.6009 - acc: 0.6944 - val_loss: 0.3287 - val_acc: 0.8982\n",
      "Epoch 3/40\n",
      " - 2s - loss: 0.5708 - acc: 0.7191 - val_loss: 0.3025 - val_acc: 0.8947\n",
      "Epoch 4/40\n",
      " - 2s - loss: 0.5521 - acc: 0.7282 - val_loss: 0.2663 - val_acc: 0.9088\n",
      "Epoch 5/40\n",
      " - 2s - loss: 0.5252 - acc: 0.7503 - val_loss: 0.2592 - val_acc: 0.9035\n",
      "Epoch 6/40\n",
      " - 2s - loss: 0.4775 - acc: 0.7803 - val_loss: 0.2274 - val_acc: 0.9175\n",
      "Epoch 7/40\n",
      " - 2s - loss: 0.4695 - acc: 0.7879 - val_loss: 0.2104 - val_acc: 0.9211\n",
      "Epoch 8/40\n",
      " - 2s - loss: 0.4774 - acc: 0.7847 - val_loss: 0.2089 - val_acc: 0.9175\n",
      "Epoch 9/40\n",
      " - 2s - loss: 0.4474 - acc: 0.7917 - val_loss: 0.1981 - val_acc: 0.9316\n",
      "Epoch 10/40\n",
      " - 2s - loss: 0.4692 - acc: 0.7828 - val_loss: 0.1845 - val_acc: 0.9421\n",
      "Epoch 11/40\n",
      " - 2s - loss: 0.3917 - acc: 0.8207 - val_loss: 0.1855 - val_acc: 0.9368\n",
      "Epoch 12/40\n",
      " - 2s - loss: 0.4050 - acc: 0.8194 - val_loss: 0.1712 - val_acc: 0.9404\n",
      "Epoch 13/40\n",
      " - 2s - loss: 0.3735 - acc: 0.8371 - val_loss: 0.1584 - val_acc: 0.9421\n",
      "Epoch 14/40\n",
      " - 2s - loss: 0.3872 - acc: 0.8305 - val_loss: 0.1413 - val_acc: 0.9561\n",
      "Epoch 15/40\n",
      " - 2s - loss: 0.3495 - acc: 0.8542 - val_loss: 0.1405 - val_acc: 0.9526\n",
      "Epoch 16/40\n",
      " - 2s - loss: 0.3301 - acc: 0.8611 - val_loss: 0.1444 - val_acc: 0.9509\n",
      "Epoch 17/40\n",
      " - 2s - loss: 0.3329 - acc: 0.8630 - val_loss: 0.1669 - val_acc: 0.9421\n",
      "Epoch 18/40\n",
      " - 2s - loss: 0.3534 - acc: 0.8491 - val_loss: 0.1847 - val_acc: 0.9386\n",
      "Epoch 19/40\n",
      " - 2s - loss: 0.2935 - acc: 0.8832 - val_loss: 0.1565 - val_acc: 0.9474\n",
      "Epoch 20/40\n",
      " - 2s - loss: 0.3137 - acc: 0.8636 - val_loss: 0.1439 - val_acc: 0.9544\n",
      "Epoch 21/40\n",
      " - 2s - loss: 0.2531 - acc: 0.8924 - val_loss: 0.1212 - val_acc: 0.9614\n",
      "Epoch 22/40\n",
      " - 2s - loss: 0.2549 - acc: 0.8924 - val_loss: 0.1484 - val_acc: 0.9526\n",
      "Epoch 23/40\n",
      " - 2s - loss: 0.2731 - acc: 0.8908 - val_loss: 0.1172 - val_acc: 0.9596\n",
      "Epoch 24/40\n",
      " - 2s - loss: 0.2450 - acc: 0.9006 - val_loss: 0.0926 - val_acc: 0.9807\n",
      "Epoch 25/40\n",
      " - 2s - loss: 0.2147 - acc: 0.9176 - val_loss: 0.1309 - val_acc: 0.9596\n",
      "Epoch 26/40\n",
      " - 2s - loss: 0.2081 - acc: 0.9179 - val_loss: 0.1038 - val_acc: 0.9702\n",
      "Epoch 27/40\n",
      " - 2s - loss: 0.2466 - acc: 0.9047 - val_loss: 0.1263 - val_acc: 0.9614\n",
      "Epoch 28/40\n",
      " - 2s - loss: 0.2399 - acc: 0.9044 - val_loss: 0.1027 - val_acc: 0.9719\n",
      "Epoch 29/40\n",
      " - 2s - loss: 0.2111 - acc: 0.9167 - val_loss: 0.0893 - val_acc: 0.9772\n",
      "Epoch 30/40\n",
      " - 2s - loss: 0.1854 - acc: 0.9315 - val_loss: 0.0992 - val_acc: 0.9719\n",
      "Epoch 31/40\n",
      " - 2s - loss: 0.2086 - acc: 0.9170 - val_loss: 0.1239 - val_acc: 0.9667\n",
      "Epoch 32/40\n",
      " - 2s - loss: 0.1714 - acc: 0.9331 - val_loss: 0.0962 - val_acc: 0.9754\n",
      "Epoch 33/40\n",
      " - 2s - loss: 0.1638 - acc: 0.9388 - val_loss: 0.1187 - val_acc: 0.9684\n",
      "Epoch 34/40\n",
      " - 2s - loss: 0.1909 - acc: 0.9230 - val_loss: 0.0835 - val_acc: 0.9825\n",
      "Epoch 35/40\n",
      " - 2s - loss: 0.1517 - acc: 0.9489 - val_loss: 0.0961 - val_acc: 0.9772\n",
      "Epoch 36/40\n",
      " - 2s - loss: 0.1548 - acc: 0.9419 - val_loss: 0.1004 - val_acc: 0.9719\n",
      "Epoch 37/40\n",
      " - 2s - loss: 0.1527 - acc: 0.9457 - val_loss: 0.1262 - val_acc: 0.9649\n",
      "Epoch 38/40\n",
      " - 2s - loss: 0.1295 - acc: 0.9549 - val_loss: 0.1073 - val_acc: 0.9702\n",
      "Epoch 39/40\n",
      " - 2s - loss: 0.1667 - acc: 0.9372 - val_loss: 0.0671 - val_acc: 0.9877\n",
      "Epoch 40/40\n",
      " - 2s - loss: 0.1305 - acc: 0.9501 - val_loss: 0.0713 - val_acc: 0.9842\n"
     ]
    }
   ],
   "source": [
    "#Then speed things up a little\n",
    "f_model.compile(optimizer=Adam(4e-5), loss = 'binary_crossentropy', metrics=['accuracy'])\n",
    "hist = f_model.fit_generator(batch_generator(x_train, y_train, 32), \n",
    "                           validation_data=(x_test, y_test), \n",
    "                           verbose=2, epochs=40,\n",
    "                           steps_per_epoch=x_train.shape[1]//32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_model.save_weights('f_model.dat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_execution_state": "busy",
    "_uuid": "31d3be9df5d7387bf68eb9ca2c1c73c1590c8681"
   },
   "source": [
    "#Evaluate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "d8a74931a02d8ceb43c03f9f687f934581e11e9d"
   },
   "source": [
    "First we look at convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "020d5851-762a-d02c-77fa-319c3af6a4af",
    "_execution_state": "idle",
    "_uuid": "6c22b94c662b6b25fd33c85fd05892aca54e5d86"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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k+vahQzcweTJQrlzhx779NrB5M+3BOW6c6vvOzqb6+Zgx3DiLMSmpVQN3cXGBi4tLvq8FBgYqPXbz5s0lj4pphYMDkJ6eo9axnp7AlCnA/PmAszPg66v8uMOHaau3K1fo84YN1Uv6jDHt45WYLM933wHvvQd89lnBpfg3btCuP926UZll715K+l9/DSQkSBMvY6aOEzjLY2EBbNsGlC9P9fD0dCAlBZg8Gfjf/2jLtnnzgNhYoHdvKqMIQaUUbp7FmP4ZRwJfvBjl/vlH6ihMQu3awM8/U4nE25sW/Hz/PTBkCHDtGpVZctdq1a1Li4L276crcsaYfhlHAt++HbW+/JJHzPSke3fgq6/oirthQ+DsWWDDBuCttwoeO24c0LIlMHYs8Py5/mNlzJQZRwKfMgVlb98GfvlF6khMxqxZNCvlxAmgbdvCj7OwANauBZKSKOkzxvTHOBJ4nz7IaNKE3q+/fCl1NCZBJgOaNy84b1yZdu2oDr5qFTXRYozph3EkcJkMjwICgFu3gB9/lDoapsS33wK1agEjR/JrLGP6YhwJHECqszPQoQNlCm6fZ3CsrIAVK4BLl4DFi6WOhjHTYDQJHDLZqwbWP/wgdTRMid696WPmTHqzxBjTLeNJ4ADg5kbLBGfPpknKzOAsX04Dm6NH625ueHw8DbLynwAzdcaVwGUyKqEkJQGrV0sdDVOiTh16fT14ENi6Vfv3f/Ys0LEjMH06D4cwZlwJHKArcA8PWhL4n377VzP1jB5NwxXDh2t35mdYGHVZLF8eeOcdqqTxClBmyowvgQN0Ff74Mb1fZwbH3JxWZ7ZvT02xpkyhHYFKIiSEtn9r1oymKgYGAhcvUsdFxkyVcSbwDh2Anj1pjfezZ1JHw5SoUYM6F44aBSxYQMvyNflVKRSUrMeNowHSY8cAW1vgo4+AChV4PJuZNuNM4ACNYj17BixZInUkrBCWljRUsXo1JfOOHYF//1X/9mlpQN++wLJl1MJ2505K2gBQpQowcCCVaFJTdRM/Y4bOeBN4q1ZAv36UwJOTpY6GFWHUKODIEfo1degA/P676tskJVG9OzycKmWLF1Np5nUjR1Ly3rZNN3EzZuiMN4EDNOE4NZVKKcygOTsD585RB8Pc6ldGhgzXr1Ny37ABmDED8PMD3N2p1h0XB+zbR8v0lenYEWjalMsozHSVeFd6STVtCgweTJdogYHK2+Uxg+HgAJw8CXz6KfUYBxrn+75MRr9CBwfaOGLiRKB168LvTyYDRoyg+nhMDPVuYcyUGPcVOECXbQoFTVFIS5M6GqZCxYq0ifL69UBAwEP89BMNTN66BWRkAPfuAadOUW27qOSda+hQ6k/OV+HMFBl/And0pCLo2bM0qsWdlAyeTEalklGjkvHxx1TrrlePBj2Ly9qahkJ+/plXZjLTY/wJHKD5ZatXA7/9Ru+peXWHSRkxgiYk7doldSSM6VfpSOAATUmYORP46Sdg2jSpo2F65OJCb8S4jMJMTelJ4ADwzTfA558D8+cDS5dKHQ3Tk9zBzBMnaOaKKseOUa8Wxoxd6UrgMhnNSOnXj1Z+6KKbEjNIw4YBZcoA69YVfowQtGzA1RXo358XADHjp1YCj4qKQvfu3eHh4YG1a9cW+P7WrVvh7e2NXr16YfDgwbhx44bWA1WbuTmNaLm40LP68GHpYmF6U7Mm0KsXVdCU7feRnQ188QUQFESLiVJTaWUnY8ZMZQJXKBSYNWsW1q1bh4iICISHhxdI0N7e3ti/fz/CwsLg7++PuXPn6ixgtZQrRytAmjShtdjnzkkbD9OLESNotefevfm/npJCi4dWrwYmTaJSS+PGXDNnxk9lAo+JiYGDgwPs7e1haWkJLy8vHDlyJN8xlSpVyvv/ixcvIFNnJ1xdq1qV1mzb2ACensD9+1JHxHTM3Z1Wer6emO/cATp3Bv74A1i7lhprmZsD/v7AX38BV65IFi5jJaYygcvlctjZ2eV9bmtrC7lcXuC4LVu2wN3dHd9//z2+/vpr7UapqVq1KIn/9x8NbvL0wlLNzIwSc2QkcPMmLQ3o0IF24TtwgK7Qc338MdXM16+XLl7GSkprS+l9fX3h6+uL/fv3Y/Xq1Zg/f77S4+LUmSagREZGhsa3tR47Frbff497ixcjxdNTo/soSkli0zVTi+299yxgbt4Qvr7p+Oef8rCxycbmzYmoXTurwAwVV9fa2LixAoYNuwFLy/wv7qb2uGkLx6YZjWMTKkRHRws/P7+8z0NDQ0VoaGihxysUCtG6dWul3zt37pyq0xUqNjZW49uK7Gwh2rcXonp1IR4+1Px+ClGi2HTMFGPz8RECEKJjRyGSkgo/7uBBOm77dv3Fpg0cm2aMNbai8qbKEoqTkxPi4+ORmJiIrKwsREREwNXVNd8x8fHxef8/duwYHBwciv9Kokvm5tTu7vlzICBA6miYji1YQJs2RUbS5g+FcXenxllFTT1kzJCpLKFYWFggODgY/v7+UCgU6NevHxwdHRESEoJmzZrBzc0NP//8M/766y9YWFjAysqq0PKJpJo2pYU+wcHAoEE054yVSu+8A6gzDGNmRj1Zpk8Hbt+mfiyMGRO1auAuLi5wcXHJ97XAwMC8/xvMoKUqU6cCu3fTgKazM1CtmtQRMYl9+ik1tNywga7aGTMmpWslpiplytAz9eFDYMIEqaNhBsDeHvjgA2DjRlrsw5gxMa0EDlCT6cmT6Rl76JDU0TAD4O9Pfci5PwozNqaXwAGqgzduTBOD//tP6miYxLy9aSm+tgYzeYtWpi+mmcDLlaMVHImJ3HqWoUwZ4JNPgP37aTNlTb14QVfz1avzMn2mH6aZwAGgUyeaUrhyJRAVJXU0TGLDh9POfD/9pNntb92iP6n166k3+ejRtFkzY7pkugkcAGbPprlj/fpRMj90SHkrO1bqNWpEE5PWrSt+x4Vff6Whlfh4uoo/e5amMvbvD/z7r07CZQyAqSfwihWBPXuAjh3pPW/37vT+t18/4McfabYKMxn+/sCNG8Dx4+odn51NM1N79QIaNACio6nrYZUqQHg4lWZ69uSaONMd007gANCyJV02JSfTs87XFzh9miYI29kB777LjaNNRL9+lHzVGcxMSgI8PGjzp5EjgZMn8y8EqluXOhonJtL9ZmXpLGxmwjiB56pQAfDyAkJD6VkXHU17bKakAB99RL1HWalWoQK9fu/aBTx/XvCpoVAAjx/TdMPWrel1/scfgTVraFz8TZ060bKD48eBUaO4GSbTPq11IyxVZDKgVSv6GDuWnq2DBgEXLgDW1lJHx3RoxAhg1Spg7Ng6qFGD3pglJwNPntDO97lJuGFDalHbvHnR9/fRR1QHnzWLZq5Onqz7n4GZDk7gqlStCmzfTrsC+PnRdi+GsGEF04mWLWkTp9Ony0AIer2uX5/2Bcn9qFGD3qxZWal3nzNmANeuUb3c0RHo00enPwIzIZzA1dGuHRU7g4KAZcuA1/rAsNJn924gLu4mmjRpopX7k8molHL7NjBkCPDnn/SmThd276YXnc6dAUtL3ZyDGQ6ugatr3DjAx4c2VeQ9NlkxlS9Pg5rVq9PKz5IsGCrMr7/S1EVXV3qn0Ls3Dem81u2ZlTKcwNUlk1H/FDs74MMPqbc4Y8VgZ0cTnp4+pT+hly+1d99paTRc07QpVfmGDAEuXqTGm/Xq0f7e48fT4iIeTC09OIEXh7U1sG0b7ZTr78/PBFZszZvTkoOoKKqJa8u33wIJCXTF3bs3sHo1lWyuXgWWLKGNK1avpk0s5s7V3nmZtDiBF1enTrSCc9cuerYwVky+vnS1vHgxsGNHye/v8mVg0SIaY3/vvVdfl8loRei4cTRj5skTYPBg2uzi119Lfl4mPU7gmpg0iZpIjx9P71MZK6aFC+lawM8PuHJF8/vJyaEyiZUVjbMXpUIF6tXSpg29iFy+rPl5mWHgBK4JMzNg0yYaKRo4EGZpaVJHxIyMpSUt8K1UiaYtpqRodj8//QScOEH7gFavrvr43MHUypVpTP7xY83OywwDJ3BN1agB/PILcPMm3h4+nNrRMVYMtWpRCeXmTWpnW9whleRkejPYuTN1flBX7do00Hn/PjBggHYHU5l+cQIvCRcXYNs2WN6+TStAfv5Z6oiYkXF2Br7/nhLqggXFu+3UqbQ6dPVqelNYHB06UM+XY8d4WYMx4wReUgMG4NaePUCLFsDQoTR/i6cYsmIYNw4YOBD48kv1e4ifPEkJOCgIcHLS7LxDhtDS/tWr6YMZH07gWpBduzZw9Cg1vNi2ja7GT52SOixmJGQyGlxs3Jha7iQkFH38y5c0cGlvT7sDlsScOdQWICCArsaZceEEri0WFsA339A6aZmM3hvPmsVbnTO1VKpErekzM6k9/ejRNNUvNbXgsZs3W+PSJWD5crpdSZib01COoyOt4uShHOPCvVC07d13aWrhF18A06dT71Fvb6BaNWqMpexfC/41MJqzHRFBNfFNm6isUaYMze3u0YNmrlapAqxcWQPe3rSRhDZYWdGLRfv2NDPlxAn602SGT63MERUVhdmzZyMnJwcDBgzAyJEj831/48aN2LlzJ8zNzWFtbY05c+agdu3aOgnYKFhZAZs307MuIKDockrZslQ7nzCB3kMzk9alC31kZlKd+8AB+pg8mT4sLemqefly7Z63YUNam/bBB3S9cfAgzRtXV0oKzS2vVasG1qzRbmysCEKF7Oxs4ebmJhISEkRmZqbw9vYW169fz3fMX3/9JdLT04UQQmzZskUEBgYqva9z586pOl2hYmNjNb6trhUZW06OEGlpQty7J8Tly0KcOCHE/v1CbN4sxLJlQvj7C1GunBCAEN7eQkRF0W30EZvEODb13b0rxPr1QgweLMS8eXd1dp4dO4SQyYTw8hIiK0u92zx9KkT79vQnDAhx/LjOwisRQ/udvq6o2IrKmypr4DExMXBwcIC9vT0sLS3h5eWFI28MlXfs2BHly5cHALRs2RJJumi1ZqxkMrqUqVWLOg117kwbJQ4ZQuupf/iBeqtMn05X6s7OVATduZO2gGEMNHfbz4/q1T4+Gq76UcOAAdQhIiKC5pbn5BR9fHIy4OZGVcOtW4E6dbIwYgSQkaGzENlrVCZwuVwOOzu7vM9tbW0hl8sLPX7Xrl1wdnbWTnSmomZN6vqfkEDbwSQn07wyR0cqhPJKC6ZHI0fS7JQtW2iKY2ELjB49ota1V67Q6s5Bg4AZM5Jw7Rq1C2K6p9XRs7CwMFy+fBk/F7GgJS4uTqP7zsjI0Pi2uqbV2N5/H+jSBZUjI2G9YQMqjB6NzIULIZ8yBWldukgbm5ZxbJrRR2y9egHXrtXE8uU2yMl5hC++yL/m/tEjc/j5OeDevTJYuTIRdeumIy4OaN06Az4+Vpg3rwratr2NRo0ydRpncZTK36mq2kx0dLTw8/PL+zw0NFSEhoYWOO7kyZPigw8+EI8fP9aolqOKsdavSiQnR4iwMCEaNqTioqenEFevGkZsWsCxaUZfseXkCPHJJ/Snt3z5q6/fvSvEO+8IUbGiEMeOFYzt0SMhqlcXokMHIbKztRePujX5whjr77RENXAnJyfEx8cjMTERWVlZiIiIgKura75jYmNjERwcjNWrV8PGxqb4ryJMOZmM5nVdvkxzy06cAJo1o+V3z55JHR0r5WQyGqLp1YuGa375hap8Li7UR+XgQfr/m6pXB5YuBU6fpoqgNnz1FVUar13Tzv2VFioTuIWFBYKDg+Hv7w9PT0/06NEDjo6OCAkJyRvMXLBgAdLT0xEYGIhevXph1KhROg/cpJQtC0ycSH+9n3xCzw5HRxptysqiOVwPHgDXrwMXLtBiogMHgN27YVHEeIVR4wVSemFhQYuLXVyAYcOoBe7jx8DhwzQeX5iPPgK6dwemTVO9slSVVauoJv/sGb2Q8D4qr9HF24HCcAlFS6KjhXB2fjVvq4gPRfnyQsybJ0Rmpn5jVINGj1tWlhCTJglRvjyVl3SE/97ye/5ciNathahWTYiinsavx3b7thAVKlDlT9OZsWFhQpiZ0QzbxYvpz3rnTs3uy1h/p0XlTV4CaIxataLGFb/+SvO3KlV69VGx4qv/A0j76ivNNxFUAAAW6ElEQVRUnjoV+PFHYOVKmjZgrBITaarDqVOArS1d5p08SY3EmE5ZWdHD/uKF+qs069YFvvuOKn7bt9OvrjjOnqXbtGlDUxTLlqUVquPH04KjkrYRKA04gRsrmYyKkyrWU99dsQJNbt2iFaFubvSMWLiQJhYbk99+Az7+mJYobt1K7+nbtaNlg2fO0I7BTKfKlqWP4ggIoNp5QADg4UF7oKjj1i1aLpG7EXTFivT1VauojPPtt6p3IDIF3MzKFHh50UDojBnUeLpxY9qQ0Rjml2dnU+NrLy+gTh0gOppehN56i57Zycm0i++LF1JHypQwN6e2t0+e0DCOOpKTqQtFdjbw++/0ZivXu+/SgqbFi4HYWN3EbEw4gZuK8uVpteeVK7Tac8IEoHVrGo0yVPfuAV270qXWyJHAX3/R4G2uVq1oE43Tp4Hhw3l0y0C1aEE7B/34IzXsjI0t/FeVkUFvKu/cAcLCqMHXm+bNoy3hvviCf+WcwE1NgwZAeDgtnUtNBbp1o/e2589LHVl+Bw9SX/ULF2hJ4Jo19CL0pj59gLlzqazy3Xf6j5OpJTiYZqV89x11lGjShDawOHfuVRLOyaEq2alT1AvuvfeU31eNGvQrP3aMfu2GIFOi9UqcwE1Rbv386lWaknjhAtC2LQ0KGkJD6A0b6D30W2/RC8tHHxV9/JQp9MwPDqZNJpnBKV+eZrbeu0dj6XXq0BZy7doBDg60ZP+zz6gF0MKF1JOlKP7+dNsJE6TfACskhOa+X7yo/3NzAjdlZcvShog3b9JKiX37qD4eEAA8fChNTCEhVA7p3h34+2/l76HfJJMBa9fSxORhw2j6AjNItWrRZhV//AHI5VRWadWKljSsW0fzvMePV30/5uY0oCmXU2VQKjdv0hBNairV5vU9rMQJnNEuAd99B9y4QX+Fq1ZRqeXbb/X33lAI6oA0bhzQty+9mBSnIXXZsjRAa2dH7y7u3tVdrEwrbGzo9TYsjBYHRUUBS5bQ67E62ralq/bly4F//tFtrMoIQee3tKQYLlygBdP6xAmcvVKrFl0KXb5MtfHgYBr2//df3Z5XCFqy9/XXtLnF9u3Fn68GUHE0PJwuh3r0oCt4ZhQqVaKNLMzNi3e72bMBa2u6qi+s9W16Og2cXr9eFo8eaa9L86ZNtAn1vHnAmDHUQHTmTP3OjuEEzgpq3BjYvZsujRISaLbKhg26GfLPyaH3zfPn0069P/5Ysi3mmjal2B89ohef3r1p5g0rlaytqZZ+6hS9YVy/nqqBH31Ev347O5pD3rQp0KtXfdSsSVfMtraAkxMtjRg8mMo2xWkG+PAhLVDq3JmuwgG6Cq9cmd7E6quVPy/kYYXz8aH3pkOHUl364EGaDaKtDROzs+mv/aefaJ7Z/Pnqv38uiocHlYNCQujZ7eREg5wzZ9KIWUllZADlypX8fphWDBtGiXvGDPrc3Bywtwfq16fFQPXq0UdS0l2UKVMHDx8i38e5c1Rx27yZavMtW6o+57hxwH//0dCL2f9fBtesSUn8o4/oTy8oSGc/8iu6WNdfGO6Fon9aiS07W4g5c4QwNxfCwUGIkydLfp+ZmeJ5t27U3GLWLK1uI5fP48dCTJwoRNmyQlhaChEYKIRcrvJmhT5uJ05QH9XgYC0Hqr5S//emgcePhTh6VIhbt4R4+VL5MUXFduOGEPb2qnu9CCHEb7/Rn+2MGQW/l5MjhI8P7ZJ47Zr68eusnSxjMDenGvXJk3S54exM71eL8z4xd5QqNJRmuXToAKtDh2hJ3TffaOfKWxkbGxpZun6drsKXL6cB2kWLil8SunaN3pVkZQGzZtEyQWYQbGxoL5R69TSrwDVoABw/TuP5bm60NkyZ1FRg1Ciaxz51asHvy2S0iVbZsjTVUdWWdCXFJRSmvg4daLLr6NE0wPnLLzSht3x55R/PnlH9OTaWatK5KlYE/vc/3J87F7XUmTOmDfb21Nx6wgTa3n3iRGpqvXChei8ejx4Bnp70YnbhAm3BPmQI/f/tt3UfP9O5evUoiXftSlW4Aweo78rrvvmGhoVOnCh8nL1WLZpN4+dH1yujR+suZk7grHisrGj5eo8ewMaNQFoaXV2/ePHqIyOD/v3/RA0fH/o398PeHpDJ8DwuDrX0HX/jxjRFMTCQrv7T02lliVkRb0ZfvKCf4d494OhRGhHbuZPa5H34IT3rLS319zMwnXn7bfp1urrSUoTffqPZMQD1TFu2jBJyUb3QAWrbv20bXSt4elJnRl3gBM404+tLH8bIzIyeiRUq0CDnixe0ikTZe++cHBrEPX0a2LUL6NiRvu7oSDNzBgyglaBLluj3Z2A6U6cOLdN3c6O2teHhtKzf358WB8+dq/o+cteWNWtGbXwOHtRNlZBr4Mw0yWQ0gXfWLJoF4+urfBnd5Mk0LXHRIlpg9Lr+/amev3QpsGePduJKSqIWB0xStWpREq9blxphDh0KXLpEb9asrNS7DwcHuj44fJjerOoCJ3BmumQyKmouXEg9VPr1o/JPrpUrKXGPHUvzxpT5/nugfXvg009p6mJJZGXRZV+rVrwIyQDY2lISb9iQ1pb166ey/X4Bn31Gg6uhobqIkBM4YzSwuXIl9Rf38QHS01Hp6FG6uvbxKXp9t6UlJX9zcyqnvP4CUFyLFtGAb+XKtFFFcV8Q7t6ly8Vt2zSPoSRycoyzv2tKCq3mWbeuwLdq1KBhj+nTaXZJcZmZ0WSlffu0EKey+9fN3TJmZEaPpve5R44A77+P2hMn0grUX35Rvb7bwYFWgVy8SIOjmrh1i8o5ffvSFAchaPTr8WP1b9+lC426+frSQLM+yeVAy5aoM2aMcW04nZFBL9LbtgEjRlDB+o3+PzY2tEioRg3NTlGuHJVkdIETOGO5PvmEEvaFC8i2tqbRq9y9vFTx8qKJwWvXFj95CkHNNCwsaAlfo0a032lCAr1nV7XbUFwcJe+UFJpr37UrzXnftKl4cWgqOZnm3f37LyofPUo/S3GvxHNyaKBY1xOnX5edTbs7HT9Oj9WXX9JUUxcXmnFkDNRfK1RyvBJT/zg2DVy8KP49frz4t3v5UghnZ9qKvTh/6zt30tK+JUsKfl0mE2LAACEUirwv53vcoqOFqF5dCFtbIS5doq+lpQnh7k63/fHH4v8cxfHsmRBt29JK1z/+EI/8/elnWbhQ/ft4+VKIoUPpdt9+q7NQ8z1uOTlCfPopnXPZsldf371biEqV6PGMitJZLEXG9oai8iYncC3g2DRTKmO7f5/aDVhbv0qoRXn+XIhatYRo2VL5GvCFCynJTJxYMLZTp4SoUoXWgL+5bjs9/VUS37hRs59FldRUITp3FsLCQojwcIrt8mUh+ven8+7dq/o+MjKE6NOHfsZGjYQoU0aIixd1Em6+3+mkSXROZS0RrlwRwtGRfq4VK3TX5qGw2N7ACVzHODbNlNrYbt6kpFyzphBXrxZ9bGAgJbvTp5V/PydHiDFjKNmsWPEqtiNHqCdLw4ZC3Lmj/Lbp6UJ4eND9b9ig+c+jzIsXQri5CWFmJsSOHXlfjo2NpfO2by9E+fJCnD1b+H2kpQmR2w8nJESIR4/oMWvZUojMTO3GK177nc6fT+ccPbrw5Pz0qRA9e9Jxn3xCP68O6TSBHz9+XHTr1k24u7uLNWvWFPj+mTNnRO/evUWTJk3E77//rlEgqpTaJ7uOcWyaKXFscXGUjGrXpoSuzPnzlABHjy76vrKzqUOSmZkQYWEiYdUqKlk0aybEgwdF3zY9nZKkTCbE+vWa/SxvyswUwsuL7nPTpnzfynvckpLonYidnfIXmGfPhHjvPfqZXo9r377Cr4xLKDY2ls4FCPHhh/nKUkopFEJMn07Ht2lDLzA6orMEnp2dLdzc3ERCQoLIzMwU3t7e4vr16/mOSUxMFHFxcWLSpEmcwA0Mx6YZrcQWE0OllLp1hUhIyP+97GyqHdva0tWeKqmpdHz58iLHwoL+//ixenGkpwvRvbt2kvjLl1QiAYQIDS3w7XyP2+XLQlhZCeHkRKWiXI8fU0K0sBBi+/aC5xgyhDpfnj9fsljfkLBsGb1gdOtWvCv8sDCK1c9Pq/G8TmfdCGNiYuDg4AB7e3tYWlrCy8sLR44cyXdMnTp10LhxY5gV1U+CMVPj5AQcOgQ8eUILdB48ePW90FBqRL1kiXr91StWpFkxdeogvXVrmu5oY6NeHOXL00Tkbt2or/vw4fljUVdODnVo2rWL+sjk7mRQmKZN6djYWOoZk51N53VxoV2f9u2jbWzetGwZNdf++GPtbOn39CmwYwdNDW3fnlbNFqd3jY8PLeTasIEaohgQlRlXLpfDzs4u73NbW1vI5XKdBsVYqdGmDa3kuH8fcHenrob379OUNQ8PmsamLltb4MoVJGzcqP567lzlylHCnDiR5qw7OlJL4PR01bdVKGiRk7s73fbbb9XbeRign3H1amrt9+mnNN0xPp4eEy8v5bepVo0W1Vy5QptwqEuhoO3/duyg7fm8vak7lbU18OGHeGlvD0REqD819HXffEPb+4wZo9+pjirovZlVXHH2LXpNRkaGxrfVNY5NMyYTW7VqqLBiBexHjUKWszNevvUWKmZk4FZQEF5q0PckIzNT89j8/FDG3R01Fy+GVXAwXq5ahYfjxiGlZ88CHRnNnzxB1d27UXX7dljev4+XNWsi+csv8bRv30L3H1P6uL33Hmr6+cFmwwYorKyQsG4dMuzsit7DrF49vNW3L6rMn4/45s2R0aJFoYeWuXMHNVasQOUjR2D2/ythhbk5MuvVQ2bz5sgYOBCZjRrhabNmsJTLadGRBqzGjUPtqVNxf84cPO/XT6P7KIzGf2+qajPR0dHC77XaT2hoqAhVUvsSQogpU6ZwDdzAcGya0Ulsv/9OuwLl7kKkIa3FFhVFtXRAiHbthPjzT5qVceqUEL6+r2J1dRVi1y4hsrI0j02hoPnWly+rH9+zZzRFsnFjquO/6d49IUaNovp0hQpCfP45TZmMjqbpierGpq6cHCHefVeIGjXUG7coBp3VwJ2cnBAfH4/ExERkZWUhIiICrq6umrzIMGbaPvgA2LuX6siTJ0sdDZUzTp+mVYj379Pn9evTLgb791ONOzaW6u39+gFlymh+LjMzagrWtKn6t6lShTa7vHqVShi5nj2jHaIaNqRSy2efATdvAqtW0WraVq0K322hJGQyYMUKam9QnNKODqlM4BYWFggODoa/vz88PT3Ro0cPODo6IiQkJG8wMyYmBs7Ozjhw4ACmT58Or8JqW4yZOk9PSkq6SDCaMDOjXqnXrlEvlgYNaID13j0aTGzSRNr4PDwoQS9eTDsOL1hALzLz51PfmKtXKam+Nk6nU61bU8+U5cupRi8xtWrgLi4ucHFxyfe1wNea9jRv3hxRUVHajYwxpj8VKtBV7utXuobi++9pRwQPD/rcywuYPRsooi6uU7Nn045MAQH0oqKr/VzVwPP+GGOGrXJlYOtW2kAjKoqmU0qVvAGgenWaiRMZSZt9SIgTOGPM8HXsSFe9uRtUSu2zz4DmzamXvDpTMXWEEzhjjBWXhQXVwRMSqB4vVRiSnZkxxoyZszPt5DN/Ps1+qVfv1fcePqRNNC9dAmJiaHfjoCCth8AJnDHGNLVgARAWRqtMW7d+lbRfXyxUsyatotUBTuCMMaapOnVow8wpU2hOfdOmQI8eVB9v3pz64dSsqbPTcwJnjLGSmDSJetrUrq16/1Qt4wTOGGMlIZNR0ywJ8CwUxhgzUpzAGWPMSHECZ4wxI8UJnDHGjBQncMYYM1KcwBljzEhxAmeMMSMlE0IIfZ3s/Pnz+joVY4yVGm3atFH6db0mcMYYY9rDJRTGGDNSnMAZY8xIGXwvlKioKMyePRs5OTkYMGAARo4cKXVIeVxdXVGxYkWYmZnB3Nwce/bskTSeadOm4dixY7CxsUF4eDgA4NmzZxg/fjzu3buH2rVrY+nSpahSpYpBxLZ8+XLs2LED1tbWAICgoKACe6/q2oMHDzB58mQkJydDJpNh4MCBGDZsmEE8boXFZgiPW2ZmJnx9fZGVlQWFQoHu3bsjICAAiYmJCAoKwrNnz9C0aVMsWLAAlpaWBhHb1KlTcebMGVSuXBkAMG/ePDSRaNNmhUKBfv36wdbWFmvWrNH8cRMGLDs7W7i5uYmEhASRmZkpvL29xfXr16UOK0/Xrl1FcnKy1GHkOXPmjLh8+bLw8vLK+9r8+fPFmjVrhBBCrFmzRixYsMBgYlu2bJlYt26dJPHkksvl4vLly0IIIf777z/RrVs3cf36dYN43AqLzRAet5ycHJGamiqEECIrK0v0799fXLhwQQQEBIjw8HAhhBDffPON2LJli8HENmXKFPH777/rPR5lNmzYIIKCgsTIkSOFEELjx82gSygxMTFwcHCAvb09LC0t4eXlhSNHjkgdlsFq165dgavEI0eOoHfv3gCA3r17448//pAiNKWxGYKaNWuiadOmAIBKlSqhfv36kMvlBvG4FRabIZDJZKhYsSIAIDs7G9nZ2ZDJZPj777/RvXt3AECfPn0keb4WFpuhSEpKwrFjx9C/f38AgBBC48fNoBO4XC6HnZ1d3ue2trYG8weca/jw4ejbty+2b98udShKJScno+b/N5SvUaMGkpOTJY4ovy1btsDb2xvTpk3D8+fPJY3l7t27iIuLQ4sWLQzucXs9NsAwHjeFQoFevXqhU6dO6NSpE+zt7WFlZQULC6rM2tnZSfZ8fTO23MdtyZIl8Pb2xpw5c5CVlSVJbHPmzMGkSZNgZkbp9+nTpxo/bgadwA3d1q1bsXfvXvzwww/YsmULzp49K3VIRZLJZAZ1JTJ48GAcPnwYYWFhqFmzJubNmydZLGlpaQgICMCXX36JSpUq5fue1I/bm7EZyuNmbm6OsLAwHD9+HDExMbh165YkcSjzZmzXrl1DUFAQDhw4gN27d+P58+dYu3at3uM6evQorK2t0axZM63cn0EncFtbWyQlJeV9LpfLYaujveU0kRuLjY0NPDw8EBMTI3FEBdnY2ODhw4cAgIcPH+YNfBmC6tWrw9zcHGZmZhgwYAAuXbokSRwvX75EQEAAvL290a1bNwCG87gpi81QHrdcVlZW6NChAy5evIiUlBRkZ2cDoFKB1M/X3Nj+/PNP1KxZEzKZDJaWlujbt68kj1t0dDQiIyPh6uqKoKAg/P3335g9e7bGj5tBJ3AnJyfEx8cjMTERWVlZiIiIgKurq9RhAQDS09ORmpqa9/+TJ0/C0dFR4qgKcnV1xb59+wAA+/btg5ubm8QRvZKbIAHgjz/+kOTxE0Lgq6++Qv369fHpp5/mfd0QHrfCYjOEx+3JkydISUkBAGRkZODUqVNo0KABOnTogIMHDwIA9u7dK8nzVVls9evXz3vchBCSPW4TJkxAVFQUIiMjsXjxYnTs2BGLFi3S+HEz+JWYx48fx5w5c/Km3Xz++edShwQASExMxBdffAGA6m09e/aUPLagoCCcOXMGT58+hY2NDcaOHQt3d3eMGzcODx48QK1atbB06VJUrVrVIGI7c+YMrl69CgCoXbs2Zs2alVd31pdz587B19cXjRo1yqtJBgUFoXnz5pI/boXFFh4eLvnjdvXqVUydOhUKhQJCCHzwwQcYM2YMEhMTMX78eDx//hxNmjTBwoUL9T6NsLDYPv74Yzx9+hRCCDRu3BgzZ87MG+yUwunTp7Fhw4a8aYSaPG4Gn8AZY4wpZ9AlFMYYY4XjBM4YY0aKEzhjjBkpTuCMMWakOIEzxpiR4gTOGGNGihM4Y4wZKU7gjDFmpP4Prn9fpv1EZZoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist.history['loss'], color='b')\n",
    "plt.plot(hist.history['val_loss'], color='r')\n",
    "plt.show()\n",
    "plt.plot(hist.history['acc'], color='b')\n",
    "plt.plot(hist.history['val_acc'], color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "62f58f451ef2de7c4895f8bdb449dae44a122871"
   },
   "source": [
    "We then use our trained net to classify the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "8ef32235-eebb-91e2-4571-1b585aa86ad4",
    "_execution_state": "idle",
    "_uuid": "62e80b4160b0bfd27bf1acdb2e5b6baed971fc01",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "non_idx = np.where(y_test[:,0] == 0.)[0]\n",
    "yes_idx = np.where(y_test[:,0] == 1.)[0]\n",
    "y_hat = f_model.predict(x_test)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "5f37a9af-ceab-8b5a-3f77-fefd6eb38710",
    "_execution_state": "idle",
    "_uuid": "4b002ba89846d5f0c25fd29466c9ceb4801fc7e5"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([y_hat[i] for i in yes_idx], 'bo')\n",
    "\n",
    "plt.plot([y_hat[i] for i in non_idx], 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "ffabc4d6783302adc67cf15ac6ca038030ff8842"
   },
   "source": [
    "These graphs show that the five positive examples all get 0.95-1.00 score. Most negative examples get score close to zero, but an unsatisfactory number are in the same range as the true positives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "088fa46309212bf4a02766ea44e918d561643718"
   },
   "source": [
    "We now choose an optimal ccossover point for classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "3d10d764-1a5f-7315-ea45-9e9481261ea4",
    "_execution_state": "busy",
    "_uuid": "38e7322f333dc2afa38186f208e5b96f4bd83fb9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crossover at 0.96 with specificity 0.99\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC area under curve is 1.00\n"
     ]
    }
   ],
   "source": [
    "y_true = (y_test[:, 0] + 0.5).astype(\"int\")\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_hat)\n",
    "plt.plot(thresholds, 1.-fpr)\n",
    "plt.plot(thresholds, tpr)\n",
    "plt.show()\n",
    "crossover_index = np.min(np.where(1.-fpr <= tpr))\n",
    "crossover_cutoff = thresholds[crossover_index]\n",
    "crossover_specificity = 1.-fpr[crossover_index]\n",
    "print(\"Crossover at {0:.2f} with specificity {1:.2f}\".format(crossover_cutoff, crossover_specificity))\n",
    "plt.plot(fpr, tpr)\n",
    "plt.show()\n",
    "print(\"ROC area under curve is {0:.2f}\".format(roc_auc_score(y_true, y_hat)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "28f895b0-597b-4f46-1a7f-7377d97ade05",
    "_execution_state": "idle",
    "_uuid": "ce60fdc5bcf2b2d08f68c22d581116383d6b6c45"
   },
   "outputs": [],
   "source": [
    "false_positives = np.where(y_hat * (1. - y_test) > crossover_cutoff)[0]\n",
    "plt.close()\n",
    "for i in non_idx[0:10]:\n",
    "    if y_hat[i] > crossover_cutoff:\n",
    "        print(i)\n",
    "        plt.plot(x_test[i])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def compare(bool_pair):\n",
    "    # from a pair= (y_predicted, y_actual) return the quadrant code\n",
    "    if bool_pair[0] == bool_pair[1]:\n",
    "        return 'TP' if bool_pair[0] else 'TN'\n",
    "    return 'FP'if bool_pair[0] else 'FN'\n",
    "    \n",
    "def F1_score(y_hat, y_true, crossover=0.5):\n",
    "#     set_trace()\n",
    "    \n",
    "    bool_true = [y[0] >= crossover for y in y_test]\n",
    "    \n",
    "    q = Counter([compare(y) for y in zip(y_hat, bool_true)])\n",
    "    \n",
    "    precision = q['TP'] / (q['TP'] + q['FP']) if q['TP'] + q['FP'] != 0 else 0.\n",
    "    recall = q['TP'] / (q['TP'] + q['FN']) if q['TP'] + q['FN'] != 0 else 0.\n",
    "    F1 = 2 * precision * recall / (precision + recall)if precision + recall != 0 else 0.\n",
    "        \n",
    "    print (\"found: \",q['TP']+q['FP'])\n",
    "    print (\"actual: \",q['TP'] + q['FN'])\n",
    "    print (\"missed: \",q['FN'])\n",
    "    print (\"precision: \",precision)\n",
    "    print (\"recall: \",recall)\n",
    "    print (\"F1: \",F1)\n",
    "    return F1\n",
    "\n",
    "def F1_score_1hot(y_hat_1hot, y_true):\n",
    "    bool_hat = [y[0] >= y[1] for y in y_hat_1hot]\n",
    "    return F1_score(bool_hat, y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('found: ', 14)\n",
      "('actual: ', 5)\n",
      "('missed: ', 0)\n",
      "('precision: ', 0)\n",
      "('recall: ', 1)\n",
      "('F1: ', 0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F1_score([y > 0.5 for y in y_hat], y_test, crossover_cutoff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0bebfd57-3ff5-90d1-79a2-4fb500116cc6",
    "_execution_state": "idle",
    "_uuid": "0e60c2111bc64417f9320ecb5f6adab298423606"
   },
   "source": [
    "# FFT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5087, 3197)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INPUT_LIB = './'\n",
    "raw_data = np.loadtxt(INPUT_LIB + 'exoTrain.csv', skiprows=1, delimiter=',')\n",
    "x_train_fft_raw = raw_data[:, 1:]\n",
    "raw_data = np.loadtxt(INPUT_LIB + 'exoTest.csv', skiprows=1, delimiter=',')\n",
    "x_test_fft_raw = raw_data[:, 1:]\n",
    "del raw_data\n",
    "x_train_fft_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "from scipy import fft\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fft_n = 20\n",
    "exFft = np.abs(fft(x_train_fft_raw[0:fft_n,:]))\n",
    "\n",
    "random.seed(42)\n",
    "fig, ax = plt.subplots(5, 4, figsize=(15,10))\n",
    "nyquist = exFft.shape[1] // 2\n",
    "for rowIdx, row in enumerate(ax):\n",
    "    for colIdx, col in enumerate(row):\n",
    "        fft_data = exFft[2*rowIdx+colIdx]\n",
    "        n = random.randint(0,300)\n",
    "        y = y_train[n]\n",
    "        col.plot(range(nyquist), fft_data[0:nyquist], color = 'red' if y else 'blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3197,)\n",
      "('x_train_fft', (5087, 1599))\n"
     ]
    }
   ],
   "source": [
    "x_train_fft_all = fft(x_train_fft_raw[1])\n",
    "print(x_train_fft_all.shape)\n",
    "nyquist = x_train_fft_all.shape[0] // 2\n",
    "x_train_fft = np.delete(np.abs(fft(x_train_fft_raw)), np.s_[nyquist:nyquist*2], axis=1)\n",
    "x_test_fft = np.delete(np.abs(fft(x_test_fft_raw)), np.s_[nyquist:nyquist*2], axis=1)\n",
    "print('x_train_fft',x_train_fft.shape)\n",
    "x_train_fft = np.stack([x_train_fft], axis=2)\n",
    "x_test_fft = np.stack([x_test_fft], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# input shape\n",
    "batch = None\n",
    "steps = 1\n",
    "input_dim = x_train_fft.shape[1]\n",
    "fft_input_shape = (input_dim,1)\n",
    "fft_input_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5087, 1599, 1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_fft.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "main_input (InputLayer)      (None, 1599, 1)           0         \n",
      "_________________________________________________________________\n",
      "L1 (CuDNNLSTM)               (None, 1599, 75)          23400     \n",
      "_________________________________________________________________\n",
      "L2 (CuDNNLSTM)               (None, 37)                16872     \n",
      "_________________________________________________________________\n",
      "M1 (Dense)                   (None, 25)                950       \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 25)                0         \n",
      "_________________________________________________________________\n",
      "D1 (Dense)                   (None, 1)                 26        \n",
      "=================================================================\n",
      "Total params: 41,248\n",
      "Trainable params: 41,248\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import CuDNNLSTM\n",
    "\n",
    "fft_inputs = Input(shape=fft_input_shape, name='main_input')\n",
    "x = CuDNNLSTM(75, return_sequences=True, name='L1')(fft_inputs)\n",
    "x = CuDNNLSTM(37, name='L2')(x)\n",
    "x = Dense(25, name='M1')(x)\n",
    "fft_final = Dropout(0.5)(x)\n",
    "output = Dense(1, name='D1')(fft_final)\n",
    "fft_model = Model(fft_inputs, output)\n",
    "fft_model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5087, 1599, 1)\n"
     ]
    }
   ],
   "source": [
    "fft_model.compile(optimizer=Adam(1e-5), loss = 'binary_crossentropy', metrics=['accuracy'])\n",
    "print(x_train_fft.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      " - 18s - loss: 3.0972 - acc: 0.4617 - val_loss: 0.5445 - val_acc: 0.7070\n",
      "Epoch 2/10\n",
      " - 16s - loss: 2.9058 - acc: 0.4739 - val_loss: 0.5402 - val_acc: 0.7474\n",
      "Epoch 3/10\n",
      " - 16s - loss: 3.1273 - acc: 0.4866 - val_loss: 0.5220 - val_acc: 0.8737\n",
      "Epoch 4/10\n",
      " - 16s - loss: 3.1265 - acc: 0.4802 - val_loss: 0.5206 - val_acc: 0.9105\n",
      "Epoch 5/10\n",
      " - 16s - loss: 2.8343 - acc: 0.4943 - val_loss: 0.5206 - val_acc: 0.9491\n",
      "Epoch 6/10\n",
      " - 16s - loss: 2.9715 - acc: 0.4739 - val_loss: 0.5398 - val_acc: 0.9368\n",
      "Epoch 7/10\n",
      " - 16s - loss: 2.9707 - acc: 0.4707 - val_loss: 0.5405 - val_acc: 0.9579\n",
      "Epoch 8/10\n",
      " - 16s - loss: 2.9950 - acc: 0.4821 - val_loss: 0.5864 - val_acc: 0.8140\n",
      "Epoch 9/10\n",
      " - 16s - loss: 2.7168 - acc: 0.4732 - val_loss: 0.5941 - val_acc: 0.8316\n",
      "Epoch 10/10\n",
      " - 16s - loss: 2.8143 - acc: 0.4841 - val_loss: 0.5773 - val_acc: 0.9228\n"
     ]
    }
   ],
   "source": [
    "hist = fft_model.fit_generator(batch_generator(x_train_fft, y_train, 32), \n",
    "                           validation_data=(x_test_fft, y_test), \n",
    "                           verbose=2, epochs=10,\n",
    "                           steps_per_epoch=x_train_fft.shape[1]//32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "fft_model.save_weights('fft_model.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "fft_model.load_weights('fft_model.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_fft = fft_model.predict(x_test_fft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('found: ', 570)\n",
      "('actual: ', 0)\n",
      "('missed: ', 0)\n",
      "('precision: ', 0)\n",
      "('recall: ', 0.0)\n",
      "('F1: ', 0.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F1_score(y_hat_fft, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "020d5851-762a-d02c-77fa-319c3af6a4af",
    "_execution_state": "idle",
    "_uuid": "6c22b94c662b6b25fd33c85fd05892aca54e5d86"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ujx5AXByOHDqE6HbtnFOPG2CAq2npUvlq9+c/O+9NT+QKc+fKwcJnnpEZjjerrAz48Udrq3rHDlmv23KwtGNH6dceP17C+q673P7kC87AAFdLcbG82RMT5U1IpGXt2smia9Omyap+aWkNf+zly8Du3bYt659+Aq5ckftDQiSkH3tMru+5RxMnW3AFBrgaFEUO/pjNMh3ZDc92TXTDJk0CFi+Whkm/frWP+KiqAvbutW1Z//ADUF4u97duLQE9bZq1K+TWWz1m3LajMcDV8Pe/Axs2yEI6nTqpXQ2RY/j5SXdgSgqwYIFMVT9yxLZlXVAgfdkA0Ly5dH08/bQ1rCMiGNY3gAHuakajnCC2b18Z+03kSR56CBgyBMjOlqnrJ07Iv/v6yuxNSzdIjx5AdDSXi7hJ3HuuVFUlM8J0OuD999l1Qp7p7bdlNMptt1nD+o47PG4SjTtggLvSokXAxo3ShRIernY1RM4RHu75Z7B3Ew1qAubn5yMlJQXJycnIycmpcf8vv/yC0aNHY8iQIRg0aBC+++47hxeqeQcPyoGZlBRphRMR3aR6W+BmsxnZ2dlYsmQJ9Ho90tPTkZSUhMirxi3/7W9/w4ABA/DYY49h//79eOKJJ7Bx40anFq4pljOA+PrKUXoepCEiB6i3BV5YWIjw8HCEhYXBz88PaWlpyLvm65FOp8OF/62DcP78eQQHBzunWq165x1Z8Oftt4HQULWrISIPUW8L3GQyISQkpPq2Xq9H4TXr/06YMAF/+MMf8OGHH6KsrAxLlixxfKVatW+fDKdKSwPGjFG7GiLyIA45iJmbm4uhQ4di7Nix+OGHHzB9+nSsW7cOXrWMsjAYDHa9Rnl5ud2PVY3ZjPAxY9DE1xcHp03DlZ9/dsjTanJfOBH3hy3uDytP3xf1Brher0dJSUn1bZPJBL1eb7PNF198gcWLFwMAunfvjoqKCpw+fRpBQUE1ni86OtquQg0Gg92PVc2CBcCuXcCKFYhKTHTY02pyXzgR94ct7g8rT9gXBQUFdd5Xbx94bGwsjEYjiouLUVlZidzcXCQlJdls065dO2zZsgUAcODAAVRUVCAwMPAmy9a4n38GZs4EBg8GRo5Uuxoi8kD1tsB9fHyQlZWFjIwMmM1mDB8+HFFRUVi4cCFiYmLQt29fPP/883jhhRewdOlS6HQ6vP7669A15pEWZjPw+9/LVOFFizjqhIicokF94ImJiUi8pgtg8uTJ1T9HRkbi008/dWxlWjZ/PrBtG/DJJ7KSGhGRE3Aut6MVFQEvvggMHy7n4iMichIGuCNduSJDBVu1kpUG2XVCRE7EtVAc6Y03ZJ3jlSsBTmYiIidjC9xR/vtf4KWXpNtkxAi1qyGiRoAB7giXL0vXSZs2Mm2eiMgF2IXiCHPmyGmhVq3iufqIyGXYAr9ZP/4IvPKKTNYZOlTtaoioEWGA34zKSuk6ueUWORcgEZELsQvlZrz6KlBYCHz1FdDYlw4gIpdjC9xeBQXA7NnSAh80SO1qiKgRYoDbo6JCgluvl5M0EBGpgF0o9nj5ZWD3bmD9eqB1a7WrIaJGii3wG7V9OzB3rpyYeMAAtashokaMAX4jysul6+TWW2XFQSIiFbEL5UZkZcmJGjZsAAIC1K6GiBo5tsAbYvt2maTz5pvAk08CyclqV0RExACvk6IAGzcC/foB990HfPedtMDfekvtyoiIALALpaaqKmDtWhnjvX070K6dteXdsqXa1RERVWOAW1y5Anz2mSxMtXs30LGjnM9yzBigaVO1qyMiqoEBXl4OLF0qJ2M4dAiIiQE++gj4zW8AH+4eInJfjTehzp+XFvaCBUBJifRzv/02MHAg4MVDA0Tk/hpfgJ88KSsH/uUvwJkzcpDy44+BBx/kOSyJSFMaT4AfOyaTb957D7h0SYYFZmYCPXqoXRkRkV08P8D37ZP+7WXLZITJyJHAc88Bt9+udmVERDfFcwP8P/+RESWffw74+gJ//CMwbRrQoYPalREROYTnBfjmzRLcubkybnvaNOCZZ4CQELUrIyJyKM8IcEWR9Ulmzwby8+UUZ6++Cowfz+VeichjaTvAzWZg9Wppce/aBYSGylDAjAygRQu1qyMiciptBnhlpUy2mTsX2LMHiIoC3n8fGDUK8PNTuzoiIpfQVoBfuiRB/eabQHExcOedwMqVwLBhgLe32tUREbmUNgL8zBkEvfeeTLg5cQJISJDx3P37c/INETVaDQrw/Px8vPbaa6iqqsKIESPwxBNP2Nw/e/ZsbNu2DQBQXl6O0tJS7Ny503FVpqQgePt2OYVZZqYEOBFRI1dvgJvNZmRnZ2PJkiXQ6/VIT09HUlISIiMjq7eZMWNG9c8rVqxAUVGRY6ucNw8HT5xAp2HDHPu8REQaVu+qTYWFhQgPD0dYWBj8/PyQlpaGvLy8OrfPzc3FwIEDHVokEhJQER3t2OckItK4elvgJpMJIVdNgtHr9SgsLKx122PHjuHo0aOIj4+v8/kMBoMdZUrXjL2P9TTcF7a4P2xxf1h5+r5w6EHM3NxcpKSkwPs6I0Ki7WxJGwwGux/rabgvbHF/2OL+sPKEfVFQUFDnffV2oej1epSUlFTfNplM0Ov1tW67fv16pKWl2VEiERHdqHoDPDY2FkajEcXFxaisrERubi6SkpJqbHfgwAGcO3cO3bt3d0qhRERkq94uFB8fH2RlZSEjIwNmsxnDhw9HVFQUFi5ciJiYGPTt2xeAtL5TU1Oh47hsIiKXaFAfeGJiIhITE23+bfLkyTa3J06c6LiqiIioXjz5IxGRRjHAiYg0igFORKRRDHAiIo1igBMRaRQDnIhIoxjgREQaxQAnItIoBjgRkUYxwImINIoBTkSkUQxwIiKNYoATEWkUA5yISKMY4EREGsUAJyLSKAY4EZFGMcCJiDSKAU5EpFEMcCIijWKAExFpFAOciEijGOBERBrFACci0igGOBGRRjHAiYg0igFORKRRDHAiIo1igBMRaRQDnIhIoxjgREQa1aAAz8/PR0pKCpKTk5GTk1PrNuvXr0dqairS0tLw7LPPOrRIIiKqyae+DcxmM7Kzs7FkyRLo9Xqkp6cjKSkJkZGR1dsYjUbk5OTgk08+QUBAAEpLS51aNBERNaAFXlhYiPDwcISFhcHPzw9paWnIy8uz2WblypUYOXIkAgICAABBQUHOqZaIiKrV2wI3mUwICQmpvq3X61FYWGizjdFoBAA8+uijqKqqwoQJE9C7d+9an89gMNhVaHl5ud2P9TTcF7a4P2xxf1h5+r6oN8Abwmw24/Dhw1ixYgVKSkowatQorF27Fq1ataqxbXR0tF2vYTAY7H6sp+G+sMX9YYv7w8oT9kVBQUGd99XbhaLX61FSUlJ922QyQa/X19gmKSkJvr6+CAsLQ4cOHapb5URE5Bz1BnhsbCyMRiOKi4tRWVmJ3NxcJCUl2WzTr18/bN++HQBw6tQpGI1GhIWFOadiIiIC0IAuFB8fH2RlZSEjIwNmsxnDhw9HVFQUFi5ciJiYGPTt2xcJCQnYvHkzUlNT4e3tjenTp6NNmzauqJ+IqNFqUB94YmIiEhMTbf5t8uTJ1T/rdDpkZmYiMzPTsdUREVGdOBOTiEijGOBERBrFACci0igGOBGRRjHAiYg0igFORKRRDHAiIo1igBMRaRQDnIhIoxjgREQaxQAnItIoBjgRkUYxwImINIoBTkSkUQxwIiKNYoATEWkUA5yISKMY4EREGsUAJyLSKAY4EZFGMcCJiDSKAU5EpFEMcCIijWKAExFpFAOciEijGOBERBrFACci0igGOBGRRjHAiYg0igFORKRRDHAiIo1qUIDn5+cjJSUFycnJyMnJqXH/qlWrEB8fj8GDB2Pw4MH4/PPPHV4oERHZ8qlvA7PZjOzsbCxZsgR6vR7p6elISkpCZGSkzXapqanIyspyWqFERGSr3hZ4YWEhwsPDERYWBj8/P6SlpSEvL88VtRER0XXU2wI3mUwICQmpvq3X61FYWFhjuw0bNmDHjh3o2LEjMjMz0a5du1qfz2Aw2FVoeXm53Y/1NNwXtrg/bHF/WHn6vqg3wBuiT58+GDhwIPz8/PDpp5/iueeew/Lly2vdNjo62q7XMBgMdj/W03BfiPPnAYMBOHz4EIYM6QhfX7Urcg98f1h5wr4oKCio8756A1yv16OkpKT6tslkgl6vt9mmTZs21T+PGDECb775pj11EtXq7FkJ6qIiYPduuS4qAo4csWzRES1aAD17Ar17y+Xee4FmzdSsmsj56g3w2NhYGI1GFBcXQ6/XIzc3F/Pnz7fZ5tdff0VwcDAAYOPGjYiIiHBOteTRzpyxhvPVQX30qHWbpk2B6GggIQG4/XagWzfgwIGjOHQoFJs2AbNmAYoC+PpKiCckSKDffz8QEKDe70bkDPUGuI+PD7KyspCRkQGz2Yzhw4cjKioKCxcuRExMDPr27YsVK1Zg48aN8Pb2RkBAAObMmeOK2hudqirg2DHgxx+boaoKCAqSi9a6Dk6dqhnSu3cDx49bt2neXIK6Tx9rUN9+O9ChA+Dtbft8BsN5WL4lnz4NfP89kJ8vl3nzgNdfB7y8gLg4a6AnJAD/a3MQaZZOURTFVS9WUFCAu+++267HekJfVkOdOQPs2QPs3Wu9tlzKympu37IlcMst1kAPCqr/dvPmgE7n3N/j5EnbkLYEtclk3aZFCwlmy8US1OHhEroNcb33xqVLwNatwKZNEuhbtlj3YZcu1kDv3Vte0xM0pr+V+qi9L8rLpavvlluAwED7nuN6uemQg5h04yoqgIMHawb1nj3AiRPW7by9gY4dJWySkoDOnQHgCNq0uQ2lpUBpqQSl5efSUnme0lLg3Lm6X79pU9tAb0joBwTUDFVFkXqvbU0XFdn+Hi1bSjCnptoGdVhYw4PaHs2by35LSpLblZXArl3WQP/iC2DxYrkvLMzaOu/dG+ja1fkfcp7k9Gl5Tx84IN8UAwOBW2+1Xlq1UrtCx7tyBSguBg4dAoxGub76519+ke3i46Xx4GgMcCdSFHkjXxvQe/fKf25VlXVbvV7CefBgue7SRa47dQL8/Gyf12C4iIY0Ki5flu6KawO+ttv//a/8fOqUbV1X8/aWP0pLoOt0cnCxtNS6TatWEs4PP2wb1KGh7hGGfn7yxxQfD0ybJr/rTz9ZAz0vD/joI9n2llskzC2BHhcH+DTivxizWY5HWELacm35+fTp6z/e39820K++tG8v1yEh7rWPq6okhGsL50OHZH+Yzdbtvb2lIdCxI5CSItcdO8r7xxncaFdp19mz1i6Oa4P60iXrds2bSyjffTfw2GPWoI6KAlq3dnxdvr7ywXDNoKHrqqqS36choW82A8OG2QZ1+/buEdQN5eUF3HGHXMaPlw/dAwckzC2hvnq1bNuypRwMtbTSe/SQbzKe5OJFCaarg9lybTTKNxgLHx85JhERAdx3nzQ2IiLkOjRUGgPHjtV++e47CcYrV2xf38tL3q91Bb2jW/OWb5C1hbPRCBw+bPs7A/Ie79hR3gMdOlhDukMHCW9XfgAxwBuoslLexLWF9NV9ul5e8p/ZuTPw4IO2ren27Z3bXeAIXl5AmzZyiYpSuxrX0+mAyEi5jB0r/3bsmIS5JdBnzpR/b9JERrpYAj0iQj6kmzWTaz8/9/swUxR5v9bVir5qxDAA6TaLiJBvH0OHWgM6IkLC6toDylcLDJT9WJeqKgnPa8P9l1/k2vJBWlvL3t/f2mqv62KZf3j6dN1dHEajbSMLANq2lTDu3l0aKFeH9G23udeHNgP8KpaQ3r8f2LdPLpafjxyx7Vpo21aCOS3NGtBdusibu0kT9X4HcrxbbwUefVQugHwD2bzZGuivvw689lrNx3l5WcPccn31z9f7txvd/upWX2WlvGdra0UfPCitbAudTlrLERFyfOLqgI6IkA9yZ30IWVrbej1w1111b3fpkjXUa7vk59fdmm/atHONgA4IsDayLN0clpDu0EE+HLSi0QV4RYV88l4dznWFdOvW0grt2RP43e+kNdG5s1yumrtEjUxQkPTxP/yw3L5wAdi2TUKkrEwC59Kl2n+2XJ8/D/z6a83tysvtq8nXV8K8SROGSCxeAAAEiklEQVSgtLSrzfu4aVNrKPftaxvQ4eHu1aKsTfPm1m9FdamtNS9912cRFxdoE9Ke9LfrkQF+syEdFSWXwED3+wpM7sffX4LREaqqJMQb+kFw7b+VlQHe3idx331tq1vT7dp5/vu4rta8wWBCdLSd4/c0QLMBXltIW36uK6Tvv19COirKGtQMaXInXl7WbpKgIPuew2A4iejoto4tjNySJgK8sBD4+ONAnDtnX0jb+4dAROTONBHgI0cCP/2krzWkLUHNkCaixkYTAf7998BPP+1Fz56d1S6FiMhtuPmoZNGyJdC6tbn+DYmIGhFNBDgREdXEACci0igGOBGRRjHAiYg0igFORKRRDHAiIo1igBMRaZTLz4lJREQ3pq5zYro0wImIyHHYhUJEpFEMcCIijXL7AM/Pz0dKSgqSk5ORk5OjdjmqOn78OEaPHo3U1FSkpaVh2bJlapekOrPZjCFDhuDJJ59UuxTVnTt3DpMmTUL//v0xYMAA/PDDD2qXpKqlS5ciLS0NAwcOxJQpU1BRUaF2SQ7n1gFuNpuRnZ2NxYsXIzc3F+vWrcP+/fvVLks13t7eeP7557F+/Xp89tln+Pjjjxv1/gCA5cuXIyIiQu0y3MJrr72GhIQEfP3111izZk2j3i8mkwnLly/Hl19+iXXr1sFsNiM3N1ftshzOrQO8sLAQ4eHhCAsLg5+fH9LS0pCXl6d2WaoJDg5Gt27dAAD+/v7o1KkTTCaTylWpp6SkBN9++y3S09PVLkV158+fx44dO6r3hZ+fH1q1aqVyVeoym80oLy/HlStXUF5ejuDgYLVLcji3DnCTyYSQkJDq23q9vlEH1tWOHj0Kg8GAuLg4tUtRzezZszFt2jR4ebn129gljh49isDAQGRmZmLIkCGYOXMmLl17OvZGRK/XY+zYsejTpw969eoFf39/9OrVS+2yHI7vfA26ePEiJk2ahBkzZsDf31/tclTxr3/9C4GBgYiJiVG7FLdw5coVFBUV4be//S3+8Y9/oFmzZo36mNHZs2eRl5eHvLw8bNq0CWVlZVizZo3aZTmcWwe4Xq9HSUlJ9W2TyQS9Xq9iReq7fPkyJk2ahEGDBuGhhx5SuxzV7Nq1Cxs3bkRSUhKmTJmCrVu3YurUqWqXpZqQkBCEhIRUfyPr378/ioqKVK5KPd9//z1CQ0MRGBgIX19fPPTQQx55UNetAzw2NhZGoxHFxcWorKxEbm4ukpKS1C5LNYqiYObMmejUqRMef/xxtctR1bPPPov8/Hxs3LgRCxYsQHx8PObNm6d2Wapp27YtQkJCcPDgQQDAli1bGvVBzPbt2+M///kPysrKoCiKx+4Ptz4npo+PD7KyspCRkQGz2Yzhw4cjKipK7bJUU1BQgDVr1qBz584YPHgwAGDKlClITExUuTJyBy+++CKmTp2Ky5cvIywsDHPmzFG7JNXExcUhJSUFQ4cOhY+PD6Kjo/HII4+oXZbDcSo9EZFGuXUXChER1Y0BTkSkUQxwIiKNYoATEWkUA5yISKMY4EREGsUAJyLSKAY4EZFG/T+JGK1NmN0UXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist.history['loss'], color='b')\n",
    "plt.plot(hist.history['val_loss'], color='r')\n",
    "plt.show()\n",
    "plt.plot(hist.history['acc'], color='b')\n",
    "plt.plot(hist.history['val_acc'], color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "8ef32235-eebb-91e2-4571-1b585aa86ad4",
    "_execution_state": "idle",
    "_uuid": "62e80b4160b0bfd27bf1acdb2e5b6baed971fc01"
   },
   "outputs": [],
   "source": [
    "non_idx = np.where(y_test[:,0] == 0.)[0]\n",
    "yes_idx = np.where(y_test[:,0] == 1.)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "5f37a9af-ceab-8b5a-3f77-fefd6eb38710",
    "_execution_state": "idle",
    "_uuid": "4b002ba89846d5f0c25fd29466c9ceb4801fc7e5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([y_hat_fft[i] for i in non_idx], 'ro')\n",
    "plt.plot([y_hat_fft[i] for i in yes_idx], 'bo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Composite model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 3197, 2)      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 3177, 8)      344         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1D)  (None, 794, 8)       0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 794, 8)       32          max_pooling1d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 774, 16)      2704        batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1D)  (None, 194, 16)      0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 194, 16)      64          max_pooling1d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 174, 32)      10784       batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 44, 32)       0           conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 44, 32)       128         max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 24, 64)       43072       batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1D)  (None, 6, 64)        0           conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "main_input (InputLayer)         (None, 1599, 1)      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 384)          0           max_pooling1d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "L1 (CuDNNLSTM)                  (None, 1599, 75)     23400       main_input[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 384)          0           flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "L2 (CuDNNLSTM)                  (None, 37)           16872       L1[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 64)           24640       dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "M1 (Dense)                      (None, 25)           950         L2[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 64)           0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 25)           0           M1[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 64)           4160        dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 89)           0           dropout_3[0][0]                  \n",
      "                                                                 dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 1)            90          concatenate_1[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 127,240\n",
      "Trainable params: 127,128\n",
      "Non-trainable params: 112\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "concatenated = concatenate([fft_final, dense2])\n",
    "answer = Dense(1, activation='sigmoid')(concatenated)\n",
    "composite_model = Model([conv_inputs, fft_inputs], answer)\n",
    "composite_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((5087, 3197, 2), (5087, 1599, 1))\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, x_train_fft.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "composite_model.compile(optimizer=Adam(1e-4), loss = 'binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "5087/5087 [==============================] - 52s 10ms/step - loss: 0.3970 - acc: 0.8319\n",
      "Epoch 2/5\n",
      "5087/5087 [==============================] - 49s 10ms/step - loss: 0.0486 - acc: 0.9925\n",
      "Epoch 3/5\n",
      "5087/5087 [==============================] - 50s 10ms/step - loss: 0.0356 - acc: 0.9927\n",
      "Epoch 4/5\n",
      "5087/5087 [==============================] - 50s 10ms/step - loss: 0.0309 - acc: 0.9927\n",
      "Epoch 5/5\n",
      "5087/5087 [==============================] - 50s 10ms/step - loss: 0.0240 - acc: 0.9925\n"
     ]
    }
   ],
   "source": [
    "hist = composite_model.fit([x_train, x_train_fft], y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "composite_model.compile(optimizer=Adam(1e-5), loss = 'binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_cell_guid": "4ba8b520-7302-747c-7f84-8a816508e651",
    "_execution_state": "busy",
    "_uuid": "0604716b9a92c9113dcd16631b6fc48ff006fd53"
   },
   "outputs": [],
   "source": [
    "def batch_generator_multi(x_train1, x_train2, y_train, batch_size=32):\n",
    "    \"\"\"\n",
    "    Gives equal number of positive and negative samples, and rotates them randomly\n",
    "    \"\"\"\n",
    "    half_batch = batch_size // 2\n",
    "    x_batch1 = np.empty((batch_size, x_train1.shape[1], x_train1.shape[2]), dtype='float32')\n",
    "    x_batch2 = np.empty((batch_size, x_train2.shape[1], x_train2.shape[2]), dtype='float32')\n",
    "    y_batch = np.empty((batch_size, y_train.shape[1]), dtype='float32')\n",
    "    \n",
    "    yes_idx = np.where(y_train[:,0] == 1.)[0]\n",
    "    non_idx = np.where(y_train[:,0] == 0.)[0]\n",
    "    \n",
    "    while True:\n",
    "        np.random.shuffle(yes_idx)\n",
    "        np.random.shuffle(non_idx)\n",
    "    \n",
    "        x_batch1[:half_batch] = x_train1[yes_idx[:half_batch]]\n",
    "        x_batch1[half_batch:] = x_train1[non_idx[half_batch:batch_size]]\n",
    "        x_batch2[:half_batch] = x_train2[yes_idx[:half_batch]]\n",
    "        x_batch2[half_batch:] = x_train2[non_idx[half_batch:batch_size]]\n",
    "        y_batch[:half_batch] = y_train[yes_idx[:half_batch]]\n",
    "        y_batch[half_batch:] = y_train[non_idx[half_batch:batch_size]]\n",
    "    \n",
    "        for i in range(batch_size):\n",
    "            sz = np.random.randint(x_batch1.shape[1])\n",
    "            x_batch1[i] = np.roll(x_batch1[i], sz, axis = 0)\n",
    "            x_batch2[i] = np.roll(x_batch2[i], sz, axis = 0)\n",
    "     \n",
    "        yield [x_batch1, x_batch2], y_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      " - 36s - loss: 1.1663 - acc: 0.5653 - val_loss: 0.0287 - val_acc: 0.9877\n",
      "Epoch 2/10\n",
      " - 33s - loss: 0.6708 - acc: 0.6891 - val_loss: 0.0341 - val_acc: 0.9789\n",
      "Epoch 3/10\n",
      " - 33s - loss: 0.4308 - acc: 0.8109 - val_loss: 0.0371 - val_acc: 0.9807\n",
      "Epoch 4/10\n",
      " - 33s - loss: 0.3249 - acc: 0.8769 - val_loss: 0.0451 - val_acc: 0.9807\n",
      "Epoch 5/10\n",
      " - 33s - loss: 0.2637 - acc: 0.9075 - val_loss: 0.0517 - val_acc: 0.9789\n",
      "Epoch 6/10\n",
      " - 33s - loss: 0.2359 - acc: 0.9167 - val_loss: 0.0551 - val_acc: 0.9807\n",
      "Epoch 7/10\n",
      " - 33s - loss: 0.1839 - acc: 0.9419 - val_loss: 0.0582 - val_acc: 0.9842\n",
      "Epoch 8/10\n",
      " - 33s - loss: 0.1690 - acc: 0.9473 - val_loss: 0.0664 - val_acc: 0.9789\n",
      "Epoch 9/10\n",
      " - 33s - loss: 0.1628 - acc: 0.9454 - val_loss: 0.0751 - val_acc: 0.9772\n",
      "Epoch 10/10\n",
      " - 33s - loss: 0.1505 - acc: 0.9517 - val_loss: 0.0688 - val_acc: 0.9807\n"
     ]
    }
   ],
   "source": [
    "hist = composite_model.fit_generator(batch_generator_multi(x_train, x_train_fft, y_train, 32), \n",
    "                           validation_data=([x_test, x_test_fft], y_test), \n",
    "                           verbose=2, epochs=10,\n",
    "                           steps_per_epoch=x_train.shape[1]//32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_cell_guid": "020d5851-762a-d02c-77fa-319c3af6a4af",
    "_execution_state": "idle",
    "_uuid": "6c22b94c662b6b25fd33c85fd05892aca54e5d86"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist.history['loss'], color='b')\n",
    "#plt.plot(hist.history['val_loss'], color='r')\n",
    "plt.show()\n",
    "plt.plot(hist.history['acc'], color='b')\n",
    "#plt.plot(hist.history['val_acc'], color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "composite_model.save_weights('composite.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist.history['loss'], color='b')\n",
    "plt.plot(hist.history['acc'], color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_cell_guid": "8ef32235-eebb-91e2-4571-1b585aa86ad4",
    "_execution_state": "idle",
    "_uuid": "62e80b4160b0bfd27bf1acdb2e5b6baed971fc01"
   },
   "outputs": [],
   "source": [
    "non_idx = np.where(y_test[:,0] == 0.)[0]\n",
    "yes_idx = np.where(y_test[:,0] == 1.)[0]\n",
    "y_hat = composite_model.predict([x_test, x_test_fft])[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_cell_guid": "5f37a9af-ceab-8b5a-3f77-fefd6eb38710",
    "_execution_state": "idle",
    "_uuid": "4b002ba89846d5f0c25fd29466c9ceb4801fc7e5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([y_hat[i] for i in yes_idx], 'bx')\n",
    "\n",
    "plt.plot([y_hat[i] for i in non_idx], 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "ffabc4d6783302adc67cf15ac6ca038030ff8842"
   },
   "source": [
    "These graphs show that the five positive examples all get 0.95-1.00 score. Also, almost all negative examples get score close to zero, except a few in the 0.9-1.0 range. This is encouraging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_cell_guid": "3d10d764-1a5f-7315-ea45-9e9481261ea4",
    "_execution_state": "busy",
    "_uuid": "38e7322f333dc2afa38186f208e5b96f4bd83fb9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ROgeOwEnGrI7AMzIyEBwcjKCgIKhUKsTHxyM1NbXZNv/5z3/wq1/9ChrNtT8sPXr0cE5aatOH3+chp7gaKxJGwMeTV4daeF4v4JwDJxmyWsD1ej0CAwMtr7VaLfR6fbNt8vLycP78eTzyyCN4+OGHkZaW5vik1Kaiiqt4++vTiBrWC7EjulizKms4AicZc8hQzWQy4cKFC9i8eTOKi4vx61//Grt374a/v3+LbXU6Xbveo76+vt37OlNnyLXqQDFMJjPmDfdETk5Op8nVGlfnUpgMGAagJD8XpX5tvy/Pl32Yyz7OymW1gGu1WhQXF1te6/V6aLXaFtuMHDkSHh4eCAoKwoABA5CXl4c777yzxfHCwtp3Y4lOp2v3vs4kdq4DOSU4fPEcXogdivvG3/xeQuxcbREl1w41evmp0Os278vzZR/msk9HcqWnp7e5zuoUSkREBPLy8pCfnw+DwYCUlBRERUU12+b+++/HsWPHAABlZWXIy8tDUBCvQXa2qwYTEndlYtAdPnjinhCx43Renv6cAydZsjoCVyqVSExMxKJFi2AymTBnzhyEhoZi7dq1CA8PR3R0NO655x4cPnwYcXFxcHd3x4svvohu3bq5In+X9tcDZ5BfdhVbn5gAlZKX9LeJ/VBIpmyaA4+MjERkZGSzZUuWLLH8XqFQYOnSpVi6dKlj01GbckuqsSHtHGbf1RcTB/Gqn9tS+/M6cJIlDtskSBAELNuRCS8Pd7wS3/nm+zodjsBJpljAJWjHiUIcPVeGFx8Yhp6+vOPSKs6Bk0yxgEtMZV0j/pCiw6igAPxyfH+x40gDR+AkU7xlT2LW7M1BWa0BHy4Yz2ZVtuIcOMkUR+ASciK/Ap8cu4j5dw9AeF+N2HGkQ60BjFcBo0HsJEQOxQIuEdeaVZ1ELz9PPB8zROw40sJ+KCRTLOASsfnoBWQVVWH59OHwU3uIHUda2A+FZIoFXAL0VfV4a99pTBlyB+IjeosdR3osPcFZwEleWMAlICk5GwaTGUkzRkCh4BeXdvNkASd5YgHv5NJOX0ZKxiU8fe9gDOjpI3YcaVJzDpzkiQW8E6tvNCFxZyYG9vTBk/eyWVW7cQqFZIrXgXdi6749i7zSOmxZ+At4Kt3FjiNdli8xOQIneeEIvJM6d7kG6749ixkj+2ByaE+x40ibyg+AgiNwkh0W8E5IEAQk7syCp9INy6azWVWHubmxHwrJEgt4J7Q74xIO5V7B/48dil5+arHjyIPanyNwkh0W8E6mqr4RrydnI6KvBr+eECx2HPlQazgHTrLDLzE7mbf2nsKVmgZ8MH8s3NmsynE8OQIn+eEIvBM5WVCJzUcvYN6EYNzZL0DsOPKi1gANLOAkLyzgnYTJLODVHSfR3ccTv586VOw48sOWsiRDLOCdxCc/XEBGQSWWTw+DxovNqhyOD3UgGWIB7wRKquuxZu8pTBrcAzNG9hE7jjzduIxQEMROQuQwLOCdwB9SdGhoNCNpZjibVTmLWgMIZsBQI3YSIodhARfZ4dwr2HmiCE9GhmDQHb5ix5EvSz8UzoOTfLCAi6jBaMLyHZno390bT903WOw48saHOpAM8TpwEW04eA7nrtTiwwXjoPZgsyqnutETnLfTk4xwBC6SC6W1eO9ALuIjeuPeob3EjiN/6uvX1XMETjLCAi6CG82qPNwUWD59uNhxugbOgZMMsYCL4MvMYhw8fRnPTx2KQA2bVbmEZQ68QtwcRA7EAu5iNQ1GJO3OxvDe/pg/kc2qXIZz4CRD/BLTxd75+jT01fX4269HQ+nOvz9dxkMNuHtyDpxkhRXEhbKKKrHp8Hk8Or4/RvfvJnacrof9UEhmbCrgaWlpiI2NRUxMDDZs2NDmdnv37sXQoUNx8uRJhwWUC7NZwLIdmejmrcJLscPEjtM1sR8KyYzVAm4ymZCUlISNGzciJSUFycnJyM3NbbFdTU0NPv74Y4wcOdIpQaXu0x/zcfxiBV6JC4PGm82qRMHHqpHMWC3gGRkZCA4ORlBQEFQqFeLj45Gamtpiu7Vr1+KJJ56Ap6enU4JK2ZWaBvzxqxz8YmB3zB7dV+w4XRcfq0YyY/VLTL1ej8DAQMtrrVaLjIyMZttkZWWhuLgY9957Lz744IPbHk+n07UraH19fbv3dSZbcr11qAQ19Y1YcKc3cnJyOk0uMYiZq6/BDZ5Vl3Gulffn+bIPc9nHWbk6fBWK2WzGm2++iTfeeMOm7cPC2veUdZ1O1+59nclarqPnSvHN2XN46t5BeGCi6+a+pXq+nOp0X6Aiu9X35/myD3PZpyO50tPT21xndQpFq9WiuLjY8lqv10Or1Vpe19bW4vTp03jssccQFRWFEydO4He/+x2/yARgMJqxbEcm+nXzwrNRoWLHIbWGc+AkK1ZH4BEREcjLy0N+fj60Wi1SUlLw1ltvWdb7+fnhhx9+sLyeN28eXnzxRURERDgnsYRsPHQOuSU1+GD+WHip2KxKdGoN0FgHmBoBd36RTNJntYArlUokJiZi0aJFMJlMmDNnDkJDQ7F27VqEh4cjOjraFTklJ7+sDu+mnsHU4VpEh2mt70DOZ7mdvgrw6SFuFiIHsGkOPDIyEpGRkc2WLVmypNVtN2/e3PFUEicIAl7blQU3hQIrZowQOw7dcON2+voKFnCSBd6J6QT7svVIzSnBc/eHom+Al9hx6IYbI3DOg5NMsIA7WG2DESt3ZWGo1g8LJg0UOw41ZWkpy2vBSR7YzMrB3k09g6LKemx79C54sFlV59J0DpxIBlhhHCinuAofHDqPh8f2w9gB3cWOQ7fy5Aic5IUF3EHMZgHLtmfCT63Ey9M6340EBM6Bk+ywgDvItvQC/PdCOZZOC0N3H5XYcag1nn7XfuUInGSCBdwBymsNeONLHcYGd8PcMf3EjkNtcXO/No3COXCSCRZwB3jzyxxU1Rux6sFwuLkpxI5Dt+PJjoQkHyzgHZRVUo9//zcfCycPxLBAf7HjkDXsh0IywgLeAY0mM/565DL6aNRYEs1mVZLAnuAkIyzgHbDp8HnkVTRixYwR8PHkJfWSwMeqkYywgLdTUcVV/OWbMxjfzxtTh7NZlWRwDpxkhAW8nVbuzoJZEPDUL3pAoeAXl5LBOXCSERbwdkjV6bE3S4/F0aHQ+rKvtKSor19GKAhiJyHqMBZwO101mLBiVxZCe/li0eQQseOQvdQaQDABhlqxkxB1GAu4nd7bfwYF5Vfx+qxwqJQ8fZJzox8Kp1FIBliB7HBGX41/fHcOs0f3xYQQPhBAkiwdCflFJkkfC7iNBEHAsh2Z8FYp8Uocm1VJlqUnOEfgJH0s4Db64qdC/HC+DC89MAw9fT3FjkPt5ckROMkHC7gNKuoMWL1Hh7v6B+CRcUFix6GOYEtZkhHePmiDNXtPobzOgI8XjmezKqlTN3mwMZHEcQRuxU8Xy7H12EU8fvdAjOijETsOdRQfq0YywgJ+G0aTGa9uz4TWT43npw4ROw45glINuHlwDpxkgQX8Nj46cgG6S1VITBgOXzarkgeFgrfTk2ywgLehuLIeb+87hcghd2BaeKDYcciR2FKWZIIFvA2vJ2fDaBaQNHMEm1XJjVrDOXCSBRbwVnx7qgQpJy/h6fsGI7iHj9hxyNHYUpZkggX8FvWNJiTuzEJITx/8TySbVckS58BJJvjN3C3+diAXF8vq8MmiX8BT6S52HHIGzoGTTHAE3sS5yzVYf/AcZo7qg7sH9xQ7DjmLOoBz4CQLNhXwtLQ0xMbGIiYmBhs2bGixftOmTYiLi0NCQgLmz5+PwsJChwd1NkEQsHxnJjw93PBqPJtVyZqnP9BYC5gaxU5C1CFWC7jJZEJSUhI2btyIlJQUJCcnIzc3t9k2YWFh+Pzzz7F7927ExsbiT3/6k9MCO8uun4twOLcUL8QORS8/tdhxyJks/VCqxc1B1EFWC3hGRgaCg4MRFBQElUqF+Ph4pKamNttmwoQJ8PLyAgCMGjUKxcXFzknrJJVXG/F6sg539tPgV78IFjsOORv7oZBMWC3ger0egYE3b2TRarXQ6/Vtbr9t2zZMmTLFMelc5K19p1BW24A/zIqAO5tVyR/7oZBMOPQqlJ07dyIzMxNbtmxpcxudTteuY9fX17d739s5faUBm48UYvowfyiri6DTFXWKXB3FXG3zLilHMIALp0+irtKz0+RqDXPZp6vlslrAtVptsykRvV4PrVbbYrvvv/8e69evx5YtW6BSqdo8XlhY+74g1Ol07d63LSazgBe/OYyefp74wyMT4K+2/wnzzsjlCMx1GwGNwAEgWBsAXM/SKXK1grnsI8dc6enpba6zOoUSERGBvLw85Ofnw2AwICUlBVFRUc22yc7ORmJiItatW4cePaTzrMgtRy/gZGEllk8f3q7iTRJlmQPnteAkbVZH4EqlEomJiVi0aBFMJhPmzJmD0NBQrF27FuHh4YiOjsaaNWtQV1eHJUuWAAB69+6N9evXOz18R5RU1ePPe09h8uCeSLizt9hxyJU4B04yYdMceGRkJCIjI5stu1GsAeDDDz90aChXWJWiQ4PRzGZVXZEnR+AkD13yTsxDZ65g189FePLeQQi5w1fsOORqbu6Ayo/9UEjyulwBr280YfnOTAT38MZT9w4SOw6Jhf1QSAa6XDOrvx88h/NXavHRb8ZD7cFmVV0WW8qSDHSpEXjelVq8/20u4u/sjcghd4gdh8Sk1rCAk+R1mQIuCAISd2VB5e6GxOnDxY5DYlP7cw6cJK/LFPA9J4uRdvoyno8ZAq0/m1V1eRyBkwx0iQJeXd+IlbuzMKKPPx6byGZVhOtz4ByBk7R1iQL+9tencbmmAX94MAJK9y7xI5M1N0bggiB2EqJ2k301yyysxEff5+GX4/tjVFCA2HGos1D7A4IJaKwTOwlRu8m6gJvMAl7dkYnuPiq8GDtM7DjUmfB2epIBWRfwrccu4uf8CrwSFwaNN5tVURO8nZ5kQLYF/HJ1A9Z8lYMJId3x4F19xY5DnY36+nQaLyUkCZNtAX9jjw5XG01YNSuCzaqoJbaUJRmQZQE/crYUXxwvxG+nhGBwLzarolZY5sBZwEm6ZFfADUYzlu04iX7dvPDMfaFix6HOinPgJAOya2b1j+/O4ezlWvzz8bHwUrFZFbXhxgicc+AkYbIageeX1eHd1DN4YEQgooa1fG4nkYWHF+Cm5AicJE02BVwQBKzYlQV3NwUSE9isiqxQKK7fjckROEmXbAr43iw99ueU4H/vH4I+AV5ixyEpYE9wkjhZFPDaBiNW7s7CsEA/PD5pgNhxSCrUGs6Bk6TJ4kvMv3xzGpcq6/HXX94FDzarIlvxsWokcZKvdrpLVfjn4Tw8Mi4IY4K7ix2HpIRz4CRxki7gZrOAZTsyofHywEsPsFkV2cmTD3UgaZN0Af8sPR/pF8rx8rRh6OajEjsOSQ3nwEniJFvAy2oNeOPLHIwb0A1zR/cTOw5JkdofMNQAJqPYSYjaRbIF/I09OtTUG7FqVgTc3NisitqBd2OSxEmygP+YV4bP0guw8J6BGBroJ3Yckir2QyGJk1wBbzSZsWx7JvoGeGFJNJtVUQfcaCnLEThJlOQK+D8PnccpfTVWJAyHt0oWl7GTWNhSliROUgW8oLwOf/nmDO4P02LqiECx45DUWaZQOAInaZJUAV+5OxsA8NoMNqsiB+AInCTOpgKelpaG2NhYxMTEYMOGDS3WGwwGPPfcc4iJicFDDz2EgoIChwc9ml+Lr7P1WBwdin7dvB1+fOqCeBUKSZzVAm4ymZCUlISNGzciJSUFycnJyM3NbbbNZ599Bn9/f3z99dd4/PHH8ec//9mhIesMRqz7oRShvXyxcPJAhx6bujBehUISZ/VbwIyMDAQHByMoKAgAEB8fj9TUVAwePNiyzf79+/HMM88AAGJjY5GUlARBEBz2MOF3U3NRUmvEe78Kh0opqVkf6szclYCHD1B0HH4GX8CcI3aiFvwKC5nLDp01l6rWG0CYw49rtYDr9XoEBt78wlCr1SIjI6PFNr179752QKUSfn5+KC8vR/fuLZtL6XQ6u0N+lVGAmBBv+DeUQKcrsXt/Z6qvr2/Xz+RszGWbEK9e8Dz9FfoTnT3VAAAIeElEQVSd/krsKK3qrPcYM5d9tN3DofPr7/Djuvw6vLAw+/8WSgkJxYWzp9u1r7PpdDrmskOnyzXgW6D6Es6eO4dBISFip2mBuezTWXMVFte2+3Ofnp7e5jqrBVyr1aK4uNjyWq/XQ6vVttjm0qVLCAwMhNFoRHV1Nbp169ausK3x8VQ6bDqGqBmvAMArAIZSAL060V8s1zGXfTprLnOpc/7VaXVCOSIiAnl5ecjPz4fBYEBKSgqioqKabRMVFYXt27cDAPbu3YsJEyaw4BIROZnVEbhSqURiYiIWLVoEk8mEOXPmIDQ0FGvXrkV4eDiio6Mxd+5cvPDCC4iJiYFGo8E777zjiuxERF2aTXPgkZGRiIyMbLZsyZIllt97enri3XffdWwyIiK6LV6TR0QkUSzgREQSxQJORCRRLOBERBKlEARBcNWb3e6CdCIiat2YMWNaXe7SAk5ERI7DKRQiIoliAScikijRC3hHHhbx97//HTExMYiNjcV3333n0lybNm1CXFwcEhISMH/+fBQWFlrWhYWFYebMmZg5cyaefPJJl+b64osvMGHCBMv7f/bZZ5Z127dvx9SpUzF16lRL6wNX5Vq9erUlU2xsLMaOHWtZ58zztXTpUkycOBHTp09vdb0gCFi1ahViYmKQkJCArKwsyzpnni9ruXbt2oWEhAQkJCTgkUceQU7OzRapUVFRSEhIwMyZMzF79myX5vrhhx8wZswYy/+vv/71r5Z11j4Dzsy1ceNGS6bp06cjLCwMFRUVAJx7vi5duoR58+YhLi4O8fHx+Oijj1ps49TPmCAio9EoREdHCxcvXhQaGhqEhIQE4cyZM8222bJli7B8+XJBEAQhOTlZWLJkiSAIgnDmzBkhISFBaGhoEC5evChER0cLRqPRZbmOHDki1NXVCYIgCP/6178suQRBEEaNGuWQHO3J9fnnnwsrV65ssW95ebkQFRUllJeXCxUVFUJUVJRQUVHhslxNffzxx8LLL79see2s8yUIgnDs2DEhMzNTiI+Pb3X9t99+KyxcuFAwm83C8ePHhblz5wqC4NzzZUuu9PR0y/t9++23llyCIAj33XefUFpa6rAs9uQ6evSo8Nvf/rbFcns/A47O1VRqaqowb948y2tnni+9Xi9kZmYKgiAI1dXVwtSpU1v83M78jIk6Am/6sAiVSmV5WERT+/fvx4MPPgjg2sMijhw5AkEQkJqaivj4eKhUKgQFBSE4OLhFn3Jn5powYQK8vLwAAKNGjWrWsdFZbMnVlkOHDmHSpEkICAiARqPBpEmTHPavFntzpaSktDmScrRx48ZBo9G0uT41NRWzZs2CQqHAqFGjUFVVhZKSEqeeL1tyjR492rLeVZ8vW3K1pSOfTUfncuXnq1evXhgxYgQAwNfXFyEhIdDr9c22ceZnTNQC3trDIm794dt6WIQt+zozV1Pbtm3DlClTLK8bGhowe/ZsPPzww/jmm28cksmeXPv27UNCQgIWL16MS5cu2bWvM3MBQGFhIQoKCjBhwgTLMmedL1vcmj0wMBB6vd6p58tet36+AGDhwoWYPXs2/v3vf7s8z4kTJzBjxgwsWrQIZ86cAeDcz5c9rl69iu+++w5Tp05tttwV56ugoAA6nQ4jR45sttyZnzGXP9BBbnbu3InMzExs2bLFsuzAgQPQarXIz8/H/PnzMWTIEPTv7/incbTmvvvuw/Tp06FSqfDpp5/ipZdewscff+yS97ZFSkoKYmNj4e7ublkm5vnq7I4ePYpt27bhk08+sSzbunUrtFotSktLsWDBAoSEhGDcuHEuyTNixAjs378fPj4+OHjwIJ5++mns27fPJe9tiwMHDmD06NEICAiwLHPF+aqtrcXixYvxyiuvwNfX16HHvh1RR+D2PCwCQLOHRdiyrzNzAcD333+P9evXY926dVCpVM32B4CgoCCMHz8e2dnZLsvVrVs3S5aHHnrI8oVJZzhfALBnzx7Ex8e32B9w/Pmyxa3Zi4uLodVqnXq+bJWTk4Nly5bhb3/7W7MHpNzI0aNHD8TExDhs6tAWvr6+8PHxAXCtS6nRaERZWVmnOF/AtQFCW58vZ52vxsZGLF68GAkJCS1G/jfe31mfMVELeEceFhEVFYWUlBQYDAbk5+cjLy8Pd955p8tyZWdnIzExEevWrUOPHj0syysrK2EwGAAAZWVl+Omnn5o9ANrZuUpKbj4zdP/+/Rg0aBAAYPLkyTh06BAqKytRWVmJQ4cOYfLkyS7LBQBnz55FVVUV7rrrLssyZ54vW0RFRWHHjh0QBAEnTpyAn58fevXq5dTzZYuioiI8++yzWLNmDQYOHGhZXldXh5qaGsvvDx8+jNDQUJflunz5MoTr9/5lZGTAbDajW7duNn8GnKm6uho//vgjoqOjLcucfb4EQcCrr76KkJAQLFiwoNVtnPkZE3UKpSMPiwgNDcW0adMQFxcHd3d3JCYmNvtnubNzrVmzBnV1dZa+6L1798b69etx9uxZrFixAgqFAoIg4IknnnBYQbIl1+bNm7F//364u7tDo9HgjTfeAAAEBATgqaeewty5cwEATz/9dLN/Zjo7F3Bt9B0XF9fsaU3OPF8A8Pzzz+PYsWMoLy/HlClT8Oyzz8JoNAIAHn30UURGRuLgwYOIiYmBl5cXVq9eDcC558uWXO+//z4qKiqwcuVKAIC7uzu++OILlJaW4umnnwYAmEwmTJ8+vcX8uDNz7d27F1u3boW7uzvUajXefvttKBSKNj8DrsoFAF9//TUmTZoEb29vy37OPl/p6enYuXMnhgwZgpkzZ1qyFhUVWbI58zPGW+mJiCRK9Bt5iIiofVjAiYgkigWciEiiWMCJiCSKBZyISKJYwImIJIoFnIhIoljAiYgk6v8AIkhaPSzKtLAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crossover at 0.79 with specificity 0.99\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC area under curve is 1.00\n"
     ]
    }
   ],
   "source": [
    "y_true = (y_test[:, 0] + 0.5).astype(\"int\")\n",
    "fpr, tpr, thresholds = roc_curve(y_true, y_hat)\n",
    "plt.plot(thresholds, 1.-fpr)\n",
    "plt.plot(thresholds, tpr)\n",
    "plt.show()\n",
    "crossover_index = np.min(np.where(1.-fpr <= tpr))\n",
    "crossover_cutoff = thresholds[crossover_index]\n",
    "crossover_specificity = 1.-fpr[crossover_index]\n",
    "print(\"Crossover at {0:.2f} with specificity {1:.2f}\".format(crossover_cutoff, crossover_specificity))\n",
    "plt.plot(fpr, tpr)\n",
    "plt.show()\n",
    "print(\"ROC area under curve is {0:.2f}\".format(roc_auc_score(y_true, y_hat)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "088fa46309212bf4a02766ea44e918d561643718"
   },
   "source": [
    "We now choose an optimal cutoff score for classification. Sklearn can help us with this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('found: ', 8)\n",
      "('actual: ', 5)\n",
      "('missed: ', 1)\n",
      "('precision: ', 0)\n",
      "('recall: ', 0)\n",
      "('F1: ', 0.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = [y>crossover_cutoff for y in y_hat]\n",
    "F1_score(predictions, y_test, crossover_cutoff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "F1 Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmark\n",
    "\n",
    "Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('x_train_fft_raw', (5087, 3197))\n",
      "('y_train_1hot', array([[1, 0],\n",
      "       [1, 0],\n",
      "       [1, 0],\n",
      "       ...,\n",
      "       [0, 1],\n",
      "       [0, 1],\n",
      "       [0, 1]]))\n"
     ]
    }
   ],
   "source": [
    "#generate one-hot classifications\n",
    "y_train_1hot = np.array([[1,0] if y > 0.5 else [0, 1] for y in y_train])\n",
    "y_test_1hot = [[1,0] if y > 0.5 else [0, 1] for y in y_test]\n",
    "\n",
    "print('x_train_fft_raw',x_train_fft_raw.shape)\n",
    "print('y_train_1hot', y_train_1hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed: 11.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "           oob_score=False, random_state=42, verbose=1, warm_start=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the model \n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Instantiate the model with 1000 trees\n",
    "rf = RandomForestRegressor(n_estimators = 100, random_state = 42, verbose=1)\n",
    "\n",
    "# Train the model on training data\n",
    "rf.fit(x_train_fft_raw, y_train_1hot )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "# predict on the test data\n",
    "predictions = rf.predict(x_test_fft_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('found: ', 0)\n",
      "('actual: ', 5)\n",
      "('missed: ', 5)\n",
      "('precision: ', 0.0)\n",
      "('recall: ', 0)\n",
      "('F1: ', 0.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# analyse\n",
    "F1_score_1hot(predictions, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_cell_guid": "8ef32235-eebb-91e2-4571-1b585aa86ad4",
    "_execution_state": "idle",
    "_uuid": "62e80b4160b0bfd27bf1acdb2e5b6baed971fc01",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "non_idx = np.where(y_test[:,0] == 0.)[0]\n",
    "yes_idx = np.where(y_test[:,0] == 1.)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_cell_guid": "5f37a9af-ceab-8b5a-3f77-fefd6eb38710",
    "_execution_state": "idle",
    "_uuid": "4b002ba89846d5f0c25fd29466c9ceb4801fc7e5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([predictions[i] for i in yes_idx], 'bo')\n",
    "\n",
    "plt.plot([predictions[i] for i in non_idx], 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "_change_revision": 0,
  "_is_fork": false,
  "kernelspec": {
   "display_name": "conda_tensorflow_p27",
   "language": "python",
   "name": "conda_tensorflow_p27"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}