p2_notebook03.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse des données de systèmes éducatifs\n",
    "Etude data - Projet 2 Nalron (Septembre 2020) OpenClassrooms / CentraleSupélec\\\n",
    "Traitement des données sur Jupyter Notebook (Distribution Anaconda)\\\n",
    "Etude réalisée en langage Python/\n",
    "\n",
    "Data source : [Données de la Banque mondiale](https://datacatalog.worldbank.org/dataset/education-statistics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACP et clustering k-means \n",
    "Rappelons les objectifs de l'entreprise, l'internationalisation des services de cours en ligne. Un choix de pays cibles devra être fait, l'aide à la décision qui passe par l'étude actuelle repose sur des hypothèses que nous poserons prochainement. La classification des pays par l'algortithme k-means semble être le bon choix de modélisation, en effet le k-means permet de rechercher efficacement une partition des données dont la variance intra-cluster est minimale. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn import cluster, metrics\n",
    "from sklearn import decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_indicators.iloc[:, 2:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_scale = preprocessing.StandardScaler().fit(X)\n",
    "X_scaled = std_scale.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "silhouettes = [] \n",
    "\n",
    "for k in range(2, 10):\n",
    "    cls = cluster.KMeans(n_clusters=k)  \n",
    "    cls.fit(X_scaled)\n",
    "    \n",
    "    silh = metrics.silhouette_score(X_scaled, cls.labels_)\n",
    "    silhouettes.append(silh)\n",
    "    \n",
    "plt.plot(range(2, 10), silhouettes, marker='o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notons que le coefficient de silhouette k=3 semble pertinent pour la réalisation du clustering, ce coefficient de silhouette n'est pas le plus élevé, mais fera ressortir des patterns plus appropiés aux objectifs attendus. De plus, k=3 ou k=4 fera très certainement ressortir des pays à forte inertie (type Chine, USA…). Le contexte métier semble favorable à un partitionnement dans lequel des fortes puissances seraient isolées pour ne pas influencer les zones dont l'inertie serait bien moins importante _(nous le verrons plus loin dans l'analyse)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=4, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = decomposition.PCA(n_components=4)\n",
    "pca.fit(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iAsOGDSs128iRIzFr1iz3TH1jx47Fzp073bNGltSl7Xav2bvvvouPPvoI3bt3R0xMDMLDw2GxWPDFF18UOdfSPPXUU/jggw/cs0ZOmzYNmzdvRocOHWCxWLB//35cuXIFBw4cAOCacfPLL7+EKIpwOBxITEzEww8/DJlMhhUrVnjM6nljQa283HjdPvroI6SlpSEpKQk6nQ6ZmZnYvn07Dh06hG7dupVaCKtZsyaqVavm7kI5d+5cZGVlQRAEfPzxxx6tsm4UFRXl8Xj48OHo2LEj5HI5BgwYcNPWhjKZDE8//bR7NtBTp06hXbt2uO+++3D58mUsX77cvW1YWJi7uHonunbtipCQEHTr1g0REREICQlBZmamx8yVZekKTURE5BckHqyfiIiIKtGHH34o6nQ6j1nmivu6fua9nJwcsV27dqXu061bN/c+ZZnJ75qVK1eKWq22xOMmJye7ty1p1khRFEWTyST269ev1Jz16tW7peslCEKRY6hUKrFz584lzix4O9ds/PjxpW4/dOjQMmffsmWLGBISUuKxWrZs6bH9woULPWaHvPFLLpeL77zzjsc+ZZldURQ9Z4282fUv73tt3rx5xe4fGRkp9urVq8RMWVlZYmBgYLH7rlmzRhTFkmeNFEXXzJEjR4686TlUr15d3LZtm8fzXj9r5PXnWNrzlfYzLZfLxW+++abE605ERORPOGskERGRHxkzZgzOnj2Lf//730hKSkJ4eDhUKhWqVauGuLg4DBs2DGvXrsWQIUPc+4SGhmLHjh1YsmQJ+vTpg1q1akGpVCIkJARxcXF44IEHsHTp0tseb2nEiBE4dOgQpkyZghYtWiAwMBBKpRLh4eHo1asXhg4dWqbjaDQafPPNN1i3bh2Sk5MRGRkJlUoFvV6PJk2aoH///nj//feLzE54M2PGjMHGjRvdreb0ej369u2Lbdu2ISkpqcT9bueaPfLII5gxYwZ69OiB+vXrQ6fTQaFQoEaNGujevTs++OADrFy5sszZe/bsiaNHj+Kll15C27ZtERwc7D5eYmIiHn/8cY/tx40bh3379mHMmDFo0KABNBoN1Go1oqOjMXr0aOzZs6fUVlh3qrzvtWeeeQb/+c9/0LRpUyiVStSoUQMjRozA77//joiIiBL3q1mzJjZt2oTu3bsjKCjols/jWku6DRs2IDk5GbVr14ZSqYROp0OLFi0wffp0HD58uNy6mS5cuBCPPfYYWrdujfDwcCiVSmg0GsTExGDEiBHYsWMH+vXrVy7PRURE5OsEURRFqUOU5vLly9iwYQNOnjyJc+fOoWnTpkhNTS11v8LCQixbtgx79uyB0+lEQkICHnnkkSJ/0OzZsweff/45Ll++jJo1a+LBBx9Ep06dKuhsiIiIiIiIiIhICj7RIuz8+fM4cOAAateufdNP72705ptv4siRIxg7dizGjx+P06dPY/78+R7bHD9+HAsWLEBcXByef/55tGnTBm+//Tb++OOP8j4NIiIiIiIiIiKSkE8Mlp+QkIB27doBABYsWICCgoJS90lPT8cff/yB1NRUNGvWDIBrkNAZM2YgLS0NLVq0AACsW7cOTZs2xaOPPgoAaN68OTIzM7F27Vq0bNmygs6IiIiIiIiIiIgqm0+0CJPJbj3mgQMHEBwc7C6CAUDDhg1Rs2ZNHDx4EABgs9lw+PBhdOzY0WPfTp06IT09HYWFhXcWnIiIiIiIiIiIvIZPFMJux4ULFxAZGVlkeWRkJC5cuAAAyMrKgsPhKLJdZGQkRFHExYsXKyUrERERERERERFVvCpbCDMajQgICCiyXKfTwWg0AgAMBoN72fUCAwPdxyAiIiIiIiIioqrBJ8YIu12CINzS8muuTaRZ3HZbtmzBli1bAACvvfbaHSYkIiIiIiIiIqLKUmULYTqdrthB9a9vKXat5deNY4FdawlWXIuypKQkJCUluR+z+ySVVVhYGHJycqSOQT6A9wrdCt4v5c9kArKz5f98yZCdLUNengyi6PkBmUwmIizMiRo1nNDrndDpROh01/51fcnlEp1EMbzpXnE6gcJCAUajAINBQGGhDEajgPx8ATk5cvz1lwx2+/+u9z+fUSI42ImaNR2oVcv1b82aTgQGihKdRdXmTfcLeTfeK3QreL9QWUVERFTYsatsISwyMhI//vhjkeUXL150z0BZq1YtyOVyXLhwwWNQ/YsXL0IQhAq98ERERFR5RBG4elVAdrYcWVkyZGe7ii0GgwzXGoBfK7ZoNKK72BIdbUeHDk6EhDhxG3P3eJWsrCzIvaQyJ5MBgYEiAgNF1KoFAI5S97nxe3jggAp//SWD0ShzrwcAQQDU6v99D2vUcP1bFb6HREREdOeqbCGsdevWWLduHY4fP44mTZoAAE6fPo2srCy0atUKAKBUKtG8eXPs2rULvXr1cu+7Y8cONGrUqNgWYUREROR9LBbg7FkFMjIUyMiQF1vgur41UevWVr9rTXTmzBnExsZKHeO2CQJQrZqIatXsaNTo5tuaTMI/rfnkyMhQ4PffXa36nE7BfSxRdBU969e3IzrajuhoBwIC/Od+ICIi8lc+UQizWCw4cOAAACA3Nxcmkwm7du0C4Cp4qdVqTJw4Ec2aNcO4ceMAAI0aNULLli3x3nvvYeTIkRAEAZ988gmaNGmCFi1auI99//33IzU1FcuWLUO7du1w4MABHDhwADNmzKj8EyUiIqIS2WzA+fPyf4pdCuTmuopdggAolSLq1XMgJsaOjh0t0OtZ0PBnWq3rfqhX7+YtzUwmARkZrnvq5581KCwU3AVUnc6JmBgHoqPtqFvXDo2mEoITERFRhfOJQtjVq1fxxhtveCy79vi9995DzZo14XQ64XQ6PbaZPHkyli9fjkWLFkEURbRp0waPPPKIxzZNmjTB008/jVWrVuH7779HzZo1MWnSJLRs2bJiT4qIiIiKcDiAixddhYkzZ+T46y+5u/WOQiGiTh1XYWLQoEJUr85iF90ZrVZEs2Z2NGtmL7KuoEBARoYCx44psHmzBlbr/8YsCw52/tOKzI66dR1QKiszNREREd0JQbw2RSLdFg6WT2XFgSGprHiv0K3wxftFFIHLl2U4c8bVsuvy5f+NWyWTiYiIcCA62tW6KyzMiVIme6YykslkRT40pNuTlye4WyaeOyeHw+G6SUURqFHDdf9GR9sRGenwqgkTboUvvraQNHiv0K3g/UJlxcHyiYiIyOdcK3gdOqTEsWNKWK3/63YWHu4qFPToYUZ4OAcxrwyBgYHIz8+XOkaVEBIiIiTEhjZtbB7LRRHIyZEhI0OBnTtVuHBBDqdTcLdobNjQjvh4G+rVc7DAS0REJBEWwoiIiOiOiSJw7pwchw4pkZ6ugN3uKnqFhzsQH29DYqIBWq3UKf3bvn37fHqwfF8gCECNGk7UqGFF+/ae62w24ORJBX7/XYU1axQQRdfsmTExruJYTIzdZ1uPERER+RIWwoiIiOiWOBzAmTMKHDqkREaGAtd629Wta0fz5jb06WPmmElEN1AqUWQ8MocDyMiQ4/BhFTZs0MLxz9j+136WGjWyQ6WSKDAREVEVxUIYERERlchmA9LTFTh8WIlz51x/NlxrxdKihQ3JySa2YiG6TXI50LChAw0bmtzLRNE1O+qhQ0r88IMGdrura2WtWq7WlU2b2ti6koiI6A6wEEZEREQAAJMJOH5ciUOHlO4B7JVKEY0a2dGxoxWDB5s4rpEPq1mzptQRqAwEAahb14G6dR3o18+1TBSBrCzXeHv//W8gLBbXD2JIiBPx8TbExdkQGMj5r4iIiMqChTAiIiI/5HAAR44osWePCrm5rpHq1WoRTZva0Lu3GbVqcbbGqqZBgwacqctHucbbcyI83IJevSzu5bm5Mhw+rMSKFToYja4f2MBAJxISbGjZ0gq1WqrERERE3ouFMCIiIj9gswFpaa7C19WrMsjlQFycq2tjWJhT6nhUCdLS0ip0KnKqfNWrO9G1qwVdu/6vOJafL2DfPhUWLgyEzSZAoxGRkGBFmzZWdqkkIiICC2FERERVksUCHDyowp49KhiNApRKEfHxNgwbVojgYHah8kdGo1HqCFQJ9HoR3btb0L27qzhmMgnYu1eJxYtdXSpVKhGtW9vQtq0VOh1fC4iIyP+wEEZERFQFXHuzu3+/ChaLq/DVurUNo0cbOXYQkR/TakUkJlqRmGgFAJjNriL50qU6FBYKUChcRfL27a0skhMRkV9gIYyIiMgHGQwC9uxRIT1dhr//DoJWK6JNGyueeMIIrZZvZqkopVIpdQTyAhoN0KGDFR06uApjVitw6JASn30W4NFtum9fiYMSERFVEBbCiIiIfMDffwvYvVuFQ4dUsNuBwEAR7dpZMW2aEwUFBVLHIx/Qtm1bDpZPRahUQEKCDQkJNgCA3Q4cParEJ5/IkJkZBEEAmjSxoUMHK2rW5HiCRETk+1gIIyIi8kJXrsjw++8qHDmihNMJBAc70b69FZMmFeD6hj1qNcA6GJXF+fPnoeVo6VQKhQJo0cKGHj2cyMkpgNMJHD+uwMaNGmRnywEAsbF2/OtfFkRGsjBGRES+h4UwIiIiL+BwAAcOKLFtmxpms4DQUCc6dLCiTx8z5HKp01FVkJmZidjYWKljkI+RyYBmzexo1swOABBF4NQpBX78UYOLF+VQKID27S3o0MEKlUrisERERGXAQhgREZFE8vMF/PKLGkeOKCGTAa1bWzF2LMf4IiLvJQiuFmGxsa7CmM0G7N6twnvvBcJqFRAdbUePHhbUqMHWYkRE5J1YCCMiIqpEp0/L8dNPGly5IkNQkIhu3czo398MQZA6GRHRrVMqgc6drejc2TX4/unTcnz1lRY5OTIEBoq46y4z4uLsfI0jIiKvwUIYERFRBbJagd9/V2H3bjVsNiAmxo6BA00IC2NrCapc8fHxMJvNUsegKq5BAwcaNDACcLV6/fVXNb75RgtBcLV67dLFioAAtnolIiLpsBBGRERUznJyZPjpJzVOn1ZApXKNnzNxYgHHzyEiv6LXi+jf39Xq1eEADh5U4r//1cFkEhAe7kBSkgVRUQ6pYxIRkZ9hIYyIiOgOiSJw5IgCv/yiQUGBa6D7Hj3MGDzYJHU0IrdDhw5xsHySjFwOJCTYkJBgAwBcuCDDTz9pkJkph0YjoksXK9q0sXJyECIiqnAshBEREd0Gk0nAtm0qHDiggtMJNGtmw0MPGREczC4/RESliYx0YuTIQgCAyQTs2KHGggVBcDhcr6fdullQrRpfT4mIqPyxEEZERFRGFy/K8MMPGly8KIdWK6JLFwumTi1gCwYiojug1QI9e1rQs6cFoggcO6bAZ58F4OpVGUJCnOjZ04yGDdmFkoiIygcLYURERDeRmyvDN9+4uu/Uru1A794WREbyDRn5nqioKKkjEJVKEIBmzexo1swOALhyRYYtW9RYvVqB0FAn+vc38zWYiIjuCAthRERENzAYBGzerMGJEwpUr+7EPfeYUa8e33iRb6tTpw5ycnKkjkF0S0JDnRgyxDXe4l9/ybBxo6tVbmSkA/37mxEayhl4iYjo1rAQRkREBMBsBrZu1WD/fiV0OhF3323GAw9wsHuqOvbu3Yv69etLHYPottWo4cTo0a5xxc6fl2PNGi1yc2WIjbXj7rvNCArimGJERFQ6FsKIiMhv2e2uAZq3b1dBqQS6dzdjxgwzBEHqZETlz2azSR2BqNzUqePA//2fEQBw4oQCS5boYDAIaN3ahh49zNBoJA5IRERei4UwIiLyK6II7NunxNatGtjtQOfOFjzzTAEU/I1IROSTGje2o3FjA0QROHBAiffeC4LNBnTqZEXnzha+vhMRkQf+WiAiIr9w7JgC332ngcnkajEwaVIB1GqpUxFVHp1OJ3UEogolCECbNja0aWODwwHs2KHC668HQSZztfht29bGFr9ERMRCGBERVV3nzsmxcaMGeXkyNG5sx2OPGREYyDFkyD+1aNGCg+WT35DLgcREKxITrbBYgJ9/1uDVVzXQakX06WNG06Z2FsWIiPwUC2FERFSlZGe7ZhW7dEmOOnUcGDLEhOrVOasY0enTpxEcHCx1DKJKp1YDffqY0aePGUajgO++02CmXGoZAAAgAElEQVT9ei1CQkT062firMBERH6GhTAiIvJ5BoOAjRs1OH1agbAwJ/r1MyEyksUvoutlZ2ezEEZ+T6cTMWiQa0bg3FwZvv1Wg5Ur5ahd24kBA0wIC+PvDiKiqo6FMCIi8llpaUp8+60GarWIe+81Y8gQk9SRiIjIR1Sv7sRDDxUCAC5elGHNGi1yc2Xo1s2Czp2t7DpJRFRFsRBGREQ+pbBQwFdfaXH6tALNm9swZQoHvSciojsTEeHEuHFGOBzAzz+r8fLLeoSHOzBoELvXExFVNSyEERGRTzh6VIGNG7WQyYABA0wYNqxQ6khEPiUhIQH5+flSxyDyanI50LOnBT17WnDhggyffhqA/HwBPXta0L49W4kREVUFLIQREZHXMpmAr7/WIj1diSZNbJgwoQBardSpiHyTwWCQOgKRT4mMdGLCBAPsduCnn9SYO1ePqCg7Bg40oVo1zkBMROSrWAgjIiKvk56uwIYNropX//4mDB7Msb+I7tSJEycQGxsrdQwin6NQAL17W9C7twXnz8uxYoUORqOAXr3MSEiwsZUYEZGPYSGMiIi8gtkMfPutFkePKhEba8OTTxoQEMBP3ImIyHvUqePApEkG2GzA999r8PLLWtSta8d995mg1/N3FhGRL2AhjIiIJHX6tBwbNmhhswm45x6Te1p7IiIib6VUAv36mdGvnxkZGXJ89JEOFouAvn3NaNnSJnU8IiK6CRbCiIio0lmtwObNGvzxhwoxMXY8/rgRgYH8JJ2oIsXExEgdgahKio52YMoUAywW1++2DRu0iIlxtRLT6fi7jYjI27AQRkRElebsWTm++EILi0XA3Xebce+9+RxbhaiS1KpVCzk5OVLHIKqy1GogOdmM5GQzTp2S4z//0cFud7V2bt7cLnU8IiL6BwthRERUoex24IcfNNi3T4U6dewYM8bIcVSIJLBz504Olk9USRo2dODppw3u8S/Xrw9Ao0Y2DBhghlbL34FERFJiIYyIiCqE0Shg9WotLl6Uo3dvM154ga2/iIjIv2g0wKBBrvEvT5xQ4J13AqHTiRg2rBChoU6p4xER+SUWwoiIqFzl5Mjw2WcBMJsFDB5ciHr1HFJHIiIiklzjxnZMn16Av/6S4dNP+XuSiEgqLIQREVG5yMiQY82aAOh0IoYP5yfdRN4mJCRE6ghEBKBGDScmTjS4W05fuKDAgAEmtGjB2SaJiCoDC2FERHRHDh5U4ptvtIiKsmPCBAMCAjj2CZE3atKkCQfLJ/IiOp2IRx4phM0GfP21Fl98ocVdd1nQrZuFQwkQEVUgFsKIiOiWiSKwdasav/2mRqtWVjz3XD4U/I1C5NWOHz+OsLAwqWMQ0Q2UStc4YgMHmvDrr2rMmaNH8+Y23HuvCUql1OmIiKoevm0hIqIys1qBDRu0OHpUie7dzUhJ4QD4RL4iLy+PhTAiLyYIQLdurhZhaWlKzJsXhIgIBwYPNkGnY2trIqLywkIYERGVymAQsGpVALKyZBgwwIQHHjBJHYmIiKjKatHChhYtbDh7Vo5FiwKhVrtmmgwL4/ibRER3ioUwIiIqUXa2awZIu901s1WdOpzZioiIqLLUq+fAtGkFyM11/T42GgU88EAhYmL4+5iI6HaxEEZEREWcOiXHunUB0OudGDnSiOrV2SWDyNd17NiRg+UT+ajq1Z0YP94Ak8k10+SnnyrQr58JrVtzpkkiolvFQhgREbnt3avE5s1a1K9vx6RJBdBqpU5EROUlKysLcrlc6hhEdAe0WhGjRhXCbgc2btRgwwYtunSxoEcPzjRJRFRWLIQREfk5pxPYskWNnTvVSEiw4vnn88H3ykRVz5kzZxAbGyt1DCIqBwoFkJxsxoABZmzfrsLcuXo0aWJDcrIJKpXU6YiIvBsLYUREfsrpBL75RoMDB1To1YszQBIREfkaQQC6dLGiSxcrjhxR4PXXg1CnjgNDhhSyIEZEVAIWwoiI/IwoulqAbdumRv/+ZgwYkC91JCIiIrpDcXF2xMUVID1dgXnz9Gjc2IaBA01Q8B0fEZEHmdQBiIio8mzbpsLMmXqo1UBqaj7atbNKHYmIKknjxo2ljkBElaBRIztefDEfjRvbMXeuHl9/rYHTKXUqIiLvwc8HiIj8wL59SmzYoEWnTlbMmsUukET+KDAwEPn5bAFK5C9atLChRQsbdu9WYdYsPRITLejZk4PqExGxRRgRURV25IgCs2bpcf68AjNn5qNPHzP/ACbyU/v27ZM6AhFJoH17K1JT86FUAjNn6rF9OwcPIyL/xhZhRERV0KlTcnz+uQ4NG9owY4brj18iIiLyT4IAdOtmQdeuFnz3nQYzZ+qRnGxCmzY2qaMREVU6FsKIiKqQ8+flWLkyALVrOzBtWj40GqkTERERkbcQBODuu83o1cuMr7/W4uuvtXjwwUI0a2aXOhoRUaVhIYyIqArIypJhxQodgoOdeOopAwICRKkjEZGXqVmzptQRiMhLyOXAffeZ0K+fCWvXBuCLLwIwbJgRDRo4pI5GRFThWAgjIvJhubkCli/XQaEAnnjCgOBgFsCIqHgNGjRATk6O1DGIyIsolcCwYYUwm4HPPw/A6tVyjBxZiKgoFsSIqOpiIYyIyAcVFAhYsUIHqxV4+OFChIZyXnQiurm0tDRERERIHYOIvJBGA4weXQijUcDKlQEoKJDh4YeNqFmTf18QUdXDQhgRkQ8xmVx/oOblyTBypBG1a/MPVCIqG6PRKHUEIvJyOp2IsWONuHpVwMcf62C3Aw8/bET16mxxTkRVBwthREQ+wGIBVq0KwIULcgwfXoh69dhlgYiIiCpGcLCICRMMuHJFhuXLdVCrgZEjjQgKYkGMiHwfC2FERF7M4QDWrdMiPV2JwYML0agRZ3UiotujVCqljkBEPiY01IkpUwy4eFGGhQsDERrqxEMPGTkrNRH5NBbCiIi81J49Knz9tQb332/C4MEmqeMQkY9r27YtB8snotsSEeHE9OkF+PNPOebN06N9eyv69DFDEKRORkR062RSByAiIk+ZmcDLLwchM1OOWbPy0bKlTepIRFQFnD9/XuoIROTj6td3ICUlHwEBTsycqUd6OttVEJHv4SsXEZGXMJuBZct0UKlkmDzZAJ2O43AQUfnJzMxEbGys1DGIqAro2tWKjh2t+OSTAPz4oxzDhgmoVo1/txCRb2AhjIhIYqIIfPedBr//rsLo0UYkJGiRk8M/JomIiMh7KZXA6NGFEMUAzJsXiDp1HBgypBByudTJiIhujl0jiYgklJ6uwMyZegQEiJg5M5+zQRIREZFPqVEDmD69APHxNqSm6rF7t0rqSEREN8UWYUREEvj7bwEffRSI0FAHXnopH5zMjYgqWnx8PMxms9QxiKiKio+3oXlzGzZs0GDuXD0efdSAiAin1LGIiIpgIYyIqBI5HMCqVQE4f16Oxx4zIjSUfyASERFR1SAIQHKyGb16WbBkiQ4KhYhRo4zQaqVORkT0P+waSURUSX7/XYXUVD3i422YPr2ARTAiqlSHDh2SOgIR+YmAABETJhhwzz1mzJ+vx7ffaiBy+FMi8hIshBERVbALF2SYO1ePy5dlmD07H/HxNqkjEREREVW4unUdSEnJR1CQEzNn6nH8ODskEZH0+EpERFRBTCZg+XId7HYBTz9dgIAAfhRKRERE/icx0YqOHa345JMAfPONFo89ZkC1avy7iIikwUIYEVE5E0Vg0yYN9u5VYfRoI+rW5UyQRCS9qKgoqSMQkR9TKIBRowpx5YoMixcHIjLSgaFDCyGXS52MiPwNu0YSEZWj48cVmDlTj6AgJ1JS8lkEIyKvUadOHakjEBEhNNSJZ58tQMuWVqSm6rFrl0rqSETkZ1gIIyIqB3l5AubPD8Lvv6uQkpKPxESr1JGIiDzs3btX6ghERG7Nm9sxe3Y+srJkmDNHjwsX+NaUiCoHu0YSEd0BUQRWr9bizz8VeOwxI2eCJCKvZbNxog4i8i6CACQnm9GrlwVLl+ogl4sYM8YIpVLqZERUlbHsTkR0m86elSMlRY8GDeyYPr2ARTAiIiKi2xAQIGL8eAOSksxITQ1GWhorYURUcdgijIjoFjkcwMcfB8BgkOHFF/OhVkudiIiodDqdTuoIREQ31bChA3PmXMXKlQH46Sc1xo41QKuVOhURVTUshBER3YITJxRYsUKH4cONiIuzSx2HiKjMWrRogZycHKljEBHdlEwGPPxwITIz5Xj1VT3uuceMDh049ioRlR92jSQiKgOrFfjgAx1+/VWNWbOusghGRD7n9OnTUkcgIiqzqCgHZs3Kx4ULcsybF4SCAkHqSERURbBFGBFRKQ4eVGLtWi0eecSIBg0cUschIrot2dnZCA4OljoGEVGZCQJw//0m/PWXDG++GYTOnS3o2dMidSwi8nFsEUZEVAKTScBbbwXiyBElZs/OZxGMiIiISAI1ajiRkpIPm03A3Ll65ObybSwR3T62CCMiKsbOnSps2qTB2LEGREZyNkgiIiIiqd19txkdOliwaFEg4uJsuPdeMwT2mCSiW8RSOhHRdQoKBMybF4TLl+WYNSufRTAiqjISEhKkjkBEdMeqVRPx/PMFqFbNidRUPS5f5ltaIro1bBFGRPSPLVvU2LlTjXHjDAgLYwGMiKoWg8EgdQQionLTtasVbdva8MEHOkRGOjB4sImtw4ioTFg+JyK/l5srw9y5ejgcAl56KZ9FMCKqkk6cOCF1BCKichUQIOLppw1o0MCOlBQ9zp6VSx2JiHwAW4QRkd8SRWDDBg2OHlVi4sQCBAeLUkciIiIiolvUtq0N8fE2fPhhIHQ6J0aOLIScNTEiKgFbhBGRX7p0SYbUVD2qV3fi+edZBCMiIiLyZWo1MH68Ae3aWTFzph4nTrDNBxEVj68ORORXRBFYtUqLS5fkmD69AAEBLIARkX+IiYmROgIRUYWLi7MjNTUfy5bpsHWrGo8+aoRKJXUqIvImbBFGRH7jzz/lSEnRo1EjO6ZMMbAIRkR+pVatWlJHICKqFAoF8NhjRvTubcacOXocPKiUOhIReREWwoioyhNF4NNPA7BxoxYvvpiPNm1sUkciIqp0O3fulDoCEVGliolxYNasfBw5osT77wfCbpc6ERF5AxbCiKhKy80VMGuWHrGxNowfb4BaLXUiIiIiIqosMhkwYkQh+vQxITVVj3PnOIo+kb/jGGFEVGVt367CTz9pMGUKB8MnIiIi8mcNGzrw0kv5WLgwEDExdiQnm6WOREQSYYswIqpybDbg3XcDcemSHC++mM8iGBERgJCQEKkjEBFJSq0GpkwxoFo1Ea+8EgSDQZA6EhFJgC3CiKhKOXtWjv/+V4dHHzUiJsYhdRwiIq/RpEkT5OTkSB2DiEhy3bpZEBdnw7x5QRg0yIRWrTh+LJE/YYswIqoy1q/XYv16LVJS8lkEIyK6wfHjx6WOQETkNcLCnJg1Kx9//KHEhx/q4HRKnYiIKgsLYUTk8woKBLz8chBCQx2YPNkAlUrqRERE3icvL0/qCEREXkUQgFGjCtGxowUzZ+px+TLfHhP5A3aNJCKftn+/El9+qcVTTxkQGsqP8oiIiIjo1sTF2TFjRgHefTcQrVpZ0bu3RepIRFSBWPImIp/kcACLF+tw5IgSs2blswhGRERERLdNqxXx7LMFcDgEzJ8fBJNJ6kREVFFYCCMin3PpkgwzZ+rRpYsFI0cWQuCEP0REperYsaPUEYiIvF7fvmaMGGHEyy/rcfw4O1ARVUUshBGRT/nuOw1WrtThhRfy0ayZXeo4REQ+IysrS+oIREQ+ISLCNZD+L7+osXJlAERR6kREVJ5YCCMin2AyCZg3LwgA8MwzBdBqJQ5ERORjzpw5I3UEIiKfIZcDY8ca0bSpDampeuTm8q0zUVXBtp5E5PWOHlXg008DMH68AbVrcywwIiIiIqocCQk2xMba8c47QejWzYzERKvUkYjoDrGsTUReSxSBFSsCsH27GrNm5bMIRkRERESVTq8X8eKL+cjJkePttwNhZS2MyKexEEZEXunKFRlSU/WIj7fh8ceNkMulTkRE5NsaN24sdQQiIp82cKAJyckmzJ6tR0YG/zgl8lXsGklEXufXX9XYtk2FZ54pQGAgRyclIioPgYGByM/PlzoGEZFPq1/fgZkz8/HBB4GoXduB++83cQZzIh/DFmFE5DWcTuD99wNx5YoMM2awCEZEVJ727dsndQQioipBqQQmTjQgPNyBV18NgtksdSIiuhUshBGRV7h6VUBqqh7du5sxcKBJ6jhERERERDfVpYsVo0cbMXt2MC5c4FtrIl/Bn1Yiklx6ugLz5wdhypQCNGtmlzoOEREREVGZREQ4kZJyFStW6LBjh0rqOERUBiyEEZGkNm3SYPNmDWbNykdICLtCEhFVlJo1a0odgYioStJogOeeK0BGhgLLlwdA5J+0RF6NhTAikoTDAbz3XiCcTmDSJANnhSQiqmANGjSQOgIRUZUlCMCIEYVo2tSOl1/Ww8SRPoi8FgthRFTp/v7bNR5YUpIZ/fpxdFEiosqQlpYmdQQioiqvfXsrxowxYM6cYGRm8pNeIm/EQhgRVarjxxVYsCAIU6cWoEkTjgdGRFRZjEaj1BGIiPxC7dpOzJx5FR9/HIDt2zluGJG3YSGMiCrNxo0a/PCDBqmp+ahWjYMnEBEREVHVpFYDzz9fgHPnFFi2jOOGEXkTFsKIqMI5HMA77wRCEICJEzkeGBGRFJRKpdQRiIj8zrBhhYiLs/0zbpggdRwiAgthRFTB8vJc44H16WPGPfdwPDAiIqm0bdtW6ghERH6pXTsbHnvMgLlz9Th/np8IE0mNhTAiqjDHjinw5ptBmDatAI0bczwwIiIpnT9/XuoIRER+KzzciZSUq/jkkwD89hvHDSOSEgthRFQhvv5ag59+co0HFhzMQRGIiKSWmZkpdQQiIr+mVgPPPVeACxfkWLqU44YRSYWFMCIqVw4H8NZbgVAqgfHjDZDxVYaIiIiIyG3oUBPi422YO5fjhhFJgW9Riajc5OUJmDlTj3vuMePuuzkeGBERERFRcdq2teGJJwyYM0ePc+c4bhhRZWIhjIjKxZEjrvHAnn22AI0acTwwIiJvEx8fL3UEIiK6Tq1aTqSmXsXnnwfg11/VUsch8hsshBHRHfvqKw1++cU1Hphez8EOiIiIiIjKQqUCnn22AJcuyfDRRzqOG0ZUCRRSByiLzMxMLFmyBOnp6dDpdOjRowcefPBByG4y+NDq1auxdu3aYtcNGzYMAwcOBAC8//77+OWXX4ps8+abbyIyMrJ8ToCoirLbgXffDURcnA1PPmmQOg4REd3EoUOHEBsbK3UMIiIqxpAhJuzfr8ScOXpMm1aAgABWxIgqitcXwgwGA+bMmYOoqCg8++yzuHz5Mj7++GOIooihQ4eWuF/Pnj3RqlUrj2V79uzBV199hdatW3ssj4yMxLhx4zyW1ahRo/xOgqgKMhoFvPZaEEaNKkTDhuwKSURERER0J9q0sSEqyoE5c/R46qkChIc7pY5EVCV5fSHshx9+gNVqxdSpUxEQEIAWLVrAZDJhzZo1GDBgAAICAordLzQ0FKGhoR7L1q1bh8jISNSvX99juVqtRqNGjSrqFIiqnOxsGd56KwhPP12AsDD+giYiIiIiKg81azqRkpKPV18NwsiRhYiN5QfOROXN68cIO3jwIFq2bOlR8OrcuTOsViuOHj1a5uMYDAakpaWhc+fOFRGTyG+cOqXAe+8F4sUX81kEIyLyIVFRUVJHICKiMtBqRcycmY8vv9Ri1y6V1HGIqhyvbxF24cIFxMXFeSwLCwuDWq3GxYsXy3ycXbt2weFwoFOnTkXWZWZmYtSoUbDZbGjQoAGGDRuGZs2a3XF2oqpm924Vtm5VIyUlHwqvf/UgIqLr1alTBzk5OVLHICKiMpDLgWnTCrBkiQ5//SXDvfeapY5EVGV4/VtZo9EInU5XZLlOp4PBUPbBubdv347o6GhERER4LI+OjkZsbCyioqKQn5+Pr7/+GnPmzMGcOXPQsGHDIsfZsmULtmzZAgB47bXXEBYWdotnRP5KoVD49P3y5ZcCzp0TMG+eE4LA6Z0rkq/fK1S5eL9QWe3btw8JCQlSxyAfwdcWKiveKxVr+nRgzRoZVq/WY9w4JwRB6kR3hvcLeQOvL4QBgFDMT7soisUuL05eXh6OHj2KESNGFFl3zz33eDxu06YNpkyZgi+++ALPPvtske2TkpKQlJTkfsxPVqmswsLCfPZ+Wb48AMHBIoYPN+HKFanTVH2+fK9Q5eP9QmVltVp5r1CZ8bWFyor3SsXr3h3Yvl2FGTPUmDy5AHK51IluH+8XKqsbGzGVJ68fI0yn08FoNBZZXlhYWGxLseLs3LkTAIrtFnkjlUqF1q1bIyMj49aCElVBTifwxhuBiImx4777TFLHISIiIiLyS507W9Gvnwlz5uhh4p/lRHfE6wthkZGRuHDhgseynJwcWCyWMlcIt2/fjiZNmtxSE8yytjYjqqrMZmD2bD369jUjMdEqdRwiIrpDZf0AkYiIvFOTJnaMHWvAnDnByM3l+1Wi2+X1hbBWrVrhjz/+gOm6sveOHTugUqnKNKB9dnY2Tp48WebZIq1WKw4ePIiYmJjbzkzk6/LyBMyeHYwnnjCgaVNO2UxEVBW0aNFC6ghERHSHatd24vnn8/HGG0E4d86H+0gSScjrC2G9evWCUqnE66+/jrS0NGzZsgVr1qxB//79ERAQ4N5u4sSJWLRoUZH9d+zYAblcjg4dOhRZV1hYiJSUFPzwww84dOgQduzYgVmzZiE3NxcDBw6s0PMi8lbnz8vx+ut6PPdcPiIinFLHISKicnL69GmpIxARUTkIChKRkpKPFSt0+OMPpdRxiHyO1w+WHxgYiJSUFHz00Uf497//DZ1Oh379+mHw4MEe2zmdTjidRd+0b9++Hc2bN4dery+yTqFQQK/X44svvsDVq1ehVCrRqFEjpKamokGDBhV2TkTe6vBhBdavD8DMmVehUkmdhoiIylN2djaCg4OljkFEROVApQJeeCEf778fiCtXZOjRwyJ1JCKfIYiiKEodwpddvHhR6gjkI7x9hpSff1YjLU2JCRMMkHl9W9GqzdvvFfIuvF+orE6ePInY2FipY5CP4GsLlRXvFel9/rkWggAMGeL9o+jzfqGy8utZI4mo4q1ercWFC3JMmsQiGBERERGRLxk61ITQUCcWLgwEm7kQlY5veYn8mCgCixbpUK2aEyNGFEodh4iIKlBCQoLUEYiIqIIkJVnQqZMFr74aBJtN6jRE3o2FMCI/ZbMBr70WhH/9y4revTmmABFRVWcwGKSOQEREFahVKxuGDy/ErFl6FBQIUsch8loshBH5IYNBwOzZegwdWog2bfiRERGRPzhx4oTUEYiIqILVr+/A008b8Oqrely+zLf7RMXhTwaRn8nKkuHll/WYPNmA6GiH1HGIiIiIiKgcVa/uxEsvXcWiRYFIT1dIHYfI67AQRuRHTp5U4P33A5GSko/QUKfUcYiIiIiIqAJotUBKSj42bNBi1y6V1HGIvAoLYUR+4uBBJdau1SIlJR9aLaeTISLyNzExMVJHICKiSiSXA1OnFuDIESW+/14tdRwir8FCGJEf2L1bha1b1XjuuQIo2DqaiKjKWbBgAZo3b47JkycjMjKy2K+6desiMjISkydPLvYYGRkZiIyMRHR0NC5evOixbvPmzYiMjPRYnpCQUOzzXCu4lbT++q9169YVm2XcuHG47777il338MMPY8SIEe7Hoiji008/Re/evREbG4u4uDj06dMHr7zyinsbs9lcYoYePXq4t3vllVc81rVu3RqPPPII0tPTS/kOACtWrPDYt2XLlhg5ciSOHj3q3uann37CE088gXbt2qFhw4bo2bMnVqxYAaeTrbSJqGIIAjBmjBE5OXJs2KCROg6RV+BbYqIq7tdfVTh8WIXJkw0QOHkMEVGVNnnyZIwcOdL9+LXXXkN+fj5eeeUVnD9/HnXq1EFoaOhNj2G1WvHBBx9g9uzZpT7f/fffj1GjRnksk8lcn7MuX74cFsv/ZiUeNmwYBg4ciMGDB7uXRUdHl+m8bmbBggV47733MH78ePzrX/+CyWRCWloaNm7ciBkzZnhsO2HCBPTu3dtjmVar9XgcGhqKpUuXQhRFnDt3Dq+//jqGDRuGrVu3Qq/Xl5rnyy+/hEwmw+XLl/Hmm2/iwQcfxC+//IKwsDCsWLECMpkMzz//PGrVqoVdu3bhpZdewsWLF/Hcc8/d8bUgIirJ8OGFWLdOi7VrtXjgAZPUcYgkxUIYURW2ZYsaf/6pwJNPGqSOQkRE5WzBggV444030K1bNyQkJAAA6tevj/r16+Pxxx9HXl4erl69imPHjiEoKAhxcXGIjY117//OO+/grbfewo8//uhRkOrYsSM++eQTTJo0CWFhYfj111/x9ttvAwA6dOiA6tWro23btrBYLAgPD3c/d9++fZGWlgYAUCqVCA4ORtOmTXHvvfdi8ODBkMvlqF27NhISEvDDDz9g9OjR7uesVq0amjZtimeffRbt27cv8ZxfeOEF/PjjjwCAqKgohIeH48qVK3jwwQfxzDPPuLfr3bs36tevj4EDB+LYsWPugtypU6cwfvz4IgWtRYsWYe7cuWjZsiWUSqX7nNq2bYvw8HA8+OCDaNq0KQBAoVCgTp06uPfeezFp0qQihbTWrVtD8U/z67i4OHTu3BmPPvooLl26hOzsbOj1eshkMowdOxZTp05FQUEBFi1ahHfffReAq5Co1+vdLcZGjRqF4ODgkm8EIqIyuv9+E77+WoNPPgnAiBGFUschkgy7RhJVURs3anDpkhyPPWaUOgoREVWgX375BZcvXy52Xb169RAQEFCkZdSFCxfwzjvvYMKECR9X5dkAACAASURBVEVaZY0aNQoajQaLFy/G22+/jWHDhkGtdo0ts2jRIsyaNQuCICAvL6/I8/bo0QMbNmzAmjVr8Nr/s3ff4VGX+frH76kpkwQCoZdIEZEWEQQhFhTBFRFQQcSyll0LupwVcT3u8Sy7+8Pd1bWgAqtHsQuCYMOOiGIBERCliQgoUkMVSDJJpnx/fyCRkJnJJJmZ75T367r2WjOZmdwTvsBw53k+z733qmPHjpo4caIuvfRSGUb1+ZTTp0/XvHnzNHnyZPl8Pl1++eXauHFjyNebnp6unj176vXXX9cdd9yhiooKffDBB/J4PJX3GT9+vP70pz+pa9eumjZtmp5++mlJ0vLlyzVp0qRqz/n6669Lkr7//vtqObt06SJJOvnkkzVv3jy9/PLLGjVqlB5//HH97W9/C5l13759kqQff/xREyZM0OzZs3XfffcpIyNDl1xyifbv369u3brJ6/UqPz9f8+bN02uvvaZHH31UZ5xxhqZPn67Bgwdry5YtIb8OAITroovK1KSJT888k2l2FMA0rAgDktCrr2bI65Wuvpqf9ABAMmvYsKFatGihpUuXBvy8xWJR+/bttXTpUn3++eeVK8ImTpyoFi1a6NZbb632mKysLP3ud7/Tf/7zH5WVlekvf/mLTjjhBC1btkw9e/ZUy5YtNXz4cHXr1k1Op1Ner1fSkVldjRo1Us+ePSu3R15wwQW69NJLdemllwacg9WtWze1adNGktSnTx/16tVLs2fPDvmarVarGjRooN69e6t3796aMmWKfvzxRz344IO6+eab9f7772vOnDl6+umndf7550s6MiNMOlKQtW3btjKzdGQ22po1a3TGGWfos88+qyz9jtq+fbskqVmzZpUrxfr27at9+/bpueee0z333COHw1EtZ3l5uW666SZJ0o033qjLL7+88nNDhgzRFVdcIafTqeXLlysjI0Mul6vy+SVp4MCBuvrqqzV06FDddttteu2110J+XwAgXIMHl+uTT9L02GMu3XxzCeNTkHJYEQYkmZdeypTNZuiyy9j7DwDJzmKxaNy4cdq0aZN8Pl/A+2RlZemqq67So48+qkOHDmnhwoV677339M9//rNa6XPU9ddfr4qKCuXk5FSWOcdLS0vTSy+9pPz8fOXn52v16tWaO3euxowZU+V+PXv21JgxY+TxeIJmlI6Uem3bttXWrVvDfPVHHJ1lNmXKFHXr1k1//vOflZ+frzPOOKPafe+++25dffXVlZnz8/M1btw42Ww2PfTQQ3I6nSorK5PX65XX69WmTZv017/+Venp6WrZsmWV5+rRo4e8Xq927txZ5Xafzyev16sXX3xRO3fulN1uryzkjtWvXz/9+OOPmj17tjp16hTwtTVv3ly33367vvzyS61fv75W3xcACOWss8rVs6dHjzySpQALdoGkRhEGJJFnn81Ubq5fw4eXmR0FABAjF110kXJzc+V2B/8ByF133SWv16tJkybpL3/5iy6++GKdeeaZQe/fsGFDOZ1Oud1ulZQE32I/atQovfPOO3rnnXfUsWNHnXfeeVVOazzqrLPOkiTt2bMn6HNVVFSoqKhITZs2DXqfQLKysiQdOShg9OjRKi8v15YtWzR06NBq35M//vGPlXmP/u/QoUMqLCysPDHT7XZXlmRnnXWWvv/+e7Vs2bJyldtRW7dulcViUZMmTarc3r59e+Xn52vixImyWCx65JFHqsxmO2r//v266aabdMopp6hz585BX9/R791XX31Vq+8LANTk9NMrdPbZ5XrggWxxeC1SCUUYkCT+7/9cys/36Te/oQQDgFRitVrVp08feTwebdq0KeB9GjRooJtvvlkzZ87UgQMH9Ne//jXkc3q9Xrndbvn9fj3//POVtxuGUblayjAMNWnSRAUFBSooKFBmZqZyc3PVoUOHas/XokULSapWqh1dPVVUVKT//d//1cGDB3X++efLbreHXD1msVjk8Xi0fv16/e1vf1NeXp5uuukm3XDDDZKOnFC5YcMGzZ07t8rjWrduXZm3oKBAVqtVW7Zs0fDhwyWpsrD661//qnnz5ukf//iHSkpKtHPnzsrX7na7tWjRIk2fPl1nn312tWH5b775pt59910VFhaqR48eGjFiRLX8brdbv/3tb2WxWPTUU09VK9kCfe9ClYgAUFc9e3p0wQVu3XtvtkL8sQskFYowIMEZhjRlSpa6d/fonHPKa34AACDpnHzyybJarZo6dWrQ+wwaNEjNmzfXpZdeWm0VUzAFBQV64oknKmdsvfXWW5WrpYqKirRs2bKwnifQoHxJKiwsVH5+vk499VTNmzdPkyZNUmFhoRo1ahS0+CktLdVHH32kE044QQMHDlRxcbGeeOKJKidBnnvuucrMzKxx8P7rr78up9OpCy64QJLUpk0bWSwWrVq1Sr169dK1116rv//973K73ZoxY4by8/PVsWNHXXHFFSooKNC///3vas/Zo0cP9ejRQ+np6bIEGLzj9Xp10003aevWrZoxY4YaNWoUMmOw7x0AREq3bl6NGuXWP/6Ro2NGKAJJi2H5QAIzDOmRR7JUWFiu007z1PwAAEBSslqtSk9P16uvvqoJEyYEvZ/dbg842D3Q/fLy8tSuXTutXr1aM2bMkCSdeeaZeueddyRJF154Ydj5jp4u6XK5qtz+zDPPqFmzZmrYsKFat24tm80m6cgw+unTp2vdunWVpzZK0uHDhyUdGXp//vnnq1mzZrJarcrLy5N0ZKC9JH377bdyu90hCz/DMDRv3jz169dPknTw4EF5PB6lpaXp/fffl9vtVkZGhi6//HLdfffdcrlcmjlzphwOh9q0aaPs7OyQr7l58+ZatWpVtdvvuOMOLVmyRHPmzFF+fn7I55B+/d6FW14CQF2ceKJXV19donvuydHddx9SGH9VAAmLIgxIUIYhPfxwls48s1y9e1OCAUCqczqdysnJ0bRp0wJ+/mjhc9TIkSPldDo1c+bMgPfv27evli9frpEjR1bep2HDhlWGxhcXF2vFihWSjqzU2rdvn1asWKEePXpUKdwWLVokqXqZc/LJJ6tp06Y68cQTddddd+mWW26RJA0ePFgFBQW68sorNX78eLVv315bt27Ve++9J4fDoVtvvbVyS+LJJ5+sYcOG6eyzz1ajRo3UsmVLPf7448rJydEll1xS5ett2bKlMu/atWu1Y8cO7dixo0rZdtT8+fM1fPhw2Ww2NW7cWLt27VJFRYUKCgpUXl6utm3bVsl8vP79+2vGjBlau3atunbtKkm6//77NWfOHN1+++3y+XyVWfbu3RvwVM1jv3fHnigJANHQrp1P1157pAz73/+lDEPyoggDEpBhSJMnZ+nss8vVqxclGACkJMPQsUd9WSwW3XTTTbr33nvVvXv3aiu/ioqKqnzs9XorV2AF8vvf/16XXHKJhg0bJrvdLm+A/TLffvuthg0bVvnxxo0btXDhQq1cubJy6P3KlSs1a9YsOZ3OgF/PMAz5fL4qRZDdbtfMmTN133336ZFHHtHevXuVm5urli1byuPxVJnLdfvtt2vBggWaP3++Dh48qMzMTJWWluree++tdtLj1KlTq20fTUtL04svvihJmjFjhhYtWiSXy6U33nijcnZYTk6ODh8+rP/85z965plnAmY+3gUXXKBWrVrp73//u2bMmCGHw1FZaj300EN66KGHqty/Xbt21Z5j165deuihh9S3b1+ddNJJQb8WAETKCSf4dP31JZo06UgZ5nSanQiIPIowIMEYhvTQQ9k699wy9exJCQYAqcq6b58s5UdmQ06YMEETJkyQ2+3WlClTtHz5cvXr109PPvlk5f03b95c5fGvv/565X+Xlpbq7LPP1sSJE3XOOedIkvr06aM77rhDDzzwgIYNG6YhQ4Zo69atWrNmjRYvXiybzaY///nPuvnmmyUdKX7y8vJ02223acuWLVq5cqU+++wzzZw5U926ddOsWbOUmZlZ7XWkp6dr+/bt1W7/6aefNGPGDD3zzDMaOHCgJOnuu+/Wp59+WuV+N9xwQ+WQ/KNuu+02TZw4Ud9//70GDBig9PR03XPPPXr22WfVp08f/etf/9Kpp56qs88+W1OmTKl8XP/+/SVJ//rXv/TEE0/o4MGDatCggSwWiy6++GLdd999QTO/99576tevn+z2I2+v09LS9Nhjj+nKK6/UsGHDdO211+ovf/mL9u/fr/nz5+uVV17R119/rUaNGumOO+7QF198oRUrVsgwDB06dEhfffWVnn/+eWVkZGjy5MnVvj8AEC35+T79/ve/rgyjDEOyoQgDEohhSA88kK3Bg8tUUEAJBgCpyLlkiSr69ZNtyxZZKiqqfC4jI0M33HBDZWETrqMrnI4fzH7bbbepZ8+emj59uu666y4dPnxYDRs21CmnnKLHHntMQ4YMqXL/hQsXauHChbLb7WrQoIG6dOmi//f//p8uu+yysGaTBcoUatVVMJMnT1ZhYaFmzJihWbNmyePxKD8/X0OGDNGNN96oTz/9VPv27dPIkSMDPv7SSy/V1KlT9c4772jMmDFhfc2jJ2Aeq1evXnr//fc1ZcoU3X///dq7d6+ys7N12mmnVRuU/8MPP2jYsGGyWq3KyclRhw4d9Pvf/17XXHONGjRoUOvvAQDUR9u2Pt1ww68zw9LSzE4ERI7F4CiaetmxY4fZEZAg8vLytHfv3jo/3u8/UoJdcEGZunenBEtm9b1WkFq4XlKMYajJoEHa8/bbavT73yt94UIVLV4sXxhD17///nudeOKJMQiJZMCfLQgX10py277dpscfd+nuuw8pPb3+z8f1gnAdP94gkqxRe2YAEeP3S/ffn60hQ9yUYACQwmybN8vx7bdK+/BDOZcskSRlvPVWWI9lxhQAoLZatfLp5puL9Y9/5KiszOw0QGRQhAFxzu+X7rsvW0OHutWtW/VBxQCA1JH22WeSJNfzz8vqdkuS0sMswrKysqKWCwCQvFq18mvs2GLdc0+OfvmrB0hoFGFAHDtagg0f7lbXrpRgAJDqjhZhaV98UXmbc9Uq2X76qcbHrlixImq5AADJrWVLv2699cjKMLfbYnYcoF4owoA4ZRjSv/+drREj3OrShRIMAFKe36+0xYslSRZP1W3y6W+/bUYiAEAKadHCr3HjivWPf2Trl0OLgYREEQbEqUceydIFF5Tp5JMpwQAAkmPtWll//jng58KdEwYAQH00a+bX2LEl+te/cuTzmZ0GqBuKMCAOTZ/u0mmnVaiggMH4AIAjnL9siwz4ua+/lm3r1pCPb9q0aaQjAQBSUKtWPl11Van+/e9sGYbZaYDaowgD4sysWRlq29arwsIKs6MAAOJIWogiTKp5e2SHDh0iGQcAkMI6dvTqwgvL9PDDWZRhSDgUYUAcefPNdDmd0uDBbLoHAByjokLOpUtD3qWm7ZGrVq2KZCIAQIrr0cOjvn0rNH26y+woQK1QhAFx4uOP03TggFWXXMKZxACAqpwrV8paw5n1zpUrZdu2LejnS0pKIh0LAJDi+vevUH6+Vy+9lGl2FCBsFGFAHFi2zKl16xz67W9LzY4CAIhDNW2LPIrTIwEAsTZ4cLnS0w3Nm5dudhQgLBRhgMnWrrVr0aI0jR1bbHYUAECcCjUo/1ihtkc6HI5IxQEAoIqLL3br55+t+uijNLOjADWiCANM9MMPNr32WqZuv/2wLBaz0wAA4pGlpETOr74K677Or76Sdfv2gJ/r3bt3JGMBAFDFb39bqvXr7Vq2zGl2FCAkijDAJDt3WvXMMy79938fkpXfiQCAIJxLl8ri9YZ9/4wg2yO3bt0aqUgAAAR0880lWrQoTWvX2s2OAgTFP78BE+zfb9HUqdn6858PiZ0qAIBQwp0PdlSw7ZHbQgzSBwAgEiwW6fbbD+u11zK1ebPN7DhAQBRhQIyVlFj0wAM5uuuuQ8rIMDsNACDe1bYIc65YEXR7JAAA0Wa1Sv/934f07LMu7dxJ5YD4w1UJxFB5ufTPf+ZowoTDys42zI4DAIhz1v375Vi7ttaPy3jnnSikAQAgPA6H9D//c0hTp2Zr/35qB8QXrkggRrzeIyXYLbcUq3Fjv9lxAAAJwPn552Hf15ebW/nfgbZHdu/ePSKZAAAIR3q6dNddh3T//dkqLuZkMMQPijAgBgxD+utfbbr66hK1auUzOw4AIEHUZluk9eBB7Zk9W6UjRsjx9dey7twZxWQAANQsO9vQHXcc1r/+laOyMrPTAEdQhAFRZhjSww9naeRIvzp2pAQDAITvaBFm2O0qufzyoPczJFn8fjk2bdLP06Zp96efylJSUuU+q1evjmZUAAACatzYr1tuOax//jNHtTgEGYgaijAgyp580qXTT69Qz57MBAMAhM+2fbtsW7eq5KqrVPTllzr4wAMyrIHfulkkHbr9dqUtWSJJ8rVtK1/HjjFMCwBAcK1a+XXNNSWaONEmP1NiYDK72QGAZDZzZqbatfOqX78Ks6MAABKMpaREez78UN4TT6y8zUhPl6W0NOD9S8aOlWHjqHoAQHzq0MGnyy7z6+GHszR+fLEsjA2DSVgRBkTJG2+kKzPT0KBB5WZHAQAkIG+nTlVKMOlIERZUebmUlhb0061bt45UNAAA6uSUUwwVFlboiSdcZkdBCqMIA6Jg4cI0HT5s1YgRbrOjAACSSKgizFIRevVxmzZtIh0HAIBa69u3Qh07ejVjRqbZUZCiKMKACFu50qH16x266qrAW1cAAKizUEVYeegVyMuXL490GgAA6mTgwHK5XIbefjvESmcgSijCgAjavt2qt97K0NixxWZHAQAkofqsCPN4PJGOAwBAnY0Y4dZPP9n0zTcOs6MgxVCEARFSUmLRtGnZ+tOfDjH4EQAQFTXOCAMAIIHcfHOJ3ngjQzt3Uk0gdrjagAjw+6X77svW+PGHQ+1aAQCgXoyMjKCfq2lrpMvFYGIAQHyxWKQ//emQHn00W6WlrCZAbFCEAREwZUqWxowpVZMmfrOjAACSWH22Rvbo0SPScQAAqLeMDGn8+MO6775s+fnnFGKAIgyopzlzMtS1q0cnn+w1OwoAIMnVpwjbtGlTpOMAABARTZv6NXp0qaZNyzI7ClIARRhQD0uWOFVSYtF55zGXBQAQfSFnhJWVhXzs7t27I5wGAIDI6dLFq5NO8uiVV4KPAQAigSIMqKMff7Rp0aI0XXNNqdlRAAApoj4rwgAAiHeDB5fr4EGrvvzSaXYUJDGKMKAODh606MknszRhwmFOiAQAxAxFGAAg2V13XYk+/DBNW7bYzI6CJEURBtSS1yvdf3+27rjjkBwOs9MAAFJJfU6N7NWrV6TjAAAQcRaLNGHCYT3xRJYOHWLVASKPIgyopYceytZ115UoN9cwOwoAIMWEnBFWQxFWXFwc4TQAAESH0ylNmHBI99+fLS9nkiHCKMKAWnjhhUwVFparQwef2VEAACko5IqwGrZGfvfdd5GOAwBA1DRqZOiaa0r08MPZZkdBkqEIA8L00UdpcjoNFRYygwUAYI6QM8JqWBEGAECi6djRpz59yjVjRqbZUZBEKMKAMGzYYNfKlQ6NHu02OwoAIIUxLB8AkGrOOqtCVqv0ySdpZkdBkqAIA2qwb59VL76YqT/+kdkqAACThZoRVkMR1r59+wiHAQAgNsaMKdWyZU5t3Gg3OwqSAEUYEEJ5ufTgg9m6887DsnF6LwDAZCFXhJWVhXxss2bNIh0HAICYue22w3ruuUzt389JkqgfijAgCMOQ7r8/R7fcUqysLE6IBACYrz7D8pcsWRLpOAAAxIzNJt1552E9+GBOTYuggZAowoAgpk936Te/cat1a06IBADEh1ArwvhXAQAg2WVnG7rppmI9+GC2DNYqoI4owoAA3n03XU2b+tS7t8fsKAAAVOLUSABAqmvb1qfzzivXM8+4zI6CBEURBhxn9WqHNm+2a/jw0LNWAACItfoUYbm5uZGOAwCAKU47rUK5uX7Nn89Jkqg9ijDgGLt2WfXqqxkaO5YTIgEA8ac+RVjnzp0jHQcAANNcfLFbGzY4tGYNJ0midijCgF94PNKjjx45IdLK7wwAQBwKWYTVMCNs/fr1kY4DAICpbrmlWHPmZOrnnzlJEuHjn/vAL6ZOzdINNxQrI4OpiwCA+BTq1MiahuUfOHAgwmkAADCX1SqNH39YkyczPB/howgDJL33Xro6dfKqXTtOiAQAxC+G5QMAUFXDhoYuucTN8HyEjSIMKW/LFpvWrnXowgsZjg8AiHMOh4wg+/cpwgAAqaqgwCOn09CyZU6zoyABUIQhpZWVSf/3f1kaN+6w2VEAAKiZxRJ8VVgNWyP79esXhUAAAMSHK68s1XvvpWvfPmoOhMYVgpT26KPZuvXWYjn5wQEAIEEEK8JqWhFWVFQUjTgAAMQFi+XIvLCHH86Sj4k3CIEiDCnrjTfS1atXhVq14k9JAEDiCFqE1bAibPPmzdGIAwBA3MjKMnTFFaV68knmhSE4ijCkpI0b7frpJ7sGDmSeCgAgwdSxCAMAIBWcfLJXeXl+ffop234QGEUYUk5pqUXPPpupsWOLzY4CAECtGRkZgT9RxqEvAABI0siRbn32WZp27aLyQHVcFUgphiFNnpylceOKZbebnQYAgNqr69bIk046KRpxAACIS7fdVqwpU7Ll9ZqdBPGGIgwp5eWXMzRgQLmaNfObHQUAgDoJWoT5/Qr1bj8rKytakQAAiDsZGYauu65Y//kPf/+hKoowpIy1a+06cMCqwkJmqAAAElewIkwKfXLkihUrohEHAIC41bGjT+3aefXBB2lmR0EcoQhDSjh82KJZszJ1440lZkcBAKBeQhVhClGEAQCQii66qExff+3U1q02s6MgTlCEIekZhvTQQ9kaP75YVq54AECCC7kijJMjAQCo5r/+67AefzyLnxdBEkUYUsALL2RqyBC3GjViLhgAIPEFPTVSobdGNm3aNBpxAACIe2lp0k03HRmeD1CEIal99ZVDPp902mkes6MAABARdV0R1qFDh2jEAQAgIbRt61OPHhV6660QIwaQEijCkLQOHLDojTcydO21pWZHAQAgYuo6I2zVqlVRSAMAQOIYPLhcGzfatWkT88JSGUUYkpLfL02enK3bbz8si8XsNAAARE5dT40sKeHAGAAAbr21WE8/nSW32+wkMAtFGJLSU0+5NGqUWw0aGGZHAQAgokLOCGNYPgAAITkc0h/+cFgPP8y8sFRFEYak88UXTrlchrp3Zy4YACD51HVGmMPhiEYcAAASTosWfhUWVmju3OA/XELyoghDUtmzx6oPPkjXmDHMBQMAJKe6zgjr3bt3FNIAAJCYzjqrXLt327R+vd3sKIgxijAkDb9fevTRLOaCAQCSWsitkSGKsK1bt0YjDgAACeumm4r14ouZcrv5B2QqoQhD0njhhUyNHOmWy8VcMABAEqvj1sht27ZFIw0AAAnLZjsyPH/atCyzoyCGKMKQFDZutKmkxKqCAuaCAQCSW11PjQQAANW1aOFXhw5effqp0+woiBGKMCQ8r1d6+uks3XBDsdlRAACIupAzwjg1EgCAWhsxwq0PP0zXoUNskUwFFGFIeE895dK115aIw7AAAKkg5IqwsrKgn+vevXs04gAAkPAsFmncuGJNncoWyVRAEYaEtmaNXU6noU6dvGZHAQAgJkIWYawIAwCgTho39qt37wq9/36IlddIChRhSFjl5dKsWZm65ppSs6MAABAzIU+NDFGErV69OhpxAABIGoMHl+urrxzau5eqJJnxq4uE9dhjWbrxxhJZuYoBACkk5IwwhuUDAFAvf/hDsaZMyZJhmJ0E0UKFgIS0bJlTzZv71Latz+woAADEFKdGAgAQPdnZhgYPLtNrrwVfgY3ERhGGhFNSYtGbb6Zr9Gi32VEAAIi5us4Ia926dTTiAACQdAoLK/TDD3Zt305lkoz4VUXCmTYtS7feWiwLJ9sCAFKRwyEjyFyAUCvC2rRpE61EAAAknVtuOazHH8+S3292EkQaRRgSyqJFaerc2aNmzfjTCACQoiyW4KvCQqwIW758eZQCAQCQfDIypEsucWvGjEyzoyDCKMKQMH7+2aJPPknTsGFlZkcBAMBUwYqwUFsjPR5PtOIAAJCUevb06OBBqzZvtpkdBRFEEYaEMXVqtv7wh8NmxwAAwHRGRuABvpYyflgEAEAk3XhjsZ5+2iUf57QlDYowJIS3305Xv37lys3lDFsAAOqyIszlckUrDgAAScvplK66qlRPPcXfo8mCIgxxb88eq1avdmjgQI6EBwBAklSHGWE9evSIUhgAAJJb585e2WzS2rV2s6MgAijCENcMQ5o6NUt/+EOx2VEAAIgbQVeEhTg1ctOmTdGKAwBA0rv22hLNnOlSiL9qkSAowhDX5s7N0JAhZcrKYkskAABH1WVr5O7du6MVBwCApGezHZkX9sQTWWZHQT1RhCFubdtm07ZtNvXtG/xNPQAAqaguK8IAAED95Of71LixX8uXO8yOgnqgCENc8vulxx93aexYtkQCAHC8YKdGsl8DAIDoGjOmVPPmZai01GJ2FNQRRRji0gsvZOqyy9xBZwEDAJDK6rI1slevXtGKAwBAyrBYpLFjizVtGlskExVFGOLOxo02ud0W9ejhMTsKAABxqS5FWHExq6wBAIiEFi386tTJo08+cZodBXVAEYa44vNJzz7r0u9+V2J2FAAA4lZdZoR999130YoDAEDKGTasTB9/nK7Dh9kimWgowhBXZszI1JgxpXIwexAAgKCCFWEqL5cMTloGACDajm6R5BTJxEMRhrhRVGTVvn1Wde3qNTsKAABxLeiKMMOQvPw9CgBALDRp4lezZj6tXs1KjkRCEYa4MX26SzfcwJZIAABqEvTUSAXfHtm+fftoxQEAIGWNGVOql1/OkM9ndhKEwJZS6wAAIABJREFUiyIMceGTT5w65RSPsrLYzgEAQE2Cbo1U8IH5zZo1i1YcAABSls0mjR5dqlmzMs2OgjBRhMF0ZWXShx+ma8iQMrOjAACQEEKtCFOQFWFLliyJUhoAAFJbt25e7dpl0549VCyJgF8lmO6ZZ1y6/voSWThsAwCAsIRcERbi5EgAABAdN95YrCeecJkdA2GgCIOpfvjBJotFys9nQzUAAGGrw9ZIAAAQPdnZhrp39+jzz51mR0ENKMJgGsM4shrsuusYkA8AQG3UZUVYbm5utOIAAABJF11UpvffTw82pQBxgiIMpnnrrXQNHlymtDSzkwAAkFhCFWHB3n137tw5SmkAAIAkWSzSddeV6Jln2CIZzyjCYIrDhy1atcqhM85g+wYAALUValh+sK2R69evj1YcAADwi3btfDIMacsWm9lREARFGEzx5JMu3XgjWyIBAKiLumyNPHDgQLTiAACAY1x3XYmeftolwzA7CQKhCEPMrVljV9OmfjVp4jc7CgAACSlkEcawfAAATJWeLg0cWK533w0xygCmoQhDTPl80qxZmRozptTsKAAAJKy6zAgDAACxc9ZZ5VqxwqniYovZUXAcu9kBwrFt2zY9/fTT2rBhg1wul84991yNGjVKVmvwHm/37t36wx/+UO32/v3767bbbqty27JlyzRr1izt2rVLTZs21ahRo9S/f/+Ivw4cKcFGj3bLxnZpAADqrC5bI/v166e9e/dGKxIAADjOjTcW68knXRo/vtjsKDhG3BdhxcXFmjRpklq3bq0777xTu3bt0gsvvCDDMHT55ZfX+Pirr75aJ510UuXHOTk5VT6/fv16Pfjggxo8eLCuu+46rVy5Uo888ohcLpcKCgoi/npS2Z49Vu3cadOVV7IaDACA+qjL1siioiLZ+EkUAAAx06yZX40a+bVunV1dunjNjoNfxH0R9sEHH6iiokITJkxQZmamevToIbfbrTlz5mjYsGHKzMwM+fiWLVuqU6dOQT//yiuv6OSTT9b1118vSerWrZu2bdumuXPnUoRF2BNPuDRuHE04AAD15nDIsNlk8fmqfy5IEbZ582adeOKJUQ4GAACOddVVpfrb33L0978fUohNbYihuP9l+Prrr1VQUFCl8CosLFRFRYXWrVtXr+f2eDxas2aN+vXrV+X2/v37a8OGDSotZeVSpHz+uVPdunmUk8OxGQAA1JvFEnRVWLCtkQAAIPZsNunSS916+eUMs6PgF3FfhG3fvl0tW7asclteXp7S0tK0Y8eOGh//n//8R6NHj9aNN96o5557ThXH/JS0qKhIPp9PrVq1qvKYVq1ayTCMsJ4fNauokN5/P13DhpWZHQUAgKRBEQYAQGI45RSPfvrJrn374r6CSQlxvzWypKRELper2u0ul0vFxcG32TkcDp1//vkqKChQRkaG1q5dqzfeeENFRUW68847Jany8cc/f1ZWVuXXPt6CBQu0YMECSdK9996rvLy8ur2wFPLww1aNH+9Xkyap/b2y2+1cLwgL1wpqg+sldVldLmnfvmq3u+x2pQe4JhwOhxo0aBCLaEgC/NmCcHGtoDZS+Xr5n/+RHnggU/fcE2CsAWIq7oswSbJYqh83ahhGwNuPys3N1e9+97vKj7t27aqGDRtq+vTp+vHHH3XCCScEfaxhGEG/7nnnnafzzjuv8mNOXwrtp59sOnQoQ7m5xUr1b1VeXh7XC8LCtYLa4HpJXU0cDjkC3O4+cECHAlwTOTk5XCsIG3+2IFxcK6iNVL9e8vMz9PbbPvXtG3ieJ351/M7ASIr7dXkulyvgyqzS0tKAK8VCOf300yUdGRYr/bry6/hZYEe/Xk2D+BGaYUhPPeXS9dczIB8AgIgLdnJkkGH5K1asiGIYAABQkxEj3Hr77fRgf1UjRuK+CGvVqpW2b99e5ba9e/eqvLy8zg3h0ZVezZo1k81mq/b8O3bskMViiWoDmQrefTdd555bHvR9OgAAqDtmhAEAkFgsFum3vy3Vc8/VblEPIivui7BTTjlF33zzjdxud+VtixcvltPpVJcuXWr1XF988YUkqX379pKOzMro1q1b5e3HPn+nTp1YEVYPbrdFy5c7dfbZvBkHACAajIzAp09RhAEAEL86dvSqrMyi7dvjvo5JWnH/nR80aJAcDoceeOABrVq1SgsWLNCcOXM0dOjQKkXVuHHj9Nhjj1V+/PLLL+v555/X0qVLtWrVKs2ePVvPPfec+vTpo/z8/Mr7XXrppVq7dq2effZZrV27Vi+++KJWrlypkSNHxvR1Jpvnn8/UdddV39IKAAAiI9iKsGD7LZo2bRrFNAAAIFzXX1+i559nVZhZ4n5YflZWliZOnKinnnpK9913n1wuly688EJddtllVe7n9/vl9/srP27VqpXefPNNffjhh6qoqFBeXp6GDRumSy65pMrjOnfurNtvv12zZ8/W/Pnz1bRpU/3Xf/2XCgoKYvL6ktG+fVaVllrUpg2nYQAAEC1Bt0YGKcI6dOiQ0gOKAQCIFy6XobZtfVq3zq4uXbxmx0k5FuPoEYmokx07dpgdIe48+GC2rr++WLm5XFrHSvUTUhA+rhXUBtdL6mr4xz8qc+7careXnXuu9r/wQrXbd+zYwfxThI0/WxAurhXUBtfLr7xeadKkHP3974fMjhKXUvrUSCSWH36wqUEDPyUYAABRVtth+YFO4QYAAOaw26X+/Sv0ySdOs6OkHIowRNSMGS5ddRVvtAEAiLbazggDAADxZfDgMi1YkK5jpjwhBijCEDFffeVQly4eBXtfDgAAIqe2p0Y6HI5oxgEAALVksUgjRrj12muB/05HdFCEISIMQ3r99QyNGOE2OwoAACmhtsPye/fuHc04AACgDk491aN16xwqKzM7SeqgCENEzJ+frkGDymTligIAICZqOyNs69at0YwDAADq6KqrSvTiiy6zY6QMagvUm9crLV7s1JlnMpMEAIBYCTojLEgRtm3btiimAQAAddWunU8HDli1f7/F7CgpgSIM9TZ7dqZGjy41OwYAACkl6IwwhuUDAJBwrr++RM89x6qwWKAIQ70UF1v00082deniNTsKAAAppbZbIwEAQPxq3NivzExDP/1kMztK0qMIQ70895xL11xTYnYMAABSTy2H5Xfv3j2aaQAAQD399releuGFTLNjJD2KMNRZUZFVPp/UsqXf7CgAAKScoFsjy8uPHOcMAAASSkaGoY4dvfrmG4fZUZIaRRjq7LnnXLr2WlaDAQBghqDD8iUpwKqw1atXRzENAACIhJEj3XrllQx+phVFFGGok++/t6tZM59ycvjdCQCAGUIVYQzMBwAgMdls0jnnlGvhwjSzoyQtijDUycyZmRozhpMiAQAwC0UYAADJacCAci1alCafz+wkyYkiDLX2xRdOnXpqhZxOs5MAAJC6Qm6NLCurdlPr1q2jmAYAAESKxSKNGuXW3LmB54GifijCUCuGIb3zTrqGDq3+BhsAAMRObVeEtWnTJppxAABABHXv7tHGjXaVllrMjpJ0KMJQK2++ma4LLyyThd+LAACYKtipkVLgImz58uXRjAMAACLsmmtK9fzzmWbHSDoUYQhbRYW0cqVTffsydwQAALOFXBFWXl7tNo/HE804AAAgwlq39snttmjvXqqbSOK7ibDNnJmpK69kQD4AAHHB4ZBhswX+XIAiDAAAJJ7rrivRs8+6zI6RVCjCEJZDhywqKrKpY0ev2VEAAMAvgq0KC7Q10uXiTTQAAImmYUNDubl+bd4c5IdfqDWKMITl2Wdduu66ErNjAACAYwQtwgKsCOvRo0e04wAAgCi46qoSzZjBD7QihSIMNdq//8hl0rSp3+QkAADgWLVZEbZp06ZoxwEAAFGQliZ16uTRmjV2s6MkBYow1GjGDGaDAQAQj4IOzA+wImz37t1RTgMAAKLlkkvcev11TpCMBIowhHTwoEVer9S4MavBAACIN0ZGRsDbA22NBAAAicvhkE44wavvv2dVWH1RhCGkmTMzNWYMq8EAAIhLtdgaCQAAEtvIkaWaMyfwD8EQPoowBFVcbFFxsVXNm7MaDACAeFSbGWG9evWKdhwAABBF6elS8+Z+/fgjJ0jWB0UYgnrpJVaDAQAQz2ozI6y4uDjKaQAAQLRdfnmpZs1iVlh9UIQhILfbon37rGrd2md2FAAAEETQFWEBirDvvvsu2nEAAECUZWYays31a/t2VoXVFUUYApo9O0OjR7MaDACAeFabrZEAACA5XHFFqV56iVVhdUURhmrKy6UdO2xq147VYAAAxDNOjQQAIPVkZxvKyDBUVESlUxd811DN3LmZGjnSbXYMAABQg6AzwgKsCGvfvn2U0wAAgFi54ooSzZzJqrC6oAhDFR6PtHmzXZ06ec2OAgAAalCbGWHNmjWLdhwAABAjubmGLBZp/35qndriO4YqXnstQxdfzGwwAAASQW2KsCVLlkQ7DgAAiKGrrirViy+yKqy2KMJQyeeT1q93qFs3VoMBAJAIGJYPAEDqysvzy+uVDh60mB0loVCEodJbb6XrwguZDQYAQKIIOiOMYfkAAKSEK64oZVZYLVGEQZLk90srVzrVq5fH7CgAACBMQU+NDLAiLDc3N9pxAABAjDVv7tfhw1YVF7MqLFwUYZAkzZ+frvPPLzM7BgAAqIXazAjr3LlztOMAAAATjBlTqpdeYlVYuCjCIMOQlixx6vTTmScCAEAiCboiLEARtn79+mjHAQAAJmjTxqe9e61yM+koLBRh0Mcfp+mcc8plYSUlAACJJdiMsABbIw8cOBDlMAAAwCyjR5dq9mxWhYWDIizFGYa0aFGazj6boboAACQaTo0EAACS1L69T9u32zgvJwwUYSlu8WKn+vdnNRgAAImoNjPCAABAchs50q25c1kVVhOKsBT3wQfpOu883iwDAJCIalOE9evXL9pxAACAiU46yatNm+zyeMxOEt8owlLYsmUOnXpqhaxcBQAAJKRgw/IDzQgrKiqKchoAAGC2iy926/XXg7w/gCSKsJT2zjsZuvDCMrNjAACAOqrNirDNmzdHOw4AADBZ9+4erVvnkM9ndpL4RRGWolavdqhrV49sNrOTAACAumJYPgAAON6FF7r11ltBTpYGRViqeuutdA0f7jY7BgAAqIeQRZhhxDgNAACIB717e7RqldPsGHGLIiwF7d5tVW6uXw6H2UkAAEC9OBwygi3vPm575EknnRSDQAAAIB4UFFRo5Ur+0R8IRVgKmjMnUyNHshoMAIBkEO72yKysrFjEAQAAceCCC8r03ntsjwyEIizFeDzSoUMW5eX5zY4CAAAiINwibMWKFbGIAwAA4oDDITVoYGjPHmqf4/EdSTHvvJOuCy7gpEgAAJKFkRH4iPRAJ0cCAIDUMWpUqebODfw+IZVRhKWYVaucOuUUj9kxAABAhARbEXb8jDAAAJBamjTx6+efrfJQAVRBEZZC1qyxq2tXfgcAAJBMwt0a2bRp01jEAQAAceQ3vynTu+8yK+xYFGEp5M03MzR0KEPyAQBIKsGKsONWhHXo0CEWaQAAQBzp2dOjb75xmh0jrlCEpYj9+63KyjLk5PoHACCphLsibNWqVbGIAwAA4kyXLh6tWWM3O0bcoAhLES+/nKFRo0rNjgEAACIs3BlhJSUlMUgDAADizdChbr31FkPzj6IISwFer7Rvn1XNm/vNjgIAACKMUyMBAEAoaWmSy2Vo/36L2VHiAkVYCvjgg3QNHsybYQAAklG4WyMdDkcs4gAAgDg0cmSp5s7NNDtGXKAISwHLlzvVu3dFzXcEAAAJJ+jWyOOKsN69e8cgDQAAiEctWvi1Z49VPp/ZScxHEZbk1q+3q1MnjyysgAQAICkFXRFWVlbl461bt8YiDgAAiFODBpXrgw+C/AAthVCEJbk33sjQsGFus2MAAIAoCXdr5LZt22IRBwAAxKnTTqvQsmVOs2OYjiIsif38s0VOp6EgM3QBAEASCFqEMSwfAAAcw2KROnXy6Lvv7GZHMRVFWBJ75ZVMjRrFajAAAJJZ0FMjK5gPCgAAqho2zK033kjt1TIUYUnK75d27rSpdWsm4QEAkMyCDss/bkVY9+7dY5AGAADEs4wMyeEwdPBg6g4SpwhLUgsXpumcc8pqviMAAEhobI0EAAC1MWqUW6+8kml2DNNQhCWpJUvS1L8/WyIAAEh24Q7LX716dSziAACAONe6tU87dtjk95udxBwUYUlo40abTjjBK0vqrnQEACBlBJsRJmaEAQCAIAYMKNNHH6WZHcMUFGFJ6PXXM3XJJQzJBwAgJbA1EgAA1FJhYYUWL6YIQxIoLrbIYpFcLsPsKAAAIAbCnRHWunXrWMQBAAAJwGKR2rXzatMmm9lRYo4iLMnMnZuhSy8tNTsGAACIkWBbI4+fEdamTZtYxAEAAAni4ovdev311BuaTxGWRAxD2rrVrhNO8JkdBQAAxEiwFWE6bkXY8uXLY5AGAAAkiqM7ydzu1BowThGWRFatcuiUUxiMCwBAKgn31EiPxxOLOAAAIIH85jduvf9+kB+qJSmKsCQyf366Bg0qMzsGAACIoXBnhAEAAByva1ev1q2zmx0jpijCkkRFheT3Bz04CgAAJKlwizCXyxWLOAAAIME0aeJXUVHq1EOp80qT3Mcfp+ncc/nJLwAAqSbojLDjtkb26NEjBmkAAECiGTbMrXnzAh++k4wowpLE8uVO9e7NfDAAAFKOwyHDXn1Lw/EzwjZt2hSrRAAAIIE0a+bX7t1WGYbZSWKDIiwJHDhgUYMGhiypddADAAD4RaBVYcdvjdy9e3es4gAAgATTrZtHa9emxqwwirAk8OabGRo61G12DAAAYJJwijAAAIBgBg8u0/z5qTF0nCIsCWzdalN+vs/sGAAAwCQB54RVMDIBAACEJyNDqqiwyOs1O0n0UYQluE2bbGrXjhIMAIBUFs6KsF69esUqDgAASEADBpRr0aI0s2NEHUVYgnv7bbZFAgCQ6gIWYcetCCsuLo5VHAAAkID69KnQl186zY4RdRRhCczvl0pKLMrJSZGjHQAAQGCBijCP58ibhV989913sUwEAAASjNUquVyGDh5M7pP4KMIS2NKlTvXty/wPAABSnZGREfgTDMwHAAC1MHSoW2+9FeR9RZKgCEtgixal6ayzeIMLAECqCzgsX9W3RwIAAITSvr1PP/5oMztGVFGEJSi32yKn05DdbnYSAABgtnCKsPbt28cqDgAASGBt2yZ3GUYRlqDefz9d559fZnYMAAAQB4IWYcdsjWzWrFms4gAAgAR20UXJvT2SIixBrV1rV9euXrNjAACAOBCsCDt2RtiSJUtilAYAACSyhg0NHTpkkZGk5/JRhCWgXbusatrUX/MdAQBASghnRRgAAEC4eveu0PLlTrNjRAVFWAKaNy9Dw4e7zY4BAADiRLBTIxmWDwAA6uKcc8q1cGGa2TGigiIswRiGtGcPK8IAAMCvwhmWn5ubG6s4AAAgwTkcks0muZNwDQ5FWIJZu9aubt08ZscAAABxJOiMsLJfD9bp3LlzjNIAAIBkMHhwmT74IMh7jARGEZZg5s9P1+DBnBYJAAB+Fc6KsPXr18cqDgAASALdu3u0erXD7BgRRxGWQLxeqaLCoiBjQAAAQIoKpwg7cOBArOIAAIAkYLFIjRv7tWdPclVHyfVqktyiRWkaMIDTnwAAQFWcGgkAAKJh2DC35s1LrtU4FGEJ5MsvnerTh9OfAABAVcFOjRSnRgIAgHpo2dKvoqLkqo6S69UkseJiizIzDVn5FQMAAMcJZ0VYv379YhUHAAAkkRNP9GrDBrvZMSKGWiVBfPxxmgYOZEg+AAAIIMiKsGOLsKKiolilAQAASWTQoDItWJA8p0dShCWINWsc6trVa3YMAAAQh8IZlr958+ZYxQEAAEmkYUNDBw9azI4RMRRhCcDjkez2Iyc2AAAAHC9YEcaMMAAAEAktWvi0Y0dyVEjJ8SqS3JIlTp1+Oqc+AQCAwIKuCCtjrAIAAKi/QYPKNX9+cmyPpAhLAEuXpqlvX36iCwAAAgtna+RJJ50UqzgAACDJtGrl086dNrNjRARFWJwzDMnrlRwOs5MAAIB4ZQQbln9MEZaVlRWrOAAAIAllZyfHrDCKsDi3apVDBQUes2MAAIA4FnRG2DGnRq5YsSJGaQAAQDIaOLBMCxcm/vZIirA49/HHaRowgPlgAAAgBLtdht1e7WZLOe8hAABAZHTu7NX69dXfbyQairA4V1pqUWamYXYMAAAQ5wKtCrNwaiQAAIgQi0VyOqssOE9IFGFxbPNmm9q185odAwAAJICARdgx71SbNm0ayzgAACAJnXFGuT7/PM3sGPVCERbHFixI16BBCV61AgCAmAg4J+yYFWEdOnSIYRoAAJCMeveu0LJlTrNj1AtFWBzbt8+qxo39ZscAAAAJoKatkatWrYplHAAAkIRsNskwJH8CVxUUYXFqzx5KMAAAED4jI6PabZayssr/LikpiWUcAACQpE49tUJffeUwO0adUYTFqSPbIstqviMAAIAYlg8AAGLjzDPL9emniTsnjCIsTv34o03t2vnMjgEAABJFDTPCHI7E/cktAACIHxkZktttkWGYnaRuKMLiUEmJRZmZCXpFAQAAU9R0amTv3r1jGQcAACSxE0/0auNGu9kx6oQiLA59/HGazjmH0yIBAED4atoauXXr1ljGAQAASWzgwDItWJCY2yMpwuLQqlUOde/uMTsGAABIIAGLsGOG5W/bti2WcQAAQBJr1MjQzz8nZqWUmKmTmMcj2e2SxWJ2EgAAkEgCnRophuUDAIAoadbMr127Eq9WSrzESe6LL5w6/XS2RQIAgNrh1EgAABBLgweXaf78AIf1xDmKsDjzxRdpOv103rQCAIDaCViEeb2S78gp1N27d491JAAAkMRat/Zp+3ab2TFqjSIsjhiG5PVKnG4OAABqK1ARJrEqDAAARE9WlqFDhxJrthNFWBxZvZoh+QAAoG6CFWEqPzJyYfXq1TFMAwAAUsG555bpo48S6/RIirA48tFHaRowgPlgAACg9oKuCCvnvQUAAIiOLl28Wrcusba1UYTFkdJSi7KyDLNjAACABMTWSAAAEGsWy5HxTon0doMiLE4UFVnVvLnP7BgAACBBGRkZgT/xy4qw1q1bxzANAABIFb17V+irr5xmxwgbRVicWLw4TYWFCVShAgCAuFLT1sg2bdrEMg4AAEgRffpUaOlSijDU0oYNdp14otfsGAAAIEHVtDVy+fLlsYwDAABSRGamIbc7cU6OpAiLI5bEuW4AAEC8CbI18mgR5vFwMjUAAIiOjAxDpaWJUWrYzQ4Qjm3btunpp5/Whg0b5HK5dO6552rUqFGyWoP3eBs3btT8+fP17bff6sCBA2rcuLHOOOMMDR8+XE7nr0v2pk2bpkWLFlV7/OTJk9WqVauovJ7j/fSTTW3aMB8MAADUHadGAgAAs/TpU6Fly5w6++z4f98R90VYcXGxJk2apNatW+vOO+/Url279MILL8gwDF1++eVBH7d48WIVFRVp+PDhatGihbZs2aLZs2dry5YtuuOOO6rct1WrVho7dmyV25o0aRKV1xM4q1OFhfF/sQAAgPgVrAg7Oizf5XLFMA0AAEglp55aoWnTsijCIuGDDz5QRUWFJkyYoMzMTPXo0UNut1tz5szRsGHDlJmZGfBxI0aMUE5OTuXHXbt2ldPp1BNPPKE9e/ZUKbrS0tLUqVOnqL+WYLZssWv0aLdpXx8AACS+YKdGHt0a2aNHD+3duzeWkQAAQIpIS5MqKhJja2Tczwj7+uuvVVBQUKXwKiwsVEVFhdatWxf0cceWYEedcMIJkqSDBw9GPGddGcaR/2c+GAAAqI+atkZu2rQplnEAAECKyckxdPBg/Jcbcb8ibPv27eratWuV2/Ly8pSWlqYdO3bU6rk2bNggi8Wili1bVrl927Ztuuaaa+TxeNShQweNGTNGXbp0qXf2cGzaZFfHjpwWCQAA6ifo1shfVoTt3r1bDRo0iGEiAACQSk4/vVxLlzo1eHB8b4+M+yKspKQk4EwLl8ul4uLisJ/n559/1quvvqqzzjqryuqydu3a6cQTT1Tr1q116NAhvfnmm5o0aZImTZqkjh07VnueBQsWaMGCBZKke++9V3l5eXV4Vb+aO9eqiy7yKy+PuR3Jzm631/t6QWrgWkFtcL2gUsOGAW/OdjjkysvT999/z7WCsPFnC8LFtYLa4HpJbueeK/3znzZdcUV8HwYY90WYJFkC7Bs0DCPg7YF4vV5NnjxZ6enpuuaaa6p8bsiQIVU+PvXUUzV+/Hi9+uqruvPOO6s913nnnafzzjuv8uP6ztrYtClbaWmHxciO5JeXl8dsFoSFawW1wfWCY7Ww22XxVl1pXrJvn0p+uUa4VhAu/mxBuLhWUBtcL8nv8OFs7d17uN7Pc/xOvkiK+xlhLpdLJSUl1W4vLS0N6/QjwzA0depUbd26VX/+85+VlZUV8v5Op1M9e/bUDz/8UOfM4fL7mQ0GAAAiJ9D2yKPD8nv16hXrOAAAIMU0buzX3r3xXTXFdzpJrVq10vbt26vctnfvXpWXl4fVED733HNatmyZ7rzzTrVq1SrsrxvuarP6WLfOri5dPFH/OgAAIDUEnBP2SxFWm5ESAAAAddG/f7kWL3aaHSOkuC/CTjnlFH3zzTdyu92Vty1evFhOp7PGgfavvfaa3n33XY0bN06dO3cO6+tVVFTo66+/Vvv27euVOxyff56m/v0rov51AABAajAyMqrddvTUyO+++y7WcQAAQIrp3Nmr9esdZscIKe5nhA0aNEjvvvuuHnjgAQ0fPly7d+/WnDlzNHTo0CpD78eNG6cuXbpo7NixkqTPPvtML730kgYMGKBGjRppw4YNlfdt3ry5cnLULOUBAAAgAElEQVRyVFpaqnvvvVdnnnmmmjdvrsOHD+vtt9/W/v37NX78+Ki/tgMHrGrUyB/1rwMAAFJDwK2R5fF9chMAAEgeVqtkGGanCC3ui7CsrCxNnDhRTz31lO677z65XC5deOGFuuyyy6rcz+/3y+//tVT65ptvJEkff/yxPv744yr3veWWWzRgwADZ7Xbl5OTo1Vdf1cGDB+VwONSpUyf97W9/U4cOHaL6ujweyR73330AAJBIQs0IAwAAiIUWLXzavt2qVq3ic+GPxTDivauLbzt27KjT45Yvd+jAAasGDeKntKmCE1IQLq4V1AbXC47VeMQIpS1bVuW2kssv18EHH5TP55PNZjMpGRINf7YgXFwrqA2ul9SwaZNNX33l1KhR7prvHERKnxqZrJYuTdPpp/MTWgAAEEEhVoQ1a9Ys1mkAAEAKat/ep82b43cLHEWYSYqLLcrOZjEeAACInIBbI8vKJElLliyJdRwAAJCCLJYj/x+v+w8pwkxQViY5nXF6RQAAgIQV8NRIZoQBAIAYO+EEr378MT5HMlCEmWDFCqd69+ZNKQAAiKxAK8JEEQYAAGKssLBCn3+eZnaMgCjCTLBsGUUYAACIvIBbI8uPHMyTm5sb6zgAACBFtW7t0/btrAjDL8rLLQqwcwEAAKBeAhZhv6wI69y5c6zjAACAFGaxSH6/2SmqowiLseJii1wu5oMBAIDIC7UibP369bGOAwAAUthJJ3n03Xfxd3okRViMLV3qVN++bIsEAACRF3BG2C+nRh44cCDGaQAAQCrr379CixfH35wwirAY++YbhwoKKMIAAEDkcWokAACIF02a+LV3b/zVTvGXKMl5vRY5nWanAAAAySjU1kgA+P/s3XlcVPX+P/DXmYUBBlDZ90XEBQNNERUTvOVWtl61q99r/TJLs6xMWq7WNcs0K0szu6Vel1t5vabZbdNsvWgquWTuaWrKIoiI7DDbOb8/RkaGGZaB2cDX8/HwgXPmLO+DCMxrPp/3h4jI2QQBkNysOxSDMCcyGAAZP+NERETkIE0FYYMHD3Z2OURERHSdCw01oLDQvYIQ96qmgztzRoFu3fSuLoOIiIg6KKs9wq4GYRcvXnRyNURERHS969tXh0OH3GtaHIMwJzp0SInkZPbpICIiIsewOiJMpwMAnD171tnlEBER0XWuZ08dTpxwr5UjGYQ50blzCsTFGVxdBhEREXVQVoMwUQT0HJFOREREzufhAeh0gqvLMMMgzIkkydgojoiIiMghrKwaCXDlSCIiInItd2qYzyDMSXQ6QKl0o395IiIi6nDERoIwaDTo0aOHc4shIiIiAhATo0dOjtzVZZgwCHOSkycV6N6d0xKIiIjIgaw1y4dx5UgfHx8nF0NEREQEJCfrcOiQ0tVlmDAIc5JDhzzQt6/O1WUQERFRB2Z11UgYp0YeOHDAydUQERERAQkJevz+O4Ow605+vhzh4WyUT0RERI7TVBBGRERE5AoKBWBwoziEQZgTsVE+EREROVJjQRhqa51bCBEREVE9Mhkgiq6uwohBmBPU1gIqFRvlExERkWM1NSIsODjYydUQERERGXXtqsfZswpXlwGAQZhTHD+uRGIi+4MRERGRgykUkBSWv2QKWi3i4+NdUBARERER0KePFr/+6h59whiEOcHhw0r06cMgjIiIiBxP8vCw2CZoNDh8+LALqiEiIiIC4uIM+OMPjgi7bly6JEdwsJtMhiUiIqIOTVKpLDdqNKiqqnJ+MUREREQw9giT3KRjFIMwIiIioo7ESp8wrhpJRERErqZQSNDrXV0FgzCHq6oS4O3tJrEnERERdXjWGuYLGg2USvfoy0FERETXp+7d9Th50vXTIxmEOdiRI0okJbE/GBERETmH1SBMq0VKSooLqiEiIiIy6tNHh0OHLHuZOhuDMAc7dEiJpCRORyAiIiLnkLy8LDdqNMjNzXV+MURERERXRUYakJcnd3UZDMIcraxMhi5dODWSiIiInEP09rbYJmg0yMvLc0E1REREREaC4OoKjBiEEREREXUgkrUgjM3yiYiIyA14eEiorXVtDQzCHKi0VECnTqKryyAiIqLrSSPN8omIiIhcLTFRhxMnXLuAD4MwBzp8WInkZDbKJyIiIuex1iwfWi2SkpKcXwwRERFRPcnJOhw6xCCswzp61IMrRhIREZFTWV01kiPCiIiIyA2EhoooKnJtw3wGYQ5UVSXAx4eN8omIiMh5GgvCjhw54oJqiIiIiNwLgzAiIiKiDkTy8rLYxmb5RERE5C68vCRUV7tuCUkGYQ5SWSnAx4eN8omIiMi5rPYI49RIIiIichPduulx9qzrpkcyCHOQs2cV6NrV4OoyiIiI6DpjdWpkTQ0iIyNdUA0RERGRufh4Pc6cUbjs+gzCHOT0aQXi4/WuLoOIiIiuM40FYVFRUS6ohoiIiMhcTIwef/zBIKzDOX9ejpgYBmFERETkXI01y9+/f78LqiEiIiIyp1IBOh17hHU4BoMApdLVVRAREdH1xmqPsJoa6HQ65xdDREREZIXguhyMQZijSJKrKyAiIqLrkbUgTMZm+URERORmRBetL8ggzAFE0bXpJhEREV2/JC8vi22CRgO1Wu2CaoiIiIgshYYaUFDgmkiKQZgDXLggR0QEV4wkIiIi57M6NVKjQXJysvOLISIiIrKiWzfXrRzJIMwBzpzhipFERETkGlZHhOl0OHPmjAuqISIiIrIUH6/H2bMMwjoMBmFERETkMtZGhOl0KCoqcn4tRERERFYEBoq4dEnukmszCHOAy5dl8Pd3Udc3IiIiuq5ZmxopaLUuqISIiIjIOq4a2QGxWT4RERG5gtUeYTqd8wshIiIickMMwhyAIRgRERG5itUeYQYD+vfr54JqiIiIiKzz8pJQU+P8AIVBmJ1VVQnw8pJcXQYRERFdp6xOjZQkVJaXu6AaIiIiIuvi4vQ4e9b5fcIYhNnZ2bMKdO3KRvlERETkInI5JLnlL5UnT592QTFERERE1sXH63H6tPNXjmQQZmdnzijQrRuDMCIiInIhpdLVFRARERE1KTZWj3PnGIS1e+fOyRETwyCMiIiIXEdiEEZERERuztMT0OnYI6zd0+kEeHi4ugoiIiK6nklWfhnp6uvrgkqIiIiIGie5oMU6gzAiIiKiDkZSqSy2hVjpG0ZERETkas4OwxiE2ZEoAoLzR/URERERmbMShO0pLXVBIURERESNCw01oLDQudEUgzA7KiiQISzM4OoyiIiI6DpnbUQYERERkbuJj9fjzBnnNsxnEGZHZ84oEB/PRvlERETkWpKnp6tLICIiImoWg7B2jkEYERERuQNrQVgXPX9HISIiIvcSHCyiqMi5fUwZhNlRcbEcgYGiq8sgIiKi65zk5WWxrRd7hBEREZGbcUWfdQZhdiQIbJZPRERErmctCDuhVrugEiIiIqKmOTtHYRBmR85e8pOIiIjIGtFK6HXFSjhGRERE5GoeHhJqapx3PQZhdqLTAUolkzAiIiJyPcnb29UlEHU448aNw8MPP2z1uVtvvRUzZ84EAMycORO33nqr2fN79+7FhAkTkJSUhG7dumHIkCGYOXMmLly40OQ1IyIirP4ZMmRIi+v++uuvERERYVETEZG7iI42IC/PeQ3znduavwMrKpIhJIT9wYiIiMj1JE6DJHIbe/fuxbhx4zBq1Ci8+eab8PT0xO+//45PP/0U+fn5CA8Pb/L4adOmYcyYMWbbVCpVi65dW1uLl156CUFBQa2un4jI0UJDDbh4UYaEBOdcj0GYnVy8KEdIiMHVZRARERFZDcKG7NmDImf9hklEJh988AESEhKwcuVKCFcb4aSnp2PKlCmQWtBbJSoqCv3792/Vtd977z2EhoYiJiYGJ0+ebNU5iIgcLSTEgF9+8XDa9Tg10k4YhBEREZG7sDY1srBLFxdUQkRlZWUICAgwhWD1WdtmL/n5+Xjvvffw8ssvO+waRET2EBIi4uJFudOuxyDMTgoLZQgN5dRIIiIicj3J09Ni25mePV1QCRElJSVh9+7dWLJkCc6fP2/z8aIoQq/Xm/0RxeZfd7z00ku44447kJSU1JqyiYicxttbQnW185aOZBBmJyUlcnTpwiCMiIiIXM9aEEZErjF9+nQMHjwYixcvRlpaGvr164fnnnsOZ86cadHxc+fORUxMjNmfWbNmNXnMrl27kJWVheeee84et0BE1KGwR5idSBIgY6xIREREbkDy8nJ1CUR0la+vLz7++GMcOHAA3333HX7++Wds2LABW7ZswZYtW5odsTV9+nTccccdZtv8/f0b3V+v12Pu3Ll48sknERwcbJd7ICLqSBiEEREREXUw1kaE9friC+ibGUVCRI2Ty+WNTkk0GAxQKBp/aSUIAlJSUpCSkgIAOHr0KMaOHYulS5di9erVTV43IiICffr0aXGd69evR1lZGcaPH4+ysjIAgE6ng8FgQFlZGby9vaFUKlt8PiKijoZBGBEREVEHYy0I88nNRakLaiHqKAICApCbm2v1uaKiIgQEBLT4XDfccAPS09Nx6tQpe5VncubMGRQUFKBv374WzyUmJmLZsmUYO3as3a9LRNQWgmCcaefANURMGIQRERERdTDWgrB9jz6KBBfUQtRRpKamYuvWrSgoKEBYWJhp+y+//IJLly5h4MCBVo8rLi5GYGCg2TZJknDu3DkEBQXZvc7Jkydj9OjRZtveffdd5OTk4LXXXkNCAr8TEJH76dJFxJUrAvz9JYdfi0GYHdTUACqV4/+xiIiIiFqCPcLoeqM8eBBeX3wBycMDkkoFeHgY/27lMVSqFj0HDw+zoQnjx4/HqlWrMHbsWDz55JOIiIjA6dOn8dZbbyElJQXDhg2zWtvTTz8NURQxZswYxMTEoKysDBs3bsTx48exYsUK036ZmZnIzs7Grl27zI7Pzc3FgQMHzLYJgoB+/foBADZt2oTMzEzs3r0bkZGRiIuLQ1xcnNn+H3/8MUpKSpCWltaWTzMRkcOEhBhw8aIc/v56h1+LQZgdFBXJERJicHUZREREREZcNZKuM7o+feDz/vvw+vJLu55XM3gwSl97DYb4eKjVanzyySdYtGgRFi5ciNLSUgQGBuLOO+/Ec889B1kjK2f9v//3//Dxxx9jyZIlKCoqgp+fH7p3745///vfyMjIMO1nMBig11u+AFyxYoVZYAYY+5Xl5OQAAERRhMFggCTxjXkiar/qgrBevRwfhAkSv2O2yYULF7B3rwdqawWkp2tcXQ65scDAQBQXF7u6DGoH+LVCtuDXC1kjz89HSGqq2baTr7wC38mTXVQRtTft8ntLbS0C/vpXqLKz23wq0c8P5bNno3rSJC4N34x2+bVCLsOvF2pMXp4cu3Z54C9/qQEAhIeHO+xa/K5uB4WFMoSGckQYERERuQdrPcIS1q51QSVETuTpiZLVq6Hr0aNNp6m++24UZWWh+v77GYIRETlJUJBxRJgzcGqkHRQWypGRwdFgRERE5B6sBWEHX3gBYVb2JeoQNBp47NsH1c6dgJXphS2hj41F2cKF0NSbrkhERM6hUgFarROWjASDMLsoL5fBz48zTImIiMg9WAvCKhs0zyZq1yQJihMnoNqxA6qdO+GRnQ1ZbW3rTqVUovKxx1AxYwbAhSaIiDo8BmF2IjgnuCQiIiJqnlwOSSaDIIquroTIbmQXLxqDrx07oPrpJ8iLitp8Ts3gwShbtAj6bt3sUCEREbUHDMKIiIiIOiK5HKgXhHlcvgwkJLiwICLbCNXV8MjONo36Uv72m93ObejSBeVz56Jm/Hi+o01EdJ1hEEZERETUAUkKBQSdzvR40KRJKDh92oUVETXDYIDy6FHTqC+P/fshaLU2n0bXowf03brB66uvrD5fNWECyp9/HpK/f1srJiIiO5LLJRgMxvfyHIlBGBEREVFHpFQCNTWmh+cmToTKheUQWSPPy7s23XHnTshKS20+hyEoCJqhQ6FJT4dm6FCIoaHw+vRTiyBMl5CAstdeg3bgQHuVT0REdhQQIOLyZRmCgx3b2oFBWBtVVgrw8WH/DSIiInIvksL817yc++8HJ0aSqwkVFVDt3m0MvrKyoPjjD5vPIXl6QjNwoDH4Sk+Hvlcvi+mNykOHzPavmDkTldOmAR4ebb4HIiJyjNBQAy5elDMIc3eFhTKEhDAIIyIiIjdj7QW/JLEfEjmXXg/lwYPwvDrqS3nwIASDwebTaJOSTCO+tAMGAFZWRq1PefgwAKD2T39C2YIFMMTEtKp8IiJynpAQEYWFMiQlOfY6DMLa6OJFOUJCbP9hTkRERORIklJpuVGn44gYcixJgvyPP65Nd9y9G7KKCptPow8PN4340t50E8SAgJYfbDBAXlSEkvfeQ+0ddzD8rWfjxo1Yu3Ytzp49C4VCgcjISKSlpWHevHmmfSIiIqweGxsbi127dllsz8rKwsiRI5u9dn5+Pt5880289dZbVp9ftmwZxo4da1GDp6cnIiMjce+992LatGlQKJp+CTtw4EDk5eVZbJfL5cjJyQFg/DzMmjULCQkJ+OGHHyCTyUz7vfzyy/jqq6/w888/AwByc3MxaNAg0/Pe3t6IiYnBgw8+iP/7v/8zu8a4cePQpUsXrFq1qskaici6kBADjhyx8vuLnTEIa6OLF+Xo08f2Jp5EREREjiSpzDuC9Zs6FZVffAGJQRjZmVBSAtVPP0G1cydUO3ZAYSWEaI6oVkOblnZtumN8fOsDLJ0Ol7ZuheTn17rjO6h33nkHb7zxBqZPn445c+agtrYWR44cwZYtW8yCMACYNm0axowZY7ZNpbLeZfDGG2/E559/bnq8b98+zJ8/H//85z8RHBxssb+fnx8++ugji+1xcXFWa6itrcV3332HhQsXQqfTYebMmc3e6z333IPJkyebbROsfD39/vvv+Oqrr3DHHXc0e86///3vGDBgAKqqqrB582Y888wzUKlUpvCOiNouMFDEpUuy5ndsIwZhbVRYKMfIkZwaSURERG7GyotWQaOB5OPjgmKoQ9Fo4LF/v6nBvfLwYQiSZNMpJJkMur59jcFXRga0N95oXODBHjw9ITUzdfJ6tG7dOkyaNAmzZ882bRs5ciRmzZplsW9UVBT69+/fovP6+fmZ7VtSUgIAuOGGGxAVFWWxv1wub9G569cwZMgQnDp1Cps3b25REBYcHNyiawwePBjvvPNOi4Kw+Ph40zmHDh2Kw4cPY/PmzQzCiOxIoQAMBseP4mUQ1kZVVQJ8fGz7wU9ERETkaA1HhP2yciV6aDQuqobaNUmC4uRJU/DlsWcPZPVWJG0pfWyscXXHjAxo0tIgderkgGKpMWVlZQgKCrLYbm2klDtKTExEdna2Xc/55JNPYsKECfj2228xYsSIFh8nCAJ69uyJEydO2LUeInIOBmFEREREHZC1ETGClu0cqIUKC+H12Wem8Et+8aLNpxA7dYJmyBBj8JWeDkN0tAMKpZZKSkrC2rVrERERgeHDh8Pf37/RfUVRhF6vN9smk8nMemm1RcNzA2i291d+fr7VEWbWSJJkcQ1BECCXy8229evXD0OHDsWyZctsCsLq6onm1zRRu8QgrI3s9LOAiIiIyK6sBmEcEUaNEGpq4PHzz6Ym98oTJ2BrNzlJqYQ2JcU46is9HbrkZKBB8ECus2DBAkyZMgVPPfUUBEFAQkICbrvtNjzyyCPw9fU123fu3LmYO3eu2bbx48dj6dKlba7jypUriLGyimd2drZZ0FUXxtX1CNu2bVuLr79y5UqsXLnSbNvgwYOxefNmi32feOIJjB8/Hjt37sTQoUMbPWdduFZZWYlNmzbh6NGj2LBhQ4vqIaKWk8kcP+OOQRgRERFRByR5eZk9jlm3DsKECS6qhtyOKEJ57BhUWVlQ7dgBj337WjViUNe9uyn40g4eDEmtdkCxZA+JiYn43//+h6ysLGRlZWHXrl1YunQpPvvsM2zfvh3qev9206dPt+ib1dQIMlv4+fnhP//5j8X2kJAQs8cNw7iHH34Yd911V4uu8ec//xkPPfSQ2TZ1I1+baWlpGDBgAN5+++0mg7CGzfdffvlls9Ukiaj9aHEQlpeXhyNHjuD06dMoLS2FTqeDj48PwsLC0LNnT/Tp0wceXIWIiIiIyC00DMJi163DpXvucVE15A7k+fmmEV8eP/0E+dWm5rYwBAYag6+rf8TwcAdUen1SHD0KeHpCHxNjv4UDGlCpVBg5ciRGjhwJANiwYQOefvppbNiwwSw4ioiIQJ8+fRxSg1wub9G568K48vJy/POf/8SqVaswdOhQ3HLLLc0eGxQUZFP9TzzxBO677z7s27ev0X3mzZuH1NRUFBcXY9myZZg/fz4GDRqE3r17t/g6ROQemgzCJEnCjh078PXXX+Ps2bNQq9WIiYmBr68vFAoFqqur8euvv+Krr76CSqVCWloa7rnnHqvL5BIRERGR8zQMwvZs3oyE2loXVUOuIFRUwGPPnmvTHc+csfkckqcnNKmpxtUd09Oh79WLvUEcRNBqEXjrrYBcDn1cHPTdukGfkAB99+7QdesGfXw80OD/dVtNnDgRr7zyCk6fPm3X89pD/TBu0KBBuOWWW/DKK6/g5ptvtnuD/5tvvhnJyclYtmwZEhISrO4TGxtrqqd///646aab8Oqrr+Kjjz6yay1E1zulEnB0J4cmg7CnnnoKAJCeno4ZM2YgIiLC6n4ajQaHDh3Cnj17kJmZiYcffhjp6en2r5aIiIiIWkT09jZ7rA0MhHD2rIuqIafQ66H89Veodu40jvr65RcIVpqSN0fXuzdko0bhyoAB0A4YYPfwhazT9euHykcfhe/y5VCeOgXlqVNmz0uCAEN0tCkg03Xvbvq75OfX7PmLi4sRGBhotu3y5cuoqKiwupqkO1EqlXjmmWfwyCOP4NtvvzWNaLOnJ554Ag899BBqW/CGQefOnfHYY4/hlVdewbFjxzgqjMiOfHxEVFU59g2XJoOwCRMmYODAgc0m7iqVCqmpqUhNTcXly5dx+fJluxZJRERERLaRfHwstsmqqlxQCTmMJEF+7pxpZUfVrl2QlZfbfBpDaKhxxFdGBjQ33QQxMBCBgYHQFhc7oGhqSsWsWfD8/nsoT5yweE6QJCjOn4fi/Hng++/NnjOEhkLfrRt0CQnGUWRXR5KJAQHA1ddyt9xyC0aNGoX09HQEBgYiLy8P77//Pry8vDB+/Hiz8+Xm5uLAgQPm1xcE9OvXDwCwadMmZGZmYvfu3RbhWnMMBoPFuQEgPDwcYWFhjR43ZswYdOvWDe+9954pCFuyZAmWLFmCnJwcs32LioqsXiMpKanRdj6jR49Gjx49sHv3bkRGRjZ7H/fffz+WL1+O999/H++8845pe2FhIb788kuL/W+//fZmz0lEgFotobLSvqM+G2oyCGtN87+AgAAEBAS0uqD2RqVy/IoGRERERLaSGowI8zl5EqiocFE1ZC/ClStQ7dplmu6oyM21+Ryitze0aWnXpjt262YKS8jFRBGVM2agy2OP2XSYvLAQ8sJCqH76yWy7ISAAlY89hqoHH8RTTz2F7du3Y+7cuSgtLUVQUBBSUlLw/vvvIzo62uy4FStWYMWKFebXkMtNgZMoijAYDJAk218LlZeX484777TY/swzz2DmzJmNHieTyTBjxgzMnDkT+/fvR0pKiqmOhj799FN8+umnFtv37duH8Eb62gmCgMcffxwzZsxo0X2o1Wo89NBDWLp0Kf72t7+ZZk/98ssvmDZtmsX++fn5LTov0fXOx8fxQZggtea7F4zfwE6fPg1JkpCQkAC/FgzH7YgWLizFAw9Uu7oMagcCAwNRzHdWqQX4tUK24NcLNcbrww/R5W9/M9tW9sILqJo+3UUVUatotfA4cMC4uuPOnVAeOgTBxl/fJZkMuj59TMGXtl8/oJlFrvi9xUFEEbKiIihyciA/f970UZ6TA0VuLuSFhfa5jKcnav7yF1Q+9BAMXbva5ZyN4dcK2YJfL9ScHTtU8PKScNddto02tUWLV42sb+/evXj33XcRHh6O2tpaFBcX4/HHH0dqaqq963N7ajVHhBEREZH7aTg18lRmJkKr+ead25MkKH7/3Rh87dgBj+xsyFrx76aPiTGu7JieDs2QIZA6d3ZAsWSNUFl5LdiqC7nqhV2CA7tAG4KCUDV5Mqruuw+Sv7/DrkNE5ChqtYjychf2CGvM+vXrMXv2bPTs2RMA8Nlnn+GDDz64LoMwHx8GYUREROR+JF9fs8cFd9yB8A0bXFQNNUV26ZKpwb1q585WjQoSO3WCZsgQU/hliI21f6FkZDBAXlBgPqIrN9cUdsld0C9Z16MHKqdORc099wAqldOvT0RkLz4+Ei5ccGGPsOeeew6TJ082BV51amtrERoaanocEhLSotU1OiK1WnR1CUREREQWRCvN8lFT4/xCyFJNDVR795r6fCmPH7f5FJJCAW3//sbgKyMDuuRkQNGq97jJCqGszPr0xZwcyPPyWrUapyNohg5F5bRp0Awbxj5vRNQh+PhIqKpyYRB222234e2330b37t1x3333mVYE+dOf/oS5c+ciNTUVGo0Gu3btwvDhwx1aqLviiDAiIiJyR9ZWjRQ4NdI1RBGK48fheTX48ti7t1XT43Tdul3r8zV4sNV/Y2ohrRby/HxjsHX1j6Je2CUrK3NKGZJMBkNEBAzR0dBHRxs/xsTAc/t2eH/2mfVjFArU3H03KqdOhb53b6fUSUTkLGq1iIoKF06NzMjIwKBBg7BlyxY8++yzGD16NO6++25MmDAB3bp1w7Fjx6BQKDB9+nQMGDDAoYW6KwZhRERE5I4kT0+zx4PGjoXYihXBqXVkFy6YT3dsxXQ5g7+/KfjS3HQTxKur0lELSBJkJSWNT1+8cAGC6JyZHWLnzmYhlyEqCoaYGOO2iAhAqWiQgtEAACAASURBVLSo3W/xYsvz+Pmh6r77UDV5MsSwMKfUTkTkbM5YNbLZ8dMqlQoTJ07ELbfcgo8++ghPPvkk/vrXv+Kmm25CSkqKQ4trDzg1koiIiNySl5fZw4ru3eFznbaycAahshIee/Zcm+54+rTN55BUKmhTU6FJT0dtejr0iYmAzLHvirdrtbVQ5OVZNqS/OsJLVlXllDIkpRKGiAhjyFUXdtUFX1FRNi9UoDh2DIqzZ02P9VFRqHroIVRPmMBRgETU4SkUgCi6OAirExwcjFmzZuH48eP417/+hW3btmHy5Mno1q2bI+tzexwRRkRERO6o4YiwY6++ioEvvOCiajoggwHKQ4eMqzvu3AmPAwda1TdKl5h4bdRXaqpFgHldE0XIiooanb7YmkUFWssQGGgxfdEQHQ1DTAwMoaGAXG63a3ldnRKpvfFGVE6bhtpbb2X/NyIiO2ryO6ooivjxxx9x+PBh6PV6xMfH49Zbb8WiRYvw/fff4/XXX0dycjImTZqEztfpksxqNYMwIiIicj8NgzAAEDgirE3k58+bgi/Vrl2t6iNlCA01reyoGToUYlCQAyptP4SqqmvBVoPpi4rcXKd9zUqentBHRVmfvhgdDUmtdkodkCQIWi2K//tfaFNS2ACfiMgBmgzC1q5di/3792P06NFQqVTIysrCgQMHsGDBAgwfPhxDhgzBpk2bkJmZiTFjxuDPf/6zs+p2G3xzhoiIiNyR1SCsFQ3ar2dCaSlUu3aZ+nwpzp+3+Ryilxe0gwcbg6+MDOgTEq6vcMNggLywsNHpi/LiYueVEhpqdUSXPioKYnCwe0xDFQSUv/SSq6sgIurQmoxxfvrpJzz++OPo168fAGDQoEGYNm0aLl68iJCQEHh5eeH+++/HiBEj8OGHHzqlYCIiIiJqAbkckiBAkIyj1xMWL4ag1bq4KDen1cLjl1+u9fk6dMjmhuqSIEDXp49x1FdGBrT9+wMeHg4q2D0IZWXmI7nqf8zPh6DTOaUOUa1udPqiPiKC006JiAhAM0GYv78/jh07ZgrCjhw5AplMBj8/P7P9wsLC8OyzzzquSiIiIiKynUwGGAwAgPAvv4QuLs7FBbkZSYLi9GlT8OWxZ0+rGqzro6Ku9fkaMgRSly4OKNaFdDrI8/OtT1/MyYGstNQpZUgyGQzh4ZYN6a+GXaK///U12o6IiFqlySBs6tSpePvtt/H9999DoVBAr9dj6tSp8OK7KURERETur14QlvW//2HIpEkuLsj1ZJcvG3t8XQ2/5AUFNp9D9PWF5qabTL2+DLGx7TuAkSTIrlwxn75YVISAU6eM0xfz820eGddaYqdOjU5fNEREdPjRdURE5HhNBmE9evTAO++8gwsXLkCv1yMsLAyeVvpNEBEREZH7keRys2lpzpqi5lZqa+Gxdy8866Y7Hjtm8ykkuRza/v1NDe51ffu2v0axtbVQ5OVZrr54dYSXrLLS4hD7rYN4jaRQwBAZaQq7TA3pr4Zd0nW6ABcRETlPsz/B5XI5oqKinFELEREREdmTvEGUcT0EYaIIxYkTphFfqr17W7XyoC4+3jTdUTt4MCRfXwcUa0eSBFlRkfXpi+fPQ15Y6LRSDAEB1ldfjImBITS0/YWIRETUobTop9CJEydQVlaGyMhIREZGWjxfUlKCH374AePGjbN7gURERETUSvUCB//duyHo9S4sxnFkBQWmlR1VO3e2aiVCQ5cu0F6d6qhJTzdOw3MzQnW15YiuutUXc3Iga0Xg1xqSSmWcqmitV1d0NCQfH6fUQURE1BpNBmHV1dVYsGABTp8+bdrWu3dvTJ8+HUFBQaZtly9fxqZNmxiEEREREbkRqV4QljRnDkS12oXV2I9QVQWPPXtM4Zfy1CmbzyF5eEA7YIAx+MrIgK53b2NPNVcyGCAvLLQ+fTEnp1UBX2tJ4eHQRkRYTl+MjoYYHOz6zxUREXVY3t6O7UvZZBD28ccfo7i4GM8//zxiY2Nx8uRJ/Pvf/8bf/vY3PPvss+jRo4dDiyMiIiKi1pOUStPfjyxciN4vv+zCatrAYIDy8GFT8OWxf3+r+p3pevW6Nt1x4EBILlgASigvN+vNZRZ25eU5rY+b6O19LeBqMH1RHxmJwKgoXHZi8EZERFRHrZYcev4mg7ADBw5g4sSJSE5OBgAMGDAAffr0wT/+8Q/Mnz8fM2bMwKBBgxxaIBERERG1Ur0grCQtDcLVFSTbA3lOzrU+X7t2QVZaavM5DMHBpuBLM3SocSSTo+l0kF+4APn586Zpi4p6I7xacx+tIQkCDOHh1ldfjImBGBDQvle6JCKiDsvHx4VBWGlpKYIb/MLg4eGBmTNn4qOPPsLSpUtx//33IyEhwaFFEhEREZHtJA8P8w2iY6catIVQVgbV7t2m8Etx7pzN5xA9PaFNS4Pmaq8vfY8e9g97JAnClSumpvT1Qy55bi7k+flOCxxFPz/rqy9GRxt7nDX89yciImoHfHxcODUyMDAQOTk5SExMtHhu0qRJ6Ny5M/71r3+hb9++DiuQiIiIiFrJnYMwnQ4eBw9ClZUF1Y4dUP76KwQb65MEAbrkZFPwpU1JAVSqttem0RinLVoZ0SXPyYGssrLt12gBSaGAITLyWthV15D+atglde7slDqIiIicydGLCzd5+t69e+PHH3/E6NGjrT5/++23o3PnzvjHP/7hkOKIiIiIqPXqjwjLGDYMjp1o0AxJgvzMGXheHfHlsWdPqwIlfWSkaaqj5qabIPn7t6oW2aVLjU9fLCyEIDnns2Xw9782iqvB9EVDWJjjXw0QERG5GUevx9LkT9bbbrsNhw8fRmVlJXwaWQb5pptugr+/P44dO+aQAomIiIiodaR6o6Mu3H47wr/8EpAkp/WGkpWUwGPnzmvTHS9csPkcoq8vNGlppl5fhri4FtUv1NQYV1q0Nn3x/HnIamtbc0s2k1Qq6CMjjcFWgxFdhuhoSI38jk3kDOPGjUOXLl2watUqi+duvfVW9OjRA0uXLsXMmTNx8uRJbNu2zfT83r178dZbb+HEiROoqqpCSEgIBgwYgGeffRbh4eGNXjMiIsLq9tjYWOzatavR4xYvXoxt27YhLy8PkiQhPj4ejzzyCO666y4b7piI2gOXBmHh4eFNfhOrk5iYaHX6JBERERG5juTpafr7708/bQzCtFr7TB+0prYWHvv2QXU1/FIePWrzyCpJLofuxhtRm5EBzdCh0N14o/VRUaIIWUFBo9MX5Zcu2emmmmcICbG++mJ0NMSQEMf/Rk/kZHv37sW4ceMwatQovP/++9DpdPj999/x6aefIj8/v9nXkNOmTcOYMWPMtqma+b5UUVGB8ePHo3v37pDL5fjqq6/w6KOPQi6X4/bbb2/zPRGR+5DJXNgsn4iIiIjar/pBWB1BozEbKda2C0hQnDhhHPG1cyc8srNbNdJKHxcHTUaGcdTX4MGQ/PyMtVZUQPHbb9bDrrw8CFqtfe6jGaKXV+PTF6OiIHl5OaUOInfxwQcfICEhAStXrkRQUBCKi4uRnp6OKVOmQGpB+B0VFYX+/fvbdM2XXnrJ7HFGRgZOnjyJzZs3Mwgj6mBcOiKspebPnw9JkjB37lx7nI6IiIiI7MBaQCNUVJiCptaQFRaaRnypdu5s1cgrsXNnY4+vIUOg694dglYLRU4OlL/8Aq9PPzWO6MrJgfzKlVbXaQtJEGAIC2t0+qIYGOi06aRE7UFZWRkCAgIgWPl/YW2bo3Tp0gU6nc5p1yMi52gXQZgkSS1K/omIiIjIeeqPCOs9ezYAQCgvBxrp0WONUF0Nj+xs4+qOO3dCefKk7XUoFNB37QpDWBgkLy/IKiqgPHQInlu3QjAYbD5fa4i+vtBfDboswq6ICMdNFyVqA1lREVRZWdDcfDPEgABXl2OSlJSEZcuWYcmSJZgyZQr8bAzXRVGEXq832yaTySBrwatfvV6PqqoqfP/999ixYwcXbiPqgNpFEMaRYERERETuR/L2Nv3d99QpAICsshJNRk8GA5RHjxqDrx074HHgQKumIEoqFSCKEHQ6CHo9lKdOQXm1BkeQ5HIYIiPNQ656Pbukzp05qovaHTEoCOoPP0Tnp56Crn9/1I4YgdoRI6Dv3t2lX8/Tp0/Hvn37sHjxYixevBghISEYMWIEpk6divj4+GaPnzt3rsVryPHjx2Pp0qVNHnfgwAHceeedAACFQoFXXnkFo0ePbv2NEJFbEgT2CENeXh7WrFmDU6dOQa1W4+abb8b48eObfceguroa69atw759+yCKIvr374/JkyfD19fXbL99+/bhP//5DwoLCxEcHIzx48cjLS3NkbdERERE5HD1p0Zmf/IJMoYNg6y83GI/eW6uaWVH1U8/QVZa2uZrCxpNm8/RkKFLl0anLxrCw6031SdqzwQBFZmZCPi//4PH/v3w2L8ffq++Cn10NGqHD4dmxAhoBg0CPDycWpavry8+/vhjHDhwALt378aPP/6IDRs2YMuWLdiyZQuSkpKaPH769Om44447zLb5+/s3e91evXph69atKCsrw/fff48XXngBvr6+uPvuu9t0P0TkXtxuRJher8fOnTuRl5cHQRAQGxuLtLS0Fg1jbY3KykrMnz8fkZGRePbZZ1FYWIgPP/wQkiRhwoQJTR67ZMkSXLhwAdOmTYNMJsP69evxxhtv4OWXXzbt89tvv+HNN9/EyJEjMXnyZBw8eBBvv/021Go1+vTp45B7IiIiInIGUa222CZUVkIoL4dq925T+KX44w8XVGdJ8vAwa0LfMOySGryZSXQ90KSnQ9u/PzwOHDBtU+TkwGfNGvisWQPRxwea9HTUjhgBzS232DSFUi6XQxRFq88ZDAYomgiXBUFASkoKRo8ejeLiYhw9ehRjx47F0qVLsXr16iavGxER0arXWt7e3qbj0tPTUVFRgQULFjAII+pgXBqELV26FH5+fnjwwQcBAPn5+Vi4cCFKSkoQFBQESZLw1Vdf4dNPP8WcOXMQ4IB5699++y20Wi0yMzPh7e2N5ORk1NTUYNOmTbjzzjvhXW/If32nTp3CoUOHMG/ePCQmJgIwvsswZ84cHD58GMnJyQCATz75BL169TLd4w033IC8vDxs3ryZQRgRERG1bz4+Fps6vfACZFeuQHBRf1dDYKAx5KqbxhgRAbHuY0hI47/9SpKxv1ljmrsfBz/f7CQ1F9dn8/NaLWQlJU67fof7/Nn5+eo//9ksCKtPVlkJr61b4bV1KyRBgK5fv2tTKHv0aHIKZUBAAHJzc60+V1RUZNPruxtuuAHp6ek45cAp0A0lJSVh48aN0Ol0UCqVTrsuETmWXO7Y8zcZhB07dgxTpkwxPV6zZg28vb3x4osvIjg4GABQWFiIxYsXY926dcjMzLR7gb/++iv69OljFngNGTIE69evx/Hjx5GSkmL1uIMHD6JTp06mEAwAunXrhuDgYPz6669ITk6GTqfD0aNHMXnyZLNj09LS8I9//APV1dWNBm1ERERE7k6s93tM2BdfAADk9cMNF5AXF0NeXAyPgwddWgc1L9TVBZDNBEmCx4ED8DhwAH6LFkEfFWU+hbLBohCpqanYunUrCgoKEBYWZtr+yy+/4NKlSxg4cKDV6xQXFyMwMNBsmyRJOHfuHIKCgux/Y43Yt28fwsLCGIIRdTAymQt7hFVXV6NTp06mx7/99hsyMzNNIRgAhIaG4t5778W7777rkALz8/PRu3dvs22BgYFQqVS4cOFCk8dFWFkRKSIiAvn5+QCAixcvwmAwWOwXEREBSZJw4cIFdOvWzQ53QUREROR8Ur0RYd3ffNOFlRCRKyhyc+Gzdi181q6FqFZDk5FhDMZuuQViYCDGjx+PVatWYezYsXjyyScRERGB06dP46233kJKSgqGDRtm9bxPP/00RFHEmDFjkJycjJycHGzcuBHHjx/HihUrTPtlZmYiOzsbu3btMjs+NzcXBxqMcBMEAf369QMAbNq0CZmZmdi9ezciIyORl5eHp556Cvfccw+io6NRXV2Nbdu24bPPPsOrr75q308aEbmco9cCaTIICw0NxZkzZ9CrVy8AgKenp9U55KIoOqxHWFVVFdRW+luo1WpUVlY2eZy10VxqtRpFRUUAYDq+4fl9rv7SWFVVZXH8d999h++++w4AsGjRIvz++++m5+qaQh45csS0LTIyElFRUdi/fz90Op3pesnJyThz5oypFgDo378/KisrcbLesuRdu3ZFSEgI9uzZY9rWpUsX9OzZE7/99huuXLli2j548GBcvHgRZ8+eNW3r0aMHfHx8zH7QBAcHIz4+HocPHzbdo1KpREpKCnJzc5GXl8d7csA91X2tdKR76oj/Tu5wTwUFBUhKSupQ99QR/53c5Z6OHDli+nnWUe6pI/47ueyehg6F3+uvY1dqqmmb/+7dSJozB0cWLkRJvcWBMoYNw4Xbb8fvTz9t2tZ79mz4njqF7E8+MW0L++ILdH/zTRxYsQKVPXoAADyKizF43Dice+ABnH/gAdO+/aZOBQD8snKlaVvMunWIXbcOezZvhvbqiBKfkyfRf9o0nMrMREG9BtqDxo5FRffuOFbvhW7C4sUI//JLZP3vf7wn3hPvycZ7qvT1xfF+/YArV4ArV9C1a1f8+OOPeOSRRzB//nyUl5cjICAA9957LyZNmoQzZ84AAMrLy6FQKGAwGHD27FkMHz4cX3/9NZYsWYKioiJ4e3sjLi4Ob7zxBoYOHYrAwEAcPnwYV65cQU1NDc6dO2f6vgcAK1asMAvMAEAmk+GHH35AZGQk1Gq16Vo1NTWQJAkxMTFYsmQJLl26BB8fH8TGxmLz5s0YMmRIx/9e3oHuqe41UUe6p4747+TqezKu/3FtAJa9CZLU+IT07777DuvXr0dmZiZuuOEGbNq0CdnZ2cjMzER4eDgA48irxYsXIy4uDk888YTdC5w4cSLuu+8+3HbbbWbbp02bhmHDhmHixIlWj5s/fz48PT3xzDPPmG1ftmwZLl26hPnz5+O3337D3Llz8frrryM2Nta0T0FBAZ588km88MILpl5ijWlqVBpRfYGBgSguLnZ1GdQO8GuFbMGvF2oJ4fJlnCopQUYjozuIqGOSlEpo0tKMjfSHD4chKsru1+DPIbIFv16oJY4eVWDkSMcFYU2OCBs+fDiKi4vxyiuvIDQ0FNHR0SguLsasWbNMUyZLS0sRFxeHB+q9W2FParXa6sis6upqqyPF6h9XUVFhsb3+SLG6kV/V1dUW+wBgfzAiIiLqGK6+72no1AmCKAI6HQSt1vj365yoVAKenhA9PQFPT0hN/IGXl9X9UNefqJm5HFJzcz3aOhekreev97yvr6/l79J2PH9rnm/289ccF9fflueVx4/D9623mj7+KoO/PzS33GIMvzIyzKZIExG1B6Lo2LmRTQZhADBhwgQMGTIEWVlZOH36NLp06QJJkuDj44OIiAj069cPqampDpsaWb+nV53i4mJoNBrTqLTGjvv+++8ttl+4cAEDBgwAAISEhEAulyM/P9+sqf6FCxcgCEKT5yciIiJqL6TAQCgrK3Hx+HHzJzQayIuKIC8ogKygAPKCAsjz8yHPzTVuKyqCvKQEgl7vmsKdQKbTATodZFbeQG0pycMDoq8vJF/fax/9/K599PGB6OcH1D3v52e+v58fJG9vxzdFsYE6MBA1HLXhNrw2bWryeV2PHsaVIocPh65fP8cvuUZE5ECOfp+u2SAMAKKiojBp0iTHVtKIvn374vPPP0dNTQ28vLwAALt374aHh4dZeNXQjTfeiE8++QS//fYbevbsCQA4c+YMLl68iL59+wIwzkO94YYbkJ2djREjRpiO3b17N7p3784RYURERNRhpKSkWE5HUalgiIpqerqUKEJWUgJZYaExKKv3UVZYaHxcWAhZWZljb8CNCVot5JcvA5cvt/ocklx+LRi7GpyZBWoNgjOrgZqvL+CgN6fJdRRHj8Jr+3azbZJSCe2gQabwyxAT06pzb9y4EWvXrsXZs2ehUCgQGRmJtLQ0zJs3z7RP/YXFPD09ERsbi/vuuw/3339/o4MhfvzxR7PXj507d0bPnj3xzDPPYNCgQWb77tmzB8uXL8fBgweh0WgQFxeHiRMn4oEHHoC8XqD3wQcfYPbs2abHgYGBSE5OxuzZs62+Ljx69ChGjRrV7Ofg+PHjptlOn3/+OdasWYPjx49DkiQkJibiwQcfxF133QUAKCsra/I1aJ3t27cjLy8PU6ZMwb59+zjAgshGbhGEudKIESOwbds2LF68GHfddReKioqwadMm3H777WZB1eOPP47ExERMnz4dANC9e3f06dMHy5cvx3333QdBELB+/Xr07NnTrO/X2LFjMW/ePKxbtw4DBgzAwYMHcfDgQcyZM8fp90pERETkKLm5uaY3FW0ik0EMDIQYGAj9DTc0uptQXW0cVVYXjhUUmAVldSPMOB3TOsFggFBaCllpaZvOI/r4WIxEswjQGjyu26duu2mqJ7mFuimRhi5doLn5ZuOUx2HDjMFnG7zzzjt44403MH36dMyZMwe1tbU4cuQItmzZYhaEAcb+zGPGjEFNTQ22b9+O559/HpIkYfLkyU1eY8WKFQgLC0NJSQlWrFiBiRMnYvv27ejevTsAYxCXmZmJkSNHYsmSJfDx8cHOnTuxYMECZGdnY+XKlRAajJT873//C5lMhsLCQixZsgTjx49HVlYWAq8uFlAnPj4en3/+uenxiRMn8Nxzz+Htt99GXFycaXtdu5xXX30V7777LiZOnIiZM2dCEAR89dVXeOyxx3D8+HHMnj0bPj4+Zue8fPkyJk+ejOeffx4DBw40u3b9JuZEZBuXB2ElJSX44YcfUF5ebnqHwKfBPPO8vDysXr0aL774ot0L9PHxwdy5c7F69Wq89tprUKvVGDNmDO69916z/URRtFjRcubMmfjXv/6F9957D5IkoV+/fhbfrHv27IlZs2Zh48aN+OabbxAcHIwnnngCffr0sfu9EBEREblKXl4eEhISHHZ+ydsbhvh4GOLjG99Jr4fs0iXLUWV10zKvPpbV1DiszpaSZDLjVEVRhND42lJuR1ZZCVRWQl5Q0OpziJ6eEDp3RpBabZy26eNjFpg1OUqtLkzz9HSrqZ7tlayoCPpu3VD8yCPQ9u9v1ymP69atw6RJk8xGWY0cORKzZs2y2DcqKgr9+/cHANx00004deoUPvjgg2aDsN69e5tCp9TUVKSkpGDDhg148cUXkZOTgzlz5mDcuHFYunSp6ZghQ4agT58+eOihh/Dhhx/i/vvvNzvnjTfeCIVCYTr/kCFD8Pnnn+PBBx8028/Ly8tUMwAYDAYAxtd/NzQI9Xft2oXly5dj9uzZmDFjhml7RkYGoqKisGjRImRkZCAtLc3snHULp3Xt2tVsOxG1jSS5sEdYQUEB5syZA4PBgKCgIHz//ffYuHEjpk+fjpSUFNN+NTU1ON6w54QdRUZGNhuyvfvuuxbb1Go1Hn30UTz66KNNHpuamorUesuKExEREZEDKBQQw8IghoVBd+ON1veRJAjl5Y0GZaZtbZiG2BItGbkmKpXGkVTe3sam+QqFcWqiJAF6PQSNBkJNDWQVFRBqax1arz3JamuBwkK0ZVyYpFSaB2TWpnvWC9msTfeU1OrrPkxTnD4NbUoKtH372r3vV1lZGYKCgiy2NxyBZU1ycjLWrl1r0/U6deqEmJgY00ip9evXQxRFPPvssxb73nrrrejXrx/WrFljEYTVFxsbC19fX+Tm5tpUS0Nr1qxBSEgIHn74YYvnHn74YaxZswZr1qxBWlpam65DRC3j0hFh69evR3h4uGkYaHl5OT744AMsXrwYkyZNwu233+7Y6oiIiIjo+iIIkDp1gr5TJ+iv9nm1SqOB/OJFYzBmLSgrKID84kUIOp3DSpXpdEBJifFPIySZDGJwMAwhITAEBEDs0sU4ykqtNq46qVQCcjmE2lrIKishVFRAVl5u/rGiwvjRykrq7krQ6SBv5nPTHEkms1yAwNb+aT4+7bpxvD4uDiGDBkH09UXN3XejZuxY6Pr2tUtAmJSUhLVr1yIiIgLDhw+Hv79/i4/Nzc1FcHCwTdfT6XQoLCw0DUDIzs5GUlJSo/2zRo8ejYULF+Ly5csICAiwus+VK1dQWVlpcy0NZWdnY9SoUVCpVBbPeXp6YtiwYfj222/bdA0iajmXBmGnTp3C1KlTTVMh/fz8MGPGDHTv3h1r167FpUuXmh0OS0RERESul5SUhNp2NCqpWSoVDNHRMERHN75Pw0b/1sKywkLIyssdVqYgiqZrNkXs1AmG0FAYQkMhhoZCHx9vemwIC4MYGgqxUycIVVXGYKy83BiclZdfe1wXmNV9rB+k1e1fUdFu+rQJogihrKzNCzGIanWjK3k21z+t7qOr+qaJYWGoGTMG3p99Bp+1a+Gzdi30Xbuietw41IwdC0NkZKvPvWDBAkyZMgVPPfUUBEFAQkICbrvtNjzyyCPwbdB/TBRF6PV61NbWYtu2bdi6dSseeuihZq9hMBig1+tRUlKCt956C1euXMHo0aMBAIWFhU1OJ4y8em+FhYVmQVjdFMfCwkK8+OKLUCgUGD58uM33X0ej0aC0tNR0vcZquXLlCjQajdWwjIjs6+p/c4dpMgjTarVW/6OPHDkS/v7+ePvtt82+mRERERERuQ1bG/03EpTJCwsd3uhfdjXwUZ482eg+koeHcWTZ1bDMEBZ2LSxLTDQ+DgkBPDwav5AkQaiqMg/Q6gdqVwM0b50OmkuXrAdqFRUQ9HoHfBYcQ1ZVBVRVNRtGNkXy9DQfmdbISLSGCw+YhWmt7JtW9eCD8P7sM9Njxdmz8Hv9dfi9/jo0gwahZuxY1Nx+OyQ/P5vOm5iYiP/973/IyspCVlYWdu3ahaVLl+Kzzz7D9u3boVar+CL6ggAAIABJREFUTfvOnTsXc+fOBWCcOjlu3DhkZmY2e42MjAzT3318fDBv3jykp6fbVGdDXbt2Nf09MDAQS5cubVP/Q6kd9QAkul64dERYeHg4Tpw4gaSkJIvnUlJS8MILL+C1117DmTNnHFYgEREREbXdkSNHHNosvz1rcaP/oqJrq2DWTcmsP7KsoMDYX8tBBK0WitxcKJrph2QICLAMyq72ZjONOgsLg9jIlDQAUAUGorS42PqTkgTU1poFY3UhWl1wVvecUFl57XG9UE2oqHDo58rehNpayGtrgUuXWn0OSak0rupZf/RZC6d76uLjobTymkuVnQ1VdjY6/f3vqB0xAtVjx0IzbFiLR7CpVCqMHDkSI0eOBABs2LABTz/9NDZs2GA24mv69Om444474Onpiejo6BavQLt69WqEhYWhc+fOiIyMhLzeNNXQ0NAmV1asey40NNRs+xdffAGFQoGAgACEh4e3qKdZUzw9PdG5c+dma+nSpQtHgxE5iUub5fft2xc//PAD7rnnHiitfDPt0aMHXnrpJSxcuNBhBRIRERERuZxCATE8HGJ4OBrtOiZJEMrKroVlBQWWI8sKCoy9sxxIfvky5JcvQ3nsWKP7iF5eZsGYISzMNAXTEBoKJCYam/9b668lCICXF0QvLyA4GK2ewaLVWk7vbOl0z7qP7a1v2pUrwJUr9j93bS28vvgCXl98AUNAwLV+YsnJNo1CmzhxIl555RWcPn3abHtERAT69Oljc109evQwrRrZ0KBBg7By5UoUFBQgLCzM4vlvvvkGCQkJFv3BkpOTTatG2sugQYOQlZVldeqjRqPBjh07MGjQILtek4ga59IRYXfccQcGDx7c5HDRqKgovPbaa00m6EREREREHZ4gQOrcGfrOnVvd6N8Unjm60X9NDWRnz0Jx9myj+4TJ5RCDgq6NLKsXlNUfZSa1cHSQBQ8PiP7+gL9/68M0g8E46qx+cNbIdM9G+6e1o75pLSG/fBk+q1fDZ/Vq6Lp1Q824caj5859hiIgw26+4uBiBgYFm2y5fvoyKigqrq0na21//+lf885//xBtvvIG33nrL7LlvvvkG+/fvx6JFixxeBwA8+OCDuPfee7F69Wo8+uijZs+tXr0ahYWFePDBB51SCxG5uEeYl5cXoqKimj2Jn58fEhMT7VYUEREREdlXU42gyclsaPQvLyi4NgWzflBWN8LMkY3+DQbbGv1fDczE+qPM6qZh+vvbZaVDC3I5pE6dYOjUqfXnkCQI1dWWI9HqLTDQ6Eqe9fd3YHDZWsrTp6FctAh+ixZBM3gwqseNQ+2YMZB8fXHLLbdg1KhRSE9PR2BgIPLy8vD+++/Dy8sL48ePt+k6/fv3x6hRo2yaKRQdHY2FCxciMzMT5eXluPfee6FWq7Fr1y6sWLECt912GyZNmmRTHd988w0eeughbNu2Db17927xcUOGDMGMGTPw6quv4ty5cxgzZgwAYNu2bVi/fj1mzJiBtLQ0m2qp88MPP6Bz585m23r37t3oSDkiAmpqXDg18sSJE+jVq5dNJ6yurkZxcTGim/rBTkREREROFRUVheLGej6R+6nX6B9W+vXWEaqqLIIyU7N/d2z038ioMkNoaPON/h1FECCp1ZDUaohWpui1iCQBGo1lkFZRYeqTZhGoWRvJ5sC+acpjx+Dl5QVZVRWq/vpXPPXUU9i+fTvmzp2L0tJSBAUFISUlBe+//77Nr+UMBoNpNUdb/OUvf0F0dDSWL1+OJ598ElqtFnFxcZgzZw4eeOABm/t/iaIIg8HQqgb4s2fPRu/evbF69Wp8+umnAIBevXph+fLluOuuu2w+X53nnnvOYtu8efPw8MMPt/qcRB1dZaXMoecXpCa+S0ydOhUhISH405/+hNTUVPj4+DR6ot9++w27d+/Gjh07MGnSpDYtYdueXLhwwdUlUDsRGBjIFyDUIvxaIVvw64Va6ty5c4iNjXV1GeQKDRv91x9VdnVqpqyw0C2a1xsCAy1HldUPz8LCIPn6OmZ0mTvQ6SymcMrz89F51iybw0xDaCi0qanQDBwIbWqqcbquzPjicuPGjVi7di3Onj0LhUKByMhIpKWlYd68eabjI+pNpfT09ERsbCzuu+8+3H///ZDJrr1Irf9z6McffzQbxdW5c2f07NkTzzzzjEWPrT179mD58uU4ePAgNBoN4uLiMHHiRDzwwANmTfU/+OADzJ492+x6ycnJmD17dpOzkn7++Wd89NFH+OWXX3Du3DlMmTIFL7/8stk+ZWVlVs8xbNgwrF+/3vR45cqV+Oabb3Dy5EkYDAYkJibimWeewcCBAxu9/rhx47Bnzx4sW7YMY8eOtbrPqlWrzD7n1iQmJuLbb78FYOxXtmrVKnzyySc4f/48vLy8kJqaipkzZzbbw+3rr7/GlClTsG/fPoQ3WCjjk08+wRNPPIHjx4+jU6dOOHr0KEaNGgVvb2/8/PPP8Pf3b3RfW/H3FmqJVavUePHFNoz0bUaTI8KWL1+OrVu3YvPmzVi1ahXCwsIQFRUFX19fKJVKVFVV4dKlS/jjjz+g1Wpx44034u9//zvim1pxh4iIiIicTueG07bISWxt9F9QgE6Vlag+fdoUlJlGmTm60X9xMeTFxcDRo43uI3p7WwRlpvCs7u9BQdYb/bs7pRKSvz8M9YIHv5dealEIpu/a1RR6aQcONE69tRIYvvPOO3jjjTcwffp0zJkzB7W1tThy5Ai2bNliEcpMmzYNY8aMQU1NDbZv347nn38ekiRh8uTJTdayYsUKhIWFoaSkBCtWrMDEiROxfft2dO/eHYAxiMvMzMTIkSOxZMkS+Pj4YOfOnViwYAGys7OxcuVKi9Fg//3vfyGTyVBYWIglS5Zg/PjxyMrKsuhzVmf37t04dOgQBgwYgNLS0ibrzczMREZGhulxw4Bn6dKluOeeezB16lR4eHjg3//+N8aPH4///Oc/VqdMfv75/2fvzsOjLu/1j9+TyTJZCFs2SMIWEAwkgAQUtNgKKnJAuwAVQYSDS1Vcu+jx9KfdVDyn9VBrWwUtSw/lAGKF4ga4i4CCCqHIjkpYEpawzGSbzMzvD0xKyEJCZub5zsz7dV1cMpPJzJ3JF4Sb5/k8K+odONCQG264QZdccknt7SVLlujvf/+7Fi1aVHtfQkKCpDMl2KRJk1RYWKi77rpLgwcPVmlpqRYsWKAbbrhBs2fPrj3901/Kyso0e/ZsPfzww359XuB8nE6DWyNjY2P13e9+VzfccIMKCwu1detW7du3TwcPHlRVVZWSkpLUuXNnXX755Ro8ePAFNcIAAAAADDtn0L83JUXOhlZtVFT8a9D/WSvKardhfvNfW3V1wKJGlZWdd9C/7+xB/03MLrvgQf8BFlVcLF+bNoo6dEiJf/lLvY/7oqLk7tu3tvSqGjLkTPnXDPPmzdPkyZPrrLK65ppr9OCDD9Z7bHZ2tgYNGiRJuuKKK7Rz504tWLDgvEXY2TOwhgwZooKCAi1atEiPPfaYvv76az3yyCMaN26cZs2aVfs5l19+ufr3769bb71Vf/3rXzVlypQ6zzlw4MDa0yL79u2ryy+/XCtWrGh0iP19992nBx54oPa5m9K1a9far7MhH374YZ1VUcOHD9fIkSP14osv1ivCysvL9etf/1oPPfSQfvKTnzT5umlpaUpLS6u9/cEHH8hutzeY5dlnn9X69ev197//XYMHD669/7rrrtMtt9yiBx54QGvXrq03j6w1hg4dqvnz5+uuu+5ScnKy354XOB+v12ARVsNmsyk/P1/5+fkBDQMAAIDASExMNB0B4cDhkKdrV3m6dm38MV6voo4da/BUzKiz55idPh2wmHUG/X/2WeNR27b916mY56wqC/ig/ybYqquVNniwvG3ayFZdLZ/DoaqBA/9VfA0aJF8TY2uacvLkyQZPhWzOPK78/HzNnTu3Ra/Xtm1bde3aVUVFRZKkhQsXyuv16mc/+1m9x1533XW65JJL9Je//KVeEXa2bt26qU2bNtq/f3+jjzl7+2ZrnV2C1Tx3bm5ug6//zDPPqGvXrho9evR5i7Dm8ng8mjt3rkaNGlWnBKvJ8tBDD+naa6/VsmXLNH36dL+8piTddtttuueee/SXv/xF999/v9+eFzCtWUUYAAAAQlt+fj5zWRAcUVHypqaeWaHU3EH/jZVlJSWyXcDg82ZHrRn0v317o4/xxcWdGfTfSFHm6dRJnrQ0vw7692Rmyt2rl+I++USSVDlkiFzTpqlyxIhWb/nMy8vT3LlzlZmZqZEjR9YreZqyf//+OiuYmsPtduvw4cMaMmSIJGn9+vXKy8urN6eqxqhRo/TEE0/o2LFj6tixY4OPKS0tldPpbHGWxvziF7/QAw88oHbt2unf/u3f9J//+Z9Nzsf2er367LPP6q0G++qrrzRnzhy98sorfslVY+fOnSotLdW1117b4Mf79eun7OxsrV+/3q9FWPv27XXzzTfrhRde0O233167TRMIdRRhAAAAEWDPnj2MsYCl+BIT5enZU56ePRt/UM2g/0aKsmAM+rdVVir6668V/fXXTT6udtD/WUVZ7aD/b+5ryaD/ssmTa4swx/vvy/H++6rOzlbZlCly3XijfC0osM72+OOPa/r06XrggQdks9nUq1cvjR49Wj/60Y/Upk2bOo/1er2qrq5WRUWFXn/9db322mu69dZbz/saHo9H1dXVOn78uJ5++mmVlpZq1KhRkqTDhw83uQ0xKyur9nFnF2E1p1IePnxYjz32mKKjo1t9QFt0dLQmTZqkq666Sm3bttXGjRv1hz/8Qbt27dJLL73U6OfNmTNH+/fv180331zn/scee0zf//731a9fP508ebJV2c52+PBhSf96bxqSlZVV+zh/uuOOOzRv3jwtWLBAP/rRj/z+/IAJFGEAAAARoKSkhCIMoae5g/5PnPjXqZg1WzLPXll26JDspaUBjdqiQf81ZdlZ88pqV5l9M+i/YvRoeR95RFEuV+3nR+/fr+THH1eb3/1O5ddfL9fUqXKf57TAc+Xm5urdd9/Ve++9p/fee09r167VrFmztHz5cr355pt1tlE/+uijevTRRyWd2To5btw4/fjHPz7va5w9eD4pKUm/+MUvNHz48BblPFePHj1qf56SkqJZs2apV69erXrOxMRE/dd//Vft7aFDhyo7O1t33323PvroowYH4a9bt04zZ87UfffdV2d00DvvvKMNGzbogw8+aFUmq0lLS9ONN96o2bNna+rUqabjAH5BEQYAAAAgdNls8rVvr+r27VV98cWNP+6sQf81K8nqrDQ7dEj24mJrDPpPS5MnI0OKiWnwMbaKCiUsWaKEJUtUNXCgXFOnqnzsWCkurlk54uLidM0119SeMrho0SL95Cc/0aJFi+qs+Lrzzjs1duxYORwOdenSRfHNPFzgxRdfVKdOndSuXTtlZWXJftZ2zoyMjNp5YQ2p+VhGRkad+//xj38oOjpaHTt2VOfOnZs10+xCjBo1Sna7XYWFhfWKsB07dmj69OkaM2ZMvcMFHn30Ud16662KiYnRyZMndfqbGXjl5eU6ffp0vdV2LVHzXpzvfctrYityzffA28AJpDWr7WoOIzjXXXfdpYULF+r//u//WvV1AFZBEdZKPl/QZ2cCAAAAaKlWDvqvLc+CMej/m9dujtjPPlPsZ58p+Ve/UtnEiSqbMkWezMwWvebEiRP1m9/8Rrt3765zf2Zmpvq3cMWZJPXu3bv21MhzXXbZZZo9e7YOHTqkTp061fv4qlWr1KtXr3rzwfLz8xstavzJZrPJ5/PVK9oOHDigSZMmqW/fvvrd735X7+N79+7V008/raeffrrO/Q899JBmzpyprU2sFDyfiy66SO3bt9eqVas0fvz4eh/ftm2bioqKdNtttzX6HDXvZ0lJSb0tliUlJXI4HI0eqpKZmakf/OAH+vOf/9zg6aJAqKEIa6XycpsSEgI3wBMAAMAfBg0apFOnTpmOAVhbcwf9O511t2CeU5QFY9D/uezHjqnNs88q6U9/UsU118h1yy2q+ta36v2r/dGjR5WSklLnvmPHjun06dMNnibpb5MmTdILL7yg//7v/65XGq1atUobN27UzJkzA56jMW+88Ya8Xm+dbY+lpaWaNGmS2rVrpxdffFGxDRyMsHTp0jq3XS6Xpk6dqnvuuUcjRoxoVSa73a5p06bpf/7nf7Rp06Y6M9Z8Pp9mzpyptm3b6gc/+EGjz5Gbm6s2bdrozTff1CWXXFLnY6tWrdKll17aZIYZM2Zo6dKlTc5OA0LFeYuw48eP6+2339apU6eUlZWlYcOG1TtBo6ioSC+++KIee+yxgAW1KqeTIgwAAFif0+k0HQEIG76kJFX37Ck1Nejf7T4z6P+soqx22P9Zt21+HvRv83oV/8Ybin/jDbl79lTZLbeobPz4M4P6JY0YMULXXnuthg8frpSUFBUVFem5555TfHx8g6uNmjJo0CDdcMMNtXPEmqNLly564okn9OMf/1inTp3ShAkTlJiYqLVr1+r555/X6NGjNXny5BblWLVqlW699Va9/vrr6tu3r6QzA+Y3btwoSSorK9OXX36plStXKjY2tnZL6HPPPaeioiJddtllatu2rT799FM9++yzuuKKK3TZZZdJkqqrqzV16lQVFRXpmWee0a5du2pfNzo6unbF3LnbKGuG5ffq1UuDBw+uvf+Xv/ylli5d2uIVYjNmzNC6des0adIk3XXXXRo8eLBOnDihBQsWaN26dZo9e7batWtX+/jbbrtNRUVFev311yVJDodDM2bM0FNPPaWqqioNHz5c5eXlWrJkiTZv3lyvyDtX9+7dNXbsWL+fiAmY0GQRdujQIT3yyCPyeDxKTU3VW2+9pcWLF+vOO+9UQUFB7ePKy8u1bdu2gIe1IqfTJj+d2gsAABAwO3bsaPVgaQAtEBMjb2amvJmZzR/0f9aqspgdOxT7TZFzwRF271bb//f/lPTHP+rko4+q4vrr9cADD+jNN9/Uo48+qhMnTig1NVUFBQV67rnn1KVLlxY9v8fjqZ0v1RI//OEP1aVLFz377LO67777VFVVpe7du+uRRx7R1KlTWzz/y+v1yuPxyHfWCrzPP/9cd9xxR+3tt956S2+99ZaSk5P1xRdfSDozgH/lypV6+eWX5XK5lJGRoVtuuaXOgQAul6u2UDt36+HZz9VcF/qexcXFaeHChZo9e7aWLVumWbNmKT4+XkOGDNHy5cvrbWGtObnzbDNmzFC7du00b948zZ8/X3a7Xf3799eiRYs0ZMiQ82a49957tXz58jrvM+Bvwbi8bL4mruLf/va3Ki0t1X/8x38oKSlJp06d0oIFC/Thhx9q8uTJGjNmjCRp165d+vnPf67FixcHPrHFvPnmEeXlNfq/NqBWSkqKjh49ajoGQgDXClqC6wXNtWvXLoowNBu/t5jX7p57lPDyyy3+vOrMTLn79lV1375yf/PDk50dsMHGXCtoCa4XnI/LZdOCBQn69a8Dd9J1kyvCdu7cqdtvv712K2RycrJmzJihiy66SHPnztWRI0c0bdq0gIULBU4nk/IBAAAA+E/cO++ctwTzRUerulev2rLL3bev3Lm58rVvH6SUAOB/TqdNiYmBXRbWZBFWVVWluAaO4L3mmmvUoUMH/f73v1dpaalGjRoVsIBWRxEGAABCQY8ePUxHANAMNpdLbR96qM593qSkOoVXdd++cvfqJTkchlICQGA4nTYlJRkswjp37qwvvvhCeQ2cmFJQUKCf//zneuqpp7Rnz56ABbQ6lyvKdAQAAIDzSk9PZzsKEAIS/vpXVefmqnz8+LpbG6P4eweA8OdyRSkpyRvQ12jyd9MBAwbo7bffltvd8Ays3r1765e//KW83sCGtDJWhAEAgFCwbt060xEANIPrRz/S8XnzdPqnP1XF6NHydO1KCQYgYhjfGjl27FgNHTq0yVMhsrOz9dRTT6moqMjv4UIBRRgAAAAAAEDrOZ02ZWQEdrFVk0VYfHy8srOzz/skycnJys3N9VuoUFJWRhEGAAAAAADQWi5XlBITqwP6Gk2usf3Nb36jgwcP1rlv69atqqioCGioUOLzUYQBAADra89JcgAAwOKCMSy/ySKssLBQZWVltbe9Xq9+/etf1yvHAAAAYG19+vQxHQEAAKBJLpfhIgwAAADhYfv27aYjAAAANKmszKaEBIowAAAAtFJpaanpCAAAAOdlC/AEqgsqwmyBTgUAAAAAAAD4WZOnRkrS448/LrvdXue+X/3qV/Xuk6QXXnjBf8lChC+wK/YAAAAAAAAiQjAOJGyyCBs3blzAAwAAACDwhg4dqqNHj5qOAQAAYFSTRdj48eODlSNk2e0+VVdL0eddWwcAAGBOcXFxgyv6AQAAIgnD8lspNdWrI0d4GwEAgLXt3bvXdAQAAIBGBWv0FA1OK6Wne1RczL+uAgAAAAAAXKhTp2xq08Yb8NehCGuljAwvRRgAAAAAAEArFBfblZ7uCfjrUIS1Unq6R4cO8TYCAABr6927t+kIAAAAjTp8OEoZGawIs7yUFK+OHWNFGAAAsLakpCTTEQAAABpVXGxXRgYrwiwvKip4A90AAAAu1KZNm0xHAAAAaFRxsV1paRRhAAAAAAAACHOVlTbFxwf+dSjCAAAAAAAAEBEowgAAACJAWlqa6QgAAADGUYT5QUyMT5WVplMAAAA0Licnx3QEAAAA4yjC/CAjw6OSEk6OBAAA1rVlyxbTEQAAABrk9QbvtSjC/CA93avDh3krAQCAdblcLtMRAAAAGlRaGqUOHQJ/YqREEeYX6ekeFRezIgwAAAAAAKClDh+OUnp6cJaFUYT5AUUYAACwupiYGNMRAAAAGlRcbFd6OivCQkb79j6dOMFbCQAArKugoMB0BAAAgAYVF9uVkUERFjJsNtMJAAAAmrZ//37TEQAAABpUXByltDS2RoYUn890AgAAgMYVFRWZjgAAANAgj8emYE1xoAgDAAAAAABARKAIAwAAAAAAgDHB3GVHEeYnCQk+uVwMCwMAANaUl5dnOgIAAIBxFGF+kp7uUXExbycAAAAAAEBzVVdLdnvwloTR3PjJmSLMbjoGAABAgwoLC01HAAAAqOfo0SilpATnxEiJIsxvMjK8FGEAAAAAAAAtUFxsV3q6J2ivRxHmJ+npHh0+zNsJAAAAAADQXMXFUcrIYEVYyGnTxqfTp3k7AQCANWVlZZmOAAAAUE9RUbQ6d2ZFGAAAAPwoOzvbdAQAAIB6jh2LUseOrAgDAACAH23cuNF0BAAAgAbZbMF7LYowP3I4fCovN50CAACgPrfbbToCAACAcRRhftS9e7X27Ys2HQMAAAAAAMDyyspsSkjwBfU1KcL8KCenWrt3U4QBAADrSUxMNB0BAACgjr177erevTqor0kR5kfdulXryy8pwgAAgPXk5+ebjgAAAFDHnj3RysmhCAtZ8fFSZWUQJ7wBAAA00549e0xHAAAAqOPLL6PVrRtFWEgL5kkHAAAAzVVSUmI6AgAAQB1ut01xccF9TYowP/P5zvwAAAAAAABA40z0JxRhfpaW5lFxMW8rAAAAAABAY0wtIqKx8bOePau1Zw8D8wEAgLUMGjTIdAQAAIBahw5FqVMnT9BflyLMz3JyKMIAAID1OJ1O0xEAAABqmTgxUqII87v0dK+Ki+2mYwAAANSxY8cO0xEAAABqUYSFCU6NBAAAAAAAaNqRI3alpnqD/roUYQAAAAAAAAg6E4uJKMICIC7Op4oK0ykAAAD+pUePHqYjAAAA1DK1o44iLAC6davWl18yMB8AAFhHenq66QgAAACSpPJym+LifEZemyIsAHJyqrV7N0UYAACwjnXr1pmOAAAAIEnau9eu7t2DPyhfoggLiB49PNq3jyIMAAAAAADgXHv2RKtnT4qwsBEf71NlJcdHAgAAAAAAnOvLL6PVrRtFWFjxmdnqCgAA0KD27dubjgAAACBJqqy0yeEw89oUYQFEGQYAAKyiT58+piMAAAAYRxEWIKmpHh05wtsLAACsYfv27aYjAAAAGF80RFMTIDk51dqzh4H5AADAGkpLS01HAAAAUHFxlNLTPcZenyIsQCjCAAAAAAAA6tqzJ1o5OWYG5UsUYQHTqZNXhw/bTccAAAAAAACwjN27KcLCks1mft8rAABAjaFDh5qOAAAAoOJiu9LTvcZenyIsgKKiJI+5ba8AAAC1iouLTUcAAACQdGbxkCkUYQGUk1OtvXuZEwYAAMzbu3ev6QgAACDClZdLcXFmt89RhAVQfn6VPv88xnQMAAAAAAAA4/75zxj17es2moEiLIC6d/foyy9ZEQYAAAAAALBlS6wGDKAIC1sm97wCAACcrXfv3qYjAACACHf0aJRSUswNypcowgIuOtont9myEwAAQElJSaYjAAAAGEcRFmC9e1drxw62RwIAALM2bdpkOgIAAIhgTqdNSUlmB+VLFGEB17+/W5s3x5qOAQAAAAAAYExhYYz69asyHYMiLNA6d/bo4EG76RgAAAAAAADGbN4co/x887OjKMICjIH5AADACtLS0kxHAAAAEezUqSi1a8fWyIgQG+tTRYXpFAAAIJLl5OSYjgAAAGAcRVgQ5Oa6tW1bjOkYAAAggm3ZssV0BAAAEKFKS21q185rOoYkirCg6N/frS1bKMIAAIA5LpfLdAQAABChtmyJVf/+5ueDSRRhQZGW5tWRIwzMBwAAAAAAkWfr1hj160cRFlF85ufBAQCACBYTw+p0AABghstlU2KiNYoRirAgSUz0yeXiCEkAAGBGQUGB6QgAAADGUYQFSV6eW4WF/EssAAAwY//+/aYjAACACFRSEqW0NI/pGLUowoIkL69KmzdThAEAADOKiopMRwAAABFo8+YY5edbYz6YRBEWNO3b+3TyJG83AAAAAAAb9pArAAAgAElEQVSIHNu2xSg3lyIMAAAAAAAAYa6y0iaHw3SKf6EIC6K2bb06cYKB+QAAIPjy8vJMRwAAABHGZ42DIuugCAui/Hy3tmxhThgAAAAAAAh/Bw7YlZlpnUH5EkVYUJ05OTLWdAwAABCBCgsLTUcAAAARZvPmGPXvX2U6Rh0UYUGUlOSTy8XWSAAAAAAAEP527IhW797VpmPUQREWZFbcHwsAAAAAAOBvbrdNMRabEEURFmTZ2R59/bXddAwAABBhsrKyTEcAAAARpKpKiomx3mogirAgu/zySq1dG2c6BgAAiDDZ2dmmIwAAgAjy2WexuuQSt+kY9VCEBVmXLh599RUrwgAAQHBt3LjRdAQAABBBNmyI1ZAh1hqUL1GEBZ3tm1n5zAoDAADB5HZb719kAQBA+CovtykhwXrlB0WYAb16VWv37mjTMQAAAAAAAPyuvNwmh8N6JZhEEWbEmTlhsaZjAACACJKYmGg6AgAAiBCffGLNbZESRZgRGRleFRczJwwAAARPfn6+6QgAACBCbNoUo0suoQjDWWw2yes1nQIAAESKPXv2mI4AAAAiRGWlTXFxplM0jCLMkL593dq2jTlhAAAgOEpKSkxHAAAAEeDkSZuSk605H0yiCDNm6NBKrV1r0XoUAAAAAADgAmzYEKvLLqs0HaNRFGGGdOjg04kTvP0AAAAAACB8bNkSq/x8t+kYjQqJvXlr1qzRihUrdOzYMWVlZWny5MnKy8tr8nNWr16t9evX6+uvv1ZVVZW6dOmicePGqX///nUeN2HChHqf26tXLz3++ON+/RoaEh0tud1STEzAXwoAAES4QYMG6dSpU6ZjAACAMOfxnOk7rMrC0c5Yu3at5syZo/Hjx6tPnz569913NXPmTD355JPq0qVLo5/38ssva8CAAbr22mvlcDj0/vvv64knntBPf/pTFRQU1HnsmDFjdNlll9Xejo+PD9jXc7b+/au0eXOMCgqs25QCAIDw4HQ6TUcAAABh7ujRKHXsaO2TAS1fhC1ZskRXXnmlxo0bJ0nKzc3Vvn379Morr+jee+9t9POeeuopJScn197Oz8/X4cOH9eqrr9YrwtLS0nTRRRcF5gtowqWXVmnBgkSKMAAAEHA7duxQr169TMcAAABh7KOPYjVsmHXng0kWnxFWXFysQ4cOadiwYbX3RUVFaejQofr888+b/NyzS7Aa3bp108mTJ/2e80K1aePT6dM20zEAAAAAAABabfv2GPXpU206RpMsvSLswIEDkqTOnTvXuT8zM1NOp1OnTp1qsPBqzK5du5SVlVXv/qVLl2revHlKTEzUoEGDNGXKFCUlJbUufDPFxflUXi4FaTcmAAAAAABAQPh8UpSll1xZvAhzuVySpMTExDr319x2Op3NLsLefvtt7du3TzfffHOd+6+88koNGjRIycnJ2rt3r5YtW6avvvpKTz75pKIa+O6tWbNGa9askSTNnDlTKSkpLf66zjZihE27d8frO9/xtep5YH3R0dGtvl4QGbhW0BJcL2gun8/HtYJm4/cWNBfXClqC6yW87d8v9eoVpZSUONNRmhT0IqysrEylpaXnfVxmZmbtz222hrcPNnb/ufbu3au5c+dq9OjR6tevX52P3X333bU/z83NVWZmpp588klt3LhRQ4YMqfdcI0eO1MiRI2tvHz16tFkZGtOrl/T880nKy2OAbbhLSUlp9fWCyMC1gpbgekFzpaamcq2g2fi9Bc3FtYKW4HoJbytXxuuSS6p09Kin1c917s5Afwp6EbZu3To9//zz533ckiVLald+uVwuJSQk1H6ssZViDSkuLtaTTz6pfv36acqUKed9/IABA+RwOLRv374GizB/i4+XKiuZEwYAAAJr3bp1DMsHAAABs3dvtMaNKzcd47yCXoSNGDFCI0aMaNZja1aFHThwQKmpqbX3HzhwQElJSefdFnny5Ek9/vjjSk1N1f3339/gVsdz1awya+5qM39ITPTJ6bQpKYntkQAAAAAAILT4fGd+BLFKuWCWHmGWnp6uTp06af369bX3eb1erV+/XgMGDGjycysqKvTkk09Kkh5++GHFxTVvj+rnn3+uiooK9ejR48KDt9Cll1Zqw4bYoL0eAAAAAACAv+zbZ1f37tY+LbKGpYswSRo/frzeeecdLVu2TFu3btWf//xnHTp0SN/97ndrH7Nt2zbdeOON2rZtW+19v/3tb/XVV19pwoQJOnz4sHbu3Fn7o8aaNWv0/PPP66OPPtLWrVu1YsUKzZo1Sz179tQll1wStK9xwAC3Pv+cIgwAAARO+/btTUcAAABhau3aOF1+eZXpGM1i6VMjJemKK65QRUWFli9frmXLlik7O1sPP/ywunTpUvsYn88nr9crn+9fWwu3bNkiSXrmmWfqPeeSJUsknVlx9t5772nDhg0qLy9Xu3btNHz4cN14443N2kbpLzExUnVoFKcAACBE9enThwHFAAAgIA4csCsrq/VD8oPB8kWYVP+kxnP17du3ttyqce7thuTl5SkvL6/V+fyhXTuvjh+3qUMH5oQBAAD/2759O0fWAwAAv/N6Q2M2WA3Lb42MFMOGVWn9+ubNMQMAAGip0tJS0xEAAEAY2rEjWn36uE3HaDaKMIvIzXVr69YY0zEAAAAAAACabe3aOA0bFhrzwSSKMMuw2yWP58xxowAAAAAAAKHgyJEopaZ6TcdoNoowC8nPd2vLFlaFAQAA/xs6dKjpCAAAIMwcP25Tu3ahU4JJFGGW8u1vV+qdd5gTBgAA/K+4uNh0BAAAEGbWrHHo6qsrTcdoEYowC0lM9Km8PISOWgAAACFj7969piMAAIAws2dPtHr2rDYdo0UowiymWzeP9u2zm44BAAAAAADQqPJymxyO0Bt0ThFmMVdfXaHVqx2mYwAAAAAAADTqgw9iNXx4aG2LlCjCLCclxatjx/i2AAAA/+rdu7fpCAAAIIx8+mmsBg50m47RYjQuFtSxo1dHj/KtAQAA/pOUlGQ6AgAACBPV1ZLNJkWFYHURgpHDH9sjAQCAv23atMl0BAAAECY2bozVkCFVpmNcEIowC+re3aMvv2RgPgAAAAAAsJ61a+M0bFjozQeTKMIsKyHBJ5fLZjoGAAAAAABALZ9PqqqS4uJMJ7kwFGEW9Z3vVOrdd0P0qgIAAJaTlpZmOgIAAAgDX3wRrYsvrjYd44JRhFlUXp5bhYUxpmMAAIAwkZOTYzoCAAAIA2+/7dBVV1WYjnHBKMIsymaToqMld+idRAoAACxoy5YtpiMAAIAwcPq0TcnJPtMxLhhFmIUNGVKpDRtiTccAAABhwOVymY4AAABC3IEDdnXu7DEdo1Uowixs6NAqrVvHnDAAAAAAAGDeqlVxuuaa0N0WKVGEWVpMjFRdfeZEBgAAgNaIiWH2KAAAaJ3Dh+3q1MlrOkarUIRZXF6eW1u38gdXAADQOgUFBaYjAACAEFZaalO7dqFdgkkUYZb37W9X6p132B4JAABaZ//+/aYjAACAEPbWWw6NGFFpOkarUYRZXFKSTy6XzXQMAAAQ4oqKikxHAAAAIWzXrmj16lVtOkarUYSFgK5dPfrqK7vpGAAAAAAAIAKVl0sOh0+2MFinQxEWAq6+ukKrVztMxwAAAAAAABHoww/jdMUVob8tUqIICwmpqV4dPcq3CgAAXLi8vDzTEQAAQIj69NNYXXKJ23QMv6BdCREdOnh17BjfLgAAAAAAEDwez5n/2sNkYhPNSogYObJCq1dzeiQAALgwhYWFpiMAAIAQtGlTrAoKqkzH8BuKsBDRo4dHe/dGm44BAAAAAAAiyLvvhs98MIkiLKSkp3t16BDfMgAAAAAAEHiV3/RfcWG0QY1WJYRcf325VqyINx0DAACEoKysLNMRAABAiHn7bYdGjKgwHcOvKMJCSGrqmYH5Pp/pJAAAINRkZ2ebjgAAAELMZ5/FhM1pkTUowkJMXp5bW7fGmI4BAABCzMaNG01HAAAAIeT48Si1b++VzWY6iX9RhIWYq6+u0KpVDtMxAABAiHG7w+tfcwEAQGCtWOHQ2LHhtS1SoggLOQ6H5PFI/FkWAAAAAAAEysGDdmVleUzH8DuKsBD0ne9U6p13wujIBgAAEHCJiYmmIwAAgBCxe3e0cnKqTccICIqwEFRQUKWNG2NNxwAAACEkPz/fdAQAABAiXn3VodGjw29bpEQRFpJsNik52acTJ8JsYh0AAAiYPXv2mI4AAABCgNcrlZXZ1KaNz3SUgKAIC1Fjx5Zr5cp40zEAAECIKCkpMR0BAACEgI8+itWwYVWmYwQMRViI6trVo6+/tpuOAQAAAAAAwsgHH8TpiisqTccIGIqwENa1q0f79lGGAQAAAACA1isrs8nh8MkexlUDRVgIGzOG7ZEAAKB5Bg0aZDoCAACwuNdfd+i668JzSH4NirAQ1ratT06nTV6v6SQAAMDqnE6n6QgAAMDiduyIVp8+1aZjBBRFWIgbMqRKH38cazoGAACwuB07dpiOAAAALOzQoSilp4f/ShuKsBB35ZWVevfdONMxAAAAAABACFuxIl7XX19uOkbAUYSFuOhoKSZGKg//axUAAAAAAASAzycdOxal1FRWhCEEXHttuVatcpiOAQAALKxHjx6mIwAAAIsqLIxRXp7bdIygoAgLA337Vuuf/4wxHQMAAFhYenq66QgAAMCiVq1y6Oqrw/u0yBoUYWHAZpNSUrwqLubbCQAAGrZu3TrTEQAAgAW53ZLHIzkiZKMZzUmYuP76cq1YEW86BgAAAAAACCHvvBOnq66qNB0jaCjCwkRGhlclJXw7AQAAAABA823cGKuCgirTMYKG5iSM5OZW65//jDYdAwAAWFD79u1NRwAAABZz4oRNbdv6ZLOZThI8FGFhZNSoCr3xBtsjAQBAfX369DEdAQAAWMzKlfEaM6bcdIygoggLI/HxPnk8UnlkXcMAAKAZtm/fbjoCAACwEJ9P2rcvWl27ekxHCSqKsDBzww0MzQcAAPWVlpaajgAAACzkk09iNWRI5MwGq0ERFmZ6967Wzp0x8vlMJwEAAAAAAFa1Zk2cRo6sMB0j6CjCwtDgwVX65JNY0zEAAAAAAIAFHToUpdRUr+x200mCjyIsDF19dYVWr44zHQMAAFjI0KFDTUcAAAAW8dJLCRo3rsx0DCMowsKQ3S6lpHh16BDfXgAAcEZxcbHpCAAAwAIqKyWXy6b27SNzphJNSZgaP75ML72UYDoGAACwiL1795qOAAAALOAf/4jX2LHlpmMYQxEWpjp08Mnlsqmy0nQSAAAAAABgFV98EaO+fatNxzCGIiyMjRlTrpUr403HAAAAAAAAFvDZZzEaMKDKdAyjKMLCWL9+1dq2LcZ0DAAAYAG9e/c2HQEAABj2xhsOjRpVYTqGURRhYa5//yp99hllGAAAkS4pKcl0BAAAYNCRI1Fq396rmAivCCjCwtx111XojTccpmMAAADDNm3aZDoCAAAwaOnSBI0bF7lD8mtQhIW5mBipbVufjh7lWw0AAAAAQCRyu6WTJ21KSfGajmIc7UgEGD++TEuXMjQfAAAAAIBI9NprDl13XWTPBqtBERYBUlO9OnEiSm636SQAAMCUtLQ00xEAAIAhW7bEasAASgGJIixijBpVoddfZ1YYAACRKicnx3QEAABgwNat0erblxKsBkVYhBg40K3Nm2NNxwAAAIZs2bLFdAQAAGDAq6/Ga8wYhuTXoAiLILm5bm3dGm06BgAAMMDlcpmOAAAAguz4cZsSE32KZV1MLYqwCDJmTLlWrmRoPgAAAAAAkWDp0gSNG1dmOoalUIRFkLg4KTHRp+PHbaajAACAIIuJiTEdAQAABFF1tXT0aJQyMrymo1gKRViEGTeuTC+9lGA6BgAACLKCggLTEQAAQBCtXu3QNddUmo5hORRhEaZTJ6+OHImSx2M6CQAACKb9+/ebjgAAAIJo48ZYFRRUmY5hORRhEejqqyu1erXDdAwAABBERUVFpiMAAIAg2bEjWhdd5JaNyUj1UIRFoMGDq/TJJxwZAQAAAABAOFqxIl7XX19uOoYlUYRFIJtNuugit7ZtizYdBQAAAAAA+NHx4zbFxfkUH286iTVRhEWo732vXH//O78qAACIFHl5eaYjAACAIFi4MFE33VRmOoZlUYRFqNhYKTvbo9277aajAAAAAAAAPzh50qaqKptSUrymo1gWRVgEmzChTEuXJpiOAQAAgqCwsNB0BAAAEGCLFiXopptcpmNYGkVYBHM4pLQ0r77+mlVhAAAAAACEMpfLplOnotSpE6vBmkIRFuEmTizTokWsCgMAAAAAIJQtWpSgiROZDXY+FGERLiHBp7ZtvTp4kEsBAIBwlpWVZToCAAAIkPJy6ciRKGVne0xHsTzaD2jSpDItWpRoOgYAAAig7Oxs0xEAAECALF6coAkTWA3WHBRhUJs2PsXF+XTkCJcDAADhauPGjaYjAACAAKiqkg4csCsnh9VgzUHzAUnSTTe5tHAhs8IAAAhXbrfbdAQAABAAy5Yl6Ac/KDcdI2RQhEGS1KGDT5J0/LjNcBIAAAAAANAc1dXS7t3R6tOn2nSUkEERhlqTJpXpb39jVhgAAOEoMZH/xwMAEG6WL4/XDTewGqwlKMJQKzXVq8pKm06dYlUYAADhJj8/33QEAADgRx6PtHVrjPLzGX/QEhRhqGPiRJf+9jdmhQEAEG727NljOgIAAPCj115zaPRoVoO1FEUY6ujc2auTJ6NUVsaqMAAAwklJSYnpCAAAwE98PmnTplgNHsxqsJaiCEM9EyeWadEiVoUBAAAAAGBFq1fH6eqrK0zHCEkUYainSxePSkqiVM4KSwAAAAAALMXnk9aujdOwYVWmo4QkijA0aMKEMi1ZwqowAADCxaBBg0xHAAAAfvD++3G68spK2ZhodEEowtCgnByPiorsqqJgBgAgLDidTtMRAACAH7z9dpy+851K0zFCFkUYGvX975fr5ZfjTccAAAB+sGPHDtMRAABAK61fH6vLLqtiNVgrUIShURdfXK2dO2NUXW06CQAAAAAAeOMNh669liH5rUERhiZ997vlWr6cVWEAAAAAAJi0aVOMBg6sUhRNTqvw9qFJ+flubd0aI6/XdBIAANAaPXr0MB0BAAC0wsqV8RozhtVgrUURhvMaPbpcr73mMB0DAAC0Qnp6uukIAADgAm3dGq0+fdyy200nCX0UYTivwYPd+uSTWHk8ppMAAIALtW7dOtMRAADABVq2LEHf/3656RhhgSIMzfK975Vr2TJmhQEAAAAAEEwffxyrAQOqFBNjOkl4oAhDswwY4NbOnTEqp4AGAAAAACAofD5p5UqHxo5lNpi/UISh2aZMcWnBgkTTMQAAwAVo37696QgAAKCFXnvNoVGjKjgp0o94K9FsXbp45HLZdOwYlw0AAKGmT58+piMAAIAWqKqSPvkkVsOGVZmOElZoNNAiU6e6NHcuq8IAAAg127dvNx0BAAC0wKJFCZo4scx0jLBDEYYW6dDBp7Ztvdq3jzNbAQAIJaWlpaYjAACAZjp92qZDh+zq3bvadJSwQxGGFrv5ZpcWLmRVGAAAAAAAgTBvXqKmTnWZjhGWKMLQYg6HdPHFbn36KWe3AgAAAADgT4cORclmkzIyvKajhCWKMFyQ732vXK+8Ei+fz3QSAADQHEOHDjUdAQAANAOrwQKLIgwXJCpKGjmyQqtXx5mOAgAAmqG4uNh0BAAAcB5ffBGtrCyPkpJYdRIoFGG4YMOHV2nt2jhVM7sPAADL27t3r+kIAADgPBYvTtCNN3JSZCBRhKFVJkwo05IlCaZjAAAAAAAQ0j78MFaXXlqlGMZxBxRFGFqlb99qffWVXS6XzXQUAAAAAABCktcrrVrl0KhRFaajhD2KMLTalCkuzZ+faDoGAABoQu/evU1HAAAAjVi+PF5jx5bLxhqTgKMIQ6tlZnrldkslJVxOAABYVVJSkukIAACgAZWVUmFhjAYPdpuOEhFoLuAX06a5NG8eq8IAALCqTZs2mY4AAAAasHBhoiZNcpmOETEowuAXyck+paV5tHt3tOkoAAAAAACEhBMnbDp2LEo5OR7TUSIGRRj85qabyrRwISdIAgAAAADQHHPnJmraNFaDBRNFGPwmNlYaOLBKGzbEmo4CAADOkZaWZjoCAAA4y/79dsXH+5SS4jUdJaJQhMGvxo6t0KuvOuTzmU4CAADOlpOTYzoCAAA4y1//mqBbbikzHSPiUITBr2w2afToCq1c6TAdBQAAnGXLli2mIwAAgG8UFsaoR49qxceziiTYKMLgd5ddVqVPP41VVZXpJAAAoIbLxfwRAACswOeTli6N17hx5aajRKSQOOJvzZo1WrFihY4dO6asrCxNnjxZeXl5TX7OkiVL9NJLL9W7/5FHHtGAAQNqb7vdbi1atEjvv/++KisrlZubq+nTpzNHo5VuuqlMf/tbgqZOZZknAAAAAAA13nknTsOHVyo6JBqZ8GP5t33t2rWaM2eOxo8frz59+ujdd9/VzJkz9eSTT6pLly5Nfm5CQoIeeeSROvdlZWXVuT137lytX79et9xyi5KTk7V06VL95je/0W9/+1vFxjL0/UL16lWtFSviVVISpbQ0Bv8BAGBaTEyM6QgAAES8igrp3Xfj9MtfnjIdJWJZfmvkkiVLdOWVV2rcuHHq16+f7rrrLmVkZOiVV1457+fa7XZddNFFdX4kJCTUfvzYsWN6++23dcstt+jKK6/UwIED9ZOf/ERHjhzRBx98EMgvKyLcfrtTc+Ykmo4BAAAkFRQUmI4AAEDEmzs3UdOnu2SzmU4SuSxdhBUXF+vQoUMaNmxY7X1RUVEaOnSoPv/881Y//+bNmyVJl156ae19HTp0UJ8+ffTZZ5+1+vkjXZs2PuXnu/Xhh6ysAwDAtP3795uOAABARNu71y6bTera1WM6SkSzdBF24MABSVLnzp3r3J+ZmSmn06lTp5peSuhyuTR9+nRNnDhRP/vZz7Rhw4Y6Hz948KA6duwoh6PuCYeZmZk6ePCgH74CjBlToVWrHKqsNJ0EAIDIVlRUZDoCAAARy+eT5s1L1LRpHF5jmqVnhNWcbpSYWHd7Xc1tp9Op5OTkBj83IyNDkydPVrdu3VRRUaHVq1frd7/7nX784x/XrgBzOp11tkrWSEpKavRkpTVr1mjNmjWSpJkzZyolJeXCvrgI8uCD0uLFaXrwwcieFRYdHc31gmbhWkFLcL2guXbt2sW1gmbj9xY0F9cKWiKSr5eXXorS+PE+ZWZG5tdvJUEvwsrKylRaWnrex2VmZtb+3NbI5tnG7pek4cOH17k9aNAg/fznP9dLL71UZytkQ8/h8/kafd6RI0dq5MiRtbePHj3a6GNxRrt2ksuVqE2bKiJ6CWhKSgrXC5qFawUtwfWCluBaQXPxewuai2sFLRGp18vp0zatXZuk//zP04rAL/+CnLsz0J+CXoStW7dOzz///Hkft2TJktqVXy6Xq87KrcZWijXFZrPp0ksv1cKFC+X1ehUVFdXoyi+Xy9Wi58b5/fu/u/TEE8n6xS9OMRQQAAAD8vLyVFFRYToGAAARZ/bsJN1+O1sirSLoRdiIESM0YsSIZj22ZlXYgQMHlJqaWnv/gQMHlJSU1Oi2yObq3Lmzjh07poqKijpzwg4ePBjQ9jESxcVJI0ZU6LXXHPq3f+MP4QAAAACA8FdYGKP0dI9SUyN7VJCVWHpYfnp6ujp16qT169fX3uf1erV+/XoNGDCgRc/l8/n08ccfq1u3boqKOvNl9+/fX5L08ccf1z7u+PHj+uKLLzRw4EA/fAU42/DhVfr001g5nSwJAwAg2AoLC01HAAAgong80uLF8Zo4scx0FJzF0sPyJWn8+PH6wx/+oNTUVPXu3VvvvfeeDh06pHvvvbf2Mdu2bdOvfvUrPfroo8rNzZUkPfbYY7r00kuVmZmpyspKvfXWW9q1a5d++tOf1n5ex44dddVVV2n+/PmSpOTkZC1dulSpqan61re+FdwvNELcfrtTc+Yk6oEHnKajAAAAAAAQMIsWJeiHPyyX3W46Cc5m+SLsiiuuUEVFhZYvX65ly5YpOztbDz/8sLp06VL7GJ/PJ6/XW2fIfUZGhl577TWVlpYqKipK3bt318MPP1xvpde0adMUFxen+fPnq6qqSrm5ubrvvvsUGxsbtK8xkqSne9Wxo1f//Ge0+vatNh0HAAAAAAC/O3IkSsXFduXlsRrMamy+po5IxHkdPHjQdISQ4/FIv/hFsn75y1OKsvTmXP+K1BNS0HJcK2gJrhc0V3l5ueLj403HQIjg9xY0F9cKWiKSrpfHH2+je+91qk0bKpcLEci57RFUQ8Aq7HZp3LhyLV6ccP4HAwAAv8jOzjYdAQCAiLB2bazy8tyUYBZFEQYj+vd3a/9+u44e5RIEACAYNm7caDoCAABhr7JSevNNh8aOrTAdBY2ghYAxd9xxZnA+AAAIPLfbbToCAABhb+7cRE2d6pLNZjoJGkMRBmPatvXp4ourtX49BxMAAAAAAELbV1/Z5fNJPXp4TEdBEyjCYNQNN5TrtdccqqoynQQAgPCWmMgqbAAAAsXnk/7yl0RNm+YyHQXnQREGo2w2acoUl+bP5w/nAAAEUn5+vukIAACErddfd2jEiEo5HKaT4HwowmBcz54eVVbatH+/3XQUAADC1p49e0xHAAAgLDmdNm3aFKvhwytNR0EzUITBEqZPd+rFF1kVBgBAoJSUlJiOAABAWJozJ1G33eY0HQPNRBEGS4iPl668slJvvsk6UgAAAABAaNi2LVodOniVkeE1HQXNRBEGy/jOdyq1YUOsyso4ZxYAAAAAYG1er7RoUYImTy4zHQUtQBEGS7ntNqeef54tkgAA+NugQYNMRwAAIKwsXpyg73+/XHbGXYcUijBYSqdOXpgelEsAACAASURBVHXu7NHHH8eajgIAQFhxOpldAgCAv3z9tV3FxVEaONBtOgpaiCIMljNhQrlWrnTI5WKLJAAA/rJjxw7TEQAACAter/T880m6807+kSkUUYTBcmw26e67nfrjH5NMRwEAAAAAoI758xM0caJLcXGmk+BCUITBktLTvcrNdevdd/mdBQAAAABgDTt3Rquy0qZ+/apNR8EFogiDZY0ZU6EPP4zTiRNskQQAoLV69OhhOgIAACHN7Zbmz0/Urbe6TEdBK1CEwdLuvvu0nn22jekYAACEvPT0dNMRAAAIaS+8kKhp05yKjjadBK1BEQZLa9/ep2HDKrVypcN0FAAAQtq6detMRwAAIGRt3hyjhASfevb0mI6CVqIIg+VddVWl/vnPGJWUcLkCAAAAAIKrvFxaujReN99cZjoK/IBmASFhxowzp0j6fKaTAAAAAAAiyXPPJenOO12KokEJC3wbERISE30aPbpCL70UbzoKAAAhqX379qYjAAAQctati1VWlkeZmWyJDBcUYQgZl15apQMH7CoqspuOAgBAyOnTp4/pCAAAhBSn06Y33nBo3Lhy01HgRxRhCCk/+pFTzz2XKK/XdBIAAELL9u3bTUcAACCkPPtskmbMcMpmM50E/kQRhpDicEg//GG5FixIMB0FAICQUlpaajoCAAAhY82aOPXv71ZqKqswwg1FGEJOXp5bFRU27d4dbToKAAAAACDMHD9u04YNsbruugrTURAAFGEISdOnuzRvXoKqq00nAQAAAACEk2efbaMZM5ymYyBAKMIQkmJipKlTy/TCC4mmowAAEBKGDh1qOgIAAJa3fLlD3/52pdq29ZmOggChCEPI6tmzWg6HT4WFMaajAABgecXFxaYjAABgaYcPR2nnzhgNH15pOgoCiCIMIW3KlDItXhyvCrZuAwDQpL1795qOAACAZfl80p/+lKS772ZLZLijCENIi4qS7rjDpeeeSzIdBQAAAAAQohYvjtfYseVKSGBLZLijCEPIy872qFMnjz7+ONZ0FAAAAABAiPnqK7tKSuwaPNhtOgqCgCIMYWHChHK9+qpDLpfNdBQAACypd+/epiMAAGA5Ho80Z06i7riDLZGRgiIMYcFmk+6+26k//IEtkgAANCQpif9HAgBwrvnzEzVxYpni4kwnQbBQhCFspKV5VVBQpRUrHKajAABgOZs2bTIdAQAAS9m0KUZRUT717VttOgqCiCIMYWXkyErt2xet3bvtpqMAAAAAACzq+HGb/vGPeN1yS5npKAgyijCEnbvvdmru3CSVlzMvDAAAAABQl9crzZrVRg8+eFo2/toYcSjCEHaio6V77jmtWbOYhQIAQI20tDTTEQAAsIQXXkjUhAllSk72mY4CAyjCEJYyMry64opKLV0abzoKAACWkJOTYzoCAADGffRRrJKTverXj7lgkYoiDGHrW9+q0rFjUdq2Ldp0FAAAjNuyZYvpCAAAGFVSEqW333bohz8sNx0FBlGEIazdfrtLixYl6PRpNn4DACKby+UyHQEAAGM8HumZZ5KYCwaKMIS3qCjp/vudevrpNvKx/RsAAAAAItKf/5ykKVPKlJDAXwwjHUUYwl7Hjl6NHl2h//3fBNNRAAAwJiYmxnQEAACMeOedOGVmenTRRcwFA0UYIsTgwVVyu236/HP+EgAAiEwFBQWmIwAAEHQHDkRpw4ZYfe97zAXDGRRhiBjTprn08svxOnGCDeEAgMizf/9+0xEAAAgqt1v605/a6P77T5uOAguhCEPEsNmkBx88raefbiOv13QaAACCq6ioyHQEAACC6tlnk3TbbU45HKaTwEoowhBR2rXz6Qc/KNfcuYmmowAAAAAAAuSNNxzq06da3bp5TEeBxVCEIeL07++Ww+HThg2xpqMAAAAAAPzsyy/t2rYtWtddV2E6CiyIIgwR6aabyvTmmw4dPcovAQBAZMjLyzMdAQCAgKuokObMSdKMGU7TUWBRtACISDXzwmbNSpKHlbIAAAAAEBZ+//s2uusup2LZAIRGUIQhYiUl+TRpUplmz2ZeGAAg/BUWFpqOAABAQL3ySrwGD65SZiarHdA4ijBEtIsvrlZqqlfvvx9nOgoAAAAA4ALt2hWt/fvtuuqqStNRYHEUYYh448aVa+3aWB06xC8HAAAAAAg1ZWU2zZ+foDvvZC4Yzo+/+QOS7r/fqWefbSO323QSAAACIysry3QEAAD8zueTZs1K0j33OBUdbToNQgFFGCApPt6nf/93p/74xyTTUQAACIjs7GzTEQAA8LslS+J15ZWVSk/3mo6CEEERBnwjJ8ejiy6q1sqVDtNRAADwu40bN5qOAACAX336aYxOn47S5ZdXmY6CEEIRBpxl9OgKFRXZ9fnnMaajAADgV272/wMAwsiBA3a9/rpD06e7TEdBiKEIA85xxx0u/eMf8TpwgF8eAAAAAGA1TqdNf/rT/2fvzsOjKu/3j9+zJJnJJANJIEgW9rCERSUoggpWqBsqWkFRQbRYBakWKm64sAgFqXyptforxdYqLhVEBWu1luIecCuIyCaLCGEJYZvMZCaZ7ffHlNiwGUKSM8v7dV1eJZOZcKc8OZm55zmfk6YJE8plMhmdBrGGV/rAEUwm6Z57XHrqqXR5PBxVAQDxweFwGB0BAIBTFgxKs2al69e/LldKitFpEIsowoBjsNmk8ePLNWtWukLMXAQAxIEePXoYHQEAgFP25JNpuvHGCmVl8UINdUMRBhxH8+YhDRtWoSef5EqSAIDYt3nzZqMjAABwShYssOv00/3q1ClgdBTEMIow4AS6dAmoa1e/Xn3VbnQUAABOSWlpqdERAACos+LiZPl8Jv3kJ5VGR0GMowgDfsTAgZVyu01asSLZ6CgAAAAAkHC2brXo449TNGJEhdFREAcowoBaGDmyQu+/n6LvvrMYHQUAAAAAEsbBgyb9+c8OjR/PFSJRPyjCgFowmaS77y7XM884dOgQR18AQOwpKioyOgIAACfF75cefzxd99xTrqQko9MgXlCEAbWUlBQpw37723QFmM0IAIgxbrfb6AgAANRaOCzNmZOuUaM8atIkbHQcxBGKMOAkZGSEdcstHs2Zk250FAAATsqGDRuMjgAAQK3Nn5+q88+vVNu2QaOjIM5QhAEnqX37oPr2rdQLL6QaHQUAAAAA4s6yZSmy2cLq06fK6CiIQxRhQB2ce26VkpLCev/9FKOjAAAAAEDcWL/eqtWrk3TttV6joyBOUYQBdXTddV6tXJmkjRutRkcBAOBHtWvXzugIAACcUFmZWS+/nKo772SuJRoORRhwCu66y60XXkjVvn38KAEAoluLFi2MjgAAwHH5fJHh+PfeWy6Lxeg0iGe8egdOgcUi3Xtvuf7v/9JVWWl0GgAAjm/58uVGRwAA4JjCYenxx5264w63HA6uEImGRREGnKK0tLDGjHFr9ux0hTlmAwAAAMBJmTfPocsu8yo3lytEouFRhAH1IC8vqIsv9unPf3YYHQUAAAAAYsY//mFTy5ZB9ezpNzoKEgRFGFBPior8at48qHfesRkdBQCAo2RkZBgdAQCAGr76Kknbtll0xRU+o6MggVCEAfVo8GCfNm+26uuvk4yOAgBADZ07dzY6AgAA1XbtMmvxYrtGj/YYHQUJhiIMqGdjxri1eLFdW7ZwqRMAQPRYv3690REAAJAk7d9v0pNPpuuee1wymYxOg0RDEQbUM7NZuu8+l/76V4d27eJHDAAQHQ4cOGB0BAAA5PGY9NvfOnX//S7Z7UanQSLiVTrQAJKSpIkTXfrDH9K1fz8/ZgAAAABQWSn95jdOTZhQLqczbHQcJCheoQMNxGaT7r/fpd/+Nl1uN/t9AQAAACSuQEB68EGL7rjDrayskNFxkMAowoAGlJ4e1oQJ5ZoxwykfF0IBABioT58+RkcAACSocFiaNStdv/hFULm5QaPjIMFRhAENLCsrpDvuKNeDD1oUCBidBgCQqPbs2WN0BABAAgqHpd/9Lk1XXOFTp05GpwEowoBGkZsb0m23BTVrVrpC7AIGABhgy5YtRkcAACSgefMcOuecKnXv7jc6CiCJIgxoNJ06SVdc4dPvfpemMHMhAQAAAMS5l15KVdu2AfXpU2V0FKAaRRjQiLp396tv3yr96U8Oo6MAAAAAQINZvNim1NSwfvrTSqOjADVQhAGN7JxzqtS+fUAvvphqdBQAQALpxGAWAEAj+fe/U1RebtZVV3mNjgIchSIMMMDAgZVKSwtp8WKb0VEAAAkiLS3N6AgAgATw6afJ+vZbq4YPrzA6CnBMFGGAQQYP9qm83KylS1OMjgIASABffvml0REAAHFuzRqrPvkkWbff7jE6CnBcFGGAgYYPr9CmTVZ9+mmy0VEAAAAAoM42b7Zo8WK7xo1zy2QyOg1wfBRhgMFuv92jTz5J1po1VqOjAAAAAMBJ27nTrOeec+i++8plpmVAlGOJAgYzmaRx49xavNiuzZstRscBAMSp7OxsoyMAAOLQ/v1mPfVUuiZOdMnKe/uIARRhQBQwm6X77ivXc885VFLCjyUAoP61b9/e6AgAgDhTXm7S44+n64EHXLJxHTDECF5xA1HCapUmTnTp6afTtW8fP5oAgPq1evVqoyMAAOKIzyfNnOnUhAnlSksLGx0HqDVebQNRxGaTHnjApccfT1d5ORMmAQD1x+PhCl4AgPoRCEi/+Y1TY8eWKzMzZHQc4KRQhAFRJi0trHvucWnGDKe8XqPTAAAAAMAPQiHpscfSdfPNHuXkUIIh9lCEAVEoMzOsO+8s14wZTvn9RqcBAMSDpKQkoyMAAGJcOCzNmZOuwYO9atcuaHQcoE4owoAo1bJlSD//uUfTpjlVVWV0GgBArOvVq5fREQAAMSwcln73uzT17+9Tt24Bo+MAdUYRBkSxNm2ClGEAgHqxfft2oyMAAGJUZCdYmvr3r1SvXpyygthGEQZEudatg7r11kgZVllpdBoAQKzasWOH0REAADEoHJZmz07XT35SqZ49KcEQ+yjCgBjQqlVQv/iFR9OnU4YBAAAAaBzhsPT44+n66U99OvNMSjDEB4owIEbk5wd1++2RnWE+n9FpAAAAAMSzUEiaNStdl1zi0+mnU4IhflCEATEkNzeo0aPdlGEAgJPWvXt3oyMAAGLE4RJs0CCvunenBEN8oQgDYkxubkh33BEpw7xeo9MAAAAAiCehkPTYY+m64govV4dEXKIIA2JQTk5IY8e6NW1aE3m9JqPjAABiwNdff210BABAlAuFpJkz0zV4sFddu1KCIT5RhAExqmXLkO68s/y/O8MowwAAAADUXTAYKcGuvtqrwkJKMMQvijAghp12Wkh33VWuRx91yuOhDAMAAABw8gIBacYMp372M6+6dKEEQ3yjCANiXIsWIf3615GdYS4XZRgA4Njy8vKMjgAAiEJVVdK0aU4NG1ahzp0pwRD/KMKAONCsWUj33uvSjBlO7d/PjzUA4Gj5+flGRwAARBmfL1KC3XKLRx06UIIhMfCKGYgTGRlhTZzo0uOPp6u0lB9tAEBNX3zxhdERAABRpKLCpGnTnBo92q3WrYNGxwEaDa+WgTiSnh7WQw+59MQT6Sop4ccbAPADv99vdAQAQJQoL4+UYL/6lVs5OSGj4wCNilfKQJxJTQ3r4YcPae7cNG3bZjE6DgAAAIAocuCASb/5jVMTJrjUvDklGBIPRRgQh2w26eGHXXr2WYc2bbIaHQcAEAUcDofREQAABisrM2vWLKceeMClzMyw0XEAQ1CEAXEqKUl66CGX/va3VK1dSxkGAImuR48eRkcAABho1y6z/u//0vXQQy45nZRgSFwUYUAcs1qliRNdevNNu1auTDI6DgDAQJs3bzY6AgDAINu3W/T002l6+OFDcjgowZDYKMKAOGc2S/feW67330/Rp58mGx0HAGCQ0tJSoyMAAAywebNFf/6zQw8/7JLdbnQawHgUYUACMJmkcePcWrUqSe+8YzM6DgAAAIBG8J//JOmVV1L10EMuJfOeOCCJIgxIGCaTdPvtHnk8Js2fn2p0HAAAAAAN6N13I2eEPPBAuayMDAaqUYQBCeaaa7xq3TqoOXPSFOJqyQCQMIqKioyOAABoJC++mKpDh8waM8Yjk8noNEB0oQgDElC/fpX66U99evRRpyorjU4DAGgMbrfb6AgAgAYWCklPPJGm3Nyghg71Gh0HiEoUYUCC6tYtoFtv9WjKlCY6eJC3iQAg3m3YsMHoCACABlRZKU2b5tSFF1bqggt4txs4HoowIIHl5gZ1770uzZrlVEmJxeg4AAAAAOrg0CGTpk5toltu8ah7d7/RcYCoRhEGJLimTcOaNOmQnnnGoTVrmKIJAAAAxJKSErMee8ypCRNcys8PGh0HiHoUYQCUkiI9/LBLS5fa9OGHKUbHAQA0gHbt2hkdAQBQz9autWrevDQ98sghZWSEjY4DxISY2P6xdOlSLVmyRPv27VNeXp6GDx+u7t27n/AxY8eO1d69e4/5ublz5yojI0OSdO211x71+YKCAk2fPv3UgwMxxGyWxo1za/78VC1aZNc11zBcEwDiSYsWLVRWVmZ0DABAPfnoo2R98UWyHnnEJTNbXIBai/oi7JNPPtG8efM0dOhQde7cWe+//75mzpypGTNmqFWrVsd93IQJE+T31zw3eu7cuTKbzdUl2GGXX365zjnnnOqP7XZ7/X4TQAwZMaJC77xj0x//6NDtt3O5ZQCIF8uXL1dBQYHRMQAA9eD11+1yuUwaP54rAgMnK+qLsAULFqh///4aMmSIJKmwsFBbt27VG2+8obvuuuu4j2vbtm2Njw8ePKiSkhINGzbsqPtmZ2erY8eO9RsciGGXXOLTF18kaebMdN1zT7msUX+kAAAAAOJfOCzNm+dQXl5QI0dyBgdQF1G9gXLPnj3atWuX+vbtW32b2WxWnz59tGrVqpP6WsXFxQqHwzr33HPrOyYQl3r18uvaays0dapTHg/bwgAAAAAjBQLSrFnpOvNMvy67zGd0HCBmRXURVlJSIknKycmpcXtubq7cbrdcLletv1ZxcbE6duyo5s2bH/W5hQsXatiwYRo1apSefvppud1sLwUkqX37oO66y63p053auzeqDxcAgB9x5GgIAEDsqKgwaepUp665xquzzqoyOg4Q06L6hCePxyNJcjgcNW4//LHb7ZbT6fzRr7N37159++23uvnmm4/6XP/+/VVUVCSn06ktW7Zo0aJF2rZtm2bMmCEzEwcBNWsW0kMPuTRzZrpuusmjDh24JDMAxKLOnTszLB8AYlBZmVmzZ6dr/PhyZWeHjI4DxLxGL8IqKip04MCBH71fbm5u9Z9Nx5nWfbzbj/TJJ5/IZDKpT58+R31u7Nix1X8uLCxUbm6uZsyYoS+++EJnn332UfdfunSpli5dKkmaOXOmmjVrVqsMgNVqjen18vvfS1On2nXppSH16cOlmRtSrK8VNC7WC2prw4YN6tSpk9ExECM4tqC2WCsN69tvpblzLZozJ6i0tEyj45wy1guiQaMXYcuXL9fcuXN/9H4LFiyo3vnl8XiUmppa/bnj7RQ7nuLiYnXt2lVNmzb90fueccYZstls2rp16zGLsIEDB2rgwIHVH/POKmqrWbNmMb9e7rwzMpxz06agLr2UuQQNJR7WChoP6wW1tX//ftYKao1jC2qLtdJwvvwySe++a9N995XL55N8cfD0m/WC2jpyRFZ9avQibMCAARowYECt7nt4V1hJSUmN2V4lJSVKS0ur1WmRO3fu1HfffafRo0fX6u88vMustrvNgERiMkm33ebR66/bNX9+qkaMqDA6EgAAABB3/vlPm7Zutej++8vFS1OgfkX1EKwWLVqoZcuWWrFiRfVtoVBIK1as0BlnnFGrr/Hxxx/LarWqd+/etbr/qlWr5PP51K5duzplBhLB1Vd71bp1QHPmpCnEmAIAAACg3rzwQqrKy00aPdpDCQY0gKguwiRp6NCheu+997Ro0SKtWbNG/+///T/t2rVLV111VfV91q5dq2HDhmnt2rVHPX758uU644wzjnka5dKlSzV37lwVFxdrzZo1WrJkiX73u9+pQ4cO6tmzZ4N+X0Cs69evShdf7NPkyU65XPyGBoBod6xZqQCA6FFVJT32WLpatQpoyBCv0XGAuBXVV42UpPPOO08+n0+LFy/WokWLlJ+fr/vvv1+tWrWqvk84HFYoFFI4XHOA93fffaeSkhINGTLkmF+7RYsW+uCDD/Tpp5/K6/WqadOm6tevn4YNG8YVI4FaKCwMaNy4cs2ala6RIytUUBAwOhIA4Dj27Nkji8VidAwAwDGUlpr1xBPpuu02t1q35irtQEMyhY9sj3BSdu7caXQExIh4HgwZCEh/+EOaOncO6JJL4mCKp8Hiea2g/rFeUFvffvutCgoKjI6BGMGxBbXFWjl1X36ZpH/8w6677y5Xamp8vzxnvaC2GnJYPtueAJwyq1UaN84tv1966inmhgEAAAC18cordq1enaSHHnLFfQkGRAuKMAD15oorfPrJTyJzww4dYm4YAAAAcCyVldLMmenKyQnqllsqGIoPNCKKMAD1qrAwoPHjy/X44+nauDHqxxACQMLo1KmT0REAAJJ27zZrypQmuvHGCp1/fpXRcYCEQxEGoN5lZIQ1ebJL//ynTW+/bTM6DgBAUlpamtERACDhff55kubNS9PDD7uUn89QfMAIFGEAGoTFIt15p1uhkPTkk2kK8nseAAz15ZdfGh0BABLayy+n6ptvIvPA7HbmgQFGoQgD0KAGDfLppz+NzA07eJDhBwAAAEgsh+eB5ecHdPPNzAMDjEYRBqDBde4c0N13l2v27HStX8/cMAAAACSGXbsi88CGD6/QeecxDwyIBhRhABpF06aRuWFLl9r0978zNwwAGlt2drbREQAgoXz2WbL+8heHHn74kPLymBMCRAuKMACNxmKRfvlLtywW6YknmBsGAI2pffv2RkcAgIQQDksvvpiqdeusmjixXHa70YkA/C+KMACN7tJLfbrkksjcsAMHGJIAAI1h9erVRkcAgLjn80XmgbVtG9DIkcwDA6IRRRgAQ3TqFNCECeWaMydda9cyNwwAGprH4zE6AgDEtZ07zZo6tYluuqlCffsyDwyIVhRhAAzTpElkbth779n05pvMDQMAAEBs+vTTZP31r5F5YLm5zP8AohlFGABDmc3S2LFuJSVJc+akKRAwOhEAxKekpCSjIwBA3AmHpRdeSNXGjVY98ADzwIBYQBEGICpccolPl18emRu2fz/DFACgvvXq1cvoCAAQVw7PA+vQIaARI5gHBsQKijAAUaOgIKB77y3XH/6QruLiZKPjAEBc2b59u9ERACBubNhg1aOPNtHIkR6dcw7zwIBYQhEGIKo4nWE9/LBLO3ZY9Ic/pMnvNzoRAMSHHTt2GB0BAGJeOCy9+GKq3nsvRVOnHlJOTsjoSABOEkUYgKhjMknXXuvVJZd4NXlyE23bZjE6EgAAABLc/v0mTZ7sVMeOfo0e7ZGFp6hATLIaHQAAjqdDh6AeeeSQnn46TW3bBnXVVV6jIwEAACABffJJspYts+nXvy5XkyZho+MAOAXsCAMQ1VJSpPHj3crICGn69HS53UwhBYC66N69u9ERACDm+P3S73+fpp07LXroIRclGBAH2BEGICb071+prl39mjUrXVdf7dWZZzI8DAAAAA3nu+8smjcvTaNGudWuXdDoOADqCTvCAMSMZs1CmjLFpdWrkzRvnkMhZpMCQK19/fXXRkcAgJjx+ut2vfGGXZMmHaIEA+IMRRiAmGIySSNHVqhv30o98ohTu3ZxGAMAAED9KC83ado0p7Kygho3zq3kZKMTAahvnBoJICZ17RrQgw+W6/e/T9OZZ1bpoosqjY4EAACAGPbll0lavNiuX/3KrawsTj0A4hVbKQDELLs9rPvuK1cwaNKsWenyclFJADiuvLw8oyMAQFQKBqU//cmhtWuTNGWKixIMiHMUYQBi3qWX+jR8uEfTpzu1bh0bXQHgWPLz842OAABRZ+dOsyZNcur88ys1YkSFTFygHIh7FGEA4kJOTmSQ/ocfpmj+/FSFubI1ANTwxRdfGB0BAKLKO+/Y9OKLDj34oEtdugSMjgOgkVCEAYgbFot0++0eFRb6NWmSU/v3c4gDgMP8fr/REQAgKni9Jj32WLrM5rDuuadcdrvRiQA0Js4hAhB3ior8KigI6Ikn0tW/v0/9+lUZHQkAAABRYO1aq15+OVV33OFWy5bMAgMSEdslAMQlpzOshx92qazMot/9Lk1VdGEAEpzD4TA6AgAYJhyWnnsuVZ98kqLJk12UYEACowgDENd+9jOvrrrKq8mTm2jzZovRcQDAMD169DA6AgAYYu/eyED8Hj38+sUvPLLwlBBIaJwaCSDutWkT1JQph/TMMw4lJUk33+yRlaMfgASzefNmNWnSxOgYANBowmHp9dft2rTJqnvuKVd6OldTAsCOMAAJIilJGjPGo/PPr9TkyU6tXUsTBiCxlJaWGh0BABpNSYlFkyY51bJlUPfeSwkG4Ae8EgSQUDp1CmjKFJfmz0/VsmU2/eIXbqWkGJ0KAAAA9SEUkl56KVVlZWY98ICLK0ICOApFGICEY7FIN99coW3bLJo2zamrrvKqqMhvdCwAAACcgs2bLXr2WYeGDvXq9NN5bgfg2Dg1EkDCat06qKlTXdq0yarZs9NVUWEyOhIANJiioiKjIwBAgwgEpHnzHPrXv2x65BEXJRiAE2JHGICEZjJJ113n1a5dZs2ala4BA3w6//wqo2MBQL1zu91GRwCAevfNN1a99JJDI0d61LFjwOg4AGIAO8IAQFLLliFNmuTSgQNmzZiRrkOH2B0GIL5s2LDB6AgAUG98PunJJ9P0+efJmjr1ECUYgFpjRxgAKCPepAAAIABJREFU/JfJJF15pU/nnVelP/whTb16+XXxxT6jYwEAAOB/fP55kpYssevWWz1q3TpodBwAMYYdYQBwhMzMkB58sFxWa1hTpzq1dy+HSgAAAKN5PCY9/ni6tmyxaupUFyUYgDphRxgAHMeAAZU6++wq/fGPaWrfPqCrr/bKxBmTAGJUu3btjI4AAHX24YcpWrYsRaNHu3XaaSGj4wCIYWxzAIATSE8P6557ypWTE9SkSU6VlFiMjgQAddKiRQujIwDASTt40KQZM9Llcpk0aZKLEgzAKWNHGADUwjnnVOn006v0pz+lKSsrpBtuqJCZtxIAxJDly5eroKDA6BgAUGtvv23TypVJGj3arczMsNFxAMQJXsYBQC3Z7dKvfuVW9+5+TZrk1ObN7A4DAACob6WlZk2Z4lRKSlgTJ5ZTggGoV+wIA4CTdPrpfhUW+vXnPzv03nvSzTd7ZOVoCgAAcErCYWnRIru2brXq7rvLlZZGAQag/rEjDADqIClJGj3ao379KjV5slNffZVkdCQAOKGMjAyjIwDAcX33nUWTJjmVnx/UPfdQggFoOBRhAHAKOnYMaMoUl9ats2rGjHTt3cthFUB06ty5s9ERAOAoHo9JTzyRpn/+06aJE13q3bvK6EgA4hwn8wDAKbJYpGHDvDp0yKS//MWhJk3CGj7co+Rko5MBwA/Wr1+vZs2aGR0DACRFToN8/XW71q2z6uc/96hlS64GCaBxsHUBAOpJkyZhjR/v1vnnV2raNKfeey/F6EgAUO3AgQNGRwAASdJXXyXpkUecys8P6MEHyynBADQqijAAqGcFBZHTJf1+adIkp7Zu5eqSAAAAe/eaNWNGutavt2rKFJfOOstvdCQACYhTIwGgAZhM0kUXVap//0o995xDFRUm3Xqrh8GvAAAg4VRVSfPnO3TokEl33OFWkyY8HwJgHIowAGhAKSnSbbd5tHu3Wb//fZo6dgzoZz/zysx+XACNrE+fPiorKzM6BoAE8957KXrvvRSNGFGhgoKA0XEAgFMjAaAxnHZaSBMnlqtNm4AmTXJq1aokoyMBSDB79uwxOgKABLJli0WTJzvl90tTprgowQBEDXaEAUAj6tXLr549/Vq40K6337Zp1CiPsrMZEAug4W3ZskUFBQVGxwAQ59xuk555xqHU1LAeeMClFK4dBCDKUIQBQCMzm6XrrvPK5TLpz392KD09rJtu8ig52ehkAAAAdRMKSa+9ZtfGjVb9/OcenXYab/QBiE4UYQBgEKczrPHj3dq0yaLp053q169SAwZUGh0LAADgpKxcmaTXXrNr8GCvhgzxGh0HAE6IGWEAYLAOHYKaPNmlUEiaNMmpjRuNTgQgHnXq1MnoCADiTGmpWQ89ZNHGjVZNmeJSr15+oyMBwI9iRxgARAGTSfrpTyvVr1+lFi3K1p49abr1Vo/S07m8OID6kZaWJpfLZXQMAHGgqkp6/nmHystNuvfeoPx+doEBiB0UYQAQRVJSpLvuCumbbyr05JNp6tAhoCFDvDKzfxfAKfryyy8Zlg/glP373yn68MMUjRjhUYcOQTVpYlNZmdGpAKD2eGkFAFGoRYuQJk4sV4cOAU2e7NT776cozOYwAABgkK++StKkSU6FQtLkyS516BA0OhIA1Ak7wgAgivXs6deZZ/r173+naNIkpy6/3Kezz64yOhYAAEgQGzZY9corqSos9OuRR1yyWIxOBACnhiIMAKKcySQNHFipCy+s1Ftv2TR5slM/+5lXPXowkBZA7WVnZxsdAUAM+e47i156KVWtWgV1//0uJScbnQgA6gdFGADECLNZuuIKny691KfXXrPrjTfsuu66CnXqFDA6GoAY0L59e5UxyAfAj9i506z58x1q1iyk8ePLZbcbnQgA6hdFGADEGKtVuvZar6qqvHrllVS9+qpdN95YoTZtmNUB4PhWr16tnJwco2MAiFJlZWY9/3yq7Pawxo51Ky2N4aQA4hNFGADEqORkacSICnm9Jr34Yqr27TNrxAiPcnJCRkcDEIU8Ho/REQBEoYMHTXr+eYfCYemWWzzKyKAAAxDfKMIAIMbZ7WHdeqtH5eUmvfBCqrxek0aOrFBWFoUYAAA4No/HpPnzU+XxRN5Iy87meQOAxEARBgBxIj09rDFjPNq/36T58x0ym6WbbvKoSRPe2QUgJSUlGR0BQBTw+aSXX05VaalFw4dXKDeX0QoAEgtFGADEmczMsH71K7dKS8165pk0ORwhjRhRIYeDQgxIZL169WJYPpDA/H5p4cJUbd1q0fXXV6hdOwowAImJIgwA4lR2dkh3312uHTss+v3v09SyZVDDhlXIZjM6GQAjbN++XXYu/wYknGBQWrzYrjVrkjR0aIVuuIGrTQNIbBRhABDn8vKCeuCBcm3ebNHs2elq1y6oIUMqxFlSQGLZsWOHCgoKjI4BoJGEw9I779j02WfJuvJKr372M6/RkQAgKlCEAUCCaN8+qAcfLNfatVbNmOFU9+5+XXmlVxaL0ckAAEB9CYelDz5I0XvvpeiSS3yaNMlldCQAiCpmowMAABpXYWFAjzziUuvWAT36qFP/+IdNIS4UBQBATAuHpeLiZE2e7FQoJE2e7FKfPlVGxwKAqMOOMABIUD17+tWzp1+ffpqsadOcKigI6OqrmSEGxKvu3bvL5/MZHQNAPQsGI6dAfv55svr0qdKkSS6Z2e4AAMdFEQYACa537yr17l2lDRusmjMnXRkZIQ0bVqGmTbnKJAAA0crrlRYtStXWrVZdcolPkydzCiQA1AZFGABAktSpU0APPFCuXbvMevZZh8Jh6brrKpSby3mTQDz4+uuvGZYPxIEDB0z6299SdfCgWUOHejV8eIXRkQAgplCEAQBqaNkypPHj3XK5Ik+09+0z66qrvOrShcutAwBglB07LHrllVRZrWENG1ahFi14owoA6oIiDABwTE5nWLfd5lFlpfTGG3YtXJiqgQN96tOnSiaT0ekAAEgM33xj1eLFdmVnh3TbbW6lpzO6AABOBUUYAOCEUlKk667zKhTyaunSFE2d6lRRUZUuvdQni8XodABqKy8vz+gIAGopHJY+/jhZy5bZ1KWLXxMmlCs52ehUABAfKMIAALViNksXXVSpiy6q1OefJ+k3v3GqTZuAhgypkN1udDoAPyY/P19lZWVGxwBwAoGA9NZbNq1alaxzz63Uww9zBUgAqG8UYQCAk3bWWX6ddZZfmzZZ9cQT6XI6wxo2zKPMTE7XAKLVF198oTZt2hgdA8AxeL0mLVhg1/btVl12mVeDB3MFSABoKBRhAIA669AhoPvvL9eePWbNn++Q32/SdddVKD8/aHQ0AEfw+/1GRwBwhH37zHr55VR5PCYNHVqhdu24AiQANDSKMADAKWvRIqRf/cott9ukV15J1Z49Zl15pVfdunGlSQAAjrRtm0ULFqQqJSWs66+vUPPmXAESABoLRRgAoN6kpYU1apRHVVXSkiV2LVqUqp/8xKfzz+dKk4DRHA6H0RGAhLd6dZKWLLErNzegO+5wy+FgpAAANDaKMABAvUtOloYM8eqaa7xatixF06c7lZcX0NVXe9WkCU/6ASP06NGDYfmAAbxe6e9/t2vDhiQVFvp1330uJSUZnQoAEhdFGACgwZhM0oABlRowoFLbt1v03HMOVVSYdNFFPp15pp9dYkAj2rx5s5o0aWJ0DCBhbNpk1eLFdoVC0uWXezV0qNfoSAAAUYQBABpJfn5Qd93llt8v/fOfNv3jH3a1bh3ZJZaWxi4xoKGVlpZShAENrLJSevttu77+OkkdOgQ0Zoxbqan8jgOAaEIRBgBoVElJ0uWX+3T55T5t3WrRvHkOVVWZdNllPnXvzlXtAACxZ8sWixYvtlf/PrvqKnZ/AUC0oggDABimbdugxo93q7JSeucdmxYvtqt9+4AGD/byDjoAIKod3uG8cmWy2rQJ6NZbPUpP53cXAEQ7ijAAgOFSUqTBg30aPNinTZus+uMfHQoGTRo0yKvCwoDR8YC4UFRUJJfLZXQMIOZ9/71Fr79ul9dr0sUX+zRokIuZlwAQQyjCAABRpUOHgH79a7e8Xumtt+x67bVUdezo1xVXeGW3G50OiF1ut9voCEDMCgSkpUtt+vzzZOXnB3TzzR6uggwAMYoiDAAQlex2acgQrySv1q+36qmn0mUySVde6VVBAbvEgJO1YcMGFRQUGB0DiCklJWa99lqq3G6TBg6s1EMPsfsLAGIdRRgAIOp17hxQ587lqqgw6c03bXrllVR17erXZZd5lZJidDoAQDwJBqX33kvRihUpatkyqBtv9Cgzk91fABAvKMIAADEjNTWs666L7BJbs8aqJ55IV1JSWFde6VX79kGj4wEAYtju3ZHdXwcPmvSTn1TqwQfZ/QUA8YgiDAAQk7p1C6hbt3K53SYtXmzXK69YlJ8f1KBBXt65B46hXbt2RkcAoo7HY9K779q0bp1VzZqFdN11FcrKChkdCwDQgCjCAAAxLS0trBtvrJAkbdtm0SuvpOrAAbM6dw7o4ot9cjgoxQBJatGihcrKyoyOARiuslL64IMUffllsuz2sC66yKerrvKy+wsAEgRFGAAgbrRuHdSYMR5J0rp1Vs2b55DXa1LPnn5dcIGPeWJIaMuXL2dYPhJWMCgtX56sjz9OkdUq9e/v0/33l1N+AUACoggDAMSlLl0C6tLFrXBY+s9/kvTkk+kKBKRzz61U375VsliMTggAaEjhsLRqVZKWLbPJ75f69KnShAnlsvIKCAASGr8GAABxzWSSior8KiryKxCQiotT9NvfpstqlS680Kczz/SzIwAA4sjGjVa9/bZNFRUmnX66X2PHlstmMzoVACBaUIQBABKG1Sr161epfv0q5fNJ779v08yZNqWmhnXJJT516hQwOiLQYDIyMoyOADSY7dsteustmw4cMKtDh4B+/nOP0tOZEQkAOBpFGAAgIdls0iWX+HTJJT653Sb98582vfaaXRkZIV12mU+tWgWNjgjUq86dOzMsH3GlrMysv//dpl27LMrJCWroUC9XfAQA/CiKMABAwktLC+uaa7ySpP37zXrrLZu2b4+8sBo0yKfmzXlhhdi3fv16NWvWzOgYwCkpLzfp7bdt2rzZqqyskAYN8ik3lzcuAAC1RxEGAMD/yMwMacSICklSSYlZr79u1759ZrVrF9Cll/rkdHKqDWLTgQMHKMIQk7xe6d//tmn16iSlp4d16aU+XXut1+hYAIAYRREGAMBx5OaGdNttHknSpk1WPf+8Q+XlJrVsGdTAgZXKy2MXAgA0hLIys5YtS9HmzValpIQ1YEClBg3ycXETAMApowgDAKAWOnQI6Je/dEuSdu4069//tmnHDotstrDOO69KPXtWyWIxOCQAxKhwWPrmG6s++MAml8ukZs1CuvBCdn4BAOofRRgAACcpJ+eH0ye9Xqm4OEWzZ6crGJQKC/3q379STZtyCiWiS58+fRiWj6ji9Zr00UfJWrUqWaGQ1LWrXyNGeDgFHQDQoCjCAAA4BXa7NGBApQYMqFQ4LK1bZ9XLL6fq0CGzMjJCuvDCSnXoEOB0Hhhuz549srBtEQYrKbFo6dIU7dp1eEdtpe6+u5wdtQCARkMRBgBAPTGZpMLCgAoLA5KkffvMeu+9FC1aZJfVKp19dpV6965USorBQZGQtmzZooKCAqNjIMEEg9J//pOkjz9Okc9nUm5uZMYiV3oEABiFIgwAgAaSlRXSkCGR+TZ+v/T558l66qk0VVWZ1LZtQBdeWKnmzUMGpwSA+nXokEkffJCitWuTZDZLPXtWafRoj+x2TnkEABiPIgwAgEaQlCT17Vulvn2rJElbtli0ZIlde/ealZYWVv/+lerWzc8plABiTjgsbd5s0bJlNu3bZ5bTGdYFF/h0xRVc5REAEH0owgAAMEC7dkG1a+eRJJWXm/Thhyl66y2bJOnMM/3q27dS6ensnkD96dSpk0IhdiCifni9kV2un3+eIr9fat8+oGuu8SorizUGAIhuFGEAABgsPT2sQYN8GjTIp1BIWrUqSS+8kKrycrMsFqlbN7/OPrtSGRkUY6i7tLQ0uVwuo2MgRnk8Jn3xRbJWrkxSVZVJKSlh9epVpTvvLFdystHpAACoPYowAACiSGSejl89e/olSYGAtGZNkhYsiFyJ0mSSunTx65xzqtSsGTsvUHtffvklw/JRa4cOSUuXpmj16iT5/SY5HJHia/Rot2w2o9MBAFB3FGEAAEQxq1U64wy/zjgjUowFg9K6dVYtXmxXWZlZDodFeXl29e5dqZYtKcYA1M3+/SZ99lmK1qxJUjAonXaaWV27hvTLX7rZ8QUAiCsUYQAAxJDIqZIBdesWkCRlZqZo+XK/3n3Xpj17LAqHpbZtA+rTp0r5+UGD0wKIVnv3mrV8ebI2bEhSKCRlZITUu3eVBg70yWqVmjVLUVmZ3+iYAADUO4owAABimNksdeoUUKdOkWIsHI5ckfKjj1K0fXukGGvVKqhzzqlU27ZBruCWwLKzs42OAAPt3GnWihUp2rQp8vS/WbOQeveu1OWX+2Q2GxwOAIBGRBEGAEAcMZmk9u2Dat++QlKkGNuxw6Lly5O1cGHk1/5ppwV1zjlV6tgxQDGWQNq3b6+ysjKjY6ARhMPS9u2Rn/vvvov83LdsGfm5v/pqLz/3AICERhEGAEAcM5mk/Pyg8vO91bft3GnWp5+maPFiuyTJZgursNCv7t39atGCOWPxavXq1crJyTE6BhrA/v0mrVmTpLVrk+RymWvsBL32WoovAAD+F0UYAAAJJicnpKuv/qEY83pNWrfOqnfesam01CJJslrD6tQpoO7d/crL45TKeODxeIyOgHqwZ4+5uvTy+SI/mE2bhtStm1833FAhpzNscEIAAKIbRRgAAAnObg+rZ0+/evb8YTB2VZW0caNVH3+coh07IuWY2Sx16OBX165+tWsXZK4Q0IDCYamkxKKvv07S+vVW+f2R0is7O6ju3f269VaP7HZKLwAAThZFGAAAOEpycs2rU0pSMCht3mzVV18l6403rAqFIqdetm4d2TlWUBCQlWcWUSspKcnoCDiOUEjautWiNWuStGlTkoL/veBrfn5Q3btXacAAn5KTjc0IAEC84OkqAACoFYtF6tgxoI4dfyjHQiHp++8ju1beftumQCCyayUnJ7JrpVMnv2w2oxLjf/Xq1Yth+VEgEJC+/daqr79O0rZtkafiJpPUtm1A3br5NWiQj0IZAIAGxK9ZAABQZ2az1KZNUG3aBKtvC4cjA/m/+SZJ77+fpqoqk8LhyByjtm0Dats2oFatguxwaWTbt2+X3W43OkbCCAYjpzZu3WrVli0W7d1rkckkWSxhdegQ0FlnVWnIEC+nGAMA0MgowgAAQL0ymaTc3JBycyt10UWV1bcfOGDS1q2RUyvffNNSvXssHJaaNw+qbdug2rYNKC8vKIvFqPTxa8eOHSooKDA6RlwJhaRdu8zautWqrVut2r37h4VrNku5uQG1axfU5Zf71KxZiItOAAAQBSjCAABAo8jICCsjo+ZQfilShO3bFykTVqxIVkmJRcFgpDEwmSLDwdu2Dahdu4BatgyxgwaNKhyW9u49XHZZVFISmY8nRcquFi0i63PAAJ9OO431CQBAtIv6Iqy4uFjFxcXauHGjDh48qDvuuEMXXHBBrR67fv16Pf/889q2bZuaNm2qQYMG6bLLLqtxH7/fr5dfflkffvihKisrVVhYqFGjRik7O7sBvhsAAHAkk0lq1iykZs2qdNZZNT8XDku7d0dKiPfft2n3bkt1CRHZeRasPt0yO5sdN6i7/ftN2rIlsrNrx44fdixKkR2L7doFde65VcrJ8bJjEQCAGBb1RdiKFSu0d+9e9ezZU8uWLav143bv3q3p06erqKhIN9xwgzZt2qTnn39eKSkpGjBgQPX9nn32Wa1YsUIjR46U0+nUwoULNW3aND3++ONKZngJAACGMpmkli1DatmySn37VtX4XDAo7dpl0ZYtFr3zjk2lpZbqx0hSRkZI2dlBtWgR+d/s7JBSU8ON/S1Eje7du8vn8xkdwxA+n7R3r0V79pir/3f/fnONnYeHZ9j16lWlq64KiotsAgAQn6K+CBs3bpzMZrN8Pt9JFWFLlixRZmam7rzzTlksFnXr1k1lZWV69dVXdeGFF8pkMmnfvn1atmyZxowZo/79+0uSWrdurbFjx+qjjz6qUZgBAIDoYrFIeXlB5eUF1a9fzZIsFJIOHjRrzx6zSkst+uyzZO3da5HX+8NcMilSgKSmhv9blEXKsuzsoDIywuwui3LhsFReblJpqaX637m01KzycnON+0hSSkq4+t83Pz+goqKQsrJC7OwCACABRX0RZq7joIWVK1fqvPPOk+V/nuH07dtX7777rrZv365WrVrpq6++kiT17t27+j6ZmZnq3LmzVq5cSREGAECMMpulzMyQMjND6tIlcML7ejym6iJl40arPv44RQcPmmuUZZJktYbVvHlIzZv/sMusefOQrFH/bCri66+/jolh+cFgZGZcaekP5VZpqUVVVUc3k2lpoep/i+7d/WrePCinkxITAAAcX4w8dTs5Pp9P+/btU25ubo3b8/LyJEklJSVq1aqVdu7cqaysLNlsthr3y83N1dq1axstLwAAMI7DEVa7dpEZUCdSVSWVlZmrdyCtXp2ksjKzAgFTdfFiMtXcbXb4zzZbWA5HSA5H+H/+++HjtLSwUlNDstkUcyVOOBz5/8bjMcnjMcvjMcntNqmi4oc/ezyRj71ek8Lhmv/fHHb4NrM5rMzMUPXuvIKCgJo3D+qIp2sAAAB1EpdFWEVFhSQpNTW1xu0Oh0OS5PF4JElut/uo+0hSWlpa9X0AAAAkKTlZyskJKScndFKPC4cjM6qOVQyVlh7+s0lut1mVlaajHisdXRwdryw7XCSZTJFdcT/8F9bAgdJTT6UpFFL1f+GwFAqZTvj1jvV3HpkrOTlS5h1Z9mVlBWuUfTYbu7UAAICxGr0Iq6io0IEDB370fkfu5qoLUy2eaR3rPuEj36L8H0uXLtXSpUslSTNnzlROTk7dAyLhsF5QW6wVnAzWC2qnv/47EhWoFY4tqC3WCk4G6wVGq9sArlOwfPlyjR8//kf/OxWHd3kduavr8MeHd4Ydb+eXx+Opvs+RBg4cqJkzZ2rmzJm6//77TyknEgvrBbXFWsHJYL2gtlgrOBmsF9QWawUng/WC2mrItdLoO8IGDBjQ4EPobTabsrKytHPnzhq3l5SUSPpht1lOTo727dsnn89XY07Yzp07aakBAAAAAADiTKPvCGssZ555pj777DOFQj/M8SguLlZWVpby8/MlSaeffrok6bPPPqu+z/79+7Vu3TqdeeaZjRsYAAAAAAAADcoyefLkyUaHOJEdO3Zo7dq1+v777/XZZ58pMzNTVVVVcrlcat68uSRp7dq1uvPOO1VYWFh9W25urhYvXqwdO3YoLS1Ny5cv15IlSzR8+HC1a9dOUuQUyv379+utt95SRkaGDhw4oGeeeUZWq1W/+MUvZLFYfjTf4a8F1AbrBbXFWsHJYL2gtlgrOBmsF9QWawUng/WC2mqotWIKn2gyfBRYsGCBXn311aNuLyws1OEO75tvvtGUKVM0adIkde3atfo+69ev13PPPafvv/9eTZs21aBBg3TZZZfV+Dp+v18vvfSSPvzwQ1VVVamwsFCjRo1SdnZ2g35fAAAAAAAAaFxRX4QBAAAAAAAA9aHRh+XHiuLiYhUXF2vjxo06ePCg7rjjDl1wwQW1euz69ev1/PPPa9u2bSfcifbyyy/rww8/VGVlJTvRYtzSpUu1ZMkS7du3T3l5eRo+fLi6d+9+wseMHTtWe/fuPebn5s6dq4yMDEnStddee9TnCwoKNH369FMPDkPUZb0cb3fsxIkTdcYZZ1R/zLElvtRlrfzrX//SihUr9P3336uqqkqtWrXSkCFDqudiHsaxJTbt2LFDf/nLX7Rx40Y5HA5deOGFGjp0qMzmE499raio0F//+ld9/vnnCoVCKioq0i233KL09PQa9/v888/1t7/9Tbt371Z2draGDh2qvn37NuS3hAZUl/WyadMmvfvuu1q3bp0OHDigrKwsnXfeeRo8eLCSk5Or7/fUU0/pgw8+OOrxc+bMqb4wFWJHXdZKaWmpfvnLXx51e9++fTVu3Lgat3FsiS91WS/Hey4rSddff72uvvpqSRxb4s3u3bu1ZMkSffvtt/r+++/VpUsX1WY6V0M/b6EIO44VK1Zo79696tmzp5YtW1brx+3evVvTp09XUVGRbrjhBm3atEnPP/+8UlJSalwt89lnn9WKFSs0cuRIOZ1OLVy4UNOmTdPjjz9e40kGot8nn3yiefPmaejQoercubPef/99zZw5UzNmzFCrVq2O+7gJEybI7/fXuG3u3Lkym83VJdhhl19+uc4555zqj+12e/1+E2g0dV0vUmSu4cSJE2vclpeXV+Njji3xo65r5bXXXtMZZ5yhiy++WDabTR9++KF+85vf6J577lGvXr1q3JdjS2xxu9169NFHlZeXp3vvvVe7d+/W/PnzFQ6HNWzYsBM+ds6cOdq5c6duv/12mc1mvfjii/rtb3+rqVOnVt9n/fr1mj17ti666CLdcsstWrlypZ544gk5HI6jilREv7qul+LiYu3Zs0eDBw9Wy5YttW3bNr3yyivatm2bJkyYUOO+ubm5GjNmTI3bDs/rRew4lWOLJI0YMUKdOnWq/tjpdNb4PMeW+FLX9TJgwIAab95KkRJj8eLFR12ojmNL/Ni+fbtWrlypgoICBQKBWj+uoZ+3UIQdx7hx42Q2m+Xz+U6qCFuyZIkyMzN15513ymKxqFu3biorK9Orr76qCy+8UCaTSfv27dOyZcs0ZswY9e/fX5JqWBPdAAAUSElEQVTUunVrjR07Vh999FGNwgzRb8GCBerfv7+GDBkiKTK/buvWrXrjjTd01113Hfdxbdu2rfHxwYMHVVJScsxfINnZ2erYsWP9Boch6rpeJMlisZxwHXBsiS91XSuPPfZYjRchPXr00O7du/XWW28dVYRxbIkt//rXv1RVVaW7775bqamp6tGjh7xerxYuXKgrr7xSqampx3zcxo0b9dVXX2ny5MkqLCyUJGVmZmrixIlavXq1evToIUlatGiRunTpop///OeSpG7dumnHjh169dVXebEag+q6Xq666qoax5CuXbsqOTlZf/rTn7R3794aL0ZTUlI4hsSBuq6Vw3Jyck64Dji2xJe6rpesrCxlZWXVuG3RokXKzc1VmzZtatzOsSV+FBUV6ayzzpIkzZ49W+Xl5T/6mMZ43nLiffQJ7MdOMTielStX6uyzz65xxcm+fftq37592r59uyTpq6++kiT17t27+j6ZmZnq3LmzVq5ceQqp0dj27NmjXbt21dh+aTab1adPH61ateqkvlZxcbHC4bDOPffc+o6JKFGf6+VYOLbEj1NZK0e+Ey9Jbdq00aFDh+o9JxrXqlWrdPrpp9d4kXHuueeqqqpKa9euPe7jVq5cqSZNmlQ/mZSkDh06KDs7u3o9+f1+rVmzRn369Knx2L59+2rjxo2qqKio5+8GDa2u6+V4xxBJHEfiVF3XSm1wbIk/9bVe3G63Vq9ezWufOFeXXqUxnrewI6we+Xw+7du376hzlw+fulRSUqJWrVpp586dysrKks1mq3G/3NzcU/5lg8ZVUlIiKfJO2P/Kzc2V2+2Wy+U65hPKYykuLlbHjh2Pue134cKF+utf/yqHw6GioiLddNNNSktLO/VvAI3qVNeLx+PRqFGjVFFRofz8fF1zzTU1Si+OLfGjPo8tkvTtt98edRqtxLEl1pSUlNS4OrYkNWvWTCkpKdq5c+cJH3esuSq5ubnVa23Pnj0KBoNH3S83N1fhcFg7d+5Uhw4d6uG7QGOp63o5lo0bN8pkMh11TNqxY4dGjhwpv9+v9u3b6/rrr6/xwgWx4VTXytNPPy23260mTZro3HPP1fXXX189joFjS/ypr2PLihUrFAwGjznPiWNLYmuM5y0UYfXocOt45HZQh8MhKfIiVoq038faMpqWllZ9H8SGw/9eh/+NDzv8sdvtrtWL1b179+rbb7/VzTfffNTn+vfvr6KiIjmdTm3ZskWLFi3Stm3bNGPGjDrvXIQxTmW9nHbaaRo+fLjatGkjn8+nf/3rX5o9e7buvvvu6jKMY0v8qK9jiyQtW7ZMW7du1YgRI2rczrEl9ng8nqPWhBRZF263+4SPO9axweFwqLS0VJKqH3/k1z9cjHIMiT11XS9HOnjwoF577TX169evxjpq27atCgoKlJeXJ5fLpTfffFOPPvqoHn30UYqNGFPXtZKUlKSLL75Yp59+uux2u7755hstXrxYe/bs0b333iuJY0s8qq9jyyeffKK2bdseVbBzbEFjPG9JmCKsoqJCBw4c+NH71ceVKEwmU53uEw6HT/nvxqmry1o53r95bdaCFPlFYDKZjtraKUWuLnlYYWGhcnNzNWPGDH3xxRc6++yza/X10XAaa73069evxsdFRUV66KGH9Oqrr9bYFcaxJXoZcWzZsmWLnn32WV122WXq1q1bjc9xbIlNx/sZ/7E1Ude1dPj4Uds1h+hS1/VyWCAQ0Jw5c2Sz2TRy5Mganzvyiug9e/bU+PHj9dprr1WXIIgddVkrGRkZGjVqVPXHXbt2VdOmTfXMM8/ou+++O2ru05Ff+3h/L6LfqR5bDhw4oLVr1+rGG2886nMcWyA1/POWhCnCli9frrlz5/7o/RYsWFDnv+Nwa3lk+3jkO/vH251xvHYdjetk1sr/7vb739b6eLs5jqe4uLj6ycOPOeOMM2Sz2bR161ZerEYBI9aLFDm49+7dWy+++KJCoZDMZjPHlijX2Gtlz549mjFjhrp166abbrrpR+/PsSX6ORyOY/6MV/z/9u49psnrjQP4l1Jsx00EbbmI4K1hBRHQKcmmspsm02zGLYpB3cg0c4jO7Z8R54XMGNwWNmNMSFzJRHcBRNAtSEBwwBQRMQEEFBC8FcyKIjgtpWD7++OXvvEdiFAYCP1+EhPe0/c957Q+HJrnfc85en2/MeHk5NTn4rRPx5flDuq/19SwtPe8xbLpxWNtvFiYzWYcOnQId+7cwd69e587bXrChAkIDQ3F5cuXre4zjY6hxsrTwsPDodFo0NTUBH9/f44t49BwxMuFCxcAoM9pkf/GscX2jMT3FptJhL355pv/+Y5pcrkcHh4eveZGW+axWu7ye3t74/79+zAYDKK1fFpaWno9GkojbzCxYvk/bW5uFq3t1dzcDGdn5wFNXWppacHNmzexefPmAbVpyW7zDtqLYaTjpT8cW15sIxkrHR0d2LdvH6ZMmSLsgvw8HFtefE+vjWFx7949dHV19fs77uPjg4KCgl7lLS0twk5OSqUS9vb2aG5uFq3D0tLS0ufaUPTiszZeLFJSUnDp0iXs2rVrUDMmOIaMPUONlb5Y4oBjy/gzHPFy/vx5BAQEYPLkyQNul2OL7RiJ7y1cBGSYhYaGoqysDCaTSSgrKSmBh4cHfH19AUDYyrOsrEw4p62tDVevXkVoaOjIdpiGRKlUwsvLC6WlpUKZyWRCaWkpQkJCBlTHuXPnIJVKRdPb+lNRUQGDwYAZM2ZY1WcaPcMRLxZmsxllZWXw9/cXkhwcW8aPocSKwWBAQkICACAuLg4ymWxAbXJsefGFhISgsrISnZ2dQllJSQkmTJjQ7yLCoaGhaG9vx7Vr14SyxsZG/P3330I8OTg4ICgoSBRzlvpVKhWf2hiDrI0XAMjKykJOTg62bt2KgICAAbVnNBpRUVHBMWQMGkqs/JtlDLHEAceW8Weo8aLT6dDQ0DDg3SI5ttiekfjeYh8fHx8/7D0fB7RaLWpra3H79m2UlZXB3d0dRqMRDx8+FO7O19bWYuvWrVCr1UKZj48PTp06Ba1WC2dnZ1y4cAG///471q1bJ/zyOjo6oq2tDdnZ2Zg0aRIePHgAjUYDqVSKTZs2wd7eftTeNw2ei4sL0tLSIJFIYDKZkJGRgatXryI2NhYTJ04E0HesWGg0GsyePRuvv/56r7rz8/Nx5swZGI1GdHR0oLS0FD/99BP8/PwQFRXFOyNjkLXxsmfPHnR2dkKv1+P27ds4duwYamtrsWnTJuGOB8eW8cXaWNm/fz8aGhoQHR0Ns9mM+/fvC/88PDwAcGwZq3x9fZGfn4+amhpMmjQJV65cwa+//ooVK1aIkt1bt27FrVu3hLumHh4eqK+vx9mzZ4Un1zUaDaZOnYrIyEjhOoVCgePHj+PRo0dwcHBAXl4eioqK8Mknn8DT03PE3y8NjbXxcu7cOSQnJyMiIgKBgYGiMcTBwQEymQx6vR579+5FT08P9Ho9Ghsb8eOPP+Lu3buIiYmBu7v7aL1tsoK1sZKeno7Lly/DYDCgra0NhYWFSE9Px/z580XrPHFsGV+sjReLM2fOoLa2Fp9++mmvm3UcW8afrq4ulJeXQ6vVorKyEo8fP8bEiROh1WoxZcoUSKXSUfneYjNTIwerpKQEGRkZwnFubi5yc3OhVqthyR2azWaYTCbRQtSenp746quvkJKSgoSEBLi5uWH9+vW9psNER0dDJpMhJSUFRqMRarUan332mbDVMI0dr732GgwGA06dOoUTJ07A19cXcXFxmDZtmnBOX7ECADdv3kRzczM++OCDPutWKpUoKirCxYsX0dnZCTc3NyxevBiRkZHc1W2MsjZePD09cfr0aTx48AASiQTTp09HXFxcrye9OLaMH9bGSlVVFQDg4MGDveq0rIPJsWVscnZ2xu7du5GcnIxvvvkGTk5OWL58OVavXi06z2QyiZ5MB4Dt27cjJSUFSUlJMJvNCAsLQ3R0tOicgIAAfPHFF0hLS0NeXh4UCgW2bdsmPG1KY4u18VJZWQkAKCwsRGFhoejcmJgYREREQCqVwtXVFZmZmejo6ICDgwNUKhXi4+Mxc+bM//y90fCyNlZ8fHzwxx9/oKCgAEajEZMnT8a7776LVatWia7j2DK+DOVvEfD/aZFBQUF9LvPAsWX86ejowPfffy8qsxwfOnQICoViVL632Jm5nRgREREREREREdkA3vYlIiIiIiIiIiKbwEQYERERERERERHZBCbCiIiIiIiIiIjIJjARRkRERERERERENoGJMCIiIiIiIiIisglMhBERERERERERkU1gIoyIiIiIiIiIiGyCdLQ7QERERDQepKenIyMjo1f5nDlzsGvXLgBASUkJSkpKUF9fj/b2dsTExCAiImJA9Xd1dSE2Nhaff/451Gq1UN7a2opffvkFlZWV6O7uhpeXF6KiohASEgIAqKqqQkFBARoaGvDw4UMolUq8/fbbWLp0KSSS/u+Jrl69ulfZ7NmzsW/fPuG4rq4Ohw8fRltbG1599VVER0fD3t5eeL2oqAjZ2dnYv3+/qL0LFy4gNTUVP/zww3P7QURERDRcmAgjIiIiGiaOjo7YsWNHrzKL0tJStLa2IiwsDGfPnh1U3Tk5OVAoFKIk2L1797Bz5074+fkhJiYGMpkMN2/ehNFoFM7Jz8+H0WhEZGQkPDw8cO3aNRw9ehQ6nQ4bNmx4brsrVqxAeHi4cPzSSy8JP/f09ODAgQMIDw9HUFAQNBoN/P398dZbbwEADAYDfvvtN2zbtq1XsmvhwoVITU1FcXHxgJOBREREREPFRBgRERHRMLG3t4dKpXrm69u3b4dEIoHBYBhUIsxkMiE3Nxfvv/++qPznn3+GUqlEXFyckGgKDg4WnbNx40a4uroKx4GBgejq6kJ2djbWrl0LBweHfttWKBTPfE8tLS3Q6/VYv349JBIJ7ty5g6qqKiERlpWVBZVKJUreWUgkEixevBg5OTlMhBEREdGI4XPoRERERCPE2imA1dXVaGtrw8KFC4UyvV6PixcvPneK49NJMIvp06eju7sbnZ2dVvXHoqenB1KpVGhfJpOhp6cHAKDT6ZCXl4d169Y98/qFCxfixo0buH379pD6QURERDRQfCKMiIiIaBg9efJEdCyRSGBnZzekOqurq+Ht7Q0XFxehrKmpCU+ePIGdnR127dqF69evY+LEiVi2bBlWrlzZb5t1dXVwcXHpM0n2b8ePH8eRI0fg5OSEefPmYcOGDXB2dgYAeHt7o6enB8XFxQgODkZxcTHmzZsH4P9Pqy1btgwKheKZdU+dOhVOTk6orq7GtGnTBvpxEBEREVmNiTAiIiKiYfLPP/9g7dq1orKdO3f2mq44WE1NTfD19RWVtbe3AwAOHz6MpUuXYs2aNaipqUFqaiocHR2xbNmyPuvSarU4c+YM3nvvvee2u2TJEsybNw+urq5oamrCiRMncOvWLSQkJEAikUAul+PDDz9EUlISnjx5gpkzZ+Kdd95BbW0t6urqEBMT89w2/Pz8cP369QF8CkRERERDx0QYERER0TBxdHQUdoi08Pb2HnK97e3tUCqVojKz2QwACA0NRVRUFAAgKCgI9+/fx8mTJ/tMhD169AiJiYnw8/PDqlWrntvuli1bhJ/VajV8fHyQkJCA8vJyLFiwAADwxhtvIDw8XNiR0mw248iRI4iKioJcLkdqairOnj0LmUyG9evXC9dZuLi4CEk9IiIiov8aE2FEREREw8Te3h4zZ84c9nq7u7t7LWpvmZ4YGBgoKg8KCkJhYSH0er1ox0qj0YjvvvsO3d3d2LNnD6TSwX8NDAkJgVwux40bN0QJLUdHR6GtgoICSKVSLFq0COXl5Th//jz2798PnU6HhIQEqFQquLm5Cdc6ODigu7t70H0hIiIisgYXyyciIiJ6wTk7O+Px48eiMh8fn36veXoBfZPJhIMHD+LOnTvYsWOHKBE1GJZ1x561/pher0daWho++ugj2NnZobq6Gq+88grc3d0REBAALy+vXtMgHz9+DCcnJ6v6Q0RERDRYTIQRERERveC8vLyg0+lEZQqFAr6+vrhy5Yqo/MqVK1AqlZDL5UKZRqNBRUUFvvzyyyFN1ayoqIDBYMCMGTP6fD0jIwPBwcFQqVRCmdFoFH7u6urqdU1ra+uwTB8lIiIiGghOjSQiIiIaIVqtFlqtVkgONTY2Qi6Xw9XVFWq1+pnXBQQE4NKlSzCZTKInvdasWYPExEQcO3YMc+fORU1NDYqLixEbGyuck5mZifz8fGEnyfr6euG1qVOnClMav/76awDA7t27AQD5+flobGzEnDlzhMXyMzMzMWvWLISFhfXq4927d/Hnn38iMTFRKFOr1UhKSkJgYCB0Oh1aW1sxa9Ys4XWDwYDm5masWbNmUJ8jERERkbWYCCMiIiIaISUlJcjIyBCOc3NzkZubC7Vajfj4+GdeN3/+fCQnJ6Ourg4vv/yyUL5gwQJs2bIFWVlZOH36NCZPnoyPP/4YixYtEs6pqqoCAJw8eRInT54U1btnzx5hjTGTySR6TalUoqioCBcvXkRnZyfc3NywePFiREZGipJxFkePHsXy5cvh7u4u6l9DQwM0Gg3kcjliY2NF0zKrqqogk8kwd+7c/j42IiIiomFjZ7ZsOUREREREL6xvv/0W7u7u2Lhx42h3ZdgcOHAAcrkcmzdvHu2uEBERkY3gGmFEREREY8CqVavw119/4dGjR6PdlWFx7949lJeXY+XKlaPdFSIiIrIhfCKMiIiIaIzIy8uDSqWCv7//aHdlyOrr63H37l0sWbJktLtCRERENoSJMCIiIiIiIiIisgmcGklERERERERERDaBiTAiIiIiIiIiIrIJTIQREREREREREZFNYCKMiIiIiIiIiIhsAhNhRERERERERERkE/4HMO8Gj8P3uKcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = pca.components_\n",
    "\n",
    "fig = plt.subplots(figsize=(20,20))\n",
    "plt.xlim(-1,1)\n",
    "plt.ylim(-1,1)\n",
    "\n",
    "plt.quiver(np.zeros(pc.shape[1]), np.zeros(pc.shape[1]),\n",
    "           pc[0,:], pc[1,:], \n",
    "           angles='xy', scale_units='xy', scale=1, color='r', width= 0.004)\n",
    "\n",
    "for i, (x, y) in enumerate(zip(pc[0, :], pc[1, :])):\n",
    "    plt.text(x, y, df_indicators.iloc[:, 2:].columns[i], fontsize=15)\n",
    "    \n",
    "circle = plt.Circle((0,0), 1, facecolor='none', edgecolor='b')\n",
    "plt.gca().add_artist(circle)\n",
    "\n",
    "plt.plot([-1,1],[0,0],color='silver',linestyle='--',linewidth=1)\n",
    "plt.plot([0,0],[-1,1],color='silver',linestyle='--',linewidth=1)\n",
    "\n",
    "plt.title('Cercle des corrélations', fontsize=20)\n",
    "plt.xlabel('F{} ({}%)'.format(1, round(100*pca.explained_variance_ratio_[0],1)))\n",
    "plt.ylabel('F{} ({}%)'.format(2, round(100*pca.explained_variance_ratio_[1],1)))\n",
    "\n",
    "plt.savefig(\"p2_00_graphic/cercle_correlation.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.62510885 0.8874847  0.96036348 0.98660659]\n"
     ]
    }
   ],
   "source": [
    "print(pca.explained_variance_ratio_.cumsum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notons que plus de 88% de la variance expliquée se retrouve dans les deux premières composantes principales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls = cluster.KMeans(n_clusters=4)\n",
    "cls.fit(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_kmeans = cls.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_projected = pca.transform(X_scaled)\n",
    "plt.figure(figsize=(20, 20))\n",
    "\n",
    "plt.scatter(X_projected[:, 1], X_projected[:, 2], c=cls.labels_)\n",
    "plt.xlabel('F{} ({}%)'.format(1, round(100*pca.explained_variance_ratio_[0],1)))\n",
    "plt.ylabel('F{} ({}%)'.format(2, round(100*pca.explained_variance_ratio_[1],1)))\n",
    "plt.title(\"Projection en 4 clusters des {} individus sur le 1er plan factoriel\".format(X_projected.shape[0]))\n",
    "plt.savefig('p2_00_graphic/pca_projection.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IT.NET.USER.P2</th>\n",
       "      <th>NY.GDP.PCAP.CD</th>\n",
       "      <th>SP.POP.TOTL</th>\n",
       "      <th>SP.POP.1524.TO.UN</th>\n",
       "      <th>UIS.E.3</th>\n",
       "      <th>UIS.E.4</th>\n",
       "      <th>SE.TER.ENRL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>cluster0</td>\n",
       "      <td>1.258389</td>\n",
       "      <td>1.171855</td>\n",
       "      <td>-0.136778</td>\n",
       "      <td>-0.188526</td>\n",
       "      <td>-0.158090</td>\n",
       "      <td>-0.122061</td>\n",
       "      <td>-0.140692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cluster1</td>\n",
       "      <td>-0.664201</td>\n",
       "      <td>-0.609345</td>\n",
       "      <td>6.374361</td>\n",
       "      <td>7.724582</td>\n",
       "      <td>7.700869</td>\n",
       "      <td>7.583737</td>\n",
       "      <td>3.920354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cluster2</td>\n",
       "      <td>-0.560206</td>\n",
       "      <td>-0.523231</td>\n",
       "      <td>-0.146370</td>\n",
       "      <td>-0.107063</td>\n",
       "      <td>-0.123830</td>\n",
       "      <td>-0.143614</td>\n",
       "      <td>-0.110156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cluster3</td>\n",
       "      <td>1.549379</td>\n",
       "      <td>1.560635</td>\n",
       "      <td>5.466174</td>\n",
       "      <td>1.324915</td>\n",
       "      <td>1.660805</td>\n",
       "      <td>2.230974</td>\n",
       "      <td>7.340002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          IT.NET.USER.P2  NY.GDP.PCAP.CD  SP.POP.TOTL  SP.POP.1524.TO.UN  \\\n",
       "cluster0        1.258389        1.171855    -0.136778          -0.188526   \n",
       "cluster1       -0.664201       -0.609345     6.374361           7.724582   \n",
       "cluster2       -0.560206       -0.523231    -0.146370          -0.107063   \n",
       "cluster3        1.549379        1.560635     5.466174           1.324915   \n",
       "\n",
       "           UIS.E.3   UIS.E.4  SE.TER.ENRL  \n",
       "cluster0 -0.158090 -0.122061    -0.140692  \n",
       "cluster1  7.700869  7.583737     3.920354  \n",
       "cluster2 -0.123830 -0.143614    -0.110156  \n",
       "cluster3  1.660805  2.230974     7.340002  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids = cls.cluster_centers_\n",
    "pd.DataFrame(centroids, columns=indicators, index=['cluster0', 'cluster1', 'cluster2', 'cluster3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "idk = np.argsort(cls.labels_)\n",
    "\n",
    "df_cls = pd.DataFrame(df_indicators.index[idk], cls.labels_[idk]).reset_index()\n",
    "df_cls = df_cls.rename(columns={'index':'cluster'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130, 11)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Currency Unit</th>\n",
       "      <th>IT.NET.USER.P2</th>\n",
       "      <th>NY.GDP.PCAP.CD</th>\n",
       "      <th>SE.TER.ENRL</th>\n",
       "      <th>SP.POP.1524.TO.UN</th>\n",
       "      <th>SP.POP.TOTL</th>\n",
       "      <th>UIS.E.3</th>\n",
       "      <th>UIS.E.4</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghan afghani</td>\n",
       "      <td>38.178801</td>\n",
       "      <td>5504.733085</td>\n",
       "      <td>509422.0</td>\n",
       "      <td>81269137.0</td>\n",
       "      <td>396801806.0</td>\n",
       "      <td>6129824.0</td>\n",
       "      <td>561081.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albanian lek</td>\n",
       "      <td>365.928222</td>\n",
       "      <td>47836.417233</td>\n",
       "      <td>1393260.0</td>\n",
       "      <td>8737415.0</td>\n",
       "      <td>44618462.0</td>\n",
       "      <td>2167444.0</td>\n",
       "      <td>3778.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>Euro</td>\n",
       "      <td>806.500709</td>\n",
       "      <td>567066.339642</td>\n",
       "      <td>3633.0</td>\n",
       "      <td>54179.0</td>\n",
       "      <td>1173712.0</td>\n",
       "      <td>13692.0</td>\n",
       "      <td>3612.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>ATG</td>\n",
       "      <td>East Caribbean dollar</td>\n",
       "      <td>528.556704</td>\n",
       "      <td>177271.659708</td>\n",
       "      <td>5067.0</td>\n",
       "      <td>77883.0</td>\n",
       "      <td>1371307.0</td>\n",
       "      <td>17940.0</td>\n",
       "      <td>9638.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruban florin</td>\n",
       "      <td>657.122823</td>\n",
       "      <td>280141.788190</td>\n",
       "      <td>28915.0</td>\n",
       "      <td>193343.0</td>\n",
       "      <td>1492667.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country Name Country Code          Currency Unit  IT.NET.USER.P2  \\\n",
       "0          Afghanistan          AFG         Afghan afghani       38.178801   \n",
       "1              Albania          ALB           Albanian lek      365.928222   \n",
       "2              Andorra          AND                   Euro      806.500709   \n",
       "3  Antigua and Barbuda          ATG  East Caribbean dollar      528.556704   \n",
       "4                Aruba          ABW          Aruban florin      657.122823   \n",
       "\n",
       "   NY.GDP.PCAP.CD  SE.TER.ENRL  SP.POP.1524.TO.UN  SP.POP.TOTL    UIS.E.3  \\\n",
       "0     5504.733085     509422.0         81269137.0  396801806.0  6129824.0   \n",
       "1    47836.417233    1393260.0          8737415.0   44618462.0  2167444.0   \n",
       "2   567066.339642       3633.0            54179.0    1173712.0    13692.0   \n",
       "3   177271.659708       5067.0            77883.0    1371307.0    17940.0   \n",
       "4   280141.788190      28915.0           193343.0    1492667.0    50000.0   \n",
       "\n",
       "    UIS.E.4  cluster  \n",
       "0  561081.0        2  \n",
       "1    3778.0        2  \n",
       "2    3612.0        0  \n",
       "3    9638.0        2  \n",
       "4     124.0        0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cls = pd.merge(df_indicators, df_cls, on='Country Name')\n",
    "display(df_cls.shape)\n",
    "display(df_cls.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39 pays dans le cluster 0\n",
      "2 pays dans le cluster 1\n",
      "88 pays dans le cluster 2\n",
      "1 pays dans le cluster 3\n"
     ]
    }
   ],
   "source": [
    "print(str(len(df_cls[df_cls['cluster'] == 0]['Country Name'])) + \" pays dans le cluster 0\")\n",
    "print(str(len(df_cls[df_cls['cluster'] == 1]['Country Name'])) + \" pays dans le cluster 1\")\n",
    "print(str(len(df_cls[df_cls['cluster'] == 2]['Country Name'])) + \" pays dans le cluster 2\")\n",
    "print(str(len(df_cls[df_cls['cluster'] == 3]['Country Name'])) + \" pays dans le cluster 3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Andorra', 'Aruba', 'Australia', 'Austria', 'Bahrain', 'Barbados',\n",
       "       'Belgium', 'Brunei Darussalam', 'Canada', 'Cyprus',\n",
       "       'Czech Republic', 'Denmark', 'Estonia', 'Finland', 'France',\n",
       "       'Germany', 'Hong Kong SAR, China', 'Iceland', 'Ireland', 'Israel',\n",
       "       'Italy', 'Japan', 'Kuwait', 'Latvia', 'Liechtenstein',\n",
       "       'Luxembourg', 'Malta', 'Netherlands', 'New Zealand', 'Norway',\n",
       "       'Portugal', 'Qatar', 'Singapore', 'Slovak Republic', 'Slovenia',\n",
       "       'Spain', 'Sweden', 'Switzerland', 'United Kingdom'], dtype=object)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checklist countries from cluster 0\n",
    "df_cls0 = df_cls[df_cls['cluster'] == 0]['Country Name'].unique()\n",
    "df_cls0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Aruba', 'Australia', 'Austria', 'Bahrain', 'Belgium', 'Canada',\n",
       "       'Cyprus', 'Czech Republic', 'Denmark', 'Estonia', 'Finland',\n",
       "       'France', 'Germany', 'Hong Kong SAR, China', 'Iceland', 'Ireland',\n",
       "       'Israel', 'Italy', 'Japan', 'Kuwait', 'Latvia', 'Luxembourg',\n",
       "       'Malta', 'Netherlands', 'New Zealand', 'Norway', 'Portugal',\n",
       "       'Qatar', 'Singapore', 'Slovak Republic', 'Slovenia', 'Spain',\n",
       "       'Sweden', 'Switzerland', 'United Kingdom'], dtype=object)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.intersect1d(df_cls0, country_evolution['Country Name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['China', 'India'], dtype=object)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cluster 1\n",
    "df_cls1 = df_cls[df_cls['cluster'] == 1]['Country Name'].unique()\n",
    "df_cls1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Antigua and Barbuda', 'Azerbaijan',\n",
       "       'Bangladesh', 'Belarus', 'Belize', 'Bhutan', 'Botswana', 'Brazil',\n",
       "       'Bulgaria', 'Burkina Faso', 'Cabo Verde', 'Cambodia', 'Cameroon',\n",
       "       'Cayman Islands', 'Chad', 'Colombia', 'Comoros', 'Cuba',\n",
       "       'Djibouti', 'Egypt, Arab Rep.', 'Eritrea', 'Ethiopia', 'Fiji',\n",
       "       'Gambia, The', 'Georgia', 'Ghana', 'Greece', 'Grenada', 'Guyana',\n",
       "       'Hungary', 'Iran, Islamic Rep.', 'Iraq', 'Jamaica', 'Kazakhstan',\n",
       "       'Kenya', 'Kyrgyz Republic', 'Lao PDR', 'Lebanon', 'Lesotho',\n",
       "       'Liberia', 'Lithuania', 'Madagascar', 'Malawi', 'Malaysia',\n",
       "       'Maldives', 'Mali', 'Mauritania', 'Mauritius', 'Moldova',\n",
       "       'Mongolia', 'Morocco', 'Namibia', 'Nicaragua', 'Niger', 'Oman',\n",
       "       'Pakistan', 'Panama', 'Paraguay', 'Peru', 'Philippines', 'Poland',\n",
       "       'Romania', 'Russian Federation', 'Rwanda', 'Samoa', 'Seychelles',\n",
       "       'Sierra Leone', 'South Africa', 'Sri Lanka', 'St. Kitts and Nevis',\n",
       "       'St. Lucia', 'Suriname', 'Swaziland', 'Syrian Arab Republic',\n",
       "       'Tanzania', 'Thailand', 'Tonga', 'Trinidad and Tobago', 'Tunisia',\n",
       "       'Uganda', 'Ukraine', 'Uruguay', 'Vanuatu', 'West Bank and Gaza',\n",
       "       'Yemen, Rep.', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cluster 2\n",
    "df_cls2 = df_cls[df_cls['cluster'] == 2]['Country Name'].unique()\n",
    "df_cls2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Albania', 'Azerbaijan', 'Bangladesh', 'Belarus', 'Belize',\n",
       "       'Bhutan', 'Brazil', 'Bulgaria', 'Burkina Faso', 'Cabo Verde',\n",
       "       'Cambodia', 'Cameroon', 'Chad', 'Colombia', 'Comoros', 'Cuba',\n",
       "       'Egypt, Arab Rep.', 'Ethiopia', 'Gambia, The', 'Georgia', 'Ghana',\n",
       "       'Greece', 'Guyana', 'Hungary', 'Iran, Islamic Rep.', 'Iraq',\n",
       "       'Jamaica', 'Kazakhstan', 'Kenya', 'Kyrgyz Republic', 'Lao PDR',\n",
       "       'Lebanon', 'Lesotho', 'Liberia', 'Lithuania', 'Madagascar',\n",
       "       'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Mauritius', 'Moldova',\n",
       "       'Mongolia', 'Morocco', 'Namibia', 'Nicaragua', 'Niger', 'Pakistan',\n",
       "       'Panama', 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Romania',\n",
       "       'Russian Federation', 'Rwanda', 'Samoa', 'Sierra Leone',\n",
       "       'South Africa', 'St. Lucia', 'Suriname', 'Swaziland',\n",
       "       'Syrian Arab Republic', 'Tanzania', 'Thailand', 'Tonga',\n",
       "       'Trinidad and Tobago', 'Tunisia', 'Uganda', 'Ukraine', 'Uruguay',\n",
       "       'Vanuatu', 'West Bank and Gaza', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Interaction with \"country_evolution\"\n",
    "np.intersect1d(df_cls2, country_evolution['Country Name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United States'], dtype=object)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cluster 3\n",
    "df_cls3 = df_cls[df_cls['cluster'] == 3]['Country Name'].unique()\n",
    "df_cls3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c_inertie</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Currency Unit</th>\n",
       "      <th>cluster</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>32.400587</td>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>LIE</td>\n",
       "      <td>Swiss franc</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>17.659858</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>LUX</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>14.513534</td>\n",
       "      <td>Norway</td>\n",
       "      <td>NOR</td>\n",
       "      <td>Norwegian krone</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>9.761808</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CHE</td>\n",
       "      <td>Swiss franc</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>8.279202</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>ISL</td>\n",
       "      <td>Iceland krona</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>8.092809</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DNK</td>\n",
       "      <td>Danish krone</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>7.638824</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SWE</td>\n",
       "      <td>Swedish krona</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>7.247820</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DEU</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>6.589247</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NLD</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6.285542</td>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Australian dollar</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>6.016032</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>QAT</td>\n",
       "      <td>Qatari riyal</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>5.820680</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>5.682041</td>\n",
       "      <td>Japan</td>\n",
       "      <td>JPN</td>\n",
       "      <td>Japanese yen</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>5.159518</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Pound sterling</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>5.073659</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>Canadian dollar</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>4.045122</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>IRL</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.970897</td>\n",
       "      <td>Austria</td>\n",
       "      <td>AUT</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>3.633685</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>NZL</td>\n",
       "      <td>New Zealand dollar</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>3.593722</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>SGP</td>\n",
       "      <td>Singapore dollar</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>3.489876</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>BEL</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.846941</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>2.770407</td>\n",
       "      <td>France</td>\n",
       "      <td>FRA</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>2.545390</td>\n",
       "      <td>Hong Kong SAR, China</td>\n",
       "      <td>HKG</td>\n",
       "      <td>Hong Kong dollar</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>2.202699</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>EST</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>1.786514</td>\n",
       "      <td>Kuwait</td>\n",
       "      <td>KWT</td>\n",
       "      <td>Kuwaiti dinar</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>1.627958</td>\n",
       "      <td>Slovak Republic</td>\n",
       "      <td>SVK</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>1.334477</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>SVN</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.326960</td>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>BRN</td>\n",
       "      <td>Brunei dollar</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>1.318351</td>\n",
       "      <td>Spain</td>\n",
       "      <td>ESP</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.281531</td>\n",
       "      <td>Barbados</td>\n",
       "      <td>BRB</td>\n",
       "      <td>Barbados dollar</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1.269609</td>\n",
       "      <td>Italy</td>\n",
       "      <td>ITA</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>1.165081</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Latvian lats</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.044126</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>CYP</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.009679</td>\n",
       "      <td>Israel</td>\n",
       "      <td>ISR</td>\n",
       "      <td>Israeli new shekel</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>1.003962</td>\n",
       "      <td>Malta</td>\n",
       "      <td>MLT</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.860063</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>BHR</td>\n",
       "      <td>Bahraini dinar</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.847547</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>CZE</td>\n",
       "      <td>Czech koruna</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.847021</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruban florin</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.564899</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>PRT</td>\n",
       "      <td>Euro</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     c_inertie          Country Name Country Code       Currency Unit  \\\n",
       "68   32.400587         Liechtenstein          LIE         Swiss franc   \n",
       "70   17.659858            Luxembourg          LUX                Euro   \n",
       "87   14.513534                Norway          NOR     Norwegian krone   \n",
       "114   9.761808           Switzerland          CHE         Swiss franc   \n",
       "50    8.279202               Iceland          ISL       Iceland krona   \n",
       "32    8.092809               Denmark          DNK        Danish krone   \n",
       "113   7.638824                Sweden          SWE       Swedish krona   \n",
       "43    7.247820               Germany          DEU                Euro   \n",
       "83    6.589247           Netherlands          NLD                Euro   \n",
       "5     6.285542             Australia          AUS   Australian dollar   \n",
       "96    6.016032                 Qatar          QAT        Qatari riyal   \n",
       "39    5.820680               Finland          FIN                Euro   \n",
       "58    5.682041                 Japan          JPN        Japanese yen   \n",
       "123   5.159518        United Kingdom          GBR      Pound sterling   \n",
       "23    5.073659                Canada          CAN     Canadian dollar   \n",
       "54    4.045122               Ireland          IRL                Euro   \n",
       "6     3.970897               Austria          AUT                Euro   \n",
       "84    3.633685           New Zealand          NZL  New Zealand dollar   \n",
       "103   3.593722             Singapore          SGP    Singapore dollar   \n",
       "12    3.489876               Belgium          BEL                Euro   \n",
       "2     2.846941               Andorra          AND                Euro   \n",
       "40    2.770407                France          FRA                Euro   \n",
       "48    2.545390  Hong Kong SAR, China          HKG    Hong Kong dollar   \n",
       "36    2.202699               Estonia          EST                Euro   \n",
       "61    1.786514                Kuwait          KWT       Kuwaiti dinar   \n",
       "104   1.627958       Slovak Republic          SVK                Euro   \n",
       "105   1.334477              Slovenia          SVN                Euro   \n",
       "17    1.326960     Brunei Darussalam          BRN       Brunei dollar   \n",
       "107   1.318351                 Spain          ESP                Euro   \n",
       "10    1.281531              Barbados          BRB     Barbados dollar   \n",
       "56    1.269609                 Italy          ITA                Euro   \n",
       "64    1.165081                Latvia          LVA        Latvian lats   \n",
       "30    1.044126                Cyprus          CYP                Euro   \n",
       "55    1.009679                Israel          ISR  Israeli new shekel   \n",
       "76    1.003962                 Malta          MLT                Euro   \n",
       "8     0.860063               Bahrain          BHR      Bahraini dinar   \n",
       "31    0.847547        Czech Republic          CZE        Czech koruna   \n",
       "4     0.847021                 Aruba          ABW       Aruban florin   \n",
       "95    0.564899              Portugal          PRT                Euro   \n",
       "\n",
       "     cluster  rank  \n",
       "68         0   1.0  \n",
       "70         0   2.0  \n",
       "87         0   3.0  \n",
       "114        0   4.0  \n",
       "50         0   5.0  \n",
       "32         0   6.0  \n",
       "113        0   7.0  \n",
       "43         0   8.0  \n",
       "83         0   9.0  \n",
       "5          0  10.0  \n",
       "96         0  11.0  \n",
       "39         0  12.0  \n",
       "58         0  13.0  \n",
       "123        0  14.0  \n",
       "23         0  15.0  \n",
       "54         0  16.0  \n",
       "6          0  17.0  \n",
       "84         0  18.0  \n",
       "103        0  19.0  \n",
       "12         0  20.0  \n",
       "2          0  21.0  \n",
       "40         0  22.0  \n",
       "48         0  23.0  \n",
       "36         0  24.0  \n",
       "61         0  25.0  \n",
       "104        0  26.0  \n",
       "105        0  27.0  \n",
       "17         0  28.0  \n",
       "107        0  29.0  \n",
       "10         0  30.0  \n",
       "56         0  31.0  \n",
       "64         0  32.0  \n",
       "30         0  33.0  \n",
       "55         0  34.0  \n",
       "76         0  35.0  \n",
       "8          0  36.0  \n",
       "31         0  37.0  \n",
       "4          0  38.0  \n",
       "95         0  39.0  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "di = np.sum(X_scaled**2,axis=1)\n",
    "ctr_indiv_inertie = pd.DataFrame({'c_inertie':di, 'Country Name': df_cls['Country Name'], \n",
    "                                  'Country Code': df_cls['Country Code'],\n",
    "                                  'Currency Unit': df_cls['Currency Unit'],\n",
    "                                  'cluster': df_cls['cluster']}).sort_values(by='c_inertie', ascending=False)\n",
    "\n",
    "ctr_indiv_inertie = ctr_indiv_inertie[ctr_indiv_inertie['cluster'] == 0]\n",
    "ctr_indiv_inertie['rank'] = ctr_indiv_inertie['c_inertie'].rank(ascending=False)\n",
    "ctr_indiv_inertie\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c_inertie</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Currency Unit</th>\n",
       "      <th>cluster</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>13.473581</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>PHL</td>\n",
       "      <td>Philippine peso</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>9.594084</td>\n",
       "      <td>Iran, Islamic Rep.</td>\n",
       "      <td>IRN</td>\n",
       "      <td>Iranian rial</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>6.725724</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>RUS</td>\n",
       "      <td>Russian ruble</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>5.457240</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazilian real</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3.883976</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>BGD</td>\n",
       "      <td>Bangladeshi taka</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>3.302816</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>PAK</td>\n",
       "      <td>Pakistani rupee</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>2.374716</td>\n",
       "      <td>Eritrea</td>\n",
       "      <td>ERI</td>\n",
       "      <td>Eritrean nakfa</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>2.304426</td>\n",
       "      <td>Niger</td>\n",
       "      <td>NER</td>\n",
       "      <td>CFA franc</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>2.297960</td>\n",
       "      <td>Liberia</td>\n",
       "      <td>LBR</td>\n",
       "      <td>Liberian dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>2.286494</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>ETH</td>\n",
       "      <td>Ethiopian birr</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>2.273170</td>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>SLE</td>\n",
       "      <td>Sierra Leonean leone</td>\n",
       "      <td>2</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>2.244426</td>\n",
       "      <td>Chad</td>\n",
       "      <td>TCD</td>\n",
       "      <td>CFA franc</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>2.239962</td>\n",
       "      <td>Egypt, Arab Rep.</td>\n",
       "      <td>EGY</td>\n",
       "      <td>Egyptian pound</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>2.226041</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>UKR</td>\n",
       "      <td>Ukrainian hryvnia</td>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>2.199798</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>MDG</td>\n",
       "      <td>Malagasy ariary</td>\n",
       "      <td>2</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>2.185187</td>\n",
       "      <td>Malawi</td>\n",
       "      <td>MWI</td>\n",
       "      <td>Malawi kwacha</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>2.173715</td>\n",
       "      <td>Mali</td>\n",
       "      <td>MLI</td>\n",
       "      <td>CFA franc</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>2.126312</td>\n",
       "      <td>Mauritania</td>\n",
       "      <td>MRT</td>\n",
       "      <td>Mauritanian ouguiya</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>2.123629</td>\n",
       "      <td>Comoros</td>\n",
       "      <td>COM</td>\n",
       "      <td>Comorian franc</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>2.082081</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>BFA</td>\n",
       "      <td>CFA franc</td>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>2.053847</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>KHM</td>\n",
       "      <td>Cambodian riel</td>\n",
       "      <td>2</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>2.043529</td>\n",
       "      <td>Djibouti</td>\n",
       "      <td>DJI</td>\n",
       "      <td>Djibouti franc</td>\n",
       "      <td>2</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>1.977799</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>RWA</td>\n",
       "      <td>Rwandan franc</td>\n",
       "      <td>2</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>1.967185</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>South African rand</td>\n",
       "      <td>2</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>1.946864</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>TZA</td>\n",
       "      <td>Tanzanian shilling</td>\n",
       "      <td>2</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>1.897307</td>\n",
       "      <td>Lesotho</td>\n",
       "      <td>LSO</td>\n",
       "      <td>Lesotho loti</td>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.886110</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghan afghani</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>1.825525</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>U.S. dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.810645</td>\n",
       "      <td>Gambia, The</td>\n",
       "      <td>GMB</td>\n",
       "      <td>Gambian dalasi</td>\n",
       "      <td>2</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>1.806611</td>\n",
       "      <td>Lao PDR</td>\n",
       "      <td>LAO</td>\n",
       "      <td>Lao kip</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.800366</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>CMR</td>\n",
       "      <td>CFA franc</td>\n",
       "      <td>2</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.780459</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>IRQ</td>\n",
       "      <td>Iraqi dinar</td>\n",
       "      <td>2</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>1.683569</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>NIC</td>\n",
       "      <td>Nicaraguan gold cordoba</td>\n",
       "      <td>2</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>1.673262</td>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>VUT</td>\n",
       "      <td>Vanuatu vatu</td>\n",
       "      <td>2</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>1.670298</td>\n",
       "      <td>Samoa</td>\n",
       "      <td>WSM</td>\n",
       "      <td>Samoan tala</td>\n",
       "      <td>2</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>1.630770</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>UGA</td>\n",
       "      <td>Ugandan shilling</td>\n",
       "      <td>2</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.601574</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>GHA</td>\n",
       "      <td>New Ghanaian cedi</td>\n",
       "      <td>2</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>1.597259</td>\n",
       "      <td>Mongolia</td>\n",
       "      <td>MNG</td>\n",
       "      <td>Mongolian tugrik</td>\n",
       "      <td>2</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.584923</td>\n",
       "      <td>Bhutan</td>\n",
       "      <td>BTN</td>\n",
       "      <td>Bhutanese ngultrum</td>\n",
       "      <td>2</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>1.574488</td>\n",
       "      <td>Namibia</td>\n",
       "      <td>NAM</td>\n",
       "      <td>Namibian dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.536476</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KEN</td>\n",
       "      <td>Kenyan shilling</td>\n",
       "      <td>2</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>1.515751</td>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Yemeni rial</td>\n",
       "      <td>2</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>1.481690</td>\n",
       "      <td>Swaziland</td>\n",
       "      <td>SWZ</td>\n",
       "      <td>Swaziland lilangeni</td>\n",
       "      <td>2</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>1.471914</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>MYS</td>\n",
       "      <td>Malaysian ringgit</td>\n",
       "      <td>2</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>1.450558</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>LKA</td>\n",
       "      <td>Sri Lankan rupee</td>\n",
       "      <td>2</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.316769</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>BWA</td>\n",
       "      <td>Botswana pula</td>\n",
       "      <td>2</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>1.250893</td>\n",
       "      <td>Tonga</td>\n",
       "      <td>TON</td>\n",
       "      <td>Tongan pa'anga</td>\n",
       "      <td>2</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>1.191600</td>\n",
       "      <td>Kyrgyz Republic</td>\n",
       "      <td>KGZ</td>\n",
       "      <td>Kyrgyz som</td>\n",
       "      <td>2</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.145736</td>\n",
       "      <td>Guyana</td>\n",
       "      <td>GUY</td>\n",
       "      <td>Guyana dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>1.079590</td>\n",
       "      <td>Paraguay</td>\n",
       "      <td>PRY</td>\n",
       "      <td>Paraguayan guarani</td>\n",
       "      <td>2</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>1.074650</td>\n",
       "      <td>Poland</td>\n",
       "      <td>POL</td>\n",
       "      <td>Polish zloty</td>\n",
       "      <td>2</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.063388</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>CPV</td>\n",
       "      <td>Cabo Verde escudo</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>1.047444</td>\n",
       "      <td>Syrian Arab Republic</td>\n",
       "      <td>SYR</td>\n",
       "      <td>Syrian pound</td>\n",
       "      <td>2</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.042106</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>FJI</td>\n",
       "      <td>Fijian dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.935577</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>GEO</td>\n",
       "      <td>Georgian lari</td>\n",
       "      <td>2</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.917407</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>CUB</td>\n",
       "      <td>Cuban peso</td>\n",
       "      <td>2</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.916682</td>\n",
       "      <td>Cayman Islands</td>\n",
       "      <td>CYM</td>\n",
       "      <td>Cayman Islands dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.893462</td>\n",
       "      <td>Moldova</td>\n",
       "      <td>MDA</td>\n",
       "      <td>Moldovan leu</td>\n",
       "      <td>2</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>0.891521</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>THA</td>\n",
       "      <td>Thai baht</td>\n",
       "      <td>2</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.848144</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>KAZ</td>\n",
       "      <td>Kazakh tenge</td>\n",
       "      <td>2</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.840001</td>\n",
       "      <td>Belize</td>\n",
       "      <td>BLZ</td>\n",
       "      <td>Belize dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>0.828225</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>PSE</td>\n",
       "      <td>Israeli new shekel</td>\n",
       "      <td>2</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>0.827306</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>SUR</td>\n",
       "      <td>Suriname dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.802688</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>HUN</td>\n",
       "      <td>Hungarian forint</td>\n",
       "      <td>2</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.794173</td>\n",
       "      <td>Maldives</td>\n",
       "      <td>MDV</td>\n",
       "      <td>Maldivian rufiyaa</td>\n",
       "      <td>2</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.745945</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LTU</td>\n",
       "      <td>Lithuanian litas</td>\n",
       "      <td>2</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.741771</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albanian lek</td>\n",
       "      <td>2</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>0.708312</td>\n",
       "      <td>St. Kitts and Nevis</td>\n",
       "      <td>KNA</td>\n",
       "      <td>East Caribbean dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.685037</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>JAM</td>\n",
       "      <td>Jamaican dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.654220</td>\n",
       "      <td>Mauritius</td>\n",
       "      <td>MUS</td>\n",
       "      <td>Mauritian rupee</td>\n",
       "      <td>2</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.643002</td>\n",
       "      <td>Grenada</td>\n",
       "      <td>GRD</td>\n",
       "      <td>East Caribbean dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>0.629060</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>TUN</td>\n",
       "      <td>Tunisian dinar</td>\n",
       "      <td>2</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>0.562755</td>\n",
       "      <td>St. Lucia</td>\n",
       "      <td>LCA</td>\n",
       "      <td>East Caribbean dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.560009</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>BLR</td>\n",
       "      <td>Belarusian rubel</td>\n",
       "      <td>2</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.551200</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>MAR</td>\n",
       "      <td>Moroccan dirham</td>\n",
       "      <td>2</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.526752</td>\n",
       "      <td>Panama</td>\n",
       "      <td>PAN</td>\n",
       "      <td>Panamanian balboa</td>\n",
       "      <td>2</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.515428</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>BGR</td>\n",
       "      <td>Bulgarian lev</td>\n",
       "      <td>2</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.512336</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>AZE</td>\n",
       "      <td>New Azeri manat</td>\n",
       "      <td>2</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.508061</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>ATG</td>\n",
       "      <td>East Caribbean dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.505925</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>LBN</td>\n",
       "      <td>Lebanese pound</td>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>0.493661</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>TTO</td>\n",
       "      <td>Trinidad and Tobago dollar</td>\n",
       "      <td>2</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>0.479015</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>URY</td>\n",
       "      <td>Uruguayan peso</td>\n",
       "      <td>2</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>0.452087</td>\n",
       "      <td>Seychelles</td>\n",
       "      <td>SYC</td>\n",
       "      <td>Seychelles rupee</td>\n",
       "      <td>2</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.439397</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>COL</td>\n",
       "      <td>Colombian peso</td>\n",
       "      <td>2</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.417671</td>\n",
       "      <td>Oman</td>\n",
       "      <td>OMN</td>\n",
       "      <td>Rial Omani</td>\n",
       "      <td>2</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.381077</td>\n",
       "      <td>Greece</td>\n",
       "      <td>GRC</td>\n",
       "      <td>Euro</td>\n",
       "      <td>2</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.360191</td>\n",
       "      <td>Peru</td>\n",
       "      <td>PER</td>\n",
       "      <td>Peruvian new sol</td>\n",
       "      <td>2</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.321650</td>\n",
       "      <td>Romania</td>\n",
       "      <td>ROU</td>\n",
       "      <td>New Romanian leu</td>\n",
       "      <td>2</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     c_inertie          Country Name Country Code               Currency Unit  \\\n",
       "93   13.473581           Philippines          PHL             Philippine peso   \n",
       "52    9.594084    Iran, Islamic Rep.          IRN                Iranian rial   \n",
       "98    6.725724    Russian Federation          RUS               Russian ruble   \n",
       "16    5.457240                Brazil          BRA              Brazilian real   \n",
       "9     3.883976            Bangladesh          BGD            Bangladeshi taka   \n",
       "89    3.302816              Pakistan          PAK             Pakistani rupee   \n",
       "35    2.374716               Eritrea          ERI              Eritrean nakfa   \n",
       "86    2.304426                 Niger          NER                   CFA franc   \n",
       "67    2.297960               Liberia          LBR             Liberian dollar   \n",
       "37    2.286494              Ethiopia          ETH              Ethiopian birr   \n",
       "102   2.273170          Sierra Leone          SLE        Sierra Leonean leone   \n",
       "25    2.244426                  Chad          TCD                   CFA franc   \n",
       "34    2.239962      Egypt, Arab Rep.          EGY              Egyptian pound   \n",
       "122   2.226041               Ukraine          UKR           Ukrainian hryvnia   \n",
       "71    2.199798            Madagascar          MDG             Malagasy ariary   \n",
       "72    2.185187                Malawi          MWI               Malawi kwacha   \n",
       "75    2.173715                  Mali          MLI                   CFA franc   \n",
       "77    2.126312            Mauritania          MRT         Mauritanian ouguiya   \n",
       "28    2.123629               Comoros          COM              Comorian franc   \n",
       "19    2.082081          Burkina Faso          BFA                   CFA franc   \n",
       "21    2.053847              Cambodia          KHM              Cambodian riel   \n",
       "33    2.043529              Djibouti          DJI              Djibouti franc   \n",
       "99    1.977799                Rwanda          RWA               Rwandan franc   \n",
       "106   1.967185          South Africa          ZAF          South African rand   \n",
       "116   1.946864              Tanzania          TZA          Tanzanian shilling   \n",
       "66    1.897307               Lesotho          LSO                Lesotho loti   \n",
       "0     1.886110           Afghanistan          AFG              Afghan afghani   \n",
       "129   1.825525              Zimbabwe          ZWE                 U.S. dollar   \n",
       "41    1.810645           Gambia, The          GMB              Gambian dalasi   \n",
       "63    1.806611               Lao PDR          LAO                     Lao kip   \n",
       "22    1.800366              Cameroon          CMR                   CFA franc   \n",
       "53    1.780459                  Iraq          IRQ                 Iraqi dinar   \n",
       "85    1.683569             Nicaragua          NIC     Nicaraguan gold cordoba   \n",
       "126   1.673262               Vanuatu          VUT                Vanuatu vatu   \n",
       "100   1.670298                 Samoa          WSM                 Samoan tala   \n",
       "121   1.630770                Uganda          UGA            Ugandan shilling   \n",
       "44    1.601574                 Ghana          GHA           New Ghanaian cedi   \n",
       "80    1.597259              Mongolia          MNG            Mongolian tugrik   \n",
       "14    1.584923                Bhutan          BTN          Bhutanese ngultrum   \n",
       "82    1.574488               Namibia          NAM             Namibian dollar   \n",
       "60    1.536476                 Kenya          KEN             Kenyan shilling   \n",
       "128   1.515751           Yemen, Rep.          YEM                 Yemeni rial   \n",
       "112   1.481690             Swaziland          SWZ         Swaziland lilangeni   \n",
       "73    1.471914              Malaysia          MYS           Malaysian ringgit   \n",
       "108   1.450558             Sri Lanka          LKA            Sri Lankan rupee   \n",
       "15    1.316769              Botswana          BWA               Botswana pula   \n",
       "118   1.250893                 Tonga          TON              Tongan pa'anga   \n",
       "62    1.191600       Kyrgyz Republic          KGZ                  Kyrgyz som   \n",
       "47    1.145736                Guyana          GUY               Guyana dollar   \n",
       "91    1.079590              Paraguay          PRY          Paraguayan guarani   \n",
       "94    1.074650                Poland          POL                Polish zloty   \n",
       "20    1.063388            Cabo Verde          CPV           Cabo Verde escudo   \n",
       "115   1.047444  Syrian Arab Republic          SYR                Syrian pound   \n",
       "38    1.042106                  Fiji          FJI               Fijian dollar   \n",
       "42    0.935577               Georgia          GEO               Georgian lari   \n",
       "29    0.917407                  Cuba          CUB                  Cuban peso   \n",
       "24    0.916682        Cayman Islands          CYM       Cayman Islands dollar   \n",
       "79    0.893462               Moldova          MDA                Moldovan leu   \n",
       "117   0.891521              Thailand          THA                   Thai baht   \n",
       "59    0.848144            Kazakhstan          KAZ                Kazakh tenge   \n",
       "13    0.840001                Belize          BLZ               Belize dollar   \n",
       "127   0.828225    West Bank and Gaza          PSE          Israeli new shekel   \n",
       "111   0.827306              Suriname          SUR             Suriname dollar   \n",
       "49    0.802688               Hungary          HUN            Hungarian forint   \n",
       "74    0.794173              Maldives          MDV           Maldivian rufiyaa   \n",
       "69    0.745945             Lithuania          LTU            Lithuanian litas   \n",
       "1     0.741771               Albania          ALB                Albanian lek   \n",
       "109   0.708312   St. Kitts and Nevis          KNA       East Caribbean dollar   \n",
       "57    0.685037               Jamaica          JAM             Jamaican dollar   \n",
       "78    0.654220             Mauritius          MUS             Mauritian rupee   \n",
       "46    0.643002               Grenada          GRD       East Caribbean dollar   \n",
       "120   0.629060               Tunisia          TUN              Tunisian dinar   \n",
       "110   0.562755             St. Lucia          LCA       East Caribbean dollar   \n",
       "11    0.560009               Belarus          BLR            Belarusian rubel   \n",
       "81    0.551200               Morocco          MAR             Moroccan dirham   \n",
       "90    0.526752                Panama          PAN           Panamanian balboa   \n",
       "18    0.515428              Bulgaria          BGR               Bulgarian lev   \n",
       "7     0.512336            Azerbaijan          AZE             New Azeri manat   \n",
       "3     0.508061   Antigua and Barbuda          ATG       East Caribbean dollar   \n",
       "65    0.505925               Lebanon          LBN              Lebanese pound   \n",
       "119   0.493661   Trinidad and Tobago          TTO  Trinidad and Tobago dollar   \n",
       "125   0.479015               Uruguay          URY              Uruguayan peso   \n",
       "101   0.452087            Seychelles          SYC            Seychelles rupee   \n",
       "27    0.439397              Colombia          COL              Colombian peso   \n",
       "88    0.417671                  Oman          OMN                  Rial Omani   \n",
       "45    0.381077                Greece          GRC                        Euro   \n",
       "92    0.360191                  Peru          PER            Peruvian new sol   \n",
       "97    0.321650               Romania          ROU            New Romanian leu   \n",
       "\n",
       "     cluster  rank  \n",
       "93         2   1.0  \n",
       "52         2   2.0  \n",
       "98         2   3.0  \n",
       "16         2   4.0  \n",
       "9          2   5.0  \n",
       "89         2   6.0  \n",
       "35         2   7.0  \n",
       "86         2   8.0  \n",
       "67         2   9.0  \n",
       "37         2  10.0  \n",
       "102        2  11.0  \n",
       "25         2  12.0  \n",
       "34         2  13.0  \n",
       "122        2  14.0  \n",
       "71         2  15.0  \n",
       "72         2  16.0  \n",
       "75         2  17.0  \n",
       "77         2  18.0  \n",
       "28         2  19.0  \n",
       "19         2  20.0  \n",
       "21         2  21.0  \n",
       "33         2  22.0  \n",
       "99         2  23.0  \n",
       "106        2  24.0  \n",
       "116        2  25.0  \n",
       "66         2  26.0  \n",
       "0          2  27.0  \n",
       "129        2  28.0  \n",
       "41         2  29.0  \n",
       "63         2  30.0  \n",
       "22         2  31.0  \n",
       "53         2  32.0  \n",
       "85         2  33.0  \n",
       "126        2  34.0  \n",
       "100        2  35.0  \n",
       "121        2  36.0  \n",
       "44         2  37.0  \n",
       "80         2  38.0  \n",
       "14         2  39.0  \n",
       "82         2  40.0  \n",
       "60         2  41.0  \n",
       "128        2  42.0  \n",
       "112        2  43.0  \n",
       "73         2  44.0  \n",
       "108        2  45.0  \n",
       "15         2  46.0  \n",
       "118        2  47.0  \n",
       "62         2  48.0  \n",
       "47         2  49.0  \n",
       "91         2  50.0  \n",
       "94         2  51.0  \n",
       "20         2  52.0  \n",
       "115        2  53.0  \n",
       "38         2  54.0  \n",
       "42         2  55.0  \n",
       "29         2  56.0  \n",
       "24         2  57.0  \n",
       "79         2  58.0  \n",
       "117        2  59.0  \n",
       "59         2  60.0  \n",
       "13         2  61.0  \n",
       "127        2  62.0  \n",
       "111        2  63.0  \n",
       "49         2  64.0  \n",
       "74         2  65.0  \n",
       "69         2  66.0  \n",
       "1          2  67.0  \n",
       "109        2  68.0  \n",
       "57         2  69.0  \n",
       "78         2  70.0  \n",
       "46         2  71.0  \n",
       "120        2  72.0  \n",
       "110        2  73.0  \n",
       "11         2  74.0  \n",
       "81         2  75.0  \n",
       "90         2  76.0  \n",
       "18         2  77.0  \n",
       "7          2  78.0  \n",
       "3          2  79.0  \n",
       "65         2  80.0  \n",
       "119        2  81.0  \n",
       "125        2  82.0  \n",
       "101        2  83.0  \n",
       "27         2  84.0  \n",
       "88         2  85.0  \n",
       "45         2  86.0  \n",
       "92         2  87.0  \n",
       "97         2  88.0  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "di = np.sum(X_scaled**2,axis=1)\n",
    "ctr_indiv_inertie = pd.DataFrame({'c_inertie':di, 'Country Name': df_cls['Country Name'], \n",
    "                                  'Country Code': df_cls['Country Code'],\n",
    "                                  'Currency Unit': df_cls['Currency Unit'],\n",
    "                                  'cluster': df_cls['cluster']}).sort_values(by='c_inertie', ascending=False)\n",
    "\n",
    "ctr_indiv_inertie = ctr_indiv_inertie[ctr_indiv_inertie['cluster'] == 2]\n",
    "ctr_indiv_inertie['rank'] = ctr_indiv_inertie['c_inertie'].rank(ascending=False)\n",
    "ctr_indiv_inertie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nos groupes sont-ils réellement distincts? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test d'adéquation de Kolmogorov-Smirnov :**\n",
    "On peut tester l’adéquation de la variable 'IT.NET.USER.P2' à une loi normale à l’aide de *Kolmogorov-Smirnov*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=0.122, p=0.060\n",
      "On ne peut pas rejeter H0 pour des niveaux de test de 3%\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ks_2samp\n",
    "\n",
    "stat, p = ks_2samp(df_indicators['IT.NET.USER.P2'],\n",
    "          list(np.random.normal(np.mean(df_indicators['IT.NET.USER.P2']), \n",
    "          np.std(df_indicators['IT.NET.USER.P2']), 1000)))\n",
    "print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "\n",
    "alpha = 0.03\n",
    "if p > alpha:\n",
    "    print('On ne peut pas rejeter H0 pour des niveaux de test de 3%')\n",
    "else:\n",
    "    print('H0 est rejetée à un niveau de test de 3%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le test de Kolmogorov-Smirnov répond positivement à une loi normale. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test de comparaison de deux clusters dans le cas gaussien.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_test1 = df_cls[df_cls['cluster'] == 2]['IT.NET.USER.P2']\n",
    "cluster_test2 = df_cls[df_cls['cluster'] == 0]['IT.NET.USER.P2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=0.259, p=0.611\n",
      "On ne rejette donc pas H0, l’égalité des variances au niveau de test 5%\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import bartlett\n",
    "stat, p = bartlett(cluster_test1, cluster_test2)\n",
    "print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "\n",
    "alpha = 0.05\n",
    "if p > alpha:\n",
    "    print('On ne rejette donc pas H0, l’égalité des variances au niveau de test 5%')\n",
    "else:\n",
    "    print('H0 est rejetée au niveau de test 5%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=-17.344, p=0.000000000\n",
      "H0 l'hypothèse d’égalité des moyennes est rejetée au niveau de test 5%\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "stat, p = ttest_ind(cluster_test1, cluster_test2, equal_var=True)\n",
    "print('Statistics=%.3f, p=%.9f' % (stat, p))\n",
    "\n",
    "alpha = 0.05\n",
    "if p > alpha:\n",
    "    print('On ne rejette donc pas H0, l’égalité des moyennes de nos 2 clusters au niveau de test 5%')\n",
    "else:\n",
    "    print('H0 l\\'hypothèse d’égalité des moyennes est rejetée au niveau de test 5%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Les tests statistiques réalisés permettent de vérifier que nos clusters ne suivent pas la même distribution, en effet l'hypothèse d'égalité des moyennes a été rejetée dans le cadre du test précédent. Les clusters identifiés sont distincts.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recommandations des pays à clibler…\n",
    "Ces préconisations tiennent compte des corrélations entre nos variables, comme nous avons pu le voir dans le cercle des corrélations. Pour rappel, il se dégage deux principaux critères _socio-démographique_ et _business_ propre au métier de l'entreprise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il n'est pas envisageable de recueillir l'ensemble des pays de ces clusters. La cohérence métier de l'entreprise doit permettre d'affiner les résultats proposés, le bon sens des décideurs permettra la sélection finale. Ceci étant, les premières recommandations sont les suivantes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Première solution, un développement \"plus rapide\" tourné vers l'Union Européenne :**\n",
    "\n",
    "A partir des hypothèses proposées et de leur représentation dans l'inertie totale, les pays cibles prioritaires seraient : **Liechtenstein, Luxembourg, Norvège, Suisse, Islande, Suisse, Allemagne, (Royaume-Uni).**\n",
    "\n",
    "Ces pays sont non seulement dans l'Union Européenne (facilité monétaire, culturelle, etc…), et répondent à une position dominante en terme de pouvoir d'achat de la population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Seconde solution, un développement en dehors de l'Union Européenne :**\n",
    "\n",
    "A partir des hypothèses proposées et de leur représentation dans l'inertie totale, les pays cibles seraient : **Canada, Australie, Nouvelle-Zélande, Qatar, Japon.** _A noter également, un potentiel marché à affiner pour la Chine, l'Inde, et les USA._\n",
    "\n",
    "Ces pays sont plus difficiles d'accès de part leur différence culturelle, une barrière linguistique possible, mais répondent à un potentiel métier intéressant. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Autre solution prenant en compte une perspective d'évolution sur d'ici 2030 :**\n",
    "\n",
    "Très fort potentiel pour la **Chine, l'Inde, et les USA**. Ceci étant, les pays suivants, avec une pénétration marché plus facile, seraient à travailler pour un positionnement d'ici 2030 : **Allemagne, Royaume-Uni, Canada, Japon.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quelque soit la ou les décisions d'internalisation de l'entreprise, ces critères sont issus d'une réflexion globale tenant compte de critère sociaux démographiques, technologiques et métiers. ***L'analyse n'exclue pas une étude complémentaire portant sur les contraintes concurrentielle avant le lancement d'une plateforme hors France, surtout pour des pays comme la Chine, l'Inde et les USA.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
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            var html_ebf0940a822847b492d23c95117420f3 = $(`<div id="html_ebf0940a822847b492d23c95117420f3" style="width: 100.0%; height: 100.0%;"><strong>Prioritaire</strong></div>`)[0];
            popup_3f1657f9a29a47ffb745f8187ff97c91.setContent(html_ebf0940a822847b492d23c95117420f3);
        

        marker_58d6a2537f534b17b7efaa9e8bd59fce.bindPopup(popup_3f1657f9a29a47ffb745f8187ff97c91)
        ;

        
    
    
            marker_58d6a2537f534b17b7efaa9e8bd59fce.bindTooltip(
                `<div>
                     <strong>LUXEMBOURG</strong>
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_93043b2c816c474b841aaa711654a2c0 = L.marker(
                [64.5731537, 11.5280364],
                {}
            ).addTo(map_5925c47c762c406dbfd51dc6178bdc26);
        
    
            var icon_357c8c890d964323b183244048fb6f3d = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_93043b2c816c474b841aaa711654a2c0.setIcon(icon_357c8c890d964323b183244048fb6f3d);
        
    
        var popup_dcc3c431898143abb3d881a5c9be46ba = L.popup({"maxWidth": "100%"});

        
            var html_469d603907b1485083d572a02bed6979 = $(`<div id="html_469d603907b1485083d572a02bed6979" style="width: 100.0%; height: 100.0%;"><strong>Prioritaire</strong></div>`)[0];
            popup_dcc3c431898143abb3d881a5c9be46ba.setContent(html_469d603907b1485083d572a02bed6979);
        

        marker_93043b2c816c474b841aaa711654a2c0.bindPopup(popup_dcc3c431898143abb3d881a5c9be46ba)
        ;

        
    
    
            marker_93043b2c816c474b841aaa711654a2c0.bindTooltip(
                `<div>
                     <strong>NORVEGE</strong>
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_ce9c5298a4624e9998fa2932885605ea = L.marker(
                [46.7985624, 8.2319736],
                {}
            ).addTo(map_5925c47c762c406dbfd51dc6178bdc26);
        
    
            var icon_22b085a1b45b4eee9135faff02e2163f = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_ce9c5298a4624e9998fa2932885605ea.setIcon(icon_22b085a1b45b4eee9135faff02e2163f);
        
    
        var popup_83bb37c8101b47f5a6cd6ae90ebcc082 = L.popup({"maxWidth": "100%"});

        
            var html_1b9fa3f663ae48c58a4c70ae5c2469c2 = $(`<div id="html_1b9fa3f663ae48c58a4c70ae5c2469c2" style="width: 100.0%; height: 100.0%;"><strong>Prioritaire</strong></div>`)[0];
            popup_83bb37c8101b47f5a6cd6ae90ebcc082.setContent(html_1b9fa3f663ae48c58a4c70ae5c2469c2);
        

        marker_ce9c5298a4624e9998fa2932885605ea.bindPopup(popup_83bb37c8101b47f5a6cd6ae90ebcc082)
        ;

        
    
    
            marker_ce9c5298a4624e9998fa2932885605ea.bindTooltip(
                `<div>
                     <strong>SWITZERLAND</strong>
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_c3964888a3bd41ec80e02778b1120c77 = L.marker(
                [64.9841821, -18.1059013],
                {}
            ).addTo(map_5925c47c762c406dbfd51dc6178bdc26);
        
    
            var icon_5c19aeea13a04ba6841c62ad58d15626 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_c3964888a3bd41ec80e02778b1120c77.setIcon(icon_5c19aeea13a04ba6841c62ad58d15626);
        
    
        var popup_3f5ca5e12c8b496fafbb9646e135f8c5 = L.popup({"maxWidth": "100%"});

        
            var html_1afc435c23ef4f69bbf90886d75a1cf9 = $(`<div id="html_1afc435c23ef4f69bbf90886d75a1cf9" style="width: 100.0%; height: 100.0%;"><strong>Prioritaire</strong></div>`)[0];
            popup_3f5ca5e12c8b496fafbb9646e135f8c5.setContent(html_1afc435c23ef4f69bbf90886d75a1cf9);
        

        marker_c3964888a3bd41ec80e02778b1120c77.bindPopup(popup_3f5ca5e12c8b496fafbb9646e135f8c5)
        ;

        
    
    
            marker_c3964888a3bd41ec80e02778b1120c77.bindTooltip(
                `<div>
                     <strong>ICELAND</strong>
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_93b5b2ada2e9409a9f369e2ec80836c1 = L.marker(
                [51.0834196, 10.4234469],
                {}
            ).addTo(map_5925c47c762c406dbfd51dc6178bdc26);
        
    
            var icon_dd362de73fd747d59146cf21a1f0b4d7 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_93b5b2ada2e9409a9f369e2ec80836c1.setIcon(icon_dd362de73fd747d59146cf21a1f0b4d7);
        
    
        var popup_f64256b6816b44919d8663b44c0097fb = L.popup({"maxWidth": "100%"});

        
            var html_a5187f53f9e740d28b30afd6ca7a629a = $(`<div id="html_a5187f53f9e740d28b30afd6ca7a629a" style="width: 100.0%; height: 100.0%;"><strong>Prioritaire</strong></div>`)[0];
            popup_f64256b6816b44919d8663b44c0097fb.setContent(html_a5187f53f9e740d28b30afd6ca7a629a);
        

        marker_93b5b2ada2e9409a9f369e2ec80836c1.bindPopup(popup_f64256b6816b44919d8663b44c0097fb)
        ;

        
    
    
            marker_93b5b2ada2e9409a9f369e2ec80836c1.bindTooltip(
                `<div>
                     <strong>GERMANY</strong>
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_13f3a236e6b7491b843924a32e681c79 = L.marker(
                [54.7023545, -3.2765753],
                {}
            ).addTo(map_5925c47c762c406dbfd51dc6178bdc26);
        
    
            var icon_f50936dda731488bbb90d1194d35dc18 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_13f3a236e6b7491b843924a32e681c79.setIcon(icon_f50936dda731488bbb90d1194d35dc18);
        
    
        var popup_998f5c6aec6b4448a5b25ce74c4e066e = L.popup({"maxWidth": "100%"});

        
            var html_e6549d494d614e6e9662478aee396812 = $(`<div id="html_e6549d494d614e6e9662478aee396812" style="width: 100.0%; height: 100.0%;"><strong>Prioritaire</strong></div>`)[0];
            popup_998f5c6aec6b4448a5b25ce74c4e066e.setContent(html_e6549d494d614e6e9662478aee396812);
        

        marker_13f3a236e6b7491b843924a32e681c79.bindPopup(popup_998f5c6aec6b4448a5b25ce74c4e066e)
        ;

        
    
    
            marker_13f3a236e6b7491b843924a32e681c79.bindTooltip(
                `<div>
                     <strong>UNITED KINGDOM</strong>
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_222233c12b854496a4e03826976927e4 = L.marker(
                [61.0666922, -107.9917071],
                {}
            ).addTo(map_5925c47c762c406dbfd51dc6178bdc26);
        
    
            var icon_984e735df7c64547a35127b921e782ad = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
            );
            marker_222233c12b854496a4e03826976927e4.setIcon(icon_984e735df7c64547a35127b921e782ad);
        
    
            marker_222233c12b854496a4e03826976927e4.bindTooltip(
                `<div>
                     <strong>CANADA</strong>
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_e177e77449a44ea7b1b55ee2880a483e = L.marker(
                [-24.7761086, 134.755],
                {}
            ).addTo(map_5925c47c762c406dbfd51dc6178bdc26);
        
    
            var icon_01f4ca0cf39e49fa85a757e8762a408c = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
            );
            marker_e177e77449a44ea7b1b55ee2880a483e.setIcon(icon_01f4ca0cf39e49fa85a757e8762a408c);
        
    
            marker_e177e77449a44ea7b1b55ee2880a483e.bindTooltip(
                `<div>
                     <strong>AUSTRALIA</strong>
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_63e0a4794116469cafa9dd039de7d67a = L.marker(
                [-41.5000831, 172.8344077],
                {}
            ).addTo(map_5925c47c762c406dbfd51dc6178bdc26);
        
    
            var icon_d64b5a2ed1a7423a977215ab14233137 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
            );
            marker_63e0a4794116469cafa9dd039de7d67a.setIcon(icon_d64b5a2ed1a7423a977215ab14233137);
        
    
            marker_63e0a4794116469cafa9dd039de7d67a.bindTooltip(
                `<div>
                     <strong>NEW ZEALAND</strong>
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_2fcb01be600441cb94f43b06ea7dbc4d = L.marker(
                [25.3336984, 51.2295295],
                {}
            ).addTo(map_5925c47c762c406dbfd51dc6178bdc26);
        
    
            var icon_697d218447e84f62aea964cc82941284 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
            );
            marker_2fcb01be600441cb94f43b06ea7dbc4d.setIcon(icon_697d218447e84f62aea964cc82941284);
        
    
            marker_2fcb01be600441cb94f43b06ea7dbc4d.bindTooltip(
                `<div>
                     <strong>CANADA</strong>
                 </div>`,
                {"sticky": true}
            );
        
    
            var marker_7a78775a95c34382ae941185af4d0137 = L.marker(
                [36.5748441, 139.2394179],
                {}
            ).addTo(map_5925c47c762c406dbfd51dc6178bdc26);
        
    
            var icon_8b180d121016469b90f0b9183d6b65ff = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
            );
            marker_7a78775a95c34382ae941185af4d0137.setIcon(icon_8b180d121016469b90f0b9183d6b65ff);
        
    
            marker_7a78775a95c34382ae941185af4d0137.bindTooltip(
                `<div>
                     <strong>JAPAN</strong>
                 </div>`,
                {"sticky": true}
            );
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" \"allowfullscreen\" \"webkitallowfullscreen\" \"mozallowfullscreen\"></iframe>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fb7243ce3d0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "from branca.element import Figure\n",
    "\n",
    "# Creating Basemap\n",
    "fig=Figure(width=950,height=550)\n",
    "m=folium.Map(location=[38.9597594,34.9249653], tiles='cartodbpositron',zoom_start=2)\n",
    "fig.add_child(m)\n",
    "\n",
    "###PRIORITY TARGET COUNTRIES\n",
    "#LIECHTENSTEIN\n",
    "folium.Marker(location=[47.1416307, 9.5531527],\n",
    "              popup='<strong>Prioritaire</strong>',\n",
    "              tooltip='<strong>LIECHTENSTEIN</strong>',\n",
    "              icon=folium.Icon(color='blue',icon='info-sign')).add_to(m)\n",
    "#LUXEMBOURG\n",
    "folium.Marker(location=[49.8158683, 6.1296751],\n",
    "              popup='<strong>Prioritaire</strong>',\n",
    "              tooltip='<strong>LUXEMBOURG</strong>',\n",
    "              icon=folium.Icon(color='blue',icon='info-sign')).add_to(m)\n",
    "#NORWAY\n",
    "folium.Marker(location=[64.5731537, 11.5280364],\n",
    "              popup='<strong>Prioritaire</strong>',\n",
    "              tooltip='<strong>NORVEGE</strong>',\n",
    "              icon=folium.Icon(color='blue',icon='info-sign')).add_to(m)\n",
    "#SWITZERLAND \n",
    "folium.Marker(location=[46.7985624, 8.2319736],\n",
    "              popup='<strong>Prioritaire</strong>',\n",
    "              tooltip='<strong>SWITZERLAND</strong>',\n",
    "              icon=folium.Icon(color='blue',icon='info-sign')).add_to(m)\n",
    "#ICELAND\n",
    "folium.Marker(location=[64.9841821, -18.1059013],\n",
    "              popup='<strong>Prioritaire</strong>',\n",
    "              tooltip='<strong>ICELAND</strong>',\n",
    "              icon=folium.Icon(color='blue',icon='info-sign')).add_to(m)\n",
    "\n",
    "#GERMANY\n",
    "folium.Marker(location=[51.0834196, 10.4234469],\n",
    "              popup='<strong>Prioritaire</strong>',\n",
    "              tooltip='<strong>GERMANY</strong>',\n",
    "              icon=folium.Icon(color='blue',icon='info-sign')).add_to(m)\n",
    "\n",
    "#UNITED KINGDOM\n",
    "folium.Marker(location=[54.7023545, -3.2765753],\n",
    "              popup='<strong>Prioritaire</strong>',\n",
    "              tooltip='<strong>UNITED KINGDOM</strong>',\n",
    "              icon=folium.Icon(color='blue',icon='info-sign')).add_to(m)\n",
    "\n",
    "###NON-PRIORITY TARGET COUNTRIES\n",
    "#CANADA\n",
    "folium.Marker(location=[61.0666922, -107.9917071],\n",
    "              tooltip='<strong>CANADA</strong>',\n",
    "              icon=folium.Icon(color='orange',icon='info-sign')).add_to(m)\n",
    "\n",
    "#AUSTRALIA\n",
    "folium.Marker(location=[-24.7761086, 134.755],\n",
    "              tooltip='<strong>AUSTRALIA</strong>',\n",
    "              icon=folium.Icon(color='orange',icon='info-sign')).add_to(m)\n",
    "\n",
    "#NEW ZEALAND\n",
    "folium.Marker(location=[-41.5000831, 172.8344077],\n",
    "              tooltip='<strong>NEW ZEALAND</strong>',\n",
    "              icon=folium.Icon(color='orange',icon='info-sign')).add_to(m)\n",
    "\n",
    "#QATAR\n",
    "folium.Marker(location=[25.3336984, 51.2295295],\n",
    "              tooltip='<strong>CANADA</strong>',\n",
    "              icon=folium.Icon(color='orange',icon='info-sign')).add_to(m)\n",
    "\n",
    "#JAPAN\n",
    "folium.Marker(location=[36.5748441, 139.2394179],\n",
    "              tooltip='<strong>JAPAN</strong>',\n",
    "              icon=folium.Icon(color='orange',icon='info-sign')).add_to(m)\n",
    "\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![folium](p2_00_image/folium.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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 },
 "nbformat": 4,
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